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This review of recent scholarship (RRS) paper is a follow-up of the first, published in this journal in 2014. For this RRS paper, we identified and included 304 mixed-methods research (MMR) papers published in 20 top-tier applied linguistics (AL) journals. We used a six-pronged quality and transparency framework to review and analyze the MMR studies, drawing on six quality frameworks and transparency discussions in the MMR literature. Using the quality and transparency framework, we report on: (1) which sources AL MMR researchers use to frame their studies, (2) how explicitly they explain the purpose and design structure of the MMR studies, (3) how transparently they describe method features (sampling procedures, data sources, and data analysis), and (4) how they integrate quantitative and qualitative data and analyses and construct meta-inferences. The results of the analyses will be reported and will show how MMR has developed and is represented in the published articles in the second decade of the twenty-first century. The discussion of the results will also highlight the areas future AL MMR researchers need to consider to make their studies and reports more rigorous and transparent.

Sensor arrays, which draw inspiration from the mammalian olfactory system, are fundamental concepts in high-throughput analysis based on pattern recognition. Although numerous optical sensor arrays for various targets in aqueous media have demonstrated their diverse applications in a wide range of research fields, practical device platforms for on-site analysis have not been satisfactorily established. The significant limitations of these sensor arrays lie in their solution-based platforms, which require stationary spectrophotometers to record the optical responses in chemical sensing. To address this, this review focuses on paper substrates as device components for solid-state sensor arrays. Paper-based sensor arrays (PSADs) embedded with multiple detection sites having cross-reactivity allow rapid and simultaneous chemical sensing using portable recording apparatuses and powerful data-processing techniques. The applicability of office printing technologies has promoted the realization of PSADs in real-world scenarios, including environmental monitoring, healthcare diagnostics, food safety, and other relevant fields. In this review, we discuss the methodologies of device fabrication and imaging analysis technologies for pattern recognition-driven chemical sensing in aqueous media. [Display omitted] • Cross-reactive sensors are applied to paper-based arrays for simultaneous detection. • Portable digital recorders can be used for detecting various responses. • Imaging analysis techniques can accelerate accurate data processing. [ABSTRACT FROM AUTHOR]

Hypericum perforatum L. (St. John's wort) is one of the world's most consumed medicinal plants for treating depression and psychiatric disorders. Counterfeiting can occur in the medicinal plant trade, either due to the lack of active ingredients or the addition of substances not mentioned on the labels, often without therapeutic value or even harmful to health. Hence, 43 samples of St. John's wort commercially acquired in different Brazilian regions and other countries were analyzed by paper spray ionization mass spectrometry (PS‐MS) and modeled by principal component analysis. Hence, samples (plants, capsules, and tablets) were extracted with ethanol in a solid–liquid extraction. For the first time, PS‐MS analysis allowed the detection of counterfeit H. perforatum samples containing active principles typical of other plants, such as Ageratum conyzoides and Senna spectabilis. About 52.3% of the samples were considered adulterated for having at least one of these two species in their composition. Furthermore, out of 35 samples produced in Brazil, only 13 were deemed authentic, having only H. perforatum. Therefore, there is a clear need to improve these drugs' quality control in Brazil. [ABSTRACT FROM AUTHOR]

We propose a printed 384‐well microtiter paper‐based fluorescent chemosensor array device (384‐well microtiter PCAD) to simultaneously categorize and discriminate saccharides and sulfur‐containing amino acids for food analysis. The 384‐well microtiter PCAD requiring 1 μL/4 mm2 of each well can allow high‐throughput sensing. The device embedded with self‐assembled fluorescence chemosensors displayed a fingerprint‐like response pattern for targets, the image of which was rapidly captured by a portable digital camera. Indeed, the paper‐based chemosensor array system combined with imaging analysis and pattern recognition techniques not only successfully categorized saccharides and sulfur‐containing amino acids but also classified mono‐ and disaccharide groups. Furthermore, the quantitative detectability of the printed device was revealed by a spike and recovery test for fructose and glutathione in a diluted freshly made tomato juice. We believe that the 384‐well microtiter PCAD using the imaging analysis system will be a powerful sensor for multi‐analytes at several categorized groups in real samples. [ABSTRACT FROM AUTHOR]

Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them. This is partly explained by the fact that the most severe worldwide pandemic emerged amid the technological advances recently achieved, and also considering the technical facilities to deal with the large amount of data produced in this context. Even though several of these works describe important advances, we cannot overlook the fact that others only use well-known methods and techniques without a more relevant and critical contribution. Hence, differentiating the works with the most relevant contributions is not a trivial task. The number of citations obtained by a paper is probably the most straightforward and intuitive way to verify its impact on the research community. Aiming to help researchers in this scenario, we present a review of the top-100 most cited papers in this field of investigation according to the Google Scholar search engine. We evaluate the distribution of the top-100 papers taking into account some important aspects, such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and finally, the dataset and code availability.

