Search Results (9,135)

This study presents an analysis of patterns in mean monthly air temperature increases in Poland using the deep learning model Neural Basis Expansion Analysis for Time Series (N-BEATS) algorithm. The dataset comprises mean monthly temperatures recorded between 1951 and 2024 at eight meteorological stations across Poland. The research was conducted in two phases. In the first phase, the 74-year period was divided into two distinct intervals: one characterized by relative temperature stability, and the other by a marked upward trend. In the second phase, the N-BEATS neural network was employed to extract temporal patterns directly from the data and to forecast future temperature values. The results confirm the capacity of machine learning methods to identify persistent climate trends and demonstrate their utility for long-term monitoring and prediction. Full article

This paper investigates the symmetric and asymmetric effects of renewable and non-renewable energy on Greece’s economic growth within an extended Cobb–Douglas production function for 1990–2022. The study is motivated by the rising role of renewable energy and the need to determine whether the energy–growth nexus is linear or nonlinear, an issue of central importance for policy. The Brock–Dechert–Scheinkman (BDS) test confirms the nonlinearity of the variables, while Zivot–Andrews unit root tests with structural breaks capture crisis-related disruptions. The Wald test indicates that renewable energy has an asymmetric long-run relationship with growth, whereas non-renewables exert symmetric effects. To model these dynamics, the Nonlinear Autoregressive Distributed Lag (NARDL) framework is applied. Results show that in the long run, positive shocks to renewable energy enhance growth, while both positive and negative shocks to non-renewables have symmetric impacts. In the short run, only non-renewable energy shocks significantly affect growth. Asymmetric causality analysis reveals a bidirectional relationship between positive renewable shocks and growth, suggesting a virtuous cycle of renewable expansion and economic performance. The study contributes by providing the first systematic evidence for Greece on the nonlinear energy–growth nexus, advancing empirical modeling with NARDL and break-adjusted tests, and highlighting the heterogeneous growth effects of renewable versus non-renewable energy. Full article

Nuclear energy plays a critical role in global decarbonisation, but its expansion raises concerns about the proliferation risks associated with conventional fuel cycles. This study addresses this challenge by evaluating Am-241 doping as a method to enhance the intrinsic proliferation resistance of nuclear fuel. Using full-core simulations across Pressurised Water Reactors (PWRs), Boiling Water Reactors (BWRs), and Molten Salt Reactors (MSRs), the research assesses the impact of Am-241 on isotopic composition, reactor performance, and safety. The results show that Am-241 reliably increases the Pu-238 fraction in spent fuel above the 6% threshold, which significantly complicates its use in nuclear weapons. Additionally, Am-241 serves as a burnable poison, reducing the need for conventional absorbers without compromising operational margins. Economic modelling indicates that the levelised cost of electricity (LCOE) increases modestly, with the most notable impact observed in MSRs due to continuous doping requirements. The project concludes that Am-241 doping offers a passive, fuel-intrinsic safeguard that complements existing verification regimes. Adoption of this approach may require adjustments to regulatory frameworks, particularly in fuel licencing and fabrication standards, but could ultimately support the secure expansion of nuclear energy in regions with heightened proliferation concerns. Full article

Suboptimal placement of charging infrastructure is a major barrier to the transition to sustainable transportation, even with the growing popularity of electric vehicles (EVs). The research addresses this challenge by proposing a novel hybrid genetic algorithm (GA) to solve the NP-hard Multiple-Choice Multidimensional Knapsack Problem (MMKP) for computationally derived optimal charging station placement and configurations in Sapporo, Japan. The methodology leverages high-granularity human flow data to identify charging demand and a Traveling Salesperson Problem (TSP)-based encoding to prioritize potential station locations. A greedy heuristic then decodes this prioritization, selecting charger configurations that maximize service capacity within a defined budget. The results reveal that as the budget increases, the network evolves through distinct phases of concentrated deployment, expansion, and saturation, with a nonlinear increase in covered demand, indicating diminishing returns on investment. The findings demonstrate the efficacy of the proposed model in providing a strategic roadmap for urban planners and policymakers to make cost-effective decisions that maximize charging demand coverage and accelerate EV adoption. Full article

