Digital Twin Concept, Method and Technical Framework for Smart Meters

Smart meters connect smart grid electricity suppliers and users. Smart meters have become a research hotspot as smart grid applications like demand response, power theft prevention, power quality monitoring, peak valley time of use prices, and peer-to-peer (P2P) energy trading have grown. But, as the carriers of these functions, smart meters have technical problems such as limited computing resources, difficulty in upgrading, and high costs, which to some extent restrict the further development of smart grid applications. To address these issues, this study offers a container-based digital twin (CDT) approach for smart meters, which not only increases the user-facing computing resources of smart meters but also simplifies and lowers the overall cost and technical complexity of meter changes. In order to further validate the effectiveness of this method in real-time applications on the smart grid user side, this article tested and analyzed the communication performance of the digital twin system in three areas: remote application services, peer-to-peer transactions, and real-time user request services. The experimental results show that the CDT method proposed in this paper meets the basic requirements of smart grid user-side applications for real-time communication. The container is deployed in the cloud, and the average time required to complete 100 P2P communications using our smart meter structure is less than 2.4 seconds, while the average time required for existing smart meter structures to complete the same number of P2P communications is 208 seconds. Finally, applications, the future development direction of the digital twin method, and technology architecture are projected

Pre-Operative Workup of Cochlear Implant

Hearing loss, an increasing problem across the globe, results in an important solution in the form of cochlear implants, highlighting the critical need for effective interventions. This review involves analyzing 46 relevant publications via databases such as PubMed and Google Scholar, providing current insights into pre-operative issues. Studies through databases such as PubMed and Google Scholar, ensuring contemporary insights into the pre-operative considerations. The pre-operative evaluation encompasses medical history, covering prenatal events and immediate post-natal health, along with physical examinations and complete audiometric assessments. High-Resolution Computed Tomography (HRCT) and Magnetic Resonance Imaging (MRI) emerge as crucial imaging techniques, guiding surgical planning and electrode placement. Brainstem Evoked Response Audiometry (BERA) supplements inconclusive MRI data, while vestibular screening aids in candidate selection. Cochlear duct length determination, often assessed through imaging techniques, contributes to optimal electrode array selection. Models in cochlear implant research, spanning computational, animal, tissue engineering, and physical models, further enhance our understanding and refinement of cochlear implant designs. In conclusion, this comprehensive pre-operative workup plays a significant role in assessing patient health, identifying causes of deafness, and contributing to the overall success of cochlear implantation, a transformative solution for profound hearing impairment

Infective agents in diabetic foot ulcers and their sensitivity patterns

Background: Diabetic-foot syndrome is a difficult & debilitating complication of inadequately regulated Diabetes Mellitus. Attributed to neural & vascular pathology, the condition is further potentiated by glycemic healing impairment.  A wide array of microorganisms have been implicated & sensitivity-guided antibiotics are essential to save both limb as well as to minimize rampant microbial resistance. Present study aims to determine the culture & sensitivity pattern of bacteria in stated cohort of patients at a Surgical Unit. Materials & Methods: This prospective cohort study was conducted over a period of 1 year-duration at a tertiary-care-Hospital. All patients presenting with diabetic-foot who had not been subjected to empiric antibiotic-therapy were enrolled. Demographic & lesion-based variables were studied and the Culture & Sensitivity pattern was evaluated and statistically analyzed. Results:100 patients were included in the study,of which 80 were male (mean-age 60.8±12.7 years) & rest female (mean-age 58.4±11.3-years).35% cultures yielded no growth. Remaining cases showed following pathogens in descending order of incidence. Maximal sensitivity was also reported as mentioned. 1) Staphylococcus-aureus & Klebseilla-Pneumoenae– Piperacillin/Tazobactam,2) Pseudomonas-Aerugionas-Cefotaxime,3)E-coli–Amikacin& Sulbactam,4) Proteus -Gentamicin, 5) Streptococci– Amikacin and 6) Bacteroides – Cefoperazone & Aztreonam. Of 71 cases, 70  had aerobic-organisms isolates & only 1 had anaerobic-isolate.   Conclusions: Six pathogens were identified in present study of which Staphylococcus-Aureus was the most prevalent as well as the most resistant. Streptococci & Gram-negative Organisms were observed in remaining cases. While formulation of an adequate antibiotic regime is rendered difficult by resistance & mixed infections, targeted antibiotic administration is decisively crucial to achieve optimal & timely outcome in diabetic foot. &nbsp

Smart Meter Development Using Digital Twin Technology for Green Energy Distribution Optimization

