Dryer Section(12)Optimisation of the Air Conditions in the Dryer Section low

MAXIMISING LIFETIME VALUEUpgrade Today to Meet the Demands of TomorrowAchieving Maximum Value from Your Spray Dryer • M• E xpanded capacity • I ncreased availability • A • T • I mproved safety • V alidated processes• R • R educed emissionsWhy Modernise?Upgrading your spray dryers can yield great rewards. Y our plant will be more efficient, your people and plant will be safer, and you will be well positioned to take advantage of new market opportunities. Plus, you can get it all done with minimal CAPEX. Identifying Your NeedsEquipped with proven techniques refined over decades of experience, SPX FLOW Rebuild & Upgrade specialists visit your site to assess your operations and identify areas where the greatest impact can be achieved. We then provide full installation, commissioning, and startup services, or can supervise your teams to ensure they are doing things right.TAPPING THE HIDDEN VALUE IN YOUR SPRAY DRYER PLANTEfficient spray drying reduces energy use and otheroperating costs while improving process stability, yield, and hygiene.A carefully planned rebuild or upgrade scenario improves performance and extends the life of your equipment.Our spray dry process experts apply a unique methodology to rejuvenate under-performing air heaters, dehumidification, and heat recovery systems. They also make your process smarter with software that self-corrects process anomalies and dramatically compresses product delivery schedules via dual feed lines.With SPX FLOW, you can rest assured you are getting maximum return on your assets.PLANTEFFICIENCY321ABCDRebuilding and upgrading is not simply about fixing what is broken. Our customers often want to raise their operations to a new level - perhaps to modify a product or meet an emerging market trend. Whatever optimisation is required, SPX FLOW has the experts and equipment necessary to make it happen.Product Preparation and Air ParametersF eed (water % + solids %)A ir inlet (temperature and moisture content)A ir outlet (temperature and moisture content)E nd product (solids % + moisture content %)OPTIMISE PROCESSES AND ENVIRONMENTAL PROTECTION65246Safety is a critical factor for most rebuild and upgrade projects. An inefficient operation can put your people and plant at risk, and negatively affect profitability. SPX FLOW helps identify and eliminate potential hazards while offering solutions that make your operation safer than ever.IMPROVE SPRAY DRYER PLANTSAFETYWhere most spray dryer solution providers provide expertise in just a few areas and rely on third-party solutions to address your needs, SPX FLOW delivers a single solution from beginning to end. The result is a system composed of high-quality components that work seamlessly together to deliver maximum value. Additionally, we offer a single point of contact so that you always know an expert is on the job. Our goal is to deliver lower operating costs with increased performance and higher production value. That is the SPX FLOW difference.A SINGLE SOLUTION FROM INQUIRY TO ACCEPTANCES ol u De l iv P ro po So l u• W • I • E • D • L• M • M • MY criteria:• C • P • M • S • ISPX FLOW, Inc. reserves the right to incorporate our latest design and material changes without notice or obligation. Design features, materials of construction and dimensional data, as described in this bulletin, are provided for your information only and should not be relied upon unless confirmed in writing. Please contact your local sales representative for product availability in your region. The green “ ” is a trademark of SPX FLOW, Inc.For more information, visit .©2022 SPX FLOW, Inc.ANH_Spray-Dryer-Plant-Rebuild-and-Upgrade_BRO_0822_GB Version: 08/2022。

4.1 The size of a penetrant indication depends on:渗透剂的显示大小取决于：A.the size of the discontinuity. 不连续的大小B.the discontinuity entrapment efficiency.C.the technique of penetrant testing. 渗透检测技术D.all of the above. 上面全部E.only a and c above. 只有上面的a和b4.2 The most sensitive application technique for dry developer is:对干燥显像剂最为敏感的应用技术是：A.Immersion. 浸入B.a dust cloud. 粉尘云C.A fluidized bed. 流化床D.A dust cloud —electrostatic gun. 粉尘云—静电抢4.3 one advantage of having a little background fluorescence is that it:小背景荧光的一个优点是它：A.reduces the contrast of the indication. 减少现象的对比度B.Indicates that the part was not overwashed. 显示不被清洗的部分C.Reveals an excessive emulsification time. 显示过度乳化时间D.Is hard to remove when a highly sensitive penetrant is applied to rough or porous surfaces.很难去除用在粗糙或多孔表面的高灵敏度渗透剂4.4 the water wash test is used to evaluate the washing properties of penetrant and emulsifiers. The spray nozzle is held 30 cm(12 in.) from the wash surface. The water pressure must not exceed:水洗涤实验用于评估渗透剂和乳化剂的洗涤性能。

4.5.6.7.The dryer contains residual water.Make sure that the airinlet remains unobstructedClean and level2Care and cleaningDryer housing, control panelRemove detergent residues immediately.Wipe with a soft, damp cloth.Do not clean with a water jet.During drying, water may accumulate between the window and the seal.Moisture sensorThe dryer is equipped with stainless steel moisture sensors.Moisture sensors measure how damp the laundry is.A thin layer of limescale may build up on the moisture sensors afterprolonged use.This must be removed regularly.1.Open the door and clean the moisture sensors with a dampsponge that has a rough surface.Do not use steel wool or abrasive materials.Cover for steam nozzle in the drum (depending on the model)After prolonged operation, limescale and fluff may accumulate on the cover of the steamnozzle. Clean regularly:1.Open the door and remove the cover with a screwdriver.Clean the cover under running water.2.Push and click the cover back in place.Clean the filter in the refresher tank if it is dirty (depending on the model)Regularly clean the filter under running water a This prevents stubborn deposits/dirt frombuilding up.1.Remove the condensation container.2.Remove the filter and clean it under running water.3.Re-insert the filter and push in the condensation container until itlocks into place.Heat exchangerThe dryer's heat exchanger is automatically cleaned during drying andbeing rinsedFilter in the condensation containerThe filter is cleaned automatically when the condensation container is emptied.However, the filter should still be cleaned regularly under running water1.Product number Production numberTrust the expertise of the manufacturer. Contact us for assistance. In this way, you can restassured that the repair will be carried out by trained service technicians using original spareparts.Optional accessories5Reversing the door1622.1.7384953.1.2.106119406 / 9000695672*695672*6Reversing the door1116121713181.3.2.141915201.2.16211.2.271.2.3.281.2.1. 2.ContentsPageʋEnvironmentally-friendly disposal . . . . . .1ʋInstallation . . . . . . . . . . . . . . . . . . . . . . . .1ʋFrost protection / Transport. . . . . . . . . . .1ʋCare and cleaning . . . . . . . . . . . . . . . 2/3ʋAfter-sales service. . . . . . . . . . . . . . . . . .4ʋOptional accessories. . . . . . . . . . . . . . . .4ʋTechnical data . . . . . . . . . . . . . . . . . . . .4ʋReversing the door . . . . . . . . . . . . . . . 5/7Read these instructions before operating the dryer.You must also observe the separate instruction manual.Keep all the documents in a safe place for future reference, or to pass on to the next owner.4.5.6.7.The dryer contains residual water.Make sure that the airinlet remains unobstructedClean and level2 Care and cleaningDryer housing, control panelRemove detergent residues immediately.Wipe with a soft, damp cloth.Do not clean with a water jet.During drying, water may accumulate between the window and the seal.Moisture sensorThe dryer is equipped with stainless steel moisture sensors.Moisture sensors measure how damp the laundry is.A thin layer of limescale may build up on the moisture sensors afterprolonged use.This must be removed regularly.1.Open the door and clean the moisture sensors with a dampsponge that has a rough surface.Do not use steel wool or abrasive materials.Cover for steam nozzle in the drum (depending on the model)After prolonged operation, limescale and fluff may accumulate on the cover of the steamnozzle. Clean regularly:1.Open the door and remove the cover with a screwdriver.Clean the cover under running water.2.Push and click the cover back in place.Clean the filter in the refresher tank if it is dirty (depending on the model)Regularly clean the filter under running water a This prevents stubborn deposits/dirt frombuilding up.1.Remove the condensation container.2.Remove the filter and clean it under running water.3.Re-insert the filter and push in the condensation container until itlocks into place.Heat exchangerThe dryer's heat exchanger is automatically cleaned during drying andbeing rinsedFilter in the condensation containerThe filter is cleaned automatically when the condensation container is emptied.However, the filter should still be cleaned regularly under running water1.Product number Production numberTrust the expertise of the manufacturer. Contact us for assistance. In this way, you can restassured that the repair will be carried out by trained service technicians using original spareparts.Optional accessories5Reversing the door1622.1.7384953.1.2.106119406 / 9000695672*695672*6Reversing the door1116121713181.3.2.141915201.2.16211.2.271.2.3.281.2.1. 2.ContentsPageʋEnvironmentally-friendly disposal . . . . . .1ʋInstallation . . . . . . . . . . . . . . . . . . . . . . . .1ʋFrost protection / Transport. . . . . . . . . . .1ʋCare and cleaning . . . . . . . . . . . . . . . 2/3ʋAfter-sales service. . . . . . . . . . . . . . . . . .4ʋOptional accessories. . . . . . . . . . . . . . . .4ʋTechnical data . . . . . . . . . . . . . . . . . . . .4ʋReversing the door . . . . . . . . . . . . . . . 5/7Read these instructions before operating the dryer.You must also observe the separate instruction manual.Keep all the documents in a safe place for future reference, or to pass on to the next owner.。

