Search Results (2,005)

In immersive digital devices, high environmental complexity can lead to rendering delays and loss of interactive details, resulting in a fragmented experience. This paper proposes a lightweight NeRF (Neural Radiance Fields) modeling and multimodal perception fusion method. First, a sparse hash code is constructed based on Instant-NGP (Instant Neural Graphics Primitives) to accelerate scene radiance field generation. Second, parameter distillation and channel pruning are used to reduce the model’s size and reduce computational overheads. Next, multimodal data from a depth camera and an IMU (Inertial Measurement Unit) is fused, and Kalman filtering is used to improve pose tracking accuracy. Finally, the optimized NeRF model is integrated into the Unity engine, utilizing custom shaders and asynchronous rendering to achieve low-latency viewpoint responsiveness. Experiments show that the file size of this method in high-complexity scenes is only 79.5 MB ± 5.3 MB, and the first loading time is only 2.9 s ± 0.4 s, effectively reducing rendering latency. The SSIM is 0.951 ± 0.016 at 1.5 m/s, and the GME is 7.68 ± 0.15 at 1.5 m/s. It can stably restore texture details and edge sharpness under dynamic viewing angles. In scenarios that support 3–5 people interacting simultaneously, the average interaction response delay is only 16.3 ms, and the average jitter error is controlled at 0.12°, significantly improving spatial interaction performance. In conclusion, this study provides effective technical solutions for high-quality immersive interaction in complex public scenarios. Future work will explore the framework’s adaptability in larger-scale dynamic environments and further optimize the network synchronization mechanism for multi-user concurrency. Full article

Fracture nonunion remains an unresolved complication after extremity fracture, with notable costs to patient quality of life and health systems. Nonunion is defined by the inability of fracture ends to unite without evidence of progressive healing over time. Approximately 2 to 10% of all fractures go onto nonunion, with increased rates observed in specific fracture locations and patient populations. Despite advances in fixation techniques and bone grafting, current treatments remain limited and frequently fail to restore durable bone healing. In this review, the current state of emerging biologic and bioengineering therapies for nonunion will be summarized, with a focus on how these advances may shift treatment from palliative reconstruction toward durable healing. Biological therapies such as growth factors, stem cells, and gene-modified constructs show promise but face challenges of short half-life, inconsistent efficacy, and safety concerns. Emerging approaches, including controlled-release scaffolds, immunomodulatory materials, stem cell-derived exosomes, and gene therapy platforms, offer opportunities to more precisely restore the osteogenic, angiogenic, and immunologic environment required for union. Full article

In order to surmount the characteristics of high steel consumption and cost in prefabricated buildings, as a novel structural component, reinforced concrete vierendeel sandwich plates (RC-VSP) could be effectively employed. However, RC-VSP is restricted by complex construction procedures and rigorous quality control demands. Reliable reinforcement connections are the keys to their prefabrication. This study employed the methods of 1:1 full-scale comparative tests and numerical analysis through finite- element modeling. It compared the mechanical behaviors of the continuous reinforcement control group and the upset sleeve assembly group under four-point cyclic bending conditions. It analyzed how sleeves’ distribution influences structural stress states and crack propagation processes. The results show a superior ductility and damage resistance, on the basis of the components’ attenuation amplitude of the secant stiffness remains around 50% after the loading test with a deflection of 1/100, and the equivalent damping ratio is greater than 13%. Furthermore, the high similarity of the strain responses demonstrated the connection achieves prefabricated structures’ “equivalent performance to cast-in-place ones”. Additionally, the sleeve joints have slightly better stiffness, minor stress concentration at sleeve ends. This study offers robust experimental and theoretical support for the integrated prefabricated application of RC-VSP and further facilitates the development of building structures toward higher efficiency and lower carbon emissions. Full article

Farmland improvement has become an overwhelmingly favorable policy in developing countries, being expected to leverage a sustainable agricultural total factor productivity (ATFP) to increase their agricultural competitiveness. Worldwide farmland improvement projects are experiencing an evolution from single goals to comprehensive goals (e.g., comprehensively improve the farmland quality by decreasing farmland abandonment and fragmentation and meanwhile improving soil–water conditions and machinery affordability). However, performances of comprehensive farmland improvement projects have been questioned, especially considering its implementary complexity and regional heterogeneity. This study applies a continuous difference-in-difference (DID) method to China’s provincial panel data (2005–2020) to analyze the impact of the high-standard farmland construction policy (which started China’s national project on comprehensive farmland improvement) on ATFP. Results show the policy significantly increases ATFP by 0.101 units. Moreover, parallel trend and robustness test results indicate the policy effect has stability and continuity. Heterogeneity analysis results show the policy effect is greater in major grain-producing regions than non-major grain-producing regions, the central regions than western or eastern regions, and regions with high disease—pest control and soil—water conservation levels than areas with low levels. Mechanism analysis results show the policy effect is achieved through three paths—operation scale increase (mediating effect size is 16.13%), planting structure adjustment (mediating effect size 12.80%), and agricultural disaster reduction (mediating effect size 13.74%). Thus, this study advocates sustainable and specialized high-standard farmland construction: it suggests post-construction policies maintaining high-standard farmland quality and detailed policies considering different regions’ heterogeneity. Full article

