Search Results (13,700)

A living organism can be regarded as a dissipative, self-organizing physical system operating far from thermodynamic equilibrium. Such systems can be effectively described within the framework of Markov jump processes subjected to an external driving force that sustains the system away from equilibrium—leading, in the special case of stabilization, to a non-equilibrium steady state (NESS). By combining the Markov formalism with concepts from stochastic thermodynamics, we demonstrate the temporal evolution of entropy in such systems: entropy decreases during growth and development, stabilizes at maturity under NESS conditions, and subsequently increases during aging, death, and decomposition. This characteristic trajectory, which we term the entropy bathtub, highlights the universal thermodynamic structure of living systems. We further show that the system exhibits continuous yet time-dependent positive entropy production, in accordance with fundamental thermodynamic principles. Perturbations of the driving force—whether reversible or irreversible—naturally capture the impact of external stressors, providing a conceptual analogy to pathological processes in biological organisms. Although the model does not introduce fundamentally new elements to the physics of life, it offers a simple tool for exploring entropy-driven mechanisms in living matter. Full article

While circuit parameter optimization has matured significantly, the systematic discovery of novel circuit topologies remains a bottleneck in analog design automation. This work presents SymXplorer, an open-source Python framework designed for automated topology exploration through symbolic modeling of analog components. The framework enables a component-agnostic approach to architecture-level synthesis, integrating stability analysis and higher-order filter exploration within a streamlined API. By modeling non-idealities as lumped parameters, the framework accounts for physical constraints directly within the symbolic analysis. To facilitate circuit sizing, SymXplorer incorporates a multi-objective optimization toolbox featuring Bayesian optimization and evolutionary algorithms for simulation-in-the-loop evaluation. Using this framework, we conduct a systematic search for differential Common-Gate (CG) Bandpass Transimpedance Amplifier (TIA) topologies tailored for 5G New Radio (NR) Radio-over-Fiber applications. We propose a novel, orthogonally tunable Bandpass TIA architecture identified by the tool. Implementation in 65 nm CMOS technology demonstrates the efficacy of the framework. Post-layout results exhibit a tunable gain of 30–50 dB$\Omega$, a center frequency of 3.5 GHz, and a tuning range of 500 MHz. The design maintains a power consumption of less than 400 $\mathsf{\mu }$W and an input-referred noise density of less than 50 pA/$\sqrt{\mathrm{Hz}}$ across the passband. Finally, we discuss how this symbolic framework can be integrated into future agentic EDA workflows to further automate the analog design cycle. SymXplorer is open-sourced to encourage innovation in symbolic-driven analog design automation. Full article

Diffraction-based velocity analysis is a key data interpretation technique in geophysical exploration, typically relying on the geometric characteristics, energy distribution, or propagation paths of diffraction waves. The hyperbola-based method is a classical strategy in this category, which extracts depth-dependent velocity (or dielectric properties) by correlating the hyperbolic shape of diffraction events with subsurface parameters for characterizing subsurface structures and material compositions. In this study, we propose a layer-stripping velocity analysis method applicable to ground-penetrating radar (GPR) and lunar-penetrating radar (LPR) data, with two main innovations: (1) replacing traditional local optimization algorithms with an intuitive parallelism check scheme, eliminating the need for complex nonlinear iterations; (2) performing depth-progressive velocity scanning of radargram diffraction signals, where shallow-layer velocity analysis constrains deeper-layer calculations. This strategy avoids misinterpretations of deep geological objects’ burial depth, morphology, and physical properties caused by a single average velocity or independent deep-layer velocity assumptions. The workflow of the proposed method is first demonstrated using a synthetic rock-fragment layered model, then applied to derive the near-surface dielectric constant distribution (down to 27 m) at the Chang’e-4 landing site. The estimated values range from 2.55 to 6, with the depth-dependent profile revealing lunar regolith stratification and interlayer material property variations. Consistent with previously reported results for the Chang’e-4 region, our findings confirm the method’s applicability to LPR data, providing a new technical framework for high-resolution subsurface structure reconstruction. Full article

