Search Results (14,022)

Emergency communication networks play a crucial role in disaster relief operations. Current automated deployment strategies based on rule-driven or heuristic algorithms struggle to adapt to the dynamic and heterogeneous network environments in disaster scenarios, while manual command deployment is constrained by personnel expertise and response time requirements, leading to suboptimal trade-offs between deployment efficiency and reliability. To address these challenges, this study proposes a novel deep reinforcement learning framework with a fully convolutional value network architecture, which achieves breakthroughs in multi-dimensional spatial decision-making through end-to-end feature extraction. This design effectively mitigates the “curse of dimensionality” inherent in traditional reinforcement learning methods for topology planning. Experimental results demonstrate that the proposed method effectively accomplishes the planning tasks of emergency communication hub elements, significantly improving deployment efficiency while maintaining robustness in complex environments. Full article

Small and medium-sized enterprises (SMEs) in the manufacturing sector often struggle to make effective use of production data due to fragmented systems and limited digital infrastructure. This paper presents a case study of implementing an AI-enhanced digital twin in a custom sportswear manufacturing SME developed under the AI and Robotics Estonia (AIRE) initiative. The solution integrates real-time production data collection using the Digital Manufacturing Support Application (DIMUSA); data processing and control; clustering-based data analysis; and virtual simulation for evaluating improvement scenarios. The framework was applied in a live production environment to analyze workstation-level performance, identify recurring bottlenecks, and provide interpretable visual insights for decision-makers. K-means clustering and DBSCAN were used to group operational states and detect process anomalies, while simulation was employed to model production flow and assess potential interventions. The results demonstrate how even a lightweight AI-driven system can support human-centered decision-making, improve process transparency, and serve as a scalable foundation for Industry 5.0-aligned digital transformation in SMEs. Full article

The global shift toward sustainable transportation has gained increasing interest, promoting the use of electric vehicles (EVs) as an environmentally friendly alternative to conventional vehicles as a result of a complex interaction between economic incentives, social dynamics, and environmental imperatives. This study is based on the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) to understand the key factors influencing consumers’ intentions in the Sultanate of Oman toward adopting electric vehicles. It is based on a mixed methodology combining quantitative data from a questionnaire of 448 participants, analyzed using ordinal logistic regression, with qualitative thematic analysis of in-depth interviews with 18 EV owners. Its results reveal that performance expectations, trust in EV technology, and social influence are the strongest predictors of EV adoption intentions in Oman. These findings suggest that some issues related to charging infrastructure, access to maintenance services, and cost-benefit ratio are key considerations that influence consumers’ intention to accept and use EVs. Conversely, recreational motivation is not a statistically significant factor, which suggests that consumers focus on practical and economic motivations when deciding to adopt EVs rather than on their enjoyment of driving the vehicle. The findings of this study provide valuable insights for decision-makers and practitioners to understand public perceptions of electric vehicles, enabling them to design effective strategies to promote the adoption of these vehicles in the emerging sustainable transportation market of the future. Full article

This study discusses the role of organizational pro-environmental behavior in driving sustainable development. Studies of green practices highlight their capacity to achieve ecological goals while delivering economic sustainability with business strategies for sustainable businesses and advancing environmental sustainability law. It also considers how the development of artificial intelligence, resource management, big data analysis, blockchain, and the Internet of Things enables companies to maximize supply efficiency and address evolving environmental regulations and sustainable decision-making. Through digital technology, businesses can facilitate supply chain transparency, adopt circular economy practices, and produce in an equitable and environmentally friendly manner. Additionally, intelligent business management practices, such as effective decision-making and sustainability reporting, enhance compliance with authorities while ensuring long-term profitability from a legal perspective. Integrating business innovation and digital technology within legal entities enhances economic efficiency, reduces operational costs, improves environmental sustainability, reduces paper usage, and lowers the carbon footprint, creating a double-benefit model of long-term resilience. The policymakers’ role in formulating policy structures that lead to green digital innovation is also to ensure that economic development worldwide is harmonized with environmental protection and international governance. Using example studies and empirical research raises awareness about best practices in technology-based sustainability initiatives across industries and nations, aligning with the United Nations Sustainable Development Goals. Full article