Analysis of the uncertainties affecting predictions of clay contents from VNIR/SWIR hyperspectral dataC.Gomez a ,A.P.A.Drost a ,J.-M.Roger ba IRD,UMR LISAH (INRA-IRD-SupAgro),F-34060Montpellier,France bIRSTEA,UMR ITAP,Montpellier,Francea b s t r a c ta r t i c l e i n f o Article history:Received 3April 2014Received in revised form 25September 2014Accepted 29September 2014Available online xxxxKeyword:Airborne hyperspectral imagery VNIR/SWIR spectroscopy UncertaintyMultivariate calibration Clay content mappingVisible,Near-Infrared and Short Wave Infrared (VNIR/SWIR,350–2500nm)hyperspectral imaging spectroscopy can be used to develop maps of estimated soil properties.The performance of the estimations obtained from regression models is usually assessed with figures of merit such as the standard error of calibration,the standard error of prediction or the ratio of performance deviation.All of these parameters are estimated during the model building and validation stages to evaluate the global model performance.Beyond these global indicators,the evaluation of the uncertainty that affects predictions is a major trend in analytical chemistry and chemometrics but not yet in hyperspectral imagery.Several approximate expressions and resampling methods have been proposed to estimate the prediction uncertainty when using multivariate calibrations from laboratory spectra.Based on these propositions,this paper studies a process of mapping and analyzing the uncertainties that affect soil property predictions obtained from VNIR/SWIR airborne data using several methods.An application to real VNIR/SWIR airborne data of clay content was used to compare the methods.The different cases yielded insights into the sources of uncertainty and showed that uncertainty analysis can guide the user to better sampling,better calibration and ultimately better mapping.©2014Elsevier Inc.All rights reserved.1.IntroductionFor two decades,laboratory Visible,Near-Infrared and Short Wave Infrared (VNIR/SWIR,350–2500nm)spectroscopy has been proven to be a good alternative to costly physical and chemical laboratory soil analyses for estimating a large range of soil properties (e.g.,Ben-Dor &Banin,1995;Cécillon et al.,2009;Viscarra Rossel,Walvoort,McBratney,Janik,&Skjemstad,2006).These studies led to the use of VNIR/SWIR hyperspectral imaging spectroscopy to estimate soil properties.VNIR/SWIR hyperspectral imaging spectroscopy can provide a global view of a study area at a high spatial resolution and can characterize several soil properties simultaneously.Ben-Dor,Patkin,Banin,and Karnieli (2002)began this field of research by applying multivariate calibration statistics to remotely-sensed data.Since then,VNIR/SWIR hyperspectral imaging spectroscopy has bene fited from an increas-ing number of methodologies developed in lab soil property predic-tion studies,including partial least square regression (e.g.,Gomez,Lagacherie,&Coulouma,2008a ),support vector machine regression (e.g.,Stevens et al.,2010),multiple linear regression (e.g.,Bayer,Bachmann,Müller,&Kaufmann,2012),stepwise multiple linear regression (e.g.,Lu,Wang,Niu,Li,&Zhang,2013)and regression rules (Minasny &McBratney,2008).Several studies have successfully mapped soil properties such as clay,calcium carbonate,iron,and soilorganic carbon (e.g.,Ben-Dor et al.,2007;Gomez,Viscarra Rossel,&McBratney,2008b;Gomez,Coulouma,&Lagacherie,2012a;Selige,Bohner,&Schmidhalter,2006;Stevens et al.,2010).Regardless of the regression method,the number of samples in the calibration database,the scale of study or the studied soil properties,the quality of mapping results are expressed using figures of merit such as the standard error of calibration (SEC)and standard error of prediction (SEP),which are referred to as the root mean squared error of calibration (RMSEC)and root mean squared error of prediction (RMSEP),respectively.The coef ficient of determination (R 2)or the ratio of performance deviation (RPD)is also used to measure the accuracy of multivariate model predictions.As mentioned in McBratney and Minasny (2013),the R 2and RPD give dependent information and the use of only one of both indicators is enough.The ratio of performance to interquartile (RPIQ),which is the ratio of the inter-quartile (IQ =Q3−Q1)to the RMSEP,was recently proposed as an alternative to the RPD to better take into account the shape of the distribution (Bellon-Maurel,Fernandez-Ahumada,Palagos,Roger,&McBratney,2010).All of these parameters are estimated during the model building and validation stages to evaluate the global model performance.Selige et al.(2006)used the partial least square re-gression (PLSR)method with a calibration database of 72soil samples to map sand content over a 7km 2area from HYMAP VNIR/SWIR hyperspectral data.Gomez,Lagacherie,and Bacha (2012b)used the PLSR method with a calibration database of 95soil samples to mapRemote Sensing of Environment 156(2015)58–70E-mail address:cecile.gomez@ird.fr (C.Gomez)./10.1016/j.rse.2014.09.0320034-4257/©2014Elsevier Inc.All rightsreserved.Contents lists available at ScienceDirectRemote Sensing of Environmentj o u r n a l h om e p a g e :w w w.e l s e v i e r.c o m /l o c a t e /r s eclay content over a300km2area from AISA-Dual VNIR/SWIR hyperspectral data.Both studies expressed the quality of the mapping results using the performance of their PLSR models(calculation of the figures of merit)and a visual pedological evaluation of the soil maps.Fi-nally,several studies have used geostatistical analysis to study the spa-tial structure of the predicted soil property(e.g.,Gomez et al.,2012a; Schwanghart&Jarmer,2011).In addition to the global performance indicators,visual pedological evaluations and geostatistical analysis,the uncertainty of a prediction must be assessed to better characterize the quality of prediction maps. The“uncertainty”of a prediction is defined as“a parameter associated with the result of a measurement that characterizes the dispersion of the values that could reasonably be attributed to the measurand”(AFNOR NFX07-001:1994)and is related to the variance of the predic-tion.The uncertainty can be calculated for all new predictions,while the RMSEP and other classicalfigures of merit can be calculated only from points for which the reference values are known.In the PLS regression context,the uncertainty of predictions wasfirst studied by Höskuldsson(1988),Phatak,Reilly,and Penlidis(1993) and Denham(1997),who used the hypothesis of negligible errors in pre-dictors.Those studies were expanded by Faber and Kowalski(1997),who included errors in the predictors under the general errors-in-variables model.A drawback to their approach is that the original expression is de-rived assuming that the errors in the predictors have a constant variance (the homoscedastic case).Faber and Bro(2002)proposed a new expres-sion that accommodated heteroscedastic and correlated errors;however, the expression was derived assuming that the errors in the predictors are identically and independently distributed(i.i.d.),which the authors conjectured applied to most types of heteroscedasticity.Fernandez-Ahumada,Roger,and Palagos(2012)proposed a new expression for the variance of the prediction that could be adapted to any linear calibration model,such as PLSR.This formulation respects the specificities of spec-trometry and particularly the spectral error structure that is induced by the high colinearity of the variables.Based on these studies,this paper proposes an analysis process of the uncertainty that affects predictions obtained from VNIR/SWIR airborne data.A bootstrap procedure is used to calculate the variance of the predic-tions of each VNIR/SWIR airborne spectrum that represents the sum of all of the sources of uncertainty and can be considered as the“true”variance. In addition,the distance between the spectral predictors and the spectral calibration samples is calculated as two uncertainty expressions:i)in a Principal Component Analysis(PCA)space(Mahalanobis distance)and ii)in the multivariate model space(leverage).Finally,the expression for the variance of prediction proposed by Fernandez-Ahumada et al. (2012)is adapted to the VNIR/SWIR airborne data.The adaptation of this formula allows the spatial dimension of these remote sensing data to be taken into account and allows the spatialization of the total variance of predictions and each term of the formula,including i)the variance of the predictions due to the multivariate model,ii)the variance of the pre-dictions due to the spectra and iii)the interaction between these two ef-fects.The proposed analysis process uses the spatial dimension of the VNIR/SWIR airborne data to express the uncertainties in the form of maps.This process of uncertainty analysis is applied to VNIR/SWIR AISA-Dual airborne data acquired over an area of Tunisia,for which an esti-mated clay content map was obtained by Gomez et al.