Estimating the relative position and velocity of micro aerial vehicles (MAVs) using visual signals is a critical issue in numerous tasks. However, traditional relative motion estimation algorithms suffer severely from non-Gaussian noise interference and have limited observability, making it difficult to meet the practical requirements of complex dynamic scenarios. To address this dilemma, this paper proposes a Multimodal Decoupled Spatiotemporal Adaptive Network (MDSAN). Designed for air-to-air scenarios, MDSAN achieves high-precision relative pose and velocity estimation of dynamic MAVs while overcoming the observability limitations of traditional algorithms. In detail, MDSAN is collaboratively composed of two core sub-modules: Modality-Specific Convolutional Normalization (MSCN) blocks and Spatiotemporal Adaptive State (STAS) blocks. Specifically, MSCN uses custom convolution kernels tailored to three modalities—visual, physical, and geometric—to separate their features. This prevents interference between modalities and reduces non-Gaussian noise. STAS, built on a state-space model, combines two key functions: it tracks long-term MAV motion trends over time and strengthens the synergy between different modal features across space. Adaptive weights balance these two functions, enabling stable estimation, even when traditional methods struggle with low observability. Furthermore, MDSAN adopts a full-vision multimodal fusion scheme, completely eliminating the dependence on wireless communication and reducing hardware costs. Extensive experimental results demonstrate that MDSAN achieves the best performance in all scenarios, significantly outperforming existing motion estimation algorithms. It provides a new technical path that balances high precision, high robustness, and cost-effectiveness for technologies such as MAV swarm perception. Full article

Agentic artificial intelligence (AI) is emerging as a paradigm for next-generation smart grids, enabling autonomous decision-making, adaptive coordination, and resilient control in complex cyber–physical environments. Unlike traditional AI models, which are typically static predictors or offline optimizers, agentic AI systems perceive grid states, reason about goals, plan multi-step actions, and interact with operators in real time. This review presents the latest advances in agentic AI for power systems, including architectures, multi-agent control strategies, reinforcement learning frameworks, digital twin optimization, and physics-based control approaches. The synthesis is based on new literature sources to provide an aggregate of techniques that fill the gap between theoretical development and practical implementation. The main application areas studied were voltage and frequency control, power quality improvement, fault detection and self-healing, coordination of distributed energy resources, electric vehicle aggregation, demand response, and grid restoration. We examine the most effective agentic AI techniques in each domain for achieving operational goals and enhancing system reliability. A systematic evaluation is proposed based on criteria such as stability, safety, interpretability, certification readiness, and interoperability for grid codes, as well as being ready to deploy in the field. This framework is designed to help researchers and practitioners evaluate agentic AI solutions holistically and identify areas in which more research and development are needed. The analysis identifies important opportunities, such as hierarchical architectures of autonomous control, constraint-aware learning paradigms, and explainable supervisory agents, as well as challenges such as developing methodologies for formal verification, the availability of benchmark data, robustness to uncertainty, and building human operator trust. This study aims to provide a common point of reference for scholars and grid operators alike, giving detailed information on design patterns, system architectures, and potential research directions for pursuing the implementation of agentic AI in modern power systems. Full article

Finding Noether symmetries for time-dependent damped dynamical systems remains a significant challenge. This paper introduces a complete geometric algorithm for determining all Noether point symmetries and first integrals for the general class of Lagrangians $L=A\left(t\right)L_0$, which model motion with general linear damping in a Riemannian space. We derive and prove a central Theorem that systematically links these symmetries to the homothetic algebra of the kinetic metric defined by $L_0$. The power of this method is demonstrated through a comprehensive analysis of the damped Kepler problem. Beyond recovering known results for constant damping, we discover new quadratic first integrals for time-dependent damping $\varphi \left(t\right)=\gamma /t$ with $\gamma =-1$ and $\gamma =-1/3$. We also include preliminary results on the Noether symmetries of the damped harmonic oscillator. Finally, we clarify why a time reparameterization that removes damping yields a physically inequivalent system with different Noether symmetries. This work provides a unified geometric framework for analyzing dissipative systems and reveals new integrable cases. Full article

