Search Results (128)

Urban and regional ecosystems are increasingly challenged by the compounded effects of climate change and intensive land use. In this study, a predictive assessment framework for ecosystem resilience in the Beijing–Tianjin–Hebei region was developed by integrating multi-source remote sensing data, with the aim of quantitatively evaluating the coupled effects of climate change and land use change on future ecosystem resilience. In the first stage of the study, the SD-PLUS coupled modeling framework was employed to simulate land use patterns for the years 2030 and 2060 under three representative combinations of Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Building upon these simulations, ecosystem resilience was comprehensively evaluated and predicted on the basis of three key attributes: resistance, adaptability, and recovery. This enabled a quantitative investigation of the spatio-temporal dynamics of ecosystem resilience under each scenario. The results reveal the following: (1) Temporally, ecosystem resilience exhibited a staged pattern of change. From 2020 to 2030, an increasing trend was observed only under the SSP1-2.6 scenario, whereas, from 2030 to 2060, resilience generally increased in all scenarios. (2) In terms of scenario comparison, ecosystem resilience typically followed a gradient pattern of SSP1-2.6 > SSP2-4.5 > SSP5-8.5. However, in 2060, a notable reversal occurred, with the highest resilience recorded under the SSP5-8.5 scenario. (3) Spatially, areas with high ecosystem resilience were primarily distributed in mountainous regions, while the southeastern plains and coastal zones consistently exhibited lower resilience levels. The results indicate that climate and land use changes jointly influence ecosystem resilience. Rainfall and temperature, as key climate drivers, not only affect land use dynamics but also play a crucial role in regulating ecosystem services and ecological processes. Under extreme scenarios such as SSP5-8.5, these factors may trigger nonlinear responses in ecosystem resilience. Meanwhile, land use restructuring further shapes resilience patterns by altering landscape configurations and recovery mechanisms. Our findings highlight the role of climate and land use in reshaping ecological structure, function, and services. This study offers scientific support for assessing and managing regional ecosystem resilience and informs adaptive urban governance in the face of future climate and land use uncertainty, promotes the sustainable development of ecosystems, and expands the applicability of remote sensing in dynamic ecological monitoring and predictive analysis. Full article

The integration of the Internet of Things (IoT) into mobile agent technology has fundamentally transformed the landscape of e-commerce by enabling intelligent, adaptive, and energy-efficient solutions. In this paper, we present a new system for integrating the information-sharing capability of IoT-enabled devices with the advanced abilities of mobile agents for the optimal utilization of energy when conducting e-commerce activity. The mobile agents are used as a mediating agent in the transaction and will capture operation data to share with stakeholders (not in the transaction) who might be able to provide services in association with that transaction. The operational data is collected, stored, and analyzed in real-time via IoT devices, facilitating adaptive decision-making while providing continuous monitoring of the system and servicing to improve energy management, efficiency, and operational performance. The combined IoT and energy capacity will enhance data sharing and provide more energy-efficient activities. The evaluation of the system was completed through simulations, as well as through real-world scenarios, achieving a decrease of approximately 27.8% in total energy consumption and savings of over 30% on operational costs. Moreover, the proposed architecture achieved a reduction of up to 38.9% for response times for resource management, under load, while also demonstrating a 50% reduction in response time for real-time event handling. Therefore, the effects of the proposed approach have been proven to be effective through simulations and real-world case studies, showing improvements in energy consumption and costs, as well as flexibility and adaptability. The findings of this study show that this framework not only minimizes energy consumption but also maximizes scalability, responsiveness to user demands, and robustness against variability in an e-commerce workload. This effort illustrates the potential for extending the lifetimes of e-commerce infrastructures and developing sustainable e-commerce models, demonstrating how IoT-based architectures can facilitate better resource allocation while achieving sustainability goals. Full article

Technical debt (TD) has emerged as a significant concern in the development of AI/ML applications, where rapid experimentation, evolving objectives, and complex data pipelines often introduce hidden quality and maintainability issues. Within this broader context, AI/ML competition platforms face heightened risks due to time-constrained environments and evolving requirements. Despite its relevance, TD in such competitive settings remains underexplored and lacks systematic investigation. This study addresses two research questions: (RQ1) What are the most significant types of technical debt recorded in AI-based systems? and (RQ2) How can we measure the technical debt of an AI-based competition platform? We present a scoping review of 100 peer-reviewed publications related to AI/ML competitions, aiming to map the landscape of TD manifestations and management practices. Through thematic analysis, the study identifies 18 distinct types of technical debt, each accompanied by a definition, rationale, and example grounded in competition scenarios. Based on this typology, a stakeholder-oriented assessment framework is proposed, including a detailed questionnaire and a methodology for the quantitative evaluation of TD across multiple categories. A novel contribution is the introduction of Accessibility Debt, which addresses the challenges associated with the ease and speed of immediate use of the AI/ML competition platforms. The review also incorporates bibliometric insights, revealing the fragmented and uneven treatment of TD across the literature. The findings offer a unified conceptual foundation for future work and provide practical tools for both organizers and participants to systematically detect, interpret, and address technical debt in competitive AI settings, ultimately promoting more sustainable and trustworthy AI research environments. Full article