The project presented in this paper aims to formulate a recommendation framework that consolidates the higher education students' particulars such as their academic background, current study and student activity records, their attended higher education institution's expectations of graduate attributes and self-assessment of their own generic competencies. The gap between the higher education students' generic competence development and their current statuses such as their academic performance and their student activity involvement was incorporated into the framework to come up with a recommendation for the student activities that lead to their generic competence development. For the formulation of the recommendation framework, the data mining tool Orange with some programming in Python and machine learning models was applied on 14,556 students' activity and academic records in the case higher education institution to find out three major types of patterns between the students' participation of the student activities and (1) their academic performance change, (2) their programmes of studies, and (3) their English results in the public examination. These findings are also discussed in this paper.

Purpose: This paper defines and assesses student learning patterns under the influence of problem-based learning (PBL) and their classification into a reasonable minimum number of classes. Study utilizes PBL implemented in an undergraduate Statistics and Operations Research course for techno-management students at a private university in India. Design/methodology/approach: Study employs an in situ experiment using a conceptual model based on learning theory. The participant's end-of-semester GPA is Performance Indicator. Integrating PBL with classroom teaching is unique instructional approach to this study. An unsupervised and supervised data mining approach to analyse PBL impact establishes research conclusions. Findings: The administration of PBL results in improved learning patterns (above-average) for students with medium attendance. PBL, Gender, Math background, Board and discipline are contributing factors to students' performance in the decision tree. PBL benefits a student of any gender with lower attendance. Research limitations/implications: This study is limited to course students from one institute and does not consider external factors. Practical implications: Researchers can apply learning patterns obtained in this paper highlighting PBL impact to study effect of every innovative pedagogical study. Classification of students based on learning behaviours can help facilitators plan remedial actions. Originality/value: 1. Clustering is used to extract student learning patterns considering dynamics of student performances over time. Then decision tree is utilized to elicit a simple process of classifying students. 2. Data mining approach overcomes limitations of statistical techniques to provide knowledge impact in presence of demographic characteristics and student attendance.

Discrimination of nitroarenes with hydrophobic dyes in a polar (H 2 O) environment is difficult but possible via a lab-on-chip, with polymeric dyes immobilized on paper or nylon membranes. Here arrays of 12 hydrophobic poly(p -phenyleneethynylene)s (PPEs), are assembled into a chemical tongue to detect/discriminate nitroarenes in water. The changes in fluorescence image of the PPEs when interacting with solutions of the nitroarenes were recorded and converted into color difference maps, followed by cluster analysis methods. The variable selection method for both paper and nylon devices selects a handful of PPEs at different pH-values that discriminate nitroaromatics reliably. The paper-based chemical tongue could accurately discriminate all studied nitroarenes whereas the nylon-based devices represented distinguishable optical signature for picric acid and 2,4,6-trinitrotoluene (TNT) with high accuracy. [Display omitted] • Discrimination of nitroarenes was possible via a lab-on-chip, with dyes immobilized on paper or nylon membranes. • Hydrophobic poly (p -phenyleneethynylene)s (PPEs) were assembled into a chemical tongue. • Nylon-based devices distinguish picric acid and TNT with high accuracy. [ABSTRACT FROM AUTHOR]

Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them. This is partly explained by the fact that the most severe worldwide pandemic emerged amid the technological advances recently achieved, and also considering the technical facilities to deal with the large amount of data produced in this context. Even though several of these works describe important advances, we cannot overlook the fact that others only use well-known methods and techniques without a more relevant and critical contribution. Hence, differentiating the works with the most relevant contributions is not a trivial task. The number of citations obtained by a paper is probably the most straightforward and intuitive way to verify its impact on the research community. Aiming to help researchers in this scenario, we present a review of the top-100 most cited papers in this field of investigation according to the Google Scholar search engine. We evaluate the distribution of the top-100 papers taking into account some important aspects, such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and finally, the dataset and code availability. [ABSTRACT FROM AUTHOR]

This paper provides a longitudinal analysis of the understanding of repetition patterns by 24 Spanish children ages 3, 4 and 5, through representation and the type of justification. A mixed quantitative and qualitative study is conducted to establish bridges between algebraic thinking and computational thinking by teaching repetition patterns in technological contexts. The data are obtained using: (a) participant observations; (b) audio-visual and photographic records; and (c) written representations, in drawing format, from the students. The analysis involves, on the one hand, a statistical analysis of the representations of patterns, and on the other, an interpretive analysis to describe the type of justification that children use in technological contexts: "elaboration", "validation", "inference" and "prediction or decision-making". The results show that: (a) with respect to the representation of patterns, errors decreased by 27.3% in 3-to-5-year-olds, with understanding and correct representation of repetition patterns gaining prominence in more than 50% of the sample from the age of 4; (b) on the type of justification used, it is evident that in 3-and-4-year-olds, "elaboration" predominates, and at 5, progress is made towards "validation". We conclude that it is necessary to design learning sequences connected with theory and upheld through practice, and that foster the active role of the teacher as a promoter of teaching situations that help spur the beginning of computational and algebraic thinking.