Background/Objectives: This study examines prisoners’ quality of life by investigating which aspects of imprisonment conditions—including perceptions of the physical environment—best predict overall satisfaction with the prison (OSP). A key question is whether the staff–prisoner relationship is the single most important dimension, which is frequently emphasized in the literature but has scarcely been tested quantitatively. Methods: Data stemmed from a survey conducted in three closed prisons in Norway in 2022 (response rate: 63%, n = 163). The dependent variable was assessed by asking: “Generally speaking, on a scale from 1 to 10, how satisfied are you with this prison?” This outcome was regressed on seven subscales from the Prison Climate Questionnaire and four single-item measures of the physical environment that have been shown to influence health and well-being. Results: As expected, the quality of staff–prisoner relationships had a unique statistical impact on OSP. Ratings of the outdoor areas and the view from the cell were about equally strong predictors. No statistically independent effects were observed for perceived quality of relationships with fellow prisoners, reintegration measures, receiving visits, personal safety, autonomy, access to natural light and a global rating of the prison building (noise, temperature, layout, etc.). Conclusions: This study further emphasizes the importance of staff–prisoner relationships in shaping prisoners’ experiences and perceptions of imprisonment. Moreover, it provides new insights into the significance of the physical environment for prisoners’ overall perceptions of prison quality, which is likely to affect their mental health and well-being. These findings have potential implications for the design and siting of new correctional facilities and for improving the quality of existing ones. Full article

The traditional ultrasonic bonding technique for IGBT T2 copper terminals often causes physical damage to ceramic substrates, severely compromising the reliability of power modules. Meanwhile, T2 copper laser welding faces inherent challenges including low laser absorption efficiency and unstable molten pool dynamics. To address these issues, this study targets the high-quality connection of IGBT T2 copper terminals and proposes a welding solution integrating a Fiber-Diode Hybrid Laser system with galvo-scanning technology. Comparative experiments between galvo-scanning and traditional oscillation methods CNC scanning were conducted under sinusoidal and circular trajectories to explore the regulation mechanism of welding quality. The results demonstrate that CNC scanning lacks precision in thermal input control, resulting in inconsistent welding quality. Galvo-scanning enables precise modulation of laser energy distribution and molten pool behavior, effectively reducing spatter and porosity defects. It also promotes the transition from columnar grains to equiaxed grains, significantly refining the weld microstructure. Under the sinusoidal trajectory with a welding speed of 20 mm/s, the Lap-shear strength of the galvo-scanned joint reaches 277 N/mm², outperforming all CNC-scanned joints. This research proposes a non-contact welding strategy targeted at eliminating the mechanical failure mechanism associated with conventional ultrasonic bonding of ceramic substrates. It establishes the superiority of galvo-scanning for precision welding of high-reflectivity materials and lays a foundation for its potential application in new energy vehicle power modules and microelectronic packaging. Full article

Given the central importance of maritime logistics to global trade, accurate and efficient vessel motion forecasting is essential for strengthening supply chain resilience and improving operational efficiency. However, traditional physical and statistical models often fail to effectively capture the multivariate, noisy, and strongly coupled nature of maritime dynamics. In this manuscript, we adapt the DSformer architecture for ship motion forecasting, leveraging its dual sampling and dual-attention design to address the multi-scale and cross-variable dependencies inherent in maritime data. Across three real-world datasets, the adapted DSformer reduces prediction error by 23% and training time by 70% compared with 13 state-of-the-art (SOTA) baselines. Moreover, we identify a consistent relationship between sampling strategies and sea states, where dense sampling performs best under stable conditions, whereas moderately sparse sampling with multi-head attention improves robustness under turbulent environments. These results apply the algorithm’s new capabilities to the daily management of maritime logistics. By adapting the architecture to real-world operational settings and optimizing its key parameters, the approach enables efficient, real-time vessel forecasting and decision support across global supply chains. Full article

