Search Results (11,261)

This study examines the impact of varying pulse conditions on the properties of titanium diboride (TiB₂) coatings deposited by high-power impulse magnetron sputtering (HiPIMS). The coatings were prepared on steel substrates using an industrial-scale system. During the experiments, the HiPIMS frequency and pulse width were systematically varied to examine their influence on the coating’s microstructural, mechanical, and tribological properties. The obtained results show a correlation between process parameters and coating performance. A maximum hardness of 39.7 GPa and a coefficient of friction (CoF) as low as 0.68 were achieved. The best combination of mechanical properties was observed for coatings prepared in a frequency range of 600–1000 Hz and with a pulse width of 50 µs. Notably, the optimal tribological properties and surface roughness were obtained at 800 Hz and a 50 µs pulse width. This work demonstrates that fine-tuning HiPIMS pulse conditions is crucial for achieving high-quality TiB₂ coatings with enhanced functional performance. Full article

In response to the practical demands of high rate, long distance, low cost and miniaturized equipment for underwater wireless communication, an underwater wireless optical communication experimental system with Fresnel lenses as optical receiving antennas has been established. Using 488 nm and 520 nm lasers as the test light sources, the relationship curves between the focusing performance of several Fresnel lenses with different light transmission aperisions and focal lengths after passing through the underwater channel and the lens surface, laser wavelength, and incident angle were obtained. The influence of the laser incident angle on the focusing spots of 488 nm and 520 nm lasers was measured. The experimental results indicate that the Fresnel lens exhibits excellent light concentration performance, with the overall system concentration efficiency being higher than that of conventional lenses, significantly enhancing the received optical power in underwater wireless optical communication systems. Additionally, configuring the sawtooth surface as the incident surface of the Fresnel lens can improve the concentration efficiency by approximately 1% to 5% compared to using a smooth incident surface. Full article

With environmental pollution and energy shortages becoming increasingly severe, developing efficient energy conversion and storage technologies is crucial. Cu₂Se has garnered significant attention as a thermoelectric material due to its abundant raw materials, low cost, and high thermoelectric figure of merit (ZT). This paper reviews the synthesis methods and application progress of Cu₂Se in the energy field. Regarding synthesis, various methods such as solid-state synthesis, hydrothermal synthesis, and ion exchange can be employed to control its microstructure and properties. In applications, Cu₂Se demonstrates significant potential in thermoelectric conversion by harnessing the Seebeck effect to convert waste heat into electricity. Simultaneously, its high carrier mobility and favorable electrochemical properties make it promising for energy storage systems like sodium-ion batteries and aqueous batteries. Furthermore, this material holds considerable potential in emerging fields such as flexible wearable devices and high-efficiency thermoelectric power generation systems. Future research should continue optimizing its comprehensive properties to advance the practical application of Cu₂Se in energy conversion and storage. Full article

