Search Results (9,010)

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

The V/G criterion is a critical indicator for monitoring dynamic changes during Czochralski silicon single crystal (Cz-SSC) growth. However, the inability to measure it in real time forces reliance on offline feedback for process regulation, leading to imprecise control and compromised crystal quality. To overcome this limitation, this paper proposes a novel soft sensor modeling framework that integrates both mechanism-based knowledge and data-driven learning for the real-time prediction of the crystal quality parameter, specifically the V/G value (the ratio of growth rate to axial temperature gradient). The proposed approach constructs a hybrid prediction model by combining a data-driven sub-model with a physics-informed mechanism sub-model. The data-driven component is developed using an attention-based dynamic stacked enhanced autoencoder (AD-SEAE) network, where the SEAE structure introduces layer-wise reconstruction operations to mitigate information loss during hierarchical feature extraction. Furthermore, an attention mechanism is incorporated to dynamically weigh historical and current samples, thereby enhancing the temporal representation of process dynamics. In addition, a robust ensemble approach is achieved by fusing the outputs of two subsidiary models using an adaptive weighting strategy based on prediction accuracy, thereby enabling more reliable V/G predictions under varying operational conditions. Experimental validation using actual industrial Cz-SSC production data demonstrates that the proposed method achieves high-prediction accuracy and effectively supports real-time process optimization and quality monitoring. Full article

Mesh-agnostic models have advantages in processing flow field data with various topologies and densities, and they can easily incorporate partial differential equations. Beyond physics-informed neural networks, mesh-agnostic models have been studied for data-driven predictions of simple flows. In this study, a data-driven mesh-agnostic model is proposed to predict the transonic flow field of various supercritical airfoils. The model consists of two subnetworks, i.e., ShapeNet and HyperNet. ShapeNet is an implicit neural representation used to predict spatial bases of the flow field. HyperNet is a simple neural network that determines the weights of these bases. The input of ShapeNet is extended to ensure accurate prediction for different airfoil geometries. To reduce overfitting while capturing shock waves and boundary layers, a multi-resolution ShapeNet combining two activation functions is proposed. Additionally, a physics-guided loss function is proposed to enhance accuracy. The proposed model is trained and tested on various supercritical airfoils under different free-stream conditions. Results show that the model can effectively utilize airfoil samples with different grid sizes and distributions, and it can accurately predict the shock wave and boundary layer velocity profile. The proposed mesh-agnostic model can be used as a decoder in any conventional models, contributing to their application in complex and three-dimensional geometries. Full article

Accurate displacement field measurement by holographic interferometry requires robust analysis of high-density fringe patterns, which is hindered by speckle noise inherent in any interferogram, no matter how perfect. Conventional skeletonization methods, such as edge detection algorithms and active contour models, often fail under these conditions, producing fragmented and unreliable fringe contours. This paper presents a novel skeletonization procedure that simultaneously addresses three fundamental challenges: (1) topology preservation—by representing the fringe family within a physics-informed, finite-dimensional parametric subspace (e.g., Fourier-based contours), ensuring global smoothness, connectivity, and correct nesting of each fringe; (2) extreme noise robustness—through a robust strip integration functional that replaces noisy point sampling with Gaussian-weighted intensity averaging across a narrow strip, effectively suppressing speckle while yielding a smooth objective function suitable for gradient-based optimization; and (3) sub-pixel accuracy without phase extraction—leveraging continuous bicubic interpolation within a recursive quasi-optimization framework that exploits fringe similarity for precise and stable contour localization. The method’s performance is quantitatively validated on synthetic interferograms with controlled noise, demonstrating significantly lower error compared to baseline techniques. Practical utility is confirmed by successful processing of a real interferogram of a bent plate containing over 100 fringes, enabling precise displacement field reconstruction that closely matches independent theoretical modeling. The proposed procedure provides a reliable tool for processing challenging interferograms where traditional methods fail to deliver satisfactory results. Full article

