Search Results (8,258)

Abstract: This position paper presents a novel perspective on addressing the challenges of digital transformation in higher education through the development of a qualification-based learning model (QBLM) qualification management methodology. It argues that the rapid pace of technological advancement and the resulting need for continuous upskilling and reskilling necessitate a more dynamic and adaptive approach to human-resource management and development. The paper posits that by extending QBLM through the integration of artificial intelligence (AI) and machine learning (ML), a more effective system for analyzing competence requirements and designing personalized learning pathways can be created. The paper proposes a three-fold approach: (1) developing the FPHR ontology to support semantic annotation of HR qualifications in higher-education institutions (HEIs), (2) integrating this ontology into QBLM to ensure the machine-readability of qualifications, and (3) modeling a knowledge-based production process for HRs in skills-based learning. This paper outlines the current state of the art, presents conceptual models, and describes planned proof-of-concept implementations and evaluations. It contends that this approach will significantly enhance the effectiveness of human-resource development in the rapidly evolving digital knowledge society. By presenting this position, the paper aims to stimulate discussion and collaboration within the academic community on innovative approaches to qualification management in higher education. The work addresses critical issues arising from technological development and offers a forward-thinking solution to bridge the gap between current and future skill requirements in industry and academia. Full article

Rate of penetration (ROP) is crucial for evaluating drilling efficiency, with accurate prediction essential for enhancing performance and optimizing parameters. In practice, complex and variable downhole environments pose significant challenges for mechanistic ROP equations, resulting in prediction difficulties and low accuracy. Recently, data-driven machine learning models have been widely applied to ROP prediction. However, these models often lack mechanistic constraints, limiting their performance to specific conditions and reducing their real-world applicability. Additionally, geological variability across wells further hinders the transferability of conventional intelligent models. Thus, combining mechanistic knowledge with intelligent models and enhancing model stability and transferability are key challenges in ROP prediction research. To address these challenges, this paper proposes a Mechanism-Data Fusion and Transfer Learning method to construct an intelligent prediction model for ROP, achieving accurate ROP predictions. A multilayer perceptron (MLP) was selected as the base model, and training was performed using data from neighboring wells and partial data from the target well. The Two-stage TrAdaBoost.R2 algorithm was employed to enhance model transferability. Additionally, drilling mechanistic knowledge was incorporated into the model’s loss function as a constraint to achieve a fusion of mechanistic knowledge and data-driven approaches. Using MAPE as the measure of accuracy, compared with conventional intelligent models, the proposed ROP prediction model improved prediction accuracy on the target well by 64.51%. The model transfer method proposed in this paper has a field test accuracy of 89.71% in an oilfield in China. These results demonstrate the effectiveness and feasibility of the proposed transfer learning method and mechanistic–data integration approach. Full article

Evaluating and integrating local ecological knowledge held by fishers into decision-making processes has the potential to significantly enhance fisheries management. The present study aimed to collect information on fishery practices and to assess the ecological knowledge of local professional fishers through interviews, evaluating its importance in managerial design. As a case study, Polyphytos Reservoir in Greece, which supports substantial fisheries, was selected. During the summer of 2023, thirty-seven interviews were conducted to document fishing efforts, methods, catches, biological information, and fishers’ perspectives on lake management, and economic/demographic details. In addition to gathering data on fishing activities, the study seeks to understand fishers’ perspectives on managerial deficiencies and necessities, thereby revealing their valuable ecological knowledge. The integration of this knowledge into decision-making processes can empower stakeholders and enhance local participation in fishery management. Ultimately, this approach has the potential to address long-standing conflicts, foster inclusive processes, and ensure better collective outcomes. Full article

In the Sahel region of West Africa, land degradation has raised concerns about the threat of desertification, leading to the establishment of the United Nations Convention to Combat Desertification (UNCCD) in 1994. Over time, the focus has shifted from simply combating desertification to a more comprehensive international program focused on preserving the health of our land by offsetting any damage with restoration efforts by 2030 to sustain ecosystem functions and services. This balancing process—which is in line with the Sustainable Development Goals (SDGs)—is known as Land Degradation Neutrality (LDN). We examine Land Degradation Neutrality (LDN) patterns, namely degradation and rehabilitation processes, by integrating participatory mapping with high-resolution satellite imagery with local stories, observations, historical records, and existing studies. The data elicited an understanding of the processes driving land degradation and adaptation strategies among three distinct ethnic groups of crop and livestock farmers in the village of Yallé in southern Burkina Faso. Some of these people were originally from this region, while others moved from places where the land was already degraded. Participants in the study had diverse experiences and perceptions of land degradation, its drivers, and adaptation strategies, which were influenced by their ethnicity, livelihood activities, and life experiences. These differences highlight the impact of cultural and socioeconomic factors on how people view land degradation, as well as the role of local knowledge in managing the environment. The study emphasizes the necessity of incorporating ethnoecological perspectives into projects focused on Land Degradation Neutrality (LDN) to better understand land degradation and improve land management. This integration can significantly contribute to strengthening global sustainability and community resilience. Full article

