Search Results (8,575)

This study, aimed at producing a contextualised positive youth development framework for socially and economically disadvantaged youths in Singapore, is an initial youth project phase for profiling this population through explanatory mixed-methods research. The online survey was conducted for 843 secondary school students. The questionnaire was guided by two PYD models—5/6 Cs and 40 developmental assets—based on Buenconsejo and Datu’s integrative ecological perspective. After data collection, Exploratory Factor Analysis (EFA) was conducted to find the underlying factors by using the Principal Axis Factoring (PAF) extraction method and Promax with Kaiser Normalisation rotation. A 31-item survey questionnaire was finalised after the EFA, identifying an underlying seven-factor structure to establish the Integrated Positive Youth Development (IPYD) framework. This study provided groundbreaking application of PYD in a non-Western context and strategic insights into how the strengths of disadvantaged youths in Singapore could be encouraged in a manner that is culturally knowledgeable. Full article

Early intervention is pivotal for optimizing neurodevelopmental outcomes in children with language delay, where increased language stimulation can optimize therapeutic outcomes. Extending speech–language therapy from clinical settings to the home is a promising strategy; however, practical barriers and a lack of scalable, customizable home-based models limit the implementation of this approach. The integration of AI-powered digital interactive tools could bridge this gap. This pilot feasibility study adopted a single-arm pre–post (before–after) design within a two-phase, mixed-methods framework to evaluate a generative AI-powered interactive platform supporting home-based language therapy in children with either idiopathic language delay or autism spectrum disorder (ASD)-related language impairment: two conditions known to involve heterogeneous developmental profiles. The participants received clinical language assessments and engaged in home-based training using AI-enhanced tablet software, and 2000 audio recordings were collected and analyzed to assess pre- and postintervention language abilities. A total of 22 children aged 2–12 years were recruited, with 19 completing both phases. Based on 6-week cumulative usage, participants were stratified with respect to hours of AI usage into Groups A (≤5 h, n = 5), B (5 < h ≤ 10, n = 5), C (10 < h ≤ 15, n = 4), and D (>15 h, n = 5). A threshold effect was observed: only Group D showed significant gains between baseline and postintervention, with total words (58→110, p = 0.043), characters (98→192, p = 0.043), type–token ratio (0.59→0.78, p = 0.043), nouns (34→56, p = 0.043), verbs (12→34, p = 0.043), and mean length of utterance (1.83→3.24, p = 0.043) all improving. No significant changes were found in Groups A to C. These findings indicate the positive impact of extended use on the development of language. Generative AI-powered digital interactive tools, when they are integrated into home-based language therapy programs, can significantly improve language outcomes in children who have language delay and ASD. This approach offers a scalable, cost-effective extension of clinical care to the home, demonstrating the potential to enhance therapy accessibility and long-term outcomes. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly integral to construction site management, supporting monitoring, inspection, and data collection tasks. Effective UAV path planning is essential for maximizing operational efficiency, particularly in complex and dynamic construction environments. While previous BIM-based approaches have explored representation models such as space graphs, grid patterns, and voxel models, each has limitations. Space graphs, though common, rely on predefined spatial spaces, making them less suitable for projects still under construction. Voxel-based methods, considered well-suited for 3D indoor navigation, suffer from three key challenges: (1) a disconnect between the BIM and voxel models, limiting data integration; (2) the computational cost and time required for voxelization, hindering real-time application; and (3) inadequate support for 4D BIM integration during active construction phases. This research introduces a novel framework that bridges the BIM–voxel gap via an enriched voxel map, eliminates the need for repeated voxelization, and incorporates 4D BIM and additional model data such as defined workspaces and safety buffers around fragile components. The framework’s effectiveness is demonstrated through path planning simulations on BIM models from two real-world construction projects under varying scenarios. Results indicate that the enriched voxel map successfully creates a connection between BIM model and voxel model, while covering every timestamp of the project and element attributes during path planning without requiring additional voxel map creation. Full article

