Search Results (7,023)

The assembly quality of scaffolding systems directly governs the safety of personnel on construction sites. According to construction safety statistics, scaffolding-related accidents account for approximately 30–40% of construction fatalities globally, with geometric assembly deviations being a contributing factor in over 60% of scaffold collapse incidents. Traditional scaffolding inspections rely heavily on manual measurements, which are inherently inefficient, hazardous, and difficult to scale comprehensively. This study presents an automated evaluation framework that integrates computer vision with structural mechanics simulations. First, an object detection model based on the SegFormer encoder architecture is developed to precisely identify scaffolding standards, ledgers, and couplers against complex site backgrounds. Its hierarchical Transformer encoder and global self-attention mechanism enable the model to capture long-range topological relationships, achieving a mean Average Precision (mAP@0.5) of 95.2% on a custom dataset with an inference speed of 45 FPS per 640 × 640 image patch. For complete high-resolution frame processing including tiling and geometric extraction, the end-to-end pipeline requires approximately 8–12 s per frame. Second, a simplified Hough transform with a restricted parameter domain is introduced. Integrated with a dual-track image processing workflow, this algorithm performs sub-pixel centerline fitting to automatically extract critical geometric parameters, including lift height and bay width, maintaining a relative measurement error within 3.5% compared to manual ground truth. Finally, a parameterized finite element model is established. An automated mapping middleware dynamically injects the extracted as-built parameters into the simulation environment. Comparative simulation analysis indicates that a 14.7% deviation in standard lift height, coupled with an initial tilt defect of 1/150, precipitates a 22.4% reduction in the predicted structural stability factor, illustrating the framework’s capability for assessing relative capacity degradation between design intent and as-built conditions. This framework establishes a robust, closed-loop pipeline spanning visual perception and structural safety assessment, indicating potential for automated construction site safety management. Full article

Reliable stored-grain pest monitoring is essential for smart granaries, yet probe-based field images pose three coupled bottlenecks: tiny and densely distributed pests in complex backgrounds, costly bounding-box annotation, and limited edge-side computing resources. To address these bottlenecks in a targeted manner, this study proposes GrainPest-SSL, an integrated framework comprising a field dataset, a lightweight detector, and a pseudo-label purification-based semi-supervised pipeline. First, to overcome the lack of realistic training data, a GrainPest dataset with 1000 field images and 21,676 annotated pest instances is constructed using multiple self-developed monitoring probes deployed in a large wheat flat granary, capturing systematic pest-monitoring images from different in-bin locations rather than a single fixed imaging point. Second, to improve small-target detection under resource constraints, a YOLOv8n-CAEMA detector is designed with a P2 detection head and tail-inserted Coordinate Attention (CA) and Efficient Multi-scale Attention (EMA), achieving 0.840 mAP@0.5 under full supervision with only 2.932 M parameters. Third, to reduce annotation dependence without adding inference-stage complexity, an offline Teacher–Student strategy with Pseudo-Label Purification Filtering (PPLF) refines pseudo-labels using confidence, size, and aspect-ratio priors; under the 30% labeled setting, GrainPest-SSL improves mAP@0.5 from 0.738 to 0.799 and mAP@0.5:0.95 from 0.322 to 0.369 on average over three random seeds. Comparisons with representative agricultural pest detectors and semi-supervised object detection (SSOD) methods further confirm the balanced accuracy–efficiency performance of GrainPest-SSL under label-limited conditions. The deployed Student detector further achieves 13.6 FPS in FP16 mode on a Jetson Orin Nano Dev Kit under the 10 W power mode, supporting scheduled pest inspection, early infestation screening, and intelligent warning in smart granary monitoring systems. Full article

