2004b), Transformations in information supply

ICES-003Issue 4February 2004Spectrum Management and Telecommunications PolicyInterference-Causing Equipment StandardDigital ApparatusTable of ContentsPage1.General (1)2.Definitions (2)3.Instrumentation (3)4.Method of Measurement (3)5.Limits (3)6.Procedural Requirements (4)7.Reference Publication (4)Annex (5)1.General1.1This Interference-Causing Equipment Standard sets out the technical requirements relative tothe radiated and conducted radio noise emissions from digital apparatus.1.2.1Subject to Sections 1.2.2 and 1.2.3, Sections 3 to 7 apply to every digital apparatus, the modelof which is newly manufactured in or imported into Canada, except digital apparatusmanufactured or imported solely for export purposes.1.2.1.1 A transition period ending June 1, 2004 is provided, within which compliance with eitherICES-003 Issue 3 or ICES-003 Issue 4 will be accepted. After June 1, 2004, only compliancewith ICES-003 Issue 4 will be accepted.1.2.2Sections 3 to 7 do not apply to digital apparatus used:(a)in a transportation vehicle;(b)as an electronic control, either by a public utility or in an industrial plant;(c)in a power system, either by a public utility or in an industrial plant;(d)as test equipment, including an oscilloscope and a frequency counter, in an industrial,commercial or medical environment;(e)as a medical computing device, under the direction of a licensed health care practitioner;(f)in machinery, apparatus or equipment:(i)the primary function of which is to apply energy to a process or material through theaction of an electric motor or a resistive heating element;(ii)that draws a steady-state current that does not exceed:(A)in the case of an electric motor, 20 A RMS;(B)in the case of an electric heating element, used either alone or in conjunction withan electric motor, 50 A RMS;(iii)that operates from an alternating current voltage supply that does not exceed 150 V RMS; or(iv)where the machinery, apparatus or equipment is a portable tool and has an input power that does not exceed 2 kW.(g)in central office telephone equipment operated by a telecommunications common carrier ina central office;(h)in a device having a power consumption not exceeding 6 nW;(i)in a device in which both the highest frequency generated and the highest frequency usedare less than 1.705 MHz and which neither operates from, nor contains provision foroperation while directly or indirectly connected to the AC power lines;(j)solely for demonstration and exhibition purposes; or(k)as a prototype unit.1.2.3(1)Sections 3 to 7 do not apply to units or models of digital apparatus for which themanufacturer, importer or owner has been granted a special permission by the Minister.(2)The Minister may grant a special permission where:(a)the manufacturer, importer or owner has presented a written application giving:(i)the reasons for the request;(ii)an analysis based on sound engineering principles showing that the unit ormodel of digital apparatus will not pose a significant risk toradiocommunication; and(iii) a guarantee of compliance with all the conditions the Minister may set in thespecial permission.(b)and, the Minister is satisfied that the unit or model will not pose a significant risk toradiocommunication.(3)The special permission is valid only if:(a)the unit bears a label stating that it is operating under special permission and settingout the conditions of that special permission; and(b)the unit complies with all conditions set out in the special permission.(4)The Minister may revoke or amend the special permission granted under subsection (2) atany time without prior notice.2.Definitions2.1In this Standard,"digital apparatus" means an electronic apparatus that generates and uses timing signals at arate in excess of 10,000 pulses per second and that utilizes radio frequency energy for thepurpose of performing functions including computations, operations, transformations,recording, filing, sorting, storage, retrieval and transfer, but does not include an ISM(industrial, scientific or medical) radio frequency generator."Class A digital apparatus" means a model of digital apparatus for which, by virtue of itscharacteristics, it is highly unlikely that any units of the model will be used in a residentialenvironment, which includes a home business. Characteristics considered to be applicable inthis assessment include: price, marketing and advertising methodology, the degree to which the functional design inhibits applications suitable to residential environments or any combination of features which would effectively preclude its use in a residential environment."Class B digital apparatus" means any model of digital apparatus that cannot qualify as ClassA digital apparatus.3.Instrumentation3.1Instrumentation shall be in accordance with the publication referred to in Section 7.1.4.Method of Measurement4.1 A representative type or model of each digital apparatus shall be tested in accordance with themeasurement method described in the publication referred to in Section 7.1.4.2The field intensity of radiated radio noise emissions may be measured at a distance other thanthat prescribed in subsection 4.1 but in such case the measurement result shall be extrapolatedto the prescribed distance in accordance with the publication referred to in Section 7.1.5.Limits5.1The limits of radio noise set out in Sections 5.2 to 5.5 do not apply to a unit of digital apparatuswhile it is being tested for compliance with this Standard.5.2The voltage of radio noise emissions that are conducted along the power supply lines of aClass A digital apparatus shall not exceed the limits specified in Table 1 of the publicationreferred to in Section 7.1, within the indicated frequency range.5.3The voltage of radio noise emissions that are conducted along the power supply lines of aClass B digital apparatus shall not exceed the limits specified in Table 2 of the publicationreferred to in Section 7.1, within the indicated frequency range.5.4The field intensity of radio noise emissions that are radiated from a Class A digital apparatusshall not exceed the limits specified in Table 5 of the publication referred to in Section 7.1,within the indicated frequency range.5.5The field intensity of radio noise emissions that are radiated from a Class B digital apparatusshall not exceed the limits specified in Table 6 of the publication referred to in Section 7.1,within the indicated frequency range.6.Procedural Requirements6.1 A record of the measurements and results, showing the date that the measurements werecompleted, shall be retained by the manufacturer or importer for a period of at least five years from the date shown in the record and made available for examination on the request of theMinister.6.2 A written notice indicating compliance must accompany each unit of digital apparatus to theend user. The notice shall be in the form of a label that is affixed to the apparatus. Wherebecause of insufficient space or other constraints it is not feasible to affix a label to theapparatus, the notice may be in the form of a statement included in the user's manual. Asuggested text for the notice, in English and in French, is provided in the Annex.7.Reference Publication7.1This Standard refers to the following publication and where such reference is made, it shall beto the edition listed below.Canadian Standards Association Standard CAN/CSA-CEI/IEC CISPR 22:02, "Limits andMethods of Measurement of Radio Disturbance Characteristics of Information TechnologyEquipment."Issued under the authority ofIndustry CanadaR.W. McCaughernActing Director GeneralSpectrum EngineeringAnnexSuggested text for the notice indicating compliance with this Standard:This Class [*] digital apparatus complies with Canadian ICES-003.Cet appareil numérique de la classe [*] est conforme à la norme NMB-003 du Canada. [*] Insert either "A" or "B" but not both as appropriate for the equipment requirements.。

