Search Results (5,287)

It has always been a research topic for some meteorologists to design a new and reasonable calculation scheme of the intensity of the Tibetan Plateau (TP) summer monsoon (TPSM). Existing indices are defined based on dynamic factors. However, the intensity of the TPSM can also be influenced by thermal factors. We therefore propose defining a TPMI in terms of horizontal temperature advection within the main body of the TP. This provides a new index that directly quantifies the extent to which the thermal forcing in the TP region regulates the monsoon system. The new index emphasizes the importance of the atmospheric asymmetry structure in measuring TPSM strength, represents the variability of the TPSM circulation system, effectively reflects the meteorological elements, and accurately represents the climate variation. Tropospheric temperature (TT) and TPSM are linked by the new index. These significant centers of correlation are characterized by alternating positive and negative phases along the Eastern European Plain, across the Turan Plain, and into southwestern and northeastern China. The correlation coefficients are found to be significantly out of phase between high and low altitudes in the vertical direction. This research broadens our minds and helps us to develop a new approach to measuring TPSM strength. It can also predict extreme weather events in advance based on TPMI changes, providing a scientific basis for disaster warnings and the management of agriculture and water resources. Full article

Due to their low permeability, the location of natural fractures is key to the successful development of buried hill reservoirs. Due to the high degree of rock fragmentation and strong absorption of seismic waves at the top of buried hill formations, it is hard to identify the distribution of natural fractures inside a buried hill using conventional seismic methods. To overcome this difficulty, this study proposes a natural fracture identification technology for buried hill reservoirs that combines a continuum damage mechanics model with finite element numerical simulation. A 3D numerical solution workflow is established to determine the natural fracture distribution in target buried hill reservoirs. By constructing a geological model of a block, reconstructing the orogenic history, developing a 3D finite element model, and performing numerical simulations, the multi-stage orogenic processes experienced by buried hill reservoirs and the resultant natural fracture formation are replicated. This approach yields 3D numerical results of natural fracture distribution. Using the G-Block in the Zhongyuan Oilfield as a case study, the natural fracture distribution in a buried hill reservoir composed of mixed lithologies, including marble and Carboniferous formations, within the faulted G6-well group is analyzed. The results include plane views of the contour of damage variable SDEG, which represents the fracture distribution within the subsurface layer at 600 m intervals below the buried hill surface, as well as a vertical sectional view of the contour of SDEG’s distribution along specified well trajectories. By comparison with the results of the fracture distribution obtained with logging data, a consistency of 87.5% is achieved. This indicates the reliability of the numerical results for natural fractures obtained using the technology presented here. Full article

The growing use of composites in automotive and aerospace fields highlights the need for effective joining of dissimilar materials. Adhesive bonding offers significant advantages over traditional methods. Therefore, comprehensively exploring the relationship between multiple design variables and joint strength, and subsequently achieving accurate prediction of joint strength based on this understanding, is essential for enhancing the effectiveness and efficiency of adhesive joint structural optimization. However, the joint—the critical yet weakest part—has strength governed by complex structural variables that are not fully understood, limiting optimization potential. Based on the effectiveness of finite element simulation in tensile fracture mechanics, this study developed a deep neural network (DNN). Combining the DNN model with a genetic algorithm (GA), both single-objective and multi-objective optimization were conducted. The single-objective optimization focused solely on maximizing joint strength, while the multi-objective GA further quantified the Pareto optimal trade-offs between joint strength and bond area, identifying compromise solutions. The effectiveness of the optimized parameters was validated, demonstrating higher efficiency and accuracy compared to traditional optimization methods such as response surface methodology (RSM). This integrated approach provides a robust framework for predicting joint strength and achieving effective optimization of bonded structures. Full article

