Search Results (10,139)

State-of-charge (SoC) balancing across multiple battery energy storage systems (BESS) is a central challenge in renewable-rich mini-grids. Heterogeneous battery capacities, differing states of health, stochastic renewable generation, and variable loads create a high-dimensional uncertain control problem. Conventional droop-based SoC balancing strategies are decentralized and computationally light but fundamentally reactive and limited, whereas model predictive control (MPC) is insightful but computationally intensive and prone to modeling errors. This paper proposes a Hierarchical Predictive–Adaptive Control (HPAC) framework for SoC balancing in mini-grids using deep reinforcement learning. The framework consists of two synergistic layers operating on different time scales. A long-horizon Predictive Engine, implemented as a federated Transformer network, provides multi-horizon probabilistic forecasts of net load, enabling multiple mini-grids to collaboratively train a high-capacity model without sharing raw data. A fast-timescale Adaptive Controller, implemented as a Soft Actor-Critic (SAC) agent, uses these forecasts to make real-time charge/discharge decisions for each BESS unit. The forecasts are used both to augment the agent’s state representation and to dynamically shape a multi-objective reward function that balances SoC, economic performance, degradation-aware operation, and voltage stability. The paper formulates SoC balancing as a Markov decision process, details the SAC-based control architecture, and presents a comprehensive evaluation using a MATLAB-(R2025a)-based digital-twin simulation environment. A rigorous benchmarking study compares HPAC against fourteen representative controllers spanning rule-based, MPC, and various DRL paradigms. Sensitivity analysis on reward weight selection and ablation studies isolating the contributions of forecasting and dynamic reward shaping are conducted. Stress-test scenarios, including high-volatility net-load conditions and communication impairments, demonstrate the robustness of the approach. Results show that HPAC achieves near-minimal operating cost with essentially zero SoC variance and the lowest voltage variance among all compared controllers, while maintaining moderate energy throughput that implicitly preserves battery lifetime. Finally, the paper discusses a pathway from simulation to hardware-in-the-loop testing and a cloud-edge deployment architecture for practical, real-time deployment in real-world mini-grids. Full article

This paper presents the construction of exact wave solutions for the generalized nonlinear Schrödinger equation (NLSE) with second-order spatiotemporal dispersion using the modified exponential rational function method (mERFM). The NLSE plays a vital role in various fields such as quantum mechanics, oceanography, transmission lines, and optical fiber communications, particularly in modeling pulse dynamics extending beyond the traditional slowly varying envelope estimation. By incorporating higher-order dispersion and nonlinear effects, including cubic–quintic nonlinearities, this generalized model provides a more accurate representation of ultrashort pulse propagation in optical fibers and oceanic environments. A wide range of soliton solutions is obtained, including bright and dark solitons, as well as trigonometric, hyperbolic, rational, exponential, and singular forms. These solutions offer valuable insights into nonlinear wave dynamics and multi-soliton interactions relevant to shallow- and deep-water wave propagation. Conservation laws associated with the model are also derived, reinforcing the physical consistency of the system. The stability of the obtained solutions is investigated through the analysis of modulation instability (MI), confirming their robustness and physical relevance. Graphical representations based on specific parameter selections further illustrate the complex dynamics governed by the model. Overall, the study demonstrates the effectiveness of mERFM in solving higher-order nonlinear evolution equations and highlights its applicability across various domains of physics and engineering. Full article

Hydroxyl-terminated polybutadiene (HTPB) is widely used in polyurethane binders, adhesives, and elastomers, but its low polarity and unsaturated backbone limit adhesion and long-term stability. Epoxidation presents a promising approach to addressing these limitations. However, most prior studies have focused on high-cis polybutadiene (PB), and systematic tuning of epoxidation in industrial low-cis HTPB has not been thoroughly examined. In this work, the epoxidation conversion of low-cis HTPB was systematically controlled by varying the equivalent amount of 3-chloroperbenzoic acid (m-CPBA). Conversion was governed solely by oxidant stoichiometry, while reaction time, concentration, and temperature had minimal effect, consistent with rapid, mixing-controlled epoxidation. Selective modification of 1,4-cis and 1,4-trans units enabled direct evaluation of how epoxidation degree influences polyurethane network formation and performance. Polyurethanes derived from epoxidized HTPB (EHTPB-PU) exhibited a clear correlation between epoxidation degree and network formation. Mechanical, adhesion, and chemical-resistance measurements revealed optimal performance at 10% epoxidation, where polarity and network compactness are effectively balanced. At this level, polyurethanes showed enhanced tensile strength, broad substrate adhesion, and increased resistance to acidic, basic, polar, and nonpolar environments, along with reduced water uptake. These results identify moderate epoxidation as a practical and efficient strategy for improving HTPB-based polyurethane materials. Full article

