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SPM12Manual The FIL Methods Group (and honorary members)John AshburnerGareth BarnesChun-Chuan ChenJean DaunizeauGuillaume FlandinKarl FristonStefan KiebelJames KilnerVladimir LitvakRosalyn MoranWill PennyAdeel RaziKlaas StephanSungho TakPeter ZeidmanDarren GitelmanRik HensonChloe HuttonVolkmar GlaucheJérémie MattoutChristophe PhillipsFunctional Imaging LaboratoryWellcome Trust Centre for NeuroimagingInstitute of Neurology,UCL 12Queen Square,London WC1N3BG,UKJuly20,20162ContentsI Temporal processing171Slice Timing191.1Data (20)1.1.1Session (20)1.2Number of Slices (20)1.3TR (20)1.4TA (20)1.5Slice order (20)1.6Reference Slice (21)1.7Filename Prefix (21)II Spatial processing232Realign252.1Realign:Estimate (25)2.1.1Data (25)2.1.2Estimation Options (26)2.2Realign:Reslice (27)2.2.1Images (27)2.2.2Reslice Options (27)2.3Realign:Estimate&Reslice (28)2.3.1Data (28)2.3.2Estimation Options (28)2.3.3Reslice Options (29)3Realign&Unwarp313.1Data (34)3.1.1Session (34)3.2Estimation Options (34)3.2.1Quality (34)3.2.2Separation (34)3.2.3Smoothing(FWHM) (34)3.2.4Num Passes (34)3.2.5Interpolation (35)3.2.6Wrapping (35)3.2.7Weighting (35)3.3Unwarp Estimation Options (35)3.3.1Basis Functions (35)3.3.2Regularisation (35)3.3.3Reg.Factor (35)3.3.4Jacobian deformations (35)3.3.5First-order effects (36)3.3.6Second-order effects (36)3.3.8Re-estimate movement params (36)3.3.9Number of Iterations (36)3.3.10Taylor expansion point (36)3.4Unwarp Reslicing Options (36)3.4.1Resliced images(unwarp)? (36)3.4.2Interpolation (37)3.4.3Wrapping (37)3.4.4Masking (37)3.4.5Filename Prefix (37)4Coregister394.1Coregister:Estimate (39)4.1.1Reference Image (39)4.1.2Source Image (40)4.1.3Other Images (40)4.1.4Estimation Options (40)4.2Coregister:Reslice (40)4.2.1Image Defining Space (40)4.2.2Images to Reslice (40)4.2.3Reslice Options (40)4.3Coregister:Estimate&Reslice (41)4.3.1Reference Image (41)4.3.2Source Image (41)4.3.3Other Images (41)4.3.4Estimation Options (41)4.3.5Reslice Options (42)5Segment435.1Data (44)5.1.1Channel (44)5.2Tissues (45)5.2.1Tissue (45)5.3Warping&MRF (46)5.3.1MRF Parameter (46)5.3.2Clean Up (46)5.3.3Warping Regularisation (47)5.3.4Affine Regularisation (47)5.3.5Smoothness (47)5.3.6Sampling distance (48)5.3.7Deformation Fields (48)6Normalise496.1Normalise:Estimate (49)6.1.1Data (50)6.1.2Estimation Options (50)6.2Normalise:Write (51)6.2.1Data (51)6.2.2Writing Options (52)6.3Normalise:Estimate&Write (52)6.3.1Data (52)6.3.2Estimation Options (53)7Smooth577.1Images to Smooth (57)7.2FWHM (57)7.3Data Type (57)7.4Implicit masking (57)7.5Filename Prefix (57)III fMRI Statistics598fMRI model specification618.1Timing parameters (61)8.1.1Units for design (62)8.1.2Interscan interval (62)8.1.3Microtime resolution (62)8.1.4Microtime onset (62)8.2Data&Design (63)8.2.1Subject/Session (64)8.3Factorial design (65)8.3.1Factor (66)8.4Basis Functions (66)8.4.1Canonical HRF (66)8.4.2Other basis sets (66)8.5Model Interactions(Volterra) (67)8.6Directory (67)8.7Global normalisation (67)8.8Explicit mask (67)8.9Serial correlations (68)8.10Reviewing your design (68)9fMRI model estimation719.1Select SPM.mat (71)9.2Method (71)9.2.1Classical (71)9.2.2Bayesian1st-level (72)9.2.3Bayesian2nd-level (75)9.3Outputfiles (76)9.3.1Classical1st-level (76)9.3.2Bayesian1st-level (76)9.4Model comparison (76)10Factorial design specification7910.1Directory (80)10.2Design (80)10.2.1One-sample t-test (80)10.2.2Two-sample t-test (80)10.2.3Paired t-test (81)10.2.4Multiple regression (81)10.2.5One-way ANOVA (82)10.2.6One-way ANOVA-within subject (83)10.2.7Full factorial (84)10.2.8Flexible factorial (86)10.2.9Partitioned model (88)10.3Covariates (88)10.3.1Covariate (88)10.4Multiple covariates (89)10.5Masking (90)10.5.1Threshold masking (90)10.5.2Implicit Mask (90)10.5.3Explicit Mask (91)10.6Global calculation (91)10.6.1Omit (91)10.6.2User (91)10.6.3Mean (91)10.7Global normalisation (91)10.7.1Overall grand mean scaling (91)10.7.2Normalisation (92)IV EEG/MEG9311SPM for MEG/EEG overview9511.1Welcome to SPM for M/EEG (95)11.2Changes from SPM8to SPM12 (96)12EEG/MEG preprocessing–Reference9712.1Conversion of data (97)12.2Converting arbitrary data (99)12.3The M/EEG SPM format (99)12.4Preparing the data after conversion and specifying batch inputs (100)12.4.1Prepare(batch) (104)12.5Integration of SPM and Fieldtrip (104)12.6Loading data into workspace (104)12.7The meeg object (104)12.7.1Constructor meeg (105)12.7.2Array-like interface (105)12.7.3display (105)12.7.4Number methods (105)12.7.5Reading and manipulation of information (105)12.7.6Reading of information (108)12.7.7Manipulations of the data on disk (109)12.7.8Struct-like interface (110)12.8SPM functions (110)12.8.1Epoching the data:spm_eeg_epochs (110)12.8.2Filtering the data:spm_eeg_filter (111)12.8.3Baseline correction:spm_eeg_bc (111)12.8.4Artefact detection and rejection:spm_eeg_artefact (111)12.8.5Downsampling:spm_eeg_downsample (112)12.8.6Rereferencing:spm_eeg_montage (112)12.8.7Grand mean:spm_eeg_grandmean (112)12.8.8Merge:spm_eeg_merge (112)12.8.9Multimodal fusion:spm_eeg_fuse (113)12.8.10Cropping:spm_eeg_crop (113)12.8.11Combine planar:spm_eeg_combineplanar (113)12.8.12Data reduction:spm_eeg_reduce (113)12.8.13Time-frequency decomposition:spm_eeg_tf (113)12.8.14Rescaling and baseline correction of time-frequency:spm_eeg_tf_rescale.11412.8.15Averaging over time or frequency:spm_eeg_avgtime,spm_eeg_avgfreq..11412.8.16Averaging:spm_eeg_average (114)12.8.17Contrast over epochs:spm_eeg_contrast (114)12.8.18Copy:spm_eeg_copy (115)12.8.19Remove bad trials:spm_eeg_remove_bad_trials (115)12.9.1Data visualization (116)12.9.2Source reconstructions visualization (116)12.9.3Script generation (117)13Analysis in sensor space11913.0.4Output (119)13.0.5Smoothing (120)143D source reconstruction:Imaging approach12114.1Introduction (121)14.2Getting started (122)14.3Source space modeling (122)14.4Coregistration (123)14.5Forward computation(forward) (124)14.6Inverse reconstruction (125)14.7Summarizing the results of inverse reconstruction as an image (126)14.8Rendering interface (127)14.9Group inversion (127)14.10Batching source reconstruction (127)14.11Appendix:Data structure (127)15Localization of Equivalent Current Dipoles12915.1Introduction (129)15.2Procedure in SPM12 (130)15.2.1Head and forward model (130)15.2.2VB-ECD reconstruction (130)15.2.3Result display (131)16Dynamic Causal Modelling for M/EEG13316.1Introduction (133)16.2Overview (134)16.3Calling DCM for ERP/ERF (134)16.4load,save,select model type (135)16.5Data and design (135)16.6Electromagnetic model (136)16.7Neuronal model (136)16.8Estimation (137)16.9Results (137)16.10Cross-spectral densities (138)16.10.1Model specification (138)16.10.2The Lead-Field (138)16.10.3Connections (138)16.10.4Cross Spectral Densities (138)16.10.5Output and Results (139)16.11Induced responses (139)16.11.1Data (139)16.11.2Electromagnetic model (139)16.11.3Neuronal model (139)16.11.4Wavelet transform (139)16.11.5Results (139)16.12Phase-coupled responses (140)16.12.1Data (140)16.12.2Electromagnetic model (140)16.12.3Neuronal model (140)16.12.4Hilbert transform (140)V Utilities143 17Display Image14517.1Image to Display (146)18Check Registration14918.1Images to Display (149)19Rendering15119.1Surface Extraction (151)19.1.1Input Images (151)19.1.2Surfaces (151)19.2Surface Rendering (152)19.2.1Objects (152)19.2.2Lights (153)20Image Calculator15520.1Input Images (155)20.2Output Filename (155)20.3Output Directory (155)20.4Expression (156)20.5Additional Variables (156)20.5.1Variable (156)20.6Options (156)20.6.1Data Matrix (156)20.6.2Masking (156)20.6.3Interpolation (156)20.6.4Data Type (157)21Import15921.1DICOM Import (159)21.1.1DICOMfiles (159)21.1.2Output directory (160)21.1.3Directory structure (160)21.1.4Protocol namefilter (160)21.1.5Conversion options (160)21.2MINC Import (160)21.2.1MINCfiles (160)21.2.2Options (160)21.3ECAT Import (161)21.3.1ECATfiles (161)21.3.2Options (161)21.4PAR/REC Import (161)21.4.1PARfiles (161)21.4.2Options (161)22De-face Images16322.1Images to de-face (163)23Deformations16523.1Composition (165)23.1.1Dartelflow (165)23.1.2Deformation Field (166)23.1.3Identity(Reference Image) (166)23.1.4Identity(Bounding Box and Voxel Size) (166)23.1.5Imported_sn.mat (167)23.1.6Inverse (167)23.2.1Save Deformation (167)23.2.2Pullback (168)23.2.3Pushforward (168)23.2.4Surface (170)23.2.5Save Jacobian Determinants (170)VI Tools17124FieldMap Toolbox17324.1Introduction (173)24.2Presubtracted Phase and Magnitude Data (173)24.2.1Data (173)24.3Real and Imaginary Data (175)24.3.1Data (176)24.4Phase and Magnitude Data (176)24.4.1Data (176)24.5Precalculated FieldMap(in Hz) (176)24.5.1Data (176)24.6Apply VDM (177)24.6.1Data (177)24.6.2Reslice Options (177)24.7Creating Field Maps Using the FieldMap GUI (178)24.7.1Createfield map in Hz (178)24.7.2Create voxel displacement map(VDM)and unwarp EPI (181)24.8Using the FieldMap in Batch scripts (182)24.9Using the VDMfile with Unwarp (183)24.10Appendices (183)24.10.1Processing Hzfield maps (183)24.10.2Converting Hzfield map to VDM (184)24.10.3Matchingfield map data to EPI data (184)25Dartel Tools18525.1Initial Import (186)25.1.1Parameter Files (186)25.1.2Output Directory (187)25.1.3Bounding box (187)25.1.4Voxel size (187)25.1.5Image option (187)25.1.6Grey Matter (187)25.1.7White Matter (187)25.1.8CSF (187)25.2Run Dartel(create Templates) (187)25.2.1Images (187)25.2.2Settings (187)25.3Run Dartel(existing Templates) (189)25.3.1Images (189)25.3.2Settings (189)25.4Normalise to MNI Space (190)25.4.1Dartel Template (190)25.4.2Select according to (190)25.4.3Voxel sizes (191)25.4.4Bounding box (191)25.4.5Preserve (191)25.4.6Gaussian FWHM (191)25.5Create Warped (191)25.5.2Images (192)25.5.3Modulation (192)25.5.4Time Steps (192)25.5.5Interpolation (192)25.6Jacobian determinants (192)25.6.1Flowfields (192)25.6.2Time Steps (192)25.7Create Inverse Warped (193)25.7.1Flowfields (193)25.7.2Images (193)25.7.3Time Steps (193)25.7.4Interpolation (193)25.8Population to ICBM Registration (193)25.8.1Dartel Template (193)25.9Kernel Utilities (193)25.9.1Kernel from Images (194)25.9.2Kernel from Flows (194)26Shoot Tools19526.1Run Shooting(create Templates) (195)26.1.1Images (196)26.2Run Shoot(existing Templates) (196)26.2.1Images (196)26.2.2Templates (196)26.3Kernel Utilities (196)26.3.1Kernel from velocities (196)26.3.2Generate Scalar Momenta (197)26.3.3Kernel from Images (197)27Longitudinal Registration19927.1Pairwise Longitudinal Registration (199)27.1.1Time1Volumes (200)27.1.2Time2Volumes (200)27.1.3Time Difference (200)27.1.4Noise Estimate (200)27.1.5Warping Regularisation (200)27.1.6Bias Regularisation (200)27.1.7Save Mid-point average (201)27.1.8Save Jacobian Rate (201)27.1.9Save Divergence Rate (201)27.1.10Deformation Fields (201)27.2Serial Longitudinal Registration (201)27.2.1Volumes (201)27.2.2Times (202)27.2.3Noise Estimate (202)27.2.4Warping Regularisation (202)27.2.5Bias Regularisation (202)27.2.6Save Mid-point average (203)27.2.7Save Jacobians (203)27.2.8Save Divergence (203)28Old Normalise20528.1Old Normalise:Estimate (206)28.1.1Data (206)28.1.2Estimation Options (206)28.2Old Normalise:Write (207)28.2.1Data (207)28.2.2Writing Options (208)28.3Old Normalise:Estimate&Write (208)28.3.1Data (208)28.3.2Estimation Options (209)28.3.3Writing Options (210)29Old Segment21129.1Data (212)29.2Output Files (212)29.2.1Grey Matter (215)29.2.2White Matter (215)29.2.3Cerebro-Spinal Fluid (215)29.2.4Bias Corrected (215)29.2.5Clean up any partitions (215)29.3Custom (215)29.3.1Tissue probability maps (215)29.3.2Gaussians per class (216)29.3.3Affine Regularisation (216)29.3.4Warping Regularisation (216)29.3.5Warp Frequency Cutoff (217)29.3.6Bias regularisation (217)29.3.7Bias FWHM (217)29.3.8Sampling distance (217)29.3.9Masking image (217)VII Data sets and examples21930Auditory fMRI data22130.1Preamble(dummy scans) (221)30.2Spatial pre-processing (223)30.2.1Realignment (223)30.2.2Coregistration (223)30.2.3Segmentation (223)30.2.4Normalise (227)30.2.5Smoothing (227)30.3Model specification,review and estimation (231)30.3.1Estimate (231)30.4Inference (231)30.4.1Contrast manager (235)30.4.2Masking (235)30.4.3Thresholds (235)30.4.4Files (236)30.4.5Maximum Intensity Projections (236)30.4.6Design matrix (238)30.4.7Statistical tables (238)30.4.8Plotting responses at a voxel (239)31Face fMRI data24331.1Spatial pre-processing (243)31.1.1Display (243)31.1.2Realignment (243)31.1.3Slice timing correction (248)31.1.4Coregistration (248)31.1.5Segmentation (248)31.1.6Normalise (251)31.1.7Smoothing (251)31.2Modelling categorical responses (253)31.2.1Estimate (255)31.2.2Inference for categorical design (255)31.2.3Statistical tables (255)31.2.4F-contrasts (257)31.2.5F-contrasts for testing effects of movement (261)31.3Modelling parametric responses (261)31.3.1Estimate (263)31.3.2Plotting parametric responses (263)31.4Bayesian analysis (266)31.4.1Specification (266)31.4.2Estimation (267)31.4.3Inference (268)32Face group fMRI data27132.1Introduction (271)32.2Data (271)32.3Canonical HRF (272)32.4Informed basis set (274)32.4.1Nonsphericity (276)32.4.2Informed Results (276)32.4.3T-and F-contrasts (280)32.5FIR basis set (282)32.5.1Nonsphericity again (283)32.5.2FIR Results (286)33Mixed Effects Analysis29133.1Introduction (291)34Verbal Fluency PET data29334.1Introduction (293)34.2Single subject (293)34.3Multiple subjects (294)34.3.1Subject and Condition design (296)34.3.2Subject and Time design (296)34.3.3Subject by Condition design (298)34.3.4Contrast manager (300)34.3.5Masking and thresholds (302)34.3.6MIPs and results tables (303)34.3.7Small volume correction (305)34.3.8Extracting data from regions (305)34.3.9Inclusive Masking (307)35Dynamic Causal Modeling for fMRI31135.1Theoretical background (311)35.2Bayesian model selection (314)35.3Practical example (315)35.3.1Defining the GLM (316)35.3.2Extracting time series (317)35.3.3Specifying and estimating the DCM (318)35.3.4Comparing models (321)36Psychophysiological Interactions(PPI)32536.1Theoretical background (325)36.2Psycho-Physiologic Interaction Analysis:Summary of Steps (327)36.3Practical example (327)36.3.1GLM analysis-Design setup and estimation (328)36.3.2GLM analysis-Results (332)36.4GLM analysis-Extracting VOIs (334)36.5PPI analysis-Create PPI variable (334)36.5.1PPI GLM analysis-Design setup and estimation (336)36.5.2PPI analysis-Results (337)36.5.3PPI analysis-Plotting (338)37Bayesian Model Inference34137.1Background (341)37.2Data (341)37.3Analysis (342)37.3.1Single Family (342)37.3.2Bayesian Model Averaging (345)37.3.3Family level inference (345)37.3.4Summary Statistics and Group Analyses (345)37.4BMS.matfile (345)37.4.1Model level results (349)37.4.2Family level results (349)37.4.3Bayesian model averaging(BMA) (349)37.5model_space.matfile (350)38Dynamic Causal Modelling for resting state fMRI35138.1Theoretical background (351)38.2Practical example (353)38.2.1Defining the GLM (353)38.2.2Extracting time series (355)38.2.3Specifying and estimating the DCM (355)39MEG source localisation36139.1Overview (361)39.2Simulation (361)39.3Imaging solutions for evoked or induced responses (363)39.3.1IID(minimum norm) (363)39.3.2Smooth priors(COH) (364)39.3.3The Multiple sparse priors algorithm (364)39.3.4Making summary images (366)39.3.5Other MSP options (366)39.4Dipolefitting to the average (367)39.4.1Load/preview the data (367)40EEG Mismatch negativity data37140.1Preprocessing (371)40.1.1Simple conversion and reviewing (371)40.1.2Preparing batch inputs (372)40.1.3Preprocessing step by step (373)40.1.4Automatisation of preprocessing (376)40.2Sensor space analysis (378)40.2.1Batching statistics (379)40.3Source reconstruction (379)40.3.1Mesh (379)40.3.2Coregister (381)40.3.3Forward model (381)40.3.4Invert (381)40.3.5Batching source reconstruction (382)40.4Dynamic Causal Modeling (386)41Advanced topics in M/EEG artefact removal39141.1Artefact marking (391)41.2Reviewing marked artefacts (392)41.3Trial rejection based on marked artefacts (392)41.4Explicit artefact exclusion in robust averaging (393)41.5Topography-based artefact correction (393)41.6Fieldtrip visual artefact rejection (398)42Multimodal,Multisubject data fusion40142.1Overview (401)42.2Getting Started (402)42.3Preprocessing M/EEG data (402)42.3.1Convert(and epoch) (402)42.3.2Prepare (404)42.3.3Downsample (404)42.3.4Baseline Correction (405)42.3.5Deleting intermediate steps(optional) (405)42.3.6Merging(concatenating runs) (406)42.3.7Prepare(a montage for re-referencing the EEG) (406)42.3.8Montage (407)42.4Evoked analysis (407)42.4.1Crop (407)42.4.2Artefact detection (408)42.4.3Combine Planar Gradiometers (408)42.4.4Trial averaging (408)42.4.5Contrasting conditions (408)42.4.6Time-Sensor images (410)42.5Scalp-Time Statistics across trials within one subject (413)42.5.1Model Specification (413)42.5.2Model Estimation (413)42.5.3Setting up contrasts (413)42.6Time-Frequency Analysis(Evoked and Induced power) (414)42.6.1Wavelet estimation (416)42.6.2Crop (416)42.6.3Average (417)42.6.4Baseline rescaling (417)42.6.5Contrasting conditions (417)42.6.6Creating2D time-frequency images (417)42.6.7Model Specification,Estimation and Contrasts (420)42.7fMRI Preprocessing and Statistics (422)42.7.2Normalisation/Segmentation of T1images (422)42.7.3Coregistration of mean EPI(fMRI)to T1(sMRI) (422)42.7.4Application of Normalisation parameters to EPI data (422)42.7.5Smoothing (423)42.7.6Creating a1st-level(fMRI)GLM (423)42.7.7Model Estimation (423)42.7.8Setting up contrasts (423)42.7.9Group Statistics on fMRI data (425)42.8Source Reconstruction (425)42.8.1Create Head Model (427)42.8.2Model Inversion (428)42.8.3Time-frequency contrasts (428)42.8.4Group Statistics on Source Reconstructions (431)42.9Group Source Reconstruction (433)42.9.1Group Statistics on Source Reconstructions (433)42.10Group MEEG Source Reconstruction with fMRI priors (433)42.10.1Group Statistics on Source Reconstructions (435)42.11References (435)42.12Acknowledgements (437)43DCM for Induced Responses43943.1Data (439)43.2Getting Started (439)43.3Setting up DCM (439)43.3.1load,save,select model type (440)43.3.2Data and design (440)43.3.3Electromagnetic model (442)43.4Neuronal model (444)43.5Estimation (445)43.6Results (446)43.6.1Frequency modes (446)43.6.2Time modes (446)43.6.3Time-Frequency (446)43.6.4Coupling(A-Hz) (446)43.6.5Coupling(B-Hz) (446)43.6.6Coupling(A-modes) (446)43.6.7Coupling(B-Hz) (446)43.6.8Input(C-Hz) (447)43.6.9Input(u-ms) (447)43.6.10Dipoles (447)43.6.11Save as img (447)43.7Model comparison (447)44DCM for Phase Coupling44944.1Data (449)44.2Getting Started (449)44.3Data and design (449)44.4Electromagnetic model (450)44.5Neuronal model (450)44.6Results (451)45DCM for Cross Spectral Densities:Anaesthesia Depth in Rodent Data45545.1Overview (455)45.2Main Results (456)45.3Using the Graphical User Interface to Obtain those Results (456)45.3.1The data (456)45.3.2Dynamic Causal Modelling of Cross Spectral Densities (456)45.3.3Comparing models using Bayesian Model Selection (460)46DCM for fNIRS46346.1Example:Motor Execution and Imagery Data (464)46.2SPM Analysis (464)46.3Specifying and Estimating the DCM (464)47Using Dartel47147.1Using Dartel for VBM (471)47.1.1Using Spatial→Segment (471)47.1.2Using Dartel Tools→Run Dartel(create Template) (472)47.1.3Using Dartel Tools→Normalise to MNI Space (472)47.2Spatially normalising functional data to MNI space (474)47.2.1An alternative approach for using Dartel to spatially normalise to MNI Space47747.3Warping Images to Existing Templates (479)47.4Warping one individual to match another (479)VIII Batch Interface48348Batch tutorial48548.1Single subject (485)48.1.1Study specific input data (486)48.1.2Necessary processing steps (486)48.1.3Add modules to the batch (486)48.1.4Configure subject-independent data (486)48.1.5Dataflow (488)48.1.6Entering subject-specific data (491)48.2Advanced features (491)48.2.1Multiple sessions (491)48.2.2Processing multiple subjects in GUI (493)48.2.3Command line interface (493)48.2.4Modifying a saved job (495)49Developer’s guide49749.1SPM and Matlabbatch code organisation (497)49.1.1Code organisation (497)49.1.2Interfaces between SPM and Matlabbatch (497)49.2Configuration Code Details (497)49.2.1Virtual Outputs (498)49.2.2SPM Startup (498)49.2.3Defaults Settings (498)49.3Utilities (499)49.3.1Batch Utilities (499)49.3.2MATLAB Code Generation (499)49.3.3Configuration Management (499)IX Bibliography501。
ABAQUS/CAE 常问界面操作(转自SimWe仿真论坛) 2009-08-01 21:40 | (分类: 默认分类)前处理：1 如何显示梁截面（如何显示三维梁模型）a）无论是运算还是默认显示，ABA中的梁都是一条线，很多人想看梁截面（一般一个星期有人问一次）。
显示梁截面：view->assembly display option->render beam profiles，自己调节系数/viewthread.php?tid=835478&page=1#pid1531086b）后处理到底能不能显示梁截面？在deformed shape和undeformed shape都能用上面的方法显示梁截面，在应力云图（contour）不能显示。
c）也经常有人问起如何显示壳单元的厚度/thread-865887-1-1.html2 怎么在局部坐标系下建立参考点在前处理中，已经建立了局部坐标系，如何在局部坐标系中建立参考点？这个有点麻烦，看看konadoul图文并茂的示例吧。
/viewthread.php?tid=863389&highlight=%D7%F8%B1%EA%CF%B 53 Documentaion（help文件）不能搜索首先保证你准确的安装了Documentaion（先安装Documentaion再安装程序），其次有问题你可以重新安装一次Documentaion。
如果你和我一样比较懒不想安装，看看下面的方法是否管用吧。
1）控制面板---服务找到texis 双击查看是不是automatic，如果不是就设置为automatic2）你可以用这个：http://name:2080/v6.8/ 注：name是你的计算机名；6.8是版本号，比如你用6.6的就改为6.6.（我的在自从不能搜索之后我就一直这么用的）/viewthread.php?tid=861085&extra=page%3D12%26amp%3Bfilter %3Dtype%26amp%3Btypeid%3D68/viewthread.php?tid=6988794 建立几何模型草绘sketch的时候，发现画布尺寸太小了1）这个在create part的时候就有approximate size，你可以定义合适的（比你的定性尺寸大一倍）；如果你已经在sketch了，可以在edit菜单－－sketch option －－grid更改2）这里如果你选择constriant标签，还能更改尺寸精度5 想输出几何模型part步，file，outport－－part想导入几何模型？part步，file，import－－part6 如何定义局部坐标系Tool－Create Datum－CSYS－－建立坐标系方式－－选择直角坐标系or柱坐标系or 球坐标7 如何在局部坐标系定义载荷laod－－Edit load－－CSYS－Edit（在BC中同理）选用你定义的局部坐标系8 如何定义随变载荷amplitude 这个不多说了，强烈建议看《常见问题2.0》小康大侠图示空间变载：/thread-867236-1-5.html （强烈推荐照此演示操作一回）/viewthread.php?tid=861727&highlight=%CB%E6%CA%B1%BC %E4%B1%E4%BB%AF%B5%C4%D4%D8%BA%C9設定於空間中變化的負載：A; v% |!/viewthrea ... p;page=1#pid1556636加载梯形载荷：/viewthread.php?tid=870350&extra=page%3D1%26amp%3Bfilter%3Dty pe%26amp%3Btypeid%3D689 怎么知道模型单元数目（一共有多少个单元）在mesh步，mesh verify 可以查到单元类型，数目以及单元质量一目了然Query---element 也可以查询的。
人工智能是一门新兴的具有挑战力的学科。
自人工智能诞生以来,发展迅速,产生了许多分支。
诸如强化学习、模拟环境、智能硬件、机器学习等。
但是,在当前人工智能技术迅猛发展,为人们的生活带来许多便利。
下面是搜索整理的人工智能英文参考文献的分享，供大家借鉴参考。
人工智能英文参考文献一：[1]Lars Egevad,Peter Str?m,Kimmo Kartasalo,Henrik Olsson,Hemamali Samaratunga,Brett Delahunt,Martin Eklund. The utility of artificial intelligence in the assessment of prostate pathology[J]. Histopathology,2020,76(6).[2]Rudy van Belkom. The Impact of Artificial Intelligence on the Activities ofa Futurist[J]. World Futures Review,2020,12(2).[3]Reza Hafezi. How Artificial Intelligence Can Improve Understanding in Challenging Chaotic Environments[J]. World Futures Review,2020,12(2).[4]Alejandro Díaz-Domínguez. How Futures Studies and Foresight Could Address Ethical Dilemmas of Machine Learning and Artificial Intelligence[J]. World Futures Review,2020,12(2).[5]Russell T. Warne,Jared Z. Burton. Beliefs About Human Intelligence in a Sample of Teachers and Nonteachers[J]. Journal for the Education of the Gifted,2020,43(2).[6]Russell Belk,Mariam Humayun,Ahir Gopaldas. Artificial Life[J]. Journal of Macromarketing,2020,40(2).[7]Walter Kehl,Mike Jackson,Alessandro Fergnani. Natural Language Processing and Futures Studies[J]. World Futures Review,2020,12(2).[8]Anne Boysen. Mine the Gap: Augmenting Foresight Methodologies with Data Analytics[J]. World Futures Review,2020,12(2).[9]Marco Bevolo,Filiberto Amati. The Potential Role of AI in Anticipating Futures from a Design Process Perspective: From the Reflexive Description of “Design” to a Discussion of Influences by the Inclusion of AI in the Futures Research Process[J]. World Futures Review,2020,12(2).[10]Lan Xu,Paul Tu,Qian Tang,Dan Seli?teanu. Contract Design for Cloud Logistics (CL) Based on Blockchain Technology (BT)[J]. Complexity,2020,2020.[11]L. Grant,X. Xue,Z. Vajihi,A. Azuelos,S. Rosenthal,D. Hopkins,R. Aroutiunian,B. Unger,A. Guttman,M. Afilalo. LO32: Artificial intelligence to predict disposition to improve flow in the emergency department[J]. CJEM,2020,22(S1).[12]A. Kirubarajan,A. Taher,S. Khan,S. Masood. P071: Artificial intelligence in emergency medicine: A scoping review[J]. CJEM,2020,22(S1).[13]L. Grant,P. Joo,B. Eng,A. Carrington,M. Nemnom,V. Thiruganasambandamoorthy. LO22: Risk-stratification of emergency department syncope by artificial intelligence using machine learning: human, statistics or machine[J]. CJEM,2020,22(S1).[14]Riva Giuseppe,Riva Eleonora. OS for Ind Robots: Manufacturing Robots Get Smarter Thanks to Artificial Intelligence.[J]. Cyberpsychology, behavior and social networking,2020,23(5).[15]Markus M. Obmann,Aurelio Cosentino,Joshy Cyriac,Verena Hofmann,Bram Stieltjes,Daniel T. Boll,Benjamin M. Yeh,Matthias R. Benz. Quantitative enhancement thresholds and machine learning algorithms for the evaluation of renal lesions using single-phase split-filter dual-energy CT[J]. Abdominal Radiology,2020,45(1).[16]Haytham H. Elmousalami,Mahmoud Elaskary. Drilling stuck pipe classification and mitigation in the Gulf of Suez oil fields using artificial intelligence[J]. Journal of Petroleum Exploration and Production Technology,2020,10(10).[17]Rüdiger Schulz-Wendtland,Karin Bock. Bildgebung in der Mammadiagnostik –Ein Ausblick <trans-title xml:lang="en">Imaging in breast diagnostics—an outlook [J]. Der Gyn?kologe,2020,53(6).</trans-title>[18]Nowakowski Piotr,Szwarc Krzysztof,Boryczka Urszula. Combining an artificial intelligence algorithm and a novel vehicle for sustainable e-waste collection[J]. Science of the Total Environment,2020,730.[19]Wang Huaizhi,Liu Yangyang,Zhou Bin,Li Canbing,Cao Guangzhong,Voropai Nikolai,Barakhtenko Evgeny. Taxonomy research of artificial intelligence for deterministic solar power forecasting[J]. Energy Conversion and Management,2020,214.[20]Kagemoto Hiroshi. Forecasting a water-surface wave train with artificial intelligence- A case study[J]. Ocean Engineering,2020,207.[21]Tomonori Aoki,Atsuo Yamada,Kazuharu Aoyama,Hiroaki Saito,Gota Fujisawa,Nariaki Odawara,Ryo Kondo,Akiyoshi Tsuboi,Rei Ishibashi,Ayako Nakada,Ryota Niikura,Mitsuhiro Fujishiro,Shiro Oka,Soichiro Ishihara,Tomoki Matsuda,Masato Nakahori,Shinji Tanaka,Kazuhiko Koike,Tomohiro Tada. Clinical usefulness of a deep learning‐based system as the first screening on small‐bowel capsule endoscopy reading[J]. Digestive Endoscopy,2020,32(4).[22]Masashi Fujii,Hajime Isomoto. Next generation of endoscopy: Harmony with artificial intelligence and robotic‐assisted devices[J]. Digestive Endoscopy,2020,32(4).[23]Roberto Verganti,Luca Vendraminelli,Marco Iansiti. Innovation and Design in the Age of Artificial Intelligence[J]. Journal of Product Innovation Management,2020,37(3).[24]Yuval Elbaz,David Furman,Maytal Caspary Toroker. Modeling Diffusion in Functional Materials: From Density Functional Theory to Artificial Intelligence[J]. Advanced Functional Materials,2020,30(18).[25]Dinesh Visva Gunasekeran,Tien Yin Wong. Artificial Intelligence in Ophthalmology in 2020: A Technology on the Cusp for Translation and Implementation[J]. Asia-Pacific Journal of Ophthalmology,2020,9(2).[26]Fu-Neng Jiang,Li-Jun Dai,Yong-Ding Wu,Sheng-Bang Yang,Yu-Xiang Liang,Xin Zhang,Cui-Yun Zou,Ren-Qiang He,Xiao-Ming Xu,Wei-De Zhong. The study of multiple diagnosis models of human prostate cancer based on Taylor database by artificial neural networks[J]. Journal of the Chinese Medical Association,2020,83(5).[27]Matheus Calil Faleiros,Marcello Henrique Nogueira-Barbosa,Vitor Faeda Dalto,JoséRaniery Ferreira Júnior,Ariane Priscilla Magalh?es Tenório,Rodrigo Luppino-Assad,Paulo Louzada-Junior,Rangaraj Mandayam Rangayyan,Paulo Mazzoncini de Azevedo-Marques. Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging[J]. Advances in Rheumatology,2020,60(1078).[28]Balamurugan Balakreshnan,Grant Richards,Gaurav Nanda,Huachao Mao,Ragu Athinarayanan,Joseph Zaccaria. PPE Compliance Detection using Artificial Intelligence in Learning Factories[J]. Procedia Manufacturing,2020,45.[29]M. Stévenin,V. Avisse,N. Ducarme,A. de Broca. Qui est responsable si un robot autonome vient à entra?ner un dommage ?[J]. Ethique et Santé,2020.[30]Fatemeh Barzegari Banadkooki,Mohammad Ehteram,Fatemeh Panahi,Saad Sh. Sammen,Faridah Binti Othman,Ahmed EL-Shafie. Estimation of Total Dissolved Solids (TDS) using New Hybrid Machine Learning Models[J]. Journal of Hydrology,2020.[31]Adam J. Schwartz,Henry D. Clarke,Mark J. Spangehl,Joshua S. Bingham,DavidA. Etzioni,Matthew R. Neville. Can a Convolutional Neural Network Classify Knee Osteoarthritis on Plain Radiographs as Accurately as Fellowship-Trained Knee Arthroplasty Surgeons?[J]. The Journal of Arthroplasty,2020.[32]Ivana Nizetic Kosovic,Toni Mastelic,Damir Ivankovic. Using Artificial Intelligence on environmental data from Internet of Things for estimating solar radiation: Comprehensive analysis[J]. Journal of Cleaner Production,2020.