Background: This narrative review examines the role of artificial intelligence (AI) in bowel sound analysis for the diagnosis and management of inflammatory bowel disease (IBD). Inflammatory bowel disease (IBD), encompassing Crohn’s disease and ulcerative colitis, presents a significant clinical burden due to its unpredictable course, variable symptomatology, and reliance on invasive procedures for diagnosis and disease monitoring. Despite advances in imaging and biomarkers, tools such as colonoscopy and fecal calprotectin remain costly, uncomfortable, and impractical for frequent or real-time assessment. Meanwhile, bowel sounds—an overlooked physiologic signal—reflect underlying gastrointestinal motility and inflammation but have historically lacked objective quantification. With recent advances in artificial intelligence (AI) and acoustic signal processing, there is growing interest in leveraging bowel sound analysis as a novel, non-invasive biomarker for detecting IBD, monitoring disease activity, and predicting disease flares. This approach holds the promise of continuous, low-cost, and patient-friendly monitoring, which could transform IBD management. Objectives: This narrative review assesses the clinical utility, methodological rigor, and potential future integration of artificial intelligence (AI)-driven bowel sound analysis in inflammatory bowel disease (IBD), with a focus on its potential as a non-invasive biomarker for disease activity, flare prediction, and differential diagnosis. Methods: This manuscript reviews the potential of AI-powered bowel sound analysis as a non-invasive tool for diagnosing, monitoring, and managing inflammatory bowel disease (IBD), including Crohn’s disease and ulcerative colitis. Traditional diagnostic methods, such as colonoscopy and biomarkers, are often invasive, costly, and impractical for real-time monitoring. The manuscript explores bowel sounds, which reflect gastrointestinal motility and inflammation, as an alternative biomarker by utilizing AI techniques like convolutional neural networks (CNNs), transformers, and gradient boosting. We analyze data on acoustic signal acquisition (e.g., smart T-shirts, smartphones), signal processing methodologies (e.g., MFCCs, spectrograms, empirical mode decomposition), and validation metrics (e.g., accuracy, F1 scores, AUC). Studies were assessed for clinical relevance, methodological rigor, and translational potential. Results: Across studies enrolling 16–100 participants, AI models achieved diagnostic accuracies of 88–96%, with AUCs ≥ 0.83 and F1 scores ranging from 0.71 to 0.85 for differentiating IBD from healthy controls and IBS. Transformer-based approaches (e.g., HuBERT, Wav2Vec 2.0) consistently outperformed CNNs and tabular models, yielding F1 scores of 80–85%, while gradient boosting on wearable multi-microphone recordings demonstrated robustness to background noise. Distinct acoustic signatures were identified, including prolonged sound-to-sound intervals in Crohn’s disease (mean 1232 ms vs. 511 ms in IBS) and high-pitched tinkling in stricturing phenotypes. Despite promising performance, current models remain below established biomarkers such as fecal calprotectin (~90% sensitivity for active disease), and generalizability is limited by small, heterogeneous cohorts and the absence of prospective validation. Conclusions: AI-powered bowel sound analysis represents a promising, non-invasive tool for IBD monitoring. However, widespread clinical integration requires standardized data acquisition protocols, large multi-center datasets with clinical correlates, explainable AI frameworks, and ethical data governance. Future directions include wearable-enabled remote monitoring platforms and multi-modal decision support systems integrating bowel sounds with biomarker and symptom data. This manuscript emphasizes the need for large-scale, multi-center studies, the development of explainable AI frameworks, and the integration of these tools within clinical workflows. Future directions include remote monitoring using wearables and multi-modal systems that combine bowel sounds with biomarkers and patient symptoms, aiming to transform IBD care into a more personalized and proactive model. Full article

Sleep monitoring is critical for diagnosing and treating sleep disorders. Although polysomnography (PSG) remains the clinical gold standard, its complexity, discomfort, and lack of portability limit its applicability for long-term and home-based monitoring. To overcome these challenges, this study introduces a novel flexible headband system designed for multi-modal physiological signal acquisition, incorporating dry electrodes, a six-axis inertial measurement unit (IMU), and a temperature sensor. The device supports eight EEG channels and enables wireless data transmission via Bluetooth, ensuring user convenience and reliable long-term monitoring in home environments. To rigorously evaluate the system’s performance, we conducted comprehensive assessments involving 13 subjects over two consecutive nights, comparing its outputs with conventional PSG. Experimental results demonstrate the system’s low power consumption, ultra-low input noise, and robust signal fidelity, confirming its viability for overnight sleep tracking. Further validation was performed using the self-collected HBSleep dataset (over 184 h recordings of the 13 subjects), where state-of-the-art sleep staging models (DeepSleepNet, TinySleepNet, and AttnSleepNet) were applied. The system achieved an overall accuracy exceeding 75%, with AttnSleepNet emerging as the top-performing model, highlighting its compatibility with advanced machine learning frameworks. These results underscore the system’s potential as a reliable, comfortable, and practical solution for accurate sleep monitoring in non-clinical settings. Full article

