Search Results (55,546)

Excavation adjacent to operating urban rail transit faces formidable deformation control challenges. To address this, a parametric collaborative optimization framework integrating micro steel pipe pile isolation and temporary intermediate partition wall reinforcement is proposed. Taking a foundation pit project at Shangdi Station of Beijing Metro Line 13 as a case study, a three-dimensional finite element model was established using the Hardening Soil constitutive model and calibrated with field monitoring data. Optimization analysis reveals that micro-pile spacing is the dominant factor controlling local rail settlement, while intermediate partition wall thickness primarily dictates global surface settlement. By balancing stringent safety limits with construction economy through a multi-objective evaluation, the preferred support configuration was calculated to be 273 mm diameter micro-piles at 500 mm spacing, combined with a 300 mm-thick partition wall. This collaborative configuration successfully truncates lateral soil displacement, reducing maximum rail settlement by over 55% and surface settlement by 53.6% compared to the baseline. Field monitoring results show high consistency with the numerical predictions (RMSE = 0.1438 mm), confirming the reliability of the proposed parametric collaborative optimization framework. Ultimately, this framework provides a validated, quantitative design methodology and a practical reference for support design in constrained excavations adjacent to existing sensitive infrastructure. Full article

Green finance seeks to reconcile economic expansion with environmental protection and may, by relaxing financing constraints on clean-energy projects, contribute to lower energy poverty. Using provincial panel data from China over 2010–2019, this study examines the relationship between green finance development and energy poverty and evaluates potential spatial spillovers. The results show that green finance development is negatively associated with energy poverty, and this relationship remains statistically robust in dynamic-panel specifications estimated using system generalized method of moments (system GMM). Mechanism analyses further provide suggestive evidence that this negative association may operate partly through greater energy-supply investment and improved energy-infrastructure conditions. Spatial econometric evidence also indicates the presence of spillover effects: improvements in green finance in one province are associated with lower energy poverty in neighboring provinces. These findings imply that efforts to eradicate energy poverty should explicitly incorporate green finance, recognize regional heterogeneity in green finance development, and improve the transmission of green finance into tangible investment in clean energy and energy infrastructure. Interprovincial policy coordination is also warranted given spatial interdependence. Full article

The paper starts by questioning the highly influential but extremely misleading characterizations of Plato and Hegel by Bertrand Russell and Karl Popper. It is argued that mathematical assumptions concerning the ancient problem of the incommensurability of continuous and discrete quantities underlie the ways in which Russell and Popper portray the metaphysics of Plato and Hegel—Popper explicitly, and Russell implicitly, presupposing a particular response to this problem by broadening the concept of number to include irrational numbers. Recent work on Plato, however, suggests a different strategy for responding to this ancient conundrum, one that involves a mediated “duality” of the continuous and discrete that Hegel would later generalize to a duality of determinate and indeterminate aspects of cognition more generally. This Platonic alternative had originated with the Pythagorean natural philosopher Philolaus of Croton and would later be expressed in modern mathematics in a non-Cartesian way of applying numerical metrics to geometric figures in disciplines such as projective geometry. Such an alternative approach to both quantitative and conceptual incommensurability, I claim, had influenced Plato’s later conception of philosophical method that would be adopted by Hegel via the intermediary of Leibniz, the first modern “idealist”. Understanding the actual mathematics modeling philosophical concepts for Plato and Hegel becomes crucial for understanding the philosophical claims of modern idealism. Full article

