Search Results (13,740)

This study analyzes the impact of socio-demographic factors on citizens’ perceptions of the functioning of local-level inclusion mechanisms, focusing on four dimensions: information, participation, transparency, and effectiveness. A mixed-methods approach is employed, combining: (1) a large-scale survey with 885 residents in three municipalities (Patos, Elbasan, and Mat) and (2) in-depth interviews with mayors, municipal councilors, and social service managers. The quantitative analysis was conducted through binary logistic regression models in SPSS version 27, as well as ordered logistic regression, examining the impact of socio-demographic factors such as age, education level, gender, employment status, and area of residence on the four dimensions of the study and the Inclusion Index. The qualitative component analyzes how local officials address citizen inclusion in key social policy areas such as employment, education, housing, social assistance, and social services. The results show that residence is the strongest predictor, with citizens in urban areas reporting higher levels of information, transparency, and effectiveness of participatory processes. Employment status is also associated with more positive perceptions, while gender and educational level show limited and inconsistent effects. Qualitative findings suggest that these differences are mediated by structural and institutional factors, such as infrastructure, administrative capacity and access to information. The study contributes to the literature on democratic innovation and participatory governance by showing that the impact of demographic factors on civic engagement is mediated by institutional and territorial conditions, particularly in developing countries. Full article

Over the past few decades, extensive research has been conducted on software reliability growth models based on the non-homogeneous Poisson process. However, most existing studies rely on the premise of perfect debugging, failing to fully consider key factors such as potential error introduction, the diversity of failure types, and dynamic changes in the testing environment. They also neglect the systematic analysis of the testing and repair processes. This disconnection between theoretical assumptions and practical application scenarios makes it difficult for these models to accurately depict the complex phenomena in real testing processes. To address these limitations, this study proposes an integrated NHPP-based SRGM combining an imperfect debugging mechanism, the fault detection process (FDP) and fault correction process (FCP), fault heterogeneity, and change-point analysis. The model introduces dynamic correction intensity linked to pending faults, classifies faults into simple (instantly corrected) and complex (queued for FCP), and models detection and correction rates as piecewise functions before and after change points, capturing realistic scheduling logic and synchronized effects of strategy, tools, and personnel changes. On this basis, a comprehensive and optimized software release strategy is further proposed. This strategy accounts for detection costs during testing, failure repair costs, and comprehensive costs from post-release failures. Its aim is to minimize full life cycle costs while meeting the reliability targets, thus providing software project managers with a scientifically grounded and practically reliable decision-making basis leveraging the integrated modeling innovations. Full article

The clean and low-carbon transition of new-type power systems imposes increasingly stringent demands on green technology innovation among enterprises along the power industry chain. Identifying the drivers and potential remedies for green innovation bubble can offer China-originated solutions to the sustainable development of the global power sector. This paper focuses on Chinese power industry chain enterprises over the period 2016–2023. Drawing on the AMO framework, a three-dimensional analytical framework encompassing ability, motivation, and opportunity is developed. Double machine learning (DDML) is employed to perform benchmark regression and causal identification. Subsequently, gradient boosting trees (GBT) combined with SHAP interpretability analysis are applied to uncover nonlinear relationships and heterogeneous transmission pathways among key variables. The results indicate that energy-saving policies and green financial policies significantly inhibit the formation of the green innovation bubble in power industry chain enterprises. Specifically, these policies curb the green innovation bubble via three channels: an innovation incentive management mechanism, a peer imitation and convergence mechanism, and an industrial chain technology spillover mechanism. Upstream enterprises exhibit greater sensitivity to direct regulatory measures and backward technology spillovers from energy-saving and green finance policies, whereas midstream enterprises are more reliant on peer carbon emission pressure. The findings are validated through cross-verification among DDML, mechanism analysis, and interpretable analysis. The results provide empirical evidence and policy implications for optimizing energy-saving and green finance policies and for precisely deflating the green innovation bubble. Full article

