Search Results (2,744)

Reliable estimation of the State of Health (SOH) in lithium-ion batteries is critical to battery system security and dependability. However, existing Physics-Informed Neural Networks (PINNs) have drawbacks like single-feature physical constraints, rigid fixed-weight fusion of multi-feature constraints and insufficient time-series degradation modeling. To solve these problems, this study proposes an Adaptive-Weight PINN (AW-PINN) optimized by the Grey Wolf Optimizer (GWO) algorithm, which features a dual-LSTM parallel structure and takes incremental capacity peaks and charged capacity as dual physical constraints. A weight generator LSTM adaptively learns weights for monotonicity losses without manual intervention, and GWO globally optimizes physical loss weights to balance data fitting accuracy and prediction physical consistency. Validated on ${\mathrm{LiCoO}}_2$, NCA, and NCM batteries from CALCE and Tongji University datasets via comparative, ablation, and small-sample experiments, AW-PINN shows superior predictive performance (average RMSE = 0.0076; MAE = 0.0065; MAPE = 0.0072), robustness, and generalization. It integrates battery degradation physics with deep learning, retaining strong fitting capability while enabling physical interpretability. Full article

The northern Vietnam shelf, particularly the area adjacent to the Red River Fault Zone, is characterized by complex geology and active neotectonics. However, the patterns of degassing and the origins of hydrocarbon gases in this region remain poorly understood. In particular, the potential links between deep-seated fluid migration, fault systems, and gas anomalies in island groundwater systems have not been systematically investigated. This study presents preliminary results of dissolved methane, its homologues (C₂–C₅), helium, hydrogen, and carbon dioxide measurements in groundwater from Co To Island (Northern Vietnam), with the aim of identifying gas origins and assessing structural controls on fluid migration. A significant methane anomaly was discovered, with concentrations reaching up to 10% by volume in the northwestern part of the island. The hydrocarbon homologous series is traced up to pentane (C₅), and CO₂ content is also elevated, with a maximum of 5.4%. The average He concentration of 10.8 ppm significantly exceeds atmospheric equilibrium values, with maximum recorded concentrations of 18 ppm for He and 34.5 ppm for H₂. Stable carbon isotope analysis of methane (δ¹³C-CH₄ values ranging from −50.2‰ to −49.7‰ VPDB), combined with the presence of a complete C₁–C₅ hydrocarbon series and elevated mantle/crustal tracers (He, H₂), indicates a predominantly thermogenic/metamorphogenic origin for the gases, ruling out a purely biogenic source. The spatial distribution of anomalies is structurally controlled, closely associated with the NE-SW trending Co To Fault system and its intersections with subsidiary faults, as corroborated by recent electrical resistivity tomography data. These findings indicate intensive, focused gas leakage from a deep-seated source, likely related to thermogenic/metamorphic processes and active fault-mediated degassing. The results highlight the significant hydrocarbon potential of the region and underscore the critical role of neotectonic activity in controlling fluid migration pathways in island aquifer systems. Full article

This study proposes a tension-spring-based unidirectional rotational stiffness mechanism (TS-URM) and its implementation in a Unidirectional Series Elastic Actuator (USEA). Unlike conventional bidirectional rotary SEAs, the proposed design is structurally optimized for unidirectional torque transmission, improving deformation utilization efficiency in pulling-type applications. An analytical model was derived to establish the geometric relationship between spring elongation and rotational deformation, enabling explicit formulation of the torque–angle relationship. The influence of the installation angle on stiffness linearity was systematically analyzed, and a multilayer spring configuration was optimized to achieve a target rotational stiffness of approximately 42 Nm/rad. A preload adjustment mechanism was incorporated to eliminate nonlinear behavior in the initial operating region. Experimental results validated the analytical model and demonstrated stable unidirectional force control up to 130 N with steady-state errors within 1 N. The proposed mechanism provides predictable stiffness characteristics and an efficient structural solution for compact USEA systems. Full article