As technology advances, virtual reality (VR) is increasingly being integrated into healthcare education to enhance learning outcomes. This systematic literature review and meta-analysis examined the effectiveness of virtual reality-based healthcare education. Randomized controlled trials (RCTs) published over the past 10 years were retrieved from 10 databases using VR, healthcare, and education as the primary keywords. Following the inclusion and exclusion criteria, 45 studies were included in the final analysis. A meta-analysis was performed to analyze the effects of VR in terms of knowledge, skill, and attitude. The results revealed that the use of VR significantly improved the knowledge (SMD: 0.28, 95% CI: 0.18–0.39, p < 0.001) and skill scores (SMD: 0.23, 95% CI: 0.11–0.34, p < 0.001), shortened the skill performance time (SMD: −0.59, 95% CI: −0.82 to −0.35, p < 0.001), and improved the satisfaction (SMD: 0.65, 95% CI: 0.48–0.81, p < 0.001) and confidence levels (SMD: 0.60, 95% CI: 0.41–0.80, p < 0.001). The in-depth analysis highlighted the significant potential of VR and provided practical implications in educational settings. In conclusion, effectively integrating VR with traditional educational methods is necessary to enhance both the quality of learning and the overall competence of healthcare professionals. Full article

This paper introduces a novel technology for the monolithic integration of GaN-based vertical and lateral devices. This approach is groundbreaking as it facilitates the drive of high-power GaN vertical switching devices through lateral GaN HEMTs with minimal losses and enhanced stability. A significant challenge in this technology is ensuring electrical isolation between the two types of devices. We propose a new isolation method designed to prevent any degradation of the lateral transistor’s performance. Specifically, high voltage applied to the drain of the vertical GaN power FinFET can adversely affect the lateral GaN HEMT’s performance, leading to a shift in the threshold voltage and potentially compromising device stability and driver performance. To address this issue, we introduce a highly doped n⁺ GaN layer positioned between the epitaxial layers of the two devices. This approach is validated using the TCAD-Sentaurus simulator, demonstrating that the n⁺ GaN layer effectively blocks the vertical electric field and prevents any depletion or enhancement of the 2D electron gas (2DEG) in the lateral GaN HEMT. To our knowledge, this represents the first publication of such an innovative isolation strategy between vertical and lateral GaN devices. Full article

Background: As large language models continue to expand in size and diversity, their substantial potential and the relevance of their applications are increasingly being acknowledged. The rapid advancement of these models also holds profound implications for the long-term design of stimulus-responsive materials used in drug delivery. Methods: The large model used Hugging Face’s Transformers package with BigBird, Gemma, and GPT NeoX architectures. Pre-training used the PubChem dataset, and fine-tuning used QM7b. Chemist instruction training was based on Direct Preference Optimization. Drug Likeness, Synthetic Accessibility, and PageRank Scores were used to filter molecules. All computational chemistry simulations were performed using ORCA and Time-Dependent Density-Functional Theory. Results: To optimize large models for extensive dataset processing and comprehensive learning akin to a chemist’s intuition, the integration of deeper chemical insights is imperative. Our study initially compared the performance of BigBird, Gemma, GPT NeoX, and others, specifically focusing on the design of photoresponsive drug delivery molecules. We gathered excitation energy data through computational chemistry tools and further investigated light-driven isomerization reactions as a critical mechanism in drug delivery. Additionally, we explored the effectiveness of incorporating human feedback into reinforcement learning to imbue large models with chemical intuition, enhancing their understanding of relationships involving -N=N- groups in the photoisomerization transitions of photoresponsive molecules. Conclusions: We implemented an efficient design process based on structural knowledge and data, driven by large language model technology, to obtain a candidate dataset of specific photoswitchable molecules. However, the lack of specialized domain datasets remains a challenge for maximizing model performance. Full article

This study explores the impact of artificial intelligence (AI) tools on the purchase intentions of members of Generation Z (Gen Z) in online shopping, using an adapted technology acceptance model (TAM). It incorporates exposure to AI, usage of AI, and knowledge about AI, alongside the existing TAM parameters of perceived usefulness of AI (PUAI) and perceived ease-of-use of AI (PEUAI). A 38-item questionnaire was distributed, yielding data from 1128 Gen Z respondents. Partial least squares structural equation modeling (PLS-SEM) and importance–performance analysis (IPA) were applied to examine the hypothesized relationships. The study identified significant direct effects of exposure, use, and knowledge on PUAI and PEUAI, and that these effects affected consumers’ purchase intentions. Indirect effects analysis revealed that PUAI and PEUAI mediate between AI exposure, use, knowledge, and purchase intentions, suggesting that greater understanding of and familiarity with AI enhance the propensity to engage in AI-powered online transactions. The ease of integrating AI into daily life and perceived AI utility enhance purchase intentions. The study offers insights for online retailers leveraging AI technologies in an effort to enhance consumer purchase experiences, emphasizing the potential of AI to positively influence choices while enhancing trust, familiarity, and the overall user experience. Full article