[33]Lauren Fried,Andrea Tan,Shirin Bajaj,Tracey N. Liebman,David Polsky,Jennifer A. Stein. Technological advances for the detection of melanoma: Part I. Advances in diagnostic techniques[J]. Journal of the American Academy of Dermatology,2020.[34]Mohammed Amoon,Torki Altameem,Ayman Altameem. Internet of things Sensor Assisted Security and Quality Analysis for Health Care Data Sets Using Artificial Intelligent Based Heuristic Health Management System[J]. Measurement,2020.[35]E. Lotan,C. Tschider,D.K. Sodickson,A. Caplan,M. Bruno,B. Zhang,Yvonne W. Lui. Medical Imaging and Privacy in the Era of Artificial Intelligence: Myth, Fallacy, and the Future[J]. Journal of the American College of Radiology,2020.[36]Fabien Lareyre,Cédric Adam,Marion Carrier,Juliette Raffort. Artificial Intelligence in Vascular Surgery: moving from Big Data to Smart Data[J]. Annals of Vascular Surgery,2020.[37]Ilesanmi Daniyan,Khumbulani Mpofu,Moses Oyesola,Boitumelo Ramatsetse,Adefemi Adeodu. Artificial intelligence for predictive maintenance in the railcar learning factories[J]. Procedia Manufacturing,2020,45.[38]Janet L. McCauley,Anthony E. Swartz. Reframing Telehealth[J]. Obstetrics and Gynecology Clinics of North America,2020.[39]Jean-Emmanuel Bibault,Lei Xing. Screening for chronic obstructive pulmonary disease with artificial intelligence[J]. The Lancet Digital Health,2020,2(5).[40]Andrea Laghi. Cautions about radiologic diagnosis of COVID-19 infection driven by artificial intelligence[J]. The Lancet Digital Health,2020,2(5).人工智能英文参考文献二：[41]K. Orhan,I. S. Bayrakdar,M. Ezhov,A. Kravtsov,T. ?zyürek. Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans[J]. International Endodontic Journal,2020,53(5).[42]Avila A M,Mezi? I. Data-driven analysis and forecasting of highway traffic dynamics.[J]. Nature communications,2020,11(1).[43]Neri Emanuele,Miele Vittorio,Coppola Francesca,Grassi Roberto. Use of CT andartificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology.[J]. La Radiologia medica,2020.[44]Tau Noam,Stundzia Audrius,Yasufuku Kazuhiro,Hussey Douglas,Metser Ur. Convolutional Neural Networks in Predicting Nodal and Distant Metastatic Potential of Newly Diagnosed Non-Small Cell Lung Cancer on FDG PET Images.[J]. AJR. American journal of roentgenology,2020.[45]Coppola Francesca,Faggioni Lorenzo,Regge Daniele,Giovagnoni Andrea,Golfieri Rita,Bibbolino Corrado,Miele Vittorio,Neri Emanuele,Grassi Roberto. Artificial intelligence: radiologists' expectations and opinions gleaned from a nationwide online survey.[J]. La Radiologia medica,2020.[46]?. ? ? ? ? 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Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges.[J]. Korean journal of radiology,2020,21(5).[55]Mateen Bilal A,David Anna L,Denaxas Spiros. Electronic Health Records to Predict Gestational Diabetes Risk.[J]. Trends in pharmacological sciences,2020,41(5).[56]Yao Xiang,Mao Ling,Lv Shunli,Ren Zhenghong,Li Wentao,Ren Ke. CT radiomics features as a diagnostic tool for classifying basal ganglia infarction onset time.[J]. Journal of the neurological sciences,2020,412.[57]van Assen Marly,Banerjee Imon,De Cecco Carlo N. Beyond the Artificial Intelligence Hype: What Lies Behind the Algorithms and What We Can Achieve.[J]. Journal of thoracic imaging,2020,35 Suppl 1.[58]Guzik Tomasz J,Fuster Valentin. Leaders in Cardiovascular Research: Valentin Fuster.[J]. 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Journal of Cleaner Production,2020,258.[66]Siva Teja Kakileti,Himanshu J. Madhu,Geetha Manjunath,Leonard Wee,Andre Dekker,Sudhakar Sampangi. Personalized risk prediction for breast cancer pre-screening using artificial intelligence and thermal radiomics[J]. Artificial Intelligence In Medicine,2020,105.[67]. Evaluation of Payer Budget Impact Associated with the Use of Artificial Intelligence in Vitro Diagnostic, Kidneyintelx, to Modify DKD Progression:[J]. American Journal of Kidney Diseases,2020,75(5).[68]Rohit Nishant,Mike Kennedy,Jacqueline Corbett. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda[J]. International Journal of Information Management,2020,53.[69]Hoang Nguyen,Xuan-Nam Bui. Soft computing models for predicting blast-induced air over-pressure: A novel artificial intelligence approach[J]. Applied Soft Computing Journal,2020,92.[70]Benjamin S. Hopkins,Aditya Mazmudar,Conor Driscoll,Mark Svet,Jack Goergen,Max Kelsten,Nathan A. 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Petroleum Exploration and Development Online,2020,47(2).[86]Alberto Arenal,Cristina Armu?a,Claudio Feijoo,Sergio Ramos,Zimu Xu,Ana Moreno. Innovation ecosystems theory revisited: The case of artificial intelligence in China[J]. Telecommunications Policy,2020.[87]T. Som,M. Dwivedi,C. Dubey,A. Sharma. Parametric Studies on Artificial Intelligence Techniques for Battery SOC Management and Optimization of Renewable Power[J]. Procedia Computer Science,2020,167.[88]Bushra Kidwai,Nadesh RK. Design and Development of Diagnostic Chabot for supporting Primary Health Care Systems[J]. Procedia Computer Science,2020,167.[89]Asl? Bozda?,Ye?im Dokuz,?znur Begüm G?k?ek. Spatial prediction of PM 10 concentration using machine learning algorithms in Ankara, Turkey[J]. Environmental Pollution,2020.[90]K.P. Smith,J.E. Kirby. Image analysis and artificial intelligence in infectious disease diagnostics[J]. 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职称英语理工类A级-22(总分：100.00，做题时间：90分钟)一、{{B}}第1部分：词汇选项{{/B}}(总题数：15，分数：15.00)1.The city has decided to do away with all the old buildings in its center.（分数：1.00）A.get rid of √B.set upC.repairD.paint解析：[解析] 本题考查的是对短语的认知。
这句话的意思是：这个城市决定拆除市中心的所有旧建筑。
本题考察对词组do away with的掌握，意为废除、去掉。
如：I have to do away with my bad habit．我得改掉我的坏习惯。
选项中A．get rid of除去；B．set up设立；C．repair修理；D．paint油漆。
因此选A．get rid of，例句：How can you get rid of this oxide coating?你们该怎样除去这些氧化性涂料?2.The soldier displayed remarkable courage in the battle.（分数：1.00）A.placedB.showed √C.pointedD.decided解析：[解析] 本题考查的是对动词的认知。
这句话的意思：是战士们在战争中表现出非凡的勇气。
本题考察对动词词汇的了解，A．place放置；B．show显示；C．point指引；D．decide决定。
句中语态是一般过去时，谓语应用动词的过去式。
displayed意思为表现，故选B。
3.He is certain that the dictionary is just what I want.（分数：1.00）A.sure √B.angryC.doubtfulD.worried解析：[解析] 本题考查的是对形容词的认知。
这句话的意思是：他很确定这个字典就是我想要的。
安装指南SOLIDWORKS PDM 2020/SOLIDWORKS Manage 2020 /Visualize内容法律声明 (8)1SOLIDWORKS PDM和SOLIDWORKS Manage安装指南 (11)2安装概述 (13)必要的安装组件 (14)可选安装组件（仅对于SOLIDWORKS PDM Professional） (16)SOLIDWORKS PDM调用情形 (16)系统要求 (19)安装摘要 (19)安装帮助 (20)3安装和配置SQL Server (21)支持SQL Server2016、2017和2019 (21)安装SQL Server2016、2017或2019 (22)安装SQL Server2016、2017或2019之前 (22)执行SQL Server2016、2017或2019安装 (22)安装SQL Server2016、2017或2019之后 (25)验证SQL Server2016、2017或2019安装 (25)升级到SQL Server2016、2017或2019 (25)执行SQL Server2016、2017或2019的升级 (26)升级到SQL Server2016、2017或2019之后 (28)SQL Server2014支持 (28)安装SQL Server2014 (28)安装SQL Server2014之前 (28)执行SQL Server2014安装 (29)安装SQL Server2014之后 (33)验证SQL Server2014安装 (33)升级到SQL Server2014 (33)向SQL Server2014升级 (33)升级到SQL Server2014之后 (35)SQL Server疑难解答 (36)客户端不能处理文件库 (36)SOLIDWORKS PDM管理功能失败 (36)SOLIDWORKS PDM不能连接到服务器 (36)更改SQL Server登录帐户 (37)创建新的SQL登录帐户 (37)对SOLIDWORKS PDM存档使用新的SQL登录名 (37)向SQL用户授予访问现有SOLIDWORKS PDM文件库数据库的db_owner权限 (38)SQL权限不足 (39)4安装和配置SQL Server Express (40)安装和管理SQL Server2014Express (40)安装SQL Server2014Express之前 (40)安装SQL Server Express数据库引擎 (40)安装SQL Server2014Express之后 (44)验证SQL Server2014Express安装 (45)将SQL Server Express2014升级到SQL Server2014 (45)5安装SOLIDWORKS PDM (46)下载安装介质 (47)通过SOLIDWORKS安装管理程序启动安装 (47)通过InstallShield向导启动安装 (47)安装SOLIDWORKS PDM数据库服务器 (48)安装数据库服务器之前 (49)执行数据库服务器安装 (49)安装SOLIDWORKS PDM存档服务器 (50)安装存档服务器之前 (51)执行存档服务器安装 (51)为客户端/服务器通信打开端口 (55)在WAN环境中添加存档服务器 (56)安装和配置SolidNetWork许可 (57)安装SolidNetWork License Manager (57)激活SolidNetWork许可 (58)在防火墙环境中使用SolidNetWork许可服务器 (59)SolidNetWork许可管理 (59)许可 (62)安装SOLIDWORKS PDM Web2（仅限SOLIDWORKS PDM Professional） (66)安装Web2之前 (66)执行Web2Server安装 (70)在运行Web2的IIS服务器上创建库视图 (70)配置SOLIDWORKS PDM Web2 (71)安装SOLIDWORKS PDM Web API服务器（仅限SOLIDWORKS PDM Professional） (79)执行Web API服务器安装 (80)安装SOLIDWORKS PDM客户端 (80)安装客户端之前 (81)使用安装向导安装客户端 (82)安装eDrawings (84)将SOLIDWORKS PDM客户端/服务器从Standard升级到Professional (84)启用日志记录以疑难解答安装问题 (84)创建SOLIDWORKS PDM客户端管理映像 (85)使用Active Directory调用客户端 (85)调用SOLIDWORKS PDM时激活日志记录 (87)执行脚本化SOLIDWORKS PDM无声安装 (87)6使用SOLIDWORKS安装管理程序 (91)SOLIDWORKS PDM (91)PDM服务器组件列表 (92)了解PDM客户端之间的区别 (93)安装PDM服务器之前 (94)使用SLDIM安装PDM服务器 (94)使用SLDIM安装PDM客户端 (96)7创建和分发文件库视图 (97)库的生成 (97)先决条件 (97)添加存档服务器 (98)登录存档服务器 (98)生成库 (99)为文件库配置SolidNetWork许可服务器 (104)使用视图设置向导创建文件库视图 (104)启用带Windows防火墙广播 (107)多个用户配置文件使用共享文件库视图 (107)在终端服务器上使用SOLIDWORKS PDM (108)创建文件库视图设置文件 (109)脚本化文件库视图设置 (109)使用Microsoft Windows Active Directory分发文件库视图 (110)查找SOLIDWORKS PDM库ID (111)接收分发的文件库视图 (112)在WAN环境中分发文件库视图 (112)手动配置向SOLIDWORKS PDM客户端公布的存档服务器 (112)手动指定SOLIDWORKS PDM设置组策略 (113)SOLIDWORKS PDM设置策略选项 (113)8将库从Standard升级到Professional (117)将SolidNetWork许可从Standard升级到Professional (117)激活Professional许可 (118)升级Standard库 (119)将SOLIDWORKS PDM客户端/服务器从Standard升级到Professional (119)升级文件库之后 (119)9配置内容搜索（仅对于SOLIDWORKS PDM Professional） (120)内容搜索概述 (120)建议的计算机配置 (121)将Windows搜索用于内容搜索 (121)安装Windows搜索服务 (122)在Windows7上启用Windows搜索服务 (122)在Windows Server2016和更高版本上启用Windows搜索服务 (122)设置Windows搜索 (122)对Windows搜索的存档进行索引 (123)核实Microsoft Indexing Service安装 (123)监控和调整Microsoft Indexing Service (123)索引SOLIDWORKS PDM Professional文件库存档 (124)在非SQL Server系统上配置索引服务 (125)使用索引服务器名更新文件库数据库 (125)更改数据库服务器登录帐户 (125)更改SQL Server登录帐户 (126)添加索引服务器过滤器 (126)压缩的存档(gzip)过滤器 (127)管理文件库索引目录 (127)删除文件库索引 (127)10备份和还原文件库 (129)备份文件库数据库 (129)备份SOLIDWORKS PDM主数据库 (130)备份存档服务器设置 (130)备份存档文件 (131)安排数据库备份时间 (131)启动SQL Server代理 (131)设置数据库备份的维护计划（仅限SOLIDWORKS PDM Professional） (132)还原文件库 (134)还原SQL Server文件库数据库 (134)核实ConisioMasterDb还原 (135)还原存档服务器和文件库存档 (135)11升级SOLIDWORKS PDM (136)关于Enterprise PDM升级 (136)升级之前 (136)确定当前版本 (137)确定已经应用了哪些更新 (138)升级存档服务器 (138)升级数据库服务器 (138)安装或升级SolidNetWork License Manager (139)升级文件库 (140)升级文件库数据库 (140)升级文件库档案 (141)在SOLIDWORKS PDM中升级Toolbox (147)12升级SOLIDWORKS文件 (149)升级SOLIDWORKS文件 (149)所需升级实用程序软件 (150)系统要求 (150)安装文件版本升级实用程序 (151)准备升级 (151)选取版本设定 (152)生成文件的新版本 (153)覆盖文件的现有版本 (156)进行尝试性文件升级 (164)运行升级实用程序 (164)生成和使用工作指南文件 (165)完成被中断的升级 (166)升级之后 (166)升级日志的文件名格式 (167)管理备份文件 (167)备份文件生成 (167)从备份恢复未正确升级的版本 (168)13其它配置 (169)管理SQL事务日志大小 (169)更改到简单恢复模式 (169)减少事务日志的大小 (169)将SOLIDWORKS PDM配置为仅使用IP地址进行通信 (170)更新存档服务器以使用IP地址进行通信 (170)更新SQL服务器以使用IP地址进行通信 (170)更新SOLIDWORKS PDM客户端以使用IP地址进行通信 (170)核实IP地址通信 (171)将服务器组件移到另一个系统 (171)将文件复制到新服务器 (171)配置已经移动的SQL文件库数据库 (172)移动SolidNetWork License Manager (173)移动SOLIDWORKS PDM数据库服务器 (173)配置已移动的存档服务器 (173)更新客户端注册表项 (174)更新复制设定（仅对于SOLIDWORKS PDM Professional） (175)更新管理设置（仅限SOLIDWORKS PDM Professional） (175)核实服务器的移动 (176)14安装SOLIDWORKS Manage Professional (177)SOLIDWORKS Manage服务器组件列表 (177)SOLIDWORKS Manage客户端类型和许可证 (178)SOLIDWORKS Manage的先决条件和系统要求 (179)使用SOLIDWORKS安装管理程序来安装SOLIDWORKS Manage (180)安装SOLIDWORKS Manage服务器 (180)安装SOLIDWORKS Manage客户端 (180)编辑SOLIDWORKS Manage配置文件 (181)15配置SOLIDWORKS Manage Professional和Microsoft IIS (182)首次登录到SOLIDWORKS Manage (182)使用SOLIDWORKS PDM的SOLIDWORKS Manage插件 (185)使用SOLIDWORKS的SOLIDWORKS Manage插件 (185)配置IIS (185)验证IIS文件服务器 (186)IIS故障排除 (187)法律声明©1995-2019,Dassault Systemes SolidWorks Corporation属于Dassault Systèmes SE公司，该公司位于175Wyman Street,Waltham,Mass.02451USA。
大规模科研项目越来越无法由一个机构独立完成而必须由来自不同机构的人员和资源协作完成他们共同形成所谓的虚拟组织这些组织是结构化的不同成员应该具有不同的权限网格是一种重要的支持虚拟组织的系统但目前网格系统只包含基础的安全服务可扩展性较差对虚拟组织的支持在规模和复杂度上资源管理者只能根据用户在虚拟组织中的身份管理用户权限Shi bbol et h提供跨域的单点登录并采用基于属性的授权框架为网格中碰到的问题提供了很好的解决方案虚拟组织试图用这种方法来取代用户身份使资源管理者不必关心虚拟组织中的所有用户而只需知道他们的属性即可如数据分析者软件开发者等G r i dShi b项目的目标就是整合网格与高校校园网络基础设施使之更具拓展性和灵活性G r i dShi b项目是由N SF中间件项目NM I NS F Mi d d l e wa r e I ni t i at i ve提供100万美元资助由美国国家超级计算应用中心N CSA N at i onal Cent er of Supercom put i ng牵头还包括来自芝加哥大学美国阿尔贡国家实验室Shi bbol et h开发组的成员目的是整合N S F资助的两大重要软件Int er net2的Shi bbol et h和网格软件G l obus 工具包最著名的网格开源软件由芝加哥大学和美国阿尔贡国家实验室共同开发简称G T实现利用这两种软件构建的系统间的互通该项目于2004年12月1日启动为期两年项目成立一年多以来已经实现了基本的系统间互通使基于GT的网格系统可以从Shi bbol et h服务获取网格用户的属性信息并基于这些信息进行访问控制项目实施的第二年将侧重于提高现有服务的性能关注于解决高层次管理问题如名字空间联盟的管理信任配置管理及网格与Shi bbol et h组件元数据管理等G r i dShi b使网格虚拟组织和高等教育组织间可以进行安全的属性信息交换它为GT提供了基于属性的授权完整的G r i dShi b工具包由G T和Shi bbol et h下的两个插件组成该软件支持推和拉两种操作模式在推模式下用户预先提供其属性在初始阶段获取属性信息并报告给网格服务提供者在拉模式下当用户得到验证后网格服务提供者通过反向信道交换的方式从用户所在的管理域获取用户属性信息在这两种情况下网格服务提供者会根据其获取的用户属性信息进行授权目前G r i dShi b项目已完成的工作包括X.509标准与SA M L标准的整合以及GT授权框架X.509标准与SA M L标准的整合包括三个方面分别是G T4.0插件Shi bbol et h1.3插件及属性交换过程普通的GT系统采用的是X.509安全标准而Sh i bb ol e t h系统采用的是OA SI S的SA M L安全标准两个插件解决了两种标准间的转换问题现在GT被越来越多的系统网格项目采用这些网格项目有不同的特征采用不同的技术与机制GT必须建立能够灵活支持用户定义的格式和使用模式尤其是在属性与授权策略方面GT授权框架采用一致的方式处理不同的机制并综合不同来源的授权结果形成对每次服务访问的决策目前的授权框架只是一个简单的实现不完全支持基于属性的授权也不支持细粒度的授权功能完整的授权框架正在开发中并将出现在G T的下一个重要版本G T4.2中G ri dShi b项目已发布了测试版本并于2005年12月实现了在访问网格A ccessG ri d上的应用2006年G ri dShi b计划实现Shi bbol et h采用SA M L与网格安全标准X.509的无缝整合并实现对在线网格CA认证的整合未来其研究的内容将包括身份管理服务I d e n t i t yProvi der基于数据库名称映射设计并开发名称映射管理工具交互式名称注册服务元数据仓库及管理工具等编译欧海峰INTE RNET2的网格探求大规模项目协作新模式G rid S h ib让校园网更具拓展性学生通过NC SA上的网格共享资源学习多媒体技术研究下一代互联网40中国教育网络2006.1-2。
●提供廣泛的訓練及學習函式●提供Simulink的類神經網路block●可自動將MATLAB產生的類神經網路物件轉成Simulink 模型●提供前處理及後處理函式改善類神經網路訓練及效能●提供視覺化函式更容易瞭解類神經網路之效能(Preceptron)、倒傳遞網路(Backpropagation)、自組織映射網路(SOM)、徑向基網路(Radial Basis Network)、學習向量量化網路(LVQN)..等.◎ Curve Fitting Toolbox(曲線契合工具箱)產品特色●可透過圖形化使用者介面或指令列操作各種功能●可作資料前置處理的例行程序設定，例如資料排序、分割、平滑化、及除去界外值(outlier)等●擁有線性或非線性參數契合模型的龐大的函式庫，與最佳化的起始點(starting points)以及非線性模型參數的解題器●多樣的線性和非線性契合方法，包括最小平方法、加權最小平方法、或強韌契合程序(robustfitting procedures) (上述全部支援限制係數範圍或不限制係數範圍的功能)●客製化的線性或非線性模型發展●使用Splines或內插法(interpolants)進行非參數(Nonparametric)契合●支援內插法、外插法、微分以及積分計算簡介1.功能就是可將客戶的資料畫成圖形，接著提供現有標準的一些數學式子來找出符合這圖形的方程式，例如y=ax+bx^2+cx^3。
2.裡面有提供許多的數學式子，也可讓User自定自己的數學函式，而此工具箱可幫客戶算出數學式子的係數(如a,b,c等)3.提供polynomial, exponential, Fourier, rational等數學model給客戶Note: 此套工具不能作曲面的契合，即是如果客戶要求要作y=ax1+bx2 找出a與b，是不可以的，因為此工具箱只能找出單一變數x的係數。
建議他們購買OP and SP 解決他們的問題。
收稿日期：２０１９ １１ １９；修回日期：２０１９ １２ ２７ 基金项目：国家自然科学基金资助项目（６１７７２０８１）；科技创新服务能力建设—科研基地建设—北京实验室—国家经济安全预警工程北京实验室项目（ＰＸＭ２０１８＿０１４２２４＿００００１０）；国家重点研发计划课题（２０１８ＹＦＢ１４０２９０１）作者简介：侯晋升（１９９４ ），男，山西太原人，硕士研究生，主要研究方向为中文信息处理；张仰森（１９６２ ），男（通信作者），山西运城人，教授，博导，博士（后），主要研究方向为中文信息处理、人工智能（ｚｈａｎｇｙａｎｇｓｅｎ＠１６３．ｃｏｍ）；黄改娟（１９６４ ），女，山西运城人，高级实验师，主要研究方向为智能信息处理；段瑞雪（１９８４ ），女，河北石家庄人，讲师，博士，主要研究方向为自然语言处理、意图理解、问答系统．基于多数据源的论文数据爬虫技术的实现及应用侯晋升１，张仰森１，２ ，黄改娟１，段瑞雪１，２（１．北京信息科技大学智能信息处理研究所，北京１００１０１；２．国家经济安全预警工程北京实验室，北京１０００４４）摘 要：在使用单个数据源进行论文数据采集的过程中，存在着数据全面性不足、数据采集速度因网站访问频率限制而受限等问题。
针对这些问题，提出了一个基于多数据源的论文数据爬虫技术。
首先，以知网、万方数据、维普网、超星期刊四大中文文献服务网站为数据源，针对检索关键词完成列表页数据的爬取与解析；然后通过任务调度策略，去除各数据源之间重复的数据，同时进行任务的均衡；最后采用多线程对各数据源进行论文详情信息的抓取、解析与入库，并构建网页进行检索与展示。
实验表明，在单个网页爬取与解析速度相同的情况下，该技术能够更加全面、高效地完成论文信息采集任务，证实了该技术的有效性。
