Search Results (777)

This paper designs a statistical channel state information-based pinching antenna system for short-packet communication (SPC). To maximize the average maximal achievable rate (MAR) under physical collision-avoidance constraints, we formulate a highly non-convex geometry optimization problem, which is solved by our proposed novel phase-domain proximal policy optimization (PPO) framework. Unlike conventional coordinate-based approaches, the agent operates in a dual-component trigonometric phase domain, and the generated phase actions are mapped to feasible antenna positions via a customized phase-domain action mapping, which fundamentally avoids the $0/2\pi$ phase discontinuity and ensures stable learning. To evaluate the reliability of SPC, we derive a tractable statistical characterization of the received signal-to-noise ratio based on a mixture Gamma approximation over spatially correlated Rician fading channels, leading to a closed-form approximation for the average block error rate (BLER). A bisection search algorithm is further developed to minimize the required blocklength under the target reliability constraint. Simulation results demonstrate that the proposed phase-domain PPO scheme significantly outperforms the conventional algorithms in terms of average MAR, average BLER, and blocklength efficiency, with the performance gain becoming more pronounced as the number of antennas per waveguide increases. Full article

Aircraft taxiing speed control along predefined airport surface routes is a constrained single-aircraft longitudinal control problem involving heterogeneous route geometry, action smoothness, and terminal velocity feasibility. Existing learning-based taxiing controllers commonly use a unified policy for the whole route, which may be insufficient for handling straight-segment propulsion, curved-segment speed regulation, and action discontinuities near straight–curve transitions. This paper proposes SegCoord-Taxi, a geometry-aware segmented deep reinforcement learning framework for taxiing speed control. The route is decomposed into straight segments, curved segments, and transition boundary zones. A Straight-Segment Policy (SSP) and a Curved-Segment Policy (CSP) generate geometry-dependent base acceleration commands, a Switch Residual Adapter (SRA) provides local residual correction near transition regions, and a Route-Level Feasibility Projection (RFP) maps the coordinated action into an executable acceleration satisfying route-level feasibility constraints. Experiments on departure taxiing routes at Chengdu Tianfu International Airport (ZUTF) included baseline comparison, ablation analysis, projection diagnostics, sensitivity analysis, and a trajectory-level case study. On the evaluated ZUTF case-study routes, SegCoord-Taxi achieves the lowest final velocity on the test set, $0.336\pm 0.017 \mathrm{m}/\mathrm{s}$, compared with $0.732\pm 0.061 \mathrm{m}/\mathrm{s}$ for the unified Proximal Policy Optimization (PPO) controller and 0.586 m/s for the curvature-aware constrained optimizer. The complete framework also reduces switch action jump from $1.022\pm 0.017 \mathrm{m}/{\mathrm{s}}^2$ to $0.429\pm 0.004 \mathrm{m}/{\mathrm{s}}^2$ in the ablation study. These results indicate improved terminal feasibility and transition-region smoothness in the evaluated single-airport case-study setting under an explicit efficiency–smoothness–feasibility trade-off. Future work will extend the framework to multi-aircraft and multi-airport settings under operational uncertainty. Full article

The paper addresses an inverse problem for a nonlinear Volterra integral equation of the first kind with a piecewise continuous kernel whose discontinuity curves are the unknown functions. Such models arise in the theory of developing systems, power systems with energy storage, and related applications. We develop an iterative scheme based on the Newton–Kantorovich linearisation of the nonlinear integral operator and obtain explicit recurrent formulas for the discontinuity curve. Both the full Newton-like and a modified (simplified) iterative process are constructed, and their local convergence is proved under natural smoothness and smallness conditions. The performance and accuracy of the method are illustrated by several model problems with known and unknown exact solutions. The algorithm demonstrates rapid convergence and is robust with respect to the choice of the initial approximation. Full article

