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Precise assessment of harvest yield is a difficult field of work. The equipment and programming stage to foresee the harvest yield relies on different components like climate, soil fruitfulness, genotype, and different collaborating wards. The assignment is unpredictable inferable from the information that should be gathered in volumes to comprehend crop yield through remote sensor organizations and distant detecting. This paper audits the previous 15 years of exploration work in the improvement of assessing crop yield utilizing profound learning calculations. The meaning of examining progressions utilizing profound learning methods will help in dynamic for foreseeing the harvest yield. The cross breed mix of profound learning with distant detecting and remote sensor organizations can give accuracy agribusiness later on.

Depending on the different types of raw materials used to produce hanji, a Korean traditional handmade paper, there can be significant differences in the durability and mechanical properties of the final product. In this study, near-infrared spectroscopy (NIR) combined with multivariate statistical methods were used to confirm the classification possibility of hanji based on the various type of raw materials. The hanji papers were prepared from paper mulberry trees, cooking agents, and mucilage. Altogether, a total of 60 hanji spectra were collected by NIR. Then, the 60 spectra were grouped into four categories: the control, paper mulberry, cooking agent, and mucilage type based on each of the types of raw materials contained in the hanji. Three different classification algorithms – partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), and random forest (RF) – were used to classify the hanji types. The best hanji material classification performance was obtained when the hanji samples were classified according to paper mulberry type, wherein the prediction accuracies of PLS-DA, SVM, and RF were 100%, 100%, and 98%, respectively. These results suggested that NIR in combination with multivariate statistical methods can be used for hanji material classification.

The dating of documents is one of the pending issues to be resolved in many fields. Although there are a variety of analytical methodologies focused on the inks of documents to estimate the dating of documents, the analysis of documents itself attracted little attention. A non-destructive method based on Fourier transformed infrared (FTIR) was proposed in this research to estimate the dating of documents. First, FTIR spectra of journals dated from 1940 to 1980, naturally aged and conserved in the China Agriculture University library were collected. Second, the least squares support vector machines (LS-SVM) was applied to distinguish documents of different dating, and the accuracy was 99.26%. In order to find interested wavenumber that influence the dating process of documents, sparse partial least squares (sPLS) was applied to select informative variables. The average of selected variables was 483 after 100 runs, and the selected variables were focused on the absorption peaks of inorganic components and cellulose. Splicing sPLS with LS-SVM (sPLS–LS-SVM) built model to see the effective of selected variables. Average accuracy of sPLS–LS-SVM model was 99.34%, even the best result could reach 100.00% after 100 circle times. The present work indicates that the possibility of FTIR combined with chemometrics can estimate the dating of documents accurately. Additionally, the wavenumber which influence the dating of documents are mostly focused on cellulose and inorganic components.

Different types of paper and different basis weight of the same paper are selected in order to study the relationship between paper and color reproduction. Comparing with the reference data set, the influence of paper on color reproduction is analyzed from the color difference and color gamut. The results show that the colorimetric value of paper is more closer to the reference data set, the average color difference of patches is more smaller. The color reproduction ability of offset paper is weaker than that of other paper in the experiment. The color reproduction ability of different basis weight of the same paper is also slightly different. Therefore, the type and basis weight of the paper affect the color reproduction. In the process of printing proofs, it is considered that paper has a certain impact on color reproduction.

Paper is non-uniform; thus, it is difficult to ascertain whether the slight differences in near-infrared absorption spectra are caused by the difference in paper type or the paper’s non-uniformity. We used near-infrared hyperspectral imaging to measure the spectra of many different points quickly for eight different paper samples. Next, we analysed the data using machine learning and constructed a model to ascertain whether the differences in the spectra are due to the difference in paper type or the paper’s non-uniformity. Even papers with very similar spectral shapes were distinguished with high accuracy with this method. As the number of spectra used to construct the classification model increased, the accuracy of the classification model increased. With 6840 spectra per sample, the accuracy exceeded 85%, and with 68,400 spectra per sample, the accuracy for identification was 87.8%.

Studying of paper carriers of information has become increasingly important in forensic practice. This is caused by growing number of bogus or in other manner fraudulently alternated documents. An identification of individual paper sheets and studying their differences are the parts of forensic analysis of multiple sheets documents. This paper focuses on the possibilities of processing of mathematical and statistical data obtained by measurements of FT-IR spectral characteristics of individual paper sheets in a studied document. The spectral data were used to create data matrix which contains values of absorbance and interval of wavenumber. The method of differentiation is based on the comparison of maximum absorbance values within the range of wavenumbers from 1562 to 1215 cm−1. This spectrum range is specific in occurrence of absorption bands that belong to calcium carbonate CaCO3—an element of paper added into it in relatively high amounts in the form of filler which content or percentage occurrence may be different considering to the paper producer or production technology. Based on the above-said, the paper sheets originating from different producers or from various production technology units can be differentiated one from another using a robust statistical analysis.

