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Medium-voltage (MV) cables often experience a shortened lifespan attributed to insulation breakdown resulting from accelerated aging and anomalous operational and environmental stresses. While partial discharge (PD) measurements serve as valuable tools for assessing the insulation state, complexity arises from the presence of diverse discharge sources, making the evaluation of PD data challenging. The reliability of diagnostics for MV cables hinges on the precise interpretation of PD activity. To streamline the repair and maintenance of cables, it becomes crucial to discern and categorize PD types accurately. This paper presents a comprehensive review encompassing the realms of detection, feature extraction, artificial intelligence, and optimization techniques employed in the classification of PD signals/sources. Its exploration encompasses a variety of sensors utilized for PD detection, data processing methodologies for efficient feature extraction, optimization techniques dedicated to selecting optimal features, and artificial intelligence-based approaches for the classification of PD sources. This synthesized review not only serves as a valuable reference for researchers engaged in the application of methods for PD signal classification but also sheds light on potential avenues for future developments of techniques within the context of MV cables.

This paper remedies the lack of comparison between studies specifically addressing partial discharge (PD) localization using electrical techniques. It identifies all the elements in need in each technique as well as the equations leading to a precise determination of the discharge site in a cable with a certain length and documents several circuit models set to simulate various types of PD. From the details in this paper, different detection methods can be combined based on the specific requirements of each method for detecting PD. This work thoroughly evaluates several electrical PD detection approaches, including time-based, frequency band, and electromagnetic time reversal (EMTR). Additionally, it gathers circuit modeling for various types of PD along cables to improve detection accuracy. It is evident that all time-dependent methods, despite their simplicity and requiring only a small number of components, face challenges when applied to long cables. This is primarily due to their reliance on signal propagation time. The authors provide profound insights into suggestions for future study areas. This review will provide essential insights and serve as a foundation for researchers to develop more effective methods for detecting PD in cables.

Partial discharge (PD) is an electrical discharge which is one of the most critical breakdown factor that is affecting the electrical equipment. The loss of the power will affect consumers and system operation. High voltage (HV) transformer is one of the equipment's subjected to phenomena PD. In this paper reviews an application of acoustic methods in transformer and piezoelectric sensors application on PD detection in HV transformer. Based on this review, the new design in acoustic sensor is required in order to improve the sensitivity and bandwidth for PD detection at HV transformer. The valuable parameter such as materials, size, and PD frequency range were discussed in this paper and can be used for early stage on designing new acoustic sensor. This detection method given some benefits on preventing the power electrical system from breakdown.

Silicon carbide (SiC) power modules are increasingly being used in high-voltage and high-frequency applications due to their superior electrical and thermal qualities. However, the issue of the partial discharge (PD) phenomenon raises serious reliability difficulties resulting in insulation failure, performance degradation, and potential device collapse. This paper provides a thorough assessment of the current PD detection strategies in SiC power modules. The issues provided by SiC devices’ distinct operational features, such as high switching frequencies and higher voltage stresses, which hinder PD detection and mitigation, are widely investigated. This review compares the effectiveness, benefits, and limitations of various detection methods, emphasizing the need for better strategies to ensure long-term reliability and performance. This study gives an in-depth overview of the numerous forms of PD phenomena that occur in power modules, including internal and surface discharges, as well as how they appear under various detection systems. It examines the performance of several methods for power module technologies such as SiC. To address these PD issues, this article proposes ways to improve reliability and detection accuracy.

In the world, tomato is a significant economic crop. However, it is easily affected by various diseases. Misprediction of disease is caused since many prevailing methodologies focused on the tomato plant’s specific portion. Thus, by employing deep learning (DL) multivariate normal DL neural network (MNDLNN) classifier, the study has proposed a framework for tomato plant disease (PD) detection. Firstly, the input images’ colours are transmitted into HSI format. Next, from the images, the green pixels are masked, and healthy and unhealthy regions are isolated. Next by deploying the region of interest (ROI), the fruit and root are detected. Then, by utilizing the rectilinear K-means (KM) clustering (RKMC) algorithm, the unhealthy regions are segmented. Afterwards, by utilizing random motion squirrel search optimization (RMSSO), the essential features are extracted. Finally, MNDLNN effectively detects and classifies the disease types. The results revealed that the proposed framework performed the disease detection process more precisely than other top-notch methodologies.

The abnormal deposition of protein in the brain is the central factor in neurodegenerative disorders (NDs). These detrimental aggregates, stemming from the misfolding and subsequent irregular aggregation of α-synuclein protein, are primarily accountable for conditions such as Parkinson’s disease, Alzheimer’s disease, and dementia. Two-photon-excited (TPE) probes are a promising tool for the early-stage diagnosis of these pathologies as they provide accurate spatial resolution, minimal intrusion, and the ability for prolonged observation. To identify compounds with the potential to function as diagnostic probes using two-photon techniques, we explore three distinct categories of compounds: Hydroxyl azobenzene (AZO-OH); Dicyano-vinyl bithiophene (DCVBT); and Tetra-amino phthalocyanine (PcZnNH2). The molecules were structurally and optically characterized using a multi-technique approach via UV-vis absorption, Raman spectroscopy, three-dimensional fluorescence mapping (PLE), time-resolved photoluminescence (TRPL), and pump and probe measurements. Furthermore, quantum chemical and molecular docking calculations were performed to provide insights into the photophysical properties of the compounds as well as to assess their affinity with the α-synuclein protein. This innovative approach seeks to enhance the accuracy of in vivo probing, contributing to early Parkinson’s disease (PD) detection and ultimately allowing for targeted intervention strategies.

