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In the last two decades, hyperspectral images have been studied for use in food quality analysis using neural network-based chemometric techniques such as radial basic neural network (RBNN) in classification tasks or multilayer perceptron (MLP) for prediction ones. However, recently, deep learning techniques are receiving special interest as those based on recurrent neural networks (RNN) or gated recurrent units for their feature extraction capacity. A kind of RNN are networks based on long short-term memory (LSTM), which are promising for improving the capabilities of hyperspectral images in food analysis. In this sense, a comparative study on using LSTM-based networks is done, comparing it to RBNN in the discrimination of fish fillets subjected to freeze-thaw cycles. The samples for the study were fresh mackerel fillets subjected to two successive cycles of freezing and thawing. Hyperspectral images of fillets were acquired, both fresh and in each freeze-thaw cycle, and spectral profiles were acquired in the range of 400–1000 nm. Complete spectral profiles were used to generate classification models; these were subsequently optimized using the relevant wavelengths determined by feature selection neighborhood component analysis. The models were trained using a k-cross-fold validation strategy (k = 5), repeating the process thirty times. The models were evaluated, confusion matrices were determined, and their classification metrics were compared. The training process results showed that the LSTM networks had superior potential to the RBNN networks, achieving an average F-measure greater than 0.89 and an accuracy greater than 0.93. This study shows that deep learning techniques can be used to tell the difference between fish fillets that have been frozen and thawed several times. These techniques could also be used to establish systems to control the transport and conservation processes of this type of food. [ABSTRACT FROM AUTHOR]

This paper introduces a new approach to controlling Pressure Swing Adsorption (PSA) using a neural network controller based on a Model Predictive Control (MPC) process. We use a Hammerstein–Wiener (HW) model representing the real PSA process data. Then, we design an MPC-controlled model based on the HW model to maintain the bioethanol purity near 99 % molar fraction. This work proposes an Artificial Neural Network (ANN) that captures the dynamics of the PSA model controlled by the MPC strategy. Both controllers are validated using the HW model of the PSA process, showing great performance and robustness against disturbances. The results show that we can follow the desired trajectory and attenuate disturbances, achieving the purity of bioethanol at a molar fraction value of 0.99 using the ANN based on the MPC strategy with 94 % of fit in the control signal and a 97 % fit in the purity signal, so we can conclude that our ANN can be used to attenuate disturbances and maintain purity in the PSA process. [ABSTRACT FROM AUTHOR]

Regardless of whether the articles they examined were ultimately published, the editors would like to express their appreciation and thank the following reviewers for the time and dedication that they have shown I Water i : es 1 B Disclaimer/Publisher's Note: b The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). Thanks to the efforts of our reviewers in 2022, the median time to first decision was 18 days and the median time to publication was 41 days. I Water i was able to uphold its high standards for published papers due to the outstanding efforts of our reviewers. [Extracted from the article]

This research work aims to identify the prevalent anchors in the professional accounting career using the Schein scale and to describe the prevalent anchors by defining the values, attitudes, aptitudes, skills, and interests. Career anchors are defined by the competence, motivation, and values a person has to perform a particular job in an organization and are present throughout their working life. When determining the soft and hard competencies of the professional profile, universities must consider the career anchors essential for graduates' work performance. To determine which anchors dominate the competencies of the graduate profile, two universities in Latin America with a degree in accounting were selected. The study was organized in two stages: first, the operationalization of the research was conducted, including the description of the instrument through the application of 40 questions divided into Schein's eight anchors. Samples were selected based on the convenience of the authors: one university in Peru and another in Colombia. The sample includes all students enrolled in the accounting major, and the data were coded and processed. In the second stage, data analysis was performed by grouping parameters, analysis of variance, explanatory analysis using a test for the best clustering algorithm, statistical testing, and discussion of the findings. The predominant anchors in the two universities are creativity, entrepreneurship, and lifestyle. The selected universities placed considerable emphasis on training future accountants with an innovative spirit, integrity, and social commitment without neglecting the professional requirements. This approach allows students to undertake challenges and new businesses in their field of work. [ABSTRACT FROM AUTHOR]

