Search Results (1,149)

Optoelectronic motion capture systems provide objective and high-resolution measurements of upper limb kinematics. The hand-to-mouth movement is closely related to motor development in children. The “Rab Hand-to-Mouth protocol” (BTS Bioengineering) is widely used; however, its seated configuration constrains elbow posture and may limit the ecological validity of the movement. In this study, we propose a methodological adaptation of the protocol in a standing position to allow a more physiological elbow configuration and to increase the dynamic range of elbow and shoulder motion. The objective was to characterize kinematic patterns of the hand-to-mouth movement in typically developing children aged 4 to 9 years using this adapted setup. This study was designed as a descriptive analysis and does not aim to provide formal validation of the standing protocol against the original seated configuration. An observational study that included 40 children was conducted. Motion data were acquired using eight optoelectronic cameras (sampling frequency: 250 Hz) and 17 reflective markers placed on the trunk and upper limbs. Kinematic patterns and spatiotemporal parameters were computed using dedicated motion analysis software. No significant differences were observed between dominant and non-dominant limbs in spatiotemporal parameters, whereas kinematic differences were minimal and limited to trunk rotation, as identified by Statistical Parametric Mapping (SPM). Some isolated statistically significant associations with age were identified in specific spatiotemporal variables; however, these variables showed low coefficients of determination (R²), indicating limited explanatory power of age. Overall, kinematic parameters did not exhibit consistent age-related patterns. These findings provide preliminary descriptive data for hand-to-mouth kinematics in a standing condition, which may contribute to the future development of assessment protocols. However, the limited sample size and the absence of pathological populations restrict the direct generalization of these findings. Future studies should evaluate the applicability of this approach in clinical cohorts and explore its integration into sensor-based and data-driven models for movement analysis. Full article

This article presents an integrated design studio pedagogy for flood-resilient urban waterfronts that employs groundscape strategies, treating the ground as an active design medium to generate hybrid structures integrating landscape, architecture, and infrastructure. Implemented at the Fay Jones School of Architecture and Design (Fall 2024), the studio challenged students to transform North Little Rock’s flood-vulnerable riverfront by replacing conventional levee infrastructure with ground-based public architectural interventions. The study adopts a pedagogical case-study approach, examining a studio cohort in which all projects were developed under shared site conditions, design constraints, and instructional frameworks. Five assignments progressed from collaborative precedent analysis to individual technical development, integrating computational modeling, performance simulations, and expert consultations across structural, envelope, MEP, and site engineering. Student work is analyzed through comparative sectional diagrams and selected in-depth project studies to evaluate how groundscape functioned as a shared solution type for multiscalar integration. The results show that groundscape operates productively when tested against specific site constraints rather than deployed as a generalized esthetic. In response to flood elevations, degraded ecology, and limited public access, students developed distinct ground-based operations—such as embedding, lifting, and integrating flood walls as spatial thresholds—demonstrating architecture’s capacity to mediate between civic space, environmental performance, and flood protection. By situating groundscape within a problem-oriented pedagogy, the study consolidates modernist, postmodern, and contemporary groundscape discourse and demonstrates how architectural education can engage productively with climate-adaptation challenges. Full article

Background/Objectives: Visuomotor interception requires aligning action with the future state of moving targets under sensory and motor delays. This constraint provides a tractable framework to examine how predictive and feedback-driven processes interact. This narrative review evaluates theoretical and empirical accounts of interception, with emphasis on how prediction and online control are integrated across behavioral and neural levels. Methods: We conducted a narrative synthesis of behavioral, eye-tracking, computational, and neurophysiological studies on visuomotor interception. Literature was identified through searches of PubMed, Web of Science, and Google Scholar using search terms including “visuomotor interception,” “predictive motor control,” “eye–hand coordination,” “time-to-contact,” “sensorimotor delay,” and related combinations. Studies published between 1986 and 2026 were considered, with emphasis on peer-reviewed empirical and theoretical work. Preprints were included only when directly relevant and are identified as such. The review compares internal model, ecological, and hybrid frameworks, and organizes evidence around spatial (“where”) and temporal (“when”) components of control. Results: Across paradigms, interception behavior is not well accounted for by purely predictive or reactive mechanisms. Instead, trajectories reflect a continuous interaction between anticipatory guidance and online correction. Spatial and temporal components show partial dissociation across tasks and manipulations. Available evidence supports the involvement of distributed circuits, including parietal, frontal, cerebellar, and subcortical systems, while indicating that eye movements play an active role in both information sampling and motor planning. Conclusions: Interception is best understood as the product of interacting biological, environmental, and learned constraints. Similar behavioral signatures can arise from distinct mechanisms, arguing against a unitary account. Progress requires integrating behavioral analyses with model-based and neural approaches to dissociate underlying computations. Full article

