Search Results (6,620)

Dense low-altitude multi-UAV operations require conflict detection methods that can rapidly identify potential conflicts while preserving detection reliability under realistic motion constraints. This paper proposes an Adaptive Cylindrical-Pruning Decision Tree Method (AC-DTPM) for short-term conflict detection in dense low-altitude UAV traffic. A realistic UAV motion model is first constructed by considering speed limits, acceleration constraints, climb/descent limits, response delay, positioning error, and wind disturbance. Based on this model, a double-layer cylindrical protection zone is established, including a core conflict zone and an outer warning zone. The proposed AC-DTPM improves the basic Decision Tree Pruning Method through two mechanisms: adaptive time segmentation, which adjusts trajectory-segment length according to UAV speed and local traffic density, and cylindrical warning-zone lower-bound pruning, which eliminates impossible conflict candidates by separately evaluating horizontal and vertical distance lower bounds during tree search. Simulation experiments were conducted for 100–800 UAVs under locally dense and crossing-route scenarios. At 800 UAVs, AC-DTPM reduced the number of evaluated candidate pairs from 601.0 with the basic DTPM to 178.8, corresponding to a 70.3% reduction and a candidate compression ratio of <!-- MathType@Translator@5@5@MathML2 (no namespace).tdl@MathML 2.0 (no namespace)@ --> Full article

The proliferation of high-definition video data necessitates highly efficient processing pipelines for real-time edge analytics. However, traditional object detection architectures rely exclusively on pixel-domain inputs, which renders the computationally prohibitive decoding phase a latency bottleneck. In this paper, we propose a novel dual-phase framework designed to achieve fast and efficient object detection directly within the partially decoded compressed-domain data. First, we introduce a partial decoding paradigm featuring the Low-Frequency Spectral Prioritization method on the encoder side. By systematically discarding high-frequency residual coefficients and retaining only a sparse subset of fundamental spatial frequencies, this method dramatically reduces transmission payloads and accelerates the standard decoding process. Second, to recover the structural fidelity lost due to the intentional omission of residual data, we employ a multi-granularity cross-domain knowledge distillation architecture. This strategy aligns global contextual features, foreground boundary attention maps, and final response logits, transferring rich representational capacities from a high-performing pixel-domain teacher network to a lightweight compressed-domain student network. Comprehensive experiments utilizing RetinaNet, FCOS, and GFL object detection networks on the COCO-mini dataset demonstrate the superiority of the proposed framework. By retaining fundamental residual coefficients within the HEVC pipeline, the proposed method reduces average decoding latency while improving the mAP score by $+0.96\%$ over the conventional fully decoded pixel-domain baseline on the COCO-mini dataset. Full article

Plant-derived extracellular vesicles (PDEVs), also referred to as plant exosomes or exosome-like nanovesicles, have emerged as promising natural bioactive nanoparticles for cosmetic and regenerative applications. Owing to their biocompatibility, intrinsic bioactive cargo, and ability to interact with mammalian cells, PDEVs are increasingly investigated as agents for skin rejuvenation, wound healing, photoprotection, pigmentation control, and skin barrier enhancement. However, the available evidence remains fragmented across different plant sources and experimental models. This review aimed to summarize and critically evaluate the current evidence regarding the cosmetic and regenerative properties of PDEVs. Experimental studies investigating the effects of PDEVs on skin cells, reconstructed skin models, animals, or human subjects were systematically identified and analyzed. The available evidence consistently demonstrated that PDEVs promote skin regeneration and tissue repair. The most frequently reported effects included enhanced keratinocyte and fibroblast proliferation and migration, accelerated wound closure, increased collagen synthesis, reduced oxidative stress, activation of antioxidant defense pathways, and suppression of inflammatory responses. Additional studies reported improvements in skin barrier function, hydration, photoprotection, pigmentation control, and cellular senescence. Collectively, the available studies demonstrate consistent regenerative, antioxidant, anti-inflammatory, and skin-protective effects of PDEVs. Overall, PDEVs represent multifunctional bioactive nanomaterials with substantial potential for cosmetic applications. While clinical translation remains limited by regulatory and standardization challenges, cosmetic use appears to offer a more immediate route toward commercialization. Further standardization, mechanistic studies, and clinical investigations are required to support the broader implementation of PDEV-based technologies. Full article

