Search Results (2,458)

Background: Fundamental movement skills (FMS) underpin lifelong physical activity (PA) and health, yet many children are failing to meet age-appropriate standards. Caregivers hold a critical influence over children’s motor development, but little is known about what helps or hinders family participation, including messaging. This study explored the determinants of family FMS engagement in the United Kingdom (UK) during early childhood, addressing unexplored gaps in how guidance reaches families and the role of grandparents in supporting children’s motor development. Methods: Twenty-three semi-structured interviews were conducted with 15 caregivers and 8 educators, including 4 grandparents and 2 family hub practitioners who offered original insights. Eleven children aged 3–5 years completed a flexible draw-and-tell task, enabling inclusion of rarely represented 3-year-olds. Thematic analysis was deployed. Results: Families and outdoor spaces were pivotal to children’s movement opportunities. However, awareness and understanding of FMS and UK PA guidance were poor, even among educators, disrupting dissemination of information to families. Greater emphasis on PA and FMS concepts within professional development, alongside clearer signposting to resources, more visible public-facing campaigns, and digital formats, could improve how families receive these messages. Tensions emerged between parents’ concerns about grandparents’ physical capability and grandparents’ belief that they could adapt to support children’s development. Unexpectedly, no children drew technology despite screen time frequently displacing active play, hinting at its normalisation and regulatory role in children’s lives. Conclusions: To enhance family understanding, value, and participation in FMS, UK policy must evolve to become more visible, relatable, and responsive to diverse family needs. Full article

The annual occupational personal ultraviolet radiation (UVR) exposure of outdoor workers is vital for several purposes, including non-melanoma skin cancer risk assessment and the recognition of UVR-related pathologies as occupational diseases. Estimations of annual personal exposure (PE) are based on measurements, which are influenced by the measuring period respectively by the start and end time of the measurements, and PEs gained from different periods may differ noticeably. Therefore, we present a method that recalculates PE measurements to any other period (time and duration) during the day, and which is also applicable for measured ambient UVR to determine the relative personal UVR exposure (ERTA). The application shows the necessity of considering not only duration but especially time, as noon hours contribute differently than morning and evening hours. The uncertainties of recalculations are within ±5% if the measuring or target periods last at least 5 h and noon hours are covered. Furthermore, we propose a method to calculate annual PE using ERTA. The application for Austria shows that depending on the work-time model (working hours, working times) and date of holidays, annual PE may differ by up to 30%. Additionally, interannual variability of 16% within a ten-year period suggests avoiding a single year for consideration. Full article

Reliable change detection in indoor environments remains a challenge for autonomous robotic systems using 3D LiDAR. Existing methods often require manual annotation, computationally intensive architectures, or focus on outdoor scenes. This paper presents SPICD-Net, a lightweight Siamese PointNet framework for indoor 3D change detection trained exclusively on synthetically generated anomalies, eliminating manual labeling. The framework offers three deployment-oriented contributions: a three-class Siamese formulation separating no-change, changed, and geometrically inconsistent tile pairs; a pre-FPS anomaly injection strategy that aligns synthetic training with inference-time preprocessing; and a stochastic-gated Chamfer-statistics branch that complements learned embeddings with explicit geometric cues under consumer-grade hardware constraints. Evaluated on 14 controlled simulation experiments in an indoor corridor dataset, SPICD-Net achieved aggregated Precision = 0.86, Recall = 0.82, F1-score = 0.84, and Accuracy = 0.96, with zero false positives in the no-change baseline and mean inference time of 22.4 s for a 172-tile map on a single consumer GPU. Additional robustness experiments identified registration accuracy as the main operational prerequisite. A limited real-world validation in one unseen room (four scans, 67 tiles) achieved Precision = 0.583, Recall = 1.000, and F1 = 0.737. Full article