Conventional dental materials with organised crystal structures exhibit limitations in corrosion resistance, bioactivity, and drug delivery capability. In contrast, amorphous nanomaterials offer potential advantages in overcoming these limitations due to their unique structural properties. They are characterised by a non-crystalline, disordered atomic structure and are similar to a solidified liquid at the nanoscale. Among the amorphous nanomaterials used in dentistry, there are five major categories: calcium-, silicon-, magnesium-, zirconia-, and polymer-based systems. This study reviewed these amorphous nanomaterials by investigating their synthesis, properties, applications, limitations, and future directions in dentistry. These amorphous nanomaterials are synthesised primarily through low-temperature methods, including sol–gel processes, rapid precipitation, and electrochemical etching, which prevent atomic arrangements into crystalline structures. The resulting disordered atomic configuration confers exceptional properties, including enhanced solubility, superior drug-loading capacity, high surface reactivity, and controlled biodegradability. These characteristics enable diverse dental applications. Calcium-based amorphous nanomaterials, particularly amorphous calcium phosphate, demonstrate the ability to remineralise tooth enamel. Silicon-based amorphous nanomaterials function as carriers that can release antibacterial agents in response to stimuli. Magnesium-based amorphous nanomaterials are antibacterial and support natural bone regeneration. Zirconia-based amorphous nanomaterials strengthen the mechanical properties of restorative materials. Polymer-based amorphous nanomaterials enable controlled release of medications over extended periods. Despite the advances in these amorphous nanomaterials, there are limitations regarding material stability over time, precise control of degradation rates in the oral environment, and the development of reliable large-scale manufacturing processes. Researchers are creating smart materials that respond to specific oral conditions and developing hybrid systems that combine the strengths of different nanomaterials. In summary, amorphous nanomaterials hold great promise for advancing dental treatments through their unique properties and versatile applications. Clinically, these materials could improve the durability, bioactivity, and targeted drug delivery in dental restorations and therapies, leading to better patient outcomes. Full article

With the rapid deployment of high-speed maglev transportation systems worldwide, the operational velocity, electromagnetic complexity, and channel dynamics have far exceeded those of conventional rail systems, imposing more stringent requirements on real-time capability, reliability, and interference robustness in wireless communication. In maglev environments exceeding 600 km/h, the channel becomes predominantly line-of-sight with sparse scatterers, exhibiting strong Doppler shifts, rapidly varying spatial characteristics, and severe interference, all of which significantly degrade the stability and convergence performance of traditional beamforming algorithms. Adaptive smart antenna technology has therefore become essential in high-mobility communication and sensing systems, as it enables real-time spatial filtering, interference suppression, and beam tracking through continuous weight updates. To address the challenges of slow convergence and high steady-state error in rapidly varying maglev channels, this work proposes a new Fractional Proportionate Normalized Least Mean Square (FPNLMS) adaptive beamforming algorithm. The contributions of this study are twofold. (1) A novel FPNLMS algorithm is developed by embedding a fractional-order gradient correction into the power-normalized and proportionate gain framework of PNLMS, forming a unified LMS-type update mechanism that enhances error tracking flexibility while maintaining $\mathcal{O}\left(L\right)$ computational complexity. This integrated design enables the proposed method to achieve faster convergence, improved robustness, and reduced steady-state error in highly dynamic channel conditions. (2) A unified convergence analysis framework is established for the proposed algorithm. Mean convergence conditions and practical step-size bounds are derived, explicitly incorporating the fractional-order term and generalizing classical LMS/PNLMS convergence theory, thereby providing theoretical guarantees for stable deployment in high-speed maglev beamforming. Simulation results verify that the proposed FPNLMS algorithm achieves significantly faster convergence, lower mean square error, and superior interference suppression compared with LMS, NLMS, FLMS, and PNLMS, demonstrating its strong applicability to beamforming in highly dynamic next-generation maglev communication systems. Full article

