Search Results (221)

Rapid, reliable, and energy-efficient data collection is essential for disaster response, where terrestrial communication networks may be disrupted or unavailable. Unmanned Aerial Vehicles (UAVs) provide a flexible means of collecting critical sensing data, but their operation is constrained by limited onboard energy, stochastic wireless conditions, complex three-dimensional environments, and stringent latency requirements. This paper presents a structured multi-UAV framework that separates mission optimisation into spatial, temporal, and safety layers. In the spatial layer, a three-dimensional Travelling Salesman Problem with Neighbourhoods (3D-TSPN) formulation enables UAVs to collect data by entering valid sensing regions rather than visiting exact sensor coordinates. An Age of Information (AoI)-aware Genetic Algorithm (GA) optimises the sensor-visitation sequence, while Rapidly Exploring Random Tree Connect (RRT-Connect) generates obstacle-aware feasible paths in the three-dimensional environment. In the temporal layer, a Lyapunov-based controller selects between local processing and binary offloading to a single Mobile Edge Computing (MEC) node according to queue backlog, processing delay, energy consumption, information freshness, wireless-link feasibility, and task deadlines. In the safety layer, continuous-time conflict detection and bounded temporal or spatial adjustments are used to monitor and mitigate inter-UAV and obstacle-related risks. The framework is evaluated under stochastic wireless, mobility, computation, and obstacle conditions using 20 independent random seeds. Across the corresponding 20 proposed-policy runs, it achieves a 100% mission-validity rate, complete sensor coverage, no dropped tasks, and zero final collision or near-miss events. Compared with planning-oriented and MEC-oriented baselines, the proposed framework achieves lower information age, average delay, processing delay, energy consumption, and system cost under the evaluated conditions, while maintaining reliable multi-UAV coordination. The layered design also clarifies the contribution of each component: 3D-TSPN provides spatial flexibility, the AoI-aware GA improves route sequencing, RRT-Connect supports obstacle-aware path feasibility, Lyapunov control enables queue-aware processing decisions, and safety monitoring supports coordinated multi-UAV operation. These results indicate that integrating spatial planning, computation control, and safety coordination within a clearly separated layered architecture can provide an effective solution for multi-UAV disaster-response data collection in complex three-dimensional environments. Full article

Engagement in the arts, particularly developing skills in an art form, represents a cognitively rich activity involving attention, memory, executive function, and sensorimotor coordination. Despite widespread assumptions that artistic abilities such as drawing are innate, they are highly trainable through targeted learning. Different techniques exemplify how structured observational drawing exercises can reshape perceptual and cognitive processing. For example, the negative space drawing technique directs attention to the shapes surrounding an object rather than the object itself. Specific techniques can map onto underlying cognitive processes, and this article examines the translational potential of learning to draw as a cognitive intervention. Due to the Arts’ aesthetic nature, the benefits extend beyond cognitive and can impact modifiable salutogenic factors at large. A toolkit of drawing techniques is provided, with information on the underlying cognitive processes. By connecting the cognitive neuropsychology of observational drawing to cognitive aging, this work contributes to a growing body of evidence supporting the arts as a vehicle for brain health, with particular promise in an aging global population. As technology transforms the landscape of arts and aesthetics, the cognitive value of embodied artmaking is underscored. Full article

