Search Results (9,270)

Dam monitoring tracks environmental variables (water level, temperature) and structural responses (deformation, seepage, and stress) to assess safety and performance. Structural health monitoring (SHM) refers to the systematic observation and analysis of the structural condition over time, and it is essential in maintaining the safety, functionality, and long-term performance of dams. This review examines monitoring data applications, covering structural health assessment methods, historical motivations, and key challenges. It discusses monitoring components, data acquisition processes, and sensor roles, stressing the need to integrate environmental, operational, and structural data for decision making. Key objectives include risk management, operational efficiency, safety evaluation, environmental impact assessment, and maintenance planning. Methodologies such as numerical modeling, statistical analysis, and machine learning are critically analyzed, highlighting their strengths and limitations and the demand for advanced predictive techniques. This paper also explores future trends in dam monitoring, offering insights for engineers and researchers to enhance infrastructure resilience. By synthesizing current practices and emerging innovations, this review aims to guide improvements in dam safety protocols, ensuring reliable and sustainable dam operations. The findings provide a foundation for the advancement of monitoring technologies and optimization of dam management strategies worldwide. Full article

Japanese encephalitis virus (JEV) is a mosquito-borne zoonotic flavivirus that circulates primarily within animal populations and occasionally spills over to humans, causing severe neurological disease. While humans are terminal hosts, veterinary species such as pigs and birds play essential roles in viral amplification and maintenance, making JEV fundamentally a veterinary infectious disease with zoonotic potential. This review summarizes the current understanding of JEV transmission dynamics from a veterinary and ecological perspective, emphasizing the roles of amplifying hosts and animal surveillance in controlling viral circulation. Recent genotype shifts and viral evolution have raised concerns regarding vaccine effectiveness and regional emergence. National surveillance systems and animal-based monitoring strategies are examined for their predictive value in detecting outbreaks early. Veterinary and human vaccination strategies are also reviewed, highlighting the importance of integrated One Health approaches. Advances in modeling and climate-responsive surveillance further underscore the dynamic and evolving landscape of JEV transmission. By managing the infection in animal reservoirs, veterinary interventions form the foundation of sustainable zoonotic disease control. Full article

As agricultural land continues to expand, the conversion of forests to farmland has intensified, significantly altering the structure and function of agroecosystems. However, the dynamic ecological responses and their interactions with economic outcomes remain insufficiently modeled. This study proposes an integrated framework that combines a dynamic food web model with the Eco-Economic Benefit and Sustainability (EEBS) model, utilizing empirical data from Brazil and Ghana. A system of ordinary differential equations solved using the fourth-order Runge–Kutta method was employed to simulate species interactions and energy flows under various land management strategies. Reintroducing key species (e.g., the seven-spot ladybird and ragweed) improved ecosystem stability to over 90%, with soil fertility recovery reaching 95%. In herbicide-free scenarios, introducing natural predators such as bats and birds mitigated disturbances and promoted ecological balance. Using XGBoost (Extreme Gradient Boosting) to analyze 200-day community dynamics, pest control, resource allocation, and chemical disturbance were identified as dominant drivers. EEBS-based multi-scenario optimization revealed that organic farming achieves the highest alignment between ecological restoration and economic benefits. The model demonstrated strong predictive power ($R^2$ = 0.9619, RMSE = 0.0330), offering a quantitative basis for green agricultural transitions and sustainable agroecosystem management. Full article

