In the era of digital transformation, datacenters and High Performance Computing (HPC) Systems have emerged as the backbone of global technology infrastructure, powering essential services across various industries, including finance and healthcare. Therefore, ensuring the uninterrupted service of these datacenters has become a critical challenge. Thermal anomalies pose a significant risk to datacenter operation, potentially leading to hardware deterioration, system downtime, and catastrophic failures. This threat is exacerbated by the growing number of datacenters, increased power density, and heat waves fostered by global warming. Detecting thermal anomalies in datacenters involves several challenges. Large-scale data collection is difficult, requiring diverse monitoring signals from thousands of nodes over long periods. The absence of labeled data complicates the identification of normal and abnormal states. Establishing accurate classification thresholds to minimize false positives and negatives is another significant hurdle. Traditional statistical methods often fail to capture temporal dependencies and complex correlations in monitoring signals. Additionally, finding anomalies at both the system and subsystem levels adds to the complexity. Deploying machine learning models in production environments presents technical and operational challenges, making real-time anomaly detection a demanding task. This paper introduces ThermADNet, a Thermal Anomaly Detection framework that combines statistical rules-based methods with Deep Neural Network (DNN) techniques for thermal anomaly detection in datacenters. ThermADNet utilizes a semi-supervised learning approach by training on a ”semi-normal” dataset, addressing the challenges of large-scale data collection, semi-normal dataset identification, and classification threshold establishment. This framework’s efficacy is validated by its success in identifying real physical thermal failure events within a Tier-0 datacenter, pinpointing anomalies at both the system and subsystem levels, including compute nodes and datacenter infrastructure. In the critical evaluation window covering the July 28 failure, ThermADNet achieves precision and recall up to 0.97, with F1-scores as high as 0.97. By providing detailed information about anomalies, the framework clarifies the characteristics and reasoning behind the DNN outputs, thereby building trust in the AI model and ensuring that users can understand and rely on the system’s decisions. By offering a sophisticated method for thermal anomaly detection, ThermADNet significantly contributes to enhancing datacenter reliability and efficiency. This advancement supports the uninterrupted operation of critical HPC systems, averting considerable economic and societal losses.

This paper proposes a novel technical architecture for university business software systems in the era of large language models focused on digital intelligence empowerment. The architecture addresses limitations in traditional university data governance frame-works by adopting a ”large platform + small applications” approach, implementing a design solution of “one support platform + three atomic component groups + one service portal.” The intelligent connection infrastructure platform serves as the architectural foundation, supporting the development of atomic data, atomic business, and atomic capability components. These components collaboratively deliver personalized services through an intelligent service portal. Based on this architecture, a complete software ecosystem featuring agile data governance and intelligent technology empowerment can be constructed. The expected application scenarios and analysis demonstrates that this architecture overcomes traditional data governance constraints while fully leveraging intelligent technology capabilities. It supports the sustainable evolution of intelligent business application ecosystems and delivers substantial technical infrastructure for the digital transformation and intelligence enhancement initiatives within higher education.

Gene therapy for Haemophilia B has received FDA approval, offering patients a transformative therapeutic option. However, effective communication about the benefits, risks, long-term efficacy and follow-up of gene therapy remains essential for informed decision-making. This study aimed to explore the diverse expectations, concerns and perspectives of patients with Haemophilia B, their caregivers and healthcare professionals (HCPs) regarding gene therapy and to identify strategies for improving communication. A prospective qualitative study was conducted using semi-structured interviews with male patients aged ≥12 years with moderate or severe Haemophilia B (factor level ≤ 2%), their caregivers and HCPs (physicians, nurses, social workers, advanced practice providers and pharmacists). Interviews were audio-recorded, transcribed and analysed thematically. Thirty participants were interviewed, including 15 patients (mean age, 21.1 years), caregivers and 15 HCPs across the United States. Patients and caregivers emphasised five themes: (1) current challenges; (2) hope and optimism; (3) concerns and skepticism; (4) the complex emotional challenges of decision-making and (5) preferences for transparent, patient-friendly communication. HCPs identified four complementary themes: (1) variable patient knowledge; (2) the need for transparency in discussing 'curative' language; (3) factors influencing treatment decisions (trust, prior experiences and financial concerns) and (4) strategies to improve communication (clear language, visuals and testimonials). Stakeholders view gene therapy as both promising and uncertain. Targeted educational interventions, transparent communication and patient-centred decision discussions are essential to fill knowledge gaps and support informed consent in this transformative era of treatment for Haemophilia B.

