Driveline infections remain a major complication of mechanical circulatory support (MCS) systems. This study evaluated the surgical implantation of a novel wireless transcutaneous energy transfer implant designed to eliminate driveline-associated infections by achieving durable cutaneous integration. Nine pigs were used across 4 trials to optimize implant geometry and surgical methods. Early prototypes caused skin necrosis due to excessive stretching, which was mitigated by increasing pocket size but led to seroma formation. A final implant design with reduced cross-sectional area and refined surgical approach eliminated both necrosis and seroma, achieving stable skin integration during a 14-day follow-up. Infections occurred in some cases, primarily attributed to limitations of the animal model. These findings demonstrate the feasibility of safe cutaneous integration of a wireless transcutaneous implant and establish the basis for long-term preclinical evaluation toward fully implantable ventricular assist device systems without percutaneous drivelines.

Ohmic heating (OH) enables rapid and uniform volumetric heating but remains challenging to model in heterogeneous food systems containing yield-stress matrices. This study proposes and validates, at bench scale, a simplified lumped circuit-analogy model to predict temperature evolution in a static multiphase configuration composed of potato puree (viscoplastic, non-convective) containing meatball inclusions. While CFD/FEM-based frameworks can provide detailed electro-thermal field resolution, their computational cost limits real-time or iterative process optimization. The proposed circuit-analogy model offers a rapid and physically consistent alternative for preliminary design and predictive analysis. The model was validated experimentally under different conductivity ratios and particle configurations using a 50 Hz, 40 V ohmic heating system. Conductivity matching reduced temperature gradients from 30–39 °C to 6–15 °C by eliminating electric-field shadowing that otherwise reduced local heating rates by ≈17%. The model reproduced puree temperatures with RMSE < 2 °C and, in the asymmetric configuration, conservatively under-predicted meatball core temperatures by up to ~10 °C at the end of treatment, while remaining robust to plausible variability in key thermophysical properties (electrical conductivity and volumetric heat capacity). Predictive scale-up simulations in a scaled-up chamber (19.6 × 10.6 × 12 cm; eight inclusions; 120 V), for which no pilot-scale experimental verification was performed, indicated improved heating uniformity (ΔT ≤ 6 °C) and attainment of the target core temperature for lethality (74 °C) without puree over-processing under the idealized inclusion arrangement considered. These results demonstrate that conductivity mismatch is the dominant driver of non-uniform OH in viscous, non-convective foods. Overall, the circuit-based framework provides a rapid, physically grounded, and computationally efficient tool to support preliminary process design and conservative safety assessment in multicomponent ready-to-eat systems. • A solid-like continuous phase limits bulk convection in multiphase foods. • A lumped‑element circuit framework captures conduction‑dominated heating. • Conductivity mismatch drives non‑uniform heating in heterogeneous foods. • Solid-inclusion arrangement modulates current focusing and heating uniformity.

Optimal control of reliability failure risk contagion dynamics in nuclear power systems

Hamiltonian neural networks (HNNs) represent a promising class of physics-informed deep learning methods that utilize Hamiltonian theory as foundational knowledge within neural networks. However, their direct application to engineering systems is often challenged by practical issues, including the presence of external inputs, dissipation, and noisy measurements. This paper introduces a novel framework that enhances the capabilities of HNNs to address these real-life factors. We integrate port-Hamiltonian theory into the neural network structure, allowing for the inclusion of external inputs and dissipation, while mitigating the impact of measurement noise through an output-error (OE) model structure. The resulting output error port-Hamiltonian neural networks (OE-pHNNs) can be adapted to tackle modeling complex engineering systems with noisy measurements. Furthermore, we propose the identification of OE-pHNNs based on the subspace encoder approach (SUBNET), which efficiently approximates the complete simulation loss using subsections of the data and uses an encoder function to predict initial states. By integrating SUBNET with OE-pHNNs, we achieve consistent models of complex engineering systems under noisy measurements. In addition, we perform a consistency analysis to ensure the reliability of the proposed data-driven model learning method. We demonstrate the effectiveness of our approach on system identification benchmarks, showing its potential as a powerful tool for modeling dynamic systems in real-world applications.