The dynamic reliability of steam-turbine governing systems is essential for the safe operation of nuclear power units. As a key regulating and protection component, the reheat valve must complete rapid closure under abnormal operating conditions. This study addresses the closing timeout problem observed in a nuclear reheat-valve oil-motor actuator after domestic substitution, with particular attention to sluggish motion and discontinuous closing at small openings. A coupled hydraulic–mechanical model was then established by integrating the coned-disc spring assembly, hydraulic circuit, cartridge valve, gear–rack transmission, and load resistance based on the mathematical model. The model was used to identify the dominant parameters controlling the fast-closing process, and the optimization strategy was subsequently verified by experiments on an actual actuator platform. The results show that coned-disc spring degradation is a critical source of closing timeout risk. When the equivalent elastic modulus decreases to approximately 195 GPa, the fast-closing time approaches the critical limit of 0.8 s, while further degradation results in evident timeout. The C0 throttling orifice has the strongest influence on the effective closing time by governing the pressure-relief capacity of the working chamber. A coordinated correction strategy, involving coned-disc spring force compensation and throttling parameter adjustment, restores the closing margin, shortens the fast-closing time to 0.78 s, and improves closing smoothness. This work provides the practical guidance for design verification, field commissioning, and domestic improvement of nuclear reheat-valve oil-motor actuator systems. Full article

Exchange-rate forecasting requires modelling non-stationary dynamics, heavy-tailed shocks, and complex temporal dependencies. However, forecasting performance in emerging-market currencies is fundamentally constrained by intrinsic dynamical instability, while most existing approaches are evaluated primarily through predictive accuracy rather than the predictability limits of the underlying system. This paper develops a predictability-aware framework that combines nonlinear dynamical diagnostics with a Lévy-driven neural stochastic differential equation model. Drift and diffusion are parameterized by neural networks and driven by $\alpha$-stable Lévy motion, enabling the representation of non-Gaussian fluctuations, abrupt shocks, and regime changes. To learn under discontinuous dynamics, we introduce a structurally constrained training objective based on a strong-form discretization of the underlying SDE. To characterise intrinsic predictability, we employ phase-space reconstruction and maximal Lyapunov exponent estimation. These diagnostics are interpreted as finite-sample measures of trajectory divergence and effective instability in a stochastic system, rather than evidence of low-dimensional deterministic chaos—a distinction motivated by well-documented limitations of chaos testing in financial data. Experiments on multiple West African currency pairs demonstrate competitive short-horizon forecasting performance relative to econometric and neural baselines while providing a principled framework for analysing predictability degradation under heavy-tailed stochastic dynamics. Across currencies and model classes, forecasting accuracy deteriorates beyond horizons comparable to the estimated Lyapunov time, suggesting that forecast degradation reflects intrinsic dynamical instability rather than model-specific limitations. The results support the view that reliable exchange-rate prediction is fundamentally a short-horizon problem and illustrate how stochastic dynamical modelling and predictability diagnostics can be combined to characterise forecasting limits in heavy-tailed financial systems. Full article

Underground mine emergencies compromise fixed communication infrastructure exactly when situational awareness is most critical for effective rescue operations. Existing LoRa mesh protocols fail in underground mines because they ignore the structured topology of tunnel networks, specifically the waveguide effect along straight galleries, severe signal discontinuity at junctions, and the dead-end geometry of working faces. This paper presents the Topology-Aware Concurrent LoRa (TACL) mesh protocol, in which each node autonomously infers its structural role from local RF observations and packet header information, without GPS, pre-loaded mine maps, or central coordination. Role classification resolves the contender estimation problem $\left(N_h\right)$ left open in the prior concurrent transmission literature, enabling provably bounded timing offsets before transmission. TACL assigns a spreading factor (SF)12 to dead-end source nodes for maximum link robustness and SF7–SF10 to relay nodes to create the inter-SF orthogonality margin required for concurrent decoding at junction nodes. Monte Carlo simulation of over 2000 trials yields TACL a PDR of 80.5% versus near-zero for all three baselines, confirming that topology-aware SF diversity is the necessary and sufficient mechanism to prevent junction collision collapse. Hardware deployment at the Missouri S&T Experimental Mine yields a 4.0× PDR improvement over the topology-agnostic concurrent transmission (CT)-fixed baseline, a median end-to-end latency of 1815 ms with 84× tighter latency spread than ALOHA-based protocols and 2.5× lower energy per delivered packet. These results establish that explicit exploitation of underground mine topology is essential for reliable, predictable, and energy-efficient emergency mesh communications in post-disaster underground mine scenarios. Full article

