Regional decision-making in urban planning, environmental management, and related fields requires the reliable and extensible integration of diverse geographic data and models. Traditional expert-driven approaches rely mainly on the manual configuration of data and models by human users, which is inefficient and suffers from knowledge gaps. Agents based on large language models (LLMs) offer the potential to integrate resources and automate computational processes, but face challenges in handling heterogeneous geospatial data and models and often produce unreliable outputs. We propose the regional decision-making agent (RDMA), which is a multiagent collaborative framework that enhances LLM-based agents with structured resource management and reliability mechanisms to support complex, multistep decision-making processes. The RDMA comprises three modules: data governance, model governance, and agent scheduling. To mitigate LLM hallucinations, three strategies are implemented: knowledge-enhanced retrieval-augmented generation (RAG), confidence self-assessment, and system transparency. Performance evaluations demonstrate high success rates and accuracy across diverse tasks at both the model configuration and workflow composition levels. Ablation studies confirm the essential contributions of quality control and knowledge-enhanced retrieval mechanisms. Case studies in urban planning and stormwater management validate RDMA’s ability to deliver actionable insights. This work advances AI-driven geographic analysis and provides a scalable solution for sustainable regional governance.

The proliferation of digitalization, along with advanced computational techniques, in the healthcare ecosystem has expedited the process of patient care, treatment, and disease diagnosis globally. Medical research, especially involving computational techniques, is heavily dependent on the availability of high-quality datasets generated at the point of care for effective translational research. Our study aims to understand the state of the digital ecosystem (i.e., digitalization, usage of electronic health records (EHRs), and medical data) for the purpose of improving healthcare services and research in hospitals. We conducted a questionnaire-based survey at 16 upper-primary health care centers and public hospitals in the district of Bikaner, Rajasthan, India, to understand the current practices of medical data digitalization and data repository development. The survey results have been analyzed using Principal Component Factor Analysis (PCFA) and statistical tests, including Cronbach’s Alpha, the Kaiser-Meyer-Olkin (KMO) measure, and Bartlett’s test of sampling adequacy, which indicate that the state of digitalization is in its initial phase. Among technical professionals, 35.6% agreed that digitalization has been implemented, while 12.3% remained neutral and 52.1% disagreed. For the same, 41.4% agreed, 13.0% remained neutral, and 45.6% disagreed among non-technical professionals. These highlight that almost half of the groups recognize slow progress in this area, implying that digitalization is still in its initial phase. Our study also indicates that the lack of access to structured and semi-structured medical datasets is a key barrier to applying Artificial Intelligence (AI) and Machine Learning (ML) in Indian healthcare research, where these technologies could play a crucial role in improving healthcare diagnostics, outcome prediction, enhancing clinical decision-making, etc., for better healthcare services, esp. in resource-constrained settings.

This study aims to re-evaluate the contractual issue between SUNGSIMDANG, a renowned bakery in Daejeon, and KORAIL Retail, focusing on SUNGSIMDANG’s managerial characteristics as reflected in its accounting performance and financial statements. During the period of disagreement between the two companies, media reports primarily highlighted figures such as KORAIL Retail’s 17% commission rate and SUNGSIMDANG’s proposed monthly payment of 100 million KRW. However, these numerical claims lacked explicit accounting-based analytical grounds. Against this backdrop, the present study analyzes the managerial characteristics of SUNGSIMDANG—an enterprise that has not only produced bread but also contributed to shaping Daejeon’s cultural identity and creating value beyond its products—through its financial statements. By adopting an accounting-based perspective, the study seeks to provide a more systematic understanding of this real-world case. The significance of this research lies in its presentation of a management case study analyzed through an accounting lens, thereby offering a more concrete understanding of an actual business issue. It also contributes to expanding accounting research by examining a familiar, unlisted company, an area where such case analyses remain limited. Furthermore, the study underscores the importance of adopting an accounting-based approach as a rational foundation for analyzing and resolving inter-firm disputes in the future.