Quantitative assessment of ecological resilience is crucial for understanding regional ecological security and provides a scientific basis for ecosystem protection and management decisions. Previous studies on ecological resilience evaluation predominantly focused on ecosystem resistance and recovery capacity under external threats. To address this gap, we propose an innovative assessment framework integrating landscape internal structure indicators—habitat suitability (HS), landscape connectivity (SHDI), and landscape diversity (LCI)—into the resilience paradigm. This approach enables the adjustment of landscape patterns, optimization of energy/material flows, and direct enhancement of ecosystem functions to improve regional ecological resilience. Using the ecological barrier area in northern Qinghai as a case study, we employed geographic grid technology to evaluate ecological resilience levels from 2000 to 2020. Combined with geological disaster risk assessment, ecological regionalization was established. The FLUS model was then applied to simulate land use changes under inertia development (ID) and ecological protection (EP) scenarios, projecting future ecological resilience dynamics. Key findings specific to the study area include: (1) In northern Qinghai, grassland degradation was prominent (2000–2020), primarily converting to barren land. (2) Landscape connectivity and diversity declined, leading to a 6% reduction in ecological resilience over twenty years. (3) Based on ecological resilience and geological disaster risk, three ecological management zones were delineated: prevention and protection areas (40.94%), key supervision areas (38.77%), and key ecological restoration areas (20.09%). (4) Compared with 2020, ecological resilience in 2030 decreased by 23.38% under the ID scenario and 14.28% under the EP scenario. The EP scenario effectively mitigated the decline of resilience. This study offers a novel perspective for ecological resilience assessment and supports spatial optimization of land resources to enhance ecosystem sustainability in ecologically vulnerable regions. Full article

This study aims to evaluate soil erosion mitigation strategies in Poland’s agricultural landscapes by applying the Revised Soil Loss Equation (RUSLE) model to identify high-risk areas where excessive soil loss adversely affects agricultural sustainability and productivity. Scenario assessments were conducted to evaluate the effectiveness of specific conservation practices—contour farming, reduced tillage, and cover crops—by simulating changes in the C-factor (cover-management factor) and P-factor (support practices factor) within the RUSLE framework. The research revealed heightened soil erosion rates during the summer months, particularly in regions with steep slopes and loess formations. Analysis indicated that annual soil loss from arable lands in Poland totals approximately 4.65 Mt yr⁻¹ and that contour farming, reduced tillage, and cover crops could collectively reduce this amount by up to 47%, with the highest reduction observed during the summer period. These findings highlighted the urgent need for stakeholders to adopt sustainable land management strategies. By quantifying the impact of these management practices on soil erosion rates, the study provided insights into the effectiveness of soil conservation measures in reducing erosion risks within Poland’s agricultural landscapes. This study emphasizes the importance of adopting sustainable land management strategies to preserve soil integrity and maintain agricultural productivity in Poland. Full article

In the rapidly evolving landscape of transportation technologies, hydrogen vehicle networks integrated with photovoltaic (PV) systems represent a significant advancement toward sustainable mobility. However, the integration of such technologies also introduces complex cybersecurity challenges that must be meticulously managed to ensure operational integrity and system resilience. This paper explores the intricate dynamics of cybersecurity in PV-powered hydrogen vehicle networks, focusing on the real-time challenges posed by cyber threats such as False Data Injection Attacks (FDIAs) and their impact on network operations. Our research utilizes a novel hierarchical robust optimization model enhanced by Recursive Neural Networks (RNNs) to improve detection rates and response times to cyber incidents across various severity levels. The initial findings reveal that as the severity of incidents escalates from level 1 to 10, the response time significantly increases from an average of 7 min for low-severity incidents to over 20 min for high-severity scenarios, demonstrating the escalating complexity and resource demands of more severe incidents. Additionally, the study introduces an in-depth examination of the detection dynamics, illustrating that while detection rates generally decrease as incident frequency increases—due to system overload—the employment of advanced RNNs effectively mitigates this trend, sustaining high detection rates of up to 95% even under high-frequency scenarios. Furthermore, we analyze the cybersecurity risks specifically associated with the intermittency of PV-based hydrogen production, demonstrating how fluctuations in solar energy availability can create vulnerabilities that cyberattackers may exploit. We also explore the relationship between incident frequency, detection sensitivity, and the resulting false positive rates, revealing that the optimal adjustment of detection thresholds can reduce false positives by as much as 30%, even under peak load conditions. This paper not only provides a detailed empirical analysis of the cybersecurity landscape in PV-integrated hydrogen vehicle networks but also offers strategic insights into the deployment of AI-enhanced cybersecurity frameworks. The findings underscore the critical need for scalable, responsive cybersecurity solutions that can adapt to the dynamic threat environment of modern transport infrastructures, ensuring the sustainability and safety of solar-powered hydrogen mobility solutions. Full article

