Search Results (522)

Let ${\rho }_i\in \mathbb{R}$ such that $0<\left|{\rho }_i\right|<1$, where $i\in \left\{1,2,3\right\}$. For an expanding real matrix $M\in M_3\left(\mathbb{R}\right)$ and a three-element integer digit set D, let ${\mu }_{M,D}$ be the self-affine measures. In this paper, we prove that if there are two different numbers $i,j\in \left\{1,2,3\right\}$ such that ${{\rho }_i}^{-1}$ is equal to ${{\rho }_j}^{-1}$, and they belong to a rational number with an odd numerator and odd denominator, then there exist at most four mutually orthogonal exponential functions in $L^2\left({\mu }_{M,D}\right)$, and four is the best number. Full article

Sheared granular media—fault gouges, landslide shear zones—develop power-law grain size distributions whose fractal dimension D saturates near 2.5–3.0, a limit classically attributed to the geometric postulate that a grain breaks only when loaded by neighbours of comparable size. That postulate has never been reduced to scaling laws. We therefore prescribe D rather than generate it: six truncated fractal gradings (D = 1.0–3.5, 0.4–4 mm) are sheared to a strain of 80 in three-dimensional discrete-element ring shear simulations at 100 kPa. The grain fractality propagates into the contact network with an amplified exponent, N(≥R*) $\propto$ R*^−αc with α_c = 1.25D, bounded between the faithful inheritance and random-pairing limits; the exponential force tail narrows linearly, β = 0.139D + 0.43; and the like-size coarse contacts vanish as f_LL $\propto$ e^−4.23D, decaying as the cube rather than the square of the coarse number fraction, which quantifies the cushioning by the fine matrix. The box-counting dimension of the strong force skeleton is near-universal (D_f ≈ 1.64–1.80). Combining these laws, a breakage opportunity index Θ $\propto$ exp(−4.65D) collapses by three orders of magnitude between D = 1 and D ≈ 2.6; the contact topology outweighs the force redistribution ten-fold. The comminution is self-arresting: the terminal fractal dimension is a topological, not an energetic, limit. Full article

A consistent computational framework is presented that integrates Markov chain modeling, decision optimization, and cost–benefit analysis for the life-cycle management of engineering assets. The approach combines deterioration modeling with a complete economic evaluation and optimization of maintenance decisions. Each condition state of the system is associated with possible actions such as do-nothing, preventive maintenance, major repair, and replacement, each defined by its own transition matrix or generator describing state changes. The expected one-step reward is formulated as the difference between benefits and all relevant costs including operating, action, and failure costs. The optimization problem is expressed as a discounted Markov decision process and solved by linear programming. The resulting stationary policy specifies the optimal decision rule for every state. Both discrete-time and continuous-time variants are implemented. Transition matrices and generator matrices are linked using a matrix exponential mapping for the selected step length. Under state-dependent policies, the discrete step model and the continuous-time feedback model may lead to different long-run state shares. This is caused by different decision timing. The continuous-time variant also provides reliability indicators such as survival and hazard. It can also provide mean time to absorption under an absorbing failure interpretation. Simulation under the optimal policy yields state trajectories, present values of benefits and costs, and key economic indicators such as net present value, benefit–cost ratio, equivalent annual cost, and equivalent annual net benefit. The framework forms a unified and practical tool that connects reliability analysis, Markov optimization, and life-cycle cost–benefit evaluation for long-term infrastructure management. Full article

Unauthorized unmanned aerial vehicle (UAV) intrusions in sensitive areas such as airports have made accurate UAV detection and localization a pressing need. Acoustic sensing is passive and weather-independent, but conventional microphone arrays require many elements and a large aperture. This paper proposes an acoustic vector sensor (AVS)-based method, termed Covariance Enhancement and Confidence-guided Tracking for 3D Acoustic Localization (CECT-3DAL). A single AVS measures the sound pressure and three-axis particle velocity at one point. Adaptive diagonal loading improves the robustness of the covariance matrix at a low signal-to-noise ratio (SNR). An exponential spectral enhancement strategy sharpens the spatial spectrum peaks for direction estimation, and an eigenvalue-ratio-based confidence drives confidence-weighted smoothing of the angle sequences. Meanwhile, a dual-sensor geometric model provides a closed-form three-dimensional solution. In simulations, the azimuth and elevation root-mean-square errors (RMSEs) were below 1.5° for SNR above 4 dB. In an anechoic chamber, confidence-weighted smoothing reduced the azimuth and elevation standard deviations from 4.34° and 2.63° to 1.46° and 0.86°. In field experiments, the hovering azimuth stayed within a 90% span of 2–3.5°, with an average horizontal RMSE of 0.209 m against a GPS reference, and trajectories under various flight modes remained continuous and smooth. The proposed method offers a compact, passive, and low-cost solution for counter-UAV acoustic surveillance. Full article