Supply Chain Management relies on human consensus in decision-making to avoid emergent problems like the bullwhip effect. Some routine consensus processes, especially those that are time-intensive, can be automated. Previously proposed supply chain automation solutions for consensus-seeking and coordination faced computational challenges, resulting in high entry barriers. Recent advances in Generative AI, particularly Large Language Model agents (LLM agents), could overcome these barriers. This paper explores how LLM agents can automate consensus-seeking in supply chains. We introduce a series of novel, supply chain-specific consensus-seeking frameworks and validate the effectiveness of our approach through a case study in inventory management, where agents that represent companies in a supply chain are able to balance selfish goals with systemic outcomes through conversation. Our results show that introducing LLM-based consensus-seeking frameworks reduces bullwhip effects. When equipped with appropriate tools, LLM agents can minimise bullwhip better than restocking policies and centralised demand approaches. Additionally, when LLM agents are handled within a negotiation framework, their behaviour converges to best practices in the supply chain literature on how to lessen the bullwhip effect. To provide a foundation for further advancements in LLM-based autonomous supply chain solutions, we open-source our code.

Electronic patient-reported outcome measures (ePROMs) have a robust evidence base showing that when they are routinely collected and actioned in clinical settings, they enhance patient-centered care and clinical decision-making. However, their integration into routine oncology care has often been hindered due to system and individual-level resistance as well as barriers to implementation, warranting a targeted change management approach. This case study presents the statewide change management approach to the implementation of an electronic patient-reported measures (ePRM) system in New South Wales (NSW), Australia. This study describes the process of statewide change management, guided by the ADKAR (Awareness, Desire, Knowledge, Ability, Reinforcement) change management model to support adoption and sustainability. A retrospective case study methodology is used. Change management strategies employed during the implementation of the ePRM system across NSW cancer services were elicited through stakeholder interviews and meetings with the change management team. Each phase of the ADKAR framework was mapped to implementation activities, stakeholder engagement efforts, and system refinements. Identified barriers were coded using the Consolidated Framework for Implementation Research (CFIR), and corresponding mitigation strategies were aligned with the Expert Recommendations for Implementing Change (ERIC) taxonomy. The structured application of the ADKAR model enabled the change management team to anticipate and address a range of barriers. Strategies included tailored stakeholder engagement to build awareness and desire, role-specific training to develop knowledge, site-based troubleshooting and workflow adaptation to support ability, and performance monitoring to reinforce long-term use. Barriers such as perceived redundancy and time constraints were effectively mitigated through context-specific strategies mapped to relevant CFIR and ERIC constructs. The resulting implementation checklist provides a practical guide for organizations undertaking similar digital health transformations. Structured change management enabled the successful implementation of a digital health innovation-the ePRM system-at scale. By combining proactive planning, iterative design, and embedded support, digital health interventions can achieve sustained adoption. These findings offer actionable guidance for policymakers and health care leaders seeking to embed digital tools such as ePROMs into routine clinical care. http://links.lww.com/IJEBH/A493.

Human parvovirus B19 (B19V) infection during pregnancy can lead to a range of adverse outcomes such as miscarriage, premature delivery, fetal hydrops, severe anemia, myocarditis, heart failure, and even fetal demise, posing significant risks to maternal and fetal health. The aim of this study was to establish a more efficient method for detecting B19V in amniotic fluid and to explore and optimize early diagnosis and treatment strategies for fetal B19V infection. Intrauterine transfusion (IUT) was performed due to the occurrence of severe fetal anemia and hydrops. Amniotic fluid was obtained for genetic detection. Metagenomic next-generation sequencing (mNGS) and bioinformatic analysis were performed on the amniotic cells to identify the viral genome. In this study, the B19V genome was identified in the amniotic cells of the suspected case, with three viral coding sequences mapped. The coverage density reached 99.9% of the viral sequences. No other pathogen sequences, including bacteria, fungi, parasites, chlamydia, mycoplasma, rickettsia and other viruses, were identified. Our study confirmed the diagnosis of fetal B19V infection in a suspected case via amniotic fluid virus genome detection. It is the first time to exhibit the clinical application of mNGS to systematically detect the B19V genome in amniotic fluid in prenatal practice, and to achieve good results in combination with clinical management. The study highlighted the importance of comprehensive management of B19V fetal infection and demonstrated the advantages and wide application prospects of mNGS in intrauterine infection diagnosis.