The Yellow River Delta is one of China’s most ecologically fragile regions, experiencing prolonged pressures from rapid urbanization and ecological degradation. Existing research, however, has predominantly focused on constructing ecological security patterns under single scenarios, with limited systematic multi-scenario comparisons and insufficient statistical support. To address this gap, this study proposes an integrated framework of “land use simulation—multi-scenario ecological security pattern construction—statistical comparative analysis.” Using the PLUS model, three scenarios were constructed—Business-as-Usual (BAU), Priority Urban Development (PUD), and Priority Ecological Protection (PEP)—to simulate land use changes by 2040. Habitat quality assessment, Multi-Scale Pattern Analysis (MSPA), landscape connectivity, and circuit theory were integrated to identify ecological source areas, corridors, and nodes, incorporating a novel hexagonal grid partitioning method. Statistical significance was evaluated using parametric tests (ANOVA, t-test) and non-parametric tests (permutation test, PERMANOVA). Analysis indicated significant differences in ecological security patterns across scenarios. Under the PEP scenario, ecological source areas reached 3580.42 km² (12.39% of the total Yellow River Delta), corresponding to a 14.85% increase relative to the BAU scenario and a 32.79% increase relative to the PUD scenario. These gains are primarily attributable to stringent wetland and forestland protection policies, which successfully limited the encroachment of construction land into ecological space. Habitat quality and connectivity markedly improved, resulting in the highest ecosystem stability. By contrast, the PUD scenario experienced an 851.46 km² expansion of construction land, resulting in the shrinkage of ecological source areas and intensified fragmentation, consequently increasing ecological security risks. The BAU scenario demonstrated moderate outcomes, with a moderately balanced spatial configuration. In conclusion, this study introduces an ecological restoration strategy of “five zones, one belt, one center, and multiple corridors” based on multi-scenario ecological security patterns. This provides a scientific foundation for ecological restoration and territorial spatial planning in the Yellow River Delta, while the proposed multi-scenario statistical comparison method provides a replicable methodological framework for ecological security pattern research in other delta regions. Full article

Post-disaster underground (UG) mine environments are characterized by complex and rapidly changing conditions, adding extra attenuation to propagating electromagnetic (EM) waves. One such complex condition is the extreme generation of dust and sudden rise in humidity contributing to extra attenuation effects to propagating waves, especially under varying airborne humidity and dust levels. The existing wave propagation prediction models, especially those that factor in the effect of dust particles, are deterministic in nature, limiting their ability to account for uncertainties, especially during emergency conditions. In this work, the vector parabolic equation (VPE) model is modified to include dust attenuation effects. Using the complex permittivity of dust as a random variable, the Karhunen–Loève (KL) expansion is used to generate random samples of permittivity along the drifts for which each realization is solved using deterministic VPE method. The model is validated using a modified Friis method and experimentally obtained data from literature. The findings show that accounting for dust and humidity effects stochastically captures the extra losses that would have otherwise been lost using deterministic methods. The proposed framework offers key insights for designing resilient underground wireless systems, strengthening miner tracking, and improving safety during emergencies. Full article

Globally escalating electricity demand necessitates substantial power grid capacity expansion. Current transmission line capacity enhancement technologies are seriously constrained by factors including limited accuracy of computational models, elevated line losses, requirements for new line construction, and substantial capital investment. Capacity-uprating conductors, recognized for their superior current-carrying performance and cost-effective retrofitting, represent one of the most viable solutions for transmission augmentation. However, their large-scale deployment remains impeded by increased line losses and high costs. This review systematically analyses critical constraints on transmission line ampacity through computational modeling and elucidates conductor heat dissipation pathways. Based on this foundation, we synthesize recent advancements in capacity-uprating conductors across three key dimensions: structural optimization, material engineering, and passive radiative cooling technologies. We concurrently evaluate their applications in power transmission projects and explore promising future development directions. This review aims to provide a theoretical foundation, guiding next-generation capacity enhancement solutions for grid modernization. Full article

