Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)
The Journal of Knowledge Learning and Science Technology (JKLST) is a reputable, peer-reviewed academic journal that operates on an Open Access model. The journal aims to provide a comprehensive and reliable source of information on groundbreaking discoveries and the latest advancements in various areas of the field. It welcomes original research papers, review papers, case reports, short communications, and other relevant contributions. The review process of JKLST is rigorous and ensures the highest quality of published content. Manuscripts are reviewed by the journal's editorial board members or external experts who are knowledgeable in the respective fields. The review process involves obtaining approval from at least three independent reviewers, followed by final approval from the editor. This meticulous evaluation guarantees the validity, significance, and credibility of the published work. The editorial team of JKLST is committed to managing the entire submission, review, revision, and publication process efficiently. They work closely with authors to ensure a smooth and timely publication experience. The journal's online platform provides unrestricted access to its content, allowing researchers and scholars from around the world to benefit from valuable insights and findings without any barriers or subscription requirements. JKLST strives to maintain its reputation as a reliable and influential platform for disseminating knowledge and promoting scholarly discussions in the fields of knowledge acquisition, learning methodologies, and science and technology. By fostering collaboration and facilitating the exchange of ideas, the journal contributes to the advancement of the scientific community and the development of innovative solutions in these domains.
Journal of Knowledge Learning and Science Technology
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The impact of Artificial Intelligence (AI) applications on teaching quality and learner satisfaction at foreign language centers in Ho Chi Minh City
In the context of the strong educational digital transformation, mastering digital platforms and the application of Artificial Intelligence (AI) at foreign language centers in Vietnam, especially in Ho Chi Minh City (HCMC), is becoming an inevitable trend. Many nations globally have identified education as a top priority sector for AI application. In the United States, according to the 2024 educational technology report, 60% of teachers use AI tools in teaching management. In South Korea, since 2023, the Ministry of Education has piloted the use of AI for primary school students and recorded that 30% of students achieved effectiveness in personalization. In Vietnam, the Ministry of Education and Training is implementing two crucial projects: one involves digital transformation and AI application in teaching and learning; the second is to establish English as the second language in schools. However, quantitative studies assessing the impact of AI on teaching quality and learner satisfaction at foreign language centers in HCMC remain limited. This research will construct and propose a Structural Equation Model (SEM) to test the relationship between AI application, teaching quality, and learner satisfaction. Drawing upon the SERVQUAL theory, TAM, and international research on AI in education, the study proposes four hypotheses regarding the direct and mediating effects among the variables. The paper is expected to provide a crucial reference framework for foreign language centers in their strategy to integrate AI into teaching effectively, humanely, and sustainably.
Artificial intelligence (AI) in education
Artificial intelligence (AI) in education, teaching quality, learner satisfaction, foreign language centers, structural equation model (SEM), technology acceptance model (TAM), SERVQUAL/ HEdPERF, AI-integrated teaching quality model
Copyright (c) 2025 Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) https://creativecommons.org/licenses/by/4.0
Socratic Political Heritage - Ethical Challenges and the Role of Dialogue in Contemporary Society
Western philosophy has failed to find a successor to Socrates in political and ethical thought. Contemporary political ethics and civic engagement debates would still depend on his commitment to questioning, moral inquiry, and civic engagement. This paper portrays Socrates' political heritage in terms of ethical challenges and the dialogue as a means for dealing with the modern sociological predicament. It examines how the works of 20th-century philosophers such as Hannah Arendt, Michel Foucault, and Hans-Georg Gadamer reveal that the living Socratic principle is as much of a problem to be solved. This research paper evaluates the Socratic dialogue's role in resisting authoritarianism and developing democratic deliberation in contemporary politics. Finally, the paper affirms that Socratic ethics, concentrating on self-examination and critical dialogue, continues to be a strong instrument for working out contemporary political and ethical difficulties.
The Impact of Basel II Regulations on Firms' Access To Credit: Empirical Evidence From Asian Countries
This study evaluates the impact of Basel II on corporate credit access based on credit ratings, using panel data of 1,295 firms across 17 Asian countries from 2004-2015. System GMM estimation results show that after Basel II implementation, firms with high credit risk (rated BB- and below) experienced a 19.94%-23.30% reduction in credit access compared to higher-rated firms. The study also finds that factors such as operating cash flow, financial leverage, and revenue size significantly impact firms' borrowing decisions in the Basel II context. Based on these findings, the author proposes policy implications including establishing a government credit guarantee system, adjusting risk weights appropriate to Asian market characteristics, reforming the credit rating system, and encouraging banks to develop internal risk assessment capabilities.
