Context Of Applications Research Articles

CDAS: A Secure Cross-Domain Data Sharing Scheme Based on Blockchain

In the current context of the wide application of Internet of Things (IoT) technology, cross-domain data sharing based on industrial IoT (IIoT) has become the key to maximizing data value, but it also faces many challenges. In response to the security and privacy issues in cross-domain data sharing, we proposed a cross-domain secure data sharing scheme (CDAS) based on multiple blockchains. The scheme first designs the cross-domain blockchain in layers and assists the device in completing the data sharing on the chain through the blockchain layer close to the edge device. In addition, we combine smart contract design to implement attribute-based access control (ABAC) and anonymous identity registration. This method simplifies device resource access by minimizing middleware confirmation, double-checking device access rights, and preventing redundant requests caused by illegal access attempts. Finally, in terms of data privacy and security, IPFS is used to store confidential data. In terms of ensuring data sharing security, searchable encryption (SE) is applied to the overall data sharing and improved. Users can find the required data by searching the ciphertext links in the blockchain system to ensure the secure transmission of private data. Compared with the traditional ABAC scheme, we have added modules for data privacy protection and anonymous authentication to further protect user data privacy. At the same time, compared with the access control scheme based on attribute encryption, our scheme has certain advantages in the time complexity calculation of key algorithms such as policy matching and encryption algorithm. At the same time, with the assistance of the edge blockchain layer, it can reduce the burden of limited computing resources of the device. This scheme can solve the security and efficiency problems of cross-domain data sharing in the industrial Internet of Things through security and experimental analysis.

Exploring DUA Methodology in Teaching Technical Vocabulary for Cutting and Sewing in English: A Qualitative Study in Technical Schools of Ecuador

This qualitative study explores the application of Universal Design for Learning (DUA) methodology in teaching technical vocabulary for cutting and sewing in English within Ecuador's technical secondary education system, specifically at Colegio Fiscal Técnico Industrial Febres Cordero. The research focuses on third-year baccalaureate students (n=30) who are developing specialized vocabulary skills essential for their technical careers. Despite the growing importance of English proficiency in technical fields, research on specialized vocabulary acquisition methods in vocational education remains limited in Latin American contexts. Through semi-structured interviews with teachers (n=5), focus group discussions with students, classroom observations, and analysis of instructional materials, this research examines how DUA principles are understood, adapted, and implemented in this specific educational environment. The findings reveal that teachers at Febres Cordero creatively adapt DUA principles to accommodate diverse learning needs while negotiating challenges including limited resources and the integration of traditional terminology. Students demonstrate enhanced engagement and vocabulary retention when multiple representation modes are utilized, particularly through visual-tactile connections between English terminology and physical materials. The study identifies sociocultural dimensions affecting technical vocabulary acquisition, including local textile traditions and the practical application contexts most relevant to students' future employment opportunities. This research contributes to understanding how flexible, multimodal approaches like DUA can be contextually adapted for technical vocabulary instruction while addressing the specific educational realities and needs of students at “Colegio fiscal técnico Industrial Febres Cordero”. Implications for teacher training, curriculum development, and culturally responsive ESL pedagogy in Ecuador's technical education system are discussed.

KNOWLEDGE MANAGEMENT IN FINTECH RISK MANAGEMENT: A BIBLIOMETRIC ANALYSIS AND FUTURE RESEARCH DIRECTIONS

Knowledge management plays a crucial role in Fintech risk management, enabling financial institutions and regulatory bodies to navigate emerging challenges in an increasingly digitalized financial ecosystem. As financial technology continues to disrupt traditional financial services, the ability to systematically acquire, organize, and apply knowledge is essential for mitigating risks associated with cybersecurity threats, regulatory compliance, fraud detection, and operational inefficiencies. This study presents a bibliometric analysis of recent literature, identifying key trends, methodologies, and research gaps in Fintech risk management through the lens of Knowledge Management. By synthesizing scholarly sources, the paper highlights how theoretical underpinnings, methodological approaches, and contextual applications contribute to knowledge creation, organization, sharing, and application in this domain. Additionally, the study evaluates how Fintech firms integrate artificial intelligence, blockchain, and big data analytics within Knowledge Management frameworks to enhance risk management effectiveness. Moreover, the research emphasizes the need for empirical studies that validate risk mitigation measures and explores how regulatory bodies can foster a knowledge-driven approach to financial risk management. The findings offer future research directions that aim to strengthen Knowledge Management strategies in Fintech, ensuring continuous learning, adaptation, and resilience in financial ecosystems. Keywords: Knowledge Management, Fintech, Risk Management, Bibliometric Analysis, Financial Technology, Future Research Directions.

