Search Results (138,374)

Accurate walking trajectory estimation is critical for monitoring activity levels in healthcare and occupational safety applications. Ultra-Wideband (UWB) technology has emerged as a key solution for indoor human activity and trajectory tracking. However, its performance is fundamentally limited by Non-Line-of-Sight (NLOS) errors and kinematic drift during turns. To address these challenges, this study introduces a novel integrated IMU-UWB framework for walking trajectory estimation in NLOS scenarios involving turning gait. The algorithm integrates an error-state Kalman filter (ESKF) and a phase-aware turning correction module. Experiments were carried out to evaluate the effectiveness of this framework. The results show that the presented framework demonstrates significant improvements in walking trajectory estimation, with a smaller mean absolute error (7.0 cm) and a higher correlation coefficient, compared to the traditional methods. By effectively mitigating both NLOS-induced ranging errors and turn-related drift, this system enables reliable indoor tracking for healthcare monitoring, industrial safety, and consumer navigation applications. Full article

This paper presents the development and implementation of a shopfloor visualization-oriented digitalization framework for heterogeneous industrial equipment, aimed to enhance sustainable performance in manufacturing environments. The proposed solution addresses a critical challenge in modern industry: the integration of legacy and modern equipment into a unified, real-time monitoring and control system. In this paper, a modular and scalable architecture that enables data acquisition from equipment with varying communication protocols and technological maturity was designed and implemented, utilizing Industrial Internet of Things (IIoT) gateways, protocol converters, and Open Platform Communications Unified Architecture (OPC UA). A key contribution of this work is the integration of various data sources into a centralized visualization platform that supports real-time monitoring, anomaly detection, and performance analytics. By visualizing operational parameters—including energy consumption, machine efficiency, and environmental indicators—the system facilitates data-driven decision-making and supports predictive maintenance strategies. The implementation was validated in a real industrial setting, where the solution significantly improved transparency, reduced downtime, and contributed to measurable energy efficiency gains. This research demonstrates that visualization-oriented digitalization not only enables interoperability among heterogeneous assets, but also acts as a catalyst for achieving sustainability goals. The developed methodology and tools provide a replicable model for manufacturing organizations seeking to transition toward Industry 4.0 in a resource-efficient and future-proof manner. Full article

Sewer systems are essential for sustainable infrastructure management, influencing environmental, social, and economic aspects. However, sewer network capacity is under significant pressure, with many systems overwhelmed by challenges such as climate change, ageing infrastructure, and increasing inflow and infiltration, particularly through groundwater infiltration (GWI). Current research in this area has primarily focused on general sewer performance, with limited attention to high-resolution, spatially explicit assessments of sewer exposure to GWI, highlighting a critical knowledge gap. This study responds to this gap by developing a high-resolution GWI assessment. This is achieved by integrating fuzzy-analytical hierarchy process (AHP) with geographic information systems (GISs) and machine learning (ML) to generate GWI probability maps across the Dawlish region, southwest United Kingdom, complemented by sensitivity analysis to identify the key drivers of sewer network vulnerability. To this end, 16 hydrological–hydrogeological thematic layers were incorporated: elevation, slope, topographic wetness index, rock, alluvium, soil, land cover, made ground, fault proximity, fault length, mass movement, river proximity, flood potential, drainage order, groundwater depth (GWD), and precipitation. A GWI probability index, ranging from 0 to 1, was developed for each 1 m × 1 m area per season. The model domain was then classified into high-, intermediate-, and low-GWI-risk zones using K-means clustering. A consistency ratio of 0.02 validated the AHP approach for pairwise comparisons, while locations of storm overflow (SO) discharges and model comparisons verified the final outputs. SOs predominantly coincided with areas of high GWI probability and high-risk zones. Comparison of AHP-weighted GIS output clustered via K-means with direct K-means clustering of AHP-weighted layers yielded a Kappa value of 0.70, with an 81.44% classification match. Sensitivity analysis identified five key factors influencing GWI scores: GWD, river proximity, flood potential, rock, and alluvium. The findings underscore that proxy-based geospatial and machine learning approaches offer an effective and scalable method for mapping sewer network exposure to GWI. By enabling high-resolution risk assessment, the proposed framework contributes a novel proxy and machine-learning-based screening tool for the management of smart cities. This supports predictive maintenance, optimised infrastructure investment, and proactive management of GWI in sewer networks, thereby reducing costs, mitigating environmental impacts, and protecting public health. In this way, the method contributes not only to improved sewer system performance but also to advancing the sustainability and resilience goals of smart cities. Full article

