Search Results (6,089)

Accurate egocentric distance estimation in vista space depends on the interaction between perceptual encoding and cognitive recalibration. This study examined how iterative, feedback-based learning modulates spatial accuracy, perceptual bias, and task efficiency in large-scale environments. A total of 133 participants (mean age = 26.3 ± 7.44 years) performed distance estimations on three outdoor targets (134 m, 575 m, 1463 m) using a mobile web application providing immediate corrective feedback (too short/too long). Six variables were analyzed: first estimate (FE), error of first estimate (EFE), mean estimate (ME), error of mean estimate (EME), number of attempts (NAs), and trial duration (TD). Given the non-normal data distribution, nonparametric tests were applied (Friedman and Wilcoxon signed-rank tests with Bonferroni correction). All variables showed significant within-subject effects across distances (p < 0.001). Post hoc analyses indicated that EFE and EME differed significantly between all target pairs (p < 0.0167), revealing a shift from slight overestimation at 134 m to increasing underestimation at 575 m and 1463 m. NA was significantly higher for the farthest target (p < 0.0167), indicating greater cognitive load and iterative correction effort. TD differed significantly only between consecutive distances (p < 0.0167), suggesting non-linear temporal adaptation. These results demonstrate that iterative feedback improves perceptual stability and efficiency but does not remove distance compression. The consistent bias and adaptive response patterns support a feedback-driven, binary search-like recalibration mechanism. The proposed mobile paradigm offers a scalable and valid approach for assessing perceptual–cognitive calibration in both natural and virtual spatial contexts. Full article

Wastewater treatment systems retain a significant proportion of microplastics (MPs) derived from domestic and industrial discharges; however, these emerging pollutants are not completely removed and tend to accumulate in the biological sludge generated during the treatment process. In this study, three biological-type wastewater treatment plants (WWTPs) located in Acapulco, Mexico, were analyzed. The concentrations of MPs in the biological sludge ranged from 830 to 9300 particles/L. Using differential scanning calorimetry (DSC), the predominant polymers identified were high-density polyethylene (HDPE), polyethylene terephthalate (PET), and polypropylene (PP). It was estimated that the monthly concentrations of MPs in the sludge could reach up to 5.36 × 10⁹ particles/L, while the annual concentrations could rise to 3.55 × 10¹⁰ particles/L. These findings highlight the urgent need to review and update the regulatory framework related to the use of residual sludge for agricultural purposes, since high loads of MPs and their transfer pose a potential risk to soil quality, ecosystem health, and long-term environmental sustainability. Full article

This paper presents the design, modeling and control of a flywheel actuator for mobile platform stabilization. A Lagrangian-based model couples platform mechanics with DC-motor electromechanics. Analytical calculations estimate natural frequencies, damping and actuator limits. Numerical simulations in Python 3.12 evaluate cascade and state-feedback controllers for suppressing free oscillations and rejecting external disturbances. Additional studies examine filtering to improve measurement quality and unloading strategies to avoid actuator saturation. The results validate the proposed control architecture and demonstrate its applicability to robotic and energy systems operating under dynamic loads. Full article

During deepwater completion and testing, the platform and riser system are subjected to long-term motions induced by ocean currents, which may cause structural damage and potential failure of the landing string. This study investigates the mechanical and fatigue performance of a subsea Christmas tree and landing string under environmental conditions of the LH11-1 Oilfield in the South China Sea. A global–local simulation framework is used to build a coupled dynamic model of the riser–landing string system and a local model for the landing string, considering load-transfer characteristics, current profiles, periodic features, and two representative environmental conditions (typhoon and non-typhoon). For seventeen typical operating scenarios, the strength of the riser–landing string system is evaluated, and wave-induced and vortex-induced fatigue analyses are performed for the key components. The stress distribution strongly depends on operating conditions, but local strength results confirm that stresses in the primary landing string components remain below allowable limits in all scenarios. Fatigue analysis indicates that the most severe wave-induced damage in the riser occurs at its bottom section, with a fatigue life of about 15.12 years, while in the landing string, it is concentrated near the lower end, with an estimated life of about 52.68 years. The maximum vortex-induced fatigue damage occurs near the riser surface region, with a corresponding fatigue life of about 18.52 years. Full article