Reproductive efficiency is a critical determinant of productivity and profitability in sheep farming. Traditional selection methods have largely relied on phenotypic traits and historical reproductive records, which are often limited by subjectivity and delayed feedback. Recent advancements in artificial intelligence (AI), including video tracking, wearable sensors, and machine learning (ML) algorithms, offer new opportunities to identify behavior-based indicators linked to key reproductive traits such as estrus, lambing, and maternal behavior. This review synthesizes the current research on AI-powered behavioral monitoring tools and proposes a conceptual model, ReproBehaviorNet, that maps age- and sex-specific behaviors to biological processes and AI applications, supporting real-time decision-making in both intensive and semi-intensive systems. The integration of accelerometers, GPS systems, and computer vision models enables continuous, non-invasive monitoring, leading to earlier detection of reproductive events and greater breeding precision. However, the implementation of such technologies also presents challenges, including the need for high-quality data, a costly infrastructure, and technical expertise that may limit access for small-scale producers. Despite these barriers, AI-assisted behavioral phenotyping has the potential to improve genetic progress, animal welfare, and sustainability. Interdisciplinary collaboration and responsible innovation are essential to ensure the equitable and effective adoption of these technologies in diverse farming contexts. Full article

This study conducts a comparative evaluation of Turkiye’s carbon emission profile from both sectoral and global perspectives. Utilizing 2022 data from 76 countries, it applies two widely recognized multi-criteria decision-making (MCDM) methods: MEREC, for determining objective weights of criteria, and PROMETHEE II, for ranking countries based on these criteria. All data used in the analysis were obtained from the World Bank, a globally recognized and credible statistical source. The study evaluates seven criteria, including carbon emissions from the energy, transport, industry, and residential sectors, along with GDP-related indicators. The results indicate that Turkiye’s carbon emissions, particularly from industry, transport, and energy, are substantially higher than the global average. Moreover, countries with higher levels of industrialization generally rank lower in environmental performance, highlighting a direct relationship between industrial activity and increased carbon emissions. According to PROMETHEE II rankings, Turkiye falls into the lower-middle tier among the assessed countries. In light of these findings, the study suggests that Turkiye should implement targeted, sector-specific policy measures to reduce emissions. The research aims to provide policymakers with a structured, data-driven framework that aligns with the country’s broader sustainable development goals. MEREC was selected for its ability to produce unbiased criterion weights, while PROMETHEE II was chosen for its capacity to deliver clear and meaningful comparative rankings, making both methods highly suitable for evaluating environmental performance. This study also offers a broader analysis of how selected countries compare in terms of their carbon emissions. As carbon emissions remain one of the most pressing environmental challenges in the context of global warming and climate change, ranking countries based on emission levels serves both to support scientific inquiry and to increase international awareness. By relying on recent 2022 data, the study offers a timely snapshot of the global carbon emission landscape. Alongside its contribution to public awareness, the findings are expected to support policymakers in developing effective environmental strategies. Ultimately, this research contributes to the academic literature and lays a foundation for more sustainable environmental policy development. Full article

With the increasing prevalence of autonomous vehicles (AVs), drivers’ spatial cognition and takeover performance have become critical to traffic safety. This study investigates the effects of landmark salience—specifically visual and structural salience—on drivers’ spatial cognition and takeover behavior in autonomous driving scenarios. Two simulator-based experiments were conducted. Experiment 1 examined the impact of landmark salience on spatial cognition tasks, including route re-cruise, scene recognition, and sequence recognition. Experiment 2 assessed the effects of landmark salience on takeover performance. Results indicated that salient landmarks generally enhance spatial cognition; the effects of visual and structural salience differ in scope and function in autonomous driving scenarios. Landmarks with high visual salience not only improved drivers’ accuracy in making intersection decisions but also significantly reduced the time it took to react to a takeover. In contrast, structurally salient landmarks had a more pronounced effect on memory-based tasks, such as scene recognition and sequence recognition, but showed a limited influence on dynamic decision-making tasks like takeover response. These findings underscore the differentiated roles of visual and structural landmark features, highlighting the critical importance of visually salient landmarks in supporting both navigation and timely takeover during autonomous driving. The results provide practical insights for urban road design, advocating for the strategic placement of visually prominent landmarks at key decision points. This approach has the potential to enhance both navigational efficiency and traffic safety. Full article

