Search Results (1,731)

Firms increasingly coordinate lot-sizing, distribution center (DC) location, and joint replenishment over time to reduce costs. This paper studies this integrated problem under the (R, S) policy with demand, which stochasticity varies from period to period. We build a model where only the timing of replenishment is the core decision; all else follows from it. To solve efficiently, we design a hybrid differential evolution algorithm with a random neighborhood search. Experiments show our algorithm outperforms eight benchmark methods in solution quality and speed. A sensitivity analysis reveals how key parameters affect the total cost, replenishment frequency, and the number of DCs. Higher ordering costs reduce replenishment frequency, and larger DC setup costs lead to fewer DCs. However, fewer DCs do not always lower the total cost—when dealers are geographically dispersed, more DCs can reduce the overall total system cost. These insights help managers balance the cost components in a supply chain network design. Full article

Reconciling economic growth with environmental sustainability and energy security is a defining challenge for resource-constrained emerging economies. This study examines whether Jordan follows the Environmental Kuznets Curve (EKC) and the Energy Kuznets Curve (EnKC)—two hypotheses positing that as an economy grows, its environmental degradation and energy consumption follow an inverted U-shaped curve in relation to per capita GDP—as counterparts to the original Kuznets curve. While these relationships have been investigated in cross-country settings, little attention has been given to individual emerging economies such as Jordan, where energy and environmental issues are among the most pressing challenges of the new century. The existence of EKC and EnKC curves is tested using a “time-varying parametric (TVP) framework”—specifically, the unobserved components model (UCM), utilizing annual data from 1980 to 2024. Further tests are carried out to validate the nonlinearity hypothesis using the variable-addition test and non-nested model selection tests. Moreover, we augment the EKC and EnKC by incorporating trade openness and urbanization as control variables. For robustness, we support the UCM results with the Dynamic OLS (DOLS) long-run estimator. The results support the EnKC across the entire battery of tests, with a turning point of roughly USD 4000–4650 depending on specification. For the EKC, the OLS quadratic estimation does not exhibit a clear inverted-U; however, once a stochastic trend (UCM) or appropriate covariates (Trade, Urban) are introduced, the inverted-U re-emerges with a turning point near USD 4149–4874. This study contributes novel empirical evidence on the EKC and EnKC for Jordan using a TVP framework. Whereas prior studies have explored the EKC in Jordan, this study systematically validates both the energy and environmental variants of the Kuznets curve using robust econometric strategies. The results offer valuable policy insights for sustainable development in Jordan and other resource-constrained emerging economies facing analogous development–environment trade-offs within international climate transition frameworks. Full article

Real-time kinematic (RTK) positioning in open-pit mining environments is critically compromised by non-line-of-sight (NLOS) signals and anisotropic multipath effects induced by pit walls, haul roads, and industrial infrastructure. Conventional elevation-dependent stochastic models fail to discriminate between geometrically favorable low-elevation satellites and those subject to directional obstruction, resulting in degraded ambiguity resolution and decimeter-level positioning errors that undermine safety-critical deformation monitoring. This paper presents an adaptive RTK positioning framework utilizing azimuth-integer-based elevation masking to explicitly model site-specific obstruction geometry. The proposed method discretizes the horizontal plane into 360 integer-degree sectors, extracts minimum elevation angles per sector from 24 h line-of-sight (LOS) data, and constructs a smoothed 360°mask profile via moving-window filtering. A virtual elevation-angle transformation is introduced to normalize satellite geometry relative to the local mask, enabling adaptive down-weighting of diffraction-susceptible observations within the stochastic model without requiring multi-day satellite repeat arcs or hardware modifications. The approach was validated using 54 h of BDS data collected at eight monitoring stations within the Wangjialing open-pit mine, China. Implementation of the mask model engendered a selective 8.1% reduction in satellite participation (15.66 to 14.39 satellites) while significantly enhancing observation quality. The ambiguity validation ratio improved by 19.5% (from 9.43 to 11.27 in the experimental project), and the fix success rate increased from 92.4% to 97.2% (exceeding the 95% reliability threshold at all stations). The RMS errors in the east, north, and up directions improved by 34.8% to 65.2%, 28.7% to 77.0%, and 44.8% to 70.8%, respectively, with the most dramatic gains observed at stations subject to severe azimuthal obstruction (e.g., ZDH6 vertical RMS: from 50.7 mm to 14.8 mm). By explicitly modeling anisotropic obstruction geometry through discrete angular sampling, the proposed method achieves sub-centimeter positioning accuracy and robust ambiguity resolution in challenging mining environments without additional hardware or empirical threshold tuning, offering a cost-effective solution for large-scale, real-time deformation monitoring systems. Full article

