Search Results (24,506)

To address the problems of closed-loop task organization, strong corridor constraints, and path failure after local disturbances in unmanned aerial vehicle (UAV) inspection of gas-insulated switchgear (GIS) rooms, this paper proposes a topology-and-corridor-guided bias-suppressed D* (TCG-BS-D*) method for closed-loop three-dimensional (3D) path planning and local replanning. The proposed method constructs a structured guidance model based on the inspection-corridor topology, generates local 3D path segments according to a predetermined inspection sequence, and forms a nominal closed-loop inspection path through bias suppression and path regularization. Meanwhile, for local maintenance blockage and dynamic disturbance scenarios, an alternative local replanning strategy is applied to the affected path segments. Simulation results show that, under the static closed-loop inspection condition, the proposed method achieves a total path length of 700.22 m, a total inspection time of 269.32 s, an average safety clearance of 8.18 m, 37 large-angle turns, a corridor adherence rate of 80.73%, and a task completion rate of 100%, showing superior performance in inspection efficiency, safety margin, trajectory regularity, and corridor consistency. Under the local blockage condition, the replanned path introduces path-length and time increments of 71.29 m and 25.88 s, respectively, while maintaining the minimum safety clearance at 1.52 m and increasing the corridor adherence rate to 83.91%. Under dynamic disturbance conditions, the minimum dynamic safety clearance is improved from −2.71 m to 17.84 m, effectively eliminating the local dynamic collision risk. The results demonstrate that the proposed method can balance closed-loop path-generation efficiency, corridor-structure consistency, safety margin, and adaptability to local disturbances, providing an effective solution for UAV inspection path planning in GIS rooms. Full article

Three-dimensional UAV operations require path planning methods that can jointly maintain route efficiency, threat avoidance, and trajectory smoothness under spatially distributed and time-varying constraints. To address this problem, this paper develops an integrated Dual-Layer Adaptive T-perturbation and Opposition-based Multi-Objective Particle Swarm Optimization framework, termed DATO-MOPSO, for 3D UAV path planning in complex threat environments. The method integrates a dual-layer adaptive inertia-weight and velocity-regulation mechanism with symmetric T-perturbation, an elite quasi-opposition-based learning strategy for diversity recovery and feasible local exploitation, and an archive-driven simulated annealing rule for stagnation-aware personal-best updating. A three-objective model minimizing path length, threat exposure, and path smoothness is established, and comparative experiments against MOPSO, ZAMOPSO, NSGA-II, and SPEA2 are conducted in both static and dynamic environments, together with statistical and ablation analyses. In the static scenario, DATO-MOPSO achieved the highest mean HV and stable repeated-run performance, but its IGD was comparable to ZAMOPSO with higher computational cost. In the dynamic scenario, DATO-MOPSO showed its main advantage, achieving the highest mean HV and the lowest mean IGD with statistically significant HV and IGD improvements over all baselines. Overall, DATO-MOPSO is most advantageous in time-varying complex threat environments, whereas its static-scenario advantages are accompanied by higher computational cost. Full article

Multimodal recommender systems are challenged by heterogeneous modality noise and coarse-grained feature fusion. Specifically, existing frequency-domain methods typically apply symmetric filtering across modalities, ignoring their distinct spectral characteristics. Consequently, symmetric filtering cannot simultaneously satisfy the denoising requirements of visual features and the semantic preservation requirements of textual features, leading to suboptimal multimodal representations. Meanwhile, current fusion strategies mainly operate at the instance level with static modality weights, lacking flexibility to dynamically adjust feature channels for user-specific collaborative contexts. To address these issues, this paper proposes MFA-GCN, a multimodal recommendation framework that combines asymmetric spectral filtering, multiview graph enhancement, and behavior-guided channel attention. For visual modalities, a multiscale frequency-domain module integrating 1D convolution and self-attention is adopted to suppress high-frequency disturbances while preserving informative structures. For textual modalities, a lightweight complex-domain scaling strategy is introduced to adjust spectral energy while maintaining semantic consistency. In addition, auxiliary user–user and item–item graphs are constructed to supplement sparse user–item interactions and provide richer collaborative signals. A behavior-guided channel attention mechanism is further used to dynamically refine multimodal representations. Experiments on three public Amazon datasets demonstrate that MFA-GCN consistently outperforms several representative baselines. Full article

