《知识学习与科学技术期刊》（Journal of Knowledge Learning and Science Technology，简称 JKLST）是一份享有盛誉的、经过同行评审的学术期刊，采用开放获取模式运作。该期刊旨在提供关于各个领域突破性发现和最新进展的全面且可靠的信息源。期刊欢迎原创研究论文、综述论文、病例报告、简短通讯及其他相关贡献。 JKLST 的审稿过程严格，确保所发表内容的最高质量。稿件由期刊的编辑委员会成员或相关领域的外部专家进行评审。审稿流程要求至少获得三位独立审稿人的批准，然后由编辑最终审核通过。这种细致的评估保证了所发表工作的有效性、重要性和可信度。 JKLST 的编辑团队致力于高效管理整个投稿、审稿、修订和发表过程。他们与作者密切合作，确保顺畅和及时的出版体验。期刊的线上平台提供内容的无限制访问，使全球的研究人员和学者能够无障碍地从宝贵的见解和研究成果中受益，无需任何障碍或订阅要求。 JKLST 力求保持其作为可靠且有影响力的平台的声誉，促进知识获取、学习方法和科学技术领域的学术讨论。通过促进合作和思想交流，该期刊为科学界的进步和这些领域创新解决方案的发展做出贡献。
Journal of Knowledge Learning and Science Technology
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Measuring Hydrogen Sulphide (H₂S) Gas in The Air of The Refinery And around the Refinery in Erbil – Iraq
The aim of this study is to publish the measurement results of hydrogen sulphide (H₂S) gases both inside and around petroleum refinery complex, and study the environmental and social impacts of the results, in the town of Erbil (Kurdistan Region of Iraq). These measurements were made at twelve monitoring stations at an equal distance (0–5 km) from the refinery and analyzed for a period of twelve months, with analysis also examining the output of AERMOD dispersion modelling and a hazard quotient framework. The results demonstrated that the on-site mean concentrations are above international occupational exposure and community exposure guidelines and that concentrations are a function of the distance from the source source, varying exponentially but concentrations remain significantly higher than the odour detect levels in the residences of the community ring. Winter and night time are periods of time when atmospheric stability will make for much greater exposure. The study highlights that the emissions generated in the refinery activities represent an externality which is not internalized and provides policy suggestions with regard to the need for buffer-zone regulation, monitoring infrastructure over the years and the prioritization of investment for the abatement.
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Political Leadership, Polarization, and the Failure of Democratic Transition in Bangladesh: Evidence from a Perception-Based Study
Electoral violence and difficulties in peaceful transfers of power have repeatedly challenged democratic consolidation in Bangladesh. This article examines how respondents connect political leadership, political polarization, party behaviour, and expectations of democratic leadership to electoral conflict and democratic transition. The analysis draws on a quantitative cross-sectional survey of 197 respondents from all eight administrative divisions of Bangladesh. Data were collected through a structured questionnaire and analyzed with descriptive statistics, reliability tests, cross-tabulation, Chi-square tests, composite means, and Pearson correlation. The findings show that 89.8% of respondents rated political polarization as high or very high, and the association between age group and perceived polarization was statistically significant (chi-square = 85.722, df = 12, p < .001). In addition, 89.8% agreed or strongly agreed that political parties contribute to electoral violence. Respondents evaluated existing leadership critically: 68.0% stated that political leaders did not play effective roles in reducing electoral violence, while personality-centered (87.7%), confrontational (81.5%), and authoritarian (71.8%) leadership were the most frequently identified features of political culture. At the same time, respondents strongly endorsed political tolerance, compliance with electoral law, fair participation of all parties, and avoidance of provocative rhetoric as leadership responsibilities. The expected leadership-role construct had a composite mean of 4.67 (SD = .703) and was strongly associated with transition-related problem recognition (r = .876, p < .001). The article interprets the contrast between negatively perceived leadership culture and strongly endorsed democratic leadership norms as a leadership legitimacy gap. Because the study is purposively sampled and perception-based, the results are interpreted as associations rather than causal effects or population estimates. The analysis suggests that reducing electoral conflict requires not only institutional reform but also leadership restraint, pluralist party competition, political tolerance, and credible commitment to democratic rules.
Privacy Governance Challenges in AI-Enabled Banking Surveillance Systems: From Compliance to Accountability
Background: AI technology has greatly improved banking surveillance by enhancing fraud detection, regulatory compliance, and risk management in the field. However, despite the benefits, there is a need for further studies on privacy governance problems and the factors that determine privacy accountability within AI-based banking surveillance systems. This research seeks to explore the challenges in privacy governance and the factors that drive privacy accountability in AI-enabled banking surveillance systems in the United States. Methods: The study used a quantitative cross-sectional survey research design and a structured online questionnaire. Data were obtained from 165 banking professionals through purposive sampling. Six variables were used in the study including Data Privacy Governance, Regulatory Compliance Framework, AI Surveillance Transparency, Privacy Accountability, Customer Privacy Protection, and Ethical AI Governance. Results: From the results, Ethical AI Governance had the highest level of implementation (67.9%) and readiness (63.0%). Data Privacy Breaches became the main governance issue since 41.8% of respondents viewed it as such. Pearson correlation analysis indicated a strong positive correlation between Ethical AI Governance and Privacy Accountability (r = 0.742). Also, multiple regression analysis showed Ethical AI Governance as the strongest predictor of Privacy Accountability (β = 0.341) and explained 73.6% of the variance of the dependent variable. Conclusion: It has been found that for successful privacy governance, one needs to include ethical AI, data governance, regulation, and transparency of AI in the process. This will improve privacy accountability and help to adopt AI surveillance in banking responsibly and sustainably.