The collision dynamics of binary seawater droplets are pivotal in marine engineering applications, like spray desalination and engine cooling. While high-fidelity simulations can resolve these dynamics, they are computationally prohibitive for rapid design and analysis. This study introduces the first interpretable machine learning (ML) framework to predict and elucidate the collision outcomes of head-on binary seawater droplets. A high-fidelity numerical dataset, generated via Modified Coupled Level Set-VOF (M-CLSVOF) simulations across a broad Weber number (We) range, serves as the foundation for training multiple classifiers. Among the tested algorithms, the Random Forest model achieved superior performance with 96.2% accuracy. The model’s predictions precisely identified the critical Weber number for the transition from coalescence to reflexive separation at We ≈ 22.3 for seawater. Moving beyond black-box prediction, we employed SHapley Additive exPlanations (SHAP) to quantitatively deconstruct the model’s decision-making process. SHAP analysis confirmed the dominance of the Weber number (75% contribution) and revealed the context-dependent role of the Reynolds number (25% contribution) in modulating the collision outcome. Furthermore, a comparative analysis with freshwater droplets quantified a 6% elevation in the critical Weber number for seawater, attributed to salinity-induced modifications in fluid properties. Finally, a machine-learned regime map in the We-Ohnesorge space was constructed, delineating the coalescence and separation boundaries. This work establishes ML as a powerful, interpretable surrogate model that not only delivers rapid, accurate predictions but also extracts fundamental physical insights, offering a valuable paradigm for optimizing marine spray systems. Full article

The predominant approach in the realm of industrial process monitoring and control involves the utilization of HMI (Human–Machine Interface) interfaces and conventional SCADA (Supervisory Control and Data Acquisition) systems. This limitation restricts user mobility, interaction with industrial equipment, and process status assessment. In the context of Industry 4.0, the ability to monitor and control industrial processes in real time is paramount. The present paper designs and implements a system for monitoring and controlling an industrial assembly line based on mixed reality. The technology employed to facilitate communication between the system and the industrial line is S7.Net. These elements facilitate direct communication with the industrial process equipment. The system facilitates the visualization of operating parameters and the status of the equipment utilized in the industrial process and its control. All data is superimposed on the physical environment through virtual operational panels. The system functions independently, negating the necessity for intermediate servers or other complex structures. The system’s operation is predicted on a series of algorithms. These instruments facilitate the automated analysis of industrial process parameters. These devices are utilized to ascertain the operational dynamics of the industrial line. The experimental results were obtained using a real industrial line. These models are employed to demonstrate the performance of data transmission, the identification of the system’s operating states, and the system’s ability to shut down in the event of operating errors. The proposed system is designed to function in a variety of industrial environments within the paradigm of Industry 4.0, facilitating the utilization of multiple virtual interfaces that enable user interaction with various elements through which the assembly process is monitored and controlled. Full article

Slag entrainment within the mold is a significant cause of surface defects in continuously cast slabs. As a key component for controlling molten steel flow, the structure of the submerged entry nozzle directly influences the flow field characteristics and slag entrainment behavior within the mold. This paper employs a 1:4-scale water–oil physical model combined with numerical simulation to investigate the effects of elliptical and circular submerged entry nozzles on slag entrainment behavior in a wide slab mold under different casting speeds and immersion depths. High-speed cameras were used to visualize meniscus fluctuations and oil droplet entrainment processes. An alternating control variable method was employed to quantitatively delineate a slag-free “safe zone” and a “slag entrainment zone” where oil droplets fall, determining the critical casting speed and critical immersion depth under different operating conditions. The results show that, given the nozzle immersion depth and slag viscosity, the maximum permissible casting speed range without slag entrainment can be obtained, providing a reference for industrial production parameter control. The root mean square (RMS) of surface fluctuations was introduced to characterize the activity of the meniscus flow. It was found that the RMS value decreases with increasing nozzle immersion depth and increases with increasing casting speed, showing a good correlation with the frequency of slag entrainment. Numerical simulation results show that compared with elliptical nozzles, circular nozzles form a more symmetrical flow field structure in the upper recirculation zone, with a left–right vortex center deviation of less than 5%, resulting in higher flow stability near the meniscus and thus reducing the risk of slag entrainment. 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