ABSTRACT Industry 5.0 marks a transformative era in manufacturing, with Collaborative robots (Cobots) positioned at the core of this paradigm shift. Cobots are designed to work alongside human operators to enhance productivity and efficiency in manufacturing environments, yet they face several deployment barriers, including technological limitations, integration complexities, financial constraints, and workforce adaptation issues. This study identifies the barriers to Cobot deployment in developing countries, with a focus on Pakistan’s manufacturing sector, using a sequential exploratory design that integrates qualitative analysis, ISM – MICMAC modelling, and SEM validation. Interviews with industry stakeholders identify five thematic categories of barriers: social, organizational, ethical, economic, and regulatory. The findings reveal both commonly reported barriers and three novel, context-specific barriers: lack of business case (B7), lack of vendor support (B11), and lack of infrastructure and energy reliability (B8). The integrated ISM – MICMAC – SEM framework identifies the hierarchical relationships among barriers and validates their overall influence. This novel framework serves as a decision-support tool enabling engineering managers and policymakers to prioritize strategies and strengthen organizational readiness for Cobot deployment. The empirically grounded framework addresses a key literature gap and provides a theoretical foundation for future research.

Breast and ovarian cancers are major public health challenges, with rising breast cancer incidence and persistently high ovarian cancer mortality. Approximately 10% of breast cancers and up to 25% of ovarian cancers are attributable to germline pathogenic variants (also called mutations) in cancer susceptibility genes, most notably BRCA1/2, but several other genes also contribute to risk of ovarian and breast cancers. Identification of pathogenic variants enables targeted prevention and early detection strategies, including enhanced surveillance and risk-reducing surgery, which can significantly decrease cancer incidence, morbidity, and mortality. We seek to review the specific risks of breast and ovarian cancer-associated genes and management strategies. Advances in sequencing technology, reduced sequencing costs, and legislative protections against genetic discrimination have expanded access to genetic testing, and clinical guidelines have broadened the pool of individuals recommended for testing. Despite these advances, most individuals with hereditary cancer syndromes remain undiagnosed, limiting the reach of precision cancer prevention. Underutilization of genetic testing is particularly pronounced among underserved populations, perpetuating disparities in health outcomes. Emerging health care delivery models have improved uptake and reduced barriers and can be considered for integration into obstetrics and gynecology practices. Ongoing research into the fallopian tube origin of high-grade serous ovarian cancer has prompted research that may expand risk-reducing surgical options. Collectively, these innovations mark a transformative era in hereditary cancer care, emphasizing access, multidisciplinary integration, and the urgent need for novel delivery models to ensure that the benefits of genetic testing reach all individuals at risk.

In the current era of digital transformation and global sustainability, 21st-century skills including technological literacy and environmental awareness are crucial competencies for students. Artificial intelligence (AI)-based teaching integrated with sustainability values ​​is seen as an effective, creative, and engaging pedagogical strategy to improve these skills in physical education. The background of this research is based on the limitations of previous studies, the majority of which only focused on basic manual techniques without considering the impact of infrastructure on the environment or the efficiency of data-based evaluation. This study aims to systematically analyze the literature on the role of AI in evaluating handball measurement test instruments and managing environmentally friendly infrastructure to support the Sustainable Development Goals (SDGs) targets. The method used is a Systematic Literature Review (SLR) with PRISMA design, including analysis of Scopus and ERIC databases using VOSviewer and Harzing's Publish or Perish software. The results show that AI integration has a significant positive impact on the objectivity of motor assessment and the efficiency of facility management, while scaffolding variables in teachers' digital literacy are key factors influencing the success of the implementation. The findings reveal that facilities not based on sustainability principles fail to provide long-term contributions to students' psychomotor development in a global context. In conclusion, this new paradigm demands a repositioning of the physical education curriculum that synergizes smart technology with green facility management to create an inclusive and sustainable future for sport. Keywords: Artificial Intelligence, Handball, Physical Education, Facilities and Infrastructure, Sustainable Development Goals (SDGs).