This paper presents a novel framework, artificial intelligence-enabled intelligent assistant (AIIA), for personalized and adaptive learning in higher education. The AIIA system leverages advanced AI and natural language processing (NLP) techniques to create an interactive and engaging learning platform. This platform is engineered to reduce cognitive load on learners by providing easy access to information, facilitating knowledge assessment, and delivering personalized learning support tailored to individual needs and learning styles. The AIIA’s capabilities include understanding and responding to student inquiries, generating quizzes and flashcards, and offering personalized learning pathways. The research findings have the potential to significantly impact the design, implementation, and evaluation of AI-enabled virtual teaching assistants (VTAs) in higher education, informing the development of innovative educational tools that can enhance student learning outcomes, engagement, and satisfaction. The paper presents the methodology, system architecture, intelligent services, and integration with learning management systems (LMSs) while discussing the challenges, limitations, and future directions for the development of AI-enabled intelligent assistants in education. Full article

The purpose of the qualitative multiple case study was to explore the strategies some supply chain managers of the United Nations (UN) use to leverage operational efficiencies in the UN humanitarian programs. As a result, communities supporting sustainability and peace or those fostering economic development will be able to respond effectively to humanitarian crises. Moreover, the UN can remain operational and engage in political and conflict-reduction interventions integral to economic and social recovery and sustainability. This study applied the qualitative multiple case study through semi-structured interviews with nine supply chain managers in the United Nations, direct observations, document analysis, and artifacts. The research is grounded in the dynamic capability theory (DCT). The research revealed several strategies that supply chain managers of the UN use to ensure operational efficiencies grouped around three themes: (a) analytical, innovation, and knowledge management strategies; (b) effective supply chain management leadership strategies; and (c) risk management strategies. This study is one of the first to apply generic findings of humanitarian supply chain studies to the United Nations, the global organization with diverse mandates that continuously strives to achieve efficiencies required by donors providing financial support, thus remaining operational. The study’s results could help leaders in the various humanitarian organizations who operate in vulnerable environments and under strict scrutiny from donors to deliver their aid programs most efficiently by understanding dynamic capabilities. Previous studies indicate the lack of strategic frameworks applicable to the United Nations that could improve decision-making at the strategic, tactical, and operational levels, facilitate collaboration among supply chain stakeholders, and reduce the costs of the operational performance of the supply chain system in the UN. Full article

In the evolving global market, new business conditions necessitate that enterprises adapt and construct organizational structures grounded in new principles and the implementation of contemporary management methods. This is particularly crucial for enterprises in transitional economies, which need to be highly flexible and innovative to meet the increasing demands of users swiftly, employ modern management techniques, and gain a competitive edge. The modern business environment assumes that there are very few products, technologies, services, knowledge areas, or procedures unavailable to interested groups worldwide. This study examines the influence of modern management methods and techniques (MMMTs), human resource management (HRM), quality management (QM), and intellectual capital management (ICM) on the sustainable market performance (SMPC) of these enterprises. A structured survey was conducted among 146 managers from various Serbian industrial enterprises, and the data were analyzed using descriptive statistics, Pearson correlation analysis, linear regression, and multicollinearity tests. The results revealed significant positive correlations between MMMTs, HRM, QM, ICM, and SMPC, with quality management having the highest impact. These findings provide valuable insights for improving business competitiveness in Serbia’s industrial sector. The results also support the development of an integrated model for sustainable management practices in transitional economies. Full article

Stress resistance is highly associated with longer and healthier lifespans in various model organisms, including nematodes, fruit flies, and mice. However, we lack a complete understanding of stress resistance in humans; therefore, we investigated how stress resistance and longevity are interlinked in humans. Using more than 180 databases, we identified 541 human genes associated with stress resistance. The curated gene set is highly enriched with genes involved in the cellular response to stress. The Reactome analysis identified 398 biological pathways, narrowed down to 172 pathways using a medium threshold (p-value < 1 × 10⁻⁴). We further summarized these pathways into 14 pathway categories, e.g., cellular response to stimuli/stress, DNA repair, gene expression, and immune system. There were overlapping categories between stress resistance and longevity, including gene expression, signal transduction, immune system, and cellular responses to stimuli/stress. The categories include the PIP3-AKT-FOXO and mTOR pathways, known to specify lifespans in the model systems. They also include the accelerated aging syndrome genes (WRN and HGPS/LMNA), while the genes were also involved in non-overlapped categories. Notably, nuclear pore proteins are enriched among the stress-resistance pathways and overlap with diverse metabolic pathways. This study fills the knowledge gap in humans, suggesting that stress resistance is closely linked to longevity pathways but not entirely identical. While most longevity categories intersect with stress-resistance categories, some do not, particularly those related to cell proliferation and beta-cell development. We also note inconsistencies in pathway terminologies with aging hallmarks reported previously, and propose them to be more unified and integral. Full article