关键词：网络爬虫；多源数据源；多线程；信息处理；数据展示中图分类号：ＴＰ３９１．１ 文献标志码：Ａ 文章编号：１００１ ３６９５（２０２１）０２ ０３７ ０５１７ ０５ｄｏｉ：１０．１９７３４／ｊ．ｉｓｓｎ．１００１ ３６９５．２０１９．１１．０６７１ＩｍｐｌｅｍｅｎｔａｔｉｏｎａｎｄａｐｐｌｉｃａｔｉｏｎｏｆｐａｐｅｒｄａｔａｃｒａｗｌｅｒｔｅｃｈｎｏｌｏｇｙｂａｓｅｄｏｎｍｕｌｔｉｐｌｅｄａｔａｓｏｕｒｃｅｓＨｏｕＪｉｎｓｈｅｎｇ１，ＺｈａｎｇＹａｎｇｓｅｎ１，２ ，ＨｕａｎｇＧａｉｊｕａｎ１，ＤｕａｎＲｕｉｘｕｅ１，２（１．ＩｎｓｔｉｔｕｔｅｏｆＩｎｔｅｌｌｉｇｅｎｔＩｎｆｏｒｍａｔｉｏｎ，ＢｅｉｊｉｎｇＩｎｆｏｒｍａｔｉｏｎＳｃｉｅｎｃｅ＆ＴｅｃｈｎｏｌｏｇｙＵｎｉｖｅｒｓｉｔｙ，Ｂｅｉｊｉｎｇ１００１０１，Ｃｈｉｎａ；２．ＮａｔｉｏｎａｌＥｃｏｎｏｍｉｃＳｅｃｕｒｉｔｙＥａｒｌｙＷａｒｎｉｎｇＥｎｇｉｎｅｅｒｉｎｇＢｅｉｊｉｎｇＬａｂｏｒａｔｏｒｙ，Ｂｅｉｊｉｎｇ１０００４４，Ｃｈｉｎａ）Ａｂｓｔｒａｃｔ：Ｔｈｅｒｅａｒｅｍａｎｙｐｒｏｂｌｅｍｓｉｎｔｈｅｐｒｏｃｅｓｓｏｆｃｏｌｌｅｃｔｉｎｇｐａｐｅｒｄａｔａｕｓｉｎｇｓｉｎｇｌｅｄａｔａｓｏｕｒｃｅ，ｓｕｃｈａｓｉｎｓｕｆｆｉｃｉｅｎｔｄａｔａｃｏｍｐｒｅｈｅｎｓｉｖｅｎｅｓｓａｎｄｌｉｍｉｔｅｄｄａｔａｃｏｌｌｅｃｔｉｏｎｓｐｅｅｄｄｕｅｔｏｗｅｂｓｉｔｅａｃｃｅｓｓｆｒｅｑｕｅｎｃｙｌｉｍｉｔａｔｉｏｎ．Ａｉｍｉｎｇａｔｔｈｅｓｅｐｒｏｂｌｅｍｓ，ｔｈｉｓｐａｐｅｒｐｒｏｐｏｓｅｄａｐａｐｅｒｄａｔａｃｒａｗｌｉｎｇｔｅｃｈｎｏｌｏｇｙｆｏｒｍｕｌｔｉ ｄａｔａｓｏｕｒｃｅｓ．Ｆｉｒｓｔｌｙ，ｉｔｕｓｅｄｔｈｅｆｏｕｒＣｈｉｎｅｓｅｄｏｃｕｍｅｎｔｓｅｒｖｉｃｅｗｅｂ ｓｉｔｅｓ ＨｏｗＮｅｔ，ＷａｎｆａｎｇＤａｔａ，Ｗｅｉｐｕ，ａｎｄＣｈａｏｘｉｎｇａｓｄａｔａｓｏｕｒｃｅｓ，ｃｏｍｐｌｅｔｅｄｔｈｅｔａｓｋｏｆｃｒａｗｌｉｎｇａｎｄｐａｒｓｉｎｇｌｉｓｔｐａｇｅｄａｔａｆｏｒｔｈｅｓｅａｒｃｈｋｅｙｗｏｒｄｓ．Ｔｈｅｎｉｔｕｓｅｄｔｈｅｔａｓｋｓｃｈｅｄｕｌｉｎｇｓｔｒａｔｅｇｙｔｏｒｅｍｏｖｅｒｅｐｅａｔｅｄｄａｔａａｎｄｂａｌａｎｃｅｔｈｅｔａｓｋｓ．Ｆｉｎａｌｌｙ，ｉｔｕｓｅｄｍｕｌｔｉ ｔｈｒｅａｄｓｆｏｒｅａｃｈｄａｔａｓｏｕｒｃｅｔｏｃｒａｗｌ，ｐａｒｓｅａｎｄｓｔｏｒｅｔｈｅｄｅｔａｉｌｉｎｆｏｒｍａｔｉｏｎｏｆｔｈｅｐａｐｅｒｓ，ａｎｄｂｕｉｌｔａｗｅｂｓｉｔｅｆｏｒｓｅａｒｃｈａｎｄｄｉｓｐｌａｙ．Ｅｘｐｅｒｉｍｅｎｔｓｓｈｏｗｔｈａｔｕｎｄｅｒｔｈｅｓａｍｅｃｒａｗｌｉｎｇａｎｄｐａｒｓｉｎｇｓｐｅｅｄ，ｔｈｉｓｔｅｃｈｎｏｌｏｇｙｃａｎｃｏｍｐｌｅｔｅｔｈｅｐａｐｅｒｉｎｆｏｒｍａｔｉｏｎｃｏｌｌｅｃｔｉｏｎｔａｓｋｍｏｒｅｃｏｍｐｒｅｈｅｎｓｉｖｅｌｙａｎｄｅｆｆｉｃｉｅｎｔｌｙ，ｗｈｉｃｈｐｒｏｖｅｓｔｈｅｅｆｆｅｃｔｉｖｅｎｅｓｓｏｆｔｈｉｓｔｅｃｈｎｏｌｏｇｙ．Ｋｅｙｗｏｒｄｓ：Ｗｅｂｃｒａｗｌｅｒ；ｍｕｌｔｉｐｌｅｄａｔａｓｏｕｒｃｅ；ｍｕｌｔｉｔｈｒｅａｄｉｎｇ；ｉｎｆｏｒｍａｔｉｏｎｐｒｏｃｅｓｓｉｎｇ；ｄａｔａｄｅｍｏｎｓｔｒａｔｉｏｎ０ 引言大数据技术从兴起之初到日益成熟，在各行各业都发挥出巨大的作用；借着大数据的东风而再一次焕发出生命力的人工智能领域近些年更是取得了一个又一个的重大突破，在科研与应用方面创造出了巨大的价值，人们逐渐意识到数据已是当下最重要的资源。
User ManualCP2KA program package to performMolecular Dynamics SimulationsThe CP2K developers groupCP2K program release1.0March17,2003ETH Zurich,Swiss Center for Scientific Computing,Switzerland University of Zurich,Physical Chemistry Institute,SwitzerlandDisclaimerPlease note that this manual is not complete.Basically it refers to the CP2K program re-lease1.0,but the CP2K program package is continuously improved and extended.There-fore the ultimate reference is always the CP2K source code.Please cite this manual as:The CP2K developers group,CP2K User Manual(Release1.0),Zurich(2003).This version of the manual was compiled by Matthias Krack(krack@cscs.ch).CopyrightCP2K:A program package to perform molecular dynamics simulationsCopyright c 2003The CP2K developers groupThis program is free software;you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation;either version2of the License,or(at your option)any later version.This program is distributed in the hope that it will be useful,but WITHOUT ANY WARRANTY;without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PUR-POSE.See the GNU General Public License for more details.You should have received a copy of the GNU General Public License along with this program;if not,write to the Free Software Foundation,Inc.,675Mass Ave,Cambridge,MA02139,USA.CONTENTS3 Contents1Introduction5 2Installation6 3Running CP2K73.1Inputfiles (7)3.1.1Fist (7)3.1.2Quickstep (7)4Input description84.1General rules (8)4.2Section&CP2K (8)4.2.1Required keywords (8)4.2.2Optional keywords (9)4.3Section&IO (9)4.4Section&CELL (9)4.4.1Required keywords (9)4.4.2Optional keywords (10)4.5Section&COORD (10)4.6Section&KIND (10)4.6.1Required keywords (10)4.6.2Optional keywords (11)4.7Section&DFT (11)4.8Section&QS (12)4.9Section&SCF (13)4.10Section&PRINT (15)5Input examples195.1Argon atom (19)5.2Water molecule (20)6Methods216.1GPW method (21)References23 Index244CONTENTS5 1IntroductionThe CP2K project was started in2000at the Max-Planck institute for solid state research in Stuttgart.Now it is continued at the ETH Zurich(CSCS)and at the University of Zurich.The current members of the CP2K developers group are•Thomas Chassaing(University of Zurich)•Harald Forbert(University of Bochum)•J¨u rg Hutter(University of Zurich)•Matthias Krack(ETH Zurich/CSCS)•William Kuo(LLNL)•Fawzi Mohamed(ETH Zurich/CSCS)•Christopher J.Mundy(LLNL)•Ari P.Seitsonen(University of Zurich)•Gloria Tabacchi(Universit`a degli studi dell’Insubria,Como)•Joost VandeV ondele(University of Zurich)62Installation 2InstallationYou can download the current version of the CP2K code fromhttp://developer.berlios.de/project/?group id=129using CVS or FTP which also allow to update your current CP2K version.Alternatively, you can directly download the full CP2K tarball which you have to uncompress with $gunzip cp2k.tar.gzThen extract the archivefile with$tar-xvf cp2k.tarIn order to generate an executable change to the directory with the makefile$cd cp2k/makefilesand run GNU make which is on LINUX systems simply make$makeThe default make will create a serial optimized(sopt)executable which is equivalent to make soptOther choices are$make sdbg(serial executable for debugging)$make pdbg(parallel executable for debugging)$make popt(optimized parallel executable)You can remove all the stuff generated by make with$make distcleanIf you want to remove only one version completely use e.g.$make sopt/realcleanor$make sopt/cleanto get rid of the objectfiles only.After a successful compilation you mayfind the corre-sponding executable in the directorycp2k/exe/ architecture nameThere are some test inputs in the directorycp2k/testsand the next section describes how to run the example inputs.7 3Running CP2KThe CP2K program is started with the command$cp2k.sopt inputfile > outputfileThe start command for the parallel CP2K version depends on the parallel execution en-vironment of the underlying architecture,e.g.with LINUX/MPICH you may start4pro-cesses with$mpirun-np4cp2k.popt inputfile > outputfilewhereas on IBMs(AIX)you have to type something like$poe cp2k.popt inputfile -procs4> outputfile3.1InputfilesThe methods implemented in CP2K may require different additional inputfiles.3.1.1Fist3.1.2Quickstep•Potentialfile(default name:POTENTIAL)•Basis setfile(default name:BASIS SET)The Gaussian basis set format and all the atomic potential formats are explained in the corresponding default databasefiles.84Input description 4Input description4.1General rules•Warning:Do not expect the input to be logic.The programmers logic may be different from yours.•Warning:This input description may not refer to the actual version of the program you are using.Therefore the ultimate and authoritative input guide is the source code.•The input is free format and is not case sensitive except where especially stated.•Empty lines and white spaces at the beginning of a line are ignored.•All characters in a line following the comment character are ignored.The default comment character is#.•The CP2K inputfile is divided into input sections which are started and terminated with the section keywords listed below.•Each section keyword starts with the section character.The default section character is&.•The order of the keywords inside an input section is arbitrary except where espe-cially stated.•For some keywords there are one or even more alias names which are given below as a comma-separated list.•Lists enclosed in{}imply that you have to choose exactly one of the items.•Lists enclosed in[]imply that you can choose any number of items(optional keywords).•There are several possibilities to define afloating point number real ,e.g.0.05,5.0E-2,5.e-2,1/20,or50/1000.Also the specification of an integer numberinteger is allowed where afloating point number real is requested,but not vice versa.•Strings string with special characters like blanks have to be delimited by"".4.2Section&CP2KThe global CP2K section.4.2.1Required keywordsPROGRAM{FIST,QUICKSTEP}Defines the methods which is used for the calculation.4.3Section&IO94.2.2Optional keywordsFFTLIB{FFTESSL,FFTSG,FFTSGI,FFTW}Defines the library which is used for the Fast Fourier Transformations(FFT).The availability of the libraries depends on the architecture and/or installation,but at least the FFTSG library is available which is included in the CP2K distribution.default:FFTSGIOLEVEL{ integer }Global print level.See also input section&PRINT.default:0PP LIBRARY PATH{ path name }Path to the directory with the pseudo potential databasefiles.Default: the current working directoryPROJECT{ string }Name of the actual project.default:project4.3Section&IOIn this section the names of input and/or outputfiles can be modified.BASIS SET FILE{ file name }Name of the Gaussian basis set databasefile.default:BASIS SETPOTENTIAL FILE{ file name }Name of the potential databasefile.default:POTENTIALRESTART FILE{ file name }Name of the restartfile.default:RESTART4.4Section&CELLThis section is always needed to define the simulation cell.4.4.1Required keywordsABC{ real real real }Lengths of the vectors a,b,and c which define the orthorhombic simulation cell.The unit of length is defined by the keyword UNIT.104Input description4.4.2Optional keywordsUNIT{ANGSTROM,BOHR,SCALED ANGSTROM,SCALED BOHR} Defines the unit of length for the simulation cell and it also applies to the definition of the atomic coordinates in the input section&COORD.Moreover,all lengths and distances in the output are printed using this unit.default:BOHR4.5Section&COORDEach non-empty input line in this section defines an atom of the considered system.The first entry in each line has to correspond to an atomic kind name defined by a&KIND section which can be a string or an integer number.The kind name has to follow a set of three real numbers defining the x,y,and z coordinates of the atom.4.6Section&KINDThis section has to be defined for each atomic kind in a Q UICKSTEP run.The name of the kind has to be defined right after the&KIND section keyword on the same input line.The kind name is referenced by the&COORD section.Alternatively,the atomic number of the kind can be defined as an integer number,e.g.&KIND6for carbon which is equivalent to&KIND CIn general,any string can be defined for an atomic kind&KIND stringwhich allows to define different atomic kinds for the same element e.g.carbon with different orbital basis sets&KIND C-DZVP&KIND C-TZVPOne or more&KIND sections are required for a Q UICKSTEP run.4.6.1Required keywordsORBITAL BASIS SET,BASIS SET,BAS{ string }Name of the Gaussian orbital basis set which has to be read from the Gaussian basis set databasefile(see section&IO).POTENTIAL,POT{ string }Name of the atomic potential which has to be read from the potential databasefile(see section&IO).4.7Section&DFT114.6.2Optional keywordsELEMENT SYMBOL,ELEMENT{ string }Defines the element to which the atomic kind belongs.ATOMIC MASS,MASS{ real }Defines an atomic mass different from the default atomic mass,e.g.for the definition of isotopes.PAO MIN BAS{list of integer }Definition of the projected atomic orbital(PAO)basis.Indices of the PAOs with respect to the full basis set.4.7Section&DFTIn this section the configuration of a density functional calculation(DFT)can be modi-fied.CHARGE{ integer }The total charge of the system.default:0CORRELATION-FUNCTIONAL,C-FUNCTIONAL{LYP,P86,NONE} Name of the requested correlation functional.default:PADEEXCHANGE-CORRELATION-FUNCTIONAL,XC-FUNCTIONAL,FUNCTIONAL {BLYP,BP86,LDA,PADE,PBE,NONE}Name of the requested exchange-correlation functional for a density functional calcu-lation.default:PADEEXCHANGE-FUNCTIONAL,X-FUNCTIONAL{B88,SLATER,NONE} Name of the requested exchange functional.default:NONEDENSITY CUTOFF{ real }Cutoff for the calculation of the density.default:1.0E-10GRADIENT CUTOFF{ real }Cutoff for the calculation of the gradients.default:1.0E-8GRID{CUTOFF,MESH,PLANE WAVES}Definition of the integration grid.CUTOFF{ real }Definition of the plane waves cutoff.124Input description MESH{ integer integer integer }Explicit definition of the grid size.PLANE WAVES,PWdefaultKINETIC-ENERGY-FUNCTIONAL,KE-FUNCTIONAL{NONE}Name of the requested kinetic energy functional.default:NONELSDA spin-polarized(unrestricted Kohn-Sham)calculation is requested. MULTIPLICITY,MULTIP{ integer }The multiplicity,i.e.the number of unpaired electrons in the system plus one.default:1for an even and2for an odd number of electrons4.8Section&QSProgram parameters Q UICKSTEPCUTOFF{ real }Plane waves cutoff of the largest grid in Rydberg.default:320EPS DEFAULT{ real }Defines a default threshold value.All threshold values of Q UICKSTEP are set to this value.EPS CORE CHARGE{ real }Threshold value for the interaction range of the atomic core charge distributions.default:1.0E-12EPS GVG RSPACE,EPS GVG{ real }Threshold value for the integration of the Hartree potential on the real space grid.default:1.0E-6EPS PGF ORB{ real }Threshold value for interaction range of the primitive Gaussian-type orbital functions.default:1.0E-6EPS PPL{ real }Threshold value for the interaction range of the local part of the GTH pseudo potential.default:1.0E-12EPS PPNL{ real }Threshold value for the interaction range of the non-local part of the GTH pseudo potential.default:1.0E-124.9Section&SCF13 EPS RHO{ real }Threshold values for EPS RHO GSPACE and EPS RHO GSPACE.default:1.0E-8EPS RHO GSPACE{ real }Threshold value for the calculation of the electronic charge density in Fourier space.default:1.0E-8EPS RHO RSPACE{ real }Threshold value for the calculation of the electronic charge density in real space.default:1.0E-8PROGRESSION FACTOR,PROFAC{ real }Progression factor for the generation of the multi-grid levels.default:2.0RELATIVE CUTOFF,REL CUTOFF{ real }Relative plane waves cutoff for each multi-grid level.Values less than20.0give inaccurate results and values greater than30.0are used for reference calculations (save).default:25.0METHOD{GPW}Method used by Q UICKSTEP.GAPW is not available yet.default:GPWMULTI GRID{list of integer }The plane waves cutoffs for each multi grid level in Rydberg.The number of grid levels is defined by the keyword NGRID LEVEL.NGRID LEVEL,NGRID{ integer }Number of the multi-grid levels.default:4PAOUse the projected atomic orbital(PAO)method.default:no PAO4.9Section&SCFThis section defines the parameters for the configuration of the self consistentfield(SCF) procedure which is used for the wavefunction optimization.ARPACK ONThe ARPACK eigensolver is used in a parallel run which requires a proper installation of the ARPACK library.default:no ARPACK usage144Input description CHOLESKY ON,CHOLESKY OFFDecides whether the Cholesky decomposition is used in the eigensolver or not.default:CHOLESKY ONDENSITY GUESS,SCF GUESS,GUESS{ATOMIC,CORE}Defines the type of guess which is employed to generate thefirst density matrix.default:ATOMICDENSITY MIXING,MIXING{ real }Factor for the mixing of the old and new density matrix during the wavefunction opti-mization.default:0.4(i.e.40%of the new and60%of the old density are used)EPS DIIS{ real }The DIIS procedure is switched on,if the maximum DIIS error vector element is below this threshold value.default:0.1EPS EIGVAL{ real }Threshold value for eigenvector quenching when S−1/2is used as the orthogonalization matrix in the eigensolver.default:1.0E-5EPS JACOBI{ real }Pseudo diagonalization(Jacobi rotations)[1]is used,if the maximum difference be-tween the corresponding density matrix elements of two consecutive SCF iteration steps is smaller than the specified threshold value.default:0.0EPS SCF{ real }SCF convergence criterion,i.e.the maximum difference between the corresponding density matrix elements of two consecutive SCF iteration steps.default:1.0E-5JACOBI THRESHOLD{ real }Threshold value for a Kohn-Sham matrix matrix element in the MO basis to perform a Jacobi rotations,if pseudo diagonalization is used.default:1.0E-7LEVEL SHIFT{ real }Shift value for the unoccupied(virtual)molecular orbitals(MOs)in atomic units.default:0.0MAX DIIS{ integer }Maximum size of the SCF DIIS buffer.default:04.10Section&PRINT15 MAX SCF{ integer }Maximum number of SCF iteration steps.default:30NREBUILD{ integer }Number of SCF steps between two full calculations of the electronic charge density.default:1OTAn orbital transformation approach instead of a diagonalization is used for the wave-function optimization during the SCF iteration procedure.SMEAR{ real }Window size in atomic units with respect to the eigenvalue of the highest occupied molecular orbital(HOMO)for the smearing of the occupation numbers.default:0.0WORK SYEVX{ real }Defines the amount of additional work space for the PDSYEVX routine from the SCALAPACK library.A value between0.0and1.0is accepted.