International audience; We report a pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) method capable of analyzing traditional East Asian papers. The method proposed is based on rapid and easy single step Py-GC/MS analysis that can be carried out with a minimum amount of matter, in the few g range. Three reference papers manufactured from kozo (Broussonetia kazinoki Siebold & Zucc.), mitsumata (Edgeworthia chrysantha Lindl.) and gampi (Wikstroemia sikokiana Franch. & Sav.) with the traditional hand paper making processes were examined. The method allows discrimination between terpenic and steroid compounds, which were revealed as chemical markers of origin of the plant fibers. Each paper investigated was found to have characteristic pyrolysis fingerprints that were unique to the traditional handmade paper, demonstrating the potential for differentiation of these biochemical components of fiber plants on East Asian papers towards identification and conservation of cultural heritage. The investigation on Py-GC/MS was extended to liquid extraction followed by GC/MS analysis to characterize the biochemical components of fiber plants. The main contribution of this study is to provide molecular criteria for discriminating plant species used for traditional East Asian hand papermaking. Py-GC/MS complements efficiently microscope identification especially for adverse cases. A case study of archaeological Chinese paper painting artefacts was thereafter successfully investigated to address informative potential and efficiency of the criteria of identification on ancient and degraded East Asian paperworks.

Diffuse reflectance ultraviolet-visible-near-infrared (UV-Vis-NIR) spectroscopy is applied as a means of differentiating various types of writing, office, and photocopy papers (collected from stationery shops in India) on the basis of reflectance and absorbance spectra that otherwise seem to be almost alike in different illumination conditions. In order to minimize bias, spectra from both sides of paper were obtained. In addition, three spectra from three different locations (from one side) were recorded covering the upper, middle, and bottom portions of the paper sample, and the mean average reflectivity of both the sides was calculated. A significant difference was observed in mean average reflectivity of Side A and Side B of the paper using Student's pair t-test. Three different approaches were used for discrimination: (1) qualitative features of the whole set of samples, (2) principal component analysis, and (3) a combination of both approaches. On the basis of the first approach, i.e., qualitative features, 96.49% discriminating power (DP) was observed, which shows highly significant results with the UV-Vis-NIR technique. In the second approach the discriminating power is further enhanced by incorporating the principal component analysis (PCA) statistical method, where this method describes each UV-Vis spectrum in a group through numerical loading values connected to the first few principal components. All components described 100% variance of the samples, but only the first three PCs are good enough to explain the variance (PC1 = 51.64%, PC2 = 47.52%, and PC3 = 0.54%) of the samples; i.e., the first three PCs described 99.70% of the data, whereas in the third approach, the four samples, C, G, K, and N, out of a total 19 samples, which were not differentiated using qualitative features (approach no. 1), were therefore subjected to PCA. The first two PCs described 99.37% of the spectral features. The discrimination was achieved by using a loading plot between PC1 and PC2. It is therefore concluded that maximum discrimination of writing, office, and photocopy paper could be achieved on the basis of the second approach. Hence, the present inexpensive analytical method can be appropriate for application to routine questioned document examination work in forensic laboratories because it provides nondestructive, quantitative, reliable, and repeatable results.

Many complex physical systems exhibit a rich variety of discrete behavioural modes. Often, the system complexity limits the applicability of standard modelling tools. Hence, understanding the underlying physics of different behaviours and distinguishing between them is challenging. Although traditional machine learning techniques could predict and classify behaviour well, typically they do not provide any meaningful insight into the underlying physics of the system. In this paper we present a novel method for extracting physically meaningful clusters of discrete behaviour from limited experimental observations. This method obtains a set of physically plausible functions that both facilitate behavioural clustering and aid in system understanding. We demonstrate the approach on the V-shaped falling paper system, a new falling paper type system that exhibits four distinct behavioural modes depending on a few morphological parameters. Using just 49 experimental observations, the method discovered a set of candidate functions that distinguish behaviours with an error of 2.04%, while also aiding insight into the physical phenomena driving each behaviour. © 2019 Howison et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

