Search Results (112,521)

This study investigates the influence of surface roughness on the performance of polymer electrolyte membrane fuel cells (PEMFCs) through computational simulations using COMSOL Multiphysics. Two distinct gas flow channel (GFC) models of serpentine and parallel GFC structures were analysed, featuring various surface roughness levels to examine their impact on gas pressure and velocity dynamics. Rough surfaces are modeled using trigonometric functions to replicate machining-induced variations. Finite element simulations were conducted, assessing the time-dependent relationship between gas pressure and velocity while considering different electrode phase potentials as a function of surface roughness. Rough surfaces generally enhance mass transport, water management, and current distribution compared to smooth surfaces. The results indicated that a surface roughness of approximately 1 µm optimizes PEMFC performance by balancing pressure and velocity, enhancing electrochemical reactions, and reducing excessive pressure drops within the cell. Notably, the 0.7 V operating voltage was found to be the most efficient, achieving rapid stabilization of pressure and velocity levels swiftly. The findings underscore the importance of precise control over GFC roughness to enhance PEMFC performance gains in commercial applications, especially when multiple cells are stacked to achieve high power outputs. Full article

The development of new technologies has significantly enhanced the monitoring and analysis of network traffic. Modern solutions like the Extended Berkeley Packet Filter (eBPF) demonstrate a clear advancement over traditional techniques, allowing for more customized and efficient filtering. These technologies are crucial for influencing system performance as they operate at the lowest layer of the operating system, such as the kernel. Network-based Intrusion Detection/Prevention Systems (IDPS), including Snort, Suricata, and Bro, passively monitor network traffic from terminal access points. However, most IDPS are signature-based and face challenges on large networks, where the drop rate increases due to limitations in capturing and processing packets. High throughput leads to overheads, causing IDPS buffers to drop packets, which can pose serious threats to network security. Typically, IDPS are targeted by volumetric and multi-vector attacks that overload the network beyond the reception and processing capacity of IDPS, resulting in packet loss due to buffer overflows. To address this issue, the proposed solution, iKern, utilizes eBPF and Virtual Network Functions (VNF) to examine and filter packets at the kernel level before forwarding them to user space. Packet stream inspection is performed within the iKern Engine at the kernel level to detect and mitigate volumetric floods and multi-vector attacks. The iKern detection engine, operating within the Linux kernel, is powered by eBPF bytecode injected from user space. This system effectively handles volumetric Distributed Denial of Service (DDoS) attacks. Real-time implementation of this scheme has been tested on a 1Gbps network and shows significant detection and reduction capabilities against volumetric and multi-vector floods. Full article

In the context of power-line scenarios characterized by complex backgrounds and diverse scales and shapes of targets, and addressing issues such as large model parameter sizes, insufficient feature extraction, and the susceptibility to missing small targets in engineering-vehicle detection tasks, a lightweight detection algorithm termed CER-YOLOv5s is firstly proposed. The C3 module was restructured by embedding a lightweight Ghost bottleneck structure and convolutional attention module, enhancing the model’s ability to extract key features while reducing computational costs. Secondly, an E-BiFPN feature pyramid network is proposed, utilizing channel attention mechanisms to effectively suppress background noise and enhance the model’s focus on important regions. Bidirectional connections were introduced to optimize the feature fusion paths, improving the efficiency of multi-scale feature fusion. At the same time, in the feature fusion part, an ERM (enhanced receptive module) was added to expand the receptive field of shallow feature maps through multiple convolution repetitions, enhancing the global information perception capability in relation to small targets. Lastly, a Soft-DIoU-NMS suppression algorithm is proposed to improve the candidate box selection mechanism, addressing the issue of suboptimal detection of occluded targets. The experimental results indicated that compared with the baseline YOLOv5s algorithm, the improved algorithm reduced parameters and computations by 27.8% and 31.9%, respectively. The mean average precision (mAP) increased by 2.9%, reaching 98.3%. This improvement surpasses recent mainstream algorithms and suggests stronger robustness across various scenarios. The algorithm meets the lightweight requirements for embedded devices in power-line scenarios. Full article

