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Heart disease is among the leading causes of mortality globally. Predicting cardiovascular disease is a major difficulty in clinical data analysis. AI has been demonstrated to be powerful in deciding and anticipating an enormous measure of information created by the health domain. We provide a unique method for finding essential traits employing machine learning approaches in this paper, which enhances the effectiveness of identifying heart diseases. Decision tree (DT), support vector machine (SVM), artificial neural network (ANN), and K-nearest neighbor (KNN) are the classification techniques used to create the proposed system. Ensemble stacking integrates the four classification models to create a single best-fit predictive model using logistic regression. Many explorations have been directed at the identification of cardiac infection; however, the exactness of the outcomes is poor. Accordingly, to further enhance the efficiency, Moth-Flame Optimization (MFO) algorithm is proposed. The feature selection strategies are used to improve the classification accuracy while shortening the execution time of the classification system. Medical data are used to assess the probability of heart disease based on BP, age, gender, chest ache, cholesterol, blood sugar, and other variables. Results revealed that the proposed system excelled other existing models, obtaining 99% accuracy in the Cleveland dataset.

In recent times, nutrition recommendation system has gained increasing attention due to their need for healthy living. Current studies on the food domain deal with a recommendation system that focuses on independent users and their health problems but lack nutritional advice to individual users. The proposed system is developed to suggest nutritional food to people based on age and gender predicted from their face image. The designed methodology preprocesses the input image before performing feature extraction using the deep convolution neural network (DCNN) strategy. This network extracts D -dimensional characteristics from the source face image, followed by the feature selection strategy. The face’s distinctive and identifiable traits are chosen utilizing a hybrid particle swarm optimization (HPSO) technique. Support vector machine (SVM) is used to classify a person’s age and gender. The nutrition recommendation system relies on the age and gender classes. The proposed system is evaluated using classification rate, precision, and recall using Adience dataset and UTKface dataset, and real-world images exhibit excellent performance by achieving good prediction results and computation time.

This paper proposes a blend of three techniques to select COVID-19 testing centers. The objective of the paper is to identify a suitable location to establish new COVID-19 testing centers. Establishment of the testing center in the needy locations will be beneficial to both public and government officials. Selection of the wrong location may lead to lose both health and wealth. In this paper, location selection is modelled as a decision-making problem. The paper uses fuzzy analytic hierarchy process (AHP) technique to generate the criteria weights, monkey search algorithm to optimize the weights, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to rank the different locations. To illustrate the applicability of the proposed technique, a state named Tamil Nadu, located in India, is taken for a case study. The proposed structured algorithmic steps were applied for the input data obtained from the government of India website, and the results were analyzed and validated using the government of India website. The ranks assigned by the proposed technique to different locations are in aligning with the number of patients and death rate.

Lately, optical systems have exponential traffic growth because of multimedia service. In addition, the ambiguity in anticipating the traffic sources as varied as there are continual changes in content suppliers over the Internet. Multi-domain elastic optical networks (MD-EONs) improve the versatility of the system, provide a better facility, and good interoperability to create managerial areas directed by various transporters. Since the clients in different spaces can dispatch cross-area physical-layer assaults to space, this paper focuses the assault based on attentive administration provisioning in one area of an MD-EON. A practical situation concentrated on considering the light paths. Routing and spectrum assignment (RSA) utilize Bat algorithm (BA) to control the spectrum usage and security-level of the space. The echolocation is considered as provisioning method between space solicitation, structure, and utility capacities for the prey (target). The bat finds the prey through echolocation. The proposed BA model accomplishes attack aware provisioning proficiently by assessing with broad imitations, and the presentation displays improved outcomes. Data loss and block probability ratio is the presentation frameworks which prove that BA-based RSA furnish upgraded results in contrast with different techniques. Also the same system validated using priority-based Concept Results compared with existing approaches.