This study presents a novel integrated methodology for optimizing forest fire emergency rescue vehicle scheduling through the synergistic combination of a multi-criteria demand urgency grading framework and mechanistic fire spread propagation modeling, enhancing spatiotemporal resource allocation efficiency under evolving wildfire scenarios. The research focuses on three core aspects: First, a multi-dimensional demand urgency evaluation system is established, incorporating fire threat, response efficiency, and path factors. Subjective and objective weights are determined through fuzzy analytic hierarchy process and entropy method, respectively, while grey relational analysis TOPSIS method is employed for prioritizing affected areas. The model’s validity is verified using wildfire data from the Greater Khingan Mountains. Second, a multi-objective vehicle scheduling model is developed, combining total rescue time, cost, and urgency ranking index via weighted sum method. A fire spread model is innovatively introduced to dynamically adjust urgency classification, with genetic algorithm (GA) and Genetic Simulated Annealing Algorithm (GASA) designed for solution optimization. Finally, empirical analysis of 13 fire cases in the Greater Khingan Mountains (2020) demonstrates that GASA outperforms GA, achieving 17% reduction in rescue time, 1% cost savings, 22% shorter travel distance, and 0.7% improvement in urgency ranking. Incorporating the fire spread model enhances the urgency ranking index by 10.78%, where the improvement is defined as the percentage increase in the achieved objective function value f3 compared to the solution obtained without dynamic fire propagation information. By integrating dynamic urgency assessment with intelligent algorithms, this research constructs a spatiotemporal-aware emergency scheduling framework aligned with forest fire evolution patterns, providing theoretical foundations and practical strategies to enhance rescue efficiency and resource allocation, with significant implications for disaster management. Full article

The need for the rapid advancement of lithium-based energy storage technologies continues to outpace progress in materials development and manufacturing, creating a widening gap between laboratory-scale innovation and industrial deployment. There is a need to examine the key materials and processing challenges that limit the performance, cost-effectiveness, and sustainability of next-generation lithium batteries. For material considerations, many commonly used electrodes face issues of volumetric expansion and performance degradation over charging cycles. To address these issues, binders are a crucial component to consider as they adhere active materials to the electrodes, and their structure can be altered to mitigate undesirable effects from these components. Hence, the selection and exploration of alternative binders are becoming increasingly important in the pursuit of longer-lasting and safer Li-batteries. From a manufacturing perspective, current production lines rely on multistep, energy-intensive processes, e.g., from slurry-mixing to cell assembly, that elevate costs and complicate scale-up. Emerging chemistries incorporating nanomaterials or solid-state components face additional barriers related to yield, process control, and defect management, all of which can exacerbate safety risks related to processing during production and thermal runaway in produced batteries. End-of-life considerations, including disassembly, recycling, and the safe handling of toxic materials, further contribute to the technological and logistical complexity of large-scale deployment. The field is moving toward sustainable material alternatives, more efficient and adaptive manufacturing routes, and advanced technologies such as solid-state electrolytes and nanostructured electrodes. Together, these developments provide a roadmap for overcoming current bottlenecks and enabling the next generation of high-performance, safe, and sustainable lithium battery technologies. This review examines the progress made in finding alternative materials and synthesis methods for the optimization of lithium battery cells, with a focus on the development of novel binders, slurry synthesis and manufacturing framework. In addition, the advantages and limitations of the alternative binder materials and processes are also explored, with a focus on scalability for manufacturing, safety concerns, sustainability and end-of-life challenges. Full article

Background: The transition from the parenteral to subcutaneous application of systemic oncological therapy represents one of the most important innovations in modern oncology, as it affects the quality of life of patients as well as the organization of work and the management of health services. The introduction of this change requires effective leadership, interdisciplinary cooperation, and the adaptation of existing processes in healthcare organizations. Methods: We conducted a systematic review of the professional and scientific literature, considering the purpose and goal of this research. We used electronic databases: Wiley Online Library, PubMed, COBBIS.SI, and Google Scholar web browser. Papers from 2017 to 2025 were considered and processed using meta-synthesis. Results: Recent studies confirm that the subcutaneous administration of immunotherapy and targeted therapy is as effective and safe as parenteral immunotherapy, while significantly reducing treatment time and improving patient experience. Discussion: The transition to subcutaneous application provides an opportunity to transform oncology care. From a management perspective, the introduction of subcutaneous application requires systematic change management, staff training, process adaptation, and interdisciplinary cooperation. The sustainable implementation of innovations depends on organization, communication, and management support. Full article