International audience; An analytical approach based on the multivariate analysis of on-line pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) data is proposed for the identification of traditional East Asian handmade papers from different fiber material origins. This approach utilized several biomarkers detected during Py-GC/MS analysis of the paper samples. At first, the total ion chromatogram (TIC) was taken as the response and then the extracted ion chromatograms (EICs) were considered to improve the discrimination of papers. The influence of different data pretreatment (raw responses vs normalized values) including different weighting of the variables (weighting as 1 vs weight as 1/STD, where STD stands for standard deviation) for principal component analysis was also investigated. The results showed that compared to the commonly used microscopy techniques, the Py-GC/MS technique proved to be able to discriminate against handmade paper materials that have similar microscopic morphologies such as Morus species vs. Broussonetia species. The data pretreatment influenced the PCA modeling: the analysis based on normalized values showed more interpretable PCA group features for Moraceae species. PCA without weighting resulted unsurprisingly in discrimination through the presence of high intensity response biomarkers, while when applying weight as 1/STD, PCA loading plot was shown to provide a group of compounds, most of them being present at low levels, to be discriminating. Additionally, the characteristic EICs can provide data matrix for statistical analysis avoiding the interference from co-eluting compound and background compared to the data matrix obtained from the TIC. As a result, a quick Py-GC/MS based handmade paper identification procedure using PCA modeling of characteristic EICs was for the first time proposed in the identification of traditional East Asian handmade papers. This procedure could be very beneficial in cultural heritage applications.

Multi-sensor object detection and tracking on a highway scene with radar measurements is presented. The estimation algorithm is the random finite set based Bernoulli filter, working in the Bayesian framework. The recursion for calculating the Bayes estimation is implemented as a particle filter. A method is presented for calculating the likelihoods, suitable for particle filtering performed with moving sensors, assuming additive Gaussian measurement noise. In our approach, for calculating the posterior estimate of the object state, the measurement likelihoods are computed in the state space, instead of the measurement space, by mapping each measurement to the global coordinate system. The map consists of a nonlinear and an affine part. While the affine transformation trivially preserves the Gaussian nature, the nonlinear is well-proven to be approximated as affine too. This approach allows the particles to be drawn directly from the state space, hence the evaluation of the measurement model is not needed, which saves computational power.

Although flexible neural networks FNNs have been used more successfully than classical neural networks CNNs in many industrial applications, nothing is rigorously known about their properties. In fact they are not even well known to the systems and control community. In the first part of this paper, existing structures of and results on FNNs are surveyed. In the second part FNNs are examined in a theoretical framework. As a result, theoretical evidence is given for the superiority of FNNs over CNNs and further properties of the former are developed. More precisely, several fundamental properties of feedforward and recurrent FNNs are established. This includes the universal approximation capability, minimality, controllability, observability, and identifiability. In the broad sense, the results of this paper help that general use of FNNs in systems and control theory and applications be based on firm theoretical foundations. Theoretical analysis and synthesis of FNN-based systems thus become possible. The paper is concluded by a collection of topics for future work.

Perfumes are cosmetic products with high added value and worldwide consumption, which make them a potential target for counterfeiting. A novel, simple and rapid method was developed for the differentiation of samples of authentic and counterfeit perfumes by employing paper spray mass spectrometry (PS-MS) combined with multivariate supervised classification models: Partial Least Squares Discriminant Analysis (PLS-DA) and Soft Independent Modelling of Class Analogies (SIMCA). Samples of authentic (n = 29, consisting of 10 different brands from several batches and from the same producer) and seized counterfeit (n = 31) perfumes were analysed by PS-MS in the positive ionization mode and within a mass range of m/z 150–1000. An initial unsupervised exploratory model (Principal Component Analysis – PCA) provided a rough visual separation between the two classes. In contrast, PLS-DA and SIMCA provided good predictions, with low false positive and false negative rates for both models. The interpretation of informative vectors, i.e. regression coefficients and Variable Importance in Projection (VIP) scores obtained from the PLS-DA model allowed the detection of diagnostic ions for authentic and counterfeit samples. Some of the most discriminant ions for counterfeit perfumes were suggested to be attributed to compounds with allergenic properties.

This discussion provides our reaction to the article by Greven and Scheipl. It contains an overview of their article and a description of the many areas of research that remain open and could benefit from further methodological and computational development.

In this paper a new method is proposed for intelligent sort of battery from waste paper based on Invariant Moments and Fractal Dimension (IMFD). In IMFD, a new distinctive feature that based on the edge contours is extracted to describe the target picture. At first invariant moments technique is used to extract Hus seven moments as the global descriptors from the target image. Then the fractal dimensions are extracted as local invariant descriptors by using Fractal Dimension. At last, a combing descriptor is built according the distinctive feature, which combines the global descriptor and the local descriptor together. The features are highly distinctive, and can be matched with high probability against a large database of features. The practical tests performed in this article show that the proposed method has a significant effect on increasing the stability, the accuracy of classification, and also can effectively against the huge-information during the image processing.