Mountain glaciers constitute sensitive indicators of climate variability and represent essential freshwater reservoirs for downstream ecosystems and societies. Although glacier change in the Greater Caucasus has been extensively investigated at regional scales, detailed long-term reconstructions of individual glacier systems remain comparatively scarce. In this study, we reconstruct two centuries of evolution of Georgia’s largest Lekhziri Glacier and document the recent formation of its proglacial lake. The Little Ice Age glacier extent (AD ~1820) was reconstructed from moraine complexes, whereas later glacier outlines were derived from historical topographic maps, Landsat, SPOT, PlanetScope, and unmanned aerial vehicle (UAV) data spanning 1890–2025. Glacier terminus elevation was analysed using the Copernicus Digital Elevation Model, whereas long-term climatic variability was evaluated from summer air temperature and winter precipitation records obtained from the local meteorological station. The results demonstrate that Lekhziri Glacier experienced substantial and accelerating retreat during the past two centuries. Glacier area decreased from 46.67 ± 2.53 km² in ~1820 to 28.31 ± 0.62 km² in 2025, representing a net reduction of −18.36 km² (−39.34%) with an average annual decrease of −0.19% yr⁻¹. Area loss accelerated markedly after 2000, reaching −0.66% yr⁻¹ during 2000–2014 and −0.87% yr⁻¹ during 2014–2025. Over the last two centuries, the glacier terminus retreated by approximately −4.78 km, equivalent to an average retreat rate of −32.4 m yr⁻¹, while the terminus elevation migrated upward by nearly 600 m, from ~1718 to ~2317 m a.s.l. The glacier also underwent pronounced structural fragmentation after 2014, fundamentally altering its geometry and flow configuration. Concurrently, a proglacial lake first identified in 2019 with an area of 10,805 m² expanded rapidly to 28,947 m² in 2022 and 38,260 m² in 2025, with its continued existence confirmed by UAV observations in 2026. Meteorological records indicate a persistent increase in summer air temperature since the mid-twentieth century, accompanied by a decline in winter precipitation, consistent with the observed acceleration of glacier recession. This study provides one of the most comprehensive reconstructions of glacier evolution to date for an individual glacier in the Greater Caucasus and contributes new insights into the changing dynamics of glacierized mountain landscapes under continued climate warming. Full article

Accurate state of health (SOH) estimation of lithium-ion batteries is critical to the reliability of electric vehicles. However, under dynamic operating conditions, conventional feature extraction based on fixed time window often exhibits poor generalization, as it fails to account for the multi-timescale parameter couplings inherent in the non-stationary voltage responses. To address this issue, this paper proposes a Bayesian Adaptive Time Window Optimization (BATWO) framework for feature extraction in battery SOH estimation. Within this framework, the time window length is treated as a learnable structural parameter and is adaptively optimized via Bayesian optimization to identify the most informative observation timescale for extracting degradation-sensitive statistical features under given operating conditions. Evaluations on a cycle-aging dataset containing 69 lithium-ion battery samples subjected to distinct dynamic operating profiles show that the optimal time window lengths vary significantly, ranging from 500 s to 27,630 s. The BATWO framework achieves an average root-mean-square error (RMSE) of 2.07% and a mean absolute error (MAE) of 1.45%, outperforming the best fixed time window strategy by reducing the RMSE and MAE by 2.35% and 2.68%, respectively. Moreover, compared with LSTM- and Transformer-based models without feature extraction, the BATWO framework reduces training time by over 97%. These results highlight the superior generalization capability and computational efficiency of the BATWO framework, demonstrating its great potential for practical deployment in battery management system. Full article

In this study, we propose a mathematical model for the dynamics of atmospheric carbon dioxide (CO₂) with human population growth and an age-structured vehicle population. The positivity and boundedness of the model’s solutions are verified, and the global stability of an interior equilibrium point is proved. We calibrate the model parameters using the least-squares method. Simulations of the model show good alignment with the reported atmospheric CO₂ data. Moreover, the sensitivity analysis shows that human-related emission factors and emissions from old vehicles are the main contributors to CO₂ accumulation. The results of this study recommend that reducing anthropogenic emissions, accelerating the retirement of old vehicles, and strengthening carbon sequestration initiatives be considered to mitigate future atmospheric CO₂ accumulation. Full article

Accurate state-of-health (SoH) prediction is vital for safe and efficient battery management, enabling extended lifespan and improving technologies such as electric vehicles and stationary energy storage systems. In this work, a graph neural network-based deep learning framework is proposed to predict the SoH of a commercial nickel–manganese–cobalt oxide (NMC) lithium-ion technology. Health indicators obtained from the in-house-generated experimental aging dataset are used to train and validate the model across batteries subjected to diverse operating conditions. The hybrid architecture combines graph neural networks (GraphSAGE) with convolutional neural networks (CNN) and long short-term memory (LSTM) blocks, capturing both local structural relationships and temporal patterns in the battery data. Evaluation results show strong predictive performance, achieving an R² of 0.989 and a mean squared error of 3.23 × 10⁻⁶. These findings suggest that the proposed methodology could be deployed as a useful diagnostic tool. Full article