There is an increasing interest and growing practice in Citizen Science (CS) that goes along with the usage of websites for communication as well as for capturing and processing data and materials. From an educational perspective, it is expected that by integrating information about CS in a formal educational setting, it will inspire teachers to create learning activities. This is an interesting case for using bots to automate the process of data extraction from online CS platforms to better understand its use in educational contexts. Although this information is publicly available, it has to follow GDPR rules. This paper aims to explain (1) how CS communicates and is promoted on websites, (2) how web scraping methods and anonymization techniques have been designed, developed and applied to collect information from online sources and (3) how these data could be used for educational purposes. After the analysis of 72 websites, some of the results obtained show that only 24.8% includes detailed information about the CS project and 48.61% includes information about educational purposes or materials.

As we move further into the digital age, the acquisition of digital literacy (DL) and computational thinking (CT) skills is emerging internationally as an essential goal for students in contemporary school curricula. As the world becomes more uncertain and volatile due to impacts of artificial intelligence (AI), international unrest, climate change, global economic instability and unforeseen catastrophes such as the Coronavirus (COVID-19) pandemic, it is necessary to review, revise and refine school education curricula and policies. The issue of what is essential for students to learn, and how they learn it, is of growing importance to international organisations such as the Organisation for Economic Co-operation and Development (OECD) and the United Nations Educational, Scientific and Cultural Organisation (UNESCO) and is emerging as a significant driver for educational reform across the globe. The growing prominence of CT and DL skills across many industry sectors has prompted recent changes to international assessment frameworks such as the Programme for International Student Assessment (PISA) and the Trends in International Mathematics and Science Study (TIMSS). This paper will briefly discuss specific examples of alternative approaches to addressing CT in national curricula for the compulsory years of schooling and explain how CT has been adopted as a driver for mathematics curriculum change in Australia.

This study investigated the influence of CThink4CS2 Module on computational thinking (CT) skills of form four chemistry students. The CThink4CS[superscript]2 Module integrated CT with the Engineering Design Process (EDP) in chemistry class. This study utilized quantitative research methods and quasi-experimental design. Quantitative data were collected using the Computational Thinking Skill Test (CTST) which consisted of algorithmic reasoning, abstraction, decomposition, and pattern recognition constructs. A total of 73 students were in the treatment group (n=39) and control group (n=34). Experimental data were described by means of descriptive analysis and inferential analysis employing two-way MANOVA analysis. The results of the analysis indicated significant differences in CT skills between groups; students in the treatment group demonstrated better results compared to those in the control group. The paper provides insight into the integration of CT and EDP as effective pedagogical strategies for inculcating CT skills.

This paper describes how the notion of the strongly didactic contract can serve to characterize the teaching adopted to implement a task in probability. It is particularly focused on the reality of mathematical work performed by students and teachers. For this research, classroom sessions were developed in an in-service teacher training course designed (and adapted) according to the Japanese Lesson Study model. Through the combined use of the Theory of Didactical Situations (TDS) and the Theory of Mathematical Working Spaces (ThMWS), a coding of the sessions observed was developed. Based on this coding, different patterns emerged which gave each session a specific rhythm and identity from which it was possible to recognize and characterize different strongly didactic contracts. The study highlights the difference between the potential contracts intended by the teachers and those observed in practice. The tools, and especially the coding, developed for the study could be used for future research on instructional situations or in-service teacher training.

This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading Italian university.

Indeed, the printed 384-well microtiter paper-based fluorescent chemosensor array system combined with imaging analysis and pattern recognition techniques successfully not only categorized saccharides and sulfur-containing amino acids but also classified mono- and disaccharide groups. Paper, sensors, pattern recognition, self-assembly, food analysis Keywords: paper; sensors; pattern recognition; self-assembly; food analysis EN paper sensors pattern recognition self-assembly food analysis 1 1 1 08/19/22 20220815 NES 220815 B A facile paper-based device b embedded with self-assembled fluorescent chemosensors displayed a fingerprint-like response pattern for targets, the image of which was rapidly captured by a portable digital camera. [Extracted from the article]

The massive amount of text related to education provides rich information to support education in many aspects. In the meantime, the vast yet increasing volume of text makes it impossible to analyze manually. Text mining is a powerful tool to automatically analyze large-scaled texts and generate insights from the texts. However, many educational scholars are not fully aware of whether text mining is useful and how to use it in their studies. To address this problem, we reviewed the literature to examine the educational research that used text mining techniques. Specifically, we proposed an educational text mining workflow and focused on identifying the articles' bibliographic information, research methodologies, and applications in alignment with the workflow. We selected 161 articles published in educational journals from 2015 to 2020. We find that text mining is becoming more popular and essential in educational research. The conclusion is that we can employ three steps (text source selection, text mining techniques application, and educational information discovery) to use text mining in educational studies. We also summarize different options in each step in this paper. Our work should help educational scholars better understand educational text mining and provide support information for future research in text mining for educational contexts.

