Search Results (952)

Stick–slip vibration, reversal, axial impact, and dynamic instability are major challenges in deep drilling operations and are closely associated with nonlinear bit–rock interaction. To investigate these phenomena, this study develops a nonlinear axial–torsional coupled dynamic model of a drill-string system by integrating rock surface morphology evolution with a Stribeck dry friction model. The drill string is discretized into a distributed lumped-parameter model with coupled axial and torsional degrees of freedom. A surface morphology matrix is introduced to simulate the rock-cutting process, while the Stribeck friction model is employed to characterise the nonlinear frictional behaviour at the bit–rock interface. Time-domain simulations, bifurcation analysis, and frequency spectrum analysis are performed to investigate the dynamic responses of the system. The results indicate that rock surface morphology evolution significantly influences the contact conditions and frictional behaviour at the bit–rock interface, and together with dry friction induces transitions among steady-state, multi-periodic, and chaotic motions. Stick–slip vibration is accompanied by axial impact, bit bounce, and a reduction in the dominant torsional vibration frequency. In addition, variations in both driving and frictional parameters can trigger dynamic instability and state transitions. The proposed model provides an effective framework for analysing nonlinear drilling dynamics and offers theoretical guidance for drill-string vibration suppression, drilling parameter optimisation, and efficient drilling in complex formations. Full article

Aflatoxins and fumonisins are two of the most prevalent and toxicologically significant mycotoxins contaminating global food supplies, particularly maize and groundnuts. Although several regulated mycotoxins contribute to food safety concerns, this review focuses on aflatoxins and fumonisins because they frequently co-occur in maize and maize products. Their widespread prevalence, distinct toxicological mechanisms, and combined health effects necessitate an integrated exposure and risk assessment. This review critically evaluates the current state of exposure assessment and its implications for human health. We examine the evolution of sample preparation techniques, highlighting the transition from traditional liquid–liquid extraction to advanced approaches such as QuEChERS and green extraction technologies that can handle the divergent physicochemical properties of lipophilic aflatoxins and hydrophilic fumonisins. Analytical methods are compared, from the robust but limited HPLC-FLD to the multi-analyte capabilities of LC-MS/MS and the emerging potential of aptamer-based biosensors. Furthermore, the review addresses the critical challenge of modified mycotoxins that evade routine detection yet may contribute to total toxicity. By synthesizing data on biomarkers of exposure and the mechanisms of co-exposure, we discuss the complex interplay between these toxins in the etiology of hepatocellular carcinoma and neural tube defects. The review concludes that mitigating the public health burden of mycotoxins requires a holistic strategy that integrates HRMS for non-targeted analysis with human biomonitoring to capture the accurate individual-level exposure. Full article

Large language models (LLMs) built upon the Transformer architecture have achieved remarkable performance in natural language understanding, text generation and logical reasoning, while their internal working mechanisms remain poorly interpreted. This paper establishes a systematic mathematical analysis framework tailored for decoder-only Transformer LLMs, based on linear algebra, tensor analysis, probability theory, information theory, optimization dynamics and geometric deep learning. We conduct rigorous mathematical modeling and theoretical deduction on core modules including word embedding, position encoding, self-attention, feed-forward networks, training optimization and generalization reasoning, and explore the mathematical nature of semantic representation, contextual correlation, knowledge storage and logical inference within models. In this paper, we strictly distinguish between classic established Transformer theories and our original mathematical derivations and conclusions. Distinct from existing fragmented theoretical studies, this work presents six targeted novel contributions beyond conventional Transformer theories: (1) we construct the first full-process unified mathematical framework covering all core modules and the entire lifecycle of Transformer-based LLMs; (2) we provide strict mathematical proof to verify that single-head self-attention is essentially a kernel weighted average operation in reproducing kernel Hilbert space and derive the low-rank and sparse properties of attention weights; (3) we establish a high-dimensional non-convex optimization dynamics model for pre-training and mathematically prove that model training converges to flat local minima; (4) we derive a tighter upper bound of generalization error and quantify the quantitative relationship among model parameters, sequence length, training data scale and generalization performance; (5) we characterize the latent space as a low-curvature smooth Riemannian manifold and model logical reasoning as geometric transformation on this manifold; (6) we design multi-group controlled experiments on mainstream datasets to quantitatively validate all above theoretical conclusions. This paper further summarizes the inherent mathematical limitations of current Transformer LLMs and proposes feasible theoretical optimization paths, referring to state-of-the-art research published from 2021 to 2026. The outcomes of this research can provide solid mathematical theoretical support for improving model interpretability, optimizing network structures and boosting practical performance, and facilitate the transition of LLM research from empirical engineering practice to theory-driven development. Full article