This study aims to analyze the role of the digitization of Accounting Information Systems in improving the operational efficiency of business entities through a conceptual literature review approach. The method used is a literature review with a thematic-critical analysis of various relevant scientific literature in the fields of accounting and information systems. The results of the study indicate that the digitization of the AIS through automation, cloud computing, and real-time systems can improve operational efficiency by accelerating the flow of information, reducing recording errors, and lowering operational costs. Additionally, digital systems enhance the accuracy and transparency of financial reports, supporting more effective managerial decision-making. However, the implementation of digitalization still faces challenges such as data security, technological infrastructure limitations, and low human resource readiness. A synthesis of the literature indicates that the success of AIS digitalization is largely determined by the integration of technology and the organization’s overall readiness. Therefore, AIS digitalization is positioned as a strategic instrument in enhancing the efficiency and competitiveness of business entities in the era of digital transformation

Digital transformation has transformed various aspects of people’s lives, yet not all groups have been able to adapt effectively, particularly older adults who are vulnerable to digital exclusion. This study aims to examine the forms of the digital divide among older adults and the role of Generation Z as mediators in bridging access to digital services. The method employed is a descriptive qualitative approach through a literature review, analysing various relevant academic sources. The findings indicate that the digital divide among the elderly is multidimensional, encompassing limited access to technology, low digital literacy, limited technical skills, information gaps, psychological factors, and digital service designs that are not yet inclusive. Furthermore, social factors such as limited social networks also reinforce digital exclusion among older adults. In this context, Generation Z plays a crucial role as mediators who not only provide technical assistance but also act as technology interpreters, facilitators of informal learning, and providers of psychological support. Interactions between older adults and Generation Z reflect the role of social capital in enhancing digital inclusion through trust and interpersonal relationships. Therefore, intergenerational collaboration is a key strategy in creating an inclusive digital transformation and empowering older adults to adapt independently in the digital age.

The convergence of nanotechnology and immunotherapy has ushered in a transformative era in cancer treatment, offering new strategies to overcome pharmacokinetic limitations and immune evasion associated with conventional therapies. While immunotherapy, spanning checkpoint inhibitors, adoptive cell transfer, and cancer vaccines, has revolutionized oncology, its efficacy remains constrained by the immunosuppressive tumor microenvironment (TME), off-target toxicity, and poor biodistribution of therapeutic agents. This review elucidates how engineered nanoparticles (NPs) are redefining immune-oncology by enabling the precise delivery of immunomodulators, antigens, and genetic payloads to target cells, while reprogramming the TME to convert "cold" tumors into immunogenic "hot" landscapes. A literature search was conducted using PubMed, Scopus, and Google Scholar. The review was performed in a narrative and non-systematic manner, focusing on studies addressing nanotechnology-enhanced cancer immunotherapy. We dissect the physicochemical and functional versatility of NPs, emphasizing size-, charge-, and ligand-dependent strategies to enhance lymph node targeting, APC activation, and sustained cargo release. Innovations in metallic, lipid-based, and biomimetic NPs are highlighted, including gold and lipid-based NPs for enhanced immune responses. Furthermore, we explore combinatorial approaches, such as NP-mediated co-delivery of checkpoint inhibitors and chemotherapeutics, which amplify cytotoxic T-cell responses and mitigate systemic toxicity. Clinical advancements, including Nab-Paclitaxel and mRNA-loaded lipid NPs, underscore the translational potential of these platforms, with trials demonstrating improved survival and manageable adverse profiles. However, challenges persist in optimizing targeting precision, scalability, and long-term safety. Integrating breakthroughs in material science, immunology, and bioengineering, this review charts a roadmap for next-generation nano-immunotherapies, advocating patient-specific designs and multimodal regimens. As the field strides toward clinical maturity, nanotechnology is poised to unlock the full potential of immunotherapy, paving the way for adaptive, immune-guided, and potentially curative cancer therapies.

