ABSTRACT The charging power is being augmented to enhance the ease of electric automobiles. Nonetheless, high‐power charging may lead to significant and apparent issues with battery heat production. Consequently, a prominent research concern is achieving an optimal equilibrium between rapid charging and preserving battery performance. An LCLC‐compensated combination inductive and capacitive wireless power transfer (COMPT) system for battery charging is a novel system in this work. Inductive and capacitive power transfer (IPT and CPT) systems are combined in the circuit design. A detailed analysis of the combined circuit architecture’s working principle clarifies the link between circuit parameters and system power. For inductive and capacitive coupling design, finite‐element analysis is used. The connector plate equivalent circuit model will be created for 100 kW. Design and implementation of the COMPPT system will demonstrate combined inductive and capacitive coupling. Both the inductive and capacitive couplers are 500 mm × 500 mm. Many air gaps are investigated for each coupler, ranging from 150 to 750 mm. Combining IPT and CPT systems yields COMPT’s output power. The prototype will generate 95.46 kW of power with 95.7% efficiency at a 1‐MHz switching frequency and operate better under misalignment than the IPT System. The inductive‐capacitive combination COMPT technology may be suitable for electric battery charging. The special environmental conditions that affect energy transfer, such as heat, rain, snow, and other important major environmental factors that affect energy transfer this research examines several lithium‐ion, lead‐acid, and nickel‐metal hydride batteries, using experiments to analyze the impact of pulse charging and constant current charging on battery performance. A suggested assessment methodology assesses the viability of various charging techniques, including rapid charging for power batteries, based on charging duration, rechargeable capacity, temperature variations while charging, and battery life degradation during cyclic charging.

Abstract The remote monitoring and fault diagnosis system can enable equipment suppliers to reduce maintenance costs, thereby saving manpower and material resources, and changing from passive defense to active; it can actively find problems and timely solve equipment after‐sales service and maintenance technical support under global market conditions, so as to improve product competitiveness. In the functional design of the system, the management of maintenance work orders is the core function of the whole system. Its main contents include: query on work orders, that is, query on unprocessed work orders; the establishment of the worksheet, which can set the founder, fault type, creation time, recommended maintenance personnel, etc. Receiving the order means that the maintainer selects the most suitable work order for maintenance according to the specific conditions on the work order. The administrator can view the specific contents of the work order and the work conditions of the maintenance personnel, and can issue work instructions to the maintenance personnel manually. In terms of system logic, the device layer includes the connections between terminal devices such as sensors, actuators, production equipment and control systems. The system uses a variety of networks and industrial buses to connect the terminal equipment, so as to achieve the normal control of data flow between the equipment. The support layer includes equipment failure prediction, maintenance work order recommendation, user management, work order management, interface settings, etc. It is responsible for the sorting and storage of instrument data, fault diagnosis and prediction of equipment, and puts forward suggestions for maintenance work. The data layer is the most basic data storage module, including file loading in the database, creation and analysis of query structured database statements. In the process of fault diagnosis, the overall correct rate of Bayesian network diagnosis with multi‐source information layer was 94.3%. The intelligent detection scheme of remote fault diagnosis proposed in this paper effectively improved the service level of the system, and provided forward‐looking information for equipment maintenance, performance diagnosis, upgrading and transformation, thus promoting the transformation of enterprise service strategy. This article is protected by copyright. All rights reserved.

Abstract The emergence of wireless sensor network (WSN) makes the communication between people and things more extensive, and provides a new way for people to obtain information and more accurate real‐time communication. However, the regional distance in WSN is still a big problem. This method can not only optimize the cluster structure, but also reduce the number of member nodes in the cluster, the cluster head receiving energy, and reduce the data transmission distance between nodes. At the same time, the number of packets received by the base station increases, so that more sensing information can be obtained. The simulation results showed that compared with the smart Ethernet protection (SEP) protocol and the low energy adaptive clustering hierarchy (LEACH) protocol, the number of first node deaths (FND) of the grey wolf optimizer (GWO) algorithm was 153 and 233 times different from those of the SEP and LEACH protocols respectively at 100m*100m. In the case of 200m*200m, compared with that of SEP and LEACH, the number of FND of GWO was 152 and 200 times different. Finally, in the case of 300m*300m, compared with that of SEP and LEACH, the number of FND of GWO was 142 and 158 times different. The GWO algorithm has a higher node ARE ratio in a wider region. In the same 1000 rounds, the node ARE of GWO was 61%, while the node ARE of SEP and LEACH protocols were 7% and 3%, respectively. From the above results, it can be found that the GWO algorithm has been greatly improved in terms of network energy efficiency, network lifetime, etc., and can still work normally under larger scale monitoring. The innovation of this article lies in the localization problem of fixed node mobile sensor networks, proposing a mobile sensor network localization method based on mobile error nodes. This can optimize the distance between nodes and improve the positioning accuracy of the algorithm in both distributed and centralized situations. This article is protected by copyright. All rights reserved.