Mating disruption (MD) is an environmentally friendly pest management approach that uses synthetic pheromones to interfere with insect mate location and reproduction. This review summarizes current progress in the application of MD for stored-product pests, with emphasis on Lepidoptera (Plodia interpunctella Hübner and Ephestia kuehniella Zeller (Pyralidae)) and Coleoptera (Sitophilus spp. (Curculionidae)). For moth pests, numerous studies have demonstrated substantial suppression of mating and population growth under both laboratory and field conditions, particularly when MD is integrated with sanitation, monitoring and other IPM measures. Conversely, MD applications against beetles have been less successful due to their aggregation-based communication and lower volatility of their pheromones. Advances in pheromone formulation technology, including polymer dispensers, microencapsulated sprays and aerosol emitters, have improved pheromone stability and controlled release, although achieving uniform coverage in large and aerated storage environments remains challenging. The integration of MD with biological control, temperature management and reduced fumigant use offers promising directions for sustainable pest suppression. Continued development of smart-release devices, long-term field validation and integration with automated monitoring systems will further enhance the feasibility and cost-effectiveness of MD. Overall, MD represents a key behavioral component in reducing pesticide reliance and promoting sustainable management of stored-product pests. Full article

This work presents a data-driven framework for early detection of polymer melt instability in industrial injection moulding using Long Short-Term Memory (LSTM) time-series models. The study uses six months of continuous production data comprising approximately 280,000 injection cycles collected from a fully operational thermoplastic injection line. Because melt behaviour evolves gradually and conventional threshold-based monitoring often fails to capture these transitions, the proposed approach models temporal patterns in torque, pressure, temperature, and rheology to identify drift conditions that precede quality degradation. A physically informed labelling strategy enables supervised learning even with sparse defect annotations by defining volatile zones as short time windows preceding operator-identified non-conforming parts, allowing the model to recognise instability windows minutes before defects emerge. The framework is designed for deployment on standard machine signals without requiring additional sensors, supporting proactive process adjustments, improved stability, and reduced scrap in injection moulding environments. These findings demonstrate the potential of temporal deep-learning models to enhance real-time monitoring and contribute to more robust and adaptive manufacturing operations. Full article

Organ-on-a-chip (OoC) or tissue-on-a-chip (ToC) technologies represent a significant advancement in enabling modeling of human organ and tissue physiology for medical study, although further development is required for these technologies to reach widespread adoption. OoC/ToC are three-dimensional (3D) microfluidic platforms that overcome limitations of traditional two-dimensional (2D) cell culture or animal models, providing an alternative environment for disease study, drug interactions, and tissue regeneration. The design of these systems is complex, requiring advanced fabrication techniques and careful selection of biomaterials with consideration of material toxicity, optical clarity, stability, and flexibility. A key innovation in this field is the multi-organ-on-a-chip (MOC) technology, which links multiple organ systems on a single platform. This enables the study of systemic diseases and the complex communication between organs, which is not possible with single-organ models. Furthermore, OoC/ToC technology holds immense potential for personalized medicine. By using patient-specific cells, these devices can create disease models that reflect an individual’s unique genetic and phenotypic variations, paving the way for tailored therapeutic interventions. The integration of real-time sensors within these devices also facilitates high-throughput screening and accelerates drug discovery. While the development and optimization of these systems is still in its early stages, OoC/ToC technologies have already demonstrated promise in a number of translational research applications. Full article

This study addresses the trajectory tracking problem for unmanned hovercrafts operating under unknown time-varying environmental disturbances and actuator saturation. To balance real-time performance with control accuracy, an event-triggered adaptive nonlinear model predictive control (EANMPC) method is proposed. The approach dynamically adjusts the prediction horizon based on tracking error and incorporates an event-triggering mechanism to reduce unnecessary control updates. This design significantly alleviates computational burden while maintaining robust tracking performance. Furthermore, a rigorous input-to-state stability proof is provided without resorting to local linearization. Simulation results under two distinct trajectories demonstrate that the proposed method achieves superior tracking accuracy and reduces computational cost by 57% compared to conventional NMPC. The framework thus offers a practical and efficient control solution for underactuated hovercraft systems operating in complex maritime environments. Full article