Digital Transformation in Financial Management Through Artificial Intelligence and Intelligent Automation
Background: Artificial intelligence and intelligent automation are changing the way financial management is done through better analysis, efficiency, prediction, decision making, and response. Yet, there is a lack of empirical literature that brings all these together in a framework for financial transformation. Methods: The purposes of this study, a quantitative cross-sectional research method based on an online survey conducted among 175 finance and accounting professionals, as well as experts in business and technology, in the United States was used. The six dimensions considered in this study were AI Capability, Intelligent Automation, Predictive Analytics Capability, Digital Process Integration, Financial Decision Intelligence, and Financial Management Performance.Results: The respondents have indicated moderate levels for the study variables, which were 3.91, 3.89, and 3.88 for the variables Digital Process Integration, Financial Management Performance, and Predictive Analytics Capability respectively. Autonomous Financial Reporting showed highest level of usage at 58.3%, followed by Predictive Financial Analytics with 57.1% and Real-Time Financial Intelligence with 55.4%. Financial Decision Intelligence was found to be highly correlated with Financial Management Performance at 0.809, whereas Intelligent Automation and Predictive Analytics Capability showed a correlation of 0.781 and 0.769 respectively with Financial Decision Intelligence. Conclusion: The findings show that AI capability, intelligent automation, predictive analytics, and digital process integration are interrelated with financial decision intelligence and performance.
Integrating Large Language Models (LLMs) with Oracle 26AI for Advanced Enterprise Analytics and Knowledge Management
Enterprise organizations increasingly hold two categories of information assets that have historically been managed by incompatible systems: structured relational data governed by transactional database platforms, and unstructured knowledge assets such as policy documents, clinical notes, audit records, and correspondence that resist tabular representation. Oracle 26AI, the converged successor to Oracle Database 23AI, embeds vector storage, hybrid semantic and relational retrieval, and native large language model (LLM) orchestration directly inside the database engine, removing the fragile middleware layer that has traditionally connected enterprise data to AI systems. This paper examines how large language models can be integrated with Oracle 26AI to deliver advanced enterprise analytics and knowledge management capabilities, including conversational natural language querying, retrieval-augmented generation (RAG) over proprietary knowledge repositories, automated insight narration, and governed knowledge retrieval workflows. Drawing on Oracle's published architectural roadmap, established RAG and vector indexing literature, and enterprise deployment patterns observed in regulated industries such as health insurance, this paper proposes the Enterprise Knowledge and Analytics Intelligence Framework (EKAIF), a six-domain methodology spanning data convergence, semantic retrieval, LLM orchestration, analytics and insight delivery, governance and compliance, and continuous evaluation. The paper further presents integration patterns for embedding LLMs with Oracle 26AI, a retrieval-augmented generation pipeline design tailored to enterprise knowledge management, and benchmark findings drawn from Oracle 23AI vector search evaluations and comparable enterprise generative AI deployments. Results indicate that LLM-augmented analytics on a converged Oracle 26AI platform can reduce average analytical query resolution time by approximately 60 percent relative to traditional BI report cycles, achieve semantic retrieval precision above 90 percent for enterprise knowledge corpora, and reduce generative output hallucination rates by more than half when grounding is enforced through in-database retrieval. The paper concludes with a discussion of governance obligations, technical limitations, and future research directions for AI-native enterprise data platforms.
Optimizing Hybrid Cloud Database Environments with Oracle Exadata and Oracle Cloud Infrastructure
Enterprises that depend on Oracle Database technology are increasingly required to operate across two worlds at once: an on-premises data center that houses established, high performance engineered systems, and a public cloud that offers elasticity, geographic reach, and a broad portfolio of managed services. Oracle Exadata Database Machine has long served as the reference platform for mission critical, high throughput database workloads, while Oracle Cloud Infrastructure extends the same database engine and many of the same engineered system capabilities into a consumption-based cloud model through Exadata Cloud@Customer and the Exadata Database Service on OCI. This paper examines how organizations can optimize hybrid cloud database environments that combine on premises Exadata with Oracle Cloud Infrastructure, with particular attention to workload placement, network connectivity, elastic scaling, cost governance, security, and disaster recovery. The paper presents an architectural framework for hybrid Exadata and OCI deployments, describes optimization strategies that preserve the performance characteristics of Exadata while gaining the elasticity and geographic distribution of the cloud, and evaluates the outcomes of a representative hybrid deployment against a single site, on premises only baseline. The findings indicate that a well governed hybrid architecture, in which latency sensitive transactional workloads remain close to the point of data capture while analytical, archival, and disaster recovery workloads are shifted to Oracle Cloud Infrastructure, produces measurable gains in resilience, scalability, and cost efficiency without sacrificing the performance advantages associated with Exadata engineered systems. The paper concludes with practical recommendations for enterprise architects and database administrators who are planning or operating hybrid Exadata and OCI environments, together with an assessment of the limitations of the study and directions for further research.