Observing building changes in remote sensing images plays a crucial role in monitoring urban development and promoting sustainable urbanization. Mainstream change detection methods have demonstrated promising performance in identifying building changes. However, buildings have large intra-class variance and high similarity with other objects, limiting the generalization ability of models in diverse scenarios. Moreover, most existing methods only detect whether changes have occurred but ignore change types, such as new construction and demolition. To address these issues, we present a building change-type detection network (BCTDNet) based on the Segment Anything Model (SAM) to identify newly constructed and demolished buildings. We first construct a dual-feature interaction encoder that employs SAM to extract image features, which are then refined through trainable multi-scale adapters for learning architectural structures and semantic patterns. Moreover, an interactive attention module bridges SAM with a Convolutional Neural Network, enabling seamless interaction between fine-grained structural information and deep semantic features. Furthermore, we develop a change-aware attribute decoder that integrates building semantics into the change detection process via an extraction decoding network. Subsequently, an attribute-aware strategy is adopted to explicitly generate distinct maps for newly constructed and demolished buildings, thereby establishing clear temporal relationships among different change types. To evaluate BCTDNet’s performance, we construct the JINAN-MCD dataset, which covers Jinan’s urban core area over a six-year period, capturing diverse change scenarios. Moreover, we adapt the WHU-CD dataset into WHU-MCD to include multiple types of changing. Experimental results on both datasets demonstrate the superiority of BCTDNet. On JINAN-MCD, BCTDNet achieves improvements of 12.64% in IoU and 11.95% in F1 compared to suboptimal methods. Similarly, on WHU-MCD, it outperforms second-best approaches by 2.71% in IoU and 1.62% in F1. BCTDNet’s effectiveness and robustness in complex urban scenarios highlight its potential for applications in land-use analysis and urban planning. Full article

Economic systems face critical challenges, including widening income inequality, unemployment driven by automation, mounting public debt, and environmental degradation. This study introduces Economocracy as a transformative framework aimed at addressing these systemic issues by integrating democratic principles into economic decision-making to achieve social equity, economic efficiency, and environmental sustainability. The research focuses on two core mechanisms: Economic Productive Resets (EPRs) and Economic Periodic Injections (EPIs). EPRs facilitate proportional redistribution of resources to reduce income disparities, while EPIs target investments to stimulate job creation, mitigate automion-related job displacement, and support sustainable development. The study employs a theoretical and analytical methodology, developing mathematical models to quantify the impact of EPRs and EPIs on key economic indicators, including the Gini coefficient for inequality, unemployment rates, average wages, and job displacement due to automation. Hypothetical scenarios simulate baseline conditions, EPR implementation, and the combined application of EPRs and EPIs. The methodology is threefold: (1) a mathematical–theoretical validation of the Cycle of Money framework, establishing internal consistency; (2) an econometric analysis using global historical data (2000–2023) to evaluate the correlation between GNI per capita, Gini coefficient, and average wages; and (3) scenario simulations and Difference-in-Differences (DiD) estimates to test the systemic impact of implementing EPR/EPI policies on inequality and labor outcomes. The models are further strengthened through tools such as OLS regression, and Impulse results to assess causality and dynamic interactions. Empirical results confirm that EPR/EPI can substantially reduce income inequality and unemployment, while increasing wage levels, findings supported by both the theoretical architecture and data-driven outcomes. Results demonstrate that Economocracy can significantly lower income inequality, reduce unemployment, increase wages, and mitigate automation’s effects on the labor market. These findings highlight Economocracy’s potential as a viable alternative to traditional economic systems, offering a sustainable pathway that harmonizes growth, social justice, and environmental stewardship in the global economy. Economocracy demonstrates potential to reduce debt per capita by increasing the efficiency of public resource allocation and enhancing average income levels. As EPIs stimulate employment and productivity while EPRs moderate inequality, the resulting economic growth expands the tax base and alleviates fiscal pressures. These dynamics lead to lower per capita debt burdens over time. The analysis is situated within the broader discourse of institutional economics to demonstrate that Economocracy is not merely a policy correction but a new economic system akin to democracy in political life. Full article