(only for paral-lel runs using SCALAPACK and an eigensolver with diagonalization.default:0.04.10Section&PRINTThis sections allows for detailed output control when running Q UICKSTEP.There are 5predefined print levels:0,1,2,3,and4which correspond to the keywords NO,LOW, MEDIUM,HIGH,and DEBUG or FULL.The print level has to be defined right after the &PRINT section keyword on the same input line,e.g.&PRINT LOWwhich is equivalent to&PRINT1The following keywords may be used based on the selected print level to requested an additional output or to suppress an output selectively by using the prefix NO for the keyword,e.g.at print level LOW the atomic coordinates are listed in the output which may be inconvenient for large sytems,thus simply request NO COORDINATES in the&PRINT section.The default print level is LOW.ANGLESPrint the angles between all atom triples in the simulation cell.Warning:That is much output for large systems.ATOMIC COORDINATES,COORDINATES,COORDPrint all atomic coordinates together with the some atomic kind information.164Input description BASIC DATA TYPESPrint informations about the basic data types like REAL,INTEGER,or LOGICAL. BASIS SETS,BASIS SET,BASISPrint the Gaussian basis set information,i.e.all Gaussian function exponents and the corresponding contraction coefficients as read from the Gaussian basis set database file.Furthermore,the normalized contraction coefficients are printed.BLACS INFOPrint the process grid information of BLACS(Basic linear algebra subprograms) CARTESIAN MATRICESPrint all operator matrices in the Cartesian instead of the spherical representation. CELL PARAMETERS,CELLPrint the simulation cell data like the cell vectors,cell volume etc.CORE HAMILTONIAN MATRIX,H MATRIXPrint the core Hamiltonian matrix.CORE CHARGE RADII,CORE RADIIPrint the radius of the core charge distribution for each atomic kind.DENSITY MATRIX,P MATRIXPrint the density matrix.DERIVATIVESPrint thefirst derivatives of the operator matrices.DFT CONTROL PARAMETERSPrint the DFT control parameters as defined in the&DFT section.DIIS INFORMATIONPrint information about the SCF DIIS procedure.DISTRIBUTIONPrint the distribution and the sparsity of the overlap matrix(only parallel version). EACH SCF STEPPrint the requested energies,densities,or matrices for each SCF iteration step.E DENSITY CUBEPrint the electronic charge density as a cubefile.FORCESPrint the atomic force contributions for all atoms.HOMOPrint the highest occupied molecular orbital(HOMO)as a cubefile.4.10Section&PRINT17 INTERATOMIC DISTANCES,DISTANCESPrint a matrix with the interatomic distances.Warning:That is much output for large systems.KIND RADIIPrint the maximum interaction radius of each atomic kind.KINETIC ENERGY MATRIX,T MATRIXPrint the kinetic energy integral matrix.KOHN SHAM MATRIXPrint the Kohn-Sham matrix.LUMOPrint the lowest unoccupied molecular orbital(LUMO)as a cubefile.MEMORYPrint informations about the memory usage of the CP2K program.MO EIGENVALUESPrint the eigenvalues of the molecular orbitals(MOs).MO EIGENVECTORS,MOSPrint the eigenvectors,eigenvalues,and the occupation numbers of the molecular or-bitals(MOs).MO OCCUPATION NUMBERSPrint the occupation numbers and the eigenvalues of the molecular orbitals(MOs). NEIGHBOR LISTSPrint all neighbor lists.Warning:That is much output for large systems.ORTHO MATRIXPrint the orthogonalisation matrix used to transform the Kohn-Sham matrix. OVERLAP MATRIXPrint the overlap matrix.PGF RADIIPrint the interaction radii of all primitive Gaussian-type functions.PHYSICAL CONSTANTS,PHYSCONPrint the values of all physical constants used in the program.POTENTIALSPrint a detailed atomic potential information for each atomic kind.PPL RADIIPrint the interaction radii of the local part of the Goedecker-Teter-Hutter(GTH)pseudo potential[2,3].184Input description PPNL RADIIPrint the interaction radii of the non-local projector functions of the Goedecker-Teter-Hutter(GTH)pseudo potential[2,3].PROGRAM BANNERPrint a program banner.PROGRAM RUN INFORMATIONPrint informations about the current program run.PW GRID INFORMATIONPrint detailed informations about the used plane waves grid.RADIIPrint all interaction radii for each atomic kinds.SCFPrint the SCF iteration.SCF ENERGIESPrint all contributions to the total SCF energy.SET RADIIPrint the interaction radii of all Gaussian orbital sets.SPHERICAL HARMONICSPrint the transformation matrices between Cartesian and spherical function.TIMING INFORMATIONPrint timing information depending on the IOLEVEL defined in the&CP2K section. TITLEPrint the title.TOTAL DENSITIESPrintTOTAL NUMBERSPrint the total number of atoms,shell sets,basis functions,projectors etc.V HARTREE CUBEPrint the Hartree potential as a cubefile.W MATRIXPrint the energy weighted density matrix used for the force calculation.19 5Input examples5.1Argon atom&CP2KPROGRAM QuickstepIOLEVEL10FFTLIB FFTSGRUN_TYPE WFN_OPT&END&DFTFUNCTIONAL PADE&END&QSCUTOFF300REL_CUTOFF30&END&SCFGUESS ATOMICEPS_DIIS0.1MAX_DIIS4EPS_SCF 1.0E-6MAX_SCF30MIXING0.4&END&PRINT mediumNO_BLACS_INFO&END&KIND ArBASIS_SET DZVP-GTH-PADEPOTENTIAL GTH&END&CELLUNIT ANGSTROMABC12.012.012.0&END&COORD180.0000000.0000000.000000&END205Input examples5.2Water molecule&CP2KPROGRAM QuickstepIOLEVEL10FFTLIB FFTSGRUN_TYPE GEO_OPT&END&DFTFUNCTIONAL Pade&END&QSCUTOFF200&END&SCFGUESS ATOMICMIXING0.4EPS_SCF 1.0E-5&END&PRINT medium&END&KIND HBASIS_SET DZV-GTH-PADEPOTENTIAL GTH&END&KIND OBASIS_SET DZVP-GTH-PADEPOTENTIAL GTH&END&CELLUNIT ANGSTROMABC10.010.010.0&END&COORDH0.000000-0.7571360.520545O0.0000000.000000-0.065587H0.0000000.7571360.520545&END21 6Methods6.1GPW methodThe electronic energy functional for a molecular or crystalline system in the framework of the Gaussian plane waves(GPW)method[4]using the Kohn-Sham formulation of density functional theory(DFT)[5,6]is defined asE el[n]=E T[n]+E V[n]+E H[n]+E XC[n](1)=∑µνPµν ϕµ(r)|−12∇2|ϕν(r) +∑µνPµν ϕµ(r)|V PP loc(r)|ϕν(r) +∑µνPµν ϕµ(r)|V PP nl(r,r )|ϕν(r ) +4πΩ∑|G|<G C ˜n∗(G)˜n(G)G2+˜n(r)εXC[˜n]d rwhere E T[n]is the kinetic energy,E V[n]is the electronic interaction with the ionic cores, E H[n]is the electronic Hartree(Coulomb)energy and E XC[n]is the exchange-correlation energy.The electronic densityn(r)=∑µνPµνϕµ(r)ϕν(r)(2) is expanded in a set of contracted Gaussian functionsϕµ(r)=∑id iµg i(r)(3)Pµνis a density matrix element,g i(r)is a primitive Gaussian function and d iµis the corresponding contraction coefficient.An auxiliary basis set of plane waves is used as an intermediate basis set to describe theelectronic charge density˜n(r)=1Ω∑|G|<G Cn(G)e i Gr(4)which is used for the calculation of the density dependent contributions E H[n]and E XC[n].Ωis the volume of the periodic simulation cell.The plane wave expansion is truncated by the specification of a cut-off value for the kinetic energyE C=12G2C(5)of the plane waves.Since the G=0term of the Hartree energy is treated with the Ewald method,the nuclear charges are represented by a Gaussian charge distribution and not by point charges.226Methods The GPW method works like pure plane waves methods with atomic pseudo potentials (PP),since an expansion of Gaussian functions with large exponents is numerically not efficient or even not feasible.The current implementation of the GPW method uses only the pseudo potentials of G¨o-decker,Teter,and Hutter(GTH)[2]which are available for the whole periodic table[3]. The separable dual-space GTH pseudo potentials consist of a local partV PP local(r)=−Zionrerf αPP r +4∑i=1C PP i √αPP r 2i−2exp − αPP r 2 (6)withαPP=1√PPlocaland a non-local partV PP nl(r,r )=∑lm ∑i jr|p lm i h l i j p lm j|r (7)with the Gaussian-type projectorsr|p lm i =N l i Y lm(ˆr)r l+2i−2exp−12rrl2as shown in Eq.1resulting in a fully analytical formulation which requires only the def-inition of a small parameter set for each element.Moreover,the GTH pseudo potentials are transferable and norm-conserving.Nevertheless,plane waves methods employ this pseudo potential type only for reference calculations or if no other reliable pseudo poten-tials are available,since this type requires relative high cut-off values,i.e.more plane waves.However,in the framework of the GPW method there are no such limitations, since all contributions are integrals over Gaussian functions which can be calculated ana-lytically.REFERENCES23 References[1]J.J.P.Stewart,P.Cs´a sz´a r,and P.Pulay,put.Chem.3,227(1982).[2]S.G¨o decker,M.Teter,and J.Hutter,Phys.Rev.B54,1703(1996).[3]C.Hartwigsen,S.G¨o decker,and J.Hutter,Phys.Rev.B58,3641(1998).[4]G.Lippert,J.Hutter,and M.Parrinello,Mol.Phys.92,477(1997).[5]P.Hohenberg and W.Kohn,Phys.Rev.B136,864(1964).[6]W.Kohn and L.J.Sham,Phys.Rev.A140,1133(1965).24INDEX IndexARPACK,13filesbasis set,7potential,7 General Public License,2 GNU,2GPL,2inputexamples,19general rules,8special characters,8 input sections,optional&DFT,11&IO,9&PRINT,15&QS,12&SCF,13input sections,required&CELL,9&COORD,10&CP2K,8&KIND,10 keyword argumentsANGSTROM,10ATOMIC,14BLYP,11BOHR,10BP86,11CORE,14FFTESSL,9FFTSGI,9FFTSG,9FFTW,9FIST,8GAPW,13GPW,13LDA,11PADE,11PBE,11QUICKSTEP,8SCALED ANGSTROM,10SCALED BOHR,10keywords,optionalV HARTREE CUBE,18ANGLES,15ARPACK ON,13ATOMIC COORDINATES,15ATOMIC MASS,11BASIS SETS,16BASIS SET FILE,9BASIS,16BLACS INFO,16CARTESIAN MATRICES,16CELL PARAMETERS,16CELL,16CHARGE,11CHOLESKY OFF,14CHOLESKY ON,14COORDINATES,15COORD,15CORE CHARGE RADII,16CORE RADII,16CUTOFF,12DENSITY GUESS,14DENSITY MATRIX,16DENSITY MIXING,14DERIVATIVES,16DFT CONTROL PARAMETERS,16DIIS INFORMATION,16DISTANCES,17DISTRIBUTION,16EACH SCF STEP,16ELEMENT SYMBOL,11EPS CORE CHARGE,12EPS DEFAULT,12EPS DIIS,14EPS EIGVAL,14EPS GVG RSPACE,12EPS GVG,12EPS JACOBI,14EPS PGF ORB,12INDEX25EPS PPL,12EPS PPNL,12EPS RHO GSPACE,13EPS RHO RSPACE,13EPS RHO,13EPS SCF,14EXCHANGE-CORRELATION-FUNCTIONAL,11E DENSITY CUBE,16 FFTLIB,9FORCES,16FUNCTIONAL,11GUESS,14HOMO,16H MATRIX],16 INTERATOMIC DISTANCES,17 IOLEVEL,9JACOBI THRESHOLD,14 KINETIC ENERGY MATRIX,17 KOHN SHAM MATRIX,17 LEVEL SHIFT,14LSD,12LUMO,17MAX DIIS,14MAX SCF,15MEMORY,17METHOD,13MIXING,14MOS,17MO EIGENVALUES,17MO EIGENVECTORS,17MO OCCUPATION NUMBERS,17 MULTIPLICITY,12 MULTIP,12MULTI GRID,13NEIGHBOR LISTS,17NGRID LEVEL,13NGRID,13NREBUILD,15ORTHO MATRIX,17OT,15OVERLAP MATRIX,17PAO MIN BAS,11PAO,13POTENTIALS,17POTENTIAL FILE,9PPL RADII,17PPNL RADII,18PP LIBRARY PATH,9PROFAC,13PROGRAM BANNER,18PROGRAM RUN INFO,18PROGRESSION FACTOR,13PROJECT,9PW GRID INFORMATION,18P MATRIX,16RADII,18RELATIVE CUTOFF,13REL CUTOFF,13RESTART FILE,9SCF ENERGIES,18SCF GUESS,14SCF,18SET RADII,18SMEAR,15SPHERICAL HARMONICS,18TIMING INFORMATION,18TITLE,18TOTAL DENSITIES,18TOTAL NUMBERS,18T MATRIX,17UNIT,10WORK SYEVX,15W MATRIX,18XC-FUNCTIONAL,11KIND RADII,17 keywords,requiredABC,9BASIS SET,10BAS,10ORBITAL BASIS SET,10POTENTIAL,10PROGRAM,8PDSYEVX,15 SCALAPACK,15。