Understanding the dynamic response and failure mechanisms of rock slopes during earthquakes is crucial in sustainable geohazard prevention and mitigation engineering. The initiation of landslides involves complex interactions between seismic wave propagation, dynamic rock mass behavior, and crack network evolution, and these interactions are heavily influenced by the slope geometry, lithology, and structural parameters of the slope. However, systematic studies remain limited due to experimental challenges and the inherent variability of landslide scenarios. This study employs Discrete Element Method (DEM) modeling to comprehensively investigate how geological structure parameters control the dynamic amplification and deformation characteristic of typical bedding/anti-dip layered slopes consist of parallel distributed rock masses and joint faces, with calibrated mechanical properties. A soft-bond model (SBM) is utilized to accurately simulate the quasi-brittle rock behavior. Numerical results reveal distinct dynamic responses between bedding and anti-dip slopes, where local amplification zones (LAZs) act as seismic energy concentrators, while potential sliding zones (PSZs) exhibit hindering effects. Parametric analyses of strata dip angles and thicknesses identify a critical dip range where slope stability drastically decreases, highlighting high-risk configurations for earthquake-induced landslides. By linking the slope failure mechanism to seismic risk reduction strategies, this work provides practical guidelines for sustainable slope design and landslide mitigation in tectonically active regions. Full article

The information on the seasonal variability of the chemical composition of the Arctic rivers is necessary for the proper assessment of the status of river runoff and the influence of anthropogenic and natural factors. Spring freshet is an especially important period for the Arctic rivers with a sharp maximum of water discharge. The Kolyma River is the least studied large river with a basin located solely in the permafrost zone. The change in the concentration of dissolved organic carbon (DOC), major, trace, and rare earth (RE) elements was studied at the peak and waning of the spring freshet of 2024 in the lower reaches of the Kolyma River. The concentration of elements was determined in filtrates <0.45 μm and in suspended solids > 0.45 μm. The content of coarse colloids (0.05–0.45 μm) was estimated by the intensity of dynamic light scattering (DLS). It was shown that the freshet peak is characterized by a minimal specific conductivity, concentration of major cations, and chemical elements migrating mainly in solution (Li, Sr, and Ba). During the freshet decline, the concentration of these elements increases with dynamics depending on the water exchange. The waters from the Kolyma River main stream have a maximal content of coarse colloids and concentration of <0.45 μm forms of hydrolysates (Al, Ti, Fe, Mn, REEs, Zr, Y, Sc, and Th), DOC, P, and heavy metals (Cu, Ni, Cd, and Co) at the freshet peak. A decrease of 8–10 times for hydrolysates and coarse colloids (0.05–0.45 μm) and of 3–6 times for heavy metals was observed at the freshet waning during the first half of June. This indicates a large-scale accumulation of easy soluble forms of hydrolysates, DOC, and heavy metals in the seasonal thawing topsoil layer on the catchment upstream in the previous summer, with a flush out of these elements at the freshet peak of the current year. In the large floodplain watercourse Panteleikha River, the change in concentration of major cations and REEs, Zr, Y, Sc, and Th at the freshet is less accented compared with the Kolyma River main stream due to a slower water exchange. Yet, <0.45 μm forms of Fe, Mn, Co, As, V, and P show an increase of 4–6 times in the Panteleikha River in the second half of June compared with the freshet peak, which indicates an additional input of these elements from the thawing floodplain landscapes and bottom sediments of floodplain watercourses. The concentration of the majority of chemical elements in suspended matter (>0.45 μm) of the Kolyma River is rather stable during the high-water period. The relative stability in the chemical composition of the suspended solids means that the content of the suspension and not its composition is the key to the share of dissolved and suspended forms of chemical elements in the Kolyma River runoff. Full article

Reliable estimation of operational modal parameters is essential in structural health monitoring (SHM), particularly when these parameters serve as damage-sensitive features. Modern distributed monitoring systems, often employing digital MEMS accelerometers, must account for timing uncertainties across sensor networks. Clock irregularities can lead to non-deterministic sampling, introducing uncertainty in the identification of modal parameters. In this paper, the effects of timing variability throughout the network are propagated to the final modal quantities through a Monte-Carlo-based framework. The modal parameters are identified using the covariance-driven stochastic subspace identification (SSI-COV) algorithm. A finite element model of a steel cantilever beam serves as a test case, with timing irregularities modeled probabilistically to simulate variations in sensing node clock stability. The results demonstrate that clock variability at both intra-node and inter-node levels significantly influences mode shape estimation and introduces systematic biases in the identified natural frequencies and damping ratios. The confidence intervals are calculated, showing increased uncertainty with greater timing irregularity. Furthermore, the study examines how clock variability impacts damage detection, offering metrological insights into the limitations of distributed vibration-based SHM systems. Overall, the findings offer guidance for designing and deploying monitoring systems with independently timed nodes, aiming to enhance their reliability and robustness. Full article