Innovative applications of high-resolution airborne imaging are explored for detecting grapevine diseases. Driven by the motivation to enhance early disease detection, the method’s effectiveness lies in its capacity to identify isolated cases of grapevine yellows (Flavescence dorée and Bois Noir) and trunk disease (Esca complex), crucial for preventing the disease from spreading to unaffected areas. Conducted over a 17 ha vineyard in the Forlì municipality in Emilia-Romagna (Italy), the aerial survey utilized a photogrammetric camera capturing centimeter-level resolution images of the whole area in 17 minutes. These images were then processed through an automated analysis leveraging RGB-based spectral indices (Green–Red Vegetation Index—GRVI, Green–Blue Vegetation Index—GBVI, and Blue–Red Vegetation Index—BRVI). The analysis scanned the 1.24 · 10⁹ pixels of the orthomosaic, detecting 0.4% of the vineyard area showing evidence of disease. The instances, density, and incidence maps provide insights into symptoms’ spatial distribution and facilitate precise interventions. High specificity (0.96) and good sensitivity (0.56) emerged from the ground field observation campaign. Statistical analysis revealed a significant edge effect in symptom distribution, with higher disease occurrence near vineyard borders. This pattern, confirmed by spatial autocorrelation and non-parametric tests, likely reflects increased vector activity and environmental stress at the vineyard margins. The presented pilot study not only provides a reliable detection tool for grapevine diseases but also lays the groundwork for an early warning system that, if extended to larger areas, could offer a valuable system to guide on-the-ground monitoring and facilitate strategic decision-making by the authorities. Full article

Reproductive health literacy (RHL) is essential to women’s ability to make informed reproductive health (RH) decisions and is a key determinant of RH outcomes. Resettled refugee women often experience poorer RH outcomes, yet there is limited research on their RHL and its influence on RH decision-making. This scoping review aims to (1) to evaluate existing methods for measuring RHL among resettled refugee women and (2) to characterize the relationship between RHL, RH decision-making, behavior, and outcomes among refugee women residing in high-income countries. A search of peer-reviewed literature published in English found limited direct measurement of RHL. Measurement methods were primarily qualitative or based on unvalidated survey instruments, limiting comparability and generalizability. The current methodologies do not adequately capture RH knowledge or RHL proficiency. A range of additional factors were found to influence RH decision-making and behavior, supporting the need for a means to accurately measure RHL. Further quantitative research is needed to clarify the extent to which RHL and knowledge influence RH behavior and outcomes. The development of a culturally relevant, validated RHL instrument that integrates knowledge and contextual influences would support healthcare providers and public health agents in serving and designing effective interventions for refugee women post-resettlement. Full article

