Search Results (10,383)

Reducing the consumption of energy-intensive cement and promoting the resource utilization of industrial waste are two critical challenges that should be urgently addressed to achieve the goals of carbon neutrality and green sustainable development in the building materials field. Among these, the massive stockpiling of industrial waste such as fly ash and silica fume poses serious threats to the environment and human health, making their efficient utilization an urgent need to alleviate environmental pressure. This study employs the ice-template method to incorporate fly ash and silica fume as functional components into a cement-based system, fabricating a novel composite material. This material features a layered porous structure, which not only reduces cement usage but also results in a lighter weight. The introduction of the ice-templating method successfully constructed an anisotropic lamellar structure, leading to significant enhancements in flexural strength and toughness—by approximately 26.6% and 30%, respectively, vertical to the lamellae compared to conventional dense cement. Meanwhile, the hybrid blend of silica fume and fly ash effectively improved the deformability of the material, as evidenced by a notable increase in compressive failure strain. These excellent behaviors of mechanical properties are attributed to the formation of a multi-scale microstructure characterized by “macroscopically continuous and microscopically dense” features. Moreover, this specific microstructure offers greater advantages in sound absorption performance. The acoustic impedance tube tests demonstrate that the noise reduction coefficient of the novel cement-based material incorporating fly ash and silica fume is improved by 82%, holding promising applications in noise reduction for the construction and transportation fields. This research provides a feasible pathway for the high-value application of industrial solid waste in low-carbon materials. Full article

To achieve high-sensitivity and intrinsically safe monitoring of hydrogen energy systems, a multi-wavelength fiber Bragg grating sensor based on the hydrogen-induced exothermic effect of Pt/WO₃ nanomaterials was designed. This structure takes advantage of the catalytic effect of Pt on hydrogen molecules to trigger a thermal effect in a hydrogen environment, resulting in a redshift in the grating’s reflected wavelength. Experiments show that the sensor has a good linear response (R² > 0.98) within a hydrogen concentration range of 0.5% to 3.5%, with stable and repeatable response and recovery processes. The multi-channel fiber structure enables synchronous detection of different central wavelengths, significantly enhancing the system’s scalability and distributed measurement capabilities. The research results indicate that this method has potential application value in hydrogen energy safety monitoring and intelligent sensor networks. Full article

Implementing effective water control measures in horizontal wells is essential for sustaining stable production in offshore oilfields. However, due to complex reservoir geological characteristics and diverse water control technologies, significant variability exists in the effectiveness of such measures, posing challenges for strategy selection. To address this gap, this study establishes a comprehensive and standardized multi-dimensional indicator system for describing treated wells, integrating geological, operational, and production parameters—an aspect seldom systematized in previous research. A major innovation of this work lies in developing a hybrid correlation evaluation framework that combines Pearson, Spearman, and canonical correlation analyses, enabling a more robust quantification of the relationships between influencing factors and water control effectiveness. This framework not only identifies the dominant indicators but also mitigates the limitations of single-method correlation analysis. Building upon these insights, the study proposes a machine learning-driven prediction system using Random Forest and Gradient Boosting algorithms, achieving classification accuracy exceeding 80% and effective regression prediction of measure duration. This represents a practical advancement over traditional empirical or single-feature decision approaches. The results reveal that overall field water cut percentage, 30-day pre-treatment water cut percentage, and daily liquid production are the key indicators governing treatment performance. Furthermore, water control measures in edge water reservoirs show significantly better performance than those in bottom water reservoirs. The developed prediction model provides a generalizable, data-driven decision-support tool, offering significant value for optimizing water control technologies in offshore horizontal wells. Full article

