Search Results (10,370)

Coastal zones face mounting pressures from rapid urban expansion and ecological degradation, posing significant challenges to achieving synergistic carbon storage and emissions reduction under China’s “dual carbon” goals. Yet, the identification of spatially explicit zones of carbon synergy (high storage–low emissions) and conflict (high emissions–low storage) in these regions remains limited. This study integrates the PLUS (Patch-generating Land Use Simulation), InVEST (Integrated Valuation of Ecosystem Services and Trade-offs), and OPGD (optimal parameter-based GeoDetector) models to evaluate the impacts of land-use/cover change (LUCC) on coastal carbon dynamics in China from 2000 to 2030. Four contrasting land-use scenarios (natural development, economic development, ecological protection, and farmland protection) were simulated to project carbon trajectories by 2030. From 2000 to 2020, rapid urbanization resulted in a 29,929 km² loss of farmland and a 43,711 km² increase in construction land, leading to a net carbon storage loss of 278.39 Tg. Scenario analysis showed that by 2030, ecological and farmland protection strategies could increase carbon storage by 110.77 Tg and 110.02 Tg, respectively, while economic development may further exacerbate carbon loss. Spatial analysis reveals that carbon conflict zones were concentrated in major urban agglomerations, whereas spatial synergy zones were primarily located in forest-rich regions such as the Zhejiang–Fujian and Guangdong–Guangxi corridors. The OPGD results demonstrate that carbon synergy was driven largely by interactions between socioeconomic factors (e.g., population density and nighttime light index) and natural variables (e.g., mean annual temperature, precipitation, and elevation). These findings emphasize the need to harmonize urban development with ecological conservation through farmland protection, reforestation, and low-emission planning. This study, for the first time, based on the PLUS-Invest-OPGD framework, proposes the concepts of “carbon synergy” and “carbon conflict” regions and their operational procedures. Compared with the single analysis of the spatial distribution and driving mechanisms of carbon stocks or carbon emissions, this method integrates both aspects, providing a transferable approach for assessing the carbon dynamic processes in coastal areas and guiding global sustainable planning. Full article

In the digital economy era, artificial intelligence, big data, and 5G are widely applied across various industries. The deep integration of digitalization and traditional sectors has been facilitated by this trend, which has injected new momentum into industrial development. In this context, this paper employs panel data from 29 Chinese provinces that span the years 2017 to 2023. This paper transcends the constraints of current research by integrating the digital economy with the export of new energy vehicles. Furthermore, this paper provides a regional analysis of this impact, thereby contributing to the existing literature. The following are the conclusions: (1) The export of new energy vehicles is substantially stimulated by the development of the digital economy. (2) Exports are indirectly facilitated by the digital economy, which promotes technological innovation and financial services. (3) The digital economy shows a significantly greater impact on the export of new energy vehicles in the eastern and inland areas than in other regions. Based on these discoveries, the paper suggests four critical policy recommendations: expanded openness, technological innovation, intelligent digital marketing, and government support. The objective is to foster the sustainable growth of China’s new energy vehicle export trade. This paper offers theoretical support for the sustainability of Chinese enterprises’ competitiveness in the international market. It also provides policymakers and industry stakeholders with practical advice. Full article

Clarifying the spatiotemporal relationship between urban ecosystem services and changes in landscape patterns is essential, as it has significant implications for balancing ecological protection with socio-economic development. However, existing studies have largely focused on the one-sided impact of landscape patterns on either the supply or demand of ESs, with limited investigation into how changes in these patterns affect the growth rates of both supply and demand. The central urban area, characterized by complex urban functions, intricate land use structures, and diverse environmental challenges, further complicates this relationship; yet, the spatiotemporal differentiation patterns of ecosystem services’ supply–demand dynamics in such regions, along with the underlying influencing mechanisms, remain insufficiently explored. To address this gap, the present study uses Shenyang’s central urban area, China as a case study, integrating multiple data sources to quantify the spatiotemporal variations in landscape pattern indices and five ecosystem services: water retention, flood regulation, air purification, carbon sequestration, and habitat quality. The XGBoost model is employed to construct non-linear relationships between landscape pattern indices and the supply–demand ratios of these services. Using SHAP values and LOWESS analysis, this study evaluates both the magnitude and direction of each landscape pattern index’s influence on the ecological supply–demand ratio. The findings outlined above indicate that: there are distinct disparities in the spatiotemporal distribution of landscape pattern indices at the patch type level. Additionally, the changing trends in the supply, demand, and supply–demand ratios of ecosystem services show spatiotemporal differentiation. Overall, the ecosystem services in the study area are developing negatively. Further, the impact of landscape pattern characteristics on ecosystem services is non-linear. Each index has a unique effect, and there are notable threshold intervals. This study provides a novel analytical approach for understanding the intricate relationship between landscape patterns and ESs, offering a scientific foundation and practical guidance for urban ecological protection, restoration initiatives, and territorial spatial planning. Full article