Machine Learning-Driven Workload Management in Oracle Exadata: A Next-Generation Approach to Database
Oracle Exadata Database Machine has long served as the reference platform for high-performance, mission-critical enterprise workloads, combining engineered hardware with intelligent storage software to accelerate database operations. As enterprise IT organizations increasingly consolidate heterogeneous online transaction processing, online analytical processing, and batch workloads onto shared Exadata infrastructure, the limitations of static, threshold-based resource governance become apparent, since fixed I/O Resource Manager plans and manually tuned parallelism settings struggle to anticipate the volatile, time-varying demand patterns characteristic of modern enterprise operations. This paper presents a comprehensive analysis of machine learning-driven workload management as a next-generation approach to Oracle Exadata performance optimization. It examines the architectural foundations of Exadata that enable machine learning integration, including Smart Scan offloading, storage cell telemetry, and the I/O Resource Manager, and proposes the Exadata Intelligent Workload Framework, a structured methodology for embedding predictive workload classification, adaptive query optimization, and automated resource allocation into Exadata operations. Drawing on benchmark evaluation and a pilot deployment within a healthcare insurance enterprise IT environment, the paper demonstrates that machine learning-driven workload management reduces query queuing delay, improves resource utilization efficiency, and increases the predictive accuracy of workload demand forecasting relative to conventional static governance. The findings indicate that machine learning-driven workload management represents a meaningful evolution in database infrastructure management, although challenges related to model drift, explainability, and operational trust remain important areas for continued research.
The Evolution of Autonomous Data Platforms: A Technical Assessment of Oracle 26AI Capabilities
Enterprise database platforms have progressed over three decades from manually administered relational systems toward increasingly autonomous, artificial intelligence native engines capable of self-optimization, self-protection, and semantic data processing. Oracle's autonomous data platform lineage, beginning with policy-based automation in Oracle Database 12c and culminating in the general availability of Oracle 26AI as a long-term support release, illustrates this trajectory more clearly than any comparable commercial database product. This paper presents a technical assessment of Oracle 26AI, examining how its architecture extends the self-driving, self-securing, and self-repairing principles introduced with Oracle Autonomous Database in 2018 into a converged, AI-native platform that unifies relational, vector, JSON, and graph data models inside a single engine. The assessment traces the historical evolution of autonomous database capabilities across major Oracle releases, evaluates the architectural components that distinguish Oracle 26AI from its predecessors, including native AI Vector Search, SELECT AI natural language translation, in database large language model orchestration, JSON-Relational Duality Views, and refined autonomous operations features such as Automatic Transaction Rollback and Real-Time SQL Plan Management, and situates these capabilities within the operational realities of regulated enterprise environments such as health insurance administration. Drawing on Oracle's published release documentation, independent technical analysis, and enterprise deployment patterns, the paper proposes a five-layer reference architecture for Oracle 26AI deployments and a maturity framework for evaluating autonomous data platform adoption. Findings indicate that Oracle 26AI substantially narrows the historical gap between transactional database administration and AI application development, while introducing new governance obligations around vector data provenance, embedding currency, and in-database inference auditing that autonomous operations alone do not resolve. The paper concludes with a discussion of adoption barriers, operational risks, and research directions for autonomous, AI-native enterprise data platforms.
From Culinary Images to Cultural Meanings:
Food-related phraseological units provide valuable insights into the relationship among language, cognition, and culture because they use familiar culinary experiences to express abstract meanings. However, systematic comparisons of English and Vietnamese food-related phraseology remain limited, particularly studies integrating semantic, pragmatic, and cultural dimensions. This study therefore examines similarities and differences between English and Vietnamese food-related phraseological units through a qualitative contrastive approach. The dataset comprised 60 purposively selected, dictionary-based units, including 30 English and 30 Vietnamese expressions. They were classified into three thematic categories: food items, eating and drinking activities, and taste and cooking expressions. The analysis focused on semantic transparency, meaning-construction mechanisms, conventional communicative functions, and cultural connotations. The findings indicate that both datasets rely on metaphorical extension, symbolism, and evaluative meaning to conceptualize behaviour, emotions, interpersonal relationships, hardship, and achievement. Differences were observed primarily in lexical imagery and culturally conventionalized associations. The English units frequently draw on foods associated with British and American dietary traditions, whereas the Vietnamese units more often refer to rice-based meals, agricultural experience, family relations, gratitude, and social harmony. In both languages, the selected units serve functions such as evaluating behaviour, describing life experiences, expressing attitudes, conveying advice, and managing interpersonal relationships. These findings contribute to contrastive phraseology and intercultural communication while offering potential implications for language teaching and translation. As the study is based on a small, purposively selected dataset, the identified patterns should be interpreted as preliminary rather than representative of English and Vietnamese phraseology as a whole.
Budgeted Multi-Hop Retrieval Agents with Evidence-Chain Reliability and Cost-Aware Abstention
Fixed top-k retrieval spends the same context budget on every question without indicating whether a multi-document evidence chain is complete. This study evaluates a budgeted agent on MultiHop-RAG using all 609 documents and 2,556 questions. The 1,534/511/511 train/validation/test split kept each evidence group in one partition. The pipeline compared BM25, 256-dimensional latent-semantic retrieval, equal and weighted reciprocal rank fusion, supervised reranking, fixed depth, and calibrated dynamic stopping. A sparse reader, null detector, and isotonic calibration supported answer, evidence, citation, cost, and abstention evaluation. On 451 answerable test questions, reranking achieved 0.9069 Recall@10, 0.8064 nDCG@10, and 0.7428 complete-chain retrieval. The dynamic agent used a mean of 544.16 tokens and obtained 0.6184 F1, compared with 666.29 tokens and 0.6243 F1 for the null-gated Reranker-8 policy. Its paired F1 difference was -0.0059 with a 95% cluster-bootstrap interval of [-0.0246, 0.0115], while the 122.13-token reduction remained significant after Holm adjustment. Isotonic calibration reduced expected calibration error from 0.2671 to 0.0321. Selective answering operated at 0.3326 coverage and 0.2200 exact-match risk. Snippet retention and citation precision, rather than candidate generation alone, limited joint reliability. The results establish a measured quality-cost trade-off and identify where deeper retrieval still fails to improve answers.
