Search Results (882)

This paper presents a hierarchical model architecture for the analysis and optimization of Fuel Cell Electric Vehicles (FCEVs). The model encompasses the levels of cell, stack, and complete vehicle, which are interconnected through clearly defined transfer parameters. At the cell level, electrochemical and thermodynamic processes are mapped, the results of which are aggregated at the stack level into characteristic maps such as current–voltage curves and efficiency profiles. These maps serve as interfaces to the vehicle level, where the electric powertrain—comprising the fuel cell, energy storage, electric motor, and auxiliary consumers—is integrated. Special attention is given to the trade-off between the lifetime and dynamics of the fuel cell, which is methodically captured through variable parameter vectors. The transfer parameters enable consistent and scalable modelling that considers both detailed cell and stack information as well as vehicle-side requirements. On this basis, various vehicle configurations can be evaluated and optimized with regard to efficiency, lifetime, and drivability. Full article

This paper proposes an event-triggered extension of duty-ratio-based model predictive direct speed control (DR-MPDSC) for permanent magnet synchronous motor (PMSM) drives in electric vehicle (EV) applications. The main contribution is the development of an event-triggered execution framework specifically tailored to DR-MPDSC, in which control updates are performed only when the speed tracking error violates a prescribed condition, rather than at every sampling instant. Unlike conventional MPDSC and time-triggered DR-MPDSC schemes, the proposed strategy achieves a significant reduction in control execution frequency while preserving fast dynamic response and closed-loop stability. An optimized duty-ratio formulation is employed to regulate the effective application duration of the selected voltage vector within each sampling interval, resulting in reduced electromagnetic torque ripple and improved stator current quality. An extended Kalman filter (EKF) is integrated to estimate rotor speed and load torque, enabling disturbance-aware predictive speed control without mechanical torque sensing. Furthermore, a unified field-weakening strategy is incorporated to ensure wide-speed-range operation under constant power constraints, which is essential for EV traction systems. Simulation and experimental results demonstrate that the proposed event-triggered DR-MPDSC achieves steady-state speed errors below 0.5%, limits electromagnetic torque ripple to approximately 2.5%, and reduces stator current total harmonic distortion (THD) to 3.84%, compared with 5.8% obtained using conventional MPDSC. Moreover, the event-triggered mechanism reduces control update executions by up to 87.73% without degrading transient performance or field-weakening capability. These results confirm the effectiveness and practical viability of the proposed control strategy for high-performance PMSM drives in EV applications. Full article

This review addresses the pivotal challenge in distributed-drive electric vehicle (DDEV) dynamics control: how to optimally distribute braking and steering forces during combined maneuvers to simultaneously enhance lateral stability, safety, and energy efficiency. The over-actuated nature of DDEVs presents a unique opportunity for precise torque vectoring but also introduces complex coupled dynamics, making vehicles prone to instability such as rollover during aggressive steering–braking scenarios. Moving beyond a simple catalog of methods, this work provides a structured synthesis and evolutionary analysis of chassis control methodologies. The problem is first deconstructed into two core control objectives: lateral stability and longitudinal braking performance. This is followed by a critical analysis of how integrated control architectures resolve the inherent conflicts between them. The analysis reveals a clear trajectory from independent control loops to intelligent, context-aware coordination. It further identifies a paradigm shift from the conventional goal of merely maintaining stability toward proactively managing stability boundaries to enhance system resilience. Furthermore, this review highlights the growing integration with high-level motion planning in automated driving. By synthesizing the current knowledge and mapping future directions toward deeply integrated, intelligent control systems, it serves as both a reference for researchers and a design guide for engineers aiming to unlock the full potential of the distributed drive paradigm. Full article

