Search Results (871)

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

This work introduces Uni-Removal, an innovative two-stage framework that effectively addresses the critical challenge of domain adaptation in unified image restoration. Contemporary approaches often face significant performance degradation when transitioning from synthetic training environments to complex real-world scenarios due to the substantial domain discrepancy. Our proposed solution establishes a comprehensive pipeline that systematically bridges this gap through dual-phase representation learning. In the first stage, we implement a structured multi-teacher knowledge distillation mechanism that enables a unified student architecture to assimilate and integrate specialized expertise from multiple pre-trained degradation-specific networks. This knowledge transfer is rigorously regularized by our novel Instance-Grained Contrastive Learning (IGCL) objective, which explicitly enforces representation consistency across both feature hierarchies and image spaces. The second stage introduces a groundbreaking output distribution calibration methodology that employs Cluster-Grained Contrastive Learning (CGCL) to adversarially align the restored outputs with authentic real-world image characteristics, effectively embedding the student model within the natural image manifold without requiring paired supervision. Comprehensive experimental validation demonstrates Uni-Removal’s superior performance across multiple real-world degradation tasks including dehazing, deraining, and deblurring, where it consistently surpasses existing state-of-the-art methods. The framework’s exceptional generalization capability is further evidenced by its competitive denoising performance on the SIDD benchmark and, more significantly, by delivering a substantial 4.36 mAP improvement in downstream object detection tasks, unequivocally establishing its practical utility as a robust pre-processing component for advanced computer vision systems. Full article

The freshwater drum (Aplodinotus grunniens) is a promising aquaculture species due to its strong environmental adaptability, tolerance to low temperatures, rapid growth rate, high nutritional value, high-quality texture (garlic-clove-shaped flesh), and absence of intermuscular bones. Nevertheless, processing technologies related to freshwater drum remain largely unexplored. Salting pretreatment serves as a viable strategy for enhancing the quality attributes of frozen fish products. This study investigated the effects of different sodium chloride (NaCl) pickling concentrations (0.25, 1, and 3 mol/L) on the physicochemical properties and quality attributes of frozen–thawed freshwater drum (Aplodinotus grunniens). Results indicated that elevated NaCl concentrations (1–3 mol/L) significantly (p < 0.05) shortened the transit time through the maximum ice crystal formation zone during freezing, effectively mitigating structural damage to myofibrillar networks. As the NaCl concentration increased from 0 to 3 mol/L, the water content decreased from 71.26 ± 0.22% to 68.64 ± 0.50%, while the salt content increased from 0.31 ± 0.01% to 8.46 ± 0.12%. Pickling pretreatment markedly enhanced water-holding capacity and improved texture profiles, including hardness, springiness, gumminess, and chewiness. Histological analysis revealed preserved myofibril integrity in high-salt-treated samples, supported by reduced fluorescence intensity of myofibrillar proteins, indicating mitigated freeze-induced denaturation. Low-field NMR confirmed salt-induced redistribution of water states, with decreased free water proportion. Our results identify that pretreatment with NaCl at concentrations ≥ 1 mol/L is an effective strategy to preserve the post-thaw quality. Due to 3 mol/L NaCl resulting in a relatively high salt content, 1 mol/L NaCl pretreatment is more suitable for maintaining the quality of freeze–thawed freshwater drums. Full article

This study investigates stochastic bifurcation in a generalized tristable Rayleigh–Duffing oscillator with fractional inertial force under both additive and multiplicative recycling noises. The system’s dynamic behavior is influenced by its inherent spatial symmetry, represented by the potential function, as well as by temporal symmetry breaking caused by fractional memory effects and recycling noise. First, an approximate integer-order equivalent system is derived from the original fractional-order model using a harmonic balance method, with minimal mean square error (MSE). The steady-state probability density function (sPDF) of the amplitude is then obtained via stochastic averaging. Using singularity theory, the conditions for stochastic P bifurcation (SPB) are identified. For different fractional derivative’s orders, transition set curves are constructed, and the sPDF is qualitatively analyzed within the regions bounded by these curves—especially under tristable conditions. Theoretical results are validated through Monte Carlo simulations and the Radial Basis Function Neural Network (RBFNN) approach. The findings offer insights for designing fractional-order controllers to improve system response control. Full article

