Search Results (6,683)

Human activity recognition (HAR) in smart environments plays a critical role in applications such as healthcare monitoring, intelligent transportation systems, and ambient assisted living; however, existing approaches are limited by their inability to effectively handle heterogeneous multimodal sensor data, capture long-range temporal dependencies, and generalize across diverse real-world environments under domain shifts. In this work, we present DMART-HAR, a Dynamic Multimodal Activity Recognition Transformer framework that unifies structured multimodal representation learning, transformer-based temporal modeling, cross-modal interaction, and adversarial domain adaptation within a single architecture. Specifically, the developed method introduces a sensor tokenization mechanism to encode heterogeneous IoT data into a unified representation space, followed by a transformer encoder to capture global contextual dependencies, while a cross-modal attention module enables deep interaction among sensor modalities and an adversarial domain adaptation strategy enhances robustness to unseen environments. Extensive experiments on benchmark datasets, including CASAS, PAMAP2, and Opportunity, demonstrate that DMART-HAR consistently outperforms both conventional baselines and recent state-of-the-art methods, achieving accuracy/F1-scores of 94.3%/92.8%, 96.2%/94.7%, and 89.8%/88.1%, respectively, and consistently outperforms the strongest competing approaches under cross-domain evaluation settings. These findings demonstrate the effectiveness of modeling temporal dynamics, multimodal relationships, and domain invariance simultaneously, establishing DMART-HAR as a scalable and robust solution for real-world HAR applications. Full article

Mapless navigation often removes global maps while retaining localization-derived goal vectors or bearings. We study a stricter setting in which a mobile robot observes only local LiDAR, scalar goal range, and short histories of executed actions; neither pose nor goal direction is provided to the policy. We introduce ACR-Nav, an action-conditioned range navigation framework that converts scalar-range evolution into closed-loop progress information. Its range–action history associates each distance change with the motion that produced it, while the sectorized LiDAR captures local geometry and short-term obstacle motion. A LiDAR-only safety filter provides immediate collision intervention, and a static-to-mixed curriculum stabilizes learning. A lightweight multilayer–perceptron is optimized with Proximal Policy Optimization (PPO), while the ACR-Nav formulation itself remains optimizer-agnostic. In corridor simulations, ACR-Nav achieved 93.2%, 80.4%, and 84.4% success in static, mixed, and dynamic environments. Removing the safety filter reduced success by 15.2, 14.6, and 16.0 percentage points in static, mixed, and dynamic environments, respectively, and random-goal tests yielded 91.2% and 81.4% success in static and mixed settings. Topology-shift experiments further quantified adaptation to an L-shaped corridor. The results show that action-conditioned scalar-range evolution can support goal-directed, segment-level navigation within locally straight corridor passages without exposing robot pose or target bearing to the policy. Full article

Vertical shear of the zonal wind across the equatorial tropopause over the Indian Ocean to the Maritime Continent is caused by a combination of the quasi-biennial oscillation (QBO) of the stratosphere and the seasonal cycle and interannual variability of the upper troposphere. The Madden–Julian Oscillation (MJO) has been previously observed to be more active during the easterly than the westerly phase of the QBO. Kelvin waves interacting with the background flow explain most of the propagation characteristics of the MJO in the equatorial upper troposphere. This work assesses the hypothesis that Kelvin wave propagation under conditions of easterly wind in both the upper troposphere and stratosphere maintains the upper tropospheric MJO circulation, but that this signal is disrupted with westerly wind shear that likely includes critical layers that would prevent Kelvin wave energy from passing. Linear regression of reanalysis data against an MJO index shows more coherent downward propagating Kelvin waves during easterly shear and no Kelvin-wave-like signal near the tropopause during conditions expected to include critical layers there. Historical analysis of this vertical shear shows that it is the primary focus of enhanced MJO variance with the easterly QBO, yielding the seasonally enhanced signal December through February and the erratic variability from year to year due to tropospheric contributions to shear. A wavenumber frequency spectrum analysis of lower stratospheric zonal wind shows that power shifts from high to low frequency between QBO westerly to easterly phases, consistent with Kelvin waves propagating at the phase speed range of the MJO during easterly QBO. Full article