The Role of Intelligent ERP Systems in Preventing Corporate Fraud and Strengthening Financial Governance
In an era marked by increasing corporate fraud and financial misconduct, organizations are under mounting pressure to adopt advanced technological solutions that enhance transparency and accountability. This study investigates the role of intelligent Enterprise Resource Planning (ERP) systems, integrated with artificial intelligence (AI), machine learning (ML), and real-time analytics, in preventing corporate fraud and strengthening financial governance. Drawing on both theoretical and empirical insights, the research explores how intelligent ERP systems contribute to early fraud detection, internal control enhancement, and compliance with governance standards. Using a mixed-method approach that combines a review of recent literature with data obtained from finance professionals and ERP users across various industries, the study identifies critical features such as automated anomaly detection, predictive risk modeling, and continuous auditing mechanisms that significantly reduce opportunities for fraudulent activities. Findings indicate that organizations leveraging intelligent ERP systems report improved financial integrity, better decision-making, and stronger adherence to governance policies. The study concludes that intelligent ERP systems not only serve as technological tools but also as strategic enablers of ethical and sustainable financial management. Practical and policy recommendations are provided to guide organizations and regulators in leveraging intelligent ERP capabilities for enhanced corporate governance and fraud mitigation.
Integration of Nanomaterial-Based Filtration and Smart Gas Sensing Systems for Enhanced Safety in Aviation Maintenance Environments
Maintenance workers in aviation constantly work with dangerous substances like hydrazine, JP-8, and AVGAS. The exposure to these chemicals over a long period of time can lead to the development of serious health problems. The current safety regulations in the field are still inadequate to provide complete protection for the workers during refueling and maintenance activities. This study explores how wearable technologies equipped with nanomaterial filters and gas detection sensors can improve both safety and productivity in the aviation industry. Nanomaterials such as graphene, carbon nanotubes (CNTs), and titanium dioxide (TiO₂) provide superior filtration performance because of their large surface area and ability to neutralize toxic compounds. In combination with electrochemical and metal-oxide sensors, these systems can continuously monitor air quality and instantly alert users when harmful gases are detected. The literature review and the available technologies are the major sources of information that this paper uses to justify the benefits of hybrid systems in aviation workplaces in terms of safety, efficiency, and eco-friendliness. It also presents the challenges that are still faced in the areas of weight, power consumption, and reliability over the long term, which are the reasons for the small-scale adoption. The main point is that when one combines nanomaterial filtration with advanced detection technology, the result will be a great reduction of occupational hazards and the increase of aviation safety equipment's lifespan.
Assessing the Factors of Knowledge Management Practice in Banking Systems of Bangladesh
Purpose: This study investigates the factors influencing knowledge management (KM) practices in the banking sector of Bangladesh using exploratory factor analysis (EFA). The research aims to identify the underlying dimensions of KM perceptions and agreements among bank managers. Design/methodology/approach: Data were collected from 174 respondents through a structured questionnaire and analyzed using principal component analysis. The Kaiser-Meyer-Olkin (KMO) measure confirmed sampling adequacy (KMO = 0.951), and Bartlett’s test of sphericity was significant (χ2 = 1635.549, p < 0.001). Findings: The study identified 3 key factors, including “management support and alignment”, “collaboration and culture”, and “resources and documentation”. The ANOVA results reveal statistically significant differences among bank types in perceptions of access to KM resources (p = .024) and the impact of KM culture on employee performance (p = .035). The findings reveal a moderate agreement on KM’s role in improving operations, though gaps remain in alignment and collaboration practices. Originality: This is the first initiative in Bangladesh that can guide banks in enhancing KM practices to achieve organizational objectives.