A Bose-Fermi mixture, consisting of both bosons and fermions, exhibits distinctive quantum coherence and phase transitions, offering valuable insights into many-body quantum systems. The ground state, as the system’s lowest energy configuration, is essential for understanding its overall behavior. In this study, we introduce the Bose-Fermi Energy-based Deep Neural Network (BF-EnDNN), a novel deep learning approach designed to solve the ground-state problem of Bose-Fermi mixtures at zero temperature through energy minimization. This method incorporates three key innovations: point sampling pre-training, a Dynamic Symmetry Layer (DSL), and a Positivity Preserving Layer (PPL). These features significantly improve the network’s accuracy and stability in quantum calculations. Our numerical results show that BF-EnDNN achieves accuracy comparable to traditional finite difference methods, with effective extension to two-dimensional systems. The method demonstrates high precision across various parameters, making it a promising tool for investigating complex quantum systems. Full article

Efficient and stable path planning in dynamic and obstacle-dense environments, such as large-scale structure assembly measurement, is essential for improving the practicality and environmental adaptability of mobile robots in measurement and quality inspection tasks. However, traditional reinforcement learning methods often suffer from inefficient use of experience and limited capability to represent policy structures in complex dynamic scenarios. To overcome these limitations, this study proposes a method named DPDQN-TER that integrates Transformer-based sequence modeling with a multi-branch parameter policy network. The proposed method introduces a temporal-aware experience replay mechanism that employs multi-head self-attention to capture causal dependencies within state transition sequences. By dynamically weighting and sampling critical obstacle-avoidance experiences, this mechanism significantly improves learning efficiency and policy performance and stability in dynamic environments. Furthermore, a multi-branch parameter policy structure is designed to decouple continuous parameter generation tasks of different action categories into independent subnetworks, thereby reducing parameter interference and improving deployment-time efficiency. Extensive simulation experiments were conducted in both static and dynamic obstacle environments, as well as cross-environment validation. The results show that DPDQN-TER achieves higher success rates, shorter path lengths, and faster convergence compared with benchmark algorithms including Parameterized Deep Q-Network (PDQN), Multi-Pass Deep Q-Network (MPDQN), and PDQN-TER. Ablation studies further confirm that both the Transformer-enhanced replay mechanism and the multi-branch parameter policy network contribute significantly to these improvements. These findings demonstrate improved overall performance (e.g., success rate, path length, and convergence) and generalization capability of the proposed method, indicating its potential as a practical solution for autonomous navigation of mobile robots in complex industrial measurement scenarios. Full article

Background: In recent decades, research on Hepatitis C Virus (HCV) drug-resistant variants has expanded; however, critical gaps remain in our understanding of global contributions, emerging trends, and future research directions. Here, we present a bibliometric analysis to understand the research themes and trends in research related to HCV drug-resistant variants published between 1999 and 2025. Methods: Publications related to HCV drug-resistant variants published between 1999 and 2025 were searched on the Web of Science and Scopus databases. Publication metadata and content-based data were extracted and analyzed using Bibliometrix and VOSviewer for keyword co-occurrence plot and cluster analysis. Results: The analysis of 653 articles revealed a clear paradigm shift, driven by the introduction of direct-acting antivirals (DAAs), which led to a significant surge in annual publications, peaking between 2014 and 2018. This shift in focus led to an emphasis on DAA efficacy, resistance mechanisms, and advanced genotyping. The United States was the most productive country, with the highest number of publications (n = 134) and citations (n = 6458). The University of São Paulo was the most productive institution (n = 40), while Antimicrobial Agents and Chemotherapy published the highest number of articles in this field (n = 40). Susser S. was the most productive researcher. Collaboration networks were found to be predominantly centered in high-income countries. Analysis of studies on minority variants showed that most studies originated from Europe and the United States, identifying low-frequency resistance-associated substitutions (RASs) such as A156V, D168V, Y93H, and S282T, with prevalence ranging from <1% to 35%, which were frequently associated with viral breakthrough and reduced treatment response. Conclusions: The field successfully transitioned to the DAA era, but research output and collaboration networks were primarily driven by high-income countries, leaving a critical gap in data from Low- and Middle-Income Countries (LMICs). Closing this gap by integrating LMIC data is the next essential step to ensure global elimination strategies are effective for all countries from different income strata. Full article

