Search Results (1,562)

Urban morphology plays a decisive role in regulating microclimate and outdoor thermal comfort in arid cities, where extreme heat and intense solar radiation amplify thermal stress. This study examines the influence of four contrasting urban fabrics in Béchar (Algerian Sahara): the vernacular Ksar, the regular-grid colonial fabric, a modern large-scale residential estate, and low-density detached housing, on local microclimatic conditions. An integrated methodological framework is adopted, combining qualitative morphological analysis, quantitative indicators including density, porosity, height-to-width ratio, and sky view factor, in situ microclimatic measurements, and high-resolution ENVI-met simulations performed for the hottest summer day. Results show that compact urban forms, characterized by low sky view factor values, markedly reduce radiative exposure and improve thermal performance. The vernacular Ksar, exhibiting the lowest SVF, records the lowest mean radiant temperature (approximately 45 °C) and the most favorable average comfort conditions (PMV = 3.77; UTCI = 38.37 °C), representing a reduction of about 3 °C, while its high-thermal-inertia earthen materials ensure effective nocturnal thermal recovery (PMV ≈ 1.06; UTCI = 27.8 °C at 06:00). In contrast, more open modern fabrics, including the colonial grid, large-scale estates, and low-density housing, experience higher thermal stress, reflecting vulnerability to solar exposure and limited thermal inertia. Validation against field measurements confirms model reliability. These findings highlight the continued relevance of vernacular bioclimatic principles for sustainable urban design in arid climates. Full article

Ganoderma sichuanense is a widely studied medicinal mushroom, but the production of its antioxidant proteins has been scarcely evaluated. We assess the influence of different concentrations of culture media components under submerged fermentation, with and without agitation, on production of proteins with antioxidant activity from the mycelial biomass of G. sichuanense. Protein extracts were characterized by scanning electron microscopy, X-ray diffraction, and attenuated total reflectance Fourier-transform infrared spectroscopy. They were also analyzed for total protein and phenolic contents, antioxidant activities (ABTS^•+, DPPH^•, chelating ability, and reducing power), and electrophoretic profiles by SDS-PAGE. The most active extract was tested for cytoprotective potential under H₂O₂-induced oxidative stress in Saccharomyces cerevisiae. Growth kinetics of the best fermentation condition were also analyzed. Microstructural differences ranged from fibrillar to aggregated forms, depending on cultivation. Crystallinity was unaffected, but chemical differences and secondary structure organization were confirmed by infrared spectroscopy. The extract from the static culture with 10 g·L⁻¹ glucose, 5 g·L⁻¹ yeast extract, and 2.5 g·L⁻¹ soy peptone (referred as CM1S) showed the highest protein and phenolic contents and the strongest antioxidant activity (IC₅₀ = 4.8 and 24.0 µg of protein·mL⁻¹ for ABTS^•+ and DPPH^•, respectively). SDS-PAGE revealed higher protein band intensities in static cultures. CM1S showed potential to protect yeast cells from oxidative stress. The Gompertz model estimated a specific growth rate of 0.0068 h⁻¹ in CM1S. The findings highlight a cultivation strategy that modulates fungal metabolism and improves the recovery of antioxidant proteins from G. sichuanense biomass. Full article

