The controversy which surrounded Red Bull team principal Christian Horner earlier this year "had an impact" on their staff, according to a long-serving ex-F1 engineer.

I am 100% on-board with the London Overground being split into six different lines with individual names. It is infuriating to see there are delays on the Overground and have no clear idea of whether they might be on a...

In 2022, Google expanded support for Product structured data, enabling enhanced product experiences in Google Search. Then, in 2023 we added support for shipping and returns structured data. Today, we are adding structured data support for Product variants, allowing merchants to easily show more variations of the products they sell, and show shoppers more relevant, helpful results. Providing variant structured data will also complement and enhance merchant center feeds, including automated feeds.

La IA generativa está aquí para quedarse. Debemos conocer todo su potencial y usarla en nuestro trabajo diario, pero tomando las debidas precauciones. Nos confesamos creyentes en la Inteligencia Artificial. No está aquí para sustituirnos, sino para ayudarnos a hacer mejor nuestro trabajo: pero no...

La entrada Los riesgos (no tan evidentes) de la IA en la redacción de documentos jurídicos aparece primero en Traducción Jurídica.

In this paper we present an approach to improving navigability of a hierarchically structured Web content. The approach is based on an integration of a tagging module and adoption of tag clouds as a navigational aid for such content. The main idea of this approach is to apply tagging for the purpose of a better highlighting of cross-references between information items across the hierarchy. Although in principle tag clouds have the potential to support efficient navigation in tagging systems, recent research identified a number of limitations. In particular, applying tag clouds within pragmatic limits of a typical user interface leads to poor navigational performance as tag clouds are vulnerable to a so-called pagination effect. In this paper, a solution to the pagination problem is discussed, implemented as a part of an Austrian online encyclopedia called Austria-Forum, and analyzed. In addition, a simulation-based evaluation of the new algorithm has been conducted. The first evaluation results are quite promising, as the efficient navigational properties are restored.

One of the challenges in Dynamic Software Product Line (DSPL) is how to support the coordination of multiple instances of a service feature. In particular, there is a need for a decentralized decision-making capability that will be able to seamlessly integrate new instances of a service feature without an omniscient central controller. Because of the need for decentralization, we are investigating principles from self-organization in biological organisms. As an initial proof of concept, we have applied three bio-inspired techniques to a simple smart home scenario: quorum sensing based service activation, a firefly algorithm for synchronization, and a gossiping (epidemic) protocol for information dissemination. In this paper, we first explain why we selected those techniques using a set of motivating scenarios of a smart home and then describe our experiences in adopting them.

Faster flowing glaciers could help predict nearby volcanic activity  Phys.org

By Hasan Basri Cifci[1] In the world of 2014, the Commission of Facebook/WhatsApp merger case[2] concluded that integration and interoperation of Facebook and WhatsApp were unfeasible. However, Facebook integrated its three subsidiaries (WhatsApp, Instagram, and Facebook) under its brand in

In order to solve the problems of long prediction consumption time and many prediction iterations existing in traditional prediction models, an e-commerce growth prediction model based on grey Markov chain is proposed. The Scrapy crawler framework is used to collect a variety of e-commerce data from e-commerce websites, and the feedforward neural network model is used to clean the collected data. With the cleaned e-commerce data as the input vector and the e-commerce growth prediction results as the output vector, an e-commerce growth prediction model based on the grey Markov chain is built. The prediction model is improved by using the background value optimisation method. After training the model through the improved particle swarm optimisation algorithm, accurate e-commerce growth prediction results are obtained. The experimental results show that the maximum time consumption of e-commerce growth prediction of this model is only 0.032, and the number of iterations is small.

The problem of dispatching is key to management of shared bicycles. Considering the number of borrowing and returning events during the dispatching period, optimisation plans of shared bicycles dispatching are studied in this paper. Firstly, the dispatching model of shared bicycles is built, which regards the dispatching cost and lost demand as optimised objectives. Secondly, the solution algorithm is designed based on non-dominated Genetic Algorithm. Finally, a case is given to illustrate the application of the method. The research results show that the method proposed in the paper can get optimised dispatching plans, and the model considering borrowing and returning during dispatching period has better effects with a 39.3% decrease in lost demand.

