Search Results (8,883)

The green renovation of existing residential buildings is a key way for the construction industry to achieve sustainable development and the dual carbon goals of China, which makes it urgent to make collaborative decisions among multiple stakeholders. However, because of divergent interests and risk perceptions among governments, energy service companies (ESCOs), and owners, the implementation of green renovation is hindered by numerous obstacles. In this study, we integrated prospect theory and evolutionary game theory by incorporating core prospect-theory parameters such as loss aversion and perceived value sensitivity, and developed a psychologically informed tripartite evolutionary game model. The objective was to provide a theoretical foundation and analytical framework for collaborative governance among stakeholders. Numerical simulations were conducted to validate the model’s effectiveness and explore how government regulation intensity, subsidy policies, market competition, and individual psychological factors influence the system’s evolutionary dynamics. The findings indicate that (1) government regulation and subsidy policies play central guiding roles in the early stages of green renovation, but the effectiveness has clear limitations; (2) ESCOs are most sensitive to policy incentives and market competition, and moderately increasing their risk costs can effectively deter opportunistic behavior associated with low-quality renovation; (3) owners’ willingness to participate is primarily influenced by expected returns and perceived renovation risks, while economic incentives alone have limited impact; and (4) the evolutionary outcomes are highly sensitive to parameters from prospect theory, The system’s evolutionary outcomes are highly sensitive to prospect theory parameters. High levels of loss aversion (λ) and loss sensitivity (β) tend to drive the system into a suboptimal equilibrium characterized by insufficient demand, while high gain sensitivity (α) serves as a key driving force for the system’s evolution toward the ideal equilibrium. This study offers theoretical support for optimizing green renovation policies for existing residential buildings in China and provides practical recommendations for improving market competition mechanisms, thereby promoting the healthy development of the green renovation market. Full article

The growing complexity of production systems in the technology sector demands advanced tools to ensure efficiency, flexibility, and cost-effectiveness. This study presents the development of a simulation model for a selective soldering line at a technology manufacturing company in Poland, created during an engineering internship. Using FlexSim 24.2 software, the real production process was replicated, including input/output queues, manual insertion (MI) stations, soldering machines, and quality control points. Special emphasis was placed on implementing dynamic process logic via ProcessFlow, enabling detailed modeling of token flow and system behavior. Through experimentation, various configurations were tested to optimize process time and the number of soldering pallets in circulation. The results revealed that reducing pallets from 12 to 8 maintains process continuity while offering cost savings without impacting performance. An intuitive operator panel was also developed, allowing users to adjust parameters and monitor outcomes in real time. The project demonstrates that simulation not only supports operational decision-making and resource planning but also enhances interdisciplinary communication by visually conveying complex workflows. Ultimately, the study confirms that simulation modeling is a powerful and adaptable approach to production optimization, contributing to long-term strategic improvements and innovation in technologically advanced manufacturing environments. Full article

This theoretical study rigorously investigates how generative artificial intelligence reshapes decision-making in social entrepreneurship by modeling the opportunity search process through the lens of optimal stopping theory. Social entrepreneurs often face high uncertainty and resource constraints, requiring them to strategically balance the cost of continued searching with the chance of identifying socially impactful opportunities. This study develops a formal model that captures two core mechanisms of generative AI: reducing search costs and increasing the probability of mission-aligned opportunity success. The theoretical analysis yields three key findings. First, generative AI accelerates the optimal stopping point, allowing social entrepreneurs to act more quickly on high-potential opportunities by lowering cognitive and resource burdens. Second, the influence of increased success probability outweighs that of reduced search costs, underscoring the strategic importance of insight quality over efficiency in socially embedded contexts. Third, the benefits of generative AI are amplified in uncertain environments, where it helps navigate complexity and mitigate information asymmetry. These insights contribute to a deeper conceptual understanding of how intelligent technologies transform the cognitive and strategic dimensions of social entrepreneurship, and they offer empirically testable propositions for future research at the intersection of AI, innovation, and mission-driven opportunity pursuit. Full article