Background/Objectives: The use of physical restraints in adult and pediatric intensive care units (ICUs) is common yet controversial. While restraints are intended to prevent treatment interference or self-harm, they pose significant physical, psychological, and ethical risks. Nurses in intensive care units play a key role in decisions about restraint application, but there is a global lack of validated tools to assess their knowledge, attitudes, and practices, particularly in non-English-speaking contexts. Aim of this study was to develop and validate a questionnaire for assessing knowledge, attitudes, and practices (KAP) of ICU nurses regarding restraint use in adult and pediatric settings. Materials and Methods: A multi-method psychometric validation study was conducted across both adult and pediatric ICU settings at two hospitals in northern Italy. Questionnaire development included literature review, expert consultation, and iterative content and face validity assessments. Reliability was tested using test–retest methods, and construct validity was explored through exploratory factor analysis. The study followed COSMIN guidelines. Results: The final CON-Ti-IT questionnaire comprised 29 items across three subscales: Practices, Attitudes, and Knowledge. It demonstrated strong content validity (CVI = 0.96) and good internal consistency for the Practices subscale (Cronbach’s α = 0.89). Internal consistency for the Attitudes (α = 0.51) and Knowledge (α = 0.47) subscales was lower, reflecting the broader conceptual variability of these domains. Exploratory factor analysis confirmed the structural validity of the tool and led to the removal of three items with low factor loadings. Conclusions: This study presents the first validated tool specifically designed to evaluate ICU nurses’ KAP on restraint in adult and pediatric settings. While developed and validated in Italy, it could undergo cross-cultural adaptation and translation for use in other languages and healthcare systems. Its strong psychometric properties support its application in future research, and the data collected through its use can serve both to improve patient care and to provide a foundation for targeted educational initiatives. Full article

Point cloud data, with its rich information and high-precision geometric details, holds significant value for urban road infrastructure surveying and management. To overcome the limitations of manual classification, this study employs deep learning techniques for automated point cloud feature extraction and classification, achieving high-precision object recognition in road scenes. By integrating the Princeton ModelNet40, ShapeNet, and Sydney Urban Objects datasets, we extracted 3D spatial coordinates from the Sydney Urban Objects Dataset and organized labeled point cloud files to build a comprehensive dataset reflecting real-world road scenarios. To address noise and occlusion-induced data gaps, three augmentation strategies were implemented: (1) Farthest Point Sampling (FPS): Preserves critical features while mitigating overfitting. (2) Random Z-axis rotation, translation, and scaling: Enhances model generalization. (3) Gaussian noise injection: Improves training sample realism. The PointNet++ framework was enhanced by integrating a point-filling method into the preprocessing module. Model training and prediction were conducted using its Multi-Scale Grouping (MSG) and Single-Scale Grouping (SSG) schemes. The model achieved an average training accuracy of 86.26% (peak single-instance accuracy: 98.54%; best category accuracy: 93.15%) and a test set accuracy of 97.41% (category accuracy: 84.50%). This study demonstrates successful road scene point cloud classification, providing valuable insights for point cloud data processing and related research. Full article

Globally, buildings are responsible for around 32% of energy consumption and 34% of greenhouse gas emissions. One reason for this is the poor energy efficiency of much of the current building stock. Around 75% of today’s buildings are projected to still be in use in 2050, highlighting the importance of retrofitting existing buildings for energy efficiency. Such a strategy presents substantial opportunities to decrease global energy consumption and greenhouse gas emissions. While building retrofit projects have been implemented in many developed countries, studies in hot-humid climates and developing countries are still lacking. The challenges posed by hot-humid climates make developing the right energy retrofit strategies even more difficult. This study reviews and analyses previous energy retrofit studies and optimisations in building energy retrofit that used multi-objective optimisation methods, especially in hot-humid climate regions, using a bibliometric mapping tool called “VOSviewer” (version 1.6.20). The study also follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework for systematic reviews. This literature review highlights the paucity of research related to Multi-Objective Optimisation building-energy retrofit for buildings in countries with hot-humid climates and aims to identify the optimal strategies for energy retrofitting buildings in hot-humid climates using an optimisation method. The results of this study will significantly impact stakeholders’ decision-making processes, enabling them to identify the most advantageous objectives and energy efficiency measures for retrofitting buildings. Full article

