Search Results (14,356)

Battery Energy Storage Systems (BESSs) are increasingly deployed in grid-scale applications, electric mobility, and renewable integration, where safety, reliability, and longevity are critical. Thermal runaway remains one of the most severe failure modes in lithium-ion batteries, often triggered by complex interactions between electrochemical, thermal, and mechanical phenomena. This paper presents an extended hybrid Physics-Informed Neural Network (PINN) framework for thermal anomaly prediction and early detection of runaway precursors in BESS. The proposed architecture integrates governing physical laws, specifically the Bernardi heat generation equation and Fick’s diffusion law, within a deep learning pipeline composed of a physics module, a temporal Bi-LSTM, and an attention mechanism for explainability, which may represent an obstacle in the application of deep learning algorithms. Beyond the initial formulation, the extended version presented here provides a deeper theoretical background, an expanded methodological justification, a more comprehensive comparison with state-of-the-art approaches, and a detailed discussion on scalability, uncertainty, and deployment challenges. The results for synthetic yet physically consistent datasets represent a proof of concept of the PINN approach, which can achieve superior generalization, robustness to noise, and interpretability compared to purely data-driven baselines, achieving an accuracy above 90% and an AUC of 0.95. The framework contributes to proactive safety management in cyber-physical energy systems and establishes a foundation for real-time, physics-aware anomaly detection in safety-critical BESS applications, e.g., marine transportation contexts and port environments. Full article

In the framework of mean-field approximation, the influence of social contacts on the spread of an epidemic in a population of constant size is discussed. The key feature of the proposed model is that it includes two infection–transmission mechanisms depending on the physical nature of the contact between people. We separate the transfer mechanism related directly to the movement of people (the so-called transport processes) from the one occurring when the relative velocity between individuals is negligible (the so-called social contacts). Based on the developed physicochemical analogy, this approach allows us to derive, in a unified manner, expressions for the rate constants of infection–transmission of different nature. The resulting transmission rate constants are used to modify the SEIS model to examine the influence of social activity on the formation of an endemic equilibrium in the population under consideration. The frequency of social contacts is estimated using Dunbar’s approach and a direct statistical calculation based on the binomial distribution. These relations are then used to discuss the formation of quasi-stationary states, which can be interpreted as endemic equilibria. A qualitative analysis of the resulting dynamical regimes is carried out. The necessary conditions for the existence of this equilibrium, depending on both social and medical–biological factors, are also derived. The analytical results are illustrated by numerical simulations. The present results should be interpreted as a necessary step to establish a link between purely transport and social mechanisms of epidemic development. Full article

Solanum lycopersicum is highly sensitive to water deficits, which negatively affect photosynthesis and increase oxidative stress. Although chitosan nanoparticles (ChNPs) offer a sustainable solution, research on their effects on this species is scarce. This study evaluated whether ChNPs mitigate the physiological and biochemical effects of water deficit on S. lycopersicum seedlings. Thirty-day-old seedlings were grown under greenhouse conditions, and two irrigation levels were established: 80% of substrate water-holding capacity (well-watered, WW), and 50% of water-holding capacity (mild-to-moderate water deficit, WD). Spherical ChNPs with a size of 39.52 ± 10.9 nm were suspended in 1% acetic acid and foliar-applied at 0, 60, or 120 mg L⁻¹. After 10 days, biomass accumulation, chlorophyll fluorescence parameters (F_v′/F_m′, ΦPSII, and ETR), gas exchange, and non-enzymatic antioxidant traits were determined. Even under this early-stage stress regime, water deficit significantly reduced shoot and root biomass, net photosynthesis, and stomatal conductance, while increasing lipid peroxidation. Foliar application of ChNPs, particularly at 60 mg L⁻¹, restored dry matter production and improved photochemical efficiency and electron transport rate by 14%; likewise, net CO₂ assimilation increased by 11.7%. In addition, this dose enhanced antioxidant activity and total phenols by 66% and 1.6-fold, respectively. ChNPs at 60 mg L⁻¹ mitigated the effects of WD in S. lycopersicum by increasing antioxidant and photosynthetic performances. Nevertheless, additional molecular studies, including enzymatic antioxidant characterization and compatible solute profiling, are required to elucidate the mechanisms involved. Full article