Advances in sensing technologies are increasingly transforming cutting operations by enabling data-driven condition monitoring, predictive maintenance, and process optimization. This review surveys recent developments in sensing modalities for cutting systems, including vibration sensors, acoustic emission sensors, optical and vision-based systems, eddy-current sensors, force sensors, and emerging hybrid/multi-modal sensing frameworks. Each sensing approach offers unique advantages in capturing mechanical, acoustic, geometric, or electromagnetic signatures related to tool wear, process instability, and fault development, while also showing modality-specific limitations such as noise sensitivity, environmental robustness, and integration complexity. Recent trends show a growing shift toward hybrid and multi-modal sensor fusion, where data from multiple sensors are combined using advanced data analytics and machine learning to improve diagnostic accuracy and reliability under changing cutting conditions. The review also discusses how artificial intelligence, Internet of Things connectivity, and edge computing enable scalable, real-time monitoring solutions, along with the challenges related to data needs, computational costs, and system integration. Future directions highlight the importance of robust fusion architectures, physics-informed and explainable models, digital twin integration, and cost-effective sensor deployment to accelerate adoption across various manufacturing environments. Overall, these advancements position advanced sensing and hybrid monitoring strategies as key drivers of intelligent, Industry 4.0-oriented cutting processes. Full article

This study presents a process-informed framework for assessing the differential credibility of diverse downscaling methodologies, including both statistical (simple and complex) and dynamical approaches. The methods evaluated include a convolutional neural network (CNN), the Locally Constructed Analog Method (LOCA), the Statistical DownScaling Model (SDSM), quantile delta mapping (QDM), simple interpolation with bias correction, and two regional climate models. As proof of concept, we apply the framework to evaluate the physical consistency of processes associated with wet-day occurrence at a site in the southern USA Great Plains. Additionally, we introduce a relative credibility metric that quantifies cross-method performance and outlines how this framework can be extended to other variables, regions, and downscaling applications. Results show that all downscaling methods perform credibly when the parent global climate model (GCM) performs credibly. However, complex statistical methods (CNN, LOCA, SDSM) tend to exacerbate GCM errors, while simpler methods (QDM, interpolation + bias correction) generally preserve GCM credibility. Dynamical downscaling, by contrast, can mitigate inherited biases and improve overall process-level credibility. These findings underscore the importance of process-based evaluation in downscaling assessments and reveal how downscaling model complexity interacts with GCM quality. Full article

Studying the origin of life requires identifying chemical and physical processes that could have supported early self-replicating and evolving molecular systems. Besides the requirement of information storage and transfer, an essential aspect is an energy source that could have thermodynamically driven the formation and replication of these molecular assemblies. Chemical energy sources such as cyclic trimetaphosphate are attractive because they could drive replication with relatively simple catalysts. Here, we focus on cyclic trimetaphosphate (cTmp), and compare its solubility in water to linear triphosphate, pyrophosphate, and phosphite when Mg²⁺ or Ca²⁺ are present. These solubilities are important for facilitating the reactions under prebiotically plausible conditions. The results showed that cTmp was soluble even at molar concentrations of Mg²⁺ and little precipitation with 200 mM Ca²⁺. In contrast, pyrophosphate and linear triphosphate precipitated efficiently even at low divalent metal ion concentrations. The precipitation of phosphate was pH-dependent, showing similar precipitation with Mg²⁺ and Ca²⁺ at a prebiotically plausible pH of 6.5. Phosphite was soluble at high Mg²⁺ concentrations but started precipitating with increasing Ca²⁺ concentration. At conditions that model Archaean seawater, cTmp was the most soluble of these compounds. Together, this experimental overview may help to identify promising conditions for lab-based investigations of phosphate-based energy metabolisms in early life forms. Full article

Background: Ultra-processed food (UPF) consumption has increased significantly in Latin America and Spain, impacting both health and environmental sustainability. To our knowledge, this is the first multicenter study to examine the association between UPF consumption and sustainable lifestyle behaviors in Latin America and Spain. Objective: To evaluate the association between UPF consumption and sustainable lifestyle behaviors in Latin America and Spain. Methods: This was an observational, analytical, multicenter, cross-sectional study. A validated, self-administered online questionnaire was distributed in 14 countries between March 2023 and January 2024. The survey collected sociodemographic data, UPF intake (classified using the NOVA system), body mass index and sustainable lifestyle behaviors (food, transport, environment). Multivariate linear regression models were applied to assess associations, adjusting for age, sex, smoking, physical activity and BMI. Results: Among 6009 adults (mean age: 34.98 ± 12.55; 79.5% women), those with the highest consumption of UPF (fast food, beverages and juices, salty snacks and sweet snacks) were significantly more likely to be in the least sustainable quartile compared to those who did not consume these food products ((OR = 2.51; 95% CI: 1.79–3.54), (OR = 1.82; 95% CI: 1.50–2.22), (OR = 1.51; 95% CI: 1.32–1.73) and (OR = 1.42; 95% CI: 1.20–1.67), respectively, with p values < 0.001). Conclusions: High consumption of ultra-processed foods (UPFs) is inversely associated with sustainable lifestyles. These findings position UPF consumption not only as a health problem but also as a key indicator of unsustainable lifestyles. Full article