Climate change is reshaping veterinary parasite transmission by altering thermal and hydrological suitability, environmental stage persistence, vector and intermediate host ecology, and contact across livestock–wildlife–companion animal interfaces. These effects are nonlinear; while warming may extend transmission in some systems, heat, desiccation, habitat loss, or disrupted hydrology can reduce the risk or concentrate transmission in local refugia. This critical narrative review compares pasture-transmitted helminths, snail-borne trematodes, environmentally transmitted protozoa, vector-borne parasites, and multi-host cycles. We propose an attribution framework that classifies observed changes across four dimensions (geographic range, seasonal timing, transmission intensity, and host-interface structure) and evaluates them through five analytical filters: suitability, parasite life-cycle response, vector or intermediate-host response, host-interface change, and surveillance artifacts. This framework prevents improved detection, land-use change, animal movement, management shifts and improved detection from being mistaken for climate-driven emergence. We also propose a climate–refugia paradox hypothesis, requiring empirical validation, in which drought or heat may reduce unselected parasite refugia and intensify selection for anthelmintic resistance. Finally, we connect a tiered diagnostic approach from field tools to reference molecular surveillance to support attribution-aware, risk-based One Health strategies that protect animal production, biodiversity, and public health. Full article

Third-party news-source factuality scorecards are valuable but increasingly fragile. Web pages change, access conditions shift and underlying datasets may disappear. The challenge is therefore not only benchmark imperfection but also benchmark sustainability as credibility datasets, search interfaces and platform reputation signals become harder to access reproducibly. This study investigates whether a fixed large language model (LLM) scoring procedure can generate durable, replayable outlet-level trust scores that align with a frozen external factuality benchmark rather than objective ground truth. Fifty-two English-language news outlets were assessed across nine predefined trust dimensions and compared with a frozen Media Bias Fact Check (MBFC) factuality snapshot. Raw LLM scores were rank-aware but compressed (Pearson’s $r=0.801$, Spearman’s $\rho =0.843$, full-cohort mean GAP $=0.221$). An affine calibration fitted on 42 training outlets increased full-cohort Pearson alignment to $r=0.828$ and reduced mean GAP to $0.090$; on the fixed ten-outlet validation fold, mean GAP fell from $0.162$ to $0.063$. Across 1000 additional stratified 42/10 splits, median validation GAP was $0.078$ (central 95% split range $0.048$–$0.110$). Wikipedia lead and source-weighted web enrichment did not outperform the calibrated archival path in the retained data. The Step 4 unweighted web-search meter improved on the Wikipedia-lead meter (Pearson’s $r=0.697$, Spearman’s $\rho =0.576$, full-cohort mean GAP $=0.200$; fixed-validation GAP $=0.146$) but remained below the calibrated archival path. RSS monitoring is reported separately as an asymmetric, bounded adverse-event signal rather than a second factuality benchmark. These findings support calibrated LLM trust vectors as a potentially useful archival proxy while highlighting benchmark dependence, sampling constraints, model sensitivity and the importance of reproducible data provenance. Full article