Classification is a crucial task for reintroducing end-of-life fabrics as raw materials in a circular process, thus reducing reliance on dyeing processes. In this context, this review explores the evolution of automated and semi-automated colour classification methods, emphasizing the transition from deterministic techniques to advanced methods, with a focus on machine learning, deep learning, and particularly Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). These technologies show potential for improving accuracy and efficiency. The results highlight the need for enriched datasets, deeper AI integration into industrial processes, and alignment with circular economy objectives to enhance sustainability without compromising industrial performance. Tested against a case study, the different architectures confirmed the state-of-the-art statements demonstrating that they are effective in classification, with better performance reached by CNN-based methods, which outperforms other methods in most colour families, with an average accuracy of 86.1%, indicating its adaptability for this task. The adoption of the proposed AI-based colour-classification roadmap could be effective in reducing dyeing operations, lower costs, and improve sorting efficiency for textile SMEs. Full article

This paper presents a real-time handover and link assignment framework for low-Earth-orbit (LEO) satellite networks operating in dense urban canyons. The proposed Markov chain-guided simulated annealing (MCSA) algorithm optimizes user-to-satellite assignments under dynamic channel and capacity constraints. By incorporating Markov chains to guide state transitions, MCSA achieves faster convergence and more effective exploration than conventional simulated annealing. Simulations conducted in Ku-band urban canyon environments show that the framework achieves an average user satisfaction of about 97%, providing an approximately 10% improvement over genetic algorithm (GA) results. It also delivers 10–15% higher resource utilization, lower blocking rates comparable to integer linear programming (ILP), and superior runtime scalability with linear complexity $O(k·|U|·|S\left|\right)$. These results confirm that MCSA provides a scalable and robust real-time mobility management solution for next-generation LEO satellite systems. Full article

This study investigates spatiotemporal b value variations and seismic interaction networks preceding the M_wg 7.1 Dingri earthquake that struck southern Tibet on 7 January 2025. Using relocated earthquake catalogs (2021–2025) and dual-method analysis combining b value mapping with Granger causality network modeling, we reveal systematic precursory patterns. Spatial analysis shows that the most significant b value reduction (Δb > 0.5) occurred north of the mainshock epicenter at seismogenic depths (5–15 km), closely aligning with subsequent aftershock concentration zones. Granger causality analysis reveals a progressive network simplification: from 73 causal links among 28 nodes during the background period (2021–2023) to 49 links among 34 nodes pre-mainshock (2023–2025) and finally to 6 localized links post-rupture. This transition from distributed system-wide interactions to localized “locked-in” dynamics reflects the stress concentration onto the primary asperity approaching critical failure. The convergence of b value anomalies and network evolution provides a comprehensive framework linking quasi-static stress states with dynamic system behavior. These findings offer valuable insights for understanding earthquake nucleation processes and improving seismic hazard assessment in the Tibetan Plateau and similar complex tectonic environments. Full article