In the last 30 years, several distinguished researchers have proposed and discussed different fuzzy versions of well-known Saaty's Analytic Hierarchy Process (AHP). The paper recently published by K. Zhü in the European Journal of Operational Research heavily criticizes the fuzzy approaches to AHP, claiming the fallacy of all of them. Therefore, it seems to be necessary to clarify whether the criticisms are well-founded or not. The present paper aims to rebut Zhü's claims by showing that the evidences and the reasonings in his paper are very poor and far from proving the fallacy of fuzzy AHP.

In this paper, we achieved the floating 3D image based on integral photography theory to display the image in an oblique viewing angle on portable device. Considering the repeated image overlapping problem in the integral image system, it can be solved by adjusting the receiving angle. In the future, the floating 3D image could integrate with the 3D interactive system to provide more realistic visual experience.

The purpose of this study is to develop a descriptive paper test item which can evaluate elementary school students’ HGA (scientific Hypothesis Generating Ability) and to propose a counting formula that can easily assess student’s HGA objectively and quantitatively. To make the test item can possibly evaluate all the students from 6th graders to 3rd graders, the ‘rabbit’s ear’ item is developed. Developed test item was distributed to four different elementary schools in Seoul. Total 280 students who were in the 6th grade solved the item. All the students’ reponses to the item were analyzed. Based on the analyzed data evaluation factors and evaluation criteria are extracted to design a Hypothesis Generating ability Quotient (HGQ). As the result ‘Explican’s Degree of Likeness’ and ‘Hypothesis’ Degree of Explanation’ are chosen as evaluation factors. Also precedent evaluation criteria were renewed. At first, Explican’s Degree of Likeness evaluation criterion was turned four levels into three levels and each content of evaluation criterion is also modified. Secondly, new evaluation factor ‘Hypothesis’ Degree of Explanation’ was developed as combined three different evaluation criteria, ‘level of explican’, ‘number of explican’ and ‘structure of explican’. This evaluation factor was designed to assess how the suggested hypothesis can elaborately explain the cause of one phenomenon. Newly designed evaluation factors and evaluation criteria can assess HGA more in detail and reduce the scoring discordant through the markers. Lastly, Developed counting formula is much more simple than precedent Kwon’s equation for evaluating the Hypothesis Explanation Quotient. So it could help easily distinguish one student’s scientific hypothesis generating ability.Key words : scientific hypothesis generating ability, evaluation factor, evaluation criteria, hypothesis generating ability quotient, counting formula

Multi-projection three-dimensional (3D) display system with dual projection using uniaxial crystal is proposed. The dual projection means that multiple images are generated by double refraction in the uniaxial crystal according to the polarization state of a single projection unit. The concept of dual projection method is equivalent to physically separated two projectors. We apply the dual projection method in the multi-projection 3D display and double the number of viewing zones while maintaining the number of projectors. Through the experiment, we implement the four-view multi-projection 3D display providing super multi-view condition with two projection units. Experimental results verify the feasibility of the proposed system.

This study investigated the effects of bending curvature of simulated e-paper displays (−10 cm, plane and 10 cm) and ambient illuminance (50, 500, 6000 and 12,000 lx) on the visual performance of young and elderly users for simulated electronic-paper displays. In total, 24 people comprising 12 young and 12 elderly users participated in this study. Young users demonstrated significantly enhanced visual performance using the simulated electrophoretic ink (E ink) e-paper display with ambient illuminance set at 50 and 500 lx; however, participants did not demonstrate significantly different visual performance using the 2 simulated e-paper displays with ambient illuminance set at 6000 and 12,000 lx. The bending curvature did not exhibit significant effects on the visual performance of young participants under various ambient illuminances. The elderly participants exhibited significantly enhanced visual performance using the simulated E ink e-paper display only when ambient illuminance was set at 50 lx; however, participants did not exhibit significantly different visual performance using the two simulated e-paper displays with ambient illuminance set at other levels. Elderly users exhibited significantly enhanced visual performance when the bending curvature was set at 10 cm, with ambient illuminance set at 50 lx. Elderly users also demonstrated significantly enhanced visual performance under bending curvature settings of 10 cm and plane, with ambient illuminance set at 500 lx. However, the bending curvature of the simulated e-paper did not exhibit significant effects on the visual performance of elderly participants under ambient illuminance settings of 6000 and 12,000 lx.