Background and Objectives: Muscle symptoms are the most visible adverse event attributed to statins, but terminology is often imprecise. Most patients report myalgia or nonspecific aches, whereas objective myopathy, inflammatory or necrotizing myositis, rhabdomyolysis, and anti-HMGCR immune-mediated necrotizing myopathy are uncommon and clinically distinct entities. To provide a clinically oriented narrative synthesis of statin-associated muscle symptoms (SAMS) and severe statin-associated myotoxicity, and to propose a practical prevention, evaluation, and management algorithm. The classification of muscle events is used to standardize terminology and avoid diagnostic confusion, not to create a new formal taxonomy. Materials and Methods: A clinically oriented narrative review was performed using PubMed, Google Scholar, and major society documents published from January 2021 to April 2026. Eligible sources addressed SAMS, statin myopathy/myositis, rhabdomyolysis, anti-HMGCR immune-mediated necrotizing myopathy, nocebo/drucebo effects, pharmacogenetics, drug interactions, diagnosis, or management. The final evidence set comprised 55 verifiable sources, including blinded randomized or n-of-1/crossover evidence; meta-analyses; clinical statements and reviews; pharmacovigilance analyses; pharmacogenetic guidance; mechanism-focused reviews; anti-HMGCR series; and lipid-lowering guideline/treatment studies. Because the review was narrative, no pooled estimate or formal PRISMA screening log was generated. Results: Blinded evidence indicates only a small absolute excess of muscle pain with statins, concentrated mainly in the first year of therapy, and that most muscle symptoms reported during statin therapy are not pharmacologically caused by the statin. N-of-1 and crossover trials show that symptom intensity is often similar during statin and placebo periods, consistent with an important nocebo/drucebo contribution. Severe muscle toxicity can nevertheless occur, especially when systemic statin exposure is increased by a high dose, interacting drugs, frailty, renal or hepatic impairment, hypothyroidism, transporter or metabolic genotypes, or intense unaccustomed exercise. Statin choice matters chiefly through dose, pharmacokinetics, and interaction burden. Conclusions: SAMS are common as reported clinical problems, but confirmed statin-caused muscle injury is substantially less frequent than routine clinical attribution suggests. Permanent discontinuation should be reserved for carefully assessed cases. A structured approach—baseline risk assessment, selective CK measurement, exclusion of alternative causes, correction of modifiable risks, dechallenge/rechallenge, statin switching, dose reduction, and combination with non-statin therapy—preserves cardiovascular benefit while protecting the rare patient with genuine toxicity. Full article

A probability-based model (PBM) is developed to predict the evolution of a population of impinging droplets on a solid substrate and the eventual transition between dropwise and filmwise regimes. A dedicated heat transfer model is designed, in order to estimate the evaporating mass flux when the solid substrate is heated. Statistical information such as the droplet size distribution and the influence of surface wettability, required by the PBM, are derived using a previously developed high-fidelity individual-based model (IBM). The PBM is verified with the high-fidelity model for a small patch of solid substrate. Then, validation with experimental evidence from the literature is carried out in the case of in-flight ice on the NACA0012 airfoil. Results show that the present PBM is capable of investigating in-flight ice problems and can be integrated with a CFD analysis of the air flow past an airfoil flying through a cloud of supercooled droplets to predict the possible onset of ice accretion on the airfoil surface. Compared to Messinger-like models, the influence of surface morphology on runback water flow is incorporated in the PBM through the modeling of a discontinuous wetting layer, contributing to the design of passive and active anti-icing systems. Full article

Bedding fractures strongly influence pore structure and anisotropic flow capacity in laminated shale oil reservoirs, but conventional porosity–permeability relationships cannot adequately explain permeability differences caused by bedding orientation and fracture connectivity. This problem represents an important gap in shale oil reservoir evaluation because cores with similar porosity may exhibit markedly different permeability when bedding-fracture connectivity and flow direction differ. The main question addressed in this study is how bedding-fracture structures in paired horizontal and vertical LGS shale oil cores selected from the same depth intervals influence porosity, permeability, and permeability anisotropy. To answer this question, this study establishes a quantitative framework linking X-ray computed tomography-derived bedding-fracture structure, three-dimensional fractal dimension, and stress-sensitive permeability anisotropy in LGS shale oil cores. Paired horizontal and vertical cores from the same depth intervals were tested under confining pressures of 10–50 MPa. X-ray computed tomography reconstruction was used to extract bedding-fracture volume fraction $V_f,$ fracture number $N_b$, fracture density ${\rho }_b$, connectivity index $C_b$, and three-dimensional box-counting fractal dimension $D_3$. The H-series cores exhibit much higher bedding-parallel permeability than the V-series cores, although their porosity ranges partly overlap. At 10 MPa, the average permeability of the H-series is 0.24402 mD, approximately 21.7 times that of the V-series 0.01127 mD. As confining pressure increases from 10 to 50 MPa, the average permeability decreases by approximately 97.1% for the H-series and 96.5% for the V-series, indicating strong stress sensitivity of bedding-fracture-controlled flow channels. The $D_3$ values range from 2.16 to 2.63 for the H-series and from 2.12 to 2.56 for the V-series. Higher $D_3$, $V_f$, and $C_b$ enhance permeability when bedding fractures are aligned with the flow direction, whereas complex but discontinuous bedding structures may still result in low bedding-normal permeability. A fractal-corrected porosity–permeability model incorporating $\phi$, $V_f$, $C_b$, and $D_3$ is proposed to improve permeability interpretation beyond porosity alone. This study demonstrates that permeability anisotropy in LGS shale oil cores is controlled by the combined effects of pore–fracture volume, directional connectivity, fractal complexity, and stress-induced fracture closure. Full article