Background: Early hematoma expansion is a major determinant of poor outcomes after spontaneous intracerebral hemorrhage (ICH). Identifying reliable predictors of hematoma expansion may facilitate risk stratification and timely interventions. This study aimed to evaluate clinical, laboratory, and radiological factors associated with early hematoma expansion within 24 h. Methods: We retrospectively analyzed consecutive patients with spontaneous ICH admitted to a tertiary hospital in Korea between 2009 and 2021. Inclusion criteria were aged ≥ 18 years, primary spontaneous ICH, baseline non-contrast CT (NCCT), and follow-up CT within 24 h. Clinical, laboratory, and medication histories were collected, and NCCT/CT angiography (CTA) imaging markers (spot sign, blend sign, hypodensity, swirl sign, black hole sign, island sign, mean hematoma density) were evaluated. Early hematoma expansion was defined as an absolute volume increase ≥6 cm³ or a relative increase ≥33% on follow-up CT. Multivariate logistic regression identified independent predictors. Results: Among 899 screened patients, 581 met inclusion criteria (mean age 61.6 years; 59.7% male). Seventy-eight patients (13.4%) experienced early hematoma expansion. Independent predictors included CTA spot sign (adjusted OR 9.001, 95% CI 4.414–18.354), blend sign (OR 3.054, 95% CI 1.349–6.910), mean hematoma density <60 HU (OR 2.432, 95% CI 1.271–4.655), male sex (OR 2.902, 95% CI 1.419–5.935), and statin use (OR 2.990, 95% CI 1.149–7.782). Prior antiplatelet therapy was associated with a reduced risk of hematoma expansion (OR 0.118, 95% CI 0.014–0.981). Conclusions: Early hematoma expansion occurred in 13.4% of patients and was predicted by a combination of CTA and NCCT markers, as well as clinical and pharmacological factors. Spot sign remained the strongest predictor, while NCCT features such as blend sign and low hematoma density also provided practical prognostic value. These findings underscore the multifactorial pathophysiology of ICH expansion and highlight the importance of integrating imaging, clinical, and therapeutic variables into prediction models to improve early risk stratification and guide targeted interventions. Full article

Frequent channel migrations of the Yellow River, coupled with increasing human disturbances, have driven significant land cover changes in the Yellow River Delta (YRD) over time. Accurate estimation of aboveground biomass (AGB) and clarification of the impact of land cover changes on AGB are crucial for monitoring vegetation dynamics and supporting ecological management. However, field-based biomass samples are often time-consuming and labor-intensive, and the quantity and quality of such samples greatly affect the accuracy of AGB estimation. This study developed a robust AGB estimation framework for the YRD by synthesizing 4717 field-measured samples from the published scientific literature and integrating two critical ecological indicators: leaf area index (LAI) and length of growing season (LGS). A random forest (RF) model was employed to estimate AGB for the YRD from 2001 to 2022, achieving high accuracy (R² = 0.74). The results revealed a continuous spatial expansion of AGB over the past two decades, with higher biomass consistently observed in western cropland and along the Yellow River, whereas lower biomass levels were concentrated in areas south of the Yellow River. AGB followed a fluctuating upward trend, reaching a minimum of 204.07 g/m² in 2007, peaking at 230.79 g/m² in 2016, and stabilizing thereafter. Spatially, western areas showed positive trends, with an average annual increase of approximately 10 g/m², whereas central and coastal zones exhibited localized declines of around 5 g/m². Among the changes in land cover, cropland and wetland changes were the main contributors to AGB increases, accounting for 54.2% and 52.67%, respectively. In contrast, grassland change exhibited limited or even suppressive effects, contributing −6.87% to the AGB change. Wetland showed the greatest volatility in the interaction between area change and biomass density change, which is the most uncertain factor in the dynamic change in AGB. Full article