Local Interpretable Model-agnostic Explanations (LIME) is a widely used technique for interpreting individual predictions of complex “black-box” models by fitting a simple surrogate model to synthetic perturbations of the input. However, its standard perturbation strategy of sampling features independently from a Gaussian distribution often generates unrealistic samples and neglects inter-feature dependencies. This can lead to low local fidelity (poor approximation of the model’s behavior) and unstable explanations across different runs. This paper presents CoLIME, which is a copula-based perturbation generation framework for LIME, designed to capture the underlying data distribution and inter-feature dependencies more accurately. The framework employs bivariate (2D) copula models to jointly sample correlated features while fitting suitable marginal distributions for individual features. Furthermore, perturbation localization strategies were implemented, restricting perturbations to a defined local radius and maintaining specific property values to ensure that the synthesized samples remain representative of the actual local environment. The proposed approach was evaluated on a network intrusion detection dataset, comparing the fidelity and stability of LIME under Gaussian versus copula-based perturbations, using Ridge regression as the surrogate explainer. Empirically, for the most dependent feature pairs, CoLIME increases mean surrogate fidelity by 21.84–50.31% on the merged CIC-IDS2017/2018 dataset and by 29.28–60.24% on the UNSW-NB15 dataset. Stability is similarly improved, with mean Jaccard similarity gains of 3.78–5.45% and 1.95–2.12%, respectively. These improvements demonstrate that dependency-preserving perturbations provide a significantly more reliable foundation for explaining complex network intrusion detection models. Full article

Climate change in the Pannonian region is accelerating a shift toward autumn sowing of cool-season grain legumes (pea, faba bean, lentil, chickpea, lupine) to achieve higher yields, greater biomass production, enhanced nitrogen fixation, improved soil cover, and superior resource use efficiency compared with spring sowing. However, successful overwintering depends on the availability of robust winter-hardy cultivars. This review synthesizes recent breeding advances, integrating traditional approaches—such as germplasm screening, hybridization, and field-based selection—with genomics-assisted strategies, including genome-wide association studies (GWAS), quantitative trait locus (QTL) mapping, marker-assisted selection (MAS), and CRISPR/Cas-mediated editing of CBF transcription factors. Key physiological mechanisms—LT₅₀ determination, cold acclimation, osmoprotectant accumulation (sugars, proline), and membrane stability—are assessed using field survival rates, electrolyte leakage assays, and chlorophyll fluorescence measurements. Despite challenges posed by genotype × environment interactions, variable winter severity, and polygenic trait control, the release of cultivars worldwide (e.g., ‘NS-Mraz’, ‘Lavinia F’, ‘Ghab series’, ‘Pinklevi’, and ‘Rézi’) and ongoing breeding programs demonstrate substantial progress. Future breeding efforts will increasingly rely on genomic selection (GS), high-throughput phenomics, pangenomics, and G×E modeling to accelerate the development of climate-resilient legume cultivars, ensuring stable and sustainable production under increasingly unpredictable winter conditions. Full article

Enterprises in industries such as coking and metallurgy possess extensive industrial equipment requiring real-time monitoring and timely fault detection. Transmitting all monitoring data to servers or cloud platforms for processing presents challenges, including substantial data volumes, high latency, and significant bandwidth consumption, thereby compromising the monitoring system’s real-time performance and stability. This paper proposes a cloud–edge collaborative approach for edge feature extraction in equipment monitoring. A three-tier collaborative architecture is established: “edge pre-processing-cloud optimization-edge iteration”. At the edge, lightweight time-domain and frequency-domain feature extraction modules are employed based on equipment structure and failure mechanisms to rapidly pre-process and extract features from monitoring data (e.g., equipment vibration), substantially reducing uploaded data volume. The cloud node constructs a diagnostic feature library through threshold self-learning and data-driven model training, then disseminates optimized feature extraction parameters to the edge node via this threshold learning mechanism. The edge node dynamically iterates its feature extraction capabilities based on updated parameters, enhancing the capture accuracy of critical fault features under complex operating conditions. Verification and demonstration applications were conducted using an enterprise’s online equipment monitoring system as the experimental scenario. The results indicate that the proposed method reduces data transmission volume by 98.21% and required bandwidth by 98.25% compared to pure cloud-based solutions, while effectively enhancing the monitoring system’s real-time performance. This approach significantly improves equipment monitoring responsiveness, reduces demands on network bandwidth and data transmission, and provides an effective technical solution for equipment health management within industrial IoT environments. Full article

Poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) has become one of the most influential materials in neural engineering, offering high electrical conductivity, mechanical softness, and stable processing in complex aqueous media. Beyond these well-known merits, recent studies indicate that PEDOT:PSS can be regarded as a bio-solid electrolyte interphase (bio-SEI) that governs the interactions between neural probes and biological tissue. In this framework, PEDOT:PSS functions as a selective and adaptive interphase that mediates ion and electron transport, buffers mechanical mismatch, and mitigates chemical or biological degradation at the device-tissue boundary. This review critically summarizes the progress in molecular design, synthesis, and post-treatment strategies that enhance PEDOT:PSS stability and compatibility within physiological environments. Developments such as polydopamine-assisted adhesion, zwitterionic modification, and hybridization with soft hydrogels have expanded its role from a passive coating to an active, self-regulating interphase that prolongs implant performance. We further discuss how the hierarchical structure of PEDOT:PSS—from its molecular organization to device-level morphology—contributes to long-term electrochemical and biological stability. By treating PEDOT:PSS as an intrinsic bio-SEI rather than a simple conductive coating, this perspective highlights its central role in the development of durable, biocompatible, and intelligent neural interfaces for next-generation implantable electronics. Full article

To enable automatic extraction of high-precision paths for intelligent orchard operations, a path detection method targeting the fruit-tree dripline is proposed. The method integrates 2D-LiDAR, RTK-GNSS, and an electronic compass, achieving time synchronization, coordinate-frame construction, and extrinsic calibration. Point clouds are rotation-normalized via least-squares trajectory fitting; ground segmentation and statistical filtering suppress noise; segment-wise extremal edge points, together with an α-shape-based concave hull algorithm, fit and generate the dripline path; and inverse rotation restores the result to the orchard-local coordinate frame. Field experiments demonstrated that the method accurately extracts dripline paths in orchard environments; relative to manual measurements, the overall mean absolute error was 0.23 m and the root-mean-square error was 0.30 m. Across different travel speeds, the system exhibited good adaptability and stability, meeting the path-planning requirements of precision orchard operations. Full article

Efficient interaction between PHP (Hypertext Preprocessor) applications and MySQL databases is essential for the performance of modern web systems. This study systematically compares the two most widely used PHP APIs for working with MySQL databases—MySQLi (MySQL Improved extension) and PDO (PHP Data Objects)—under identical experimental conditions. The analysis covers execution time, memory consumption, and the stability and variability of results across different types of SQL (Structured Query Language) queries (simple queries, complex JOIN, GROUP BY/HAVING). A specialized benchmarking tool was developed to collect detailed metrics over several hundred repetitions and to enable graphical and statistical evaluation. Across the full benchmark suite, MySQLi exhibits the lowest mean wall-clock execution time on average (≈15% overall). However, under higher query complexity and in certain connection-handling regimes, PDO prepared statement modes provide competitive latency with improved predictability. These results should be interpreted as context-aware rankings for the tested single-host environment and workload design, and as a reusable benchmarking framework intended for replication under alternative deployment models. Statistical analysis (Kruskal–Wallis and Mann–Whitney tests) confirms significant differences between the methods, while Box-plots and histograms visualize deviations and the presence of outliers. Unlike earlier studies, this work provides a controlled and replicable benchmarking environment that tests both MySQLi and PDO across multiple API modes and isolates the impact of native versus emulated prepared statements. It also evaluates performance under complex-query workloads that reflect typical reporting and analytics patterns on the ClassicModels schema. To our knowledge, no previous study has analyzed these factors jointly or provided a reusable tool enabling transparent comparison across PHP–MySQL access layers. The findings provide empirical evidence and practical guidelines for choosing the optimal API depending on the application scenario, as well as a tool that can be applied for further testing in various web environments. Full article