Search

Due to the energy turnaround in German politics, it is necessary to integrate more and more wind and solar energy into the existing energy system. In particular, power generation is changing from a previously centralized to a decentralized structure, which also has consequences for requirements for safe, reliable and efficient grid operation. Generation and utilization characteristics will become more dynamic and flexible in the future. Increased demand for the measurement, control and automation of voltage and electricity will require the further development of grid infrastructure, the expansion of storage capacity and the introduction of information and communication technology (ICT)-based energy management (Appelrath et al., 2012). Utilities therefore need to know what migration paths into the future of a smart energy grid could look like. And this against the background of which technologies have to be installed, in which order this can happen and which dependencies have to be considered. The aim is to create roadmaps to the modern Smart Grid for two case studies. Within the framework of the Green Access project (Projekt Green Access, 2019), and (Flore & Kumm, 2020), a maturity model and, based on this, migration paths were developed for this purpose, which describe a path from one development stage to the next. It describes the necessary development steps that have to be implemented in the context of migration paths. These migration paths have been developed for a specially designed maturity model and describe the technologies used to move from one maturity level to the next. Finally, there will be a comparison of the developed migration paths of the two case studies., (© 2020 Published by Elsevier Ltd.)

The metaverse concept has been evolving from static, pre-rendered virtual environments to a new frontier: the real-time metaverse. This survey paper explores the emerging field of real-time metaverse technologies, which enable the continuous integration of dynamic, real-world data into immersive virtual environments. We examine the key technologies driving this evolution, including advanced sensor systems (LiDAR, radar, cameras), artificial intelligence (AI) models for data interpretation, fast data fusion algorithms, and edge computing with 5G networks for low-latency data transmission. This paper reveals how these technologies are orchestrated to achieve near-instantaneous synchronization between physical and virtual worlds, a defining characteristic that distinguishes the real-time metaverse from its traditional counterparts. The survey provides a comprehensive insight into the technical challenges and discusses solutions to realize responsive dynamic virtual environments. The potential applications and impact of real-time metaverse technologies across various fields are considered, including live entertainment, remote collaboration, dynamic simulations, and urban planning with digital twins. By synthesizing current research and identifying future directions, this survey provides a foundation for understanding and advancing the rapidly evolving landscape of real-time metaverse technologies, contributing to the growing body of knowledge on immersive digital experiences and setting the stage for further innovations in the Metaverse transformative field. [ABSTRACT FROM AUTHOR]

Financial fraud negatively impacts organizational administrative processes, particularly affecting owners and/or investors seeking to maximize their profits. Addressing this issue, this study presents a literature review on financial fraud detection through machine learning techniques. The PRISMA and Kitchenham methods were applied, and 104 articles published between 2012 and 2023 were examined. These articles were selected based on predefined inclusion and exclusion criteria and were obtained from databases such as Scopus, IEEE Xplore, Taylor & Francis, SAGE, and ScienceDirect. These selected articles, along with the contributions of authors, sources, countries, trends, and datasets used in the experiments, were used to detect financial fraud and its existing types. Machine learning models and metrics were used to assess performance. The analysis indicated a trend toward using real datasets. Notably, credit card fraud detection models are the most widely used for detecting credit card loan fraud. The information obtained by different authors was acquired from the stock exchanges of China, Canada, the United States, Taiwan, and Tehran, among other countries. Furthermore, the usage of synthetic data has been low (less than 7% of the employed datasets). Among the leading contributors to the studies, China, India, Saudi Arabia, and Canada remain prominent, whereas Latin American countries have few related publications. [ABSTRACT FROM AUTHOR]

As the digital transformation accelerates in our society, open data are being increasingly recognized as a key resource for digital innovation in the public sector. This study explores the following two research questions: (1) Can a machine learning approach be appropriately used for measuring and evaluating open data utilization? (2) Should different machine learning models be applied for measuring open data utilization depending on open data attributes (field and usage type)? This study used single-model (random forest, XGBoost, LightGBM, CatBoost) and multi-model (stacking ensemble) machine learning methods. A key finding is that the best-performing models differed depending on open data attributes (field and type of use). The applicability of the machine learning approach for measuring and evaluating open data utilization in advance was also confirmed. This study contributes to open data utilization and to the application of its intrinsic value to society. [ABSTRACT FROM AUTHOR]