The Late Cretaceous to Paleocene magmatic evolution along the southern margin of the Lhasa Terrane records a critical transition from oceanic subduction to continental collision, yet its western segment remains underexplored. This study presents integrated petrographic, zircon U–Pb geochronological, zircon Hf isotopic, whole-rock geochemical, and Sr–Nd isotopic data for three distinct phases of intermediate to felsic intrusions from the Qiuwo area in the western segment of the southern Lhasa terrane. The results reveal three distinct magmatic pulses: an early granodiorite emplaced at 89.9 ± 0.75 Ma, followed by a diorite crystallizing at 68.6 ± 0.56 Ma, and a late-stage granodiorite forming at 56.75 ± 0.43 Ma. All three rock units are metaluminous to weakly peraluminous (A/CNK < 1.1), sodic (Na₂O > 3.2 wt.%), and dominated by amphibole, with zircon saturation temperatures of 737–786 °C, consistent with I-type granitoid affinity. All units are metaluminous (A/CNK = 0.92–1.00), calc-alkaline to high-K calc-alkaline, and enriched in LILE (K, Th, Rb) while depleted in HFSE (Nb, Ta, P, Ti), with moderate ΣREE (81–130 ppm), elevated (La/Yb)_N (9.3–15.8), and negative Eu anomalies (δEu = 0.70–0.89). The early granodiorite is Na-rich (Na₂O/K₂O = 1.6), whereas the Paleocene granodiorite shows elevated K₂O (3.2 wt.%) and reduced Na₂O/K₂O (~1.0), reflecting progressive crustal thickening and increasing magmatic differentiation. Zr and Hf are relatively enriched, and Sr/Y ratios decrease from 39 to 21, consistent with evolving magmatic conditions from deeper crustal melting in the Late Cretaceous to shallower, more evolved sources in the Paleocene. Zircon Hf isotopes reveal consistently positive εHf(t) values (+10.4 to +4.9), indicating derivation from juvenile basaltic lower crust. Sr–Nd isotopic data further demonstrate a systematic evolution: εNd(t) decreases from +2.7 to −0.1, while (⁸⁷Sr/⁸⁶Sr)_i increases from 0.7044 to 0.7055, reflecting progressive incorporation of ancient crustal components into the magma source from the early Late Cretaceous to the Paleocene. These findings indicate that the Qiuwo intrusions formed by partial melting of a juvenile basaltic lower crust, with increasing crustal contamination during ascent and emplacement. The temporal progression of magmatism—spanning the waning stages of Neo-Tethyan subduction to the initial India–Eurasia collision (~55 Ma)—supports a model in which slab breakoff and lithospheric delamination triggered decompression melting of the lower crust, while assimilation of older crustal materials intensified as the continental collision progressed. This work provides key geochemical evidence for the transition from arc to post-collisional magmatism in the western Gangdese belt and refines the timing and mechanism of crustal growth in southern Tibet. Full article

To address active power fluctuations in power grids induced by high renewable energy penetration and overcome the limitations of existing probabilistic power flow (PPF) methods that ignore generator frequency regulation constraints, this paper proposes a segmented stochastic power flow modeling method and an efficient analytical framework that incorporates the actions and capacity constraints of regulation units. Firstly, a dual dynamic piecewise linear power injection model is established based on “frequency deviation interval stratification and unit limit-reaching sequence ordering,” clarifying the hierarchical activation sequence of “loads first, followed by conventional units, and finally automatic generation control (AGC) units” along with the coupled adjustment logic upon reaching limits, thereby accurately reflecting the actual frequency regulation process. Subsequently, this model is integrated with the State-Independent Linearized Power Flow (DLPF) model to develop a segmented stochastic power flow framework. For the first time, a deep integration of unscented transformation (UT) and regulation-aware power allocation is achieved, coupled with the Nataf transformation to handle correlations among random variables, forming an analytical framework that balances accuracy and computational efficiency. Case studies on the New England 39-bus system demonstrate that the proposed method yields results highly consistent with those of Monte Carlo simulations while significantly enhancing computational efficiency. The DLPF model is validated to be applicable under scenarios where voltage remains within 0.95–1.05 p.u., and line transmission power does not exceed 85% of rated capacity, exhibiting strong robustness against parameter fluctuations and capacity variations. Furthermore, the method reveals voltage distribution patterns in wind-integrated power systems, providing reliable support for operational risk assessment in grids with high shares of renewable energy. Full article

The study examines the theoretical and practical dimensions of Community-Supported Agriculture (CSA). Its objective is to assess whether social capital theory explains food producers’ engagement in CSA and whether this is reflected in practice. The research is based on a critical review of the relevant literature and on empirical investigations conducted in Poland among CSA producers using the CAWI method in 2024. The findings indicate that social capital theory plays a fundamental role in explaining the mechanisms underpinning CSA, with significant implications for the development of local food systems and for policies supporting small farms. This suggests the need for stronger institutional support aimed at enhancing trust and cooperation between food producers and consumers. Unfortunately, due to the low level of social capital in Poland, the CSA model remains only a niche complement to traditional forms of agriculture, functioning primarily as an alternative for a narrow group of socially and environmentally conscious consumers and small clusters of producers. Full article

This paper examines how entrenched dichotomies between natural/cultural and tangible/intangible heritage shape conservation policy in contexts where material, ecological, and socio-cultural dimensions are deeply interdependent. Despite calls for more integrated frameworks, limited empirical research examines how such categorical divisions operate within living rural heritage environments or how they align with community perspectives and inform conservation policies. This paper addresses this gap by analysing the persistence of rigid heritage taxonomies in officially designated traditional villages in central rural Greece exploring how heritage officials and residents perceive and value their heritage. Drawing on qualitative fieldwork across six villages—including interviews, focus groups, and consultations with heritage professionals—it demonstrates that these categorical divisions fail to reflect local understandings of heritage as a living socio-ecological system. For residents, ecological conditions, built forms, agricultural practices, and social relations are interdependent and tied to livelihoods, land stewardship, and communal identity. Findings show that the Authorized Heritage Discourse (AHD) reinforces artificial separations that are misaligned with grassroots perceptions and hinder integrated planning. This mismatch has direct implications for sustainability: governance models that fragment nature, culture, and community obscure socio-ecological processes and limit the development of meaningful sustainability strategies. By foregrounding experiential and affective dimensions of heritage, the paper advances debates on community-centred and context-responsive heritage management. It reframes rural heritage as a co-produced, evolving system of material, social, and ecological relations, positioning rural landscapes as critical arenas for addressing sustainability challenges and offering new empirical insights into an underexplored dimension of heritage research. The study further advocates for governance approaches that embed local ontologies and lived knowledge into policy and practice, fostering more inclusive, resilient, and socio-ecologically grounded heritage frameworks capable of supporting long-term sustainable development. Full article