ENHANCING STUDENTS’ ENGLISH ORAL COMMUNICATION SKILLS THROUGH ROLE-PLAY ACTIVITIES: A CASE STUDY AT HANOI UNIVERSITY OF NATURAL RESOURCES AND ENVIRONMENT
Abstract: Developing oral communication competence remains a persistent challenge for Vietnamese university students, particularly among non-English majors who often exhibit limited fluency despite years of instruction. This study investigates the effectiveness of role-play activities in enhancing students’ English speaking skills at Hanoi University of Natural Resources and Environment (HUNRE). A quantitative case study design was employed with 56 first-year students at the A2 CEFR level. Data were collected through pre- and post-intervention speaking tests, assessed with an adapted analytic rubric, and a post-intervention questionnaire. Findings revealed a statistically significant improvement in students’ speaking performance after an eight-week intervention (t(55) = -5.16, p < .001), with mean scores increasing from 7.02 to 7.35. Questionnaire results further indicated that students perceived role play as engaging, motivating, and beneficial for vocabulary development and communicative confidence. However, challenges such as limited vocabulary, insufficient background knowledge, time constraints, and nervousness were also reported. The results highlight the dual nature of role play: while it fosters measurable gains in oral proficiency, its effectiveness depends on appropriate scaffolding, contextual preparation, and sufficient classroom time. The study contributes empirical evidence on role play in tertiary EFL contexts, offering practical implications for curriculum design, teacher training, and the integration of communicative tasks in general English courses. Future research should explore long-term effects and technology-enhanced role play to broaden its pedagogical impact.
Advertising as a Linguistic Tool: A Syntactic Study of English and Vietnamese Brand Slogans
Advertising slogans are one of the most influential tools for building brand identity and attracting consumer attention in today’s competitive market. Despite their widespread use, many marketing professionals continue to face challenges in crafting effective slogans, often leading to weak associations between brands and their slogans. This study aims to investigate the distinctive syntactic features of English and Vietnamese advertising slogans, with the purpose of identifying both similarities and differences between the two languages. A descriptive and comparative research design was employed, analyzing a corpus of advertising slogans collected from English and Vietnamese newspapers and magazines. The findings reveal that slogans in both languages frequently employ simplicity, brevity, and elliptical structures—such as noun phrases, adjective phrases, and verb phrases—though English slogans show a greater tendency toward ellipsis and dynamic expressions. Additionally, repetition at the syntactic level emerges as a significant device in both languages, enhancing memorability and persuasive power. These results provide valuable implications for teaching and learning business English, offering learners and teachers deeper insights into the linguistic mechanisms of advertising language. Furthermore, the study proposes practical guidelines for advertisers and copywriters to design effective slogans that strengthen brand recognition and consumer engagement.
Reinforcement Learning for Autonomous Data Pipeline Optimization in Cloud-Native Architectures
Efficient data pipeline management is critical for cloud-native architectures, where data velocity, volume, and variety challenge traditional orchestration methods. This study proposes a Reinforcement Learning (RL)-based framework for autonomous optimization of data pipelines, enabling dynamic task scheduling, resource allocation, and failure recovery without human intervention. The framework models pipeline operations as a sequential decision-making problem, where an RL agent learns optimal policies to maximize throughput, minimize latency, and reduce operational costs. Experiments conducted on simulated and real-world cloud-native workloads demonstrate that the RL-optimized pipelines achieve significant performance improvements compared to conventional static and heuristic-based scheduling strategies. This approach highlights the potential of intelligent, self-adaptive data pipelines for scalable, resilient, and cost-efficient cloud-native data processing.
Federated Reinforcement Learning for Adaptive Fraud Behavior Analytics in Digital Banking
The rapid growth of digital banking has been paralleled by increasingly sophisticated fraud attempts that adapt to detection mechanisms. Traditional centralized fraud detection models often face challenges such as data privacy concerns, scalability limitations, and delayed adaptability to emerging fraud patterns. To address these issues, this study proposes a Federated Reinforcement Learning (FRL) framework for adaptive fraud behavior analytics in digital banking. The framework enables multiple financial institutions to collaboratively train fraud detection agents without sharing sensitive customer data, thereby preserving privacy and regulatory compliance. By leveraging reinforcement learning, the model continuously adapts to dynamic fraud strategies through feedback-driven policy optimization. Experimental results demonstrate that the proposed FRL approach achieves superior detection accuracy, reduced false positives, and faster adaptation to novel fraud patterns compared to conventional machine learning and federated learning baselines. This research highlights the potential of FRL as a scalable and privacy-preserving solution for combating financial fraud in the era of decentralized and intelligent banking ecosystems.