Large language models (LLMs) now match or exceed human performance on many open-ended language tasks, yet they continue to produce fluent but incorrect statements, which is a failure mode widely referred to as hallucination. In low-stakes settings this may be tolerable; in regulated or safety-critical domains such as financial services, compliance review, and client decision support, it is not. Motivated by these realities, we develop an integrated mitigation framework that layers complementary controls rather than relying on any single technique. The framework combines structured prompt design, retrieval-augmented generation (RAG) with verifiable evidence sources, and targeted fine-tuning aligned with domain truth constraints. Our interest in this problem is practical. Individual mitigation techniques have matured quickly, yet teams deploying LLMs in production routinely report difficulty stitching them together in a coherent, maintainable pipeline. Decisions about when to ground a response in retrieved data, when to escalate uncertainty, how to capture provenance, and how to evaluate fidelity are often made ad hoc. Drawing on experience from financial technology implementations, where even rare hallucinations can carry material cost, regulatory exposure, or loss of customer trust, we aim to provide clearer guidance in the form of an easy-to-follow tutorial. This paper makes four contributions. First, we introduce a three-layer reference architecture that organizes mitigation activities across input governance, evidence-grounded generation, and post-response verification. Second, we describe a lightweight supervisory agent that manages uncertainty signals and triggers escalation (to humans, alternate models, or constrained workflows) when confidence falls below policy thresholds. Third, we analyze common but under-addressed security surfaces relevant to hallucination mitigation, including prompt injection, retrieval poisoning, and policy evasion attacks. Finally, we outline an implementation playbook for production deployment, including evaluation metrics, operational trade-offs, and lessons learned from early financial-services pilots. Full article

Hymenopellis raphanipes is a high-value edible fungus with a short shelf life and high perishability, which poses significant challenges for quality control and safety assurance throughout its supply chain. Ensuring effective traceability is essential for improving production management, strengthening consumer trust, and supporting brand development. This study proposes a comprehensive traceability system tailored to the full lifecycle of Hymenopellis raphanipes, addressing the operational needs of producers and regulators alike. Through detailed analysis of the entire supply chain, from raw material intake, cultivation, and processing to logistics and sales, the system defines standardized traceability granularity and a unique hierarchical coding scheme. A multi-layered system architecture is designed, comprising a data acquisition layer, network transmission layer, storage management layer, service orchestration layer, business logic layer, and user interaction layer, ensuring modularity, scalability, and maintainability. To address performance bottlenecks in traditional systems, a multi-chain collaborative traceability model is introduced, integrating a mainchain–sidechain storage mechanism with an on-chain/off-chain hybrid management strategy. This approach effectively mitigates storage overhead and enhances response efficiency. Furthermore, data integrity is verified through hash-based validation, supporting high-throughput queries and reliable traceability. Experimental results from its real-world deployment demonstrate that the proposed system significantly outperforms traditional single-chain models in terms of query latency and throughput. The solution enhances data transparency and regulatory efficiency, promotes sustainable practices in green agricultural production, and offers a scalable reference model for the traceability of other high-value agricultural products. Full article