Rural resilience building has gained increasing scholarly attention, yet existing literature overlooks the temporal dynamics of resilience evolution and lacks an integrative framework to explain cross-level mechanisms. This paper uses a longitudinal case study to explore how rural resilience transitions from a low-equilibrium to a high-equilibrium state and how neo-endogenous practices emerge in a weak institutional context. The study reveals three key findings. First, the village’s resilience evolved through three phases—institutional intervention, community capital activation, and resilience self-reinforcement—driven by co-evolutionary interactions between an enabling government and the rural community. This process is marked by chain effects of multidimensional community capital (e.g., cultural capital enhancing social capital) and overflow effects from resilience amplification (e.g., multi-scalar network). Second, exogenous resources and endogenous community capital are critical in the neo-endogenous model, but their synergy relies on vertical institutional interventions that foster horizontal networks and enhance communities’ resource absorption capacity. Third, the government enables resilience building by creating a support ecosystem that transitions from institutionally bundled resources to a higher-order composite space, facilitated by urban–rural interactions and community restructuring. The study makes three theoretical contributions: (1) it proposes an analytical framework integrating an enabling government, community capital, and ecosystem upgrading, thus advancing beyond the current community capital-centric paradigm; (2) it introduces a three-phase process model that unpacks spatiotemporal interactions across urban-rural interfaces, multi-scalar networks, and state-community relations, addressing the limitations of static factor-based analyses; (3) it reconceptualizes the role of government as an “enabling government” that mediates local and extra-local resource interfaces, challenging the neo-endogenous theories’ neglect of institutional agency. These insights contribute to rural resilience scholarship through a complex adaptive systems lens and offer policy implications for synergistic urban-rural revitalization. Full article

The rapid transition to electric vehicles (EVs) requires a charging infrastructure that is both efficient and equitable. Conventional planning approaches, which often deploy chargers in proportion to current EV density, fail to account for the diverse characteristics of EV owners and the evolving patterns of adoption across different regions and time periods. This paper introduces an integrated, data-driven framework that addresses these limitations through three stages: segmentation of the EV market, spatio-temporal adoption forecasting for each segment, and optimizing charger placement through a constrained optimization model. The proposed optimization model incorporates equity constraints to ensure minimum service coverage for all user segments while maximizing overall utilization within a fixed budget. Methodologically, the paper contributes a transparent, reproducible framework that unifies user segmentation, geographically resolved adoption forecasting, and an equity-constrained MILP for charger placement. Applying this approach to a dataset of EV registrations in Washington State from 2010 to 2025 and extending it to projections through 2030 demonstrate important improvements in demand coverage. Overall coverage increases from 76.0% to 96.1% compared to a proportional-allocation baseline. More importantly, the proposed framework ensures a minimum of 70% coverage for all user segments. The presented approach is portable to other regions and budget scenarios. These findings show the potential for strategic, data-informed infrastructure planning that balances efficiency and equity, providing actionable insights for policymakers and network operators in the EV transition. Full article

This study investigates the stress-dependent creep behavior of a CZ1 Zr alloy exhibiting two distinct microstructural states induced by different annealing treatments. Creep tests were conducted at 380 °C under applied stresses of 140 MPa and 260 MPa. CZ1-2 (fully recrystallized), characterized by coarse grains and low dislocation density, demonstrated superior creep resistance under low stress due to suppressed dislocation activity and diffusion-dominated deformation. Stress exponent analysis revealed n = 5 for CZ1-1 (partially recrystallized) and n = 10 for CZ1-2, confirming a mechanism transition from steady-state dislocation climb to power-law breakdown. TEM characterization provided direct evidence of evolving dislocation networks, stacking faults, and second-phase particle redistribution. These findings underscore the critical role of microstructural conditioning in governing creep pathways and provide a mechanistic basis for tailoring Zr alloys to stress-specific service environments in advanced nuclear applications. Full article