There are almost no studies that investigate the flexural behavior of existing reinforced concrete (RC) beams with insufficient concrete strength using machine learning methods. This study investigates the flexural response of low-strength concrete (LSC) RC beams reinforced exclusively with steel rebars, focusing on the effectiveness of three different longitudinal reinforcement configurations. Nine beams, each measuring 150 × 200 × 1100 mm and cast with C10-grade low-strength concrete, were divided into three groups according to their reinforcement layout: Group 1 (L2L) with two Ø12 mm rebars, Group 2 (L3L) with three Ø12 mm rebars, and Group 3 (F10L3L) with three Ø10 mm rebars. All specimens were tested under three-point bending to evaluate their load–deflection characteristics and failure mechanisms. The experimental findings were compared with ML approaches. To enhance predictive understanding, several ML regression models were developed and trained using the experimental datasets. Among them, the Light Gradient Boosting, K Neighbors Regressor and Adaboost Regressor exhibited the best predictive performance, estimating beam deflections with R² values of 0.89, 0.90, 0.94, 0.74, 0.84, 0.64, 0.70, 0.82, and 0.72, respectively. The results highlight that the proposed ML models effectively capture the nonlinear flexural behavior of RC beams and that longitudinal reinforcement configuration plays a significant role in the flexural performance of low-strength concrete beams, providing valuable insights for both design and structural assessment. Full article

Switching to electric vehicles (EVs) could help reduce air pollution in cities. This is especially important for cities in Russia that have grown quickly because of industry, like those in Siberia, where environmental problems are particularly acute. However, several factors continue to hinder the rapid expansion of EVs on the market, such as an additional strain on the energy infrastructure, which threatens to cause power outages. This study proposes a model for estimating the electricity consumption by EVs in the largest Russian cities, taking into account the technical characteristics of the EV fleet and climatic conditions. The calculations indicate that if 15% of the current car fleet are replaced by EVs, electricity consumption in the 16 largest cities in Russia would increase by 2.2 TWh per year in total. The estimated additional demand in particular cities varies between 33 mln and 769 mln kWh per year, depending on the number of vehicles and the local climate. Furthermore, we conducted an intra-day simulation of electricity consumption from EVs in a conditional Russian city with a population of over one million people. Three scenarios for the power grid load have been developed: (A) the maximum scenario, in which all EVs have a battery level of 0%; (B) the medium scenario, where EVs’ state of charge is distributed between 0% and 100%, and (C) the minimum scenario, involving charging scheduling that allows only EVs with a battery level of 20% or less to charge. The findings show that replacing just 15% of the car fleet with electric vehicles will trigger an increase in current daily household urban consumption of 28.4% in scenario (C), 75.6% in scenario (B) and 141.8% in scenario (A). Consequently, even in Russia’s largest cities, the further proliferation of EVs requires large-scale investments in power infrastructure. An additional 1 mln kWh used by EVs per day may require $160.7 mln investments in energy facilities and urban distribution networks. These findings highlight the necessity of a more thorough cost–benefit analysis of widespread electric vehicle adoption in densely populated urban areas. Full article