In real-world industrial settings, obtaining class-balanced fault data is often difficult. Imbalanced data across categories can degrade diagnostic accuracy. Time-series Generative Adversarial Network (TimeGAN) is an effective tool for addressing one-dimensional data imbalance; however, when dealing with multiple fault categories, it faces issues such as unstable training processes and uncontrollable generation states. To address this issue, from the perspective of data augmentation and classification, a gradient-penalized Conditional Time-series Generative Adversarial Network with a Multi-Scale Importance-aware Network (CTGAN-MSIN) is proposed in this paper. Firstly, a gradient-penalized Conditional Time-Series Generative Adversarial Network (CTGAN) is designed to alleviate data imbalance by controllably generating high-quality fault samples. Secondly, a Multi-scale Importance-aware Network (MSIN) is constructed for fault classification. The MSIN consists of the Multi-scale Depthwise Separable Residual (MDSR) and Scale Enhanced Local Attention (SELA): the MDSR network can efficiently extract multi-scale features, while the SELA network is capable of screening out the most discriminative scale features from them. Finally, the proposed method is validated using the HUST bearing dataset and the axial piston pump dataset. The results show that under the data imbalance ratio of 15:1, the CTGAN-MSIN achieves diagnostic accuracies of 98.75% and 96.50%, respectively, on the two datasets and outperforms the comparison methods under different imbalance ratios. Full article

This article describes a model for optimizing traffic flow control and generating traffic signal phases based on the stochastic dynamics of traffic and the percolation properties of transport networks. As input data (in SUMO), we use lane-level vehicle flow rates, treating them as random processes with unknown distributions. It is shown that the percolation threshold of the transport network can serve as a reliability criterion in a stochastic model of lane blockage and can be used to determine the control interval. To calculate the durations of permissive control signals and their sequence for different directions, vehicle queues are considered and the time required for them to reach the network’s percolation threshold is estimated. Subsequently, the lane with the largest queue (i.e., the shortest time to reach blockage) is selected, and a phase is formed for its signal control, as well as for other lanes that can be opened simultaneously. Simulation results show that when dynamic traffic signal control is used and a percolation-dynamic model for balancing road traffic is applied, lane occupancy indicators such as “congestion” decrease by 19–51% compared to a model with statically specified traffic signal phase cycles. The characteristics of flow dynamics obtained in the simulation make it possible to construct an overall control quality function and to assess, from the standpoint of traffic network management organization, an acceptable density of traffic signals and unsignalized intersections. Full article

This study presents a comprehensive construction monitoring program for a geometrically complex, large-span spatial tubular truss system within a typical center steel exhibition hall. To ensure construction quality and structural integrity throughout the entire process, the monitoring strategy was rigorously aligned with the actual construction sequence. Real-time vertical displacement measurements were acquired at critical structural members and joints. A detailed finite element model of the entire structure was developed to systematically analyze the structural behavior of herringbone columns, primary and secondary trusses, and temporary supports during both installation and removal phases. Displacement patterns at key locations were investigated, and a global stability assessment was performed. Results demonstrate close agreement between finite element predictions and field measurements, confirming the rationality and reliability of the construction scheme. The structural system exhibited excellent stability across all construction stages, satisfying both architectural aesthetics and structural safety requirements. This study provides practical insights for construction control of similar large-span steel structures. Full article

The construction of prefabricated timber buildings is a topic of growing interest, although research has primarily focused on technological aspects, while the users’ perspective remains underexplored. Accordingly, this paper aims to map the existing literature on prefabricated wooden buildings from a user-centered perspective, considering the whole-building scale. A systematic literature search of publications between 2010 and 2025 was conducted following PRISMA guidelines, identifying relevant studies. A bibliometric analysis was then performed to map key research themes, which were further examined through a scoping review. Four main themes emerged, i.e., indoor comfort, indoor air quality, sustainability and energy efficiency, and building architectural design. The findings highlight numerous aspects that should be considered in prefabricated timber buildings design, including thermal, vibrational and acoustic comfort, air pollutant and ventilation control, user behavior in relation to energy use, and spatial design based on users’ needs. However, the limited number of existing studies makes comprehensive evaluation difficult. Furthermore, the results emphasize the need for multidisciplinary approaches to adequately integrate user experience into the design of these buildings. Full article