Machine Learning-Driven Body Composition Analysis for Predicting Clinical Outcomes
This research article explores the application of machine learning techniques in analyzing body composition metrics to predict clinical outcomes. By leveraging advanced algorithms and large datasets, the study aims to improve the accuracy of body composition assessments, which are crucial for personalized medicine and effective treatment plans. The findings suggest a significant correlation between machine learning-derived body composition metrics and various clinical outcomes, demonstrating the potential of these technologies in enhancing healthcare delivery.
Performance-Based Governance of Trade Corridors: Designing a Balanced Scorecard Integrating the TCEI Dimensions
Within the boundaries of the current paper, it is suggested that performance-based governance of trade corridors should be approached with an incorporation of the three dimensions of the Trade Corridor Efficiency Index (TCEI) which includes time efficiency, infrastructure quality and geopolitical risk into a Balanced Scorecard model. The framework makes use of these dimensions of TCEI and converts them into strategic Key Performance Indicators (KPIs) to evaluate and enhance the performance of trade corridor governance. Through real-life case studies and the simulation model, the research identifies how the proposed model can make the infrastructure more efficient, align policies, and make up the resilience in geopolitically and infrastructural affected corridors. The results prove the applicative power of the TCEI-based Balanced Scorecard to serve as a decision-making tool, help to detect gaps in the performance, and inspire the specific investment. The study provides a unique instrument the policymakers and the logistics entities can use to evaluate the performance of trade corridors and the enhancement of global trade environment.
The supply chains that are present worldwide
Using Shadowing Technique to Improve English-Majored Sophomores’ English Speaking Skills at Hanoi University of Natural Resources and Environment
Abstract This research investigates the effectiveness of the shadowing technique in improving the English-speaking skills of second-year English-majored students at Hanoi University of Natural Resources and Environment (HUNRE). The study was motivated by the observation that many students struggle with fluency, pronunciation, and confidence in speaking English, as reflected in the average scores of the Speaking components in the Listening-Speaking 1 and 2 courses. Although the shadowing technique has been proven to be beneficial in enhancing speaking proficiency in various international contexts, limited studies have been conducted within the Vietnamese educational setting, particularly at HUNRE. The study employed a mixed-method approach. A pre-survey was conducted with 66 students to assess their speaking habits, challenges, and awareness of the shadowing technique. Based on these findings, 60 students were introduced to the technique and practiced it over a six-week period using structured materials. A post-survey, designed around linguistic, cognitive, and sociocultural theoretical perspectives, was then administered to evaluate improvements in fluency, pronunciation, automaticity, and learner motivation. In addition, in-depth interviews were conducted with six students representing different proficiency levels to explore their experiences and perceptions in greater detail. Keywords Shadowing technique, Speaking skills, Fluency, Pronunciation, EFL learners, second-year English-majored students at HUNRE.
Disciplines: Applied Linguistics; English Language Teaching
Keywords: Shadowing technique, Speaking skills, English-majored students, Language learning strategies, Higher education, Vietnam EFL context
Optimizing Hot Standby Redundancy Using AI for Network Traffic Balancing and Failover Management
This research explores the application of artificial intelligence (AI) techniques to optimize hot standby redundancy mechanisms in network systems, focusing on enhancing traffic balancing and failover management. By leveraging AI-driven predictive analytics and adaptive algorithms, the proposed approach dynamically distributes network traffic across primary and standby nodes to minimize latency and maximize resource utilization. The system proactively detects potential failures and orchestrates seamless failover processes, thereby improving network reliability and reducing downtime. Experimental results demonstrate significant improvements in traffic throughput, failover response time, and overall system resilience compared to traditional redundancy models. This study provides a robust framework for implementing intelligent redundancy solutions in modern high-availability networks.
AI-Agent Driven Test Environment Setup and Teardown for Scalable Cloud Applications
The increasing complexity of cloud-native and distributed applications has intensified the need for efficient, scalable, and automated testing environments. Traditional manual or script-based test environment setup and teardown processes often struggle to keep pace with rapid deployment cycles and dynamic infrastructure changes. This paper proposes an AI-agent–driven framework that automates the provisioning, configuration, and dismantling of test environments in cloud ecosystems. Leveraging machine learning for resource optimization and intelligent orchestration, the AI agents dynamically allocate computing resources, configure application dependencies, and execute teardown procedures to minimize cost and idle time. The framework supports integration with popular CI/CD pipelines, enabling seamless scaling for multi-tenant and high-availability applications. Experimental evaluations demonstrate significant reductions in environment setup time, operational overhead, and infrastructure costs, while improving testing reliability and repeatability. The proposed approach offers a robust, adaptive solution for organizations aiming to accelerate development cycles without compromising quality in scalable cloud application testing.