This paper proposed an improved method based on adaptive with threshold wavelet transform denoising, according to image is often affected by noise pollution in the process of acquisition and transmission, compared with the advantages and disadvantages of traditional digital filtering method. It extracts the structure information and details of the image. It can adaptively select wavelet transform of optimal decomposition level and soft threshold to achieve the optimal noise reduction effect. The Simulation results demonstrate that this theory can effectively filter Gaussian noise and Salt and pepper noise, at the same time well protect the image details and achieve better visual effects.

In this paper we introduce a notion of a t-filter on residuated lattices which is a generalization of several special types of filters. We provide some basic properties of t-filters and show how particular results about special types of filters (e.g. Extension property, Triple of equivalent characteristics, and Quotient characteristics) are uniformly covered by this simple general framework.

Effects of character size under ambient illuminances and light sources on legibility of electronic paper displays (electrophoretic display and cholesteric liquid crystal display) were studied and compared with paper. Sixty subjects participated in a letter-search task in the experiment. The results showed that search speed depends on the illuminance but not light source. Search speed increased as the illuminance increase from 300, 700 to 1500 lx. Search speed also increased with the increase of character size, from character height of 1.4 mm (9.6 min visual angle), 2.2 mm (15.1 min visual angle) to 3.3 mm (22.7 min visual angle), and the increase leveled off at 4.3 mm (29.6 min visual angle). The effect of character size on accuracy was also significant. Accuracy increased with the increase of character size. However, the effect of illuminance and light source on accuracy was not statistically significant. Based on the results of this study, it seems that E-paper displays may need greater illumination (700 lx or higher), greater character size (3.3 mm or 22 min of visual angle).

International audience; This paper investigates the classification of photographic paper tex-tures using visual descriptors. Such classification is called fine grain due to the very low inter-class variability. We propose a novel image representation for photographic paper texture categorization, relying on the incorporation of a powerful local descriptor into an efficient higher-order model deviation where texture is represented by com-puting statistics on the occurrences of specific local visual patterns. We perform an evaluation on two different challenging datasets of photographic paper textures and show such advanced methods in-deed outperforms existing descriptors.

In the present work, the analytical potential of laser induced breakdown spectroscopy (LIBS) for the discrimination of paper types and prints made by digital printers on these papers was assessed. Six different paper types (including standard office papers, color paper and non-bleached paper), and eight printers (including laser and inkjet, as well as color and black printers) were included in the study. Only one or two laser shots were delivered to each sample in order to cause minimal sample destruction and provide maximum spatial resolution. The statistical evaluation of LIBS spectra was performed by multiple methods including three comparative functions (linear correlation, sum of squared deviations and overlapping integral) as well as two advanced statistical methods (multivariate curve resolution alternating least squares (MCR-ALS) combined with classification tree and discriminant analysis (DA)). The best classification results were obtained with the newly introduced MCR-ALS/DA approach, which proved to be 96.3% accurate in the identification of the paper type and 83.3% accurate in the identification of the printer type, based on the LIBS spectrum collected from just a single laser shot to the sample. It was found that the UV part of the LIBS spectrum can be used most efficiently for the discrimination.

There exist low recognition speed and non-ideal recognition effect in some recognition algorithms of paper currency value. One of very important reasons is that the shooting angle makes the image inclined. This paper firstly analyses the binarization processing of RMB 100-Yuan image and then the method of acquiring straight lines in image is discussed. Thus, the inclination angle of image is calculated by using the obtained straight lines. Finally, through rotation transformation, the inclination image is corrected. The experimental results show that this algorithm has a good corrected effect to the inclination image of paper currency and improves the image recognition effect.

Fiducial markers are used in image processing to determine locations of interest based on fixed points of reference. There are a number of applications for these markers across various fields ranging from advertising to radiation therapy. The four-square fiducial markers discussed in this manuscript allow for the determination of locations of interest on digital images. These new markers are easily detected, provide information about image orientation, and allow for local color sampling. The markers are intended for use in a pharmaceutical assessment process in which images of colorimetric chemical tests are taken by a smart-phone in the field, uploaded to a database, and analyzed to collect quantitative information about the colors resulting from the tests. KEYWORDS: Image Processing; Digital Imaging; Fiducial Markers; Image Thresholding; Color Calibration; MATLAB; Paper Analytical Devices

This second part (out of three) of a series of position papers on triangular norms (for Part I see Triangular norms. Position paper I: basic analytical and algebraic properties, Fuzzy Sets and Systems, in press) deals with general construction methods based on additive and multiplicative generators, and on ordinal sums. Also included are some constructions leading to non-continuous t-norms, and a presentation of some distinguished families of t-norms.