Behavioral authentication (BA) systems verify user identity claims based on unique behavioral characteristics using machine learning (ML)-based classifiers trained on user behavioral profiles. Although effective, ML-based BA systems face serious privacy threats, including profile inference and reconstruction attacks. This paper presents RUIP-BA (Renewable, Unlinkable, and Irreversible Privacy-Preserving Behavioral Authentication), a non-cryptographic framework designed for settings where computational resources may be limited. Random Projection (RP) maps behavioral profiles into lower-dimensional protected templates while approximately preserving utility-relevant geometry, and local Differential Privacy (DP) injects calibrated stochastic perturbations to provide formal privacy protection. The proposed design jointly targets the ISO/IEC 24745 requirements of renewability, unlinkability, and irreversibility. We provide complete algorithmic realizations for enrollment, verification, template renewal, unlinkability testing, and GAN-based adversarial privacy evaluation. We also introduce rigorous formal privacy derivations and proofs under explicit assumptions, including formal security games, information-theoretic theorem-level guarantees, Cramér–Rao lower bounds for irreversibility, full Jensen–Shannon divergence derivations for unlinkability, and a GAN Nash-equilibrium attack bound. Comprehensive dimensionality ablation across all three modalities confirms robust utility at compact template sizes, and an expanded analysis of the privacy–utility trade-off under varying $ϵ$ values is provided. Experiments on voice, swipe, and drawing datasets show authentication accuracy above $96\%$ while sharply limiting feature recoverability under strong GAN-based attacks. All reported FAR/FRR figures are single-session best-case estimates; cross-session longitudinal evaluation remains future work. RUIP-BA provides a scalable, mathematically grounded, and deployment-ready privacy-preserving BA solution. Full article

This is a narrative review that explores the development of non-implantable vestibular devices designed to address postural instability, particularly in aging populations and patients with vestibular hypofunction. It establishes that balance relies on complex sensory integration and that the functional decline of this system creates a significant medical need. Three principal technological strategies are examined: sensory substitution devices, galvanic vestibular stimulation (GVS), and immersive visual feedback systems. Sensory substitution devices, which convert balance data into auditory, tactile, or electrotactile cues, demonstrate significant promise. Examples like vibrotactile belts provide feedback that reduces postural sway, enhancing stability and patient confidence. Parallel to this, GVS—using electrical currents applied to the mastoids—emerges as a potent non-invasive method to modulate vestibular pathways, improving balance control and even inducing neuroplastic changes, especially with stochastic “noisy” signals. The most recently developed devices include augmented and virtual reality technologies that offer innovative visual feedback, creating enriched rehabilitation environments that accelerate recovery by promoting sensory reweighting and neural adaptation. This review concludes that while implantable prostheses are advancing, non-invasive devices offer versatile, affordable, and complementary solutions for balance restoration. The future success of non-invasive alternatives hinges on developing more sophisticated stimulation protocols that account for the complexity of natural movement and individual patient contexts, expanding therapeutic options for vestibular disorders. Full article