Urban Form (UF), as a synthesis of urban functions and socioeconomic elements, is closely associated with Anthropogenic Carbon Emissions (ACE) and has important implications for low-carbon urban planning. As a key national economic strategy region, the Yangtze River Economic Belt (YREB) exhibits pronounced heterogeneity in urban development, highlighting the urgent need to elucidate the interaction mechanisms between UF and ACE to support carbon reduction strategies. This study employs nighttime light data and carbon emission records from 2002 to 2022 in the YREB. By integrating Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT), we developed a neural network ensemble model (RSG-Net) to analyze the impacts and driving mechanisms of UF on ACE. The results indicate the following: (1) Over the past two decades, total ACE in the YREB increased by 196%, displaying a three-phase trajectory of rapid growth, deceleration, and rebound. (2) The RSG-Net model achieved superior predictive performance, with an R² of 0.93, an RMSE of 1.96 × 10⁶ t, an RPD of 3.69, and a PBIAS of 4.53%. (3) Based on Pearson correlation analysis and SHAP (Shapley Additive Explanations) feature importance, beyond economic and demographic indicators, the most influential UF indicators are ranked as Number of Urban Patches (NP), Normalized Difference Vegetation Index (NDVI), and Construction Land Concentration (CLC). These findings demonstrate that the RSG-Net model can not only predict ACE but also identify key UF factors and explain their interrelationships, thereby providing technical support for the formulation of urban carbon reduction strategies. Full article

Computer viruses remain a persistent technological challenge in information security. They require mathematical frameworks that realistically capture their propagation in digital networks. Classical continuous-time, integer-order models often overlook two key aspects of cyber environments: their inherently discrete nature and the memory-dependent effects of networked interactions. In this work, we introduce a fractional-order discrete computer virus (FDCV) model, derived from a three-dimensional continuous integer-order formulation and reformulated into a two-dimensional fractional discrete framework. We analyze its rich dynamical behaviors under both commensurate and incommensurate fractional orders. Leveraging a comprehensive toolbox including bifurcation diagrams, Lyapunov spectra, phase portraits, the 0–1 test for chaos, spectral entropy, and $C_0$ complexity measures, we demonstrate that the FDCV system exhibits persistent chaos and high dynamical complexity across broad parameter regimes. Our findings reveal that fractional-order discrete models not only enhance the dynamical richness compared to integer-order counterparts but also provide a more realistic representation of malware propagation. These insights advance the theoretical study of fractional discrete systems, supporting the development of potential technologies for cybersecurity modeling, detection, and prevention strategies. Full article

Enhanced Rock Weathering (ERW) is a promising carbon dioxide removal (CDR) strategy that accelerates mineral dissolution, sequestering atmospheric CO₂ while improving soil health. This study builds on prior applications of soil calcimetry by investigating its ability to resolve short-term carbonate fluxes and rainfall-modulated weathering dynamics in wollastonite-amended croplands. Conducted over a single growing season (May–October 2024) in temperate row-crop fields near Port Colborne, Ontario—characterized by fibric mesisol soils (Histosols, FAO-WRB)—this study tests whether calcimetry can distinguish between dissolution and precipitation phases and serve as a proxy for weathering flux within the upper soil horizon, under the assumption that rapid pedogenic carbonate cycling dominates alkalinity retention in this soil–mineral system. Monthly measurements of soil pH (Milli-Q and CaCl₂) and calcium carbonate equivalent (CCE) were conducted across 10 plots, totaling 180 composite samples. Results show significant alkalinization (p < 0.001), with average pH increases of ~+1.0 unit in both Milli-Q and CaCl₂ extracts over the timeline. In contrast, CCE values showed high spatiotemporal variability (−2.5 to +6.4%) without consistent seasonal trends. The calcimetry-derived weathering proxy, log (Σ ΔCCE/Δt), correlated positively with pH (r = 0.652), capturing net carbonate accumulation, while the kinetic dissolution rate model correlated strongly and negatively with pH (r ≈ −1), reflecting acid-promoted dissolution. This divergence confirms that the two metrics capture complementary stages of the weathering–precipitation continuum. Rainfall strongly modulated short-term carbonate formation, with cumulative precipitation over the previous 7–10 days enhancing formation rates up to a saturation point (~30 mm), beyond which additional rainfall yielded diminishing returns. In contrast, dissolution fluxes remained largely independent of rainfall. These results highlight calcimetry as a direct, scalable, and dynamic tool not only for monitoring solid-phase carbonate formation, but also for inferring carbonate migration and dissolution dynamics. In systems dominated by rapid pedogenic carbonate cycling, this approach captures the majority of alkalinity fluxes, offering a conservative yet comprehensive proxy for CO₂ sequestration. Full article