LIMA: Towards building a non-invasive and stealthy real-world adversarial attack model for traffic sign recognition systems.

A data-driven dynamic control strategy based on an enhanced random forest model for active air-cooled battery thermal management systems

A fluid vacation model for renewable-powered electric vehicle charging systems: Performance evaluation and cost-optimal battery sizing

Does frequency of use enhance ChatGPT user satisfaction? The role of perceived functional capabilities and AI interaction preference

The restoration efficiency can be significantly improved through the coordination of transmission and distribution systems. However, this process is hindered by challenges related to information aggregation, model complexity, and the uncertainty introduced by the penetration of renewable generation (RG). For this purpose, this paper proposes a novel distributed load restoration model for integrated transmission and distribution systems (ITDS) using the robust model projection method (RMPM). To achieve the distributed approach, the non convex restoration model for active distribution systems (ADSs) is first reformulated as a second-order cone programming (SOCP) problem using a sequential SOCP algorithm (SSA). The resulting high-dimensional SOCP model for ADSs is then projected into a low-dimensional space via a vertex-searching method (VSM). Next, the uncertainty of distributed RG in ADSs is considered, and the robust feasible region for the ADS restoration model is determined using the column and constraint generation (CCG) algorithm. This robust, convex feasible region is then integrated into the transmission system (TS) restoration model, which is formulated as a two-stage, three-level robust model considering the RG uncertainty. By adopting this restoration scheme, optimal coordination between ADSs and TSs is achieved with limited information exchange, thereby alleviating the communication burden. Moreover, the model's solvability can be ensured due to its low-dimensional, convex structure. Finally, the effectiveness of the proposed method, as well as its superiority over existing approaches, is validated through numerical experiments.

Stress is a physiological response mechanism that enables humans to react to perceived threats through a fight-or-flight response. While beneficial in acute situations, prolonged exposure to stress can lead to significant physical and mental health issues, making early and reliable detection essential. Although many existing approaches achieve high accuracy by relying on numerous physiological signals and features, such solutions are often unsuitable for Internet of Medical Things (IoMT) applications that increasingly rely on edge computing paradigms. In these scenarios, stress detection models must operate directly on resource-constrained devices with limited computational and energy budgets. Therefore, this work proposes a lightweight and efficient methodological framework for stress detection, specifically designed for edge-based IoMT deployment. Eight supervised Machine Learning (ML) algorithms were evaluated: Random Forest (RF), LightGBM, CatBoost, XGBoost, Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and a Multilayer Perceptron (MLP). All models were trained using Heart Rate Variability (HRV) and respiratory features extracted from the WESAD dataset. The proposed framework combines population-level training with subject-specific adaptation and evaluates model performance under progressive dimensionality reduction using subsets of 15, 10, 8, 6, and 4 features. The proposed two-stage framework demonstrates that subject-specific adaptation significantly improves stress detection performance. XGBoost achieved the highest balanced accuracy (95.1% ± 4.7%) using 10 features, outperforming the configuration with all 15 variables. Crucially, the study identifies a reduced set of 6 features as the optimal deployment configuration; despite its further reduced feature set, it showed no statistically significant performance loss compared to the 10-feature model (95% CI: -0.0078, 0.0068) and maintained a 99.6% probability of outperforming the best models from all other architectures evaluated. The results show that accurate and personalized stress detection is feasible using reduced feature sets, enabling efficient, interpretable, and real-time deployment of ML models in wearable and IoMT-based monitoring systems.