It is inevitable for basketball players to have physical injury in sports. Reducing basketball injury is one of the main aims of the study of basketball. In view of this, this paper proposes a monocular vision and FMS injury prediction model for basketball players. Aiming at the limitations of traditional FMS testing methods, this study introduces intelligent machine learning methods. In this study, random forest algorithm was introduced into OpenPose network to improve model node occlusion, missed detection or false detection. In addition, to reduce the computational load of the network, the original OpenPose network was replaced by a lightweight OpenPose network. The experimental results show that the average processing time of the proposed model is about 90 ms, and the output video frame rate is 10 frames per second, which can meet the real-time requirements. This study analysed the students participating in the basketball league of the College of Sports Science of Nantong University, and the results confirmed the accuracy of the injury prediction of college basketball players based on FMS scores. It is hoped that this study can provide some reference for the research of injury prevention of basketball players.

Food quality is defined as a collection of properties that differentiate each unit and influences acceptability degree of food by users or consumers. Owing to the nature of food, food quality prediction is highly significant after specific periods of storage or before use by consumers. However, the accuracy is the major problem in the existing methods. Hence, this paper presents a BEFA_DRNN approach for accurate food quality prediction using dairy products. Firstly, input data is fed to data normalisation phase, which is performed by min-max normalisation. Thereafter, normalised data is given to feature fusion phase that is conducted employing DNN with Canberra distance. Then, fused data is subjected to data augmentation stage, which is carried out utilising oversampling technique. Finally, food quality prediction is done wherein milk is graded employing DRNN. The training of DRNN is executed by proposed BEFA that is a combination of BES and FA. Additionally, BEFA_DRNN obtained maximum accuracy, TPR and TNR values of 93.6%, 92.5% and 90.7%.

Elections hold immense significance in shaping the leadership of a nation or organisation, serving as a pivotal moment that influences the trajectory of the entity involved. Despite their centrality to modern democratic systems, elections face a significant hurdle: widespread mistrust in the electoral process. This pervasive lack of confidence poses a substantial threat to the democratic framework, even in the case of prominent democracies such as India and US, where inherent flaws persist in the electoral system. Issues such as vote rigging, electronic voting machine (EVM) hacking, election manipulation, and polling booth capturing remain prominent concerns within the current voting paradigm. Leveraging blockchain for electronic voting systems offers an effective solution to alleviate the prevailing apprehensions associated with e-voting. By incorporating blockchain into the electoral process, the integrity and security of the system could be significantly strengthened, addressing the current vulnerabilities and fostering trust in democratic elections.

Disease pandemics, once thought to be historical relics, are now again challenging healthcare systems globally. Of essential importance is sufficiently investing in preparedness and responsiveness, but approaches to such investments vary significantly by country. These variations provide excellent opportunities to learn and prepare for future pandemics. Therefore, we examine digital health infrastructure and the state of healthcare and public health services in relation to pandemic preparedness and responsiveness. The research focuses on two countries: South Africa and the USA. We apply case analysis at the country level toward understanding digital health policy preparedness and responsiveness to a pandemic. We also provide a teaching note at the end for use in guiding students in this area to formulate digital health policy recommendations for pandemic preparedness and responsiveness.

The research uses a machine learning approach to appraising the validity of customer aptness for a loan. Banks and non-banking financial companies (NBFC) face significant non-performing assets (NPAs) threats because of the non-payment of loans. In this study, the data is collected from Kaggle and tested using various machine learning models to determine if the borrower can repay its loan. In addition, we analysed the performance of the models [K-nearest neighbours (K-NN), logistic regression, support vector machines (SVM), decision tree, naive Bayes and neural networks]. The purpose is to support decisions that are based not on subjective aspects but objective data analysis. This work aims to analyse how objective factors influence borrowers to default loans, identify the leading causes contributing to a borrower's default loan. The results show that the decision tree classifier gives the best result, with a recall rate of 0.0885 and a false- negative rate of 5.4%.