Fracturing is the dominant stimulation technique for low-porosity, low-permeability dolomite gas reservoirs, yet the lack of systematic laboratory research on multistage alternating acid etching mechanisms restricts field construction parameter optimization. Targeting the low-permeability Xixiangchi Formation dolomite reservoir in the eastern Sichuan Basin, this work develops a high-temperature, high-pressure core acid etching system coupled with 3D surface scanning. A reliable lab-to-field parameter conversion is established based on the Reynolds and Froude similarity criteria. Four-factor three-level orthogonal tests are conducted to quantify the impacts of pad fluid-to-acid viscosity ratio, total acid volume, pumping rate, and alternating injection stages on JRC-characterized wall roughness and fracture conductivity. The results show an identical factor dominance ranking for both indicators: viscosity ratio > pumping rate > injection stages > total acid volume. The optimal stimulation scheme is determined as a 50:1 viscosity ratio, 120 mL total acid volume, 12.54 mL/min laboratory pumping rate (equivalent to 8 m³/min in field operations), and 3 alternating injection stages. An elevated viscosity ratio intensifies viscous fingering, induces heterogeneous dolomite dissolution, and forms abundant irregular asperities on fracture surfaces. These self-supporting rough structures sustain stable seepage channels and markedly improve conductivity, verifying the positive roughness-conductivity correlation and revealing the core mechanism of heterogeneous etching-driven conductivity enhancement. The findings provide direct experimental support and parameter guidance for multistage alternating acid fracturing design in the Xixiangchi Formation and analogous tight dolomite reservoirs. Full article

Accurate detection of citrus leaf and fruit diseases is important for precision orchard management. However, natural orchard images often contain small disease symptoms, leaf and fruit overlap, illumination variation, and cluttered backgrounds, making reliable detection challenging. This study proposes LDTC-YOLO (LDTC denotes Lightweight Detection for Typical Citrus Diseases), a lightweight YOLOv8n-based detection model for typical citrus leaf and fruit diseases under natural orchard conditions. To improve detection accuracy and model compactness, LDTC-YOLO integrates an Adaptive Feature Pyramid Network for cross-level feature fusion, Coordinate Attention for disease-region feature enhancement, a Lightweight Shared Convolutional Detection head for reducing parameter redundancy, and Wise-IoU for bounding-box regression optimization. In addition, a self-collected handheld citrus disease dataset, HOCD-4, was constructed using close-range smartphone images captured under natural illumination. The dataset covers leaf and fruit symptoms of four typical citrus diseases: Huanglongbing/citrus greening, black spot, canker, and melanose. On the HOCD-4 test set, averaged across the four disease categories, LDTC-YOLO achieved precision, recall, mAP@0.5, and mAP@0.5:0.95 values of 0.915, 0.843, 0.894, and 0.648, respectively. These results indicate that LDTC-YOLO improves detection performance while maintaining a compact and efficient model profile, providing a potential reference for citrus disease detection under natural orchard conditions; however, its performance on actual mobile or embedded edge devices remains to be validated. Full article

Visibility graphs map time series to networks whose combinatorial structure encodes fractality, recovering the Hurst exponent of self-affine processes. We ask what the visibility construction carries beyond this fractal content. We lift the visibility graph to a 1-varifold, a measure on position and direction space from geometric measure theory, and equip it with a multiscale positive definite kernel. The lift embeds visibility graphs of unequal size in a common Hilbert space and yields a channel-resolved measure of cross-series geometric alignment. On a 25.8-year daily panel of thirteen commodity and currency layers, we define a relative alignment contrast that compares commodity currencies and safe haven currencies in their geometric alignment with the commodity complex. During global risk-off episodes the contrast is large and positive: commodity currencies import commodity shock geometry far beyond a time-shift independence benchmark, while the Japanese yen remains near geometric independence and the franc is confounded by a managed regime. The contrast is significant under three stress definitions with autocorrelation robust inference, holds as a continuous dose response, survives the removal of any single crisis, withstands moment, fractal, and topological controls, is direction-consistent across sixteen specifications, and collapses under a time-shift placebo. Detrended fluctuation analysis explains only two percent of it, so the reconfiguration is geometric information beyond fractality at this horizon, and a scaling exponent of the kernel mass separates a fractal-free component from a fractal-driven one. For investors, financial institutions, and policymakers, the contrast is a real-time structural diagnostic of flight to safety: it marks when commodity currencies stop diversifying the commodity complex while genuine safe havens still do, signaling through a channel that second-moment risk measures are built to miss. Full article