Accurate traffic flow forecasting is essential for the stable operation and efficient scheduling of intelligent transportation systems. The key lies in identifying the complex spatio-temporal dependencies within the road network structure. In the real world, traffic data are often noisy and incomplete due to sensor failures, communication interruptions, and other unexpected disturbances. To overcome these challenges, this paper proposes an adaptive spatio-temporal self-supervised traffic flow forecasting method based on contrastive learning (ASTSS-CL). At the graph level, structural perturbations are generated by combining node centrality with nonlinear probabilities, while a learnable temporal-periodic parameter matrix and an attention-based fusion mechanism are introduced to adaptively optimize adjacency relationships. At the temporal level, complementary augmentations are designed in both the time and frequency domains. Dynamic interpolation captures continuous traffic variations, while wavelet decomposition and node-adaptive frequency masking balance low-frequency trends and high-frequency details; random masking further improves robustness to missing observations and disturbances. In addition, spatial heterogeneity learning and contrastive consistency learning are jointly employed to enhance representation quality. Experiments on the PeMS04 and PeMS08 datasets show that ASTSS-CL achieves MAE, RMSE, and MAPE values of 17.95, 28.86, and 12.07% on PeMS04, and 13.78, 22.05, and 9.46% on PeMS08, respectively, outperforming the best-performing baseline. These results validate the effectiveness of the proposed method and demonstrate its potential to support traffic management and the operation of intelligent transportation systems. Full article

This study aimed to investigate the regulatory effect and cryoprotective mechanism of melatonin (MT) on the physiological functions of Lactobacillus plantarum FQR during freezing and freeze-drying. Results indicated that the addition of 5 mg/mL MT as a cryoprotectant maximized the freeze-drying survival rate to 32.04 ± 2.14%. MT effectively alleviated low-temperature and freeze-drying stress by reducing extracellular alkaline phosphatase activity, enhancing intracellular lactate dehydrogenase activity, and decreasing extracellular β-galactosidase activity without significant differences. Higher survival rates in defining medium further suggested that MT reduced damage to cell wall and membrane structures during lyophilisation, decreased membrane permeability, and preserved cellular physiological functions. In addition, MT supported cellular energy metabolism and protein synthesis, enhanced transmembrane potential to facilitate ATP transport, and helped maintain intracellular and extracellular pH balance. The prepared freeze-drying protectant containing 69.80 mg/mL exopolysaccharides (EPS) and 4.25 mg/mL MT showed better protective effects than the control group. MT also increased bound water content, lowered the freezing point of the solution, and inhibited ice crystal formation. Transcriptomic analysis revealed that amino acid biosynthesis, amino acid metabolism, and ABC transport systems were the primary pathways affected by MT treatment. These findings demonstrate that MT improves freeze-drying tolerance by maintaining membrane integrity, regulating cellular metabolism, and enhancing oxidative stress resistance. Given its natural biosynthetic origin, generally recognized as safe (GRAS) status, and absence of residual solvents or allergenic proteins, MT can be safely considered for incorporation into food and nutraceutical products. This study underscores the practical relevance of MT as a functional component in compound cryoprotectants, providing a feasible strategy to enhance the viability, stability, and industrial applicability of Lactobacillus plantarum during freeze-drying and storage. Full article

Industrial transformation in resource-based regions (RBRs) is a global challenge. Shanxi is a typical resource-based province in China. The long-term exploitation of coal resources has posed huge challenges to its ecological protection and high-quality development. Breaking away from the single-city perspective, this study focuses on the regional scale and comparative analysis and attempts to construct a novel three-dimensional analytical framework, namely, “industrial characteristics, industrial layout, and industrial policies”, to explore the industrial transformation path of typical RBRs. The results indicate the following: (1) Shanxi does not have obvious advantages in terms of resource endowment, with a severely heavy industrial structure and strategic emerging industries still in the initial stage of development. At the national strategic level, it is still necessary to strengthen the application of the “pioneer and pilot” policies and mechanisms for innovation. (2) In the context of high-quality development, Shanxi needs to clarify the industrial transformation orientation. For agriculture, the focus should be placed on characteristic and efficient development. For industrial development, priority should be given to upgrading advantageous industries and cultivating emerging industries. For the tertiary industry, it is necessary to form a development pattern of “new producer services + characteristic tourism”. In terms of regional development layout, Shanxi should establish a macro-pattern to promote inter-regional coordinated development. (3) In the new period, Shanxi should accelerate the construction of transportation systems to improve the convenience of inter-regional cooperation. It is essential to increase investment in education and scientific research so as to enhance the overall social innovation capacity. Meanwhile, differentiated regional development policies should be adequately supplied to drive the high-quality evolution of local industries. Focusing on the regional scale, the new logical analysis paradigm can provide theoretical references for RBRs to clarify the direction of industrial transformation and formulate transformation policies. Full article