The transition process from synchronous condenser pump (SCP) mode to pumping mode determines the response rapidity of the startup procedure and operational stability of pump-turbines; however, the complex gas–liquid interaction and transient hydraulic characteristics during this process remain insufficiently understood. To address this, this study develops a numerical framework for the SCP-to-pumping transition process, incorporating the full-passage system, a multiscale mesh strategy for coupling mainstream and clearance flow regions, and a gas–liquid two-phase flow model based on the Volume of Fluid (VOF) method. The reliability of the numerical model is verified through comparison with model experiments, demonstrating good agreement between simulations and experimental data. Based on the validated model, the transient evolution of hydraulic forces, pressure pulsations, and internal flow structures is systematically analyzed. Axial force analysis reveals a significant internal equilibrium; the crown bears a maximum instantaneous fluctuation of approximately 2800 kN. Conversely, the radial force is primarily dominated by blade hydraulic thrust (1294 kN), showing distinct anisotropic behavior. The runner blade channels and the upper draft tube region are identified as critical areas with intense pressure fluctuations, with peak-to-peak pressure amplitudes reaching 45~48 m and 54 m head, respectively. Furthermore, reducing the duration of the exhaust process constitutes the main strategy for accelerating the transition and mitigating prolonged high-amplitude force and pressure fluctuations. The findings provide new insights into the transient hydraulic mechanisms of SCP-to-pumping transitions and offer guidance for optimizing transition control strategies in pumped-storage units. Full article

Ground-penetrating radar (GPR) and electrical resistivity tomography (ERT) are physically complementary non-destructive evaluation methods for reinforced concrete, yet their integrated diagnostic use has been limited by the absence of controlled, ground-truth-validated calibration of the joint-signature space. This paper presents a laboratory calibration programme in which a single C30/37 reinforced concrete beam (3000 mm × 300 mm × 200 mm, three T12 bars at 35 mm cover, CEM I 42.5N, w/c = 0.50) was sequentially conditioned through four controlled deterioration states—intact reference (Model A), water-filled saw-cut crack (Model B), full saturation by seven-day top-surface ponding (Model C), and chloride-induced active corrosion (Model D). Seven RES2DINV inverted ERT sections at three electrode spacings (a = 7, 15, and 30 mm) and three 800 MHz GPR profiles were acquired across the four known ground-truth conditions. The intact-reference resistivity ρ₀ = 558 Ω·m (full-section median of the mlab dataset at a = 7 mm) and GPR-calibrated velocity v = 0.095 ± 0.008 m/ns (from hyperbola fitting at 35 mm rebar cover) establish the absolute baselines. The four conditions produce systematically distinct joint signatures: Model A exhibits uniform high resistivity with clean rebar hyperbolae and no anomalous reflections; Model B produces a localised ERT low-ρ anomaly (ρ_min = 1.46 Ω·m) co-located with a negative-polarity (R = −0.68) GPR crack-mouth reflection confirming water-fill; Model C produces pervasive low-ρ with a smooth depth gradient and 50–65% GPR amplitude attenuation (−6.0 to −9.1 dB); Model D produces the same bulk GPR signatures as Model C but with a critically different ERT spatial texture—a heterogeneous near-surface layer above a sharp boundary at z ≈ 40 mm with depth-preferential low-ρ concentrated at rebar level. This depth-preferential signature, quantified here by a reproducible Depth-Preferential Index (DPI), is the primary ERT-only diagnostic criterion distinguishing active corrosion from pervasive saturation. For the Model C versus Model D distinction, the GPR response is non-discriminating; this high-risk distinction is resolved exclusively by the ERT depth-preferential criterion. The calibration demonstrates that GPR and ERT are physically non-redundant in the strict sense: neither method alone can unambiguously discriminate all four states, but their combination yields correct classification within the controlled laboratory conditions and subject to the stated qualification conditions. The corrosion state was confirmed at the regime level (chloride above the depassivation threshold, under accelerated polarisation) but was not quantified electrochemically, so the depth-preferential signature is interpreted as an indirect spatial proxy for active corrosion rather than a measurement of corrosion rate. Seven failure modes are quantitatively characterised and embedded in the framework as a priori qualification conditions. The calibrated reference values (ρ₀, A₀, Stage 2 thresholds, depth-preferential criterion) are specific to the laboratory mix and curing history and require local Stage 1 recalibration for field application. Full article