J.Dairy Sci.90:1122–1132©American Dairy Science Association,2007.Evaluating Mid-infrared Spectroscopy as a New Technique for Predicting Sensory Texture Attributes of Processed CheeseC.C.Fagan,*1C.Everard,*C.P.O’Donnell,*G.Downey,†E.M.Sheehan,‡C.M.Delahunty,§andD.J.O’Callaghan ʈ*Biosystems Engineering,UCD School of Agriculture,Food Science and Veterinary Medicine,University College Dublin,Earlsfort Terrace,Dublin 2,Ireland†Teagasc,Ashtown Food Research Centre,Dublin 15,Ireland‡Department of Nutritional Sciences,University College Cork,Cork,Ireland§Department of Food Science,University of Otago,PO Box 56,Dunedin 9015,New Zealand ʈTeagasc,Moorepark Food Research Centre,Fermoy,Co.Cork,IrelandABSTRACTThe objective of this study was to investigate the po-tential application of mid-infrared spectroscopy for de-termination of selected sensory attributes in a range of experimentally manufactured processed cheese samples.This study also evaluates mid-infrared spectroscopy against other recently proposed techniques for pre-dicting sensory texture attributes.Processed cheeses (n =32)of varying compositions were manufactured on a pilot scale.After 2and 4wk of storage at 4°C,mid-infrared spectra (640to 4,000cm −1)were recorded and samples were scored on a scale of 0to 100for 9attributes using descriptive sensory analysis.Models were devel-oped by partial least squares regression using raw and pretreated spectra.The mouth-coating and mass-form-ing models were improved by using a reduced spectral range (930to 1,767cm −1).The remaining attributes were most successfully modeled using a combined range (930to 1,767cm −1and 2,839to 4,000cm −1).The root mean square errors of cross-validation for the models were 7.4(firmness;range 65.3),4.6(rubbery;range 41.7),7.1(creamy;range 60.9),5.1(chewy;range 43.3),5.2(mouth-coating;range 37.4),5.3(fragmentable;range 51.0),7.4(melting;range 69.3),and 3.1(mass-forming;range 23.6).These models had a good practical utility.Model accuracy ranged from approximate quantitative predic-tions to excellent predictions (range error ratio =9.6).In general,the models compared favorably with previously reported instrumental texture models and near-infrared models,although the creamy,chewy,and melting models were slightly weaker than the previously reported near-infrared models.We concluded that mid-infrared spec-troscopy could be successfully used for the nondestruc-tive and objective assessment of processed cheese sen-sory quality.Received April 12,2006.Accepted October 30,2006.1Corresponding author:colette.fagan@ucd.ie1122Key words:descriptive sensory analysis,processed cheese,mid-infrared spectroscopy,chemometricsINTRODUCTIONOver 18million tonnes of cheese were produced world-wide in 2004,and processed cheese is an important seg-ment of this market (Wohlfarth and Richarts,2005).The United States,the largest producer of processed cheese (where 20%of all cheese consumed is processed cheese),produced 1,092,000tonnes in 2003(Wohlfarth and Ric-harts,2005).In the same year,the 25countries of the European Union produced 655,000tonnes of processed cheese (Wohlfarth and Richarts,2005).Consumer preference for a food product is principally determined by its sensory characteristics.Accurate mon-itoring and control of sensory properties will facilitate the production of high-quality products.A number of factors determine the final quality and sensory proper-ties of processed cheese (Caric´and Kala ´b,1993).These include the processing conditions used during manufac-ture,the composition of the ingredients,and the propor-tions of those ingredients added to the blend.Sensory profiling allows various quality attributes to be identified and their intensity determined (Brown et al.,2003).Sensory attributes are traditionally assessed by descriptive sensory evaluation using trained panel-ists.However,this is a time-consuming and expensive process that may lack objectivity (Blazquez et al.,2006).Although instrumental techniques such as texture pro-file analysis (TPA )and the 3-point bend test are avail-able for determining the texture attributes of food prod-ucts,these laboratory-based techniques are time-con-suming and require the use of skilled personnel in their execution (Blazquez et al.,2006).Therefore,considerable interest exists in the development of instrumental tech-niques to enable more objective,faster,and less expen-sive assessments of cheese quality to be made,including sensory aspects (Downey et al.,2005).Such a technique would assist producers to maximize yields,increasePREDICTION OF CHEESE SENSORY TEXTURE ATTRIBUTES1123throughput and efficiency,reduce labor costs,and opti-mize product quality,consistency,and customer satisfac-tion.Critical points in the manufacturing process could be monitored to ensure that the final product would meet required specifications.Recently,Kealy (2006)examined cream cheese using TPA,one of the main instrumental techniques for tex-ture measurement,and compared the results with those of a trained taste panel.Although a reasonably strong correlation was found between the taste panel results and TPA-derived hardness and adhesiveness parame-ters,the correlation for cohesiveness was not straightfor-ward.Everard (2005)also investigated the prediction of sensory attributes of processed cheese from instrumen-tal texture attributes derived from TPA,a compression test,and a 3-point bend test.He could predict the texture attributes of firmness,rubbery,creamy,chewy,frag-mentable,and mass-forming with a good level of accu-racy (Everard,2005).Spectroscopic analysis in combination with predictive mathematical models,developed using multivariate data analysis techniques such as partial least squares (PLS )regression,have potential use in controlling and monitoring the quality of raw materials through to the final product in food processing.In particular,infrared spectroscopy has been applied as an objective and nonde-structive technique to provide a rapid and real-time anal-ysis of both composition and quality (Downey,1998;Lefier et al.,2000;Ozen and Mauer,2002;Blazquez et al.,2004).Blazquez et al.(2006)modeled the sensory attributes of processed cheese using near-infrared re-flectance spectroscopy and PLS regression.They found that it was possible to model a number of attributes including firmness,melting,rubbery,and creamy.Two other studies have investigated the prediction of sensory attributes in natural cheese.Downey et al.(2005)and Sørensen and Jepsen (1998)successfully demonstrated that near-infrared spectroscopy in conjunction with PLS regression can be used to predict several sensory attri-butes of Cheddar and Danbo cheeses,respectively.Mid-infrared spectroscopy has been most widely used for de-termination of the fat and protein contents of cheese (Chen and Irudayaraj,1998).Irudayaraj et al.(1999)also investigated the use of mid-infrared spectroscopy to follow texture development in Cheddar cheese during ripening.They demonstrated that springiness could be successfully correlated with a number of bands in the mid-infrared spectra.Research has shown that mid-in-frared spectroscopy is a useful technique for characteriz-ing the changes in proteins during cheese ripening (Ma-zerolles et al.,2001).Pillonel et al.(2003)also found that mid-infrared spectroscopy may be successfully applied to the discrimination of Emmental cheese based on geo-graphic origin.Journal of Dairy Science Vol.90No.3,2007Table 1.Quantity of ingredients (g/kg)used in the production of experimental processed cheese samples Sample Emulsifyingnumber(s)Cheddar Butter Water salt1,10838.70.0161.39.72838.70.0151.619.43,11838.70.0141.929.04,12838.751.6112.99.75,13838.751.6100.019.46,14838.751.690.329.07,15838.7100.061.39.78838.7100.051.619.49,16838.7100.041.929.017,25848.451.6103.29.718,26838.751.6100.019.419,27829.051.6100.029.020,28751.645.2203.29.721,29745.245.2203.219.422,30738.745.2200.025.823,31651.638.7303.216.124,32645.238.7303.222.6No data are currently available on the application of mid-infrared spectroscopy to determine the sensory attributes in processed cheese,or regarding evaluation of mid-infrared spectroscopy in comparison with other technologies in such an application.Therefore,the objec-tives of this study were to investigate the use of mid-infrared spectroscopy in predicting sensory texture attri-butes using a range of experimentally manufactured pro-cessed cheese samples and to compare the models devel-oped with those recently modeled using near-infrared spectra and instrumental texture attributes.These newly presented data allow for the critical evaluation of mid-infrared spectroscopy as a rapid,nondestructive technique for predicting the sensory texture attributes of processed cheese.MATERIALS AND METHODSProcessed Cheese SamplesThirty-two processed cheese batches were manufac-tured in a pilot plant at Moorepark Food Research Cen-tre,Cork,Ireland.The ingredients and formulations are listed in Table 1.The formulations,which were selected to provide samples with compositional ranges that ex-tended beyond those used commercially by processed cheese manufacturers,provided samples with a wide range of sensory characteristics.The ingredients were mixed for 1min in a jacketed cooker (Stephan UMM/SK5Universal cooker;Stephan u So ¨hne GmbH &Co.,Hameln,Germany).The blend was cooked at 80°C for 2min by indirect steam heating.During cooking,the blend was stirred constantly using a knife at 300rpm and a baffle mixer at 80rpm.The cooked blend was stored in food-grade plastic containers (225g capacity),FAGAN ET AL.1124Table2.Vocabulary of sensory attributes,their definitions,and mastication phases used to carry out the sensory analysis of processed cheese samplesSensoryattribute Definition Mastication phaseFirmness The extent of the initial resistance offered by the cheese,Phase1:Judged on thefirst chew using the front teeth ranging from“soft”to“firm”Rubbery The extent to which the cheese returns/springs to its Phase2:Assessed during thefirst2to3chews initial form after biting,ranging from“a little”to“a lot”Creamy The texture associated with cream that has been whipped,ranging from“a little”to“a lot”Chewy The effort needed to break down the structure of the cheese,Phase3:Judged in the middle phase of mastication ranging from“a little”to“a lot”Mouth-coating The extent to which the cheese clings to the insideof the mouth(roof,teeth,tongue,gums),ranging from“a little”to“a lot”Fragmentable Breaks down to smaller versions of itself,ranging from Phase4:Probably judged toward the end of the chewing “a little”to“a lot”Melting The extent to which the cheese melts in the mouth;smooth,velvet fullness in the mouth,ranging from“a little”to“a lot”Mass-forming The extent to which the cheese form a bolus or massin the mouth after chewing,ranging from“a little”to“a lot”Greasy/oily The extent to which a greasy/oily residue is deposited Phase5:Judged at the end of the chewing sequence in the mouth after the cheese is broken down,rangingfrom“a little”to“a lot”which were lidded,allowed to cool,and placed in storage at4°C for4wk.The independent compositional variables for samples1to16were fat and emulsifying salt,and the variables for samples17to32were moisture and emulsifying salt.Descriptive sensory analysis and mid-infrared spectroscopy were carried out at2and4wk postmanufacture.Sensory AnalysisA panel of10assessors(9females and1male),aged 35to55yr old,were selected and recruited in1998and 2000according to international standards(International Organization for Standardization,1993).The panel de-veloped a vocabulary of9texture terms:firmness,rub-bery,creamy,chewy,mouth-coating,fragmentable, melting,mass-forming,and greasy/oily(Table2),which they used to assess each sample.Samples were prepared for analysis in duplicate by removing them from storage and preparing two5-g cubes.These samples were left to equilibrate to room temperature(21°C).Each equili-brated sample was presented to assessors in a glass tumbler covered with a clock glass and labeled with a randomly selected3-digit code.Assessors were provided with deionized water and unsalted crackers to cleanse their palate between tastings.The assessors scored the samples for each attribute by marking on unstructured 100-mm line scales labeled at both ends with extremes of each attribute.The intensity of each of the descriptive terms was recorded using the Compusense v.4.0sensory data acquisition software(Guelph,Ontario,Canada).At each time point,the descriptive sensory analysis took Journal of Dairy Science Vol.90No.3,2007place over2d.The order of tasting was balanced to account for the order of presentation and carryover ef-fects(MacFie et al.,1989).All assessments were con-ducted in individual booths at the sensory laboratory at University College,Cork,which complies with interna-tional standards for the design of test rooms(Interna-tional Organization for Standardization,1988).Mid-Infrared SpectroscopyMid-infrared spectra were collected over the range of 4,000to640cm−1,with a resolution of8cm−1,using an ATI Mattson Infinity Series Fourier transform spectro-photometer(ATI Mattson,Madison,WI)controlled by WinFirst software(ATI Mattson).The sample accessory used for sample presentation was an attenuated total reflectance ZnSe crystal(Graseby Specac Ltd.,Kent, UK),with an incidence angle of45°and6internal reflec-tions.Sixty-four interferograms were coadded before Fourier transformation.Prior to mid-infrared analysis, samples were removed from storage and left to equili-brate to room temperature.This was confirmed prior to analysis using a digital thermocouple(Sensor-Tech Ltd., Co.Louth,Ireland).Processed cheese samples were wiped across the attenuated total reflectance crystal to ensure even and immediate contact.Triplicate spectra were captured for each sample and replicate spectra were averaged prior to data analysis.Multivariate Data AnalysisMultivariate data analysis was carried out using The Unscrambler software(v.8.0;Camo A/S,Oslo,Norway).PREDICTION OF CHEESE SENSORY TEXTURE ATTRIBUTES1125Figure 1.Typical mid-infrared spectrum of processed cheese.Principal component analysis of the spectra was used to examine the spectral data set for any possible outliers.Models for the prediction of sensory attributes were de-veloped using PLS regression and confirmed by cross-validation.Prior to PLS regression,spectra were pre-treated using multiplicative scatter correction (MSC ),first derivative (Savitzky-Golay,2data points each side),second derivative (Savitzky-Golay,4data points each side),and each derivative plus MSC (Geladi et al.,1985).The potential of the models to predict the sensory attri-butes was evaluated using the root mean square error of cross-validation (RMSECV ),correlation coefficient (r )and the number of PLS loadings (#L ).The range error ratio (RER )was used to determine the practical utility of the models (Williams,1987).It was calculated by di-viding the range in the reference data of a given attribute by the prediction error for that attribute.The ratio of prediction error to deviation (RPD )was calculated by dividing the standard deviation of the reference data by RMSECV.RESULTS AND DISCUSSIONMid-Infrared SpectraA number of studies have assigned the main cheese constituents (fat,protein,moisture)to specific bands in the mid-infrared spectra (Chen and Irudayaraj,1998;Chen et al.,1998;Irudayaraj and Yang,2000;Mazerolles et al.,2001).The positions of these bands are indicated in a typical mid-infrared spectrum of processed cheese from this study (Figure 1).The results of principal component analysis of the spectra were investigated to determine whether any in-fluential outliers were present in the data set.An influ-ential outlier is a sample that has both a high residual and high leverage.A high residual means that the model,Journal of Dairy Science Vol.90No.3,2007Table 3.Statistical summary of sensory attributes (n =64)Sensory attribute Mean Range SD Firmness 37.6 5.6–70.921.9Rubbery 23.3 2.9–44.614.4Creamy 35.710.7–70.722.6Chewy24.8 2.8–46.114.6Mouth-coating 33.117.4–54.810.0Fragmentable 25.6 1.4–52.420.0Melting40.113.2–82.523.6Mass-forming 10.5 1.8–25.4 5.5Greasy/oily36.628.5–43.83.9which nevertheless fits the other samples quite well,poorly describes the sample.Leverage measures the dis-tance from the projected sample to the center or mean point.If a sample has a high leverage,it is exerting a stronger influence on the model than the remaining samples.According to these criteria,no outlier was found.Previous research has recommended that prior to analysis,a portion of the mid-infrared spectra (1,800to 2,700cm −1)might be omitted because of its low signal-to-noise ratio (Pillonel et al.,2003).This approach was used in this study,with the region 1,775to 2,830cm −1having a low signal-to-noise ratio,and was therefore omitted from analysis.In a preliminary investigation of the spectra,the region 640to 923cm −1was found to be of limited use in predicting sensory attributes and was also omitted.Therefore,only spectral data in the ranges of 930to 1,767cm −1and 2,839to 4,000cm −1were used for the multivariate data analysis.Predication of Sensory Texture Attributes by Mid-infrared SpectroscopyA summary of the values scored by the taste panel for each of the 9sensory attributes is shown in Table 3.The table highlights the high degree of variability in the data,which should support the development of robust models.Models were developed using 1)the combined spectral ranges of 930to 1,767cm −1and 2,839to 4,000cm −1,and 2)930to 1,767cm −1.The spectra were used in a number of forms:raw,MSC,first derivative,second derivative,and MSC plus each derivative step,giving 12models for each sensory attribute.A second derivative step offered no improvement in model accuracy for any attribute;hence,those prediction results are not shown.The RMSECV,r,and #L values obtained from the models developed are given in Table 4for the combined spectral range or the 930to 1,767cm −1range.These parameters allow for assessment of model strength.The preferred predictive model for an attribute (highlighted in bold in Table 4)was that which produced the lowest RMSECVFAGAN ET AL.1126Table4.Summary of partial least squares prediction results for sensory attributes using mid-infrared spectra1Scatter correctedRaw data Scatter corrected First derivative+first derivative Sensoryattribute Spectral range,cm−1r RMSECV#L r RMSECV#L r RMSECV#L r RMSECV#L Firmness930–1,767and2,839–4,0000.8810.570.947.4110.928.8100.9010.07 Rubbery930–1,767and2,839–4,0000.92 5.540.95 4.590.95 4.650.95 4.65 Creamy930–1,767and2,839–4,0000.947.840.947.860.957.150.947.55 Chewy930–1,767and2,839–4,0000.92 5.770.93 5.460.89 6.550.94 5.17 Mouth-coating930–1,7670.84 5.4100.85 5.3110.84 5.4100.85 5.211 Fragmentable930–1,767and2,839–4,0000.95 6.190.96 5.390.94 6.540.96 5.87 Melting930–1,767and2,839–4,0000.947.940.957.560.957.750.957.47 Mass-forming930–1,7670.83 3.1100.83 3.1100.83 3.190.63 4.34 Greasy/oily930–1,767and2,839–4,0000.56 3.230.54 3.320.59 3.240.56 3.23 1Preferred model in bold.RMSECV=root mean square error of cross-validation;#L=number of partial least squares loadings.and highest r values.It was also desirable for the pre-ferred model to incorporate the lowest#L possible. The results showed that only2of the models(mouth-coating and mass-forming)were improved when the re-duced spectral range(930to1,767cm−1)was used.None of the preferred models was developed using raw spectral data(i.e.,accuracy was improved by the application of a pretreatment).Thefirmness and fragmentable attri-butes were most successfully modeled using MSC spec-tra.The application of afirst derivative step resulted in the preferred models of rubbery,creamy,mass-forming, and greasy/oily.The most accurate models for the chewy, melting,and mouth-coating attributes were achieved when the spectra were subjected to scatter correction and afirst derivative.In conjunction with the RMSECV,r,and#L,the prac-tical utility of the models can also be assessed using the RER.Models with RER of less than3have little practical utility;RER values of between3and10indicate limited to good practical utility;and values above10show that the model has a high utility value(Williams,1987).The preferred models for predicting thefirmness,rubbery, creamy,chewy,mouth-coating,fragmentable,melting, and mass-forming attributes(shown in bold in Table4) had RMSECV values of between3.1and7.4and resulted in corresponding RER values of between7.2and9.6, indicating that the models had good practical utility. Therefore,these attributes had the potential to be pre-dicted by mid-infrared spectroscopy and multivariate data analysis.The greasy/oily attribute was not success-fully modeled(RER=4.8),possibly because of the small range displayed by the samples analyzed,and will there-fore not be discussed further.A graphical display of the preferred regression model for each attribute(high-lighted in bold in Table4)is shown in Figure2A to2H. Figure2shows that there is minimal scatter in the plots, as indicated by the high r values(0.83to0.96),and that the regression lines also have slopes close to1(0.77to 0.96)and low intercepts(1.0to5.9),demonstrating a Journal of Dairy Science Vol.90No.3,2007goodfit(Figure2).The accuracy of each model can be evaluated using the coefficients of determination(R2) between the predicted and measured values,as stated by Williams(2003).The models for mass-forming and mouth-coating both provided approximate quantitative predictions because their R2lay in the range of0.66to 0.81.Good predictions were achieved for the attributes firmness,rubbery,creamy,and chewy,with R2values of between0.82and0.90.The fragmentable model was considered to be excellent,having an R2greater than 0.91.The#L must also be taken into account.This ranged from5to11for the selected models.The models forfirmness,fragmentable,mouth-coating,and mass-forming incorporated a relatively high number of load-ings(9to11),which may have implications for their robustness,because the lower#L,the more robust the model.Thefirst3loadings of the models,which accounted for greater than90%of the variation in the spectral data,are plotted in Figure3A to3H.Although a number of preferred models were developed using spectra pre-treated with afirst derivative step,interpretation of the loadings associated with these models was difficult.This was because the observed peaks and valleys did not follow the raw spectral pattern.However,second deriva-tive steps were very helpful in spectral interpretation because in this form,band intensity and peak location were maintained with those in the raw spectral pattern. Therefore,although the second derivative step did not improve any of the prediction models,the loadings pre-sented for rubbery,creamy,and mass-forming were ob-tained using second derivative spectra and those for chewy,mouth-coating,and melting were obtained using MSC second derivative spectra.The loading plots pre-sented forfirmness and fragmentable were obtained us-ing MSC spectra.Figure3A to3H shows the relation-ships among the loadings used in the prediction model and the different wavenumbers.If a wavenumber had a large positive or negative loading,this meant that thePREDICTION OF CHEESE SENSORY TEXTURE ATTRIBUTES1127Figure2.Linear regression plots of actual vs.predicted sensory attributes of(A)firmness,(B)rubbery,(C)creamy,(D)chewy,(E)mouth-coating,(F)fragmentable,(G)melting,and(H)mass-forming.RER=range error ratio.Journal of Dairy Science Vol.90No.3,20071128FAGAN ET AL.Figure3.Loading plots for partial least squares models of the sensory attributes of(A)firmness,(B)rubbery,(C)creamy,(D)chewy, (E)mouth-coating,(F)fragmentable(G)melting,and(H)mass-forming.Journal of Dairy Science Vol.90No.3,2007PREDICTION OF CHEESE SENSORY TEXTURE ATTRIBUTES1129wavenumber was important for the attribute concerned.Therefore,they assisted in summarizing the relationship between the spectra and the predicted attribute and provided an aid to interpreting the molecular basis for predicting an attribute.The important loadings were distributed across the full spectral range used in predicting each attribute (Fig-ure 3).There was considerable structure present in all of the loading plots.In comparing the plots produced using similar spectral treatments,that is,MSC (Figure 3A and 3F),second derivative (Figure 3B and 3C),and MSC plus second derivative (Figure 3D and 3G),it was apparent that differences existed in the relative impor-tance of various regions of the spectra in predicting the different sensory attributes.For example,the region around 3,200to 3,900cm −1was shown to be of greater importance in predicting the attributes of firmness (Fig-ure 3A),chewy (Figure 3D),and melting (Figure 3G)than for the other attributes.This region of the spectra corresponded with a broad moisture absorption peak.The loadings incorporated into the firmness model ex-plained the variation across almost the full spectral range used (Figure 3A).Peaks and valleys occurred at around 1,095,1,160,and 1,269to 1,396cm −1,associated with the vibration of C–H,C–O bonds of carbohydrates;1,739,2,846,and 2,931cm −1,associated with lipids;and around 1,554,1,604,and 1,646cm −1,which are known to correspond with amides I and II.The amide I and amide II regions of the spectra were also important in predicting chewy,with peaks observed in the loading plot in the region of 1,504,1,547,1,639,and 1,655cm −1(Figure 3D).The loadings for the chewy model were also found to explain variation in the moisture absorp-tion region (3,359to 3,907cm −1),the peaks associated with lipids (1,739and 2,927cm −1),and the region around 1,080cm −1.The most important regions of the spectra for predicting mouth-coating were the regions associated with the vibration of C–H,C–O bonds of carbohydrates (987,1,072to 1,095,1,176,and 1,334to 1,427cm −1)and lipids (1,732,1,739,and 1,751cm −1;Figure 3E).Peaks were also observed in the amide I and II region (1,542,1,562,and 1,655cm −1).A number of major peaks were clearly identified in the fragmentable loading plot (Fig-ure 3F).These were 1,110,1,169,1,242,and 1,462cm −1,and 1,743,2,854,and 2,924cm −1,which corres-ponded with the vibration of the C–H,C–O bonds of carbohydrates and lipids,respectively.Emulsifying salts chelate calcium,which plays an important role in the 2-dimensional structure of processed cheese.They also aid in the dispersion of proteins,which contributes to the emulsification of fat.In this study,the emulsifying salt used was disodium phosphate.The effect of increasing the phosphate concentration was 2-fold,namely,an in-creasing ability to chelate calcium and an incremental Journal of Dairy Science Vol.90No.3,2007increase in the pH of cheese.The interaction between these 2effects (emulsifying salt and pH)will result in increased firmness of the cheese.However,this result is also dependent on the moisture content because mois-ture acts as a plasticizer in processed cheese and de-creases the concentration of the dispersed phase,hence decreasing the firmness of the processed cheese.Greater firmness is also attributed to a higher concentration of protein,and increases in the fat and water contents weaken the protein structure,thereby decreasing the firmness of the processed cheese.This explains the im-portance of the fingerprint (991to 1,400cm −1),lipid,amide,and moisture-absorption regions in predicting the firmness,chewy,fragmentable,and mass-forming attributes.The regions of the spectra that were most important in predicting the attributes of rubbery (Figure 3B)and creamy (Figure 3C)were all associated with lipids (1,739,1,743,2,846,2,858,2,916,2,919cm −1).Minor peaks were also observed in the 1,079to 1,173,1,542,and 3,556to 3,907cm −1spectral regions,which are associated with the vibration of the C–H,C–O bonds;amide II;and moisture absorption,respectively.These peaks were of particular importance in predicting the rubbery and creamy attributes,because fat in cheese has the effect of preventing the protein network of the cheese matrix from forming a tough,dense structure (Lawlor et al.,2001).The loadings for the melting model (Figure 3G)ex-plained variation in a number of different regions,in-cluding the amide I and II regions (1,547,1,654cm −1),lipid regions (1,751,2,927cm −1),and moisture-absorp-tion region (3367to 3907cm −1).All factors that influence either the content or distribution of fat,or the strength of the protein network are known to influence cheese meltability (Lefevere et al.,2000).This accounts for the significance of the moisture,amide,and lipid regions of the spectra in predicting melting.The regions of the spectra that were the most im-portant in predicting mass-forming were found to be 1,092and 1,130cm −1(C–H,C–O bond vibrations);1,535,1,547,1,646,and 1,647cm −1(amides I and II);and 1,736,1,743,and 1,751cm −1(lipids;Figure 3H).This indicates the role that the fat content and protein structure has in determining the mass-forming potential of pro-cessed cheese.These results highlight the importance of different regions across the entire spectral range used in pre-dicting the sensory textural attributes of processed cheese.The importance of different spectral regions in predicting sensory attributes is related to the effects of the formulation and composition on processed cheese texture.Changes in the formulation and composition of。