Conventional analysis of nuclear power plant accident transient responses heavily relies on physical simulation programs, whose computational time significantly exceeds the actual accident response duration, thereby severely hindering real-time safety assessment. This paper proposes a novel framework that integrates a Transformer-based Variational Autoencoder (VAE) with knowledge distillation for the fast prediction of nuclear power plant (NPP) accident transient responses. The approach involves constructing a latent space to extract essential features from transient response data using an Encoder–Decoder model based on the VAE architecture. A key innovation is the establishment of a direct mapping between plant operating condition parameters and the latent space, enabling the one-step generation of accident transients without iterative sequential prediction. The Encoder and Decoder leverage Self-Attention and Cross-Attention mechanisms to enhance feature extraction and conditional generation. Furthermore, the Encoder is distilled into a Mapper network, which predicts the latent features directly from the operating conditions, resulting in an efficient Mapper–Decoder pipeline for rapid prediction. The proposed model was evaluated against traditional Long Short-Term Memory (LSTM) and fully connected neural networks. Experimental results demonstrate that the proposed framework achieves superior performance in predicting NPP accident transients, indicating its strong potential for efficient safety analysis and system design optimization. Full article

Ultraviolet (UV) insect traps enable continuous field pest monitoring, but their images contain severe scale variation, dense and overlapping targets, incomplete specimens, background stains, insect fragments, non-target insects, inter-class similarity, and long-tailed distributions, leading to missed detections, false positives, and class confusion. To address these challenges, we constructed a real-world UV insect-trap pest dataset comprising 12,426 high-resolution images and 38,003 annotated instances from 36 pest categories, and propose FCD-DETR, a foreground-aware and context-enhanced Detection Transformer based on RT-DETR-R18. The main distinction of FCD-DETR lies in a progressive enhancement design tailored to UV insect-trap images, where FADM first reduces foreground–background confusion by decoupling pest foreground cues from multi-scale semantic information, C2f_CFBlock strengthens local-detail and contextual representations in the lightweight backbone, and DHSA further alleviates high-level semantic ambiguity through dynamic-range histogram self-attention in the Transformer encoder. On the proposed dataset, FCD-DETR achieved 64.42% mAP@0.5 and 41.33% mAP@50:95, improving the baseline by 5.25 and 5.11 percentage points, respectively, and outperforming representative CNN- and Transformer-based detectors. Comprehensive experimental analyses confirm that FCD-DETR improves foreground discrimination and detection robustness in complex UV insect-trap scenarios. Full article

Integrating the “programmable” characteristics of smart materials with 3D printing technology enables the integration of structural design and manufacturing, showing broad application prospects in flexible electronics, aerospace, biomedicine, and other fields. Magnetically controlled smart fluids are characterized by flexible solid–liquid conversion, high driving efficiency, and high safety. By harnessing the distinctive characteristics of this material, manufacturing and actuation approaches for intelligent structures can be further diversified. Inspired by the sol–gel transformation mechanism of protoplasm, this paper proposes a composite 3D printing method for magnetic and non-magnetic materials. A magnetically controllable binary suspension system with strong thixotropic properties was constructed, and its microscopic self-assembly structure was characterized. The yield behavior, linear viscoelastic properties, and thixotropic recovery performance of the magnetic thixotropic fluid (MTF) were investigated through steady and dynamic rheological measurements, and the optimal rheological parameters for printing were determined. A 3D printing platform with coordinated control of a magnetic field and a motion system was built to further study and optimize the printing process. The supporting characteristics of the MTF on a silicone film and the deformation of the printed composite structure under a gradient magnetic field were studied. The composite 3D printing and its application in soft robotics may provide new insights for space exploration, biomedicine, military reconnaissance, and many other fields. Full article