The hospitality sector plays a crucial role in the tourism industry, undergoing a transformation driven by the intersection of service innovation and environmental practices. Competitiveness in this sector requires adaptation to market demands, with a focus on service innovation and environmental sustainability. This research aims to analyze the relationship between service innovation, environmental practices, and sustainable competitive advantage in Brazilian hospitality establishments. A quantitative and descriptive approach was applied to 300 individuals who stayed in Brazil. Data collection was conducted through an online questionnaire, utilizing the Snowball Sampling technique. The data collection was between 15 February and 20 June 2024. Data analysis was performed using Structural Equation Modeling, which enabled the examination of multiple variables and the verification of hypothetical relationships. The research results validated the hypotheses tested, demonstrating that service innovation and environmental practices have a positive influence on sustainable competitive advantage in hospitality establishments. An important finding in the research refers to the correlation between these constructs, which highlights the importance of integrated strategies that consider innovation and environmental sustainability as key elements for organizational success in the hospitality sector. With its theoretical contribution, this research developed a framework for analyzing the relationships between the constructs. Full article

The construction of large underground caverns fundamentally differs from building and above ground civil infrastructure projects due to their complex geometries and variable geological conditions. These projects are complex and challenging because a large amount of data is generated from dispersed, independent, and heterogeneous sources. The underground construction industry often uses traditional project management techniques to manage complex interactions between these data sources that are hardly linked, and independent decisions are often made without considering all the relevant aspects. In this context, cavern construction exhibits uncertainties and risks due to unforeseen circumstances, an intricate design, and ineffective information management. Existing research has considered general BIM semantic models at the design stage; however, the digital transformation of cavern construction remains underdeveloped and fails to integrate digital construction throughout the project lifecycle. To address that gap, a novel BIM-based multi-model cavern information modeling framework is presented here to improve project management, construction, and delivery by integrating multiple interlinked data models and project performance data for large underground cavern construction. Data models of cavern construction processes are linked to propose an extension of the Industry Foundation Classes (IFC) schema based on the cavern-specific elements, relationships, and property set definitions. To illustrate the potential of the proposed framework, a theoretical application to the powerhouse cavern construction is presented. The results indicate that the framework has significant potential to improve construction efficiency and safety and establish a robust foundation for the digital transformation of underground cavern projects. The theoretical implementation on the Neelum–Jhelum powerhouse cavern showed that the framework enabled a 92 m cavern realignment to avoid fault zones, achieved a 12.4% reduction in rock bolt usage, and a 9.8% reduction in shotcrete volume. These quantitative improvements illustrate its potential to enhance safety, reduce material costs, and optimize construction efficiency compared to conventional workflows. Full article

Spatial environments influence users’ behavioral patterns and psychological perceptions, affecting health outcomes—a professional consensus in architecture, particularly within healthy buildings. Growing attention to spatial design’s health benefits has rapidly increased quantitative research. Relationships between spatial elements (e.g., green spaces, water features, facilities) and health indicators (e.g., emotional state, mental health, physical activity) are increasingly clear. Due to collective behavior patterns on campuses, the space–health relationship is particularly pronounced. This paper examines campus open spaces via meta-analysis to explore spatial elements’ relative influence on health outcomes. After a chronological review of qualitative research, it cross-sectionally extracts quantitative data. The independent variable (“campus open space”) is categorized into natural landscapes, service facilities, and built environment (design organization). The dependent variable (“health”) is subdivided into physical health, mental health, and positive social adaptation. The main conclusions of the study are as follows: Campus open spaces significantly impact student health, with the built environment exerting the strongest influence. Combining landscape/facility elements with spatial guidance yields more significant results. Furthermore, based on the calculated impact factor data for each element, this study has developed an evaluation scale that could serve as an empirical foundation for future assessments of campus health benefits, thereby guiding health-oriented campus spatial design. Full article