Aim: With the intensification of population aging, the public health challenges posed by multimorbidity have become increasingly severe. This study employs bibliometric analysis to elucidate research hotspots and trends in the field of multimorbidity against the backdrop of global aging. The immediate aim is to systematically map the intellectual landscape and evolving patterns in multimorbidity research. The ultimate long-term aim is to provide a scientific basis for optimizing chronic disease prevention systems and guiding future research directions. Methods: The study adopted the descriptive research method and employed a bibliometric approach, analyzing 8129 publications related to multimorbidity from the Web of Science Core Collection. Using CiteSpace, we constructed and visualized several knowledge structures, including collaboration networks, keyword co-occurrence networks, burst detection maps, and co-citation networks within the multimorbidity research domain. Results: The analysis included 8129 articles from 2004 to 2024, published across 1042 journals, with contributions from 740 countries/regions, 33,931 institutions, and 40,788 authors. The five most frequently occurring keywords were prevalence, health, older adult, mortality, and risk. The top five contributing countries globally were the United States, the United Kingdom, Germany, China, and Spain. Five pivotal research trajectories delineate the intellectual architecture of this field: ① Evolution of Disease Cluster Management: Initial investigations (2013–2014) prioritized disease cluster coordination within general practice settings, particularly cardiovascular comorbidity management through primary care protocols and self-management strategies. ② Paradigm Shifts in Health Impact Assessment: Multimorbidity outcome research demonstrated sequential transitions—from physical disability evaluation (2013) to mental health consequences like depression (2016), culminating in current emphasis on holistic health indicators including frailty syndromes (2015–2019). ③ Expansion of Risk Factor Exploration: Analytical frameworks evolved from singular physical activity metrics (2014) toward comprehensive lifestyle-related determinants encompassing behavioral and environmental dimensions (2021). ④ Emergence of Polypharmacy Scholarship: Medication optimization studies emerged as a distinct research stream since 2016, addressing therapeutic complexities in multimorbidity management. ⑤ Frontier Investigations: Cutting-edge directions (2019–2021) feature cardiometabolic multimorbidity patterns and their dementia correlations, signaling novel interdisciplinary interfaces. Conclusions: The prevalence of multimorbidity is on the rise globally, particularly in older populations. Therefore, it is essential to prioritize the prevention of cardiometabolic conditions in older adults and to provide them with appropriate and effective health services, including disease risk monitoring and community-based chronic disease care. Full article

Extracellular particles (EPs), an umbrella term encompassing membrane-enclosed extracellular vesicles (EVs) and non-vesicular extracellular particles ([NVEPs], previously described as extracellular condensates [ECs]) contain a complex cargo of biomolecules, including DNA, RNA, proteins, and lipids, reflecting the physiological state of their cell of origin. Identifying proteins associated with EPs that regulate host responses to physiological and pathophysiological processes is of critical importance. Here, we report the findings of our study to gain insight into the proteins associated with NVEPs. We used samples from human semen, the rat brain, and the rhesus macaque (RM) brain and blood to assess the physical properties and proteome profiles of NVEPs from these specimens. The results show significant differences in the zeta potential, concentration, and size of NVEPs across different species. We identified 938, 51, and 509 total proteins from NVEPs isolated from rat brain tissues, RM blood, and human seminal plasma, respectively. The species-specific protein networks show distinct biological themes, while the species-conserved protein interactome was identified with six proteins (ALB, CST3, FIBA/FGA, GSTP1, PLMN/PLG, PPIA) associated with NVEPs in all samples. The six NVEP-associated proteins are prone to aggregation and formation of wide, insoluble, unbranched filaments with a cross-beta sheet quaternary structure, such as amyloid fibrils. Protein-to-function analysis indicates that the six identified proteins are linked to the release of dopamine, immune-mediated inflammatory disease, replication of RNA viruses, HIV-HCV co-infection, and inflammation. These interesting findings have created an opportunity to evaluate NVEPs for their potential use as biomarkers of health and disease. Additional in-depth studies are needed to clarify when and how these proteins sustain their physiological role or transition to pathogenic roles. Full article