ABSTRACT Stress, a common response to challenging situations, has become pervasive in contemporary daily life due to various factors. Persistent stress can weaken the human immune system, increasing the risk of chronic stress and contributing to a range of physical and mental health disorders. Therefore, timely detection of stress in its early stages is crucial for preventing adverse health outcomes. Physiological signals offer insights into the body's stress‐induced changes and can be leveraged for stress detection applications. Among these signals, the photoplethysmogram (PPG) signal stands out due to its advantages. This article introduces an innovative stress detection model based on dilated convolutional neural networks (Dilated CNNs), a deep learning algorithm. This model distinguishes between an individual's stressed and non‐stressed states by analysing PPG signals without requiring pre‐processing, denoising, or feature extraction. Leveraging the Empatica E4 PPG signals from the Wearable Stress and Affect Detection (WESAD) dataset, the authors developed and evaluated the model, achieving remarkable results: a test accuracy of 93.56% and an area under the curve (AUC) of 96.52%. These outcomes are particularly noteworthy given the streamlined data preparation process and methodological simplicity. Beyond enabling early stress diagnosis, this advancement holds promise for enhancing overall health and well‐being in the fast‐paced and intricate world. Additionally, its simplicity makes it suitable for real‐time stress detection and integration into wearable devices.

ABSTRACTThe demand for fast, precise, and sensitive food safety methods is growing as consumers increasingly rely on online food delivery services. Food products shipped from South America to Europe can spend more than 21 days in transit, often resulting in deterioration, mould, or pathogen growth. Similar risks apply in the food service industry, where food may have been stored or handled improperly, leading to foodborne illness. This paper presents the Edispotter, an IoT‐based food quality monitoring system designed to address these and similar issues. Using Raspberry Pi and ESP32, the Edispotter collects essential food quality data through various sensors. The data are processed and stored in a Redis database within an Amazon Web Services (AWS) cloud environment, providing real‐time food‐based status updates via an Android application.

ABSTRACT Precision agriculture (PA) plays an essential role in resource use and crop yields while minimising environmental impact through data‐driven farming techniques. The combination of unmanned aerial vehicles (UAVs), the Internet of Things (IoT) and wireless sensor networks (WSNs) has significantly transformed the current state of farming, enabling decisions based on data, predicting outcomes and precise control. This review presents the current developments, challenges and complementary advantages of these technologies to improve agricultural efficiency and sustainability in a comprehensive manner. The search timeframe of this search is 2019–2025. The analysis of the WSN‐based systems begins with the analysis of sensing technologies, communication protocols (LoRa, Sigfox, Wi‐Fi, Bluetooth, ZigBee, NB‐IoT and RFID), sensor architecture, energy consumption and path‐loss models, which affect the data transmission in an agricultural setting. It highlights the weaknesses of WSN deployment, such as power consumption and coverage. Second, the use of UAVs in crop monitoring, irrigation, pest detection and resource optimisation is reviewed with references to the incorporation of sensing and data analytics algorithms and the challenges associated with UAV use, such as the short flight duration and energy consumption. Third, IoT‐based frameworks are researched in the context of their roles in the PA of real‐time monitoring, automated controls and smart decision‐making. The findings suggest that a network of WSNs, UAVs and the IoT can be used to enhance monitoring quality, data quality and resource utilisation by multiple orders of magnitude. However, such barriers as energy consumption, connectivity differences, complexity of data and costs will be topical. Overall, the WSN‐UAV‐IoT combo is a potentially fruitful direction that could assist PA to take a step forward in terms of productivity, sustainability and environmental friendliness.

ABSTRACTThe rapid advancements in vehicular ad hoc networks (VANETs) call for development of effective networking schemes. Managing heterogenous traffic in VANETs becomes a critical challenge, especially when dealing with critical scenarios. In this paper, we present a novel dynamic fragmentation‐based MAC protocol, DFROG‐MAC for Internet of Things (IoT) applications in VANET environment. This protocol is focused on facilitating prioritised heterogenous traffic in sensor networks and hence, can offer a distinguished quality of service for various application areas such as vehicular, industrial or body sensor networks. DFROG‐MAC deploys fragmentation scheme for low priority data, so the high priority data may interrupt and access channel without needing to wait for the complete transmission of lower priority data. The fragment size for the lower priority data is dynamically adjusted at the runtime based on the frequency of urgent traffic arrival. This dynamic approach helps to ensure that the channel does not remain idle, and lower priority traffic could be served quickly, in the absence of urgent traffic. Two types of traffic priorities, normal and urgent have been used for performance evaluation of FROG‐MAC and DFROG‐MAC, over Contiki platform, with the scope of this study focused on vehicle‐to‐infrastructure (V2I) single‐hop communication. The delay and throughput both have been found to improve for DFROG‐MAC due to the possibility of dynamic fragment size selection.

ABSTRACTThis research presents a novel square hollow‐core photonic crystal fibre (PCF) sensor designed for the detection of food‐grade oils in the terahertz (THz) frequency range. The sensor’s effectiveness is quantitatively evaluated using COMSOL Multiphysics, a sophisticated simulation tool that employs finite element methodology (FEM) to model complex interactions within the fibre structure. Simulation outcomes reveal that, under optimal geometric parameters, the proposed sensor achieves an exceptional relative sensitivity of 98.27% for various edible oils at an ideal frequency of 2.2 THz, significantly outperforming existing technologies. Additionally, the sensor exhibits minimal confinement loss of 1.428 × 10−8 dB/m and a low effective material loss of 0.004246 cm−1, facilitating accurate detection of slight refractive index variations related to the chemical compositions of different oils. This high sensitivity enables non‐destructive testing, allowing for the analysis of oils without compromising their composition or quality, thereby maintaining the integrity of food products. Ultimately, the proposed PCF sensor enhances food safety monitoring and paves the way for advanced applications in the food industry, ensuring consumers receive high‐quality products.