Machine learning-based prediction of ambient CO2 and CH4 concentrations with high temporal resolution in Seoul metropolitan area.

Machine learning-based prediction of ambient CO2 and CH4 concentrations with high temporal resolution in Seoul metropolitan area.

KKN-P: Perancangan Alat Presensi Otomatis dengan menggunakan ID Card Elektronik bagi Pemerintahan Desa Lebakrejo

Appropriate technology is an innovation designed to provide practical and efficient solutions in various aspects of life in the context of the world of work or education, one of the applications of appropriate technology that is increasingly popular is the use of attendance automation tools. By utilizing various technologies, such as fingerprint, face or card scanning, attendance automation systems not only reduce human error, but also minimize the time required in attendance recording. Appropriate technology research uses qualitative methods that aim to understand the context, process and impact of technology application in society. This method focuses more on collecting descriptive and in-depth data, such as in-depth interviews and group discussions as well as observation and documentation. In addition, this technology also supports transparency, data security, and facilitates attendance management and monitoring. Overall, the application of appropriate technology-based attendance tools is very beneficial in increasing productivity, accountability, and operational efficiency

Imaging for inflammatory neuropathies.

Imaging for inflammatory neuropathies.

Advanced Java/J2EE design patterns for scalable enterprise applications: A systematic approach

This article presents a systematic examination of design patterns within the Java/J2EE ecosystem, focusing on their application in enterprise-scale software development. Through article analysis of empirical studies and industry research, the article explores the evolution of design patterns from their architectural origins to their current implementation in enterprise environments. The article explores core architectural patterns including Model-View-Controller, Singleton, Data Access Object, Factory, and Microservices, providing evidence-based assessment of their impacts on maintainability, performance, and scalability. Performance optimization strategies, including lazy initialization, caching mechanisms, load balancing, and memory management techniques, are evaluated within the context of enterprise applications. The article incorporates case studies across diverse industry verticals, offering quantitative metrics on pattern effectiveness and implementation approaches. The article concludes with an analysis of emerging trends in pattern-based architecture, particularly integration with cloud-native paradigms and AI-driven development, providing practical recommendations for enterprise architects and developers.

Performance Measurement of Gesture-Based Human-Machine Interfaces Within eXtended Reality Head-Mounted Displays.

This paper proposes a method for measuring the performance of Human-Machine Interfaces based on hand-gesture recognition, implemented within eXtended Reality Head-Mounted Displays. The proposed method leverages a systematic approach, enabling performance measurement in compliance with the Guide to the Expression of Uncertainty in Measurement. As an initial step, a testbed is developed, comprising a series of icons accommodated within the field of view of the eXtended Reality Head-Mounted Display considered. Each icon must be selected through a cue-guided task using the hand gestures under evaluation. Multiple selection cycles involving different individuals are conducted to derive suitable performance metrics. These metrics are derived considering the specific parameters characterizing the hand gestures, as well as the uncertainty contributions arising from intra- and inter-individual variability in the measured quantity values. As a case study, the eXtended Reality Head-Mounted Display Microsoft HoloLens 2 and the finger-tapping gesture were investigated. Without compromising generality, the obtained results show that the proposed method can provide valuable insights into performance trends across individuals and gesture parameters. Moreover, the statistical analyses employed can determine whether increased individual familiarity with the Human-Machine Interface results in faster task completion without a corresponding decrease in accuracy. Overall, the proposed method provides a comprehensive framework for evaluating the compliance of hand-gesture-based Human-Machine Interfaces with target performance specifications related to specific application contexts.