This study presents a structural design sensitivity analysis and metamodeling framework for a 30-ft class modular pontoon boat utilizing High-Density Polyethylene (HDPE) and an aluminum alloy (Al-5083) frame. Finite element analysis (FEA) was performed under the design load conditions specified by the Korean Register (KR) rules for high-speed light craft. Based on the FEA simulation results, a design sensitivity analysis was conducted using an L₈₁(3¹¹) orthogonal array design matrix (OADM) to identify the quantitative influence of structural thicknesses on the hull weight and maximum stresses. The best design combination among the OADM experiments successfully achieved a 31.9% weight reduction while strictly satisfying the allowable stress criteria. Furthermore, Kriging metamodels with four different correlation functions (Gaussian, Exponential, Matérn linear, and Matérn cubic) were constructed to predict the structural responses efficiently. A comparative analysis of the approximation accuracy revealed that the Matérn linear function provided the most robust predictive performance, yielding the highest average cross-validation coefficient of determination (R²) of 0.971 across all performance metrics. The predictive accuracy of the selected metamodel was further verified by leave-one-out cross-validation in terms of root mean square error (RMSE) and mean absolute error (MAE). The findings confirm that the Kriging metamodel employing the Matérn linear correlation function is highly suitable for capturing the complex structural behavior of hybrid-material marine structures. Full article

Personal voice activity detection (PVAD) identifies whether each detected speech frame originates from a designated target speaker. Modern PVAD systems are typically trained offline and then deployed with frozen model parameters and a fixed, pre-enrolled speaker embedding, leaving them unable to adapt to distribution shifts at inference time such as unseen acoustic environments, changing speaking styles, or mismatches between enrollment and test conditions. Test-time training (TTT) and test-time adaptation have shown promise in language, vision, and several speech tasks, yet their behavior on PVAD has not been studied. In this work, we present an empirical study of two complementary test-time adaptation mechanisms built on top of the recently proposed FDE-Mamba backbone. The first is a VAD-gated TTT adapter, which instantiates the TTT-Linear formulation within the personalization pathway and augments it with a VAD-probability gate and exponential moving-average stabilization, adapting an internal weight matrix on the speaker-conditioned feature stream of each test utterance. The second is TEA (Test-time Embedding Adaptation), a scheme that keeps all model parameters frozen and instead adapts the target speaker d-vector itself via self-supervised objectives at inference time, directly targeting enrollment–test mismatch. We evaluate both mechanisms on the LibriSpeech PVAD benchmark across two backbones (LSTM-based FDE-RNN and Mamba-based FDE-Mamba), reporting category-wise average precision, mean average precision (mAP), accuracy, recall, precision, and real-time factor. We further isolate the effect of a post-hoc Gaussian smoothing step and report that, of all the components we examine, this task-agnostic smoothing accounts for the largest single accuracy gain on the FDE-Mamba backbone (accuracy $89.87\%\to 90.47\%$); the test-time adaptation mechanisms contribute a separate, smaller gain that is concentrated on speaker-discrimination metrics (mAP, precision) rather than on accuracy. Overall, the proposed test-time adaptation yields a consistent but modest improvement over the FDE-Mamba baseline (mAP $0.9605\to 0.9641$, precision $0.881\to 0.899$), while slightly reducing recall and increasing inference cost when TEA is enabled. Through ablation studies, we quantify the independent and combined contribution of each component and characterize the recall–precision trade-off introduced by adaptation. These findings, together with a discussion of their limitations and cost–benefit profile, provide a measured baseline and design insights for future work on adaptive PVAD, particularly under stronger acoustic and enrollment mismatches than those captured by the LibriSpeech protocol. Full article