Precise curvilinear path tracking remains a persistent challenge for autonomous agricultural machines, where conventional Model Predictive Control (MPC) suffers from poor adaptability to varying curvatures and high computational overhead in unstructured farmland environments. This paper proposes a soft-constrained MPC framework enhanced by a two-layer fuzzy architecture and Recursive Least Squares filtering to address these limitations simultaneously. The first fuzzy layer dynamically adjusts the MPC prediction horizon in response to real-time path curvature, enabling proactive steering on complex curved trajectories. The second fuzzy layer tunes the state weighting matrix online based on lateral and heading deviations, improving transient tracking accuracy without increasing computational cost. Recursive Least Squares filtering is further integrated to suppress sensor noise and compensate for tire slip dynamics inherent to farmland operation. The proposed framework is validated using MATLAB simulations on both constant-curvature semicircular paths and variable-curvature S-curve trajectories at operational speeds of 2.0 and 2.5 m/s, followed by outdoor field trials on a scaled autonomous robot platform. Simulation results demonstrate average tracking error reductions of 52.7–55.9% on constant-curvature paths and 10.8–18.2% on variable-curvature paths compared to fixed-parameter soft-constrained MPC. Field experiments confirm practical viability, achieving an RMS lateral error of 0.131 m over a 50 m curved route on natural terrain. These results demonstrate that the hierarchical decomposition of adaptation objectives yields substantial accuracy gains while preserving real-time feasibility on resource-constrained embedded platforms. Full article

People spend most of their time indoors, highlighting the importance of indoor environmental quality for health and wellbeing. While previous studies have shown that exposure to nature can benefit wellbeing, much of this research has focused on outdoor environments, and less is known about how indoor plants and their spatial characteristics influence human perceptions and experiences. This paper reports on a survey study exploring how perceived health and wellbeing are influenced by indoor plants and human preferences for their characteristics, spatial arrangement, and other features within indoor environments. Indoor plants serve as visual and multisensory environmental stimuli. By examining the relationship between indoor plants, preferences, perceptions, visual comfort, multisensory experiences, and wellbeing, the study aims to understand these influences. The questionnaires include multiple-choice questions, yes-no questions, and open-ended questions, allowing the collection of both quantitative and qualitative data. The survey findings highlight the unique benefits of indoor plants, emphasising their potential to enhance wellbeing in ways that outdoor nature may not fully replicate in indoor settings. One significant finding of this study is that scattering indoor plants throughout a space can enhance the connection to nature through three-dimensional spatial interaction, potentially improving wellbeing. This arrangement may serve as a bridge to the outdoors, providing a psychological link to the natural environment. Crucial preference factors also include the complexity and coherence of indoor plants’ appearance, such as colour, shape, and size. The results further indicate that students prefer indoor plants over other elements such as cut flowers, fake plants, or artificial plant representations. The findings indicate that caring for indoor plants may foster emotional engagement, a sense of fulfilment, and place attachment through everyday interaction. In public spaces, plants may also enhance feelings of refuge and perceived security. These findings provide practical recommendations for designing indoor environments that enhance student wellbeing and human–environment interaction. Full article

Background: Outdoor time in early childhood education and care (ECEC) settings provides important opportunities for children’s physical activity. Evidence is limited on whether different organizational outdoor conditions influence not only activity intensity but also the contextual characteristics of children’s movement. Methods: An observational study was conducted using the Observational System for Recording Physical Activity in Children—Preschool Version (OSRAC-P). The study was conducted in two public ECEC centers. Not all children were observed across all three conditions due to the field-based design. A total of 7440 observation intervals were analyzed from preschool children across three outdoor conditions (structured educator-led physical activity, outdoor free play, and outdoor free play with additional portable equipment) using a momentary time-sampling protocol (10 s observation + 50 s recording), resulting in one interval per minute. Physical activity intensity, activity type, equipment use, and social context were coded. Contextual differences were analyzed using chi-square tests with standardized residuals, and activity intensity using linear mixed-effects models. Results: No significant differences were found between outdoor conditions in physical activity intensity, sedentary behavior, and moderate-to-vigorous physical activity (all p > 0.05). About one-third of the variance in activity intensity was attributable to individual differences between children (ICC ≈ 33%). Differences were observed in contextual characteristics. Structured activity involved more locomotor activities and greater adult involvement, with 49.4% of intervals occurring in groups with an educator present. Free play with portable equipment showed more manipulative activities, greater equipment use, and mostly peer interactions without adult presence (55.5%), while free play without additional equipment involved more stationary behavior and activities without equipment (46.9%). Conclusions: Although physical activity intensity did not differ across conditions, the structure, material context, and social organization of children’s activity varied, highlighting the practical importance of intentionally combining different outdoor activity formats to support diverse movement patterns in ECEC settings. Full article