High-resolution three-dimensional (3D) radar imaging of non-coherent point target clusters faces significant challenges, particularly severe angular glint induced by the simultaneous presence of dual targets or co-channel interference (CCI) within the antenna mainlobe. Conventional monopulse systems often struggle to resolve such overlapping sources, particularly under conditions of high power disparity between signal components. To overcome the Rayleigh resolution limit, this paper proposes a polarimetric 3D imaging framework for three-channel monopulse radar by leveraging joint polarimetric-angular diversity. By exploiting the intrinsic instability of spatial parameter estimates induced by snapshot-to-snapshot echo envelope fluctuations, a cost function based on fluctuation minimization is constructed. Furthermore, an optimized oblique projection (OP) strategy is developed to decouple overlapped echoes in the joint domain, thereby effectively extracting stable angular features of non-coherent sources under various stochastic scattering scenarios (e.g., Swerling models). Extensive simulations demonstrate that, compared with traditional MPV, Seung, and Blair methods, the proposed approach consistently achieves superior estimation precision and robustness, especially in challenging scenarios characterized by low signal-to-noise ratios (SNR), limited snapshots, and restricted polarimetric diversity. Moreover, experimental validation using real-world data from a 45-m civilian vessel and an active non-cooperative radio frequency (RF) source confirms the practical effectiveness of the algorithm in resolving extended targets in the presence of strong non-coherent background emissions. This work provides a reliable solution for high-fidelity 3D imaging of point target clusters in environments characterized by dense targets and complex electromagnetic interference. Full article

This Perspective is dedicated to the memory of Professor Joseph Mark (Joe) Gani (1924–2016) and Professor Christopher Charles (Chris) Heyde (1939–2008), two scholars whose intellectual leadership profoundly shaped applied probability, mathematical statistics, and their interface with finance, insurance, and risk management. Their contributions extend beyond specific technical results to the development of research cultures grounded in probabilistic rigor, empirical relevance, and methodological transparency. We emphasize three enduring themes central to modern quantitative risk analysis. First, the systematic incorporation of heavy-tailed and non-Gaussian features in stochastic modeling, reflecting persistent empirical deviations from classical Gaussian assumptions in financial data. Second, the development of stochastic and time-series methodologies capable of handling dependence structures, including conditional heteroskedasticity and long-range dependence. Third, the principled integration of probabilistic modeling with data-driven and machine learning approaches, ensuring predictive performance is accompanied by interpretability and robustness. We situate these contributions within contemporary challenges in financial risk management, including systemic risk, environmental, social and governance (ESG) considerations, and climate finance. In particular, climate-related financial risks arise from both physical impacts (such as extreme weather events and long-term environmental change) and transition dynamics associated with the shift toward a low-carbon economy (including policy, technological, and market adjustments). These sources of risk introduce additional forms of dependence, nonlinearity, and model uncertainty, particularly in high-dimensional, data-rich settings. This Perspective highlights a forward-looking research agenda that preserves the foundational principles of applied probability while adapting them to modern financial systems characterized by real-time information flows and evolving risk structures. This legacy continues to shape how financial risk is modeled, measured, and understood in increasingly complex and interconnected environments. Full article

This paper is concerned with the problems of mean-square global dissipativity and global asymptotic stability for a class of stochastic fractional-order memristive BAM neural networks with leakage terms and mixed time-varying delays, including discrete delays and distributed delays. By using differential inclusion theory, stochastic analysis, matrix measure approach, and Lyapunov stability theory combined with linear matrix inequalities (LMIs), several new sufficient conditions are derived to ensure the mean-square global dissipativity and global asymptotic stability of the considered system. Compared with the existing results, the obtained stability and dissipativity criteria are less conservative due to the adoption of matrix measure and fractional-order differential inequalities. The proposed model simultaneously incorporates stochastic perturbations, memristive discontinuity, leakage effects, and mixed delays, which makes it more consistent with actual engineering scenarios such as pattern recognition and intelligent control. Finally, a numerical example is provided to demonstrate the effectiveness and correctness of the theoretical results. Full article

Reliable solar energy harvesting demands advanced control strategies capable of maintaining thermal precision despite inherent environmental unpredictability. This research addresses the critical challenge of temperature regulation in the solar furnace system, which is hindered by severe non-linearities and stochastic environmental uncertainties. The study aims to transition Fractional-Order PID (FOPID) control from theoretical design to reliable industrial application by accounting for the Uncertain Design Vector (UDV) during the tuning phase. A Reliability-Based Design Optimization (RBDO) framework is proposed, utilizing a hybrid Multi-Objective Imperialist Competitive Algorithm (MOICA) integrated with Monte Carlo Analysis (MCAR). This approach simultaneously optimizes the Maximum Sensitivity (Ms), the integral of Time-weighted Absolute Error (ITAE) and their sensitivities, while ensuring physical realizability through the FOPID structure. Crucially, the simulation results demonstrate that the RBDO-tuned FOPID design achieves optimal performance levels comparable to deterministic methods while significantly reducing the overall system sensitivity by 35% to 55% compared to both deterministic and literature-based methods (GA-FOPID and PSO-FOPID). The study concludes that integrating probabilistic reliability into multi-objective metaheuristics provides a robust control strategy for high-temperature solar facilities, effectively mitigating the performance degradation caused by real-world parameter fluctuations and ensuring consistent operational stability. Full article