Semantic segmentation of crowdsourced street-level imagery plays a critical role in urban analytics by enabling pixel-wise understanding of urban scenes for applications such as walkability scoring, environmental comfort evaluation, and urban planning, where robustness to geometric transformations and projection-induced symmetry variations is essential. This study presents a comparative evaluation of two primary families of semantic segmentation models: transformer-based models (SegFormer and Mask2Former) and prompt-based models (CLIPSeg, LangSAM, and SAM+CLIP). The evaluation is conducted on images with varying geometric properties, including normal perspective, fisheye distortion, and panoramic format, representing different forms of projection symmetry and symmetry-breaking transformations, using data from Google Street View and Mapillary. Each model is evaluated on a unified benchmark with pixel-level annotations for key urban classes, including road, building, sky, vegetation, and additional elements grouped under the “Other” class. Segmentation performance is assessed through metric-based, statistical, and visual evaluations, with mean Intersection over Union (mIoU) and pixel accuracy serving as the primary metrics. Results show that LangSAM demonstrates strong robustness across different image formats, with mIoU scores of 64.48% on fisheye images, 85.78% on normal perspective images, and 96.07% on panoramic images, indicating strong semantic consistency under projection-induced symmetry variations. Among transformer-based models, SegFormer proves to be the most reliable, attains higher accuracy on fisheye and normal perspective images among all models, with mean IoU scores of 72.21%, 94.92%, and 75.13% on fisheye, normal, and panoramic imagery, respectively. LangSAM not only demonstrates robustness across different projection geometries but also delivers the lowest segmentation error, consistently identifying the correct class for corresponding objects. In contrast, CLIPSeg remains the weakest prompt-based model, with mIoU scores of 77.60% on normal images, 59.33% on panoramic images, and a substantial drop to 59.33% on fisheye imagery, reflecting sensitivity to projection-related symmetry distortions. Full article

This study established a marine atmospheric corrosion prediction model by comparing the corrosion behavior of 7075 aluminum alloy in neutral salt spray tests and outdoor exposure tests conducted in the coastal atmosphere of Hainan. The results show that severe rusting occurred after 96 h of neutral salt spray testing, with loose white cluster-like corrosion products mainly composed of Al(OH)₃ and Al₂O₃. The thickening of the corrosion product layer slowed down the corrosion process, following a nonlinear power-law kinetic relationship. In the later stage, potential dropped sharply due to product layer spallation, and recovered as new corrosion products formed, confirming that the stability of the product layer is critical for protection. Under coastal atmospheric exposure, the composition of corrosion products was similar to that observed in the salt spray test, but the actual corrosion rate was affected by environmental dynamic equilibrium. The acceleration factor of the neutral salt spray test corresponding to the same corrosion amount in the Hainan marine atmosphere exhibited a declining trend, reflecting that differences in the protective nature of the corrosion product layer were influenced by environmental factors. Electrochemical analysis indicated that both tests showed similar current–potential synergistic variation mechanisms dominated by product layer stability. In summary, while the neutral salt spray test effectively simulates the chloride-induced corrosion mechanism in marine atmospheres, its kinetic model cannot directly predict real corrosion behavior through a simple acceleration factor, as environmental complexity must be considered. Full article

LGBTQ+ persons and related issues remain barely visible in German academia, reflecting limited acceptance as well as heteronormative and discriminatory structures. This invisibility negatively affects career trajectories, well-being, and protection from discrimination of LGBTQ+ academics, while also hindering research on LGBTQ+-related social issues, injustices and forms of disadvantage. In addition, LGBTQ+ students lack important role models. To examine this exploratory finding more systematically, this project was carried out in collaboration with the Magnus Hirschfeld Federal Foundation, collecting written responses from 26 German academic institutions to a set of open-ended questions delivered by email on the visibility of LGBTQ+ persons. The responses were analysed using qualitative content analysis. Our findings show that most institutions perceive the visibility of LGBTQ+ academics as low and recognise a need for new networking opportunities. Germany thus provides a valuable example of the dynamics of LGBTQ+ inclusion in continental Europe, where diversity has historically played a less prominent role than in US and UK contexts, and where cross-national comparisons suggest significant variation in levels of inclusivity. Full article

