Search Results (27,842)

The Arteni volcanic complex (Armenia) represents a distinctive volcanic landscape characterized by well-preserved pyroclastic deposits, rhyolitic domes, extensive obsidian flows, and significant archaeological evidence. This study aims to evaluate the geoheritage value of the complex and to develop a scientifically grounded geotouristic trail model based on the targeted selection of representative sites. Field-based investigations were integrated with a simplified semi-quantitative assessment of selected sites and Geographic Information System (GIS)-supported spatial analysis, including topographic, viewshed, and accessibility analyses. The results allowed for the selection of nine representative sites, effectively representing the principal stages of volcanic evolution, including explosive eruptions, lava flow emplacement, and dome formation. Spatial analysis demonstrates that the selected sites enable the development of a coherent, accessible, and scientifically meaningful geotouristic route while balancing scientific representativeness with visitor accessibility and safety. In addition, the widespread occurrence of obsidian and associated archaeological artifacts highlights the combined geological and cultural significance of the area. The proposed approach provides a transferable framework for the development of geotourism in volcanic regions and contributes to geoheritage conservation, geoeducation, and sustainable regional development. Full article

Accurate prediction of contaminant transport and self-purification processes in rivers remains challenging because pollutant dispersion, biochemical reactions, and hydrodynamic conditions interact across multiple spatial scales. This study aims to develop and compare mathematical models for soluble contaminant transport and biodegradable organic matter removal in channels and rivers. Unsteady advection–diffusion–reaction equations were formulated for one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) transport scenarios and solved through numerical techniques based on the transformation of partial differential equations into systems of ordinary differential or algebraic equations. In parallel, the classical Streeter–Phelps model and an extended formulation incorporating turbulent diffusion were implemented to evaluate organic load degradation and oxygen deficit dynamics. Simulations were performed using a Matlab R2019a-based computational framework under representative hydraulic and reaction conditions obtained from literature data and empirical correlations. The results showed that, under specific conditions, the 3D model reproduced trends comparable to those predicted by the 2D model, while the latter approached the behavior of the 1D formulation. The Streeter–Phelps model predicted an organic load removal efficiency of 97.74%, a purification index of 1.9564, a critical time of 18.43 h, and a critical distance of 6.93 km. These findings provide a useful framework for river water-quality assessment and support future applications involving complex hydrodynamic and pollutant-loading scenarios. Full article

Background: Cutaneotrichosporon jirovecii is an under-characterized basidiomycetous yeast within the family Trichosporonaceae. Its taxonomic placement, ecological distribution, and functional potential remain incompletely understood because genome-scale resources for C. jirovecii and closely related lineages are limited. Methods: We characterized strain H0426_7, a C. jirovecii-related yeast recovered from a human fecal sample, using ITS-based type-strain comparison, ITS phylogenetic analysis, whole-genome sequencing, average nucleotide identity analysis, read-level assessment of public C. jirovecii-labeled datasets, and comparative functional annotation. Antifungal susceptibility was assessed using the Sensititre YeastOne plate. Results: The ITS sequence of H0426_7 closely matched type-strain material of C. jirovecii, including CBS 6864 and its equivalent deposits. The ITS-based tree placed H0426_7 adjacent to CBS 6864 with bootstrap support of 87%. The final draft genome comprised 38.66 Mb in 1974 contigs, with a GC content of 63.76% and BUSCO completeness of 80.0%. ANI analysis showed that H0426_7 was genomically distinct from the recognized Cutaneotrichosporon species included in the ANI analysis but highly similar to two unclassified feces-derived strains, P10-008 and PK4640, with ANI values exceeding 98.8%. Two public datasets labeled as C. jirovecii showed anomalously low ANI values with H0426_7; read-level taxonomic profiling indicated low target-fungal read proportions, suggesting that these datasets are unsuitable as definitive genome-level references. CAZyme annotation identified 285 family assignments in H0426_7, representing 278 non-redundant predicted proteins, including relatively high GH5 and GH31 counts, suggesting candidate carbohydrate-utilization features shared with the H0426_7/P10-008/PK4640 lineage. Conclusions: H0426_7 is best described as a C. jirovecii-related Cutaneotrichosporon isolate pending availability of a high-quality genome assembly from the C. jirovecii type strain. This study expands genome-scale resources for underrepresented basidiomycetous yeasts and provides a comparative framework for future taxonomic, ecological, and functional studies of feces-associated Cutaneotrichosporon lineages. Full article