Chymosin is an aspartyl protease widely used in the food industry for milk coagulation during cheesemaking. Although recombinant production has replaced natural extraction from rennet, current heterologous expression systems still face significant challenges, including low solubility, costly purification steps, and enzyme instability after activation. To address these limitations, we sought to develop a more efficient and economical production strategy for bovine chymosin by cloning its codon-optimized prochymosin A gene into Escherichia coli using the pBAD/His vector under the control of the L-arabinose-inducible PBAD promoter. Overexpression of the recombinant gene resulted in the formation of inclusion bodies, which were solubilized with NaOH and refolded by dilution and pH adjustment with glycine. The folded prochymosin was then activated by acidification. To simplify the downstream process and improve enzyme recovery, different immobilization strategies were explored to combine activation, purification, and immobilization in a single step. While polymeric agarose-based supports showed low immobilization efficiency (<20%) due to pore clogging, magnetic nanoparticles completely overcame these limitations, achieving nearly 100% immobilization yield and retaining about 85% of enzymatic activity. This integrated magnetic-based approach provides a cost-effective and scalable alternative for the production and stabilization of active chymosin. Full article

Focusing on the challenges posed by the strong randomness, weak manifestation, and difficulty in diagnosing intermittent faults (IFs) in high-density integrated circuits (HDICs)—often induced by bonding wire defects—this paper takes the GPIO interfaces of a typical DSP chip as the research object. It systematically analyzes the response characteristics of intermittent short-circuit and open-circuit faults and proposes a hybrid intelligent diagnosis method based on the Sparrow Search Algorithm-optimized Variational Mode Decomposition and Attention-based Support Vector Machine (SSA–VMD–Attention–SVM). A dedicated fault injection circuit is designed to accurately replicate IFs and acquire the power supply current response signals. The Sparrow Search Algorithm (SSA) is employed to adaptively optimize the parameters of Variational Mode Decomposition (VMD) for effective extraction of frequency-domain features from fault signals. A three-level attention mechanism is introduced to adaptively weight multi-domain features, thereby highlighting the key fault components. Finally, the Support Vector Machine (SVM) is utilized to achieve high-precision fault classification under small-sample conditions. Experimental results demonstrate that the proposed method achieves a diagnostic accuracy of 97.78% for intermittent short-circuit and open-circuit faults in the GPIO interfaces of the DSP chip, significantly outperforming traditional methods and exhibiting notable advantages in terms of diagnostic accuracy, robustness, and interpretability. Full article

Weed management has become a critical agricultural practice, as weeds compete with crops for nutrients, host pests and diseases, and cause major economic losses. The invasive weed Ambrosia artemisiifolia (common ragweed) is particularly problematic in Hungary, endangering crop productivity and public health through its fast proliferation and allergenic pollen. This review examines the current challenges and impacts of A. artemisiifolia while exploring sustainable approaches to its management through precision agriculture. Recent advancements in unmanned aerial vehicles (UAVs) equipped with advanced imaging systems, remote sensing, and artificial intelligence, particularly deep learning models such as convolutional neural networks (CNNs) and Support Vector Machines (SVMs), enable accurate detection, mapping, and classification of weed infestations. These technologies facilitate site-specific weed management (SSWM) by optimizing herbicide application, reducing chemical inputs, and minimizing environmental impacts. The results of recent studies demonstrate the high potential of UAV-based monitoring for real-time, data-driven weed management. The review concludes that integrating UAV and AI technologies into weed management offers a sustainable, cost-effective, mitigate the socioeconomic impacts and environmentally responsible solution, emphasizing the need for collaboration between agricultural researchers and technology developers to enhance precision agriculture practices in Hungary. Full article