As urbanization accelerates and climate change intensifies, the ecological integrity of urban riparian forests faces growing threats, underscoring the need for a systematic framework to guide their sustainable management. To address this gap, we developed a causal framework by applying text mining and sentence classification to 1001 abstracts from previous studies, structured within the DPSIR (Driver–Pressure–State–Impact–Response) model. The analysis identified six dominant thematic clusters—water quality, ecosystem services, basin and land use management, climate-related stressors, anthropogenic impacts, and greenhouse gas emissions—which reflect the multifaceted concerns surrounding urban riparian forest research. These themes were synthesized into a structured causal model that illustrates how urbanization, land use, and pollution contribute to ecological degradation, while also suggesting potential restoration pathways. To validate its applicability, the framework was applied to four major urban streams in Seoul, where indicator-based analysis and correlation mapping revealed meaningful linkages among urban drivers, biodiversity, air quality, and civic engagement. Ultimately, by integrating large-scale text mining with causal inference modeling, this study offers a transferable approach to support adaptive planning and evidence-based decision-making under the uncertainties posed by climate change. Full article

This research aims to identify and prioritize key planning elements for the redevelopment of such housing complexes by incorporating perspectives from both experts (supply-side) and residents (demand-side). To achieve this, a hybrid multi-criteria decision-making framework was developed by integrating the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). A total of 25 planning elements were identified through Focus Group Interviews and organized into five domains: legal and institutional reforms, project feasibility, residential conditions, social integration, and complex design. The AHP was used to assess the relative importance of each element based on responses from 30 experts and 130 residents. The analysis revealed a clear divergence in priorities: experts emphasized feasibility and regulatory considerations, while residents prioritized livability and spatial quality. Subsequently, the TOPSIS method was applied to evaluate four real-world redevelopment cases. From the supply-side perspective, Seoul A District received the highest score (0.58), whereas from the demand-side perspective, Gyeonggi D District ranked highest (0.69), illustrating the differing priorities of stakeholders. Overall, Gyeonggi D District emerged as the most favorable option in the combined evaluation. This research contributes a structured and inclusive decision-making framework for the regeneration of public housing. By explicitly comparing and quantifying the contrasting preferences of key stakeholders, it underscores the critical need to balance technical feasibility with resident-centered values in future redevelopment initiatives. Full article

Outdoor air pollution poses a major global health risk, yet monitoring remains insufficient, especially in regions with limited infrastructure. Solar-powered monitors could allow for increased coverage in regions lacking robust connectivity. However, reliable sample collection can be challenging with these systems due to extreme temperatures and insufficient solar energy. Proper planning can help overcome these challenges. Air Sampler Solar and Thermal Optimization for Reliable Monitoring (Air-STORM) is an open-source tool that uses meteorological and solar radiation data to identify temperature and solar charging risks for air pollution monitors based on the target deployment area. The model was validated experimentally, and its utility was demonstrated through illustrative case studies. Air-STORM simulations can be customized for specific locations, seasons, and monitor configurations. This capability enables the early detection of potential sampling risks and provides opportunities to optimize monitor design, proactively mitigate temperature and power failures, and increase the likelihood of successful sample collection. Ultimately, improving sampling success will help increase the availability of high-quality outdoor air pollution data necessary to reduce global air pollution exposure. Full article

Adverse drug events (ADEs) significantly impact patient safety and health outcomes. Manual ADE detection from clinical narratives is time-consuming, labor-intensive, and costly. Recent advancements in large language models (LLMs), including transformer-based architectures such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pretrained Transformer (GPT) series, offer promising methods for automating ADE extraction from clinical data. These models have been applied to various aspects of pharmacovigilance and clinical decision support, demonstrating potential in extracting ADE-related information from real-world clinical data. Additionally, chatbot-assisted systems have been explored as tools in clinical management, aiding in medication adherence, patient engagement, and symptom monitoring. This narrative review synthesizes the current state of LLMs in ADE detection from a clinical perspective, organizing studies into categories such as human-facing decision support tools, immune-related ADE detection, cancer-related and non-cancer-related ADE surveillance, and personalized decision support systems. In total, 39 articles were included in this review. Across domains, LLM-driven methods have demonstrated promising performances, often outperforming traditional approaches. However, critical limitations persist, such as domain-specific variability in model performance, interpretability challenges, data quality and privacy concerns, and infrastructure requirements. By addressing these challenges, LLM-based ADE detection could enhance pharmacovigilance practices, improve patient safety outcomes, and optimize clinical workflows. Full article