This paper introduces a novel plasmon refractive index nanosensor structure based on Fano resonance. The structure comprises a metal–insulator–metal (MIM) waveguide with an inverted rectangular cavity and a circle minus a small internal circle plus a rectangular cavity (CMSICPRC). This study employs the finite element method (FEM) to analyze the sensing characteristics of the structure. The results demonstrate that the geometrical parameters of specific structures exert a considerable influence on the sensing characteristics. Simulated experimental data show that the maximum sensitivity of this structure is 3240 nm/RIU, with a figure of merit (FOM) of 52.25. Additionally, the sensor can be used in biology, for example, to detect the concentration of hemoglobin in blood. The sensitivity of the sensor in this application, according to our calculations, can be 0.82 nm∙g/L. Full article

This work proposes a simple and affordable technology for the manufacturing of a miniature end-face fiber-optic temperature sensor based on a Fabry–Perot interferometer formed from a transparent UV-curable resin. For the manufactured working prototype of the sensor, the sensitivity and operating temperature range were determined, and the methods for their enhancement were proposed. Due to its small size, the proposed type of sensor can be used in high-precision and minimally invasive temperature measurements, in biology for microscale sample monitoring, and in medicine during operations using high-power lasers. A microwave photonic method is proposed that enables the interrogation of the sensor without using an optical spectrum analyzer. Full article

Cellulose microspheres have a wide range of applications due to their unique properties and versatility. Various preparation methods have been explored to tailor these microspheres for specific applications. Among these methods, the acetate method using cellulose acetate is well known. However, replacement of the acetate group through the butyrate group significantly extends the variety of morphological properties. In the present work, microspheres based on cellulose acetate butyrate are being developed with modified characteristics in terms of particle size, porosity, surface morphology and the inner structure of the microspheres. While the inner structure of cellulose acetate microspheres is predominantly porous, microspheres prepared from cellulose acetate butyrate are mainly filled or contain several smaller microspheres. Carbon materials from cellulose acetate butyrate microspheres exhibit a high specific surface area of 567 m² g⁻¹, even without further activation. Activation processes can further increase the specific surface area, accompanied by an adaptation of the pore structure. The prepared carbons show promising results in symmetrical supercapacitors with aqueous 6 M KOH electrolytes. Activated carbons derived from cellulose acetate butyrate microspheres demonstrate an energy density of 12 Wh kg⁻¹ at a power density of 0.9 kW kg⁻¹ . Full article

Insulated gate bipolar transistors (IGBTs) are widely used in power electronic devices, and their health prediction problems have attracted much attention in the field of power electronic equipment health management. The performance degradation of IGBT gate oxide is one of the most important failure modes. In order to analyze this failure mechanism and the ease of implementation of a monitoring circuit, the gate leakage current of IGBTs was selected as the fault precursor parameter for the degradation of their gate oxide performance, and feature selection and fusion were carried out by using time domain characteristic analysis, grayscale correlation, Mahalanobis distance, Kalman filter, and other methods. Thus, a health indicator was obtained to characterize the degradation of IGBT performance, which was used to indicate the degree of aging of the IGBT gate oxide layer. In this paper, we propose an improved degradation prediction model called MultiScaleFormer, inspired by advanced design ideas of the iTransformer network architecture, combined with the health parameters of IGBTs to construct a degradation prediction model for the IGBT gate oxide layer. MultiScaleFormer showed the highest fitting accuracy compared with the Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Support Vector Regression (SVR), Gaussian Process Regression (GPR), CNN-LSTM, and Transformer models in our experiment. The mean absolute error (MAE) of the MultiScaleFormer prediction was as low as 0.0087. Extraction of the health indicator and the construction and verification of the degradation prediction model were carried out on the dataset released by the NASA-Ames Laboratory. These results demonstrate the feasibility of the gate leakage current as a fault precursor parameter for IGBT gate oxide failure, and the feasibility and accuracy of the MultiScaleFormer prediction model for IGBT performance degradation. Full article