Odometry estimation, which calculates the trajectory of a moving object across timeframes, is a critical and time-consuming function in SLAM (Simultaneous Localization and Mapping) systems. Although LiDAR-based sensing is most popular for outdoor and long-range applications because of its ranging accuracy, the sparsity of laser point cloud poses a significant challenge to feature extraction and matching in odometry estimation. In this paper, we investigate odometry estimation from two aspects, i.e., algorithm optimization, and system design/implementation. In algorithm optimization, we present an image feature-assisted odometry estimation scheme that leverages the richness of image information captured by a companion camera to enhance the accuracy of laser point cloud matching. This also serves as a screening mechanism to reduce the matching size and lower the computing complexity for a higher estimation rate. In addition, various schemes, such as adaptive threshold in image feature point selection, principal component analysis (PCA)-based plane fitting for laser point interpolation, and Gauss–Newton optimization for calculating the transform matrix, are also employed to improve the accuracy of odometry estimation. The performance of improved odometry estimation is verified using an existing FLOAM (Fast Lidar Odometry and Mapping) framework. The KITTI dataset for autonomous vehicles with ground truth was used as the test bench. Simulation results indicate that the translation error and rotation error can be reduced by 16.6% and 1.3%, respectively. Computing complexity, measured as the software execution time, also reduced by 63%. In system implementation, a hardware/software (HW/SW) co-design strategy was adopted, where complexity profiling was first conducted to determine the task partitioning and time-consuming tasks are offloaded to a hardware accelerator. This facilitates real-time execution on a resource-constrained embedded platform consisting of a microprocessor module (Raspberry Pi) and an attached FPGA board (Pynq Z2). Efficient hardware designs for customized DSP functions (adaptive threshold and PCA) were developed in an FPGA capable of completing one data frame in 20ms. The final system implementation met the target throughput of 10 estimations per second, and can be scaled up further. Full article

Noise disturbances can cause conflicts in several areas, such as residences, civil constructions, highways, subways, and airports, measured by different scales of acoustic comfort for community well-being evaluation. These disturbances also have signatures such as frequency, amplitude, and temporal patterns to compare acoustic comfort with real-time parameters. In addition, acoustic sensors should be chosen based on accuracy, price, and calibration method, and acoustic insulation should be applied with the aim of achieving reliable measurements in indoor and outdoor environments for sustainable urban living. In some situations, the lack of noise control can lead to several human disorders, from hearing loss to cardiovascular complications. Therefore, legislation and regulation should be carefully studied and applied to achieve an equilibrium between energy-efficient and healthy building designs in entertainment, work, and rest activities with measured parameters visualized through the design of interface tools that should enable the collection and organization of sound data, with proper presentation for the final user. Finally, intellectual property registrations bring recent industrial applications with aspects of noise mitigation. All these features constitute noise disturbance mitigation in a multi-dimensional integration framework of technology, health, and law to improve the quality of life in human-centric environments. Full article

This paper presents an onboard, markerless perching system for a quadrotor UAV, validated in outdoor flight experiments, to reduce hovering energy during long-endurance unmanned missions. Existing autonomous landing research predominantly focuses on planar surfaces, cooperative environments with visual markers, or specialized hardware, limiting scalability to scenarios requiring detection and perching on thin rod-like targets in uncooperative outdoor settings. This study proposes a markerless perching system for autonomously perching a drone on a hurdle’s horizontal bar. The system employs a single-axis gimbal camera, altitude LiDAR, and ToF sensor, integrating perception, post-processing, and control. On the perception side, we augment a YOLOv12n-based segmentation model with a high-resolution P2 pathway for small-object detection and apply module compression for real-time inference on edge devices. Robustness is improved by jointly utilizing the full hurdle and horizontal bar while constructing negative samples to suppress false positives. On the control side, a state machine controller leverages centroid coordinates, orientation, and distance measurements to achieve a stable long-range approach and precise close-range alignment. Experiments on a Jetson Orin NX-based system demonstrate successful perching in all six outdoor flight tests. Ablation studies quantitatively analyze each component’s contribution to perching success rate and completion time. This research validates perching technology’s practical applicability through outdoor markerless perching on thin 3D structures. Full article