The accelerated rate of Industry 4.0 development has turned traditional manufacturing systems into highly networked, smart, and data-driven settings, thus making decision-making processes exceptionally complicated. Smart manufacturing systems are characterized by a number of conflicting criteria, interdependencies, and uncertainty, and thus require powerful and systematic decision-support tools. This paper is a systematic review of the use of multi-criteria decision making (MCDM) in smart manufacturing systems within the Industry 4.0 paradigm. A systematic literature review methodology is followed, which includes database selection, a keyword-based search, and inclusion and exclusion criteria based on the PRISMA framework. The analyzed literature is categorized into major areas of application, such as technology choice, supplier selection, production optimization, sustainability measurement, and risk management. Moreover, a comparative study of the popular application of MCDM techniques, including AHP, ANP, DEMATEL, TOPSIS, and hybrid methods, is conducted to outline their strengths and weaknesses and their applicability to various decision settings. The research points out key research gaps, such as the lack of full integration of artificial intelligence, inadequate treatment of uncertainty, and the absence of real-time decision frameworks. Lastly, possible future research directions are suggested, focusing on the creation of hybrid and AI-enhanced MCDM models for smart manufacturing systems. This review presents important lessons for researchers and practitioners who are interested in adopting effective decision-making models in Industry 4.0 settings.

Abstract We present a method for constructing dynamical models of stellar systems described by distribution functions and constrained by discrete-kinematic data. We implement various improvements compared to earlier applications of this approach, demonstrating with several examples that it can deliver meaningful constraints on the mass distribution even in situations where the density profile of tracers and the selection function of the kinematic catalog are unknown. We then apply this method to the Milky Way nuclear star cluster, using kinematic data (line-of-sight velocities and proper motions) for a few thousand stars within 10 pc from the central black hole, accounting for the contributions of the nuclear stellar disk and the Galactic bar. We measure the mass of the black hole to be 4 × 10 6 M ⊙ with a 10% uncertainty, which agrees with the more precise value obtained by the GRAVITY instrument. The inferred stellar mass profile depends on the choice of kinematic data, but the total mass within 10 pc is well constrained in all models to be (2.0–2.3) × 10 7 M ⊙ . We make our models publicly available as part of the Agama software framework for galactic dynamics.

Abstract Gravitational-wave astronomy has revealed that close binaries with compact companions are widespread. Long gamma-ray bursts (LGRBs) from massive star collapse face persistent challenges in achieving the rapid core rotation required by the collapsar model. Binary interaction via tidal spin-up offers a natural solution; recent population synthesis studies suggest a substantial fraction of LGRBs may originate from close binaries with a compact companion. In this scenario, supernova ejecta from the primary can be accreted by the companion, potentially launching a second relativistic jet after a delay set by the binary separation. We develop a comprehensive model for these double-jet systems, analyzing the dynamics of the second jet and its interaction with the first. The resulting observational signatures depend critically on the Lorentz factor ratio, the alignment angle, and the time delay. For aligned jets, two regimes arise: a fast second jet producing multiple gamma-ray triggers with distinct spectral/polarization evolution, and a slow second jet where its emission appears as an X-ray flare followed by an afterglow plateau from energy injection. For misaligned jets, the observed signal ranges from normal gamma-ray bursts (GRBs) with late-time radio structures to fast X-ray transients followed by off-axis rebrightening. These features have observational parallels in existing GRB data. High-resolution radio interferometry with SKA, time-resolved polarimetry with eXTP, and multiwavelength surveys with Einstein Probe and SVOM will test these predictions, providing constraints on the evolution of close massive binaries as progenitors of GRBs and gravitational-wave sources.