The rising interest in quinoa (Chenopodium quinoa Willd.) is due to its high protein content and gluten-free condition; nonetheless, the presence of foreign bodies in quinoa processing facilities is an issue that must be addressed. As a result, convolutional neural networks have been adopted, mostly because of their data extraction capabilities, which had not been utilized before for this purpose. Consequently, the main objective of this work is to evaluate convolutional neural networks with a learning transfer for foreign bodies identification in quinoa samples. For experimentation, quinoa samples were collected and manually split into 17 classes: quinoa grains and 16 foreign bodies. Then, one thousand images were obtained from each class in RGB space and transformed into four different color spaces (L*a*b*, HSV, YCbCr, and Gray). Three convolutional neural networks (AlexNet, MobileNetv2, and DenseNet-201) were trained using the five color spaces, and the evaluation results were expressed in terms of accuracy and F-score. All the CNN approaches compared showed an F-score ranging from 98% to 99%; both color space and CNN structure were found to have significant effects on the F-score. Also, DenseNet-201 was the most robust architecture and, at the same time, the most time-consuming. These results evidence the capacity of CNN architectures to be used for the discrimination of foreign bodies in quinoa processing facilities. [ABSTRACT FROM AUTHOR]

In combinatorial optimization, the more complex a problem is, the more challenging it becomes, usually causing most research to focus on creating solvers for larger cases. However, real-life situations also contain small-sized instances that deserve a researcher's attention. For example, within a web development context, a developer might face small combinatorial optimization cases that fall in the following situations to solve them: (1) the development of an ad hoc specialized strategy is not justified; (2) the developer could lack the time, or skills, to create the solution; (3) the efficiency of naive brute force strategies might be compromised due to the programming paradigm use. Similar situations in this context, combined with a recent increasing interest in optimization information from databases, open a research area to develop easy-to-implement strategies that compete with those naive approaches and do not require specialized knowledge. Therefore, this work revises Structured Query Language (SQL) approaches and proposes new methods to tackle combinatorial optimization problems such as the Portfolio Selection Problem, Maximum Clique Problem, and Graph Coloring Problem. The performance of the resulting queries is compared against naive approaches; its potential to extend to other optimization problems is studied. The presented examples demonstrate the simplicity and versatility of using a SQL approach to solve small optimization problem instances. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 virus that causes COVID-19 affects the respiratory tract and is highly infectious. Those patients who knew that the disease could cause death or that their healing process is quite painful because of the symptoms and conditions developed extreme stress, anxiety, and depression, which aggravated the effects of the disease. Therefore, it is vital to conduct research to analyze these effects and generate self-help and support mechanisms during the disease process. This paper presents exploratory analysis related to stress, coping attitudes, emotional responses, and sources of support that were vital in patients affected by COVID-19; the focus of this study is the consideration of the spiritual factor, which may influence religious resilience that allows for a positive attitude and tenacity. To carry out this research, interviews were conducted with patients who had suffered from COVID-19 disease, and the collected information was processed using text-mining techniques using a two-phase methodology. The first phase is based on the Colaizzi method. Interview responses were coded through the search for patterns in the key phrases, and these codes were grouped, forming semantic relationships. In the second phase, natural-language processing algorithms (WordCloud, WordEmbedding, sentiment analysis of opinions) were used, summarizing the interviews in relevant factors of the patient's experience during the disease. Spiritual resilience stood out the most of all key phrases of the code group tables. Likewise, words such as security, confidence, tranquility, and peace indicated that the patients took a positive attitude towards the symptoms and complications of the disease. Therefore, it is important to be the resilience to face a crisis process, and one of the factors that generated such resilience in COVID-19 patients was religious faith, which was expressed in the interviews using the factors of security, trust, promises of healing, tranquility, and the impossibility of discouragement. All this contributed to the positive attitude of the interviewees during the process of recovery from the disease. [ABSTRACT FROM AUTHOR]