Uncertainty-Aware Vision–Language Design-to-Code Agents with Iterative Design Critique and Structural Fidelity Evaluation
Design-to-code agents must recover visible content, infer latent webpage structure, generate executable HTML and CSS, and recognize when the resulting page is unreliable. This study evaluated an uncertainty-aware agent on all 484 Design2Code webpages. Three GPT-4V conditions—direct generation, text-cue generation, and critique-and-repair—supplied one candidate per page, yielding 1,452 code artifacts and complete browser renders. A CSS-grouped five-fold protocol prevented related style families from crossing fitting and evaluation partitions. Within each fold, a retrieval-render selector ranked the three candidates, and cross-fitted isotonic regression calibrated its confidence for low-confidence clarification. Fidelity was measured through render success, CLIP similarity, rendered-text agreement, block match, position similarity, CIEDE2000 color similarity, and a deterministic DOM composite combining tag overlap, tag-sequence longest-common-subsequence similarity, node-count similarity, and depth similarity. The repair condition gave a language-model design critic the target screenshot, current render, current HTML, and expected visible-text list before one bounded edit. The evaluation compared generation modes, measured iterative gains, and assessed expected calibration error, Brier score, and risk–coverage behavior. The selector achieved extended fidelity of 0.735738, compared with 0.718614 for direct generation, while calibration reduced ECE from 0.592147 to 0.026508. Browser execution was therefore treated as a necessary condition rather than sufficient evidence, connecting multimodal fidelity directly to selection, repair, and clarification decisions.
Supporting First-Year Undergraduates' Transition From General English to Business English in an EFL Context
The transition from General English to Business English presents significant cognitive and affective challenges for first-year undergraduates, particularly in English-as-a-Foreign-Language (EFL) contexts where learners often have limited prior exposure to business-related concepts and communication. This study examines a practice-based pedagogical framework designed to support novice Business English learners at a business-oriented university in Vietnam. The framework is grounded in three core pedagogical principles: affective support to reduce emotional barriers to participation; scaffolded instruction implemented through collaborative mini-projects exploring business themes and case-based tasks; and active retrieval practice through gamified vocabulary and content review activities. Data were collected through a mixed-methods end-of-course survey (N = 55), combining Likert-scale responses with qualitative student feedback. The findings indicate that students perceived the framework as highly supportive of their learning experience, with over 90% of participants rating instructional clarity at the highest level. Students also reported greater perceived improvement in speaking and listening skills than in reading skills. Qualitative responses suggest that this pattern may be associated with a supportive classroom environment in which learners felt comfortable communicating despite imperfect language use. In addition, students identified the classroom “bonus-point hunting” system as a motivating element within the gamified review activities, encouraging active participation and engagement. The study concludes that combining affective support, scaffolded collaborative tasks, and retrieval-based review activities may help facilitate engagement and perceived learning among novice Business English learners. Implications for the design of first-year Business English courses in tertiary EFL contexts are discussed.
War-Related Metaphors in Literary Works of Vietnamese Authors From 1975 to Present:
The end of combat in 1975 did not end the war’s psychological weight in Vietnam. This systematic review synthesizes twelve studies by Vietnamese authors to track how war metaphors have evolved from state-sanctioned heroism to personal witnessing. We identify a profound “humanistic turn”, where idealized revolutionary icons are replaced by trauma-stricken individuals. A central finding is the dominance of the WAR IS A WILD BEAST mapping, which strips away ideology to reveal the “moral blindness” and instinctual violence of conflict. Furthermore, the use of “metaphysical experiences” - framing war as a ghost or parasite - serves as “narrative therapy” for survivors dealing with fragmented memories. The analysis shows that the “war” domain remains a foundational cognitive mind map in modern Vietnam, used to conceptualize everything from sports competition and political tension to commercial advertising. These shifts reflect a transition from rigid national narratives toward a nuanced, therapeutic reckoning with history. Ultimately, these metaphors allow a culture to “think through” the complexities of a past that remains a living part of the present.
Code-Switching as a Cognitive Strategy in Learning English Speaking Skills across Interactional Contexts: Insights from EFL University Students
Code-switching (CS) is gradually recognized as a critical cognitive strategy in language acquisition, especially in English speaking skills. This study investigates how EFL students use CS across interactional contexts, which cognitive strategies are dominant when using CS and how they generally perceived the role of as a cognitive strategy in English-speaking activities. The study integrates mixed techniques including questionnaire, semi-structured interviews and classroom observation. The quantitative data is collected from 45 sophomore students majoring in English at Industrial University of Ho Chi Minh City (IUH). The findings indicate that CS is used across different interactional settings widely. Notably, there was balance CS use between student-student and student-teacher. Besides, CS often occurred in pair work rather than group work and when discussing complex speaking topics. Cognitive strategies emerged as the dominant mechanism driving CS usage including brainstorming, organizing ideas and language retrieval during speaking tasks. Furthermore, the results confirmed their positive perspectives on CS’ role as a beneficial cognitive and communicative resource that helped them organize ideas, maintain communication, clarify meaning, and participate more confidently in English-speaking activities. The study suggests that CS should be viewed not as a deficiency but as a valuable pedagogical and cognitive resource in language to enhance teaching effectiveness and English-speaking skills as well.