The Substantive Justice in Regional Elections: A Philosophical and Sociological Comparison of Asian, European, And African Countries

Substantive justice in regional elections, which focuses on election outcomes that reflect social justice, inclusiveness, and fair distribution of power, requires a contextual approach in Asia, Europe, and Africa to address the unique challenges of each region and create more inclusive and just political systems. This study aims to explore the philosophical and sociological understanding and application of the concept of substantive justice and its impact on political representation and inclusiveness in regional elections in Asian, European, and African countries. This study uses normative legal methods with legislative, conceptual, and comparative approaches to analyze the concept of substantive justice and its application in regional elections in Asia, Europe, and Africa, relying on qualitative analysis of primary, secondary, and tertiary legal materials. The results of the study show that substantive justice in regional elections focuses on outcomes that reflect social justice, equality, and inclusiveness, with highly contextual applications in Asia, Europe, and Africa. In Europe, this concept is influenced by John Rawls’ theory of justice as fairness, with a proportional electoral system that is effective in representing the diversity of society. In Asia, values ​​of social harmony and cultural plurality influence the implementation of substantive justice, although challenges such as money politics and social inequality remain significant. In Africa, the philosophy of Ubuntu encourages community solidarity, but obstacles such as corruption and ethnic conflict hinder its implementation. In general, affirmative action policies, ongoing reforms, and public education are needed to strengthen inclusiveness and political representation at the local level in various regions.

ENHANCING IN-HOSPITAL MORTALITY PREDICTION VIA PERSONALIZED FEDERATED LEARNING IN MULTI INSTITUTIONAL ICUS

A fresh and promising plan to advance machine learning (ML) at many independently dispersed healthcare facilities is provided by federated learning (FL), a paradigm for resolving the difficulties of applying ML to private distributed data. Its effectiveness may be hampered, however, by the non-IID and uneven distribution of the data, which may even make the institutions less inclined to take part in its training. In order to retain the original non-IID and unbalanced data distribution, this research investigated the issue of an in-hospital mortality prediction job under a real multi-center ICU electronic health record database. Prior to proposing a personalised FL (PFL) solution called POLA to address the issue, it first examined the cause of the baseline FL's performance decrease in this data setting. POLA is a customised one-shot, two-step FL technique that may provide very effective customised models for every individual participant. In studies, the suggested POLA approach was contrasted with two other PFL techniques, and the findings show that it not only considerably lowers the number of communication cycles but also enhances FL's prediction performance. Additionally, because of its generality and flexibility, it may be expanded to other comparable cross-silo FL application contexts.

Developing entrustable professional activities for residents in physical and rehabilitation medicine: A Delphi study.

Entrustable professional activities (EPAs) are set to assess residents' capacity to perform professional units close to the context of their future clinical practice. The aim was to define a concise list of EPAs covering the broad specialty of Physical and Rehabilitation Medicine (PRM) to implement a systematic and consistent assessment of trainees' competencies during their residency. A list of EPAs was developed based on a literature review and working group. This list was then submitted to academic PRM professors for validation via a Delphi process. Two rounds were planned with intermediate and final discussion meetings by an external steering committee. Ten EPAs were developed in a first draft. Each of the seven parts of the EPAs obtained validation through the Delphi process. A list of 11 clinical contexts of application was approved to integrate the EPAs in the different areas of PRM. The next step will be the deployment of these EPAs for the validation of residency internships.

AI in the German Media: Narratives of AI-in-Particular and AI-in-General in German Media Reporting About Artificial Intelligence

The continuing high level of media attention to advances in AI has raised scholarly concerns about hype. However, existing scholarship on AI hype considers its object of study, ‘AI’, as given and stable, rather than tracing how it is discursively constructed. This paper presents a qualitative discourse analysis of four years of AI coverage in German newspapers. It traces how the object of AI itself is constructed in discourse, rather than assuming it to be monolithic and stable. We identify two distinct constructions of AI: AI-in-general constructs AI as a promising future technology, while AI-in-particular zooms in on local application contexts in which AI is already being used. We argue that AI-in-general, and its narratives foregrounding a global technology race, reflect crucial elements of hype and also serve as a pervasive backdrop for the contextual narratives of AI-in-particular. In terms of broader debates about hype, we therefore conclude that contextual narratives are not a panacea against hype.