数学: 科学的王后和仆人Mathematics: Queen and Servant of Science北京理工大学叶其孝本文的题目是已故的美国科学院院士、著名数学家、数学史学家和科普作家Eric Temple Bell(贝尔, 1883, 02, 07 ~ 1960, 12, 21)于1951年写的一本书的书名Mathematics: Queen and Servant of Science (数学: 科学的王后和仆人). 该书主要是为大学生和非数学领域的人士写的, 介绍纯粹和应用数学的各个方面, 更着重在说明数学科学的极端重要性.The Mathematical Association of America, 1996, 463 pages实际上这是他1931年写的The Queen of the Sciences (科学的王后)和1937年写的The Handmaiden of the Sciences (科学的女仆)这两本通俗数学论著的合一修订扩大版.Eric Temple Bell Alexander Graham Bell (1847 ~ 1922) 按常识的理解, 女王是优美、高雅、无懈可击、至尊至贵的, 在科学中只有纯粹数学才具有这样的特点, 简洁明了的数学定理一经证明就是永恒的真理, 极其优美而且无懈可击;另一方面, 科学和工程的各个分支都在不同程度上大量应用数学, 这时数学科学就是仆人, 这些仆人是否强有力, 用起来是否得心应手是雇佣这些仆人的主人最为关心的事. 事实上, servant这个字本身就有“供人们利用之物, 有用的服务工具”的意思. 毫无疑问, 我们的目的不是为数学争一个好的名分, 而是想说明数学是怎样通过数学建模来解决各种实际问题的; 数学(数学建模)的极端重要性, 以及探讨正确认识和理解数学科学的作用对于发展我国科学技术、经济以及教育, 从而争取在21世纪把我国真正建设成为屹立于世界民族之林的强国，乃至个人事业发展的至关重要性. 当然, 我们也希望说明王后和仆人集于一身并不矛盾. 历史上, 很多特别受人尊敬的科学家, 不仅仅是由于他们的科学成就, 更因为他们的科学成就能够服务于人类.数学是科学的王后, 算术是数学的王后. 她常常放下架子为天文学和其他科学效劳, 但是在所有情况下, 第一位的是她(数学)应尽的责任. (高斯)Mathematics is the Queen of the Sciences, and Arithmetic the Queen of Mathematics. She often condescends to render service to astronomy and other natural sciences, but under all circumstance the first place is her due.— Carl Friedrich Gauss (卡尔·弗里德里希·高斯, 1777, 4, 30 ~ 1855, 2, 23)From: Bell, Eric T., Mathematics: Queen and Servant of Science, MAA, 1951, p.1;Men of Mathematics, Simon and Schuster, New York, 1937, p. xv.***************************************************自古以来，数学的发展始终与科学技术的发展紧密相连，反之亦然. 首先, 我们来看一下导致我们现在这个飞速发展的信息社会的19、20世纪几乎所有重大科学理论的发展和完善过程中数学(数学建模)所起到的不可勿缺的作用.数学研究的成果往往是重大科学发明的催生素(仅就19、20世纪而言, 流体力学、电磁理论、相对论、量子力学、计算机、信息论、控制论、现代经济学、万维网和互联网搜索引擎、生物学、CT、甚至社会政治学领域等). 但是20世纪上半世纪, 数学虽然也直接为工程技术提供一些工具, 但基本方式是间接的: 先促进其他科学的发展, 再由这些科学提供工程原理和设计的基础. 数学是幕后的无名英雄.现在, 数学无处不在, 数学和工程技术之间,在更广阔的范围内和更深刻的程度上, 直接地相互作用着, 极大地推动了科学和工程科学的发展, 也极大地推动了技术的发展. 数学不仅是幕后的无名英雄, 很多方面开始走向“前台”. 但是对数学的极端重要性迄今尚未有共识, 取得共识对加强一个国家的竞争力来说是至关重要的.硬能力―一位美国朋友谈及对未来中国人的看法: 20年后, 中国年轻人会丢了中国人现在的硬能力, 他们崇拜各种明星, 不愿献身科学, 不再以学术研究为荣, 聪明拔尖的学生都去学金融、法律等赚钱的专业; 而美国人因为认识到其硬能力(例如数学)不行, 进行教育改革, 20年后, 不但保持了其软实力即非专业能力的优势, 而且在硬能力上赶上中国人.‖“正在丢失的硬实力”, 鲁鸣, 《青年文摘》2011年第5期动向：美国很多州新办STEM高中, 一些大学开始开设STEM课程等.STEM = Science + Technology + Engineering + Mathematics2012年2月7日公布的美国总统科技顾问委员会给总统的报告，参与超越：培养额外的100万具有科学、技术、工程和数学学位的大学生(Engage to Excel: Producing One Million Additional College Graduates with Degrees in Science, Technology, Engineering, and Mathematics)The Mathematical Sciences in 2025, the National Academies Press, 2013人们使用的数学科学思想、概念和方法的范围在不断扩大的同时，数学科学的用途也在不断扩展. 21世纪的大部分科学与工程将建立在数学科学的基础上.This major expansion in the uses of the mathematical sciences has been paralleled by a broadening in the range of mathematical science ideas and techniques being used. Much of twenty-first century science and engineering is going to be built on a mathematical science foundation, and that foundation must continue to evolve and expand.数学科学是日常生活的几乎每个方面的组成部分.互联网搜索、医疗成像、电脑动画、数值天气预报和其他计算机模拟、所有类型的数字通信、商业和军事中的优化问题以及金融风险的分析——普通公民都从支撑这些应用功能的数学科学的各种进展中获益，这样的例子不胜枚举.The mathematical sciences are part of almost every aspect of everyday life. Internet search, medical imaging, computer animation, numerical weather predictions and othercomputer simulations, digital communications of all types, optimization in business and the military, analyses of financial risks —average citizens all benefit from the mathematical science advances that underpin these capabilities, and the list goes on and on.调查发现:数学科学研究工作正日益成为生物学、医学、社会科学、商业、先进设计、气候、金融、先进材料等许多研究领域不可或缺的重要组成部分. 这种研究工作涉及最广泛意义下数学、统计学和计算综合，以及这些领域与潜在应用领域的相互作用. 所有这些活动对于经济增长、国家竞争力和国家安全都是至关重要的，而且这种事实应该对作为整体的数学科学的资助性质和资助规模产生影响. 数学科学的教育也应该反映数学科学领域的新的状况.Finding: Mathematical sciences work is becoming an increasingly integral and essential component of a growing array of areas of investigation in biology, medicine, social sciences, business, advanced design, climate, finance, advanced materials, and many more. This work involves the integration of mathematics, statistics, and computation in the broadest sense and the interplay of these areas withareas of potential application. All of these activities are crucial to economic growth, national competitiveness, and national security, and this fact should inform both the nature and scale of funding for the mathematical sciences as a whole. Education in the mathematical sciences should also reflect this new stature of the field.****************************************************************为了以下讲述的方便, 我们先来了解一下什么是数学建模.数学模型(Mathematical Model)是用数学符号对一类实际问题或实际发生的现象的(近似的)描述.数学建模(Mathematical Modeling)则是获得该模型并对之求解、验证并得到结论的全过程.数学建模不仅是了解基本规律, 而且从应用的观点来看更重要的是预测和控制所建模的系统的行为的强有力的工具.数学建模是数学用来解决各种实际问题的桥梁.↑→→→→→→→→↓↑↓↑↓↓↑↓←←←←←通不过↓↓通过)定义：数学建模就是上述框图多次执行的过程数学建模的难点观察、分析实际问题, 作出合理的假设, 明确变量和参数, 形成明确的数学问题. 不仅仅是翻译的问题; 涉及的数学问题可能是复杂、困难的, 求解也许涉及深刻的数学方法. 如何作出正确的判断, 寻找合适、简洁的(解析或近似) 解法; 如何验证模型.简言之:合理假设、模型建立、模型求解、解释验证.记住这16个字, 将会终生受用.数学建模的重要作用：源头创新当然数学建模也有局限性, 不能单独包打天下, 因为实际问题是非常复杂的, 需要多学科协同解决.在图灵(A. M. Turing)的文章: The Chemical Basis of Morphogenesis (形态生成的化学基础), Philosophical Transactions of the Royal Society of London (伦敦皇家学会哲学公报), Series B (Biological Sciences),v.237(1952), 37-72.1. 一个胚胎的模型. 成形素本节将描述一个正在生长的胚胎的数学模型. 该模型是一种简化和理想化, 因此是对原问题的篡改. 希望本文论述中保留的一些特征, 就现今的知识状况而言, 是那些最重要的特征.1. A model of the embryo. MorphogensIn this section a mathematical model of the growing embryo will be described. This model will be asimplification and an idealization, and consequently a falsification. It is to be hoped that the features retained for discussion are those of greatest importance in the present state of knowledge.想单靠数学建模本身来解决重大的生物学问题是不可能的，另一方面，想仅仅依靠实验来获得对生物学的合理、完整的理解也是极不可能的. There is no way mathematical modeling can solve major biological problems on its own. On the other hand, it ishighly unlikely that even a reasonably complete understanding could come solely from experiment.—— J. D. Murray, Why Are There No 3-Headed Monsters? Mathematical Modeling in Biology, Notices of the AMS,v. 59 (2012), no. 6, p.793.自古以来公平、公正的竞赛都是培养、选拔人才的重要手段, 科学和数学也不例外.中学生IMO (国际数学奥林匹克(International Mathematical Olympiad), 1959 ～)北美的大学生Putnbam数学竞赛(1938 ～)全国大学生数学竞赛(2010 ～)Mathematical Contest in Modeling (MCM, 1985 ～)美国大学生数学建模竞赛Interdisciplinary Contest in Modeling (ICM, 1999～)美国大学生跨学科建模竞赛China Undergraduate Mathematical Contest in Modeling (CUMCM, 1992～) 中国大学生数学建模竞赛中国大学生参加美国大学生数学建模竞赛情况中国大学生数学建模竞赛情况在以下讲述中涉及物理方面的具体的数学模型 (问题)的叙述和初步讨论可参考《物理学与偏微分方程》, 李大潜、秦铁虎编著, (上册, 1997; 下册, 2000), 高等教育出版社.Seven equations that rule your world (主宰你生活的七个方程式), by Ian Stewart, NewScientist, 13 February 2012.Fourier transformation 2ˆ()()ix f f x e dx πξξ∞--∞=⎰Wave equation 22222u u c t x ∂∂=∂∂ Ma xwell‘s equation110, , 0, H E E E H H c t c t∂∂∇⋅=∇⨯=-∇⋅=∇⨯=∂∂Schrödinger‘s equation ˆψH ψi t∂=∂Ian Stewart, In Pursuit of the Unknown:17 Equations That Changed the World (追求对未知的认识：改变世界的17个方程), Basic Books, March 13, 2012.目录(Contents)Why Equations? /viii1. The squaw on the hippopotamus ——Pythagoras‘sTheorem/12. Shortening the proceedings —— Logarithms/213. Ghosts of departed quantities —— Calculus/354. The system of the world ——Newton‘s Law ofGravity/535. Portent of the ideal world —— The Square Root ofMinus One/736. Much ado about knotting ——Euler‘s Formula forPolyhedra/837. Patterns of chance —— Normal Distribution/1078. Good vibrations —— Wave Equation/1319. Ripples and blips —— Fourier Transform/14910. The ascent of humanity —— Navier-StokesEquation/16511. Wave in the ether ——Maxwell‘s Equations/17912. Law and disorder —— Second Law ofThermodynamics /19513. One thing is absolute —— Relativity/21714. Quantum weirdness —— Schrödinger Equation/24515. Codes, communications, and computers ——Information Theory/26516. The imbalance of nature —— Chaos Theory/28317. The Midas formula —— Black-Scholes Equation/195Where Next?/317Notes/321Illustration Credits/330Index/331相对论Albert Einstein(1879, 3, 14 ～1955, 4, 18)20世纪最伟大的科学成就莫过于Einstein(爱因斯坦)的狭义和广义相对论了, 但是如果没有Minkowski (闵可夫斯基)几何、Riemann(黎曼)于1854年发明的Riemann几何, 以及Cayley(凯莱), Sylvester(西勒维斯特)和Noether(诺特)等数学家发展的不变量理论, Einstein的广义相对论和引力理论就不可能有如此完善的数学表述. Einstein自己也不止一次地说过.早在1905年, 年仅26岁的爱因斯坦就已提出了狭义相对论. 狭义相对论推倒了牛顿力学的质量守恒、能量守恒、质量能量互不相关、时空永恒不变的基本命题. 这是一场真正的科学革命.为了导出狭义相对论，爱因斯坦作出了两个假设：运动的相对性(所有匀速运动都是相对的)和光速为常数(光的运动例外, 它是绝对的). (1)狭义相对性原理,即在所有惯性系中, 物理学定律具有相同的数学表达形式;(2)光速不变原理,真空中光沿各个方向传播的速率都相等，与光源和观察者的运动状态无关.时空不是绝对独立的.由此可以导出一些推论: 相对论坐标变换式和速度变换式, 同时的相对性, 钟慢尺缩效应和质能关系式等.他的好友物理学家P.Ehrenfest指出实际上还蕴涵着第三个假设, 即这两个假设是不矛盾的. 物体运动的相对性和光速的绝对性, 两者之间的相互制约和作用乃是相对论里一切我们不熟悉的时空特征的根源.(部分参阅李新洲:《寻找自然之律--- 20世纪物理学革命》, 上海科技教育出版社, 2001.)1907 年德国数学家H. Minkowski (1864 ～1909) 提出了―Minkowski 空间‖,即把时间和空间融合在一起的四维空间1,3R. Minkowski 几何为Einstein 狭义相对论提供了合适的数学模型.“没有任何客观合理的方法能够把四维连续统分离成三维空间连续统和一维时间连续统. 因此从逻辑上讲, 在四维时空连续统(space- time continuum)中表述自然定律会更令人满意. 相对论在方法上的巨大进步正是建立在这个基础之上的, 这种进步归功于闵可夫斯基(Minkowski).”—Albert Einstein, The Meaning of Relativity, 1922, Princeton University Press. 中译本, 阿尔伯特·爱因斯坦著, 相对论的意义, (普林斯顿科学文库(Princeton Science Library) 1), 郝建纲、刘道军译, 上海科技教育出版社, 2001, p. 27.有了Minkowski 时空模型后, Einstein 又进一步研究引力场理论以建立广义相对论. 1912 年夏他已经概括出新的引力理论的基本物理原理, 但是为了实现广义相对论的目标, 还必须寻求理论的数学结构, Einstein 为此花了 3 年的时间, 最后, 在数学家M. Grossmann 的介绍下学习掌握了发展相对论引力学说所必需的数学工具—以Riemann几何和Ricci, Levi - Civita的绝对微分学, 也就是Einstein 后来所称的张量分析.“根据前面的讨论, 很显然, 如果要表达广义相对论, 就需要对不变量理论以及张量理论加以推广. 这就产生了一个问题, 即要求方程的形式必须对于任意的点变换都是协变的. 在相对论产生以前很久, 数学家们就已经建立了推广的张量演算理论. 黎曼(Riemann)首先把高斯(Gauss)的思路推广到了任意维连续统, 他很有预见性地看到了……进行这种推广的物理意义. 随后, 这个理论以张量微积分的形式得到了发展, 对此里奇(Ricci)和莱维·齐维塔(Tulio Levi-Civita, 1873～1941)做出了重要贡献. ”—阿尔伯特·爱因斯坦著, 相对论的意义, 郝建纲、刘道军译, 上海科技教育出版社, 2001, p. 57.从数学建模的角度看, 广义相对论讨论的中心问题是引力理论, 其基础是以下两个假设: 1. (等效原理)惯性力场与引力场的动力学效应是局部不可分辨的，(或说引力和非惯性系中的惯性力等效)；2. (广义相对性原理) 一切参考系都是平权的，换言之，客观的真实的物理规律应该在任意坐标变换下形式不变——广义协变性(即一切物理定律在所有参考系[无论是惯性的或非惯性的]中都具有相同的形式)。
DELVER: Real-Time, ExtensibleAlgorithms出处AbstractPeer-to-peer communication and semaphores have garnered minimal interest from both computational biologists and mathematicians in the last several years. In fact, few cyberinformaticians would disagree with the investigation of rasterization. In this position paper, we investigate how write-back caches can be applied to the emulation of object-oriented languages.Table of Contents1) Introduction2) Design3) Implementation4) Experimental Evaluation∙ 4.1) Hardware and Software Configuration∙ 4.2) Experiments and Results5) Related Work6) Conclusion1 IntroductionThe refinement of linked lists is a confusing question. To put this in perspective, consider the fact that foremost researchers always use suffix trees to fulfill this ambition. A practical problem in steganography is the exploration of operating systems. To what extent can redundancy be enabled to fulfill this intent?We examine how superpages can be applied to the synthesis of the Turing machine. Our heuristic creates Internet QoS. Nevertheless, the study of I/O automata might not be the panacea that end-users expected. Combinedwith Lamport clocks, it harnesses a novel framework for the synthesis of superblocks.To our knowledge, our work in this position paper marks the first framework improved specifically for flip-flop gates. Existing semantic and lossless applications use multimodal technology to allow RPCs. Existing electronic and cacheable algorithms use the refinement of model checking to request event-driven models [,,,]. It should be noted that our application is maximally efficient. This combination of properties has not yet been studied in previous work.Our main contributions are as follows. To begin with, we verify that though neural networks can be made large-scale, authenticated, and replicated, virtual machines can be made interactive, distributed, and homogeneous. Continuing with this rationale, we verify that Boolean logic and the transistor are largely incompatible. Of course, this is not always the case.The roadmap of the paper is as follows. To begin with, we motivate the need for linked lists. Furthermore, we prove the robust unification of lambda calculus and reinforcement learning []. We place our work in context with the related work in this area. Ultimately, we conclude.2 DesignOur methodology relies on the confirmed methodology outlined in the recent acclaimed work by Johnson in the field of linear-time theory. Consider the early methodology by Kumar and Jackson; our framework is similar, but will actually achieve this mission. The model for DELVER consists of four independent components: interrupts, multimodal modalities, "smart" configurations, and self-learning configurations. This may or may not actually hold in reality. Rather than synthesizing compact epistemologies, DELVER chooses to study active networks.Figure 1: DELVER's relational provision.DELVER relies on the extensive design outlined in the recent well-known work by G. Watanabe et al. in the field of networking. This is a significant property of our methodology. Consider the early methodology by Alan Turing et al.; our methodology is similar, but will actually solve this grand challenge. Similarly, Figure 1details DELVER's client-server synthesis. Thus, the model that our algorithm uses is feasible.Figure 2: The architectural layout used by DELVER.Reality aside, we would like to evaluate a design for how DELVER might behave in theory. Furthermore, we assume that operating systems and the producer-consumer problem can synchronize to surmount this problem. While information theorists always assume the exact opposite, our methodology depends on this property for correct behavior. The question is, will DELVER satisfy all of these assumptions? The answer is yes.3 ImplementationThough many skeptics said it couldn't be done (most notably K. Ramanathan et al.), we construct a fully-working version of our system. DELVER requires root access in order to learn self-learning symmetries. DELVER is composed of a collection of shell scripts, a virtual machine monitor, and a virtual machine monitor. It was necessary to cap the instruction rate used by our system to 2498 teraflops. One is not able to imagine other solutions to the implementation that would have made implementing it much simpler.4 Experimental EvaluationOur evaluation represents a valuable research contribution in and of itself. Our overall evaluation methodology seeks to prove three hypotheses: (1) that e-commerce no longer influences complexity; (2) that interrupt rate stayed constant across successive generations of Apple Newtons; and finally (3) that an approach's user-kernel boundary is not as important as an application's legacy ABI when maximizing median power. An astute reader would now infer that for obvious reasons, we have decided not to refine effective response time. Along these same lines, we are grateful for discrete 16 bit architectures; without them, we could not optimize for usability simultaneously with usability constraints. Third, our logic follows a new model: performance matters only as long as security constraints take a back seat to popularity of replication. We hope to make clear that our patching the ABI of our distributed system is the key to our evaluation method.4.1 Hardware and Software ConfigurationFigure 3: Note that seek time grows as block size decreases - a phenomenon worthemulating in its own right.Our detailed performance analysis necessary many hardware modifications. We scripted a simulation on MIT's 2-node overlay network to quantify T. U. Qian's synthesis of neural networks in 1999 []. To start off with, physicists added 2Gb/s of Ethernet access to our system to understand the effective RAM speed of DARPA's human test subjects. We removed 100MB of flash-memory from the NSA's metamorphic overlay network to understandtechnology. Had we prototyped our trainable overlay network, as opposed to emulating it in courseware, we would have seen duplicated results. Along these same lines, we removed more USB key space from CERN's network to prove collectively linear-time methodologies's impact on the work of Soviet chemist Ivan Sutherland. Next, we quadrupled the hit ratio of our human test subjects to consider information. On a similar note, we removed 25 CPUs from our 10-node overlay network to understand our mobile telephones []. In the end, we removed some NV-RAM from our human test subjects to disprove the paradox of cryptoanalysis.Figure 4: Note that latency grows as work factor decreases - a phenomenon worthimproving in its own right.We ran DELVER on commodity operating systems, such as Ultrix Version 0.8.0, Service Pack 9 and AT&T System V. all software components were hand assembled using Microsoft developer's studio linked against trainable libraries for