Longnan is the largest olive cultivation area in China. The unique microclimates in Longnan make it an ideal testing ground for climate-resilient cultivation strategies with broader applications across similar regions, yet predictive models linking weather to oil quality remain scarce. This study establishes a climate suitability evaluation model for olive cultivation in central Longnan based on meteorological data and olive quality data in the Fotanggou planting base. Four key climatic factors are identified: cumulative sunshine hours during the fruit coloring to ripening period, average temperature during the fruit coloring to harvesting period, number of cloudy and rainy days during the harvesting period, and relative humidity during the fruit setting to fruit enlargement period. Olive oil quality is graded into three levels (Excellent III, Good II, Fair I) based on acidity, linoleic acid, and peroxide value using K-means clustering. A climate suitability index is developed by integrating these factors, with weights determined via principal component analysis. The model is validated against an olive quality report from the Dabao planting base, showing an 80% match rate. From 1991 to 2023, 87.9% of years exhibit suitable or moderately suitable conditions, with 100% of years in the past decade (2014–2023) reaching “Good” or “Excellent” levels. This model provides a scientific basis for evaluating and predicting olive oil quality, supporting sustainable olive industry development in Longnan. This model provides policymakers and farmers with actionable insights to ensure the long-term sustainability of olive industry amid climate uncertainty. Full article

This study focuses on realistic modeling of forming load and microstructural evolution during hot metal extrusion, emphasizing the effects of friction models and hydraulic press behavior. Rather than merely predicting load magnitudes, the objective is to replicate actual press operation by integrating a load limit response into finite element modeling (FEM). By applying Coulomb and shear friction models under both constant and hydraulically controlled press conditions, the resulting impact on grain size evolution during deformation is examined. The hydraulic press simulation features a maximum load threshold that dynamically reduces die velocity once the limit is reached, unlike constant presses that sustain velocity regardless of load. P91 steel is used as the material system, and the predicted grain size is validated against experimentally measured data. Incorporating hydraulic control into FEM improves the representativeness of simulation results for industrial-scale extrusion, enhancing microstructural prediction accuracy, and ensuring forming process reliability. Full article

Digitalization is rapidly reshaping the global construction industry; however, its adoption in developing countries, such as Pakistan, remains limited and uneven. Hence, this study investigates and evaluates the current status of digital technology integration in Pakistan’s construction industry, with a primary focus on key tools, implementation challenges, and necessary policy interventions. Using a three-phase mixed-method approach involving a literature review, expert interviews, and a nationwide survey, this research identifies Building Information Modeling, Geographic Information Systems, and E-Procurement as essential technologies with strong potential to improve transparency, efficiency, and collaboration. However, adoption is hindered by a lack of awareness, limited technical expertise, and the absence of a cohesive national policy. This study also highlights that the private sector shows greater readiness compared to the public sector; however, systemic barriers persist across both sectors. Based on stakeholder insights, a three-part policy strategy was also proposed. This includes establishing a national regulatory framework, investing in capacity-building programs, and providing financial or institutional incentives to encourage the adoption of these measures. The findings emphasize that digitalization is not just a technical upgrade; it represents a pathway to improved governance and more efficient infrastructure delivery. With timely and coordinated policy action, the construction industry in Pakistan can align itself with global innovation trends and move toward a more sustainable and digitally empowered future. Full article

Driven by the need for sustainable and efficient energy systems, the optimal management of distributed generation, including photovoltaic systems and battery energy storage systems within prosumer households, is of crucial importance. This requires a comprehensive cost–benefit analysis to assess their viability. In this study, an optimization model formulated as a mixed-integer linear programming problem is proposed to evaluate the integration of battery storage systems for 10 prosumers on the radial feeder in Croatia and to quantify the benefits both from the prosumers’ perspective and that of the reduction in grid losses. The results show significant annual cost reductions for prosumers, totaling EUR 1798.78 for the observed feeder, with some achieving a net profit. Grid losses are significantly reduced by 1172.52 kWh, resulting in an annual saving of EUR 216.25 for the distribution system operator. However, under the current Croatian market conditions, the integration of battery storage systems is not profitable over the entire lifetime due to the high initial investment costs of EUR 720/kWh. The break-even analysis reveals that investment cost needs to decrease by 52.78%, or an inflation rate of 4.87% is required, to reach prosumer profitability. This highlights the current financial barriers to the widespread adoption of battery storage systems and emphasizes the need for significant cost reductions or targeted incentives. Full article