The cornea is an avascular, immune-privileged tissue critical to maintaining transparency, optimal light refraction, and protection from microbial and immunogenic insults. Corneal neovascularization (CoNV) is a pathological sequela of multiple anterior segment diseases and presents a major cause for reduced visual acuity and overall quality of life. Various aetiologies, including infection (e.g., herpes simplex), inflammation (e.g., infective keratitis), hypoxia (e.g., contact lens overuse), degeneration (e.g., chemical burns), and trauma, disrupt the homeostatic avascular microenvironment, triggering an overactive compensatory response. This response is governed by a complex interplay of pro- and anti-angiogenic factors. This review investigates the potential for these mediators to serve as therapeutic targets. Current therapeutic strategies for CoNV encompass topical corticosteroids, anti-VEGF injections, fine-needle diathermy, and laser modalities including argon, photodynamic therapy and Nd:YAG. Emerging therapies involve steroid-sparing immunosuppressants (including cyclosporine and rapamycin), anti-fibrotic agents and advanced drug delivery systems, including ocular nanosystems and viral vectors, to enhance drug bioavailability. Adjunctive therapy to attenuate the protective corneal epithelium prior to target neovascular plexi are further explored. Gene-based approaches, such as Aganirsen (antisense oligonucleotides) and CRISPR/Cas9-mediated VEGF-A editing, have shown promise in preclinical studies for CoNV regression and remission. Given the multifactorial pathophysiology of CoNV, combination therapies targeting multiple molecular pathways may offer improved visual outcomes. Case studies of CoNV highlight the need for multifaceted approaches tailored to patient demographics and underlying ocular diseases. Future research and clinical trials are essential to elucidate optimal therapeutic strategies and explore combination therapies to ensure better management, improved treatment outcomes, and long-term remission of this visually disabling condition. Full article

To address three core deficiencies of the existing research on ZnO varistors (incomplete full-lifecycle datasets, insufficient characterization robustness due to the lack of multi-parameter complementarity, and disconnected remaining life prediction and failure threshold determination), this study proposes a comprehensive technical solution for ZnO varistor remaining life prediction. An 8/20 μs impulse current accelerated deterioration experiment was designed to construct a full-lifecycle dataset (441 sets of data) covering nine same-batch ZnO varistors from their initial state to complete failure. Five core electrical parameters (varistor voltage U_1mA, nonlinear coefficient α, leakage current I_L, parallel resistance R_p, parallel capacitance C_p) were fused, and principal component analysis (PCA) was adopted for dimensionality reduction to form a high-robustness characterization feature (correlation coefficient with deterioration degree = 0.96). A combined model of Particle Swarm Optimization-Support Vector Regression (PSO-SVR) and Isolation Forest (iForest) was established to realize “quantitative prediction–qualitative threshold” collaboration. Experimental results show that the PSO-SVR model achieves high-precision remaining life prediction (test set R² = 0.9726, MSE = 0.00142) and the iForest model accurately identifies the failure threshold (AUC = 0.984, accuracy = 95.9%). The combined model reaches an overall accuracy of 99.89%, effectively solving the core deficiencies of the existing research and providing key technical support for SPD-condition monitoring and operation and maintenance decisions in energy systems. Full article

To address the problem that transformer inrush currents under no-load and energization conditions can easily trigger misoperations of differential protection, this paper proposes a multi-feature identification method for transformer inrush current based on adaptive variational mode decomposition. Traditional methods typically rely on fixed physical features or single criteria, making them sensitive to operating condition variations and prone to misclassification or missed detection under complex disturbances, with limited generalization capability. The proposed method first performs adaptive VMD decomposition of current waveforms under different operating conditions. On this basis, time-domain, frequency-domain, and nonlinear features are extracted to comprehensively characterize the signal’s amplitude, spectral, and complexity information. Then, by combining the ReliefF algorithm with forward stepwise feature selection, the method reduces feature dimensionality while maintaining high discriminative power and low redundancy. Using the VMD-ReliefF-EEFO-SVM classification model, the approach achieves efficient and accurate discrimination between inrush currents and fault currents. Simulation results demonstrate that the proposed identification method adapts well to various operating conditions and exhibits strong robustness and versatility. Full article

Recent advances in deep learning have had a significant impact on biomedical applications, driving precise actions in automated diagnostic processes. However, integrating neural networks into medical devices requires meeting strict requirements regarding computing power, energy efficiency, reconfigurability, and latency, essential conditions for real-time inference. Field-Programmable Gate Array (FPGA) architectures provide a high level of flexibility, performance, and parallel execution, thus making them a suitable option for the real-world implementation of machine learning (ML) and deep learning (DL) models in systems dedicated to the analysis of physiological signals. This paper presents a review of intelligent algorithms for electrocardiogram (ECG) signal classification, including Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Convolutional Neural Networks (CNNs), which have been implemented on FPGA platforms. A comparative evaluation of the performances of these hardware-accelerated solutions is provided, focusing on their classification accuracy. At the same time, the FPGA families used are analyzed, along with the reported performances in terms of operating frequency, power consumption, and latency, as well as the optimization strategies applied in the design of deep learning hardware accelerators. The conclusions emphasize the popularity and efficiency of CNN architectures in the context of ECG signal classification. The study aims to offer a current overview and to support specialists in the field of FPGA design and biomedical engineering in the development of accelerators dedicated to physiological signals analysis. Full article