In resource-constrained edge agricultural environments, the accurate recognition of toxic weeds poses dual challenges related to model lightweight design and the few-shot generalization capability. To address these challenges, a multi-strategy recognition framework is proposed, which integrates a lightweight backbone network, a pseudo-labeling guidance mechanism, and a contrastive boundary enhancement module. This approach is designed to improve deployment efficiency on low-power devices while ensuring high accuracy in identifying rare toxic weed categories. The proposed model achieves a real-time inference speed of 18.9 FPS on the Jetson Nano platform, with a compact model size of 18.6 MB and power consumption maintained below 5.1 W, demonstrating its efficiency for edge deployment. In standard classification tasks, the model attains 89.64%, 87.91%, 88.76%, and 88.43% in terms of precision, recall, F1-score, and accuracy, respectively, outperforming existing mainstream lightweight models such as ResNet18, MobileNetV2, and MobileViT across all evaluation metrics. In few-shot classification tasks targeting rare toxic weed species, the complete model achieves an accuracy of 80.32%, marking an average improvement of over 13 percentage points compared to ablation variants that exclude pseudo-labeling and self-supervised modules or adopt a CNN-only architecture. The experimental results indicate that the proposed model not only delivers strong overall classification performance but also exhibits superior adaptability for deployment and robustness in low-data regimes, offering an effective solution for the precise identification and ecological control of toxic weeds within intelligent agricultural perception systems. Full article

With the increasing penetration of photovoltaic systems in low-voltage distribution networks, new operational challenges arise for distribution system operators. This article focuses on a comprehensive analysis of the impact of single-phase and three-phase photovoltaic systems on voltage magnitude, voltage unbalance, and currents flowing through distribution lines. The steady-state operation was calculated using EA-PSM simulation software, and the assessment of the impact of photovoltaic systems on the network was carried out using the international standard EN 50160. Simulation results show that a high penetration of photovoltaic systems causes significant changes in the network’s voltage profile. The study also includes a proposal of measures aimed at mitigating the adverse effects of decentralized generation in photovoltaic systems on the distribution network. Among the most effective measures is the selection of an appropriate conductor cross-section for distribution lines. The results also indicate that, in terms of negative impact on the network, it is preferable to prioritize three-phase connection over single-phase connection, because for the same impact on the network, three-phase photovoltaic systems can inject several times more power into the network compared to single-phase systems. These and other findings may be beneficial, especially for distribution system operators in planning the operation and development of networks. Full article

Green hydrogen is increasingly recognized as a pivotal energy carrier in the global transition toward low-carbon energy systems. Beyond its established applications in industry and transportation, the development of green hydrogen could accelerate its integration into the power generation sector, thus enabling a more sustainable deployment of renewable energy sources. Vietnam, endowed with abundant renewable energy potential—particularly solar and wind—has a strong foundation for green hydrogen. This emerging energy source holds significant potential to support the strategic objectives in recent national energy policies, aligning with the country’s socio-economic development. However, despite this promise, the integration of green hydrogen into Vietnam’s energy system remains limited. This paper provides a critical review of the current landscape of green hydrogen in Vietnam, examining both the opportunities and challenges associated with its production and deployment. Special attention is given to regulatory frameworks, infrastructure readiness, and economic viability. Additionally, the study also explores the potential of green hydrogen in enhancing energy security within the context of the national energy transition. Full article

This paper proposed an adaptive bandwidth Phase-Locked Loop (PLL) that integrates integer-N and fractional-N switching for energy-efficient RF synthesis in IoT and mobile applications. The architecture exploits wide-bandwidth integer-N mode for rapid lock acquisition, then seamlessly transitions to narrow-bandwidth fractional-N mode for high-resolution synthesis and noise optimization. The architecture features a bandwidth-reconfigurable loop filter with intelligent switching control that monitors phase error dynamics. A novel adaptive digital noise filter mitigates $\Delta \Sigma$ quantization noise, replacing conventional synchronous delay lines. The multi-loop structure incorporates a high-resolution digital phase detector to enhance frequency accuracy and minimize jitter across both operating modes. With 180 nm CMOS technology, the PLL consumes 13.2 mW, while achieving $-119$ dBc/Hz in-band phase noise and 1 $p{s}_{rms}$ integrated jitter. With an operating frequency range at 2.9–3.2 GHz from a 1.8 V supply, the circuit achieves a worst case fractional spur of −62.7 dBc, which corresponds to a figure of merit (FOM) of −228.8 dB. Lock time improvements of 70% are demonstrated compared to single-mode implementations, making it suitable for high-precision, low-power wireless communication systems requiring agile frequency synthesis. Full article

