Search Results (13,557)

Türkiye’s forest governance exhibits a persistent policy–implementation gap rooted in a governance paradox: while the Ecosystem-Based Functional Planning (EBFP) system promotes ecological integrity and adaptive management, the foundational Forest Law No. 6831 (1956) still legitimizes extractive uses under a broad “public interest” doctrine. This contradiction has enabled 94,148 permits covering 654,833 ha of forest conversion, while marginalizing nearly seven million forest-dependent villagers from decision-making. The study applies a doctrinal and qualitative document-analysis approach, integrating legal, institutional, and socio-economic dimensions. It employs a comparative design with five EU transition countries—Poland, Romania, Bulgaria, Czechia, and Greece—selected for their shared post-socialist administrative legacies and diverse pathways of forest-governance reform. The analysis synthesizes legal norms, policy instruments, and institutional practices to identify drivers of reform inertia and regulatory capture. Findings reveal three interlinked failures: (1) institutional and ministerial conflicts that entrench centralized decision-making and weaken environmental oversight—illustrated by the fact that only 0.97% of Environmental Impact Assessments receive negative opinions; (2) economic and ecological losses, with foregone ecosystem-service values exceeding EUR 200 million annually and limited access to carbon markets; and (3) participatory deficits and social contestation, exemplified by local forest conflicts such as the Akbelen case. A comparative SWOT analysis indicates that Poland’s confrontational policy reforms triggered EU infringement penalties, Romania’s fragmented legal restitution fostered illegal logging networks, and Greece’s recent modernization offers lessons for gradual legal harmonization. Drawing on these insights, the paper recommends comprehensive Forest Law reform that integrates ecosystem-service valuation, climate adaptation, and transparent participatory mechanisms. Alignment with the EU Nature Restoration Regulation (2024/1991) and Biodiversity Strategy 2030 is proposed as a phased transition pathway for Türkiye’s candidate-country obligations. The study concludes that partial reforms reproduce systemic contradictions: bridging the policy–law divide requires confronting entrenched political-economy dynamics where state actors and extractive-industry interests remain institutionally intertwined. Full article

Wastewater Treatment Plants (WWTPs) rely on automatic control strategies to regulate pollutant concentrations and comply with environmental standards. Among them, Proportional Integral (PI) controllers are widely adopted for their simplicity and robustness, yet their effectiveness is limited by the nonlinear and time-varying dynamics of biological processes. In this work, Long Short-Term Memory (LSTM)-based Artificial Neural Network (ANN) PI controllers are proposed as data-driven replacements for conventional PIs in key WWTP feedback loops. Using the Benchmark Simulation Model No. 1 (BSM1), ANN controllers were trained to replicate the behavior of default nitrate and nitrite nitrogen ($S_{NO,2}$) and dissolved oxygen ($S_{O,5}$) loops, under both time-agnostic and time-aware strategies with three- and four-input configurations. The four-input time-aware model delivered the best results, reproducing PI behavior with high accuracy (coefficient of determination, $R^2\approx 0.99$) and considerably reducing control errors. For instance, under storm influent conditions, the $S_{O,5}$ controller reduced the Integral of Squared Error ($\mathrm{ISE}$) and Integral of Absolute Error ($\mathrm{IAE}$) by 84.7% and 68.4%, respectively, compared with the default PI. Beyond loop-level improvements, a Transfer Learning (TL) extension was explored: the trained $S_{O,5}$ controller was directly applied to additional aerated reactors ($S_{O,3}$ and $S_{O,4}$) without retraining, replacing fixed aeration and demonstrating adaptability while reducing design effort. Plant-wide evaluation with the $S_{NO,2}$ loop and three dissolved oxygen loops ($S_{O,3}$–$S_{O,5}$), all controlled by LSTM-based PI controllers, under storm influent conditions, showed further reductions in the Effluent Quality Index ($\mathrm{EQI}$) and the Overall Cost Index ($\mathrm{OCI}$) by 0.84% and 1.47%, respectively, highlighting simultaneous gains in effluent quality and operational economy. Additionally, the actuator and energy analyses showed that the LSTM-based controllers produced realistic and smooth control signals, maintained consistent energy use, and ensured stable overall operation, confirming the practical feasibility of the proposed approach. Full article