Introduction: Ecological networks (ENs) are critical frameworks designed to protect biodiversity, enhance habitat connectivity, and provide ecosystem services in fragmented landscapes. Urban ecological networks (UENs) adapt this concept to address the challenges posed by urbanization, habitat fragmentation, and climate change. Methods: This systematic review follows the PRISMA methodology, with the search strategy applied across PubMed, Scopus, and Web of Science. Articles published until 29 July 2025, were evaluated based on their alignment with One Health domains: human, animal, and ecosystem health. The included studies underwent independent review and quality assessment using the Newcastle–Ottawa Scale. Results: Only nine of the 228 articles that were found satisfied the requirements for inclusion. These studies examined UENs’ effects on biodiversity, species migration, and climate resilience but lacked direct evaluation of human health impacts. Key findings highlighted the role of ecological corridors in improving habitat connectivity, promoting biodiversity, and mitigating climate-related fragmentation. Conclusions: While UENs show significant potential to enhance biodiversity and urban resilience, their direct impacts on human health remain underexplored. Future interdisciplinary research should focus on quantifying these links and integrating UENs into urban planning to address ecological and Public Health challenges under a One Health framework. Full article

Unmanned Aerial Vehicles (UAVs) face collision risks due to Beyond Visual Line of Sight operations. Therefore, UAV Traffic Management (UTM) systems are used to manage and monitor UAV flight paths. However, centralized UTM systems are susceptible to various security attacks and are inefficient in managing flight data from different service providers. It further fails to provide low-latency communication required for UAV real-time operations. Thus, this paper proposes to integrate Drone-to-Drone (D2D) communication protocol into a secure public blockchain-based UTM system to enable direct communication between UAVs for efficient collision avoidance. The D2D protocol is designed using SHA256 hash function and bitwise XOR operations. A proof of concept has been built to verify that the UTM system is secure by enabling authorized service providers to view sensitive flight data only using legitimate secret keys. The security of the protocol has been analyzed and has been proven to be secure from key disclosure, adversary-in-the-middle, replay, and tracking attacks. Its performance has been evaluated and is proven to outperform existing studies by having the lowest computation cost of 0.01 ms and storage costs of 544–800 bits. Full article

Artificial intelligence (AI) is playing an increasingly important role in driving sustainable change across coastal and marine environments. Artificial intelligence offers strong support for environmental decision-making by helping to process complex data, anticipate outcomes, and fine-tune day-to-day operations. In busy coastal zones such as the Mediterranean where tourism and boating place significant strain on marine ecosystems, AI can be an effective means for marinas to reduce their ecological impact without sacrificing economic viability. This research examines the contribution of artificial intelligence toward the development of environmental sustainability in marina management. It investigates how AI can potentially reconcile economic imperatives with ecological conservation, especially in high-traffic coastal areas. Through a focus on the impact of social and technological context, this study emphasizes the way in which local conditions constrain the design, deployment, and reach of AI systems. The marinas of Ibiza and Monaco are used as a comparative backdrop to depict these dynamics. In Monaco, efforts like the SEA Index^® and predictive maintenance for superyachts contributed to a 28% drop in CO₂ emissions between 2020 and 2025. In contrast, Ibiza focused on circular economy practices, reaching an 85% landfill diversion rate using solar power, AI-assisted waste systems, and targeted biodiversity conservation initiatives. This research organizes AI tools into three main categories: supervised learning, anomaly detection, and rule-based systems. Their effectiveness is assessed using statistical techniques, including t-test results contextualized with Cohen’s d to convey practical effect sizes. Regression R² values are interpreted in light of real-world policy relevance, such as thresholds for energy audits or emissions certification. In addition to measuring technical outcomes, this study considers the ethical concerns, the role of local communities, and comparisons to global best practices. The findings highlight how artificial intelligence can meaningfully contribute to environmental conservation while also supporting sustainable economic development in maritime contexts. However, the analysis also reveals ongoing difficulties, particularly in areas such as ethical oversight, regulatory coherence, and the practical replication of successful initiatives across diverse regions. In response, this study outlines several practical steps forward: promoting AI-as-a-Service models to lower adoption barriers, piloting regulatory sandboxes within the EU to test innovative solutions safely, improving access to open-source platforms, and working toward common standards for the stewardship of marine environmental data. Full article

