Search Results (2,662)

The relationship between forest ecosystem services and human health has emerged as a key topic in forest economics and health policy research. This study develops a spatial modelling framework to quantify the health benefits of forest ecosystem services and proposes policy mechanisms to incorporate these benefits into governmental health strategies. Using county-level panel data from 66 administrative units in Zhejiang Province, China, covering the period 2013–2023, we analyse the relationship between forest-mediated air purification services and two population health outcomes: the incidence of respiratory diseases and cardiovascular disease mortality. We employ a Spatial Durbin Model (SDM) to estimate both direct and spatial spillover effects across county boundaries. The findings indicate that forest ecosystem services exert significant negative effects on adverse health outcomes, with spillover effects extending beyond administrative boundaries. The monetised health benefit of forests is estimated at approximately RMB 1108.6 per hectare per year, substantially exceeding current ecological compensation standards and suggesting systematic undervaluation of forest health services. Heterogeneity analysis reveals that health benefits are greater in urbanised regions and among vulnerable population groups, including the elderly. These findings provide an empirical basis for reforming health-oriented ecological compensation mechanisms and offer implications for sustainable land use governance aligned with SDG 3 (Good Health and Well-being) and SDG 15 (Life on Land). Full article

Functionally graded materials (FGMs) effectively alleviate residual stress induced by physical property mismatch at dissimilar material interfaces through a graded transition in composition or structure. Among these, the matching of the coefficient of thermal expansion (CTE) is a core indicator for ensuring the service reliability of the joint. Traditional composition design relies on empirical trial-and-error, which makes it difficult to efficiently identify the optimal path in a high-dimensional composition space. This study proposes a data-driven, machine learning-assisted composition design method. Based on a high-precision dataset covering 15 elements and 747 CTE data points, six typical regression models were systematically evaluated. The results show that the random forest (RF) model achieves the best performance, with a coefficient of determination (R²) of 0.929 and a root mean square error (RMSE) of 0.658 on the test set. Using the SHapley Additive exPlanations (SHAP) method, the lattice constant (c), Young’s modulus (YM), and temperature (T) were identified as the key physical descriptors governing the thermal expansion behavior. Experimental validation shows that the CTE prediction deviation of the model for the high-performance Fe-based alloy Norem02 in the range of 20–300 °C is only 0.89%. Based on this framework, the composition of the 316L/Norem02 transition layer was successfully designed in this study. This effectively reduced the interfacial thermal expansion mismatch. Consequently, it provides a reliable theoretical basis for the rational design of dissimilar material interfaces under extreme service conditions. Full article

This study analyzed online review data to examine user requirements for audiovisual products and to compare requirement salience and satisfaction across traditional and emerging product contexts. We collected 86,213 Chinese-language reviews of Skyworth TVs, Xiaomi TVs, and Xiaomi projectors from JD.com. LDA topic modeling was used to identify major user requirement areas, and Logistic Regression, Random Forest, and Support Vector Machine (SVM) models were compared for sentiment classification, with the tuned SVM model retained for downstream analysis. The results show that user discussions primarily concern audiovisual experience, cost performance, service quality, design aesthetics, and intelligent operation. Skyworth TVs receive particularly strong evaluations for picture and sound quality (97.89% positive sentiment), whereas Xiaomi TVs are more strongly associated with cost-effectiveness and smart features (94.05% positive sentiment). Xiaomi projectors attract attention for portability but receive lower satisfaction ratings on core audiovisual performance and intelligent operation. These findings suggest that traditional manufacturers should continue strengthening core performance while improving service responsiveness, whereas emerging brands should build on their technological advantages while further enhancing their product reliability and user experience. Full article