Computer-aided diagnosis is a research area of increasing interest in third-level pediatric hospital care. The effectiveness of surgical treatments improves with accurate and timely information, and machine learning techniques have been employed to assist practitioners in making decisions. In this context, the prediction of the discharge diagnosis of new incoming patients could make a difference for successful treatments and optimal resource use. In this paper, a computer-aided diagnosis system is proposed to provide statistical information on the discharge diagnosis of a new incoming patient, based on the historical records from previously treated patients. The proposed system was trained and tested using a dataset of 1196 records; the dataset was coded according to the International Classification of Diseases, version 10 (ICD10). Among the processing steps, relevant features for classification were selected using the sequential forward selection wrapper, and outliers were removed using the density-based spatial clustering of applications with noise. Ensembles of decision trees were trained with different strategies, and the highest classification accuracy was obtained with the extreme Gradient boosting algorithm. A 10-fold cross-validation strategy was employed for system evaluation, and performance comparison was performed in terms of accuracy and F-measure. Experimental results showed an average accuracy of 84.62%, and the resulting decision tree learned from the experience in samples allowed it to visualize suitable treatments related to the historical record of patients. According to computer simulations, the proposed classification approach using XGBoost provided higher classification performance than other ensemble approaches; the resulting decision tree can be employed to inform possible paths and risks according to previous experience learned by the system. Finally, the adaptive system may learn from new cases to increase decisions' accuracy through incremental learning. [ABSTRACT FROM AUTHOR]

In marketing, there is a notable lack of models, which leads to the loss of potential customers since there is no feedback for improvement in the marketing strategy. In the face of the Internet revolution, this study used digital channels and interaction to achieve potential customer retention through a Funnel; this allowed a potential client to go through the acquisition, conversion, and retention stages. In addition, activities were developed that allow the potential user to become a retained client. Thus, considering the stages of a marketing strategy, a non-probabilistic sample was used, composed of a pre-test period with 1088 users and a post-test period with 1450 users. Overall, there was a significant improvement in the time a client remained observing the website per session, with a 20.6% efficiency; furthermore, there was a 36.25% efficiency increase of the user conversion process per session in the digital channel. Likewise, a 46.43% efficiency increase was measured in visits to the digital channel, which confirms that the proposed marketing strategy significantly improves previous marketing strategies. Therefore, it is concluded that the proposal based on the elements of insight aligned with the measurement proposed by Google Analytics Key Performance Indicators, which was used to test and observe the improvements to continue the feedback, taking advantage of the characteristic of the digital channel with its two-way participation. [ABSTRACT FROM AUTHOR]

Although knowledge of the microstructure of food of vegetal origin helps us to understand the behavior of food materials, the variability in the microstructural elements complicates this analysis. In this regard, the construction of learning models that represent the actual microstructures of the tissue is important to extract relevant information and advance in the comprehension of such behavior. Consequently, the objective of this research is to compare two machine learning techniques—Convolutional Neural Networks (CNN) and Radial Basis Neural Networks (RBNN)—when used to enhance its microstructural analysis. Two main contributions can be highlighted from this research. First, a method is proposed to automatically analyze the microstructural elements of vegetal tissue; and second, a comparison was conducted to select a classifier to discriminate between tissue structures. For the comparison, a database of microstructural elements images was obtained from pumpkin (Cucurbita pepo L.) micrographs. Two classifiers were implemented using CNN and RBNN, and statistical performance metrics were computed using a 5-fold cross-validation scheme. This process was repeated one hundred times with a random selection of images in each repetition. The comparison showed that the classifiers based on CNN produced a better fit, obtaining F1–score average of 89.42% in front of 83.83% for RBNN. In this study, the performance of classifiers based on CNN was significantly higher compared to those based on RBNN in the discrimination of microstructural elements of vegetable foods. [ABSTRACT FROM AUTHOR]

Software testing is an essential activity to ensure the quality of software systems. Combinatorial testing is a method that facilitates the software testing process; it is based on an empirical evidence where almost all faults in a software component are due to the interaction of very few parameters. The test generation problem for combinatorial testing can be represented as the construction of a matrix that has certain properties; typically this matrix is a covering array. Covering arrays have a small number of tests, in comparison with an exhaustive approach, and provide a level of interaction coverage among the parameters involved. This paper presents a repository that contains binary covering arrays involving many levels of interaction. Also, it discusses the importance of covering array repositories in the construction of better covering arrays. In most of the cases, the size of the covering arrays included in the repository reported here are the best upper bounds known, moreover, the files containing the matrices of these covering arrays are available to be downloaded. The final purpose of our Binary Covering Arrays Repository (BCAR) is to provide software testing practitioners the best-known binary test-suites. [ABSTRACT FROM AUTHOR]