Noise contamination is a common challenge in the analysis of time series data, where stochastic perturbations can obscure deterministic dynamics and complicate the interpretation of signals from chaotic and physiological systems. Reliable identification of noise regimes and their intensity is therefore essential for robust analysis of dynamical and biomedical signals, where incorrect attribution of stochastic perturbations can lead to misleading interpretations of system behavior. For this reason, the present study examines the role of complexity-based descriptors for identifying stochastic perturbations in time series and analyzes how these metrics respond to different noise regimes across heterogeneous dynamical systems. A supervised learning approach based on complexity descriptors was developed to analyze controlled perturbations in multiple signal types. Gaussian, pink, and low-frequency noise disturbances were injected at predefined intensity levels into the Rössler and Lorenz chaotic systems, the Hénon map, and synthetic electrocardiogram signals, while AR(1) processes were used for validation on inherently stochastic signals. From these systems, eighteen entropy-based, fractal, statistical, and singular value decomposition-based complexity metrics were extracted from either raw signals or reconstructed phase spaces. These features were used to perform three classification tasks that capture different aspects of noise characterization, including detecting the presence of noise, identifying the perturbation type, and discriminating between different noise intensities. In addition to predictive modeling, the study evaluates the complexity profiles and feature relevance of the metrics under varying perturbation regimes. The results show that no single complexity metric consistently discriminates noise regimes across all systems. Instead, system-specific relevance patterns emerge. Under given experimental constraints (data partitioning, machine learning algorithm, etc.), Approximate Entropy provides the strongest discrimination for the Lorenz system and the Hénon map, the Coefficient of Variation, Sample and Permutation Entropy dominate classification for ECG signals, and the Condition Number and Variance of first derivative together with Fisher Information are most informative for the Rössler system. Across all datasets, the proposed framework achieves an average accuracy of 99% for noise presence detection, 98.4% for noise type classification, and 98.5% for noise intensity classification. These findings demonstrate that complexity metrics capture structural and statistical signatures of stochastic perturbations across a diverse set of dynamic systems. Full article

Synthetic dyes discharged from the textile and dyeing industry present a significant environmental and health hazard due to their inherent toxicity, environmental persistence, and potential carcinogenicity. Microbial degradation has garnered significant interest as a cost-effective and eco-friendly strategy for dye wastewater treatment in recent years. The study systematically evaluated the decolorization performance, degradation pathways, and detoxification effects of three bacterial strains, including Rhodopseudomonas palustris gh32, Bacillus cereus HL7, and Bacillus safensis X64, on the dye indigo carmine (IC) and three azo dyes: reactive black 5 (RB5), direct black G (DBG), and direct blue 15 (DB15). The degradation mechanisms were elucidated through UV-Vis spectroscopy, UPLC-Orbitrap-HRMS analysis, and enzyme activity assays. Molecular docking simulations were employed to investigate the interactions between key redox enzymes (such as laccase, tyrosinase, and azoreductase) and the dye molecules. The results demonstrated that the strain-specific enzymatic systems effectively disrupted the dye structures. Significant detoxification effects were further confirmed through a series of bio toxicity assays involving Escherichia coli, Bacillus subtilis, plant seeds, and erythrocytes. The addition of Fe³⁺, sodium citrate, or yeast extract significantly enhanced both the decolorization efficiency and enzyme activity. This study provides an in-depth understanding of the bacterial dye degradation process at the mechanistic level, highlighting the potential of customized bacterial systems for eco-friendly dye wastewater treatment. It offers theoretical support for elucidating the mechanisms of bacterial dye degradation and advancing bioremediation technologies. Full article