(2012b).First, all of the uncertainty expressions are calculated and analyzed in a vali-dation database.Then,all of the uncertainty expressions are calculated, mapped and analyzed in a test area.Specific cases highlight several interesting characteristics of these uncertainty expressions.2.Materials2.1.NotationsIn this paper,capital bold characters are used for matrices;e.g.,A.Small bold characters are used for column vectors;e.g.,a i will denote the i th column of A.Row vectors are denoted by the transpose notation;e.g.,a′.Lowercase non-bold italic characters are used for scalar variables;e.g.,indices i.Uppercase non-bold italic characters are used for scalar constants;e.g.,the number of samples N.When needed,the matrix dimensions are indicated;e.g.,A(N×P). The trace of a square matrix A is noted as tr(A).2.2.Site descriptionThe study area is located in the Cap Bon region in northern Tunisia (36°24′N to36°53′N;10°20′E to10°58′E),60km east of Tunis (Fig.1a).This300km2area includes the Lebna catchment,which is mainly rural(N90%)and is devoted to the cultivation of cereals in addition to legumes,olive trees,vineyards and natural vegetation for animals.The region is characterized by rolling hills with elevations between0and226m.The climate varies from humid to semi-arid with inter-annual precipitation of600mm and inter-annual potential evapotranspiration of1500mm.The soil pattern of the Lebna catch-ment is mainly the result of variations in lithology.The variations in the bedrock between Miocene sandstone and marl cause large varia-tions in the physical and chemical soil properties(Zante,Collinet,& Pepin,2005).Furthermore,the distance between successive sandstone outcrops decreases significantly from the ocean to the mountains,this also causes variations in the soil property patterns(Gomez et al., 2012b).The soil materials were redistributed laterally along the slopes during the Holocene,which adds to the complexity of the soil patterns. The main soil types are regosols and eutric regosols(9.6%),which are preferentially associated with sandstone outcrops,and calcic cambisols and vertisols,which preferentially formed on marl outcrops and low-lands.The southeastern region of the study area has aflatter landscape with sandy Pliocene deposits that yield calcosols and rendzinas.2.3.VNIR/SWIR hyperspectral dataOn November2,2010,an AISA-Dual airborne hyperspectral image was acquired for the study area with a spatial resolution of5m (Fig.1b).The area of the image is approximately12km×24km.The AISA-Dual spectrometer measured the reflected radiance in359non-contiguous bands covering the400-to2450-nm spectral domain with 4.6nm bandwidths between400and970nm and6.5nm bandwidths between970and2450nm.The instantaneousfield of view(IFOV) was24°.The radiance units were converted to reflectance units using ASD spectrometer measurements of uniform surfaces(parking lots, asphalt,concrete)that were collected at the same time as the overflight. An empirical line correction method was used to calibrate eachflight line to the reflectance.Topographic corrections were performed using a digital elevation model constructed from the ASTER data and ground control points.In this study,we removed the spectral bands in the blue part of the spectral domain(between400and484nm)due to noise in these bands as well as the spectral bands between1339and 1464nm and between1772and2004nm due to vibrational–rotational H2O absorption bands.Consequently,280AISA-Dual spectral bands were retained.When the image was acquired(November2010)the bare soils represented46.3%of the study area.The rest of the area was covered by urbanization,water and vegetation consisting mainly of olive trees, native forests,green plants and vineyards.To isolate the bare soil areas,the pixels with normalized difference vegetation index(NDVI) values greater than an expert-calibrated threshold were masked;a value of0.20was determined after considering twenty parcels that had been visually inspected in thefield.Water areas were also masked using an expert-calibrated threshold;pixels with a reflectance of less than8%at1665nm were removed.Finally,urban areas were masked using a map of urban areas.An initial predicted clay content map over the Lebna catchment was obtained from a multivariate model using these AISA-Dual airborne data(Gomez et al.,2012b).59C.Gomez et al./Remote Sensing of Environment156(2015)58–70Fig.1.a)Location of the hyperspectral image (black rectangle)in Tunisia,b)location of the Kamech catchment (black rectangle)over the hyperspectral AISA-Dual image (in white the masked areas:vegetated-,urban-,and waterareas).Fig.2.Map of bare soils (black areas)over the Kamech catchment.Three studied pixels are located on the center fields (Pix_Soil 1,Pix_Soil 2,Pix_Soil 3),and three other studied pixels are located on the border fields (Pix_BoundaryField 1,Pix_BoundaryField 2,Pix_Urban ).60 C.Gomez et al./Remote Sensing of Environment 156(2015)58–70This paper focuses on a test area for the analysis of maps of the uncertainty expressions.This test area is a6.67km2area that is centered on the Kamech catchment,which contained a high percentage of bare soils during image acquisition and exhibited contrasting soil patterns(Figs.1b,2).The Kamech experimental catchment belongs to a long-term environmental research observatory called OMERE (Mediterranean observatory of water and rural environment)which aims to study the anthropogenic impacts on water and sediment budgets at catchment scale(e.g.,Mekki,Albergel,Ben Mechlia,& Voltz,2006;Raclot&Albergel,2006).The bare soils represent49.2%of the test area and10,705AISA-Dual pixels.In this area,six pixels were selected to study the expressions of uncertainty that affect the predic-tions proposed in this paper.Three of these test pixels are located in the centers of bare soilfields,and the other three are located at the boundaries offields(Fig.2).2.4.Soil sample databaseA dataset of262soil samples was collected from the Lebna catch-ment between2008and2010.All of the samples were composed of five sub-samples collected to a depth of5cm within a10×10m square centered on the geographical position of the sampling plot as recorded by a Garmin GPS instrument.The sampling strategy to collect the data followed a stratified scheme.The study area was divided in72grids of 2×2km.From two to six soil samples were collected per grid,over 60grids of the72designed grids.The remaining12grids corresponded to the Lebna lake,a Sebkha area and some grids inaccessible by cars at the bottom of the Djebel(West border of the study area).Among these262soil samples,129samples were collected infields that were bare during the hyperspectral data acquisition in November2010;58 were collected in June2008,30were collected in October2009,and 41were collected in November2010.These129samples were located over55of the72designed grids,with one to three soil samples per grid.These129soil samples constituted the soil database used in this study.After homogenizing the sample,approximately20g was devoted to the soil property analysis.The initial samples were air-dried and sieved with a2mm sieve prior to being transported to the laboratory for analysis.The clay content(granulometric fraction b2μm)was deter-mined using a pipette method(method NF X31–107,particle size dis-tribution by sedimentation,Baize&Jabiol,1995).The clay contents of the129soil samples vary between46and777g/kg and follow a normal distribution.3.Prediction model:PLSRThe partial least square regression(PLSR)method(Wold,Sjöström, &Eriksson;,2001)was used to establish relationships between the soil clay content and the VNIR/SWIR hyperspectral imaging data.The spectroscopic and chemometric analyses were implemented in R (Version1.17).Prior to the quantitative statistical analysis,the reflectance was converted into“pseudo absorbance”(log[1/reflectance]).Noise reduction was achieved through standard pre-treatments,including a Savitzky–Golayfilter with second-order polynomial smoothing and window widths of30nm(Savitzky&Golay,1964)for noise removal and a standard normal variate correction(Barnes,Dhanoa,& Lister,1993)for additive and multiplicative effect removal.The dataset was divided into a calibration set(97samples, or3/4of the total dataset,denoted BD_Calib)and a validation set(32samples,or1/4of the total dataset,denoted BD_Valid). The reference values(soil property)of the dataset were sorted in ascending order.The method starts by selecting the sample with the lowest reference value and putting it in a validation set. The next three samples are then put in the calibration set,and the procedure is continued by alternately placing the next sample in the validation set and the following three samples in the calibration set.Following this process of subdivision of the data, the distributions of the calibration and validation database were similar.Because a limited number of samples were available, a leave-one-out cross-validation procedure was adopted to verify the prediction capability of the PLSR model for the calibration set(Wold,1978).Each time,N−1samples were used to build the regression model from all N samples within the dataset. Based on this model,the value for the soil property of the sample that was not used to develop the model was predicted.This procedure was repeated for all N samples and resulted in predictions for all of the calibration samples.Outliers are commonly defined as observations that are not con-sistent with the majority of the data(Chiang,Pell,&Seasholtz, 2003;Pearson,2002),such as observations that deviate significantly from normal values.An outlier can be defined as a spectral outlier, which is a sample that is spectrally different from the other samples, or a concentration outlier,which is a predicted value with a residual difference that is significantly greater than the mean of the predicted values.One method for identifying spectral outliers uses the princi-ple of the Mahalanobis distance(Mark&Tunnell,1985)applied to PCA-reduced data.In this study,a value of3was selected to identify outliers based on the Mahalanobis distance.The analysis to detect outliers was performed for all of the samples in the calibration set, and the detected spectral and concentration outliers were deleted from the calibration set.In this paper,we only provide a brief description of the PLSR model, which is fully detailed in Wold et al.