Computational Thinking with Computer Vision: Developing AI Competency in an Introductory Computer Science Course

Developing competency in artificial intelligence is becoming increasingly crucial for computer science (CS) students at all levels of the CS curriculum. However, most previous research focuses on advanced CS courses, as traditional introductory courses provide limited opportunities to develop AI skills and knowledge. This paper introduces an introductory CS course where students learn computational thinking through computer vision, a sub-field of AI, as an application context. The course aims to achieve computational thinking outcomes alongside critical thinking outcomes that expose students to AI approaches and their societal implications. Through experiential activities such as individual projects and reading discussions, our course seeks to balance technical learning and critical thinking goals. Our evaluation, based on pre-and post-course surveys, shows an improved sense of belonging, self-efficacy, and AI ethics awareness among students. The results suggest that an AI-focused context can enhance participation and employability, student-selected projects support self-efficacy, and ethically grounded AI instruction can be effective for interdisciplinary audiences. Students' discussions on reading assignments demonstrated deep engagement with the complex challenges in today's AI landscape. Finally, we share insights on scaling such courses for larger cohorts and improving the learning experience for introductory CS students.

A conceptual framework and review of multi-method approaches for 3D markerless motion capture in sports and exercise

ABSTRACT The increasing diversity in motion capture technologies necessitates a structured approach to review and compare different systems. This paper presents a conceptual framework based on a review of existing motion capture methodologies, ranging from single-camera configurations to multi-camera systems enhanced with depth sensing and computer vision technology. The framework encompasses three distinct approaches: 1) single-camera with depth estimation, 2) single-camera with depth sensors, and 3) multiple cameras. Each method is detailed in terms of setup procedures, calibration techniques, advantages and disadvantages, as well as data processing workflows. The paper provides a framework and guide that can be adapted to different research and application contexts for sports and exercise, ensuring accurate and reliable 3D markerless motion capture. This framework aims to assist researchers, analysts and scientists in choosing the most suitable configuration based on their sport, specific requirements and/or constraints. By outlining the processes and considerations for each setup, this paper serves as a methodological guide, facilitating broader adoption and standardisation of advanced 3D motion capture technologies for sports and exercise. Although empirical data is not included in this paper, the focus on procedural guidelines demonstrates methodological rigour and practical implementation for 3D markerless motion capture research in sports and exercise.

Real-Time Data-Driven Method for Bolt Defect Detection and Size Measurement in Industrial Production

To enhance the automatic quality monitoring of bolt production, YOLOv10, Intel RealSense D435, and OpenCV were integrated to leverage GPU parallel computing capabilities for defect recognition and size measurement. To improve the model’s effectiveness across various industrial production environments, data augmentation techniques were employed, resulting in a trained model with notable precision, accuracy, and robustness. A high-precision camera calibration method was used, and image processing was accelerated through GPU parallel computing to ensure efficient and real-time target size measurement. In the real-time monitoring system, the average defect prediction time was 0.009241 s, achieving an accuracy of 99% and demonstrating high stability under varying lighting conditions. The average size measurement time was 0.021616 s, and increasing the light intensity could reduce the maximum error rate to 1%. These results demonstrated that the system excelled in real-time performance, accuracy, robustness, and efficiency, effectively addressing the demands of industrial production lines for rapid and precise defect detection and size measurement. In the dynamic and variable context of industrial applications, the system can be optimized and adjusted according to specific production environments and requirements, further enhancing the accuracy of defect detection and size measurement tasks.

Navigating Vocabulary Learning in Mobile-Assisted Language Learning: Mapping Benefits and Addressing Challenges

The proliferation of mobile technologies has fueled the growth of mobile-assisted language learning (MALL), providing learners with opportunities to customize their language learning experiences. Numerous studies have investigated the impact of MALL on enhancing language skills such as listening, speaking, reading, and writing, with a particular focus on vocabulary development. However, in addition to the benefits, the use of MALL is accompanied by specific challenges. This study systematically synthesizes existing literature to map the benefits and challenges associated with vocabulary learning within MALL contexts. An analysis of 76 scholarly articles retrieved from the Web of Science Core Collection reveals that MALL substantially facilitates vocabulary acquisition (43%), promotes learner motivation (24%), improves retention of vocabulary items (15%), and offers authentic contexts for vocabulary application (6%). Identified challenges encompass technological constraints of mobile devices and platforms, potential negative impacts associated with smartphone usage, user skepticism regarding the effectiveness of MALL, and external environmental pressures. To fully realize the potential of MALL, further empirical research is necessary to deepen understanding of its advantages and to develop effective strategies for mitigating identified challenges.