Thin-film thermocouples (TFTCs) offer conformal sensing junctions with minimal thermal mass, enabling rapid transient response and direct deposition on curved or moving components, which are difficult to achieve using conventional wire thermocouples in applications such as high-speed machining, electric powertrain thermal management, and fuel-cell monitoring. In practical deployment, the effective accuracy of a TFTC can also be affected by the measurement setup used for calibration and testing, particularly lead-wire material transitions, cold-junction compensation, and wiring-related thermoelectric offsets. This study presents a systematic static calibration and performance evaluation of NiCr-based TFTCs under standardised laboratory conditions, with repeated measurements across the 20–260 °C range using both copper leads and matched compensation wires. The thermoelectric output exhibits excellent linearity; temperature reconstruction against a traceable standard reference yields a maximum deviation of approximately 0.27 °C, with root-mean-square and relative errors within tight bounds. Short-term extended-range verification up to 1000 °C confirms detectable thermoelectric signal generation under the present test conditions. A calibration data packet framework containing the calibrated TFTC sample, wiring configuration, calibration coefficients, validity range, and a GUM-compliant uncertainty budget is proposed to support consistent interpretation of calibration results in future digital integration. The study therefore provides a structured calibration workflow and uncertainty-reporting basis for the tested flexible NiCr-based TFTC configurations, supporting further reliability assessment, material-level characterisation, and digital integration. Full article

Quantitative phenotyping of pepper seedlings is important for greenhouse plug tray seedling cultivation, but it remains constrained by inefficient manual monitoring, complex greenhouse backgrounds, and growth-stage-dependent discrepancies between two-dimensional image traits and actual leaf biomass. In this study, a cascaded vision framework with stage-specific morphological correction was developed for nondestructive seedling phenotyping. The framework integrated Visual Dynamic Momentum YOLO (VDM-YOLO) for individual seedling localization and growth-stage recognition, Variance Guided Strip Ghost Gated UNet (VSG-UNet) for lightweight, high-resolution leaf segmentation, and a stage-aware correction model for leaf dry biomass estimation. In performance evaluation, VDM-YOLO achieved a mean average precision at an intersection over union threshold of 0.5 (mAP_0.5) of 89.27%, improving mAP_0.5 by 1.82 percentage points over YOLOv12. VSG-UNet achieved a mean intersection over union (mIoU) of 83.9% and a Dice coefficient of 81.8%, while reducing floating point operations (FLOPs) and parameters by 44.2% and 61.2%, respectively, compared with U-Net. After stage-aware calibration, the coefficient of determination (R²) between segmented area and leaf dry weight increased from 0.764 to 0.813, and the root mean square error (RMSE) decreased from 0.0210 g to 0.0190 g. These results demonstrated that the proposed framework provided a proof of concept approach based on RGB images for the nondestructive assessment of leaf area and leaf dry biomass in pepper seedlings under restricted experimental conditions. Full article

Retrofitting existing buildings has become a critical strategy for reducing energy consumption, improving thermal comfort, and achieving carbon reduction targets in the built environment. Among retrofit measures, wall insulation plays a pivotal role in minimizing heat loss and enhancing building energy efficiency. Conventional insulation materials, although effective, are often associated with high embodied energy, limited recyclability, and environmental concerns. Consequently, bio-based composite materials derived from natural fibers, agricultural residues, and renewable binders have emerged as promising sustainable alternatives. However, the moisture sensitivity of lignocellulosic materials remains a major challenge that can compromise thermal performance, durability, and long-term service life. This review provides a comprehensive and critical assessment of bio-based hydrophobic composite panels for wall insulation in retrofit applications. Unlike previous reviews that have primarily examined bio-based insulation materials, natural-fiber composites, or hydrophobic modifications separately, this study integrates these interconnected research domains within a unified framework. The review systematically examines raw material selection, composite panel manufacturing processes, hydrophobic surface-engineering strategies, thermal and moisture-related performance, durability characteristics, retrofit implementation approaches, and sustainability considerations. The analysis demonstrates that hydrophobic modification significantly reduces moisture uptake, enhances dimensional stability, and preserves thermal-insulation performance under varying environmental conditions. Natural-fiber-based composites, including hemp, flax, jute, bamboo, coconut fiber, and agricultural residues, exhibit competitive thermal conductivity (λ) values while offering reduced environmental impacts compared with conventional insulation materials. Furthermore, the integration of advanced hydrophobic treatments improves resistance to water penetration, biological degradation, and freeze–thaw damage, thereby increasing the long-term reliability of retrofit insulation systems. Full article