Managing translation and interpreting services for large-scale international events presents significant challenges, particularly in implementing an integrated management model that synergizes both modalities. Research in this area, especially practice-oriented case studies, remains limited. This paper addresses this gap by conducting an in-depth case study of the translation management for the 6th World Buddhist Forum, a major event involving 80 professional translators and interpreters, 8 languages, and 389 manuscripts. Drawing on participant observation and interviews, the study analyzes the project's entire lifecycle, which was structured into five distinct phases: translator training, team mobilization, remote translation, on-site services, and post-event knowledge consolidation. The findings reveal a unique management model that inherits and develops the traditional Buddhist scripture "translation forum" (yichang). This legacy was manifested in its collaborative ethos, clear division of labor, and the strategic embedding of the translation team within the event's core organizational structure rather than as an external vendor. Furthermore, the project implemented a human-centered management approach rooted in the Buddhist principles of wisdom and compassion, fostering strong team cohesion and motivation. A robust emphasis on systematic knowledge management—through pre-forum training, the creation of a dynamic knowledge base, and post-forum debriefing—ensured quality, consistency, and long-term capacity building. This study aims to transcend its specific religious context to offer a universal framework for language service management. It demonstrates how integrating "value-driven" leadership with "task-driven" efficiency can resolve common bottlenecks—such as resource constraints and siloed workflows—in large-scale international events. The proposed model offers actionable implications for organizers of large-scale international events seeking to balance operational rigor with humanistic care.

Climate change is affecting human health in ways that exceed the scope of conventional public health law. As climate change drives new and evolving threats to human health, the public health legal framework should also evolve to address health risks that cut across socioecological systems. Shifts in precipitation and higher temperatures will change the range and scale of disease vectors and other health stressors, and the societal response to some impacts might cause unintended health problems. Current public health laws are ill-equipped to transcend jurisdictional and bureaucratic boundaries and address indirect health risks. In this Personal View, we use a case study of mosquito-borne disease control in Queensland, Australia, to illustrate how a socioecological approach to public health law reform could promote coordinated multisectoral regulation of a specific climate change-related health impact. Managing mosquito-borne disease in Queensland already necessitates laws that extend beyond traditional public health, including the incorporation of environmental impact assessments into public health legislation; the integration of health assessments into planning and environmental laws; the facilitation of interagency cross-referral and closer oversight of public health management of mosquito control; and promotion of community education and engagement. We suggest that public health adaptation aligned with a Planetary Health perspective will require additional recognition of the interconnections between social and ecological systems and more holistic conceptions of public health law.

This research develops an innovative mathematical framework that unifies classical and modern approaches to stochastic differential equations (SDEs) driven by irregular paths. We introduce a novel Newton-Cotes integration method that bridges Young integration and rough path theory, providing comprehensive solutions for processes with Hölder continuous sample paths. The theoretical foundation establishes existence, uniqueness, and regularity results across the entire roughness spectrum. Our methodology offers practical advantages through adaptive numerical schemes with proven convergence rates and robust parameter estimation techniques combining maximum likelihood and Bayesian approaches. The framework’s real-world utility is demonstrated through a detailed case study of groundwater management in Senegal, where our model achieves a 52%improvement in prediction accuracy over traditional methods. This enhancement enables more reliable drought early warnings and sustainable water resource planning in semi-arid regions facing climate uncertainty. The unified approach has broad applicability across scientific domains dealing with irregular data patterns, including finance, environmental science, and engineering.

Techno-economic optimization of e-methanol production integrated with oxy-fuel power plants: an adaptive power management case study in Australia

Background: We introduce two cases of progressive diabetic nephropathy that were successfully managed with combination therapy including RAS blocker(Losartan), DPP-4 inhibitor(Linagliptin), pentoxifyllin. In case 1(Diabetes History 12 years, female, age 50 years, eGFR: 62.38mL/min/1.73m2, proteinuria:3.3g/d), her eGFR and proteinuria deteriorated(eGFR: 50.54mL/min/1.73m2, proteinuria:4.3 g/d, annual eGFR change: -17.76mL/min/1.73m2) after 8 months using of amlodipine and atenolol as BP control agents, we replaced them with losartan and pentoxifylline and could observe some improvement in eGFR change (annual eGFR change: -11.46mL/min/1.73m2) with slight reduction in proteinuria(3.7g/d). Therefore, we added linagliptin which finally led to significant improvement both in eGFR(55.65 mL/min/1.73m2) and proteinuria(2.5g/d). In case 2(Dibetes History 10 years, male, age 64 years, eGFR: 59.07 mL/min/1.73m2, proteinuria: 2.9g/d), he had controled his blood pressure with amlopdine and atenolol but his protienuria and eGFR did worsend after 1 year (eGFR:49.92 mL/min/1.73m2, proteinruia: 3.6g/d), therefore we replaced amlodipine and atenolol with losartan and added linagliptin for renoprotective and glycemic control measure but there was no significant improvement in eGFR(44.58 mL/min/1.73m2) so we added pentoxifylline which finally led to improvement in eGFR(53.89 mL/min/1.73m2).