Abstraction in business processes (BP) modeling arises from the recognition of similarities to the detriment of its differences. However, teaching modeling to beginning students in the context of process management is a hard task to perform, given the high level of abstraction required for these students to develop. This paper uses BP fragments to facilitate the teaching of BP modeling. Thus, two different process models were used from a BP Office. This observation resulted in the fragments being abstracted and defined by a group of students, later another group applied the third model. Thus, two fragments were abstracted, specified, and reused. In addition to the approach itself and the template used to define the fragments here presented, this research's main contribution is the role that abstraction plays in BP learning and modeling skills. Finally, the fragments were validated by ten specialists, who emphasized the feasibility of using these fragments.

Educational data mining is an emerging interdisciplinary research area involving both education and informatics. It has become an imperative research area due to many advantages that educational institutions can achieve. Along these lines, various data mining techniques have been used to improve learning outcomes by exploring large-scale data that come from educational settings. One of the main problems is predicting the future achievements of students before taking final exams, so we can proactively help students achieve better performance and prevent dropouts. Therefore, many efforts have been made to solve the problem of student performance prediction in the context of educational data mining. In this paper, we provide readers with a comprehensive understanding of student performance prediction and compare approximately 260 studies in the last 20 years with respect to i) major factors highly affecting student performance prediction, ii) kinds of data mining techniques including prediction and feature selection algorithms, and iii) frequently used data mining tools. The findings of the comprehensive analysis show that ANN and Random Forest are mostly used data mining algorithms, while WEKA is found as a trending tool for students' performance prediction. Students' academic records and demographic factors are the best attributes to predict performance. The study proves that irrelevant features in the dataset reduce the prediction results and increase model processing time. Therefore, almost half of the studies used feature selection techniques before building prediction models. This study attempts to provide useful and valuable information to researchers interested in advancing educational data mining. The study directs future researchers to achieve highly accurate prediction results in different scenarios using different available inputs or techniques. The study also helps institutions apply data mining techniques to predict and improve student outcomes by providing additional assistance on time.

The large rates of students' failure is a very frequent problem in undergraduate courses, being even more evident in exact sciences. Pointing out the reasons of such problem is a paramount research topic, though not an easy task. An alternative is to use Educational Data Mining techniques (EDM), which enables one to convert data from educational database into useful information, in order to understand and improve teaching and learning processes. In this way, the objective of this paper is to propose mathematical models based on EDM techniques to estimate the probability of a student in a mathematics degree course at IFSP (Federal Institute of São Paulo) to fail in exact sciences disciplines, and later on, indicate which aspects contribute significantly for the Students' failure rates in these branches. We present three logistic regression models that which were applied based on socioeconomic data and student performance over 4 years. For interpretation and evaluation of such models, odds ratio, ten-fold Cross Validation method and the metrics: accuracy, sensitivity, specificity and area under the ROC curve (AUC) were used. It was noted that through Cross Validation, the models achieved accuracy values accounting for over 70%, sensitivity over 70%, specificity over 60% and AUC over 0.75. Analyzing the predictive variables of these models, we identified that factors such as advantage age, rates of failure through the course and attendance in initial semesters can increase the probability of failure in exact science disciplines in the analyzed course.

The need for a knowledge-based society has perpetuated an increasing demand for higher education around the globe. Recently, there has been an increase in the demand for Computer Science professionals due to the rise in the use of ICT in the business, health and education sector. The enrollment numbers in Computer Science undergraduate programmes are usually high, but unfortunately, many of these students drop out from or abscond these programmes, leading to a shortage of Computer Science professionals in the job market. One way to diminish if not completely eradicate this problem is to identify students who are at risk of dropping out and provide them with special intervention programmes that will help them to remain in their programmes till graduation. In this paper, data mining techniques were used to build predictive models that can identify student dropout in Computer Science programmes, more specifically focusing on freshmen attrition since a significant number of dropout occurs in the first year of university studies. The predictive models were built for three stages of the first academic year using five classification algorithms which were Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, and K-Nearest Neighbour. The models used past five years of institutional data stored in university's repositories. Results show that the Naïve Bayes model performed better in stage 1 with an AUC of 0.6132 but in stages 2 and 3, the overall performance of the Logsitic Regression models were better with an AUC of 0.7523 and 0.8902, respectively.