Highly deviated wells commonly exhibit large errors in horizon calibration because the logging path follows an inclined borehole trajectory, whereas post-stack seismic processing effectively treats wave propagation as vertical. This mismatch has received limited attention. Here, we performed horizon calibration and velocity-model building for highly deviated wells drilled in the Mahu Sag, Junggar Basin, and obtained three key findings. First, the assumed vertical travel path in post-stack data is the primary cause of the initial mis-tie for highly deviated wells. Second, calibration in the deviated interval requires a strategy distinct from that of vertical wells and may involve substantial stretching or squeezing of the original logs to achieve a consistent time-depth relationship. Third, the map-view projection of a highly deviated well is essentially linear; relative to vertical wells, it provides denser in situ velocity constraints and, with pseudo-well control, supplies 2D velocity information along the well-trajectory plane, thereby improving velocity-field modeling. Validation against drilling data showed that this workflow improved well ties and refined the velocity model, providing practical guidance for geological well planning and reducing drilling risk. Full article

Code smells are indicators of potential design problems in software systems and are commonly used to guide refactoring activities. Recent advances in representation learning have enabled the use of embedding-based models for analyzing source code, offering an alternative to traditional approaches based on manually engineered metrics. However, the effectiveness of different embedding representations for multiclass code smell detection remains insufficiently explored. This study presents an empirical comparison of embedding models for the automatic detection of three widely studied code smells: Long Method, God Class, and Feature Envy. Using the Crowdsmelling dataset as an empirical basis, source code fragments were extracted from the original projects and transformed into vector representations using two embedding approaches: a general-purpose embedding model and the code-specialized CodeBERT model. The resulting representations were evaluated using several machine learning classifiers under a stratified group-based validation protocol. The results show that CodeBERT consistently outperforms the general-purpose embeddings across multiple evaluation metrics, including balanced accuracy, macro F1-score, and Matthews correlation coefficient. Dimensionality reduction analyses using PCA and t-SNE further indicate that CodeBERT organizes code smell instances in a more structured latent representation space, which facilitates the separation of smell categories. In particular, CodeBERT achieved a macro F1-score of 0.8619, outperforming general-purpose embeddings (0.7622) and substantially surpassing a classical TF-IDF baseline (0.4555). These findings highlight the value of this study as a controlled multiclass evaluation of embedding representations and demonstrate the practical value of domain-specific representations for improving automated code smell detection and class separability in real-world software engineering scenarios. Full article

The main objective of this study is to produce a systematic literature review that analyses key performance indicators (KPI) in the context of efficient and sustainable building renovation. Efficiency and sustainability, in combination with building renovation, are important themes due to the increasing need for creating sustainable renovations worldwide. The identification and monitoring of KPIs is fundamental in decision-making processes, but also in the monitoring of short-term and long-term project goals. In the current academic literature, existing research gaps, especially in the social aspects of sustainability and research, have also been analyzed in terms of regional differences in the approach to each KPI. The systematic literature review examined 29 studies published between 2014 and 2024, based on a literature search conducted in 2024, using databases such as Scopus and Web of Science, with the final search performed in June 2024. The inclusion criteria focused on peer-reviewed studies addressing KPIs in sustainable building renovation, while studies not directly related to renovation processes or lacking KPI analysis were excluded. The research results show that the majority of studies focus on economic and environmental factors, which are the most commonly addressed, while research on other KPIs is significantly behind. The results were synthesized using a qualitative comparative analysis of identified KPI categories. This study also highlights the importance of addressing effective and sustainable renovation for historic buildings with a focus on heritage preservation and the need to further analyze the use of KPIs with a focus on historic buildings. The limitations include the limited number of studies and the underrepresentation of social sustainability aspects. Full article