(1) Background: Motorcyclists remain disproportionately represented in road-traffic fatalities and serious injuries worldwide, yet the behavioral factors associated with their crash involvement are still incompletely understood. (2) Methods: This study integrates several established behavioral theories—Human Information Processing (HIP), Reason’s Generic Error-Modelling System (GEMS), the Theory of Planned Behavior (TPB), and Protection Motivation Theory (PMT)—into a single mixed-theory framework in order to examine simultaneously how risk behavior and protective behavior are associated with self-reported motorcycle crash involvement. A cross-sectional survey was administered to 2910 active motorcyclists using a Modified Motorcycle Rider Behavior Questionnaire (MRBQ) to capture four dimensions of risk behavior. (3) Results: A second-order confirmatory factor analysis (CFA) confirmed that the four risk dimensions load onto a single higher-order motorcyclist risk behavior construct, and the full measurement model demonstrated good reliability, convergent validity, and discriminant validity. Structural equation modeling (SEM) showed excellent fit. Motorcyclist risk behavior was positively and significantly associated with crash involvement, whereas protective behavior was negatively associated with it; because protective equipment mainly reduces injury severity rather than preventing crashes, this inverse relationship is interpreted as an indirect association rather than a direct reduction in crash occurrence, and both hypotheses were supported. (4) Conclusions: The findings support the value of integrating error-based and motivation-based theories when modeling motorcyclist safety and highlight the need for generationally tailored interventions that simultaneously reduce risky riding and promote consistent protective behavior. Full article

As generative artificial intelligence (GenAI) increasingly permeates educational contexts, understanding the factors driving learners’ Behavioral Intention (BI) toward GenAI-powered tools has become critical. This study integrates the Technology Acceptance Model (TAM), the Task-Technology Fit (TTF) framework, and privacy and ethical risk considerations to explore the determinants of Chinese higher education students’ Behavioral Intention to adopt these tools. Data were collected from 716 students via a structured self-reported questionnaire. A multi-stage analytical approach was employed by integrating structural equation modeling (SEM) with artificial neural networks (ANN) and support vector regression (SVR). SEM was first utilized to validate the theoretical hypotheses and the measurement model. Subsequently, ANN and SVR models were constructed to explore non-linear relationships and rank the importance of core predictors for Behavioral Intention, including Perceived Ease of Use (PEU), Privacy and Ethical Concerns (PEC), Perceived Technical Features (PTF), and TTF. The modeling performance of the two algorithms was then rigorously compared. The SEM results indicate that PTF exerts an indirect impact on Behavioral Intention via the sequential mediation of Task-Technology Fit and Perceived Usefulness (PU), while PEU positively influences both Perceived Usefulness and Behavioral Intention. Notably, PEC did not exhibit a significant negative effect on users’ Attitude (ATT) or Behavioral Intention. These findings were further elucidated by the machine learning analyses, where PTF and PEU emerged as the dominant predictors, whereas the non-linear contribution of PEC was marginal. Furthermore, SVR outperformed ANN in terms of predictive accuracy and model stability. This study demonstrates the efficacy of combining theoretical modeling with machine learning techniques to elucidate the adoption mechanisms of GenAI in higher education. In addition, preliminary teaching observations in undergraduate mathematics and logistics management courses link quantitative results with actual learning scenarios. We acknowledge that future research should validate these patterns using observed behavioral data. Full article

This paper investigates secure self-triggered time-varying formation control for quadrotor swarm systems against sequential multi-link scaling attacks, which can be implemented in a self-triggered and fully distributed manner. Firstly, based on the outer-loop position and velocity control model of quadrotors, a fully distributed secure time-varying formation control protocol is constructed under sequential multi-link scaling attacks with three characteristics: distributed, sequential, and scalable, and the design criteria for fully distributed secure time-varying formation control are provided. Then, combining the inner and outer-loop control principles of quadrotors, by constructing Euler angle loop controllers and angular velocity controllers, the conversion of the control input of the outer-loop position and velocity to the inner-loop attitude control is achieved, and fully distributed secure time-varying formation control algorithms for quadrotor UAV swarm systems under attacks are proposed. Finally, the effectiveness and applicability of the fully distributed secure consensus method in the formation control of quadrotor UAV swarms is verified through flight experiments using a quadrotor UAV swarm flight test platform. The research results provide a useful reference for the practical application of the fully distributed secure cooperative control theory. Full article