Conventional truck emission accounting methods based on average activity levels and static emission factors are increasingly inadequate for dynamic regulation and policy comparison at high spatiotemporal resolution. This review synthesizes recent progress in dynamic truck emission estimation from four perspectives: multi-source data support, key feature extraction, physics-constrained emission modeling, and governance-oriented applications. The literature was collected from Web of Science Core Collection and ScienceDirect for the period 2014–2026, supplemented by backward reference checking, and was analyzed through a progressive framework linking data, features, models, and governance tasks. Unlike previous reviews that usually discuss emission inventories, conventional emission models, or data-driven prediction methods separately, this review highlights an integrated governance-oriented chain that connects multi-source data fusion, mechanism-related feature construction, physics-constrained modeling, and environmental management applications. Existing studies suggest that multi-source data, including GPS trajectories, on-board diagnostics (OBDs), on-board monitoring (OBM), portable emissions measurement system (PEMS) measurements, traffic flow monitoring, and road network attributes, provide an important basis for representing real-world operating processes. Meanwhile, key features have expanded from surface-level variables such as vehicle velocity to mechanism-related factors, including payload, road grade, engine operating conditions, vehicle-specific power, and roadway context. Truck emission modeling has also evolved from unconstrained or weakly constrained approaches toward frameworks that place greater emphasis on physical consistency, interpretability, and result credibility. In parallel, application scenarios have extended from emission quantification to high-emission vehicle identification, dynamic inventory development, hotspot detection, policy comparison, and transport optimization. These developments can support policymakers, transportation planners, and environmental agencies in moving from aggregate emission accounting toward targeted and process-based truck emission governance. Current research, however, still faces challenges related to data consistency, model generalizability, uncertainty propagation, and real-time application. Future work should focus on standardized datasets, hybrid AI–physics modeling frameworks, uncertainty-aware validation, real-time deployment in intelligent transportation systems, and improved links between dynamic estimation and practical environmental management. Full article

Preceramic polymers, especially silicon oxycarbide (SiOC) and silicon carbonitride (SiCN) ceramics, have gained significant attention due to their wide range of applications in many fields, particularly in energy storage devices beyond conventional lithium-ion batteries (LIBs). This review focuses on the synthesis, structural characteristics, and properties of SiOC and SiCN ceramics as electrodes for battery applications. Furthermore, their promising applications as electrode materials for energy storage systems are explored, along with the most recent advances in the development of such materials and their use in lithium-ion batteries (LIBs), lithium-sulfur batteries (LSBs), potassium-ion batteries (PIBs), sodium-ion batteries (SIBs), and supercapacitors. This review addresses the distinct advantages of SiOC and SiCN ceramics, including high thermal stability, mechanical robustness, and adaptable microstructures. It also examines the challenges associated with the commercialization of these ceramics, including issues related to electronic conductivity and ion transport pathways. Full article

The Internet of Vehicles (IoV) is rapidly emerging as a core component of intelligent transportation systems, enabling real-time communication among vehicles, infrastructure, and cloud platforms. However, the increasing interconnectivity of vehicular systems and the advancement of quantum computing introduce significant security challenges to existing cryptographic mechanisms. Conventional schemes such as RSA and Elliptic Curve Cryptography (ECC) are vulnerable to quantum attacks and are computationally inefficient for resource-constrained vehicular environments. To address these limitations, this paper proposes a Double-Ring Hybrid Sparse NTRU (DRH-SNTRU) framework, a lightweight and quantum-resistant cryptographic scheme for secure IoV communication. The proposed framework introduces three key enhancements: (i) controlled-support sparse polynomial structures to reduce polynomial multiplication complexity while improving entropy distribution; (ii) a double-ring algebraic architecture that separates key operations from message processing to enhance structural security and minimize coefficient leakage; and (iii) hybrid ephemeral keys derived from contextual entropy to strengthen forward secrecy and adaptive security. An optional ciphertext evaluation mechanism is further incorporated to detect malformed and replayed ciphertexts prior to decryption. Security analysis demonstrates that the proposed framework achieves IND-CPA security under the hardness assumption of the NTRU lattice problem and can be extended to resist chosen-ciphertext attacks through the integrated validation mechanism. Experimental benchmarking across polynomial dimensions N = 512 to 8192 demonstrates that DRH-SNTRU achieves low setup overhead below 3 μs, efficient decryption latency of approximately 305.64 μs at N = 8192, and compact sparse private key representation of only 117 bytes at higher dimensions. Compared with Standard NTRUEncrypt, NTRU-HRSS, and Ring-LWE Encryption, the proposed framework demonstrates improved decryption efficiency, lightweight storage overhead, and enhanced ciphertext integrity protection while maintaining practical scalability for resource-constrained post-quantum IoV environments. Full article