Urban forests support wildlife populations across North America and the world. Yet, challenges remain for research and practice to integrate wildlife habitat as a core component of the myriad objectives that urban foresters manage. Ecosystem services have been adopted as a dominant paradigm in urban forestry for both advocacy and management, yet accounting for contributions to wildlife habitat does not fit squarely within typical ecosystem service frameworks. The i-Tree program, a suite of urban forest ecosystem service models and tools developed by the US Forest Service, presented an opportunity to link widely used urban forest assessment field protocols with indicators of suitable habitat. In this reflection piece, we demonstrate how the i-Tree Wildlife project assessed whether urban forest structural assessment methods could be applied to assess wildlife habitat provision, operationalizing the fundamental question “How do urban forests support wildlife?” We describe the development process for integrating bird habitat suitability models for 12 species present in the northeastern US, ten native and two non-native birds, into the flagship i-Tree Eco tool. We offer reflections, challenges, and opportunities from this process. Ultimately, the improvement of ecosystem assessment tools like i-Tree can assist practitioners who aim to manage healthy and productive urban forests that benefit people and wildlife. Full article

This Editorial introduces the Special Issue “Spatial Planning and Land-Use Management: 2nd Edition” and discusses the eight articles published in it. Taken together, these contributions demonstrate that contemporary spatial planning and land-use management can no longer be understood as narrowly regulatory or sector-specific activities. Rather, they must be approached as integrative and adaptive practices capable of mediating between ecological integrity, territorial cohesion, infrastructure provision, social justice, public health, and participatory governance. The Special Issue brings together case studies from China, the United States, Australia, Iran, Portugal, Slovakia, and Belgium, as well as comparative evidence from peri-urban landscapes, and spans a wide range of spatial scales, from neighbourhoods and urban forests to metropolitan green belts, urban agglomerations, peri-urban territories, and ecoregions. Several major lines of inquiry emerge across the volume. First, the articles reaffirm the need for multiscale planning frameworks able to connect local action with regional and supra-regional structures. Second, they broaden the understanding of infrastructure by including not only transport and urban facilities, but also ecological, green, and even nocturnal infrastructures. Third, they show that many of today’s most difficult planning questions arise in spaces of transition and overlap, especially peri-urban areas, where conflicts among land uses, ecosystem services, development pressures, and governance arrangements become particularly acute across sectors and across spatial and temporal scales. Fourth, they underline that planning effectiveness increasingly depends on participation, co-design, and cooperation among diverse actors, including civic initiatives and local communities. Overall, the Special Issue highlights spatial planning as a strategic field of action through which societies can address land-use conflicts, reconcile environmental and social objectives, and design more sustainable, resilient, and liveable territories. Full article

In the assessment of bearing capacity for in-service bridge pile foundations, static load tests are costly, destructive, and difficult to scale. The traditional dynamic formula approach relies heavily on an empirical dynamic–static conversion coefficient that introduces considerable uncertainty. To address these limitations, this study proposes a non-destructive evaluation method for pile foundation bearing capacity based on measured dynamic stiffness and machine learning algorithms. Using data from a highway bridge inspection project, a dataset comprising 680 piles was compiled, including measured dynamic stiffness, geometric parameters, and design load information. An end-to-end binary classification model was constructed to map multidimensional physical features to an engineering decision target, namely, whether the bearing capacity meets the design requirement. The performance of several algorithms was compared, including logistic regression, random forest, and gradient boosting decision tree (GBDT). Among the evaluated models, the GBDT model demonstrated the best capability for capturing the complex nonlinear pile–soil interactions. On an independent test set, it achieved an accuracy of 96.3% and an F1 score of 0.96, with a very low false-negative rate, satisfying the high precision required for engineering safety screening. Feature importance analysis indicates that measured dynamic stiffness contributed approximately 42% to the classification outcome, establishing it as the dominant indicator for detecting capacity deficiencies and reinforcing its physical relevance as a key health indicator for pile foundations. This study demonstrates that data-driven methods can effectively circumvent the uncertainty associated with traditional empirical coefficients, providing a promising approach to the health monitoring and rapid evaluation of in-service bridge pile foundations. Full article