Most retinal and optic nerve diseases pose significant threats to vision, primarily due to irreversible retinal neuronal cell death, a permanent change, which is a critical factor in their pathogenesis. Conditions such as glaucoma, retinitis pigmentosa, diabetic retinopathy, and age-related macular degeneration are the top four leading causes of blindness among the elderly in Japan. While standard treatments—including reduction in intraocular pressure, anti-vascular endothelial growth factor therapies, and retinal photocoagulation—can partially delay disease progression, their therapeutic effects remain limited. To address these shortcomings, a range of neuroprotective and regenerative therapies, aimed at preventing retinal neuronal cell loss, have been extensively studied and increasingly integrated into clinical practice over the last two decades. Several of these neuroprotective therapies have achieved on-label usage worldwide. This narrative review introduces several neuroprotective and regenerative therapies for retinal and optic nerve diseases that have been successfully translated into clinical practice, providing foundational knowledge and success stories that serve as valuable references for researchers in the field. Full article

Coronary aneurysms are typically defined as sections of a coronary artery where the diameter is more than 1.5 times that of an adjacent normal segment. In rare circumstances, these aneurysms can become exceedingly large, leading to the classification of giant coronary artery aneurysms. Despite their occurrence, there is no clear consensus on the precise definition of giant coronary artery aneurysms, and their etiology remains somewhat ambiguous. Numerous potential causes have been suggested, with atherosclerosis being the most prevalent in adults, accounting for up to 50% of cases. In pediatric populations, Kawasaki disease and Takayasu arteritis are the primary causes. Although often discovered incidentally, coronary artery aneurysms can lead to severe complications. These complications include local thrombosis, distal embolization, rupture, and vasospasm, which can result in ischemia, heart failure, and arrhythmias. The optimal approach to medical, interventional, or surgical management of these aneurysms is still under debate and requires further clarification. This literature review aims to consolidate current knowledge regarding coronary artery aneurysms’ pathophysiology, emphasizing their definition, causes, complications, and treatment strategies. Recent research has begun to explore the molecular mechanisms involved in the formation and progression of coronary artery aneurysms. Various molecules, such as matrix metalloproteinases (MMPs), inflammatory cytokines, and growth factors, play crucial roles in the degradation of the extracellular matrix and the remodeling of vascular walls. Elevated levels of MMPs, particularly MMP-9, have been associated with the weakening of the arterial wall, contributing to aneurysm development. Inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α) and interleukins (IL-1β and IL-6) have been implicated in promoting inflammatory responses that further degrade vascular integrity. Additionally, growth factors such as vascular endothelial growth factor (VEGF) may influence angiogenesis and vascular remodeling processes. Understanding these molecular pathways is essential for developing targeted therapies aimed at preventing the progression of coronary artery aneurysms and improving patient outcomes. Full article

Large language models (LLMs) are transforming the medical landscape by enhancing access to information, diagnostics, treatment customization, and medical education, especially in areas like Gastroenterology. LLMs utilize extensive medical data to improve decision-making, leading to better patient outcomes and personalized medicine. These models are instrumental in interpreting medical literature and synthesizing patient data, facilitating real-time knowledge for physicians and supporting educational pursuits in medicine. Despite their potential, the complete integration of LLMs in real-life remains ongoing, particularly requiring further study and regulation. This review highlights the existing evidence supporting LLMs’ use in Gastroenterology, addressing both their potential and limitations. Recent studies demonstrate LLMs’ ability to answer questions from physicians and patients accurately. Specific applications in this field, such as colonoscopy, screening for colorectal cancer, and hepatobiliary and inflammatory bowel diseases, underscore LLMs’ promise in improving the communication and understanding of complex medical scenarios. Moreover, the review discusses LLMs’ efficacy in clinical contexts, providing guideline-based recommendations and supporting decision-making processes. Despite these advancements, challenges such as data completeness, reference suitability, variability in response accuracy, dependency on input phrasing, and a lack of patient-generated questions underscore limitations in reproducibility and generalizability. The effective integration of LLMs into medical practice demands refinement tailored to specific medical contexts and guidelines. Overall, while LLMs hold significant potential in transforming medical practice, ongoing development and contextual training are essential to fully realize their benefits. Full article