Hands-on cybersecurity training allows students and professionals to practice various tools and improve their technical skills. The training occurs in an interactive learning environment that enables completing sophisticated tasks in full-fledged operating systems, networks, and applications. During the training, the learning environment allows collecting data about trainees' interactions with the environment, such as their usage of command-line tools. These data contain patterns indicative of trainees' learning processes, and revealing them allows to assess the trainees and provide feedback to help them learn. However, automated analysis of these data is challenging. The training tasks feature complex problem-solving, and many different solution approaches are possible. Moreover, the trainees generate vast amounts of interaction data. This paper explores a dataset from 18 cybersecurity training sessions using data mining and machine learning techniques. We employed pattern mining and clustering to analyze 8834 commands collected from 113 trainees, revealing their typical behavior, mistakes, solution strategies, and difficult training stages. Pattern mining proved suitable in capturing timing information and tool usage frequency. Clustering underlined that many trainees often face the same issues, which can be addressed by targeted scaffolding. Our results show that data mining methods are suitable for analyzing cybersecurity training data. Educational researchers and practitioners can apply these methods in their contexts to assess trainees, support them, and improve the training design. Artifacts associated with this research are publicly available.

Mathematical giftedness (MG) is an intriguing phenomenon, the nature of which has yet to be sufficiently explored. This study goes a step further in understanding how MG is related to expertise in mathematics (EM) and general giftedness (G). Cognitive testing was conducted among 197 high school students with different levels of G and of EM. Based on our previous studies, we perceive MG as a combination of G and EM. Exploratory factor analysis of test results revealed five main cognitive factors: visual-serial processing (VSP); arithmetic abilities (AA); pattern recognition (PR); auditory working memory (AWM); visual-spatial working memory (VSWM); and Structural equation modeling (SEM) based on the factor analysis revealed clear differences in the role of cognitive abilities as predictors of EM, G, and MG. The study demonstrates that visual components are especially important for the development of EM and that G students are less dependent on their visual cognitive processing. Based on the study results, we argue that EM, G, and MG, which are often considered equivalent characteristics, are interrelated but different in nature. The paper presents a research-based justification that not any gifted is an expert in mathematics and not any expert in mathematics is gifted.

Purpose: The purpose of the study is to build predictive models for early detection of low-performing students and examine the factors that influence massive open online courses students' performance. Design/methodology/approach: For the first step, the author performed exploratory data analysis to analyze the dataset. The process was then followed by data pre-processing and feature engineering (Step 2). Next, the author conducted data modelling and prediction (Step 3). Finally, the performance of the developed models was evaluated (Step 4). Findings: The paper found that the decision trees algorithm outperformed other machine learning algorithms. The study also confirms the significant effect of the academic background and virtual learning environment (VLE) interactions feature categories to academic performance. The accuracy enhancement is 17.66% for decision trees classifier, 3.49% for logistic regression classifier and 4.89% for neural networks classifier. Based on the results of "CorrelationAttributeEval" technique with the use of a ranker search method, the author found that the "assessment_score" and "sum_click" features are more important among academic background and VLE interactions feature categories for the classification analysis in predicting students' academic performance. Originality/value: The work meets the originality requirement.

The rise of online proctoring potentially provides more opportunities for item harvesting and consequent brain dumping and shared "study guides" based on stolen content. This has increased the need for rapid approaches for evaluating and acting on suspicious test responses in every delivery modality. Both hiring proxy test takers and studying unauthorized test content (e.g., "study guides" or brain dumps) result in characteristic patterns of responses, many of which are detectable through collusion analysis. The ability to identify and rapidly revoke test results are one component of stopping test takers from engaging in these behaviors, both in online proctored and test center testing. Existing collusion analyses have typically taken the approach of evaluating all response pairs sequentially, potentially requiring several days to evaluate a set of test results. This paper demonstrates matrix-based methods for quickly calculating exact overlap counts for large data sets, as well as approaches for determining criteria for flagging suspicious results or invalidating results. We discuss and compare the results for simulations and probability calculations and discuss the operational implications of these decisions.

License plate detection is an important task in Intelligent Transportation Systems (ITS) and has a wide range of applications in vehicle management, traffic control, and public safety. In order to improve the accuracy and speed of mobile recognition, an improved lightweight YOLOv5s model is proposed for license plate detection. First, an improved Stemblock network is used to replace the original Focus layer in the network, which ensures strong feature expression capability and reduces a large number of parameters to lower the computational complexity; then, an improved lightweight network, ShuffleNetv2, is used to replace the backbone network of the YOLOv5s, which makes the model lighter and ensures the detection accuracy at the same time. Then, a feature enhancement module is designed to reduce the information loss caused by the rearrangement of the backbone network channels, which facilitates the information interaction in the feature fusion process; finally, the low‐, medium‐ and high‐level features in the Shufflenetv2 network structure are fused to form the final high‐level output features. Experimental results on the CCPD dataset show that compared to other methods this paper obtains better performance and faster speed in the license plate detection task, in which the average precision mean value reaches 96.6%, and can achieve a detection speed of 43.86 frame/s, and the parameter volume is reduced to 5.07 M. [ABSTRACT FROM AUTHOR]