The further promotion of cycling is a key component for each city to reach its sustainability goals. To make decisions to improve comfort or safety for cyclists, the amount of motorized traffic should be taken into account. Therefore, traffic data play a crucial role not only in the construction of roads but also in cycling planning. This data source provides insights essential for road infrastructure development and optimizing various modes of transportation, such as bike paths. However, processing municipal traffic data becomes a challenge when stationary traffic-counting stations lack geo-referencing in relational databases. In this case, the locations of traffic counters are solely displayed on a PDF-based site map without inherent geo-referencing, and the geo-coordinates are not stored in any relational database. The absence of geo-references poses a significant hurdle for traffic-planning experts in decision-making processes. Hence, this study aims to address this issue by finding a suitable approach to extract the geo-coordinates from the site maps. Several potential solutions are discussed and compared in terms of time dimension, usability, extensibility, error treatment and the accuracy of results. Leveraging the open-source tool QGIS, geo-coordinates may be successfully extracted from the PDF-based site maps, resulting in the creation of a GeoTIFF file incorporating coordinates and the rotated site map. Geo-coordinates can then be derived from the GeoTIFF files using x and y coordinates, computed through the rotation matrix formula. Over 1400 measurement locations may be extracted based on the preferred approach, facilitating more informed decision-making in traffic planning. [ABSTRACT FROM AUTHOR]

The morphological complexity of urban environments results in a high spatial and temporal variability of the urban microclimate. The consequent demand for high-resolution atmospheric data remains a challenge for atmospheric research and operational application. The recent widespread availability and increasing adoption of low-cost mobile sensing offer the opportunity to integrate observations from conventional monitoring networks with microclimatic and air pollution data at a finer spatial and temporal scale. So far, the relatively low quality of the measurements and outdoor performance compared to conventional instrumentation has discouraged the full deployment of mobile sensors for routine monitoring. The present study addresses the performance of a commercial mobile sensor, the MeteoTracker (IoTopon Srl), recently launched on the market to quantify the microclimatic characteristics of the outdoor environment. The sensor follows the philosophy of the Internet of Things technology, being low cost, having an automatic data flow via personal smartphones and online data sharing, supporting user-friendly software, and having the potential to be deployed in large quantities. In this paper, the outdoor performance is evaluated through tests aimed at quantifying (i) the intra-sensor variability under similar atmospheric conditions and (ii) the outdoor accuracy compared to a reference weather station under sub-optimal (in a fixed location) and optimal (mobile) sensor usage. Data-driven corrections are developed and successfully applied to improve the MeteoTracker data quality. In particular, a recursive method for the simultaneous improvement of relative humidity, dew point, and humidex index proves to be crucial for increasing the data quality. The results mark an intra-sensor variability of approximately ± 0.5 °C for air temperature and ± 1.2 % for the corrected relative humidity, both of which are within the declared sensor accuracy. The sensor captures the same atmospheric variability as the reference sensor during both fixed and mobile tests, showing positive biases (overestimation) for both variables. Through the mobile test, the outdoor accuracy is observed to be between ± 0.3 to ± 0.5 °C for air temperature and between ± 3 % and ± 5 % for the relative humidity, ranking the MeteoTracker in the real accuracy range of similar commercial sensors from the literature and making it a valid solution for atmospheric monitoring. [ABSTRACT FROM AUTHOR]

Purpose: The research aims at examining educators' perceptions, attitudes and behavioral intentions toward learning analytics (LA) and the role of self-instruction within the proposed model for LA adoption. Design/methodology/approach: A quantitative approach is utilized in which a questionnaire is designed as a tool for data collection and partial least squares structural equation modeling (PLS-SEM) is used for data analysis and model testing. Findings: Results show that performance expectancy and effort expectancy have a significant effect on educators' attitudes, which in turn significantly affect educators' behavioral intentions. Self-instruction mediates the relationship between educators' attitudes and behavioral intentions. The attitude towards LA mediates the relationship between LA performance expectancy and educators' self-instruction. The research model explains 54% of the variance in learning analysis adoption. Originality/value: Findings open a path for research on pedagogical factors affecting LA adoption and guide education managers toward facilitating LA adoption. The tested model contributes to LA and teaching and learning literature by highlighting the role of educators' self-instruction in LA adoption. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, this paper explores the vital role of advanced technologies, including Cyber–Physical Systems (CPS), Big Data, Internet of Things (IoT), digital twins, and Artificial Intelligence (AI), in enhancing data valorization and management within industries. These technologies are integral to addressing the challenges of producing highly customized products in mass, necessitating the complete digitization and integration of information technology (IT) and operational technology (OT) for flexible and automated manufacturing processes. The paper emphasizes the importance of interoperability through Service-Oriented Architectures (SOA), Manufacturing-as-a-Service (MaaS), and Resource-as-a-Service (RaaS) to achieve seamless integration across systems, which is critical for the Industry 4.0 vision of a fully interconnected, autonomous industry. Furthermore, it discusses the evolution towards Supply Chain 4.0, highlighting the need for Transportation Management Systems (TMS) enhanced by GPS and real-time data for efficient logistics. A guideline for implementing CPS within Industry 4.0 environments is provided, focusing on a case study of real-time data acquisition from logistics vehicles using CPS devices. The study proposes a CPS architecture and a generic platform for asset tracking to address integration challenges efficiently and facilitate the easy incorporation of new components and applications. Preliminary tests indicate the platform's real-time performance is satisfactory, with negligible delay under test conditions, showcasing its potential for logistics applications and beyond. [ABSTRACT FROM AUTHOR]