Aim/Purpose: Electronic examinations have some inherent problems. Students have expressed negative opinions about electronic examinations (e-examinations) due to a fear of, or unfamiliarity with, the technology of assessment, and a lack of knowledge about the methods of e-examinations. Background: Electronic examinations are now a viable alternative method of assessing student learning. They provide freedom of choice, in terms of the location of the examination, and can provide immediate feedback; students and institutions can be assured of the integrity of knowledge testing. This in turn motivates students to strive for deeper learning and better results, in a higher quality and more rigorous educational process. Methodology : This paper compares an e-examination system at FUT Minna Nigeria with one in Australia, at the University of Tasmania, using case study analysis. The functions supported, or inhibited, by each of the two e-examination systems, with different approaches to question types, cohort size, technology used, and security features, are compared. Contribution: The researchers’ aim is to assist stakeholders (including lecturers, invigilators, candidates, computer instructors, and server operators) to identify ways of improving the process. The relative convenience for students, administrators, and lecturer/assessors and the reliability and security of the two systems are considered. Challenges in conducting e-examinations in both countries are revealed by juxtaposing the systems. The authors propose ways of developing more effective e-examination systems. Findings: The comparison of the two institutions in Nigeria and Australia shows e-examinations have been implemented for the purpose of selecting students for university courses, and for their assessment once enrolled. In Nigeria, there is widespread systemic adoption for university entrance merit selection. In Australia this has been limited to one subject in one state, rather than being adopted nationally. Within undergraduate courses, the Nigerian scenario is quite extensive; in Australia this adoption has been slower, but has penetrated a wide variety of disciplines. Recommendations for Practitioners: Assessment integrity and equipment reliability were common issues across the two case studies, although the delivery of e-examinations is different in each country. As with any procedural process, a particular solution is only as good as its weakest attribute. Technical differences highlight the link between e-examination system approaches and pedagogical implications. It is clear that social, cultural, and environmental factors affect the success of e-examinations. For example, an interrupted electrical power supply and limited technical know-how are two of the challenges affecting the conduct of e-examinations in Nigeria. In Tasmania, the challenge with the “bring your own device” (BYOD) is to make the system operate on an increasing variety of user equipment, including tablets. Recommendation for Researchers: The comparisons between the two universities indicate there will be a productive convergence of the approaches in future. One key proposal, which arose from the analysis of the existing e-examination systems in Nigeria and Australia, is to design a form of “live” operating system that is deployable over the Internet. This method would use public key cryptography for lecturers to encrypt their questions online. Impact on Society : If institutions are to transition to e-examinations, one way of facilitating this move is by using computers to imitate other assessment techniques. However, higher order thinking is usually demonstrated through open-ended or creative tasks. In this respect the Australian system shows promise by providing the same full operating system and software application suite to all candidates, thereby supporting assessment of such creative higher order thinking. The two cases illustrate the potential tension between “online” or networked reticulation of questions and answers, as opposed to “offline” methods. Future Research: A future design proposition is a web-based strategy for a virtual machine, which is launched into candidates’ computers at the start of each e-examination. The new system is a form of BYOD externally booted e-examination (as in Australia) that is deployable over the Internet with encryption and decryption features using public key cryptography (Nigeria). This will allow lecturers to encrypt their questions and post them online while the questions are decrypted by the administrator or students are given the key. The system will support both objective and open-ended questions (possibly essays and creative design tasks). The authors believe this can re-define e-examinations as the “gold standard” of assessment.

Aim/Purpose: The dynamic nature of the information systems (IS) field presents educators with the perpetual challenge of keeping course offerings current and relevant. This paper describes the process at a College of Business (COB) to redesign the introductory IS course to better prepare students for advanced business classes and equip them with interdisciplinary knowledge and skills demanded in today’s workplace. Background: The course was previously in the Computer Science (CSC) Department, itself within the COB. However, an administrative restructuring resulted in the CSC department’s removal from the COB and left the core course in limbo. Methodology: This paper presents a case study using focus groups with students, faculty, and advisory council members to assess the value of the traditional introductory course. A survey was distributed to students after implementation of the newly developed course to assess the reception of the course. Contribution: This paper provides an outline of the decision-making process leading to the course redesign of the introductory IS course, including the context and the process of a new course development. Practical suggestions for implementing and teaching an introductory IS course in a business school are given. Findings: Focus group assessment revealed that stakeholders rated the existing introductory IS course of minimal value as students progressed through the COB program, and even less upon entering the workforce. The findings indicated a complete overhaul of the course was required. Recommendations for Practitioners: The subject of technology sometimes requires more than a simple update to the curriculum. When signs point to the need for a complete overhaul, this paper gives practical guidance supplemented with relevant literature for other academicians to follow. Recommendation for Researchers: Students are faced with increasing pressure to be proficient with the latest technology, in both the classroom where educators are trying to prepare them for the modern workplace, as well as the organization which faces an even greater pressure to leverage the latest technology. The newly designed introductory IS course provides students, and eventually organizations, a better measure of this proficiency. Future Research: Future research on the efficacy of this new course design should include longitudinal data to determine the impact on graduates, and eventually the assessment of those graduates’ performance in the workplace.