Organizational patterns can serve as a teaching strategy for instructors and as a learning tool for students to develop their expository writing skills, which are commonly required for assignments (for example, laboratory reports and research papers) in Food Science courses and in their future careers. The article discusses the importance of organizational patterns for teaching expository writing through an interdisciplinary collaboration. The teaching collaboration occurred with professors from Food Science, English, and Anthropology in an introductory Food Science course (FSCI 232) taught at California State University Long Beach (CSULB). In FSCI 232, students learned how to use organizational patterns to interpret and explain the content of an infographic obtained from the Food Technology magazine, published by the Institute of Food Technologists (IFT). The infographic "Global Obesity's Expanding Girth, the World is Getting Fatter" served as a visual stimulus to help students identify these patterns, focusing on inquiry and analysis of scientific data and skills required for technical writing. Furthermore, the article illustrates those other potential applications of organizational patterns using the infographic could extend to interdisciplinary content (that is, Food Anthropology), which facilitates the development of cultural competency and sensitivity in food systems. Additionally, the article provides sample activities for teachers to use in their classrooms. To summarize, organizational patterns can serve as an effective teaching strategy to enhance students' writing skills across Food Science and related disciplines.

The main scientific results of the 16th International Conference on Pattern Recognition and Information Processing (PRIP-2023), Minsk, Republic of Belarus, October 2023, are reviewed and analyzed. The history of this series of conferences is outlined, and its significant role in the development of the theory and practice of image analysis, pattern recognition, and artificial intelligence is indicated. A list of articles in the special issue is provided, prepared from reports selected by the PRIP-2023 Program Committee. [ABSTRACT FROM AUTHOR]

Derek Holton and Duncan Symons present the first in a series of three papers that address sequences and their patterns. Their explorations lead to classroom teaching suggestions that involve creative thinking.

Featured Application: This paper proposes a method of monitoring the condition of three-phase asynchronous induction motors running with no load based on computer analysis of the instantaneous active electrical power. This paper experimentally reveals some of the resources offered by the instantaneous active electric power in describing the state of three-phase AC induction asynchronous electric motors (with a squirrel-cage rotor) operating under no-load conditions. A mechanical power is required to rotate the rotor with no load, and this mechanical power is satisfactorily reflected in the constant and variable part of instantaneous active electric power. The variable part of this electrical power should necessarily have a periodic component with the same period as the period of rotation of the rotor. This paper proposes a procedure for extracting this periodic component description (as a pattern by means of a selective averaging of instantaneous active electrical power) and analysis. The time origin of this pattern is defined by the time of a selected first passage through the origin of an angular marker placed on the rotor, detectable by a proximity sensor (e.g., a laser sensor). The usefulness of the pattern in describing the state of the motor rotor has been demonstrated by several simple experiments, which show that a slight change in the no-load running conditions of the motor (e.g., by placing a dynamically unbalanced mass on the rotor) has clear effects in changing the shape of the pattern. [ABSTRACT FROM AUTHOR]

Research on social psychology has revealed the existence of an affective mechanism in a human group, which is the group members spread their emotions to one another, the emotions of the group members form the group emotion, and the group emotion as a powerful force shapes the group members’ emotions. Current group emotion recognition methods focus on how the emotions of the group members form the group-level emotion but rarely take into account how the group emotion feeds back to the group members instantaneously. This paper proposes a new graph convolutional network architecture to characterize this unique affective mechanism for group emotion recognition. We regard the group members as the nodes of the graph and introduce a pseudo node into the graph to represent the role of the group. This paper uses graph convolutional networks to model the emotional interactions within the group from a static image and constructs an effective emotional representation at the group level for recognition. Experiment results on three widely used datasets for group emotion recognition show that our proposed method achieved superior performance in terms of recognition accuracy compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The implementation of Deep Learning (DL) Prediction techniques for Human Age Prediction (HAP) has been widely researched and studied to prevent, treat, and extend life expectancy. While most algorithms rely on facial images, MRI scans, and DNA methylation for training and testing, they are seldom implemented due to a lack of significant validation and evaluation in real-world scenarios, low performance, and technical challenges. To address these issues, this paper proposes the Data, Classification Technique, Prediction, and View (DCPV) taxonomy, which outlines the primary components required to implement and validate a deep learning model for predicting human age. By providing a common baseline for end-users and researchers, this taxonomy offers a clearer view of the constituents of deep learning prediction approaches, enabling the development of similar systems in the health domain. In contrast to existing machine learning methods, the proposed taxonomy emphasizes the value of deep learning practices based on performance, accuracy, and efficiency in predicting human age. To validate the DCPV taxonomy, the study examines 31 state-of-the-art research journal articles within the HAP system domain, assessing the taxonomy's performance, accuracy, robustness, and model comparisons. [ABSTRACT FROM AUTHOR]

In biometric identification, various physiological and behavioral traits such as fingerprints, facial features, iris patterns, and voice have been extensively explored. This paper introduces human body odour as a biometric trait for person identification. Body odour, composed of volatile organic compounds unique to each individual, presents a challenge for biometric authentication. The study explores the scientific basis of human body odour, including its chemical composition and uniqueness. The paper presents the enrollment process for capturing and storing body odour data, emphasizing the potential of electronic nose (E-nose) devices for scent detection and pattern recognition. This paper discusses the advantages of body odour biometrics, including its resistance to masking by artificial scents and its potential to reduce password administration costs. The proposed odour biometric system offers a non-intrusive and reliable means of person identification, particularly in scenarios where traditional biometric modalities may be impractical or ineffective. [ABSTRACT FROM AUTHOR]