Triply periodic minimal surface (TPMS) structures provide high surface area to volume ratios and tunable conduction pathways, but predicting their thermal behavior across different metallic materials remains challenging because multi-material experimentation is costly and full-scale simulations require extremely fine meshes to resolve the complex geometry. This study develops a physics-informed neural network (PINN) that reconstructs steady-state temperature fields in TPMS Gyroid structures using only two experimentally measured materials, Aluminum and Silver, which were tested under identical heat flux and flow conditions. The model incorporates conductivity ratio physics, Fourier-based thermal scaling, and complete spatial temperature profiles directly into the learning process to maintain physical consistency. Validation using the complete Aluminum and Silver datasets confirms excellent agreement for Aluminum and strong accuracy for Silver despite its larger temperature gradients. Once trained, the PINN can generalize the learned behavior to nine additional metals using only their conductivity ratios, without requiring new experiments or numerical simulations. A detailed heat transfer analysis is also performed for Magnesium, a lightweight material that is increasingly considered for thermal management applications. Since no published TPMS measurements for Magnesium currently exist, the predicted Nusselt numbers obtained from the PINN-generated temperature fields represent the first model-based evaluation of its convective performance. The results demonstrate that the proposed PINN provides an efficient, accurate, and scalable surrogate model for predicting thermal behavior across multiple metallic TPMS structures and supports the design and selection of materials for advanced porous heat technologies. Full article

Spine stereotactic radiotherapy and radiosurgery (SSRS) techniques, encompassing both fractionated stereotactic treatments and single-fraction radiosurgery, are widely used for the management of spinal metastases due to their ability to deliver highly conformal radiation while limiting dose to adjacent critical structures. Clinical outcomes following SSRS, including durable local control and acceptable toxicity, have been reported previously in multiple institutional series. In this manuscript, we describe the clinical workflow used to deliver SSRS at a high-volume academic center, with emphasis on the medical physics processes that support routine clinical practice. Key elements of the workflow include patient selection, treatment region-specific immobilization, CT and MRI-based simulation, treatment planning, patient-specific quality assurance, and image-guided treatment delivery. Rather than presenting new outcome data, this work provides a descriptive overview of how established SSRS techniques are integrated into day-to-day clinical care. Full article

Path planning, as a key technology in unmanned aerial vehicle (UAV) systems, affects the overall efficiency of task completion and is often limited by energy consumption, obstacles, and maneuverability in complex application environments. Traditional algorithms have insufficient performance in nonlinear, multimodal, and multiconstraints problems. Based on this, this paper proposes an improved exponential-trigonometric optimization (ETO) to solve a 3D smooth path planning model based on a spherical Bezier curve. Firstly, a fixed arc length resampling strategy is proposed to address the issue of the insufficient adaptability of existing path smoothing methods to dynamic threats. Generate a uniformly distributed set of reference points along the Bezier curve and combine it with spherical projection to improve the safety and efficiency of the flight path. On this basis, establish a total cost function that includes four types of costs. Secondly, a new ETO variant called IETO is proposed by introducing the alpha evolution strategy, noise and physical attack strategy, and opposition-based cross teaching strategy into ETO. Then, the effectiveness of IETO for addressing various optimization problems is showcased through population diversity analysis, ablation analysis, and benchmark experiments. Finally, the results of the simulation experiment indicate that IETO stably provides shorter and smoother safe paths for UAVs in three elevation maps with different terrain features. Full article