Search‐based software engineering involves the application of optimisation methods to solve software engineering problems. One of the most significant difficulties in testing software systems is the effort needed to build the test suites required to validate a software system, which efficiently exposes faults. Given the importance of the software testing stage, a specific sub‐area known as search‐based software testing has become relevant in recent years. In this work, a search‐based software testing algorithm for constructing covering arrays is proposed. A covering array is a combinatorial structure that can be used as a set of test cases. By utilising this algorithm, the authors reduce the size of 893 test suites. [ABSTRACT FROM AUTHOR]

Software testing has become a critical component of the modern software development process. Therefore, a lot of research has been done in this area in recent years, and as a result new algorithms, methodologies, and tools have been created. One of the most used testing strategies is pairwise testing; this technique ensures that all possible combinations of values between any two input parameters are covered by at least one test. In this work, a new algorithm called add factor and stochastic optimisation (AFSO) is used to build small pairwise test suites for non‐prime‐power orders. Starting from an orthogonal array of order v∈{10,12,14,15,18,20,21,22,24}, AFSO iteratively adds a factor and then reduces to zero the number of uncovered combinations by means of a simulated annealing algorithm. The results of the AFSO algorithm improved the size of 92 pairwise test suites with non‐prime‐power orders. One of these improved test suites is used in a real‐word application to show the usefulness of the new results. [ABSTRACT FROM AUTHOR]

A correction to the article "Supercomputing and grid computing on the verification of covering arrays," published in a previous issue is presented.

Software test suites based on the concept of interaction testing are very useful for testing software components in an economical way. Test suites of this kind may be created using mathematical objects called covering arrays. A covering array, denoted by CA(N; t, k, v), is an N × k array over with the property that every N × t sub-array covers all t-tuples of at least once. Covering arrays can be used to test systems in which failures occur as a result of interactions among components or subsystems. They are often used in areas such as hardware Trojan detection, software testing, and network design. Because system testing is expensive, it is critical to reduce the amount of testing required. This paper addresses the Optimal Shortening of Covering ARrays (OSCAR) problem, an optimization problem whose objective is to construct, from an existing covering array matrix of uniform level, an array with dimensions of (N − δ) × (k − Δ) such that the number of missing t-tuples is minimized. Two applications of the OSCAR problem are (a) to produce smaller covering arrays from larger ones and (b) to obtain quasi-covering arrays (covering arrays in which the number of missing t-tuples is small) to be used as input to a meta-heuristic algorithm that produces covering arrays. In addition, it is proven that the OSCAR problem is NP-complete, and twelve different algorithms are proposed to solve it. An experiment was performed on 62 problem instances, and the results demonstrate the effectiveness of solving the OSCAR problem to facilitate the construction of new covering arrays. [ABSTRACT FROM AUTHOR]

The objective of this research was to develop a methodology for optimizing multilayer-perceptron-type neural networks by evaluating the effects of three neural architecture parameters, namely, number of hidden layers (HL), neurons per hidden layer (NHL), and activation function type (AF), on the sum of squares error (SSE). The data for the study were obtained from quality parameters (physicochemical and microbiological) of milk samples. Architectures or combinations were organized in groups (G1, G2, and G3) generated upon interspersing one, two, and three layers. Within each group, the networks had three neurons in the input layer, six neurons in the output layer, three to twenty-seven NHL, and three AF (tan-sig, log-sig, and linear) types. The number of architectures was determined using three factorial-type experimental designs, which reached 63, 2 187, and 50 049 combinations for G1, G2 and G3, respectively. Using MATLAB 2015a, a logical sequence was designed and implemented for constructing, training, and evaluating multilayer-perceptron-type neural networks using parallel computing techniques. The results show that HL and NHL have a statistically relevant effect on SSE, and from two hidden layers, AF also has a significant effect; thus, both AF and NHL can be evaluated to determine the optimal combination per group. Moreover, in the three study groups, it is observed that there is an inverse relationship between the number of processors and the total optimization time. [ABSTRACT FROM AUTHOR]