A systematic investigation was conducted on the laser shock forming (LSF) process of carbon steel Q355ME sheets and practical skin components, focusing on the influence of absorption layer types, laser energy, and impact cycles on forming capacity and surface properties. Three kinds of absorbing layers were compared in the experiment: no absorbing layer, 0.1 mm aluminum foil and 0.12 mm black tape. The results show that when the black tape is used as the absorbing layer, the forming effect is the best, the arc height value reaches 2.63 mm, and the radius of curvature is 1066 mm. Using 0.1 mm thick black tape as the absorption layer and laser parameters of 10% overlap rate, 15 ns pulse width, 4 mm spot, and 1064 nm wavelength, the single impact of 13 J, 15 J, and 17 J, and one, two, and three impacts of 15 J energy were carried out on the plate. It was found that the increase in laser energy and impact times resulted in increases in deformation, surface roughness, microhardness, and residual stress of the plate. The surface work hardening phenomenon of Q355ME plate after laser shock slowed down the increase in these performance parameters. The experimental results show that the laser energy is linearly positively correlated with the residual stress in a certain energy range. Under the optimized laser process parameters, the forming error of the actual skin parts is controlled within ± 0.4 mm, the surface residual stress increases by 368.9%, and the surface microhardness increases by 10.4%. The ultra-high strain plastic deformation and grain refinement on the surface of the sheet were caused by multiple laser shock peenings, which confirmed that LSF technology can improve the formability of carbon steel skin parts and improve its surface properties. Full article

The integration of Artificial Intelligence (AI) technologies in students’ lives necessitates the systematic incorporation of foundational AI literacy into educational curricula. Students are challenged to develop conceptual understanding of computational frameworks such as Machine Learning (ML) algorithms and Decision Trees (DTs). In this context, unplugged (i.e., computer-free) pedagogical approaches have emerged as complementary to traditional coding-based instruction in AI education. This study examines the pedagogical effectiveness of an instructional intervention employing unplugged activities to facilitate conceptual understanding of DT algorithms among 47 9th-grade students within a Computer Science (CS) curriculum in Greece. The study employed a quasi-experimental design, utilizing the Structure of Observed Learning Outcomes (SOLO) taxonomy as the theoretical framework for assessing cognitive development and conceptual mastery of DT principles. Quantitative analysis of pre- and post-intervention assessments demonstrated statistically significant improvements in student performance across all evaluated SOLO taxonomy levels. The findings provide empirical support for the hypothesis that unplugged pedagogical interventions constitute an effective and efficient approach for introducing AI concepts to secondary education students. Based on these outcomes, the authors recommend the systematic implementation of developmentally appropriate unplugged instructional interventions for DTs and broader AI concepts across all educational levels, to optimize AI literacy acquisition. Full article