Dissolved oxygen (DO) is a primary environmental regulator of shrimp physiology and behavior in intensive aquaculture systems. Whether shrimp acoustic emissions quantitatively reflect oxygen-driven behavioral modulation under operational pond conditions, however, remains uncertain due to the difficulty of isolating biologically relevant signals from complex soundscapes. In this study, passive acoustic monitoring was conducted in commercial outdoor ponds culturing Litopenaeus vannamei. A periodic-coding non-negative matrix factorization approach was applied to separate putative shrimp-associated acoustic components from broadband background noise and to obtain stable time–frequency representations of acoustic activity. Temporal variations in the extracted acoustic intensity were compared with simultaneously measured DO concentrations. Rather than relying on global correlation, phase-specific analyses revealed that the putative shrimp-associated acoustic component exhibited consistent positive associations with DO dynamics during both rising and declining phases, whereas background noise showed only weak and non-coherent relationships with DO. These results indicate that the observed acoustic–oxygen relationship is non-stationary and context-dependent. Given the observational nature of the study and potential confounding influences (e.g., aeration and other environmental factors), these findings, which are based on observations from a single pond over a limited recording period (62.85 h) under specific operational conditions, should be interpreted with caution and regarded as a proof-of-concept rather than evidence of general applicability. Nevertheless, the results are consistent with the hypothesis that population-level acoustic activity may reflect environmentally modulated behavioral responses. This highlights the potential of soundscape-based approaches as non-invasive tools for supporting aquaculture monitoring, while emphasizing the need for further validation under controlled and multi-site conditions. Full article

Waste separation in private households remains difficult to promote, particularly in urban contexts, where anonymity limits informal social monitoring. This proof-of-concept study tested, for the first time, self-administration of images of “watching eyes” as an intervention. About 22% of all households living in the district of Riedberg in Frankfurt/Main, Germany, received a letter asking residents to attach eye cues to kitchen and outdoor waste bins to prompt appropriate separation of organic from residual waste. Objective data from weighed collection trucks showed a measurable behavioral effect compared to control conditions, with a 5–8% increase in biowaste volumes. While this study does not allow causal inference because waste was measured only at the group level, it does suggest that, when applied by residents themselves, social nudges might enhance self-awareness about environmentally conscious behavior. Accompanying survey responses displayed ceiling effects, presumably because only highly motivated individuals participated. Importantly, some signs of reactance were also observed, with some participants perceiving the intervention as intrusive and regulatory. Although low-cost and easy to apply, self-administration of watching-eyes cues requires careful communication and attention to psychological reactions to avoid resistance while encouraging the formation and maintenance of target habits in private environments. Full article

Accurate modeling of outdoor wireless propagation in dense urban environments is essential for smart city connectivity. Deterministic ray-tracing techniques provide high-fidelity multipath insight; however they suffer from high computational cost and limited scalability in large 3D environments. This work proposes a hybrid framework combining MATLAB-based (MATLAB 2024b 24.2.0.2773142, 64-bit, 22 October 2024) ray tracing and Machine Learning for scalable Wi-Fi 7 channel analysis. A large dataset is generated over a realistic university campus across multiple frequency bands, transmit powers, and reflection/diffraction configurations. Several regression models are evaluated, with emphasis on transformer-based architectures. The FT-Transformer achieves a Mean Absolute Error (MAE) of $3.49$ dB, RMSE of $5.36$ dB, and an $R^2$ of $99.63\%$ for validation, reducing computation time from months of simulation to seconds at inference. The framework enables accurate and efficient surrogate modeling for network planning and digital twin applications. Full article