Post-disaster reconstruction often prioritises the rapid delivery of housing and infrastructure, yet the long-term everyday performance of commercial environments embedded in recovery plans remains poorly understood. This study investigates why broadly similar post-disaster commercial blocks in Yenikent (Sakarya, Türkiye) have developed markedly different levels of spatial vitality and long-term everyday use. Yenikent is a state-led satellite city developed after the 1999 Marmara Earthquake, where residential, administrative and service functions were relocated to higher ground as part of a planned secondary urban centre. Twenty-five years later, these purpose-built commercial centres display markedly different levels and forms of everyday use, ranging from active neighbourhood service hubs to abandoned urban voids. Using a multiple-case, mixed-methods design, the study analyses all twelve post-disaster commercial clusters (eighteen buildings) through sectional and morphological analysis, a four-indicator Spatial Vitality Matrix (stationary activity, pedestrian flow, physical permeability, and active occupancy), and 88 semi-structured interviews with businesses, users, and neighbourhood headmen. The findings identify three dominant trajectories: (i) relatively robust service hubs sustained by institutional and neighbourhood anchors, (ii) fragile clusters where vitality is concentrated along accessible ground-level edges while upper or internal spaces shift towards storage, institutional, or ancillary uses, and (iii) functionally obsolete complexes associated with peripheral siting, poor topographic adaptation, and weak accessibility. The results further demonstrate that formal occupancy is not necessarily equivalent to spatial vitality: sectional relationships with the terrain, entrance legibility, façade permeability, and the integration of anchors into shared circulation are closely associated with whether commercial spaces sustain everyday activity. Interview evidence additionally reveals case-specific differences in perceived safety and gendered use of internal corridors and courtyards. The study supports a section-sensitive approach to post-disaster commercial development that prioritises topographic adaptation, legible access, active edges, and the integration of everyday anchors into shared spatial networks. Full article

Ankle postural control is essential for standing stability immediately after sit-to-stand (STS), a movement associated with falls in older adults. Although triceps surae stretching is commonly used before exercise, its immediate effects on post-STS postural sway remain unclear. This exploratory study investigated the effects of static (SS), dynamic (DS), and ballistic stretching (BS) on post-STS postural sway and directional shifts in the anteroposterior extremes of center-of-pressure (CoP) after STS in a repeated-measures design with 10 healthy young adults (8 males, 2 females, mean ± SD age: 20.1 ± 0.3 years, age range 20–21 years). On three separate days, participants performed STS at a same-day pre-stretching baseline and after one of the stretching modalities. A force plate measured CoP in the anteroposterior direction (CoPap) during 0–10, 0–3, and 3–10 s after standing, and the standard deviation of CoPap was calculated as an index of post-STS postural sway. For the primary outcome measure (0–3 s), directional shifts in the anteroposterior extremes of CoPap during the first trial were assessed based on the change in the anterior and posterior positions of CoPap from the pre-stretching baseline to the post-stretching condition. Two-factor repeated-measures ANOVA revealed no significant main effects or interactions across all time intervals (all p ≥ 0.187). For the primary outcome—the 0–3-s interval—no significant effects were observed for stretch modality (F(2, 18) = 1.177, p = 0.331, partial η² = 0.116), trial (F(2, 18) = 0.314, p = 0.735, partial η² = 0.034), or their interaction (Greenhouse–Geisser-corrected F(1.75, 15.72) = 1.170, p = 0.329, partial η² = 0.115). Directional shifts in CoPap extremes showed 95% confidence intervals crossing zero across all modalities, with modest mean changes (<3%FL) and substantial inter-individual variability. In conclusion, acute triceps surae stretching did not significantly alter post-STS postural sway variability in this sample of healthy young adults, while preliminary descriptive findings highlight the importance of considering individual variability and directional CoP behavior in dynamic stability assessments. Full article