Background/Objective: The COVID-19 pandemic profoundly transformed social life worldwide, indiscriminately affecting individuals across all age groups. Children have not been exempted from the risk of severe illness and death caused by COVID-19. Objective: This paper sought to describe the clinical findings, laboratory and imaging results, and hospital care provided for severe and critical cases of COVID-19 in unvaccinated children, with or without severe asthma, hospitalized in a public referral service for COVID-19 treatment in the Brazilian state of Pernambuco. Methods: This was a case series study of severe and critical COVID-19 in hospitalized, unvaccinated children, with or without severe asthma, conducted in a public referral hospital between March 2020 and June 2021. Results: The case series included 80 children, aged from 1 month to 11 years, with the highest frequency among those under 2 years old (58.8%) and a predominance of males (65%). Respiratory diseases, including severe asthma, were present in 73.8% of the cases. Pediatric multisystem inflammatory syndrome occurred in 15% of the children, some of whom presented with cardiac involvement. Oxygen therapy was required in 65% of the cases, mechanical ventilation in 15%, and 33.7% of the children required intensive care in a pediatric intensive care unit. Pulmonary infiltrates and ground-glass opacities were common findings on chest X-rays and CT scans; inflammatory markers were elevated, and the most commonly used medications were antibiotics, bronchodilators, and corticosteroids. Conclusions: This case series has identified key characteristics of children with severe and critical COVID-19 during a period when vaccines were not yet available in Brazil for the study age group. However, the persistence of low vaccination coverage, largely due to parental vaccine hesitancy, continues to leave children vulnerable to potentially severe illness from COVID-19. These findings may inform the development of public health emergency contingency plans, as well as clinical protocols and care pathways, which can guide decision-making in pediatric care and ensure appropriate clinical management, ultimately improving the quality of care provided. Full article

Environmental monitoring plays a key role in understanding and mitigating the effects of climate change, pollution, and resource mismanagement. The growth of printed sensor technologies offers an innovative approach to addressing these challenges due to their low cost, flexibility, and scalability. Printed sensors enable the real-time monitoring of air, water, soil, and climate, providing significant data for data-driven decision-making technologies and policy development to improve the quality of the environment. The development of new materials, such as graphene, conductive polymers, and biodegradable substrates, has significantly enhanced the environmental applications of printed sensors by improving sensitivity, enabling flexible designs, and supporting eco-friendly and disposable solutions. The development of inkjet, screen, and roll-to-roll printing technologies has also contributed to the achievement of mass production without sacrificing quality or performance. This review presents the current progress in printed sensors for environmental applications, with a focus on technological advances, challenges, applications, and future directions. Moreover, the paper also discusses the challenges that still exist due to several issues, e.g., sensitivity, stability, power supply, and environmental sustainability. Printed sensors have the potential to revolutionize ecological monitoring, as evidenced by recent innovations such as Internet of Things (IoT) integration, self-powered designs, and AI-enhanced data analytics. By addressing these issues, printed sensors can develop a better understanding of environmental systems and help promote the UN sustainable development goals. Full article

Malignant gastric outlet obstruction (MGOO) is a serious complication arising from advanced gastric or pancreatic head cancer, significantly impairing patients’ quality of life by disrupting oral intake and inducing severe gastrointestinal symptoms. With benign causes such as peptic ulcer disease on the decline, malignancies now account for 50–80% of gastric outlet obstruction (GOO) cases globally. This review outlines the pathophysiology, evolving epidemiology, and treatment modalities for MGOO. Therapeutic approaches include conservative management, endoscopic stenting, surgical gastrojejunostomy (GJ), stomach partitioning gastrojejunostomy (SPGJ), and endoscopic ultrasound-guided gastroenterostomy (EUS-GE). While endoscopic stenting offers rapid symptom relief with minimal invasiveness, it has higher rates of re-obstruction. Surgical options like GJ and SPGJ provide more durable palliation, especially for patients with longer expected survival. SPGJ, a modified surgical technique, demonstrates reduced incidence of delayed gastric emptying and may improve postoperative oral intake and survival compared to conventional GJ. EUS-GE represents a promising, minimally invasive alternative that combines surgical durability with endoscopic efficiency, although long-term data remain limited. Treatment selection should consider patient performance status, tumor characteristics, prognosis, and institutional resources. This comprehensive review underscores the need for individualized, multidisciplinary decision-making to optimize symptom relief, nutritional status, and overall outcomes in patients with MGOO. Full article