This article presents a method for calibration of dynamic vehicle weighing systems (WIM—Weigh-In-Motion) involving the calibration of all WIM stations operating within a given road network segment as a single process. A key assumption of the method is the presence of at least one scale with significantly higher accuracy than the calibrated systems in this part of road network. This reference scale function may be played by a static scale, slow-pass scale (LS-WIM—Low-Speed WIM) for measurement of vehicle axle load or by a selected WIM system with heightened accuracy. Both the reference scale and all systems undergoing calibration must be equipped with a system for the automatic recognition of vehicle registration number plates. The reference scale makes it possible to determine axle load values considered as benchmark values. Then, for each vehicle weighed on the reference scale and subsequently on any WIM system operating within the analysed area, the relative difference between the reference result and the WIM system measurement is calculated with respect to the reference value. This difference forms the basis for the operation of the algorithm estimating the coefficients of the static characteristic of the calibrated WIM system (so-called calibration coefficients), which are then used to determine corrected weighing results. The estimation of the coefficients is updated after each identified vehicle that has previously been weighed on the reference scale is considered. The article presents both the results of simulations and experimental studies concerning the proposed spatial method of calibration. The results obtained allow for an assessment of the effectiveness of the proposed solution. As can be seen from the analyses conducted, this method leads to a significant reduction in systematic error of vehicle weight measurement. Unfortunately, it does not eliminate random errors. The spatial calibration approach described in this paper has certain limitations. The main ones include the impact of ANPR system errors on calibration effectiveness, cases where a vehicle is unloaded or loaded between WIM stations, and the propagation of systematic errors from the reference systems to the other WIM systems. A significant advantage of the proposed spatial calibration method is that it can operate effectively using weighing data from a single reference WIM system and does not require heavy traffic volumes. Full article

This paper provides a comprehensive review of recent studies on event-triggered control schemes for DC microgrids. Several event-triggered mechanisms (ETMs) are thoroughly discussed, including static, dynamic, self-triggered, and edge-based algorithms. Considering the strengths and weaknesses of these algorithms, it is found that although such ETMs can decrease communication burden in the system, they are also susceptible to communication delays, Zeno behaviour, sensitivity to control parameter changes in triggering conditions, and inability to adapt to the fluctuating nature of renewable energy sources (RESs). Furthermore, this article examines implementation challenges, including data packet loss, quantisation effects, actuator faults, and a lack of cybersecurity measures, to provide readers with a clear vision of future trends in this field. Based on the main findings of the investigation, this review paper proposes possible areas for future research, highlighting the need for event-triggered control schemes that operate in discrete time, handle delays, and adapt to varying operating conditions. Other concepts, including adaptive control parameters for triggering conditions based on machine learning, the adoption of advanced cybersecurity measures, and data-aware transmission approaches that consider both communication frequency and total data volume, are also discussed. To conduct a comprehensive review of all the above-mentioned ETMs, several databases, including IEEE Xplore, Elsevier, and MDPI, were searched using the main keywords in this field, such as event-triggered, self-triggered, and edge-based ETMs, in conjunction with DC microgrids. This facilitated an in-depth analysis of such control schemes, including their strengths and weaknesses, providing readers with a strong basis for selecting a proper control scheme suited to their future research. Full article

GNSS array receivers suffer tracking degradation under array nonidealities such as element-position perturbations, channel amplitude/phase errors, and slowly varying manifold mismatch. Conventional blind anti-jamming suppresses interference, but adaptive weight fluctuations can propagate into the correlator domain, increasing cross-branch correlation, causing Early/Late metric imbalance, and reducing Prompt phase consistency. Existing noncoherent combining methods mainly convert multi-branch correlator outputs into scalar energy metrics for code tracking, leaving the carrier loop’s complex Prompt input insufficiently constrained. To address this problem, we propose a blind adaptive joint code–carrier channel-combining method for nonideal arrays. After first-stage anti-jamming, the method estimates an Early/Late correlator-domain covariance matrix and reuses it as a shared statistical constraint. In the code loop, this matrix drives whitened noncoherent energy combining with closed-loop gain normalization to stabilize the DLL discriminator scale. In the carrier loop, it is combined with a Prompt-derived coherent direction to form a covariance-constrained PLL complex input. Simulations under wideband interference, static array errors, and dynamic mismatch show that the proposed J-WNCC reduces both code-phase error and carrier-phase jitter, improving joint tracking robustness in nonideal array environments. Ablation results further reveal a dominant-effect separation: DLL gain normalization mainly calibrates the whitened code-discriminator scale, whereas coherent Prompt combining mainly reconstructs the complex PLL input. Full article