(2001).The PLSR model is devel-oped from a training set of N observations(number of spectra in the calibration dataset)with K X-variables(number of wavelengths in the spectra)denoted x k(k=1,…,K),and M Y-variables(number of soil properties)denoted y m(m=1,…M).These training data form the two matrices X and Y of dimensions(N×K)and(N×M), respectively.As in all factorial methods,the main principle of PLSR is:i)tofind a subspace of the spectral spaceℝK on which the spectra are projected,yielding a matrix of N scores T(N×k);and ii)to per-form a linear regression between T and Y.Thefirst step is carried out iteratively by searching loadings u and v fromℝK andℝM,respec-tively,such that cov2(Xu,Yv)is maximal.The scores T are thus given by T=XU.The linear regression between T and Y produces an esti-mate^Y¼T T0TÀÁ−1T0Y,so replacing T by XUfinally yields^Y¼XU U0X0XUÀÁ−1U0X0Y.Thefinal regression coefficients are given by b=U(U′X′XU)−1U′X′Y.In the case where Y contains only one re-sponse,b is a K-vector,which is usually called the“b-coefficients”and is generally analyzed as a spectrum.The k columns of U are also K-vectors and are called latent variables.The prediction performances of the PLSR models were evaluated using the coefficient of determination R cal2and R val2of the predicted values against the measured values in the calibration and validation sets,respectively.The root mean square errors of calibration(RMSEC) and the root mean square errors in the validation set(RMSEP)were also analyzed for all of the models.Finally,the proportion of the valida-tion data falling within the prediction interval prediction interval was analyzed.This prediction interval was calculated assuming that the prediction was normally distributed,as±2×SD(^y),where^y is the predicted value.If the uncertainty was correctly specified,then95%of the data should fall within the prescribed limit.4.Measurements of uncertainty affecting predictionsThe estimation^y of the y value for a new sample p can be written as: ^y¼f Xc;Yc;Model;xðÞð1Þwhere(Xc,Yc)contains the calibration spectra and the calibration responses,x is the spectrum of the sample p and Model represents the calibration action,including the preprocessing and the choice of61C.Gomez et al./Remote Sensing of Environment156(2015)58–70dimensions.Thus,each prediction relies on a chain of operations,which each adds a source of uncertainty:—The uncertainty of the spectra was assumed to be identical for the calibration and test spectra;this is mainly due to the device repeatability(Ud)and the spatial positioning(Us)—The uncertainty of the reference lab values(Uy)—The uncertainty of the model building(Um)is caused by two main factors,with Um=Uc+Ul.Uc is related to the choice made to build the calibration set,and Ul is related to the choice of model dimension.In this study,the following choices were made:Ud was considered to be negligible in comparison with Us,and Uy was ,Uc and Ul were computed by means of a bootstrap procedure(Efron,1982).Seven expressions related to the uncertainty that affected the predictions were computed and are described in the following sections.4.1.Bootstrap procedureA bootstrap procedure was performed to obtain a variance value that integrated all of the sources of uncertainty described previously.Fig.3a describes the workflows and can be summarized as:1st step:N replacement samples were drawn from the calibration dataset.2nd step:For each of the N selected samples,the associated AISA-Dual spectrum was drawn among a grid of3×3pixels that wascentered on the location of the selected sample p.The1st and2nd steps are grouped in the box called“double-drawing of the BD_Calib”,in Fig.3a.3rd step:The number of the latent variable was drawn from3to7.4th step:For each pixel p for which the estimated clay content value is searched,the associated AISA-Dual spectrum x was drawnfrom a grid of3×3pixels centered on pixel p(Fig.3c).Thisstep is the same for the validation dataset BD_Valid and forthe entire image.The1st step allows estimating a part of uncertainty of the model building(Um),caused by the choice made to build the calibration set(Uc).The2nd and4th steps allow estimating a part of uncertainty of the spectra due to spatial positioning(Us).Indeed,we assumed errors on AISA-Dual data georeferencing and collected soil samples georeferencing.Drawing randomly a pixel among a grid of3×3pixels pixels centered on the pixel p allowed to take into account these georeferencing errors in the uncertainty estimation due to the spatial positioning(Us).We used the following assumptions:—Symmetric probability of the error,centered at the central pixel p,—Maximum probability for the central pixel p,—Zero probability beyond the grid of3×3pixels centered on the pixel p.So the draw of AISA-Dual spectrum among the grid of3×3pixels follows a multinomial law,with the round probability values:0.27for the central pixel,0.09for the eight neighbor's pixels,and0for the pixels out of the grid of3×3pixels centered on the pixel p.The3rd step allows estimating a part of uncertainty of the model building(Um),caused by the choice of model dimension(Ul). Indeed,the choice of the number of latent variables is a critical step in a PLSR model building,because this choice may stronglyimpact Fig.3.Workflow of the soil property mapping associated to the uncertainty expression calculation.62 C.Gomez et al./Remote Sensing of Environment156(2015)58–70the performance of the PLSR model.To take into account this imperfect step in the PLSR model building and the bootstrap process, we choose to draw the number of latent variables following these assumptions:—Symmetric probability,centered at5,—Maximum probability for5,—Zero probability below3and above7.So the number of the latent variable is drawn from a multinomial law with the probability values:0.05for3and7,0.2for4and6,and 0.4for5.These four steps were performed R times and produced R bootstrap (training)datasets,R PLSR models,R prediction values for each new pixel and(R×K)b-coefficients.The number of iterations R was999. At each bootstrap iteration,the predicted outcomes were calculated by cross-validation,and the corresponding performance indicators R cal2and RMSEC were calculated for the calibration set.The predicted outcomes and corresponding performance indicators R val2and RMSEP were also calculated for the validation set at each of the R bootstrap iterations.4.2.Expressions of the uncertainty using the bootstrap procedureThe bootstrap procedure was used to obtain a variance value var^yðÞBS,where no assumptions were considered.At the end of the R iterations,the variance var^yðÞBS of the R predictions was calculated for each pixel of the AISA-Dual image(Fig.3a and d).When the boot-strap procedure integrates all of the sources of uncertainty(spectra, reference lab values,model building),the variance var^yðÞBS represents the sum of all of the sources of uncertainty and is considered as the “true”variance:var^yðÞBS¼UdþUsþUyþUm:ð2Þ4.3.Expressions of the uncertainty using spectral distance4.3.1.Mahalanobis distanceThe Mahalanobis distance(MD)can be used to detect outliers,which are observations located at the periphery of the data cloud that can have a stronger than average influence on thefitted model.In the framework of linear regression,the further a sample is from the center of the model, the higher its prediction uncertainty.The MD of a spectrum is calculated independently of the bootstrap procedure(Fig.3b)as the distance of this spectrum to the center of the calibration set in a PCA-reduced dimension.The MD of a VNIR/SWIR spectrum x is calculated from the calibration database as:MD xðÞ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffix−μðÞ0S−1x−μðÞqð3Þwhere x(K×1)is a VNIR/SWIR spectrum,μ(K×1)is the mean of the N calibration VNIR/SWIR spectra,and S(K×K)is the variance–covariance matrix of the N calibration spectra.The principle of the Mahalanobis distance is described in detail in Mark and Tunnell(1985).Generally, a MD value greater than3is considered to reflect an outlier to the calibration set(Mark&Tunnell,1985).The MD is related to the uncertainty of the model building(Um)with the hypothesis that the PCA space is similar to the PLSR space.4.3.2.LeverageThe leverage(H)can also be used to diagnose how atypical a new vector of a predictor is.The leverage value of a spectrum x is the distance of this spectrum to the center of the calibration set within the multivar-iate model.The leverage measures the variation within the multivariate model,so the H measurement is dependent on the model,in contrast to the Mahalanobis distance.Although the H measurement is dependent on the model,it is calculated independently of the bootstrap procedure (Fig.3b).The leverage of a VNIR/SWIR spectrum x is calculated as (Martens,1991):H xðÞ¼t0x T0TÀÁ−1t xð4Þwhere x(K×1)is a VNIR/SWIR spectrum,t x(K×1)is the score vector of x for the multivariate model,and T(N×A)is the X-score matrix of the multivariate model(see Section3).The leverage is related to the uncertainty of the model building(Um).4.4.Expressions of the uncertainty using the formula of Fernandez-Ahumada et al.(2012)4.4.1.Total variance of predictionsAs a measure of the uncertainty of the estimations made by the PLSR model,the variance model proposed by Fernandez-Ahumada et al. (2012)was applied in this study.This proposal for variance modeling is based on the errors-in-variables(EIV)model that was initially proposed by Faber and Kowalski(1997),which considers all sources of uncertainty that affect the predictors,the dependent variables and the model coeffi-cients.The difference between the classical EIV model and the variance model proposed by Fernandez-Ahumada et al.(2012)is that the latter model is constructed based on assumptions related to the predictors, which are the spectral bands.The formulation is described in detail in Fernandez-Ahumada et al.(2012)and can be written as:var^yðÞAhumada¼1þ1b0Σx bþz0Σb zþ1þ1trΣxΣbðÞþσ2labð5Þwhere^y is the prediction,z is the centered x spectrum used for the prediction,b is the K-vector of the b-coefficients of the model,N is the number of calibration samples,Σb is the variance–covariance matrix of the b-coefficients,Σx is the variance–covariance matrix associated to the spectrum x,andσlab2is the laboratory y variance.The four terms of Eq.(5),from left to right,are:T1)the hyperspectral data uncertainty,T2)the model coefficients uncertainty,T3)the depen-dency between the spectral and model uncertainties and T4)the labora-tory reference measurement uncertainty.Term T4is constant over the entire image and is small compared to the other three terms.In this study,the variance model was confined to thefirst three terms:var^yðÞAhumada¼1þ1Nb0Σx bþz0Σb zþ1þ1NtrΣxΣbðÞ:ð6ÞEach term of Eq.(6)and their calculations are described in the following sections.4.4.2.Variance of predictions due to the spectrum xThefirst term of the formula of Fernandez-Ahumada et al.(2012) (denoted T1;Fig.3d)expresses the variance of the predictions caused by the uncertainty of the hyperspectral data x corresponding to the pixel p:T1¼1þ1Nb0Σx bð7Þwhere N is the number of samples in the calibration set,b is the model coefficients(here,the mean of the R b-coefficient vectors obtained in the bootstrap procedure),andΣx is a variance–covariance matrix that describes the uncertainty of the spectrum x.In this study,the variance–covariance matrixΣx was calculated as follows:Let G be the matrix in which each line is a spectrum that belongs to the grid of3×3pixels centered on the pixel p and centered columnwiseΣx¼18G0Gð8Þ63C.Gomez et al./Remote Sensing of Environment156(2015)58–70。