In 3 kV DC systems, the pantograph–catenary interface and auxiliary converter unit (ACU) are among the critical high-voltage subsystems, where electrical transients and thermal overload conditions frequently lead to service disruptions. This paper presents a case study on the application of machine-learning-based predictive maintenance to a 3 kV DC electric train, with a specific focus on the pantograph and ACU. A 2-year period of operational data collected from a passenger rail fleet was analysed using a hybrid data sampling strategy to capture both operational conditions and events associated with failures. Logistic Regression (LR), and Random Forest (RF) were trained and evaluated using standard performance metrics. The RF model achieved superior predictive performance, with an accuracy of approximately 93%, a precision of 0.91, a recall of 0.88, and an F1-score of 0.89, outperforming the baseline across all metrics. The analyses demonstrated that anomalies in electrical arcing, line voltage, and ACU current and temperature frequently preceded recorded fault events, confirming that failures arise from subsystems interactions and that it is critical for such parameters to be monitored. The results demonstrate the technical feasibility and practical value of integrating machine learning into EMU maintenance practice, enabling earlier detection of degradation, more targeted interventions, and a transition towards condition-based maintenance. Full article

The rapid expansion of ski tourism in Northeast China has triggered extensive land use and land cover change (LULCC), yet the micro-scale spatial mechanisms linking historical land conversion to the accessibility of tourist services remain largely unquantified. This study addresses this gap by integrating annual 30 m CLCD land cover data with GIS network analysis of Points of Interest (POIs) around 30 major ski resorts (2018–2023). Specifically, it makes a novel distinction between the accessibility outcomes of construction-oriented and agriculture-oriented land transitions. Results indicate that while forest-to-construction conversion significantly predicts reduced travel distances to services (e.g., hotels: r = −0.532, p < 0.01), a distinct and previously unreported agri-tourism synergy emerges: forest-to-cropland conversion is positively associated with higher per capita tourist spending (r = 0.366, p < 0.05). This finding challenges the conventional zero-sum view of land use competition and suggests that cultivated landscapes can function as complementary tourism assets. These empirical patterns provide an evidence-based framework for integrated land-transport planning in emerging winter sports destinations. Full article

To protect the endangered Northern Spotted Owl, the U.S. Fish and Wildlife Service established extensive conservation areas across the Pacific Northwest (PNW). While this policy effectively contributed to the preservation of an endangered species, it also generated significant short- and long-term impacts on the U.S. forestry market. This study investigates the impact of federal timber harvesting restrictions in the Pacific Northwest in the early 1990s on the U.S. softwood market, particularly on softwood planting in the South. By constructing and analyzing a panel dataset covering 537 counties in seven southern U.S. states from 1977 to 2007, the research finds that timber-harvesting restrictions triggered by the listing of the Northern Spotted Owl as threatened led to a significant increase in softwood planting rates in the Southern U.S. Previous studies have shown that set-asides can shift timber harvesting from one region to another and raise prices in the short term. This study illustrates a different outcome of set-asides: tree planting. We argue that accounting for long-term investment responses, such as tree planting, is critical when evaluating the impacts of forest policies, as these can significantly alter estimates of net carbon balance and overall market outcomes. Full article

New-energy plants and battery energy-storage stations increasingly depend on software-defined supervision, remote maintenance, and event-driven control, which makes cyber protection inseparable from operational responsiveness. This study presents a calibrated context-aware Security-as-a-Service orchestration framework, denoted SECaaS-CARO, for station-oriented adaptive risk control. The framework separates field assets, control services, security services, and an adaptive decision layer, and it uses a monotone nine-indicator risk score whose weights are calibrated from the training split rather than fixed heuristically. A validation-based threshold search maps that score to low-, medium-, and high-intensity service chains so that protection strength changes with session context instead of remaining static. A reproducible semi-synthetic dataset containing 17,000 station sessions was used to emulate operator login, remote maintenance, gateway misuse, and malicious command scenarios. Across 10 independently resampled 5000-session test streams, SECaaS-CARO achieved an F1 score of 0.973, a blocking success of 0.965, and the highest deployment utility of 1.173 while reducing mean latency to 21.28 ms compared with 27.06 ms for Logistic-Fixed and 28.15 ms for RandomForest-Fixed. The results indicate that an interpretable calibrated service-orchestration policy can preserve near-supervised detection quality while materially improving deployment-oriented efficiency for new-energy and energy-storage stations. Full article