Aim/Purpose: This study reports the outcome of how a first pilot semester introductory programming course was designed to provide tangible evidence in support of the concept of Student Ownership of Learning (SOL) and how the outcomes of this programming course facilitate effective student learning. Background: Many instructors want to create or redesign their courses to strengthen the relationship between teaching and learning; however, the researchers of this study believe that the concept of Student Ownership of Learning (SOL) connects to student engagement and achievement in the classroom setting. The researchers redesigned the introductory programming course to include valuable teaching methods to increase Student Ownership of Learning and constructive approaches such as making students design an authentic mobile app project as individuals, partners, or within teams. The high quality of students’ projects positioned them as consultants to the university IT department. Methodology: This paper employs a case study design to construct a qualitative research method as it relates to the phenomenon of the study’s goals and lived experiences of students in the redesigned introductory programming course. The redesigned course was marketed to students as a new course with detailed description and elements that were different from the traditional computer science introductory programming course requirement. The redesigned introductory programming course was offered in two sections: one section with 14 registered students and the other section with 15 registered students. One faculty member instructed both sections of the course. A total of 29 students signed up for the newly redesigned introductory programming course, more than in previous semesters, but two students dropped out within the first two weeks of the redesigned course making a total of 27 students. The redesigned coursework was divided into two parts of the semester. The first part of the semester detailed description and elements of the coursework including a redesigned approach with preparation for class, a quiz, and doing homework in class, which gives students control of decisions whenever possible; and working with each other, either with a partner or in a team. The second part of the semester focuses on students designing a non-trivial working mobile app and presenting their developing mobile app at a significant public competition at the end of the semester. Students developed significantly complex mobile apps and incorporated more complex functionality in their apps. Both Management Information System (MIS) major students and Computer Science major students were in the same course despite the fact that MIS students had never taken a programming course before; however, the Computer Science students had taken at least one course of programming. Contribution: This study provides a practical guide for faculty members in Information Technology programs and other faculty members in non-Computer Science programs to create or redesign an introductory course that increases student engagement and achievement in the classroom based on the concept of Student Ownership of Learning (SOL). This study also deepens the discussion in curriculum and instruction on the value to explore issues that departments or programs should consider when establishing coursework or academic programs. Findings: This study found two goals evidently in support to increase Student Ownership of Learning (SOL). The first goal (Increase their ownership of learning SOL) showed that students found value in the course contents and took control of their learning; therefore, the faculty no longer had to point out how important different programming concepts were. The students recognized their own learning gap and were excited when shown a programming concept that addressed the gap. For example, student comments were met with “boy, we can really use this in our app” instead of comments about how complex they were. The coursework produced a desired outcome for students as they would get the knowledge needed to make the best app that they could. The second goal (Develop a positive attitude toward the course) showed positive results as students developed a more positive attitude towards the course. Student actions in the classroom strongly reflected a positive attitude. Attendance was almost 100% during the semester even though no points for attendance were given. Further evidence of Student Ownership of Learning and self-identity was students’ extensive use of the terminology and concept of the course when talking to others, especially during the public competition. Students were also incorporating their learning into their identities. For example, teams became known by their app such as the Game team, the Recipe team, and the Parking team. One team even made team t-shirts. Another exciting reflection of the Student Ownership of Learning which occurred was the learning students did by themselves. Recommendations for Practitioners: Practitioners can share best practices with faculty in different departments, programs, universities, and educational consultants to cultivate the best solution for Student Ownership of Learning based on student engagement and achievement in the classroom setting. Recommendation for Researchers: Researchers can explore different perspectives with scholars and practitioners in various disciplinary fields of study to create or redesign courses and programs to reflect Student Ownership of Learning (SOL). Impact on Society: Student Ownership of Learning is relevant for faculty and universities to incorporate in the creation or redesigning of coursework in academic programs. Readers can gain an understanding that student engagement and achievement are two important drivers of Student Ownership of Learning (SOL) in the classroom setting. Future Research: Practitioners and researchers could follow-up in the future with a study to provide more understanding and updated research information from different research samples and hypotheses on Student Ownership of Learning (SOL).