超声增强的输送的物料进入并通过皮肤翻译Ultrasound-enhanced delivery of materials into and through the skinA method for enhancing the permeability of the skin or other biological membrane to a material such as a drug is disclosed. In the method, the drug is delivered in conjunction with ultrasound having a frequency of above about 10 MHz. The method may also be used in conjunction with chemical permeation enhancers and/or with iontophoresis.图片(11)权利要求(21)We claim:1. A method for enhancing the rate of permeation of a drug medium into a selected intact area of an individual's body surface, which method comprises:(a) applying ultrasound having a frequency of above 10 MHz to said selected area, at an intensity and for a period of timeeffective to enhance the permeability of said selected area;(b) contacting the selected area with the drug medium; and(c) effecting passage of said drug medium into and through said selected area by means of iontophoresis.2. The method of claim 1, wherein said ultrasound frequency is in the range of about 15 MHz to 50 MHz.3. The method of claim 2, wherein said ultrasound frequency is in the range of about 15 to 25 MHz.4. The method of claim 1, wherein said period of time is in the range of about 5 to 45 minutes.5. The method of claim 4, wherein said period of time is in the range of about 5 to 30 minutes.6. The method of claim 1, wherein said period of time is less than about 10 minutes.7. The method of claim 1, wherein said intensity of said ultrasound is less than about 5.0W/cm.sup.2.8. The method of claim 7, wherein said intensity of said ultrasound is in the range of about 0.01 to 5.0 W/cm.sup.2.9. The method of claim 8, wherein said intensity of said ultrasound is in the range of about 0.05 to 3.0 W/cm.sup.2.10. The method of claim 1, wherein said area of the stratum corneum is in the range of about 1 to 100 cm.sup.2.11. The method of claim 10, wherein said area of the stratum corneum is in the range of about 5 to 100 cm.sup.2.12. The method of claim 11, wherein said area of the stratum corneum is in the range of about 10 to 50 cm.sup.2.13. The method of claim 1 wherein said drug medium comprises a drug and a coupling agent effective to transfer said ultrasound to the body from an ultrasound source.14. The method of claim 13 wherein said coupling agent is a polymer or a gel.15. The method of claim 13 wherein said coupling agent is selected from the group consisting of glycerin, water, and propylene glycol.16. The method of claim 1 wherein said drug medium further comprises a chemical permeation enhancer.17. The method of claim 1, wherein steps (a) and (b) are carried out approximately simultaneously.18. The method of claim 1, wherein step (b) is carried out before step (a).19. The method of claim 1, wherein step (a) is carried out before step (b).20. The method of claim 1, wherein the ultrasound is applied continuously.21. The method of claim 1, wherein the ultrasound is pulsed.说明This application is a division of application Ser. No. 07/844,732 filed Mar. 2, 1992, now U.S. Pat. No. 5,231,975 which is a divisional of application Ser. No. 07/484,560, now U.S. Pat. No. 5,115,805, filed Feb. 23, 1990.TECHNICAL FIELDThis invention relates generally to the field of drug delivery. More particularly, the invention relates to a method of enhancing the rate of permeation of topically, transmucosally or transdermally applied materials using high frequency ultrasound.BACKGROUNDThe delivery of drugs through the skin ("transdermal drug delivery" or "TDD") provides many advantages; primarily, such a means of delivery is a comfortable, convenient and non-invasiveway of administering drugs. The variable rates of absorption and metabolism encountered in oral treatment are avoided, and other inherent inconveniences--e.g., gastrointestinal irritation and the like--are eliminated as well. Transdermal drug delivery also makes possible a high degree of control over blood concentrations of any particular drug.Skin is a structurally complex, relatively impermeable membrane. Molecules moving from the environment into and through intact skin must first penetrate the stratum corneum and any material on its surface. They must then penetrate the viable epidermis, the papillary dermis, and the capillary walls into the blood stream or lymph channels. To be so absorbed, molecules must overcome a different resistance to penetration in each type of tissue. Transport across the skin membrane is thus a complex phenomenon. However, it is the stratum corneum, a layer approximately 5-15 micrometers thick over most of the body, which presents the primary barrier to absorption of topical compositions or transdermally administered drugs. It is believed to be the high degree of keratinization within its cells as well as their dense packing and cementation by ordered, semicrystalline lipids which create in many cases a substantially impermeable barrier to drug penetration. Applicability of transdermal drug delivery is thus presently limited, because the skin is such an excellent barrier to the ingress of topically applied materials. For example, many of the new peptides and proteins now produced as a result of the biotechnology revolution cannot be delivered across the skin in sufficient quantities due to their naturally low rates of skin permeability.Various methods have been used to increase skin permeability, and in particular to increase the permeability of thestratum corneum (i.e., so as to achieve enhanced penetration, through the skin, of the drug to be administered transdermally). The primary focus has been on the use of chemical enhancers, i.e., wherein drug is coadministered with a penetration enhancing agent (or "permeation enhancer"). While such compounds are effective in increasing the rate at which drug is delivered through the skin, there are drawbacks with many permeation enhancers which limit their use. For example, many permeation enhancers are associated with deleterious effects on the skin (e.g., irritation). In addition, control of drug delivery with chemical enhancement can be quite difficult.Iontophoresis has also been used to increase the permeability of skin to drugs, and involves (1) the application of an external electric field, and (2) topical delivery of an ionized form of drug (or of a neutral drug carried with the water flux associated with ion transport, i.e., via "electroosmosis"). While permeation enhancement via iontophoresis has, as with chemical enhancers, been effective, there are problems with control of drug delivery and the degree of irreversible skin damage induced by the transmembrane passage of current.The presently disclosed and claimed method involves the use of ultrasound to decrease the barrier function of the stratum corneum and thus increase the rate at which a drug may be delivered through the skin. "Ultrasound" is defined as mechanical pressure waves with frequencies above 20,000 Hz (see, e.g., H. Lutz et al., Manual of Ultrasound: 1. Basic Physical and Technical Principles (Berlin: Springer-Verlag, 1984)).As discussed by P. Tyle et al. in Pharmaceutical Research 6(5):355-361 (1989), drug penetration achieved via "sonophoresis" (the movement of drugs through skin under theinfluence of an ultrasonic perturbation; see D. M. Skauen and G. M. Zentner, Int. J. Pharmaceutics 20:235-245 (1984)), is believed to result from thermal, mechanical and chemical alteration of biological tissues by the applied ultrasonic waves. Unlike iontophoresis, the risk of skin damage appears to be low.Applications of ultrasound to drug delivery have been discussed in the literature. See, for example: P. Tyle et al., supra (which provides an overview of sonophoresis); S. Miyazaki et al., J. Pharm. Pharmacol. 40:716-717 (1988) (controlled release of insulin from a polymer implant using ultrasound); J. Kost et al., Proceed. Intern. Symp. Control. Rel. Bioact. Mater.16(141):294-295 (1989) (overview of the effect of ultrasound on the permeability of human skin and synthetic membranes); H. Benson et al., Physical Therapy 69(2):113-118 (1989) (effect of ultrasound on the percutaneous absorption of benzydamine); E. Novak, Arch. Phys. Medicine & Rehab. 