An evaluation of regional landscape ecological risk (LER) in Harbin, a key center city in Northeast China, is crucial for the long-term sustainability of its ecological and economic development. This study aims to (1) assess the spatiotemporal patterns of LER in Harbin from 2000 to 2020, (2) identify the key natural and human driving factors influencing LER, and (3) project future landscape ecological risk trends under multiple land use scenarios. To achieve these objectives, land use data from 2000, 2010, and 2020 were analyzed using landscape pattern indices to characterize ecological risk patterns. GeoDetector was applied to quantify the spatial differentiation and factor contributions to LER. Furthermore, the PLUS model was employed to simulate land use change and assess future LER patterns under three scenario settings. Moran’s I was used to evaluate spatial autocorrelation. The results indicate the following: (1) Between 2000 and 2020, cultivated land and woodland were the two most prevalent land types in Harbin, with the majority of land use shifts occurring between these two groupings. The main changes to the landscape were a continuous increase in development land and a steady decrease in unused area. (2) The overall LER in Harbin has been trending downward over the last 20 years, primarily falling within the medium-risk range. Marked spatial heterogeneity in LER was observed, displaying a distribution pattern of “high in the west and north, low in the east and south”. The majority of the riskiest regions were concentrated around bodies of water. (3) The Moran’s I indices for LER in Harbin were 0.798, 0.828, and 0.852, respectively, indicating significant spatial autocorrelation. The local clustering patterns were mainly defined by “High–High” and “Low–Low” agglomeration patterns. (4) Among natural factors, DEM exhibited the greatest explanatory strength for LER in Harbin, and the interaction between DEM and annual precipitation was recognized as the dominant force driving spatial disparities in LER. (5) Among the three projected scenarios for 2030, the ecological priority scenario showed a slower rate of decrease in ecological land, suggesting that this scenario is an effective approach for improving landscape ecological conditions. The findings offer a theoretical foundation and scientific guidance for LER management in Harbin and similar regions. Full article

Infrastructure projects that transform ecosystems and landscapes, such as hydropower developments, are essential for economic growth but pose significant socio-environmental challenges. Addressing these complexities requires advanced, dynamic management strategies. This study presents the Bayesian integrated risk mitigation model (BIRMM), a novel probabilistic framework designed to augment traditional environmental impact assessments. BIRMM enables comprehensive risk evaluation, scenario-based analysis, and mitigation planning, empowering stakeholders to make informed decisions throughout project lifecycles. BIRMM integrates socio-environmental and economic risks using a three-dimensional risk assessment approach grounded in a Bayesian belief network influence diagram. It provides a holistic view of risk interactions by capturing interdependencies across spatial, temporal, and magnitude dimensions. Through simulation of risk dynamics and adaptive evaluation of mitigation strategies, BIRMM offers actionable insights for resource allocation, enhancing project resilience, and minimizing socio-environmental disruptions. The framework was validated using the Balakot Hydropower Project in Pakistan. BIRMM successfully simulated proposed risks and assessed mitigation strategies under varying scenarios, demonstrating its reliability in navigating complex socio-environmental challenges. The case study highlighted its potential to support adaptive decision-making across all project phases. With its versatility and practical ease, BIRMM is particularly suited for large-scale energy, transportation, and urban development projects. By bridging gaps in traditional methodologies, BIRMM advances sustainable development practices, promotes equitable stakeholder outcomes, and establishes itself as an indispensable decision-support tool for modern infrastructure projects. Full article