Aim/Purpose: One of the main objectives of higher education institutions is to provide a high-quality education to their students and reduce dropout rates. This can be achieved by predicting students’ academic achievement early using Educational Data Mining (EDM). This study aims to predict students’ final grades and identify honorary students at an early stage. Background: EDM research has emerged as an exciting research area, which can unfold valuable knowledge from educational databases for many purposes, such as identifying the dropouts and students who need special attention and discovering honorary students for allocating scholarships. Methodology: In this work, we have collected 300 undergraduate students’ records from three departments of a Computer and Information Science College at a university located in Saudi Arabia. We compared the performance of six data mining methods in predicting academic achievement. Those methods are C4.5, Simple CART, LADTree, Naïve Bayes, Bayes Net with ADTree, and Random Forest. Contribution: We tested the significance of correlation attribute predictors using four different methods. We found 9 out of 18 proposed features with a significant correlation for predicting students’ academic achievement after their 4th semester. Those features are student GPA during the first four semesters, the number of failed courses during the first four semesters, and the grades of three core courses, i.e., database fundamentals, programming language (1), and computer network fundamentals. Findings: The empirical results show the following: (i) the main features that can predict students’ academic achievement are the student GPA during the first four semesters, the number of failed courses during the first four semesters, and the grades of three core courses; (ii) Naïve Bayes classifier performed better than Tree-based Models in predicting students’ academic achievement in general, however, Random Forest outperformed Naïve Bayes in predicting honorary students; (iii) English language skills do not play an essential role in students’ success at the college of Computer and Information Sciences; and (iv) studying an orientation year does not contribute to students’ success. Recommendations for Practitioners: We would recommend instructors to consider using EDM in predicting students’ academic achievement and benefit from that in customizing students’ learning experience based on their different needs. Recommendation for Researchers: We would highly endorse that researchers apply more EDM studies across various universities and compare between them. For example, future research could investigate the effects of offering tutoring sessions for students who fail core courses in their first semesters, examine the role of language skills in social science programs, and examine the role of the orientation year in other programs. Impact on Society: The prediction of academic performance can help both teachers and students in many ways. It also enables the early discovery of honorary students. Thus, well-deserved opportunities can be offered; for example, scholarships, internships, and workshops. It can also help identify students who require special attention to take an appropriate intervention at the earliest stage possible. Moreover, instructors can be aware of each student’s capability and customize the teaching tasks based on students’ needs. Future Research: For future work, the experiment can be repeated with a larger dataset. It could also be extended with more distinctive attributes to reach more accurate results that are useful for improving the students’ learning outcomes. Moreover, experiments could be done using other data mining algorithms to get a broader approach and more valuable and accurate outputs.

Aim/Purpose: This exploratory qualitative case study examines the perceptions of high-school learners of English regarding a pedagogical intervention involving progressive reduction of captions (full, sentence-level, keyword captions, and no-captions) in enhancing language learning. Background: Recognizing the limitations of caption usage in fostering independent listening comprehension in non-captioned environments, this research builds upon and extends the foundational work of Vanderplank (2016), who highlighted the necessity of a comprehensive blend of tasks, strategies, focused viewing, and the need to actively engage language learners in watching captioned materials. Methodology: Using a qualitative research design, the participants were exposed to authentic video texts in a five-week listening course. Participants completed an entry survey, and upon interaction with each captioning type, they wrote individual reflections and participated in focus group sessions. This methodological approach allowed for an in-depth exploration of learners’ experiences across different captioning scenarios, providing a nuanced understanding of the pedagogical intervention’s impact on their perceived language development process. Contribution: By bridging the research-practice gap, our study offers valuable insights into designing pedagogical interventions that reduce caption dependence, thereby preparing language learners for success in real-world, caption-free listening scenarios. Findings: Our findings show that learners not only appreciate the varied captioning approaches for their role in supporting text comprehension, vocabulary acquisition, pronunciation, and on-task focus but also for facilitating the integration of new linguistic knowledge with existing background knowledge. Crucially, our study uncovers a positive reception towards the gradual shift from fully captioned to uncaptioned materials, highlighting a stepwise reduction of caption dependence as instrumental in boosting learners’ confidence and sense of achievement in mastering L2 listening skills. Recommendations for Practitioners: The implications of our findings are threefold: addressing input selection, task design orchestration, and reflective practices. We advocate for a deliberate selection of input that resonates with learners’ interests and contextual realities alongside task designs that progressively reduce caption reliance and encourage active learner engagement and collaborative learning opportunities. Furthermore, our study underscores the importance of reflective practices in enabling learners to articulate their learning preferences and strategies, thereby fostering a more personalized and effective language learning experience. Recommendation for Researchers: Listening comprehension is a complex process that can be clearly influenced by the input, the task, and/or the learner characteristics. Comparative studies may struggle to control and account for all these variables, making it challenging to attribute observed differences solely to caption reduction. Impact on Society: This research responds to the call for innovative teaching practices in language education. It sets the stage for future inquiries into the nuanced dynamics of caption usage in language learning, advocating for a more learner-centered and adaptive approach. Future Research: Longitudinal quantitative studies that measure comprehension as captions support is gradually reduced (full, partial, and keyword) are strongly needed. Other studies could examine a range of individual differences (working memory capacity, age, levels of engagement, and language background) when reducing caption support. Future research could also examine captions with students with learning difficulties and/or disabilities.