In an era of rapid digital transformation, the reliance on traditionally inherited faith is increasingly challenged, necessitating adaptive, student-centred pedagogical approaches such as Outcome-Based Education (OBE). Despite its growing implementation, the effectiveness of OBE in fostering genuine spiritual growth remains under-explored, particularly regarding the subjective lived experiences of students. This study employs Interpretative Phenomenological Analysis (IPA) to explore how twelve Generation Z students navigate their faith transformation within an OBE-based Catholic Religious Education (CRE) framework. Data were gathered through open-ended questionnaires and semi-structured, in-depth interviews with these twelve participants, subsequently analysed to identify patterns of meaning-making and reflective processes. The findings reveal a significant transition from ‘inherited faith’ – rooted in family tradition – to a ‘personal faith’ forged through reflection on authentic life experiences. Furthermore, while the digital natives value technology as a supportive ecosystem, they regard physical, in-person interaction as the primary catalyst for spiritual development. Conceptually, this research contributes to a humanist-reflective model of OBE that integrates technology as a supplementary tool, rather than a primary indicator of spiritual growth, within the contemporary Catholic educational landscape.

ABSTRACT This study investigates conceptions of life and death during the culturally integrative period at Northern Wei Pingcheng by combining stable isotope and mortuary analyses. For the broader population at Qilicun, the enduring importance of kinship is reflected in both shared diets in life and joint interment after death, demonstrating a tangible continuity between life and death. For Xianbei elites, as seen in tomb M29, the constructed mortuary narrative selectively integrates agrarian, nomadic, and external cultural elements, strategically reworking life history into a legitimized posthumous identity. This study reveals how life and death were materially expressed through mortuary practices across different social strata during a culturally transformative era.

Background. The use of big data in higher education planning is a strategic issue in the era of digital transformation, demanding data-driven decision-making. Purpose. Aims. This research aims to analyze the characteristics and sources of big data, its use in national education planning, the challenges it faces, and the strategies for its strengthening. Method. The research uses a qualitative, descriptive-exploratory design, employing interviews with resource persons from the National Development Planning Agency and documentation studies. Results. The results of the study show that big data in higher education has complex characteristics, is large and diverse, and is dynamic. Its use has been employed in mapping human resource needs and policy formulation, but it remains descriptive. The main challenges faced are data fragmentation, limited system integration, human resource capacity, and data governance. Conclusion. The ideal model emphasizes cross-sectoral integration based on analytics technology. Therefore, it is necessary to strengthen digital infrastructure, human resource capacity, data integration policies, and data-based culture to support more effective and sustainable higher education planning.

Challenges of Digital Services in Public Administration in the Era of Digital Transformation: A Systematic Literature Review

The integration of Artificial Intelligence (AI) in education is increasingly urgent in the era of digital transformation, yet AI literacy levels among Indonesian educators remain insufficiently mapped. This study aims to analyse AI literacy levels among primary and secondary school teachers in the context of digital teaching material development in Yogyakarta. Using a descriptive-qualitative design with an observational approach, the study involved 161 educators across educational levels observed during four AI, AR, and VR workshop sessions in Yogyakarta. Data were collected through structured observation and field notes, and analysed using reflexive thematic analysis. Results revealed significant differences in generative AI exposure: all teachers with less than five years of teaching experience (49 teachers, 100%) had been exposed to generative AI, while 72.32% of senior teachers (81 out of 112) had comparable exposure. Thematic analysis identified three main themes: (1) varying patterns of AI exposure and technology readiness, (2) barriers to AI adoption encompassing infrastructure, mindset, and competency factors, and (3) preferences for practice-based training. These findings are interpreted through the Technology Acceptance Model (TAM) and Diffusion of Innovation frameworks, contributing to theoretical understanding of intergenerational gaps in educational technology adoption in Indonesia.