In the context of fifth-generation (5G) communications and the dawn of sixth-generation (6G) networks, a surged societal demand on bandwidth and data rate and more stringent commercial requirements on transmission efficiency, cost, and reliability are increasingly evident and, hence, driving the maturity of reconfigurable millimeter-wave (mmW) and terahertz (THz) devices and systems, in particular, liquid crystal (LC)-based tunable solutions for delay line phase shifters (DLPSs). However, the field of LC-combined electronics has witnessed only incremental developments in the past decade. First, the tuning principle has largely been unchanged (leveraging the shape anisotropy of LC molecules in microscale and continuum mechanics in macroscale for variable polarizability). Second, LC-enabled devices’ performance has yet to be standardized (suboptimal case by case at different frequency domains). In this context, this work points out three underestimated knowledge gaps as drawn from our theoretical designs, computational simulations, and experimental prototypes, respectively. The first gap reports previously overlooked physical constraints from the analytical model of an LC-embedded coaxial DLPS. A new geometry-dielectric bound is identified. The second gap deals with the lack of consideration in the suboptimal dispersion behavior in differential delay time (DDT) and differential delay length (DDL) for LC phase-shifting devices. A new figure of merit (FoM) is proposed and defined at the V-band (60 GHz) to comprehensively evaluate the ratios of the DDT and DDL over their standard deviations across the 54 to 66 GHz spectrum. The third identified gap deals with the in-depth explanation of our recent experimental results and outlook for partial leakage attack analysis of LC phase shifters in modern eavesdropping. Full article

Modern engineering systems require reliable and timely Fault Detection and Diagnosis (FDD) to ensure operational safety and resilience. Traditional model-based and rule-based approaches, although interpretable, exhibit limited scalability and adaptability in complex, data-intensive environments. This survey provides a systematic overview of recent studies exploring Machine Learning (ML) and Artificial Intelligence (AI) techniques for FDD across industrial, energy, Cyber-Physical Systems (CPS)/Internet of Things (IoT), and cybersecurity domains. Deep architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, and Graph Neural Networks (GNNs) are compared with unsupervised, hybrid, and physics-informed frameworks, emphasizing their respective strengths in adaptability, robustness, and interpretability. Quantitative synthesis and radar-based assessments suggest that AI-driven FDD approaches offer increased adaptability, scalability, and early fault detection capabilities compared to classical methods, while also introducing new challenges related to interpretability, robustness, and deployment. Emerging research directions include the development of foundation and multimodal models, federated learning (FL), and privacy-preserving learning, as well as physics-guided trustworthy AI. These trends indicate a paradigm shift toward self-adaptive, interpretable, and collaborative FDD systems capable of sustaining reliability, transparency, and autonomy across critical infrastructures. Full article

In the preliminary design stage of wind farms, the theoretical energy output must be adjusted by multiple reduction factors to estimate the actual grid-connected power. As renewable energy becomes increasingly integrated into electricity markets, the conventional approach using static, averaged reduction coefficients for annual yield estimation can no longer meet the market’s demand for high-resolution power time series. Addressing this gap, the novelty of this paper lies in shifting the focus from total annual estimation to hourly-level dynamic allocation. This paper proposes a time-series reduction coefficient evaluation method based on the time-varying entropy weight method (TV-EWM). Under the assumption that the total annual reduction quantity adheres to standard design specifications, this method utilizes long-term wind measurement data, integrates unique ultra-high-altitude wind resource characteristics, and constructs a scenario-based indicator system. By quantifying the coupling relationships between key meteorological variables and incorporating a dynamic weighting mechanism, the proposed approach achieves hourly refined reduction estimation for theoretical power output. Comparative analysis was conducted against the traditional static average reduction method. Results indicate that, compared to the traditional average reduction method, the TV-EWM approach significantly enhances the model’s ability to capture seasonal variability, increasing the coefficient of determination (R²) by 4.19% to 0.7061. Furthermore, it demonstrates higher stability in error control, reducing the Normalized Root Mean Square Error (NRMSE) by 4.51% to 15.45%. The TV-EWM more accurately captures the temporal evolution and coupling effects between meteorological elements and curtailed generation under various reduction scenarios, retains full-load operational features, and enhances physical interpretability and time responsiveness, providing a new analytical framework for market-oriented power generation assessment. Full article