The Effectiveness of Using Podcasts to Improve English Listening Skills of First-Year Students at Hanoi University of Natural Resources and Environment
This study explored the effectiveness of podcasts in enhancing English listening comprehension among first-year university students in Vietnam. A quasi-experimental design was employed with 60 participants divided into control and experimental groups. While the control group engaged in traditional listening activities using textbook-based materials, the experimental group practiced listening through curated podcast content over an eight-week period. Pre- and post-tests were administered to assess gains in listening proficiency. The results indicated a statistically significant improvement in the experimental group’s performance. Qualitative feedback gathered from reflective journals further supported these findings, revealing increased learner autonomy, motivation, and engagement with authentic language input. The study underscores the pedagogical value of integrating podcasts into language instruction, especially in contexts with limited access to native speaker interaction and authentic materials. Recommendations for instructional practice and future research are discussed.
Exploring the Tourism Products Development Theoretical Framework
This article investigates the theoretical framework for tourism product development, emphasizing its significance in enhancing the global tourism sector. As tourism increasingly drives economic growth and cultural exchange, the demand for innovative and sustainable tourism products has surged. This study defines a tourism product as a combination of tangible and intangible elements that provide unique experiences for travelers. The framework integrates insights from various disciplines, including economics, marketing, and environmental studies, to address the complexities of tourism management. Central to this framework is the sustainable tourism development model, which prioritizes economic efficiency, environmental protection, and social responsibility. The article highlights the importance of destination management, asserting that effective tourism product development relies on competitive capacity, product innovation, and stakeholder collaboration. This collaborative approach ensures that products meet diverse market needs while leveraging local resources and cultural heritage. The research also emphasizes the importance of context-specific strategies in tourism development, particularly in regions like Vietnam. Here, regional characteristics and economic integration are vital for tailoring tourism offerings to meet local and international tourist expectations. Government policies play a crucial role, with supportive regulations and infrastructure investments fostering a conducive environment for sustainable tourism growth. Furthermore, the role of digital technologies and innovations is explored, showcasing how tools like big data and online platforms can enhance customer experiences and streamline operations. The article concludes by synthesizing theoretical and practical insights into a comprehensive framework for tourism product development. This framework serves as a guide for practitioners and researchers, enabling them to navigate challenges and seize opportunities in an evolving tourism landscape. Ultimately, the goal is to contribute to the sustainable growth of the tourism sector, ensuring its resilience in a rapidly changing global environment.
Applying TBLT and The PERMA Model in Teaching Business English
Nowadays, Artificial Intelligence has placed a dominant role in teaching and learning English, which raises the question if AI will be able to replace the role of a teacher in the classroom. Facing those challenges, the teachers are supposed to do things differently based on their own contexts. Therefore, the researcher has applied TBLT and the PERMA model of flourishing (Positive emotions, Engagement, Relationships, Meaning, and Achievements) in designing meaningful tasks such as modified tasks, games, and speaking videos in teaching ESP. The tasks are required to meet some criteria including cognitive engagement, social engagement and affective engagement. At University of Economics Ho Chi Minh City in Vietnam, 170 students who major in Business Studies at intermediate level participate in the research during 90 hours for two semesters. Data collected from the observation and the questionnaire proves that this model has brought many benefits to the students in terms of learning outcomes as well as positive emotions in the learning performance. The results show more cognitive engagement, social engagement and affective engagement, particularly during the more complex tasks.
TBLT, PERMA, engagement, meaningful tasks
The Impact of Social Media Influencers on The Decision to Enroll in English Language Centers in Ho Chi Minh City
The study was conducted in Ho Chi Minh City with the aim of identifying the influencing social media influencers decision to choose English language centers. The proposed theoretical model include five independent variables: credibility, attractiveness, relevance, engagement density, and product experience. A mixed-method approach combining qualitative and quantitative analyses was employed. Based on 400 valid survey responses, the findings indicate that all factors in the model positively influenced the decision to enroll in English language centers; in descending order of impact: credibility (β_SUT = 0.278), engagement density (β_TT = 0.234), product relevance (β_SPH = 0.206), attractiveness (β_SHD = 0.159), and product experience (β_TN = 0.155). These findings serve as a scientific foundation for English language centers to develop appropriate plans and strategies when leveraging social media influencers to promote their services to learners in Ho Chi Minh City.