北京师范大学模糊系统与人工智能方向简介（讨论稿）北京师范大学模糊数学与人工智能方向是国内最早从事模糊数学及其应用研究的单位之一，可以说是国内模糊数学研究的重要基地。

早在1979年北师大数学科学学院开始就开始招收模糊数学研究方向的硕士研究生，是我国最早从事模糊数学研究的硕士学科点。

1986年，汪培庄先生牵头，以模糊数学为主申请下来应用数学博士点，这也是我国最早从事模糊数学研究的博士学科点。

迄今为止，北师大数学科学学院已培养几十名硕士和博士研究生，并且在各种工作岗位已成为骨干力量。

北京师范大学模糊系统与模糊信息研究中心暨复杂系统实时智能控制实验室创建于2000年。

现任中心主任为国家级有突出贡献中青年专家李洪兴教授。

目前，实验室拥有博导教授2人，副教授3人，博士后2人，在读博士生15人（其中具有教授职称者2人，副教授4人），硕士研究生19人。

该研究中心现有一个应用数学的博士学位授权点，应用数学和控制理论与控制工程两个硕士学位授权点。

1982年至今，北京师范大学模糊数学与人工智能研究群体先后提出并研究了因素空间、真值流推理、随机集落影、模糊计算机、模糊摄动理论、幂结构提升理论、基于变权综合的智能信息处理、模糊系统的插值表示、变论域智能计算、复杂系统建模以及知识表示的数学理论模糊计算机等一些先进的理论方法。

近期的主要研究成果包括：1）给出因素空间理论，建立知识表示的数学框架，并系统研究概念的内涵与外延表示问题，为专家经验、领域知识在软件系统中的表示与计算提供了理论基础；2）揭示了模糊逻辑系统的数学本质，给出常用模糊逻辑系统地插值表示，并系统研究了模糊逻辑系统的构造、分析以及泛逼近性等理论问题；3）提出变论域自适应智能信息处理理论，设计了基于变论域思想的一类高精度模糊控制器，在世界上第一个实现了四级倒立摆控制实物系统，经教育部组织专家鉴定，确认这是一项原创性的具有国际领先水平的重大科研成果；4）引入变权的概念，并给出基于自适应变权理论的智能信息处理方法；5）提出模糊计算机的概念，并研究了模糊计算机设计的若干理论问题；6）给出数学神经网络理论，从数学上揭示了模糊逻辑系统与人工神经网络之间的关系，首次定义了“输出返回”的模糊逻辑系统并证明了它与反馈式神经网络等价；7）提出一种基于数据集成、规则提取和模糊推理的复杂系统的建模方法，即基于模糊推理的建模方法，由此可突破障碍模糊控制理论发展的一些瓶颈问题，诸如稳定性、能控性、能观测性等的判据问题。