Real-time order arrivals and stringent timeliness demands in e-commerce pose significant challenges to production-distribution coordinated scheduling in distributed manufacturing systems. This paper tackles the integrated production and distribution scheduling problem within a dynamic distributed hybrid flow shop. We formulate a mixed-integer linear programming model aimed at minimizing average order tardiness and total operational cost. To address dynamic uncertainties, we propose an improved decomposition-based multi-objective evolutionary algorithm (I-MOEA/D) operating within a rolling horizon framework. The algorithm integrates three critical components: an urgency-based emergency window management strategy that caps computational complexity, a 2-opt local search operator that boosts vehicle routing efficiency, and acceleration techniques—including order dictionary indexing, lightweight object replication, and distance caching—that guarantee real-time responsiveness. The window size is determined as W_max = 50 through sensitivity analysis. Extensive experiments conducted on 100, 200, and 300-order scenarios with 30 independent random seeds demonstrate that I-MOEA/D markedly outperforms NSGA-II, MOALNS, and a reinforcement learning-driven hyper-heuristic (RL-HH). For 200 orders, I-MOEA/D reduces average tardiness by 18.5%, 41.0%, and 58.7% compared to NSGA-II, MOALNS, and RL-HH, respectively, while maintaining competitive cost performance (a cost gap of 16.99% relative to the static lower bound). The IGD metric achieves 0.0031, confirming good convergence and diversity. Acceleration techniques cut computation time by 56% without sacrificing solution quality. The algorithm scales efficiently with problem size. These results validate that I-MOEA/D delivers effective real-time decision support for dynamic distributed production-distribution systems. Full article

Field vibration measurements of quay-crane hoisting gearboxes are difficult to interpret because the measured response is generated under short, load-dependent cycles rather than stationary excitation. This study develops a PLC-guided method for constructing condition-tagged steady-state vibration samples from non-stationary field measurements. Multi-rate records are aligned on a common time basis; work cycles and action stages are identified from load and hoist-speed information; steady PLC candidates are selected using operating-context and local-steadiness criteria; and one-second vibration segments are fine-screened within the corresponding search intervals. The accepted segments are stored in a quality-controlled feature table and characterized by acceleration RMS, 10–1000 Hz velocity RMS, kurtosis, peak-to-peak acceleration, dominant frequency, and spectral entropy. Field data from four gearboxes yielded 1140 steady-state segments and 8912 quality-controlled segment-channel records. Stepwise CV analysis showed that action-only grouping reduced the coefficient of variation from 0.692 to 0.658, while full machine–action–channel grouping reduced it to 0.351, corresponding to a 49.35% reduction. Median acceleration RMS ranged from 0.494 to 4.805 m/s², and 10–1000 Hz velocity RMS ranged from 0.317 to 1.512 mm/s. The method provides a traceable basis for condition-aware baseline modelling and trend analysis without making unsupported fault-diagnostic claims. Full article

Endophytic bacteria of the Bacillus subtilis species complex are known for plant growth promotion and antifungal activity, although strain-specific traits remain poorly understood. This study characterized the Bacillus siamensis strain BACIII, isolated from asymptomatic soybean roots in a charcoal rot-affected area, using whole-genome analysis and phenotypic assays. Genome sequencing identified BACIII as Bacillus siamensis and revealed a metabolically versatile genome containing twelve biosynthetic gene clusters linked to antimicrobial compounds such as difficidin, fengycin, and surfactin. Additionally, genomic islands associated with mobile elements, regulation, and stress response suggest adaptive potential. Inoculation with the BACIII strain significantly accelerated germination and increased early growth, biomass accumulation, and chlorophyll content in soybean and sunn hemp (p < 0.05), whereas no significant effects were observed in cotton or sunflower, indicating host-dependent responses. In vitro assays demonstrated consistent inhibition of several phytopathogenic fungi, including Sclerotinia sclerotiorum, Fusarium spp., and Macrophomina phaseolina, with variable intensity. Overall, BACIII combines host-specific plant growth promotion with broad antifungal activity, supporting its potential for biological control in sustainable agriculture. Full article

Recent advances in vaccinology have accelerated the shift from conventional live-attenuated and inactivated vaccines toward subunit and nucleic acid-based platforms. Although these next-generation vaccines offer improved safety, rapid adaptability, and manufacturing flexibility, their relatively low intrinsic immunogenicity often requires efficient adjuvants and delivery systems. Nanoparticle-based vaccine platforms have therefore emerged as versatile tools capable of protecting antigens, improving targeted delivery, and modulating both innate and adaptive immune responses. This review summarizes the major classes of nanovaccine platforms, including lipid and polymeric nanoparticles, self-assembling protein nanostructures such as virus-like particles and ferritin nanocages, saponin-based self-assembling complexes, and inorganic nanomaterials. Particular attention is given to how vaccine performance is determined not only by material composition but also by nanoparticle physicochemical properties, biodistribution, cellular uptake, and mechanisms of immune activation. We further discuss the major challenges limiting clinical translation, including scalable manufacturing, safety evaluation, quality control, regulatory requirements, and long-term biocompatibility. Finally, emerging strategies involving hybrid and personalized nanovaccine platforms are highlighted, illustrating how nanotechnology and immunoengineering are transforming vaccine development for both prophylactic and therapeutic applications. Full article