Traditional tunable Bragg waveguide grating filters, which rely on thermo-optic or carrier effects, often face limitations such as high energy consumption, low tuning efficiency, and difficulty in achieving independent multi-parameter control. To overcome these bottlenecks, this work proposes a novel optical waveguide filter based on the heterogeneous integration of silicon nitride and the phase-change material Sb₂Se₃. The device leverages the substantial refractive index contrast between crystalline and amorphous states of Sb₂Se₃ to construct a programmable Bragg grating within the thin film layer. This is realized through laser-induced phase transition method, enabling nonvolatile manipulation of the light field. Simulation results indicate that the independent tuning of central wavelength over 19.2 nm range was achieved by adjusting the grating width and ripple width simultaneously. Likewise, the extinction ratio could be independently controlled over 22.3 dB through coordinated adjustments of the grating length and position shift. Beyond its tuning capabilities, the proposed device theoretically exhibits exceptional performance characteristics, including an ultra-low insertion loss of 0.1 dB and strong side lobe suppression. These advantages highlight the potential of this approach to provide a low energy consumption, multifunctional solution for integrated photonic devices, offering a promising pathway for the next generation of programmable photonic integrated circuits. Full article

This study proposes an adaptive multi-mode graph search algorithm that integrates spatial previewing with terminal analytics to address node proliferation and terminal oscillation in path planning for four-wheel independent steering (4WIS) vehicles under complex, low-speed conditions. By employing line-of-sight checking and the Douglas–Peucker algorithm to extract the environmental topological skeleton, the proposed method generates Predictive Spatial Profiling (PSP) fields that precisely quantify channel safety margins. Departing from conventional soft-weight arbitration, a dynamic driving state machine leverages these rigid spatial constraints to deterministically prune redundant expansion branches—including Ackermann steering, crab steering, and in-place rotation—prior to node generation. Furthermore, a comprehensive cost function incorporating a mode-switching penalty and a gradient-heading heuristic is formulated to accelerate search convergence. To circumvent reliance on traditional empirical distance thresholds, a topology-triggered, multi-dimensional terminal analytical strategy is introduced, enabling a seamless transition from discrete search node expansion to continuous curve generation near the target. Extensive simulations demonstrate that the proposed algorithm reduces both the node expansion scale and optimization time by over 80% compared with conventional unconstrained methods, while effectively mitigating chaotic motion-mode transitions. Ultimately, integrating environmental spatial dimensionality reduction with terminal analytics yields a highly efficient and smooth global path-planning solution for 4WIS vehicles. Full article

This study presents the prototype manufacturing and experimental validation of a 1 kW, 750 rpm three-phase outer-rotor permanent magnet-assisted synchronous reluctance motor (PMASynRM) designed for in-wheel electric vehicle applications. The work is based on a previously reported electromagnetic design and finite element method (FEM)-based optimization framework and focuses on the physical implementation and experimental evaluation of the proposed motor. The prototype was manufactured using M470-50A grade electrical steel laminations and arc-shaped N35H NdFeB permanent magnets embedded within a three-barrier transversally laminated anisotropic rotor structure. A custom-built experimental test bench consisting of the PMASynRM prototype, a PMSM generator with a controllable resistive load bank, a torque transducer, and a precision power analyzer was developed to evaluate motor performance under controlled operating conditions. Experimental investigations were carried out under four steady-state load conditions—no-load, 13 Nm, 20 Nm, and 26 Nm—as well as during dynamic stepwise load transitions representative of in-wheel drive operation. The measured results show good agreement with FEM predictions, with a maximum efficiency of 90.55% at nominal load and efficiency values remaining above 87% under overload conditions up to 26 Nm. Minor differences between simulation and experimental results are mainly associated with mechanical friction, bearing losses, and manufacturing tolerances that are not fully captured in the numerical model. The study provides experimental validation of an outer-rotor PMASynRM prototype under multi-load steady-state and dynamic operating conditions for in-wheel electric vehicle applications. Full article