3415120v G i n U z09:00~12:00 a I G G606G t(Lectures )»P(Seminars)iv G C g T17:00~18:00 (½i e n O)v p G32703¡F l G*****************.tw(IPE)¦U y D n I A Hu y F v g v(GPE)ªs C w H v Pi X C F v g B D n s D(¥i)µi iii i iii* Baldwin, David A. (1993). Key Concepts in International Political Economy. Brookfield: Edward Elgar.* Cox, Robert W. Production,Power and World Order: Social Forces in the Making of History (1987). New York: Columbia University Press. L AB v O M@C G@X A2004~CEichengreen, Barry (1996). Globalizing Capital: A History of the International Monetary System. Princeton: Princeton University Press.* Evans, Peter (1995). Embedded Autonomy: States and Industrial Transformation. Princeton: Princeton University.* Frieden, Jeffry A. and David A. Lake (1995 3rd edition). International Political Economy: Perspectives on Global Power and Wealth. London: Routledge. Hobson, M. John (2007). Everyday Politics of World Economy. Cambridge: Cambridge University Press.* ------ (2003). a P Y C P o A x G C* Gill, Stephen (1993). Gramsci, Historical Materialism and International Relations. Cambridge: CUP.* Gilpin, Robert (1987). The Political Economy of International Relations. Princeton: Princeton University Press. ·t A Y F v gR C x G a A1994~C* Gilpin, Robert (2001). Global Political Economy: Understanding the International Economic Order. Princeton: Princeton University Press.A x G a A2004~C* Held, David, Anthony McGrew and David Goldblatt (1999). Global Transformations: Politics, Economics and Culture. New York: Polity Press.H v A y j C x G B X A2001~CHobson, John (2004). The Eastern Origins of Western Civilization. Cambridge, Cambridge University Press. Ch1~Ch4.* Lairson, Thomas D. and David Skidmore (1997, 2003). International Political Economy: the Struggle for Power and Wealth. Belmont: Thomson Wadsworth.* Hout, Wil (1993). Capitalism and the Third World. Brookfield: Edward Elgar. Marx, Karl and Friedrich Engels (1848). The Communist Manifesto. New York: Monthly Review Press (1998 edition). ¤@C@s AG1973¦~C* Staniland, Martin (1985). What is Political Economy: A Study of Social Theory and Underdevelopment. J y C F v g C x G n A1990~C* Strange, Susan (1994). States and Markets (2 ed.) London: Pinter. ·tC a P C W G W@X A2006~C* Strange, Susan (1996). The Retreat of the State: the Diffusion of Power in the World Economy. Cambridge: CUP.* Youngs, Gillian (1999). International Relations in a Global Age. London: Polity Press. v C y N Y C x G B X A2001ii “*”¸ “**”¸iiYoungs, Gillian (2007). Global Political Economy in the Information Age.London: Routledge.B(½s)¡C P y G y@O@C Gm X A2008~C* s(½s)¡C C o i P o i C x G y A1987~CG B U GBaker, Andrew et. al ed (2005). Governing Financial Globalization: International Political Economy and Multi-level Governance. London: Routledge Press.* Crawford, Beverly (1995). Markets, States, and Democracy. Boulder: Westview Press.Enders, Walter (2006). The Political Economy of Terrorism. Cambridge, Cambridge University Press.Friedman, Milton (1962). Capitalism and Freedom. Chicago: Chicago University Press. A D P C x G j A1994~C Goddard, Roe C., Patrick Cronin and Kishore C. Dash ed (1995, 2003).International Political Economy: State-Market Relations in a Changing Global Order. Boulder: Lynne Rienner.* Hughes, Barry B (1998). Continuity and Change in World Politics. HA s F v s C x GB X A T C* Joll, James. C v C x G a A1992~CLavigne, Marie (1985). International Political Economy and Socialism.Cambridge: Cambridge University Press.Lawton, Thomas et. al ed. (2000). Strange Power: Shaping the Parameters of International Relations and International Political Economy. Burlington: Ashgate Press.* Liberman, Peter (1996). "Trading with the Enemy." International Security.Vol. 21-1, pp. 147-175.* Lipson, Charles and Benjamin J. Cohen ed (1999). Theory and Structure in International Political Economy. Mass.: MIT Press.** Luttwal, Edward N. (1990). "From Geopolitics to Geoeconomics: Logic of Conflict, Grammar of Commerce." The National Interest. Vol. 20-17, pp.17-23.** Murphy, Craig N. & Douglas R. Nelson (2001), “International Political Economy: A Tale of Two Heterodoxies”, British Journal of Politics and International Relations”, Vol. 3-3, pp. 393-412.* Palan, Ronen (2000). Global Political Economy: Contemporary Theories.London: P & F Press. F B C y F v g C x G B A2004~C* Peterson, Spike V. (2003). A Critical Rewriting of Global Political Economy.London: Routledge.Przeworski, Adam (1991). Democracy and the Market: Political and Economic Reforms in Eastern Europe and Latin America. Cambridge: Cambridge University Press.Ramo, Joshua Cooper (2004). The Beijing Consensus. London, The ForeignPolicy Centre.* Strange, Susan (1970). ‘International Economics and International Relations:A Case of Mutual Neglect’, International Affairs, Vol.46, No. 2, 1970.B i(2003)¡C F v g G z d P g s C GL C* d s(2000)¡C X G@F v g R C x G n X C* (1994)¡C T F v C x G X C* ------- (2005)¡C u P v v A D P s A446A79-102¡C * J(1844)¡C1844~g C t C x G A1990~C * J B(1995)¡C J C G H X C* j e@(1996)¡C a C T C x A Cs B(1999)¡C F v g C G H j A@BG C1-73¡C* (1995)¡C o i F v g C x G n X C* i(½s)(2003)¡C Y C x G C B@C* v s(µ)(1991)¡C c D C x G s X C* (2006)¡C z F v g o i@G v B N B v A P I C y F v A C63-88¡C* (2006)¡C u y B H v O—¥443A81-129¡CC(1999)¡C v t P C W G W H X C* F(1991)¡20%¡CG B i G30%¡C C i G(A4 in print)e v o L C s@W v@P Mw CT B i G50%¡C6,000r(¤t)´i@g Ci N s@(¨)A E e(P M)v Cn N G P CiSession I. Lecture: P(2/19)Must Read* Lairson, Thomas D. and David Skidmore (1997, 2003). “The Origins of A World Economy”, in International Political Economy: the Struggle for Powerand Wealth. Belmont: Thomson Wadsworth; Ch3, pp. 38-62.Related Readings* Friedman, Milton (1962). Capitalism and Freedom. Chicago: Chicago University Press. A D P C x G j A1994~C@A7-22¡C* Goddard, Roe C., Patrick Cronin and Kishore C. Dash ed (1995, 2003). International Political Economy: State-Market Relations in a Changing Global Order. Boulder: Lynne Rienner. Ch. 1-2, pp. 9-32.* Staniland, Martin (1985). What is Political Economy: A Study of Social Theory and Underdevelopment. J y C F v g C x G n A1990 ~C@G C* Strange, Susan (1970). ‘International Economics and International Relations:A Case of Mutual Neglect’, International Affairs, Vol.46, No. 2, 1970.* j e@(1996)¡C a C T C x A C B@CB i(2003)¡C F v g G z d P g s C GL C A1-30¡C* F(2007)¡C u S. Strange P R. Cox v O P c G H vC v F v A44C101-126¡Cs B(1999)¡C F v g C G H j A@BG C1-73¡C* (1995)¡C o i F v g C x G n X C@B C* i(½s)(2003)¡C Y C x G C B@C* F(1991)¡C F v P g X C x G a C G A9-36¡Session II. Lecture: Cherished Traditions and New Perspectives (2/26)Must Read* Lairson, Thomas D. and David Skidmore (1997, 2003), “The Economics of International Political Economy”, in International Political Economy: the Struggle for Power and Wealth. Belmont: Thomson Wadsworth; Ch2, pp.13-37.* Gilpin, Robert (2001). Global Political Economy: Understanding the International Economic Order. Princeton: Princeton University Press.A x G a A2004~C@C* Gilpin, Robert (1987). The Political Economy of International Relations. Princeton: Princeton University Press. ·t A Y F v gR C x G a A1994~C@T CRelated ReadingsHobson, M. John (2007). Everyday Politics of World Economy. Cambridge: Cambridge University Press.* Hughes, Barry B (1998). Continuity and Change in World Politics. HA s F v s C x GB XC G A395-518¡C Lawton, Thomas et. al ed. (2000). Strange Power: Shaping the Parameters of International Relations and International Political Economy. Burlington: Ashgate Press. Ch. 1, pp. 3-18.** Murphy, Craig N. & Douglas R. Nelson (2001), “International Political Economy: A Tale of Two Heterodoxies”, British Journal of Politics and International Relations”, Vol. 3-3, pp. 393-412.* Peterson, Spike V. (2003). A Critical Rewriting of Global Political Economy.London: Routledge. Ch. 1-2, pp. 1-43.* Strange, Susan (1994). States and Markets (2 ed.) London: Pinter. ·tC a P C W G W@X A2006~C B@A1-18¡C* Youngs, Gillian (1999). International Relations in a Global Age. London: Polity Press, Ch.6. v C y N Y C x G B X A2001~C CYoungs, Gillian (2007). Global Political Economy in the Information Age.London: Routledge; pp. 1-20.* (2006)¡C z F v g o i@G v B N B v A P I C y F v A C63-88¡CSession III. Seminar: Liberalism(3/5)Mandatory readings* Gilpin, Robert (1987). The Political Economy of International Relations.Princeton: Princeton University Press. t A Y F v gR C x G a A1994~C A193-260¡C* Gilpin, Robert (2001). Global Political Economy: Understanding the International Economic Order. Princeton: Princeton University Press.A x G a A2004~A K A239-282¡C* Liberman, Peter (1996). "Trading with the Enemy." International Security.Vol. 21-1, pp. 147-175.Supplemented Readings* David Ost, “Labor, Class and Democracy”, in Crawford, Beverly (1995). Markets, States, and Democracy. Boulder: Westview Press, pp. 177-203.* Ruggie, John J. (1982) “International Regimes, Transactions and Change: Embedded Liberalism in the Postwar Economic Order”, International Organization, Vol. 36-2.Galbraith, John Kenneth(1973). Economics and the Public Purpose.A g v G z P C x G w A1997~C* Smith, Adam, “The Wealth of Nations” (excerpts), in Goddard, Roe C., Patrick Cronin and Kishore C. Dash ed (1995, 2003). International Political Economy: State-Market Relations in a Changing Global Order. Boulder:Lynne Rienner; pp. 33-48. (°I K n)B i(2003)¡C F v g G z d P g s C GL C G A59-93¡CSession IV. Mercantilism (Economic Nationalism)(3/12)Mandatory readings** Luttwal, Edward N. (1990). "From Geopolitics to Geoeconomics: Logic of Conflict, Grammar of Commerce." The National Interest. Vol. 20-17, pp.17-23.* Baldwin, David A. (1993). Key Concepts in International Political Economy.Brookfield: Edward Elgar. Part One, Ch.3-Ch4, (pp. 60-73, 74-90)* (1995)¡C o i F v g C x G n X C B C 127-148¡A377-405¡C* Gilpin, Robert (2001). Global Political Economy: Understanding the International Economic Order. Princeton: Princeton University Press.A x G a A2004~C A u y g a v A445-462¡CSupplemented Readings* Krasner, Stephen, “Sovereignty”, in Goddard, Roe C., Patrick Cronin and Kishore C. Dash ed (1995, 2003). International Political Economy: State-Market Relations in a Changing Global Order. Boulder: Lynne Rienner; pp. 139-150.* Hughes, Barry B (1998). Continuity and Change in World Politics. HA s F v s C x GB X A G A395-420¡C Session V. Seminar: Marxism(3/19)Mandatory readings* Palan, Ronen (2000). Global Political Economy: Contemporary Theories.London: P & F Press. F B C y F v g C x G B A2004~A@A245-268¡CMarx, Karl and Friedrich Engels (1848). The Communist Manifesto. New York: Monthly Review Press (1998 edition). @C@s A@C G1973~A23-58¡C* Marx, Karl (1844). Economic and Philosophical Manuscripts. t A@K ~g C x G A1990~C@B G B T C5-70, 71-116.Wood, Ellen M. (1998). “The Communist Manifesto 150 Years Later”, in The Communist Manifesto. New York: Monthly Review Press (1998 edition), pp.89-112.Supplemented Readings* Marx, Karl (1995 Chinese edition). u i K v C J(²@)¡A G H X A585-689¡C* Marx, Karl (1995 Chinese edition). u F P v C J(²T)¡A G H X A298-319¡C* Lenin, V. “Imperialism: the Highest State of Capitalism” in ”Frieden, Jeffry A.and David A. Lake (1995 3rd edition). International Political Economy: Perspectives on Global Power and Wealth. London: Routledge, Ch. 7, pp.110-119.* Staniland, Martin (1985). What is Political Economy: A Study of Social Theory and Underdevelopment. J y C F v g C x G n A1990 ~C A137-176¡CB i(2003)¡C F v g G z d P g s C GL C T A94-114¡CSession VI. Seminar: the State and the Market (I)(3/26)Mandatory readings* Strange, Susan (1994). States and Markets (2 ed.) London: Pinter. ·tC a P C W G W@X A2006~A u v@A1-41¡C* Strange, Susan (1994). States and Markets (2 ed.) London: Pinter. ·tC a P C W G W@X A2006~A u v GA41-146¡CSupplemented Readings* John M. Hobson (2003). a P Y C P o A x G CSession VII. Seminar: the State and the Market (II)(4/9)Mandatory readings* Strange, Susan (1994). States and Markets (2 ed.) London: Pinter. tC a P C W G W@X A2006~A T B A147-252¡C* Strange, Susan (1996). “The Declining Authority of States”, The Retreat of the State: the Diffusion of Power in the World Economy. Cambridge: CUP. Ch1, pp.3-15.Session VIII. Seminar: the State and the Market (III)(4/16)Mandatory readings* Evans, Peter (1995). Embedded Autonomy: States and Industrial Transformation. Princeton: Princeton University, Ch. 1, pp. 3-20.* Skocpol, Theda (1985). “Bringing the State Back In: Strategies of Analysis in Current Research”, in Peter Evans, D. Rueschemeyer and ThedaSkocpol ed., Bringing the State Back In. Cambridge: Cambridge University Press, pp. 3-43.* Gilpin, Robert (2001). Global Political Economy: Understanding the International Economic Order. Princeton: Princeton University Press.A x G a A2004~C G A u a P g o i v A373-417¡CSupplemented Readings* Palan, Ronen (2000). Global Political Economy: Contemporary Theories.London: P & F Press. F B C y F v g C x G BA2004~C G A31-52¡C* F(1991)¡C F v P g X C x G a C A69-118¡CB i(2003)¡C F v g G z d P g s C GL C A188-241¡CSession IX. Seminar: the State and the Market (IV)(4/23)Mandatory readings* Youngs, Gillian (1999). International Relations in a Global Age. London: Polity Press. v C y N Y C x G B X A2001 ~A@B B CYoungs, Gillian (2007). “States and Markets: Understanding Geospatial Time”Global Political Economy in the Information Age. London: Routledge, Ch. 1, pp. 23-39.Supplementary readings* Staniland, Martin (1985). What is Political Economy: A Study of Social Theory and Underdevelopment. J y C F v g C x G n A1990 ~C(²X D)¡A67-90¡CSession X. Seminar: International Monetary System and Economic Order (I)(4/30)Mandatory readingsEichengreen, Barry (1996), “The Gold Standard”. Globalizing Capital: A History of the International Monetary System. Princeton: Princeton University Press, Ch 2, pp. 7-44.* Gilpin, Robert (1987). The Political Economy of International Relations. Princeton: Princeton University Press. ·t A Y F v gR C x G a A1994~C B K F135-192¡B345-384¡C* A u T P v A i(½s)(2003)¡C Y C x G CG A299-324¡C* (2005)C u P v v A D P s A446A79-102¡CSupplementary readingsEichengreen, Barry (1996). Globalizing Capital: A History of the International Monetary System. Princeton: Princeton University Press. Ch. 3.* Held, David, Anthony McGrew and David Goldblatt (1999). Global Transformations: Politics, Economics and Culture. New York: Polity Press.H v A y j C x G B X A2001~C T A183-290¡CSession XI. International Monetary System and Economic Order (II)(5/7)Mandatory readingsEichengreen, Barry (1996), “The Bretton Woods System”. Globalizing Capital: A History of the International Monetary System. Princeton: Princeton University Press, Ch 4, pp. 93-135.* Dash, Kishore. “The Asian Economic Crisis and the Role of IMF”, in Goddard, Roe C., Patrick Cronin and Kishore C. Dash ed (1995, 2003). International Political Economy: State-Market Relations in a Changing Global Order. Boulder: Lynne Rienner; Ch. 17, pp. 269-289.* C u P v v C V B h s A2005~y F v P wi C G m X C199-216¡CSupplementary readingsBaker, Andrew et. al. ed., (2005). Governing Financial Globalization: International Political Economy and Multi-level Governance. London: Routledge Press, Ch. 2, pp. 24-48.B i(2003)¡C F v g G z d P g s C GL C E B A343-443¡CSession XII. World System, Underdevelopment and Dependency(5/14)Mandatory readings* Baldwin, David A. (1993). Key Concepts in International Political Economy. Brookfield: Edward Elgar. Part One, Ch.8, (pp. 181-206).* Hout, Wil (1993). Capitalism and the Third World. Brookfield: Edward Elgar, Ch. 3, Ch5; pp. 52-73; 94-110.* Gilpin, Robert (2001). Global Political Economy: Understanding the International Economic Order. Princeton: Princeton University Press.A x G a A2004~C@A u a P h~v A337-372¡C * Lairson, Thomas D. and David Skidmore (1997, 2003), “Rich and Poor State in the World Economy”, in International Political Economy: the Struggle for Power and Wealth. Belmont: Thomson Wadsworth; Ch 8, pp. 211-235.* s(½s)¡C C o i P o i C x G y A1987~C C A157-182¡CSupplementary readings* Baldwin, David A. (1993). Key Concepts in International Political Economy. Brookfield: Edward Elgar. Part One, Ch.13, (pp. 290-326).* Gilpin, Robert (1987). The Political Economy of International Relations. Princeton: Princeton University Press. ·t A Y F v gR C x G a A1994~C C A297-344¡C* Hughes, Barry B (1998). Continuity and Change in World Politics. HA s F v s C x GB X A A449-503¡CB i(2003)¡C F v g G z d P g s C GL C C K A242-342¡C* (1995)C o i F v g C x G n X C T B C C43-68B 149-174¡C* (2007)¡C u o i g u G x g X@F g uv C Y A24A87-108¡C* s(½s)¡C C o i P o i C x G y A1987~C G@t z C(¯S O O A Theda Skocpol R)Session XIII. Hegemony: Old and New (I)(5/21)Mandatory readings* Gilpin, Robert (1987). The Political Economy of International Relations. Princeton: Princeton University Press. ·t A Y F v g1994~A A411-462¡C* Lairson, Thomas D. and David Skidmore (1997, 2003). “The Political Economy of American Hegemony”, in International Political Economy: the Struggle for Power and Wealth. Belmont: Thomson Wadsworth; Ch 4, pp.63-92.* Joll, James. C v C x G a A1992~C(pp. 1-127)¡CSupplementary readings* Cox, Robert W. Production, Power and World Order: Social Forces in the Making of History (1987). New York: Columbia University Press. L AB v O M@C G@X A2004~C C BA76-195¡A256-282¡C* Cox, Robert W. (1993). “Gramsci, Hegemony and International Relation: An Essay in Method”. In Stephen Gill ed. (1993). Gramsci, Historical Materialism and International Relations. Cambridge, Cambridge University Press, pp. 49-66.* Frieden, Jeffry A. and David A. Lake (1995 3rd edition). International Political Economy: Perspectives on Global Power and Wealth. London: Routledge, Ch. 15, pp. 230-254.* Kindlberger, Charles P. “The Rise of Free Trade in Western Europe”, in Frieden, Jeffry A. and David A. Lake (1995 3rd edition). International Political Economy: Perspectives on Global Power and Wealth. London: Routledge, pp. 73-89.* Strange, Susan (1994). States and Markets (2 ed.) London: Pinter. ·tC a P C W G W@X A2006~A CB i(2003)¡C F v g G z d P g s C GL C A150-187¡CC(1999)¡C v t P C W G W H X CSession XIV. Hegemony: Old and New (II)(6/4)Mandatory readingsYoungs, Gillian (2007), “Complex Hegemony in the Twenty-First Century”, Global Political Economy in the Information Age. London: Routledge, Ch 7, pp. 127-142.Ramo, Joshua Cooper (2004). The Beijing Consensus. London, The Foreign Policy Centre.* Gore, Charles (2003), “The Rise and Fall of the Washington Consensus as a Paradigm for Developing Countries”, in Goddard, Roe C., Patrick Cronin and Kishore C. Dash ed (1995, 2003). International Political Economy: State-Market Relations in a Changing Global Order. Boulder: Lynne Rienner; Ch. 19, 317-340.Supplementary readings* Lake, David. “British and American Hegemony Compared”, in Frieden, JeffryA. and David A. Lake (1995 3rd edition). International Political Economy:Perspectives on Global Power and Wealth. London: Routledge, Ch. 8, pp.120-134.B i(2003)C F v g G z d P g s C GL C T CB(s)C P y G y@O@C Gm X A2008~CSession XV. Globalization: An Introduction(6/11)Mandatory readings* Gilpin, Robert (2001). Global Political Economy: Understanding the International Economic Order. Princeton: Princeton University Press.A x G a A2004~A C445-496¡C* Held, David, Anthony McGrew and David Goldblatt (1999). Global Transformations: Politics, Economics and Culture. New York: Polity Press.H v A y j C x G B X A2001~C E C* w(2007)C u y P B b a P T w A u v C F vC A67-100¡C* (2006)C u y B H v O—443A81-129¡C* N. Lardy. ¡u J@g G v C Gill, Bates (©u O )(2006). China: the Balance Sheet. A U C x Gp g A93-147¡CSupplementary readings* Lairson, Thomas D. and David Skidmore (1997). International Political Economy: the Struggle for Power and Wealth. Belmont: Thomson Wadsworth; Ch5, pp. 95-124.* Palan, Ronen (2000). Global Political Economy: Contemporary Theories.London: P & F Press. F B C y F v g C x G B A2004~A A355-376¡C* A u y v A i(s)(2003)C Y C x G C@A271-298¡C* Beck, Ulrich C P D C L C x G CSession XV. Special Session: Guest Speaker onGlobalization and IPE(6/18)i(6,0008,000)17:30e C。