This study investigates the performance of goodness-of-fit tests for the ARA ∞ –PLP imperfect maintenance model, with a particular emphasis on entropy- and extropy-based test statistics. Test statistics are constructed using three different approaches: martingale residuals, probability integral transform, and information-based measures. Extensive simulation studies are conducted under several alternative hypotheses, including ARA 1 , ARA ∞ –LLP, QR, EGP, and Brown–Proschan models, to evaluate the empirical power of the proposed tests. In addition to numerical power comparisons, graphical analyses are employed to illustrate the behavior of the test statistics and to provide further insight into their sensitivity under different repair scenarios. The simulation results demonstrate that entropy- and extropy-based statistics generally outperform classical goodness-of-fit tests, particularly in detecting deviations from the null model under moderate and severe imperfect repair effects. The consistency observed between graphical patterns and numerical findings further confirms the robustness and interpretability of the proposed procedures. An application to a real dataset related to automobile failure times illustrates the practical effectiveness of the methodology and supports the suitability of the ARA ∞ –PLP model for real-world repairable systems.

The incorporation of artificial intelligence, multi-sensor perception, and cyber-physical control into mining operations offers tremendous opportunities for increasing productivity, safety, and sustainability. However, present frameworks focus on discrete subsystems rather than providing a unified, safety-constrained optimization method that has been verified in both surface and underground environments. This paper describes a scalable, hierarchical autonomous mining architecture that incorporates sensor fusion, edge intelligence, fleet coordination, and digital twin-based decision support. It is designed to operate in GNSS-denied conditions and extreme climatic constraints common to Nordic mining environments. A mathematical modeling approach formalizes vehicle dynamics, drilling mechanics, and multi-agent fleet coordination inside a safety-constrained multi-objective optimization formulation. The framework is validated using Monte Carlo simulation with uncertainty measurement, sensitivity analysis, and statistical hypothesis testing. The preliminary results show improvements over a typical baseline, with productivity increasing by approximately 24.3% ± 3.2%, energy consumption decreasing by 12.8% ± 2.5%, and safety risk decreasing by 48.6% ± 4.1%. A sensitivity study identifies localization accuracy, communication delay, and optimization weighting as the primary system performance drivers. The suggested framework serves as a reproducible and transferable reference model for next-generation intelligent mining systems, having direct applications to both industrial deployment and future research in autonomous resource extraction.

Strong partnerships between criminal courts and mental health providers are imperative to ensure justice for individuals involved in the forensic system. Yet these entities approach issues with different priorities, unique perspectives, and competing resource demands. The authors present a university's effort to facilitate communication among Alabama criminal courts, community providers, and the Alabama Department of Mental Health (ADMH). The authors identify specific obstacles ADMH faced while attempting to resolve the "competence crisis," that is, the long list of individuals mandated to restoration of competence to stand trial. Finally, the authors offer points of consideration for universities and mental health providers working in tandem with criminal courts.

Purpose The present article is dedicated to the analytical construction of soliton solutions for the third-order perturbed nonlinear Schrödinger equation having the Kudryashov’s law of selfphase modulation form in the absence of the group velocity dispersion term. Such a formulation is especially relevant in the context of ultrashort pulse propagation through nonlinear optical media, where higher-order dispersive and nonlinear impacts dominate. Design/methodology/approach To retrieve soliton solutions, the new Kudryashov method is employed, which has been verified to be an efficient and systematic technique for solving nonlinear evolution equations. This method facilitates the reduction of the governing partial differential equation to a solvable nonlinear ordinary differential equation through an appropriate transformation. Findings The study yields both bright and dark soliton solutions. Soliton solutions obtained under this framework are expressed in closed form, and their validity is confirmed through direct substitution. Their qualitative properties and physical dynamics are explored through comprehensive visualizations, including 2-dimensional plots, contour maps, and 3-dimensional surface diagrams. Social implications text. Originality/value The results contribute to the understanding of nonlinear pulse dynamics in the absence of the group velocity dispersion term regimes and indicate the applicability and robustness of the new Kudryashov method for handling complex nonlinear models in mathematical physics and optical communication systems.