In order to address the social, economic, and environmental challenges arising from urban development, some urban revitalization plans have been proposed. With the implementation of these plans, the spatial pattern of the region has also undergone corresponding changes. Some of the revitalization projects have driven economic growth while accompanied by ecological degradation, while others have achieved coordinated development and protection. This study selected eight urban revitalization cases, based on remote sensing (RS) and geographic information system (GIS), and used the Random Forest (RF) machine learning method to dynamically monitor the spatial changes in the region before and after revitalization through Land Use and Land Cover (LULC) analysis. The research results show that among the eight cases, only the revitalization cases located in Beijing and Swarzędz reflected an increase in water and vegetation areas, while the built-up area decreased. The other six cases located in Nanjing, Kraków, Wągrowiec, Swarzędz, Parczew, and Mosina all reflect the result of built-up areas encroaching water and vegetation areas. Full article

As climate change accelerates, environmental factors are expected to fluctuate as well. To gain insight into soil respiration (Rs) dynamics, it is essential to conduct long-term measurements of Rs alongside environmental variations. To this end, we examined Rs associated with environmental variables from 2018 to 2024 at a site located on Mt. Jeombong, which is situated in a temperate deciduous broadleaf forest. The interannual variation in Rs was not explained by soil temperature but was primarily associated with rainfall regimes. The mean Rs for April–November was substantially different during the study period and was strongly correlated with cumulative rainfall at all measurement points (R² = 0.68–0.94). These variations were largely attributed to changes in autotrophic respiration (Ra). Furthermore, Rs differed significantly between nearby measurement points (p < 0.05), despite their proximity within a 100 m by 100 m plot, apparently reflecting point-level differences in responses of Rs to environmental drivers that were likely modulated by uneven litter accumulation. Overall, at our site located in temperate deciduous forests, Rs primarily fluctuates as a result of rainfall variation, and Rs variations are strongly influenced by the heterogeneity in the litter deposition. Full article

Accurate and nondestructive monitoring of tomato growth is essential for large-scale greenhouse production; however, it remains challenging for small-fruited cultivars such as cherry tomatoes. Traditional 2D image analysis often fails to capture precise morphological traits, limiting its usefulness in growth modeling and yield estimation. This study proposes an automated phenotyping framework that integrates deep learning-based instance segmentation with high-resolution 3D point cloud reconstruction and ellipsoid fitting to estimate fruit size and ripeness from daily video recordings. These techniques enable accurate camera pose estimation and dense geometric reconstruction (via SfM and MVS), while Nerfacto enhances surface continuity and photorealistic fidelity, resulting in highly precise and visually consistent 3D representations. The reconstructed models are followed by CIELAB color analysis and logistic curve fitting to characterize the growth dynamics. When applied to real greenhouse conditions, the method achieved an average size estimation error of 8.01% compared to manual caliper measurements. During summer, the maximum growth rate (g_max) of size and ripeness were 24.14%, and 95.24% higher than in winter, respectively. Seasonal analysis revealed that winter-grown tomatoes matured approximately 10 days later than summer-grown fruits, highlighting environmental influences on phenological development. By enabling precise, noninvasive tracking of size and ripeness progression, this approach is a novel tool for smart and sustainable agriculture. Full article

Intelligent connected vehicles (ICVs) face challenges in handling intensive onboard computational tasks due to limited computing capacity. Vehicular edge computing networks (VECNs) offer a promising solution by enabling ICVs to offload tasks to mobile edge computing (MEC), alleviating computational load. As transportation systems are dynamic, vehicular tasks and MEC capacities vary over time, making efficient task offloading and resource allocation crucial. We explored a vehicle–road collaborative edge computing network and formulated the task offloading scheduling and resource allocation problem to minimize the sum of time and energy costs. To address the mixed nature of discrete and continuous decision variables and reduce computational complexity, we propose a hybrid hierarchical deep reinforcement learning (HHDRL) algorithm, structured in two layers. The upper layer of HHDRL enhances the double deep Q-network (DDQN) with a self-attention mechanism to improve feature correlation learning and generates discrete actions (communication decisions), while the lower layer employs deep deterministic policy gradient (DDPG) to produce continuous actions (power control, task offloading, and resource allocation decision). This hybrid design enables efficient decomposition of complex action spaces and improves adaptability in dynamic environments. Results from numerical simulations reveal that HHDRL achieves a significant reduction in total computational cost relative to current benchmark algorithms. Furthermore, the robustness of HHDRL to varying environmental conditions was confirmed by uniformly designing random numbers within a specified range for certain simulation parameters. Full article