Water is increasingly acknowledged as a limited and strategically critical resource, particularly in regions where hydrological imbalances are structurally persistent. Across Europe, countries such as Spain, Turkey, Italy, and Greece face recurrent water scarcity driven by precipitation regimes characterized by low annual rainfall, pronounced temporal variability, and marked spatial heterogeneity. In response to rising water demand associated with tourism, agricultural intensification, and sustained demographic pressures, Spain has implemented a series of national water-management strategies over the past two decades. Notably, the National Hydrological Plan, enacted in July 2005, introduced more than one hundred immediate actions focused on modernizing hydraulic infrastructure and reinforcing the country’s desalination capacity. Furthermore, the Royal Decree issued in December 2007 established a comprehensive regulatory framework to promote and standardize water reuse practices nationwide. Within this context, reverse osmosis has emerged as a central technology for the desalination of seawater and brackish water, as well as for advanced water-reclamation applications. This work presents a consolidated examination of Spain’s water-resource management framework, drawing on historical material and recent advances to outline the current context of desalination and water reuse. It presents operational performance data from several full-scale reverse osmosis facilities, and reviews recent technological developments in the field, including newly engineered membrane modules, innovative system architectures, and the latest generation of large-diameter RO elements. Together, these advancements illustrate the evolving role of membrane-based desalination and water reuse in supporting water security in semi-arid regions. Full article

Internet-of-Things (IoT) sensing can improve traceability, safety, and efficiency of medical waste handling, yet many deployments remain fragmented, lack an end-to-end system architecture, and do not provide the structured data pipelines needed for artificial intelligence (AI) analytics. This paper presents a layered IoT-based system design for medical waste management that integrates: (i) Espressif Systems 32 (ESP32)-based edge devices for fill-level and Global Positioning System (GPS) telemetry; (ii) secure network communication; (iii) a cloud backend for data ingestion, storage, and analytics; and (iv) operator dashboards with event-driven alerting. The architecture extends our prior GPS-enabled tracking and route optimization by adding sensor-driven state monitoring, threshold-based decision support, and a time-series data pipeline designed for future AI-driven predictive analytics. In a 30-day pilot with five containers, the system collected one reading every 15 min (14,400 total readings). The backend demonstrated efficient processing with an average Application Programming Interface (API) response time of 45 ms, sub-50 ms database write latency, and high uptime; alerts were delivered promptly upon threshold violations. Compared with a fixed-schedule baseline, the system enabled condition-based collection scheduling with zero data loss. The proposed design emphasizes modularity, fault tolerance, and integration readiness for hospital information systems, providing a practical blueprint for scalable smart-healthcare waste logistics and a foundation for machine learning-based predictive waste management. Full article

Europe’s fossil-based electricity mix has shifted rapidly in recent years, raising a practical question: can we model competitive substitution among fuels with a framework that is both predictive and interpretable? We address this by combining a compact neural network (NN) with a three-dimensional Lotka–Volterra (LV) system to study monthly EU coal, natural gas, and oil-fired generation shares from the second semester of 2017 to 2023. After converting the series to row-wise shares that sum to one, we use the first $70\%$ of the sample to learn smooth trajectories and data-driven derivatives with the NN and then estimate the LV interaction coefficients through a constrained nonlinear fit. We advance the calibrated LV system over the final $30\%$ holdout with a fourth-order Runge–Kutta (RK4) scheme and evaluate forecasts using the RMSE and MAE for each fuel share series. For comparison, we report the results against both a neural network-only forecasting baseline and a classical ARIMA benchmark, both trained on the same $70\%$ window and evaluated on the same $30\%$ holdout. The hybrid NN–LV model achieves competitive forecast errors while yielding interpretable interaction patterns consistent with substitution pressures (for example, negative cross-effects between coal and gas). Finally, we run counterfactual shock experiments to illustrate how a change in one fuel’s share propagates through the mix under the learned LV dynamics, highlighting the usefulness of embedding a simple mechanistic structure within a data-driven estimator. Full article