Industry 4.0 enables the modernisation of machines and opens up the digitalisation of processes in the manufacturing industry. As a result, these machines are ready for predictive maintenance as part of Industry 4.0 services. The benefit of predictive maintenance is that it can significantly extend the life of machines. The integration of predictive maintenance into existing production environments faces challenges in terms of data understanding and data preparation for machines and legacy systems. Current AI frameworks lack adequate support for the ongoing task of data integration. In this context, adequate support means that the data analyst does not need to know the technical background of the pilot's data sources in terms of data formats and schemas. It should be possible to perform data analyses without knowing the characteristics of the pilot's specific data sources. The aim is to achieve a seamless integration of data as information for predictive maintenance. For this purpose, the developed data-sharing infrastructure enables automatic data acquisition and data integration for AI frameworks using interoperability methods. The evaluation, based on two pilot projects, shows that the step of data understanding and data preparation for predictive maintenance is simplified and that the solution is applicable for new pilot projects. [ABSTRACT FROM AUTHOR]

Copyright of Ciencia Ergo Sum is the property of Universidad Autonoma del Estado de Mexico and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Consensus algorithms play a crucial role in facilitating decision-making among a group of entities. In certain scenarios, some entities may attempt to hinder the consensus process, necessitating the use of Byzantine fault-tolerant consensus algorithms. Conversely, in scenarios where entities trust each other, more efficient crash fault-tolerant consensus algorithms can be employed. This study proposes an efficient consensus algorithm for an intermediate scenario that is both frequent and underexplored, involving a combination of non-trusting entities and a trusted entity. In particular, this study introduces a novel mining algorithm, based on chameleon hash functions, for the Nakamoto consensus. The resulting algorithm enables the trusted entity to generate tens of thousands blocks per second even on devices with low energy consumption, like personal laptops. This algorithm holds promise for use in centralized systems that require temporary decentralization, such as the creation of central bank digital currencies where service availability is of utmost importance. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has been used in different ways in the fight against COVID-19 disease. ML models have been developed, e.g., for diagnostic or prognostic purposes and using various modalities of data (e.g., textual, visual, or structured). Due to the many specific aspects of this disease and its evolution over time, there is still not enough understanding of all relevant factors influencing the course of COVID-19 in particular patients. In all aspects of our work, there was a strong involvement of a medical expert following the human-in-the-loop principle. This is a very important but usually neglected part of the ML and knowledge extraction (KE) process. Our research shows that explainable artificial intelligence (XAI) may significantly support this part of ML and KE. Our research focused on using ML for knowledge extraction in two specific scenarios. In the first scenario, we aimed to discover whether adding information about the predominant COVID-19 variant impacts the performance of the ML models. In the second scenario, we focused on prognostic classification models concerning the need for an intensive care unit for a given patient in connection with different explainability AI (XAI) methods. We have used nine ML algorithms, namely XGBoost, CatBoost, LightGBM, logistic regression, Naive Bayes, random forest, SGD, SVM-linear, and SVM-RBF. We measured the performance of the resulting models using precision, accuracy, and AUC metrics. Subsequently, we focused on knowledge extraction from the best-performing models using two different approaches as follows: (a) features extracted automatically by forward stepwise selection (FSS); (b) attributes and their interactions discovered by model explainability methods. Both were compared with the attributes selected by the medical experts in advance based on the domain expertise. Our experiments showed that adding information about the COVID-19 variant did not influence the performance of the resulting ML models. It also turned out that medical experts were much more precise in the identification of significant attributes than FSS. Explainability methods identified almost the same attributes as a medical expert and interesting interactions among them, which the expert discussed from a medical point of view. The results of our research and their consequences are discussed. [ABSTRACT FROM AUTHOR]

In line with the new trend toward reforming the education system in the 21st century, teachers are more willing to accept changes to improve performance and achieve success in the educational process. Early Childhood Education (ECE) in Saudi Arabia has recently witnessed a huge revolution. Teacher Professional Development (PD) is one of the most influential elements in achieving quality education. The present study focuses on revealing the professional development program (PDP) obstacles faced by kindergarten teachers from their perspectives, as well as their implications for overcoming these obstacles. Based on the model of PDPs developed in this research, it is divided into three main elements: administrative, personal, and digital. The current investigation adopted an exploratory approach, where a total of (n=102) kindergarten teachers in the city of Mecca, Saudi Arabia, participated in this study by filling out a self-administered questionnaire. The results showed that all kindergarten teachers admitted that they faced several obstacles in their professional practices, which hindered the quality of their practices. Most kindergarten teachers reported a lack of motivation and inadequate training opportunities, which influenced their reluctance to participate in PDPs. However, they revealed their positive attitudes and willingness to implement various strategies and practices that contribute to their own PD. It recommends that decision makers and government agencies consider the needs and capabilities of kindergarten teachers during training and involve them in the professional planning and development process to ensure that the training outputs are more effective in their professional practice. [ABSTRACT FROM AUTHOR]