In GNSS-deprived settings, such as indoor and underground environments, research on simultaneous localization and mapping (SLAM) technology remains a focal point. Addressing the influence of dynamic variables on positional precision and constructing a persistent map comprising solely static elements are pivotal objectives in visual SLAM for dynamic scenes. This paper introduces optical flow motion segmentation-based SLAM(OS-SLAM), a dynamic environment SLAM system that incorporates optical flow motion segmentation for enhanced robustness. Initially, a lightweight multi-scale optical flow network is developed and optimized using multi-scale feature extraction and update modules to enhance motion segmentation accuracy with rigid masks while maintaining real-time performance. Subsequently, a novel fusion approach combining the YOLO-fastest method and Rigidmask fusion is proposed to mitigate mis-segmentation errors of static backgrounds caused by non-rigid moving objects. Finally, a static dense point cloud map is generated by filtering out abnormal point clouds. OS-SLAM integrates optical flow estimation with motion segmentation to effectively reduce the impact of dynamic objects. Experimental findings from the Technical University of Munich (TUM) dataset demonstrate that the proposed method significantly outperforms ORB-SLAM3 in handling high dynamic sequences, achieving a reduction of 91.2% in absolute position error (APE) and 45.1% in relative position error (RPE) on average. Full article

Geological elements are fundamental components of the Earth’s ecosystem, and accurately identifying their spatial distribution is essential for analyzing environmental processes, guiding land-use planning, and promoting sustainable development. Remote sensing technologies, combined with artificial intelligence algorithms, offer new opportunities for the efficient interpretation of geological features. However, in areas with dense vegetation coverage, the information directly extracted from single-source optical imagery is limited, thereby constraining interpretation accuracy. Supplementary inputs such as synthetic aperture radar (SAR), topographic features, and texture information—collectively referred to as sensitive features and prior knowledge—can improve interpretation, but their effectiveness varies significantly across time and space. This variability often leads to inconsistent performance in general-purpose models, thus limiting their practical applicability. To address these challenges, we construct a geological element interpretation dataset for Northwest China by incorporating multi-source data, including Sentinel-1 SAR imagery, Sentinel-2 multispectral imagery, sensitive features (such as the digital elevation model (DEM), texture features based on the gray-level co-occurrence matrix (GLCM), geological maps (GMs), and the normalized difference vegetation index (NDVI)), as well as prior knowledge (such as base geological maps). Using five mainstream deep learning models, we systematically evaluate the performance improvement brought by various sensitive features and prior knowledge in remote sensing-based geological interpretation. To handle disparities in spatial resolution, temporal acquisition, and noise characteristics across sensors, we further develop a multi-source complement-driven network (MCDNet) that integrates an improved feature rectification module (IFRM) and an attention-enhanced fusion module (AFM) to achieve effective cross-modal alignment and noise suppression. Experimental results demonstrate that the integration of multi-source sensitive features and prior knowledge leads to a 2.32–6.69% improvement in mIoU for geological elements interpretation, with base geological maps and topographic features contributing most significantly to accuracy gains. Full article

This study focuses on analyzing the generalized HSC-KdV equations characterized by variable coefficients and Wick-type stochastic (Wt.S) elements. To derive white noise functional (WNF) solutions, we employ the Hermite transform, the homogeneous balance principle, and the $F_e$ (F-expansion) technique. Leveraging the inherent connection between hypercomplex system (HCS) theory and white noise (WN) analysis, we establish a comprehensive framework for exploring stochastic partial differential equations (PDEs) involving non-Gaussian parameters (N-GP). As a result, exact solutions expressed through Jacobi elliptic functions (JEFs) and trigonometric and hyperbolic forms are obtained for both the variable coefficients and stochastic forms of the generalized HSC-KdV equations. An illustrative example is included to validate the theoretical findings. Full article