Autonomous navigation in dynamic, multi-vessel maritime environments presents a formidable challenge, demanding strict adherence to the International Regulations for Preventing Collisions at Sea (COLREGs). Conventional approaches often struggle with the dual imperatives of global path optimality and local reactive safety, and they frequently rely on simplistic state representations that fail to capture complex spatio-temporal interactions among vessels. We introduce a novel hybrid reinforcement learning framework, D* Lite + Transformer-Enhanced Soft Actor-Critic (TE-SAC), to overcome these limitations. This hierarchical framework synergizes the strengths of global and local planning. An enhanced D* Lite algorithm generates efficient, long-horizon reference paths at the global level. At the local level, the TE-SAC agent performs COLREGs-compliant tactical maneuvering. The core innovation resides in TE-SAC’s synergistic state encoder, which uniquely combines a Graph Neural Network (GNN) to model the instantaneous spatial topology of vessel encounters with a Transformer encoder to capture long-range temporal dependencies and infer vessel intent. Comprehensive simulations demonstrate the framework’s superior performance, validating the strengths of both planning layers. At the local level, our TE-SAC agent exhibits remarkable tactical intelligence, achieving an exceptional 98.7% COLREGs compliance rate and reducing energy consumption by 15–20% through smoother, more decisive maneuvers. This high-quality local control, guided by the efficient global paths from the enhanced D* Lite algorithm, culminates in a 10–32 percentage point improvement in overall task success rates compared to state-of-the-art baselines. This work presents a robust, verifiable, and efficient framework. By demonstrating superior performance and compliance with rules in high-fidelity simulations, it lays a crucial foundation for advancing the practical application of intelligent autonomous navigation systems. Full article

(1) The beach areas of Galveston, Texas, USA are heavily used for recreational activities and often experience elevated fecal indicator bacteria levels, representing a potential threat to ecosystem services, human health, and tourism-based economies that rely on suitable water quality. (2) During the span of 15 months (March 2022–May 2023), water samples that exceeded the U.S. Environmental Protection Agency-accepted alternative Beach Action Value (BAV) for enterococci of 104 MPN/100 mL were analyzed via microbial source tracking (MST) through quantitative polymerase chain reaction (qPCR) assays. The Bacteroides HF183 and DogBact as well as the Catellicoccus LeeSeaGull markers were used to detect human, dog, and gull fecal sources, respectively. The qPCR MST data were then utilized in a quantitative microbial risk assessment (QMRA) to assess human health risks. Additionally, samples collected in July and August 2022 were sequenced for 16S rRNA and matched with fecal sources through the Bayesian SourceTracker2 program. (3) Overall, 26% of the 110 samples with enterococci exceedances were positive for at least one of the MST markers. Gull was revealed to be the primary source of identified fecal contamination through qPCR and SourceTracker2. Human contamination was detected at very low levels (<1%), whereas dog contamination was found to co-occur with human contamination through qPCR. QMRA identified Campylobacter from canine sources as being the primary driver for human health risks for contact recreation for both adults and children. (4) These MST results coupled with QMRA provide important insight into water quality in Galveston that can inform future water quality and beach management decisions that prioritize public health risks. Full article

Dynamic and Adaptive solar systems demonstrate a greater potential to enhance the satisfaction of occupants, in terms of indoor environment quality and the energy efficiency of the buildings, than conventional shading solutions. This study has evaluated Dynamic and Adaptive Photovoltaic Shading Systems (DAPVSSs) through a comprehensive analysis of six shading designs in which their energy production and the comfort of occupants were considered. Energy generation, thermal comfort, daylight, and glare control have been assessed in this study, considering multiple orientations throughout the seasons, and a variety of tools, such as Rhino 6.0, Grasshopper, ClimateStudio 2.1, and Ladybug, have been exploited for these purposes. The results showed that the prototypes that were geometrically more complex, designs 5 and 6 in particular, had approximately 485 kWh higher energy production and energy savings for cooling and 48% better glare control than the other simplified configurations while maintaining the minimum daylight as the threshold (min DF: 2%) due to adaptive and control methodologies. Design 6 demonstrated optimal balanced performance for all the aforementioned criteria, achieving 587 kWh/year energy production while maintaining the daylight factor within the 2.1–2.9% optimal range and ensuring visual comfort compliance during 94% of occupied hours. This research has established a framework that can be used to make well-informed design decisions that could balance energy production, occupants’ wellbeing, and architectural integration, while advancing sustainable building envelope technologies. Full article