Internet of Medical Things (IoMT) devices are inherently vulnerable to cyberattacks, typically due to their limited processing power and memory capacity. Their widespread use in healthcare poses a significant security risk, threatening patient data privacy and the continuity of services. This study examines the effects of data imbalance correction and balancing strategies on the performance of machine and deep learning models using openly available IoMT datasets. In this context, four different balancing methods—RandomUnderSampler, SMOTE, Borderline-SMOTE, and ADASYN—were applied to three open-access IoMT datasets: ECU-IoHT, WUSTL, and CICIoMT2024. Performance analyses were conducted using five machine learning algorithms (AdaBoost, Logistic Regression, Random Forest, XGBoost, and K-Nearest Neighbor (KNN)) and two deep learning algorithms (Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN)). In the highly imbalanced binary setting of the CICIoMT2024 dataset, the combination of RandomUnderSampler and SMOTE under the balanced-training/original-testing scenario produced the strongest improvement in the binary CICIoMT2024 setting, increasing the F1-Score from the unbalanced baseline to 99.87% for Random Forest and 99.86% for XGBoost across repeated runs. However, the benefit of balancing was not universal. In datasets with stronger class separability, such as ECU-IoHT, and in several multi-class settings, the effect of balancing was limited or, in some cases, inferior to the unbalanced baseline. These findings indicate that balancing is most effective under specific conditions, particularly in highly imbalanced binary tasks, and should be validated using class-sensitive metrics rather than overall performance alone. Full article

This study examines the spatial patterns and key drivers of outdoor recreation participation in the 14 National Forests (NFs) of USDA Forest Service Region 8—covering thirteen southeastern states and El Yunque NF in Puerto Rico (15 forest units in total)—based on data from the USDA National Visitor Use Monitoring (NVUM) 2010–2014 microdata cycle. We postulated that spatial autocorrelation is statistically significant for individual recreation drivers, particularly around forest boundaries and major road networks. We test for spatial autocorrelation using Global Moran’s I and Local Indicators of Spatial Association (LISA) and identify hot spots with Getis–Ord Gi* statistics. Spatial regression models (OLS, spatial lag, and spatial error) are estimated to assess the effects of proximity to major roads and distance from forest boundaries on population-normalised visitation intensity. We find significant spatial autocorrelation in overall visitation intensity (Global Moran’s I = 0.312, p < 0.001), with high clusters observed within 50 km of forest boundaries and along major Interstate highway corridors. At least four of five key recreation drivers are significantly clustered. Our results provide spatially specific, statistically robust evidence to inform NF management. Full article

The study investigates the interrelationship between the performance of the regional economy in Italy and the multidimensionality of wellbeing, as defined by the ISTAT Benessere Equo e Sostenibile (BES) model. Based on panel data from 19 Italian regions and 2 autonomous provinces—Trentino and Bolzano (2012–2023)—the research aims to explore whether there is a link between regional GDP and the three BES dimensions: Benessere (B), Equità (E), and Sostenibilità (S). The innovative contribution of this paper is not the creation of a novel theoretical model, but a multilayered empirical approach that combines panel data methods, machine learning, and clustering. This approach makes it possible to reveal nonlinearities, complex interactions, and regional heterogeneity in BES–GDP relationships. The analysis of the Benessere dimension based on k-Nearest Neighbors reveals nonlinear dynamics related to health, mobility, security, digital access, and socio-economic conditions. Furthermore, cluster analysis identifies territorial development regimes according to the Benessere dimension. The Equità dimension is estimated using boosting regression and clustering models that emphasize the role of income, poverty risk, healthcare pressure, labour-market participation, youth exclusion, deprivation, and access to essential services. Finally, the Sostenibilità dimension is explored using boosting regression and random forest models to estimate interactions among environmental quality, climate stress, energy transition, innovation, digital skills, service reliability, and regional economic performance. The findings demonstrate a structural connection between well-being, equity, sustainability, and the economic performance of Italian regions. The results also confirm the hypothesis that Italy has multiple development regimes that differ geographically. Full article