模糊系统与数学131（2002）101-106一个基于方案模糊信息的多属性决策方法Zhi-Ping Fan a,b , Jian Ma b,∗ , Quan Zhang b信息与决策部门，工商管理学院，东北大学，沈阳110006，中国部门的信息系统，香港大学，83Tat中港大道，九龙，香港摘要本文研究了对多属性决策方案的问题。

本文提出了一种新的方法来解决决策问题，也就是决策者对于他/她的偏好方案的一个模糊的关系。

对re.ect决策者的偏好信息，构建一个最优化模型来评估属性权重然后选择最理想的替代品。

最后，用一个例子来说明所提出的问题。

2001由Elsevier Science公司出版。

关键词：多属性决策；模糊偏好信息；方案排序1 引言在多属性决策（MADM）问题里，决策者往往需要选择或者排序非均匀和相互冲突的属性[3,14]。

MADM是在现实世界中许多的情况下出现的一个决策性问题[3,6,14,19,20]，例如，在生产计划的问题上，生产速度，质量和运营成本都要考虑选择满意的方案。

大量的研究工作一直在进行一个多属性决策问题，其中之一的研究热点就是模糊集理论的应用解决不精确信息的多属性决策问题以[3,4,24]为代表的模糊条款。

通讯作者。

电话: +852-2788-8514 传真: +852-2784-4198电子邮件地址: isjian@.hk (J. Ma).0165-0114/01/$-见前页c 2001 由Elsevier科学有限公司出版PII: S0165-0114(01)00258-5在多属性决策问题里，决策者的偏好信息经常被用来排列替代品，然而，决策者的判断因形式和深度而异。

决策者可能不代表他/她所有喜欢的，或者可能代表他/她的属性或替代偏好的形式。

决策者的判断能力也各种各样。

解决多属性决策问题可以分成三类，根据决策者给出diEerent形式的偏好信息[3,14]：（1）无偏好信息[2,8,12]的方法，（2）与属性[1,5,7,9,22]信息;（3）对替代品[3,14,16,17,20,23]信息的方法。

BLACK-BOX TESTING IN THE INTRODUCTORYPROGRAMMING CLASSTamara BabaianComputer Information Systems DepartmentBentley Collegetbabaian@Wendy LucasComputer Information Systems DepartmentBentley CollegeABSTRACTIntroductory programming courses are often a challenge to both the students taking them and the instructors teaching them. The scope and complexity of topics required for learning how to program can distract from the importance of learning how to test. Even the textbooks on introductory programming rarely address the topic of testing. Yet, anyone who will be involved in the system development process should understand the critical need for testing and know how to design test cases that identify bugs and verify the correct functionality of applications. This paper describes a testing exercise that has been integrated into an introductory programming course as part of an overall effort to focus attention on effective software testing techniques.1 A comparison of the performance on a common programming assignment of students who had participated in the testing exercise to that of students who had not demonstrates the value of following such an approach.Keywords: testing, debugging, black-box method, introductory programming1 A shorter version of this paper, entitled Developing Testing Skills in an Introductory Programming Class, was presented at the 2005 International Conference on Informatics Education Research.I. INTRODUCTIONFor several years now, object-oriented languages have predominated within introductory programming courses in the Computer Science and Information Systems curricula. Programming in general does not come naturally to all students, and object-oriented concepts can be especially daunting. Students struggling to write their first programs quickly succumb to the mantra that it compiles and runs - therefore it is correct. The importance of testing is lost on these novices in their rush to submit functioning code. Integrated Development Environments (IDEs), which are invaluable in many ways, may have the unintended consequence of supporting this attitude; a simple click of a button compiles and runs code with astonishing speed (particularly to those of us who remember punch cards). It is so easy to recompile that one can fall into the trap of making changes and rerunning the program without analyzing errors and thinking through the code to address them. While syntactical errors are caught and promptly drawn to the programmer’s attention by the IDE, trapping logical errors requires careful design of test cases and thorough analysis of outputs. The necessity for these skills is often lost on the novice. A far greater risk is that the novice will become a developer who never learned the value of thorough testing. Attesting to the validity of this concern is the estimated $59.5 billion that software bugs are costing the U.S. each year [Tassey, 2002]; early detection of these errors could greatly reduce these costs [Baziuk, 1995]. As noted by Shepard et al. [2001], although testing typically takes at least 50% of the resources for software development projects, the level of resources devoted to testing in the software curriculum is very low. This is largely due to a perceived lack of available time within a semester for covering all of the required topics, let alone making room for one that may not be viewed as core to the curriculum. The motivation for the work presented here arises from the need for teaching solid testing skills right from the start. Students must learn that testing should be givenat least as much priority as providing the required functionality if they are to become developers of high-quality software.This paper describes a testing exercise that has been used successfully within an introductory programming course taught using the Java language at Bentley College. This course is part of the curriculum within the Computer Information Systems (CIS) Department, and is required for CIS majors but open to all interested students. The contents of this course are in keeping with the IS2002 Model Curriculum [Gorgone et al., 2002], which recommends the teaching of object-oriented programming and recognizes the need for testing as a required part of the coursework. While faculty readily acknowledge this need, developing a similar appreciation for testing in our students has proven far more difficult. The testing exercise described here has been found to be an effective step in this process.The next section of this paper reviews research that is relevant to the work presented here. We then provide an overview of the course and a detailed description of the testing exercise. In order to assess the impact of this exercise, we present an analysis of student performance on a related coding assignment. This paper concludes with a discussion of directions for future work.II. LITERATURE REVIEWThe low priority given to testing within the software curriculum and the need for that to change has been acknowledged in the literature. Shepard, Lamb, and Kelly [2001], who strongly argue for more focus on testing, note that Verification and Validation (V&V) techniques are hardly taught, even within software engineering curriculum. They propose having several courses on testing, software quality, and other issues associated with V&V available for undergraduates. Christensen [2003] agrees that testing should not be treated as an isolated topic, but rather should be integrated throughout the curriculum as“core knowledge.” The goal must be on producing reliable software, and he proposes that systematic testing is a good way to achieve this.Much of the relevant literature describes the use of Extreme Programming (XP) [Beck, 2000] techniques in programming courses for teaching testing. XP advocates a test-first approach in which unit tests are created prior to writing the code. For students, benefits of this approach include developing a better understanding of the project’s requirements and learning how to test one module or component at a time.XP plays a key role in the teaching guidelines proposed by Christensen [2003], which include: (1) fixing the requirements of software engineering exercises on high quality, (2) making quality measurable by teaching systematic testing and having students follow the test-driven approach of XP, and (3) formulating exercises as a progression, so that each builds on the solution to the prior exercise. These guidelines have been applied by Christensen in an advanced programming class.Allen, Cartwright, and Reis [2003] describe an approach for teaching production programming based on the XP methodology. The authors note that, “It is impossible to overstate the importance of comprehensive, rigorous unit testing since it provides the safeguard that allows students to modify the code without breaking it” [Allen et al., 2003, p. 91]. To familiarize students with the test-first programming approach, they are given a simple, standalone practice assignment at the beginning of the course for which most of their grade is based on the quality of the unit tests they write. Another warm-up assignment involves writing units tests for a program written by the course’s instructors. These exercises were found to be effective in teaching students how to write suitable tests for subsequent assignments.The approaches to teaching testing described above are very similar to the approach described in this paper. What differentiates our testing exercise andfollow-up coding assignment is that they are intended for beginning programmers, not the more experienced ones who would be found in advanced or production-level programming courses. This presents the challenge of teaching students who are only beginning to grasp the concept of programming about the importance of testing and the complexities associated with developing effective test cases.Edwards [2004] does address the issues of teaching testing in an introductory CS course and recommends a shift from trial-and-error testing techniques to reflection in action [Schön, 1983], which is based on hypothesis-forming and experimental validation. He advocates the Test Driven Development (TDD) method [Edwards, 2003], which requires, from the very first assignment, that students also submit test cases they have composed for verifying the correctness of their code. Their performance is assessed on the basis of “how well they have demonstrated the correctness of their program through testing” [Edwards, 2004, p. 27]. Edwards [2004] focuses on tools that support students in writing and testing code, including JUnit (/), DrJava [Allen et al., 2002], and BlueJ [Kölling, 2005], and on an automated testing prototype tool called Web-CAT (Web-based Center for Automated Testing) for providing feedback to students. Patterson, Kölling, and Rosenberg [2003] also describe an approach to teaching unit testing to beginning students that relies on the integration of JUnit into BlueJ. While Snyder [2004] describes an example that introduces testing to beginning programmers, his work is built around the use of an automated system for conditional compilation.What differentiates these works from our own is our explicit focus on the testing exercise itself, rather than on the different types of tools that provide assistance with testing, as a means for supporting the teaching of testing to novices. Our testing assignment requires a thorough analysis by students of the inner workings of a program for which they do not have access to the code. Theassignment’s components must therefore be carefully designed for use by beginning programmers.III. COURSE BACKGROUNDIn this section we present an overview of the Programming Fundamentals course and describe how instruction in software testing is positioned within its curriculum. This is the first programming course within the CIS Major at Bentley College, and it is taught using the Java programming language. While it is required for majors, it also attracts non-majors, with students also differing in terms of backgrounds in programming and class levels. To accommodate the majority of students enrolled in this course and prepare them for subsequent classes in software development, it is targeted towards those students who do not have any prior programming experience. The goal of this course is for students to develop basic programming and problem-solving skills. This is accomplished through lectures, in-class laboratory sessions for writing and testing code, and assignments that are completed outside of the classroom.Approximately two-thirds of the material covered in this course focuses on basic data types, control structures, and arrays. The remainder of the semester is spent introducing object-oriented programming concepts, including classes and objects, and instance versus static variables and methods. All of these concepts are reinforced through frequent programming assignments, with an assignment due every one to two weeks. Students are expected to complete all assignments on their own, without collaborating with others in the class, in accordance with our academic honesty policy. There are no group assignments in this course, as we feel that, at the introductory level, individual effort is required to absorb abstract programming concepts. Laboratory assistants and instructors are always on-hand to answer any questions with assignments and help direct student efforts without revealing solutions.Concepts related to the system lifecycle are sprinkled throughout the course to keep the students aware of the big picture and to help explain and motivate effective development practices associated with object-oriented languages. Strongly emphasized are testing and debugging techniques, the development of sound programming logic, and the writing of well-structured code. The decision to devote class time specifically to teaching program verification as part of this course arose from a curriculum revision process. Several of the faculty who teach development courses acknowledged that insufficient training in testing methodologies during the introductory programming classes was adversely impacting the students’ attitudes toward program verification in later courses. By addressing testing early and often in the sequence of courses within our major, we could help students develop proper testing techniques while stressing the important role of program validation within the system development process.As part of this effort, during the introductory lectures we stress the fact that the longest and most expensive part of the software lifecycle is spent in maintenance. We point out that maintenance expenses depend on the clarity of the code and its documentation, as well as on the robustness of the testing performed during the software development process. The formal introduction to testing and verification of software is given in the third week of the course, after most of the basic programming concepts have been covered and students are capable of composing a program with more than one possible outcome. Such an early introduction is necessary to facilitate the early application of testing techniques by students. This also serves to reinforce the importance of testing and good testing practices, which students will apply throughout the rest of the semester in their programming assignments. In addition, opportunities to develop test cases arise during completion of in-class programming exercises. These present students with the opportunity to learn from both the instructor and each other about the process of developing and implementing test cases.IV. TESTING EXERCISEIn this section, we provide a detailed description of the testing exercise that has been included in the Programming Fundamentals course. To set the stage for the testing exercise, the black-box (specification-based) method of testing was introduced in a lecture given during the third week of the course. This lecture was then followed by the testing assignment, in which the students were asked to perform black-box testing of a completed program. They were provided with a requirements specification for the program and with a compiled Java application, created by the instructor, which implemented those requirements with varying degrees of completeness and correctness. As part of their task, students would need to identify the ways in which the program failed to meet the specification. In the following sections, we describe the set of requirements for the program, the compiled code to be tested, the student deliverables and evaluation guidelines, and the instructor’s evaluation process.PROBLEM REQUIREMENTS SPECIFICATIONThe application described in the requirements specification for the testing assignment is for automating the billing process for an Internet café (see Figure 1). The specified billing rules resemble those that are typically found in contemporary commerce applications and are based on multiple dimensions, including: the time when the service was provided, the length of that service, the charges associated with the service, and whether or not the customer holds a membership in the Café-Club.In selecting the application domain for this assignment, we wanted one that would reinforce the importance of testing. An Internet café is something with which students are familiar, most likely in the capacity of a customer who would want to be sure that the café was correctly billing for its services. Students could also conceivably be owners of such an enterprise, who would be equally if notmore concerned with the correctness of the billing process. This domain should therefore contribute to the students’ motivation to verify the billing functionality.A new Internet café operates between the hours of 8 a.m. and 11 p.m. The regular billing rate for Internet usage from its computers is 25 cents per minute. In addition, the following rules apply:1. Regular (non-member) customers are always billed at the regular rate.2. Café-Club members only receive a special discount during the discount period between 8a.m. and 2 p.m.: the first two hours within that period are billed at the rate of 10 cents perminute; all time past the first two hours (but within the discount period) is billed at the rate of 20 cents per minute. Any time outside of the discount period is billed at the regular rate. 3. If the total cost exceeds $50, it is discounted by 10%.Note that rule 2 above applies to Café-Club members only and rule 3 applies to all customers. The program should help automate customer billing for the Internet café. The program should work exactly as follows.The user should be prompted to enter:1. The letter-code designating the type of the customer: 'r' or 'R' for a regular customer, 'm'or 'M' for a club member.2. The starting hour.3. The starting minute.4. The ending hour.5. The ending minute of the customer's Internet session.The starting and ending hours are to be entered based on a 24 hour clock. Your program must then output the cost in dollars of the service accordin g to the above billing rules.Figure 1. Billing Program RequirementsIt was also important to provide an application that was understandable without being trivial. The logic of the billing rules is straightforward; at the same time, there is a rich variety of situations requiring different computational processes. Several categories of test cases as well as a number of different boundary conditions are necessitated and require thorough testing to verify the correctness of the application.PROGRAM TO BE TESTEDEvery student was e-mailed a compiled Java application implementing the billing program requirements presented in Figure 1. In order to maximize independent discovery and minimize the potential for students to discuss andcopy each other’s solutions, two different billing programs were implemented. Students were informed that more than one program was being distributed, but since the names of both programs and their compiled file sizes were identical and they did not have access to the source code, they could not readily tell who else had been sent the same version.Both versions contained four logical errors that were deliberately and carefully entered into the code by the instructor. While the errors in each version were different, the scope of the input data with which students would need to test in order to identify the incorrect operations was consistent. Hence, the likelihood of finding the problems with the implementations was comparable for the two versions.ASSIGNMENT DELIVERABLES AND EVALUATION GUIDELINES There are two parts to the deliverable that students were required to submit for this assignment (see Appendix I for the complete description). The purpose of the first part is to document the set of test cases they designed and the outcomes of each of the individual tests they ran using those test cases. Test case descriptions must include a complete specification of program inputs, the correct output value (i.e., given those inputs, the cost in dollars of the service based on the business rules shown in Figure 1), and the actual output value produced by the program. The objective of the test must also be described. For example, an objective might be to: “Test regular customer outside of the discount period.” The aim of this requirement is to help the students organize their testing process and learn to identify and experiment with distinct categories of input data.Students were encouraged to design test cases for different computational scenarios and boundary conditions. While there were no explicit requirements on the number of test cases, students were told that they should only include cases with valid application data (e.g. hour values between 0 and 23, inclusive). Thiswas done to limit the scope of the problem to a manageable size for beginning programmers.The second part of the assignment is to summarize the errors identified during testing in the form of hypotheses regarding the unmet requirements of the program. An example of a hypothesis might be: “The 10% discount is not applied within the discount period.” In order for a student to form such a hypothesis, which precisely identifies the error and the circumstances in which it occurs, observations from multiple test cases must be combined. For this particular example, one must combine the results of testing for the correct application of the 10% discount rule during different periods of service for each of the customer types. Thus, students must use their analytical skills to generalize the results of individual tests to a higher level of abstraction. In order to direct the students in this analytical process, the assignment explicitly suggests that they form additional test cases to verify or refine their initial hypotheses.INSTRUCTOR’S EVALUATION OF THE ASSIGNMENTIn evaluating the first part of this assignment, student submissions were checked against a list of twenty-five categories of test cases derived by the instructor. For the assignment’s second part, the summary of findings was checked for consistency with each student’s test case results. Appendix II shows the point value assigned to each graded component of the assignment, with a maximum possible score of 10 points. The first 5.5 points were awarded based on the degree of coverage of the students’ test sets with respect to the instructor’s categorizations. The next 2 points were for the number of actual problems with the code that were correctly identified (Diagnosed problems/summary of findings). The final 2.5 points were for the completeness of the descriptions provided for each test case (Presentation). This last component refers to the format rather than the content of the tests. For example, using the interaction shown in Appendix I, the student should show the starting hour of 12and the starting minute of 0 as two separate values rather than as one value of 12:00.The majority of students precisely identified two of the four program errors. Approximately 68% of submissions received scores of 8 and above out of a possible 10, 20% scored between 6 and 8, and 12% scored below 6. The value of this assignment cannot, however, be discerned solely on the basis of the students’ performance on it; rather, it is how it influences performance on future programming assignments that is most important, as discussed next.V. ASSESSING THE IMPACT OF TRAINING IN TESTINGIn this section, we present an assessment of the results of using the previously described approach to teaching students how to test by evaluating the performance of two groups of students on a common programming assignment. Students in Group 1 were enrolled in this course in a prior semester and did not receive any class time or homework training in testing methodology. They also did not complete the testing exercise. Group 2 students were enrolled in this course in the following semester; they were given a lecture on the black-box method and completed the testing exercise (but had not yet received the instructor’s evaluations of that exercise) prior to being given the programming assignment described below. These differences in testing preparation were the only distinguishing variation between the two groups; there were no significant differences between the number of students in each group or their composition in terms of their majors and prior exposure to programming. All the students were beginning programmers enrolled for the first time in a programming course at Bentley College, and most were in either their sophomore or junior year. Attendance by students in both groups was typically 85% or more for all class sessions.The assignment given to the students was to create a program for the billing requirements specification presented in Figure 1. Both groups were given the programming assignment at approximately the same point in the course. The students’ submissions were tested against the same suite of sixteen test cases. Table 1 summarizes the results of the comparison between the two groups of students on the common programming assignment.Table 1. Students’ Performance on the Billing Requirements ProgramGroup 1: Without testing assignmentGroup 2: With testing assignmentTotal number of students enrolled 39 40Total number of submissions 25 35Percentage of students who submitted 64% 87%Median number of failed tests 5 5Percentage of submitted programs with0 errors detected8% 20% The above comparison yields interesting results. The submission rate, i.e.,the percentage of enrolled students who submitted a program that compiled and ran, is far higher for the Group 2 students, who had received instruction in testing and completed the testing exercise. Based on a two-tailed t-test comparison, the means of the number of submissions are significantly different, with p = 0.015 using the standard 0.05 significance level. This suggests that problem analysis inthe form of creating test cases brings students closer to an understanding of the algorithm being tested. Based on this increased level of understanding, studentsin Group 2 had the confidence to complete an assignment that was perceived by many in Group 1 as being too difficult.The median number of failed test cases is the same for both groups and, while the percentage of “error-free” submissions (those that passed all 16 tests)is 2.5 times higher for Group 2, the means are not significantly different (usingthe two-tailed t-test, p = 0.206). A likely explanation is that only the “best” students in Group 1 were able to complete the programming assignment, so theirperformance was similar to that of those in Group 2, in which a far greater percentage of students were able to complete the assignment.Throughout the semester, it was also observed by the instructor that the explicit lecture on testing coupled with the testing exercise had served to increase the Group 2 students’ awareness of the variety of usage scenarios that could be derived from a program specification. Students were more likely to consider different input categories and suggest test cases capturing important boundary conditions based on the specification. The instructor felt that the introduction of black-box testing to the curriculum had an overall positive impact on the students’ ability to produce robust applications.VI. DISCUSSIONWhile introducing the concept of testing and having students create test cases are not uncommon activities throughout Computer Science and Information Systems curricula, the approach described here has several unique characteristics and advantages. First of all, the testing exercise requires that students develop a set of test cases for an instructor-created, compiled program, rather than for code they wrote themselves. This approach clearly separates the testing of the code from its development and is, therefore, a purer way for students to experience black-box testing than the TDD methodology [Edwards 2003] described earlier. Since creating the set of test cases prior to working on the implementation is not enforced by the TDD method, the testing performed by students may be biased by their knowledge of the code’s structure and how they chose to implement the program. Students participating in our testing exercise did not have access to the source code and were, thus, solely dependent on the requirements specification for developing their test cases.Providing students with a program that has been carefully crafted to include observable errors enables a second unique aspect to the assignment:。