The dual active bridge (DAB) converter is one of the most common types of isolated bidirectional power converters in modern spacecraft EPS owing to its galvanic isolation, bidirectionality, soft switching, and good controllability. However, the goal of power transfer maximization often clashes with real-world spacecraft EPS constraints, namely thermal compliance, reliability, and the accuracy of simplified models used for analysis. This paper proposes a physics-informed methodology to derive the practical operating range under single phase shift (SPS) control based on a rigorous piecewise time-domain representation. From this model, the steady-state initial condition, general closed-form RMS current expression, ZVS boundary condition, and ZVS-aware loss model linked to the junction-temperature estimate are derived. The validity domain of the fundamental harmonic approximation (FHA) is evaluated against the exact model across the full (φ, k) space, and a two-dimensional operating map superposing power contours, the ZVS limit, and the thermal limit is presented. For the baseline case study at k = 1.0, the thermal constraint limits the nominal feasible upper phase shift to approximately 35°, while the broader 15–45° range remains useful for design assessment and operation toward 45° requires lower effective resistance and/or improved thermal management. The normalized SPS power-transfer curve retains the same shape under variations in L and f_s, but RMS current, losses, and thermal feasibility must be reassessed for each converter design. The resulting closed-form framework provides a steady-state feasibility-evaluation tool for spacecraft EPS design and offers a computational basis for future supervisory constraint evaluation under varying voltage, load, and thermal conditions. Full article

The rapid development of multimodal foundation models has shifted video understanding from perception-centered recognition toward more general semantic interpretation, reasoning, and decision-making over dynamic visual content. As video understanding tasks increasingly require fine-grained perception, long-range temporal modeling, multimodal grounding, and adaptive reasoning, collaboration among heterogeneous functional units, including specialized models, modules, agents, memory systems, and external tools, has emerged as an important system-level paradigm. However, existing surveys mainly organize video understanding methods by architectures, learning strategies, or task categories, leaving the collaborative structure of modern systems insufficiently examined. This survey provides a structured narrative review of multi-model collaboration in video understanding, which we formulate as collaborative video understanding. We introduce a unified analytical framework that characterizes collaborative systems through functional units, inter-unit communication mechanisms, and collaborative state representations, and organize existing methods according to their coordination dynamics into static collaboration and dynamic collaboration, with the latter further distinguished into controller-based and agent-based collaboration. We further review representative benchmarks, evaluation metrics, and empirical analysis, showing that current evaluation protocols mainly capture task-level performance but provide limited insight into collaborative organization, memory use, adaptive execution, and system-level collaborative capability. Finally, we discuss key challenges and future directions, including adaptive task decomposition, semantically aligned inter-unit communication, persistent shared memory, uncertainty-aware error containment, evidence-grounded reasoning, and collaboration-centric evaluation. By reinterpreting video understanding from a collaborative systems perspective, this survey aims to provide a structured foundation for developing more adaptive, reliable, and scalable video understanding systems. Full article

Light Detection and Ranging (LiDAR)-based forest inventory increasingly relies on diverse platforms, ranging from proximal systems including BackPack, All-Terrain Vehicle (ATV), and terrestrial laser scanning (TLS) to near-proximal systems such as uncrewed aerial vehicles (UAVs). However, differences in point density, viewing geometry, and occlusions among these acquisition systems pose challenges for processing heterogeneous LiDAR datasets using a common workflow. Traditional geometric approaches often rely on parameter tuning. On the other hand, deep learning (DL) approaches can be constrained by domain shift when applied to different sensors or forest environments. This study proposes a forest inventory pipeline for individual tree segmentation and the derivation of key forest biometrics including tree location and diameter at breast height (DBH) across heterogeneous LiDAR datasets. The pipeline uses a confidence-guided, multi-stage quality control framework that evaluates agreement between complementary tree location estimates to reduce common segmentation errors. In addition, a semi-automated procedure is developed to generate reference data for datasets lacking field measurements. The proposed workflow was evaluated using eight diverse datasets representing different platforms, sensors, acquisition patterns, and forest environments and was compared with 3DFIN, TreeLearn, and ForestFormer3D. Field reference measurements were available for a natural forest site, while the remaining datasets were evaluated using semi-automatically generated and manually refined reference data. The proposed tree detection pipeline achieved Precision ranging from 86.44% to 100%, Recall from 74.17% to 100%, and F1-scores from 81.82% to 100% across the evaluated datasets. For the Martell–BackPack dataset with independent field reference measurements, Precision, Recall, and F1-score were 97.55%, 96.95%, and 97.25%, respectively. For correctly detected trees by the proposed approach in the natural forest dataset with field measurements, DBH estimates achieved an RMSE of 2.5 cm with the total basal area underestimated by 1.88%, compared with DBH RMSE and reduction in basal area of 4.0 cm and 3.67%, respectively, for 3DFIN. Although the proposed pipeline did not achieve the highest performance in every test case, it maintained strong and generally consistent tree detection performance for the evaluated datasets. The main limitation of the proposed pipeline is its dependence on sufficient lower-stem visibility, which reduced tree detection accuracy in sparsely sampled areas. The proposed framework provides a practical workflow for LiDAR-based individual tree segmentation and DBH estimation using a fixed parameter configuration for all datasets captured by a given acquisition system. Full article