Electrocoagulation (EC) processes have emerged as an efficient solution for different inorganic and organic effluents. The main characteristics of this versatile process are its ease of operation and low sludge production. The literature indicates that EC can be successfully used as a single process or a step within a combined treatment system. If used in a combined system, this process could be employed as a pre-, a post-, or middle treatment step. Additionally, the EC process has been used in both continuous and batch modes. In most studies, EC has achieved significant improvements in the treated water quality and relatively low total energy consumption. This review presents a comprehensive evaluation and analysis of standalone and combined continuous EC processes. The influence of key operational parameters on continuous EC performance is thoroughly discussed. Furthermore, recent advancements in reactor design, modeling, and process optimization are addressed. The benefits of integrating other treatment processes with the EC process, such as advanced oxidation, membranes, chemical coagulation, and adsorption, are also evaluated. The performance of most standalone and combined EC processes used for organic pollutant treatment and published in the last 25 years is critically analyzed. This review is expected to give researchers many insights to improve their treatment scenario with recent and efficient environmental experiences, sustainability, and circular economy. The clearly presented information is expected to guide researchers in selecting efficient, cost-effective, and time-saving treatment alternatives. The findings ensure the considerable potential of continuous EC treatment processes for organic pollutants. However, more research is warranted to enhance process design, operational efficiency, scale-up, and economic viability. Full article

Urbanization and the rapid growth of e-commerce have significantly increased delivery volumes in cities, creating challenges in terms of cost, efficiency, and sustainability in last-mile delivery (LMD). To address these challenges, this paper proposes an innovative methodological framework for selecting optimal delivery strategies in urban environments, grounded in the principles of collaboration. The framework integrates an Extended FullEx method, developed to calculate criteria weights while accounting for expert reputation based on education and experience, with the MARCOS multi-criteria decision-making (MCDM) method used to rank delivery strategies. The Extended FullEx method proposed in this paper differs from the original FullEx by providing two improvements. The first concerns the introduction of the normalization procedure in the calculation of experts’ reputations, while the second addresses the different scoring of educational degrees, providing a more precise mathematical basis for the process. Four collaborative delivery strategies are evaluated against twelve sustainability-related criteria identified through an extensive literature review. The proposed framework is applied to a real-life case study in Novi Sad, Republic of Serbia. Results indicate that the most suitable delivery strategy is a hybrid model that combines the use of a consolidation center with smaller urban delivery hubs, providing practical insights for enhancing the sustainability and efficiency of urban delivery. This study contributes both methodologically, by advancing MCDM techniques, and practically, by offering decision-makers a comprehensive tool that integrates subjective expert knowledge and objective criteria assessment in the selection of sustainable LMD solutions. Full article

With the growth of the global population and the increasing scarcity of arable land, traditional agricultural production is confronted with multiple challenges, such as efficiency improvement, precision operation, and sustainable development. The progressive advancement of artificial intelligence (AI) technology has created a transformative opportunity for the intelligent upgrade of agricultural equipment. This article systematically presents recent progress in computer vision, machine learning (ML), and intelligent sensing. The key innovations are highlighted in areas such as object detection and recognition (e.g., a K-nearest neighbor (KNN) achieved 98% accuracy in distinguishing vibration signals across operation stages); autonomous navigation and path planning (e.g., a deep reinforcement learning (DRL)-optimized task planner for multi-arm harvesting robots reduced execution time by 10.7%); state perception (e.g., a multilayer perceptron (MLP) yielded 96.9% accuracy in plug seedling health classification); and precision control (e.g., an intelligent multi-module coordinated control system achieved a transplanting efficiency of 5000 plants/h). The findings reveal a deep integration of AI models with multimodal perception technologies, significantly improving the operational efficiency, resource utilization, and environmental adaptability of agricultural equipment. This integration is catalyzing the transition toward intelligent, automated, and sustainable agricultural systems. Nevertheless, intelligent agricultural equipment still faces technical challenges regarding data sample acquisition, adaptation to complex field environments, and the coordination between algorithms and hardware. Looking ahead, the convergence of digital twin (DT) technology, edge computing, and big data-driven collaborative optimization is expected to become the core of next-generation intelligent agricultural systems. These technologies have the potential to overcome current limitations in perception and decision-making, ultimately enabling intelligent management and autonomous decision-making across the entire agricultural production chain. This article aims to provide a comprehensive foundation for advancing agricultural modernization and supporting green, sustainable development. Full article