New copper(II) (C1) and palladium(II) (C2) complexes with S,O-tetradentate ligand (L) derived from thiosalicylic and thiopropionic acids were synthesized. In cell-based assays, (C1) exhibited the most pronounced activity within the tested compound series and was therefore advanced for mechanistic evaluation in 4T1 triple-negative breast cancer cells. (C1) significantly reduced 4T1 cell viability by inducing early and late apoptosis, accompanied by mitochondrial membrane depolarization and enhanced cytochrome C release. Consistently, (C1) increased the Bax/Bcl-2 ratio, promoting a pro-apoptotic shift. In parallel, (C1) triggered autophagy, as evidenced by decreased p62 and LC3B levels, induced G0/G1 cell-cycle arrest, and suppressed proliferative signaling by downregulating Ki67, cyclin D, and phosphorylated AKT. The DNA-binding studies showed moderate to strong affinity, favoring minor groove binding, with higher affinity for (C1) than for (C2). Tryptophan fluorescence quenching indicated a strong interaction with BSA via a predominantly static mechanism, more pronounced for (C1). Molecular docking at the DNA and BSA binding sites corroborated experimental findings and suggested favorable interactions between the complexes and apoptosis-related proteins (CASP3, BAX, and BCL2). The integrated experimental and computational data identify (C1) as a biologically active compound with multimodal biological effects in vitro, supporting further structural optimization and mechanistic investigation. Full article

Typhoon-induced landslides in coastal mountainous regions are controlled by the coupled effects of rainfall, wind, topography, and storm-track geometry. However, conventional static susceptibility models have limited ability to represent event-scale forcing under extreme weather conditions. This study develops a physics-guided dynamic landslide susceptibility framework and retrospectively applies it to the 2017 Tropical Cyclone Cempaka event in Pacitan Regency, Indonesia, where 743 landslides were identified. The framework integrates static terrain factors, antecedent wetness, event-scale rainfall accumulation and intensity, maximum wind speed, and a typhoon geometric exposure index derived from IBTrACS best-track information that represents track proximity, topographic shielding, rainfall-favored quadrant effects, and storm-motion effects. Under spatial block cross-validation, model performance improved progressively from the static baseline to the full-factor model, with the receiver operating characteristic area under the curve (ROC-AUC) increasing from 0.648 to 0.751, the precision–recall area under the curve (PR-AUC) reaching 0.826, and the F1-score reaching 0.744. The full-factor model also reduced missed landslide cases from 328 to 205 and concentrated predicted high-susceptibility zones along the typhoon exposure corridor. Additional parameter-sensitivity analyses further indicate that the event-based E_geo setting produced positive performance increments under the event-consistent quadrant convention. These results indicate that physically meaningful typhoon-exposure information can improve the spatial discrimination and interpretability of event-scale landslide susceptibility assessment. Full article

To enhance the precision of Discrete Element Method (DEM) simulation parameters for the grading of mechanically harvested fresh tea leaves, this study systematically measured the intrinsic physical and basic contact parameters of the Yinghong No. 9 cultivar. Addressing the distinction between primary and secondary contact interfaces during roller screening, the extreme boundary validation method was first employed to determine simplified fixed values for the contact parameters of the secondary component. Based on the measured physical angle of repose of 36.3°, Plackett–Burman screening, steepest ascent, and Box–Behnken tests were conducted sequentially to construct and optimize a second-order regression model relating significant parameters to the angle of repose. The results indicated that the static friction coefficient between tea leaves (0.723), the rolling friction coefficient between tea leaves (0.031), and the static friction coefficient between tea leaves and the PVC roller (0.547) were the key parameters affecting the angle of repose. Verification tests demonstrated that the simulated static angle of repose was 36.9° against the measured 36.3°, yielding a relative error of 1.65%. The simulated dynamic angle of repose in the rotating drum was 39.8° compared to the physical 38.3°, representing a relative error of 3.92%, and the errors in screening efficiency on the grading bench were all less than 5%. These results indicate that the calibrated parameters accurately characterize the material properties of mechanically harvested tea leaves, providing a reliable theoretical foundation for the structural optimization of grading equipment. Full article