The aim of individuals consuming health supplements is to attain a robust state through nutritional regulation. However, some unscrupulous manufacturers, motivated by profit, fraudulently incorporate drugs or unauthorized components with therapeutic effects into the product for instant product performance enhancement. The long-term use of these products may inadvertently inflict harm on human health and fail to promote nutritive healthcare. The illegal inclusion of these substances is prevalent in kidney-tonifying and sexuality-enhancing products. Developing effective analytical methods to identify these products and screen for illegal added ingredients can effectively prevent such products from reaching and remaining on the market. A target screening method for the detection and quantification of 90 phosphodiesterase type 5 inhibitors (PDE-5is) in 5 kinds of health products was developed and validated. The type of dietary supplements varied from tablets, capsules, and protein powder to wine and beverages. Sample preparation was completed with a one-step liquid phase extraction. The screening process of 90 PDE-5is was done efficiently within 25 min by ultra-high performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) using the dynamic multiple reaction monitoring (dMRM) technique. The LODs of 90 PDE-5is were detected at levels ranging from 25 to 85 ng/g or ng/mL. This novel targeting methodology was effective and can be applied to routine market supervision. Among 286 batches of samples, 8 batches were found to be positive. Three kinds of PDE-5is were first detected in healthy products. The screening method demonstrated herein will be a promising and powerful tool for rapid screening of PDE-5is. Full article

In the current electromechanical model of cantilevered piezoelectric energy harvesters, the assumption of uniform electric field strength within the piezoelectric layer is commonly made. This uniform electric field assumption seems reasonable since the piezoelectric layer looks like a parallel-plate capacitor. However, for a piezoelectric bender, the strain distribution along the thickness direction is not uniform, which means the internal electric field generated by the spontaneous polarization cannot be uniform. In the present study, a non-uniform electric field in the piezoelectric layer is resolved using electrostatic equilibrium equations. Based on these, the traditional distributed parameter electromechanical model is corrected and simplified to a practical single mode one. Compared with a traditional model adopting a uniform electric field, the bending stiffness term involved in the electromechanical governing equations is explicitly corrected. Through comparisons of predicted power output with two-dimensional finite element analysis, the results show that the present model can better predict the power output performance compared with the traditional model. It is found that the relative corrections to traditional model have nothing to do with the absolute dimensions of the harvesters, but only relate to three dimensionless parameters, i.e., the ratio of the elastic layer’s to the piezoelectric layer’s thickness; the ratio of the elastic modulus of the elastic layer to the piezoelectric layer; and the piezoelectric materials’ electromechanical coupling coefficient squared, . It is also found that the upper-limit relative corrections are only related to , i.e., the higher is, the larger the upper-limit relative corrections will be. For a PZT-5 unimorph harvester, the relative corrections of bending stiffness and corresponding resonant frequency are up to 17.8% and 8.5%, respectively. An inverse problem to identify the material parameters based on experimentally obtained power output performance is also investigated. The results show that the accuracy of material parameters identification is improved when considering a non-uniform electric field. Full article

Foreign objects such as balloons and nests often lead to widespread power outages by coming into contact with transmission lines. The manual detection of these is labor-intensive work. Automatic foreign object detection on transmission lines is a crucial task for power safety and is becoming the mainstream method, but the lack of datasets is a restriction. In this paper, we propose an advanced model termed YOLOv8 Network with Bidirectional Feature Pyramid Network (YOLOv8_BiFPN) to detect foreign objects on power transmission lines. Firstly, we add a weighted cross-scale connection structure to the detection head of the YOLOv8 network. The structure is bidirectional. It provides interaction between low-level and high-level features, and allows information to spread across feature maps of different scales. Secondly, in comparison to the traditional concatenation and shortcut operations, our method integrates information between different scale features through weighted settings. Moreover, we created a dataset of Foreign Object detection on Transmission Lines from a Drone-view (FOTL_Drone). It consists of 1495 annotated images with six types of foreign object. To our knowledge, FOTL_Drone stands out as the most comprehensive dataset in the field of foreign object detection on transmission lines, which encompasses a wide array of geographic features and diverse types of foreign object. Experimental results showcase that YOLOv8_BiFPN achieves an average precision of 90.2% and an [email protected] of 0.896 across various categories of foreign objects, surpassing other models. Full article

The proliferation of electronic loads has led to a substantial increase in harmonic emissions within low-voltage distribution networks. The accurate estimation of the expected levels of harmonics in a network is a daunting task for network operators. Stochastic-based harmonic estimation models can offer a comprehensive assessment of the expected levels of harmonics in the presence of existing and future loads, including electric vehicles and smart-grid-enabled devices. Such models offer a valuable tool for network operators to assess the potential impact of harmonics on future networks and to create sustainable design solutions to meet the increasing demand for electricity while achieving net zero targets. However, several variables associated with these estimations models involve a level of uncertainty due to their stochastic nature, leading to inaccuracies in the estimations. This paper aims to provide a more realistic estimate of these uncertainties in order to improve the outcomes of harmonic estimation models for the development of sustainable distribution networks. Full article