Accurate mapping of winter rapeseed cultivation areas is crucial for food security assessment and agricultural resource management, yet remains a persistent challenge in mountainous regions characterized by complex topography and highly fragmented field parcels. To address these challenges, this study develops a progressive deep learning framework using single growing-season data from the Hanzhong Basin. We conducted a structured comparison of remote sensing indices, machine learning, and deep learning approaches for rapeseed identification in heterogeneous landscapes. First, sensitivity analysis of the Flowering Index for Rapeseed was performed to identify the optimal parameterization, yielding high inter-class separability (ND = 0.959) during peak flowering and a threshold-based overall accuracy (OA) of 94.41%. Second, a multidimensional feature space was constructed by integrating Sentinel-2 spectral bands, image texture metrics, and topographic variables; Random Forest-based feature importance selection subsequently enhanced Support Vector Machine classification performance to an OA of 90.70%. Third, we proposed an innovative three-stage progressive UNet++ architecture: Stage1 focuses on binary rapeseed/non-rapeseed classification to establish spatial priors; Stage2 refines discrimination among spectrally similar vegetation classes (rapeseed and other vegetation); and Stage3 achieves comprehensive seven-class semantic segmentation. A weighted focal loss function combined with a weight inheritance mechanism was employed to mitigate class imbalance and facilitate inter-stage knowledge transfer. The final model attained an OA of 98.65% and a mean intersection over union of 95.29%, while effectively suppressing salt-and-pepper noise artifacts in geometrically fragmented parcels. Our findings demonstrate the substantial advantages of progressive deep learning strategies for crop monitoring in topographically constrained environments. Full article

The knowledge economy and green innovation are fundamental pillars for the transition towards sustainable production models. The objective of this study was to analyze the influence of intellectual capital, green knowledge management and environmental practices on green innovation in SMEs in Tena. A quantitative cross-sectional approach was developed, applying a structured questionnaire to a sample of 64 green enterprises. Data analysis was performed using a Partial Least Squares Structural Equation Model (PLS-SEM), after evaluating the psychometric properties of the measurement model. The results show that the model explains 40% of the variance in green innovation. It was confirmed that Environmental and Technological Practices (ETPs) have the strongest and most significant effect on innovation, followed to a lesser extent by Intellectual Capital, whose influence was positive but marginal. Green Knowledge Management did not show a statistically significant impact. It is concluded that green innovation in Amazonian enterprises depends primarily on the adoption of technological infrastructure and tangible practices, while the systematization of knowledge remains a pending challenge. Full article

In sociotechnical transitions, landscape disruptions, such as climate change, exert pressure on incumbent regimes and can trigger the emergence of niche innovations. Renewable energy communities represent one such innovation, increasingly central to European energy policy. This paper applies a critical realist method to examine the energy community niche in Portugal, drawing on a content analysis of the scientific literature and recent Horizon Europe research projects involving Portuguese actors. The analysis reveals three distinct research pathways structuring knowledge production in this niche—technology-driven, socio-governance-oriented, and infrastructure-focused. It also reveals a systemic R&D bias: incumbent actors occupy dual positions—simultaneously at the regime level and within the niche—playing central roles in learning and network formation while exhibiting limited capacity to translate innovation into institutional change and large-scale diffusion. Building on these critical realist findings, we then apply the Strategic Niche Management framework as an evaluative lens, revealing structural misalignments between components of the sociotechnical system. Together, these two analytical steps offer a novel reading of the Portuguese energy community niche, contributing to the theoretical debate on incumbent roles in transition dynamics and identifying concrete shortcomings for future R&D agenda-setting. Full article