The generative mechanisms underlying multifractal scaling in complex systems remain a fundamental unsolved problem, limiting our ability to distinguish healthy from pathological dynamics, predict system failures, or understand how scale-invariant organization emerges across vastly different physical domains. We resolve this challenge by introducing threshold sensitivity analysis—an extension of Chhabra–Jensen’s direct method—as a framework that classifies cascade types by examining how scaling reliability varies across moment orders q. Different q values systematically probe weak fluctuations (negative q) versus strong fluctuations (positive q), and the coefficient of determination ($r^2$) of partition function regressions quantifies scaling reliability at each q. Analyzing $r^2\left(q\right)$ patterns in 280 cardiac recordings (healthy controls through fatal heart failure), 200 financial time series (global equity markets and currencies, 2000–2025), and 80 climate stations (tropical to continental zones, 2000–2025), we discover a universal diagnostic signature: symmetric expansion of valid scaling behavior under relaxed $r^2$ thresholds, spanning both weak and strong fluctuations. This threshold sensitivity fingerprint—predicted by synthetic cascade simulations but never before validated empirically—uniquely identifies additomultiplicative cascades, hybrid processes that randomly alternate between additive stabilization and multiplicative amplification. Critically, this symmetric signature persists universally across domains: cardiac dynamics maintain consistent patterns across health and disease states, financial markets show varying robustness across asset classes (currencies more variable than US equities) while preserving a hybrid structure, and climate systems exhibit geographical variations (subtropical/continental stronger than tropical) without altering fundamental cascade type. These findings suggest that additomultiplicative organization is a unifying feature of complex adaptive systems, offering a resolution to decades of debate between additive and multiplicative models. The $r^2\left(q\right)$ profiling provides a mechanistic diagnostic capable of detecting early dysfunction, assessing system resilience, and revealing how environmental constraints shape—but do not determine—the fundamental principles governing multifractal complexity. Full article

The impact of large boulders transported by debris flows is a primary cause of structural damage to mitigation works. However, quantitative modeling remains difficult because of the scarcity of field measurements and the complexity of the flow medium. In this study, a theoretical model for boulder impact force in debris flows is developed using dimensional analysis based on the Buckingham theorem, subsequently simplified to two dimensionless parameters, and then validated through a series of controlled laboratory experiments. Marble spheres were used as impactors and were released to strike a rigid steel plate under three types of media: clear water, bentonite slurry, and debris flows containing particles of different size classes. The experiments were designed to isolate and quantify the influence of the flow rheology and the suspended solid phase on impact forces. The results show that the impact coefficient c is strongly governed by the debris flow yield stress, bulk density, and the size of suspended particles, following the relationship c = 0.183[τ/(rgd₁)]^−0.1(d/d₀)^0.05. Based on this relationship, a generalized formula for calculating boulder impact forces in debris flows is proposed. The model is further evaluated using field monitoring data from Jiangjiagou, Yunnan Province. The back-calculated boulder diameters fall predominantly within the range of 0.1–0.3 m (76.3–86.8%), which is consistent with field observations. These results indicate that the proposed model captures the essential physical mechanisms governing boulder impacts and provides a rational basis for selecting design parameters in debris flow mitigation engineering. The array-type piezoelectric impact sensing system designed in this study achieves high-precision and high-stability measurement of the impact force of large boulders in debris flows, providing a new sensing technology for debris flow impact monitoring. Full article