In large-scale fluid transport systems, distributed pump and valve stations must coordinate their operations to prevent overpressure while minimizing energy use and control effort. This paper presents a communication-aware, game-theoretic coordination framework in which stations act as rational agents that iteratively adjust operating setpoints based on locally computed utilities. Existing station-level pressure controllers regulate local pressures and flows, while a slower supervisory negotiation layer governs inter-station coordination using steady-state hydraulic surrogates derived from pump affinity laws and pipeline loss relationships. The proposed framework does not rely on centralized optimization or exhaustive enumeration of strategies. Instead, stations update setpoints sequentially, evaluating incremental changes in utility to determine beneficial adjustments and detect equilibrium conditions. Cooperative behavior emerges naturally when communication is available, enabling stations to internalize the hydraulic impact of their actions on neighboring stations. When communication is lost, the system transitions seamlessly to a non-cooperative mode in which each station optimizes its local objective while maintaining safe operation. Simulation studies conducted on a multi-station pipeline with mixed actuator types demonstrate measurable performance improvements over fixed-setpoint operation. Cooperative coordination reduces total system energy usage from 39.6 MW to 38.8 MW while increasing average control valve openness from 60.4% to 63.7%. Non-cooperative operation converges more rapidly but results in higher energy consumption (39.2 MW) and greater valve throttling. Under partial communication loss, the system preserves near-cooperative energy performance (38.8 MW) with a modest increase in convergence time, demonstrating robustness to degraded communication. Across all simulated scenarios, the iterative game converged to stationary operating points consistent with Nash-equilibrium behavior in non-cooperative settings and Pareto-stationary solutions in cooperative communication settings. Full article

Background/Objectives: University students commonly experience psychological distress driven by academic demands, social transitions, and financial pressures. Mindfulness-based interventions have emerged as scalable approaches to improve mental health. However, evidence comparing their effectiveness across delivery formats remains limited. This systematic review aimed to evaluate the effectiveness of mindfulness-based interventions in reducing stress, anxiety, and depression and to compare outcomes across in-person, digital, and hybrid modalities. Methods: This review followed PRISMA 2020 guidelines and included randomized controlled trials (RCTs) published between January 2020 and December 2025 on mindfulness-based interventions among university students aged 18 years and older. Intervention duration ranged from 3 days to 12 weeks, with most lasting 4 to 8 weeks, and outcomes included validated measures of stress, anxiety, or depression. Literature research was conducted in PubMed, PsycINFO, CINAHL, Scopus, and Web of Science, and two reviewers independently screened studies, extracted data, and assessed methodological quality using the Joanna Briggs Institute checklist. Results: A total of 24 RCTs were included, with the highest representation from the United States and China (n = 4 each), followed by the United Kingdom and Canada. Mindfulness-based interventions demonstrated consistent reductions in depression and generally positive effects on anxiety, while effects on stress were more variable. Digital interventions demonstrated effectiveness comparable to in-person programs, though outcomes varied by intervention structure and level of guidance. Conclusions: Mindfulness-based interventions are effective in improving mental health among university students, particularly for depression and anxiety. Multi-week programs and guided digital delivery appear to enhance effectiveness and scalability. Full article

Background: Metastasis and poor chemotherapy response have stagnated therapeutic progress in osteosarcoma (OS) for the past three decades. Defining the transition from localized to metastatic OS before overt dissemination is fundamental for improving survival. However, effective early diagnostic tools remain scarce, largely due to limited exploitation of the metastasis-associated tumor microenvironment’s own record of prior environmental and stress exposures encoded in cell-intrinsic transcriptional states. Here, we employed a supervised machine learning framework with iterative resampling and multi-stage model selection to identify molecular markers associated with metastasis in osteosarcoma and to develop a computational signature, Auto-RS. Methods: Transcriptomic and clinical data from 139 OS patients with ≥5 years of follow-up were analyzed. A LASSO–Cox framework was applied to derive a gene expression-based risk score, Auto-RS, from which a nomogram integrating age and sex was generated for individualized prognosis. Model interpretability was assessed across six independent single-cell OS patient datasets, and drug sensitivity predictions were inferred by integrating Auto-RS with the Precily algorithm to uncover actionable therapeutic vulnerabilities. Results: Auto-RS, constructed from the expression of four autophagy genes (BNIP3, MYC, PEA15, and SAR1A), served as an independent prognostic factor for overall survival (HR = 1.091; 95% CI, 1.047–1.136; p < 0.001). Time-dependent ROC analysis showed that Auto-RS was the most accurate single predictor (AUC = 0.88), exceeding metastasis (0.83), sex (0.45), and age (0.39). A basic prognostic model (BpM) incorporating metastasis status yielded a C-index of 0.741 (95% CI, 0.679–0.803). The addition of Auto-RS (CpM) improved discrimination (C-index = 0.788; 95% CI, 0.731–0.845), whereas a model without metastasis information (ApM) retained predictive ability (C-index = 0.709; 95% CI, 0.640–0.778). Single-cell analysis confirmed that Auto-RS features aligned with known metastatic trajectories, reflecting the transition from proliferative to invasive tumor states and highlighting coordinated programs among cancer-associated fibroblasts and immune cells. Drug sensitivity integration through Precily identified gemcitabine and cytarabine as FDA-approved agents predicted in silico to show greater sensitivity in the high-risk subgroup. Conclusions: We identified autophagy-mediated transcriptional ‘stress fingerprints’ that are tightly associated with OS metastasis. The Auto-RS signature, composed of BNIP3, MYC, PEA15, and SAR1A, enables early therapeutic stratification of patients independent of overt metastatic status. Moreover, Auto-RS delineates key molecular underpinnings of OS metastasis at single-cell resolution. As a practical laboratory tool, Auto-RS may represent a step toward improved risk stratification, where advances in metastasis prediction and therapeutic guidance converge to improve outcomes in OS. Full article