· 1 ·2.1 试问四进制、八进制脉冲所含信息量是二进制脉冲的多少倍？ 解：四进制脉冲可以表示4个不同的消息，例如：{0, 1, 2, 3}八进制脉冲可以表示8个不同的消息，例如：{0, 1, 2, 3, 4, 5, 6, 7} 二进制脉冲可以表示2个不同的消息，例如：{0, 1} 假设每个消息的发出都是等概率的，则：四进制脉冲的平均信息量H(X 1) = log 2n = log 24 = 2 bit/symbol 八进制脉冲的平均信息量H(X 2) = log 2n = log 28 = 3 bit/symbol 二进制脉冲的平均信息量H(X 0) = log 2n = log 22 = 1 bit/symbol 所以：四进制、八进制脉冲所含信息量分别是二进制脉冲信息量的2倍和3倍。

2.2 居住某地区的女孩子有25%是大学生，在女大学生中有75%是身高160厘米以上的，而女孩子中身高160厘米以上的占总数的一半。

假如我们得知“身高160厘米以上的某女孩是大学生”的消息，问获得多少信息量？ 解：设随机变量X 代表女孩子学历X x 1（是大学生） x 2（不是大学生） P(X) 0.25 0.75设随机变量Y 代表女孩子身高Y y 1（身高>160cm ） y 2（身高<160cm ） P(Y) 0.5 0.5已知：在女大学生中有75%是身高160厘米以上的 即：p(y 1/ x 1) = 0.75求：身高160厘米以上的某女孩是大学生的信息量 即：bit y p x y p x p y x p y x I 415.15.075.025.0log )()/()(log )/(log )/(2111121111=⎪⎭⎫⎝⎛⨯-=⎥⎦⎤⎢⎣⎡-=-=2.3 一副充分洗乱了的牌（含52张牌），试问 (1) 任一特定排列所给出的信息量是多少？(2) 若从中抽取13张牌，所给出的点数都不相同能得到多少信息量？ 解：(1) 52张牌共有52！种排列方式，假设每种排列方式出现是等概率的则所给出的信息量是：bit x p x I i i 581.225!52log )(log )(2==-=(2) 52张牌共有4种花色、13种点数，抽取13张点数不同的牌的概率如下：bit C x p x I C x p i i i 208.134log )(log )(4)(13521322135213=-=-==· 2 ·2.4 设离散无记忆信源⎭⎬⎫⎩⎨⎧=====⎥⎦⎤⎢⎣⎡8/14/1324/18/310)(4321x x x x X P X ，其发出的信息为(202120130213001203210110321010021032011223210)，求(1) 此消息的自信息量是多少？(2) 此消息中平均每符号携带的信息量是多少？ 解：(1) 此消息总共有14个0、13个1、12个2、6个3，因此此消息发出的概率是：62514814183⎪⎭⎫ ⎝⎛⨯⎪⎭⎫ ⎝⎛⨯⎪⎭⎫ ⎝⎛=p此消息的信息量是：bit p I 811.87log 2=-=(2) 此消息中平均每符号携带的信息量是：bit n I 951.145/811.87/==2.5 从大量统计资料知道，男性中红绿色盲的发病率为7%，女性发病率为0.5%，如果你问一位男士：“你是否是色盲？”他的回答可能是“是”，可能是“否”，问这两个回答中各含多少信息量，平均每个回答中含有多少信息量？如果问一位女士，则答案中含有的平均自信息量是多少？ 解： 男士：sym bolbit x p x p X H bitx p x I x p bit x p x I x p i i i N N N Y Y Y / 366.0)93.0log 93.007.0log 07.0()(log )()( 105.093.0log )(log )(%93)( 837.307.0log )(log )(%7)(22222222=+-=-==-=-===-=-==∑女士：symbol bit x p x p X H ii i / 045.0)995.0log 995.0005.0log 005.0()(log )()(2222=+-=-=∑2.6 设信源⎭⎬⎫⎩⎨⎧=⎥⎦⎤⎢⎣⎡17.016.017.018.019.02.0)(654321x x x x x x X P X ，求这个信源的熵，并解释为什么H(X) >log6不满足信源熵的极值性。