Substantial declines in acidifying emissions across Europe have led to pronounced chemical recovery of Norwegian surface waters. In recent decades, however, changes in water chemistry have increasingly coincided with climate change, complicating the attribution of observed trends to individual drivers. This study assesses whether ongoing climate change has produced detectable effects on freshwater chemistry in Norway and how these effects vary among catchments with differing sensitivities to acidification. In this study, the Model of Acidification of Groundwater In Catchments (MAGIC), which is based on current understanding of the processes governing acid–base chemistry in soils and waters, was used to simulate the effects of declining acid deposition. Deviations between observed and modelled water chemistry were provisionally interpreted as climate-related effects. However, these residuals may also reflect model or parameter uncertainty and other unaccounted-for processes. The analysis draws on long-term monitoring data (1986–2022) from 59 acid-sensitive Trend Lakes distributed across Norway, together with four Field Research Stations (1986–2020) representing contrasting hydroclimatic and biogeochemical conditions. Temporal trends were evaluated using the Mann–Kendall test and Sen’s slope estimator, while relationships between inferred climate effects and climatic variables were examined using Pearson’s correlation analysis. Across the Trend Lakes, inferred climate effects were predominantly positive for acid-neutralising capacity (ANC) and weathering-derived cations, suggesting that climate change may contribute to accelerated chemical recovery, particularly in catchments less sensitive to acidification. The inferred climate effects varied substantially among the Field Research Stations. Higher temperatures were generally associated with enhanced recovery, possibly through intensified silicate weathering, whereas increased precipitation and runoff appeared to dampen recovery. Overall, the results suggest that climate change exerts a measurable influence on freshwater chemistry in Norway, although the magnitude and direction of the response are strongly modulated by catchment-specific characteristics. While previous studies have identified climate-related influences on individual chemical variables, quantitative attempts to separate climate- and acid-deposition-related effects across a large number of acid-sensitive catchments remain rare. Here, we use deviations between observed water chemistry and MAGIC simulations of acid deposition recovery as a screening approach to investigate whether climate-related signals can be detected at the national scale and whether these signals vary among catchments with differing sensitivities to acidification. Full article

Interest in electric vehicles (EVs) is rising as the world shifts toward sustainable transportation, yet consumer adoption remains highly uneven, particularly in developing countries. This study examines how five dimensions of consumption value—functional, social, emotional, novelty, and conditional—influence consumers’ purchase intention toward EVs in Indonesia, while also testing the moderating role of infrastructure readiness. Using a quantitative approach, data were collected through an online survey with purposive sampling, yielding 455 valid responses. Partial least squares structural equation modeling (PLS-SEM) was applied to assess the measurement and structural models. The results reveal that functional, social, emotional, and conditional values significantly influence consumers’ purchase intention toward EVs, whereas novelty value has no significant effect. Infrastructure readiness also significantly moderates most consumption values, with negative coefficients indicating that limited charging access and inadequate maintenance support weaken the positive impact of consumer values on EV adoption. The findings show that although consumers value performance, social image, emotional appeal, and situational factors, poor charging infrastructure hinders purchase intention toward EVs. This study contributes to EV adoption literature by integrating consumption value theory with infrastructure readiness as a moderator. The results emphasize that developing charging infrastructure, expanding service availability, and maintaining supportive government policies are critical steps for accelerating EV adoption in emerging markets. Full article

At SAE Level 2 automation, the human driver retains full supervisory responsibility, making unobtrusive monitoring relevant for maintaining supervision under real-world driving conditions. Driver monitoring systems capable of operating robustly under such conditions are therefore essential, but wearable-based personalized approaches remain underexplored, particularly when the target labels are derived from experimental scenarios. This study presents a real-world SAE Level 2 on-road acquisition campaign and evaluates a target-driver intra-subject classification approach using non-intrusive wrist-derived signals. Physiological and motion data recorded with the Empatica E4 wristband, including blood volume pulse, electrodermal activity, heart rate, skin temperature, and triaxial wrist acceleration, were converted into image representations and processed with a frozen ResNet-50 feature extractor, principal component analysis, and a supervised classifier. The labels were scenario-derived operational driver-state classes defined from experimental phases and scenario groups. Personalization was assessed via a Leave-One-Experience-Out protocol on the target driver. Classification accuracy was 50% under external-user-only training, 54% under mixed target/external-user training, and 60% under target-driver-only training, with the target-driver-only configuration yielding the highest mean performance in the evaluated setting. For the low-demand baseline class, the one-vs.-rest classifier achieved 88.4% accuracy and an F1-score of 70%. These results provide initial evidence of the feasibility of personalized wrist-worn classification of scenario-derived operational driver-state classes under the real-world automated driving conditions evaluated in this study. Full article