Ion channels are validated drug targets, but they remain difficult to study as their pharmacology is influenced by rapid gating, conformational state transitions, cell-type-specific expression, and narrow safety margins. Recent advances in cryo-electron microscopy, structure-based in silico screening, machine-learning-guided prioritization, optical high-throughput screening, automated patch-clamp electrophysiology, and human-relevant organoid or microphysiological system (MPS) models are transforming this field. In this expanded review, we examine how these modalities can be integrated into a hybrid discovery pipeline that begins with computational triage, proceeds through scalable functional screening and state-aware electrophysiological validation, and concludes with multi-channel safety de-risking and translational analysis in complex human models. We also discuss disease-associated channel remodeling in cancer and inflammatory disorders, with an emphasis on transient receptor potential channels, voltage-gated potassium channel 1.3 (Kv1.3), Piezo channels, transmembrane protein 16A/anoctamin-1 (TMEM16A/ANO1), chloride channels, and proarrhythmic safety risks. Additionally, we highlight unresolved challenges, including bias in artificial intelligence models, incomplete conformational sampling, assay interference, organoid heterogeneity, and regulatory acceptance of MPS platforms. This review proposes a staged decision framework in which computational prioritization, scalable functional screening, direct electrophysiological confirmation, safety pharmacology, DMPK assessment, and disease-relevant human models serve as complementary filters rather than competing platforms for the identification of selective and translatable ion-channel therapeutics. Full article

Accurate identification of drilling operation states is essential for improving drilling efficiency and operational safety. However, existing methods often suffer from limited temporal feature extraction capability, weak cross-well generalization, and insufficient model interpretability. To address these issues, this study proposes a drilling-state recognition framework based on MultiHead-BiLSTM and low-rank adaptation (LoRA) transfer learning. The MultiHead-BiLSTM model combines multi-head attention with bidirectional long short-term memory to capture both critical temporal segments and global sequential dependencies in drilling time series data. To improve cross-well adaptability while reducing training computational cost, a parameter-efficient LoRA fine-tuning strategy is introduced within the transfer learning framework. In addition, SHAP-based feature attribution and attention visualization are employed to enhance model interpretability. Experimental results show that the proposed method achieves an accuracy of 95.11% and an F1-score of 94.00%, outperforming LSTM, GRU, BiLSTM, and Transformer baselines. The LoRA-based transfer strategy reduces the cross-well error rate to 1.91%, compared with 8.79% for direct transfer and 4.48–5.39% for partial-layer freezing methods. Interpretability analysis qualitatively suggests that bit depth, weight on bit, and block position contribute strongly to drilling-state discrimination, while attention visualization qualitatively suggests that the model tends to focus on operational transition periods. The proposed framework provides an effective and computationally efficient solution for intelligent drilling-state recognition and cross-well deployment. Full article

Coordinating agents in dynamic and uncertain environments remains a fundamental challenge in multi-agent systems (MAS) research, particularly in contexts where the composition of agent organizations directly affects overall system performance. While significant effort has focused on task allocation and individual agent planning, predicting the systemic impact of organizational changes and selecting optimal organizational structures under uncertainty remain less explored in MAS. This paper addresses this challenge by introducing a decision-making framework that models structural reorganization as a Markov Decision Process (MDP), where actions represent organizational structures rather than individual agent behaviors, and organizational selection is guided by the anticipated impact on the overall system state. The proposed model captures the stochastic dynamics of multi-agent intervention and diverse agent capabilities through a probabilistic transition function, while a reward function guides the selection of coalition structures that maximize operational effectiveness. The framework is solved using value iteration and evaluated on the RoboCup Rescue simulation platform. Results show that the derived optimal policy identifies, at each decision step, an appropriate coalition structure that reduces system degradation while efficiently utilizing available agents. Full article