Flight delay serves as a pivotal metric for assessing service quality in the aviation industry. Accurately estimating flight delay duration is increasingly acknowledged as a cornerstone of aviation management, with significant implications for operational efficiency, passenger satisfaction, and economic outcomes. Most existing approaches often focus on single airports or airlines and overlook the complex interdependencies within the broader aviation network, limiting their applicability for system-wide planning. To address this gap, this study proposes a novel integrated framework that combines deep learning and complex network theory to predict flight arrival delay duration from a multi-airport and multi-airline perspective. Leveraging Bayesian optimization, we fine tune hyperparameters in the XGBoost algorithm to extract critical aviation network features at both node (airports) and edge (flight routes) levels. These features, which capture structural properties such as airport congestion and route criticality, are then used as inputs for a deep kernel extreme learning machine to estimate delay duration. Numerical experiment using a high-dimensional flight dataset from the U.S. Bureau of Transportation Statistics reveals that the proposed framework achieves superior accuracy, with an average delay error of 3.36 min and a 7.8% improvement over established benchmark methods. This approach fills gaps in network-level delay prediction, and the findings of this research could provide valuable insights for the aviation administration, aiding in making informed decisions on proactive measures that contribute to the sustainable development of the aviation industry. Full article

Linear cultural heritage is characterized by complex cross-regional and multi-level features, facing severe challenges of spatial resource fragmentation and an imbalance in cultural and tourism functions. However, existing research lacks quantitative analysis regarding the non-linear driving mechanisms of spatial distribution and the misalignment of culture–tourism coupling. In this study, we construct an integrated identification–explanation–coupling–governance (IECG) theoretical framework. Taking The Great Tea Road (Jinzhong Section) as a case study, our framework integrates the CCSPM, XGBoost-SHAP machine learning interpreter, and Geodetector to systematically quantify the spatial structure of heritage and the level of culture–tourism integration. The results indicate that, (1) in terms of spatial patterns, the study area exhibits an unbalanced agglomeration characteristic of “dual-primary and dual-secondary cores,” with high-density areas showing significant orientation along rivers and roads; (2) regarding driving mechanisms, the machine learning model reveals a significant “non-linear threshold effect,” with 83% of driving factors (e.g., elevation and distance to transportation) exhibiting non-linear fluctuations in their influence on heritage distribution; and, (3) in terms of culture–tourism coupling, the overall coupling coordination degree (CCD) is low (mean 0.38), indicating significant “resource–facility” spatial misalignment. The modern number of public cultural facilities (NCF) is identified as the primary obstacle restricting the transformation of high-grade heritage into tourism products. Based on these findings, we propose adaptive zoning governance strategies. This research not only theoretically clarifies the complexity of the social–ecological system of linear heritage but also provides a generalizable quantitative method for the digital protection and sustainable tourism planning of cross-regional cultural heritage. Full article

Against the backdrop of the continuous expansion of the global aquaculture industry and the growing demand for high-quality feed protein, the development of sustainable alternative protein sources to fishmeal is crucial. Cottonseed protein, particularly cottonseed protein concentrate, has emerged as a highly promising plant-based alternative raw material due to its high protein content and cost advantages. This review systematically evaluates the application effects, challenges, and mechanisms of action of cottonseed protein in fish feed. Core analysis indicates that the primary limiting factor of cottonseed protein is the antinutritional factor free gossypol. High-level replacement (typically >30%) of fishmeal can inhibit fish growth, reduce protein deposition, and impair intestinal health. These adverse effects are closely associated with the downregulation of the hepatic mTOR signaling pathway—a central regulator of protein synthesis and cell growth—shifting the organism’s energy allocation from growth to stress adaptation. Furthermore, the unique fatty acid profile of cottonseed protein may exacerbate energy metabolism imbalance. To overcome gossypol toxicity, physical, chemical, and biological detoxification technologies have been widely applied. Among these, biological methods (such as Bacillus subtilis fermentation and CotA laccase-catalyzed degradation) are particularly outstanding, not only efficiently removing gossypol (removal rate > 90%) but also degrading macromolecular proteins into more digestible and absorbable small peptides and amino acids, significantly enhancing the nutritional value of cottonseed protein. Although the application prospects for cottonseed protein are broad, gaps remain in current research, particularly concerning the deeper metabolic pathways, nutrient utilization efficiency, and long-term impacts on metabolic homeostasis of detoxified cottonseed protein in fish. Future research needs to employ molecular nutrition and multi-omics technologies to elucidate its metabolic mechanisms and optimize detoxification processes and precision feeding strategies. Glandless cottonseed varieties, which fundamentally address the gossypol issue, are considered the most transformative development direction. Through continuous technological innovation, cottonseed protein is expected to become a core feed protein ingredient promoting the sustainable development of the global aquaculture industry. Full article