45:231-232 (1964) (enhanced penetration of lidocaine through intact skin using ultrasound); J. E. Griffin et al., Amer. J. Phys. Medicine 44(1):20-25 (1965) (ultrasonic penetration of cortisol into pig tissue); J. E. Griffin et al., J. Amer. Phys. Therapy Assoc.46:18-26 (1966) (overview of the use of ultrasonic energy in drug therapy); J. E. Griffin et al., Phys. Therapy 47(7):594-601 (1967) (ultrasonic penetration of hydrocortisone); J. E. Griffin et al., Phys. Therapy 48(12):1336-1344 (1968) (ultrasonic penetration of cortisol into pig tissue); J. E. Griffin et al., Amer. J. Phys. Medicine 51(2):62-72 (1972) (same); J. C. McElnay, Int. J. Pharmaceutics 40:105-110 (1987) (the effect of ultrasound on the percutaneous absorption of fluocinolone acetonide); and C. Escoffier et al., Bioeng. Skin 2:87-94 (1986) (in vitro study of the velocity of ultrasound in skin).In addition to the aforementioned art, U.S. Pat. Nos. 4,767,402 and 4,780,212 to Kost et al. relate specifically to the use of specific frequencies of ultrasound to enhance the rate of permeation of a drug through human skin or through a synthetic membrane.While the application of ultrasound in conjunction with drug delivery is thus known, results have for the most part been disappointing, i.e., enhancement of skin permeability has been relatively low.SUMMARY OF THE INVENTIONThe present invention provides a novel method for enhancing the rate of permeation of a given material through a selected intact area of an individual's body surface. The method comprises contacting the selected intact area with the material and applying ultrasound to the contacted area. The ultrasound preferably has a frequency of above about 10 MHz, and is continued at an intensity and for a period of time sufficient to enhance the rate of permeation of the material into and through the body surface. The ultrasound can also be used to pretreat the selected area of the body surface in preparation for drug delivery, or for diagnostic purposes, i.e., to enable non-invasive sampling of physiologic material beneath the skin or body surface.In addition to enhancing the rate of permeation of a material, the present invention involves increasing the permeability of a biological membrane such as the stratum corneum by applying ultrasound having a frequency of above about 10 MHz to the membrane at an intensity and for a period of time sufficient to give rise to increased permeability of the membrane. Once the permeability of the membrane has been increased, it is possible to apply a material thereto and obtain an increased rate of flowof the material through the membrane.It is accordingly a primary object of the invention to address the aforementioned deficiencies of the prior art by providing a method of enhancing the permeability of biological membranes and thus allow for an increased rate of delivery of material therethrough.It is another object of the invention to provide such a method which is effective with or without chemical permeation enhancers.It is still another object of the invention to minimize lag time in such a method and provide a relatively short total treatment time.It is yet another object of the invention to provide such a method in which drug delivery is effected using ultrasound.It is a further object of the invention to enable sampling of tissue beneath the skin or other body surface by application of high frequency (>10 MHz) ultrasound thereto.A further feature of the invention is that it preferably involves ultrasound of a frequency greater than about 10 MHz.Additional objects, advantages and novel features of the invention will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following, or may be learned by practice of the invention.BRIEF DESCRIPTION OF THE DRAWINGSFIGS. 1A, 1B and 1C are theoretical plots of energy dissipation within the skin barrier versus frequency of applied ultrasound.FIGS. 2, 3 and 4 are graphic representations of the amount of salicylic acid recovered from the stratum corneum after ultrasound treatment at different frequencies.FIGS. 5 and 6 represent the results of experiments similar to those summarized in FIGS. 2, 3 and 4, but with a shorter treatment time.FIGS. 7, 8, 9 and 10 are plots of enhancement versus "tape-strip number," as described in the Example.FIG. 11 illustrates the effect of ultrasound on the systemic availability of salicylic acid following topical application.DETAILED DESCRIPTION OF PREFERRED EMBODIMENTSBefore the present method of enhancing the rate of permeation of a material through a biological membrane and enhancing the permeability of membranes using ultrasound are disclosed and described, it is to be understood that this invention is not limited to the particular process steps and materials disclosed herein as such process steps and materials may, of course, vary. It is alto to be understood that the terminology used herein is used for purpose of describing particular embodiments only and is not intended to be limiting since the scope of the present invention will be limited only by the appended claims.It must be noted that as used in this specification and the appended claims, the singular forms "a", "an" and "the" include plural reference unless the context clearly dictates otherwise. Thus, for example, reference to "a drug" includes mixtures of drugs and their pharmaceutically acceptable salts, reference to "an ultrasound device" includes one or more ultrasound devices of the type necessary for carrying out the present invention, and reference to "the method of administration" includes one or more different methods of administration known to those skilled in the art or which will become known to those skilled in the art upon reading this disclosure.In one aspect of the invention a method is provided forenhancing the permeation of a given material such as a drug, pharmacologically active agent, or diagnostic agent into and/or through a biological membrane on an individual's body surface, which method comprises: (a) contacting the membrane with the chosen material in a pharmacologically acceptable carrier medium; and (b) applying ultrasound of an intensity and for a treatment time effective to produce delivery of the material through the membrane. The material is preferably a drug and it is preferable to obtain a desired blood level of the drug in the individual. The ultrasound is of a frequency and intensity effective to increase the permeability of the selected area to the applied drug over that which would be obtained without ultrasound. The ultrasound preferably has a frequency of more than 10 MHz, and may be applied either continuously or pulsed, preferably continuously. The ultrasound may be applied to the skin either before or after application of the drug medium so long as administration of the ultrasound and the drug medium is relatively simultaneous, i.e., the ultrasound is applied within about 6, more preferably within about 4, most preferably within about 2 minutes of drug application.The invention is useful for achieving transdermal permeation of pharmacologically active agents which otherwise would be quite difficult to deliver through the skin or other body surface. For example, proteinaceous drugs and other high molecular weight pharmacologically active agents are ideal candidates for transdermal, transmucosal or topical delivery using the presently disclosed method. In an alternative embodiment, agents useful for diagnostic purposes may also be delivered into and/or through the body surface using the present method.The invention is also useful as a non-invasive diagnostictechnique, i.e., in enabling the sampling of physiologic material from beneath the skin or other body surface and into a collection (and/or evaluation) chamber.The present invention will employ, unless otherwise indicated, conventional pharmaceutical methodology and more specifically conventional methodology used in connection with transdermal delivery of pharmaceutically active compounds and enhancers.In describing the present invention, the following terminology will be used in accordance with the definitions set out below.A "biological membrane" is intended to mean a membrane material present within a living organism which separates one area of the organism from another and, more specifically, which separates the organism from its outer environment. Skin and mucous membranes are thus included."Penetration enhancement" or "permeation enhancement" as used herein relates to an increase in the permeability of skin to a material such as a pharmacologically active agent, i.e., so as to increase the rate at which the material permeates into and through the skin. The present invention involves enhancement of permeation through the use of ultrasound, and, in particular, through the use of ultrasound having a frequency of greater than 10 MHz."Transdermal" (or "percutaneous") shall mean passage of a material into and through the skin to achieve effective therapeutic blood levels or deep tissue therapeutic levels. While the invention is described herein primarily in terms of "transdermal" administration, it will be appreciated by those skilled in the art that the presently disclosed and claimed methodalso encompasses the "transmucosal" and "topical" administration of drugs using ultrasound. "Transmucosal" is intended to mean passage of any given material through a mucosal membrane of a living organism and more specifically shall refer to the passage of a materialfrom the outside environment of the organism, through a mucous membrane and into the organism. "Transmucosal" administration thus includes delivery of drugs through either nasal or buccal tissue. By "topical" administration is meant local administration of a topical pharmacologically active agent to the skin as in, for example, the treatment of various skin disorders or the administration of a local anaesthetic. "Topical" delivery can involve penetration of a drug into the skin but not through it, i.e., topical administration does not involve actual passage of a drug into the bloodstream."Carriers" or "vehicles" as used herein refer to carrier materials without pharmacological activity which are suitable for administration with other pharmaceutically active materials, and include any such materials known in the art, e.g., any liquid, gel, solvent, liquid diluent, solubilizer, or the like, which is nontoxic and