Partial discharge (PD) is a symptom of insulation defect or degradation in high-voltage equipment. Thus, PD detection is an important diagnostic tool. Furthermore in practical situations, the PD can be generated from a single or multiple sources. Being able to detect and classify such PD events will help to determine the necessary corrective action to prevent insulation breakdown. To demonstrate, three different simulated discharge conditions in transformers were investigated: void, floating metal and their combination. The PD signals were captured using an ultra-high frequency (UHF) sensor and denoised using wavelet transform method by application of Matlab wavelet multi-variate denoising tool. Two types of mother wavelet, that is, db and sym, were applied to decompose the signals and extract the signal features in terms of their skewness, kurtosis and energy. These features were then used as input to train a neural network to analyse and determine the PD source type. Results show this technique is able to classify and recognise single and multiple PD source types with a high degree of success.

This study presents a new and well-validated method using a particle filter for detecting Partial Discharge (PD) in power transformers. Recognizing the limitations of traditional methods in noisy settings, our creative approach effectively merges multi-modal acoustic and Ultra - High Frequency (UHF) sensor data, enhanced by advanced signal processing and improved feature extraction techniques. At first, our basic algorithm showed higher error rates than earlier research, highlighting the necessity for major enhancements. We tackled this issue by creating an optimized particle filter algorithm, inspired by the precise Monte Carlo localization methods used in satellite navigation and deep-space tracking. This improved algorithm, supported by comprehensive MATLAB simulations and detailed quantitative analysis, significantly lowered localization errors, achieving outstanding performance gains with sub-millimeter accuracy in ideal conditions and consistently maintaining sub-6 mm precision across various testing scenarios. A key contribution is the combined use of advanced Time Difference of Arrival (TDOA) techniques with our dependable Particle Filter framework, which is improved by adaptive mechanisms tailored for transformer environments. This strong combination greatly boosts fault detection and noise immunity, while also offering outstanding resistance to environmental changes that are often faced in transformer diagnostics. When compared to traditional methods, our integrated, sensor-driven framework consistently shows better performance in terms of detection precision and localization accuracy. This improved ability is crucial for enabling dependable predictive maintenance plans, avoiding costly breakdowns, and extending the lifespan of high-voltage equipment. The proven practicality and success of this combined method emphasize its potential to revolutionize transformer condition monitoring. Future work will focus on enhancing computational efficiency for real-time uses and expanding the framework’s applicability to a broader range of high-voltage devices, thus solidifying its importance as a flexible and vital tool in modern electrical asset management.

Day by day, the demand for wireless systems is increasing while the available spectrum resources are not sufficient. To fulfil the demand for wireless systems, the spectrum hole (spectrum vacant) should be found and utilised very effectively. Cognitive radio (CR) is a device which intelligently senses the spectrum through various spectrum-sensing detectors. Based on the complexity and licensed user’s information present with CR, the appropriate detector should be utilised for spectrum sensing. In this paper, a hybrid detector (HD) is proposed to determine the spectrum hole from the available spectrum resources. HD is designed based on an energy detector (ED) and matched detector (MD). Unlike a single detector such as ED or MD, HD can sense the signal more precisely. Here, HD can work on both conditions whether the primary user (PU) information is available or not. HD is analysed under heterogeneous environments with and without cooperative spectrum sensing (CSS). For CSS, four users were used to implement OR, AND, and majority schemes under low SNR walls. To design the HD, specifications were chosen based on the IEEE Wireless Regional Area Network (WRAN) 802.22 standard for accessing TV spectrum holes. For the HD model, we achieved the best results through OR rule. Under the low SNR circumstances at −20 dB SNR, the probability of detection (PD) is maximised to 1 and the probability of a false alarm (PFA) is reduced to 0 through the CSS environment.

Exosomes emerges as indicators of the tumor microenvironment, yet their predictive utility for immunotherapy responses is limited by the insufficient sensitivity and specificity of currently available assays. Here, a multi‐level engineering strategy is presented that enables accurate exosome‐based prediction of immunotherapy responses by integrating systematic aptamer ligand tailoring, ultrasensitive ellipsometry‐based sensing, and clinically relevant tumor‐proximal fluid sampling. Aptamers specifically targeting PD‐L1 are identified through systematic evolution of ligands by exponential enrichment (SELEX), followed by truncation and computational sequence refinement to enhance binding specificity. The optimized aptamer sequence (Tr‐Apt13) is validated from molecular interaction analyses to in vitro assays, demonstrating superior target binding efficacy over conventional antibodies, attributed to dense surface immobilization and multivalent binding capability. When incorporated into an ellipsometry‐based dual‐prism solution‐immersed silicon sensor, Tr‐Apt13 enabled ultrasensitive detection of PD‐L1–expressing exosomes with a detection limit of ≈9.8 particles mL−1, exhibiting high linearity and stability. Clinical validation using bronchoalveolar lavage fluid (BALF) from lung cancer patients revealed precise discrimination between responders and non‐responders at exosome concentrations as low as 103 particles mL−1, outperforming serum‐based analysis, conventional ELISA, and tissue immunohistochemistry. Collectively, this multi‐level strategy offers a new perspective on exosome‐based diagnostics, enabling precise prediction of immunotherapy efficacy in lung cancer patients. A novel ellipsometry platform functionalized with truncated PD‐L1 aptamers are developed to enable ultrasensitive detection of BALF‐derived exosomes. Through chemical ligand tailoring, mechanical sensing optimization, and tumor‐proximal fluid sampling, the system precisely stratifies ICI responders and non‐responders, offering superior sensitivity and clinical applicability over conventional assays, with strong potential to advance personalized immunotherapy.