Post-stress cognitive impairment (PSCI) is defined as a persistent neuropsychiatric condition characterized by deficits in memory consolidation, executive functioning, and environmental interaction following exposure to violent stress. Despite its high incidence, PSCI remains underdiagnosed and lacks effective therapeutic strategies, posing a substantial societal burden and highlighting a critical gap in neuropsychiatric research. A major constraint in mechanistic studies is the persistent reliance on conventional paradigms, notably the Y-maze and novel object recognition test. Their limited sensitivity and poor translational relevance to human cognitive dysfunction, compounded by slow methodological innovation, significantly impede progress. Furthermore, the specific brain regions or neuronal populations contributing to PSCI pathogenesis are insufficiently explored. To address this, we assessed post-stress cognitive impairment in mice using a triple approach: Skinner box assays, traditional behavioral paradigms, and integrated 3D ethological analysis. This multi-method framework provides novel insights for refining animal models and advancing mechanistic understanding. Using c-Fos-based whole-brain screening, we identified the dentate gyrus (DG) as a key region involved in PSCI. Stress caused by violence markedly increased activity in DG CaMKII-expressing neurons. Chemogenetic inhibition of these neurons effectively alleviated stress-induced mild cognitive impairment phenotypes. In summary, by applying novel behavioral assessment tools, this study demonstrates that DG CaMKII neurons play a critical role in regulating post-stress cognitive impairment. Full article

The application of volumetric modular construction using Cross-Laminated Timber (CLT) has emerged as a sustainable and efficient alternative to traditional building methods, especially in residential and mid-rise structures. However, the widespread adoption of this technology remains limited due to the lack of standardized inter-modular connection systems. This paper presents a comprehensive state-of-the-art review of inter-modular connections used in volumetric CLT modular buildings. This review aims to evaluate the inter-modular connections by developing performance objectives and identifying gaps in knowledge of volumetric CLT inter-modular connections. It begins with an overview of global CLT modular construction trends, highlighting geographic distribution, structural demands, and environmental hazards such as seismic and wind exposure. Seven representative connection systems were identified from the literature and assessed using a multi-criteria framework comprising structural performance, manufacturing feasibility, on-site construction efficiency, and experimental and numerical evaluation. Each connection was scored according to defined evaluation metrics, and the results were provided to identify key strengths and limitations. The top-performing systems demonstrated superior resilience, modular adaptability, and validation through experimental testing and simulation. The paper identified critical research gaps, including limited performance data available for seismic applications, challenges in disassembly and reuse specifications, and the need for adaptable, damage-tolerant systems to enhance building structural performance. These findings provide a reference evaluation methodology for future development of inter-modular connections, to expand the applicability of volumetric CLT modular construction in moderate and high seismic and wind hazard regions. Full article

With the rapid development of the aviation industry, the complexity and intertwinement of various aviation activities have been continuously increasing, leading to the escalating of potential conflicts in limited airspace resources. Efficient airspace management is crucial for flight safety. To address the issues of conflict detection and resolution in localized high-density multi-airspace environments, this paper proposes an end-to-end integrated framework that integrates octree spatial partitioning, hierarchical conflict detection, and a conflict resolution module based on the Improved Whale Migration Algorithm (IWMA). This framework takes the airspace safety separation standards as constraints and the minimization of multi-airspace adjustment offsets as the optimization objective. The IWMA integrates diverse population structures and multi-strategy optimization technologies to balance global search and local exploration capabilities and avoid falling into local optima. Experiments on high-density conflict scenarios of different scales have verified the effectiveness and robustness of the proposed method. Simulation results show that compared with traditional detection methods and other classic optimization algorithms, the conflict detection and resolution method proposed in this paper can quickly and efficiently provide reliable solutions for multi-airspace conflict issues. Full article