which does not interact with the drug to be administered in a deleterious manner. Examples of suitable carriers for use herein include water, mineral oil, silicone, inorganic gels, aqueous emulsions, liquid sugars, waxes, petroleum jelly, and a variety of other oils and polymeric materials.By the term "pharmacologically active agent" or "drug" as used herein is meant any chemical material or compound suitable for transdermal or transmucosal administration which can either (1) have a prophylactic effect on the organism and prevent an undesired biological effect such as preventing aninfection, (2) alleviates a condition caused by a disease such as alleviating pain caused as a result of a disease, or (3) either alleviates or completely eliminates the disease from the organism. The effect of the agent may be local, such as providing for a local anaesthetic effect or it may be systemic. Such substances include the broad classes of compounds normally delivered through body surfaces and membranes, including skin. In general, this includes: anti-infectives such as antibiotics and antiviral agents; analgesics and analgesic combinations; anorexics; antihelminthics; antiarthritics; antiasthmatic agents; anticonvulsants; antidepressants; antidiabetic agents; antidiarrheals; antihistamines; antiinflammatory agents; antimigraine preparations; antinauseants; antineoplastics; antiparkinsonism drugs; antipruritics; antipsychotics; antipyretics; antispasmodics; anticholinergics; sympathomimetics; xanthine derivatives; cardiovascular preparations including potassium and calcium channel blockers, beta-blockers, and antiarrhythmics; antihypertensives; diuretics; vasodilators including general coronary, peripheral and cerebral; central nervous system stimulants; cough and cold preparations, including decongestants; hormones such as estradiol and other steroids, including corticosteroids; hypnotics; immunosuppressives; muscle relaxants; parasympatholytics; psychostimulants; sedatives; and tranquilizers. By the method of the present invention, both ionized and nonionzed drugs may be delivered, as can drugs of either high or low molecular weight.Proteinaceous and polypeptide drugs represent a preferred class of drugs for use in conjunction with the presently disclosed and claimed invention. Such drugs cannot generally be administered orally in that they Are often destroyed in the G.I.tract or metabolized in the liver. Further, due to the high molecular weight of most polypeptide drugs, conventional transdermal delivery systems are not generally effective. It is also desirable to use the methodof the invention in conjunction with drugs to which the permeability of the skin is relatively low, or which give rise to a long lag-time (application of ultrasound as described herein has been found to significantly reduce the lag-time involved with the transdermal administration of most drugs).By a "therapeutically effective" amount of a pharmacologically active agent is meant a nontoxic but sufficient amount of a compound to provide the desired therapeutic effect. The desired therapeutic effect may be a prophylactic effect, in preventing a disease, an effect which alleviates a system of the disease, or a curative effect which either eliminates or aids in the elimination of the disease.As noted above, the present invention is a method for enhancing the rate of permeation of a drug through an intact area of an individual's body surface, preferably the human skin. The method involves transdermal administration of a selected drug in conjunction with ultrasound. Ultrasound causes thermal, mechanical and chemical alterations of biological tissue, thereby enhancing the rate of permeation of a given material therethrough.While not wishing to be bound by theory, applicants propose that the use of higher frequency ultrasound as disclosed herein specifically enhances the permeation of the drug through the outer layer of skin, i.e., the stratum corneum, by causing momentary and reversible perturbations within (and thus short-term, reversible reduction in the barrier function of) the layer ofthe stratum corneum. It will be appreciated by those skilled in the art of transdermal drug delivery that a number of factors related to the present method will vary with the drug to be administered, the disease or injury to be treated, the age of the selected individual, the location of the skin to which the drug is applied, and the like.As noted above, "ultrasound" is ultrasonic radiation of a frequency above 20,000 Hz. As may be deduced from the literature cited above, ultrasound used for most medical purposes typically employs frequencies ranging from 1.6 to about 10 MHz. The present invention, by contrast, employs ultrasound frequencies of greater than about 10 MHz, preferably in the range of about 15 to 50 MHz, most preferably in the range of about 15 to 25 MHz. It should be emphasized that these ranges are intended to be merely illustrative of the preferred embodiment; in some cases higher or lower frequencies may be used.The ultrasound may be pulsed or continuous, but is preferably continuous when lower frequencies are used. At very high frequencies, pulsed application will generally be preferred so as to enable dissipation of generated heat.The preferred intensity of the applied ultrasound is less than about 5.0 W/cm.sup.2, more preferably is in the range of about 0.01 to 5.0 W/cm.sup.2, and most preferably is in the range of 0.05 to 3.0 W/cm.sup.2. The total treatment time, i.e., the period over which drug and ultrasound are administered, will vary depending on the drug administered, the disease or injury treated, etc., but will generally be on the order of about 30 seconds to 60 minutes, preferably 5 to 45 minutes, more preferably 5 to 30 minutes, and most preferably 5 to 10minutes. It should be noted that the aforementioned ranges represent suggested, or preferred, treatment times, but are not in any way intended to be limiting. Longer or shorter times may be possible and in some cases desirable. Virtually any type of device may be used to administer the ultrasound, providing that the device is callable of producing the higher frequency ultrasonic waves required by the present method. A device will typically have a power source such as a small battery, a transducer, a reservoir in which the drug medium is housed (and which may or may not be refillable), and a means to attach the system to the desired skin site.As ultrasound does not transmit well in air, a liquid medium is generally needed to efficiently and rapidly transmit ultrasound between the ultrasound applicator and the skin. As explained by P. Tyle et al., cited above, the selected drug medium should contain a "coupling" or "contacting" agent typically used in conjunction with ultrasound. The coupling agent should have an absorption coefficient similar to that of water, and furthermore be nonstaining, nonirritating to the skin, and slow drying. It is clearly preferred that the coupling agent retain a paste or gel consistency during the time period of ultrasound administration so that contact is maintained between the ultrasound source and the skin. Examples of preferred coupling agents are mixtures of mineral oil and glycerine and propylene glycol, oil/water emulsions, and a water-based gel. A solid-state, non-crystalline polymeric film having the above-mentioned characteristics may also be used. The drug medium may also contain a carrier or vehicle, as defined alone.A transdermal patch as well known in the art may be used in conjunction with the present invention, i.e., to deliver the drugmedium to the skin. The "patch", however, must have the properties of the coupling agent as described in the preceding paragraph so as to enable transmission of the ultrasound from the applicator, through the patch, to the skin.As noted earlier in this section, virtually any chemical material or compound suitable for transdermal, transmucosal or topical administration may be administered using the present method. Again, the present invention is particularly useful to enhance delivery of proteinaceous and other high molecular weight drugs.The method of the invention is preferably carried out as follows. The drug medium, i.e., containing the selected drug or drugs in conjunction with the coupling agent and optionally a carrier or vehicle material, is applied to an area of intact body surface. Ultrasound preferably having a frequency greater than about 10 MHz may be applied before or after application of the drug medium, but is preferably applied immediately before application of the drug so as to "pretreat" the skin prior to drug administration.It should also be pointed out that the present method may be used in conjunction with a chemical permeation enhancer as known in the art, wherein the ultrasound enables the use of much lower concentrations of permeation enhancer--thus minimizing skin irritation and other problems frequently associated with such compounds--than would be possible in the absence of ultrasound. The permeation enhancer may be incorporated into the drug medium or it maybe applied in a conventional transdermal patch after pretreatment of the body surface with ultrasound.The present invention may also be used in conjunction with。