Waste disposal of expended lithium-ion batteries enables recovery, recycling and reduction of greenhouse gas emissions. Complete discharge of electrochemical devices, especially release through electrode material and current collector (copper/aluminum foils). The release among electrode material particles, is the essential requirement for ensuring the energy yield of polymer electrolytes. In this work, existing technologies which used release electrode materials with spent lithium-ion batteries. Treating the underlying effects of lithium-ion battery oxidation is especially complex of the indirect pathways identified. Aging mechanisms are also divided into three degradation modes (DMs) for simplicity: loss of conductivity, a deficit of active material, and decline of lithium production. It’s launched the lithium-ion secondary battery structure and assemblies outlined all forms of recycling processes from spent lithium-ion secondary batteries and provided some examples of common mixed recycling processes. Also, the problems and prospects of their recycling technology is discussed.

The evaluation and selection of state of transformers based on their mineral oil data are quite difficult. In this study, analytical hierarchy process (AHP) and technique for order preference by similarity to ideal solutions (TOPSISs) are used as the multi-criteria decision support methodologies to provide a simple approach to rank transformers based on their mineral oil data and to help decision makers to identify the transformer, which is in the most critical state. AHP is exercised for both priority weight calculation and ranking of transformers. The AHP method is used for weight calculation, and then transformers are ranked using TOPSIS. Here, data of 69 in-service and 12 failed transformers in Tamil Nadu state in India is collected for assessment. From the results, transformers with good insulation condition are ranked at top, while the transformers are given lower ranks if they are found to have bad health. It is proved that decisions yielded by both methods are in agreement with reality.

A real-time multiprocessor chip model is also called a Network-on-Chip (NoC), and deals a promising architecture for future systems-on-chips. Even though a lot of Double Tail Sense Amplifiers are used in architectural approach, the existing DTSA with transceiver exhibits a difficulty of consuming more energy than its gouged design during various traffic condition. Novel Low Power pulse Triggered Flip Flop with DTSA is designed in this research to eliminate the difficulty. The Traffic Aware Sense amplifier MAS consists of Sense amplifiers (SA’s), Traffic Generator, and Estimator. Among various SA’S suitable (DTSA and NLPTF -DTSA) SA are selected and information transferred to the receiver. The performance of both DTSA with Transceiver and NLPTF-DTSA with transceiver compared under various traffic conditions. The proposed design (NLPTF-DTSA) is observed on TSMC 90 nm technology, showing 5.92 Gb/s data rate and 0.51 W total link power.

Nowadays, reducing heat generation in electronic devices while using microchannel cooling is used to solve this problem. Because the trend is globally marching toward the compact size, the component’s dimensions get smaller, but the warmth involved within the component increases. Studies of heat transfer rate are conducted to determine the effect of a fully heated microchannel conductor’s heat transfer performance. Experiments are performed using nanofluid Al2O3/water through a concentration percentage of 0.1% and 0.25% and deionized water through a microchannel conductor with 25 rectangular microchannel numbers with a dimension of (0.42×0.42×100) mm3. This present work deals with the effect of nanofluids and their concentration percentages. Finally, it concluded that better heat transfer performance was seen in nanofluids compared to deionized water. The reason is the high viscosity of nanofluid Al2O3/water due to these nanoparticles is deposited on the wall surface of the microchannel and outcomes trendy improvement in the heat transfer. Finally, a high concentration percentage of nanofluids revealed a practical improvement in the transfer of microchannel. As a result, 0.25% of the concentration percentage achieved a satisfactory result compared to the remaining fluids and almost 32.5% and 26% of thermal resistance decrease.

Increased power densities drive researchers to enhance and improve the life of insulation system employed in transformers. In order to reduce the oxidation process and enhance the life of liquid insulation, Anti-Oxidant (AO) additives are usually added with Liquid Insulation (LI). The main aim of this research work is to understand the behaviour of LIs with AO additives under thermally aged condition. Here, Butylated Hydroxy Toluene (BHT) AO and natural esters like Sunflower oil (SFO) and Rapeseed oil (RSO) are used for analyses and then subjected to an accelerated ageing test. Important life determining properties of inhibited and uninhibited Mineral Oil (MO), SFO and RSO samples are examined and compared. From the experimental results, it is concluded that the degradation rate of inhibited samples are lesser than that of uninhibited samples. Also, inhibited SFO samples show similar or better performance compared to MO and RSO samples.