This study proposes a digital twin (DT) approach and technical framework for smart meters to solve potential implementation and development problems and adapt to the new energy revolution trend and increase smart grid network security. DT models were deployed in the cloud and edge using a smart meter DT demonstration system. This paper evaluates the DT system's communication performance in real-time smart grid application through three dimensions: remote application service for smart grid user side, P2P transaction on the user side, and user real-time request service. This study's container-based decision tree strategy for smart meters meets the smart grid's real-time communication requirements for user-side applications

Hepatoprotective effect of desi and kabuli cultivars of Cicer arietinum L (chick peas) against carbon tetrachloride-induced toxicity in rats

Purpose: To determine the hepatoprotective potential of ethanol extracts of desi and kabuli cultivars of Cicer arietinum L. (chick peas). Methods: Hepatotoxicity was induced in rats using oral administration of carbon tetrachloride (CCl4). The rats were then orally administered different doses of the ethanol extracts of desi and kabuli cultivars of Cicer arietinum L. for 21 days. Oxidative stress parameters and hepatoprotective profiles were determined in serum samples using standard procedures. The effect of the treatments on liver histology was also determined. Results: Administration of extracts of desi and kabuli cultivars of Cicer arietinum L. to CCl4 treated rats at a dose of 300 mg/kg resulted in a significant (p ≤ 0.05) decrease in oxidative stress parameters, whereas catalase activity significantly increased (p ≤ 0.05); on the other hand, ALT and AST levels were decreased significantly (p ≤ 0.05), when compared to the control group. Conclusion: High doses of Cicer arietinum L (desi and kabuli cultivars) seem to have hepatoprotective and antioxidant effects on CCl4-induced toxicity in rats. This finding underscores the therapeutic importance of Cicer arietinum L. as a plant with hepatoprotective properties. Keywords: Cicer arietinum, Phenolics, Hepatotoxicity, Chick peas, Catalas

Anomaly Detection in Traffic Surveillance Videos Using Deep Learning

In the recent past, a huge number of cameras have been placed in a variety of public and private areas for the purposes of surveillance, the monitoring of abnormal human actions, and traffic surveillance. The detection and recognition of abnormal activity in a real-world environment is a big challenge, as there can be many types of alarming and abnormal activities, such as theft, violence, and accidents. This research deals with accidents in traffic videos. In the modern world, video traffic surveillance cameras (VTSS) are used for traffic surveillance and monitoring. As the population is increasing drastically, the likelihood of accidents is also increasing. The VTSS is used to detect abnormal events or incidents regarding traffic on different roads and highways, such as traffic jams, traffic congestion, and vehicle accidents. Mostly in accidents, people are helpless and some die due to the unavailability of emergency treatment on long highways and those places that are far from cities. This research proposes a methodology for detecting accidents automatically through surveillance videos. A review of the literature suggests that convolutional neural networks (CNNs), which are a specialized deep learning approach pioneered to work with grid-like data, are effective in image and video analysis. This research uses CNNs to find anomalies (accidents) from videos captured by the VTSS and implement a rolling prediction algorithm to achieve high accuracy. In the training of the CNN model, a vehicle accident image dataset (VAID), composed of images with anomalies, was constructed and used. For testing the proposed methodology, the trained CNN model was checked on multiple videos, and the results were collected and analyzed. The results of this research show the successful detection of traffic accident events with an accuracy of 82% in the traffic surveillance system videos.publishedVersio

Numerical optimization of (FTO/ZnO/CdS/CH₃NH₃SnI₃/GaAs/Au) perovskite solar cell using solar capacitance simulator with efficiency above 23% predicted

The presented study deals with the investigations of the methyl ammonium tin halide (CH3NH3SnI3) based perovskite solar cells for optimized device performance using solar capacitance simulations software. Several necessary parameters such as metal work functions, thickness of structural layers, charge carrier’s mobility and defect density have been explored to evaluate the device performance. Calculations reveal that for the best efficiency of device the maximum thickness of the perovskite (CH3NH3SnI3) absorber layer must be 4.2 μm. The thickness values of 0.01 μm for ZnO electron transport layer (ETL), 0.871 μm for GaAs hole transport layer and 0.001 μm for CdS buffer layer have been found which proved to be optimum for maximum power conversion efficiency (PCE) of 23.80% for the device. The variation of open circuit voltage (Voc), Short circuit current (Jsc), Fill Factor (FF %), quantum efficiency (QE) against thickness of all layers and interface defect densities in FTO/ZnO/CdS/CH3NH3SnI3/GaAs/Au composition have been critically explored and their crucial role for the device performance has been reported. Heterojunctions between ZnO-ETL and CdS buffer layers have shown improved device performance and PCE. Current investigations may prove to be useful for designing and fabrication of climate friendly, non-toxic and highly efficient solar cells