Islanded microgrids face significant frequency stability challenges due to limited system capacity, low inertia levels, and the strong variability in renewable energy sources. Traditional reliability assessment methods, often based on static power balance, struggle to comprehensively reflect frequency dynamic characteristics and their impact on power supply reliability. To address this issue, this paper proposes a sequential Monte Carlo reliability assessment method integrated with a system frequency response model. First, an SFR model for the isolated microgrid, incorporating diesel generators, gas turbines, energy storage, and wind turbines, is established. For synchronous units, a frequency deviation-based failure rate correction mechanism is introduced to characterize the impact of frequency fluctuations on equipment reliability. State transitions are achieved by integrating failure and repair rates to reach threshold values. Second, sequential Monte Carlo simulation is employed to conduct time-series simulations of annual operation. Random sampling of unit failure and repair times is used to calculate reliability metrics. MATLAB/Simulink simulation results demonstrate that system frequency fluctuations caused by power imbalance worsen unit failure rates, leading to microgrid reliability values lower than static calculations. This provides reference for planning, design, and operational scheduling of isolated microgrids. Full article

Neuromorphic circuits emulate the brain’s massively parallel, energy-efficient, and robust information processing by reproducing the behavior of neurons and synapses in dense networks. Memristive technologies have emerged as key enablers of such systems, offering compact and low-power implementations. In particular, locally active memristors (LAMs), with their ability to amplify small perturbations within a locally active domain to generate action potential-like responses, provide powerful building blocks for neuromorphic circuits and offer new perspectives on the mechanisms underlying neuronal firing dynamics. This paper introduces a novel second-order locally active memristor (LAM) governed by two coupled state variables, enabling richer nonlinear dynamics compared to conventional first-order devices. Even when the capacitances controlling the states are equal, the device retains two independent memory states, which broaden the design space for hysteresis tuning and allow flexible modulation of the current–voltage response. The second-order LAM is then integrated into a FitzHugh–Nagumo neuron circuit. The proposed circuit exhibits oscillatory firing behavior under specific parameter regimes and is further investigated under both DC and AC external stimulation. A comprehensive analysis of its equilibrium points is provided, followed by bifurcation diagrams and Lyapunov exponent spectra for key system parameters, revealing distinct regions of periodic, chaotic, and quasi-periodic dynamics. Representative time-domain patterns corresponding to these regimes are also presented, highlighting the circuit’s ability to reproduce a rich variety of neuronal firing behaviors. Finally, two unknown system parameters are estimated using the Aquila Optimization algorithm, with a cost function based on the system’s return map. Simulation results confirm the algorithm’s efficiency in parameter estimation. Full article

Extreme natural disasters can force microgrids into islanded operation, where low system inertia and asynchronous, time-varying communication delays present severe challenges to frequency stability. These challenges threaten not only short-term reliability but also the sustainable operation of renewable-dominated energy systems. Existing frequency control methods are often unable to robustly handle heterogeneous delays, thereby limiting the resilience of power systems with high shares of renewables. To address this issue, we propose a parametric Riccati equation-based frequency control method that adaptively adjusts control parameters to balance system robustness and optimality under asynchronous delays. Controller stability is guaranteed by Barbalat’s lemma. The main contributions include: (i) developing a microgrid frequency control model that incorporates asynchronous delays, (ii) designing a delay-aware controller using the parametric Riccati equation, and (iii) validating its effectiveness on a modified New England 39-bus system. Simulation results confirm that the proposed method enhances frequency stability under disaster-induced islanding scenarios. By ensuring robust and reliable operation of renewable-rich power systems, the proposed approach contributes to the sustainable integration of renewable energy, reduces blackout risks, and supports long-term environmental and socio-economic sustainability goals. Full article

