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This paper focuses on the opportunities and challenges associated with the use of artificial intelligence (AI) in academic library operations. In the quest to render fast, effective and efficient services, academic libraries have adopted different technologies in the past. Artificial intelligence technologies is the latest among the technologies currently being introduced in libraries. The technology which is considered an intelligent system, come in the form of robots and expert systems which have natural language processing, machine learning and pattern recognition capabilities. This paper examined the features of AI, the application of AI to library operations, examples of academic libraries with AI technologies in Sub-Saharan Africa, the need for AI in libraries and the challenges associated with the adoption of AI in libraries. The study concluded that AI holds a lot of prospects for the improvement of information services delivery in African academic libraries. Consequently, its adoption is a sinequanon to delivering robust library services in the Fourth Industrial Revolution (4IR).

A remarkable feature of the set of papers in this issue is the consistent pattern of tending to deal with non-linear situations as if they are linear that is shown by students widely differing in age and educational systems, and studying various topics within mathematics. Methodological issues about the difficulty of interpretation of student behavior, and the need to avoid attributing more rationality than is warranted, are raised. Suggestions about educational steps that could be taken to avoid an over-dependence on linearity are made.

Activities which may be used to investigate a variety of woven patterns are presented. Pattern predicting and analysis of different patterns are discussed. A Basic computer program for the weaving code is given. (CW)

The recognition of the creepage discharge development process of oil–paper insulation under AC–DC combined voltage is the basis for fault monitoring and diagnosis of converter transformers; however, only a few related studies are available. In this study, the AC–DC combined voltage with a ratio of 1:1 was used to develop a recognition method for the creepage discharge development process of an oil–paper insulation under a cylinder–plate electrode structure. First, the pulse current method was used to collect the discharge signals in the creepage discharge development process. Then, 24 characteristic parameters were extracted from four types of creepage discharge characteristic spectra after dimensionality reduction. Finally, based on the online sequential extreme learning machine (OS-ELM) algorithm, these characteristic parameters were used to recognize the development stage of the creepage discharge of the oil–paper insulation. The results showed that when the size of the sample training set used in the OS-ELM algorithm is close to the number of hidden layer neurons, a high recognition accuracy can be obtained, and the type of activation function has little influence on the recognition accuracy. Four stages of the creepage discharge development process were recognized using the OS-ELM algorithm; the trend was the same as that of the characteristic parameters of the entire creepage discharge development process. The recognition accuracy was 91.4%. The algorithm has a high computing speed and high accuracy and can train data in batches. Therefore, it can be widely used in the field of online monitoring and evaluation of electrical equipment status.

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]

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.

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.

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]

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]

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.

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]

Developing simple assays to identify and discriminate quinolones are highly desired for food safety, which remain a great challenge due to the interferences from food matrices and the minor variation on the molecular structure of massive quinolones. We proposed a room-temperature phosphorescence (RTP) assay for the quantitative detection and discrimination of quinolones, aided by a confinement and heating strategy. Quinolones were loaded into the porous framework of paper, which provided confinement effects on the molecular motions of quinolones. The heating process further removed the solvents, eliminating their quenching effects on excitons. These synergistic effects improved the RTP intensity and emission lifetime by 1144-fold and from nanoseconds to seconds, respectively. Three types of quinolones were quantitatively detected and discriminated through pattern recognition methods. The proposed assay showed excellent detection performance in complicated meat samples, aided by the delayed signal collecting of RTP. The reported results provided a clue to modulate the RTP properties of organic molecules, showing great potential for application prospect in food safety analysis, environmental and healthy monitoring. • A room-temperature phosphorescence assay for detection and discrimination of quinolones was proposed. • The phosphorescence performances were promoted by a confinement and heating strategy. • The phosphorescence intensity and emission lifetime were promoted by 1144 folds and from nanoseconds to several seconds. • Quinolones and their mixtures were discriminated through pattern recognition methods. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between early childhood music and maths. The emphasis is on children as intuitive pattern makers as they explore, categorise and imagine their worlds. We argue for the careful listening of childhood languages and reason that music and maths are expressive languages that young children use to investigate and experiment. This concept is based on Malaguzzi's idea of the hundred languages of children. Children's explorations of patterns, whether music, maths or other media will follow the principle of symmetry. We observe the intersectionality of language/s. Data are drawn from video and teaching materials prepared for early childhood teachers in-service training in Australia. Framing the discussion with Vygotskian and developmental theories we identify the patterns a child makes in a music discovery video and categorise the child's actions to combine both musical and mathematical concepts within the one action. The aim of the research is to revisit early childhood curriculum and pedagogy, rethinking sources of knowledge and the value of treating arts and sciences as being of equal importance as children discover and define their worlds.

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]

College students exploit information technology to cheat on papers and assignments, but for the most part university faculty employ few technological techniques to detect cheating. This paper reports on a trial of software for the detection of cheating in a large undergraduate survey class. The paper discusses the decision to adopt electronic means for screening student papers, the techniques used, the outcome, strategic concerns regarding deterrence versus detection of cheating, and the results of a survey of student attitudes about the experience. The paper advances the thesis that easily-adopted techniques not only close a sophistication gap associated with computerized cheating, but can place faculty in a stronger position than they have ever enjoyed historically with regard to the deterrence and detection of some classes of plagiarism. (Contains 3 figures and 5 notes.)

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.

The paper relic identification is a pending issue to be resolved in the field of cultural heritage. As we all known, heritage paper has significant importance in archaeological research. Nowadays, there are a variety of research methodologies focuses on the analysis of inks for dating documents. While the paper analysis attained little attention. This work is to explore the non-destructive application of ATR-FTIR technique in discrimination of paper relics. 15 types of paper spectra were collected by ATR-FTIR, which wavenumber range were range from 4000 to 650 cm−1. And the moving average smoothing and normalization was used for pretreatment analysis. Five different classification algorithms, principal component analysis-linear discriminant analysis (PCA-LDA), partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), least squares-support vector machine (LS-SVM), partial least squares-linear discriminant analysis (PLS-LDA) were selected to classify the types of paper. PLS-LDA and LS-SVM are effective techniques with 100% classification accuracy. PCA-LDA, PLS-DA and SIMCA give accuracy of 98.67%, 97.33% and 95.56%, respectively. The present experiment suggested that ATR-FTIR combining with chemometrics will be highly useful in paper identification of cultural heritage. Unlabelled Image • We compare the performances of the five algorithms in classifying 15 kinds of paper. • ATR-FTIR combine with chemometrics can classify multiple types of paper. • PLS-LDA and LS-SVM are proved to be effective technique. [ABSTRACT FROM AUTHOR]

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.