Search Results (2,150)

The use of consumer-grade cameras for stereo vision provides a cost-effective, non-contact method for measuring three-dimensional displacement in civil engineering experiments. However, obtaining accurate 3D coordinates requires accurate temporal alignment of several unsynchronized cameras, which is often lacking in consumer-grade devices. Current synchronization software methods usually only achieve precision at the frame level. As a result, they fall short for high-frequency shake table experiments, where even minor timing differences can cause significant triangulation errors. To address this issue, we propose a novel image-based synchronization method and a graphical user interface (GUI)-based software for acquiring stereo videos during shake table testing. The proposed method estimates the time lag between unsynchronized videos by minimizing reprojection errors. Then, the estimate is refined to sub-frame accuracy using polynomial interpolation. This method was validated using a high-precision motion capture system (Mocap) as a benchmark through large- and small-scale experiments. The proposed method reduces the RMSE of triangulation by up to 78.79% and achieves maximum displacement errors of less than 1 mm for small-scale experiments. The proposed approach reduces the RMSE of displacement measurements by 94.21% and 62.86% for small- and large-scale experiments, respectively. The results demonstrate the effectiveness of the proposed method for precise 3D displacement measurement with low-cost equipment. This method offers a practical alternative to expensive vision-based measurement systems commonly used in structural dynamics research. Full article

Leveling of the crane support platform plays a vital role in operational safety and lifting efficiency; it requires both precise horizontal positioning and the rational distribution of outrigger load. However, the current synchronous leveling methods mainly focus on displacement synchronization leveling while neglecting the control of outrigger load, resulting in the problem of individual outrigger overloading. To address this problem, a synchronous leveling control method with variable load constraints (SLCM-VLC) is proposed in this paper based on the framework of model predictive control. Firstly, the proposed method conducts independent outrigger modeling and decoupling of outriggers through adjacent cross-coupling; then a displacement synchronization controller (DSC) is designed to ensure efficient synchronous leveling. Secondly, a collaborative controller of displacement and force (DFCC) under variable load constraints is designed to overcome the limitations of traditional independent optimization. Subsequently, an extended state observer (ESO) is introduced to compensate for environmental disturbances and control deviations. Finally, the effectiveness of the proposed method is verified through a co-simulation using Matlab, Adams, and Solidworks. The results show that, compared with existing leveling control methods, the proposed method can achieve high precision and rapid leveling under smaller peak load, thereby extending the service life of the platform’s electric cylinders. Full article

This study provides an in-depth structural analysis of UNESCO World Heritage Apulian trulli, considering the three-layer dry-stone structure of their characteristic conical roofs. An integrated approach involving laser scanning, ground-penetrating radar, endoscopic investigation, and laboratory materials testing is used to identify and characterize the multi-wythe masonry system. A detailed finite element model is created in ANSYS to analyze seismic performance on Italian building codes. The model is validated through ambient vibration testing using accelerometric measurements. The diagnostic survey identified a three-layer system including exterior stone wythe, interior wythe, and rubble core, with compressive strength of stone averaging 2.5 MPa and mortar strength of 0.8 MPa. The seismic assessment will allow the examination of displacement patterns and stress distribution under design load conditions (ag = 0.15 g). The structural analysis demonstrates adequate performance under design loading conditions, with maximum stress levels remaining within acceptable limits for historic masonry construction. The experimental validation confirmed the finite element model predictions, with good correlation between numerical and experimental frequencies. The improvement of the overall seismic performance with the multi-wythe configuration and the role of wall thickness and geometric proportions will be taken into account. The methodology aims to provide technical evidence supporting the continued use of vernacular buildings while contributing to scientifically informed conservation practices throughout the region. Full article