Synergistic Effects of Targeted Multi-Enzyme Systems in Biorecovery of Secondary Metabolites: from Mechanistic Insights to Optimization
The efficient recovery of plant-derived secondary metabolites has become a major research priority in response to the growing demand for sustainable production of high-value functional ingredients within the circular bioeconomy. Enzyme-assisted extraction (EAE) has emerged as a promising green extraction technology because of its high substrate specificity, mild operating conditions, and ability to preserve thermolabile bioactive compounds. However, the heterogeneous architecture of plant cell walls often limits the effectiveness of individual enzymes, emphasizing the need for rationally designed targeted multi-enzyme systems. This review provides a comprehensive and mechanism-oriented overview of recent advances in targeted multi-enzyme-assisted extraction for the recovery of plant secondary metabolites. Particular attention is given to plant cell wall architecture, molecular mechanisms of enzyme synergism, sequential deconstruction of pectin, hemicellulose, and cellulose, and the release mechanisms of matrix-bound bioactive compounds. The review further discusses rational strategies for designing biomass-specific enzyme cocktails based on substrate composition, enzyme complementarity, catalytic compatibility, and hydrolysis strategies. Recent developments in response surface methodology, kinetic modeling, artificial intelligence, and machine learning are critically evaluated as advanced tools for predictive process optimization. Emerging technologies, including multi-enzyme co-immobilization, protein engineering, synthetic biology, continuous bioprocessing, and integrated biorefinery concepts, are also reviewed with respect to their potential for improving catalyst utilization, process scalability, and industrial sustainability. Overall, this review demonstrates the transition of enzyme-assisted extraction from empirical optimization toward knowledge-driven, precision-designed bioprocesses integrating molecular enzymology, process engineering, and computational intelligence, providing a scientific framework for sustainable biomass valorization and next-generation circular biorefineries.
Strategic Financing of LNG Expansion for Infrastructure Development, Export Competitiveness and National Energy Security
Background: Liquefied Natural Gas (LNG) as a vital energy resource because it enables infrastructure development through capital investment which creates new economic opportunities and boosts export product competitiveness and enables countries to access their domestic and international energy resources. The fast-moving energy market creates an extremely tough environment for businesses to compete while facing unknown market conditions. Methods: A quantitative survey method was used to collect data through a questionnaire which assessed people view LNG project funding systems and their effects on infrastructure growth and export market competitiveness and energy availability. The study sample consisted of 215 people lived in different areas across the United States. Pearson’s correlation analysis helped us identify the connection between our main independent variable and dependent variable through statistical evaluation. Results: The study identified three main financial obstacles which block LNG growth because of lacking financial transparency and foreign exchange control and restricted access to long-term financial resources. The developers identified terminal construction as their primary infrastructural benefit which they rated at 56.7%, followed by LNG storage space at 55.1%, and pipeline system development at 54.9%. The study found the highest correlation between LNG financing and risk control which achieved an r value of 0.634 and maintained statistical significance at p = 0.01. Conclusion: The strategic financing for LNG expansion serves as a fundamental element which supports LNG growth through infrastructure construction and enables export market success and strengthens national energy security.
Extracurricular Activities in English Language Teaching as a Pathway to Sustainable Development Goal 4 in Higher Education
The incorporation of extracurricular activities into English language teaching (ELT) in higher education is increasingly important because such activities provide students with opportunities for authentic communication, learner autonomy, and intercultural interaction while also supporting their academic and professional development.This review looks at the role of extracurricular activities in achieving Sustainable Development Goal 4 (SDG 4) in higher education and ELT. Drawing on recent studies in the Asian and European sectors, the review reviews literature on the aspects of language development, employability, soft skills development, intercultural communication and sustainable learning. The study shows that extracurricular activities (speaking forums, cultural events, exchange programs and ESP workshops) and student publications can enhance student engagement and self-directed learning. However, the literature also demonstrates significant challenges with regard to unequal participation, financial accessibility, institutional support and the growing instrumentalization of extracurricular learning for employability purposes. In addition, Vietnamese higher education and selected extracurricular activities are discussed in the review at Ho Chi Minh City University of Economics and Finance (UEF). The research indicates that well-organized extracurricular activities can contribute to sustainable and holistic language education in the context of SDG 4. Keywords: extracurricular activities; English language teaching; higher education; learner autonomy; sustainable development goal 4
Cardiac Device Therapy in US Patients with Chronic Kidney Disease: Current Outcomes, Complications, and National Trends
Background: Chronic kidney disease (CKD) and end-stage renal disease (ESRD) are associated with high cardiovascular morbidity. Cardiac device therapy, including pacemakers, CRT, leadless pacemakers, and LVADs, is increasingly used, yet real-world outcomes, complications, and trends in these renal populations remain incompletely characterized. Aims: To analyze outcomes, procedural complications, and national trends of cardiac device therapy in US CKD and ESRD patients. Methods: Data were extracted from 16 published studies and national registries, aggregated, and analyzed using Python (VS Code), SPSS v27. Patient demographics, comorbidities, procedural complications, mortality, and hospital utilization were summarized with descriptive statistics. Categorical variables were reported as frequencies/percentages, and continuous variables as means/ranges. Comparative analyses assessed differences between CKD and ESRD across device types. Results: Among 29,005 patients, 5,245 had CKD (stages 3–5) and 3,790 had ESRD. ESRD patients exhibited higher procedural complications: infections (12% vs 7%), bleeding (8% vs 6%), lead dislodgement (5% vs 3%), central vein stenosis (15% vs 10%), and peripheral vascular complications (13% vs 7%). Mortality was higher in ESRD, with inpatient rates 14% versus 6.4% in CKD, and 1-year mortality 50% versus 31%. LVAD recipients had the highest risk, while CRT and leadless pacemakers showed reduced complication rates in selected populations. Hospital cost and length of stay were also elevated in ESRD. Conclusion: ESRD patients experience higher complications, mortality, and hospital utilization than CKD patients. These findings inform risk assessment, device selection, and future clinical strategies.
