Search Results (2,645)

As a critical technological foundation for electric vehicles, power battery state estimation primarily involves estimating the State of Charge (SOC), the State of Health (SOH) and the Remaining Useful Life (RUL). This paper systematically categorizes battery state estimation methods into three distinct generations, tracing the evolutionary progression from single-state to multi-state cooperative estimation approaches. First-generation methods based on equivalent circuit models offer straightforward implementation but accumulate SOC-SOH estimation errors during battery aging, as they fail to account for the evolution of microscopic parameters such as solid electrolyte interphase film growth, lithium inventory loss, and electrode degradation. Second-generation data-driven approaches, which leverage big data and deep learning, can effectively model highly nonlinear relationships between measurements and battery states. However, they often suffer from poor physical interpretability and generalizability due to the “black-box” nature of deep learning. The emerging third-generation technology establishes transmission mechanisms from microscopic electrode interface parameters via electrochemical impedance spectroscopy to macroscopic SOC, SOH, and RUL states, forming a bidirectional closed-loop system integrating estimation, prediction, and optimization that demonstrates potential to enhance both full-operating-condition adaptability and estimation accuracy. This progress supports the development of high-reliability, long-lifetime electric vehicles. Full article

Both epidemiological and animal model studies have revealed that heat stroke is closely related to the development or exacerbation of dementia disorders. The most common form of dementia is Alzheimer’s disease, which is characterized by the accumulation of amyloid-β protein in the central nervous system. Notably, a whole-genome transcriptome analysis of heat stroke patients has identified the increased expression of amyloid-β precursor protein gene and the activation of amyloid processing pathways. This finding provides a molecular basis for the theory that heat stroke is a risk factor for dementia disorders. Down syndrome—a common chromosomal abnormality—is also a dementia disorder that is characterized by the overexpression of amyloid-β precursor protein gene and the accumulation of amyloid-β protein. Thus, heat stroke may also develop or exacerbate Alzheimer’s disease-like dementia in Down syndrome. For individuals with Down syndrome, heat stroke is therefore not only a life-threatening risk factor but may also be a risk factor for accelerating intellectual decline. Full article

While synthetic biology has traditionally focused on creating biological systems often through genetic engineering, emerging technologies, for example, implantable pacemakers with integrated piezo-electric and tribo-electric materials are beginning to enlarge the classical domain into what we call symbiotic synthetic biology. These devices are permanently attached to a body, although non-living or genetically unaltered, and closely mimic biological behavior by harvesting biomechanical energy and providing functions, such as autonomous heart pacing. They form active interfaces with human tissues and operate as hybrid systems, similar to synthetic organs. In this context, the present paper first presents a short summary of previous in vivo studies on piezo-electric composites in relation to their deployment as battery-less pacemakers. This is then followed by a summary of a recent theoretical work using a damped harmonic resonance model, which is being extended to mimic the functioning of such devices. We then extend the theoretical study further to include new solutions and obtain a sum rule for the power output per cycle in such systems. In closing, we present our quantitative understanding to explore the modulation of the quantum vacuum energy (Casimir effect) by periodic body movements to power pacemakers. Taken together, the present work provides the scientific foundation of the next generation bio-integrated intelligent implementation. Full article

Amid the global energy transition, rapidly expanding wind and solar installations challenge power grids with variability and uncertainty. We propose an adaptive framework for renewable energy accommodation assessment under high-dimensional uncertainties, integrating three innovations: (1) Response Surface Methodology (RSM) is adopted for the first time to construct a closed-form polynomial of renewable energy accommodation in terms of resource hours, load, installed capacity, and transmission limits, enabling millisecond-level evaluation; (2) LASSO-regularized RSM suppresses high-dimensional overfitting by automatically selecting key interaction terms while preserving interpretability; (3) a Bayesian kernel density extension yields full posterior distributions and confidence intervals for renewable energy accommodation in small-sample scenarios, quantifying risk. A case study on a renewable-rich grid in Northwest China validates the framework: two-factor response surface models achieve R² > 90% with < 0.5% mean absolute error across ten random historical cases; LASSO regression keeps errors below 1.5% in multidimensional space; Bayesian density intervals encompass all observed values. The framework flexibly switches between deterministic, sparse, or probabilistic modes according to data availability, offering efficient and reliable decision support for generation-transmission planning and market clearing under multidimensional uncertainty. Full article