In the dynamic context of the global gig economy and Saudi Arabia's Vision 2030, this study offers a novel examination of the impact of HR practices on gig workers' job satisfaction and career sustainability in Saudi Arabia. Setting itself apart from prior research, it explores the uncharted interplay between HR practices and career longevity in the Saudi gig economy. Utilizing data from 344 gig workers, the study uncovers the intermediary role of job satisfaction in connecting HR practices to career sustainability, a dimension scarcely investigated before. It further assesses the often-assumed significant effects of demographic factors such as age and gender, revealing an unexpected, non-significant moderating impact. This research finds a strong positive correlation between effective HR practices, job satisfaction, and career endurance, highlighting the transformative power of HR strategies in the Saudi gig sector. These findings are vital for policymakers and practitioners focusing on Vision 2030 goals, underscoring the need for sophisticated HR strategies tailored to the unique Saudi gig environment. By bridging a critical knowledge gap and offering actionable insights, this study contributes significantly to the academic discourse on HR dynamics in gig economies and provides a foundation for future HR policy developments. [ABSTRACT FROM AUTHOR]

Learning analytics (LA) has the potential to significantly improve teaching and learning, but there are still many areas for improvement in LA research and practice. The literature highlights limitations in every stage of the LA life cycle, including scarce pedagogical grounding and poor design choices in the development of LA, challenges in the implementation of LA with respect to the interpretability of insights, prediction, and actionability of feedback, and lack of generalizability and strong practices in LA evaluation. In this position paper, we advocate for empowering teachers in developing LA solutions. We argue that this would enhance the theoretical basis of LA tools and make them more understandable and practical. We present some instances where process data can be utilized to comprehend learning processes and generate more interpretable LA insights. Additionally, we investigate the potential implementation of large language models (LLMs) in LA to produce comprehensible insights, provide timely and actionable feedback, enhance personalization, and support teachers' tasks more extensively. [ABSTRACT FROM AUTHOR]

The design-building of IT services in data centers has been historically conducted by applying rigorous IT service design-building phases-workflows. Consequently, relevant research has been conducted upon these rigorous phases-workflows to provide theoretical foundations and practical guidance to IT service design-building architects. However, the current dynamic business-governmental environment is demanding agile approaches, and research from this perspective is still very scarce. This research, thus, aims to provide an updated review and quantitative evaluation of the main rigorous (ITIL v2011, CMMI-SVC v1.3, and ISO/IEC 20000-1:2018) and main agile (ITIL v4, VeriSM, and ISO/IEC 29110-4-3) IT service design-building phases-workflows. For this aim, we conduct a conceptual review research methodology enhanced with an analytics hierarchical process (AHP) method to assess quantitatively how well these six IT service design-building phases-workflows fit two theoretical expected rigorous and agile IT service design-building phases-workflows pro formas. We found that the ITIL v2011 and CMMI-SVC v1.3 phases-workflows fit the rigorous pro forma with a high level, and the ISO/IEC 20000-1:2018 fits a moderate level, whereas all these three ones fit a low level the agile pro forma, as it was expected. ITIL v4 and VeriSM were found to fit a high level the agile pro forma but the ISO/IEC 29110-4-3 fits a moderate level. Unexpectedly, ITIL v4 and the ISO/IEC 29110-4-3 fit a moderate and moderate levels the rigorous pro forma, but VeriSM fits a low level as it was expected. Hence, we can conclude that ITIL v4 and VeriSM provide the most agile IT service design-building phase-workflow, and CMMI-SVC v1.3 the most rigorous one. The ISO/IEC 20000-1:2018 and ISO/IEC 29110-4-3 standards are still aligned to the rigorous approach, and ITIL v4 exhibits a dual moderate rigorous and high agile profile. Hence, this research provides ITSM professionals with an updated analysis useful to guide the selection and application of the IT service design-building phase-workflow—rigorous or agile one—in data centers. This research also contributes to the IT service design-building literature with updated insights and proposes specific research avenues to advance our scientific knowledge on how to design-building IT services. [ABSTRACT FROM AUTHOR]