This study aimed to adapt and validate the Polish version of the Perceived Future Employability Scale (PFES) and verify its factor structure among university students. Drawing on Social Cognitive Career Theory and the concept of possible selves, this study analysed how students perceive their future employment opportunities. This research was conducted among 408 students (61.0% female, 39.0% male; age: M = 20.97, SD = 2.68) at Maria Curie-Skłodowska University. Exploratory factor analysis using Principal Axis Factoring with Oblimin rotation revealed a six-factor structure explaining 63.74% of total variance. Based on stringent psychometric criteria (primary loadings ≥0.50, cross-loadings <0.30), six items exhibiting weak or problematic loadings were systematically removed, yielding a refined 18-item version that maintains all 6 theoretical dimensions while improving model fit. Confirmatory factor analysis demonstrated excellent fit using DWLS estimation (CFI = 0.996, RMSEA = 0.053) and acceptable fit with ML estimation (CFI = 0.958, RMSEA = 0.062). Reliability analysis demonstrated good-to-excellent internal consistency (α = 0.756–0.903; ω = 0.754–0.893) and adequate convergent validity (AVE = 0.612–0.785). Full measurement invariance across gender was established. The final Polish PFES comprises six dimensions: perceived future network, perceived expected experiences, perceived future personal characteristics, anticipated reputation of educational institution, perceived future labour market knowledge, and perceived future skills. The PFES provides a psychometrically sound tool for career development research and interventions supporting UN Sustainable Development Goals 4 and 8. Full article

Introduction: The health care burden of chronic hepatis B virus (CHB) infection can be reduced by appropriate workup, treatment, and monitoring. Methods: As a primary objective, we determined whether adequate initial hepatitis B virus (HBV) laboratory workup in CHB patients is associated with improved CHB complications risk. Secondary outcomes assessed included: mortality, hospitalization, emergency department, and liver specialist visits. We conducted a retrospective cohort study from 1 January 2012 to 31 December 2018. Participants were followed from 12 months post index event until outcome occurrence, death, loss of eligibility, or 31 March 2023. Health administrative data from Ontario, Canada was utilized. The study cohort included individuals with at least one positive result of either hepatitis B surface antigen, hepatitis B e antigen, or HBV DNA viral load documented during the study window. The exposure of interest was defined as adequate laboratory workup, defined as having subsequent quantitative HBV DNA, and alanine aminotransferase testing completed within 12 months of the index event. CHB-related complications were assessed using previously validated diagnostic codes. Modified Poisson regression modelling was used to estimate relative risks. Results: The study cohort consisted of 30,794 CHB patients, with a mean age 45.7 years. The majority were male (53.5%) and within the lowest two income quintiles (50.2%). In total, 68.0% underwent adequate workup. Individuals with adequate workup were more likely to be older, male, urban based, and of the highest racialized and newcomer populations quintile. The risk for CHB complications was 1.50 (95% CI 1.36–1.65) times greater among those with adequate workup. By multivariable analysis, adequate workup was associated with a lower risk of mortality (RR 0.78; 95% CI 0.69–0.87), all-cause hospitalizations (RR 0.77; 95% CI 0.74–0.80), all-cause (RR 0.77; 95% CI 0.75–0.78), and liver-related (RR 0.67; 95% CI 0.60–0.75) ED visits. Conclusions: Adequate CHB clinical workup is associated with improved patient outcomes. Our findings advocate for the comprehensive evaluation of CHB patients using key laboratory tests to optimize clinical management and improve long-term health outcomes. We identified gaps in the workup of young adults, females, and those residing in rural settings, which should be addressed to ensure equity of HBV care. Full article

The digital and ecological transition of the industrial sector requires methodological tools that integrate information modelling, performance simulation, and operational decision support. In this context, the present study introduces and tests a semi-automatic BIM-to-BEM framework to optimise human–machine interaction and support critical data interpretation through Graphical User Interfaces. The objective is to propose and validate a BIM-to-BEM workflow for an existing industrial facility to enable comparative evaluation of energy retrofit scenarios. The information model, developed through an interdisciplinary federated approach and calibrated using parametric procedures, was exported in the gbXML format to generate a dynamic, interoperable energy model. Six simulation scenarios were defined incrementally, including interventions on the building envelope, Heating, Ventilation and Air Conditioning (HVAC) systems, photovoltaic production, and relamping. Results are made accessible through dashboards developed with Business Intelligence tools, allowing direct comparison of different design configurations in terms of thermal loads and indoor environmental stability, highlighting the effectiveness of integrated solutions. For example, the combined interventions reduced heating demand by up to 32% without compromising thermal comfort, while in the relamping scenario alone, the building could achieve an estimated 300 MWh reduction in annual electricity consumption. The proposed workflow serves as a technical foundation for developing an operational and evolving Digital Twin, oriented toward the sustainable governance of building–system interactions. The method proves to be replicable and scalable, offering a practical reference model to support the energy transition of existing industrial environments. Full article