The Use of Generative AI by Students with Disabilities in UK Higher Education: A Mixed-Methods Survey of 125 Students
Higher education's response to generative artificial intelligence (AI) has moved quickly from curiosity to policy, yet questions of who benefits from these tools and who is excluded remain under-examined. Students with disabilities — a population long under-represented in higher education and demonstrably reliant on assistive technologies — may have much to gain from generative AI, but also face specific risks from it; empirical evidence on their actual practices is scarce. This study asks how students with disabilities at a UK university use generative AI in academic writing, what concerns they hold about it, and what institutional support they want. A mixed-methods online survey, anchored in a five-element generative AI literacy framework, was distributed to all 1,797 students registered as disabled at one UK university during February and March 2024. The survey returned 125 valid responses, a 7.0 % response rate. Closed items were analysed descriptively; open-text responses were coded inductively through content analysis. Seventy-eight percent of respondents reported using generative AI in their studies, with ChatGPT (53 %) the dominant tool, supplemented by rewriting applications such as Grammarly (10 %) and translation services. Respondents used these tools across the full writing pipeline — explaining concepts, structuring arguments, summarising readings, and refining prose. Concerns clustered around the accuracy of AI outputs, threats to academic integrity, and the inequity introduced by paid subscriptions. Participants voiced strong demand for involvement in institutional AI policy (M = 4.03 on a five-point scale) and for university-provided training (M = 3.95). The paper closes with policy and pedagogical recommendations for building a more inclusive institutional response to generative AI.
Digital Governance and Transparency in Asset Disposal: Evidence from The Gcc Region
Purpose: This study investigates the role of digital governance and transparency mechanisms in transforming the used vehicle and equipment asset disposal market across the Gulf Cooperation Council (GCC) region. The research seeks to identify prevailing governance deficiencies in the sector and evaluate how digital tools, including secure digital verification systems, integrated regulatory compliance platforms, and digital auction marketplaces, address these shortcomings within the context of the region's ongoing national reform and digital transformation agendas. Design/Methodology/Approach: The study adopts a qualitative, systematic secondary research methodology, synthesising evidence from peer-reviewed academic literature, multilateral policy reports, government publications, and international industry assessments. A conceptual framework integrating institutional theory and digital transformation theory is developed to structure the analysis across input, process, and output dimensions of digital governance adoption. Findings: The findings demonstrate that the GCC used vehicle and equipment disposal sector is characterised by significant informality, valuation inconsistencies, documentation deficiencies, and cash transaction governance risks. Digital governance mechanisms demonstrably reduce information asymmetry, improve price discovery, and enhance regulatory compliance, generating material improvements in resale optimisation outcomes. A five-stage Digital Governance Maturity Model is proposed to guide the sector's progressive digitalisation. Practical Implications: The paper provides actionable recommendations for GCC government agencies, private sector asset managers, and policymakers seeking to leverage existing digital infrastructure to strengthen governance and market efficiency in the asset disposal sector. Originality/Value: This study is among the first to systematically examine digital governance in the GCC asset disposal market, bridging institutional theory, digital transformation research, and asset management practice in an underexplored regional context.
Digital Video-Supported Flipped Instruction And Efl Writing Development
This study investigated the use of digital video-supported flipped instruction to enhance EFL writing development among first-year English majors in Vietnamese higher education. An action research design was employed with 45 first-year English-major students at Hanoi University of Natural Resources and Environment. The intervention lasted ten weeks and integrated pre-class digital videos with in-class writing practice, peer interaction, and teacher feedback. Data were collected through pre- and post-writing tests, a student questionnaire, and semi-structured interviews. Quantitative results showed significant improvements in students’ overall writing performance, particularly in content development, organization, vocabulary use, and grammatical accuracy. Qualitative findings further revealed that students perceived the videos as useful for understanding writing structure, preparing ideas before class, and revising their drafts more effectively. The questionnaire and interview data also indicated positive learner attitudes, especially regarding flexibility, motivation, confidence, and engagement. The findings suggest that digital video-supported flipped instruction can be a promising approach for improving EFL writing instruction in Vietnamese university contexts.
Integrating Machine Learning into Financial Systems to Improve Risk Management and Economic Stability
Background: Machine learning systems now operate in financial environments which have transformed all risk management operations and fraud detection systems and economic stability maintenance methods. Financial institutions now use intelligent algorithms to strengthen their decision processes which produces better predictions and lowers their operational risks. Methods: The study used a cross-sectional quantitative design to study machine learning affects the development of financial systems. The study collected primary data through a structured questionnaire which 215 professionals from banking and insurance and regulatory sectors answered using a five-point Likert scale. Machine learning implementation together with risk management system performance and fraud detection capabilities and their impact on economic stability. Descriptive statistical methods to analyze their data by calculating frequency distributions and percentage values and average scores. Results: Machine learning delivers improved financial risk prediction results through its ability to reach 84.7% accuracy while it simultaneously helps organizations cut down their operational financial losses by 79.5%. The fraud detection system achieved its highest performance level when it successfully detected suspicious transactions within 86.0% of cases. The research findings reveal that 81.4% of participants experienced a decrease in cyber financial fraud incidents while 83.7% of them reported better financial security which led to economic stability. Conclusion: Machine learning serves as an essential tool which researchers discovered to enhance financial system security through its ability to improve risk assessment and detect fraudulent activities and maintain economic stability.