Purpose: Skills development for business students is increasingly becoming more important in business education and the workplace. In this paper, students' research skills are examined. The purpose of this paper is to identify some of the issues and challenges students face in developing research skills and how these can be addressed. Design/methodology/approach: The study combines external marker evaluation and content analysis to evaluate one hundred 2,000-word essays and identify issues and challenges associated with students' development of research skills. Findings: Results show that the essays rate well in collecting and using sources and summarising key topics but miss on integrating sources in writing and inconsistency in citations and referencing. Research limitations/implications: One of the paper's limitations is using a sample from a single course in a business school in Australia. Nonetheless, the sampled essays represent similar writing tasks elsewhere that require students to display research skills. Practical implications: Some implications for business and higher education are offered, including suggestions to address the issues and challenges raised in business education. For example, academics are encouraged to communicate clear expectations for writing tasks, provide support and show exemplars of business writing that incorporates practical research skills. Social implications: Business students who develop effective research skills contribute to society by developing skills in effectively searching and evaluating information. This ensures business graduates in industry workplaces reach considered conclusions before making recommendations that could impact people's lives. Originality/value: The study is original in its approach to investigating the nuances of research skills deficiencies by using external expert examination combined with content analysis. [ABSTRACT FROM AUTHOR]

Copyright of Jurnal Sosial Humaniora is the property of Universitas Djuanda and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Object detection (OD) coupled with active learning (AL) has emerged as a powerful synergy in the field of computer vision, harnessing the capabilities of machine learning (ML) to automatically identify and perform image-based objects localisation while actively engaging human expertise to iteratively enhance model performance and foster machine-based knowledge expansion. Their prior success, demonstrated in a wide range of fields (e.g., industry and medicine), motivated this work, in which a comprehensive and systematic review of OD and AL techniques was carried out, considering reputed technical/scientific publication databases—such as ScienceDirect, IEEE, PubMed, and arXiv—and a temporal range between 2010 and December 2022. The primary inclusion criterion for papers in this review was the application of AL techniques for OD tasks, regardless of the field of application. A total of 852 articles were analysed, and 60 articles were included after full screening. Among the remaining ones, relevant topics such as AL sampling strategies used for OD tasks and groups categorisation can be found, along with details regarding the deep neural network architectures employed, application domains, and approaches used to blend learning techniques with those sampling strategies. Furthermore, an analysis of the geographical distribution of OD researchers across the globe and their affiliated organisations was conducted, providing a comprehensive overview of the research landscape in this field. Finally, promising research opportunities to enhance the AL process were identified, including the development of novel sampling strategies and their integration with different learning techniques. [ABSTRACT FROM AUTHOR]

Commercial and public sector interests surrounding technological developments are promoting a widespread transition to autonomous vehicles, intelligent transportation systems and smart phone communications in everyday life, as part of the smart mobility agenda. There is, however, inadequate understanding about the impact of such a shift on potential users, their readiness to engage and their vision of transportation systems for the future. This paper presents the findings from a series of citizen panels, as part of a 2-year project based in south-west England, focusing on in-depth discussions regarding the future of commuting, the flow of the daily commute and the inclusion of publics in smart mobility planning. The paper makes three key propositions for researchers: enabling publics should lead to a visionary evolution in the development of sustainable transportation systems; commercial interests, public bodies and IT innovators must employ a holistic approach to mobility flows; and, processes engaging publics need to be inclusive when co-creating solutions in the transition to smart mobilities. [ABSTRACT FROM AUTHOR]

Spatial evaluation of urban environment quality (UEQ) is a key prerequisite in urban planning and development. The main goal of this study is to present a scenario-based spatial multi-criteria decision-making system for evaluating UEQ. Therefore, stakeholder involvement was conducted and eight environmental criteria and six spatial-functional criteria were identified for five districts of Tehran. The weight of the effective criteria was calculated using the analytic hierarchy process (AHP) model. Then, the ordered weighted averaging (OWA) model was used to prepare UEQ maps in different scenarios, including very pessimistic, pessimistic, intermediate, optimistic, and very optimistic. Finally, the spatial distribution of the district population in different classes of UEQ was evaluated. Among the spatial-functional and environmental criteria, the sub-criteria of population density and air pollution, respectively, had the greatest impact on UEQ. In very pessimistic, intermediate, optimistic, and very optimistic scenarios, approximately 76.7, 51.8, 36.4, 23.7, and 9.8 km2 of the studied area had unsuitable UEQ conditions, respectively. In the very pessimistic scenario, about 37,000 and 1,500,000 people lived in areas with suitable and unsuitable UEQ conditions, respectively. In the very optimistic scenario, the population increased to over 917,000 in areas with suitable UEQ and decreased to 336,000 in those with unsuitable UEQ conditions in terms of both environmental and spatial-functional criteria. The research results showed that a high percentage of the population in the study area live under unsuitable UEQ conditions, which indicates the need for attention to improving the current UEQ conditions. The proposed approach is timely to gain a better understanding of the adverse impact of climate change on human well-being in marginal societies and how climate-resilient urban planning can play a significant role. [ABSTRACT FROM AUTHOR]