Sino–U.S. trade friction (SUTF) has imposed significant shocks on economic systems and firm operations, attracting growing scholarly attention. This study investigates the impact of SUTF on corporate innovation quality and its underlying mechanism. Using the U.S. Section 301 investigation as a quasi-natural experiment, we adopt a difference-in-differences (DID) research design. The results indicate that SUTF significantly enhances corporate innovation quality, and this positive effect is partially mediated by the adoption of artificial intelligence (AI)—a general-purpose technology that reshapes traditional organizational and management systems. Moreover, the innovation-enhancing effect of SUTF is more pronounced among firms with a higher proportion of executives with IT experience and those with stronger corporate governance. These findings contribute to the literature on the economic consequences of SUTF by revealing AI adoption as a novel mechanism. This study also offers practical insights for firms navigating an era of heightened trade tensions and can inform policies aimed at fostering high-quality innovation. Full article

Artificial intelligence (AI) is reshaping tourism businesses by improving decision making, service personalization, operational efficiency, and data-driven management. Beyond these organizational benefits, AI may also strengthen firms’ capacity to cope with market volatility, demand shocks, cost pressures, and other sources of financial fragility. This study provides a systematic literature review and bibliometric analysis of 146 Web of Science articles on AI in tourism published between 2019 and 2023. Following a structured screening process, it identifies the intellectual structure, thematic evolution, and main performance-related drivers associated with AI adoption. The findings show a rapidly expanding field centered on business performance, information technology, big data, robotics, and AI-enabled service innovation. The literature suggests that AI contributes to resilience by enhancing forecasting, resource allocation, customer management, and organizational adaptability under uncertainty. However, explicitly financial perspectives—such as financial vulnerability, resilience, liquidity, solvency, and risk management—remain underdeveloped. This study contributes by reframing AI in tourism as a potential resilience-building capability rather than only a tool for service innovation. Its main limitations are the reliance on Web of Science and a fixed 2019–2023 bibliometric corpus, which future research should extend. Full article

Urban reservoirs are increasingly exposed to multiple interacting pressures associated with eutrophication, pollutant inflow, ageing sewerage and stormwater infrastructure, and climate-related hydrological instability. This issue is of growing concern because municipalities often possess fragmented monitoring and planning evidence without an operational framework for translating it into remediation action. This study develops an integrated decision-support framework for the Ternopil Reservoir based primarily on recent hydrochemical monitoring data, complemented by historical targeted sampling and local environmental and planning materials. The analysis focuses on the most informative indicators of ecological deterioration in an urban reservoir, including oxygen regime, organic pollution, nutrient-related parameters, suspended solids, and selected pollution markers. The available evidence indicates that the Ternopil Reservoir is among the most environmentally stressed water bodies within the local reservoir system, with recurrent eutrophication symptoms, seasonal water blooming, and spatially differentiated exceedances of selected water-quality indicators. The results further indicate persistent nutrient-related and organic pressure, pronounced hydrochemical tension in 2022, and hotspot vulnerability in hydraulically weak sectors of the reservoir. To address these pressures, the study proposes a four-stage monitoring-to-remediation framework that links environmental diagnosis with the identification of vulnerable zones, the prioritisation of hydraulic and hydrobiological measures, and post-remediation control. The proposed framework is intended as an operational planning tool for translating fragmented local evidence into a coherent remediation pathway for urban reservoirs under multiple environmental stressors. Full article