In the construction sector, moulding machines are crucial in producing concrete products, yet changing their mould can pose challenges for some businesses. This paper presents a case study aimed at reducing the setup time of HESS RH 600 moulding machine. Four alternatives are proposed and evaluated to achieve this goal. The first alternative involves converting internal to external activities, while the subsequent alternatives aim to improve the basic solution. These include building a canopy near the machine (alternative 2), installing an air reservoir (alternative 3), and a comprehensive approach involving building the canopy, installing the air reservoir, and adding a new forklift to facilitate the machine setup process (alternative 4). The analytic hierarchy process (AHP) heuristic method is used to select the best alternative solution based on prespecified criteria. It is found that the application of the single-minute exchange of die (SMED) solution without any further improvement is the most favourable.

Green entrepreneurial intention (GEI) in the agriculture sector signifies agricultural businesses' strong determination to embrace environmentally sustainable practices and innovative eco-friendly approaches. To understand farmers' GEI, the research applied theories of entrepreneurial events and institutional theory. A model was developed and empirically validated through structural equation modelling (SEM). A questionnaire survey was used to collect data from 211 farmers from the southern region of India. Findings revealed that perceived desirability, perceived feasibility, mimetic pressure, and entrepreneurial mindset positively influenced GEI. Entrepreneurial mindset played a mediating role in strengthening the farmers GEI. This study contributes to understanding GEI in agriculture and informs strategies for promoting sustainable farming practices.

In India, 70% of the Indian population is dependent on agriculture, yet agriculture generates only 13% of the country's gross domestic product. Several factors contribute to high levels of stress among farmers in India, such as increased input costs, draughts, and reduced revenues. The problem lies in the absence of an integrated farm advisory system. A farmer needs help to bridge this information gap, and they need it early in the crop's lifecycle to prevent it from being destroyed by pests or diseases. This research involves developing deep learning algorithms such as <i>ResNet18</i> and <i>DenseNet121</i> to help farmers diagnose crop diseases earlier and take corrective actions. By using deep learning techniques to detect these crop diseases with images farmers can scan or click with their smartphones, we can fill in the knowledge gap. To facilitate the use of the models by farmers, they are deployed in Android-based smartphones.

This paper proposes a method for predicting college students' performance based on learning behaviour data mining. The method addresses the issue of limited sample size affecting prediction accuracy. It utilises the K-means clustering algorithm to mine learning behaviour data and employs a density-based approach to determine optimal clustering centres, which are then output as the results of the clustering process. These clustering results are used as input for an attention encoder-decoder model to extract features from the learning behaviour sequence, incorporating an attention mechanism, sequence feature generator, and decoder. The characteristics derived from the learning behaviour sequence are then used to establish a prediction model for college students' performance, employing support vector regression. Experimental results demonstrate that this method accurately predicts students' performance with a relative error of less than 4% by leveraging the results obtained from learning behaviour data mining.

This paper analyses the adoption of learning analytics to predict at-risk students. A total of 233 research articles between 2004 and 2023 were collected from Scopus for this study. They were analysed in terms of the relevant types and sources of data, targets of prediction, learning analytics methods, and performance metrics. The results show that data related to students' academic performance, socio-demographics, and learning behaviours have been commonly collected. Most studies have addressed the identification of students who have a higher chance of poor academic performance or dropping out of their courses. Decision trees, random forests, and artificial neural networks are the most frequently used techniques for prediction, with ensemble methods gaining popularity in recent years. Classification accuracy, recall, sensitivity, and true positive rate are commonly used as performance metrics for evaluation. The results reveal the potential of learning analytics for informing timely and evidence-based support for at-risk students.