The last few years have witnessed an upsurge in the number of studies using Machine and Deep learning models to predict vital academic outcomes based on different kinds and sources of student-related data, with the goal of improving the learning process from all perspectives. This has led to the emergence of predictive modelling as a core practice in Learning Analytics and Educational Data Mining. The aim of this study is to review the most recent research body related to Predictive Analytics in Higher Education. Articles published during the last decade between 2012 and 2022 were systematically reviewed following PRISMA guidelines. We identified the outcomes frequently predicted in the literature as well as the learning features employed in the prediction and investigated their relationship. We also deeply analyzed the process of predictive modelling, including data collection sources and types, data preprocessing methods, Machine Learning models and their categorization, and key performance metrics. Lastly, we discussed the relevant gaps in the current literature and the future research directions in this area. This study is expected to serve as a comprehensive and up-to-date reference for interested researchers intended to quickly grasp the current progress in the Predictive Learning Analytics field. The review results can also inform educational stakeholders and decision-makers about future prospects and potential opportunities. [ABSTRACT FROM AUTHOR]

Purpose of the study: The turbulent global higher education sector poses significant marketing, branding and management challenges to institutions. In South Africa, the development of new institutions like Sol Plaatje University and the University of Mpumalanga has increased competition in the market, increasing pressure on universities to retain high-performing students in a bid to grow. This study investigates the institutional factors influencing post-graduate students' university loyalty. It examines the relationship between reputation and academic programmes, quality and competencies of academic staff, expenses and grants, facilities and physical environment, career prospects, and sports on university loyalty through the mediating role of attitude. Design/methodology/approach: Data for this quantitative study were gathered from 150 post-graduate students at the University of the Witwatersrand through a close-ended questionnaire. Data were analysed using Statistical Package for Social Science 27 (SPSS), whereby various reliability and validity tests were conducted. Structural Equation Modelling (SEM) was then used to test the hypotheses. Findings: The results obtained indicate a positive and significant relationship between reputation and academic programmes, quality and competencies of academic staff, and career prospects and attitude towards the university. A positive and significant relationship was observed between attitude towards the university and university loyalty. Recommendations/value: Universities should focus on enhancing their reputation and offering cutting-edge academic programmes that are considerably different from those offered by competitors. Quality and competencies of academic staff and maintaining academic excellence must be emphasised. This will encourage recruiters to seek graduates from the institution, resulting in greater career prospects for students. Managerial implications: By concentrating on the university's reputation and academic programmes, quality and competencies of academic staff, and providing qualifications that lead to good job prospects, positive student attitudes towards the university and, by extension, loyalty will be ensured. This will ensure that universities retain some of their high-performing students. [ABSTRACT FROM AUTHOR]

Analysis of pig posture is significant for improving the welfare and yield of captive pigs under different conditions. Detection of pig postures, such as standing, lateral lying, sternal lying, and sitting, can facilitate a comprehensive assessment of the psychological and physiological conditions of pigs, prediction of their abnormal or detrimental behavior, and evaluation of the farming conditions to improve pig welfare and yield. With the introduction of smart farming into the farming industry, effective and applicable posture detection methods become indispensable for realizing the above purposes in an intelligent and automatic manner. From early manual modeling to traditional machine vision, and then to deep learning, multifarious detection methods have been proposed to meet the practical demand. Posture detection methods based on deep learning show great superiority in terms of performance (such as accuracy, speed, and robustness) and feasibility (such as simplicity and universality) compared with most traditional methods. It is promising to popularize deep learning technology in actual commercial production on a large scale to automate pig posture monitoring. This review comprehensively introduces the data acquisition methods and sub-tasks for pig posture detection and their technological evolutionary processes, and also summarizes the application of mainstream deep learning models in pig posture detection. Finally, the limitations of current methods and the future directions for research will be discussed. [ABSTRACT FROM AUTHOR]

The dynamics of change in the market force organizations to be ready to take up challenges. The innovation of enterprises is a determinant of their development. Implementation of technological innovation involves a large number of important strategic decisions. This publication discusses the issue of implementing technological innovation in a manufacturing enterprise. The subject of the manuscript includes the creation of a comprehensive methodology of proceedings. This, in turn, includes issues that determine the effectiveness of the management process of an innovative project in a small-batch production enterprise, whose specificity requires the proposal of a methodology for creating a concept of an innovative product under the conditions of limited project resources. The presented approach provides support for the creation of an innovation concept and its proper management. The proposed methodology allows for the evaluation and selection of optimal solution variants and the development of the technical preparation of new production in terms of construction, technology, and organization. The implementation of improvements presented in the article results in an increase in the company's ability to successfully implement technological innovations, whose implementation is an essential step to increase the value and growth of the company. [ABSTRACT FROM AUTHOR]