In multi-asset UAV safety monitoring, dynamic threat assessment is challenged by delayed recognition of changes in the potentially threatened asset and unstable threat rankings under noisy observations. This paper proposes an interpretable three-dimensional dynamic threat assessment method integrating approach directionality, historical trend correction, and adaptive exponential moving average smoothing. Each UAV-protected asset pair is treated as an assessment unit to construct a many-to-many threat matrix. A basic threat score is first derived from UAV type, three-dimensional closing velocity, distance, altitude difference, and vertical approach motion. Approach directionality is then characterized using the geometric alignment between the UAV velocity and the bearing to each protected asset, with optional attitude and reachability information. The temporal trend of directionality is estimated from historical observations to capture persistent changes in approach tendency, while adaptive smoothing balances responsiveness to genuine threat transitions against suppression of noise-induced fluctuations. Comprehensive simulation experiments, including three-dimensional discrimination, Monte Carlo evaluation, observation noise, latency, missed detections, clutter, difficult motion conditions, and ablation studies, demonstrate that the proposed method provides accurate and timely threat identification while substantially improving score and ranking stability. The resulting framework offers an interpretable and computationally efficient basis for multi-UAV threat prioritization and safety-oriented situational awareness. Full article

To achieve non-contact and continuous monitoring of body length in Micropterus salmoides and overcome the stress damage and subjective error associated with traditional manual measurement, this paper proposes an improved YOLOv8-based multi-target tracking framework for intensive recirculating aquaculture systems. The system employs a geometric measurement framework based on monocular vision that achieves conversion from pixel coordinates to actual body length through camera calibration, water-surface refraction correction, and pose projection correction. Under a collaborative optimization framework integrating detection and tracking, the model incorporates multi-scale feature enhancement, lightweight re-identification (ReID), and a robust data association mechanism, which improves system stability under conditions of high fish density, variable illumination, and turbid water. A shallow feature fusion path is introduced to enhance small-target perception, and a MobileNetV3_ReID model is adopted to extract highly discriminative appearance features, which improves identity consistency while maintaining model compactness. In the data association stage, a hybrid cost matrix integrating IoU, cosine similarity, and motion consistency is constructed, and optimal matching is realized through the Hungarian algorithm. Dynamic threshold adjustment and an exponential moving-average feature-update strategy are introduced to effectively suppress identity switching. Experiments were conducted on an overhead video dataset of Micropterus salmoides collected at a recirculating aquaculture system facility. The results show that the proposed method achieves 82.7% mAP50 while maintaining a real-time throughput of 88 FPS, with MOTA reaching 76.9% and IDF1 reaching 81.5%—the latter representing an improvement of 3.2 percentage points over BoT-SORT and 5.3 percentage points over the YOLOv8 baseline tracker. The number of identity switches (IDSW) decreased from 89 in the baseline configuration to 39, a reduction of 56.2%. Crucially, these component-level improvements translate into a body length error (BLE) of 5.2 ± 1.8% (MAE = 1.35 cm, Pearson r = 0.972), representing a 38.8% improvement over the baseline BLE of 8.5% and satisfying the 5–10% tolerance required for aquaculture growth monitoring. Ablation analysis confirms that both detection enhancements (contributing −1.3% BLE) and tracking optimizations (contributing −2.0% BLE) are necessary to achieve this application-level accuracy. Full article

Schemes that use ordinary-differential-equation solvers for computing the matrix exponential of a constant matrix have generally been considered inefficient compared to other strategies such as scaling and squaring, since they do not exploit the fact that the matrix in question is constant. In this work, we devise a strategy based on the time-finite element that exploits this fact, and thus results in an extremely efficient strategy that requires the solution of a linear system of equations just once at the beginning of the algorithm. Moreover, since the matrix to be inverted is well-conditioned even when the constant matrix is ill-conditioned, the strategy yields very reliable results as demonstrated by means of several challenging examples. The approach outlined here is easily generalized to the case where the matrix is time dependent and where there is a time-dependent forcing function. Full article

Ensuring input-to-state stability (ISS) of networked control systems under simultaneous mode switching and cyber attacks is a challenging open problem, since the asynchronous interplay among continuous dynamics, event-triggered impulses, and aperiodic denial-of-service (DoS) blockages has not been addressed in a unified nonlinear framework. This paper establishes such a framework by integrating a mode-dependent event-triggered mechanism (MDETM) with the admissible edge-dependent average dwell time (AED-ADT) approach for continuous-time nonlinear impulsive switched systems. Under mild Lyapunov-based conditions, rigorous sufficient conditions are derived that (i) guarantee a strictly positive uniform inter-event lower bound $\underline{\Delta }>0$, and (ii) establish global ISS with an explicit exponential decay rate $\eta >0$ that quantifies the trade-offs among the AED-ADT limit, the DoS frequency/duration parameters $({\tau }_D,{\tau }_d)$, and the triggering coefficients $(a_k,d_k)$. The nonlinear framework is further specialized to linear systems, yielding tractable Linear Matrix Inequality (LMI) criteria. Numerical validation on a two-subsystem linear impulsive switched system confirms the theoretical predictions: the LMI solver returns $\eta =0.451$, a Zeno-free lower bound ${\Delta }_1=0.0103 \mathrm{s}$, and the state converges from $x\left(t_0\right)={[100,-100]}^{\top }$ to $\parallel x\left(10\right)\parallel =0.0011$ under an average DoS duty cycle of approximately $15\%$. Full article