Time-series Interferometric Synthetic Aperture Radar (InSAR) techniques encounter substantial reliability challenges, primarily due to the presence of gross errors arising from phase unwrapping failures. These errors propagate through the processing chain and adversely affect displacement estimation accuracy, particularly in the case of a small number of SAR datasets. This study presents a unified data fusion framework designed to enhance the detection of gross errors in multi-source InSAR observations, incorporating a robust Least Squares Adjustment (LSA) methodology. The proposed framework develops a comprehensive mathematical model that integrates the fusion of multi-source InSAR data with robust LSA analysis, thereby establishing a theoretical foundation for the integration of heterogeneous datasets. Then, a systematic, reliability-driven data fusion workflow with robust LSA is developed, which synergistically combines Multi-Temporal InSAR (MT-InSAR) processing, homonymous Persistent Scatterer (PS) set generation, and iterative Baarda’s data snooping based on statistical hypothesis testing. This workflow facilitates the concurrent localization of gross errors and optimization of displacement parameters within the fusion process. Finally, the framework is rigorously evaluated using datasets from Radarsat-2 and two Sentinel-1 acquisition campaigns over the Tianjin Binhai New Area, China. Experimental results indicate that gross errors were successfully identified and removed from 11.1% of the homonymous PS sets. Following the robust LSA application, vertical displacement estimates exhibited a Root Mean Square Error (RMSE) of 5.7 mm/yr when compared to high-precision leveling data. Furthermore, a localized analysis incorporating both leveling validation and time series comparison was conducted in the Airport Economic Zone, revealing a substantial 42.5% improvement in accuracy compared to traditional Ordinary Least Squares (OLS) methodologies. Reliability assessments further demonstrate that the integration of multiple InSAR datasets significantly enhances both internal and external reliability metrics compared to single-source analyses. This study underscores the efficacy of the proposed framework in mitigating errors induced by phase unwrapping inaccuracies, thereby enhancing the robustness and credibility of InSAR-derived displacement measurements. Full article

Virotherapy is a promising method for treating oncological diseases, including such aggressive and difficult-to-treat brain tumors such as glioblastoma. Recombinant vaccinia virus VV-GMCSF-Lact has previously shown high antitumor potential against tumor cells of varying histogenesis, including gliomas, and completed a Phase I clinical trial, demonstrating safety and good tolerability in patients with recurrent/refractory metastatic breast cancer. Investigating two types of VV-GMCSF-Lact delivery, intravenous and intratumoral, into orthotopically transplanted C6 glioma in rats, it was shown that intratumoral injection significantly increases tumor volumes in comparison with intravenous virus delivery and is accompanied by noticeable toxic effects. Extensive areas of necrotic decay of tumor tissue and its significant mixed-cell infiltration and peritumoral edema, affecting the tumor volume, were detected using H&E staining of C6 tumors after intratumoral injection of VV-GMCSF-Lact. However, only with intratumoral administration was a significant decrease in the level of the tumor cell proliferation marker Ki67 demonstrated by immunohistochemical staining. The observed toxic effects of VV-GMCSF-Lact with intratumoral administration revealed the need for dose selection, which was performed on a mouse GL261 glioma model. Results of the study allowed us to determine the viral dose that does not lead to toxic effects and can potentially increase life expectancy of mice. The data obtained show the need for careful selection of both the route of viral drug dose and administration. Full article