Background: Against the backdrop of the large-scale and intensive development of the livestock industry, enhancing the reproductive efficiency of cattle has become a crucial factor in industrial development. Holstein cows, as the most predominant dairy cattle breed globally, are characterized by high milk yield and excellent milk quality. However, their reproductive efficiency is comprehensively influenced by a variety of complex factors, and improving their reproductive performance faces numerous challenges. The ovary, as the core organ of the female reproductive system, plays a decisive role in embryonic development and pregnancy maintenance. It is not only the site where eggs are produced and developed but it also regulates the cow’s estrous cycle, ovulation process, and the establishment and maintenance of pregnancy by secreting various hormones. The normal functioning of the ovary is crucial for the smooth development of the embryo and the successful maintenance of pregnancy. Methods: Currently, traditional sequencing technologies have obvious limitations in deciphering ovarian function and reproductive regulatory mechanisms. To overcome the bottlenecks of traditional sequencing technologies, this study selected Holstein cows as the research subjects. Ovarian samples were collected from one pregnant and one non-pregnant Holstein cow, and single-nucleus transcriptome sequencing technology was used to conduct an in-depth study on the ovarian cells of Holstein cows. Results: By constructing a cell type-specific molecular atlas of the ovaries, nine different cell types were successfully identified. This study compared the proportions of ovarian cell types under different physiological states and found that the proportion of endothelial cells decreased during pregnancy, while the proportions of granulosa cells and luteal cells increased significantly. In terms of functional enrichment analysis, oocytes during both pregnancy and non-pregnancy play roles in the “cell cycle” and “homologous recombination” pathways. However, non-pregnant oocytes are also involved in the “progesterone-mediated oocyte maturation” pathway. Luteal cells during pregnancy mainly function in the “cortisol synthesis and secretion” and “ovarian steroidogenesis” pathways; non-pregnant luteal cells are mainly enriched in pathway processes such as the “AMPK signaling pathway”, “pyrimidine metabolism”, and “nucleotide metabolism”. Cell communication analysis reveals that there are 51 signaling pathways involved in the pregnant ovary, with endothelial cells, granulosa cells, and luteal cells serving as the core communication hubs. In the non-pregnant ovary, there are 48 pathways, and the interaction between endothelial cells and stromal cells is the dominant mode. Conclusions: This study provides new insights into the regulatory mechanisms of reproductive efficiency in Holstein cows. The differences in the proportions of ovarian cell types, functional pathways, and cell communication patterns under different physiological states, especially the increase in the proportions of granulosa cells and luteal cells during pregnancy and the specificity of related functional pathways, indicate that these cells play a crucial role in the reproductive process of cows. These findings also highlight the importance of ovarian cells in pathways such as “cell cycle”, “homologous recombination”, and “progesterone-mediated oocyte maturation”, as well as the cell communication mechanisms in regulating ovarian function and reproductive performance. Full article

Data annotation serves as a critical foundation for artificial intelligence (AI) and machine learning (ML). Recently, AI agents powered by large language models (LLMs) have emerged as effective solutions to longstanding challenges in data annotation, such as scalability, consistency, cost, and limitations in domain expertise. These agents facilitate intelligent automation and adaptive decision-making, thereby enhancing the efficiency and reliability of annotation workflows across various fields. Despite the growing interest in this area, a systematic understanding of the role and capabilities of AI agents in annotation is still underexplored. This paper seeks to fill that gap by providing a comprehensive review of how LLM-driven agents support advanced reasoning strategies, adaptive learning, and collaborative annotation efforts. We analyze agent architectures, integration patterns within workflows, and evaluation methods, along with real-world applications in sectors such as healthcare, finance, technology, and media. Furthermore, we evaluate current tools and platforms that support agent-based annotation, addressing key challenges such as quality assurance, bias mitigation, transparency, and scalability. Lastly, we outline future research directions, highlighting the importance of federated learning, cross-modal reasoning, and responsible system design to advance the development of next-generation annotation ecosystems. Full article