This study examines the status of workers’ rights in the era of digital transformation in the Kingdom of Saudi Arabia. Digital transformation has shifted labor relations from traditional patterns to more modern forms, while the Saudi regulator has shown clear concern for safeguarding workers’ rights, given that workers are generally the weaker party in contractual relationships. The study addresses a central question concerning the extent to which digital transformation has affected workers’ rights in Saudi Arabia, through clarifying the concept of digital transformation and analyzing the most prominent recent developments introduced in line with the digital transformation strategy under the Saudi Labor Law. The study is divided into two sections. The first examines the concept of digital transformation, its origins, characteristics, and resulting challenges. The second discusses the contributions of digital transformation to strengthening guarantees for workers’ rights, while also highlighting the main issues related to workers’ rights in the digital environment. The study aims to define digital transformation, explain its role in regulating labor practices, and identify the most prominent digital initiatives that have contributed to strengthening workers’ rights. It also seeks to demonstrate the extent to which digital transformation is consistent with the fundamental principles of the Saudi Labor Law, and to explain the challenges created by the digital environment, particularly those related to workers’ data and privacy. The study finds that digital transformation in the labor sector has introduced new tools for protecting workers’ rights and promoting transparency, such as the Qiwa platform, the Wage Protection Program, and the requirement to authenticate employment contracts so that they may serve as enforceable instruments for claiming wages in the event of non-payment by the employer. The study concludes by recommending that the Ministry of Human Resources and Social Development organize periodic training courses for employers and workers on digital transformation, its tools, and workers’ rights.

This study aims to analyze the effect of human resource (HR) development strategies on the competitiveness of higher education institutions in the era of digital transformation. The increasingly dynamic educational environment, characterized by the integration of digital technologies, requires universities to have adaptive, innovative, and competent human resources. This research employs a quantitative approach with an ex post facto design, involving lecturers and educational staff as respondents. Data were collected through questionnaires that had been tested for validity and reliability. Data analysis was conducted using linear regression to determine the relationship and influence between variables. The results indicate that HR development strategies, including digital-based training, enhancement of technological competencies, and strengthening of organizational culture, have a positive and significant effect on the competitiveness of higher education institutions. Universities that systematically and sustainably develop their human resources tend to be more competitive in facing global challenges, improving the quality of educational services, and strengthening institutional reputation. In addition, digital transformation acts as a supporting variable that strengthens the relationship between HR development and organizational competitiveness. The findings highlight the importance of formulating integrated HR development strategies aligned with institutional digitalization policies to achieve sustainable competitive advantage.

This research is motivated by obstacles in the implementation of character education at SMP Negeri 1 Loceret, particularly in the aspect of learning media that is less innovative in the era of digital transformation. The focus of this research is to develop Normapedia interactive media integrated with the SATRIA BAIK (One Day Three Goodness) program to strengthen students' moral actions in a real way. Using the Research and Development method with the ADDIE development model, this research goes through the stages of analysis, design, development, implementation, and comprehensive evaluation. The results of the validity test show a very high level of product feasibility reaching 91.87%, covering aspects of material, character, and media design. In the effectiveness test stage on 32 students, a significant increase in moral actions was found with an average score that jumped from 65.12 in the pretest to 84.56 in the posttest. This increase of 19.44 points is supported by an N-gain value of 0.71 which is in the high category and a significance of 0.000. Qualitatively, this program succeeded in increasing discipline consistency by 87% and social empathy by 81%. The main conclusion confirms that the synergy between interactive normative content and digital daily reflection features effectively builds a measurable and sustainable moral habitus. This innovation bridges the gap between students' understanding of ethical theory and their daily behavioral practices in a junior high school environment through the systematic use of technology. This medium supports the creation of a generation of intelligent and high-integrity learners.