Asteroids are remnants of primordial material from the early stages of solar system formation, approximately 4.6 billion years ago, and they preserve invaluable records of the processes underlying planetary evolution. Investigating asteroids provides critical insights into the mechanisms of planetary development and the potential origins of life. To enable efficient sample acquisition under vacuum and microgravity conditions, this study introduces a two-stage gas-driven asteroid sampling strategy. This approach mitigates the challenges posed by low-gravity environments and irregular asteroid topography. A coupled computational fluid dynamics–discrete element method (CFD–DEM) framework was employed to simulate the gas–solid two-phase flow during the sampling process. First, a model of the first-stage gas-driven sampling device was developed to establish the relationship between the inlet angle of the gas nozzle and the sampling efficiency, leading to the optimization of the nozzle’s structural parameters. Subsequently, a model of the integrated two-stage gas-driven sampling and sample-delivery system was constructed, through which the influence of the second-stage nozzle inlet angle on the total collected sample mass was investigated, and its design parameters were further refined. Simulation outcomes were validated against experimental data, confirming the reliability of the CFD–DEM coupling approach for predicting gas–solid two-phase interactions. The results demonstrate the feasibility of collecting asteroid regolith with the proposed two-stage gas-driven sampling and delivery system, thereby providing a practical pathway for extraterrestrial material acquisition. Full article

Background/Objectives: Electroencephalography (EEG) is increasingly used in pediatric anesthesia to detect abnormal brain activity such as burst suppression (BS), a marker of profound cortical inactivation. The objective of this study was to assess anesthetic depth using bilateral spectral edge frequency (SEF) and to determine the incidence of frontal cortical BS in young children undergoing cardiac surgery with extracorporeal circulation (ECC) under sevoflurane anesthesia. Methods: Twelve children, divided into two age groups (<12 months and 12–36 months), were included. EEG sensors were placed on the forehead and continuously monitored with SedLine^®. BS and SEF were analyzed using linear mixed-effects models, accounting for age group and repeated measurements across the procedure. Results: BS did not differ significantly over time. Across the full surgical procedure, higher SEF was associated with lower BS. However, children <12 months exhibited a stronger SEF–BS relationship, suggesting greater susceptibility to BS compared to older children. Before and during ECC, SEF and age group were not significantly related to BS. Random effects indicated moderate to substantial between-subject variability. Scatterplots showed a negative SEF–BS relationship overall, but weak and inconsistent associations during specific perioperative phases, underscoring the phase-dependent nature of SEF–BS dynamics. Conclusions: SEF is an age-sensitive marker of anesthetic depth during sevoflurane anesthesia with ECC, with children <12 months showing greater susceptibility to BS. These findings highlight the importance of individualized, age-adjusted anesthesia monitoring strategies in pediatric cardiac surgery. Full article

This paper presents a dual-objective Model Predictive Control (MPC) framework for fixed-wing unmanned aerial vehicles (UAVs). The framework was designed with two goals in mind: improving longitudinal motion control and optimizing the flight trajectory when connectivity and no-fly zone constraints are present. A multi-input–multi-output model derived from NASA’s Generic Transport Model (T-2) was used and linearized for controller design. We compared the MPC controller with a Linear Quadratic Regulator (LQR) in MATLAB simulations. The results showed that MPC reached the reference values faster, with less overshoot and phase error, particularly under sinusoidal reference inputs. These differences became even more evident when the UAV had to fly in windy conditions. Trajectory optimization was carried out using the CasADi framework, which allowed us to evaluate paths that balance two competing requirements: reaching the target quickly and maintaining cellular connectivity. We observed that changing the weights of the cost function had a strong influence on the trade-off between direct flight and reliable communication, especially when multiple base stations and no-fly zones were included. Although the study was limited to simulations at constant altitude, the results suggest that MPC can serve as a practical tool for UAV missions that demand both accurate flight control and robust connectivity. Future work will extend the framework to more complete models and experimental validation. Full article

Phased array technology can be realized with directional control with fixed beam steering. However, its directionally dependent beam pattern limits the efficiency of suppressing undesirable distance interference. This paper presents a guided wave frequency diverse array-based damage location method for thin-walled structures. Firstly, a guided wave frequency diverse array signal model is derived with a relatively small frequency increment that can achieve distance–direction two-dimensional focusing. Secondly, three types of receiving arrays, including a monostatic array, following array, and symmetric array, are constructed to achieve the maximum damage-induced signal amplitude. Finally, a two-dimensional multiple signal classification (MUSIC)-based damage location method is applied for damage imaging in thin-walled structures. Simulations on an aluminum plate and the experiments on an epoxy laminate plate demonstrate the validity and effectiveness of the proposed method. Full article