Background/Objectives: Chemotherapy is recommended for patients with advanced soft tissue sarcoma (STS). However, chemotherapy is less aggressive in elderly patients than in younger patients owing to comorbidities and other health problems. This multicenter study aimed to examine the outcomes of elderly patients treated with chemotherapy for advanced STS. Methods: The study cohort included 60 men and 71 women with a mean age of 73 years. The mean follow-up duration was 22.9 months. Results: As first-line treatment, the doxorubicin-containing regimen was the most frequently used. Dose reduction was more frequent in patients aged ≥ 75 years than in those aged ≤ 74 years. Complete response occurred in two patients and partial response in eight patients. The objective response rate was 8.2%. The 1- and 2-year survival rates after first-line chemotherapy were 61.8% and 40.2%, respectively. The median survival time was 19 months. In multivariate analysis, patients with performance status (PS) 2 or 3 had poorer survival than those with PS 0 or 1. The median survival times for patients with PS 0 or 1 and PS 2 or 3 were 22.1 and 4.3 months, respectively. Among 131 patients, no fatal adverse events occurred, although chemotherapy was discontinued due to adverse events in 28 patients. Conclusions: Chemotherapy for advanced STS in elderly patients may be effective in those with good PS, although it should be considered to evaluate the benefits and risks of cytotoxic chemotherapy. Full article

This paper investigates a class of electro-hydraulic servo systems with unknown nonlinear functions and parameters. To address the issues of modeling uncertainties and unmodeled dynamics, an adaptive robust nonlinear controller integrating neural networks and predefined-time prescribed performance is proposed. First, an exponential-type predefined-time prescribed performance function is designed to ensure that the system tracking error converges to a prescribed region within a predefined time. An adaptive law based on the discontinuous projection method is developed to estimate unknown parameters and compensate for them in the controller. The dynamic surface technique is introduced to overcome the “explosion of complexity” problem inherent in the traditional backstepping method. Meanwhile, neural networks are employed to approximate system nonlinearities, thereby reducing modeling errors. Finally, the stability of the closed-loop system is rigorously proved using Lyapunov theory, and numerical simulations validate the superiority of the designed controller over conventional control strategies. Full article

To address the voltage regulation problem of the DC-DC buck converter under multi-source disturbances, this paper proposes a composite anti-disturbance control strategy integrating a Chebyshev-based self-evolving fuzzy neural network (SECFNN) and an arctangent super-twisting sliding mode control (ASTSMC). First, to construct the composite anti-disturbance framework, a load algebraic reconstruction compensator (LARC) is utilized to analytically estimate real-time load dynamics, providing active feedforward compensation for extreme load steps. Second, targeting the unmodeled nonlinearities and parameter uncertainties, the SECFNN is deeply integrated into the control loop. It employs a bidirectional structural learning mechanism—dynamically growing and pruning fuzzy rules—to achieve high-precision adaptive approximation and intelligent compensation. Furthermore, serving as the robust inner-loop core of this composite strategy, the ASTSMC is introduced. By replacing the traditional discontinuous sign function with a continuous arctangent operator, it effectively mitigates sliding mode chattering while ensuring the rapid finite-time convergence of the current tracking error. Ultimately, by synergistically fusing feedforward disturbance rejection (LARC), intelligent nonlinear approximation (SECFNN), and robust tracking (ASTSMC), the proposed strategy significantly reduces transient voltage drops and achieves smoother steady-state performance. Comparative simulation experiments demonstrate the superiority of the proposed method, achieving a rapid startup settling time of 6.5 ms, limiting the maximum transient voltage drop to 15 mV, and completing dynamic reference tracking in 1.2 ms. Furthermore, hardware experimental results confirm its practical engineering feasibility, demonstrating a fast startup of 8.3 ms with zero overshoot, effectively mitigating transient voltage drops during load step changes, and completing dynamic tracking in just 2.2 ms, which verifies its reliable dynamic agility and strong robustness under various test conditions. Full article