Unloading-induced bone loss is a major medical challenge during long-duration human spaceflight, largely driven by suppressed osteoblast-mediated bone formation, and practical countermeasures are needed. Electromagnetic stimulation has shown benefits for bone repair, and its non-invasiveness supports potential space use; however, its single-modality efficacy remains limited. Here, we investigated a combined electromagnetic field (CEMF) integrating a static magnetic field (SMF, 0.4–0.6 T) and a pulsed electromagnetic field (PEMF, 0.38 ± 0.19 mT) to attenuate unloading-related bone loss and examine field-induced mechanical stimulation. Finite-element simulations mapped magnetic flux density, field gradient, induced current density, and Lorentz force density in bone tissue. CEMF was evaluated in vivo in hindlimb unloading (HLU) mice and in vitro in MC3T3-E1 osteoblasts. CEMF improved bone mineral density, trabecular and cortical microarchitecture, and mechanical properties in HLU mice, with increased osteoblast number and mineral apposition rate. In vitro, CEMF promoted osteogenic differentiation and upregulated COL1A1 and RUNX2. Transcriptome analysis suggested activation of ECM–integrin mechanical signaling and the PI3K–AKT pathway. These findings indicate that CEMF-induced multiphysics stimulation enhances osteogenic responses and may serve as a complementary, non-invasive countermeasure for spaceflight-associated bone loss. Full article

This paper presents a comparative evaluation of quarter-car-model-based modular synthesis (QCMS) and full-car-based centralized synthesis (FCCS) for active suspension control in full-car systems. FCCS explicitly accounts for the coupled vertical, pitch, and roll dynamics by incorporating the geometric configuration of the sprung mass; however, this centralized formulation increases model complexity and controller–synthesis effort. In contrast, QCMS reduces the synthesis complexity by designing local suspension controllers using a quarter-car model and applying them modularly to the four suspension corners of a full-car system. Within both synthesis frameworks, linear quadratic (LQ) static output feedback (SOF) controllers and recursive-least-squares/extended-Kalman-filter (RLS/EKF)-based controllers are developed under comparable but structurally different control objectives. In particular, the proposed FCCS framework uses the geometric symmetry of the sprung mass not merely as a modeling assumption but as an explicit force-allocation structure that transforms the desired vertical force, roll moment, and pitch moment into four suspension actuator forces. Thus, four controllers are considered: LQSOF-QCMS and RLS/EKF-QCMS as modular quarter-car-based controllers, and LQSOF-FCCS and RLS/EKF-FCCS as centralized full-car-based controllers. In addition, the computational complexity of the LQSOF- and RLS/EKF-based controllers is compared in terms of their implementation burden. The main contribution of this study is not merely to show that the full-car-based FCCS improves the suppression of coupled body motions, but to clarify, under identical control and simulation conditions, the quantitative trade-off between the modular simplicity of QCMS and the symmetry-based centralized performance of FCCS. These controllers are evaluated through CarSim-based simulations under selected representative road-profile conditions in terms of ride comfort, motion-sickness mitigation, sensor requirements, and implementation complexity. The simulation results show that QCMS offers a low-complexity and modular implementation with acceptable ride-comfort performance, whereas FCCS justifies its increased synthesis and implementation burden when the suppression of coupled vertical, pitch, and roll motions and motion-sickness-related responses is required. Full article

Dense granular materials under low confining stress and low shear velocity—conditions relevant to low-pressure powder handling, near-surface transport, and the upper layers of stored bulk solids—remain insufficiently characterized at the microstructural level. We perform three-dimensional discrete element method (DEM) simulations of annular shear of monodisperse glass spheres at σ = 1 kPa and v = 0.01 m/s, corresponding to an inertial number I ≈ 1.06 × 10⁻³ at the quasi-static limit of the dense flow regime. The steady-state friction coefficient stabilizes at μ_ss ≈ 0.78, consistent with the quasi-static limit of the μ(I) framework. The solid volume fraction decreases monotonically from φ ≈ 0.50 at the base to φ ≈ 0.35 near the top, while the tangential velocity decays exponentially with depth (decay length δ_s ≈ 10 mm). Particle trajectory tracking reveals a sharp kinematic transition near z ≈ 5–6 mm separating a quasi-rigid basal layer (z ≲ 5 mm) from an upper shear-active zone (z ≳ 6 mm). The contact force distribution follows an exponential decay P($f$/$⟨f⟩$) ∝ exp(−β·$f$/$⟨f⟩$) with β ≈ 0.45, with strong force chains selectively concentrated in the upper zone. Together, these four microstructural descriptors co-locate within a single transition band, providing quantitative benchmarks for material characterization and constitutive modelling at the lower boundary of dense flow. Full article