This study examines the use of alternative financial services in the context of COVID-19-induced economic displacement. We utilize data from the 2020 Collaborative Multiracial Post-Election Survey and the ABC-X model of family stress and coping to examine how economic displacement, prior AFS use, and sociodemographic factors collectively promote AFS utilization. This study examines four types of financial coping strategies: credit cards, payday loans, public benefits, and borrowing from family and friends. Of these, payday loan use represents the primary indicator of AFS reliance. Our findings indicate that borrowing from friends and family is a significant predictor of payday loan usage. Furthermore, prior use of AFS, such as payday loans, check-cashing services, and pawnshops, strongly predict future AFS use. This study also finds a negative relationship between ethno-racial identity and AFS use which contradicts much of the existing literature. We find that lower levels of education and living in large urban areas are predictors of AFS use. This study highlights how the pandemic exacerbated financial vulnerabilities and validates the need for education and advocacy to prepare the most vulnerable to break cycles of AFS use. Full article

To address the issue of missed detection of abnormal images caused by scarcity of defect samples and inadequate model training that characterize the current substation image inspection methods, this paper proposes a new substation image inspection method based on visual communication and combination of normal and abnormal samples. In this new method, the quality of substation equipment images is first evaluated, and images are recaptured when they are defocused and underexposed. Images are then preprocessed to eliminate the impact of noise on the algorithm. Image feature alignment is then performed to mitigate camera displacement errors that could degrade algorithmic accuracy. Subsequently, normal-labeled images are used to train the model, and a normal sample database is thus established. Built upon visual communication infrastructure with low-level quantization, the visual feature discrepancy between the current inspection images and those in the normal sample database is calculated using the Learned Perceptual Image Patch Similarity (LPIPS) metric. Through this process, the normal images are filtered out while abnormal images are classified and reported. Finally, this new method is validated at a municipal power supply company in China. When the abnormal image reporting rate is 18.9%, the abnormal image reporting accuracy rate is 100%. This demonstrates that the proposed method can significantly decrease the workload of substation operation and maintenance personnel in reviewing substation inspection images, reduce the time required for a single inspection of substation equipment, and improve the efficiency of video-based substation inspections. Full article

A series of shear interface experiments on a type of loess and rough concrete interface under conditions of different initial water contents (16%, 21%, and 26%), dry densities (1.30 g/cm³, 1.52 g/cm³, 1.70 g/cm³) and normal stresses (50 kPa, 100 kPa, 200 kPa) were conducted to further understand shear deformation and strength characteristics of a loess and rough concrete interface combined with loess deformation monitoring method of gypsum powder line method. A discrete element method (DEM) model was then established, calibrated against the experimentally obtained shear stress–displacement curves, and run to investigate the shear deformation, contact force chain and fabric evolution processes at the microscopic level. The results show the following: (1) The shear deformation and strength behaviors of the loess and rough concrete interface were significantly impacted by the initial moisture content, dry density and normal stress. (2) The shear deformation of the loess increased with the increase in initial moisture content, and decreased with dry density and normal stress. (3) The shear strength of the loess and rough concrete interface increased with the increase in dry density and normal stress, and decreased with the increase in initial moisture content. (4) The evolution of the shear deformation, contact force chain and fabric of the loess-concrete rough interface were explored and analyzed from a microstructural perspective. This study contributes insights critical to construction of the pile-loess systems in Chinese Loess Plateau regions. Full article

This study examines the socio-ecological impacts of soybean cultivation in the Brazilian Amazon, a region of critical importance for global climate regulation and biodiversity conservation. It explores how the expansion of soybean cultivation in this region since the 1990s, driven by international demand and domestic policies, has triggered a series of unsustainable socio-ecological consequences, such as deforestation, overuse of agrochemicals, displacement of indigenous communities, and land tenure conflicts. Inadequate governance, at both national and international levels, has exacerbated these challenges, undermining efforts to balance soybean cultivation with sustainable development in Brazilian Amazon. Through a mixed analysis method, this study proposes pathways for sustainable soybean production in the Amazon, including extending the Soy Moratorium to the Cerrado, strengthening indigenous land rights, enhancing international cooperation, and adopting sustainable agricultural practices such as agroforestry. These findings contributes to reconciling soybean cultivation with sustainable development in the Brazilian Amazon. Full article