Background/Objectives: The assessment of platelet kinetics (APK) is recommended for patients with chronic idiopathic thrombocytopenic purpura (chITP). The aim of this study was to examine the importance of APK as a prognostic instrument in the selection of therapy in children with chITP. Methods: Retrospective, observational research, which included chITP children who were treated and subjected to APK in Serbia for 25 years (total number was 152). Results: In the acute phase of the disease, 15% of patients had life-threatening bleeding, 15% were asymptomatic, and there were no cases of fatal bleeding. Mean platelet life was 0.89 ± 0.47 days. A total of 45% of patients had normal platelet production, and 35% had very low production. Among the patients, 55% exhibited splenic platelet sequestration, 35% had mixed sequestration, and 10% showed hepatic platelet sequestration. Platelet lifespan and production indices were less reliable parameters, due to numerous contradictory results, especially when compared with the location of platelet sequestration. Distribution of bleeding and therapy-resistant patients was dominant with mixed sequestration. Good therapy responders had dominant splenic sequestration. In the chronic phase of the disease, initial therapy was sufficient for 40–45% of patients, while another 25% required second-line therapy, regardless of platelet sequestration location. A total of 25% percent of patients had undergone splenectomy, and all of them were in stable remission. The remaining 10%, which represented the most severe cases, required all available therapies, had equally mixed and liver sequestration, and splenectomy showed no effect. Conclusions: APK may be a determining factor for the selection of splenectomy as a therapeutic option in case of predominantly splenic sequestration. Although the platelet production index has been explored in several studies, its clinical relevance remains controversial. In our findings, it did not contribute to therapeutic decision-making and may even lead to misinterpretation. The factors distinguishing the minority of bleeding and therapy-resistant patients with similar laboratory profiles remain unclear. Full article

The advent of immune checkpoint inhibitors (ICIs) has revolutionized cancer treatment, significantly improving patient outcomes across multiple malignancies. Nonetheless, these therapies are associated with immune-related adverse effects, including cardiotoxicity, which remains a critical concern. This review provides a comprehensive analysis of ICI-related cardiotoxicity, encompassing its pathophysiological mechanisms, risk factors, diagnostic modalities, and management strategies. The onset of cardiotoxicity varies widely, ranging from acute myocarditis to long-term cardiovascular complications. Early identification through clinical assessment, biomarkers, and advanced imaging techniques is crucial for timely intervention. Management strategies include high-dose corticosteroids, other immunosuppressive agents, and supportive therapies, with a focus on balancing oncologic efficacy and cardiovascular safety. Additionally, rechallenging patients with ICIs following cardiotoxic events remains a complex clinical decision requiring multidisciplinary evaluation. As immunotherapy indications expand to include high-risk populations in a curative setting too, optimizing screening, prevention, and treatment strategies is essential to mitigate cardiovascular risks. A deep understanding of the molecular and clinical aspects of ICI-related cardiotoxicity will enhance patient safety and therapeutic decision-making, underscoring the need for ongoing research in this rapidly evolving field. Full article

The development of new diagnostic technologies is accelerating, and budgetary constraints in the health sector necessitate a systematic decision-making process to acquire emerging technologies. Health Technology Assessment methodologies integrate technology, clinical efficacy, patient safety, and organizational and financial factors in this context. However, these methodologies do not include the learning curve, a critical factor in operator-dependent technologies. This study presents an evaluation model incorporating the learning curve, developed from the domains of the AdHopHTA project. Using System Dynamics (SD), the model was validated and calibrated as a case study to evaluate the use of Point-of-Care Ultrasound (POCUS) in identifying dengue. This approach allowed for the analysis of the impact of the learning curve and patient demand on the revenues and costs of the healthcare system and the cost–benefit indicator associated with dengue detection. The model assesses physician competency and how different training strategies and frequencies of use affect POCUS adoption. The findings underscore the importance of integrating the learning curve into decision-making. This study highlights the need for further investigation into the barriers that limit the effective use of POCUS, particularly in resource-limited settings. It proposes a framework to improve the integration of this technology into clinical practice for early dengue detection. Full article