Synthetic aperture radar (SAR), with its all-weather and all-day observation capabilities, plays a significant role in the field of remote sensing. However, due to the unique imaging mechanism of SAR, its interpretation is challenging. Translating SAR images into optical remote sensing images has become a research hotspot in recent years to enhance the interpretability of SAR images. This paper proposes a deep learning-based method for SAR-to-optical remote sensing image translation. The network comprises three parts: a global representor, a generator with cascaded multi-head attention, and a multi-scale discriminator. The global representor, built upon InternImage with deformable convolution v3 (DCNv3) as its core operator, leverages its global receptive field and adaptive spatial aggregation capabilities to extract global semantic features from SAR images. The generator follows the classic “encoder-bottleneck-decoder” structure, where the encoder focuses on extracting local detail features from SAR images. The cascaded multi-head attention module within the bottleneck layer optimizes local detail features and facilitates feature interaction between global semantics and local details. The discriminator adopts a multi-scale structure based on the local receptive field PatchGAN, enabling joint global and local discrimination. Furthermore, for the first time in SAR image translation tasks, structural similarity index metric (SSIM) loss is combined with adversarial loss, perceptual loss, and feature matching loss as the loss function. A series of experiments demonstrate the effectiveness and reliability of the proposed method. Compared to mainstream image translation methods, our method ultimately generates higher-quality optical remote sensing images that are semantically consistent, texturally authentic, clearly detailed, and visually reasonable appearances. Full article

Urban towns and smart city governments face increasing challenges in maintaining ecological balance as urbanization, industrial activity, and climate dynamics evolve. The degradation of ecological gardens, biodiversity parks, and waterways adversely affects ecosystem stability, air and water quality, and community well-being. Conventional urban ecological systems rely on reactive assessment methods that detect damage only after it occurs, leading to delayed interventions, higher maintenance costs, and irreversible environmental harm. This study introduces a Graph–Transformer Neural Network (GTNet) as a data-driven and predictive framework for sustainable urban ecological management. GTNet provides real-time estimation of smart city garden health, addressing the gap in proactive environmental monitoring. The model captures spatial relationships and contextual dependencies among multimodal environmental features using Dynamic Graph Convolutional Neural Network (DGCNN) and Vision Transformer (ViT) layers. The preprocessing pipeline integrates Principal Component Aggregation with Orthogonal Constraints (PCAOC) for dimensionality reduction, Weighted Cross-Variance Selection (WCVS) for feature relevance, and Selective Equilibrium Resampling (SER) for class balancing, ensuring robustness and interpretability across complex ecological datasets. Two new metrics, Contextual Consistency Score (CCS) and Complexity-Weighted Accuracy (CWA), are introduced to evaluate model reliability and performance under diverse environmental conditions. Experimental results on Melbourne’s multi-year urban garden datasets demonstrate that GTNet outperforms baseline models such as Predictive Clustering Trees, LSTM networks, and Random Forests, achieving an AUC of 98.9%, CCS of 0.94, and CWA of 0.96. GTNet’s scalability, predictive accuracy, and computational efficiency establish it as a powerful framework for AI-driven ecological governance. This research supports the transition of future smart cities from reactive to proactive, transparent, and sustainable environmental management. Full article

Multi-sensor fusion represents a primary approach for enhancing environmental perception in intelligent transportation scenes. Among diverse fusion strategies, Bird’s-Eye View (BEV) perspective-based fusion methods have emerged as a prominent research focus owing to advantages such as unified spatial representation. However, current BEV fusion methods still face challenges with insufficient robustness in cross-modal alignment and weak perception of dynamic objects. To address these challenges, this paper proposes a Bidirectional Complementary Cross-Attention Module (BCCA), which achieves deep fusion of image and point cloud features by adaptively learning cross-modal attention weights, thereby significantly improving cross-modal information interaction. Secondly, we propose a Temporal Adaptive Fusion Module (TAFusion). This module effectively incorporates temporal information within the BEV space and enables efficient fusion of multi-modal features across different frames through a two-stage alignment strategy, substantially enhancing the model’s ability to perceive dynamic objects. Based on the above, we integrate these two modules to propose the Dual Temporal and Transversal Attention Network (DTTANet), a novel camera and LiDAR fusion framework. Comprehensive experiments demonstrate that our proposed method achieves improvements of 1.42% in mAP and 1.26% in NDS on the nuScenes dataset compared to baseline networks, effectively advancing the development of 3D object detection technology for intelligent transportation scenes. Full article