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AbstractTraditional analog and digital meters are being substantially replaced with technological advances and the Internet of Things (IoT) introduction. The smart meter is highly preferred for accessing real‐time consumption, tariff calculation, and remote system control. These smart meters also prevent the majority from bypassing theft. Despite its intelligence, it cannot be 100% secure. An error in the readings can be caused by hacking or damage to meter components making the utility companies suffer significant losses. Based on past and future energy consumption data predictions on the consumer side, various methods are used in the proposed work to identify theft or anomalies in smart meter readings. Forecast‐based detection proved to be the most effective and accurate method. The primary and secondary decision models, which employ a variety of statical analyses to identify system anomalies, serve as the foundation for the energy consumption that follows the forecasting. Past 24‐h data is needed for forecasting, which is passed through different statical calculations such as RMSE, simple moving average, and Absolute Percentage Error to conclude detecting the normal values. Long short‐term memory gives high accuracy of 97% for forecasting and detecting abnormalities.

Introduction: Diabetic foot ulcers are critical complications of uncontrolled diabetes, with peripheral neuropathy, peripheral arterial disease, and immune suppression as primary contributing factors. Adenosine Deaminase (ADA), an enzyme, has been identified as a promising marker for cell-mediated immunity. Its estimation may serve as a predictive marker for immunomodulation, which, in turn, leads to complications among individuals with diabetes. Aim: To estimate serum ADA levels in diabetic patients without diabetic foot and diabetic patients with diabetic foot. Additionally, the study aimed to investigate the association of ADA serum levels with glucose, Glycated Haemoglobin (HbA1c), and the severity of wounds. Materials and Methods: This cross-sectional study was conducted in the central laboratory Department of Biochemistry at Krishna Rajendra Hospital (KR Hospital), Mysuru Medical College and Research Institute, Mysuru, India, from August to September 2021. The study included diabetic foot cases (n=45) and Type 2 diabetes patients on regular follow-up in the Department of Medicine without diabetic foot and any other complications (n=45). Venous blood samples were analysed for ADA, glucose, HbA1c, and complete blood count. The data were statistically analysed using the Mann-Whitney U test, Kruskal-Wallis test, and Spearman rank correlation test. Results: The present study observed a higher incidence of diabetic foot among males. It showed a statistically significant increase in serum ADA, HbA1C, and neutrophil count in patients with diabetic foot compared to patients with diabetes without diabetic foot ulcers, with mean Standard Deviation (SD) of 71.79±25.11, 11.53±3.11%, and 75.91±9.77%, respectively. Serum ADA exhibited a positive correlation with HbA1C and different grades of ulcers. Conclusion: The present study demonstrated a significantly increased serum ADA in diabetic ulcers and a positive correlation with different grades of ulcers. This finding contributes to a better understanding of the disease's pathogenesis at different stages. Furthermore, the positive correlation between ADA and HbA1C levels may play an important role in predicting the glycaemic and immunological status of these patients.

Abstract A drop in physical activity and a deterioration in the capacity to undertake daily life activities are both connected with ageing and have negative effects on physical and mental health. An Elderly and Visually Impaired Human Activity Monitoring (EV-HAM) system that keeps tabs on a person’s routine and steps in if a change in behaviour or a crisis might greatly help an elderly person or a visually impaired. These individuals may find greater freedom with the help of an EVHAM system. As the backbone of human-centric applications like actively supported living and in-home monitoring for the elderly and visually impaired, an EVHAM system is essential. Big data-driven product design is flourishing in this age of 5G and the IoT. Recent advancements in processing power and software architectures have also contributed to the emergence and development of artificial intelligence (AI). In this context, the digital twin has emerged as a state-of-the-art technology that bridges the gap between the real and virtual worlds by evaluating data from several sensors using artificial intelligence algorithms. Although promising findings have been reported by Wi-Fi-based human activity identification techniques so far, their effectiveness is vulnerable to environmental variations. Using the environment-independent fingerprints generated from the Wi-Fi channel state information (CSI), we introduce Wi-Sense. This human activity identification system employs a Deep Hybrid convolutional neural network (DHCNN). The proposed system begins by collecting the CSI with a regular Wi-Fi Network Interface Controller. Wi-Sense uses the CSI ratio technique to lessen the effect of noise and the phase offset. The t- Distributed Stochastic Neighbor Embedding (t-SNE) is used to eliminate unnecessary data further. The data dimension is decreased, and the negative effects on the environment are eliminated in this process. The resulting spectrogram of the processed data exposes the activity’s micro-Doppler fingerprints as a function of both time and location. These spectrograms are put to use in the training of a DHCNN. Based on our findings, EVHAM can accurately identify these actions 99% of the time.