Understanding the molecular structure and water transport behavior in anion-exchange membranes (AEMs) is essential for advancing efficient and cost-effective alkaline fuel cells. In this study, large-scale all-atom molecular dynamics simulations of QPAF-4, a promising AEM material, were performed at multiple water uptakes (λ = 2, 3, 6, and 13). The simulated systems comprised approximately 1.4 to 2.1 million atoms and spanned approximately 26 nm, thus enabling direct comparison with both wide-angle X-ray scattering (WAXS) and small-angle X-ray scattering (SAXS) experiments. The simulations successfully reproduced experimentally observed structure factors, accurately capturing microphase-separated morphologies at the mesoscale ($~$8 nm). Decomposition of the SAXS profile into atom pairs suggests that increasing water uptake may facilitate the aggregation of fluorinated alkyl chains. Furthermore, the calculated pair distribution functions showed excellent agreement with WAXS data, suggesting that the atomistic details were accurately reproduced. The water dynamics exhibited strong dependence on hydration level: At low water uptake, mean squared displacement showed persistent subdiffusive behavior even at long timescales ($~$200 ns), whereas almost normal diffusion was observed when water uptake was high. These results suggest that water mobility may be significantly influenced by nanoconfinement and strong interactions exerted by polymer chains and counterions under dry conditions. These findings provide a basis for the rational design and optimization of high-performance membrane materials. Full article

Frequent building fires seriously threaten the safety of steel structures. According to the data, fire accidents account for about 35% of the total number of production safety accidents. The collapse of steel structures accounted for 42% of the total collapse. The early warning problem of steel structure fire collapse is imminent. This study aims to address this challenge by establishing a novel early warning framework, which is used to quantify the critical early warning threshold of steel frames based on elastic modulus degradation and its correlation with ultrasonic wave velocity under different collapse modes. The sequential thermal–mechanical coupling numerical method is used in the study. Firstly, Pyrosim is used to simulate the high-fidelity fire to obtain the real temperature field distribution, and then it is mapped to the Abaqus finite element model as the temperature load for nonlinear static analysis. The critical point of structural instability is identified by monitoring the mutation characteristics of the displacement and the change rate of the key nodes in real time. The results show that when the steel frame collapses inward as a whole, the three-level early warning elastic modulus thresholds of the beam are 153.6 GPa, 78.6 GPa, and 57.5 GPa, respectively. The column is 168.7 GPa, 122.4 GPa, and 72.6 GPa. Then the three-level warning threshold of transverse and longitudinal wave velocity is obtained. The three-stage shear wave velocity warning thresholds of the fire column are 2828~2843 m/s, 2409~2434 m/s, and 1855~1874 m/s, and the three-stage longitudinal wave velocity warning thresholds are 5742~5799 m/s, 4892~4941 m/s, and 3804~3767 m/s. The core innovation of this study is to quantitatively determine a three-level early warning threshold system, which corresponds to the three stages of significant degradation initiation, local failure, and critical collapse. Based on the theoretical relationship, these elastic modulus thresholds are converted into corresponding ultrasonic wave velocity thresholds. The research results provide a direct and reliable scientific basis for the development of new early warning technology based on acoustic emission real-time monitoring and fill the gap between the mechanism research and engineering application of steel structure fire resistance design. Full article