In adaptive signal control, the upstream observation range at intersections is typically determined by empirical detector placement, lacking a data-driven adaptive mechanism. This paper addresses the question of how far upstream is sufficient for timing decisions and proposes a sensing boundary identification method based on Bayesian online learning. A microscopic simulation model coupling all signal phases is first constructed based on the Intelligent Driver Model, with a green split optimization function minimizing the total queued vehicles at cycle end. The prefix length is then modeled as a probabilistic variable, and the class separability score serves as the likelihood basis. Through recursive Bayesian posterior updates, the optimal prefix length is automatically determined. Based on the identified sensing boundary, K-nearest neighbors weighted regression enables online prediction of the green split. Experiments on 10,368 simulated scenarios demonstrate that the posterior converges to a prefix length consistent with the core queue dissipation region. Far-end free-flow vehicles contribute limited information and degrade class separability when included, validating that a longer observation range does not necessarily improve performance. The identified prefix length uses only a small fraction of the full feature dimensions while maintaining favorable prediction accuracy, achieving a good trade-off among accuracy, computational efficiency, and online adaptability. The proposed method shifts sensing boundary determination from empirical setting to data-driven identification, offering a reference for detector placement and edge controller deployment. Full article

Significant research efforts have recently focused on nanomaterial processing for gas sensors and related sensing applications. However, the major challenges in the field involve the choice of material for the sensing layer of the sensor device element, together with the right structure, assembly, and morphology through which the full sensing properties of the material can be realised. Herein, we critically review the hierarchical nanostructures of V₂O₅ nanomaterial for application in gas sensing technology. Beyond the sheet structure, which serves as the fundamental building block of the V₂O₅’smolecular arrangement, nanostructures ranging from nanobelts to nanowires, nanorods, nanoribbons, nanofibres, nanotubes, and thin films were discovered as preferred configurations and thermodynamically favourable structures, according to many synthesis processes. Ethanol (C₂H₅OH) and Nitrogen dioxide (NO₂) gases were identified as preferred molecules commonly detected by various V₂O₅ morphologies, with the nanotube structure showing preferential sensitivity and selectivity to C₂H₅OH. We also discuss perspectives from density functional theory (DFT) studies of V₂O₅ nanostructures and other (2D) materials structures for gas sensing applications. The studies highlight enhanced adsorption energy, increase conductivity, and band gap variation as a result of an upper shift in the Fermi level, all as a consequence of surface interaction between semiconductor crystal orientation and chemical molecules. Finally, our calculations of the optimised parameters for α-V₂O₅ orthorhombic structure showed good agreement with experimental and other theoretical data in the literature. The adsorption energy profile for NO₂ molecules revealed that the Ag-doped surface exhibits the most negative adsorption energy compared with the clean surface and other doped surfaces. Full article