To combat climate change, farmers want to develop sustainable agriculture that enhances food production while strengthening their capacity to cope with extreme weather events and pest and disease pressures. Promoting agroecological farming practices is a promising approach in enhancing sustainability and strengthening the climate-resilient farming systems. Recent research often overlooks to what extent the agroecological farming practices (AFP) provide a measurable advantage over non-AFP methods under increasing environmental challenges. In this regard, this study compares the extent of climate resilience between AFP mango-based farming systems and non-AFP mango-based farming systems in southern Ethiopia. AFP adopters applied ecological principles like intercropping, integrated pest management, agroforestry, canopy management, varietal diversity, and water and soil preservation to enhance biodiversity and soil health, and boost productivity and ecosystem services. The study employed a mixed-method design, drawing on the data from 395 selected households. The resilience of AFP and non-AFP farming systems was assessed by computing the 13 agroecosystem indicators of climate resilience using the Self-evaluation and Holistic Assessment of Climate Resilience of Farmers and Pastoralists (SHARP+) tool. Households in AFP mango-based farming system demonstrated greater diversification in agricultural production system compared to those in non-AFP mango farming system. The analysis of climate resilience indicators showed that the mango production systems under the AFP were more climate-robust than their conventional systems. Both the compound resilience score and the household resilience index showed that the mango farming systems under AFP substantially enhanced climate resilience. Hence, coordinated supports from the extension services, NGOs, and researchers are needed to scale up these benefits of AFP. Strengthening the AFP mango farming requires addressing the key barriers such as market access, input availability, and crop diversification strategies. This paper identifies important avenues for further AFP research in Sub-Saharan African countries. Full article

Fluorene poses ecological and health hazards that originate from biomass combustion and petroleum. However, some microorganisms can counter fluorene through complex enzymatic degradation pathways. This research aimed to explore the catalytic efficiency of enzymes on metabolites and their toxicity levels throughout the fluorene biodegradation pathway. Several web servers and software were used to characterize them and analyse molecular dockings between ligands and proteins. Fluorene and its metabolites have mild toxicities to the brain, lung, neurons, and kidneys, and consequent endpoints cause mutations, cancer, and ecotoxicity at different levels. The catalytic enzymes are well-folded, single-chained, medium-sized proteins that are acidic, thermostable, and with few exceptions, hydrophilic, cytoplasmic, non-allergenic, and nonvirulent, possessing multiple active sites. The ERRAT, PROCHECK, and VERIFY 3D tools successfully validated the SWISS-modelled 3D structures of proteins. Molecular docking results showed moderate binding affinities between proteins and ligands, ranging from −9.4 to −6.1 kcal/mol, indicating potential activities of the enzymes. This computational study supports the conventional fluorene degradation pathway and may provide a new avenue for further research. Full article

Trust decay in social media is a serious threat to user experience and platform ecology. To solve this problem, this paper focuses on triadic closure in the infrastructure of social networks and explores its mechanism in trust decay prevention. Based on the systematic comparison of the ER random graph, the BA scale-free network, a forest fire model, and complete graph approaches, two core metrics, the trust decay risk index and trust resilience index, are proposed in this paper. Combined with structural indices such as the clustering coefficient, the average path length, and the triangular closure number and its growth rate, the quantitative relationship between network structure evolution and trust decay risk is established. It is found that the forest fire model exhibits optimal trust resilience in structure due to its power-law growth characteristics of high clustering, short path length and triangular closure; the dynamic mechanism of trust decay under different network growth modes is significantly different. The validity of the theoretical framework is further supported by the verification of Sina Weibo attention relationship network data. The analysis framework of network growth evolution based on fusion triangle closure and the risk and resilience indicators defined in this paper provides a computable theoretical tool for understanding and predicting trust evolution in social media from the perspective of network structure. Full article