Incorporating renewable energy sources into the grid poses challenges due to their volatility and uncertainty in optimizing dispatch strategies. In response, this article proposes a cloud–edge collaborative scheduling strategy for distributed multi-energy systems, operating across various time scales. The strategy integrates day-ahead dispatch, intra-day optimization, and real-time adjustments to minimize operational costs, reduce the wastage of renewable energy, and enhance overall system reliability. Furthermore, the cloud–edge collaborative framework helps mitigate scalability challenges. Crucially, the strategy considers the multi-timescale characteristics of two types of energy storage systems (ESSs) and three types of demand response (DR), aimed at optimizing resource allocation efficiently. Comparative simulation results evaluate the strategy, providing insights into the significant impacts of different ESS and DR types on system performance. By offering a comprehensive approach, this strategy aims to address operational complexities. It aims to contribute to the seamless integration of renewable energy into distributed systems, potentially enhancing sustainability and resilience in energy management. Full article

Arylcyclohexylamine (ACH) compounds represent a predominant faction within new psychoactive substances. Due to their powerful dissociative effects, they are used in recreational contexts but also in situations of drug-facilitated sexual assault, and therefore, they are a constant target of analysis by forensic experts. In recent years, their consumption has been notably high, especially the use of ketamine, presenting daily challenges for laboratories in the determination of this and other ACH analogues. This review comprises the recent strategies that forensic specialists use to identify and quantify ACH compounds in the laboratory with more traditional analytical techniques and technology, and on the point-of-care testing via sensor technology. The study focuses on analogues of phencyclidine (PCP), ketamine, and eticyclidine, highlighting the consistent need for higher sensitivity in the analysis of various samples collected from real cases and simulations of possible matrices. The review also emphasises the ongoing research to develop more sensitive, quicker, and more capable sensors. Full article

Additive Manufacturing (AM) appears as a very interesting alternative to conventional production routes for alloys and metals, thanks to the fact that at the end of printing, the final product is obtained directly. The present article looks for the inclusion of duplex stainless steel 2205 (DSS-2205) in the commercial catalog of steels utilized in powder bed fusion (PBF) technologies, specifically applying the selective laser melting (SLM) technique. The main objective is to establish optimal printing parameters that reproduce the closest results to the base material properties. To achieve this, the response surface method was used in the methodology and experimental design, studying the parameters of laser power, scanning speed, and hatching distance. A reference material, machined from a hot-rolled plate, was utilized to compare the results obtained through tensile strength. Lastly, the optimal parameters have been obtained for this stainless steel. Additionally, a study of heat treatments has been developed, aiming to optimize the austenitization process, achieving an improvement in mechanical properties. A steel with mechanical properties practically identical to those of steel produced using conventional techniques has been obtained through SLM. Full article

Edge computing networks are critical infrastructures for processing massive data and providing instantaneous services. However, how to efficiently allocate resources in edge computing networks to meet the embedding requirements of service function chains has become an urgent problem. In this paper, we model the resource allocation problem in edge computing networks as a service function chain embedding problem model, aiming to optimize the resource allocation through reinforcement learning algorithms to achieve the goals of low cost, high revenue, and high embedding rate. In this paper, the basic concepts of edge computing network and service function chain are elaborated, and the resource allocation problem is transformed into a service function chain embedding problem by establishing a mathematical model, which provides a foundation for the subsequent algorithm design. In this paper, a service function chain embedding algorithm based on reinforcement learning is designed to gradually optimize the resource allocation decision by simulating the learning process. In order to verify the effectiveness of the algorithm, a series of simulation experiments are conducted in this paper and compared with other algorithms. The experimental results show that the service function chain embedding algorithm based on reinforcement learning proposed in this paper exhibits superior performance in resource allocation. Compared with traditional resource allocation methods, the algorithm achieves significant improvement in terms of low cost, high revenue, and high embedding rate. Full article