Large-scale integration of distributed energy resources (DERs) into distribution networks causes topological-operational situations with multidirectional power flows. One of the main components of distribution networks is the power transformer, which does not have the capabilities for real-time control of distribution network parameters with DERs. The use of solid-state transformers (SSTs) for connecting medium-voltage (MV) and low-voltage (LV) distribution networks of both alternating and direct current has great potential for constructing new distribution networks and enhancing the existing ones. Electricity losses in distribution networks can be reduced through the establishment of MV and LV DC networks. In hybrid AC-DC distribution networks, the SSTs can be especially effective, ensuring compensation for voltage dips, fluctuations, and interruptions; regulation of voltage, current, frequency, and power factor in LV networks; and reduction in the levels of harmonic current and voltage due to the presence of power electronic converters (PECs) and capacitors in the DC link. To control the operating parameters of hybrid distribution networks with solid-state transformers, it is crucial to develop and implement advanced control systems (CSs). The purpose of this review is a comprehensive analysis of the features of the creation of CSs SSTs when they are used in hybrid distribution networks with DERs to identify the most effective principles and methods for managing SSTs of different designs, which will accelerate the development and implementation of CSs. This review focuses on the design principles and control strategies for SSTs, guided by their architecture and intended functionality. The architecture of the solid-state transformer control system is presented with a detailed description of the main stages of control. In addition, the features of the SST CS operating under various topologies and operating conditions of distribution networks are examined. Full article

Under the background of “dual-carbon”, the development of energy internet is an inevitable trend for China’s low-carbon energy transition. This paper proposes a hydrogen-coupled electrothermal integrated energy system (HCEH-IES) operation mode and optimizes the source-side structure of the system from the level of carbon trading policy combined with low-carbon technology, taps the carbon reduction potential, and improves the renewable energy consumption rate and system decarbonization level; in addition, for the operation optimization problem of this electric–gas–heat integrated energy system, a flexible energy system based on electric–gas–heat is proposed. Furthermore, to address the operation optimization problem of the HCEH-IES, a deep reinforcement learning method based on Soft Actor–Critic (SAC) is proposed. This method can adaptively learn control strategies through interactions between the intelligent agent and the energy system, enabling continuous action control of the multi-energy flow system while solving the uncertainties associated with source-load fluctuations from wind power, photovoltaics, and multi-energy loads. Finally, historical data are used to train the intelligent body and compare the scheduling strategies obtained by SAC and DDPG algorithms. The results show that the SAC-based algorithm has better economics, is close to the CPLEX day-ahead optimal scheduling method, and is more suitable for solving the dynamic optimal scheduling problem of integrated energy systems in real scenarios. Full article

High-voltage switchgear is an important component of the power system, and its operation safety will directly affect the reliability of the power supply of the power system. At present, the operation and maintenance decision-making of the switchgear mainly relies on manual work, which has problems such as low efficiency and poor reliability of judgment results. Therefore, this paper proposes an intelligent operation and maintenance auxiliary method for high-voltage switchgear based on the combination of the Ripplenet algorithm and knowledge graph, which ensures high efficiency while improving the reliability of the results. Among them, the knowledge graph is mainly based on the Bidirectional Encoder Representations from Transformers-Whole Word Masking (BERT-wwm) algorithm, and it is constructed in a bottom-up and top-down manner. It consists of 240 nodes and 960 relationships. Based on this knowledge graph, the intelligent operation and maintenance auxiliary method of high-voltage switchgear based on Ripplenet is studied. Based on textual information such as on-site information and fault reports, the judgment reasoning of the fault type of the high-voltage switchgear and recommendations for operation and maintenance solutions are realized. The diagnostic accuracy of this method for high-voltage switchgear faults can reach 95.96%. Full article