Vehicular Internet of Things (V-IoT) networks require reliable scheduling for safety-critical communication, cooperative awareness, and cooperative perception under dynamic mobility and limited roadside infrastructure. This paper proposes MoReSP, a Mobility- and Reliability-aware Scheduling Policy for roadside unit (RSU)-assisted V-IoT networks. MoReSP uses mobility-regime inference, structured action scoring, safety projection, and episodic parameter adaptation to select among deny, grant, preempt, coexist, and handoff actions. Its multiobjective formulation jointly minimizes average delay, communication energy consumption, admission-adjusted reliability loss, a penalty for cooperative perception message (CPM) delivery/freshness, and RSU-load imbalance. The framework is evaluated under vehicle-load variation, Nagel–Schreckenberg (NaSch) density variation, and RSU-capacity scaling using admission-adjusted metrics that penalize excessive blocking and interruption. MoReSP is compared with five literature-grounded benchmark families: Age of Correlated Information (AoCI)-Heuristic, RSU-Coop, Handoff-Aware, vehicle-to-everything (V2X)-Priority, and Adaptive Learning-based Task Offloading multi-armed bandit (ALTO-MAB). Simulation results show that MoReSP achieves the lowest admission-adjusted system cost across all evaluated scenarios. At nominal RSU capacity, MoReSP reduces the system cost by 43.6% compared with the best baseline. Under high vehicle load, it reduces the cost by 54.4% at arrival scale 2.0 and maintains effective packet and CPM delivery ratios of 0.849 and 0.828, respectively. These results demonstrate that MoReSP provides a reliable and balanced scheduling solution for dynamic V-IoT environments. Full article

Finite element modeling is central to the safety assessment and structural health monitoring of transmission towers, yet conventional workflows remain labor-intensive and depend on complete engineering drawings, which are often unavailable for aging towers. This study proposes an automated finite element modeling framework based on unmanned aerial vehicle (UAV) point clouds. Multi-view stereo reconstruction with geometric–photometric depth refinement is used to generate dense three-dimensional point clouds, from which key nodal coordinates are extracted and converted into executable ANSYS APDL command streams. Material properties, boundary conditions, and loads are then assigned automatically to construct computable finite element models. This method was first validated using concrete cubes and Q235B steel members and was further evaluated on a scaled transmission tower through comparison with the commercial SmartTower workflow. The results show that the proposed approach reduces modeling time from 1.5 days to 4.3 h, corresponding to an 88% efficiency improvement, while keeping nodal coordinate errors within 10% and modal frequency errors within 3.7%. The framework satisfies the basic requirements of transmission tower numerical analysis and provides a practical non-contact solution for the rapid assessment of aging towers lacking design documentation. Full article

Older primary schools in Croatia are frequently located in densely built older settlement cores. Micro-locations surrounding school entrances are often not the result of prior urban or traffic planning; instead, they are retroactively managed through infrastructure and signalling interventions. Pupils participate in traffic as pedestrians, cyclists, e-scooter users, or passengers in cars, school buses, or public buses. The proposed integrated research approach includes: an online survey of pupils’ travel behaviour, systematic safety assessments of entrance micro-locations using the iRAP methodology, as well as field measurements and in-depth analysis of vehicle speeds, traffic flow and structure. For classes organised in two shifts, an online survey of parents (for classroom-based education) and pupils (for subject-based education) covered 56% of the pupil population. Because pupils’ travel mode is the factor most susceptible to influence through infrastructure improvements, statistical analysis was conducted using the χ²-test for the purpose of investigating relationships with the other three traffic-relevant determinants: school age group, pupils’ sex, and distance from school. Approximately three-quarters of pupils live less than 2 km from the typical school. If peak vehicle traffic does not coincide with the peak of pupil arrivals and departures during the overlap of two school shifts, part of the traffic on the school-access street may be unrelated to direct school-access activities. Vehicle-type restrictions and one-way traffic operation should be considered as measures to improve pupils’ safety. The proposed groups of measures for improving pupils’ safety include: (i) educational workshops for pupils, parents and teachers; (ii) reconstruction of school entrance micro-locations; (iii) targeted interventions in the traffic environment within a 2 km perimeter. Full article