Pacific white shrimp (Litopenaeus vannamei) is a major aquaculture product worldwide. For consumers, discriminating domestic from imported sources of shrimp meat, and individual domestic sources, can be highly desirable because of the different meat quality and environmental contamination from geographically different origins of shrimp. This study evaluated the potential of stable isotope analysis (δ¹³C, δ¹⁵N, δ²H, δ¹⁸O) with chemometric models to authenticate the origins of Pacific white shrimp sold in China. Shrimp samples from domestic (Guangxi, Fujian, Shandong, Inner Mongolia) and foreign (Ecuador) sources were analyzed, using statistical analyses. The four-isotope model achieved 89.3% cross-validation accuracy in distinguishing domestic and foreign shrimp, with an overall prediction Area Under the Curve (AUC) of 0.901 (95% CI: 0.819–0.983)—significantly outperforming single-isotope models. Differences in δ¹³C and δ¹⁵N reflected feed source variations, while δ²H and δ¹⁸O (Variable Importance in the Projection (VIP) > 1, key discriminatory indicators) mirrored geographic environmental difference. Although δ¹⁵N did not differ significantly among groups, the combination of all four isotopes reduced limitations of individual δ²H/δ¹⁸O use. This approach enhanced the precision, reliability, and applicability of stable isotope analysis for origin authentication by leveraging complementary isotopic data and robust statistical frameworks. These findings demonstrate the proposed model’s potential as a cost-effective, copyright-compliant framework for shrimp origin authentication, with implications for isotopic traceability across food science fields. Full article

This paper is a contribution to Einstein’s general relativity theory and is mostly a review of known work. It concentrates attention on four fourth-order tensors which arise on the space-time manifold describing this theory and which are very useful. These are the (Riemann) curvature tensor, the Weyl conformal tensor, the “E” tensor and the Weyl projective tensor. The first of these, the curvature tensor, plays an important role in the formulation and interpretation of Einstein’s theory. Next, the Weyl conformal tensor is introduced and its conformal properties described and with it, the Petrov classification of gravitational fields which arises from this tensor. This, in turn, gives rise to the Bel criteria for distinguishing Petrov types at a point by an alignment of certain null directions at that point. The third of these tensors, the “E” tensor, is an important tensor in calculations due to its close connection to the Ricci tensor. The fourth tensor, the Weyl projective tensor, is then described together with its properties relating to the geodesic structure of space-time. As examples of the combined usefulness of these tensors, pp-waves and generalised pp-waves are discussed and related, and a review of the geodesic structure of vacuum metrics is given. Full article

To address the urgent need for enhancing landslide risk monitoring in reservoir embankment slopes, a core component of water conservancy projects, this paper proposes the LRES-YOLO algorithm for real-time landslide detection on reservoir embankments. In LRES-YOLO, we first integrate coordinate attention into basic feature extraction convolutional blocks to form the CACBS attention module, which enhances the model’s ability to identify and locate landslide targets in complex reservoir terrain, overcoming positional information insensitivity in deep networks. Second, we add novel downsampling DP modules and ELAN-W modules to the backbone network, improving feature recognition efficiency for embankment slopes with diverse hydrological and topographical interference. Third, we optimize the feature fusion network with targeted concatenation and pooling operations, balancing semantic information enhancement with computational load reduction to mitigate overfitting in variable reservoir environments. Finally, we adopt Focal Loss and EIoU Loss to accelerate training convergence and strengthen target feature representation for small or obscured landslides on embankments. Experimental results show that LRES-YOLO outperforms traditional algorithms in detecting landslides across diverse reservoir embankment scenarios: it achieves an average improvement of 8.4 percentage points in mean mAP over the best-performing baseline across five independent trials, a detection speed of 8.2 ms per image, and memory usage of 139 MB. This lightweight design makes it suitable for edge computing devices, providing robust technical support for intelligent monitoring systems in water conservancy projects. Full article

This mixed-methods study explores the views and experiences of 55 English-language teacher candidates in Chile who designed gamified project-based lessons aimed at fostering inclusive learning and social justice in culturally diverse classrooms. Data were collected through lesson plans, semi-structured interviews, and a Likert-scale survey, and were analysed using inductive content analysis and descriptive statistics. The findings reveal that participants valued gamification for enhancing student engagement, collaboration, and critical thinking, and they perceived gains in their ability to integrate social justice themes into language teaching. However, discrepancies emerged when participants had to plan lessons that had a social justice orientation because they perceived they did not have enough competence to approach equity-oriented themes. This study adopts a justice lens that foregrounds power, agency, and digital equity in teacher candidates’ lesson-planning skills to examine how they can redistribute voice, recognise situated knowledges, and expand their capacity to act within and against structural constraints. The study underscores the need for teacher education programmes to move beyond technical and motivational uses of gamification and digital tools. From their lesson plans, teacher candidates were not simply adopting digital tools at a technical level but seem to be designing an integrated pedagogical ecosystem that aligned gamification and project-based learning. However, it is inconclusive whether they are able to design gamified PBL environments that do not reproduce existing social and educational inequalities and ensure that access and participation are carefully scaffolded. Full article