Distributed Denial-of-Service (DDoS) attacks continue to escalate in scale and complexity, posing significant threats to modern network infrastructures and cloud services. Although many machine learning and deep learning approaches have been proposed for intrusion detection, most existing studies rely on raw traffic features and binary classification, which limits their ability to capture complex temporal characteristics of multi-class DDoS attacks. To address these challenges, this study proposes an ensemble stacking framework combined with a frequency-domain feature representation for DDoS detection using the CIC-DDoS2019 dataset. Random Forest (RF), AdaBoost, and XGBoost are employed as base learners, while Logistic Regression is adopted as the meta-learner, and grid search cross-validation is used to determine the optimal hyperparameters. The main contributions of this study are threefold. First, a feature extraction pipeline integrating Fast Fourier Transform (FFT), sliding-window segmentation, and SHA256-based deduplication is proposed to capture temporal–frequency characteristics of network traffic while reducing redundant feature segments. Second, a stacking ensemble model is constructed to integrate heterogeneous classifiers and improve classification robustness across multiple attack types. Third, the proposed framework significantly improves computational efficiency by reducing feature redundancy, leading to substantial reductions in model training time. Experimental results demonstrate that the proposed FFT + SHA256 + SW stacking model achieves near-perfect detection performance, with an accuracy of 0.9997 and an F1-score of 0.9998 on the original dataset, which further improves to an accuracy of 0.9998 and an F1-score of 0.9999 when combined with SMOTE. Statistical evaluation using the Friedman test confirms that the stacking model consistently achieves the best ranking among the evaluated classifiers. The results indicate that the proposed approach provides an accurate, efficient, and scalable solution for large-scale DDoS attack detection. Full article

Universities worldwide are increasingly committing to carbon neutrality; however, most institutional climate strategies treat operational emissions forecasting and ecosystem-based carbon sequestration as separate analytical domains, leading to inconsistencies in accounting boundaries, temporal alignment, and verification practices. This study develops and demonstrates an integrated LEAP–InVEST framework that explicitly links energy-system modeling with spatial ecosystem carbon accounting within a unified monitoring, reporting, and verification (MRV)-aligned structure. The framework combines the Low Emissions Analysis Platform (LEAP) for scenario-based greenhouse gas emissions modeling with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model for spatial carbon storage assessment. A key methodological contribution lies in reconciling emission flows and carbon stock changes by converting carbon stock variations into annualized removal flows, thereby enabling consistent estimation of gross emissions, carbon removals, and net emissions while avoiding double counting across scopes. Using a university campus in Taiwan as a case study, a baseline inventory was established following ISO 14064-1 standards, and future emissions trajectories were simulated under Business-as-Usual and mitigation pathways through 2040. In parallel, land-use and land-cover data were used to quantify historical and projected carbon stocks across forest, grassland, agricultural, and built-up areas. Results indicate that electricity consumption constitutes the dominant emissions source, and that energy efficiency improvements, photovoltaic deployment, and green power procurement provide the largest mitigation potential. Although ecosystem carbon stocks remain substantial, their annual sequestration capacity offsets only a limited portion of projected emissions, reinforcing the importance of prioritizing emissions reduction before applying nature-based removals. The proposed framework provides a transferable methodological approach for institutional carbon neutrality planning by integrating emissions reduction and carbon sequestration within a coherent analytical system. By aligning energy modeling, ecosystem dynamics, and MRV principles, the framework enhances the transparency, credibility, and robustness of net-zero pathway assessment and is applicable to universities and compact urban systems seeking data-driven and verifiable decarbonization strategies. Full article