Accurate remote sensing scene classification is essential for applications such as environmental monitoring and disaster management. In real-world scenarios, particularly during emergency response and disaster relief operations, acquiring nadir-view satellite images is often infeasible due to cloud cover, satellite scheduling constraints, or dynamic scene conditions. Instead, off-nadir images are frequently captured and can provide enhanced spatial understanding through angular perspectives. However, remote sensing scene classification has primarily relied on nadir-view satellite or airborne imagery, leaving off-nadir perspectives largely unexplored. This study addresses this gap by introducing Off-nadir-Scene10, the first controlled and comprehensive benchmark dataset specifically designed for off-nadir satellite image scene classification. The Off-nadir-Scene10 dataset contains 5200 images across 10 common scene categories captured at 26 different off-nadir angles. All images were collected under controlled single-day conditions, ensuring that viewing geometry was the sole variable and effectively minimizing confounding factors such as illumination, atmospheric conditions, seasonal changes, and sensor characteristics. To effectively leverage abundant nadir imagery for advancing off-nadir scene classification, we propose an angle-aware active domain adaptation method that incorporates geometric considerations into sample selection and model adaptation processes. The method strategically selects informative off-nadir samples while transferring discriminative knowledge from nadir to off-nadir domains. The experimental results show that the method achieves consistent accuracy improvements across three different training ratios: 20%, 50%, and 80%. The comprehensive angular impact analysis reveals that models trained on larger off-nadir angles generalize better to smaller angles than vice versa, indicating that exposure to stronger geometric distortions promotes the learning of view-invariant features. This asymmetric transferability primarily stems from geometric perspective effects, as temporal, atmospheric, and sensor-related variations were rigorously minimized through controlled single-day image acquisition. Category-specific analysis demonstrates that angle-sensitive classes, such as sparse residential areas, benefit significantly from off-nadir viewing observations. This study provides a controlled foundation and practical guidance for developing robust, geometry-aware off-nadir scene classification systems. Full article

Sediment plumes threatening benthic ecosystems are one of the environmental hazards associated with seafloor interventions such as bottom trawling, cabling, dredging, and marine mining operations. This study focuses on sediment plume release from hypothetical future deep-sea mining activities, emphasizing its interaction with turbulent ocean currents in regions characterized by complex seafloor topography. In such environments, turbulent lee waves may significantly enhance the scattering of released sediments, pointing to the clear need for appropriate impact assessment frameworks. Global-scale models are limited in their ability to resolve sufficiently high Reynolds numbers to accurately represent turbulence generated by seafloor topography. To overcome these limitations and effectively assess lee wave dynamics, models must incorporate the full physics of turbulence without simplifying the Navier–Stokes equations and must operate with significantly finer spatial discretization while maintaining a domain large enough to capture the full topographic signal. Considering a seamount in the Lofoten Basin of the Norwegian Sea as an example, we present a novel numerical analysis that explores the interplay between lee wave turbulence and sediment plume dispersion using a high-resolution Large Eddy Simulation (LES) framework. We show that the turbulence occurs within semi-horizontal channels that emerge beyond the topographic highs and extend into sheet-like tails close to the seafloor. In scenarios simulating sediment release from various sites on the seamount, our model predicts distinct behavior patterns for different particle sizes. Particles with larger settling velocities tend to deposit onto the seafloor within 50–200 m of release sites. Conversely, particles with lower settling velocities are more susceptible to turbulent transport, potentially traveling greater distances while experiencing faster dilution. Based on our scenarios, we estimate that the plume concentration may dilute below 1 ppm at about 2 km distance from the release site. Although our analysis shows that mixing with ambient seawater results in rapid dilution to low concentrations, it appears crucial to account for the effects of topographic lee wave turbulence in impact assessments related to man-made sediment plumes. Our high-resolution numerical simulations enable the identification of sediment particle size groups that are most likely affected by turbulence, providing valuable insights for developing targeted mitigation strategies. Full article

 Modern biomedicine frequently contextualizes disease around isolated molecular or organ-specific mechanisms, but numerous chronic diseases, including Alzheimer’s disease, multiple sclerosis, depression, diabetes, and sepsis, share common trajectories of systemic destabilization. An increasing body of evidence indicates that health is not a property of single organs but the emergent property of interdependent feedback networks linking the microbiome, endocannabinoidome, neuroimmune system, and metabolic regulators. We propose the Endocannabinoid–Microbiota–Neuroimmune Super-System (EMN-S) as an evolutionarily conserved conceptual model that describes how these fields of influence reciprocally interact through feedback control. The microbial communities constituting the EMN-S encode environmental and dietary inputs, endocannabinoid signaling serves as an integrative regulator that synchronizes neural and immune activity, and neuroimmune circuits effectuate adaptive behaviors that alter microbiotal and lipid ecosystems. This review formalizes the EMN-S, contending that it is a unitary and cohesive model of physiological resilience, as well as offering a framework for precision feedback therapeutics. We describe how three mechanisms—encoder drift, integrator detuning, and executor overutilization—convert stabilizing negative feedback into runaway feedback cascades that underlie chronic, recurrent, and multisystemic disease. We then specify the EMN-S signature—integrated microbiome, lipidomic, and immune readouts—as an early indicator of resilience collapse and prospective preclinical state. Finally, we recapitulate the potential of AI-driven digital twins to illuminate feedback collapse, predict tipping points, and direct closed-loop intervention and treatments to restore dynamic equilibrium. By anchoring complexity in concrete and measurable feedback principles, the EMN-S shifts focus to investigate pathophysiology as opposed to reductionist lesion models of systemic derangements and embraces a systemic, empirically testable theory of stability.  Full article