3GPP TS 36.331 V13.2.0 (2016-06)Technical Specification3rd Generation Partnership Project;Technical Specification Group Radio Access Network;Evolved Universal Terrestrial Radio Access (E-UTRA);Radio Resource Control (RRC);Protocol specification(Release 13)The present document has been developed within the 3rd Generation Partnership Project (3GPP TM) and may be further elaborated for the purposes of 3GPP. The present document has not been subject to any approval process by the 3GPP Organizational Partners and shall not be implemented.This Specification is provided for future development work within 3GPP only. The Organizational Partners accept no liability for any use of this Specification. Specifications and reports for implementation of the 3GPP TM system should be obtained via the 3GPP Organizational Partners' Publications Offices.KeywordsUMTS, radio3GPPPostal address3GPP support office address650 Route des Lucioles - Sophia AntipolisValbonne - FRANCETel.: +33 4 92 94 42 00 Fax: +33 4 93 65 47 16InternetCopyright NotificationNo part may be reproduced except as authorized by written permission.The copyright and the foregoing restriction extend to reproduction in all media.© 2016, 3GPP Organizational Partners (ARIB, ATIS, CCSA, ETSI, TSDSI, TTA, TTC).All rights reserved.UMTS™ is a Trade Mark of ETSI registered for the benefit of its members3GPP™ is a Trade Mark of ETSI registered for the benefit of its Members and of the 3GPP Organizational PartnersLTE™ is a Trade Mark of ETSI currently being registered for the benefit of its Members and of the 3GPP Organizational Partners GSM® and the GSM logo are registered and owned by the GSM AssociationBluetooth® is a Trade Mark of the Bluetooth SIG registered for the benefit of its membersContentsForeword (18)1Scope (19)2References (19)3Definitions, symbols and abbreviations (22)3.1Definitions (22)3.2Abbreviations (24)4General (27)4.1Introduction (27)4.2Architecture (28)4.2.1UE states and state transitions including inter RAT (28)4.2.2Signalling radio bearers (29)4.3Services (30)4.3.1Services provided to upper layers (30)4.3.2Services expected from lower layers (30)4.4Functions (30)5Procedures (32)5.1General (32)5.1.1Introduction (32)5.1.2General requirements (32)5.2System information (33)5.2.1Introduction (33)5.2.1.1General (33)5.2.1.2Scheduling (34)5.2.1.2a Scheduling for NB-IoT (34)5.2.1.3System information validity and notification of changes (35)5.2.1.4Indication of ETWS notification (36)5.2.1.5Indication of CMAS notification (37)5.2.1.6Notification of EAB parameters change (37)5.2.1.7Access Barring parameters change in NB-IoT (37)5.2.2System information acquisition (38)5.2.2.1General (38)5.2.2.2Initiation (38)5.2.2.3System information required by the UE (38)5.2.2.4System information acquisition by the UE (39)5.2.2.5Essential system information missing (42)5.2.2.6Actions upon reception of the MasterInformationBlock message (42)5.2.2.7Actions upon reception of the SystemInformationBlockType1 message (42)5.2.2.8Actions upon reception of SystemInformation messages (44)5.2.2.9Actions upon reception of SystemInformationBlockType2 (44)5.2.2.10Actions upon reception of SystemInformationBlockType3 (45)5.2.2.11Actions upon reception of SystemInformationBlockType4 (45)5.2.2.12Actions upon reception of SystemInformationBlockType5 (45)5.2.2.13Actions upon reception of SystemInformationBlockType6 (45)5.2.2.14Actions upon reception of SystemInformationBlockType7 (45)5.2.2.15Actions upon reception of SystemInformationBlockType8 (45)5.2.2.16Actions upon reception of SystemInformationBlockType9 (46)5.2.2.17Actions upon reception of SystemInformationBlockType10 (46)5.2.2.18Actions upon reception of SystemInformationBlockType11 (46)5.2.2.19Actions upon reception of SystemInformationBlockType12 (47)5.2.2.20Actions upon reception of SystemInformationBlockType13 (48)5.2.2.21Actions upon reception of SystemInformationBlockType14 (48)5.2.2.22Actions upon reception of SystemInformationBlockType15 (48)5.2.2.23Actions upon reception of SystemInformationBlockType16 (48)5.2.2.24Actions upon reception of SystemInformationBlockType17 (48)5.2.2.25Actions upon reception of SystemInformationBlockType18 (48)5.2.2.26Actions upon reception of SystemInformationBlockType19 (49)5.2.3Acquisition of an SI message (49)5.2.3a Acquisition of an SI message by BL UE or UE in CE or a NB-IoT UE (50)5.3Connection control (50)5.3.1Introduction (50)5.3.1.1RRC connection control (50)5.3.1.2Security (52)5.3.1.2a RN security (53)5.3.1.3Connected mode mobility (53)5.3.1.4Connection control in NB-IoT (54)5.3.2Paging (55)5.3.2.1General (55)5.3.2.2Initiation (55)5.3.2.3Reception of the Paging message by the UE (55)5.3.3RRC connection establishment (56)5.3.3.1General (56)5.3.3.1a Conditions for establishing RRC Connection for sidelink communication/ discovery (58)5.3.3.2Initiation (59)5.3.3.3Actions related to transmission of RRCConnectionRequest message (63)5.3.3.3a Actions related to transmission of RRCConnectionResumeRequest message (64)5.3.3.4Reception of the RRCConnectionSetup by the UE (64)5.3.3.4a Reception of the RRCConnectionResume by the UE (66)5.3.3.5Cell re-selection while T300, T302, T303, T305, T306, or T308 is running (68)5.3.3.6T300 expiry (68)5.3.3.7T302, T303, T305, T306, or T308 expiry or stop (69)5.3.3.8Reception of the RRCConnectionReject by the UE (70)5.3.3.9Abortion of RRC connection establishment (71)5.3.3.10Handling of SSAC related parameters (71)5.3.3.11Access barring check (72)5.3.3.12EAB check (73)5.3.3.13Access barring check for ACDC (73)5.3.3.14Access Barring check for NB-IoT (74)5.3.4Initial security activation (75)5.3.4.1General (75)5.3.4.2Initiation (76)5.3.4.3Reception of the SecurityModeCommand by the UE (76)5.3.5RRC connection reconfiguration (77)5.3.5.1General (77)5.3.5.2Initiation (77)5.3.5.3Reception of an RRCConnectionReconfiguration not including the mobilityControlInfo by theUE (77)5.3.5.4Reception of an RRCConnectionReconfiguration including the mobilityControlInfo by the UE(handover) (79)5.3.5.5Reconfiguration failure (83)5.3.5.6T304 expiry (handover failure) (83)5.3.5.7Void (84)5.3.5.7a T307 expiry (SCG change failure) (84)5.3.5.8Radio Configuration involving full configuration option (84)5.3.6Counter check (86)5.3.6.1General (86)5.3.6.2Initiation (86)5.3.6.3Reception of the CounterCheck message by the UE (86)5.3.7RRC connection re-establishment (87)5.3.7.1General (87)5.3.7.2Initiation (87)5.3.7.3Actions following cell selection while T311 is running (88)5.3.7.4Actions related to transmission of RRCConnectionReestablishmentRequest message (89)5.3.7.5Reception of the RRCConnectionReestablishment by the UE (89)5.3.7.6T311 expiry (91)5.3.7.7T301 expiry or selected cell no longer suitable (91)5.3.7.8Reception of RRCConnectionReestablishmentReject by the UE (91)5.3.8RRC connection release (92)5.3.8.1General (92)5.3.8.2Initiation (92)5.3.8.3Reception of the RRCConnectionRelease by the UE (92)5.3.8.4T320 expiry (93)5.3.9RRC connection release requested by upper layers (93)5.3.9.1General (93)5.3.9.2Initiation (93)5.3.10Radio resource configuration (93)5.3.10.0General (93)5.3.10.1SRB addition/ modification (94)5.3.10.2DRB release (95)5.3.10.3DRB addition/ modification (95)5.3.10.3a1DC specific DRB addition or reconfiguration (96)5.3.10.3a2LWA specific DRB addition or reconfiguration (98)5.3.10.3a3LWIP specific DRB addition or reconfiguration (98)5.3.10.3a SCell release (99)5.3.10.3b SCell addition/ modification (99)5.3.10.3c PSCell addition or modification (99)5.3.10.4MAC main reconfiguration (99)5.3.10.5Semi-persistent scheduling reconfiguration (100)5.3.10.6Physical channel reconfiguration (100)5.3.10.7Radio Link Failure Timers and Constants reconfiguration (101)5.3.10.8Time domain measurement resource restriction for serving cell (101)5.3.10.9Other configuration (102)5.3.10.10SCG reconfiguration (103)5.3.10.11SCG dedicated resource configuration (104)5.3.10.12Reconfiguration SCG or split DRB by drb-ToAddModList (105)5.3.10.13Neighbour cell information reconfiguration (105)5.3.10.14Void (105)5.3.10.15Sidelink dedicated configuration (105)5.3.10.16T370 expiry (106)5.3.11Radio link failure related actions (107)5.3.11.1Detection of physical layer problems in RRC_CONNECTED (107)5.3.11.2Recovery of physical layer problems (107)5.3.11.3Detection of radio link failure (107)5.3.12UE actions upon leaving RRC_CONNECTED (109)5.3.13UE actions upon PUCCH/ SRS release request (110)5.3.14Proximity indication (110)5.3.14.1General (110)5.3.14.2Initiation (111)5.3.14.3Actions related to transmission of ProximityIndication message (111)5.3.15Void (111)5.4Inter-RAT mobility (111)5.4.1Introduction (111)5.4.2Handover to E-UTRA (112)5.4.2.1General (112)5.4.2.2Initiation (112)5.4.2.3Reception of the RRCConnectionReconfiguration by the UE (112)5.4.2.4Reconfiguration failure (114)5.4.2.5T304 expiry (handover to E-UTRA failure) (114)5.4.3Mobility from E-UTRA (114)5.4.3.1General (114)5.4.3.2Initiation (115)5.4.3.3Reception of the MobilityFromEUTRACommand by the UE (115)5.4.3.4Successful completion of the mobility from E-UTRA (116)5.4.3.5Mobility from E-UTRA failure (117)5.4.4Handover from E-UTRA preparation request (CDMA2000) (117)5.4.4.1General (117)5.4.4.2Initiation (118)5.4.4.3Reception of the HandoverFromEUTRAPreparationRequest by the UE (118)5.4.5UL handover preparation transfer (CDMA2000) (118)5.4.5.1General (118)5.4.5.2Initiation (118)5.4.5.3Actions related to transmission of the ULHandoverPreparationTransfer message (119)5.4.5.4Failure to deliver the ULHandoverPreparationTransfer message (119)5.4.6Inter-RAT cell change order to E-UTRAN (119)5.4.6.1General (119)5.4.6.2Initiation (119)5.4.6.3UE fails to complete an inter-RAT cell change order (119)5.5Measurements (120)5.5.1Introduction (120)5.5.2Measurement configuration (121)5.5.2.1General (121)5.5.2.2Measurement identity removal (122)5.5.2.2a Measurement identity autonomous removal (122)5.5.2.3Measurement identity addition/ modification (123)5.5.2.4Measurement object removal (124)5.5.2.5Measurement object addition/ modification (124)5.5.2.6Reporting configuration removal (126)5.5.2.7Reporting configuration addition/ modification (127)5.5.2.8Quantity configuration (127)5.5.2.9Measurement gap configuration (127)5.5.2.10Discovery signals measurement timing configuration (128)5.5.2.11RSSI measurement timing configuration (128)5.5.3Performing measurements (128)5.5.3.1General (128)5.5.3.2Layer 3 filtering (131)5.5.4Measurement report triggering (131)5.5.4.1General (131)5.5.4.2Event A1 (Serving becomes better than threshold) (135)5.5.4.3Event A2 (Serving becomes worse than threshold) (136)5.5.4.4Event A3 (Neighbour becomes offset better than PCell/ PSCell) (136)5.5.4.5Event A4 (Neighbour becomes better than threshold) (137)5.5.4.6Event A5 (PCell/ PSCell becomes worse than threshold1 and neighbour becomes better thanthreshold2) (138)5.5.4.6a Event A6 (Neighbour becomes offset better than SCell) (139)5.5.4.7Event B1 (Inter RAT neighbour becomes better than threshold) (139)5.5.4.8Event B2 (PCell becomes worse than threshold1 and inter RAT neighbour becomes better thanthreshold2) (140)5.5.4.9Event C1 (CSI-RS resource becomes better than threshold) (141)5.5.4.10Event C2 (CSI-RS resource becomes offset better than reference CSI-RS resource) (141)5.5.4.11Event W1 (WLAN becomes better than a threshold) (142)5.5.4.12Event W2 (All WLAN inside WLAN mobility set becomes worse than threshold1 and a WLANoutside WLAN mobility set becomes better than threshold2) (142)5.5.4.13Event W3 (All WLAN inside WLAN mobility set becomes worse than a threshold) (143)5.5.5Measurement reporting (144)5.5.6Measurement related actions (148)5.5.6.1Actions upon handover and re-establishment (148)5.5.6.2Speed dependant scaling of measurement related parameters (149)5.5.7Inter-frequency RSTD measurement indication (149)5.5.7.1General (149)5.5.7.2Initiation (150)5.5.7.3Actions related to transmission of InterFreqRSTDMeasurementIndication message (150)5.6Other (150)5.6.0General (150)5.6.1DL information transfer (151)5.6.1.1General (151)5.6.1.2Initiation (151)5.6.1.3Reception of the DLInformationTransfer by the UE (151)5.6.2UL information transfer (151)5.6.2.1General (151)5.6.2.2Initiation (151)5.6.2.3Actions related to transmission of ULInformationTransfer message (152)5.6.2.4Failure to deliver ULInformationTransfer message (152)5.6.3UE capability transfer (152)5.6.3.1General (152)5.6.3.2Initiation (153)5.6.3.3Reception of the UECapabilityEnquiry by the UE (153)5.6.4CSFB to 1x Parameter transfer (157)5.6.4.1General (157)5.6.4.2Initiation (157)5.6.4.3Actions related to transmission of CSFBParametersRequestCDMA2000 message (157)5.6.4.4Reception of the CSFBParametersResponseCDMA2000 message (157)5.6.5UE Information (158)5.6.5.1General (158)5.6.5.2Initiation (158)5.6.5.3Reception of the UEInformationRequest message (158)5.6.6 Logged Measurement Configuration (159)5.6.6.1General (159)5.6.6.2Initiation (160)5.6.6.3Reception of the LoggedMeasurementConfiguration by the UE (160)5.6.6.4T330 expiry (160)5.6.7 Release of Logged Measurement Configuration (160)5.6.7.1General (160)5.6.7.2Initiation (160)5.6.8 Measurements logging (161)5.6.8.1General (161)5.6.8.2Initiation (161)5.6.9In-device coexistence indication (163)5.6.9.1General (163)5.6.9.2Initiation (164)5.6.9.3Actions related to transmission of InDeviceCoexIndication message (164)5.6.10UE Assistance Information (165)5.6.10.1General (165)5.6.10.2Initiation (166)5.6.10.3Actions related to transmission of UEAssistanceInformation message (166)5.6.11 Mobility history information (166)5.6.11.1General (166)5.6.11.2Initiation (166)5.6.12RAN-assisted WLAN interworking (167)5.6.12.1General (167)5.6.12.2Dedicated WLAN offload configuration (167)5.6.12.3WLAN offload RAN evaluation (167)5.6.12.4T350 expiry or stop (167)5.6.12.5Cell selection/ re-selection while T350 is running (168)5.6.13SCG failure information (168)5.6.13.1General (168)5.6.13.2Initiation (168)5.6.13.3Actions related to transmission of SCGFailureInformation message (168)5.6.14LTE-WLAN Aggregation (169)5.6.14.1Introduction (169)5.6.14.2Reception of LWA configuration (169)5.6.14.3Release of LWA configuration (170)5.6.15WLAN connection management (170)5.6.15.1Introduction (170)5.6.15.2WLAN connection status reporting (170)5.6.15.2.1General (170)5.6.15.2.2Initiation (171)5.6.15.2.3Actions related to transmission of WLANConnectionStatusReport message (171)5.6.15.3T351 Expiry (WLAN connection attempt timeout) (171)5.6.15.4WLAN status monitoring (171)5.6.16RAN controlled LTE-WLAN interworking (172)5.6.16.1General (172)5.6.16.2WLAN traffic steering command (172)5.6.17LTE-WLAN aggregation with IPsec tunnel (173)5.6.17.1General (173)5.7Generic error handling (174)5.7.1General (174)5.7.2ASN.1 violation or encoding error (174)5.7.3Field set to a not comprehended value (174)5.7.4Mandatory field missing (174)5.7.5Not comprehended field (176)5.8MBMS (176)5.8.1Introduction (176)5.8.1.1General (176)5.8.1.2Scheduling (176)5.8.1.3MCCH information validity and notification of changes (176)5.8.2MCCH information acquisition (178)5.8.2.1General (178)5.8.2.2Initiation (178)5.8.2.3MCCH information acquisition by the UE (178)5.8.2.4Actions upon reception of the MBSFNAreaConfiguration message (178)5.8.2.5Actions upon reception of the MBMSCountingRequest message (179)5.8.3MBMS PTM radio bearer configuration (179)5.8.3.1General (179)5.8.3.2Initiation (179)5.8.3.3MRB establishment (179)5.8.3.4MRB release (179)5.8.4MBMS Counting Procedure (179)5.8.4.1General (179)5.8.4.2Initiation (180)5.8.4.3Reception of the MBMSCountingRequest message by the UE (180)5.8.5MBMS interest indication (181)5.8.5.1General (181)5.8.5.2Initiation (181)5.8.5.3Determine MBMS frequencies of interest (182)5.8.5.4Actions related to transmission of MBMSInterestIndication message (183)5.8a SC-PTM (183)5.8a.1Introduction (183)5.8a.1.1General (183)5.8a.1.2SC-MCCH scheduling (183)5.8a.1.3SC-MCCH information validity and notification of changes (183)5.8a.1.4Procedures (184)5.8a.2SC-MCCH information acquisition (184)5.8a.2.1General (184)5.8a.2.2Initiation (184)5.8a.2.3SC-MCCH information acquisition by the UE (184)5.8a.2.4Actions upon reception of the SCPTMConfiguration message (185)5.8a.3SC-PTM radio bearer configuration (185)5.8a.3.1General (185)5.8a.3.2Initiation (185)5.8a.3.3SC-MRB establishment (185)5.8a.3.4SC-MRB release (185)5.9RN procedures (186)5.9.1RN reconfiguration (186)5.9.1.1General (186)5.9.1.2Initiation (186)5.9.1.3Reception of the RNReconfiguration by the RN (186)5.10Sidelink (186)5.10.1Introduction (186)5.10.1a Conditions for sidelink communication operation (187)5.10.2Sidelink UE information (188)5.10.2.1General (188)5.10.2.2Initiation (189)5.10.2.3Actions related to transmission of SidelinkUEInformation message (193)5.10.3Sidelink communication monitoring (195)5.10.6Sidelink discovery announcement (198)5.10.6a Sidelink discovery announcement pool selection (201)5.10.6b Sidelink discovery announcement reference carrier selection (201)5.10.7Sidelink synchronisation information transmission (202)5.10.7.1General (202)5.10.7.2Initiation (203)5.10.7.3Transmission of SLSS (204)5.10.7.4Transmission of MasterInformationBlock-SL message (205)5.10.7.5Void (206)5.10.8Sidelink synchronisation reference (206)5.10.8.1General (206)5.10.8.2Selection and reselection of synchronisation reference UE (SyncRef UE) (206)5.10.9Sidelink common control information (207)5.10.9.1General (207)5.10.9.2Actions related to reception of MasterInformationBlock-SL message (207)5.10.10Sidelink relay UE operation (207)5.10.10.1General (207)5.10.10.2AS-conditions for relay related sidelink communication transmission by sidelink relay UE (207)5.10.10.3AS-conditions for relay PS related sidelink discovery transmission by sidelink relay UE (208)5.10.10.4Sidelink relay UE threshold conditions (208)5.10.11Sidelink remote UE operation (208)5.10.11.1General (208)5.10.11.2AS-conditions for relay related sidelink communication transmission by sidelink remote UE (208)5.10.11.3AS-conditions for relay PS related sidelink discovery transmission by sidelink remote UE (209)5.10.11.4Selection and reselection of sidelink relay UE (209)5.10.11.5Sidelink remote UE threshold conditions (210)6Protocol data units, formats and parameters (tabular & ASN.1) (210)6.1General (210)6.2RRC messages (212)6.2.1General message structure (212)–EUTRA-RRC-Definitions (212)–BCCH-BCH-Message (212)–BCCH-DL-SCH-Message (212)–BCCH-DL-SCH-Message-BR (213)–MCCH-Message (213)–PCCH-Message (213)–DL-CCCH-Message (214)–DL-DCCH-Message (214)–UL-CCCH-Message (214)–UL-DCCH-Message (215)–SC-MCCH-Message (215)6.2.2Message definitions (216)–CounterCheck (216)–CounterCheckResponse (217)–CSFBParametersRequestCDMA2000 (217)–CSFBParametersResponseCDMA2000 (218)–DLInformationTransfer (218)–HandoverFromEUTRAPreparationRequest (CDMA2000) (219)–InDeviceCoexIndication (220)–InterFreqRSTDMeasurementIndication (222)–LoggedMeasurementConfiguration (223)–MasterInformationBlock (225)–MBMSCountingRequest (226)–MBMSCountingResponse (226)–MBMSInterestIndication (227)–MBSFNAreaConfiguration (228)–MeasurementReport (228)–MobilityFromEUTRACommand (229)–Paging (232)–ProximityIndication (233)–RNReconfiguration (234)–RNReconfigurationComplete (234)–RRCConnectionReconfiguration (235)–RRCConnectionReconfigurationComplete (240)–RRCConnectionReestablishment (241)–RRCConnectionReestablishmentComplete (241)–RRCConnectionReestablishmentReject (242)–RRCConnectionReestablishmentRequest (243)–RRCConnectionReject (243)–RRCConnectionRelease (244)–RRCConnectionResume (248)–RRCConnectionResumeComplete (249)–RRCConnectionResumeRequest (250)–RRCConnectionRequest (250)–RRCConnectionSetup (251)–RRCConnectionSetupComplete (252)–SCGFailureInformation (253)–SCPTMConfiguration (254)–SecurityModeCommand (255)–SecurityModeComplete (255)–SecurityModeFailure (256)–SidelinkUEInformation (256)–SystemInformation (258)–SystemInformationBlockType1 (259)–UEAssistanceInformation (264)–UECapabilityEnquiry (265)–UECapabilityInformation (266)–UEInformationRequest (267)–UEInformationResponse (267)–ULHandoverPreparationTransfer (CDMA2000) (273)–ULInformationTransfer (274)–WLANConnectionStatusReport (274)6.3RRC information elements (275)6.3.1System information blocks (275)–SystemInformationBlockType2 (275)–SystemInformationBlockType3 (279)–SystemInformationBlockType4 (282)–SystemInformationBlockType5 (283)–SystemInformationBlockType6 (287)–SystemInformationBlockType7 (289)–SystemInformationBlockType8 (290)–SystemInformationBlockType9 (295)–SystemInformationBlockType10 (295)–SystemInformationBlockType11 (296)–SystemInformationBlockType12 (297)–SystemInformationBlockType13 (297)–SystemInformationBlockType14 (298)–SystemInformationBlockType15 (298)–SystemInformationBlockType16 (299)–SystemInformationBlockType17 (300)–SystemInformationBlockType18 (301)–SystemInformationBlockType19 (301)–SystemInformationBlockType20 (304)6.3.2Radio resource control information elements (304)–AntennaInfo (304)–AntennaInfoUL (306)–CQI-ReportConfig (307)–CQI-ReportPeriodicProcExtId (314)–CrossCarrierSchedulingConfig (314)–CSI-IM-Config (315)–CSI-IM-ConfigId (315)–CSI-RS-Config (317)–CSI-RS-ConfigEMIMO (318)–CSI-RS-ConfigNZP (319)–CSI-RS-ConfigNZPId (320)–CSI-RS-ConfigZP (321)–CSI-RS-ConfigZPId (321)–DMRS-Config (321)–DRB-Identity (322)–EPDCCH-Config (322)–EIMTA-MainConfig (324)–LogicalChannelConfig (325)–LWA-Configuration (326)–LWIP-Configuration (326)–RCLWI-Configuration (327)–MAC-MainConfig (327)–P-C-AndCBSR (332)–PDCCH-ConfigSCell (333)–PDCP-Config (334)–PDSCH-Config (337)–PDSCH-RE-MappingQCL-ConfigId (339)–PHICH-Config (339)–PhysicalConfigDedicated (339)–P-Max (344)–PRACH-Config (344)–PresenceAntennaPort1 (346)–PUCCH-Config (347)–PUSCH-Config (351)–RACH-ConfigCommon (355)–RACH-ConfigDedicated (357)–RadioResourceConfigCommon (358)–RadioResourceConfigDedicated (362)–RLC-Config (367)–RLF-TimersAndConstants (369)–RN-SubframeConfig (370)–SchedulingRequestConfig (371)–SoundingRS-UL-Config (372)–SPS-Config (375)–TDD-Config (376)–TimeAlignmentTimer (377)–TPC-PDCCH-Config (377)–TunnelConfigLWIP (378)–UplinkPowerControl (379)–WLAN-Id-List (382)–WLAN-MobilityConfig (382)6.3.3Security control information elements (382)–NextHopChainingCount (382)–SecurityAlgorithmConfig (383)–ShortMAC-I (383)6.3.4Mobility control information elements (383)–AdditionalSpectrumEmission (383)–ARFCN-ValueCDMA2000 (383)–ARFCN-ValueEUTRA (384)–ARFCN-ValueGERAN (384)–ARFCN-ValueUTRA (384)–BandclassCDMA2000 (384)–BandIndicatorGERAN (385)–CarrierFreqCDMA2000 (385)–CarrierFreqGERAN (385)–CellIndexList (387)–CellReselectionPriority (387)–CellSelectionInfoCE (387)–CellReselectionSubPriority (388)–CSFB-RegistrationParam1XRTT (388)–CellGlobalIdEUTRA (389)–CellGlobalIdUTRA (389)–CellGlobalIdGERAN (390)–CellGlobalIdCDMA2000 (390)–CellSelectionInfoNFreq (391)–CSG-Identity (391)–FreqBandIndicator (391)–MobilityControlInfo (391)–MobilityParametersCDMA2000 (1xRTT) (393)–MobilityStateParameters (394)–MultiBandInfoList (394)–NS-PmaxList (394)–PhysCellId (395)–PhysCellIdRange (395)–PhysCellIdRangeUTRA-FDDList (395)–PhysCellIdCDMA2000 (396)–PhysCellIdGERAN (396)–PhysCellIdUTRA-FDD (396)–PhysCellIdUTRA-TDD (396)–PLMN-Identity (397)–PLMN-IdentityList3 (397)–PreRegistrationInfoHRPD (397)–Q-QualMin (398)–Q-RxLevMin (398)–Q-OffsetRange (398)–Q-OffsetRangeInterRAT (399)–ReselectionThreshold (399)–ReselectionThresholdQ (399)–SCellIndex (399)–ServCellIndex (400)–SpeedStateScaleFactors (400)–SystemInfoListGERAN (400)–SystemTimeInfoCDMA2000 (401)–TrackingAreaCode (401)–T-Reselection (402)–T-ReselectionEUTRA-CE (402)6.3.5Measurement information elements (402)–AllowedMeasBandwidth (402)–CSI-RSRP-Range (402)–Hysteresis (402)–LocationInfo (403)–MBSFN-RSRQ-Range (403)–MeasConfig (404)–MeasDS-Config (405)–MeasGapConfig (406)–MeasId (407)–MeasIdToAddModList (407)–MeasObjectCDMA2000 (408)–MeasObjectEUTRA (408)–MeasObjectGERAN (412)–MeasObjectId (412)–MeasObjectToAddModList (412)–MeasObjectUTRA (413)–ReportConfigEUTRA (422)–ReportConfigId (425)–ReportConfigInterRAT (425)–ReportConfigToAddModList (428)–ReportInterval (429)–RSRP-Range (429)–RSRQ-Range (430)–RSRQ-Type (430)–RS-SINR-Range (430)–RSSI-Range-r13 (431)–TimeToTrigger (431)–UL-DelayConfig (431)–WLAN-CarrierInfo (431)–WLAN-RSSI-Range (432)–WLAN-Status (432)6.3.6Other information elements (433)–AbsoluteTimeInfo (433)–AreaConfiguration (433)–C-RNTI (433)–DedicatedInfoCDMA2000 (434)–DedicatedInfoNAS (434)–FilterCoefficient (434)–LoggingDuration (434)–LoggingInterval (435)–MeasSubframePattern (435)–MMEC (435)–NeighCellConfig (435)–OtherConfig (436)–RAND-CDMA2000 (1xRTT) (437)–RAT-Type (437)–ResumeIdentity (437)–RRC-TransactionIdentifier (438)–S-TMSI (438)–TraceReference (438)–UE-CapabilityRAT-ContainerList (438)–UE-EUTRA-Capability (439)–UE-RadioPagingInfo (469)–UE-TimersAndConstants (469)–VisitedCellInfoList (470)–WLAN-OffloadConfig (470)6.3.7MBMS information elements (472)–MBMS-NotificationConfig (472)–MBMS-ServiceList (473)–MBSFN-AreaId (473)–MBSFN-AreaInfoList (473)–MBSFN-SubframeConfig (474)–PMCH-InfoList (475)6.3.7a SC-PTM information elements (476)–SC-MTCH-InfoList (476)–SCPTM-NeighbourCellList (478)6.3.8Sidelink information elements (478)–SL-CommConfig (478)–SL-CommResourcePool (479)–SL-CP-Len (480)–SL-DiscConfig (481)–SL-DiscResourcePool (483)–SL-DiscTxPowerInfo (485)–SL-GapConfig (485)。