Against the backdrop of global energy transition and the continuous growth in electric vehicle (EV) market penetration, accurate forecasting of EV charging load is critically important for guaranteeing the safe and stable operation of power grids. Most existing forecasting approaches rely on artificial intelligence (AI) models trained with large-scale and continuous historical data, which imposes stringent requirements on the collection of EV charging load data. To address this issue, this paper proposes a novel method for EV charging load forecasting under small sample and discontinuous data conditions. Firstly, the differences between the daily load curves of EV charging are characterized by local dynamic time warping (LDTW) distance. And a spectral clustering algorithm based on LDTW distance (SCLD) is proposed to realize the classification of daily EV charging load patterns. Secondly, feature correlation weights (FCWs) derived from eXtreme gradient boosting (XGBoost) with one-hot encoding of input features are introduced to quantify the influences of features such as district-level attributes and weather conditions on daily EV charging load. Then, a method for determining the category of daily EV charging load based on FCWs and Hamming distance is put forward. On this basis, a daily EV charging load forecasting framework is established via weighted fitting of similar intra-class samples based on category judgment. Finally, to validate the effectiveness of the proposed method, a case study is carried out using EV charging load data and corresponding feature data of 62 typical days across 16 administrative districts in Shanghai from 2023 to 2025. The results demonstrate that the proposed method effectively addresses the challenging problem of EV charging load forecasting under small sample and discontinuous data conditions. Full article

In high-speed machining, linear toolpaths constructed from a series of short line segments are widely used but inevitably introduce tangent and curvature discontinuities at segment junctions, which may cause feedrate fluctuation and contouring error. To address this problem, this study proposes a real-time corner smoothing and feedrate interpolation method based on dual cubic Bézier transition curves and an optimal error assignment model. The main contribution lies in coupling analytical corner rounding with error allocation: the approximation error and maximum curvature of the transition curves are obtained explicitly, while the allowable tolerance is optimally distributed between approximation error and chord error so that the overall trajectory error remains within the prescribed bound. A jerk-limited look-ahead interpolator is then developed through reverse scanning and forward interpolation to satisfy geometric constraints, drive constraints, and feedrate commands. Simulation results for a three-dimensional toolpath show that the approximation error, chord error, and total trajectory error are all constrained within the preset tolerance of 0.05 mm. In the mask-machining case, the proposed method reduces the machining time to 13.9 s, corresponding to reductions of approximately 70% and 25% compared with the method without look-ahead and the method with look-ahead only, respectively. These results indicate that the proposed framework can improve motion smoothness and machining efficiency while maintaining trajectory accuracy. Full article

Effective project selection is a critical determinant of success for Design for Six Sigma (DFSS), particularly in automotive environments defined by high technical complexity and constrained resources. Because these selection tasks involve competing priorities, they are fundamentally multi-criteria decision-making (MCDA) problems that directly impact a company’s economic performance. This paper proposes a hybrid decision-support framework that integrates the Analytic Hierarchy Process (AHP) with a normalized scoring model. In this approach, classical AHP pairwise comparisons are used to derive consistent criteria weights, while project alternatives are evaluated on a 1–10 normalized scale to ensure the model remains scalable and practical for an industrial setting. The framework was empirically validated through a case study in an automotive company evaluating twelve DFSS project concepts. The results reveal that experts prioritize Product Quality (33%) and Cost/Functionality (33%) above all other factors, with these two criteria accounting for 66% of the total decision weight. Furthermore, the study established classification rules where projects scoring above 7.2 showed high implementation potential, while those below 5.2 were frequently discontinued. This structured approach enables a transparent and justifiable prioritization process that supports economic and operational sustainability by significantly reducing wasted engineering hours and prototype costs. Full article