Over the past few decades, the concept of sustainable agriculture has gained popularity. However, the notion of sustainable agriculture is highly imprecise and unclear, making its application and execution exceedingly challenging. Moreover, disagreements about what sustainability means can lead to a deeper understanding of the intricate empirical procedures and possibly debatable principles involved in any effort to achieve sustainability in agriculture. Practices to increase crop resilience, lower chemical inputs, and boost efficiency are examples of future developments. This review identifies how agricultural life cycle assessment (LCA) studies engage with climate-related metrics such as GHG emissions and land use changes, offering insights for adaptation and mitigation strategies. This review also addresses the need to synthesize existing research on how agriculture and food systems can become more environmentally friendly through LCA. LCA enables the identification of environmental hotspots within agricultural systems, therefore, guiding efforts to limit resource consumption and emissions. For this purpose, a search of a bibliographic database was carried out and the results obtained were analyzed with the open-source tool bibliometrix. There were 2328 results in total with publication years from 1993 to 2025, the latter of which refers to a pre-publication. Then, a post-processing analysis of 1411 articles was conducted and a narrative review of around 100 publications was carried out, where agricultural practices with LCA, current trends, and research gaps were explored. Finally, this paper contributes by identifying three major research gaps derived from the literature synthesis: firstly, the underrepresentation of dynamic LCA models in agriculture; secondly, the lack of geographical balance in case studies; and thirdly, the insufficient integration of socio-economic dimensions in environmental assessments. Full article

Air cycle systems, once largely replaced by vapour-compression technologies due to efficiency concerns, are now re-emerging as a viable and sustainable alternative for highly dynamic thermal applications and excel in ultra-low temperature. By using air as the working fluid, these systems eliminate the need for synthetic refrigerants and comply naturally with evolving environmental regulations. This study presents the conceptual design and simulation-based analysis of a novel air cycle machine developed for advanced automotive testing environments. The system is intended to replicate a wide range of climatic conditions—from deep winter to peak summer—through the use of fast-responding turbomachinery and a flexible control strategy. A central focus is placed on the radial turbine, which is designed and evaluated using a modular, open source framework that integrates geometry generation, off-design CFD simulation, and performance mapping. The study outlines a potential operating strategy based on these simulations and discusses a control architecture combining lookup tables with zone-specific PID tuning. While the results are theoretical, they demonstrate the feasibility and flexibility of the proposed approach, particularly the turbine’s role within the system. Full article

This paper advances a novel bio-cognitive framework for understanding how urban peripheries function as disabling environments that systematically undermine human flourishing. Drawing on recent theoretical developments in predictive processing, 4E cognition (embodied, embedded, enactive, and extended), and biology, we propose that marginalization in urban contexts emerges not merely from socio-economic deprivation but from fundamental disruptions to cognitive, physiological, and embodied processes. Our analysis illustrates how peripheral spaces operate as neuro-affective ecologies that constrain agency through the breakdown of sensorimotor coupling, the generation of persistent prediction errors, and the activation of chronic stress responses. We argue that environmental features characteristic of urban peripheries, such as fragmented infrastructure, limited affordances, and unpredictable spatial configurations, create conditions where the dynamic interplay between body, brain, and environment systematically impairs inhabitants’ capacity for effective action and adaptation. This bio-cognitive perspective challenges conventional approaches that frame peripheries primarily through geographic or policy lenses, instead revealing how spatial injustice also operates at the intersection of neural, bodily, and environmental processes. Our framework contributes to emerging debates on spatial justice by providing a scientifically grounded account of how built environments become constitutively disabling, offering new conceptual tools for policy interventions that address the embodied and cognitive dimensions of urban inequality. The implications extend beyond urban planning to fundamental questions about how environments shape human potential and the ethical imperatives of creating spaces that support rather than constrain human flourishing. Full article