Background: Renal vasculitis encompasses a heterogeneous spectrum of disorders where vascular inflammation leads to organ dysfunction. Given that renal involvement is the primary determinant of long-term morbidity, timely diagnosis and intervention are paramount. This review aims to synthesize recent pathogenic insights and evaluate how these mechanistic breakthroughs are reshaping current diagnostic and therapeutic paradigms. Methods: A narrative review of the literature was performed to analyze the pathophysiology, diagnosis, and management of pediatric renal vasculitis. The analysis synthesizes current clinical guidelines and recent trial data, highlighting the transition toward biomarker-driven precision medicine for refined disease assessment and management. Results: Diagnosis remains multimodal, necessitating the integration of clinical, laboratory, and histopathological data. In ANCA-associated vasculitis (AAV), recent evidence has challenged the traditional “pauci-immune” concept. Management of pediatric IgA vasculitis utilizes a risk-stratified approach, whereas cryoglobulinemic vasculitis requires targeted trigger elimination. Across all pediatric syndromes, there is a shift toward minimizing corticosteroid exposure and utilizing individualized frameworks. Conclusions: Despite substantial progress in targeted biological therapies and reduced corticosteroid burden, the long-term morbidity of pediatric renal vasculitis remains substantial. Outcomes are dictated by a synergy of disease-specific and patient-specific factors. Addressing persistent unmet needs in the field requires further refinement of individualized management protocols and the continued validation of dynamic biomarkers, alongside the implementation of pediatric-specific guidelines and age-appropriate outcome measures. Full article

Building inclusive, high-density cities requires understanding vulnerable groups’ public space usage. While green exposure significantly impacts urban health, existing research frequently overlooks females’ specific needs regarding streetscape visual quality, green space structures, and daily travel experiences. To address this, the study investigates spatial disparities in Suzhou’s historic district. Utilizing multi-source data and mixed modeling strategies, including Partial Least Squares and Ordinary Least Squares (PLS-OLS) and eXtreme Gradient Boosting (XGBoost), the research analyzes how streetscape perceptions and green space characteristics affect female life satisfaction and expressed sentiment. Results indicate three main findings. (1) Streetscape visual features fundamentally drive subjective evaluations. Safe significantly enhances well-being, whereas boring and lively negatively impact life satisfaction, reflecting females’ acute sensitivity to environmental oppressiveness during daily travel. (2) Park diversity elevates expressed sentiment, while patch density positively influences life satisfaction, demonstrating the vital value of fragmented greenery for daily public space usage. (3) Boring precipitously diminishes life satisfaction after surpassing a specific threshold, while park diversity elevates expressed sentiment only after crossing a critical interval. The study establishes an integrated analytical framework linking visual perception, green space structure, emotional response, and satisfaction. These findings provide targeted strategies for enhancing inclusive urban design and optimizing green space allocation to improve streetscape safety and alleviate visual oppressiveness, thereby advancing gender social justice for vulnerable groups in historic districts. Full article

Microplastics (MPs; <5 mm) represent a growing environmental concern that increasingly challenges environmental monitoring, governance, and evidence-based decision-making. This review critically examines how current scientific understanding of microplastic sources, classification, occurrence, and environmental behavior can support environmental governance. MPs are classified as primary and secondary particles; however, persistent inconsistencies in size definitions, shape descriptors, and polymer identification limit the comparability of monitoring data and constrain the development of coherent regulatory frameworks. Evidence on the occurrence of MPs in surface waters and sediments highlights widespread contamination and pronounced spatial variability, raising challenges for risk assessment and policy harmonization across regions. Key transport pathways, including atmospheric deposition, terrestrial runoff, and riverine fluxes, are analyzed to illustrate how local emissions translate into large-scale environmental impacts. Rivers emerge as key components linking sources to receptors, offering relevant points for policy intervention and management measures. The review evaluates current policy responses to microplastic pollution, identifying significant gaps in standardized monitoring, data integration, and risk assessment approaches. It emphasizes the need for stronger alignment between scientific outputs and policy requirements, including the co-production of knowledge involving scientists, regulators, and stakeholders. By outlining pathways through which scientific evidence can inform regulatory design and environmental management, this study provides actionable insights for improving policy effectiveness. Advancing harmonized methodologies and integrating science into decision-making processes are essential steps toward mitigating microplastic pollution and supporting sustainable environmental governance. Full article