Kernel row number (KRN) is a pivotal determinant for yield in maize breeding programs. However, the genetic basis underlying KRN remains largely elusive. To identify candidate genes regulating KRN, a population of 318 BC₄F₄ chromosomal segment substitution lines (CSSLs) was developed via backcrossing, with Baimaya (BMY) as the donor parent and B73 as the recurrent parent. Furthermore, a high-density genetic linkage map containing 2859 high-quality single-nucleotide polymorphism (SNP) markers was constructed for quantitative trait locus (QTL) mapping of KRN. Notably, 19 QTLs controlling KRN were detected across three environments and in the Best Linear Unbiased Prediction (BLUP) values; among these, a major-effect QTL (qKRN4.09-1) was consistently identified across all three environments and BLUP. Then, the integration of linkage mapping and transcriptome analysis of 5 mm immature ears from near-isogenic lines (NILs) uncovered a candidate gene, Zm00001eb205550. This gene exhibited significant downregulation in qKRN4.09-1^BMY, and two missense variants were detected between qKRN4.09-1^BMY and qKRN4.09-1^B73. Zm00001eb205550 exhibited preferential expression in developing ears. Moreover, the pyramiding of favorable alleles from the five stable QTLs significantly increased KRN in maize. These findings advance our genetic understanding of maize ear development and provide valuable genetic targets for improving KRN in maize breeding. Full article

Titanium dioxide (TiO₂) is widely used in coatings, plastics, rubber, papermaking, and other industries. The microstructural characteristics of its inorganic shell largely determine the overall performance of the product, significantly affecting optical behavior, dispersibility, weather resistance, and stability. Currently, coating quality evaluation in industry still relies primarily on manual inspection, lacking objective, standardized, and reproducible quantitative methods. This study focuses on lab-prepared core–shell TiO₂ powders comprising a TiO₂ core and a thin inorganic shell enriched in alumina/silica. This study presents Titanium Dioxide U-Net (TD U-Net)—a deep learning approach for transmission electron microscopy (TEM) image segmentation and shell thickness evaluation of core–shell structured TiO₂ particles. TD U-Net employs an encoder–decoder architecture that effectively integrates multi-scale features, addressing challenges such as blurred boundaries and low contrast. We constructed a dataset of 1479 TEM images processed through a six-step workflow: image collection, data cleaning, annotation, mask generation, augmentation, and cropping. Results show that TD U-Net achieves a Dice coefficient of 0.967 for segmentation accuracy and controls shell-thickness measurement error within 5%, significantly outperforming existing image-processing models. An intelligent analysis system developed from this technology has been successfully applied to titanium dioxide product quality assessment, providing an efficient and reliable automated tool for coating-process optimization and quality control. Full article

The basis of the construction industry is building materials with high-quality indicators in terms of physical, mechanical, and thermophysical characteristics, however, there are a number of issues affecting the quality of manufactured products. The development of the construction industry provides new opportunities for designing efficient construction facilities. To obtain enhanced design capabilities, it is very important to relieve the load on the structure, and this can be achieved by reducing the mass of materials without losing strength. This study investigates the enhancement of foam concrete through the combined incorporation of mineral fibers recycled from basalt insulation waste and complex polymer modifiers. The aim was to improve the material’s mechanical performance, durability, and pore structure stability while promoting sustainable use of industrial by-products. The experimental program included tests on density, compressive strength, water absorption, and thermal conductivity for mixtures of different densities (400–1100 kg/m³). Results demonstrated that the inclusion of mineral fibers and polymer modifiers significantly enhanced structural uniformity and pore wall integrity. Compressive strength increased by up to 35%, water absorption decreased by 25%, and thermal conductivity was reduced by 18% compared with the control mixture. Full article

In precast concrete shear wall structures, the joints formed during the vertical connection of precast units are referred to as the “horizontal joint”. Serving as vertical connection nodes in this structure system, the construction quality of theses horizontal joints significantly influences the structural integrity. To investigate the influence of horizontal joint quality defects on the mechanical behaviour of precast concrete shear walls, three precast concrete shear wall specimens with quality defects in different regions and three control specimens were designed. Quasi-static tests under a constant axial load were conducted to investigate the effects of defect area, location and other factors on the mechanical behaviour of the walls. Results demonstrate that the quality defects in horizontal joints significantly affect the mechanical behaviour of precast concrete shear walls. When the ratio of the quality defect area to the cross-sectional area of the boundary member reaches 100%, the yield load and peak load of the precast concrete shear wall decrease by 13% and 20%, respectively. Additionally, the structural stiffness exhibited a 13% degradation at a drift angle of 1/1000. Although the failure mode remains largely unchanged, yielding of longitudinal reinforcement in the boundary members is observed. Moreover, as the proportion of the quality defect area to the cross-sectional area decreases, its adverse effects on the mechanical behaviour of the precast concrete shear wall gradually diminish. The established numerical analysis model is shown to be reasonable and reliable. When the defective area of the horizontal joints is less than 25% of the total cross-sectional area, the quality defects essentially have no influence on the mechanical behaviour of the precast concrete shear walls. Full article