This study examines the effects of sustainability-related data requests—spurred by the EU Corporate Sustainability Reporting Directive (CSRD)—on small and medium-sized enterprises (SMEs) in the Netherlands. Using a representative survey of 431 SMEs and 48 qualitative interviews with SME representatives and business stakeholders, the research provides a comprehensive overview of their experiences in late 2024. A key finding is that most Dutch SMEs (72%) have not yet received sustainability data requests. However, SMEs embedded in international value chains report more frequent and complex data demands, particularly concerning environmental indicators like CO₂ emissions and material use. Ratings of perceived relevance reveal a disconnect between external data requests and SMEs’ internal priorities, with many SMEs prioritizing health and safety over climate metrics. While some SMEs see data requests as opportunities for improved sustainability performance and market positioning, many also experience challenges, including limited resources, fragmented IT systems, and regulatory uncertainty. The implementation of CSRD highlights the urgency of supporting SMEs in building data management capacities and standardized processes. The study recommends clearer communication of data relevance, targeted support measures, and further research into cross-national and longitudinal dynamics to foster an effective sustainability transition across value chains. Full article

Background/Objectives: Combined physical activity and nutritional interventions may produce synergistic effects on child development, but evidence from school-based programs is still limited. This systematic review investigated the impact of physical activity programs with simultaneous nutritional supplementation in school-aged children. Methods: A systematic search was conducted across five databases (PubMed, ProQuest, SCOPUS, Web of Science, and SPORTDiscus) up to June 2025. Randomized controlled trials examining combined physical activity and nutritional supplementation interventions in children aged 5–12 years recruited from schools were included. Methodological quality was rated using the Physiotherapy Evidence Database (PEDro) scale. Results: Thirteen studies (n = 3967 participants) were eligible, with program lengths ranging from 2 weeks to 24 months. Methodological quality ranged from fair to good (PEDro scores: 4 to 7/10). Combined interventions showed greater benefits than single interventions. For bone health, 2–3% higher increases in bone mineral content at loaded sites were reported with exercise combined with calcium supplementation. Body composition improvements included significant reductions in fat mass and increases in fat-free mass, with effects most pronounced in girls and slow-growing children. Motor performance, academic achievement, and sleep quality also improved with combined approaches. Effects were consistently greatest in children with baseline micronutrient deficiencies or slower growth velocity. Conclusions: School-based programs combining moderate-to-high impact physical activity with targeted nutritional supplementation yield synergistic health benefits in children. Brief interventions (2–3 sessions/week, ≤60 min) appear sufficient when adequate micronutrient provision is ensured, supporting implementation feasibility within educational settings. Full article

Copper contamination in the environment poses significant risks to both soil and human health, making the need for reliable monitoring methods crucial. In this study, we report the use of the EmStat Pico module as potentiostat to develop a portable electrochemical biosensor for copper detection, utilizing yeast Saccharomyces cerevisiae cells immobilized on a polydopamine (PDA)-coated screen-printed electrode (SPE). By optimizing the sensor design with a horizontal assembly and the volume reduction in the electrolyte solution, we achieved a 10-fold increase in current density with higher range of copper concentrations (0–300 µM CuSO₄) compared to traditional (or previous) vertical dipping setups. Additionally, the use of genetically engineered copper-responsive yeast cells further improved sensor performance, with the recombinant strain showing a 1.7-fold increase in current density over the wild-type strain. The biosensor demonstrated excellent reproducibility (R² > 0.95) and linearity over a broad range of copper concentrations, making it suitable for precise quantitative analysis. To further enhance portability and usability, a Bluetooth-enabled electrochemical platform was integrated with a web application for real-time data analysis, enabling on-site monitoring and providing a reliable, cost-effective tool for copper detection in real world settings. This system offers a promising solution for addressing the growing need for efficient environmental monitoring, especially in agriculture. Full article

This research presents the calibration of a constitutive model to replicate the cyclic performance of soils using a Bayesian framework. This study uses data from laboratory-conducted consolidated undrained isotropic cyclic triaxial tests and numerical tools to estimate optimal parameters by the application of Slice Sampling in a Bayesian analysis and to determinate the uncertainty of the model. For each calibrated parameter in the model, a probability distribution was obtained from the Markov chain. The means and the standard deviations from the distributions are compared with the laboratory results by the simulation of a series of consolidated undrained isotropic cyclic triaxial tests and a numerical model for a deposit that replicates the Wildlife’s stratigraphic characteristics. The calibrated model response offers a good approximation of the recorded data and the uncertainty due to the model is evaluated. The results of this study demonstrate that Bayesian calibration can reliably quantify parameter uncertainty, reveal parameter correlations that deterministic methods overlook, and improve confidence in liquefaction assessments. This probabilistic framework provides a robust basis for extending calibration to other soil types and site conditions. Full article