Understory vegetation plays a pivotal role in enhancing forest biodiversity, and its restoration is crucial for sustainable forest development, energy flow, and nutrient cycling. However, the dynamics of the biomass, diversity, and species composition of understory vegetation in plantations in south China, along with their key drivers, remain poorly understood. This study investigated four mature plantation types (Pinus massoniana, Pinus caribaea, Cunninghamia lanceolata, and mixed Chinese fir–broadleaf forests) in south China through plot surveys, environmental factor measurements, and structural equation modeling (SEM) to explore the diversity, biomass allocation patterns, and driving mechanisms of understory vegetation. The results demonstrated the following. (1) The introduced Caribbean pine forests exhibited higher shrub biomass than native Masson pine forests, which was driven by their high canopy openness favoring light-demanding species (e.g., Melicope pteleifolia, IV = 33.93%), but their low mingling degree limited herb diversity. (2) Masson pine forests showed superior shrub diversity due to their random spatial distribution and higher soil total potassium (TK) content. (3) Mixed Chinese fir–broadleaf forests achieved 24.50–66.06% higher herb biomass compared to coniferous monocultures, supported by high mingling degree, random spatial configuration, and phosphorus-potassium-enriched soil, with concurrently improved herb diversity. SEM revealed that stand structure (DBH, density, mingling degree) directly drove shrub diversity by regulating light availability, while herb biomass was primarily governed by soil total phosphorus (TP) and pH. Canopy-induced light suppression negatively affected herb diversity. We recommend optimizing stand density and canopy structure through thinning and pruning to enhance light heterogeneity alongside supplementing slow-release P fertilizers in P-deficient stands. This study provides theoretical support for the multi-objective management of south China plantations, emphasizing the synergistic necessity of stand structure optimization and soil amendment. Full article

The persistent fragmentation of the architecture, engineering, and construction (AEC) industry drives the pursuit of advanced and unified construction solutions. This study investigated the limited understanding and adoption of one of these solutions, the platform approach to design for manufacturing and assembly (P-DfMA) within the AEC industry. Semi-structured interviews were conducted with 14 design professionals from China and the UK to understand how they utilize this approach. Governmental policy documents were also analyzed to examine how they hinder or facilitate the adoption of P-DfMA. The results were mapped using the technology–organization–environment (TOE) framework. Challenges and adoption considerations were identified by a thematic analysis, supported by text-mining results from Voyant Tools, with the most frequent keywords visualized in charts. The findings indicate that P-DfMA adoption is conceptually fragmented within the AEC industry, with a gap between theory and practice. Technical limitations in organizational structuring and environmental misalignment hinder adoption. Challenges and considerations span five domains: design, digital, financial and procurement, organizational, and sustainability. This research offers novel insights gained by integrating multi-layered analyses of construction practice interviews and policy perspectives within the TOE framework, along with timely insights into the socio-technical dynamics shaping the future of the industry. Full article

Railway hazardous chemical transportation, a high-risk activity that endangers personnel, infrastructure, and ecosystems, directly undermines the sustainability of the transportation system and regional development. Traditional risk management algorithms, which rely on empirical rules, result in sluggish emergency responses (with an average response time of 4.8 h), further exacerbating the environmental and economic losses caused by accidents. The standalone DBSCAN algorithm only supports static spatial clustering (with unoptimized hyperparameters); it lacks probabilistic reasoning capabilities for dynamic scenarios and thus fails to support sustainable resource allocation. To address this gap, this study develops a DBSCAN–Bayesian network fusion model that identifies risk hotspots via static spatial clustering—with ε optimized by the K-distance method and MinPts determined through cross-validation—for targeted prevention; meanwhile, the Bayesian network quantifies the dynamic relationships among “hazardous chemical properties-accident scenarios-material requirements” and integrates real-time transportation and environmental data to form a “risk positioning-demand prediction-intelligent allocation” closed loop. Experimental results show that the fusion algorithm outperforms comparative methods in sustainability-linked dimensions: ① Emergency response time is shortened to 2.3 h (a 52.1% improvement), with a 92% compliance rate in high-risk areas (e.g., water sources), thereby reducing ecological damage. ② The material satisfaction rate reaches 92.3% (a 17.6% improvement), and the neutralizer matching accuracy for corrosive leaks is increased by 26 percentage points, which cuts down resource waste and lowers carbon footprints. ③ The coverage rate of high-risk areas reaches 95.6% (a 16.4% improvement over the standalone DBSCAN algorithm), with a 27.5% reduction in dispatch costs and a drop in resource waste from 38% to 11%. This model achieves a leap from static to dynamic decision-making, providing a data-driven paradigm for the sustainable emergency management of railway hazardous chemicals. Its “spatial clustering + probabilistic reasoning” path holds universal value for risk control in complex systems, further boosting the sustainability of infrastructure. Full article