Chatbots were an important research subject in the past. A chatbot is a computer program or an artificial intelligence program that participates in a conversation via auditory or textual methods. As the research on chatbots progressed, some important issues regarding them changed over time. Therefore, it is necessary to review the technology with a focus on recent advancements and core research technologies. In this paper, we introduce five different chatbot technologies: natural language processing, pattern matching, semantic web, data mining, and context-aware computer. We also introduce the latest technology for the chatbot researchers to recognize the present situation and channelize it in the right direction. [ABSTRACT FROM AUTHOR]

This paper presents a novel use of a bio-inspired retina model as a scene preprocessing stage for the recognition of letters and numbers on Chilean vehicle license plates. The goal is to improve the effectiveness and ease of pattern recognition. Inspired by the responses of mammalian retinas, this retinal model reproduces both the natural adjustment of contrast and the enhancement of object contours by parvocellular cells. Among other contributions, this paper provides an in-depth exploration of the architecture, advantages, and limitations of the model; investigates the tuning parameters of the model; and evaluates its performance when integrating a convolutional neural network and a spiking neural network into an optical character recognition (OCR) algorithm, using 40 different genuine license plate images as a case study and for testing. The results obtained demonstrate the reduction of error rates in character recognition based on convolutional neural networks (CNNs), spiking neural networks (SNNs), and OCR. It is concluded that this bio-inspired retina model offers a wide spectrum of potential applications to further explore, including motion detection, pattern recognition, and improvement of dynamic range in images, among others. [ABSTRACT FROM AUTHOR]

Predictive maintenance (PdM) is currently the most cost-effective maintenance method for industrial equipment, offering improved safety and availability of mechanical assets. A crucial component of PdM is the remaining useful life (RUL) prediction for machines, which has garnered increasing attention. With the rapid advancements in industrial internet of things and artificial intelligence technologies, RUL prediction methods, particularly those based on pattern recognition (PR) technology, have made significant progress. However, a comprehensive review that systematically analyzes and summarizes these state-of-the-art PR-based prognostic methods is currently lacking. To address this gap, this paper presents a comprehensive review of PR-based RUL prediction methods. Firstly, it summarizes commonly used evaluation indicators based on accuracy metrics, prediction confidence metrics, and prediction stability metrics. Secondly, it provides a comprehensive analysis of typical machine learning methods and deep learning networks employed in RUL prediction. Furthermore, it delves into cutting-edge techniques, including advanced network models and frontier learning theories in RUL prediction. Finally, the paper concludes by discussing the current main challenges and prospects in the field. The intended audience of this article includes practitioners and researchers involved in machinery PdM, aiming to provide them with essential foundational knowledge and a technical overview of the subject matter. [ABSTRACT FROM AUTHOR]

In order to better evaluate and promote human health, this paper analyzes the influence of different inertial-measurement-unit signals, different sensor locations, different activity intensities and different signal fusion schemes on the accuracy of physical strength consumption estimation during walking and running activities. Different pattern recognition methods, such as the Counts-based linear regression model, the typical non-linear model based on decision tree and artificial neural network, and the end-to-end convolutional neural network model, are analyzed and compared. Our findings are as follows: 1) For the locations of sensors during walking and running activities, the physical strength consumption prediction accuracy at the ankle location is higher than that at the hip location. Therefore, wearing an inertial-measurement-unit at the ankle can improve the accuracy of the model. 2) Regarding the types of activity signals during walking and running activities, the impact of accelerometer signals on hip and ankle prediction accuracy is not significantly different, while the gyroscope model is more sensitive to the location, with higher prediction accuracy at the ankle than at the hip. In addition, the physical strength consumption prediction accuracy of accelerometer signals is higher than that of gyroscope signals, and fusion of accelerometer and gyroscope signals can improve the accuracy of physical strength consumption prediction. 3) For different data analysis models during walking and running activities, the artificial neural network model that integrates different sensor locations and inertial-measurement-unit signals with different activity intensities has the lowest mean squared error for the measurement of physical strength consumption. The non-linear models based on decision tree and artificial neural network have better physical strength consumption prediction capabilities than the Counts-based linear regression model, especially for high-intensity activity energy consumption prediction. In addition, feature engineering models are generally better than convolutional neural network model in terms of overall performance and prediction results under the three different activity intensities. Furthermore, as the activity intensity increases, the performance of all physical strength consumption calculation models decreases. We recommend using the artificial neural network model based on multi-signal fusion to estimate physical strength consumption during walking and running activities because this model exhibits strong generalization ability in cross-validation and test results, and its stability under different activity intensities is better than that of the other three models. To the best of our knowledge, this paper is the first to delve deeply and in detail into methods for estimating physical strength consumption. Undoubtedly, our paper will have an impact on research related to topics such as intelligent wearable devices and subsequent methods for estimating physical strength consumption, which are directly related to physical health. [ABSTRACT FROM AUTHOR]