Uninterrupted traffic flow monitoring is a prerequisite for optimal resource allocation and minimizing vehicular emissions in smart cities. However, centralized traffic management architectures are highly vulnerable to single points of failure. When structural sensor malfunctions occur, the resulting network unobservability paralyzes dynamic signalization, triggering cascading traffic congestion, extended idling times, and severe greenhouse gas emissions. To address this cyber-ecological vulnerability, we propose the Hybrid Multi-Agent State Estimation (H-MASE) protocol, a fully decentralized decision-support framework designed from an applied systems reliability engineering perspective. By deploying PSAs and VLAs directly onto IoT-enabled edge devices at smart intersections, H-MASE leverages a hop-by-hop edge computing topology to collaboratively track macroscopic route flow dynamics. Mathematically, this distributed estimation process is formulated as a network-wide least-squares convex optimization problem, where local projection operators function as exact Distributed Gradient Descent steps to minimize the global residual sum of squares. The distributed consensus mechanism acts as a spatial variance reduction tool, effectively dampening measurement noise and stochastic demand fluctuations. Furthermore, we introduce an autonomous anomaly detection logic that isolates severe structural faults rapidly, which is mathematically structured to prevent false alarms under bounded disturbance conditions. Numerical simulations demonstrate that the protocol yields a highly resilient optimality gap (e.g., a Root Mean Square Error of merely 0.81 vehicles per estimated state) even under catastrophic hardware failures. Ultimately, H-MASE provides a robust, fail-safe data foundation for sustainable urban logistics and green-wave signalization, ensuring that smart cities maintain ecological resilience and optimal resource utilization under severe structural disruptions. Full article

Mobile EEG enables investigating brain activity during real-world behaviour, but remains susceptible to motion artefacts, limiting signal interpretability and the use of advanced analytical techniques. Methods developed for removing motion-related artefacts induced by periodic activity like cycling, walking or juggling showed degraded performance with increasing movement variability and speed. To fill this gap, we developed a method based on generalized eigenvalue decomposition (GED) to identify and suppress highly variable, non-periodic—especially transient—artefacts due to very rapid, free full body movements of different types, as they occur during sports practice. By leveraging the contrast between covariance matrices of artefactual and resting-state EEG segments, this approach isolates motion-related components for removal during multichannel EEG signal reconstruction. The method was validated on two ecological datasets featuring stereotyped head and body movements and dynamic table tennis. Comparison with state-of-the-art technique showed superior performance of our method in terms of signal-to-error ratio (SER), artefact-to-residue ratio (ARR), brain spectral power preservation and computation time. Sensitivity analysis was applied to demonstrate the method’s robustness to parameter changes. These findings highlight the potential of the proposed method as a robust, generalizable approach for motion artefact suppression in mobile EEG, particularly when applied in extreme recording conditions like during active sports activity. Full article

This study proposes a heterogeneous fusion path planning framework for unmanned aerial vehicles (UAVs) operating in complex emergency rescue and civil environments. Existing single-mechanism metaheuristics—including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithms (GAs)—suffer from fundamental limitations in three-dimensional kinematic path planning: PSO converges rapidly but stagnates at local optima due to population variance collapse; ACO offers robust local exploitation but incurs prohibitive cold-start overhead; GAs maintain diversity at the cost of expensive crossover operations. To address these complementary deficiencies simultaneously, the proposed framework introduces a spherical coordinate representation that reduces computational complexity and naturally enforces UAV kinematic constraints, combined with adaptive weight factors and a serial PSO-ACO fusion strategy, and subsequently incorporates adaptive weight factors. A serial fusion strategy is then introduced, wherein the sub-optimal trajectory generated by the Spherical PSO phase is mapped into the ACO pheromone field via a Gaussian Kernel Density Mapping (GKDM) mechanism, enabling the ACO phase to perform fine-grained local exploitation within a kinematically feasible corridor. Various constraints along the flight path are formulated into distinct cost functions, which cover aircraft track length, pitch angle variation, altitude difference variation, obstacle avoidance, and smoothness; the core task of the algorithm is to find the flight path with the minimum total cost. The proposed algorithm is dedicated to UAV path planning in complex emergency rescue environments (disaster-stricken areas, hazardous zones) and is further applicable to civil low-altitude logistics delivery, industrial facility inspection, ecological environment monitoring and urban air mobility (UAM) scenarios with complex obstacle constraints. It can effectively improve the safety and efficiency of UAVs in reaching rescue points, delivering emergency supplies, conducting disaster surveys, and completing various civil low-altitude operation tasks. Full article