The paper establishes a categorical framework linking L-quasi-internal and L-quasi-enclosed spaces with L-filter, L-grill, and L-neighborhood spaces within the context of complete lattices. We define and characterize L-quasi-internal and L-quasi-enclosed relations, demonstrating how these structures can be mutually induced by L-filter and L-grill spaces. Specifically, the study proves the existence of categorical Galois connections between the category of L-grill spaces and L-quasi-enclosed spaces, along with those between L-filter spaces and L-quasi-internal spaces. Additionally, we investigate the properties of stratified and strong L-quasi-structures, clarifying their interrelationships with L-neighborhood systems. The theoretical results are reinforced by illustrative examples and a practical application in epidemiological risk assessment, demonstrating the usefulness of the proposed framework for decision-making under uncertainty. Full article

Large language models (LLMs) are increasingly adopted in life-science research for scientific writing, coding, literature synthesis, workflow troubleshooting, and preliminary data interpretation. In microbial genomics and bioinformatics, their appeal is clear because researchers routinely integrate genome annotations, antimicrobial resistance profiles, virulence determinants, taxonomic assignments, microbiome outputs, workflow scripts, and primary literature. Yet this domain also highlights major risks, including hallucinated biological claims, inaccurate citations, irreproducible code, unsupported genotype-to-phenotype inference, and inappropriate clinical or public health framing. This narrative review examines responsible LLM use in microbial genomics as a representative life-science setting where interpretation depends on database provenance, validated workflows, expert assessment, and reproducible evidence chains. It considers applications in genome annotation, antimicrobial resistance interpretation, virulence analysis, microbiome and metagenomics workflows, coding support, and scientific writing. The review further presents MicrobeGuardGPT as a conceptual reliability framework for assessing LLM-assisted microbial genomics outputs before scientific, clinical, or public health use. By connecting task domains, evidence verification, expert validation, and reliability classification, the framework supports risk-aware LLM integration in bioinformatics. Responsible implementation will require domain-specific benchmarks, curated database linkage, transparent reporting, reproducible workflows, human oversight, and governance standards tailored to biological interpretation across research, diagnostic, surveillance, outbreak-response, educational, and translational contexts. Full article

As tunnel engineering continues to advance toward deeper, longer, and more complex projects, the risks encountered during the construction phase have evolved into a combination of various disaster types and the accumulation of multiple contributing factors. Traditional empirical and semi-empirical risk management methods are increasingly revealing shortcomings in terms of timeliness, accuracy, and the ability to process multi-source data. In recent years, driven by advancements in computing power and sensor technology, artificial intelligence algorithms (AI algorithms) such as machine learning and deep learning have been rapidly adopted in tunnel construction risk management. This paper retrieved relevant literature from the Web of Science database covering the period from 2010 to 2025. After rigorous screening, 96 highly relevant papers were selected for bibliometric analysis. This paper systematically reviews research progress from two perspectives: algorithmic models and engineering applications. The review indicates that, in terms of algorithmic models, traditional machine learning, convolutional neural network, recurrent neural network, generative adversarial network, Transformer, and graph neural network constitute a multi-level technical framework encompassing feature representation, risk perception, and intelligent decision-making. In terms of applications, AI algorithms have been widely integrated into typical scenarios such as geological hazard identification and prediction, surrounding rock stability and deformation prediction, rock burst assessment and early warning, lining defect detection and structural safety assessment, construction-induced ground settlement prediction, and tunnel gas and fire hazard prediction, significantly enhancing risk identification and early warning capabilities. However, several challenges remain, including the scarcity of high-quality datasets, the prevalence of noisy, incomplete, and heterogeneous monitoring data, insufficient coupling between model interpretability and engineering mechanisms, limited cross-project transferability, and the lack of integrated management systems for multi-hazard lifecycle control. Based on this, this paper proposes future research directions in areas such as data infrastructure development, integration of mechanism constraints, and multi-hazard collaborative modeling, aiming to provide guidance for the further development of intelligent risk management in tunnel construction. Full article