Bird's nest defect is an important cause of transmission line faults. To achieve accurate detection of bird nest defects in complex scenarios, a bird nest defect detection model for transmission lines was proposed that combines domain knowledge and occlusion reasoning networks. On the one hand, the model utilized the domain knowledge of the location of the bird's nest, using edge detection to obtain tower area information to constrain the location of candidate frames. This helps to reduce the false detection caused by complex backgrounds. On the other hand, on the basis of analyzing the occlusion characteristics of bird nests, the model employed occlusion reasoning networks that randomly erase features at the feature level to simulate the occlusion of bird nests in real scenes and improve the model's detection capability for occluded targets. Additionally, a multi‐scale feature fusion algorithm was designed in this paper to adapt the model to the scale variations of bird nests in aerial images. Experimental results demonstrate that the model outperforms advanced target detection models and other bird nest defect detection methods, with an AP50 of 78.8% and an AR10 of 72.4% for defect detection. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI) is an emerging paradigm to achieve communication between external devices and the human brain. Due to the low signal-to-noise ratio of the original electroencephalograph (EEG) signals, it is different to achieve feature extraction and feature selection, and further high classification accuracy cannot be obtained. To address the above problems, this paper proposes a pattern recognition method that takes into account sample entropy combined with a batch-normalized convolutional neural network. In addition, the sample entropy is used to extract features from the EEG signal data processed by wavelet transform and independent component analysis, and then the extracted data are fed into the convolutional neural network structure to recognize the EEG signal. Based on the comparison of experimental results, it is found that the method proposed in this paper has a high recognition rate. [ABSTRACT FROM AUTHOR]

The publication presents an introductory paper to the Special issue of the international journal Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications of the Russian Academy of Sciences. The main scientific results of the 8th International Workshop "Image Mining: Theory and Applications," held on August 21, 2022, Montreal, Canada, are presented. Historical information is given on this series of international workshops, and their significant role in the development of the theory and practice of automation of image analysis, pattern recognition, and artificial intelligence is emphasized. The list of papers of the Special issue of PRIA, prepared based on the invited and regular papers selected and recommended for publication by the Program Committee of the IMTA-VIII-2022, is presented. [ABSTRACT FROM AUTHOR]

In this paper, the aim is to classify torque signals that are received from a 3-DOF manipulator using a pattern recognition neural network (PR-NN). The output signals of the proposed PR-NN classifier model are classified into four indicators. The first predicts that no collisions occur. The other three indicators predict collisions on the three links of the manipulator. The input data to train the PR-NN model are the values of torque exerted by the joints. The output of the model predicts and identifies the link on which the collision occurs. In our previous work, the position data for a 3-DOF robot were used to estimate the external collision torques exerted by the joints when applying collisions on each link, based on a recurrent neural network (RNN). The estimated external torques were used to design the current PR-NN model. In this work, the PR-NN model, while training, could successfully classify 56,592 samples out of 56,619 samples. Thus, the model achieved overall effectiveness (accuracy) in classifying collisions on the robot of 99.95%, which is almost 100%. The sensitivity of the model in detecting collisions on the links "Link 1, Link 2, and Link 3" was 97.9%, 99.7%, and 99.9%, respectively. The overall effectiveness of the trained model is presented and compared with other previous entries from the literature. [ABSTRACT FROM AUTHOR]

China is a large consumer of meat and meat products. People's daily diets include a variety of meat, but meat food adulteration problems are common. This paper discusses the research progress of the electronic nose and near-infrared spectroscopy in the field of meat adulteration detection. Through the study of dozens of related papers in recent years, it has been found that the use of the electronic nose and near-infrared spectroscopy for meat detection has the advantages of speed, a nondestructive nature, high sensitivity, strong quantitative analysis, high automation, a wide applicability, an improved product quality, and cost reduction over the traditional detection, but it may be limited in detecting the adulteration of a specific meat, and there are issues with the life and stability of the sensors of the electronic nose in the process of detection, along with the problems of the high requirements for the modeling of the data of near-infrared spectroscopy. This paper takes adulterated meat as the research object and briefly summarizes the detection principles of the electronic nose and near-infrared spectroscopy, as well as the types of sensors applied in the electronic nose. The research progress of the electronic nose and near-infrared detection technology in meat adulteration assessment is reviewed, the advantages and disadvantages of the two in practical application are analyzed, the classification of pattern recognition methods and their applications in meat identification are described, and the feasibility and practical significance of the joint application of the two in meat adulteration detection are envisioned. Meanwhile, the challenges faced by the two in meat detection are pointed out. [ABSTRACT FROM AUTHOR]

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