Digital technologies are increasingly embedded in tourism performing arts, yet technology-intensive projects do not automatically generate profound cultural experiences. To explain this high-investment-but-low-experience dilemma, this study conceptualizes digital technology-enabled cultural experience as a configurational outcome of technological conditions and visitor-side psychological processing. Drawing primarily on the S-O-R framework and interpreted from a socio-technical systems perspective, digital technology interactivity and innovativeness are treated as stimulus conditions, while cognitive evaluation, scenario construction, and flow experience are treated as organism-level processing conditions. Psychological ownership theory is employed as a supplementary interpretive lens—rather than as a directly measured or tested mechanism—to explain why divergent configurational pathways may converge on high cultural experience through control, intimate understanding, and self-investment. Using fuzzy-set qualitative comparative analysis (fsQCA) with 540 valid questionnaires from visitors to tourism performing arts, the study finds that no single condition is necessary for high cultural experience. Instead, five sufficient configurations are identified: interaction–cognitive compensation, interaction–cognitive–flow synergy, dual-technology traction, innovation–scenario–immersion drive, and innovation–cognitive–flow compensation. The pathway to non-high cultural experience is asymmetric and is mainly characterized by the concurrent absence of technological, cognitive, scenario, and flow conditions. The findings re-specify the application of S-O-R theory in a configurational analytical context and provide a cautious socio-technical explanation of how technological and psychological conditions jointly shape cultural experience. Full article

This paper proposes a SOC-SOH joint estimation method based on adaptive weighted multi-channel LSTM Transformer fusion network (MLTA-Net). The proposed method constructs a battery health factor set which covers multi-level features, and the aging trend of batteries can be characterized from multiple dimensions. The MLTA-Net model adopts a multi-channel parallel architecture, which can analyze the different types of battery data characteristics. Short-term temporal dependencies are captured by LSTM encoder, and global operating characteristics are analyzed using Transformer multi head self-attention mechanism. Based on adaptive weighted fusion layer for feature fusion, high-precision estimation of battery state can be achieved. Experimental results on CATL-1 and CATL-2 datasets show that the proposed MLTA-Net achieves superior SOH estimation accuracy, with RMSE values of 0.286 and 0.287, MAE values of 0.151 and 0.162, MAPE values of 0.053 and 0.056, and R² values of 0.997 and 0.997, respectively. Compared with CNN-GRU, MLP-Attention, Transformer, MLP, RNN, and SVR models, the proposed method exhibits lower prediction errors and better robustness. Full article

There is a growing body of research examining the experiences of lesbian, gay, bisexual, trans, and queer (LGBTQ+) teachers in schools. However, limited empirical attention has been given to those in the early stages of their careers, particularly within the UK context, where historical and legislative dynamics continue to shape school cultures. This article addresses this gap by exploring how queer early-career teachers (QECTs) construct and navigate their personal and professional identities within heteronormative and cisnormative school environments. Drawing on a qualitative narrative inquiry with three UK-based QECTs, the study analyses written biographical narratives and semi-structured interviews using reflexive thematic analysis. The findings identify three interconnected processes shaping identity navigation: preventative barriers (including anticipated judgement, self-surveillance, and performative regulation), community as a site of affirmation and restoration, and a negotiated sense of freedom understood as context-dependent agency rather than full disclosure. The study argues that identity formation for QECTs is not a linear progression towards authenticity, but an ongoing negotiation shaped by power, precarity, and relational safety. In doing so, it extends existing research by foregrounding early-career status as a critical factor in queer teacher identity work and contributes a conceptual framing of “negotiated authenticity” within professional contexts. The article concludes by considering implications for professional sustainability, belonging, and inclusive school cultures. Full article

Underwater concrete infrastructure is gradually damaged by water scouring, chloride ingress, freeze–thaw cycles, and fatigue loading, so reliable in situ repair materials are increasingly needed. Conventional cement-based repair materials are often unsuitable for underwater use because they disperse in water, bond weakly to wet substrates, and show limited durability. Polymer-modified cementitious materials can reduce these problems by combining cement compatibility with polymer film formation and interfacial strengthening. Water-soluble polymers mainly improve fresh-state cohesion and anti-washout performance through adsorption, bridging, and flocculation regulation. In comparison, polymer emulsions and latexes are more effective after hardening, improving bonding, crack resistance, and durability through polymer films and organic–inorganic networks. For self-leveling underwater repair, the flow spread should reach at least 130 mm. For vertical repair with a 20 mm layer, a yield stress of about 360 Pa is needed to prevent sagging. Therefore, performance should not be judged by strength alone, but by constructability, interfacial water films, and pore connectivity. Future studies should consider responsive polymers, multi-component modification, standardized tests, and low-carbon binders. Full article