Cancer immunotherapy increasingly relies on nucleic acid-based vaccines, yet achieving efficient and safe delivery remains a critical limitation. Polyplexes—electrostatic complexes of cationic polymers and nucleic acids—have emerged as versatile carriers offering greater chemical tunability and multivalent targeting capacity compared to lipid nanoparticles, with lower immunogenicity than viral vectors. This review summarizes key design principles governing polyplex performance, including polymer chemistry, architecture, and assembly route—emphasizing microfluidic fabrication for improved size control and reproducibility. Mechanistically, effective systems support stepwise delivery: tumor targeting, cellular uptake, endosomal escape (via proton-sponge, membrane fusion, or photochemical disruption), and compartment-specific cargo release. We discuss therapeutic applications spanning plasmid DNA, siRNA, miRNA, mRNA, and CRISPR-based editing, highlighting preclinical data across multiple tumor types and early clinical evidence of on-target knockdown in human cancers. Particular attention is given to physiological barriers and engineering strategies—including size-switching systems, charge-reversal polymers, and tumor-penetrating peptides—that improve intratumoral distribution. However, significant challenges persist, including cationic toxicity, protein corona formation, manufacturing variability, and limited clinical responses to date. Current evidence supports polyplexes as a modular platform complementary to lipid nanoparticles in selected oncology indications, though realizing this potential requires continued optimization alongside rigorous translational development. Full article

Recent vision-language pre-training models, like CLIP, have been shown to generalize well across a variety of multitask modalities. Nonetheless, their generalization for downstream tasks is limited. As a lightweight adaptation approach, prompt learning could allow task transfer by optimizing only several learnable vectors and thus is more flexible for pre-trained models. However, current methods mainly concentrate on the design of unimodal prompts and ignore effective means for multimodal semantic fusion and label alignment, which limits their representation power. To tackle these problems, this paper designs a Hierarchical Interactive Semantic Fusion (HISF) framework for multimodal prompt learning. On top of frozen CLIP backbones, HISF injects visual and textual signals simultaneously in intermediate layers of a Transformer through a cross-attention mechanism as well as fitting category embeddings. This architecture realizes the hierarchical semantic fusion at the modality level with structural consistency kept at each layer. In addition, a Label Embedding Constraint and a Semantic Alignment Loss are proposed to promote category consistency while alleviating semantic drift in training. Extensive experiments across 11 few-shot image classification benchmarks show that HISF improves the average accuracy by around 0.7% compared to state-of-the-art methods and has remarkable robustness in cross-domain transfer tasks. Ablation studies also verify the effectiveness of each proposed part and their combination: hierarchical structure, cross-modal attention, and semantic alignment collaborate to enrich representational capacity. In conclusion, the proposed HISF is a new hierarchical view for multimodal prompt learning and provides a more lightweight and generalizable paradigm for adapting vision-language pre-trained models. Full article