The ocean harbors vast potential for oil and gas resources, positioning offshore drilling as a critical approach for future energy exploration. However, high-temperature and high-pressure offshore reservoirs present formidable challenges, as conventional water-based drilling fluids are prone to thermal degradation and rheological instability, leading to wellbore collapse and stuck-pipe incidents. Offshore oil-based drilling fluids (OBDFs), typically water-in-oil emulsions, offer advantages in wellbore stability, lubricity, and contamination resistance, yet their stability under extreme high-temperature conditions remains limited. This study reveals the enhancement of offshore OBDFs performance in harsh conditions by employing nano-SiO₂ to synergistically improve emulsion film stability and non-Newtonian rheological behavior while systematically elucidating the underlying mechanisms. Nano-SiO₂ forms a composite film with emulsifiers, reducing droplet size, enhancing mechanical strength, and increasing thermal stability. Optimal stability was observed at an oil-to-water ratio of 7:3 with 2.5% nano-SiO₂ dispersion and 4.0% emulsifier. Rheological analyses revealed that nano-silica enhances electrostatic repulsion, reduces plastic viscosity, establishes a network structure that increases yield stress, and promotes pronounced shear-thinning behavior. Macroscopic evaluations, including fluid loss, rheological performance, and electrical stability, further confirmed the improved high-temperature stability of offshore OBDFs with nano-SiO₂ at reduced emulsifier concentrations. These findings provide a theoretical basis for optimizing offshore OBDFs formulations and their field performance, offering breakthrough technological support for safe and efficient drilling in ultra-high-temperature offshore reservoirs. Full article

In this study, we examine the presence of bias in weighted least squares (WLS) estimation within the context of first-order bifurcating autoregressive (BAR(1)) models. These models are widely used in the analysis of binary tree-structured data, particularly in cell lineage research. Our findings suggest that WLS estimators may exhibit significant and problematic biases, especially in finite samples. The magnitude and direction of this bias are influenced by both the autoregressive parameter and the correlation structure of the model errors. To address this issue, we propose two bootstrap-based methods for bias correction of the WLS estimator. The paper further introduces shrinkage-based versions of both single and fast double bootstrap bias correction techniques, designed to mitigate the over-correction and under-correction issues that may arise with traditional bootstrap methods, particularly in larger samples. Comprehensive simulation studies were conducted to evaluate the performance of the proposed bias-corrected estimators. The results show that the proposed corrections substantially reduce bias, with the most notable improvements observed at extreme values of the autoregressive parameter. Moreover, the study provides practical guidance for practitioners on method selection under varying conditions. Full article

Simultaneous Localization and Mapping (SLAM) systems require accurate and globally consistent mapping to ensure the long-term stable operation of robots or vehicles. However, for the commercial applications of indoor sweeping robots, the system needs to maintain accuracy while keeping computational and storage requirements low to ensure cost controllability. This paper proposes a dual-point-cloud-registration SLAM based on line features for the mapping of a mobile robot, named DPCR-SLAM. The front-end employs an improved Point-to-Line Iterative Closest Point (PLICP) algorithm for point cloud registration. It first aligns the point cloud and updates the submap. Subsequently, the submap is aligned with the regional map, which is then updated accordingly. The back-end uses the association between regional maps to perform graph optimization and update the global map. The experimental results show that, in the application scenario of indoor sweeping robots, the proposed method reduces the map storage space by 76.3%, the point cloud processing time by 55.8%, the graph optimization time by 77.7%, and the average localization error by 10.9% compared to the Cartographer, which is commonly used in the industry. Full article