We illustrate the Efficient Forward Filtering Backward Smoothing (EFFBS) algorithm proposed by Khreich et al. (2010) for estimation of hidden Markov models. The algorithm is aimed at reducing the amount of memory required by the Baum-Welch recursions, while having the same complexity in terms of number of operations. In implementing the EFFBS algorithm we found a numerical problem that limits its applicability. We discuss this problem in detail, providing some possible explanations of the causes of the error, together with two illustrative examples.

In drug development decision-making is often supported through model-based methods, such as physiologically-based pharmacokinetics (PBPK). Global sensitivity analysis (GSA) is gaining use for quality assessment of model-informed inference. However, the inclusion and interpretation of correlated factors in GSA has proven an issue. Here we developed and evaluated a latent variable approach for dealing with correlated factors in GSA. An approach was developed that describes the correlation between two model inputs through the causal relationship of three independent factors: the latent variable and the unique variances of the two correlated parameters. The latent variable approach was applied to a set of algebraic models and a case from PBPK. Then, this method was compared to Sobol’s GSA assuming no correlations, Sobol’s GSA with groups and the Kucherenko approach. For the latent variable approach, GSA was performed with Sobol’s method. By using the latent variable approach, it is possible to devise a unique and easy interpretation of the sensitivity indices while maintaining the correlation between the factors. Compared methods either consider the parameters independent, group the dependent variables into one unique factor or present difficulties in the interpretation of the sensitivity indices. In situations where GSA is called upon to support model-informed decision-making, the latent variable approach offers a practical method, in terms of ease of implementation and interpretability, for applying GSA to models with correlated inputs that does not violate the independence assumption. Prerequisites and limitations of the approach are discussed. Supplementary Information The online version supplementary material available at 10.1007/s10928-021-09764-x.

Graphical abstract Most drugs are no longer produced in their own countries by the pharmaceutical companies, but by contract manufacturers or at manufacturing sites in countries that can produce more cheaply. This not only makes it difficult to trace them back but also leaves room for criminal organizations to fake them unnoticed. For these reasons, it is becoming increasingly difficult to determine the exact origin of drugs. The goal of this work was to investigate how exactly this is possible by using different spectroscopic methods like nuclear magnetic resonance and near- and mid-infrared spectroscopy in combination with multivariate data analysis. As an example, 56 out of 64 different paracetamol preparations, collected from 19 countries around the world, were chosen to investigate whether it is possible to determine the pharmaceutical company, manufacturing site, or country of origin. By means of suitable pre-processing of the spectra and the different information contained in each method, principal component analysis was able to evaluate manufacturing relationships between individual companies and to differentiate between production sites or formulations. Linear discriminant analysis showed different results depending on the spectral method and purpose. For all spectroscopic methods, it was found that the classification of the preparations to their manufacturer achieves better results than the classification to their pharmaceutical company. The best results were obtained with nuclear magnetic resonance and near-infrared data, with 94.6%/99.6% and 98.7/100% of the spectra of the preparations correctly assigned to their pharmaceutical company or manufacturer. Supplementary Information The online version contains supplementary material available at 10.1007/s00216-021-03249-z.

We present the basic analytical and algebraic properties of triangular norms. We discuss continuity as well as the important classes of Archimedean, strict and nilpotent t-norms. Triangular conorms and De Morgan triples are also mentioned. Finally, a brief historical survey on triangular norms is given.

Selection of the most significant variables, i.e. the wavenumber, from an infrared (IR) spectrum is always difficult to be achieved. In this preliminary paper, the feasibility of genetic algorithms (GA) in identifying most informative wavenumbers from 150 IR spectra of papers was investigated. The list of selected wavenumbers was then employed in Linear Discriminant Analysis (LDA). GA procedure was repeated 30 times to get different lists of variables. Then the performances of LDA models were estimated via leave-one-out cross-validation. A total of six to eight wavenumbers were identified to be valuable variables in the GA procedures. All the 30 LDA models achieve correct classification rates between 97.3% to 100.0%. Therefore the GA-LDA model could be a suitable tool for differentiating white papers that appeared to be highly similar in their IR fingerprints.

In this position paper, we shall undertake a critical scrutiny of the decision analysis (DA) paradigm on the basis of experience we gained from working on the bullwhip effect. We had some success with sorting out the complexities of the bullwhip by using fuzzy numbers in the bullwhip models, and we found out that this points the way to a more viable approach to sort out complex decision problems. As a positive criticism of the DA paradigm we have collected the insights we have gained in an agenda for soft decision analysis (SDA), which we hasten to add is not complete but--hopefully--an opening for focused research and continuing development.