Tritium breeding blanket is a key component of future fusion power plants, and its performance depends on the selection, fabrication, and qualification of lithium-based ceramic material. Among the proposed lithium ceramics materials, the main candidates for ceramic breeders are lithium orthosilicate (Li₄SiO₄) and lithium metatitanate (Li₂TiO₃). These advanced ceramics and their biphasic composites are the leading candidates due to their high lithium density, favorable tritium breeding ratio (TBR ≈ 1.15–1.25 with Be₁₂Ti multiplier and 90% ⁶Li enrichment), and robust thermo-mechanical behavior within the 200–900 °C operational window of helium-cooled pebble bed (HCPB) blankets. This review provides an engineering-oriented assessment covering fabrication routes (solid-state, hydrothermal, melt-based, drip casting, powder injection molding, microwave sintering, and digital light processing additive manufacturing); microstructure–property relationships and performance under neutron irradiation; and tritium generation, retention, and release as functions of chemical composition, defect structure, and operating temperature. Induced radioactivity of Li-based ceramics and key impurity elements is quantified using activation formalisms applied to WWR-K reactor conditions, providing guidance for raw-material selection and waste-management assessment. Authors’ original contributions include (i) an empirical model of pebble crush load vs. biphasic composition (R² > 0.99); (ii) two universal semi-empirical kinetic models (exponential growth and non-linear strength degradation, R² = 0.97–0.99) for nine structural and mechanical parameters of Li₂TiO₃ under He²⁺ and H⁺ irradiation; (iii) a consolidated table of Arrhenius tritium diffusion parameters from reactor experiments and DFT; and (iv) an induced radioactivity calculation for the biphasic system with two-exponential post-irradiation decay analysis. The review identifies biphasic Li₄SiO₄–Li₂TiO₃ composites with ~30 ± 5 mol.% Li₂TiO₃ as particularly promising and formulates specific data gaps and modeling needs for the reliable deployment of ceramic breeder pebbles in helium-cooled fusion blanket systems. It should be specifically noted that Li₄SiO₄ pebbles fabricated via the melt method, as an example, typically exhibit exceptionally high densities, generally exceeding 90% of the theoretical density (TD). Building on the calculation of induced radioactivity, it is crucial to consider the microstructural distribution of highly radioactive nuclides (e.g., Co, Mn) within the ceramic matrix. If these impurities segregate at grain boundaries rather than being homogeneously distributed, there is a potential pathway to develop targeted wet-chemical methods, such as selective acid leaching, to remove these impurities post-irradiation, thereby lowering the waste disposal classification. Full article

The pathological mechanism of autism spectrum disorder (ASD) exhibits dual heterogeneity: abnormal local energy metabolism and brain-wide high-order topological failure. To synergistically characterize these complex signals captured by advanced neuroimaging sensors, we propose the Quantum-Enhanced Graph Convolutional Wide Residual Network (QG-WRN), a modality-specific, decoupled parallel dual-stream architecture. In the classical branch, to accurately capture the spatial distribution of local metabolic abnormalities, we employ a wide residual network (WRN) to extract amplitude of low-frequency fluctuation (ALFF) features, leveraging its expanded feature channels to effectively mine regional neurodynamic properties. Furthermore, to overcome the representational bottlenecks of classical linear operators in parsing hidden, long-range network connections, we introduce quantum computing, exploiting its exponentially expansive state space and intrinsic low-parameter regularization mechanism. Guided by these properties, the quantum branch utilizes a variational quantum graph convolutional (QGCN) module—featuring a trainable circular encoding strategy and a hardware-efficient 4-qubit configuration—with a 2-layer nested message passing structure to process the functional connectivity (FC) matrix, harnessing quantum interference in Hilbert space to parse complex topology while effectively mitigating overfitting on small-sample medical data. A unified training scheme achieves full-dimensional fusion of node activity and topology. Achieving 68.49% accuracy, our method outperforms 10 classic and recent new baselines, providing a powerful computational intelligence tool for sensor-based ASD clinical diagnosis. Furthermore, interpretability analysis successfully maps core disease hubs to standard AAL116 atlas coordinates, providing a powerful tool for computationally aided ASD diagnosis. Full article