A city's transport and distribution system requires the effective organization of urban freight deliveries that take into consideration the economic, ecological, and social impact. Implementation of the concept of green logistics necessitates the use of environmentally friendly and energy-efficient modes of transport, which consider the existing infrastructure and the possibility of its development. The aim of this work was to facilitate the transfer to rail transport, using the existing tramway infrastructure, those shipments which previously entered the city center by road. The paper proposes a simulation model for small consignments delivered by freight trams in the city of Poznań, Poland. Operational planning is carried out in conditions of uncertainty and risk. The uncertainty is due to the stochastic nature of the demand for products. The risk is characterized by the probability that, due to technological and technical limitations, a given customer will not be fully served from the distribution center. The authors provide a decision support tool for evaluating the possible locations of tram depots and for route planning. The assignment of the routes, with the criterion of minimizing the costs for the carrier, and with a limitation on tram operating time, is based on the use of genetic algorithms, which makes it possible to obtain a solution that is close to optimal, within a reasonable time period. As a result of a series of simulation experiments and statistical data processing, the distribution laws and expected average values of the technological parameters were determined for the functioning of the city transport and distribution system under conditions of uncertainty and risk. [ABSTRACT FROM AUTHOR]

Measuring Corporate Sustainability (CS) has been identified as an important enabler for integrating sustainability into corporate practices. Different methodologies and frameworks for measuring CS have been developed in the literature with limited success, as reflected by the lack of application in the real world. Among practitioners, the effort has been on developing frameworks that provide useful indicators of the different items that need to be considered for integrating sustainability. Notwithstanding the increasing attention and progress on the subject, a cohesive and applicable measure of CS across firms, industries and geographies is still missing. This paper provides an examination of the different methodologies for measuring CS, with an analysis of their merits and limitations, as well as guidance for future research focus. The findings show a failure to coherently link the mathematical measurement and data aggregation methodologies to a well-constructed concept where the linkage between the defining features and causal relations are appropriately identified. The proposed models and mathematical techniques are not able to inform on the extent to which a corporation acts sustainably because sustainability is not being measured in its highest extension, making the results narrow, non-referential and non-comparable. Furthermore, there is confusion between developing the indicators of CS, providing their measurement and enabling their integration. [ABSTRACT FROM AUTHOR]

Purpose: The study aims to examine the connection between practices of total quality management (TQM) and sustainability in Malaysia food and beverages companies (FBC). Continuous process improvement, benchmarking, management leadership, human resources management, quality assurance, service design and information and analysis as TQM practices are considered and their relationship, respectively, with sustainable performance. Design/methodology/approach: A survey questionnaire is administered to gather responses from 303 FBC, while 98 responses are useable and subsequently analysed using partial least squares structural equation modelling. Findings: The results reveal that effective implementation of continuous process improvement, benchmarking, quality assurance, service design and information and analysis have positive and significant effect on sustainability. Research limitations/implications: The scope of the present study was limited to FBC in Malaysia, and a cross-sectional design was employed to examine the hypothesized relationships at a single point in time. Practical implications: The proposed and developed model of this study can be employed by policy and decision makers in the industry. This model can be considered by practitioners in the industry to implement critical policies in the future. Originality/value: The premises of the institutional and contingency theory are supported by re-affirming the importance of contingencies and institutions for any successful strategic practices to enhance sustainable performance by implementing TQM. [ABSTRACT FROM AUTHOR]

Cultural Heritage (CH) management software represents virtual information in various ways aiming either at usability and long-term preservation or interactivity and immersiveness. A single web-based framework that couples the organization of geospatial, multimedia and relational data with 4D visualization, Virtual Reality (VR) and Augmented Reality (AR) is presented in this paper (https://meteora.topo.auth.gr/5dmeteora.php). It comprises the 5dMeteora platform and the Content Management System (CMS) for uploading, processing, publishing and updating its content. The 5dMeteora platform integrates a responsive 3D viewer of high-resolution models in the basis of 3DHOP (3D Heritage Online Platform) and Nexus.js multi-resolution library. It offers data retrieval and interpretation mechanisms through navigation tools, clickable geometries in the 3D scene, named hotspots, and semantic organization of metadata. Its content and interactive services are differentiated, based on the scientific specialty or the field of interest of the users. To achieve the sense of spatial presence, VR and AR viewports are designed to give a clearer understanding of spatial bounds and context of 3D CH assets. The proposed CMS allows dynamic content management, automation of 3DHOP's operations regarding 3D data uploading and hotspots defining, real-time preview of the 3D scene as well as extensibility at all levels (e.g., new data types). It is built upon a MySQL Database Management System and developed with PHP scripting, backend JavaScript and Ajax controllers as well as front-end web languages. The database maintains and manages the entities of every type of data supported by the platform, while encryption methods guarantee data confidentiality and integrity. The presented work is the first valid attempt of open-source software that automates the dissemination of 3D and 2D content for customized eXtented Reality (XR) experiences and reaches multiple levels of interactivity for different users (experts, non-experts). It can meet the needs of domain experts that own or manage multimodal heritage data. [ABSTRACT FROM AUTHOR]