This work presents a closed-form approximation to the symbol error probability (SEP) for cross-quadrature amplitude modulation (cross-QAM) schemes over $\mathit{\kappa }$–$\mathit{\mu }$ fading channels. The proposed formulation enables accurate performance evaluation while avoiding computationally expensive numerical integration. The analysis covers millimeter-wave (mmWave) frequencies at 55, 60, and 65 GHz, under both line-of-sight (LoS) and non-line-of-sight (nLoS) conditions, and for multiple transmitter–receiver polarization configurations. A key contribution of this work is the experimental validation of the theoretical expression with real channel-measurement data, which confirms the applicability of the $\mathit{\kappa }$–$\mathit{\mu }$ model in realistic mmWave scenarios. Furthermore, we perform a detailed parametric study to quantify the influence of $\mathit{\kappa }$ and $\mathit{\mu }$ on adaptive modulation performance, providing practical insights for 5G and future 6G systems. The proposed framework bridges theoretical analysis and experimental validation, offering a computationally efficient and robust tool for the design and evaluation of advanced modulation schemes in generalized fading environments. Full article

The present work numerically investigates the mechanical behavior of six 6061-T6 aluminum profiles during roll bending, considering, in two specific cases, the application of the process in different bending directions (vertical and horizontal), totaling eight cases analyzed, with emphasis on the influence of multiple bending radii. Notably, two of the profiles are characterized by high geometric complexity, making their analysis particularly relevant within the scope of this study. Using the finite element method in ANSYS^® (version 2022 R2) (SOLID187 element), the study expands the previously validated model to a broader range of geometries and includes an additional validation and verification stage. The results reveal: (i) an inverse relationship between bending radius and von Mises stress, with critical values close to the material’s strength limit at smaller radii; (ii) characteristic displacement patterns for each profile, quantified through specific curve fittings; and (iii) a systematic comparison among the six profiles, highlighting stress concentrations and deformations differentiated by geometry. The simulations provide criteria for predicting forming defects and optimizing process parameters, expanding the database for industrial designs with multiple extruded profiles. Full article

This paper expands the Mallows model for use in combinatorial domains. The Mallows model is a popular distribution used to sample permutations around a central tendency but requires a unique normalizing constant for each distance metric used in order to be computationally efficient. In this paper, closed-form expressions for the Mallows model normalizing constant are derived for the Hamming distance, symmetric difference, and the similarity coefficient in combinatorial domains. Additionally, closed-form expressions are derived for the normalizing constant of the weighted Mallows model in combinatorial domains. The weighted Mallows model increases the versatility of the Mallows model by allowing granular control over likelihoods of individual components in the domain. The derivation of the closed-form expression results in a reduction of the order of calculations required to calculate probabilities from exponential to constant. Full article

Previous studies on tunnel fires have primarily focused on tunnels with flat ceilings and lacked studies on tunnels with beams. The present study is predicated on a reduced-scale tunnel model with a beam structure. Through meticulous analysis of the effects of factors such as longitudinal ventilation velocity and beam dimensions, the study unveils the distribution pattern of ceiling temperatures under the longitudinal smoke exhaust mode. The findings suggest that the presence of beams can induce turbulence in the longitudinal ventilation airflow. It has been demonstrated that the magnitude of this phenomenon is directly proportional to the spacing of the beams. This results in fluctuations in the ceiling temperature rise close to the combustion zone. The smoke storage capacity of the open cavities formed between adjacent beams is significantly affected by the beam height, thereby influencing the overall temperature rise beneath the ceiling. The greater the beam height, the higher the overall ceiling temperature rise near the combustion zone, but the lower the ceiling temperature rise downstream of the fire source. A prediction model for the longitudinal decay of ceiling temperature downstream of the fire source in tunnels with beams has been obtained. This model is related to the dimensionless beam dimension. Full article