Despite the growing recognition of sustainability in forest management, comprehensive multi-criteria evaluations of silvicultural practices remain scarce, particularly in Patagonia. In this study, we applied a multi-criteria decision analysis to evaluate the sustainability of different strip-cutting intensities in secondary Nothofagus antarctica forests in Northern Patagonia, Argentina. The performance of four management alternatives was assessed: no cutting, low cutting intensity, medium cutting intensity, and high cutting intensity. These alternatives were evaluated across 11 indicators of nature’s contributions to people. Indicator values were estimated from previous research across three contrasting sites, complemented by expert surveys to estimate weights and target values for each indicator. The results indicate that the key indicators included those associated with firewood harvesting, fire and invasions prevention, and timber species plantation performance. Medium cutting intensity consistently emerged as the most sustainable option across all sites, models, and scenarios. In contrast, no cutting performed poorly across most sites, models, and scenarios. These findings underscore the importance of integrating diverse ecological and socioeconomic indicators into forest management planning. The promotion of medium cutting intensity has the potential to enhance sustainability in N. antarctica forests, thereby contributing to the development of resilient and multifunctional landscapes in Northern Patagonia. Full article

In today’s rapidly evolving digital landscape and accelerating technological development, industrial clusters play a crucial role in fostering innovation and ensuring sustainable economic growth. However, their effectiveness largely depends on the organization of optimal interactions between the participants, which implies a balanced allocation of resources and the co-evolution of capitals within the cluster. In this paper, we introduce strategic tools designed to form cluster capital by integrating financial, technological, and intellectual resources to create a sustainable environment for technological innovation implementation. To solve the set tasks, we developed a mathematical model based on the entropy approach and network analysis methods. This was developed to model and optimize the resource distribution among the cluster participants. The application of the proposed model using the example of the PenzaStankoMash industrial machine-building cluster has shown that the optimal configuration of the actors’ capitals in clusters contributes to the creation of synergetic effects. This increases the innovation potential and overall efficiency of the system. Our modeling considered various capital allocation scenarios, leading us to conclude that a balanced approach is important. The results of this study contribute to an in-depth understanding of the mechanisms for optimizing interactions in clusters. They contain specific strategic tools for managing capitals in clusters and contribute to the development of industrial policy based on the principles of a systematic approach. Full article

Rapid urbanization and the consequent alteration in land use and land cover (LULC) significantly change the natural landscape and adversely affect hydrological cycles, biological systems, and various ecosystem services, especially in the developing world. Thus, it is vital to study the environmental conditions of a region to mitigate the negative impacts of urbanization. Out of a wide array of parameters, the Environmental Criticality Index (ECI), a relatively new concept, was used in this study, which was conducted over the Kolkata Metropolitan Area (KMA). It was derived using Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) to quantify heat-related impact. An increase in the percentage of land area under high ECI categories, from 23.93% in 2000 to 32.37% in 2020, indicated a progressive increase in criticality. The Spatio-temporal Thermal-based Environmental Criticality Consistency Index (STTECCI) and hotspot analysis identified the urban and industrial areas in KMA as criticality hotspots, consistently recording higher ECI. The correlation analysis between ECI and LULC features revealed that there exists a negative correlation between ECI and natural vegetation and agriculture, while built-up areas and ECI are positively correlated. Bare lands, despite being positively correlated with ECI, have an insignificant relationship with it. Also, the designed built-up index extracted the built-up areas with an accuracy of 89.5% (kappa = 0.78). The future scenario of ECI in KMA was predicted using Modules for Land Use Change Evaluation (MOLUSCE) with an accuracy level above 90%. The percentage of land area under low ECI categories is expected to decline from 50.02% in 2000 to 35.6% in 2040, while the percentage of land area under high ECI categories is expected to increase from 23.93% in 2000 to 36.56% in 2040. This study can contribute towards the development of tailored management strategies that foster sustainable growth, resilience, and alignment with the Sustainable Development Goals, ensuring a balance between economic development and environmental preservation. Full article

Understanding the changes in ecosystem quality caused by land use changes is critical for sustainable urban development and environmental management. This study investigates the spatial-temporal evolution of ecosystem quality in Wuhan from 2000 to 2020 and forecasts future trends under multiple land use scenarios for 2030. Using a “foundation-function-structure” assessment framework, we integrate system dynamics (SD), the Patch-generating Land Use Simulation (PLUS) model, and a neural network-based ecosystem quality inversion model to analyze land use transitions and their ecological impacts. The results indicate that rapid urban expansion has significantly contributed to the decline of cropland and forest areas, while impervious surfaces have increased, leading to notable ecological degradation. Simulations for 2030 under three scenarios—ecological protection, natural development, and economic priority—demonstrate that the ecological protection scenario yields the highest ecosystem quality, preserving landscape connectivity and mitigating degradation risks. In contrast, the economic priority scenario results in extensive urban expansion, exacerbating ecological stress. Under the ecological protection scenario from 2020 to 2023, the decline in ecosystem quality was primarily due to the expansion of urban fringes and the erosion of forest and grassland areas. The increase in ecosystem quality was mainly attributed to the transformation of early urban edge conflict zones into stable urban edge interior areas and the integration of fragmented ecological land patches. These findings highlight the need for strategic land use planning to balance economic growth and environmental conservation. This study provides a robust methodological framework for assessing and predicting ecosystem quality changes, offering valuable insights for policymakers and urban planners striving for sustainable development. Full article