This work develops a geometric and statistical framework for analyzing the structure of biological sequences and explores its implications for understanding the emergence and evolution of life. Motivated by questions concerning the transition from prebiotic chemistry to living systems, the quantification of negentropy in organic matter, and the distinction between random and biologically viable sequences, we introduce mathematical descriptors that measure deviation from linearity and related geometric irregularities of self-replicating macromolecules. These descriptors reveal a pronounced geometric separation between biological DNA and random sequences, underscoring the non-random structural organization characteristic of living systems. Using these descriptors, we compare a broad range of species across the Tree of Life and examine how geometric complexity varies between primitive and more advanced organisms. We further investigate whether these measures provide a natural way to compare organismal complexity, characterize the structure of viable sequence space, and identify potential constraints on evolutionary trajectories. The framework also offers an initial perspective on how natural selection and stochastic mutations may jointly influence genomic organization. Finally, we outline speculative connections between increasing geometric irregularity and the emergence of biological complexity, suggesting that such geometric transitions may offer insight into the origins of life and the theoretical limits of evolutionary development. Full article

The post-pandemic evolution of education involving mechatronics and machine learning has shifted the demand for robotic hardware from centralized laboratories to accessible laboratories in home environments. This paper presents a portable three-wheeled holonomic robotic platform designed for remote research and home office experimentation. The proposed system utilizes a modular design and low-cost philosophy comprising a custom embedded control system driven by an ESP32-WROOM microcontroller, which manages a closed-loop PID velocity controller using Hall effect feedback from three DC micromotors. In contrast, external nodes allow the reception, conditioning, and classification of 8-channel surface electromyography (sEMG) data sampled at 500 Hz. To address the non-stationarity and stochastic noise in raw sEMG signals, this study implements a hybrid Deep Learning (DL) architecture that complements 2D Convolutional Neural Networks (CNN) for spatial feature extraction with Long Short-Term Memory (LSTM) networks for temporal context awareness. This model decodes the neuromuscular intent of the user into real-time holonomic velocity vectors, achieving validation accuracies of 80.51% for horizontal movement, 84.86% for vertical translation, and 99.56% for the Fist/no-Fist state. By synthesizing advanced AI-based teleoperation with a portable design, this study establishes a scalable framework for the next generation of “laboratory-at-home” educational tools and research regardless of physical location. Full article

Accurate Remaining Useful Life (RUL) prediction is pivotal for the intelligent operation and maintenance of high-precision equipment. However, existing deep learning-based prognostic methods predominantly focus on point estimations and often overlook the non-Markovian characteristics and stochastic uncertainties inherent in complex mechanical degradation. To bridge this gap, this study proposes a novel uncertainty-aware hybrid prognostic framework by synergizing TCN–Transformer architectures with fractional Brownian motion (FBM). Specifically, a TCN–Transformer hybrid network is developed to adaptively learn a multi-scale drift function, effectively capturing both localized causal features and global long-range temporal dependencies. Concurrently, the FBM component is employed to model the diffusion process, explicitly accounting for the long-range dependence and inherent stochasticity of degradation. By leveraging the first hitting time (FHT) principle, an approximate analytical expression for the RUL probability density function (PDF) is derived based on an established approximation treatment for FBM-driven degradation processes, enabling robust uncertainty quantification. Experimental results on both the XJTU-SY bearing dataset and the servo tool holder power head system dataset demonstrate that the proposed method achieves superior predictive accuracy and reliable uncertainty quantification, thereby providing effective support for condition-based maintenance and intelligent decision-making. Full article