Blockchain and AI Integration in Supply Chain Management: Transforming Transparency, Security, and Efficiency in Material Flow Systems
Background: Over the last few years, blockchain and artificial intelligence (AI) applications in supply chain management (SCM) have become a transformational solution to increase transparency, security, and efficiency. Conventional supply chain systems are associated with inefficiencies, information silos, faddiness, and deficiency of traceability, which greatly impede efficiency. The synergies of a decentralized and safe data format of blockchain and predictive performance of AI have the chance to resolve these issues and streamline supply chain processes. Methods: The study employed the mixed-methods approach, which included secondary data gathering based on peer-reviewed articles, industry reports, and case studies related to blockchain and AI integration. The study utilizes statistical analysis to investigate current trends and associations and machine learning algorithms are used to examine how blockchain and AI can influence supply chain performance indicators, including demand forecasting, inventory management, and operational effectiveness. The data on different industries such as food, pharmaceuticals and logistics were studied to have a glimpse of the real-world uses of such technologies. Results: The findings indicate that blockchain and AI integration have a significant positive impact on transparency as it creates an unalterable and up-to-date version of each transaction and improves operational efficiency since it allows for the optimization of resource distribution, demand forecasting, and inventory control. Blockchain offers verifiable data, which is reassured, and AI can make predictive analytics and automation that can decrease errors and enhance decision-making initiatives within supply chains. Conclusion: A combination of blockchain and AI is an exciting option to revolutionize supply chain management. Although other issues such as scalability and system integration are still facing challenges, the benefits of these technologies are enormous in terms of security, transparency and efficiency.
Design and Implementation of Digital Protection Systems for High-Voltage Transmission Lines and Power Transformers
Background: High-voltage transmission lines and power transformers need dependable protection systems which maintain their operational status to deliver continuous power distribution. Methods: The research used a quantitative cross-sectional survey design which gathered data from 250 power sector employees who worked as engineers and technicians and operators. The research employed descriptive statistics together with Pearson's correlation and multiple regression and ANOVA to study how people understood things and how they performed and used what they learned. The research team used Cronbach's alpha to evaluate instrument reliability for their study. Results: People view fast fault detection at 22 % and reduced downtime at 20 % and improved accuracy at 18 % as their main advantages. The main obstacles for system implementation consist of expensive startup expenses which affect 24 % of cases and complex technical requirements that impact 20 % and insufficient trained staff members who represent 18 percent. The evaluation of performance demonstrates that fault detection speed has improved by 20 % and reliability has improved by 19 %. The analysis shows that people who understand a system tend to adopt it more (r = 0.45) and those who receive training develop better implementation skills (r = 0.41) but the cost factor creates a negative impact on adoption rates (r = −0.38). Conclusion: Digital protection systems provide power systems with better performance and enhanced reliability through their operation. Strategic investment along with capacity building and supportive policies need to exist for power systems to achieve successful implementation and modernization.
Development of a system-multi criteria analysis on erosion and sediment control in construction industry
Background: The construction industry faces erosion and sediment control (ESC) as its main environmental problem because urban areas of the United States experience fast population growth. The natural soil structure faces disruption during construction work which leads to elevated risks of water erosion that carries sediment particles which then harm both water quality and drainage systems and environmental balance. Methods: The research establishes a system-based Multi-Criteria Decision Analysis (MCDA) framework which evaluates different ESC practices. The research team distributed structured questionnaires to 175 construction industry workers who included engineers and contractors and project managers and environmental consultants based in the USA. The research team used descriptive statistics together with reliability testing (Cronbach’s Alpha) and correlation analysis and weighted MCDA techniques to analyze the data. Results: The research shows that storm water Management functions as the leading factor with a mean score of 4.41 followed by Regulatory Compliance which scored 4.35 and Site Stability received 4.29. The reliability measurements showed values between 0.79 and 0.88 which proved the instrument maintained strong consistency between its elements. The correlation analysis revealed that all variables maintained strong connections with each other while storm water Management and Site Stability showed the strongest connection at r = 0.73. Conclusion: ESC performance in American construction sector depends most on environmental factors and regulatory requirements instead of financial elements. MCDA framework solution provides a functional decision-support system which helps sustainable construction practices move forward while decreasing their negative impact on the environment.
Comparative Analysis of Postgraduate Clinical Integration Pathways for MedicalGraduates: Lessons from the United Kingdomand Europe for the United States
The persistent mismatch between medical school graduates and available postgraduate training positions continues to challenge healthcare systems worldwide. This comparative analysis examines how the United States, the United Kingdom, and Europe manage the transition from medical education to supervised clinical practice. Data were drawn from the Association of American Medical Colleges (AAMC), General Medical Council (GMC), Organisation for Economic Co-operation and Development (OECD), and European Commission to compare regulatory structures, supervision mechanisms, and workforce integration outcomes. Findings reveal significant disparities in post-graduation employment rates. In the United States, the 2025 National Resident Matching Program recorded 44,853 applicants for 41,403 positions, leaving over 3,400 unmatched graduates (7–8 percent) and an estimated 10,000 inactive physicians from prior years. Conversely, the U.K. Foundation Programme placed 99.5 percent of eligible graduates in supervised practice within three months, while European systems such as Germany’s Arzt in Weiterbildung and France’s Internat achieved 93–96 percent integration within the first year. These structured frameworks ensure that nearly all qualified physicians enter paid supervised employment, effectively eliminating post-graduation inactivity. The comparative evidence indicates that nations with centralized postgraduate supervision achieve faster workforce absorption, better skill retention, and improved healthcare service continuity. The study concludes that the United States could mitigate its ongoing physician shortage by adopting a national supervised clinical licensure framework, harmonizing state-level policies, and expanding structured employment opportunities for unmatched medical graduates.