Objectives: This study aimed to compare the biomechanical effects of Ballista Spring and Elastic Thread systems on impacted maxillary canines using three-dimensional finite element analysis (FEA). Materials and Methods: Finite element models were constructed from CBCT images of a human maxilla, incorporating cortical bone, spongy bone, teeth, and periodontal ligament. Two orthodontic force application methods were simulated: Ballista Spring (0.016-inch stainless steel) and Elastic Thread (0.25 mm medical-grade latex). Both systems delivered a force of 150 g to the impacted canine. Stress distribution and initial displacement patterns were analyzed using ANSYS Workbench. Results: The Ballista Spring generated a more uniform stress distribution across the periodontal ligament and cortical bone, with a maximum von Mises stress of 0.0042 MPa. The impacted canine exhibited an initial displacement of 0.015 μm, primarily in the vertical and distal directions, indicating a controlled movement path. In contrast, the Elastic Thread showed a more concentrated stress pattern with a maximum von Mises stress of 0.0035 MPa, and the impacted canine experienced 0.013 μm of displacement, accompanied by greater lateral deviation and buccal tipping of the adjacent teeth. The Ballista Spring induced higher stress levels on anchorage teeth—particularly the first molars and premolars—while the Elastic Thread exerted more localized stress around the impacted canine and adjacent structures. All observed stress values remained within physiological thresholds, indicating no immediate risk of tissue damage. Conclusions: Both systems were effective in facilitating the eruption of the impacted canines. However, the Ballista Spring provided more favorable stress distribution and controlled displacement, making it suitable for complex cases requiring anchorage preservation. The Elastic Thread, while less biomechanically efficient, remained a practical and cost-effective alternative in patients with adequate periodontal support. Full article

Approach or avoidance behaviors toward appetitive stimuli, such as alcohol and food, reflect the engagement of motivational states that are fundamental to adaptation of human behavior. Investigating early motor or neural responses to these stimuli provides valuable insights into the underlying mechanisms of these behaviors. This study employed an integrative approach combining postural and electrophysiological measures to explore the impact of alcohol consumption levels on early postural and neural responses to visual alcohol and food stimuli. The objective was to identify early automatic markers of approach or avoidance, and to examine correlations between motor and neural responses. Forty-six participants were divided into two groups (“Low” and “High”) according to their level of alcohol consumption (AUDIT scores). They were exposed to images of alcoholic beverages, non-alcoholic beverages, and appetitive or neutral foods. Postural responses were recorded using a force platform, and brain activity was measured via EEG. Displacement of the center of pressure along the anteroposterior axis, as well as the P100 and N100 components, were analyzed. “High” participants exhibited greater anterior postural displacement in response to alcohol during the first two seconds of stimulus exposure. In contrast, “Low” participants showed early avoidance responses. Significant correlations were found between event-related potential (ERP) wave latencies and postural displacement during the first second of exposure to alcohol-related stimuli. AUDIT scores were also positively correlated with early postural displacement and N100 latency following the viewing of alcoholic beverage images. Early perceptual and motor responses are modulated by alcohol consumption habits. These findings support the value of integrative EEG–posture approaches for identifying implicit motivational markers. Full article

This study uses a U-shaped aqueduct structure in a specific irrigation area as the research object to examine the damage patterns of large-span elevated U-shaped aqueduct structures under seismic action. A single-span aqueduct model that integrates fluid–structure interaction is created with the finite element program ANSYS. The incremental dynamic analysis approach is utilized to perform nonlinear dynamic time–history assessments for three types of bearings—plate rubber bearings, pot rubber bearings and lead-core rubber bearings—under conditions of an empty condition, a half-full condition and a design water level. Seismic fragility curves for the bearings and piers subjected to transverse seismic stress are developed using capacity–demand ratio models and specified damage limit states. The findings demonstrate that the likelihood of aqueduct components being damaged increases substantially as seismic intensity increases, with bearings failing before piers. Under the conditions of empty, half-full and design water levels, the structural mass increases as a result of higher water levels. This alters the dynamic response characteristics and increases the likelihood of failure in a variety of damage states. The probability of plate rubber bearings experiencing minor damage exceedance increases from 11.75% to 61.6% as the water level rises from vacant to design conditions. Lead-core rubber bearings provide better seismic isolation than plate rubber bearings and pot rubber bearings. This greatly lowers the aqueduct structure’s displacement response and damage likelihood. Under design water level circumstances, the chance of mild damage to lead rubber bearings is 8.64%, at a peak ground acceleration of 0.4 g. The damage probabilities for the pot rubber bearings and the plate rubber bearings are 80.68% and 97.45%, respectively. The research findings establish a theoretical foundation for the seismic design and damage evaluation of aqueduct structures in places with high seismic activity, ensuring the stable operation of water transfer projects and sustainable water resource utilization, presenting considerable technical applicability. Full article