Mountain resilience is the ability of mountain regions to endure, adapt to, and recover from environmental, climatic, and anthropogenic stressors. Due to their steep topography, extreme weather conditions, and unique biodiversity, these areas are particularly vulnerable to climate change, natural hazards, and human activities. This paper examines how nature-based solutions (NbSs) can strengthen slope stability and geotechnical resilience, with a specific focus on Slovenia’s sub-Alpine regions as a case study representative of the Alps and similar mountain landscapes worldwide. The proposed Climate-Adaptive Resilience Evaluation (CARE) concept integrates geomechanical analysis with geotechnical planning, addressing the impacts of climate change through a systematic causal chain that connects climate hazards, their effects, and resulting consequences. Key factors such as water infiltration, soil permeability, and groundwater dynamics are identified as critical elements in designing timely and effective NbSs. In scenarios where natural solutions alone may be insufficient, hybrid solutions (HbSs) that combine nature-based and conventional engineering methods are highlighted as essential for managing unstable slopes and restoring collapsed geostructures. The paper provides practical examples, including slope stability analyses and reforestation initiatives, to illustrate how to use the CARE concept and how NbSs can mitigate geotechnical risks and promote sustainability. By aligning these approaches with regulatory frameworks and climate adaptation objectives, it underscores the potential for integrating NbSs and HbSs into comprehensive, long-term geotechnical strategies for enhancing mountain resilience. Full article

This article explores the impacts of climate change on the rural and natural landscapes in the region of Eastern Macedonia and Thrace, northeastern Greece. The spatial distributions of the bioclimatic de Martonne Index and the phytoclimatic Emberger Index were calculated at a very high resolution (~500 m) for present conditions (1970–2000), two future time periods (2030–2060; 2070–2100), and two greenhouse gas concentration scenarios (RCP4.5; RCP8.5). The results show significant bioclimatic changes, especially in the Rhodope Mountain range and along almost the whole length of the Greek–Bulgarian border, where forests of high ecosystem value are located, together with the rural areas along the Evros river valley, as well as in the coastal zone of the Aegean Sea. The article describes the processes of bioclimatic changes that can significantly modify the study area’s landscapes. The study area reveals a shift toward xerothermic environments over time, with significant bioclimatic changes projected under the extreme RCP8.5 scenario. By 2100, de Martonne projections indicate that around 40% of agricultural areas in the eastern, southern, and western regions will face Mediterranean and semi-humid conditions, requiring supplemental irrigation for sustainability. The Emberger Index predicts that approximately 42% of natural and agricultural landscapes will experience sub-humid conditions with mild or cool winters. In comparison, 5% will face drier humid/sub-humid, warm winter conditions. These foreseen futures propose initial interpretations for key landscape conservation, natural capital, and ecosystem services management. Full article

As the global energy landscape evolves towards sustainability, the extensive usage of fossil fuels in electricity generation is progressively diminishing, while the contribution of renewable energy sources is steadily increasing. In this evolving scenario, the importance of load forecasting cannot be overstated in optimizing energy management and ensuring the efficient operation of industrial plants regardless of their scale. By accurately anticipating energy demand, industrial facilities can enhance efficiency, reduce costs, and facilitate the adoption of renewable energy technologies in the power grid. Recent studies have emphasized the pervasive utilization of machine learning-based algorithms in the field of electric load forecasting for industrial plants. Their capacity to analyze intricate patterns and enhance prediction accuracy renders them a favored option for enhancing energy management and operational efficiency. The present analysis revolves around the creation of short-term electric load forecasting models for a large industrial plant operating in Adana, Turkey. The integration of calendar, meteorological, and lagging electrical variables, along with machine learning-based algorithms, is employed to boost forecasting accuracy and optimize energy utilization. The ultimate objective of the present study is to conduct a thoroughgoing and detailed analysis of the statistical performance of the models and associated error metrics. The metrics employed include the R² and MAPE values. Full article