The expansion of conventional agricultural models in the Colombian Amazon has caused deforestation, biodiversity loss, and socio-environmental degradation. In response, agroecology and bioeconomy are emerging as key strategies to regenerate landscapes and foster sustainable production systems. We evaluated the agroecological performance of 25 farms in the Andean–Amazon transition zone of Colombia using FAO’s Tool for Agroecology Performance Evaluation (TAPE). The analysis included land cover dynamics (2002–2024), characterization of the agroecological transition based on the 10 Elements of Agroecology, and 23 economic, environmental, and social indicators. Four farm typologies were identified; among them, Mixed Family Farms (MFF) achieved the highest transition score (CAET = 60.5%) and excelled in crop diversity (64%), soil health (SHI = 4.24), productive autonomy (VA/GVP = 0.69), and household empowerment (FMEF= 85%). Correlation analyses showed strong links between agroecological practices, economic efficiency, and social cohesion. Land cover dynamics revealed a continuous decline in forest cover (12.9% in 2002 to 7.1% in 2024) and an increase in secondary vegetation, underscoring the urgent need for restorative approaches. Overall, farms further along the agroecological transition were more productive, autonomous, and socially cohesive, strengthening territorial resilience. The application of TAPE proved robust multidimensional evidence to support agroecological monitoring and decision-making, with direct implications for land use planning, rural development strategies, and sustainability policies in the Amazon. At the same time, its sensitivity to high baseline biodiversity and to the complex socio-ecological dynamics of the Colombian Amazon underscores the need to refine the methodology in future applications. By addressing these challenges, the study contributes to the broader international debate on agroecological transitions, offering insights relevant for other tropical frontiers and biodiversity-rich regions facing similar pressures. Full article

Differential tracked robots play a crucial role in various modernized work scenarios such as smart industry, agriculture, and transportation. However, these robots frequently encounter substantial challenges in trajectory tracking, attributable to substantial initial errors and dynamic environments, which result in slow convergence rates, cumulative errors, and diminished tracking precision. To address these challenges, this paper proposes a fuzzy adaptive virtual steering coefficient model predictive control (FVSMPC) algorithm. The FVSMPC algorithm introduces a virtual steering coefficient into the robot’s kinematic model, which is adaptively adjusted using fuzzy logic based on real-time positional error and velocity. This approach not only enhances the robot’s ability to quickly correct large errors but also maintains stability during tracking.The nonlinear kinematic model undergoes linearization via a Taylor expansion and is subsequently formulated as a quadratic programming problem to facilitate efficient iterative solutions. To validate the proposed control algorithm, a simulation environment was constructed and deployed on a real prototype for testing. Results demonstrate that compared to the baseline algorithm, the proposed algorithm performs excellently in trajectory tracking tasks, avoids complex parameter tuning, and exhibits high accuracy, fast convergence, and good stability. This work provides a practical and effective solution for improving the trajectory tracking performance of differential tracked robots in complex environments. Full article

With the rapid expansion of the global timber trade, accurate wood identification has become essential for regulating ecosystems and combating illegal logging. Traditional methods, largely reliant on manual analysis, are inadequate for large-scale, high-precision demands. A multi-architecture fusion network model that combines generative adversarial networks and one-dimensional convolutional neural networks aims to solve the problems in data quality and the challenges in classification accuracy existing in the classification process of pine tree species. The generative adversarial network is used to improve the data, which effectively expands the scale of the training set. Moreover, the one-dimensional convolutional neural network is utilized to extract local and global features from the spectral data, which improves the classification accuracy of the model and also makes the model more stable. The results obtained from the experiment show that MAFNet can achieve an accuracy rate of 99.63% in the classification of pine species. The model performed best on cross-sectional data. The research finds that MAFNet, relying on the strategy of integrating data enhancement and deep feature extraction, provides strong technical support for the rapid, accurate and non-destructive identification of pine species. Full article