This study investigates the influence of electrical actuation parameters on the performance of a morphing composite aerodynamic profile actuated by Shape Memory Alloy (SMA) wires. A fully coupled electro-thermo-mechanical finite element model has been developed to simulate the transient response of NiTi SMA, capturing the nonlinear interplay between temperature evolution, phase transformation, and mechanical deformation under Joule heating. The model incorporates phase-dependent material properties, heat effects, and geometric constraints, enabling accurate prediction of actuation dynamics. To validate the model, a morphing spoiler prototype has been fabricated using high-performance additive manufacturing with a carbon fibre-reinforced polymer. The SMA wires have been pretensioned and electrically actuated at different current levels (3 A and 6 A), and the resulting deformation has been recorded through video analysis with embedded timers. Experimental measurements confirmed the model’s ability to predict both actuation time and displacement, with maximum deflections of 33 mm and 40 mm corresponding to different current inputs. This integrated approach demonstrates an efficient and compact solution for active aerodynamic surfaces without the need for mechanical linkages, enabling future developments in adaptive structures for automotive and aerospace applications. Full article

Managing transition cows and preventing diseases related to this period is challenging due to the latter’s multifactorial nature. The aim of this applied observational case study is to illustrate and discuss the different aspects involved and provide an approach to analysis and the resulting management solutions using a real-life case within a 500-cow herd. The initial assessment, involving the collection of data on the level of production, animal health and behaviour, and metabolic indicators, as well as management and housing key indicators, revealed key risk factors, including overcrowding, suboptimal feeding strategies, inadequate water supply, and insufficient disease monitoring. These factors contributed to increased cases of metabolic disorders such as hypocalcemia (annual incidence 7.8%), excessive lipomobilisation, and displaced abomasum (annual incidence 5.2%). A holistic approach combining feeding adjustments, disease monitoring, facility improvements, and long-term management strategies was implemented to address these challenges. Short-term interventions, such as optimizing the dietary cation–anion balance and enhancing disease detection protocols, led to noticeable improvements. However, structural constraints and external factors, such as extreme weather conditions (heat stress) and economic limitations, created significant hurdles in achieving immediate and sustained success. The farm ultimately opted for infrastructural improvements, including a new transition cow facility, to provide a long-term solution to these recurring issues. This case highlights the complexity of transition cow management, demonstrating that long-term success depends on continuous monitoring, interdisciplinary collaboration, and adaptability in response to evolving challenges in dairy production. Full article

The karst terrain of Guangxi, China, characterized by steep slopes and thin residual soils, is highly vulnerable to rainfall-induced shallow landslides. Timely and accurate displacement forecasting is critical for early warning and risk mitigation. However, most existing systems depend on centralized computation, leading to latency and reduced responsiveness. Moreover, conventional forecasting models are often too computationally intensive for edge devices with limited processing resources. To address these constraints, we present EoML-SlideNet, a lightweight forecasting framework designed for resource-limited hardware. It decomposes displacement and triggers into trend and periodic components, then applies the Dual-Band Lasso-Enhanced Latent Variable (DBLE–LV) module to select compact, interpretable features via cross-correlation, LASSO, and VIF screening. A small autoregressive model predicts the trend, while a lightweight neural network captures periodic fluctuations. Their outputs are combined to estimate displacement. All models were evaluated on a single CPU-only workstation to ensure fair comparison. This study introduces floating-point operations (FLOPs), alongside runtime, as practical evaluation metrics for landslide displacement prediction models. A site-specific multi-sensor dataset was developed to monitor rainfall-triggered landslide behavior in the karst terrain of Guangxi. The experimental results show that EoML-SlideNet achieves 2–4 times lower MAE/RMSE than the most accurate deep learning and the lightest baseline models, while offering 3–30 times faster inference. These results demonstrate that low-complexity models can match or surpass the accuracy of deep networks while achieving latency and FLOP levels suitable for edge deployment without dependence on remote servers. Full article