Drug–drug interactions (DDIs) can cause adverse reactions or reduce the efficiency of a drug. Using computers to predict DDIs is now critical in pharmacology, as this reduces risks, improves drug outcomes and lowers healthcare costs. Clinical trials are slow, expensive, and require a lot of effort. The use of artificial intelligence (AI), primarily in the form of machine learning (ML) and its subfield deep learning (DL), has made DDI prediction more accurate and efficient when handling large datasets from biological, chemical, and clinical domains. Many ML and DL approaches are bio-inspired, taking inspiration from natural systems, and are considered part of the broader class of biomimetic methods. This review provides a comprehensive overview of AI-based methods currently used for DDI prediction. These include classical ML algorithms, such as logistic regression (LR) and support vector machines (SVMs); advanced DL models, such as deep neural networks (DNNs) and long short-term memory networks (LSTMs); graph-based models, such as graph convolutional networks (GCNs) and graph attention networks (GATs); and ensemble techniques. The use of knowledge graphs and transformers to capture relations and meaningful data about drugs is also investigated. Additionally, emerging biomimetic approaches offer promising directions for the future in designing AI models that can emulate the complexity of pharmacological interactions. These upgrades include using genetic algorithms with LR and SVM, neuroevaluation (brain-inspired model optimization) to improve DNN and LSTM architectures, ant-colony-inspired path exploration with GCN and GAT, and immune-inspired attention mechanisms in transformer models. This manuscript reviews the typical types of data employed in DDI (pDDI) prediction studies and the evaluation methods employed, discussing the pros and cons of each. There are useful approaches outlined that reveal important points that require further research and suggest ways to improve the accuracy, usability, and understanding of DDI prediction models. Full article

Volatile organic compounds (VOCs) released from automotive interior materials and exchanged with external air seriously compromise cabin air quality and pose health risks to occupants. Electronic noses (E-noses) based on metal oxide semiconductor (MOS) micro-electro-mechanical system (MEMS) sensor arrays provide an efficient, real-time solution for in-vehicle gas monitoring. This review examines the use of SnO₂-, ZnO-, and TiO₂-based MEMS sensor arrays for this purpose. The sensing mechanisms, performance characteristics, and current limitations of these core materials are critically analyzed. Key MEMS fabrication techniques, including magnetron sputtering, chemical vapor deposition, and atomic layer deposition, are presented. Commonly employed pattern recognition algorithms—principal component analysis (PCA), support vector machines (SVM), and artificial neural networks (ANN)—are evaluated in terms of principle and effectiveness. Recent advances in low-power, portable E-nose systems for detecting formaldehyde, benzene, toluene, and other target analytes inside vehicles are highlighted. Future directions, including circuit–algorithm co-optimization, enhanced portability, and neuromorphic computing integration, are discussed. MOS MEMS E-noses effectively overcome the drawbacks of conventional analytical methods and are poised for widespread adoption in automotive air-quality management. Full article

Populus euphratica is a key constructive species in desert ecosystems and plays a vital role in maintaining their stability. However, effective automated methods for accurately delineating its distribution outlines are currently lacking. This study used the mainstream area of the Tarim River as a case study and proposed a technical solution for identifying the distribution outline of Populus euphratica using multi-source thematic classification data. First, cropland thematic data were used to optimize the accuracy of the Populus euphratica classification raster data. Discrete points were removed based on density to reduce their impact on boundary identification. Then, a hierarchical identification scheme was constructed using the alpha-shape algorithm to identify the boundaries of high- and low-density Populus euphratica distribution areas separately. Finally, the outlines of the Populus euphratica distribution polygons were smoothed, and the final distribution outline data were obtained after spatial merging. The results showed the following: (1) Applying a closing operation to the cropland thematic classification data to obtain the distribution range of shelterbelts effectively eliminated misclassified pixels. Using the kd-tree algorithm to remove sparse discrete points based on density, with a removal ratio of 5%, helped suppress the interference of outlier point sets on the Populus euphratica outline identification. (2) Constructing a hierarchical identification scheme based on differences in Populus euphratica density is critical for accurately delineating its distribution contours. Using the alpha-shape algorithm with parameters set to α = 0.02 and α = 0.006, the reconstructed geometries effectively covered both densely and sparsely distributed Populus euphratica areas. (3) In the morphological processing stage, a combination of three methods—Gaussian filtering, equidistant expansion, and gap filling—effectively ensured the accuracy of the Populus euphratica outline. Among the various smoothing algorithms, Gaussian filtering yielded the best results. The equidistant expansion method reduced the impact of elongated cavities, thereby contributing to boundary accuracy. This study enhances the automation of Populus euphratica vector data mapping and holds significant value for the scientific management and research of desert vegetation. Full article