Accurate determination of elastic properties, especially Poisson’s ratio, is crucial for the design and modeling of composite materials. Traditional methods often struggle with low strain measurements and non-uniform strain distributions inherent in these anisotropic materials. This research work introduces a novel methodology that integrates Digital Image Correlation (DIC) with frequency analysis techniques to improve the precision of Poisson’s ratio determination during tensile tests, particularly at low strain ranges. The focus is on the evaluation of two distinct frequency-based approaches: Phase-Based Motion Magnification (PBMM) and Lock-in filtering. DIC + PBMM, while promising for motion amplification, encountered specific challenges in this application, particularly at very low strain amplitudes, leading to increased variability and computational demands. In contrast, the DIC + Lock-in filtering method proved highly effective. It provided stable, filtered strain distributions, significantly reducing measurement uncertainty compared to traditional DIC and other conventional methods like strain gauges and Video Extensometers. This study demonstrates the robust potential of Lock-in filtering for characterizing subtle periodic mechanical behaviors leading to a reduction of approximately 70% in the standard deviation of the measurement. This work lays a strong foundation for more precise and reliable material characterization, crucial for advancing composite design and engineering applications. Full article

For large-scale measurement of microbubble parameters on the ocean surface beneath breaking waves, a buoy-type bubble sensor (BBS) is proposed. This sensor integrates a panoramic bubble imaging sub-sensor with a circular array fiber-optic sub-sensor. The sensitive unit of the latter sub-sensor is designed via theoretical modeling and experimental validation. Theoretical calculations indicate that the optimal cone angle for a quartz fiber-optic-based sensitive unit ranges from 45.2° to 92°. A prototype array element with a cone angle of 90° was fabricated and used as the core component for feasibility experiments in static and dynamic two-phase (gas and liquid) identification. During static identification, the reflected optical power differs by an order of magnitude between the two phases. For dynamic sensing of multiple microbubble positions, the reflected optical power varies from 13.4 nW to 29.3 nW, which is within the operating range of the array element’s photodetector. In theory, assembling conical quartz fiber-based sensitive units into fiber-optic probes and configuring them as arrays could overcome the resolution limitations of the panoramic bubble imaging sub-sensor. Further discussion of this approach will be presented in a subsequent paper. Full article

Insights into motor cortex remodeling may enable the development of more effective rehabilitation strategies during the acute phase. We aim to assess the affected and unaffected motor/premotor/somatosensory cortex resting state functional connectivity (RSFC) and reactivity with continuous wave functional near-infrared spectroscopy (cw-fNIRS) in patients with ICH compared to age, sex, and comorbidity-matched subjects. We enrolled patients with acute–subacute hemispheric ICH (n = 37; two were excluded due to artifacts) and grouped them according to the side (right and left) of the stroke. Matched participants or patients with recent transient ischemic attack were enrolled as control subjects for the study (n = 44; five were excluded due to artifacts). RSFC was assessed in both affected and unaffected hemispheres by group-level seed-based (primary motor cortex, priMC) correlation analysis. FT-associated relative oxyhemoglobin (ΔHbO) changes were analyzed in affected and unaffected hemispheres with generalized linear model regression. In left hemispheric ICH, the resting state coherence between the affected priMC and the affected premotor cortex (preMC) increased (β = 0.83, 95% CI = 0.19, 1.47, p = 0.01). In contrast, in right hemispheric ICH, the coherence between the unaffected priMC and the affected preMC decreased (β = −0.6, 95% CI = −1.12, −0.09, p = 0.02). In the left hemispheric ICH, the left-hand FT was associated with increased ΔHbO over the affected preMC (β = 0.01, 95% CI = 0.003, 0.02, p = 0.01). In contrast, in right hemispheric ICH, the left-hand FT was associated with increased ΔHbO over the unaffected preMC (β = 0.02, 95% CI = 0.006, 0.04, p = 0.01). Left hemispheric preMC may be involved in motor cortex reorganization in acute ICH in either hemisphere. Further studies may be required to assess longitudinal changes in motor cortex reorganization to inform acute motor rehabilitation. Full article