Hydraulic concrete is prone to cracking and interactive propagation under complex stress, threatening its structural integrity and service life. To address limitations of traditional numerical methods (e.g., mesh dependency in FEM) and imprecision of existing meshless methods for characterizing multi-fissure interactions, this study improved SPH to model double-crack interactions in hydraulic concrete under three-point bending and clarify the underlying mechanisms. A modified SPH framework was developed by introducing a failure parameter (ξ) to refine the kernel function, enabling simulation of particle progressive failure via the Mohr–Coulomb criterion; a three-point bending numerical model of concrete beams containing double precast fissures (induced and obstacle) was established, with simulations under varying obstacle fissure angles (α = 0–75°) and distances (d = 0.02–0.06 m). The results show that the obstacle fissure angles significantly regulate the crack paths: as the α increases, the tensile stress concentration shifts from the obstacle fissure’s middle to its ends, causing cracks to deflect toward the lower end, with a reduced propagation length and lapping time; at an α = 75°, the obstacle fissure’s lower tip dominates failure, forming an “induced fissure–lower end of obstacle fissure–top” penetration mode. The fissure distances affect the stress superposition: a smaller d (e.g., 0.02 m) induces vertical propagation and rapid lapping with the obstacle fissure’s lower end, while a larger d (e.g., 0.06 m) weakens the stress at the induced fissure tip, promoting horizontal deflection toward the obstacle fissure’s upper end and transforming the failure into “upper-end dominated.” This confirms that the improved SPH method effectively simulates crack behaviors, providing insights into multi-fissure failure mechanisms and theoretical support for hydraulic structure crack control and safety evaluation. Full article

Customer segmentation is a foundation of Customer Relationship Management (CRM) and is widely regarded as a key to business development success. As the principles of sustainable development become increasingly central to business strategy, it is necessary that social, environmental, and economic considerations be incorporated into customer segmentation—even in regulated markets such as the postal market. The article develops and applies a three-dimensional (3D) segmentation model of business customers in the Slovak postal market, utilizing cluster analysis within STATISTICA analytical software for operationalization of the segmentation criteria. The 3D model reacts to the three pillars of sustainable development and is verified under real conditions at Slovak Post, plc. By adopting a systems perspective, the research places customer segmentation as an integral component of the entire socio-technical system, emphasizing the interrelatedness of organizational, social, and environmental considerations. The study illustrates how a systems-based approach to segmentation enables postal operators to uncover key customer segments, optimize resource allocation, and support competitiveness and sustainability goals. The practical applicability of the model is illustrated by its potential for application in other regulated service industries, providing a solid framework for sustainable customer management and strategic decision-making in complex environments. The research underscores the critical role of systems thinking in addressing the complex challenges of sustainable development in regulated industries. Full article

The rapid expansion of urbanization and inadequate planning have triggered a water balance crisis in many cities, manifesting as both the need for artificial lake supplementation and frequent urban flooding. Using the Xuanwu Lake watershed in Nanjing as a case study, this research aims to optimize the Blue–Green Infrastructure (BGI) network to maximize rainfall utilization within the watershed. The ultimate goal is to restore natural water balance processes and reduce reliance on artificial supplementation while mitigating urban flood risks. First, the Soil Conservation Service Curve Number (SCS–CN) model is employed to estimate the maximum potential of natural convergent flow within the watershed. Second, drawing on landscape connectivity theory, a multi-level BGI network optimization model is developed by integrating the Minimum Cumulative Resistance (MCR) model and the gravity model, incorporating both hydrological connectivity and flood safety considerations. Third, a water balance model based on the Storm Water Management Model (SWMM) framework and empirical formulas is constructed and coupled with the network optimization model to simulate and evaluate water budget performance under optimized scenarios. The results indicate that the optimized scheme can reduce artificial supplementation to Xuanwu Lake by 62.2% in June, while also ensuring effective supplementation throughout the year. Annual runoff entering the lake reaches 13.25 million cubic meters, meeting approximately 13% of the current annual supplementation demand. Moreover, under a 100-year return period flood scenario, the optimized network reduces total watershed flood volume by 35% compared to pre-optimization conditions, with flood-prone units experiencing reductions exceeding 50%. These findings underscore the optimized BGI network scheme’s capacity to reallocate rainwater resources efficiently, promoting a transition in urban water governance from an “engineering-dominated” approach to an “ecology-oriented and self-regulating” paradigm. Full article