Background: Schizophrenia is a serious mental health condition that usually begins in adolescence and often progresses to become a chronic and disabling illness. Difficulties in communication and anomalous language are considered core elements of the disorder. Several studies have demonstrated the presence of semantic deficits in individuals with schizophrenia, suggesting that these deficits may constitute a core feature of the disorder. However, research in this area remains limited, particularly among individuals at high risk of developing the disorder. The central hypothesis of this study is that individuals with schizophrenia exhibit semantic processing deficits, even when cognitive function, psychopathology, and medication are controlled for. We also hypothesize that similar, albeit milder, deficits can be observed in individuals at high risk of developing the condition. Methods: This cross-sectional study included 155 participants divided into three groups: 46 with schizophrenia, 42 at high risk due to factors like substance use and high psychopathology, and 67 controls matched by sex, age, and education. Semantic processing was assessed using the semantic relations subtest from the BETA, controlling for medication and cognitive performance as possible confounding factors. Results: the results revealed significant differences among the three groups (F = 28.543; p < 0.001); the schizophrenia group performed poorly, followed by the high-risk group, and then the control group, which showed no deficits. Error patterns were also analyzed to assess group differences, revealing that the schizophrenia group had the lowest scores and the most specific deficits. These findings highlight the relevance of semantic evaluation in schizophrenia and, more importantly, in individuals at high risk of developing the disorder, as such deficits may serve as early biomarkers. Additionally, significant correlations were found between semantic performance and variables such as medication (r = −0.342; p = 0.020), cognition (r = −0.259; p = 0.001), and psychopathology (r = −0.566; p < 0.001). Conclusions: This emphasizes the need to control these factors to avoid misinterpreting semantic deficits in both schizophrenia and high-risk groups. The present research is not without limitations; for example, the study design does not allow for conclusions of causality but rather of correlation. Full article

The future development of perovskite light-emitting diodes (LEDs) is significantly limited by the poor stability and low brightness of the pure-blue emission in the wavelength range of 460–470 nm. In this study, the Cl/Br element ratio in CsPbCl_xBr_3−x perovskite nanocrystals (NCs) was modulated to precisely control their blue emission in the 428–512 nm spectral region. Then, the undoped CsPbCl₁Br₂ and the ZnCl₂-doped CsPbCl₁Br₂ perovskite NCs were synthesized via the hot-injection method and investigated using variable-temperature photoluminescence (PL) spectroscopy. The PL emission peak of the ZnCl₂-doped CsPbCl₁Br₂ perovskite NCs exhibits a blue shift from 475 nm to 460 nm with increasing ZnCl₂ doping concentration. Additionally, the ZnCl₂-doped CsPbCl₁Br₂ perovskite NCs show a high photoluminescence quantum yield (PLQY). The variable-temperature PL spectroscopy results show that the ZnCl₂-doped CsPbCl₁Br₂ perovskite NCs have a larger exciton binding energy than the CsPbCl₁Br₂ perovskite NCs, which is indicative of a potentially higher PL intensity. To assess the stability of the perovskite NCs, high-temperature experiments and ultraviolet-irradiation experiments were conducted. The results indicate that zinc doping is beneficial for improving the stability of the perovskite NCs. The ZnCl₂-doped CsPbCl₁Br₂ perovskite NCs were post-treated using didodecylammonium bromide, and after the post-treatment, the PLQY increased to 83%. This is a high PLQY value for perovskite NC-LEDs in the blue spectral range, and it satisfies the requirements of practical display applications. This work thus provides a simple preparation method for pure blue light-emitting materials. Full article

Alginate-based materials are widely studied for biomedical use, but their limited mechanical properties and variable biocompatibility pose challenges. In this work, hybrid composites composed of alginate, calcium, and graphene oxide were fabricated using a freeze-drying method and cross-linked with calcium ions via calcium chloride at different concentrations. Structural and morphological features were assessed using SEM, EDS, ICP-MS, and BET analysis. The resulting composites exhibited a porous architecture, with calcium incorporation confirmed by elemental analysis. Surface characteristics and pore parameters were influenced by the presence of graphene oxide and the cross-linking process. The effects of the materials on haemostasis were evaluated through activated partial thromboplastin time (aPTT) and prothrombin time (PT) assays, revealing modulation of the intrinsic coagulation pathway without significant changes in the extrinsic pathway. In this study, we analysed the effect of alginate–graphene oxide composites on the viability of peripheral blood mononuclear (PBM) cells and human foreskin fibroblasts from the Hs68 cell line. We also assessed the genotoxic potential of alginate–graphene oxide composites on these cells. Our results showed no cyto- or genotoxic effects of the material on either cell type. These findings suggest the biocompatibility and safe character of alginate–graphene oxide composites for use with blood and skin cells. Full article