With the increasing penetration of renewable energy and the rising demand for power quality, the dynamic performance and accuracy of grid-voltage detection have become crucial for the control of grid-following devices such as dynamic voltage restorers (DVRs). However, the conventional moving average filter (MAF) in grid-voltage detection suffers from inherent limitations in dynamic response. To address this issue, this paper proposes a voltage-detection method, which is based on an adaptive variable-parameter filtering algorithm termed MAF-MATCH-V. First, a cascaded filter model is constructed by integrating a zero-pole matcher (MATCH) with the MAF. Frequency-domain analysis demonstrates that the MATCH compensates for the mid- and high-frequency magnitude attenuation and reduces the phase delay of the MAF, thereby accelerating the dynamic response while preserving its harmonic-rejection capability. Second, the influence of the matching coefficient on the time-domain response is investigated, and a time-varying adaptive strategy is designed to balance rapid disturbance recognition with steady-state convergence. Finally, experimental results under various non-ideal grid conditions demonstrate that the proposed method achieves superior overall performance compared with conventional approaches. Specifically, MAF-MATCH-V realizes millisecond-level event recognition and zero steady-state error convergence, making it a practical solution for the real-time control of grid-following equipment in modern power systems. Full article

Climate change affects both the quantity and quality of water resources, amplifying the water crisis, slowing progress toward achieving the Sustainable Development Goals (SDGs), and contributing to the needs of future generations. To address these challenges, this study presents an interdisciplinary synthesis of the literature on the subject, highlighting the impact of climate change on water resources (surface water and groundwater). The escalating global demand for water, driven by factors such as population growth, urbanization, and industrial development, is placing significant pressure on water resources. This situation needs sustainable management solutions to mitigate the environmental impacts associated with increased water consumption and climate change. The methodology included bibliometric analysis using VOSviewer version 1.6.19, a software tool for constructing and visualizing bibliometric networks, and systematic analysis according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. 155 records were used in this review from a total number of 1344 documents searched in Science Direct, Scopus and Google Scholar databases. The results indicate that research on the consequences of climate change on water quality remains in its infancy. This study highlights the effects of climate change on water quality indicators, including physicochemical, microbiological, and micropollutants, as well as the implications for human health and water supply infrastructure. Climatic factors, such as rising temperatures and changing precipitation patterns, are particularly important because they control processes fundamental to sustaining life on the planet. The main conclusions are that climate change accelerates the degradation of drinking water quality and amplifies public health risks. These findings highlight the need for rigorous assessments and the development of integrated adaptation strategies involving collaboration among water operators, decision-makers, the scientific community, and climate change specialists. Full article

Multifunctional metasurfaces, capable of flexible electromagnetic wave manipulation, have become a focus of research for their high integration and utility. In particular, those operating simultaneously in transmission and reflection modes have attracted growing interest, as they integrate multiple functions within a single aperture, save physical space, and further expand wave control capabilities across full space. In this work, an inspiring strategy of transmission-reflection-integrated bifunctional metasurface by hybridizing geometric phase and propagation phase is proposed. The transmission and reflection modes can be independently and flexibly controlled in full space: the co-polarized reflection under left-handed circular polarization (LCP) incidence is governed by rotation-induced geometric phase modulation, while the co-polarized transmission under right-handed circular polarization (RCP) incidence is modulated through scaling-induced propagation phase modulation. Moreover, arbitrary amplitude modulation of the co-polarized transmission under RCP incidence can be realized by incorporating lumped resistors. As a proof of concept, a bifunctional meta-device is constructed, which can generate vortex beam carrying arbitrary topological charge for LCP reflected wave and achieve high-quality holographic imaging for RCP transmitted wave. Both the simulated and experimental results validate the feasibility of the proposed strategy, which significantly enhances the integration density of multifunctional metasurfaces while reducing inter-functional crosstalk, expanding its potential applications in electronic engineering. Moreover, it can also serve as a fundamental machine learning platform, facilitating multimodal fusion and cross-modal learning in radar signals and visual imaging. Full article