The Generalized Tempered Stable (GTS) distribution extends classical stable laws through exponential tempering, preserving the power-law behavior while ensuring finite moments. This makes it especially suitable for modeling heavy-tailed financial data. However, the lack of closed-form densities poses significant challenges for simulation. This study provides a comprehensive and systematic comparison of GTS simulation methods, including rejection-based algorithms, series representations, and an enhanced Fast Fractional Fourier Transform (FRFT)-based inversion method. Through extensive numerical experiments on major financial assets (Bitcoin, Ethereum, the S&P 500, and the SPY ETF), this study demonstrates that the FRFT method outperforms others in terms of accuracy and ability to capture tail behavior, as validated by goodness-of-fit tests. Our results provide practitioners with robust and efficient simulation tools for applications in risk management, derivative pricing, and statistical modeling. Full article

The global expansion of Korean language education has created an urgent need for scalable, objective, and consistent methods for assessing the writing skills of non-native (L2) learners. Traditional manual grading is resource-intensive and prone to subjectivity, while existing Automated Essay Scoring (AES) systems often struggle with the linguistic nuances of Korean and the specific error patterns of L2 writers. This paper introduces a novel hybrid AES system designed specifically for Korean L2 writing. The system integrates two complementary feature sets: (1) a comprehensive suite of conventional linguistic features capturing lexical diversity, syntactic complexity, and readability to assess writing form and (2) a novel semantic relevance feature that evaluates writing content. This semantic feature is derived by calculating the cosine similarity between a student’s essay and an ideal, high-proficiency reference answer generated by a Large Language Model (LLM). Various machine learning models are trained on the Korean Language Learner Corpus from the National Institute of the Korean Language to predict a holistic score on the 6-level Test of Proficiency in Korean (TOPIK) scale. The proposed hybrid system demonstrates superior performance compared to baseline models that rely on either linguistic or semantic features alone. The integration of the LLM-based semantic feature provides a significant improvement in scoring accuracy, more closely aligning the automated assessment with human expert judgments. By systematically combining measures of linguistic form and semantic content, this hybrid approach provides a more holistic and accurate assessment of Korean L2 writing proficiency. The system represents a practical and effective tool for supporting large-scale language education and assessment, aligning with the need for advanced AI-driven educational technology systems. Full article

High-precision spiral trajectory tracking for aquaculture net-cage inspection is hindered by uncertain hydrodynamics, strong coupling, and time-varying disturbances acting on an underactuated autonomous underwater vehicle. This paper adapts and validates a model–data-driven learning-aided adaptive robust control strategy for the specific challenge of high-precision spiral trajectory tracking for aquaculture net-cage inspection. At the kinematic level, a serial iterative learning feedforward compensator is combined with a line-of-sight guidance law to form a feedforward-compensated guidance scheme that exploits task repeatability and reduces systematic tracking bias. At the dynamic level, an integrated adaptive robust controller employs projection-based, rate-limited recursive least-squares identification of hydrodynamic parameters, along with a composite feedback law that combines linear error feedback, a nonlinear robust term, and fast dynamic compensation to suppress lumped uncertainties arising from estimation error and external disturbances. A Lyapunov-based analysis establishes uniform ultimate boundedness of all closed-loop error signals. Simulations that emulate net-cage inspection show faster convergence, higher tracking accuracy, and stronger robustness than classical adaptive robust control and other baselines while maintaining bounded control effort. The results indicate a practical and effective route to improving the precision and reliability of autonomous net-cage inspection. Full article

Thermal bridging through cold-formed steel (CFS) studs significantly reduces the thermal performance of light gauge steel frame (LSF) wall systems, particularly in climates demanding higher thermal resistance (R-value). While thermal breaks are commonly used, they increase material costs and construction complexity. According to NCC 2022, the minimum total R-value requirement for external walls ranges between 2.8 and 3.8 m²·K/W depending on the climate zone and building class. This study therefore evaluated web-perforated steel studs as a passive strategy to enhance thermal resistance of LSF walls, analysing 120 configurations with validated 3D finite element models in Abaqus and benchmarking in THERM. The results showed that web perforations consistently improved R-values by 14 to 20%, as isotherm contours and heat flux vectors demonstrated disruption of direct heat flow through the stud, thereby mitigating thermal bridging. Although the axial compression capacity of web-perforated CFS studs decreased by 29.5%, the use of 4 mm hole-edge stiffeners restored 96.8% of the original capacity. The modified NZS 4214:2006 and ASHRAE Modified Zone methods, incorporating steel area reduction and heat flux redistribution, closely matched Abaqus predictions, with coefficients of variation (COV) below 0.009, corresponding to less than 1% relative deviation between analytical and numerical R-values. Furthermore, application of web-perforated CFS studs in five external wall systems demonstrated improved thermal resistance, ensuring compliance with NCC 2022 R-value requirements across all Australian climate zones. Overall, the findings establish web-perforated studs as an effective solution for improving the energy performance of LSF building envelopes. Full article