Towards an Integrative Model for Sustainability and Technological Innovation amidst Global Geopolitical Shifts: A case study of Rare Earth Elements Project Management in Algeria

In line with global green initiatives and the growth of Environmental, Social, and Governance (ESG) corporate sustainability standards, how supply chain management can better meet corporate sustainability goals has become a key issue today. This article examines the Tesla supplier management case study further to explain how Tesla advances its sustainability objectives through green initiatives and digital collaboration. Tesla incorporates green, circular, and low-carbon principles into the entire lifecycle management of its new energy vehicles, creating a responsible green supply chain. Concerning environmental initiatives, Tesla emphasizes the use of eco-friendly and recycled materials and is dedicated to battery recycling and reuse, to recycle over 90% of battery manufacturing waste by 2023. The Shanghai Gigafactory's industrial waste recycling rate is an impressive 93%. Tesla also utilizes digital technology to enhance supply chain management, developing a proprietary production and manufacturing control system that enables real-time collection and monitoring of quality information throughout the entire business chain. By utilizing an artificial intelligence platform to optimize process parameters and forecast quality risks, Tesla significantly enhances the efficiency and accuracy of its manufacturing process. Through the deep integration of green initiatives and digitalization, Tesla not only achieves efficient supply chain collaboration and low-carbon operations but also sets an example for global companies to reach sustainable development goals through supply chain innovation.

ABSTRACT Despite a substantial and growing literature on governance, dominant narratives remain centered on the Global North, creating significant gaps in understanding the distinct challenges of urban Southern contexts. Addressing this imbalance, our study investigates the concept and prevalence of ‘bad governance’ within urban Southern contexts. This concept is examined through a case study of solid waste management in a rapidly urbanizing African city. Employing a qualitative methodology of in-depth interviews, participant observation, and document analysis, the research posits that bad governance manifests in distinct ways in the urban South, shaped by unique socioeconomic and political realities. Prevalent characteristics include institutional weaknesses, systemic corruption, enduring colonial legacies, influential informal power structures, divergent socio-cultural attitudes, and pervasive ecological neglect. The analysis scrutinizes the roles of local government, informal waste pickers, formal and informal institutions, and communities, uncovering the systemic issues that perpetuate bad governance and hinder sustainable urban development. The findings underscore the necessity of collaborative and polycentric governance, particularly in contexts characterized by weak or, in some cases, absent formal structures. This study illuminates the specific challenges confronting the urban Global South and offers critical insights for policymakers and practitioners aiming to improve urban governance and foster sustainable urban futures.

A Comparison of Construction Accident Cognition: A Case Study of Construction Managers

Abstract The unprecedented emergency relocation of Israel’s largest rehabilitation facility in the north, Fliman Medical Center, to an underground facility during active conflict presents a unique case study in healthcare emergency management. This research addresses a critical gap in the literature on emergency preparedness for specialized healthcare facilities during crisis situations. This qualitative single-case study examines the strategic planning and execution of relocating individuals requiring ventilator support and other complex medical care amid active conflict. Data were collected through in-depth interviews with the emergency coordinator and analysis of contemporaneous documentation. Over eleven months of security challenges, the facility successfully implemented a carefully coordinated evacuation plan, safely transferring sixty medically complex patients—along with essential medical equipment and personnel—to an underground facility at Rambam Hospital within three hours. The relocation maintained operational independence and ensured the continuity of comprehensive rehabilitation services. The successful execution of this emergency relocation protocol demonstrates the feasibility of sustaining complex medical care during crisis situations. This study offers a model for emergency preparedness in specialized healthcare facilities, providing critical insights for institutions worldwide—particularly those serving medically complex populations in conflict-prone regions. The findings present practical guidelines for the safe relocation of vulnerable patients during crises, contributing to improved healthcare resilience in emergency settings.