Today, software systems are complex and have many possible configurations. A deficient software testing process often leads to unfortunate consequences, including data losses, large economic losses, security breaches, and even bodily harm. Thus, the problem of performing effective and economical testing is a key issue. Combinatorial testing is a method that can reduce cost and increase the effectiveness of software testing for many applications. It is based on constructing economical sized test‐suites that provide coverage of the most prevalent configurations. Mixed covering arrays (MCAs) are combinatorial structures that can be used to represent these test‐suites. MCAs are combinatorial objects represented as matrices having a test case per row. MCAs are small, in comparison to an exhaustive approach, and guarantee a level of interaction coverage among the parameters involved. This study presents a metaheuristic approach based on a simulated annealing (SA) algorithm for constructing MCAs. This algorithm incorporates several distinguishing features, including an efficient heuristic to generate good quality initial solutions, and a compound neighbourhood function that combines two carefully designed neighbourhood functions. The experimental design involved a benchmark reported in the literature and two real cases of software components. The experimental evidence showed that the SA algorithm equals or improves the obtained results by other approaches reported in the literature, and also finds the optimal solution in some of the solved cases. [ABSTRACT FROM AUTHOR]

The Covering Arrays (CAs) are mathematical objects with minimal coverage and maximum cardinality that are a good tool for the design of experiments. A covering array is an N× k matrix over an alphabet v s.t. each N× k subset contains at least one time each combination from {0,1,..., v−1}, given a positive integer value t. The process of ensuring that a CA contains each of the v combinations is called verification of CA. In this paper, we present an algorithm for CA verification and its implementation details in three different computation paradigms: (a) sequential approach (SA); (b) parallel approach (PA); and (c) Grid approach (GA). Four different PAs were compared in their performance of verifying a matrix as a CA; the PA with the best performance was included in a different experimentation where the three paradigms, SA, PA, and GA were compared in a benchmark composed by 45 possible CA instances. The results showed the limitations of the different paradigms when solving the verification of CA problem, and points out the necessity of a Grid approach to solve the problem when the size of a CA grows. [ABSTRACT FROM AUTHOR]

A branch of mathematics commonly used in cryptography is Galois Fields GF(pn). Two basic operations performed in GF(pn) are the addition and the multiplication. While the addition is generally easy to compute, the multiplication requires a special treatment. A well-known method to compute the multiplication is based on logarithm and antilogarithm tables. A primitive element of a GF(pn) is a key part in the construction of such tables, but it is generally hard to find a primitive element for arbitrary values of p and n. This article presents a naive algorithm that can simultaneously find a primitive element of GF(pn) and construct its corresponding logarithm and antilogarithm tables. The proposed algorithm was tested in GF(pn) for several values of p and n; the results show a good performance, having an average time of 0.46 seconds to find the first primitive element of a given GF(pn) for values of n = {2, 3, 4, 5, 8, 12} and prime values p between 2 and 97. [ABSTRACT FROM AUTHOR]

Rigorous peer-reviews are the basis of high-quality academic publishing. Thanks to the contribution of our reviewers, in 2021, the median time to first decision was 16 days and the median time to publication was 40 days. Thanks to the great efforts of our reviewers, I Sensors i was able to maintain its standards for the high quality of its published papers. [Extracted from the article]

This paper explores five multivariate techniques for information fusion on sorting the visual ripeness of Cape gooseberry fruits (principal component analysis, linear discriminant analysis, independent component analysis, eigenvector centrality feature selection, and multi-cluster feature selection.) These techniques are applied to the concatenated channels corresponding to red, green, and blue (RGB), hue, saturation, value (HSV), and lightness, red/green value, and blue/yellow value (L*a*b) color spaces (9 features in total). Machine learning techniques have been reported for sorting the Cape gooseberry fruits' ripeness. Classifiers such as neural networks, support vector machines, and nearest neighbors discriminate on fruit samples using different color spaces. Despite the color spaces being equivalent up to a transformation, a few classifiers enable better performances due to differences in the pixel distribution of samples. Experimental results show that selection and combination of color channels allow classifiers to reach similar levels of accuracy; however, combination methods still require higher computational complexity. The highest level of accuracy was obtained using the seven-dimensional principal component analysis feature space. [ABSTRACT FROM AUTHOR]