Evolutionary Strategies (ESs) are optimization metaheuristics largely adopted in Evolutionary Computation (EC). Since their introduction in the early 70s, researchers in the field have attempted to improve the efficacy of these algorithms. The most advanced ESs, such as the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) and Exponential Natural Evolution Strategies (xNESs), make use of covariance matrices storing relationships between parameters to be optimized, which enable the algorithms to fasten the search in the solution spaces. However, the computational cost of calculating covariance matrices linearly scales with the number of parameters. Recently, the OpenAI Evolutionary Strategy (OpenAI-ES) emerged as an effective ES in different domains, thanks to the parameter information stored in two momentum vectors. Furthermore, OpenAI-ES gains an advantage from the usage of symmetric sampling and weight decay techniques. In this work, I delve into the application of symmetric sampling and weight decay on CMA-ES, xNES and Separable Natural Evolution Strategies (sNESs), with the aim to improve their performance in domains in which they get stuck in local minima outcomes. Specifically, I propose three novel variants for each ES and verify their efficacy with respect to the PyBullet halfcheetah and hopper robot locomotion problems, and two collective tasks (i.e., swarm aggregation and swarm foraging). The findings reveal that symmetric sampling produces performance enhancements in all the domains, whereas the effect of weight decay varies across the considered problems. Furthermore, symmetric sampling allows ESs to keep parameter size limited, which is paramount in these scenarios. This research identifies techniques enhancing the success of modern ESs, proposes several ES variants, and discusses the relationship between algorithmic performance and task properties. Full article

This paper introduces and investigates a novel two-parameter sequence, termed the biparametric Appell extension (B-App-Ex) and denoted by ${\mathcal{B}}_n(x;\lambda ,\alpha )$. Standard classical Appell sequences often lack sufficient structural parameters, which can limit their operational flexibility in certain advanced spectral schemes. To address this limitation, we construct an enhanced operational framework by integrating a binomial structural kernel ${(1+w)}^{\lambda }$ with a linear exponential scaling $e^{\alpha xw}$ entirely within the Appell class. We provide a rigorous logical deduction of the fundamental properties of this sequence, including its explicit power series representation, a characteristic three-term recurrence relation, and a governing second-order differential equation (DEq.). A significant contribution of this work is the establishment of analytically exact connection and inverse connection formulas between the B-App-Ex basis and various classical orthogonal polynomial (COP) families. Numerical verification via a collocation-based projection framework demonstrates that these algebraic kernels achieve near-machine epsilon precision (≈${10}^{-15}$), remaining stable even for high-order approximations. Furthermore, by isolating the dilation factor $\alpha$, we establish an $O\left(N\right)$ computational complexity that offers a reduction in latency by approximately two orders of magnitude compared to classical matrix-based transformations. The results demonstrate that the proposed biparametric (Bip.) extension offers a versatile and highly optimized analytical template for modeling complex dynamic systems where structural shifting and spatial scaling must be tuned simultaneously. Full article

Fractured coal in the residual-strength stage is a primary medium for gas migration and drainage in deep mining areas. To investigate the hydro–mechanical seepage response of post-peak fractured coal under constant-pressure-difference conditions, triaxial CO₂ seepage tests were conducted on coal specimens collected from the Xinyuan Coal Mine. A Weibull-based damage constitutive model was established to characterize the confining-pressure-induced hysteresis in the damage-evolution path. The flow-rate evolution and Reynolds number analysis indicated that gas flow remained within the linear Darcy regime. A controlled-variable analysis was used to examine the competing effects governing permeability evolution. Mechanical compaction induced an exponential decrease in permeability, whereas the decrease in permeability with increasing pore pressure was interpreted, within the proposed model framework, as the combined effect of possible adsorption-induced matrix swelling and weakened gas slippage. To address the limitations of conventional constant-slip-factor models, a pressure-dependent slip modulation coefficient was introduced into a composite permeability equation incorporating effective stress, adsorption-related deformation, and dynamic gas slippage. Global nonlinear fitting yielded R² = 0.97 and an RMSE of 0.1909, with the residuals generally distributed around zero, supporting the fitting reliability of the model within the investigated stress–pressure range. Response-surface analysis identified mechanical compaction as the dominant controlling mechanism, while adsorption-related deformation and gas slippage acted as secondary correction mechanisms. The proposed framework provides a quantitative basis for distinguishing the mechanical and fluid-related effects governing permeability evolution in post-peak fractured coal. Full article