Machine learning (ML) has pervaded most areas of protein engineering, including stability and stereoselectivity. Using limonene epoxide hydrolase as the model enzyme and innov'SAR as the ML platform, comprising a digital signal process, we achieved high protein robustness that can resist unfolding with concomitant detrimental aggregation. Fourier transform (FT) allows us to take into account the order of the protein sequence and the nonlinear interactions between positions, and thus to grasp epistatic phenomena. The innov'SAR approach is interpolative, extrapolative and makes outside‐the‐box, predictions not found in other state‐of‐the‐art ML or deep learning approaches. Equally significant is the finding that our approach to ML in the present context, flanked by advanced molecular dynamics simulations, uncovers the connection between epistatic mutational interactions and protein robustness., A quick learner: Machine learning based on the innov'SAR algorithm leads to efficient selection of highly robust limonene epoxide hydrolase mutants with enhanced unfolding stability and resistance to aggregation by recognizing epistatic mutational interactions.

Paper properties, including the chemical or optical characteristics influence the quality and colorimetric characteristics of final printed colors. In this study, the effect of gloss, texture, and whiteness of the paper on the printed colors were investigated. The study was applied on 10 coated papers with different texture, gloss or whiteness, and the same grammage using two inkjet printers. It was found that the reproducibility of the printer can be affected with the paper brand, and this effect is statistically dependent on the brand of printer. The kind of paper significantly influences the light fastness of the final printed colors, and this effect statistically depends on the printer. Although, the paper's properties such as gloss and whiteness affect the gamut volume, it is not totally possible to model this effect. The effect of paper on the colorimetric values of the final printed colors is statistically significant. However, the influence of the brand of printer on the color changes via the paper difference is not statistically provable. Edge frequency technique was introduced as an appropriate method for computing texture properties of papers. The texture difference computed by this method shows a suitable correlation with the color differences caused by changing the paper's texture; however, it depends on the printer's brand. Furthermore, there is a good correlation between the Edge frequency difference and the gamut volume variation for papers with different texture while the other parameters such as gloss and whiteness are the same. © 2012 Wiley Periodicals, Inc. Col Res Appl, 38, 284–291, 2013

Paper, as an important substrate of printed matter, the performance will impact on the color reproduction. Brightness, as a key index of paper performance, affects the apparent color of paper directly. It was investigated that the printed samples with different brightness have been evaluated the influence of paper brightness on color reproduction by the methods of subjective and objective color difference evaluation. Three kinds of paper with different brightness have been selected as substrate of printed samples. The standard test color blocks have been printed to do corresponding objective color difference evaluation to different paper by CIEDE2000 color-difference formulae methods. Meanwhile, the standard test images with difference contents have been printed on three kinds of paper, which was subjected to subjective color difference evaluation for visual evaluation of standard test images in standard observe conditions. The results of subjective and objective color difference evaluation have been combined to analyze the effect of paper brightness to color reproduction, and different visual influence to images of difference content, and could provide certain reference for selecting the brightness performance of paper in color reproduction.

The application of non-destructive process analytical technologies in the area of food science got a lot of attention the past years. In this work we used hyperspectral imaging to detect mould on milk agar and cheese. Principal component analysis is applied to hyperspectral data to localise and visualise mycelia on the samples’ surface. It is also shown that the PCA loadings obtained from a set of training samples can be applied to hyperspectral data from new test samples to detect the presence of mould on these. For both the agar and cheeselets, the first three principal components contained more than 99 % of the total variance. The spatial projection of the second principal component highlights the presence of mould on cheeselets. The proposed analysis methods can be adopted in industry to detect mould on cheeselets at an early stage and with further testing this application may also be extended to other food products., Graphical abstract Image 1, Highlights • Hyperspectral imaging can be used for non-destructive analysis of contamination in food products. • Principal component analysis (PCA) was applied to milk agar and cheeselet samples to localise and visualise mould growing on the samples. • The results from PCA applied to hyperspectral data shows that the technique can be used to identify and localise growth of mould on food samples.

The author presents a different view on properties of impact measures than given in the paper of De Visscher (2011). He argues that a good impact measure works better when citations are concentrated rather than spread out over articles. The author also presents theoretical evidence that the g-index and the R-index can be close to the square root of the total number of citations, whereas this is not the case for the A-index. Here the author confirms an assertion of De Visscher. © 2012 Wiley Periodicals, Inc.