Block Chain Applications in Healthcare: A Secure and Transparent Method for Data Sharing

Medical data includes several patient records that are crucial for further study and therapy. To preserve the privacy of the data, it must be shared and maintained securely. Blockchain's tamper-proof and decentralised properties make it a popular choice for managing healthcare data. This research assesses blockchain from a number of angles in order to investigate its evolution in the healthcare industry. We examine blockchain-based strategies in various application contexts. These comprise blockchain and the Internet of Medical Things, blockchain-based federal learning, and blockchain-based electronic medical record sharing. These comprise blockchain and the Internet of Medical Things, blockchain-based federal learning, and blockchain-based electronic medical record sharing. The results show how naturally beneficial in the domain of medical data are tamper-proof and traceable blockchain and smart contracts. In order to propel the healthcare industry forward, the difficulties and potential paths of blockchain technology are finally examined.

Skeletal Editing of Polymer Backbones and Its Impact Across the Polymer Lifecycle.

ConspectusIn the last five years, interest in the precise modification of molecular cores─termed skeletal editing─has rapidly expanded in the Chemistry community. Beyond the intrinsic value of these transformations, skeletal editing also has value in the attention it brings to under-explored chemical challenges, whose solutions could transform the practice of Chemistry at large. In few contexts does this perspective ring as true as in the realm of polymers. Inspired by the revolutionary power of biologically derived machinery called CRISPR-Cas9 to edit nucleic acid polymers and, consequently, the genetic meaning encoded in them, we envisioned that skeletal editing of synthetic polymer backbones may also enable control over the structure and "meaning"─i.e., properties and function─of plastics. However, the idea of editing polymer backbones brings about numerous fundamental chemical questions that must be answered to make the vision a reality: for instance, how to constructively activate carbon-carbon and carbon-heteroatom bonds that make up typical polymer backbones and how to do so in a site-selective manner? While many fundamental questions have begun to be answered by the small molecule community, they are yet to be applied to the realm of polymers, and such adaptation often begets new scientific challenges. Moreover, as we begin to tackle these questions, we must always consider how advances in skeletal editing of polymer backbones impact the broader contexts of applications and sustainability of plastics.In this Account, we summarize our efforts to advance the skeletal editing of polymer backbones, focusing on how such methods can affect each stage of the polymer lifecycle: (1) provide an entry to previously challenging-to-access functional polymers or to existing ones but from new feedstocks, (2) evolve one type of polymer into another with associated changes in material properties, and (3) enable the breakdown of otherwise intractable polymer backbones. Along the way, we describe our rationale behind the selection and development of reactions utilized for skeletal editing. We explain how small molecule reactions often need to be adapted to suit polymeric substrates and the methodology optimizations we needed to do to accomplish our edits. We also discuss the considerations involved in the selection or design of polymeric substrates for editing with an eye toward what edits can add to polymer function and how to advance the field. We conclude with an outlook on outstanding challenges that we aim to address in future work establishing areas for future exploration within each of our topic areas.

Quy tắc ứng xử của Ủy ban của Liên hợp quốc về luật thương mại quốc tế cho trọng tài viên trong giải quyết tranh chấp đầu tư quốc tế

On December 7, 2023, the United Nations General Assembly has adopted a Code of Conduct for Arbitrators in International Investment Dispute Resolution jointly drafted by the Commission on International Trade Law (UNCITRAL) and the International Centre for Settlement of Investment Disputes (ICSID). This article analyzes the context, objectives and scope of application of the Code. It then analyzes three major rules related to independence and impartiality, restrictions on arbitrator’s double hatting activities, and disclosure obligations.

Identifying fractal geometry in Arctic marginal ice zone dynamics

Abstract The Arctic marginal ice zone (MIZ) is the transitional region between dense pack ice and open ocean. As an increasingly important component of the polar marine environment, recent investigations have focused on changes in MIZ size and location as the climate has warmed. Fractal geometry offers a universal measure of complexity, shape, and self-similarity across scales, and a powerful tool for characterizing MIZ evolution. Here we analyze the fractal dimension of the Arctic MIZ boundary and find a pronounced seasonal cycle that is repeated almost exactly each year, with a sharp maximum in late summer. The long-term trend is slight, with a decrease of less than 2% over the satellite era, while MIZ width has increased over the same period by almost 40%. Our results have important implications for climatic and ecological processes which depend critically on MIZ geometry. We demonstrate thermodynamic feedbacks through statistical analysis and provide context for future applications.