In indoor positioning systems, it is common to use existing AP deployments within buildings to build a fingerprint database, providing positioning information during the online phase. However, AP layouts inside buildings often contain a large number of redundant APs, which leads to the improvement in positioning accuracy leveling off as the number of redundant APs increases, while also increasing the computational load of indoor positioning services. To address this problem, the thesis proposes a method for calculating the AP location discrimination capability and combines the location discrimination capability with coverage to eliminate redundant APs. Experiments conducted in real indoor scenarios, as well as on the Crowdsourced dataset and the SODIndoorLoc dataset, validate the results. The results show that the redundant AP removing strategy ensures that the average positioning accuracy fluctuates by no more than 5% compared to the unfiltered case, while significantly reducing the number of APs in the fingerprint database—by 64.43%, 72.78%, and 59.62%, respectively. In the position estimation phase, this paper uses affinity propagation clustering for coarse positioning and combines Bayesian methods for fine positioning. Compared with GMM, K-Means, and the pointwise algorithm, the average positioning error of the proposed method is reduced by 11% to 39%. Full article

Adhesive fracture in layered structures is governed by subsurface crack evolution that cannot be accessed using surface-based diagnostics. Methods such as digital image correlation and optical spectroscopy measure surface deformation but implicitly assume a straight and uniform crack front, an assumption that becomes invalid for interfacial fracture with wide crack openings and asymmetric propagation. In this work, terahertz time-domain spectroscopy (THz-TDS) is combined with double-cantilever beam testing to directly map subsurface crack-front geometry in opaque adhesive joints. A strontium titanate-doped epoxy is used to enhance dielectric contrast. Multilayer refractive index extraction, pulse deconvolution, and diffusion-based image enhancement are employed to separate overlapping terahertz echoes and reconstruct two-dimensional delay maps of interfacial separation. The measured crack geometry is coupled with load–displacement data and augmented beam theory to compute spatially averaged stresses and energy release rates. The measurements resolve crack openings down to approximately 100 μm and reveal pronounced width-wise non-uniform crack advance and crack-front curvature during stable growth. These observations demonstrate that surface-based crack-length measurements can either underpredict or overpredict fracture toughness depending on the measurement location. Fracture toughness values derived from width-averaged subsurface crack fronts agree with J-integral estimates obtained from surface digital image correlation. Signal-to-noise limitations near the crack tip define the primary resolution limit. The results establish THz-TDS as a quantitative tool for subsurface fracture mechanics and provide a framework for physically representative toughness measurements in layered and bonded structures. Full article

Accurate determination of a vehicle’s centre of gravity (CoG) is fundamental to driving dynamics, safety, and engineering design. However, existing static CoG estimation methods often neglect tyre deflection and detailed wheel geometry, which can introduce significant errors, particularly in electric vehicles, where the low and concentrated mass of the battery pack increases the sensitivity of vertical CoG calculations. This study presents a refined static axle-load-based method for electric vehicles, in which the influence of tyre deformation and lifting height on the accuracy of the vertical centre of gravity coordinate is explicitly considered and quantitatively justified. To minimise human error and accelerate the evaluation process, a custom-developed Python (Python 3.13.2.) software tool automates all calculations, provides an intuitive graphical interface, and generates visual representations of the resulting CoG position. The methodology was validated on a Volkswagen e-Golf, demonstrating that the proposed approach provides reliable and repeatable results. Due to its accuracy, reduced measurement complexity, and minimal equipment requirements, the method is suitable for design, educational, and diagnostic applications. Moreover, it enables faster and more precise preparation of vehicle dynamics tests, such as rollover assessments, by ensuring that sensor placement does not interfere with vehicle behaviour. Full article