The proliferation of bullet comment systems on video-sharing platforms has created novel opportunities for covert communication. Recent research has demonstrated multiple bullet comment-based steganographic paradigms: time modulation, time attribute shifting, live broadcast game-based channels, and generative stegotext via frame comments. This paper presents Multi-Dimensional Behavioral Signature Analysis (MDBSA), a unified detection framework integrating seven behavioral dimensions to systematically characterize anomalies from four documented bullet comment steganographic paradigms. We construct a synthetic benchmark spanning six video categories with controlled steganographic injection; extract features capturing temporal, spatial, content, and structural patterns; and evaluate it using GroupKFold cross-validation to prevent data leakage from overlapping sliding windows. On this synthetic benchmark, MDBSA features combined with Gradient Boosting achieve 96.9% accuracy (F1 = 96.8%, AUC = 0.996), compared with 65.6% for logistic regression on the same features. Per-paradigm detection rates on the benchmark range from 98.4% to 100.0%. Full article

The global shift toward cleaner energy has positioned hydrogen-enriched methane (CH₄/H₂) as a practical bridge fuel. While it burns more efficiently and produces fewer carbon emissions than traditional hydrocarbons, it introduces operational and safety challenges. Hydrogen’s high reactivity and rapid burning velocity increase risks such as flashback and premature ignition. This study employs Computational Fluid Dynamics to examine the combustion behavior of methane−hydrogen blends in a 2D axisymmetric chamber based on RANS equations. Using ANSYS Fluent 19.1, the research utilizes a validated equilibrium mixture-fraction/PDF framework to ensure accuracy against physical experiments. The simulation framework successfully captures the complexity of non-premixed turbulent combustion by combining a probability density function approach with a realizable k-ε turbulence model. Moreover, this research explores how varying hydrogen concentrations and air mass flow rates, covering the full spectrum from lean to fuel-rich conditions, affect fluid dynamics, turbulence, and the development of recirculation zones. The data show that adding hydrogen fundamentally reshapes velocity fields and thermal profiles, which in turn dictate combustion efficiency and pollutant formation. It has been demonstrated that the optimal blend for combustion performance is the case containing 30% hydrogen. Furthermore, evaluations involving higher-fraction blends (approaching the 70% enrichment range) suggest that configurations exceeding this level necessitate a redesign of the injector near field to mitigate localized heat release and accelerated NO_x emissions. By identifying the operational limits for CH₄/H₂ blends in industrial settings such as steam boilers, this study offers a technical roadmap for engineering more stable, high-performance, and low-carbon energy infrastructure. Full article

Urban parks are “cool islands” for mitigating urban heat, yet most snapshot-based assessments overlook intraday cooling dynamics and divergent mechanisms across park typologies. This study examines 52 parks in Wuhan, a humid city with routine park irrigation, using thermal data from Landsat 9 and ECOSTRESS across morning, noon, and nightfall. Through stepwise analysis and blue–green classification, we quantify diurnal cooling dynamics and their drivers. While previous studies have examined diurnal (within-day) cooling, multidimensional indicators, or scale effects separately, our contribution lies in establishing a multi-temporal assessment framework that integrates temporal dynamics with blue, green, and grey park typologies to reveal how cooling patterns diverge across blue, green, and grey parks throughout the day. Results show park cooling intensity (PCI) and gradient (PCG) peak at noon, while cooling area (PCA) remains stable. Elevated cooling efficiency (PCE) at nightfall is driven not by ecological cooling, but by the rapid thermal response of impervious surfaces with low thermal inertia. Area, greenspace proportion, and building height are primary drivers, shifting from scale dominance in the morning to vegetation and building at noon, with a preliminary transition range of approximately 14–16 hm² identified for this regime shift, though this finding warrants further validation with larger samples. Based on blue–green composition, parks are categorised as blue, green, or grey, with divergent cooling dynamics due to thermophysical properties. Blue parks cool steadily all day, green parks peak at noon, while grey parks’ elevated PCE at nightfall is an apparent thermal response, not ecological cooling. Typological heterogeneity weakens models that pool all parks together, as water storage, vegetation evapotranspiration, and impervious thermal response vary across types and cancel out when pooled. Findings show that single-time-phase or full averaging insufficiently captures park cooling dynamics, underscoring the value of considering both diurnal and typological variations in climate-adaptive planning for dense cities. Full article