Using information criteria is a common method for making a decision about which model to use for forecasting. There are many different methods for evaluating forecasting models, such as MAE, RMSE, MAPE, and Theil-U, among others. After the creation of AIC, AICc, HQ, BIC, and BICc, the two criteria that have become the most popular and commonly utilised are Bayesian IC and Akaike's IC. In this investigation, we are innovative in our use of exponential weighting to get the log-likelihood of the information criteria for model selection, which means that we propose assigning greater weight to more recent data in order to reflect their increased precision. All research data is from the major stock markets' daily observations, which include the USA (GSPC, DJI), Europe (FTSE 100, AEX, and FCHI), and Asia (Nikkei).

In the realm of digital data, vendor-supplied cloud systems will still leave the user with responsibility for curation of digital data. Some of the very tasks users thought they were delegating to the cloud vendor may be a requirement for users after all. For example, cloud vendors most often require that users maintain archival copies. Beyond the better known vendor cloud model, we examine curation in two other models: inhouse clouds, and what we call "open" clouds—which are neither inhouse nor vendor. In open clouds, users come aboard as participants or partners—for example, by invitation. In open cloud systems users can develop their own software and data management, control access, and purchase their own hardware while running securely in the cloud environment. To do so will still require working within the rules of the cloud system, but in some open cloud systems those restrictions and limitations can be walked around easily with surprisingly little loss of freedom. It is in this context that REDDnet (Research and Education Data Depot network) is presented as the place where the Texas Tech University (TTU)) Libraries have been conducting research on long-term digital archival storage. The REDDnet network by year's end will be at 1.2 petabytes (PB) with an additional 1.4 PB for a related project (Compact Muon Soleniod Heavy Ion [CMS-HI]); additionally there are over 200 TB of tape storage. These numbers exclude any disk space which TTU will be purchasing during the year. National Science Foundation (NSF) funding covering REDDnet and CMS-HI was in excess of $850,000 with $850,000 earmarked toward REDDnet. In the terminology we used above, REDDnet is an open cloud system that invited TTU Libraries to participate. This means that we run software which fits the REDDnet structure. We are beginning to complete the final design of our system, and starting to move into the first stages of construction. And we have made a decision to move forward and purchase one-half petabyte of disk storage in the initial phase. The concerns, deliberations and testing are presented here along with our initial approach.

Chempound is a new generation repository architecture based on RDF, semantic dictionaries and linked data. It has been developed to hold any type of chemical object expressible in CML and is exemplified by crystallographic experiments and computational chemistry calculations. In both examples, the repository can hold >50k entries which can be searched by SPARQL endpoints and pre-indexing of key fields. The Chempound architecture is general and adaptable to other fields of data-rich science.

Numerous Internet of Things (IoT) applications require brain-empowered intelligence. This necessity has caused the emergence of a new area called cognitive IoT (CIoT). Reasoning, planning, and selection are typically involved in decision-making within the network bandwidth limit. Consequently, data minimisation is needed. Therefore, this research proposes a novel technique to extract conscious data from a massive dataset. First, it groups the data using k-means clustering, and the entropy is computed for each cluster. The most prominent cluster is then determined by selecting the cluster with the highest entropy. Subsequently, it transforms each cluster element into an informative element. The most informative data is chosen from the most prominent cluster that represents the whole massive data, which is further used for intelligent decision-making. The experimental evaluation is conducted on the 21.25 years of environmental dataset, revealing that the proposed method is efficient over competing approaches.

Aim/Purpose: Although cassava is one of the crops that can be grown during the dry season in Northeastern Thailand, most farmers in the region do not know whether the crop can grow in their specific areas because the available agriculture planning guideline provides only a generic list of dry-season crops that can be grown in the whole region. The purpose of this research is to develop a predictive model that can be used to predict suitable areas for growing cassava in Northeastern Thailand during the dry season. Background: This paper develops a decision support system that can be used by farmers to assist them determine if cassava can be successfully grown in their specific areas. Methodology: This study uses satellite imagery and data on land characteristics to develop a machine learning model for predicting suitable areas for growing cassava in Thailand’s Nakhon-Phanom province. Contribution: This research contributes to the body of knowledge by developing a novel model for predicting suitable areas for growing cassava. Findings: This study identified elevation and Ferric Acrisols (Af) soil as the two most important features for predicting the best-suited areas for growing cassava in Nakhon-Phanom province, Thailand. The two-class boosted decision tree algorithm performs best when compared with other algorithms. The model achieved an accuracy of .886, and .746 F1-score. Recommendations for Practitioners: Farmers and agricultural extension agents will use the decision support system developed in this study to identify specific areas that are suitable for growing cassava in Nakhon-Phanom province, Thailand Recommendation for Researchers: To improve the predictive accuracy of the model developed in this study, more land and crop characteristics data should be incorporated during model development. The ground truth data for areas growing cassava should also be collected for a longer period to provide a more accurate sample of the areas that are suitable for cassava growing. Impact on Society: The use of machine learning for the development of new farming systems will enable farmers to produce more food throughout the year to feed the world’s growing population. Future Research: Further studies should be carried out to map other suitable areas for growing dry-season crops and to develop decision support systems for those crops.