Tropical cyclones rank among the most destructive natural hazards globally, posing significant threats to coastal ecosystems and communities. Mangrove forests, renowned for their ecological importance and coastal protection services, are vulnerable to these disturbances, suffering structural damage, habitat loss, and disruption of vital ecosystem functions. Conventional field-based assessment methods often fall short in capturing the rapid and widespread impacts of cyclones, particularly in remote or cloud-obscured regions. This review aims to provide a comprehensive synthesis of remote sensing applications for monitoring cyclone-induced impacts on mangrove and coastal ecosystems worldwide. Through a systematic literature review of 74 peer-reviewed articles from 1990 to 2025, the study evaluates the utility of optical sensors, radar systems, and multi-sensor platforms in assessing inundation, vegetation damage, and ecosystem service loss. Key methodological advances such as time-series analysis, machine learning, and UAV-based validation are highlighted, alongside critical gaps including limited geographic coverage, weak validation practices, and minimal socio-economic integration. Notably, 75.4% of reviewed studies are concentrated in Asia, with Bangladesh and India alone accounting for 44.6% of the total literature, underscoring a pronounced geographic bias. The findings underscore the need for robust, near-real-time monitoring frameworks that combine satellite technologies with ground data and community engagement. Ultimately, the review advocates for an integrated, multi-sensor, and participatory approach to cyclone resilience, offering valuable insights for future research, disaster response planning, and sustainable mangrove management. Full article

This study investigates the thermal performance of a flat-plate phase-change thermal energy storage system, focusing on two structural innovations: a cascaded arrangement of multiple phase-change materials (PCMs) with varying melting points, and the implementation of series/parallel flow configurations. A combined numerical and experimental approach is employed to analyze dynamic charging/discharging behavior. Quantitative results indicate that the cascaded configuration (three PCMs) reduces phase-change completion time by 13% and increases cooling energy storage power from 2.00 kW to 2.43 kW during charging compared to single-PCM systems. Flow configuration significantly impacts thermal response: the parallel layout delivers more stable cooling output, while the series layout achieves faster initial cooling (reaching 6.24 °C within 1200 s, 31% faster than the parallel layout). Experimental results reveal that inlet water temperature is the most critical operating parameter, with each 2 °C increase significantly prolonging charging time. This work offers practical guidance for the design and optimization of efficient cascaded PCM thermal storage systems. Full article

This paper presents the synthesis, physicochemical characterisation, and analytical applications of chemiluminescent (CL) labels based on acridinium salts (ALs) for biomedical diagnostics. These compounds emit light as a result of oxidative reactions and represent an established class of reagents widely employed in chemiluminescence immunochemical assays (CLIAs) today. A series of structurally differentiated acridinium labels (AL1–AL5) was synthesised applying mostly original synthetic routes and purified to chromatographic purity (>90%, RP-HPLC). The compounds, including a commercial product treated as a reference, were successfully conjugated to anti-human IgG, yielding stable immunochemical reagents suitable for immunoassays with CL detection. The chemiluminescence properties of the obtained labels and their protein conjugates were investigated in aqueous buffers and in the presence of surfactants. The emission profiles exhibited characteristic flash-type kinetics with emission maxima occurring within 0.15–0.25 s after reaction initiation. The presence of surfactants more or less significantly enhanced the emission intensity, with signal increases of up to approx. 2-fold compared to surfactant-free systems. Analytical calibration demonstrated a linear response of signal derived from native labels over at least one order of magnitude of concentration, with detection limits falling in the range of 10⁻⁹–10⁻¹⁰ M, confirming the high sensitivity of the developed compounds. The experimental results were supported by theoretical studies using density functional theory (DFT), which confirmed the energetic feasibility of the CL reaction pathway and identified structural factors influencing activation barriers. Additional semiempirical calculations (PM7) indicated that the dielectric environment and proximity of ionic species can influence the reaction energetics, providing mechanistic support for the experimentally observed effects of surfactants. The results demonstrate that both molecular structure and microenvironment influence CL efficiency and kinetics of the investigated systems. The developed acridinium labels exhibit analytical performance better or comparable to commercial reagents and are fully compatible with standard immunodiagnostic conjugation protocols, confirming their suitability for use in modern chemiluminescent immunoassays. Full article