Traditional evaluations of revetment projects primarily focus on structural safety and economic analysis, which cannot comprehensively reflect the overall effectiveness of such projects. To address this issue, this paper establishes a comprehensive evaluation index system for ecological revetments based on ecosystem theory and sustainable development principles. The system is tailored for the Pinglu Canal Ecological Revetment Demonstration Project. It assesses three key aspects: structural stability, ecological health, and socioeconomic benefits. Subjective weights were calculated using the Fuzzy Analytic Hierarchy Process (FAHP). Objective weights were determined by optimizing the Projection Pursuit (PP) model with the Tent-improved Crocodile Ambush Optimization Algorithm (TCAOA). Game theory was employed to compute the combined weights. The evaluation grade of the ecological revetment project was subsequently determined using a cloud model. The results show that the cloud eigenvalues of the project’s comprehensive evaluation are (1.096, 0.209, 0.047), and the application effectiveness is rated as “Excellent”. The cloud expected values for structural stability, ecological health, and socioeconomic benefits are 1.02, 1.18, and 1.15, respectively. All of these values are at the “Excellent” level. Compared with GA-PP and PSO-PP, TCAOA-PP converges faster and more stably. It requires only 347 iterations, achieves a coefficient variation of 3.8%, and reduces computation time by 23%. By revealing the nonlinear coupling relationships among indicators, the model presented in this paper provides a methodological foundation for establishing an evaluation framework that is ecologically interpretable for bank protection. This study has important practical significance for promoting the high-quality development of inland waterways and the construction of ecological revetments. Full article

3D bird pose estimation plays a pivotal role in ecological conservation research. However, it remains a formidable challenge due to extensive joint deformation, severe self-occlusion, and the scarcity of 3D ground truth data. Therefore, practical solutions typically rely on accurate 2D keypoint detection from monocular images and subsequent 3D lifting. Although the High-Resolution Network (HRNet) has established a benchmark in 2D pose estimation by preserving high-resolution feature representations, its architecture, which relies on small convolution kernels, faces difficulties in capturing the global long-range dependencies necessary to resolve severe occlusions. To address these deficiencies, the core contributions of this work are summarized as follows: (1) We design a Gated LS-Block with a partial channel gating strategy to decouple channel mixing from spatial mixing, and extract global long-range dependencies via the proposed Large–Small Convolution (LSConv) to minimize feature redundancy. (2) We embed this block into Stage 2 of HRNet, enhancing multi-scale feature learning while slightly reducing model parameters and computational overhead; (3) To alleviate the ill-posed nature of monocular 3D lifting without paired supervision, we develop an unsupervised 3D reconstruction algorithm. Experimental results on the Animal Kingdom dataset demonstrate that our method achieves a 0.9% improvement in PCK@0.05 while reducing GFLOPs by 3.3%. These results verify that the proposed architecture enhances the model’s representation capability for bird poses while ensuring efficient inference. Meanwhile, we validate the applicability of the proposed 3D reconstruction algorithm via qualitative experiments, and further demonstrate that our unsupervised 3D lifting algorithm successfully preserves low symmetry error and robust bone length consistency with proxy metrics. Full article

With the advent of the Fourth Industrial Revolution and the rise of new forms of productive forces, the ways humans interact with space, objects, and information are being profoundly reshaped, bringing unprecedented possibilities for upgrading interactive spaces—human settlements that integrate physical and digital environments. Against this background, using the literature on interactive space research from the Web of Science (WoS) Core Collection between 1990 and 2025 as the data source, this study employs CiteSpace software to generate scientific knowledge maps, analyzing the historic development, hotspots, and trends in the research of interactive space, providing both theoretical and data support. In terms of results, a total of 458 papers were collected, demonstrating a consistent year-on-year increase. The research spans multiple fields, including computer science, architecture, ecology, physics, design, and behavioristics. Specifically, results indicate that research hotspots in interactive spaces include collaborative governance, social coexistence, and sustainable renewal, all of which are highly relevant to activating human settlements. The vitality of interactive spaces can be constructed across multiple dimensions, (for instance, enhancement based on ecology, environment, culture, and other factors of the space). However, research on interactive spaces still suffers from a lack of interdisciplinary collaboration and multi-domain integration; therefore, it is essential to strengthen cooperation among relevant fields. Current research lacks interdisciplinary integration and dynamic response mechanisms. Based on these findings, this study, through visual analysis, reveals the research hotspots and evolutionary trajectory of interactive spaces and proposes a “technology–humanism–governance” trinity framework. This system should be based on technology as the means, humanism as the guiding principle, and effective governance as the goal. It aims to explore how to leverage the service-oriented and convenient nature of technology in interactive spaces to deepen human-centric design and thereby drive the optimization of systems. Based on these findings, future research on interactive spaces should shift its design philosophy to be more human-centric, establish a multidisciplinary research system, utilize local empirical cases, and develop scalable, applicable theories to construct harmonious, open spaces, enhance human–environment relationships, and provide other countries undergoing urbanization with practical solutions. Full article