As the core binder material of asphalt pavement, the rheological properties of asphalt directly determine the service performance and service life of the pavement. Under actual service conditions, asphalt is constantly exposed to a multi-coupling environment involving temperature variation, vehicle load, and ultraviolet aging, which easily leads to irreversible rheological deterioration and induces diseases such as rutting and cracking. Aiming at the insufficient research on the rheological evolution law and microscopic damage mechanism under the coupling of the above three factors, this study took 70# base asphalt as the research object and adopted a combination of macro-performance testing and microstructure characterization. The high- and low-temperature rheological properties, permanent deformation resistance, and fatigue resistance of asphalt under multi-coupling effects were systematically evaluated through three conventional index tests: dynamic shear rheology (DSR), multiple stress creep recovery (MSCR), linear amplitude sweep (LAS) and bending beam rheology (BBR). Combined with gel permeation chromatography (GPC) and thin-layer chromatography with flame ionization detection (TLC–FID), the evolution laws of molecular distribution and chemical components were revealed, and the deterioration mechanism of multi-coupling effects was clarified. The results show that compared with the control group, after 72 h of coupling treatment, the penetration decreases by 32.6%, the softening point increases by 18.3%, and the ductility decreases by 45.8%. The high-temperature complex modulus decreases by 51.2%, the low-temperature creep stiffness increases by 76.4%, and the fatigue life decreases by 58.6% on average. At the microscopic level, obvious molecular polymerization and component weight gain occur in asphalt: the content of macromolecular components rises from 18.7% to 32.1%, asphaltene content increases from 12.3% to 25.8%, and aromatic content decreases from 42.6% to 28.3%. Temperature variation, load, and ultraviolet aging present significant deterioration effects, rather than a simple superposition of single factors. Prolonged aging and increased load aggravate the hardening of asphalt, while extreme temperature variation further weakens the rheological properties through microscopic damage. This study clarifies the internal relationship between the microscopic structure and macroscopic properties of asphalt under multi-coupling effects, improves the theory of anti-coupling damage to asphalt, and provides an important theoretical basis and experimental support for damage-resistant design, material selection, and service life prediction of asphalt pavement. Full article

The development of age-appropriate pediatric dosage forms remains an important challenge, particularly for acid-labile drugs requiring gastro-resistant protection. Pantoprazole, a proton pump inhibitor, must be protected from gastric acid until intestinal absorption; however, conventional enteric-coated tablets may be difficult to use in younger children, while manipulation of dosage forms or mixing with food can compromise dose accuracy and drug release performance. Multiparticulate systems, such as minitablets, pellets, or granules, offer flexible dosing but may still require a suitable vehicle to improve acceptability, handling, and ease of swallowing. In this study, enteric-coated pantoprazole minitablets were developed and evaluated after dispersion in selected hydrogel vehicles intended to serve as standardized alternatives to food-based carriers. Hydrogels based on hypromellose (HPMC), carbomer (CAR), and sodium alginate (SA) were characterized in terms of pH, rheological properties, firmness, acid penetration, and their effect on pantoprazole release. Dissolution performance was assessed using both conventional pharmacopoeial testing and dynamic non-pharmacopoeial conditions. Low-concentration gels prepared from high-viscosity HPMC grades showed the most favorable performance, combining suitable spoonable consistency with limited impact on drug release. Among them, 5% HPMC 65SH4000 was particularly promising, as it did not markedly delay pantoprazole release in either pharmacopoeial or dynamic dissolution testing. CAR gels provided advantageous rheological properties, including high viscosity at rest and shear-thinning behavior, and allowed efficient pantoprazole release after transition to buffer conditions; however, their interaction with enteric-coated minitablets should be further optimized with respect to gel amount, concentration, and neutralization strategy. SA gel showed strong structural persistence and delayed release under pharmacopoeial conditions, although this effect was less pronounced in the dynamic model. Overall, the findings indicate that appropriately selected hydrogels may improve the practical use of pediatric multiparticulate formulations, but their composition, pH, rheology, and interaction with enteric coatings must be carefully evaluated. Full article