Seamless localization across indoor and outdoor environments remains a fundamental challenge for wearable navigation systems, particularly those intended to assist visually impaired individuals. This challenge arises from the unreliability of GNSS signals in indoor and transitional spaces and the cumulative drift inherent to IMU–based dead reckoning. To address these limitations, this paper proposes a physics-informed GNSS–IMU sensor fusion framework that enables robust, real-time wearable navigation across heterogeneous environments. The proposed system dynamically adapts to environmental context, employing GNSS dominant localization in outdoor settings and PINN enhanced IMU-based dead reckoning during GNSS denied indoor operation. At the core of the framework is a tightly coupled Physics-Informed Neural Network (PINN) and Extended Kalman Filter (EKF), where the PINN embeds kinematic motion constraints to correct inertial drift and suppress sensor noise, while the EKF performs probabilistic state estimation and sensor fusion. The framework is implemented on a compact, energy-efficient wearable platform and evaluated using real-world indoor–outdoor pedestrian trajectories. Experimental results demonstrate improved localization accuracy, significantly reduced drift during indoor navigation, and stable indoor–outdoor transitions compared to conventional GNSS–IMU fusion methods. The proposed approach offers a practical and reliable solution for wearable assistive navigation and has broader applicability in smart mobility and autonomous wearable systems. Full article

The rare and threatened pleurocarpous semi-aquatic moss Drepanocladus sendtneri (Amblystegiaceae) was the focus of an integrative conservation approach aimed at improving knowledge of its biological and ecological characteristics and enhancing its survival prospects. The results provide insights into both the axenic and xenic propagation of this species, as well as its biomass production under ex situ conditions. The KNOP medium proved to be the most suitable for propagation, particularly when demeristemized shoot tips were cultured in an upright orientation. Exogenous application of IBA increased the production of new shoots and reduced the time required to obtain substantial biomass under axenic conditions. Following successful acclimatisation to controlled xenic laboratory conditions, the moss was able to fully develop and spread in experimental basins maintained under outdoor botanical garden conditions, with humidity carefully regulated during dry periods. Within one year, a small number of initial plantlets expanded to cover approximately 4 dm², spreading efficiently over rainwater-soaked filter paper covered with an inert plastic mesh. These results provide practical guidance for the production and ex situ maintenance of D. sendtneri, thereby supporting the development and improvement of conservation action plans for this rare and threatened moss species. Full article

In an accelerated aging experiment involving a wide range of cumulative UV-B radiant exposures (up to approximately 9.46 × 10³ J cm⁻²), the degradation state of microplastics was assessed using SEM, FTIR, Raman spectroscopy, and DSC, and correlated with the cumulative UV-B dose. Sunlight-induced photooxidation is a significant weathering mechanism for microplastics. In this study, high-density polyethylene (HDPE), low-density polyethylene (LDPE), polypropylene (PP), polyvinyl chloride (PVC), nylon, and polystyrene (PS) were exposed to UV-B radiation under controlled dry conditions at two irradiance levels (0.06 and 0.6 mW cm⁻²), covering cumulative UV-B radiant exposures of up to approximately 9.47 × 10³ J cm⁻². Degradation was evaluated using SEM, FTIR, Raman spectroscopy, and DSC, and was related to the cumulative UV-B dose (H). The extent and progression of degradation varied significantly among the polymers. Overall, FTIR provided the most sensitive assessment of photooxidative surface changes for HDPE, LDPE, PP, and PS, Raman spectroscopy was most diagnostic for PVC (particularly for dechlorination-related changes), and DSC-derived crystallinity was most informative for nylon. These dose-resolved datasets establish a reproducible reference framework (“degradation library”) to facilitate the comparative assessment of the relative photooxidative aging stage of microplastics under comparable surface UV-driven conditions. Outdoor “sunlight-equivalent” times are reported solely as order-of-magnitude contextualization due to environmental variability. Full article