Despite advancements in technology, power system faults leading to electric power interruption remain a significant issue. Efficient restoration of the power system relies on the swift classification and clearance of faults. Among the various types of faults in transmission lines, open circuit and short circuit faults are commonly encountered. This study specifically focuses on the analysis of five types of short circuit faults: line-to-line, line-to-ground, double line-to-ground, triple line, and triple line-to-ground faults. Faults can cause both power failure and power loss in transmission lines. Once a fault occurs, it is crucial to restore electricity supply promptly to prevent further losses. Therefore, the development of a system capable of accurately and swiftly detecting and removing faults is essential. Traditionally, categorizing transmission line faults required sophisticated mathematical modeling, intricate signal processing techniques, and expert knowledge to interpret the output signals. In this paper, an alternative approach is proposed, utilizing deep learning techniques for transmission line fault classification. Specifically, the paper employs techniques such as artificial neural network (ANN), long short-term memory (LSTM), with and without window regression (WR). By implementing these deep learning techniques, automatic feature extraction and signal processing are achieved, streamlining the fault classification process. The experimental results obtained from this study demonstrate an accuracy of 42.98% for ANN, 99.98% for LSTM, and 99.99% for LSTM-WR. These outcomes underscore the effectiveness of the deep learning techniques employed in accurately classifying transmission line faults, with LSTM and LSTM-WR outperforming ANN in terms of accuracy.

In humans, the immune system serves as a protective barrier against infection; however, when the immune system is out of balance, it can harm the host. Immunomodulators are chemicals or medications that have been employed in the clinic to treat an unbalanced immune response. The majority of immunological medicines in clinical use are cytotoxic. They harm the patient's quality of life by causing various side effects and being associated with higher production costs, longer lead times, and a high failure rate. Furthermore, obtaining single-compound chemicals with low toxicity, high efficacy, and selectivity for specified disorders is difficult for researchers. As a result, techniques based on alternative medicine are gaining traction in drug development, focusing on innovative natural compounds utilized to treat various disorders. Many plant molecules founded to have biologically beneficial properties. This review aimed to look at the immunomodulatory activity of plant-derived chemicals from widely used plants that treat a range of diseases worldwide.

Objective: The present article details the preparation and characterization of bio-nanocomposite films loaded with nanoclays and chitosan which are the catalysts for retaining the fresh quality of flowers, fruits, and vegetables and extending their shelf life under packed with this film. The development of new packaging films is currently an emerging field for the consumers and edible goods trading industries. Hence, novel bio packaging films with a unique combination is essential to ensure optimum characteristics for the bio packaging films, thereby obviating synthetic plastics. Methods: Synthesis and characterization of the formulated blends of carboxymethylcellulose incorporated with chitosan, nanoclays, epoxy, and polyethylene glycol with the addition of lemon leaf extract (LLE) and analysis of their antibacterial activity. Result: The results indicate that when the jasmine flower buds were packed with the synthesized film, retain their buddy state for a minimum of 5 days at room temperature, freshness, and fragrance. Conclusion: Hence, the nano-bio packaging film will ensure the transportation of daily harvested flowers, fruits, and vegetables from farms to firms, stores, and homes.

A Wireless Sensor Network is made up of a large number of small, low-power sensor nodes that are randomly or manually distributed across an uncontrolled target region. However, the WSNs' main constraint is the sensor nodes' restricted power sources. As a result, energy saving at sensor nodes is the most difficult barrier for WSNs' long-term functioning. Clustering and low-energy routing are two well-known optimization tasks that have received a lot of attention in the quest to extend the life of wireless sensor networks (WSNs). In a periodic data gathering application, the Particle Swarm Optimization (PSO) technique is employed to overcome this problem. Sensors monitor the environment on a regular basis and send data to the base station (BS), which analyses the data and draws conclusions about the field activities. A cluster is used to send the data to the base station. A cluster head receives data from sensor nodes and transmits it to the cluster head, which combines the information and sends it to the base station. The packet delivery ratios and throughput have both been enhanced. As a result, total energy consumption is reduced [ABSTRACT FROM AUTHOR]

Zinc (Zn)-composite coatings are still in demand as good corrosion barrier coatings to protect steel substrates from corrosion environment. In this article, the Ni doped SnO2 nanoparticles were synthesized and used as a composite additive for Zn-coating. The synthesis was carried out by the combustion method using citric acid as a fuel. The Zn-Ni doped SnO2 composite coating was produced on mild steel by an electroplating technique. The surface characterization and elemental analysis of the coated samples were examined by X-ray diffraction spectroscopy (XRD), scanning electron microscopic images (SEM) followed by energy dispersive spectroscopy (EDAX). The surface morphology of Zn-Ni doped SnO2 composite before and after corrosion showed a more compact surface structure with respect to the pure Zn-coat. The corrosion resistance property of the Zn-Ni doped SnO2 composite coating was studied by Tafel polarization and electrochemical impedance spectroscopy. Keywords: Ni doped SnO2 nanoparticles, Zn-composite coating, Corrosion behavior, Tafel polarization, EIS