This study investigates the complex and nonlinear interaction between the dual circulation paradigm and high-quality full employment. Moving beyond the limitations of conventional static partial equilibrium frameworks, the analysis conceptualizes this relationship as a system of three interrelated feedback loops. Drawing on system dynamics (SD) theory, a set of nonlinear differential equations is developed, with model parameters calibrated using macroeconomic data from 2010 to 2025. The simulation results yield three main findings. First, international trade, cross-border investment, and technological exchange jointly form a core reinforcing feedback loop that underpins the mutually beneficial interaction between domestic and international circulations. Second, the integrated development of education, technology, and human capital emerges as a critical state variable for overcoming the persistent trade-off between employment quantity and quality. Third, the interplay between horizontal market expansion and vertical technological advancement constitutes a dual driving mechanism that facilitates the system’s transition toward a higher-level equilibrium, with multi-factor interactions generating pronounced nonlinear multiplier effects. Overall, the study provides a quantitative basis for designing adaptive and targeted employment policies within the dual circulation framework. Full article

The fractional Whitham–Broer–Kaup model governs nonlinear wave propagation in memory-dependent media, including porous structures, viscoelastic fluids, and irregular seabeds, yet the full dynamical spectrum from quasi-periodicity to deterministic chaos, the role of stochastic forcing, and reliable identification from noisy data remains insufficiently explored, particularly how the fractional order $\beta$ influences these regimes. This study addresses these gaps through a comprehensive, multi-method dynamical analysis of a representative nonlinear oscillator embodying key FWBK features. Three-dimensional attractor visualizations, return maps, and surrogate data tests demonstrate a transition from quasi-periodic toroidal attractors to fully developed chaos via torus breakdown, confirming that observed complexity originates from deterministic nonlinearity. Poincaré sections reveal multistability and KAM-type structures, where coexisting attractors depend on initial conditions, while increasing noise progressively disrupts coherent dynamics. The OGY control method effectively stabilizes unstable periodic orbits across chaotic regimes with minimal perturbation, and Lyapunov analysis indicates that stochastic forcing attenuates chaos while enhancing dissipation. The Fokker–Planck framework shows that noise reshapes probability landscapes, driving transitions from unimodal to bimodal distributions. Comparative analysis of SINDy, JMAP and VBA highlights trade-offs in interpretability, computational efficiency, and uncertainty quantification, while an integrated Bayesian–PCE–Sobol approach quantifies parametric uncertainty and reveals time-dependent sensitivity variations. Additionally, the overlapping of soliton solutions extracted via the enhanced modified Sardar sub-equation method reveals structural relationships among soliton families and their stability under interaction. Soliton branches that maintain high overlap under noise correspond to stable regimes, while those losing coherence indicate the onset of chaos. Furthermore, while the reduced dynamics in $\eta$-space are independent of $\beta$, the fractional order controls spatial compression and temporal scaling in physical coordinates, directly influencing observable wave localization. These results imply that fractional effects can modify chaos transitions, support controllability through OGY, and influence noise–instability interactions depending on $\beta$. This framework provides a robust, transferable methodology for analyzing and controlling nonlinear oscillatory systems under deterministic and stochastic conditions, with direct applications to FWBK-based models in coastal engineering, fiber optics, and quantum interference systems. Full article

Background/Objectives: The accuracy and reliability of automated clinical analyzers are fundamental to patient safety and effective medical decision-making. Traditional quality control (QC) methods, which rely on periodic manual calibration and reactive maintenance, are inherently limited by high latency and labor costs; furthermore, they fail to provide continuous, real-time monitoring. This paper introduces a novel hybrid ensemble learning framework for the automated quality inspection of medical devices through the analysis of time-series reaction curves. Methods: Our system integrates three heterogeneous anomaly detection paradigms: an Enhanced Dynamic Time Warping (DTW) detector for robust non-linear pattern matching, a Shape Template Matching (STM) detector that mimics expert clinical logic by analyzing morphological features in a normalized shape space, and a specialized Time-series Variational Autoencoder (TimeVAE) for deep representation learning. The outputs of these detectors are fused using a weighted ensemble strategy, which is specifically designed to prioritize the minimization of false negatives—a critical requirement in medical diagnostics. Results: We evaluate our framework on a comprehensive, multi-center real-world dataset comprising seven distinct biochemical assays. Experimental results demonstrate that our proposed method achieves superior performance, attaining a $0\%$ false negative rate on CRE and DBIL assays and outperforming all baseline methods on the other five datasets. An ablation study confirms the model’s robustness even with limited training data, and a comparative analysis against eight state-of-the-art baseline methods further validates the effectiveness of our domain-optimized ensemble approach. Conclusions: The system provides a robust, interpretable, and highly automated solution for transitioning from reactive maintenance to proactive, real-time quality assurance in clinical laboratories. Full article