The modernization of global energy infrastructure within the Industry 5.0 framework requires the use of high-strength steels and reliable joining technologies to ensure safe, sustainable pipeline transport. This study focuses on the analysis of heterogeneous welded joints formed between high-strength alloy steel (34KhN2MA/EN 34CrNiMo6) and an austenitic welded seam (ER 307). While austenitic welds mitigate the risk of cold cracking, they introduce significant structural and mechanical heterogeneity. To address this, the research proposes and validates a material homogeneity criterion (MHC) derived from the LM-hardness methodology. By analyzing the statistical dispersion of macrohardness (HRC) through indicators such as the Weibull homogeneity coefficient (m) and the coefficient of variation (ν), the study establishes a quantitative approach to assess material degradation and structural uniformity across key weld zones. Results demonstrate that macrohardness profiling effectively distinguishes between structurally heterogeneous regions near the weld axis characterized by low homogeneity coefficients (m = 4.04 < 10, A_m = 0.742 < 0.878), elevated variability (ν = 29.68% > 11.6%), and high technological damageability (D = 0.92 > 0.81, j_D = 11.87 > 4.38) with pronounced step-like variation in macrohardness (HRC ∈ [12.6; 47]), on the one hand, and stabilized homogeneous zones in the base material, where m = 24.89 > 10, A_m = 0.947 > 0.878, ν = 4.39% < 11.6%, D = 0.52 ⟶ 0, j_D = 1.09 ⟶ 0, and characteristic range of HRC = 47–55, on the other hand. This methodology provides a robust, quasi-non-destructive tool for enhancing predictive maintenance, digital twins, and the overall integrity management of “smart” pipeline systems. Full article

Floating belt conveyor routes consisting of serially arranged belt conveyors, the end parts of which are mechanically attached to floating bodies, are designed for the continuous transport of extracted granular materials from water. This paper deals with the analytical determination of the position of the centre of gravity of the buoyancy force, the coordinates of which change depending on the longitudinal deflection of the floating body from the equilibrium state, which acts as a supporting element of individual conveyor belts. Analysis of the individual phases of deflection of the floating body, consisting of a pair of floats with a circular cross-section, shows that the complete submergence of one of the floats occurs at a higher value of the angle of inclination in the case when the floats are initially submerged under the surface to exactly half their diameter. On the realized experimental device, the buoyancy force was detected using strain gauges during the deflection of the floating body from the equilibrium position for three defined levels of immersion. The floating body of the experimental device consists of a pair of floats with a circular cross-section with a diameter of 80 mm. The output is a structured methodological procedure for determining the position of the centre of gravity of the displacement (centre of buoyancy) of a floating body when it deviates from the equilibrium position and a methodology for calculating the stability arm, which is a key parameter for assessing the buoyancy and stability of the body. On the basis of the laboratory measurements, the magnitude of the buoyancy force can be quantified as a function of the immersion depth of the floating body. It was found that the buoyancy force remains constant when the body deflects only when the immersion corresponds to half the diameter of a float with a circular cross-section. If the depth of the immersion is less than the radius of the float, the buoyancy force increases during deflection; however, if the immersion is greater than the radius of the float, the buoyancy force decreases. Full article