This study investigates the charging behaviors, routines, and perceptions of Swedish electric vehicle (EV) users who lack access to home charging, a group that remains underrepresented in the EV adoption literature. Based on an online survey of 250 EV users—primarily located in Gothenburg—respondents were divided into two groups: those with and those without home charging availability. Nearly half of the sample (47.6%) reported not having access to charging at home. Comparative analyses, including linear regression models, were conducted to examine differences in sociodemographic characteristics, charging patterns, and perceptions of public charging. While the two groups were similar in terms of age, gender, vehicle type, charging frequency, and minimum state of charge preferences, significant differences emerged in perceived convenience, distance, and freedom to charge. Users without home charging availability reported lower access to workplace charging and evaluated public charging as less convenient and less accessible. Charging behavior in both groups was primarily goal-oriented and triggered by minimum state of charge rather than spontaneous opportunities. The findings highlight the structural disadvantages faced by users without home charging and underline the importance of adapting public charging infrastructure and policy strategies to support a broader and more equitable transition to electric mobility. Full article

This two-year study proposes the application of industrial computed tomography (CT) as a complementary technique to conventional capacity and internal resistance measurements for evaluating not only the state of health (SOH) of different lithium-ion battery types used in electric vehicles, but also to predict its past. While commonly used assessment methods primarily focus on electrical properties of batteries, industrial CT allows non-destructive, three-dimensional visualization and systematic evaluation of internal structural changes within individual battery cells and allows to compare different lithium battery type internal structure changes. The study investigates two lithium-ion battery chemistries: lithium iron phosphate (LFP) and nickel manganese cobalt oxide (NMC). The effects of different discharge rates (1C, 2C, and 3C) on battery degradation were analyzed by comparing CT scan data obtained for the cells in their initial (new) condition and after reaching 60% SOH following cycling-induced aging. The findings provide improved understanding of the physical processes associated with battery aging under varying discharge conditions, enabling a more complete evaluation of battery health. Full article

The aim of this study is to analyse the active role of volunteers in the Italian reception system and to assess how their actions and strategies contribute to the construction of an environment that promotes the autonomy and protagonism of unaccompanied minors in the development of their migration project. Based on a theoretical framework that combines socio-educational and psychosocial approaches and theories of community participation, the study addresses the importance of volunteering as a key tool in processes of inclusion and the development of personal migration projects. A qualitative approach is adopted under the interpretative paradigm, using a collective case study carried out at the Scuola Penny Wirton in Rome. The sample was selected through purposive and convenience sampling, integrating 25 volunteer agents (68% women and 32% men), aged between 60 and 84. Semi-structured interviews were conducted. The results show that volunteers deploy flexible and personalised strategies, prioritising the creation of a climate of trust and contextualised language learning as a vehicle for integration. Likewise, the importance of emotional support, identification and attention to psychosocial needs, and the promotion of a sense of belonging is evident. However, a lack of formal mechanisms for interdisciplinary collaboration is detected. Full article

Skin aging stems from intrinsic decline and external stressors that induce oxidative stress and mitochondrial damage, ultimately lowering cellular energy production and slowing epidermal turnover to cause wrinkles, dryness, and pigment imbalances. While disodium adenosine monophosphate (AMP2Na) is hypothesized to enhance cellular adenosine triphosphate production and restore epidermal metabolism, its broader anti-aging effects have remained underexplored. To address this, a multi-tiered study integrating in vitro, ex vivo, and clinical investigations was conducted. Specifically, a 12-week exploratory clinical trial involving female participants with facial hyperpigmentation (n = 23), alongside a short-term forearm study (n = 22), suggested that the AMP2Na-containing product could reduce wrinkles and hyperpigmentation while safely improving hydration, barrier function, skin lifting, and epidermal turnover with high participant satisfaction. Mechanistically, in vitro assays on human dermal fibroblasts showed that the formulation restored antioxidant enzyme activity and mitigated senescence. Ex vivo UVB-irradiated skin explant models corroborated these findings by revealing reduced melanin levels, preserved collagen and elastin networks, and an upregulation of key structural and barrier-related proteins. Ultimately, by potentially supporting epidermal turnover and restoring barrier function through this biomimetic mechanism, the AMP2Na-containing product might offer a promising option for alleviating wrinkles, dryness, and hyperpigmentation. Future randomized, vehicle-controlled clinical trials and comprehensive laboratory studies are warranted to validate its true potential in skin rejuvenation. Full article