enabling SCSI disks. All software was hand assembled using GCC 8.0 with the help of Richard Stearns's libraries for provably exploring vacuum tubes. Our purpose here is to set the record straight. All software components were linked using Microsoft developer's studio built on James Gray's toolkit for lazily enabling average throughput. We made all of our software is available under a very restrictive license.Figure 5: These results were obtained by Adi Shamir []; we reproduce them here for clarity. Even though such a hypothesis is continuously a robust ambition, it largely conflicts with the need to provide cache coherence to hackers worldwide.4.2 Experiments and ResultsFigure 6: These results were obtained by Lee et al. []; we reproduce them here forclarity.Figure 7: The median interrupt rate of our application, as a function of hit ratio[].Is it possible to justify the great pains we took in our implementation? Yes. We ran four novel experiments: (1) we measured optical drive throughput as a function of ROM space on a Nintendo Gameboy; (2) we ran 08 trials with a simulated RAID array workload, and compared results to our bioware emulation; (3) we measured E-mail and DNS latency on our system; and (4) we compared median popularity of rasterization on the FreeBSD, L4 and L4 operating systems. All of these experiments completed without paging or unusual heat dissipation. Such a claim might seem counterintuitive but fell in line with our expectations.We first analyze the first two experiments as shown in Figure 5 [,,]. Operator error alone cannot account for these results. These instruction rate observations contrast to those seen in earlier work [], such as B. Harris's seminal treatise on I/O automata and observed ROM throughput. This is essential to the success of our work. Note that 4 bit architectures have smoother clock speed curves than do modified journaling file systems.We next turn to the first two experiments, shown in Figure 4. This is essential to the success of our work. Note the heavy tail on the CDF in Figure 5, exhibiting amplified median time since 2001. the key to Figure 3is closing the feedback loop; Figure 6shows how our heuristic's effective ROM speed does not converge otherwise. Next, note the heavy tail on the CDF in Figure 4, exhibiting amplified median instruction rate.Lastly, we discuss the first two experiments. The many discontinuities in the graphs point to muted median latency introduced with our hardware upgrades. Note how emulating Byzantine fault tolerance rather than emulating them in middleware produce smoother, more reproducible results.Furthermore, error bars have been elided, since most of our data points fell outside of 14 standard deviations from observed means.5 Related WorkWe now consider related work. Thomas [] developed a similar application, unfortunately we verified that our methodology is in Co-NP [,,,,]. Unlike many previous methods [], we do not attempt to control or provide the producer-consumer problem []. DELVER is broadly related to work in the field of hardware and architecture by Suzuki [], but we view it from a new perspective: hierarchical databases [,,,,]. Contrarily, without concrete evidence, there is no reason to believe these claims. Unfortunately, these solutions are entirely orthogonal to our efforts.Instead of architecting read-write configurations [,], we achieve this goal simply by visualizing cooperative theory []. We had our method in mind before Raj Reddy published the recent well-known work on reliable models. Our design avoids this overhead. Unlike many prior approaches, we do not attempt to synthesize or emulate the construction of Scheme []. DELVER represents a significant advance above this work. These frameworks typically require that the memory bus [] and interrupts can collaborate to accomplish this intent, and we validated here that this, indeed, is the case.We now compare our method to previous certifiable technology methods. Further, instead of architecting the development of flip-flop gates [,], we accomplish this purpose simply by controlling spreadsheets []. DELVER represents a significant advance above this work. Unlike many previous approaches, we do not attempt to develop or simulate the deployment of journaling file systems []. Instead of emulating distributed configurations [], we achieve this purpose simply by improving the partition table. All of these approaches conflict with our assumption that wireless technology and highly-available methodologies are structured [,].6 ConclusionIn our research we introduced DELVER, a methodology for superpages. Along these same lines, we also explored a framework for model checking []. Weused compact epistemologies to argue that kernels and theproducer-consumer problem can connect to overcome this riddle. We expect to see many researchers move to visualizing DELVER in the very near future.。
AbstractThe present paper describes an intelligent system AUTOPROMOD developed for automatic modeling of progressive die. The proposed system utilizes interfacing of AutoCAD and AutoLISP for automatic modeling of die components and die assembly. The system comprises eight modules, namely DBMOD, STRPRMOD, BPMOD, PPMOD, BBDSMOD, TBDSMOD, BDAMOD and TDAMOD. The system modules work in tandem with knowledge-based system (KBS) modules developed for design of progressive die components. The system allows the user firstly to model the strip-layout and then utilizes output data files generated during the execution of KBS modules of die components for automatic modeling of progressive die. An illustrative example is included to demonstrate the usefulness of the proposed system. The main feature of the system is its facility of interfacing die design with modeling. A semiskilled and even an unskilled worker can easily operate the system for generation of drawings of die components and die assembly. System modules are implementable on a PC having AutoCAD software and thus its low cost of implementation makes it affordable for small and medium sized stamping industries.本文描述了一种智能系统AUTOPROMOD开发级进模的自动建模。
NASA Technical Memorandum 112871Lyceum: A Multi-Protocol Digital Library GatewayMing-Hokng MaaMassachusetts Institute of Technology, Cambridge, Massachusetts Sandra L. EslerUniversity of Alabama, Tuscaloosa, AlabamaMichael L. NelsonLangley Research Center, Hampton, VirginiaJuly 1997National Aeronautics andSpace AdministrationLangley Research CenterHampton, Virginia 23681-0001Lyceum: A Multi-Protocol Digital Library GatewayMing-Hokng Maammaa@Massachusetts Institute of Technology Cambridge, MA 02139Sandra L. Eslersesler@University of AlabamaTuscaloosa, AL 354877Michael L. Nelsonm.l.nelson@NASA Langley Research Center, MS 124Hampton, VA 23681AbstractLyceum is a prototype scalable query gateway that provides a logically central interface to multi-protocol and physically distributed, digital libraries of scientific and technical information. Lyceum processes queries to multiple syntactically distinct search engines used by various distributed information servers from a single logically central interface without modification of the remote search engines. A working prototype (/lyceum/) demonstrates the capabilities, potentials, and advantages of this type of meta-search engine by providing access to over 50 servers covering over 20 disciplines.IntroductionInternet document and information archival, indexing, and distribution is typically embodied in widely heterogeneous and distributed information servers, employing search engines with user interfaces and query syntax that vary significantly. The incompatibility and non-interoperability between search engines and the lack of a common and unified interface between users and distributed information servers present a significant challenge to the design of meta-search engines and the indexing and retrieval of comprehensive and apropos information on the Internet. Because of this growing problem we developed Lyceum(/lyceum/),a working scalable query gateway meta-search engine that provides a common and unified interface to widely heterogeneous and distributed information servers.DesignA number of digital libraries exist on the World Wide Web (WWW). However, many overlap with other information servers and are incomplete, both in terms of the content they provide and the subject areas they cover. This requires the user to have detailed knowledge of where the various digital libraries are and what resources can be found in them. In short, users must perform extensive integration of the information they receive from various sources (Figure 1). In addition, these information servers often utilize different search engines to index their information. Various search engines, e.g., Harvest [Bowman, et al., 1995], Wide Area Information Server (WAIS) [Kahle, et al., 1992], freeWAIS-sf [Pfeifer, et al., 1995]. often require different syntax for search functions such as Boolean searches, result limiting and ranking, case sensitivity, search method, and other miscellaneous return options. This variability has typically discouraged attempts to implement meta-search engines. We have previously examined various digital library architectures and have found the distributed architecture with the contributors being the authoring organization or individuals as the most desirable architecture (Esler & Nelson, 1997). There are two primary advantages to distributing information among multiple servers versus implementing one centralized information server:1. Each information server is now responsible only for maintaining information localto an organization.2. One canonical information server or database which covers the entire spectrum ofscientific research is patently neither feasible nor desirable.What is needed is a logically central interface to physically distributed and heterogeneous databases. As a gateway server, Lyceum allows users to query syntactically distinct search engines from a single interface by formatting a userÕs query submission to conform to the appropriate search engine query syntax and options (Figure 2).Because of the rapid pace with which new information servers are established, Lyceumwas designed for scalability. The ability to add both individual information servers and other meta-search engines similar to Lyceum enables Lyceum to access many already cataloged information servers by taking advantage of pre-existing services such as NCSTRL [Davis, et al., 1995],building on the work of others rather than recompiling databases of individual information servers.Within a distributed architecture, there are two methods to aggregating digital library resources:1. Encourage the proliferation and wide spread adoption of a single digital library protocol2. Provide a protocol conversion functionality to gateway between heterogeneous resourcesFigure 1: User Performs Multiple SearchesFigure 2: Lyceum Performs Multiple Searches for the UserThe first method is what is employed by NCSTRL and NCSTRL+ [Nelson, et al., 1997]. The second method is that adopted by Lyceum. Finally, Lyceum was designed to require no special input or coordination from the remote information servers. Inclusion of and gatewaying to the remote information servers is performed without any action from the remote servers. Indeed,direct queries from users or from Lyceum are indistinguishable to the remote servers.ImplementationLyceum runs on a UNIX platform as a package of Perl Common Gateway Interface (CGI) scripts separated into client, server database, and administration scripts. Because Lyceum typically gateways to numerous information servers per query, Lyceum sends queries to information servers in parallel. This significantly reduces the userÕs idle time by shifting the time dependence of query results away from slow bottleneck servers and stalled connections towards a process where query results are returned as soon as they are available. In addition, this also prevents bad Internet connections from deadlocking the entire query. The Lyceum client,therefore, is a multi-forking client designed to gateway query requests in parallel. As users submit queries to Lyceum, the client forks and submits an individually tailored query (dependent on the requirements of each search engine) to each information server. When all requests have been forked, the client waits for the results of each query, post-formatting and merging the returned query results, and displaying the results to the user as they return. This implementation is similar to the parallel search algorithm implemented in the NASA Technical Report Server (NTRS)[Nelson & Maa, 1996].Secondly, Lyceum was written with the goal of data type independence. Because Lyceum does not explicitly maintain records of the information type archived at various information servers, Lyceum may be used to index not only scientific information, but any arbitrary indexed information. To achieve data type independence, the server database, which maintains information on each of the information servers accessed by Lyceum, simply consists of a structured directory tree (Figure 3). Renaming the directory structure automatically changes the information type that users see. Each file in this directory structure is a record for an information server, containing relevant descriptors and most importantly, the unique uniform resource locator (URL) that is used to query the serverÕs search engine. This form URL stores the syntax rules by which to format a userÕs query string for compatibility with a particular search engine. Figure 4 shows an example of the data files used to describe individual servers in Lyceum. Although new values can be added, the following values are currently used:Type : WAIS, Glimpse, Split, Other Category : aerodynamics, aeronautics, astronautics, biology, chemistry, computer,earth, economics, energy, engineering, environmental, geography, geology,hierarchy, materials, mathematics, medicine, meteorology, multi-categories,nonlinear, physical, physics, psychology, social, zoology Sub-Category : Technical-Reports, Journals, Proceedings, Bibliographies,Newsletters, OtherObjectsaeronautics. . .. . .chemistry physicsindex.html chemistry.1chemistry.2. . .Figure 3: Objects Placed in the Subject CategoriesFinally, Lyceum was designed to be as maintenance free as possible. All system administration takes place through a WWW interface, providing a flexible layer that shields the administrator from the programming code (Figure 5). All query interfaces are generated on the fly according to the contents of the server database, erasing the need for periodic updating of the server database. Because query interfaces are generated on the fly (Figures 6 & 7), delays slightly increase as the server database populates. Currently, the delay is minimal, but if LyceumÕs population grew to the point where the dynamic construction of the interface became noticeable to the user, we could switch the interface construction to be static and periodically regenerated (e.g.,every 12 hours). The search results interface is a concatenation of what the various servers would return if they had been searched individually. Typical examples are too lengthy to be placed in a typical figure, so the reader is encouraged to experiment.Figure 5: The Lyceum Administrative Interface and FunctionsName: Electronic Conference on Trends in Organic Chemisty (ECTOC)Category: chemistry Sub-Category: Proceedings Description:Home: /ectoc/Date: Fri Aug 2 10:11:40 EDT 1996Form: /cgi-bin/pursuit-ectoc?