Urban flood vulnerability is increasingly shaped by the interaction between climate change, urbanization, and spatial planning practices, particularly in Mediterranean metropolitan areas. This study develops an integrated GIS–AHP framework to assess the susceptibility component of flood vulnerability in the urban area of Thessaloniki, Greece. Using open-access geospatial data, ten indicators representing soil, hydrological, and environmental conditions are derived and spatially analyzed. The Analytic Hierarchy Process (AHP), based on expert judgment, is applied to estimate the relative importance of these indicators and to support their integration into a composite flood susceptibility index. The results reveal strong spatial heterogeneity, with high susceptibility concentrated in low-lying, densely urbanized areas and zones near drainage pathways. Among the examined factors, the Topographic Wetness Index emerges as the most influential, highlighting the persistent role of terrain-driven hydrological processes even in highly built environments. The proposed framework provides a transparent and transferable methodology for identifying flood-prone areas and supports evidence-based urban planning and climate resilience strategies. The findings contribute to the broader discussion on vulnerability and resilience in urban systems by linking spatial analysis with decision-support tools in a policy-relevant context. Full article

In recent years, an essential approach for promoting and implementing efficient sustainable construction practices has been considered through the integration of Building Information Modeling (BIM) and Life-Cycle Assessment (LCA). The introduction of OpenBIM, which is characterized by its collaborative and interoperable nature, offers an ideal framework to enhance this integration. This paper conducts a systematic review of the literature concerning the practices applied to integrate BIM and LCA, focusing on the present trends, challenges, and opportunities as well as on how the concept of OpenBIM can be applied to tackle the identified issues and gaps. Based on an intense review of the literature to identify the ways currently used to exchange data, this paper proposes a robust framework to create Information Delivery Specifications (IDS) as a solution to the identified gaps to attain an effective implementation, ultimately contributing to sustainable buildings’ practices and enhancing the integration of OpenBIM and LCA. OpenBIM emphasizes interoperability and collaboration by using open standards like Industry Foundation Classes (IFCs), which, when combined with LCA, offer a powerful method for the practice of sustainable building and provide a transparent evaluation of the environmental impacts of building materials and processes. This paper explores the definitions, key concepts, types of the exchanged data, and methods of integration and therefore provides insights into their potential in addressing the gaps that the construction industry is currently facing. The framework of integrating OpenBIM and LCA will be developed as a tool; therefore, it will combine an automated validation option by using IDS, create an enriched IFC file(s), dynamically map the data to an external LCA repositories, and incorporate feedback and reporting mechanisms. All those will be combined to address the most persistent shortcomings in the reviewed studies related to the integration of BIM and LCA. The framework will promote a holistic approach covering the early design benchmark to the detailed Whole Building LCA (WBLCA), including the operational and end-of-life phases. This next-generation workflow will align closely to the principles of OpenBIM, leading to improvement in the efficiency, accuracy, and deeper understanding of the environmental impacts by stakeholders over the construction lifecycle of buildings. Full article

The measurement and improvement of urban land use efficiency (ULUE) are crucial for sustainable development in China’s Canal Cities (CCCs). Drawing on the theories of production factors, spatial externalities, and agglomeration economy, this study proposes a framework that explicitly addresses the trade-offs and synergies of sustainable land use. A comprehensive ULUE evaluation index system was established. The super-SBM (Slack-Based Measure) and Global Malmquist–Luenberger (GML) index models were employed to assess the green efficiency of urban land use from 2002 to 2023, while Kernel Density Estimation (KDE) and the optimal parameters-based geographical detector (OPGD) model were used to investigate the spatiotemporal evolution and influencing factors of ULUE. The results reveal a distinctive V-shaped trend in efficiency, marked by significant spatial disequilibrium and predominantly technology-driven sustainable growth. Furthermore, ULUE exhibits a spatial distribution characterized by bipolar and multipolar differentiation, accompanied by concurrent concentration and dispersion, with high-value clusters dominating the spatial clustering type. Government regulation emerges as the dominant factor influencing ULUE, underscoring the pivotal role of policy intervention in guiding the sustainable development of land use. The interactions among pairs of influencing factors strengthened over time; notably, the interaction between government regulation and other factors is the strongest. Four-quadrant analysis profoundly reveals the underlying mechanism, distinguishing a high-quality, sustainable development model driven by technological innovation and a resource-dependent economic growth model. The findings provide valuable insights for promoting green development and formulating sustainable land use policies in CCCs. Full article