Phase change material (PCM)-based latent heat storage (LHS) systems help address the mismatch between renewable energy supply and thermal demand. However, their practical implementation is constrained by the strongly nonlinear and multiphysics nature of phase change, which makes high-fidelity simulations and real-time applications computationally expensive. This review examines mathematical reduced-order modeling (ROM) as an effective strategy to overcome this limitation by combining physics-based simplifications, projection methods, interpolation techniques, and data-driven models for PCM-based LHS systems. While physical simplifications (such as dimensional reduction and effective property approximations) represent an important first layer of model reduction, the primary focus of this work is on the mathematical ROM methodologies that operate on the governing equations after such physical simplifications have been applied. The review covers approaches including two-temperature non-equilibrium and analytical thermal-resistance models, Proper Orthogonal Decomposition (POD), CFD-derived look-up tables, kriging and ε-NTU grey/black-box metamodels, and machine-learning methods such as artificial neural networks and gradient-boosted regressors trained from CFD data. These ROM techniques have been applied to packed beds, PCM-integrated heat exchangers, finned enclosures, triplex-tube systems, and solar thermal components, achieving speed-ups from tens to over 80,000 times faster than full CFD simulations while maintaining prediction errors typically below 5% or within sub-Kelvin temperature deviations. A critical comparative analysis exposes the fundamental trade-off between interpretability, data dependence, and computational efficiency, leading to a practical decision-making framework that guides method selection for specific applications such as design optimization, real-time control, and system-level simulation. Remaining challenges—including accurate representation of phase change nonlinearity, moving phase boundaries, multi-timescale dynamics, generalization across geometries, experimental validation, and integration into industrial workflows—motivate a structured roadmap for future hybrid physics–machine learning developments, standardized validation protocols, and pathways toward industrial deployment.

Abstract Dynamical quantum phase transitions (DQPTs), which serve as a theoretical framework for understanding far-from-equilibrium physics in quantum many-body systems, have recently been observed experimentally. Their topological properties are typically characterized by the winding number, which acts as an order parameter. While DQPTs exhibiting both integer and half-integer jumps in the winding number have been reported, the underlying mechanisms behind these distinct topological behaviors, as well as the potential existence of other topological classes, remain open questions. To address this, we investigate DQPTs in the one-dimensional XY model under a quench protocol. We show that the observed topological diversity originates from the nature of the critical modes, which we classify into two categories: boundary modes and interior modes. Specifically, critical interior modes always lead to DQPTs with an integer winding number, while critical boundary modes always result in DQPTs characterized by a half-integer winding number. By analyzing the number and classification of critical modes, we provide a classification of the topological properties of DQPTs in the one-dimensional XY model. According to their distinct topological features, we categorize DQPTs into six types, three of which have not been previously identified in the literature. We discuss in detail the conditions associated with each type and present the corresponding dynamical phase diagrams. Our framework is not restricted to the XY model; it is applicable to other two-band models in one-dimensional systems, including the SSH model, Kitaev chain, Rice-Mele model, and Creutz model.

Problematic Internet Use (PIU) is a critical public health issue, especially among children and adolescents. The Dual Systems Model posits that imbalances between reward processing and self-control systems increase PIU vulnerability, with potential reciprocal effects of PIU on these systems. However, previous studies have primarily employed variable-centered approaches, neglecting within-system heterogeneity, and longitudinal studies on bidirectional relationships have mostly focused on single system, with limited evidence in children. The present study fills these research gaps by utilizing person-centered approaches to explore bidirectional relationships in 1341 children (9-11 years) and adolescents (13-16 years). A 1-year longitudinal survey measuring reward sensitivity, self-control, and PIU was conducted, combined with latent profile transition analysis and supervised machine learning. The results showed developmental similarities and differences. First, dual systems patterns predicted PIU in both age groups, but the underlying mechanisms were different. Low reward sensitivity and low self-control in children and adolescents increased PIU risk, while adolescents also uniquely affected by high reward sensitivity and low self-control. Second, PIU only reduced reward sensitivity and self-control over time in children. The present study enriches the Dual Systems Model by demonstrating bidirectional relationships from a person-centered perspective and informs tailored PIU interventions based on developmental stage and dual-system profiles.