Improving the energy efficiency of historic field stone masonry buildings often requires internal insulation, as external insulation is frequently restricted by heritage and architectural constraints. Internal insulation, however, alters the hygrothermal behavior of massive masonry walls and may increase moisture-related risks. This study assesses the hygrothermal performance of an internally insulated historic field stone masonry wall under past and projected future climatic conditions using long-term transient simulations. Coupled heat and moisture transfer simulations were performed with the DELPHIN software for an uninsulated reference wall and an internally insulated configuration. The analyses accounted for wind-driven rain, masonry heterogeneity, and variations in inner core composition. Past conditions were represented by a continuous 20-year measured climate dataset, while future conditions were evaluated using regional late-century climate projections (RCP2.6 and RCP8.5). Hygrothermal performance was evaluated based on moisture mass density, freeze–thaw exposure, and mold-relevant temperature–relative humidity conditions at predefined evaluation points within the wall. The results show that moisture accumulation develops gradually and cannot be reliably captured by short simulation periods. Internal insulation redistributes moisture-related risks within the wall rather than fundamentally altering the seasonal moisture regime. Freeze–thaw exposure occurs under all investigated climates, while mold-relevant humidity conditions persist at interior-adjacent locations. The findings demonstrate the importance of multi-year hygrothermal analyses when assessing moisture-related risks in internally insulated historic masonry walls. Full article

Renewable energy sources, and wind energy in particular, constitute a central pillar of energy policy at both national and European levels. Nevertheless, the deployment of onshore wind farms is frequently associated with spatial, environmental, and social conflicts, making the evaluation of existing projects imperative. The present study aimed to assess the sustainability of existing onshore wind farms in the Region of Thessaly, with particular emphasis on their spatial planning, technical characteristics, and environmental impacts. The methodological framework consists of four distinct stages: (i) identification and spatial mapping of existing wind farms in the study area, (ii) assessment of the compliance of existing wind installations with the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD–RES), (iii) application of the Rapid Impact Assessment Matrix (RIAM) to enable a systematic and comparable evaluation of the impacts of wind installations on specific environmental and anthropogenic parameters, and (iv) estimation of project hazard and operational vulnerability through the application of Operational Risk Management (ORM). Geographic Information Systems (GISs) were employed for data processing and spatial analysis. The assessment showed that 40% of the evaluated wind farms fully comply with all eleven exclusion criteria of the SFSPSD-RES, whereas the remaining 60% show partial compliance, failing to meet between one and three criteria. RIAM results indicate that the most significant adverse impacts (−D and −C) during construction are associated with morphology/soils and the natural environment, mainly due to loss/fragmentation of vegetation and disturbance of fauna, and, in some cases, in areas of increased sensitivity. During operation, the main negative effects (−D and −C) relate to landscape and visual quality, as well as continued disturbance to the natural environment. At the same time, the operation generates important positive effects (+E) on the atmospheric environment through reduced CO₂ emissions. The ORM analysis further shows that the most important risks for most wind farms arise during construction (ORM = 2 and 3), particularly from serious worker accidents during lifting, roadworks, and foundation activities. The study demonstrates that the sustainability of existing wind installations depends on a complex set of spatial, environmental, and technical factors. The proposed framework integrates spatial compliance screening, RIAM-based environmental impact assessment, and ORM-based risk and opportunity evaluation. This connection links the importance of impacts with their operational manageability during construction and operation phases, as well as across sustainability dimensions. Consequently, the study provides a more decision-focused approach for assessing existing wind farms and supporting policy development. Full article