This paper develops a belief-based dynamic optimisation framework to explain career continuation decisions in settings characterised by monopsonistic employment and asymmetric performance evaluation. Extending Holmström’s career concerns model, we consider agents who must decide whether to continue or exit their vocation based on subjective beliefs updated from noisy signals. Unlike the original framework, our model assumes a single institutional employer and limited feedback transparency, turning the agent’s decision into an optimal stopping problem governed by evolving belief thresholds. Analytical results demonstrate how greater signal noise, higher effort costs, and more attractive outside options raise the probability of exit. To validate the framework, we confront belief-based dropout decisions using original survey data from over 8000 football referees in Europe, showing that threats, unmet development expectations, and perceived stagnation significantly predict dropout. The results offer practical insights for institutions, such as sports federations, academic bodies, and civil services, on how to improve retention through increased transparency and better support structures. This study contributes to the literature by integrating optimal stopping theory and dynamic labor models in a novel context of constrained career environments. Full article

Background: The Nursing Work Environment (NWE) plays a critical role in determining the quality of care, staff well-being, and organizational performance, particularly in oncology settings. Despite increasing attention, a comprehensive synthesis of organizational factors shaping oncology NWEs has been lacking. This scoping review aimed to describe the key features of oncology NWEs and to explore the outcomes associated with these characteristics. Methods: A scoping review was conducted following the Joanna Briggs Institute guidelines. Peer-reviewed studies published in English or Italian were included without time restrictions. Literature searches were performed in MEDLINE via PubMed, CINAHL, and Scopus between January and April 2025. Results: Twenty studies met the inclusion criteria. Key organizational characteristics of oncology NWEs were grouped into the following four domains: leadership and organizational support; workload and resource availability; ethical climate and collegial relationships; and physical and structural conditions of care settings. Across the studies, a positive NWE was frequently reported to be associated with improved nurse-related outcomes and, to a lesser extent, with patient-related outcomes. However, these associations should be interpreted with caution due to the heterogeneity of contexts and the predominance of cross-sectional designs. Conclusions: The NWE is a strategic element in delivering effective, safe, and sustainable oncology care. Practical actions for nurse managers and healthcare leaders include implementing leadership training programs, ensuring adequate staffing and resource allocation, fostering open communication, and promoting interdisciplinary collaboration. These measures are essential to protect staff well-being and guarantee high-quality, patient-centered care. Full article

This article focuses on how to further explore the impact of building layout and form on the local wind environment in micro scale ventilation corridors connected to the urban scale. Taking Shijiazhuang as the research area, three typical blocks of complex building forms, including old and new ones, were selected near the built ventilation corridors. CFD numerical simulation and on-site observation experiments were conducted to analyze the impact of different building heights and layouts on the wind environment in each typical block qualitatively and quantitatively. The above can provide a reference and guidance for the construction of secondary and tertiary ventilation corridors and the spatial form design of functional buildings during urban renewal in the stock era. The results show the following: (1) average wind speed, Mean Wind Velocity ratio, and the proportion of the outdoor pedestrian comfort zone are negatively correlated with the building height, but there is a threshold for them to decrease with the increase in the building height. Observation experiments also indicate that in the background of the south wind, the internal and leeward wind environment of new high-rise residential areas is better than that of old low residential areas. (2) Regression analysis was conducted between the simulated average wind speed and the building height, indicating that regulating the average building height to be below 45 m can improve the wind environment as the building height decreases. (3) The enclosed building complex has the smallest impact distance on downstream wind speed compared to point, row, and staggered layouts, but its internal ventilation environment is relatively poor. To ensure the ventilation performance, the upper limit of the building height should be stricter, and it should be controlled within at least 40 m, especially below 30 m. (4) In the process of urban renewal in the future, it is recommended to conduct an overall ventilation efficiency evaluation for different blocks. Compared to others, increasing the height of buildings and leaving more space to increase the inter site ratio/building spacing is more beneficial for the overall ventilation environment. Full article