This study presents a transient dynamic analysis of a 3D-printed composite cantilever beam fabricated from short carbon fibre-reinforced polyamide (CF-PA-12). Particular attention is given to acceleration response, vibration damping, and energy dissipation, which govern the transient behaviour and dynamic stability of lightweight composite structures under impulsive loading. The research combines experimental modal analysis (EMA) and transient impact testing with numerical simulations based on classical laminated plate theory (CLPT). A finite element model was developed in Simulia/Abaqus and used within a modal-superposition-based transient framework incorporating experimentally identified damping ratios and measured impact forces. The proposed approach enables realistic prediction of vibration decay and time-dependent acceleration response. Good agreement between experimental and numerical results confirms the capability of the method to reproduce the dynamic behaviour of additively manufactured composite beams subjected to impact excitation. Full article

The doubly fed induction generator (DFIG) has become one of the most widely adopted technologies in modern wind energy conversion systems due to its high efficiency, flexible operation, and capability to operate under variable wind-speed conditions. Nevertheless, maintaining high power quality, reducing harmonic distortion, and ensuring reliable operation under parameter uncertainties and grid disturbances remain major challenges. Conventional proportional–integral (PI)-based control approaches often suffer from limited robustness and sensitivity to system nonlinearities, which can adversely affect the dynamic performance and operational stability of wind energy systems. To address these limitations, this paper proposes an advanced hybrid control strategy based on the integration of fuzzy logic (FL) and a third-order sliding mode controller (TOSMC) for the control of DFIG-based wind turbines. The proposed FL–TOSMC combines the robustness and fast convergence properties of sliding mode control with the adaptive capability of fuzzy logic, thereby reducing dependence on accurate mathematical models and enhancing tolerance to parameter variations and external disturbances. The developed controller is applied to maximum power point tracking (MPPT) and direct field-oriented control of the DFIG to maximize energy extraction and ensure stable power regulation. The simulation results obtained in the MATLAB/Simulink (2021) environment demonstrate that the proposed strategy improves the dynamic response of the system by reducing overshoot, accelerating error convergence, minimizing steady-state oscillations, and decreasing total harmonic distortion compared with the conventional TOSMC approach. Furthermore, the proposed controller provides enhanced tracking performance, improved robustness against parameter uncertainties, and better power-quality characteristics under various operating conditions. These results indicate that the FL–TOSMC approach is an effective control solution for improving the operational performance of DFIG-based wind energy conversion systems. Full article

Stationary human–structure interaction (HSI) can significantly modify the vibration characteristics of lightweight structures, yet it remains unclear whether a human model identified for an individual can be extended consistently to crowd conditions. This study investigates this issue through sweep-frequency tests on a single-degree-of-freedom (SDOF) rig under single-person standing, single-person seated and two- to five-person standing group conditions. The measured acceleration frequency response functions (FRFs) were fitted using a two-degree-of-freedom (2DOF) integrated HSI model to identify equivalent human/crowd and structural parameters. The experimental program involved 20 participants and 180 tests, yielding 40 averaged individual datasets and 50 averaged group-combination datasets. Clear double-peak FRF characteristics were observed under all occupied conditions, indicating coupled 2DOF behavior rather than a simple added-mass effect. Compared with seated occupants, standing occupants showed a slightly higher equivalent human natural frequency (6.76 Hz versus 6.45 Hz), but lower human damping ratio (23.37% versus 31.73%) and body mass ratio (BMR) (0.621 versus 0.729). For standing groups, the equivalent crowd damping ratio increased from 24.60% for the two-person group to 29.10% for the five-person group, and BMR increased from 0.66 to 0.80, whereas the equivalent crowd natural frequency remained within 6.74–7.02 Hz. These results support a unified but parameter-dependent 2DOF framework for stationary individuals and crowds, provided that posture- and crowd-dependent parameters are identified from the overall coupled-system FRF rather than obtained by direct averaging of individual properties. Full article