Three-dimensional (3D) epicuticular wax coverage on plant surfaces contributes to multifunctional surface properties, such as enhanced water repellence, reduced pathogen adherence, modified optical properties, and reduced insect adhesion. The diversity in wax projection morphology, size, abundance, and spatial arrangement among plant species results in a broad spectrum of anti-adhesive effects, reflecting both phylogenetic history and ecological function. This study presents a numerical model consisting of 3D tubular-shaped structures randomly deposited on a substrate and forming a highly porous layer. The simulations based on this model demonstrate a strong reduction in adhesion to the contacting insect adhesive pad. It is found that a structure formed by sufficiently long tubes, where the length is enough to support the tubes in space and build a porous 3D structure with a very low density, at relatively weak attraction to the underlying substrate, leads to the weakest adhesion. The model is constructed on the basis of our recent works combining discrete and continuous approaches in biological modeling. It mainly exploits the technique of the movable digital automata, allowing modeling of numerous numerically elastic cylinders that can be moved in 3D space, elastically collide with one another and with boundaries, and build self-consistent surface structures, which can be used to mimic nano- or microscale surface coverages of real plants. Full article

Intentional and unintentional GNSS interference can greatly affect the performance of precise timing and localization in areas such as automated driving or aviation. Nevertheless, reports show that jamming occurs near many European airports that are located close to a highway or in heavy industry areas due to broadcasting of interfering signals. To assess the impact of such potential risks, we investigated interference occurring on a section of highway located both near to an airport and close to logistics centers as part of the Austrian Security Research Program project CATCH-IN. This section of highway is of particular interest, as the highway runs in parallel to the approach path of aircraft and crosses the approach path 3.7 km before the aircraft touches down (the flight altitude is only 200 m above the ground). For this experiment, we distributed six Septentrio Mosaic x5 GNSS receivers as sensors along the highway and monitored this section for five months. We analyzed the data with AGC monitoring, CN0 monitoring, and baseband sample monitoring to identify interference along the highway that could affect sensors along the descending flight trajectory. During the period of this experiment, we saw events that we believe could cause potential safety risks and problems for aviation safety. In our analysis, we focused on the statistical evaluation of the temporal repetitions, in particular the times of day that see more interference and the frequencies at which more interference occurs. Additionally, we analyzed the performance of different algorithms for dealing with large datasets. The results provide new insight into potential monitoring stations near airports and raise awareness of potential risks and vulnerabilities in aviation safety as well as automated driving along highways. Full article

Livestock manure management contributes substantially to agricultural greenhouse gas emissions, making the adoption of low-carbon approaches urgent in ecologically sensitive regions. This study focuses on the County-wide Livestock Manure Resource Utilization Project in Danjiangkou City, the core water source area of China’s South-to-North Water Diversion Project. Based on field survey data, IPCC Guidelines, and a life cycle assessment framework, this study established a carbon accounting boundary covering excretion, collection, storage, treatment, and utilization stages. A scenario analysis was conducted to compare 2023 baseline emissions with 2026 project emissions and to quantify the carbon reduction potential. The research findings indicate that the overall carbon reduction rate following the project’s implementation reached 40.8%. However, the effectiveness varied considerably across the four management models. The Sedimentation–Crop Model and the Housing–Bedding Integrated Model, which employed integrated systemic interventions, achieved reductions of 61.50% and 60.09%, respectively. In contrast, the “124” Healthy Breeding Model and the Raised-Bedding Composting System, which relied primarily on single-stage upgrades, achieved reductions of only 32.04% and 27.70%. This disparity suggests that in decentralized livestock operations, isolated technological improvements fall short; meaningful decarbonization requires systemic interventions across the entire manure management chain. The findings provide a reference for low-carbon livestock manure management and regional development in ecologically sensitive areas. Full article