Camera tracking plays a pivotal role in augmented reality geographic information systems (AR-GIS) and location-based services (LBS), serving as a crucial component for accurate spatial awareness and navigation. Current learning-based camera tracking techniques, while achieving superior accuracy in pose estimation, often overlook changes in scale. This oversight results in less stable localization performance and challenges in coping with dynamic environments. To address these tasks, we propose a pyramid convolution-based scene coordinate regression network (PSN). Our approach leverages a pyramidal convolutional structure, integrating kernels of varying sizes and depths, alongside grouped convolutions that alleviate computational demands while capturing multi-scale features from the input imagery. Subsequently, the network incorporates a novel randomization strategy, effectively diminishing correlated gradients and markedly bolstering the training process’s efficiency. The culmination lies in a regression layer that maps the 2D pixel coordinates to their corresponding 3D scene coordinates with precision. The experimental outcomes show that our proposed method achieves centimeter-level accuracy in small-scale scenes and decimeter-level accuracy in large-scale scenes after only a few minutes of training. It offers a favorable balance between localization accuracy and efficiency, and effectively supports augmented reality visualization in dynamic environments. Full article

Insects, the most diverse group of animals on Earth, have historically evolved under strong environmental selective pressures, particularly fluctuations in atmospheric oxygen and temperature. During the Anthropocene, rapid climate change, pollution, and habitat alteration now impose new and compounded stresses, accelerating insect decline at unprecedented rates. Here, we present a conceptual framework comparing ancient environmental drivers of insect physiology, size, and diversity with modern anthropogenic stressors. This perspective reveals how contemporary pressures such as pesticide-induced hypoxia, climate-driven size alterations, and habitat fragmentation resemble and intensify ancient evolutionary constraints. We further highlight the disruption of key ecological services and the emergence of novel biotic pressures, including intensified competition and predation. Recent advances in trait-based modeling, environmental DNA analysis, remote sensing, and AI-powered monitoring offer promising avenues for assessing these complex interactions. Integrating these modern tools with historical evolutionary insights is essential for improving risk assessments, informing conservation strategies, and mitigating the cascading effects of insect diversity loss on ecosystems. Full article

Predictive maintenance (PdM) represents a significant evolution in maintenance strategies. However, challenges such as system integration complexity, data quality, and data availability are intricately intertwined, collectively impacting the successful deployment of PdM systems. Recently, large model-based agents, or agentic artificial intelligence (AI), have evolved from simple task automation to active problem-solving and strategic decision-making. As such, we propose an AI agent-enabled PdM method that leverages an agentic AI development platform to streamline the development of a multimodal data-based fault detection agent, a RAG (retrieval-augmented generation)-based fault classification agent, a large model-based fault diagnosis agent, and a digital twin-based fault handling simulation agent. This approach breaks through the limitations of traditional PdM, which relies heavily on single models. This combination of “AI workflow + large reasoning models + operational knowledge base + digital twin” integrates the concepts of BaaS (backend as a service) and LLMOps (large language model operations), constructing an end-to-end intelligent closed loop from data perception to decision execution. Furthermore, a tentative prototype is demonstrated to show the technology stack and the system integration methods of the agentic AI-based PdM. Full article

In today’s fast-moving digital economy, service start-ups are reshaping industries; however, they face intense uncertainty, limited resources, and fierce competition. This study introduces an Artificial Intelligence (AI)-powered process modeling framework designed to give these ventures a competitive edge by combining big data analytics, machine learning, and Business Process Model and Notation (BPMN). While past models often overlook the dynamic, human-centered nature of service businesses, this research fills that gap by integrating AI-Driven Ideation, AI-Augmented Content, and AI-Enabled Personalization to fuel innovation, agility, and customer-centricity. Expert insights, gathered through a two-stage fuzzy Delphi method and validated using DEMATEL, reveal how AI can transform start-up processes by offering real-time feedback, predictive risk management, and smart customization. This model does more than optimize operations; it empowers start-ups to thrive in volatile, data-rich environments, improving strategic decision-making and even health and safety governance. By blending cutting-edge AI tools with process innovation, this research contributes a fresh, scalable framework for digital-age entrepreneurship. It opens exciting new pathways for start-up founders, investors, and policymakers looking to harness AI’s full potential in transforming how new ventures operate, compete, and grow. Full article