基于心智模型理论的网络分类理解机制研究（以电子商务商品搜索为例）文献综述心智模型来自国外的mental model，在国内也有其它多种翻译，使用较多的是心智模型、心智模式、心理模型、思维模型等，本文中统一采用“心智模型”的叫法。

心智模型影响我们看待周边世界的方式，从而决定我们采取何种行动。

因为这些“想法”都存在我们的心中，影响我们的行为，所以称为“心智”；过去的经验形成我们看问题的角度，它不易察觉，不易改变，故成为“模型”[1]。

1 心智模型基本概念1.1 心智模型定义心智模型（Mental Model）概念最早是由苏格兰心理学家Kenneth Craik 在1943 年提出，用以表示一个系统的内部表征（internal representations），指那些在人们心中根深蒂固存在的，影响人们认识世界、解释世界、面对世界，以及如何采取行动的许多假设、陈见和印象[2]。

白新文等指出，心智模型的研究主要在人类工效学和认知科学（特别是认知心理学）这两个领域进行[3]，但从现有研究看，心智模型已经扩展到许多学科领域。

下面就列举几个学科，从各领域研究角度解释什么是心智模型。

1.1.1 认知心理学自从Craik(1943)第一次提出心智模型这一概念起，认知心理学很多学者就对其投入了较大关注。

大多数心理学家将心智模型当作理解人类感知、认识、决策以及构建行为的一种重要途径，与其它学科相比，对它的关注主要集中在大脑推理与概念发展上，更为从个人内心和知识状况来阐释。

个人知识的局限性、个人不能完全作出准确的预期以及个人行为受到限制。

因此,个人在决策时依赖于心理过程。

诺斯等人将这种心理过程称为心智模型,并将之定义为用于解释环境的内部表征，它由人的认知系统为应对环境的不确定性而创立[4]。

文献[4]Luria(1973)指出，心智模型介于知觉和行为之间，是最高的知识表征模式[5]。

Williams（1983）认为心智模型，也称为“知识结构”，是一些相互关联的心理对象的集合，是它们与其它对象相互关系状态，以及一系列内部因素的外显表征[6]。