Social media influencer, Influencer Impact, Influencer Marketing, Trust in third parties
Levitate or Stagnate: A Comparative Review of Maglev Implementation in China, Japan, and India
This paper compares high speed Maglev systems in China, Japan and India, focusing on the interplay between infrastructure costs, technology maturity and policy frameworks. Shanghai’s EMS based Maglev is a short term, state funded demonstration project with limited ridership and no network integration. Japan’s SCMaglev is a long term, scalable national infrastructure project with strong public private partnerships and phased implementation. Using operational data, cost estimates and national transportation plans we explain why China’s system did not scale, how Japan is preparing for long term resilience and return on investment (ROI) and whether India is strategically and institutionally prepared for Maglev adoption. We assess India’s position in this spectrum by looking at proposals like Mumbai-Pune and Delhi-Agra corridors in the context of land acquisition hurdles, fiscal constraints and technological readiness. The paper also looks at emerging opportunities like AI assisted grid management, modular Maglev fabrication and room temperature superconductors (RTSCs) as enablers of future viability. Governance models – centralized vs hybrid PPPs are analysed to highlight the role of institutional continuity and policy coherence. Despite technical advancements, challenges in energy demands, infrastructure integration and governance slow down Maglev scalability. Our study concludes with a framework for India’s pilot scale deployment and a future ready roadmap aligning Make in India with high speed mobility.
Room-Temperature Superconductors (RTSCs)
Federated Data-Mesh Quality Scoring with Great Expectations and Apache Atlas Lineage
The proliferation of decentralized data architectures like data-mesh introduces challenges in maintaining consistent data quality across federated domains. This research proposes an integrated framework for federated data quality scoring by leveraging Great Expectations (GE) for declarative data validation and Apache Atlas for lineage-driven impact analysis. The solution enables domain teams to autonomously define quality rules using GE, while Apache Atlas captures end-to-end lineage to propagate quality scores across interconnected datasets. This lineage-aware approach quantifies quality degradation risks downstream, providing a holistic view of data health in a decentralized ecosystem. Experimental results demonstrate a 40% reduction in root-cause analysis time and a 35% improvement in cross-domain trust scores. The framework supports scalable, domain-agnostic quality monitoring without central oversight, aligning with data-mesh principles of decentralization and domain ownership.
Consent-Driven Continuous Delivery with Open Policy Agent and Spinnaker
Continuous Delivery (CD) pipelines require robust governance to balance automation with compliance, security, and auditability. Traditional manual approval processes introduce bottlenecks, while static policy enforcement lacks flexibility. This research introduces a consent-driven CD framework integrating Open Policy Agent (OPA)—a declarative policy engine—with Spinnaker, a leading open-source CD platform. The framework leverages OPA's dynamic policy-as-code capabilities to automate deployment consents based on contextual rules (e.g., security scans, environment risks, or regulatory requirements). By decoupling policy logic from Spinnaker's orchestration, our approach enables granular, auditable, and real-time consent decisions without halting pipelines for human intervention. We validate the solution through a case study demonstrating reduced deployment latency by 65%, elimination of manual approval backlogs, and consistent enforcement of organizational policies. The integration establishes a scalable, compliant CD workflow adaptable to evolving operational demands, proving that policy-driven automation enhances both velocity and governance in modern DevOps environments.
AI-Powered Crisis Management: Revolutionizing Customer Service Emergencies
This study examines the potential of AI as a disruptive technology to address brand crisis customer service by specifically analyzing the ability of AI to deliver personalized, real-time responses that can enhance customer perceptions of a firm’s handling of a crisis situation. The research design employed online surveys, semi-structured interviews with customer service workers, a controlled experiment, and a secondary analysis of cases in a mixed-methods study. Among the most relevant conclusions, Artificial Intelligence has been proven to be able to reduce response times to initial crisis responses while adding a personalization capability driven by data. For basic requests, AI can offer immediacy such that information queries are not provided. They also highlight the ongoing need for human intervention in emotional and complex situations where empathy and nuances are key. This study indicates that an optimum level of customer satisfaction is achieved using a hybrid human interaction model. This study is limited by its narrow focus on specific AI technologies, relatively small sample size, lack of demographic diversity, and dependence on a simulated crisis environment. Practice implications indicate that the role of AI in mobilization should involve providing this type of information, an initial point of contact that can be reached quickly, and clear and easy pathways for moving problems to a human who can handle more complexity. Its value lies in informing companies of useful practical implications in organizing responses to disasters and building trust in AI. Keywords: Artificial intelligence, crisis management, customer service, brand crisis, emergency response, customer satisfaction, natural language processing, chatbots, sentiment analysis, personalized communication.