PURPOSE The introduction of a linear safety factor to address peak local specific absorption rate (pSAR10g ) uncertainties (eg, intersubject variation, modeling inaccuracies) bears one considerable drawback: It often results in over-conservative scanning constraints. We present a more efficient approach to define a variable safety margin based on the conditional probability density function of the effectively obtained pSAR10g value, given the estimated pSAR10g value. METHODS The conditional probability density function can be estimated from previously simulated data. A representative set of true and estimated pSAR10g samples was generated by means of our database of 23 subject-specific models with an 8-fractionated dipole array for prostate imaging at 7 T. The conditional probability density function was calculated for each possible estimated pSAR10g value and used to determine the corresponding safety margin with an arbitrary low probability of underestimation. This approach was applied to five state-of-the-art local SAR estimation methods, namely: (1) using just the generic body model "Duke"; (2) using our model library to assess the maximum pSAR10g value over all models; (3) using the most representative "local SAR model"; (4) using the five most representative local SAR models; and (5) using a recently developed deep learning-based method. RESULTS Compared with the more conventional safety factor, the conditional safety-margin approach results in lower (up to 30%) mean overestimation for all investigated local SAR estimation methods. CONCLUSION The proposed probabilistic approach for pSAR10g correction allows more accurate local SAR assessment with much lower overestimation, while a predefined level of underestimation is accepted (eg, 0.1%).

In many sports games, we would use a coin or the game of scissors-paper-stone to decide which side will begin first. We consider the game of scissors-paper-stone when two teams are composed of N respectively. We continue the game of scissors-paper-stone until the winner of the two team is decided. Using sample spaces and enumerating the elements we calculated the mean number of the game when N = 1, N = 2 and N = 3. In case of N = 1 and N = 2, we simulate the game and find the mean and variance when the repetition number n = 20; 30; 50; 100; 150; 200.

A thermal aging experiment was conducted for oil-paper insulation to evaluate the insulation's response under thermal stress. Oil-papers at different aging stages were used to create oil-paper bound gas cavity specimens, which were used to collect data on degrees of polymerization (DP) and partial discharge (PD). Evaluation of oil-paper aging was based on statistical operators of PD and factor analysis was used to extract the principle parameters of PD. Three types of fuzzy clustering approaches were used to classify PD of aged oil-paper: the fuzzy c-means, the kernel fuzzy c-means, and the possibilistic fuzzy c-means. The clustering results showed that the possibilistic fuzzy c-means clustering was capable of classifying PD of oil-paper bound gas cavity specimens. The factor analysis method was also verified to be helpful in fuzzy clustering of PD data samples by reducing number of parameters.

This paper puts across the various approaches and methods that have been proposed in the context of Fuzzy Mathematical Morphology. The underlying principles of Dilation & Erosion, the structuring elements used in various techniques, the unique variations put forth by researchers, new applications in spatial relationships, decision making, segmentation of medical images have been discussed.

In this paper, we describe the development of a system for evaluating an specific quality characteristic of recycled paper sheets using techniques of image analysis and pattern recognition. We call Bumpiness the phenomenon of interest, which is new in the literature on paper quality. This phenomenon is characterized by the appearance of macroscopic undulations on the paper sheet surface that may emerge shortly or some time after its production. We explore the detection and measurement of this defect by means of computer vision and statistical pattern recognition techniques that may allow early detection at the production site. Our goal is to give an scalar continuous measure of Bumpiness. We propose features computed from Gabor filter banks (GFB) and discrete wavelet transforms (DWT) for the characterization of paper sheet surface Bumpiness in recycled paper images. The starting point is to state the problem as a classification of the paper sheet images into two classes: low and high Bumpiness. In this setting we obtain, with both proposed texture modelling approaches (GFB and DWT), classification accuracies comparable to the agreement between human observers. The best performance is obtained using DWT features. Finally, we propose as the scalar index of Bumpines the fisher discriminant analysis (FDA) function defined on the space of the best features for the classification task. We perform an innovative validation process of this Bumpiness index, based on the ordering of random pairs of images, obtaining a very high agreement with the human observers.

This paper presents an empirical study to investigate the use of photometric stereo (PS) for micro-scale 3D measurement of paper samples. PS estimates per-pixel surface orientation from images of a surface captured from the same viewpoint but under different illumination directions. Specifically, we investigate the surface properties of paper to test whether they are sufficiently well approximated by a Lambertian reflectance model to allow veridical surface reconstruction under PS and explore the range of conditions for which this model is valid. We present an empirical setup that is used to conduct a series of experiments in order to analyse the applicability of PS at the micro-scale. In addition, we determine the best 4, 6, and 8 light source tilt (illumination) angles with respect to multi-source micro-scale PS. Furthermore, an intensity based image registration method is used to test the accuracy of the recovery of surface normals. The results demonstrate that at the micro-scale: (a) Lambert model represents well the data sets with low root mean square (RMS) error between the original and reconstructed image, (b) increasing the light sources from 4 to 8 reduces RMS error, and (c) PS can be used to extract veridical surface normals.