Aim/Purpose: Any system that aims to address the task of modeling social media communication need to deal with the usage of emojis. Efficient prediction of the most likely emoji given the text of a message may help to improve different NLP tasks. Background: We explore two tasks: emoji identification and emoji prediction. While emoji prediction is a classification task of predicting the emojis that appear in a given text message, emoji identification is the complementary preceding task of determining if a given text message includes emojies. Methodology: We adopt a supervised Machine Learning (ML) approach. We compare two text representation approaches, i.e., n-grams and character n-grams and analyze the contribution of additional metadata features to the classification. Contribution: The task of emoji identification is novel. We extend the definition of the emoji prediction task by allowing to use not only the textual content but also meta-data analysis. Findings: Metadata improve the classification accuracy in the task of emoji identification. In the task of emoji prediction it is better to apply feature selection. Recommendations for Practitioners: In many of the cases the classifier decision seems fitter to the comment content than the emoji that was chosen by the commentator. The classifier may be useful for emoji suggestion. Recommendation for Researchers: Explore character-based representations rather than word-based representations in the case of morphologically rich languages. Impact on Society: Improve the modeling of social media communication. Future Research: We plan to address the multi-label setting of the emoji prediction task and to investigate the deep learning approach for both of our classification tasks

Aim/Purpose: This study aims to answer the research question titled What are the competencies required for the Business Process Analyst (BPA) role in organizations with ERP systems in Kenya. Through 4 hypotheses, this study focuses on two specific aspects: (1) Enhancing BPM Maturity and (2) ERP implementation. Background: The emergence of complex systems and complex processes in organizations in Kenya has given rise to the need to understand the BPM domain as well as a need to analyze the new roles within organizational environments that drive BPM initiatives. The most notable role in this domain is the BPA. Furthermore, many organizations in Kenya and across Africa are making significant investments in ERP systems. Organizations, therefore, need to understand the BPA role for ERP systems implementation projects. Methodology: This study uses a sequential mixed methods approach analyzing quantitative survey data followed by the analysis of qualitative interview data. Contribution: The main contribution of this study is a description of competencies that are critical for the BPA in Kenya both in terms of enhancing BPM maturity and for driving ERP systems implementations. In addition, this study sheds light on critical BPA competencies that are perceived to be undervalued in the Kenyan context. Findings: Findings show that business process orchestration competencies are important for driving BPM maturity and for ERP systems implementations. This study found that business process elicitation, business analysis, business process improvement and a holistic overview of business thinking are often overlooked as critical competencies for BPAs but are nevertheless critical for building the BPA practitioner. Recommendations for Practitioners: From this study, practitioners such as top managers and BPAs can be enlightened on the specific competencies that require focus when carrying out BPM and when implementing ERP systems projects. Future Research: The next step is to investigate the interventions that organizations implement to build their BPA competencies. The main aim of this would be to describe those interventions that impact the requisite BPA competencies especially those competencies that were seen to be undervalued within the Kenyan context.

Aim/Purpose. In this study, we examined, from the perspective of the participants, aspects of information and communications technology (ICT) and resilience, comparing first-generation students in higher education with students whose parents had higher education. Methodology. We examined self-image, motivation, happiness, and the use of ICT. This was a quantitative study. Respondents answered a questionnaire that contained open and closed questions. The sample included 307 students from academic institutions in Israel between the ages of 18 and 64. Findings. The findings were grouped into four clusters: (a) second-generation students under the age of 25 years, members of Generation Z; (b) second-generation students over the age of 25; (c) first-generation students over the age of 25 years (the largest group in the sample), mostly members of the Generation Y; and (d) first-generation students under the age of 25. We found consistent differences on all scales between the group of first-generation students over the age of 25 years and those in the other groups. The research findings indicate that the group with the highest resilience was students who were the first generation acquiring higher education and were over 25, mostly members of the Y generation. Impact on Society. This research allows an instructive look at Generation Y and Generation Z and the academic abilities of this generation. Future Research. Future studies should examine the correlation between a sense of resilience (which was examined in this study) and academic achievement (which was not).