Trajectory planning for logistics UAVs in complex environments faces a key challenge: balancing global search breadth with fine constraint accuracy. Traditional algorithms struggle to simultaneously manage large-scale exploration and complex constraints, and lack sufficient modeling capabilities for multi-UAV systems, limiting cluster logistics efficiency. To address these issues, we propose a GWOP algorithm based on dual-layer fusion of GWO and GRPO and incorporate a graph attention network (GAT). First, CEC2017 benchmark functions evaluate GWOP convergence accuracy and balanced exploration in multi-peak, high-dimensional environments. A hierarchical collaborative architecture, “GWO global coarse-grained search + GRPO local fine-tuning”, is used to overcome the limitations of single-algorithm frameworks. The GAT model constructs a dynamic “environment–UAV–task” association network, enabling environmental feature quantification and multi-constraint adaptation. A multi-factor objective function and constraints are integrated with multi-task cascading decoupling optimization to form a closed-loop collaborative optimization framework. Experimental results show that in single UAV scenarios, GWOP reduces flight cost (FV) by over 15.85% on average. In multi-UAV collaborative scenarios, average path length (APL), optimal path length (OPL), and FV are reduced by 4.08%, 14.08%, and 24.73%, respectively. In conclusion, the proposed method outperforms traditional approaches in path length, obstacle avoidance, and trajectory smoothness, offering a more efficient planning solution for smart logistics. Full article

This study presents an exact analytical investigation into the static response of helical single-walled carbon nanotube (SWCNT) beams based on Eringen’s differential nonlocal elasticity theory, which captures nanoscale effects arising from interatomic interactions. A key contribution of this work is the derivation of the governing equations for helical SWCNT beams, based on the nonlocal Euler–Bernoulli theory, followed by their exact analytical solution using the initial value method. To the best of the authors’ knowledge, this represents the first closed-form formulation for such complex nanostructures using this theoretical framework of nonlocal elasticity theory. The analysis considers both cantilevered and clamped–clamped boundary conditions, under various concentrated force and moment loadings applied at the ends and midpoint of the helical beam. Displacements and rotational components are expressed in the Frenet frame, enabling direction-specific evaluation of the deformation behaviour. Parametric studies are conducted to investigate the influence of geometric parameters—such as the winding angle ($\alpha$) and aspect ratio ($R/d$) and the nonlocal parameter ($R/\gamma$). Results show that nonlocal elasticity theory consistently predicts higher displacements and rotations than the classical local theory, revealing its importance for accurate modelling of nanoscale structures. The proposed analytical framework serves as a benchmark reference for the modelling and design of nanoscale helical structures such as nano-springs, actuators, and flexible nanodevices. Full article

Lithium fluoride (LiF) crystals are widely employed both as optical windows transparent in the ultraviolet spectral region and as efficient personal dosimeters, with their application scope recently expanding into lithium-ion technologies. Moreover, as an alkali halide crystal (AHC), LiF serves as a model system for studying and simulating radiation effects in solids. This work identifies radiation-induced defects formed in lithium fluoride upon irradiation with swift heavy ion beams (N, O, Kr, U) and intense pulsed electron beams, investigates their thermal stability, and performs computer modeling of annealing processes. The theoretical analysis of existing experimental kinetics for $F$-centers induced by electron and heavy ion irradiation reveals considerable differences in the activation energies for interstitial migration. A strong correlation between the activation energy $E_a$ and the pre-exponential factor $X$($E_a$) is observed; notably, $X$($E_a$) is no longer constant but closely matches the potential function Ea. Indeed, with increasing irradiation dose, both the migration energy $E_a$ and pre-exponential factor $X$ decrease simultaneously, leading to an effective increase in the defect diffusion rate. Full article