Large language models (LLM) have achieved remarkable advances in natural-language understanding and content generation, and LLM-based agents demonstrate strong adaptability, flexibility, and robustness in handling complex tasks and enabling automated decision-making. Determining the operating mode of a power system requires repeated adjustments of boundary conditions to address violations. Conventional approaches include expert-driven power flow calculations and optimal power flow methods, the latter of which often lack clear physical interpretability during the iterative optimization process. This study proposes a novel paradigm for automated computation and adjustment of power system operating modes based on LLM-driven multi-agent systems. The approach leverages the reasoning capabilities of LLMs to enhance the adaptability of power flow adjustment strategies, while multi-agent coordination with power flow calculation modules ensures computational accuracy, enabling a natural-language-guided adaptive operational computation and adjustment process. The framework also incorporates retrieval-augmented generation techniques to access external knowledge bases and databases, further improving the agents’ understanding of system operational patterns and the accuracy of decision-making. This method constitutes an exploratory application of LLMs and multi-agent technologies in power system computational analysis, highlighting the considerable potential of LLMs to extend and enhance traditional power system analysis methodologies. Full article

Underground coal gasification (UCG) is a controlled combustion process of in situ coal that produces combustible gases through thermal and chemical reactions. In order to investigate the UCG induced multi-physical field evolution and overlying strata fracture propagation of deep steeply inclined coal seam (SICS), which play a vital role in safety and sustainable UCG project, this study established a finite element model based on the actual geological conditions of SICS and the controlled retracting injection point (CRIP) technology. The results are listed as follows: (1) the temperature field influence ranges of the shallow and deep parts of SICS expanded from 15.56 m to 17.78 m and from 26.67 m to 28.89 m, respectively, when the burnout cavity length increased from 100 m to 400 m along the dip direction; (2) the floor mudstone exhibited uplift displacement as a result of thermal expansion, while the roof and overlying strata showed stepwise-increasing subsidence displacement over time, which was caused by stress concentration and fracture propagation, reaching a maximum subsidence of 3.29 m when gasification ended; (3) overlying strata rock damages occurred with induced fractures developing and propagating during UCG. These overlying strata fractures can reach a maximum height of 204.44 m that may result in groundwater influx and gasification failure; (4) considering the significant asymmetry in the evolution of multi-physical fields of SICS, it is suggested that the dip-direction length of a single UCG channel be limited to 200 m. The conclusions of this study can provide theoretical guidance and technical support for the design of UCG of SICS. Full article

This work provides an overview of the mechanics of lithium-ion batteries, both from the aging and diagnostics perspective. Battery diagnostics based on mechanical measurements exploit the strong correlation between electrode lithiation and its deformation, resulting in macroscopic cell deformation. Macroscopic deformation is then a proxy for lithium concentration, enabling estimation of state of charge (SOC) and degradation indicators such as loss of active material and lithium inventory. The results demonstrate that SOC estimation algorithms based on deformation measurements are more robust than voltage-based methods, which are sensitive to temperature and aging, requiring constant updates of the algorithm parameters. Moreover, the health of the battery can be assessed through the differential expansion method even under high-current operation, providing results consistent with the traditional differential voltage method but applicable to real-world industrial applications. Mechanics plays a crucial role also in battery degradation. This work presents the application of POLIDEMO, an advanced battery aging model that explicitly accounts for mechanical degradation phenomena, providing a physics-based framework describing the coupled electrochemical–mechanical aging processes in lithium-ion batteries. It enables the prediction of key degradation indicators, including capacity fade—capturing the characteristic knee-point behavior—and the irreversible battery thickness increase associated with long-term aging. The model is validated with multiple aging datasets, demonstrating that parameters calibrated under a single operating condition can accurately predict degradation across diverse aging scenarios. Full article