Agroecology offers solutions to viticulture challenges, including climate adaptation, reducing phytosanitary product use, and preserving biodiversity and soil quality. In France, where most viticultural production is under geographical indications, strict product specifications regulate practices. Although agricultural policies continue to encourage changes in practices, the question arises as to how to encourage these changes in the context of winegrowing under protected designations of origin. This study contributes to research on viticultural agroecology with a new analytical approach to studying viticultural practices at the farm level by adapting the social–ecological system framework (SES). We conceptualise the viticultural agroecosystem as an agroecological resource system where viticultural practices are considered as interactions between winegrowers and their agroecosystems. Using a mixed-method approach, we analysed data from 34 semi-structured interviews with winegrowers in the Anjou-Saumur region of France. Our findings are twofold. First, by classifying winegrowers based on ground cover management, we reveal diverse transition pathways toward zero-herbicide viticulture, linked to environmental certifications such as Organic Agriculture, High Environmental Value, or Terra Vitis. Second, we highlight that many winegrowers consider several agroecological issues when choosing their practices. This approach provides a nuanced understanding of agro-viticultural practices, incorporating both productive and non-productive zones across small territories. Our results also allow for a better understanding of the agroecological transition by identifying the differences in individual reasoning within a collective associated with protected designations of origin.

Securing sensitive data is essential to protect against unauthorized access, breaches, and privacy violations. A decentralized blockchain ‎offers a robust solution by distributing data across multiple nodes, ensuring transparency, immutability, and enhanced protection, all without ‎relying on a central authority. Several clinical datasets are available, covering a wide range of healthcare-associated data. Encryption and ‎decryption, along with optimization, are techniques used to secure data by converting it into a form that is unintelligible without the precise ‎key or algorithm. These processes play a crucial role in data protection and privacy. These datasets are invaluable for numerous applications, ‎along with clinical studies, predictive analytics, system studies, and healthcare system optimization. The research presents an overall ‎approach to a safe data management case study, which uses Kaggle datasets. The approach begins with preprocessing using the Z-score ‎normalization method to ensure consistency in the dataset's dimensions. Use a data augmentation technique to expand the data range, thereby ‎increasing the recording duration. Thereafter, an encryption scheme is implemented to maximize the security of the dataset. The encoded ‎dataset is then placed in a decentralized blockchain, which allows for the effective utilization of its infrastructure for well-organized and ‎efficient storage methods. The decryption process is facilitated by an AALGES Exchange mechanism, which securely retrieves the original ‎data. Risk simulation occurs both before and after the data reaches the storage region, allowing for an evaluation of the system's resilience to ‎stress. This is done in the Python platform. To examine the system's performance, it simulates the analysis in detail, specifically focusing on ‎various aspects such as encryption and decryption speed, data storage capability, and minimum computational overhead‎.

Under the National Education Strengthening Strategy, the development of “curriculum-based ideological and political education” in higher education has entered a deepening phase. However, event-related majors face challenges like forced ideological incorporation, weak value guidance in practice, and oversimplified evaluation. Using the provincial demonstration course Event Planning and Management as a case, and based on the OBE (Outcome-Based Education) concept, this study innovatively constructs an HCCR curriculum-based ideological and political education goal system, which includes four dimensions: Humanistic Sentiment (cultivating cultural identity and patriotism), Career Competence (strengthening professional standards and occupational responsibility), Creative Thinking (cultivating innovation awareness and practical wisdom), and Social Responsibility (shaping international perspectives and sustainable development views). In terms of teaching methods, it adopts a combination of PBL project-driven method and case teaching, and conducts 'ideological and political education into practice' activities through internships at international exhibitions such as the Canton Fair, extending ideological education from the university to the industry. At the same time, a dual evaluation mechanism for teachers and students is established. This study provides a systematic reform plan for the ideological and political education of applied professional courses, 'goal-oriented—diverse methods—dual evaluation', not only fulfills the requirements of the National Education Strengthening Strategy to "establish an education system guided by ideological and political principles", but also providing an effective path to solve the structural contradictions in the cultivation of talents in the exhibition industry.

Monoclonal antibodies (mAbs) have emerged as one of the most important tools in modern medicine, combining scientific precision with lifesaving applications. This project explores their biology, production techniques, and industrial as well as therapeutic uses. Beginning with an overview of antibodies and the discovery of B lymphocytes are responsible for their production, the study highlights how monoclonal antibody technology was developed through hybridoma methods and later scaled in to industrial production. The project further examines the therapeutic importance of monoclonal antibodies with a special focus on cancer management, autoimmune disease and diagnostic tools. Real – world aspects such as cost, accessibility in India and expanding global market are also discussed. A case study on breast cancer has been included to illustrate how monoclonal antibodies are applied in practice, supported by clinical outcomes and industry data. Overall, this project emphasizes how a laboratory breakthrough transformed into a global healthcare solution. By combining biological principles, industrial data, and real clinical insights, the work provides a complete picture of how monoclonal antibodies are shaping the future of medicine.