Model Predictive Control (MPC) has emerged as a promising approach for energy management in hybrid electric vehicles, enabling predictive optimization of powertrain operation. The energy management problem in parallel hybrid powertrains constitutes a Mixed-Integer Nonlinear Programming (MINLP) problem, combining continuous decision variables such as torque distribution with discrete decisions including engine on/off states and clutch engagement. This problem structure presents distinct challenges for different optimization approaches. Gradient-based methods such as Sequential Quadratic Programming (SQP) solve continuous, differentiable optimization problems and require auxiliary methods to handle integer variables, while metaheuristic approaches such as Genetic Algorithms (GA) can handle the mixed-integer structure directly at the cost of increased computational effort. This study presents a systematic comparison between GA and SQP as optimization solvers within an MPC framework for a P1P3 parallel hybrid powertrain. A multi-objective cost function is formulated to simultaneously optimize system efficiency, battery state of charge management, and noise emissions. Both approaches are evaluated across the WLTC as well as a real-world RDE scenario. On the WLTC, both MPC approaches reduce fuel consumption by 0.5–1.0% and improve system efficiency by 3.7–4.6% compared to a state-of-the-art deterministic reference strategy optimized for fuel consumption. At the same time, both approaches additionally achieve substantial reductions in noise emissions compared to the deterministic reference, which was not optimized for acoustic behavior. On both cycles, the GA-based MPC achieves favorable performance compared to SQP, with the performance gap widening from the WLTC to the RDE cycle. Both methods achieve real-time capability, yet SQP reduces computational time by a factor of four compared to GA. As long as computational resources in automotive ECUs remain constrained, this efficiency advantage positions gradient-based optimization for series production applications, whereas metaheuristic methods offer greater flexibility for concept development stages with relaxed real-time requirements. The findings contribute to the understanding of optimization algorithm selection for MINLP energy management problems in hybrid electric vehicles. Full article

In Hungary, a fast-growing Empress tree hybrid (×Paulownia Clone in vitro 112) also known as Smaragdfa^® has been developed as a low-density plantation species seeking industrial utilization. Many potential industrial applications presuppose its bonding. The presence of adhesives in bonded layered assemblies, with differing climatic conditions on the internal and outer side, may induce undesired internal strains due to restricted water vapor diffusion, especially in the case of Smaragdfa as a low-density wood species. For decades, lasures have been specifically formulated with a molecular structure that allows partial vapor transmission while hindering water diffusion. Lasure-coated samples were used as control samples to identify, among the different custom-made MW adhesives, the one with diffusion properties closest to those of the lasure. Uncoated Smaragdfa wood samples were used as the baseline reference to evaluate the effect of different adhesive and coating systems on water vapor diffusion. Smaragdfa samples were prepared both uncoated and coated with different adhesive and lasure layers. Experiments were conducted following ISO 12572 and ASTM E96 standards using the cup method, with all specimens pre-conditioned to 12% moisture content. Results showed that the uncoated Smaragdfa exhibited the highest diffusion coefficient (δ = 7.02 × 10⁻¹³ kg/(m·s·Pa)) and flow rate (G = 0.055763 g/h), while the commercial adhesive-coated sample displayed an 84% reduction in diffusion capacity (δ = 1.15 × 10⁻¹³ kg/(m·s·Pa)), indicating a strong vapor-blocking effect. The lasure coating allowed partial vapor transmission, confirming its semi-permeable nature. Adhesives formulated with varying polyol molecular weights (Series 1–5) revealed a clear molecular-weight-dependent diffusion behavior: low-MW systems (S1) acted as strong diffusion barriers comparable to lasure-coated samples (SMW_L), in the same time high-MW systems (S4, S5) permitted excessive diffusion but induced microcracking, while intermediate formulations (S2, S3) achieved the most balanced performance, combining moderate diffusion with structural stability. Overall, the findings confirm that adhesive layers significantly influence water vapor transmission through Smaragdfa wood, with the degree of hindrance closely related to the molecular weight of the polyol matrix. The optimized formulations (S2, S3) demonstrate promising potential for use in bonded assemblies and engineered wood products where controlled vapor diffusion and mechanical reliability are critical in order to support reduced strains caused by water vapor. Full article