Coral reef ecosystems are declining rapidly due to climate change, disease, and anthropogenic stressors, driving the expansion of land-based coral propagation for reef restoration. A major bottleneck in these efforts is the manual measurement of coral recruit tissue area from microscopy images, which requires 2–7 min per image and limits scalability. We present a hierarchical deep learning pipeline that automates this measurement by integrating YOLO-based detection with Segment Anything Model (SAM) segmentation. YOLO localizes recruits and classifies them by developmental stage; stage-specific fine-tuned SAM models then segment live tissue using bounding box and background point prompts to suppress segmentation leakage and improve boundary precision. Surface area is computed directly from the segmented masks using pixel size extracted from image metadata. The pipeline reduces processing time to approximately 3–5 s per image—a 24–140× speedup over manual tracing. Evaluated on 3668 microscopy images from two national coral research facilities, the system achieves a mean IoU exceeding 95% and an auto-acceptance rate (AAR) of 71.51%, where predicted-to-ground-truth area ratios fall within a ±5% tolerance of expert annotation, substantially reducing manual workload while maintaining measurement reliability across species, developmental stages, and imaging conditions. This workflow addresses a critical bottleneck in restoration research and demonstrates the broader applicability of AI-based image analysis in marine ecology. Full article

Infant respiration is a physiological marker of health and wellbeing that can provide insight into sleep and wake patterns. Technological innovation presents opportunities to enhance measurements of physiological signals, which improves ecological validity and participant experiences. This is particularly true in the context of studying infant sleep, as it can be disrupted by changes in the environment and the physical sensation of unfamiliar or uncomfortable sensors. The goal of this study was to examine if a commercially available video baby monitor (Nanit system) can accurately estimate respiration during a nap relative to a commonly used cardiorespiratory sensor (Isansys Lifetouch sensor). Thirty-three infants (M = 9.7 months; range = 1–22 months) took a nap while wearing the Lifetouch sensor and Nanit Breathing Band. Infants slept in view of the Nanit camera. A computer vision algorithm applied to the video detected movement of the patterns on the fabric band worn around the infant’s torso to determine respiratory rates. The results showed strong consistency between the devices. More than 95% of the minute-by-minute respiration data fell within the limits of agreement, with little bias. Agreement was not influenced by age or nap duration, suggesting the Nanit Breathing Band provides a valid measure of respiration across infancy. Full article

The presence of corticosteroids (CSs) in aquaculture wastewater poses risks to ecological health and food safety, yet data on their lipophilicity (logK_ow) remain scarce. This study determined the logK_ow of CSs to perform a screening-level in silico environmental risk prioritization. We evaluated nine computational programs (ACD/LogP, ALOGPS 2.1, CLOGP, JChem, KOWWIN, MiLogP, MolLogP, MOSES.logP, and XLOGP3) against experimental data for 50 steroid hormones. Results showed that XLOGP3 demonstrated the highest accuracy (Adjusted R² = 0.9872; SSE = 0.1004), followed by MiLogP, ACD/LogP, and KOWWIN. Structure–lipophilicity analysis revealed that esterification and acetonide formation significantly increase logK_ow, while hydroxylation decreases it. Using the validated XLOGP3, we predicted logK_ow for 32 synthetic CSs and estimated their bioconcentration factor (BCF) and soil organic carbon–water partition coefficient (K_oc). Because experimental logK_ow data for these 32 synthetic compounds are largely unavailable, these estimates should be interpreted as preliminary prioritization indicators rather than experimentally confirmed endpoints. Heavily modified CSs like Ciclesonide and Fluocortolone 21-hexanoate exhibited high logK_ow (>4.5), log BCF (>3.0), and logK_oc (>4.0), indicating their high potential for bioaccumulation and persistent sediment adsorption. This study provides a prioritized list of high-risk CSs, serving as a preliminary tool to identify potential compounds of concern in aquaculture environments. Full article