Despite notable advancements in smart home technologies, residential energy management continues to face critical challenges. These include the complex integration of intermittent renewable energy sources, issues related to data latency, interoperability, and standardization across diverse systems, the inflexibility of centralized control architectures in dynamic environments, and the difficulty of accurately modeling and influencing occupant behavior. To address these challenges, this study proposes an intelligent multi-agent system designed to accurately estimate and control energy consumption in residential buildings, with the overarching objective of optimizing energy usage while maintaining occupant comfort and satisfaction. The methodological approach employed is a hybrid framework, integrating multi-agent system architecture with system dynamics modeling and agent-based modeling. This integration enables decentralized and intelligent control while simultaneously simulating physical processes such as heat exchange, insulation performance, and energy consumption, alongside behavioral interactions and real-time adaptive responses. The system is tested under varying conditions, including changes in building insulation quality and external temperature profiles, to assess its capability for accurate control and estimation of energy use. The proposed tool offers significant added value by supporting real-time responsiveness, behavioral adaptability, and decentralized coordination. It serves as a risk-free simulation platform to test energy-saving strategies, evaluate cost-effective insulation configurations, and fine-tune thermostat settings without incurring additional cost or real-world disruption. The high fidelity and predictive accuracy of the system have important implications for policymakers, building designers, and homeowners, offering a practical foundation for informed decision making and the promotion of sustainable residential energy practices. Full article

Background: Immune checkpoint inhibitors (ICIs) have reshaped the treatment landscape of metastatic melanoma. While combination regimens often demonstrate improved response and survival compared to monotherapy, they are also associated with a higher incidence of immune-related adverse events (irAEs). Understanding the balance between benefit and risk is essential for making informed treatment decisions, especially given the variability in reported outcomes across clinical trials. Methods: We conducted a systematic review and meta-analysis of 14 clinical trials (comprising 22 treatment arms and >5000 patients) comparing ICI monotherapy (nivolumab, ipilimumab, or pembrolizumab) and combination therapy (nivolumab + ipilimumab) in advanced melanoma. Treatment-related outcomes were synthesized using fixed-effects, random-effects, or generalized linear mixed models (GLMMs), depending on study variability. Survival data were extracted from published Kaplan–Meier curves and analyzed using longitudinal GLMMs to capture trends over time. Results: Compared to monotherapy, combination immunotherapy achieved higher clinical benefit, with an overall response of 52.2% (vs. 31.6%), a five-year overall survival of 55.7% (vs. 34.3%), and a five-year progression-free survival of 39.0% (vs. 17.2%). However, this benefit came with a higher risk of toxicity: immune-related adverse events occurred in 93.2% of patients receiving combination therapy versus in 81.9% receiving monotherapy. Differences were consistent across all individual severe toxicities. Conclusions: Combination immunotherapy offers greater long-term clinical benefit than monotherapy in metastatic melanoma but at the cost of increased toxicity. By applying models adapted to study variability, we provide more reliable estimates of treatment efficacy and risk. GLMMs provide the most robust estimates and enable the modeling of survival dynamics over time. These findings support evidence-based decision-making and highlight the value of model-informed meta-analysis in oncology. Full article

The fast expansion of cloud computing has raised carbon emissions and energy usage in cloud data centers, so creative solutions for sustainable resource management are more necessary. This work presents a new algorithm—Carbon-Aware, Energy-Efficient, and SLA-Compliant Virtual Machine Placement using Deep Q-Networks (DQNs) and Agglomerative Clustering (CARBON-DQN)—that intelligibly balances environmental sustainability, service level agreement (SLA), and energy efficiency. The method combines a deep reinforcement learning model that learns optimum placement methods over time, carbon-aware data center profiling, and the hierarchical clustering of virtual machines (VMs) depending on resource constraints. Extensive simulations show that CARBON-DQN beats conventional and state-of-the-art algorithms like GRVMP, NSGA-II, RLVMP, GMPR, and MORLVMP very dramatically. Among many virtual machine configurations—including micro, small, high-CPU, and extra-large instances—it delivers the lowest carbon emissions, lowered SLA violations, and lowest energy usage. Driven by real-time input, the adaptive decision-making capacity of the algorithm allows it to dynamically react to changing data center circumstances and workloads. These findings highlight how well CARBON-DQN is a sustainable and intelligent virtual machine deployment system for cloud systems. To improve scalability, environmental effect, and practical applicability even further, future work will investigate the integration of renewable energy forecasts, dynamic pricing models, and deployment across multi-cloud and edge computing environments. Full article