Introduction: Taxonomies capture knowledge about a particular domain in a succinct manner and establish a common understanding among peers. Researchers use taxonomies to convey information about a particular knowledge area or to support automation tasks, and practitioners use them to enable communication beyond organizational boundaries. Aims: Even though this important role of taxonomies in software engineering, their quality is seldom evaluated. Our aim is to identify and define taxonomy quality attributes that provide practical measurements, helping researchers and practitioners to compare taxonomies and choose the one most adequate for the task at hand. Methods: We reviewed 324 publications from software engineering and information systems research and synthesized, when provided, the definitions of quality attributes and measurements. We evaluated the usefulness of the measurements on six taxonomies from three domains. Results: We propose the definition of seven quality attributes and suggest internal and external measurements that can be used to assess a taxonomy's quality. For two measurements we provide implementations in Python. We found the measurements useful for deciding which taxonomy is best suited for a particular purpose. Conclusion: While there exist several guidelines for creating taxonomies, there is a lack of actionable criteria to compare taxonomies. In this article, we fill this gap by synthesizing from a wealth of literature seven, non‐overlapping taxonomy quality attributes and corresponding measurements. Future work encompasses their further evaluation of usefulness and empirical validation. [ABSTRACT FROM AUTHOR]

For English as a Foreign Language (EFL) students, automatic speech recognition (ASR) technology is the most potential assistant tool to help them improve their spoken English ability. The primary purpose of this study is to investigate learners' perceptions towards ASR technology after it is applied to traditional classrooms. This study selected 249 English majors from a university in Northeastern China as samples and divided them into a control group consisting of 124 students and an experimental group including 125 students. The participants of two groups used ASR technology in the process of oral practice, and the experimental group also added teacher's guidance compared with the control group. The teacher gives more detailed instruction in speaking based on the scores provided by ASR technology. Participants needed to complete relevant questionnaires and learning reflective journals during the experiment. The results of the study showed that both participants and instructors held positive and satisfactory attitudes towards the potential of ASR in oral training and believed that the technology could meet many of their needs such as the scoring system to help them more intuitively understand the real speaking level. The findings of this paper will give some implications to oral English teaching in the future. [ABSTRACT FROM AUTHOR]

The biological sciences community is increasingly recognizing the value of open, reproducible and transparent research practices for science and society at large. Despite this recognition, many researchers fail to share their data and code publicly. This pattern may arise from knowledge barriers about how to archive data and code, concerns about its reuse, and misaligned career incentives. Here, we define, categorize and discuss barriers to data and code sharing that are relevant to many research fields. We explore how real and perceived barriers might be overcome or reframed in the light of the benefits relative to costs. By elucidating these barriers and the contexts in which they arise, we can take steps to mitigate them and align our actions with the goals of open science, both as individual scientists and as a scientific community. [ABSTRACT FROM AUTHOR]

Climate change adaptation (CCA) evolved in the global policy framework in the early 1990s. However, it began to flourish about a decade later through a subsequent development of institutions, policies and supporting financial mechanisms. Various adaptation approaches and development practices have been evolving over the last couple of decades through a process of scrutiny, debates, and critiques. One such recent approach is called conflict-sensitive adaptation, which encompasses concepts to understand potential conflict-inflicting elements and peacebuilding aspects of adaptation interventions. This paper examines 35 peer-reviewed research articles that have analysed field data with the notion of conceptualising conflict-sensitive CCA initiatives. Emerging key results were presented and discussed in different academic forums to stimulate peer reflections and debates. We found that the understanding of conflict-sensitive adaptation has its universality in engaging with diverse stakeholders. However, practicalities were different in the cases of the global north and the global south. In the global south, there is a concentration of research in areas of pre-existing conflict in Africa and Asia, where climate change links are often assumed from IPCC reports in order to compensate for the unavailability of field data. However, from the perspective of adaptation politics and governance, there is a concerted urge for the emancipatory participation of local and marginalised populations. We argue for a need to pretest adaptation projects through a conflict lens. Decolonising the adaptation and understanding of local geography is critical in such planning. [ABSTRACT FROM AUTHOR]

In a recently published study on business process management (BPM) capabilities in the view of digitalization, project management was not named as a core capability by the corresponding Delphi panel. However, earlier (pre-digitalization) research suggests that project management is a key success factor for BPM. This contradiction could have severe impact with regard to the sustainability of process management. This article uses qualitative case study data to discuss potential reasons for this contradiction and to answer the question of how important project management is for contemporary BPM. As both traditional and agile project management knowledge was employed in the case study, results indicate that project management is indeed helpful for BPM, especially for discrete process change. Practitioners should consider project management knowledge when staffing business process optimization and digitalization initiatives. Research should develop a deeper understanding of agile and traditional project management as antecedents to sustainable BPM success and as BPM capabilities. [ABSTRACT FROM AUTHOR]