The integration of exosomes into professional cosmetics marks a significant paradigm shift from traditional passive formulations to advanced regenerative esthetics. Rather than being defined solely by their nanometric dimensions or classical association with endosomal biogenesis, these vesicles function as highly targeted intercellular messengers capable of delivering complex bioactive payloads to modulate tissue repair and collagen synthesis. While robust preclinical and clinical trials validate their remarkable potential in skin rejuvenation, hair restoration, and hyperpigmentation management, significant translational barriers remain. A critical analysis of the current literature reveals that successful clinical outcomes frequently rely on physical penetration enhancers, such as microneedling or fractional lasers, making it challenging to isolate the autonomous efficacy of topical vesicles from the trauma-induced regenerative response. Furthermore, commercial viability is dictated by stringent regulatory frameworks. In the European Union, Regulation (EC) No 1223/2009 strictly prohibits human-derived biologicals, while the US Food and Drug Administration (FDA) aggressively monitors the unsubstantiated marketing of cellular therapies. To navigate these biosafety and legal constraints, the aesthetic industry is increasingly pivoting toward non-human and legally compliant alternatives. Consequently, Plant-Derived Extracellular Vesicles (PDEVs), microbiome-derived exosomes (such as those obtained from bacterial fermentation), and bioengineered synthetic analogues have become the focal point of market innovation. A practical evaluation of the MCCM Medical Cosmetics portfolio illustrates this strategic shift, demonstrating the clinical versatility of botanical sources. To secure the long-term credibility of exosome technology, the industry must overcome current manufacturing heterogeneity by aligning with international standardization frameworks, such as the MISEV2023 guidelines, thereby ensuring reliable delivery systems, batch-to-batch consistency, and uncompromised consumer safety. This review provides a comprehensive overview of the biological mechanisms, clinical efficacy, and translational challenges associated with exosome-based cosmetics. Full article

The agricultural sector, particularly animal production, faces numerous unprecedented challenges driven by climate change, resource depletion, and an ever-growing global demand for quality food. These challenges are further compounded by the increasing environmental impact of livestock farming, including greenhouse gas emissions, overuse of water and land resources, and the destruction of vital ecosystems. Ensuring the sustainability of animal production systems while mitigating the negative environmental impacts of these factors is essential for future global food security. As the demand for animal-derived products continues to rise, there is a pressing need for innovations that can enhance productivity without compromising environmental integrity or animal welfare. Artificial intelligence (AI) has emerged as a transformative technology with the potential to revolutionize the animal production industry. AI-driven solutions offer promising avenues for optimizing production efficiency, enhancing animal health and welfare, and reducing the environmental footprint of livestock farming. Machine learning, sensor technologies, and advanced data analytics are being increasingly utilized to monitor and predict various aspects of animal farming, such as feed efficiency, disease prevention, and climate resilience. These technologies enable farmers to make data-driven decisions, fostering more sustainable and environmentally responsible practices. This review examines the integration of AI into animal production systems, emphasizing its applications in climate change mitigation, resource management, and advancing sustainability. The discussion addresses how AI technologies can be utilized to improve productivity while minimizing environmental impact and enhancing animal welfare. Additionally, the paper outlines future opportunities, challenges, and potential barriers to integrating AI technologies into livestock farming, thereby ensuring long-term sustainability amid global challenges. Full article

With growing global concern over climate risk, high-carbon enterprises are assuming an increasingly critical role in strengthening climate resilience and fostering low-carbon development. However, how climate risk disclosure shapes their carbon performance—specifically through what mechanisms and pathways—remains a pivotal yet underexplored question. To address this gap, this study constructs a panel dataset comprising Chinese listed high-carbon companies over the period 2006–2022 and employs a two-way fixed-effects econometric model to assess how climate risk disclosure affects carbon performance while investigating the underlying mediating channel. The empirical results provide robust evidence that enhanced climate risk disclosure improves the carbon performance of high-carbon enterprises. Mechanism analysis indicates that this beneficial outcome is mainly achieved through promoting green technological innovation and easing corporate financial constraints. Heterogeneity analysis further shows that the effect is stronger among smaller companies, firms operating in less concentrated industries, and those headquartered in China’s eastern region. The policy implications derived from these findings include establishing and strengthening a mandatory climate risk disclosure framework, introducing targeted incentives for green innovation and transition finance and tailoring climate risk management strategies according to firm-specific characteristics. Overall, this study underscores climate risk disclosure as a crucial factor in supporting the shift toward low-carbon operations among high-carbon enterprises. Full article