Fault-crossing ground motions, characterized by velocity pulses, permanent fault dis-placement, and non-uniform support excitation associated with fault rupture, may significantly affect the seismic performance of siphon bridges crossing active faults. This study investigates a long-span siphon arch bridge subjected to pulse-type fault-crossing ground motions. A unified stochastic ground motion model is developed by integrating nonstationary high-frequency components based on the evolutionary power spectrum with low-frequency pulse components represented by an improved Gabor wavelet, capturing forward directivity effects, permanent displacement, and differential support input at the two sides of the fault. A three-dimensional nonlinear finite element model is established in OpenSees using fiber-based beam–column elements, with hydrodynamic effects incorporated through the added mass method. Parametric analyses consider pulse phase angle (0–90°), amplitude (Mw 6.0–7.5), and frequency (0–1 Hz). Results indicate that structural responses decrease with increasing phase angle, with 0° being most unfavorable, high-lighting the dominant influence of permanent displacement. Resonance amplification occurs when pulse frequencies approach the fundamental modes of the pier (0.345 Hz) and deck (0.51 Hz), while the arch is particularly sensitive near 0.439 Hz. Water added mass reduces natural frequencies by 8–14% and significantly amplifies internal forces. These findings provide guidance for seismic design of fault-crossing siphon bridges. Full article

This paper addresses the existence of mild solutions and the approximate controllability of a class of higher-order Hilfer fractional semi-linear neutral stochastic differential equations with non-instantaneous impulses in Hilbert spaces. The system is driven by both fractional Brownian motion and Poisson jumps, thereby capturing long-range dependence as well as random discontinuities. By combining techniques from fractional calculus, stochastic analysis, and operator theory, we establish sufficient conditions for the existence of mild solutions. The analysis is carried out through the construction of suitable solution operator families and the application of Sadovskii’s fixed point theorem in an appropriate phase space framework. In addition, we investigate the controllability properties of the system and derive criteria ensuring approximate controllability of the underlying fractional neutral dynamics. The proposed approach relies on the structural properties of the higher-order Hilfer fractional derivative, estimates for stochastic integrals with respect to fractional Brownian motion, and compactness arguments adapted to non-instantaneous impulsive effects. The inclusion of Poisson jumps and neutral terms introduces significant analytical difficulties, which are overcome using refined resolvent operator techniques and fractional power estimates. An illustrative example is presented to demonstrate the applicability of the theoretical results. The results obtained generalize and unify several recent developments in the theory of fractional stochastic systems and provide a flexible framework for analyzing controlled dynamical models with memory, randomness, and impulsive behavior. Full article

Many of life’s biggest dilemmas can be summed up as a tension between holding on and letting go. The very language evokes a notion of intentionality which, for the most part, has evaded scientific understanding. How might we even get a window into it? Important insights have come from a seemingly simple task: wiggling one’s fingers to and fro to the beat of a metronome. As the metronome pace increases to some critical frequency, one coordinative pattern becomes unstable and switches spontaneously to another. Such transitions are typically preceded by critical fluctuations, a predicted feature of self-organization in complex, dynamical systems. Here we address the nature and source of these fluctuations, usually assumed to be: (1) random; (2) of external origin; and (3) of fixed magnitude. We performed an experiment in which participants were instructed to oscillate their fingers in either an in-phase or anti-phase pattern in time with a metronome and instructed them to either “hold-on” or “let-go” should they feel the pattern begin to change, yielding a 2 by 2 within-subjects design. We observed that as the metronome frequency was increased from 1.00 to 3.00 Hz, fluctuations in the relative phase between the fingers were significantly altered both by the starting coordinative pattern as well as the participant’s intention to “hold it on” or “let it go”. Specifically, the intention to hold on to the anti-phase pattern delayed the spontaneous transition to in-phase, an effect that was paired with increased fluctuations beyond the critical frequency. These observations were analyzed under the extended Haken–Kelso–Bunz (HKB) model which describes the non-linear stochastic dynamics of the order parameter (relative phase) as a gradient descent on a certain potential. Our analysis, in line with experimental results, suggests that intention transforms the HKB potential not only by stabilizing unstable coordination states but also (paradoxically) by increasing fluctuations around them. Such findings may offer new interpretative light on the relation between intention and fluctuations in the coordination dynamics of living things. Full article