Post-industrial waterfronts have become key arenas of urban transformation, where heritage, public space and social equity intersect. This study examined the Xuhui Waterfront in Shanghai under the ‘One River, One Creek’ initiative, which converted former industrial land into a continuous riverfront corridor of parks and cultural venues. The research aimed to evaluate whether this large-scale renewal enhanced social equity or produced new forms of exclusion. A tripartite analytical framework of distributive, procedural and recognitional justice was applied, combining spatial mapping, remote-sensing analysis of vegetation and heat exposure, housing price-to-income ratio assessment, and policy review from 2015 to 2024. The results showed that the continuity of the riverfront, increased greenery and adaptive reuse of industrial structures improved accessibility, environmental quality and cultural enjoyment. However, housing affordability became increasingly polarised, indicating emerging gentrification and generational inequality. This study concluded that this dual outcome reflected the fiscal dependency of state-led renewal on land-lease revenues and high-end development. It suggested that future waterfront projects could adopt financially sustainable yet inclusive models, such as incremental phasing, public–private partnerships and guided self-renewal, to better reconcile heritage conservation, public-space creation and social fairness. Full article

Galvanized steel sheets are vital anti-corrosion materials, yet their surface quality is prone to defects that impact performance. Manual inspection is inefficient, while conventional machine vision struggles with complex, small-scale defects in industrial settings. Although deep learning offers promising solutions, standard object detection models like YOLOv5 (which is short for ‘You Only Look Once’) exhibit limitations in handling the subtle textures, scale variations, and reflective surfaces characteristic of galvanized sheet defects. To address these challenges, this paper proposes S-C-B-YOLO, an enhanced detection model based on YOLOv5. First, a Squeeze-and-Excitation (SE) attention mechanism is integrated into the deep layers of the backbone network to adaptively recalibrate channel-wise features, improving focus on defect-relevant information. Second, a Transformer block is combined with a C3 module to form a C3TR module, enhancing the model’s ability to capture global contextual relationships for irregular defects. Finally, the original path aggregation network (PANet) is replaced with a bidirectional feature pyramid network (Bi-FPN) to facilitate more efficient multi-scale feature fusion, significantly boosting sensitivity to small defects. Extensive experiments on a dedicated galvanized sheet defect dataset show that S-C-B-YOLO achieves a mean average precision (mAP@0.5) of 92.6% and an inference speed of 62 FPS, outperforming several baseline models including YOLOv3, YOLOv7, and Faster R-CNN. The proposed model demonstrates a favorable balance between accuracy and speed, offering a robust and practical solution for automated, real-time defect inspection in galvanized steel production. Full article

Acceptance and Commitment Therapy (ACT) has been widely applied in clinical contexts; however, its systematic adaptation to elite sports, particularly precision-based disciplines such as shooting, remains underexplored. The present study aimed to develop and preliminarily validate an ACT-based psychological training program—the Acceptance and Commitment Performance Training for Shooters (ACPT-S)—by reframing ACT from a therapeutic intervention into a performance-oriented training framework. Using a multiphase formative evaluation design, a needs assessment was first conducted with 28 elite and collegiate shooters to identify sport-specific psychological demands. Based on these findings, a ten-session ACPT-S program was developed by integrating the six core ACT processes with shooter-specific routines, embodied exercises, and performance-relevant metaphors. The program was subsequently examined through two pilot studies: Phase 1 with four collegiate/corporate athletes and Phase 2 with 15 national-level shooters. Data were collected via session reflections, focus group interviews, and expert panel evaluations, and the Content Validity Ratio (CVR) analysis was used to assess conceptual clarity and implementation feasibility. The results indicated that ACPT-S was perceived as both feasible and contextually appropriate, with athletes reporting improvements in attentional focus, emotional acceptance, value-based motivation, and reduced anxiety. Qualitative analyses demonstrated strong engagement with ACT principles and their functional integration into shooting performance contexts, while all program components achieved CVR scores of ≥0.80, indicating a strong expert consensus. Program refinements were guided by feedback related to activity sequencing, metaphor resonance and personalization strategies. Overall, this study reconceptualizes ACT as a performance-enhancement framework rather than a purely clinical approach and introduces the ACPT-S as a novel, theory-driven, and scalable psychological training model for precision sports, providing a robust foundation for future longitudinal and comparative research. Full article