Forest tenure functions as a critical institutional mechanism globally for curbing deforestation and degradation and advancing sustainable forest administration, ultimately underpinning the provision of vital ecosystem services. However, research on robust forest tenure system models both globally and within China remains underdeveloped, hindering their potential as an effective administration tool. The study addresses this gap by conceptualizing China’s forest tenure system through the lens of systems thinking and proposing a Forest Cadastral System based on the Land Administration Domain Model (LADM). We conduct a comprehensive review of the evolution of China’s forest tenure system and an in-depth analysis of the current “person–right–land” configuration. Subsequently, we construct an integrated forest cadastral model structured around three core LADM-compliant packages: party, administrative, and spatial unit. The model is then tested in Ningbo’s forested highlands: trials confirm its efficacy in reconciling tenure security with ecological governance. The findings offer valuable insights for policymakers and practitioners engaged in forest tenure reform and administration, while advancing the academic discourse on leveraging land administration systems for ecosystem service outcomes through robust institutional mechanisms. Full article

Addressing climate change and advancing clean energy transitions demand holistic approaches that capture complex, interconnected system behaviors. This review focuses on the application of causal loop diagrams (CLDs) as a core systems-thinking methodology to understand and manage dynamic feedback within environmental, social, and technological domains. CLDs visually map the reinforcing and balancing loops that drive climate risks, clean energy adoption, and sustainable development, offering intuitive insights into system structure and behavior. Through a synthesis of empirical studies and case examples, this paper demonstrates how CLDs help identify leverage points in renewable energy policy, carbon management, and ecosystem resilience. Despite their strengths in simplifying complexity and enhancing stakeholder communication, challenges remain—including data gaps, model validation, and the integration of diverse knowledge systems. The review also examines recent innovations that improve CLD effectiveness, such as hybrid modeling approaches and digital tools that enhance transparency and decision support. By emphasizing CLDs’ unique capacity to reveal feedback mechanisms critical for climate action and energy planning, this study provides actionable recommendations for researchers, policymakers, and practitioners seeking to leverage systems thinking for transformative, sustainable solutions. Full article

Nitrous oxide (${\mathrm{N}}_2\mathrm{O}$) emissions from agriculture are rising due to increased fertilizer use and intensive farming, posing a major challenge for climate mitigation. This study introduces a novel reinforcement learning (RL) framework to optimize farm management strategies that balance crop productivity with environmental impact, particularly ${\mathrm{N}}_2\mathrm{O}$ emissions. By modeling agricultural decision-making as a partially observable Markov decision process (POMDP), the framework accounts for uncertainties in environmental conditions and observational data. The approach integrates deep Q-learning with recurrent neural networks (RNNs) to train adaptive agents within a simulated farming environment. A Probabilistic Deep Learning (PDL) model was developed to estimate ${\mathrm{N}}_2\mathrm{O}$ emissions, achieving a high Prediction Interval Coverage Probability (PICP) of 0.937 within a 95% confidence interval on the available dataset. While the PDL model’s generalizability is currently constrained by the limited observational data, the RL framework itself is designed for broad applicability, capable of extending to diverse agricultural practices and environmental conditions. Results demonstrate that RL agents reduce ${\mathrm{N}}_2\mathrm{O}$ emissions without compromising yields, even under climatic variability. The framework’s flexibility allows for future integration of expanded datasets or alternative emission models, ensuring scalability as more field data becomes available. This work highlights the potential of artificial intelligence to advance climate-smart agriculture by simultaneously addressing productivity and sustainability goals in dynamic real-world settings. Full article