Abstract— In the era of digital transformation in education, providing real-time, personalized guidance to prospective students remains a significant challenge for large universities. This paper presents PU Buddy, an AI-powered Education Career Guidance Chatbot designed specifically for Parul University, Vadodara. The system leverages the Google Gemini 2.0 Flash large language model (LLM) through the OpenRouter API to provide accurate, context-aware responses about courses, fees, hostel facilities, scholarships, admissions, and campus life. The chatbot is built usinga Flask (Python) backend and a modern HTML/CSS/JavaScript frontend, incorporating advanced features such as multimodal image analysis, voice-to-text input via the Web Speech API, session-based conversation management, and structured HTML table outputs for data-rich queries. A comprehensive knowledge base derived from the official Parul University 2025–26 Brochure is embedded as a system prompt to ensure domain-restricted, hallucination-free responses. Evaluation results indicate that PU Buddy successfully reduces student inquiry response times by an estimated 85%, handles parallel sessions with contextual continuity, and gracefully degrades to a fallback offline guide when API limits are exceeded. The system also includes a pixel-perfect university website clone with 570+ pages, serving as a standalone digital information platform. This pap. Keywords - AI Chatbot, Career Guidance, Large Language Models (LLM), Natural Language Processing (NLP), Flask, Gemini 2.0, OpenRouter API, Education Technology (EdTech), Parul University, Multimodal AI

The rapid pace of digitalization is having a transformative impact on modern supply chain management, particularly in the implementation of Digital Supply Chain Management (DSCM). The integration of digital technologies such as integrated information systems, the Internet of Things (IoT), big data analytics, and cloud-based platforms—enables companies to enhance visibility, coordination, and efficiency in managing the flow of goods and information. This study aims to explain how the integration of technology in the digital supply chain can support the optimization of modern inventory systems and improve a company’s operational performance. This research was conducted using a systematic literature review approach, examining various relevant scientific publications related to the digital supply chain and inventory management. The findings indicate that the implementation of high-performance digital technologies significantly contributes to optimizing inventory planning accuracy, accelerating decision making processes, and minimizing the risks of overstocking or stockouts. Additionally, the use of digital technologies enables companies to monitor inventory in real-time and optimize their ability to respond to changes in market demand. Therefore, the implementation of Digital Supply Chain Management is a critical strategy for organizations to optimize modern inventory systems, drive operational efficiency, and strengthen the company’s competitive capabilities in the era of digital transformation.

The rapid advancement of artificial intelligence (AI) technologies has significantly reshaped educational systems and learning environments worldwide. In particular, the integration of AI into language education has created new opportunities for improving learning efficiency, enhancing personalized instruction, and facilitating interactive communication environments. In the context of globalization and international talent development, the ability to communicate effectively in English has become an essential competence for participating in global economic, cultural, and technological exchanges. However, traditional language education models often rely on teacher-centered instruction and standardized assessments, which may not fully address the diverse learning needs of students or provide sufficient opportunities for authentic communication practice. This study explores the role of artificial intelligence in empowering the development of English competence for international talents. Drawing upon theories of technology-enhanced learning, intelligent tutoring systems, and global competence development, the paper proposes a conceptual framework that illustrates how AI technologies can support language learning through multiple application scenarios. These scenarios include intelligent language tutoring systems, AI-mediated communication environments, automated language assessment systems, and data-driven learning analytics. Based on these application scenarios, the study further proposes an AI-empowered educational model that integrates artificial intelligence technologies with innovative pedagogical approaches to support the cultivation of international talents. The model emphasizes personalized learning pathways, adaptive instruction, and global communication platforms supported by AI systems. The study contributes to theoretical discussions on AI-supported language education and provides practical insights for cultivating internationally competitive talents in the era of digital transformation.