$keywords Type: Other:+Boolean: YES POC:POCemail:POCphone:Comments:Figure 4: File Format for an ObjectFigure 6: Initial Lyceum Interface Figure 7: Lyceum Interface, 2 Subjects ChosenRelated WorkFew digital library projects attempt to build a multi-discipline digital library. The University of Illinois Urbana-ChampaignÕs portion of the Digital Library Initiative (DLI) [Shatz, et al., 1996] does, but it has explicit agreements with various journal publishers to provide their titles using a homogeneous protocol. Similarly, the NCSTRL+ project builds its multi-discipline project using a homogeneous protocol, but has the authoring organizations as participants instead of traditional publishers.Perhaps the most similar to Lyceum is StanfordÕs STARTS: Stanford Protocol Proposal for Internet Retrieval and Search project [Gravano, et al., 1997]. STARTS proposes to implement a minimally common query language and protocol based on a simple subset of the Z39.50-11995 type-101 [Z39.50, 1995] query language and the Government Information Locator Service (GILS) attribute set [Christian, 1996]. In order for a search engine to support meta-searching, it must be actively modified to support this basic query language and protocol. In contrast, Lyceum requires no modifications on the part of information servers or the search engines that are used. Rather, knowledge of the query syntax of a particular search engine enables Lyceum to format the query into an equivalent and valid query to each of the remote information servers. While this method may not provide a permanent or long-term solution to distributed document indexing and retrieval, it does provide an efficient and working interim solution.A historically similar project was the Unified Computer Science Technical Report Index (UCSTRI) [Van Heyningen, 1994]. UCSTRI would use a collection of heustrics to index various computer science department anonymous ftp servers, parsing their README and abstracts files. UCSTRI was similar to Lyceum in that both require no coordination or modification with the provider of the original server. UCSTRI is still functional, but has been superseded by NCSTRL. Discussion and Future WorkVariability in search syntax, search options, and output is inherent with different search engines. To provide a common gatewaying interface to multiple distributed information servers, Lyceum must by necessity either ignore or modify certain options that are available in certain search engines but missing in others. For example, while nearly all modern search engines provide Boolean searches, some older search engines do not. For this particular situation, in formatting the user query by the appropriate syntax rules, Lyceum filters out the Boolean syntax from query strings sent to search engines that do not support Boolean searches.Increased protocol and syntax conversion is an area for improvement with Lyceum. Other areas include obtaining user feedback about improved formatting of search and results interfaces, more automation in the areas of maintenance and resource discovery, and more sophisticated cataloging techniques. Currently, Lyceum gateways to 56 information servers spanning 22 scientific and engineering disciplines ranging from aeronautics to zoology. The information servers included range in diversity from government data servers to journal archives. While this has been sufficient to demonstrate Lyceum conceptually, in order for Lyceum to demonstrate true application, many more information servers must be included. We encourage others to test, evaluate, and contribute resources to Lyceum.ConclusionWe developed Lyceum, a working prototype of a scalable query gateway that provides a logically common and local interface to widely heterogeneous and physically distributed scientific and technical digital libraries. Lyceum allows users to query multiple, syntactically distinct search engines used by various distributed information servers from a single logically central interface without modification of the remote search engines. The current working prototype, incorporating more than 56 information servers and meta-servers across 22 scientific and technical disciplines, demonstrates the capabilities, potentials, and advantages of this type of meta-search engine. Suggestions and contributions to Lyceum are welcome. Contact the authors for more information.ReferencesBowman, C. Mic; Danzig, Peter B.; Hardy, Darren R.; Manber, Udi ; Schwartz, Michael F. & Wessels, Duane P. (1995). Harvest: A Scalable, Customizable Discovery and AccessSystem. University of Colorado Computer Science Technical Report CU-CS-732-94. Christian, Elliot (1996). GILS: What is it? Where is it going?, D-lib Magazine, December 1996, /dlib/december96/12christian.htmlDavis, James R.; Krafft, Dean B.; & Lagoze, Carl (1995). Diesnt: Building a Production Technical Report Server. Advances in Digital Libraries, Springer-Verlag, pp. 211-222. Esler, Sandra L. & Nelson, Michael L. (1997). The Evolution of Scientific and Technical Information Distribution, to appear in the Journal of the American Society forInformation Science.Gravano, L.; Chang, C.-C. K.; Garcia-Molina, H.; Paepcke, A. (1997). STARTS: Stanford Proposal for Internet Meta-Searching, Proceedings of the International Conferenceon Management of Data (SIGMOD), May 12-15, Tuscon, AZ.Kahle, Brewster; Morris, Harry; Davis, Franklin; Tine, Kevin; Hart, Clare & Palmer, Robin (1992). Wide Area Information Servers: An Executive Information Systemfor Unstructured Files. Electronic Networking: Research, Applications, and Policy,2(1), pp. 59-68.Nelson, Michael L. & Maa, Ming-Hokng (1996). Optimizing the NASA Technical Report Server, Internet Research: Electronic Networking Applications and Policy, 6(1), pp. 64-70. Nelson, Michael L.; Maly, Kurt; Shen, Stewart N. T. (1997). Buckets, Clusters, and Dienst.Old Dominion University Computer Science Technical Report 97-30 and NASATM-112877.Pfeifer, Ulrich; Fuhr, Norbert; & Huynh, Tung (1995). Searching Structured Documents with the Enhanced Retrieval Functionality of freeWAIS-sf and Sfgate. Computer Networksand ISDN Systems, 27, pp. 1027-1036.Schatz, B.; Mischo, W. H.; Cole, T. W.; Hardin, J. B.; Bishop, A. P. & Chen, H. (1996).Federating Diverse Collections of Scientific Literature. IEEE Computer, 29(5), pp. 28-36. Van Heyningen, Marc (1994). The Unified Computer Science Technical Report Index: Lessons in indexing diverse resources. Proceedings of the Second International World Wide Web Conference, Chicago, IL, October 19-21, 1994, pp. 535-543.Z39.50-1995 Maintenance Agency (1995). /z3950/agency/1995doce.htmlREPORT DOCUMENTATION PAGE Form ApprovedOMB No. 0704-0188Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188), Washington, DC 20503.1. AGENCY USE ONLY(Leave blank)2. REPORT DATEJuly 19973. REPORT TYPE AND DATES COVERED Technical Memorandum4. TITLE AND SUBTITLELyceum: A Multi-Protocol Digital Library Gateway5. FUNDING NUMBERS6. AUTHOR(S)Ming-Hokng Maa, Sandra L. Esler, Michael L. Nelson7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)NASA Langley Research CenterHampton, VA 23681-21998. PERFORMING ORGANIZATIONREPORT NUMBER9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)National Aeronautics and Space AdministrationWashington, DC 20546-000110. SPONSORING/MONITORINGAGENCY REPORT NUMBER NASA TM-11287111. SUPPLEMENTARY NOTESMing-Hokng Maa, Massachusetts Institute of Technology, Cambridge MA; Sandra L. Esler, University of Alabama, Tuscaloosa, AL; Michael L. Nelson, NASA Langley Research Center, Hampton, VA12a. DISTRIBUTION/AVAILABILITY STATEMENTUnclassifiedÐUnlimitedSubject Category 82 Distribution: NonstandardAvailability: NASA CASI (301) 621-039012b. DISTRIBUTION CODE13. ABSTRACT (Maximum 200 words)Lyceum is a prototype scalable query gateway that provides a logically central interface to multi-protocol and physically distributed, digital libraries of scientific and technical information. Lyceum processes queries to multiple syntactically distinct search engines used by various distributed information servers from a single logically central interface without modification of the remote search engines. A working prototype(/lyceum/) demonstrates the capabilities, potentials, and advantages of this type of meta-search engine by providing access to over 50 servers covering over 20 disciplines.14. SUBJECT TERMSWWW, Digital Libraries, STI, Distributed Information Retrieval 15. NUMBER OF PAGES916. PRICE CODEA0217. SECURITY CLA SSIFICATIO NOF REPORTUnclassified 18. SECURITY CLA SSIFICATIO NOF THI S PAGEUnclassified19. SECURITY CLASSIFICATIONOF ABSTRACTUnclassified20. LIMITATIONOF ABSTRACTNSN 7540-01-280-5500 Standard Form 298 (Rev. 2-89)Prescribed by ANSI Std. Z-39-18298-102。
WorkgroupMiddlewareforDistributedProjectsGailE.KaiserandStephenE.DossickColumbiaUniversityDepartmentofComputerScience1214AmsterdamAvenue,MC0401NewYork,NY10027USA212-939-7000/fax:212-939-7084kaiser,sdossick@cs.columbia.edu
ABSTRACTWehavedevelopedamiddlewareframeworkforworkgroupenvironmentsthatcansupportdistributedsoftwaredevelop-mentandavarietyofotherapplicationdomainsrequiringdocumentmanagementandchangemanagementfordistributedprojects.Theframeworkenableshypermedia-basedintegra-tionofarbitrarylegacyandnewinformationresourcesavail-ableviaarangeofprotocols,notnecessarilyknowninad-vancetousasthegeneralframeworkdevelopersnoreventotheenvironmentinstancedesigners.TherepositoriesinwhichsuchinformationresidesmaybedispersedacrosstheInternetand/oranorganizationalintranet.Theframeworkalsopermitsarangeofclientmodelsforuserandtoolinter-action,andappliesanextensiblesuiteofcollaborationser-vices,includingbutnotlimitedtomulti-participantworkflowandcoordination,totheirinformationretrievalsandupdates.Thatis,theframeworkisinterposedbetweenclients,servicesandrepositories—thus“middleware”.Weexplainhowourframeworkmakesiteasytorealizeacomprehensivecollec-tionofworkgroupandworkflowfeaturesweculledfromarequirementssurveyconductedbyNASA.KEYWORDS:Distributedsoftwaredevelopmentsupport,distributeddocumentmanagement,monitoringandmanag-ingdistributeddevelopmentprocesses,distributedchangeman-agement,workflowmanagementandcoordinationsupportindistributedprojects,Internet-basedsoftwareprocesscoordi-nationINTRODUCTIONWehavedevelopedamiddlewareframeworkfordistributedworkgroupenvironmentsfoundedonwhatwecallareferen-tialhyperbaseparadigmforrepresentingandhyper-linkingtheinformationresourcesofinteresttoacollaboratingteamintenttowardssomejointpurposeorgoal(asopposedtoin-cidentallybrowsingthesamematerialsforindependentuse,suchasmostWorldWideWebusers).Areferentialhyper-basediffersfromaconventionalhyperbaseinthatthedoc-umentsneednotresideinthesystem’sownrepository.Areferentialhyperbasediffersfromalinkbaseorlinkserverinprovidingfullobject-orienteddatabasefunctionalityinclud-ingaquerylanguageandentitytyping.
Thereferentialhyperbasesystemcommunicateswithavari-etyoflocalandremoteinformationrepositories,eachthroughitsoriginalprotocol.Userclientsandtoolsinteractwiththehyperbaseinclient/serverfashion,alsoeachthroughtheirappropriateprotocol.Thehyperbasemaycontainbothna-tiveobjects,internaltothesystem,andprotocolobjects,rep-resentingexternaldocumentsresidinginoneofthereposi-tories.Nativeobjectsareretrievedandpresentedtoclientsentirelythroughthehyperbase’sownobjectmanagementfa-cilities,butaccessingthecontentsofprotocolobjectsgener-allyinvolvesforwardingtheappropriateoperation(s)totheirhomerepositoriesandtransmittingtheresultsthroughthehy-perbasebacktotheoriginatingclient.Anextensiblesetofcollaborativeworksupportservicesareperformedexplicitlyinresponsetorequestsfromclientsand/orimplicitlyuponaccessestoobjects,independentoftheprotocol(s)used.Thesegroupspace,orgroupworkspace,servicesmaysendandre-ceiverequeststo/fromeachotheraswellasthroughthepro-tocolstobackendrepositoriesandfrontendclients.
Oursystemdoesnothardwireanyparticularprotocolsforinteractingwithbackendrepositoriesorfrontendclients.In-stead,backendaccessprotocolsprovidemeanstoreadandwritethedocumentsrequiredbytheworkgroup,andaddi-tionalpredicatesandactionsmayexposethecapabilitiesofspecificlegacyandnewinformationresources.Frontendpre-sentationprotocolstailoroursystemsothatitappearsto
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BackendHeterogeneousInformationRepositories
FrontendHeterogeneousUser/ToolClients
Client Interface
Repository Interface Hyperbase
Data Access
Protocol Accessobject layerModules
ModulesGroupspaceInterface
GroupspaceServiceProviders
GroupspaceServiceRoles
Figure1:FrameworkArchitectureclientsasiftheyarecommunicatingwiththeirowninfor-mationserver.Thususerscancontinueusingtheirfamiliarlegacysystemcommands,ifdesired,whilestillreceivingthebenefitsofthemulti-protocolworkgroupframework.
Wearecurrentlyrethinkingandreimplementingworkflowandtransaction-basedcoordinationcomponentspreviouslyintegratedwithourearlier,single-protocol(HTTP)referen-tialhyperbasesystemtargetedspecificallytodistributedsoft-waredevelopmentprojects[11].Wehavealsoportedandcontinuetodevelopthatsystem’sdistributedtoollaunchingcomponent[12].[9]describesourexperienceusingtheear-lierHTTP-onlysystemforourowncontinuingsoftwarede-velopmentefforts.Ournewmulti-protocolframeworkisin-troducedin[10].Herewebrieflydiscussthenewframeworkandourinitialimplementation,andthenfocusonhowiten-abledustorapidlyfulfillmostofthegenericworkgroupre-quirementsadaptedfromthepubliclyavailableresultsofasurveyconductedwithinNASAin1995[8].