Wheel load is a critical information source reflecting the status of vehicle load distribution and motion. Yet, existing in-wheel motor products are primarily designed as propulsion units and inherently lack the load-sensing capabilities required by intelligent vehicles. To address this research gap, this paper presents a novel intelligent electric drive wheel (i-EDW) with an integrated transmission system and a load-sensing unit (LSU). The i-EDW adopts an Axial Flux Permanent Magnet Synchronous Motor (AFPMSM), while the integrated LSU ensures high-precision measurement of six-dimensional wheel forces and moments. According to this multi-axis force information, a real-time estimation and stability control method based on the tire–road friction circle concept is proposed. Instead of the complex decoupling and multi-objective optimization with the multi-actuator systems, this paper focuses on minimizing the tire load rate of i-EDWs, which significantly advances the state of the art in terms of calculation efficiency and respond speed. To validate this theoretical framework, a full-vehicle model equipped with four i-EDWs is developed. In the MATLAB R2022A/Simulink co-simulation environment, a virtual prototype is tested under typical driving scenarios, including the straight-line acceleration and double-moving-lane (DML) steering. The simulation results prove a reliable safety margin from the friction circle boundaries, laying a solid foundation for precise motion control and improved system robustness in future intelligent vehicles. Full article

The contraction joints within paver runs are important for the design and construction of rigid aircraft pavements. These joints are typically un-doweled and sawn into the pavement to induce a crack. The joints control shrinkage cracking during curing, allow for thermal expansion and contraction, and provide load transfer through aggregate interlock joint stiffness between adjacent slabs. Aggregate interlock joint stiffness is typically modeled by assigning a spring element between two slabs that is indicative of the stiffness of the joint. However, that simplification may not accurately represent the complex interaction of irregularly shaped concrete faces and joint openings. Consequently, previous researchers have recommended modelling aggregate interlock stiffness based on physical crack shape. This research uses a novel approach to characterize crack shape through an idealized two-dimensional sinusoidal shape. Once the crack shape was defined, finite element methods were used to determine the significance of load, sublayer, and crack shape factors on load transfer values. It was determined that joint opening was the most significant factor for aggregate interlock load transfer. Future research is recommended to further validate the model against a larger data set, to confirm if the two-dimensional idealization of crack shape is an appropriate estimation of field conditions. Full article

With the rapid development of the Internet of Things (IOT), 5G, and modern power systems, demand-side loads are becoming increasingly observable and remotely controllable, which enables demand-side flexibility to participate more actively in grid dispatch and emergency support. Under extreme rainstorm conditions, however, component failure risk rises and the availability and dispatchability of demand-side flexibility can change rapidly. This paper proposes a risk-aware emergency regulation framework that translates rainstorm information into actionable multi-load aggregation decisions for urban power systems. First, demand-side resources are quantified using four response attributes, including response speed, response capacity, maximum response duration, and response reliability, to enable a consistent characterization of heterogeneous flexibility. Second, a backpropagation (BP) neural network is trained on long-term real-world meteorological observations and corresponding reliability outcomes to estimate regional- or line-level fault probabilities from four rainstorm drivers: wind speed, rainfall intensity, lightning warning level, and ambient temperature. The inferred probabilities are mapped onto the IEEE 30-bus benchmark to identify high-risk areas or lines and define spatial priorities for emergency response. Third, guided by these risk signals, a two-level coordination model is formulated for a load aggregator (LA) to schedule building air conditioning loads, distributed photovoltaics, and electric vehicles through incentive-based participation, and the resulting optimization problem is solved using an adaptive genetic algorithm. Case studies verify that the proposed strategy can coordinate heterogeneous resources to meet emergency regulation requirements and improve the aggregator–user economic trade-off compared with single-resource participation. The proposed method provides a practical pathway for risk-informed emergency regulation under rainstorm conditions. Full article