Understanding Perceptions and Practices of Translation Competence among Senior English-Major Students at Nguyen Tat Thanh University
This study delves into the translation competence of seminar students at Nguyen Tat Thanh University, offering a dual perspective from both experienced translation instructors and the students themselves. Employing a robust methodological framework that included in-depth surveys and comprehensive interviews, the research aimed to capture a nuanced understanding of their experiences. Data collected from 80 students provided critical insights into their perceptions of the current translation course, the specific challenges they encountered throughout its progression, and their perspectives on the pedagogical approaches utilized by Translation-majored faculty. Furthermore, the study also solicited valuable recommendations for future course enhancement. The findings illuminate several key areas of difficulty for students, primarily identifying lexical deficiencies, inadequate grammatical proficiency, and struggles with sentence construction as significant hurdles. Students also reported considerable challenges in comprehending implicit meaning within diverse contextual frameworks, highlighting the complexity of genuine translation. Notwithstanding these pervasive difficulties, a notable majority of students expressed general satisfaction with their perceived translation competence, suggesting a degree of confidence despite the identified obstacles. In response to these findings, the instructors offered a range of actionable recommendations, focused on optimizing the course design and implementing targeted improvements to foster enhanced translation competence among future cohorts.
This study investigates EFL teachers’ and students’ perceptions of motivational teaching strategies at a vocational school in Khanh Hoa Province. Using a mixed-methods approach, data were collected from five teachers and 300 students through questionnaires and semi-structured interviews during the 2024-2025 academic year. The findings reveal that both groups highly value strategies such as supportive teacher behavior, encouragement, recognition of effort, and a comfortable learning environment. Teachers also emphasized goal-setting and meaningful tasks, while students appreciated emotional support and structured, confidence-building activities. Although both groups shared similar views, teachers were slightly more supportive of promoting learner autonomy. Strategies involving fantasy or high difficulty were less favored. The study highlights the need for a balanced approach that blends structure, encouragement, and clear goals to enhance motivation in vocational EFL classrooms.
Geo-Techno Politics and E-Commerce in the BRICS Countries:
E-commerce, the digital exchange of goods and services, is vital in shaping global trade. Its implementation varies significantly across the BRICS nations due to the differences in technological advancement, political structures, and economic development. Investigating how these disparities influence the geopolitical dynamics of the digital economy is coined by the term Geo-Techno politics, yet the impacts are rarely mentioned. While e-commerce promises increased connectivity and innovation, contrasting regulatory approaches such as Russia's Data Localization Law and Brazil's relatively open market demonstrate deep-rooted tension in digital cooperation. Using comparative policy analysis and review of government frameworks, this study explores how state-driven agendas, data sovereignty, and inconsistent consumer protections contribute to the growing mistrust between nations and consumers and hinder the development of a unified global commerce framework. The results show that while all BRICS countries have made great strides in the development of e-commerce, diverging national priorities and regulatory models create friction in cross-border trade, affect consumer confidence, and limit collaborative innovation. This fragmentation amplifies global political divisions and highlights the need for harmonized digital policies. By identifying the geopolitical consequences of disjointed e-commerce ecosystems, this study calls for strategic cooperation among BRICS nations to develop aligned frameworks that promote trust, interoperability, and sustainable growth.
Transforming Neurological Disease Modeling and Therapy with 3D Bioprinting
Three-dimensional (3D) bioprinting is transforming neuroscience, tissue engineering, and regenerative medicine by enabling the precise fabrication of complex, functional biological structures. This review provides a comprehensive overview of the latest advances in 3D bioprinting for neural applications, including brain tumor modeling, neurodegenerative disease research, peripheral nervous system (PNS) repair, and cancer drug delivery. We discuss the principles and techniques of major bioprinting methods including extrusion-based, inkjet, laser-assisted, multimaterial, and coaxial bioprinting. The unique advantages are highlighted for engineering neural tissues and disease models. Special attention is given to the development of patient-specific brain tumor organoids, innovative scaffolds for neurodegenerative disease modeling, and bioprinted nerve guidance conduits for PNS regeneration. The review also addresses the challenges of vascularization, cell viability, and clinical translation, and explores future directions integrating 3D bioprinting with tissue engineering and digital technologies for personalized medicine. By bridging critical gaps in disease modeling and neural repair, 3D bioprinting holds the promise to revolutionize research and therapy for neurological and oncological disorders.