Unit1 Cosmic Beginnings宇宙的起源1．Where and when does the history of the Earth begin? Only in the last few decades could this question be asked with any hope of a scientific answer. 地球的历史上是何时何地开始的?只有在过去的几十年里,这个问题才有了一个比较科学的回答来解释。

Certainly one good point at which to start is the time when the materials that were to become the Earth became separated in space from materials that were to become other members of the solar system. 当然存在一个较好的说法是地球的起源时间是当组成地球的物质在宇宙中开始与太空中组成太阳系其它成员的物质分离的时候.Although the story could well commence here, a great many important questions would remain unanswered.虽然故事很可能开始在这里,许多重要的问题仍悬而未决。

Something needs to be said about the materials that make up the Earth, and this pushes the question of origin to a more remote period. 一些有必要提及的物质构成了地球,这将推动更偏远的起源问题。

Earth's partners in space must also be considered. 地球在太空的合作伙伴也必须加以考虑。

第六章（1733---1911）1733 Your ship is solely to the damage to my crane.A. To.B.C. Of.D. On.对于起重机的损坏，你船完全。

1734 Your kind attention to the above will be much ______.A. Appreciate.B.C. Appreciating.D. Being appreciated. 你对上述的友好关注将被（给予）很多的。

（非常感谢你对上述事情的关注）1735 Your kind attention to our ship will be ______.A. B. Much appreciating. C. Many appreciated. D. Many appreciating. 你对我船的友好关注将被（给予）Much可修饰动词，而Many不能修饰动词；Be much appreciated非常感谢1736 You should not the fresh water ______ three trips.A. In.B.C. To.D. For.你不应淡水三个航次。

xxh1737 You shall ______ this letter so as to signify your acknowledgement of the responsibility.A. Make out.B. Submit.C. Produce.D.你应这个信件以便表示你责任的确认。

1738 You must ______ in this area unless you have messages about the casualty.A. Keep radio silence.B.C. Keep radio be silent.D. Keep silent the radio.你必须直到你收到有关伤亡的信息。

Modelling the grain orientation of austenitic stainless steel multipass welds to improve ultrasonic assessment of structural integrity奥氏体不锈钢的多路模拟晶粒取向改善焊缝超声评价结构完整性J. Moysan a,*, A. Apfel a, G. Corneloup a, B. Chassignole bJ.莫伊桑A.阿普费尔G.科尔内卢帕 B.沙西尼奥尔aLaboratoire de Caracte´risation Non Destructive (LCND, EA 3153), Universite´ de la Me´diterrane´e, IUT Avenue Gaston Berger,13625 Aix-en-Provence Cedex, FrancebElectricite´ De France, Direction Etudes et Recherches, Les Renardie`res, 77818 MoretsurLoing, FranceReceived 1 August 2002; revised 4 February 2003; accepted 4 February 2003 AbstractKnowledge of the grain orientation quantifies the material anisotropy which helps to ensure the good ultrasonic testing of weldedassemblies and the assessment of their mechanical integrity. The model described here concerns the weld solidification of 316L stainlesssteel. The solidification of multipass welds made with a shielded electrode raises many unsolved modelling questions as it involves heat andfluid flow modelling in addition to soluteredistribution models. To overcome these difficulties we have developed the MINA model topredict the resulting grain orientations without using a complete solidification model. This model relies upon a phenomenologicaldescriptionof grain orientations from macrograph analysis. One important advance of this model is to include data reporting in the welding notebook thatensures the generality of the model. This model allows us to accurately simulate the ultrasonic testing of welded co mponents and to propose anew tool to associate welding design with the ultrasonic assessment of structural integrity.Keywords: Welding; Anisotropy; Modelling; Ultrasound; Structural integrity文摘晶粒取向量化材料各向异性的知识，有助于确保良好的焊接程序集和评估超声检测机械的完整性。

一、名词解释1、优质棉：优质棉是能充分满足和适应纺织工业生产不同类型、不同品质棉织用纱所必需的质量指标，并能为纺织工业和植棉者提供最大经济效益的原棉。

2、四桃：伏前桃：指7月15日以前开花所结的棉铃，江淮流域也以7月20日作时间限。

伏桃：指7月16日（江淮流域也用7月20日）至8月15日期间开花所结的棉铃。

早秋桃：指8月16日至8月25日（江淮流域也用8月30日）期间开花所结的棉铃。

晚秋桃：指8月26日（或8月31日）至有效开花结铃终止期开花所结的棉铃。

3、成铃时空分布：棉花群体开花结铃时间长达70天（7/上-9/10）左右，以时间为主线形成的产量和品质差异规律，就是棉花单铃产量和品质的时间分布特征。

棉花不同果枝、果节位的结铃，形成铃重、衣分及纤维品质变化的规律，就是棉花产量和品质的空间分布特征。

4、优化成铃：优化成铃就是实现棉花集中成铃期（高能转化期）与高温富照期（最佳光热季节）的高度同步，达到优质铃强势果节的高成铃强度。

5、单铃经济系数：成熟棉铃风干状态下实收单铃皮棉或籽棉重量占单铃实收全部干物重的百分率。

6、圆锥体规律：棉花现蕾的顺序是：由下而上，由内而外，以第一果节为中心，呈螺旋形由内向外，由下向上逐渐推进。

7、强（弱）势果节：强势果节的空间，结铃率和铃重高、烂铃率低的强势果节量中有84.4%座落于近主茎的1、2内围果节，外围果节仅占15.6%。

8、三同步四集中：三同步是指棉花集中成铃期与成铃最佳光热季节和肥效高峰期三者的高度同步。

集中现蕾、集中开花、集中成铃、集中吐絮简称“四集中”，这是棉花优化成铃的目标之一。

9、最佳结铃期：集中成铃期与最佳光热季节相同步的时期称之为最佳结铃期。

10、“密矮早”：大群体、小个体，多结内围铃，是增产优质棉的一个途径。

密度较高、分布均匀、植株矮化、生长整齐的高产群体。

11、“小壮高”：采取扩行降密，小群体、壮个体，增加中上部铃，更易于达到高产更高产。

1、初生根：也叫胚根、种子根。