Forested areas in Poland comprise numerous post-mining sites that hinder effective forest management. Such mining remnants may pose a threat to humans, animals, and operating forest machines. This study aimed to determine the feasibility of inventorying such man-made landforms as mining waste heaps, excavations, remnants of shallow shafts, adits, etc., using the Digital Elevation Model (DEM) based on Airborne Laser Scanning (ALS) data provided by the national agency (the Head Office of Geodesy and Cartography—HOGC) as open data. The DEM, when combined with other cartographic materials using GIS, accurately reflects the anthropogenic transformation evident in the topography. This paper presents the results of inventorying remnants of iron ore mining in the present-day forested area located between Krzepice, Kłobuck, and Częstochowa in southern Poland. The identification and inventory of post-mining landforms, mainly mounds resulting from shallow shaft mining operations, were supplemented by their digitization, automatically providing information on parameters such as perimeter (ranged in most cases from 24.3 to 159 m), surface area (46.9 to 1656 m²), length and width (7.8 to 59.2 m). The heights of the investigated structures were also read from the DEM, ranging from 0.3 to 4.1 m. Much larger structures were also identified, but they occurred accidentally (up to 23.5 m in height). In this manner, approximately 823 morphological forms were characterized, resulting in a database. Test fieldwork was then conducted to verify the DEM readings. It was proposed to calculate deformation indexes (Id [%]) for forested areas and apply them when estimating the forest management hindrance index used by the State Forests. The studied forest compartments managed by State Forests were characterized by an Id value from 0.1 to 55.5%. This type of measure provides a helpful tool in planning forestry operations in areas with diverse topography, including those transformed by mining activities. The actual environmental impact is highlighted. Forest management practices in the study area must take into consideration, in particular, topography, as well as geology and hydrology. Studies have shown that the DEM based on the ALS data is sufficiently accurate to detect even minor post-mining deformations (which may be important, in particular, in inaccessible areas). The recorded parameters can be considered when planning management, protection interventions, or reclamation activities. Full article

This study introduces the Hybrid Integrated Prediction-Error Reconstruction-based Anomaly Detection (HIPER-CHAD) model, which addresses the challenge of reliably detecting subtle anomalies in noisy multivariate indoor environmental time-series data. The main objective is to separate temporal modeling of normal behavior from probabilistic modeling of prediction uncertainty, ensuring that the anomaly score becomes robust to stochastic fluctuations while remaining sensitive to truly abnormal events. The HIPER-CHAD architecture first employs a Long Short-Term Memory (LSTM) network to forecast the next time step’s sensor readings, subsequently forming a residual error vector that captures deviations from the expected temporal pattern. A Variational Autoencoder (VAE) is then trained on these residual vectors rather than on the raw sensor data to learn the distribution of normal prediction errors and quantify their probabilistic unicity. The final anomaly score integrates the VAE’s reconstruction error with its Kullback–Leibler (KL) divergence, yielding a statistically grounded measure that jointly reflects the magnitude and distributional abnormality of the residual. The proposed model is evaluated on a real-world multivariate indoor environmental dataset and compared against eight traditional machine learning and deep learning baselines using a synthetic ground truth generated by a 99th percentile-based criterion. HIPER-CHAD achieves an F1-score of 0.8571, outperforming the next best model, the LSTM Autoencoder (F1 = 0.8095), while maintaining perfect recall. Furthermore, a time-step sensitivity analysis demonstrates that a 20-step window yields an optimal F1-score of 0.884, indicating that the proposed residual-based hybrid design provides a reliable and accurate framework for anomaly detection in complex multivariate time-series data. Full article

Rodents, as a core component of desert ecosystems and an important indicator of environmental changes, are ideal subjects for studying the impacts of fluctuations in climatic conditions on wildlife. Based on field data from the southern Alxa Desert (2014–2020), this study constructed an ecosystem structure network integrating local/metacommunities, climate, soil, and plant communities. Combined with structural equation modeling, we explored the response mechanisms of rodent communities to climatic conditions across multiple scales. The results showed the following: the α-diversity of local and metacommunities exhibited convergent seasonal patterns, with greater impacts from human disturbances than interannual effects, as well as coexisting species turnover and nesting in metacommunities. Precipitation directly affected metacommunity abundance and diversity and indirectly influenced both community types via vegetation, while temperature directly regulated community characteristics; metacommunities were formed via the coupling of local communities through species migration and habitat filtering, reflecting complex links between local and regional processes. This research provides scientific support for predicting desert ecosystem dynamics and guiding conservation management. Full article