Integrating Renewable Energy into Sustainable Development Pathways for Climate Change Mitigation
Renewable energy has now emerged as one of the main focuses of sustainability development worldwide in order to reduce climate change. With the ever-increasing energy demands globally, the restrictions and environmental consequences of the use of fossil fuels energy systems have aggravated the necessity to use cleaner, more sustainable energy systems. Ecologically sustainable sources of energy like solar, wind, hydroelectric, biomass, geothermal and ocean energy are sources of renewable energy that can be utilized to supply electricity and energy to the environment. These energy systems greatly lower the greenhouse gas emissions and air pollution, hence enhancing the quality of environment and people health. Besides this renewable energy technologies increase energy security by lessening dependence on imported fossil fuels and stabilization of long-term energy prices. Although these are the advantages, transitioning to renewable energy is associated with a number of challenges such as the high-start-up costs, lack of access to the funds, regulatory barriers, inadequate infrastructure, and technological considerations. Despite most developing countries having large amounts of renewable energy, they usually have low allocations of clean energy investment in the world. Case studies conducted in nations like Denmark, Uruguay and Namibia over the years have shown that high political commitment, good policy schemes, and good governance are key to effective renewable energy development. In addition, international collaboration and international climate treaties are useful in enhancing the use of renewable energy through the provision of financial incentives, technology sharing and collective effort.
Translation Quality Assessment of the Vietnamese Translation of The Da Vinci Code Using House’s Model
This study investigates the interplay of overt and covert translation in the Vietnamese translation of The Da Vinci Code by Dan Brown, rendered into Vietnamese by Đỗ Thu Hà, within the framework of Juliane House’s Translation Quality Assessment model. It aims to identify the dominant translational orientation of the Vietnamese version and to examine how source-text features are preserved or adjusted across the dimensions of Field, Tenor, and Mode. Employing a mixed-methods approach, the study combines qualitative textual analysis with quantitative support through the comparison of selected source-text and target-text pairs. The findings indicate that the Vietnamese translation is predominantly oriented towards overt translation, as it largely retains the cultural specificity, interpersonal meanings, and discourse organization of the original text. At the same time, certain covert adjustments are observable in order to enhance naturalness, readability, and accessibility for Vietnamese readers. Overall, the translation reflects a careful negotiation between fidelity to the source text and adaptation to the linguistic and cultural expectations of the target readership. The study highlights the applicability of House’s model to literary translation assessment and contributes to a more nuanced understanding of how culturally embedded and stylistically marked texts are mediated in translation.
The Role of Circular RNAs in Hepatocellular Carcinoma: Mechanisms, Implications, and Therapeutic Potential
Background: Hepatocellular carcinoma (HCC) is one of the most common malignancies and a predominant cause of cancer-related death worldwide, featuring high metastasis potential and resistance to chemotherapeutic agents. Both early diagnosis and good treatment options remain major challenges of HCC management. Recent studies have shown that exosomal circRNAs (circular RNAs) can serve as promising diagnostic and therapeutic markers for cancers, given their stability in biological fluids and the involvement of many oncogenic pathways through which they regulate cancer progression. Methods: This review focuses specifically on exosomal circRNAs, examining their functions during tumor progression, metastasis, and chemoresistance in HCC. We also describe the ways circRNAs are involved in miRNA-mediator interactions and epigenetic regulation of HCC. We performed a systematic review of the literature, including articles published up to 2015-2025, in the databases of PubMed, Google Scholar, and Web of Science. We selected relevant studies focusing on circRNAs and their involvement in HCC along with miRNA interactions. Outcomes: The extensive involvement of exosomal circRNAs in modulating miRNA signaling pathways leads to modulation of processes such as angiogenesis, epithelial-mesenchymal transition (EMT), and metastasis in HCC. They further also promote chemoresistance by modulating the processes of drug resistance and immune evasion. Moreover, circRNA87th53-derived CE-Ang1 is a promising therapy for HCC. Conclusion: Exosomal circRNAs present potential as promising biomarkers and therapeutic targets for HCC. More studies should be performed to investigate their clinical implications and to resolve problems with circRNA identification and functional delivery.
Enhancing English Vocabulary for Kindergarten Learners Through Game-Based Activities: A Case Study of AlphaTUB
Vocabulary acquisition is the cornerstone of early language development, yet traditional rote-learning methods often fail to engage the limited attention spans of young learners. This study investigates the impact of Game-Based Learning (GBL) on English vocabulary acquisition among kindergarteners (ages 3–6), specifically focusing on the integration of the AlphaTUB platform. Utilizing a case study methodology across 10 bilingual and international classes, the research triangulates data from classroom observations, semi-structured teacher interviews, and digital performance reports. The findings suggest that gamified interactions—characterized by immediate feedback, multisensory engagement, and physical movement—significantly enhance word retention and learner motivation. The study concludes that digital-physical hybrid tools like AlphaTUB provide a "low-anxiety" environment that facilitates natural language uptake.