Adapting Agile Methodologies to Incorporate Digital Twins in Sprint Planning, Backlog Refinement, and QA Validation
Agile approaches, which offer flexibility, iterative development, and constant feedback, have emerged as the mainstay of contemporary software and hardware development. However, managing dependencies, correctly forecasting system behavior, and evaluating features under real-world circumstances are all difficult tasks for traditional agile approaches. Real-time simulations, predictive analytics, and automated testing are made possible by digital twins (DTs), which are virtual representations of digital or physical systems. The three essential agile processes of sprint planning, backlog refinement, and QA validation are examined in this paper along with how DTs might improve them. By adopting Digital Twins, Agile teams may achieve more exact sprint predictions, risk-based backlog prioritization, and automated QA validation. DTs' capacity to model task allocation and foresee system restrictions helps in sprint planning. DT-driven dependency analysis and dynamic risk assessment enhance backlog refinement, guaranteeing that priority is in line with practical viability. Lastly, by offering virtual testing environments that identify flaws before to deployment, digital twins transform QA validation. This study synthesizes insights from existing literature, industry case studies, and empirical evidence to propose an integrated Agile-Digital Twin framework. The findings suggest that organizations implementing DTs within Agile practices experience enhanced efficiency, reduced technical debt, and improved product quality. However, challenges such as high implementation costs, integration complexity, and skill gaps must be addressed. The paper concludes by highlighting future research directions, including AI-powered Digital Twins for Agile optimization and the role of DTs in DevSecOps.
An essential component of contemporary software development is quality assurance (QA) automation, which guarantees program dependability, effectiveness, and user pleasure. Traditional QA techniques, on the other hand, frequently have trouble finding flaws early in the software development lifecycle, which raises expenses and delays releases. By predicting possible flaws before they appear, predictive analytics which is fueled by machine learning (ML) and artificial intelligence (AI) offers a revolutionary approach to QA automation. This study examines how predictive analytics might improve software quality and expedite testing procedures, hence redefining defect prevention for American businesses. This study uses a systematic methodology that combines machine learning-based defect prediction with real-world case studies, analyzing defect trends and evaluating the effectiveness of predictive models. The results show that enterprises leveraging predictive analytics in QA automation experience higher defect detection rates reduced testing overhead, and faster release cycles. The study identifies key machine learning models, such as Random Forests, Support Vector Machines (SVM), and Neural Networks, which have demonstrated significant accuracy in defect prediction. It also discusses the integration of predictive analytics within DevOps and CI/CD pipelines, enabling continuous monitoring and proactive defect prevention. Defect prediction skills will be significantly improved in the future by developments in Explainable AI (XAI), deep learning models, and Natural Language Processing (NLP). In addition to supporting data-driven decision-making, model transparency, and continuous learning frameworks, this article offers important advice for businesses looking to integrate predictive analytics into their QA procedures. U.S. businesses may go from reactive to proactive QA approaches by adopting predictive analytics, which will guarantee better software quality, lower expenses, and an enhanced user experience
The burgeoning complexity of data ecosystems demands intelligent, adaptive ETL (Extract, Transform, Load) solutions. This research introduces GenAI-Driven Semantic ETL, a novel framework leveraging Generative Artificial Intelligence (GenAI) to automate the synthesis of self-optimizing SQL and PL/SQL code for ETL workflows. By integrating semantic understanding of data schemas, transformation rules, and performance objectives, the system dynamically generates code that autonomously refines execution strategies based on runtime statistics, workload patterns, and data evolution. Key innovations include a context-aware GenAI engine that translates natural language requirements into optimized procedural logic and a feedback-driven optimization loop enabling continuous code adaptation. Evaluations on enterprise datasets demonstrate 40–65% reductions in ETL latency and 30–50% lower resource consumption compared to manually tuned pipelines, while minimizing human intervention. This work pioneers the fusion of generative AI with semantic reasoning to realize truly autonomous, efficient, and future-proof data integration systems.