Precision Livestock Farming (PLF) increasingly relies on edge-deployed sensors to monitor bovine behaviors, fostering improved welfare and management. However, behavioral data naturally expands over time and presents severe class imbalances due to animals’ predominantly sedentary routines. When continuous sequential updates are required without access to historical datasets, deep learning methods frequently succumb to catastrophic forgetting. This study introduces an ultra-lightweight (∼0.85 MB) Continual Learning (CL) architecture built upon a CNN-BiLSTM feature extractor, tailored to process multivariate Inertial Measurement Unit (IMU) streams. We exhaustively evaluated baseline Naïve Fine-Tuning against Elastic Weight Consolidation (EWC), Learning without Forgetting (LwF), and episodic Replay under three rigorous real-world paradigms: Class Incremental, Subject Incremental (domain shift), and Imbalanced Realistic scenarios. Our empirical findings expose the fragility of static paradigms: in Class Incremental expansions, Naïve Fine-Tuning collapsed to an Average Accuracy of 33.33%. Conversely, Experience Replay emerged as the most robust defense, achieving a statistically significant Average Accuracy of 74.64 ± 6.77% across multiple random seeds. Furthermore, LwF effectively mitigated structural variations across unseen animal domains (Subject Incremental) without requiring raw data buffers. Notably, under severe biological class imbalances (Imbalanced Cumulative), the architecture proved highly resilient, maintaining 98.46% Average Accuracy and retaining perfect minority class recall. This research validates the operational feasibility of deploying adaptive, privacy-preserving CL frameworks directly on low-power wearable devices for lifelong livestock monitoring. Full article

The increasing frequency of extreme weather events, particularly hailstorms, driven by climate change, poses growing threats to the resilience, environmental sustainability, and long-term performance of photovoltaic (PV) systems. This study evaluates the environmental impacts of a 12 kWp rooftop PV installation in Brescia, northern Italy, through a comparative Life Cycle Assessment (LCA) of three system configurations: a standard unprotected system (Scenario A), one equipped with a retractable polycarbonate hail-protection panel with automated weather-sensor activation (Scenario B), and one using thicker reinforced front-glass modules (Scenario C). The analysis follows a cradle-to-gate plus operational maintenance phase (30-year horizon, excluding end-of-life) system boundary and employs the ReCiPe 2016 Midpoint (H) methodology across 18 environmental impact categories. A novel integration of the Social Discount Rate (SDR) to the LCA framework—constituting a Discounted LCA (D-LCA)—incorporates both temporal discounting and risk dimensions into the environmental evaluation. A structured PESTEL-based risk taxonomy is applied to derive scenario-specific SDRs, with the Environmental risk category as the key differentiator between configurations. The static LCA identifies Scenario A as the lowest-impact option, while the D-LCA framework reverses this ranking: Scenario C achieves the highest Net Present Value of Emissions, followed by Scenario A. A negative NPV-E for Scenario B reflects the temporal cost of a large, front-loaded construction debt rather than absolute environmental harm. D-LCA framework should be interpreted as a complement to the full 18-category static LCIA profile, not a replacement. These results demonstrate that risk-informed D-LCA provides a more policy-relevant environmental sustainability assessment than static LCA for long-lived energy infrastructure subject to climate-driven operational risks. Full article

Autophagy-associated readouts in localized prostate cancer cannot be interpreted based on LC3, p62/SQSTM1, or LC3 puncta alone. In line with the concept of autophagy as a stress-response system, this review proposes a flux-aware, organelle-centered framework for assigning biological meaning to autophagy-related changes under disease-relevant stress. The framework integrates oxidative burden, lysosomal competence, selective autophagy, mitophagy, ferritinophagy, p62/SQSTM1-NRF2 signaling, ferroptosis-aware controls, and disease-stage context to distinguish four interpretive states: homeostatic quality control, adaptive tumor survival, blocked clearance, and stress-overload vulnerability. Flavonoid-associated responses are used as stress-test examples because they expose recurrent limitations in the field, including supraphysiologic exposures, limited metabolite realism, static-marker inflation, and insufficient assessment of lysosomal function. However, the framework is not restricted to dietary compounds; it applies to metabolic, pharmacological, inflammatory, androgen-related, radiation-associated, or therapy-induced perturbations in which autophagy-associated markers are altered without resolution of flux or organelle function. By linking autophagosome formation, cargo turnover, lysosomal acidification, redox buffering, and phenotype-level endpoints, this review defines a practical evidence hierarchy for interpreting autophagy in localized prostate cancer and for prioritizing translational vulnerabilities arising from organelle crosstalk. This contribution is primarily conceptual and is operationalized methodologically through flux-based evaluation criteria and translationally through disease-window-specific study-design recommendations. Full article