Greater Casablanca, one of Africa’s largest metropolitan regions, is undergoing significant spatial and demographic transformation. Yet, the underlying patterns of these dynamics remain poorly understood. This study investigates population dynamics and spatial inequalities in Greater Casablanca between 2014 and 2024. The analysis combines geospatial data, regression modeling, and clustering techniques to explore the interplay between demographic change, housing affordability, public-transport accessibility, and economic activity, providing a data-driven perspective on how these factors shape spatial inequalities and the region’s urban development trajectory. The results reveal a clear core–periphery divide. The central prefecture has lost population despite continued land consumption, while peripheral communes have experienced rapid demographic and economic expansion. This growth is strongly associated with affordable housing and high rates of new-firm formation, but it occurs where transport access remains weakest. Cluster analysis identifies four socio-spatial types, ranging from a shrinking but well-served core to fast-growing, poorly connected peripheries. The study underscores the need for integrated policy interventions to improve transport connectivity, implement inclusive housing strategies, and manage economic decentralization in ways that foster balanced and sustainable metropolitan development. By situating Greater Casablanca’s trajectory within global urbanization debates, this research extends core–periphery and shrinking-city frameworks to a North African context and provides evidence-based insights to support progress towards Sustainable Development Goal 11. Full article

The integration of wood and concrete in building structures is a well-established practice typically realized through mechanical connectors. However, the thermomechanical behavior of wood–concrete composite façades assembled via adhesive bonding remains underexplored. This study introduces a novel concept—the adhesive-bonded wood–concrete façade, termed “Hybrimur”—and evaluates the response of these façade panels under thermal gradients, with a focus on thermal bowing phenomena. Four full-scale façade prototypes (3 m high × 6 m wide), consisting of 7 cm thick concrete and 16 cm thick laminated timber (GL24h), were fabricated and tested both with and without insulation. Two reinforcement types were considered: fiberglass-reinforced concrete and welded mesh reinforcement. The study combines thermal analysis of temperature gradients at the adhesive interface with analytical and numerical methods to investigate thermal expansion effects. The experimental and numerical results revealed thermal strains concentrated at the wood–concrete interface without inducing panel failure. Thermal bowing (out-of-plane deflection) exhibited a nonlinear behavior influenced by the adhesive bond and the anisotropic nature of the wood. These findings highlight the importance of accounting for both interface behavior and wood anisotropy in the design of hybrid façades subjected to thermal loading. A tentative finite element model is proposed that utilizes isotropic wood with properties that limit the accuracy of the results obtained by the model. Full article

Habitat alteration due to agricultural expansion and heavy livestock grazing is a major threat for open natural ecosystems (ONEs). Within the Indian Thar Desert, such land-use transformations are altering native grassland habitats, with consequential effects on insect communities that perform vital ecological functions and support higher trophic levels. Between 2020 and 2022, we surveyed a 641 km² area, using belt transect and visual detection methods, to quantify insect densities at the order level across different seasons. Linear mixed-effect (LME) models revealed that the orthopteran insect densities, primarily grasshoppers, were significantly higher in grasslands compared to agriculture and barren lands and were lower in the presence of livestock grazing. Orthopteran densities were higher and showed strong seasonal dependencies, likely driven by rainfall-mediated vegetation growth during monsoons. Intense grazing and agricultural expansion reduced vegetation biomass and resource availability, which affected the insect populations negatively. These research findings underscore the urgent need to implement ecologically sensitive land management practices, including sustainable grazing regimes and grassland conservation, to maintain insect biodiversity and the broader ecological network. Given the role of insects in ecosystem functioning and their importance to conservation dependent species of, such as the critically endangered Great Indian Bustard (Ardeotis nigriceps), these findings underscore the ecological significance of preserving native grassland habitats in the Thar Desert landscape. Full article