To promote the green and low-carbon transition and achieve sustainable development in the transportation sector, virtual calibration technology was employed for the efficient and precise control of emissions from heavy-duty diesel engines and aftertreatment systems. A data-driven, semi-empirical and semi-physical simulation modeling method was proposed. By constructing core modules based on physical mechanisms and refining empirical parameters using experimental data, the method improves computational efficiency while maintaining the prediction accuracy of key parameters. Additionally, a collaborative architecture combining physical actuators and virtual sensor signals was introduced, laying the foundation for the validity of virtual calibration. By innovatively introducing a closed-loop system with real actuators and virtual sensors, the dynamic response characteristics of the control system are faithfully reproduced, providing a reliable environment for validating the results of virtual calibration. Under steady-state conditions, the results demonstrated an average relative error of 1.7% for brake-specific fuel consumption (BSFC) and 6.1% for NOx emissions. An open-loop system for the virtual calibration testing platform was constructed for steady-state calibration. Using the main injection timing and common rail pressure as independent variables, a D-optimal design was utilized to generate 43 sets of experimental points, from which a polynomial regression model was established (R² ≥ 98%). Under the constraints of NOx and pre-turbine temperature, fuel consumption in the low-load range is reduced by 0.5–3 g/kW·h, aftertreatment NOx emissions are reduced by 0.5–3 g/kW·h, and exhaust temperature is increased by 10 °C. This study establishes a complete development workflow consisting of “operating condition design-virtual optimization-bench validation,” significantly enhancing calibration efficiency and engineering applicability. This method shortens the calibration cycle and reduces the number of physical bench tests, providing the industry with a comprehensive calibration methodology tailored to engine operating conditions that is both reproducible and scalable. Full article

Emerging contaminants (ECs) encompass a wide spectrum of pollutants, from endocrine disruptors and persistent organic pollutants to microplastics and pharmaceutical residues. These contaminants often exhibit distinct chemical and physical properties compared with traditional pollutants and potentially pose risks to human health, especially as they have become pervasive in environmental and biological systems. ECs can also pose a significant threat to cardiovascular health, as they may target the ion channels that are critical to regulating cardiac excitability and contraction. However, the impact of ECs on the cardiovascular system, particularly on cardiac ion channels, remains elusive. In this review, we aim to provide an overview of the knowledge base concerning the impact of emerging contaminants on cardiac ion channels, with an emphasis on the effects of these compounds on cardiac excitability, contractility, and overall cardiovascular function. We first outline the structural and functional characteristics of ion channels, along with how these transmembrane proteins regulate cardiac physiology. Subsequently, we detail how typical ECs directly or indirectly interact with various ion channels—including sodium, calcium, potassium channels, as well as ion transporters and exchangers. Special attention is given to studies that have demonstrated cell-level responses or examined how pollutant concentration and chemical structure affect the modulation of ion channels. This review compiles recent research reports to elucidate the mechanisms by which EC exposure disrupts cardiac ion channels, potentially leading to cardiotoxicity. Moreover, the insights gathered herein illuminate critical research gaps and outline essential directions for future investigations. Full article

This study proposes an attitude stabilization method based on an improved adaptive Kalman filter (AKF). The aim is to address attitude fluctuations and rollover risks in rail-based agricultural transport platforms on hilly terrain caused by slope changes, load shifts and vibrations. A dynamic model integrating the load distribution and center-of-mass migration was established, and an adaptive noise covariance mechanism was used to precisely estimate the roll and pitch angles in real time. A dual-channel proportional–integral–derivative controller was designed for automatic leveling, and a rollover risk index (RRI) was adopted for safety evaluation. Simulations revealed the ability of the improved AKF to decrease the roll estimation (RMSE) from 1.2684° to 0.8670° and the stabilization time from 0.6250 to 0.3830 s for the roll and from 0.6930 to 0.4110 s for the pitch. Under 10–30° slope disturbances, the average RRI decreased from 0.1861 to 0.1506. Field tests further demonstrated decreases in the peak roll and pitch angles from 4.8° and 4.1° to 3.1° and 2.7°, respectively, and a decrease in the average RRI from 0.203 to 0.169. The improvements in estimation accuracy, leveling performance, and operational safety under complex disturbances indicate the strong engineering potential of the proposed method. Full article

Does public transportation infrastructure expansion necessarily improve urban air quality? Using panel data from 168 Chinese cities, this study examines the impact of public transportation infrastructure development on air quality by applying GTWR (Geographically and Temporally Weighted Regression) models to capture spatial–temporal heterogeneity. Partial Dependence Plots (PDPs) are further employed to identify nonlinear relationships, alongside mechanism analysis. The results indicate that the effects of public transportation infrastructure on air quality are significant but highly heterogeneous across cities and over time. Transport development is associated with air quality through channels related to industrial transformation and agglomeration dynamics, with the latter showing a stronger relationship. Moreover, several key variables exhibit nonlinear relationships with identifiable threshold effects. These findings suggest that the environmental benefits of public transportation infrastructure are context-dependent rather than universal. This study provides a more comprehensive understanding of transport–environment linkages and offers policy insights for optimizing urban transport systems and promoting sustainable development. Full article