The objective of the work is to improve the performance of mineral oil. To enhance the performance,mineral oil is blended with natural ester oils such as sunflower oil, palm oil,rapeseed oil and Olive oil. The key parameters of the blending mixtures are measured such as breakdown voltage, viscosity, flash point, fire point and acidity as per standards. In order to verify the repeatability and reproducibility of the above parameters, repeated measurements are taken. With the increase in mixing ratio, the performance of samples gets increased to some extent, after that, it started decreasing. The selection of optimal sample concentration based on the considered parameters is quite tricky. Therefore, it is vital to determine the optimal sample concentration based on the enhanced electrical, physical and thermal properties with respect to the mixture concentration. In this work, the selection of optimal mixed liquid insulation is performed by a grey relational analysis (GRA) method after carefully measuring the properties. It determines, Mineral oil and Sunflower oil mixing ratio 10:90 produces desirable performance compared to other samples. Validation has also been carried out on the optimized sample concentration. It is concluded that grey relational analysis is a possible method to determine the optimal sample concentration of mixed liquid insulation.

The failure free operation of transformers is a main factor on economic and safety aspects as per the industrial consumer's point of view. Basically, the transformer is one of the prime equipment in power system network because of its unkind operating condition at different circumstances like higher operating temperature, overloading and continuous operation under uneven outdoor environmental conditions. Above said operating conditions lead to unpredicted failure in the transformer and this directly has an effect on reliability of the power system network. This work aims to investigate the failure rate of transformer liquid insulation (LI)–solid insulation (SI) system at various selective environmental conditions, by considering relative humidity of the air as a frequently variable environmental factor. For investigation, mineral oil (MO) and natural ester like sunflower oil (SFO) and rapeseed oil (RSO) are chosen as the LI system and Kraft paper is chosen as the SI system. The humidified samples are aged at 140 °C up to 1000 h at closed condition. Critical parameters of both LI and SI are periodically measured at an interval of 200 h. Weibull distribution statistics method is used to predict the failure rate of the insulation system with respect to time and relative humidity of the air. From the analysis, it is inferred that the degradation rate of the transformer LI–SI increases with an increase in relative humidity of the air. It is also noted that the LI-MO degrades rapidly when compared with other natural esters.

The introduction of ester oils replaces existing liquid insulation system (mineral oil) in transformers. Since ester oils are eco-friendly and biodegradable in nature. In this work, mineral oil and rapeseed oil are used as base fluids. Further, MO and RO are blended together at various ratios from 10 to 90%. The important characteristics like breakdown voltage, acidity, and dielectric loss of the oil samples are analyzed. Using fuzzy logic method, best optimistic liquid insulation sample is identified.

While considering the current scenario, in this world power demand goes on increases day by day. Frequent power outages occur in high voltage transmission line due to the deprived performance of polluted insulators; this affects overall operation of power system and may indirectly impinge on the growth of production sector. Many researchers are keenly taking efforts to provide highly reliable and stable power to neediest. In this paper, A.C pollution flashover performance of disc type glass insulator and composite long rod insulators investigation under various artificial pollutions by varying Equivalent Salt Density Deposition (ESDD) levels. Here, we use different types of pollution methods like binding method, dipping method and spraying methods with different types of pollutants concentration. Based on dimensional analysis, four different Mathematical models have been developed to predict the A.C pollution Flashover Voltage (FOV) of insulators. Both the experimental and mathematically modeled results are compared; it’s observed that mathematical model 3 yields better results.

Transmission plays a major role in the field of electricity. The structure used in the transmission should be taken care from the particles that affects the performance. This paper reveals the effect of combination of pollutants such as sand and salt, sugar and ash in the characteristics of the bushing. The parameters considered to expose the characteristics are Breakdown voltage (BDV) and the Leakage current (LC). On comparing the parameters with the normal bushing parameters we can infer the effects of pollutants over the performance of the bushing. When the pollution severity exceeds, it has the adverse effects over the performance of the bushing.

Maintaining continuous power supply is the major anxiety of power engineers throughout the world. Power transformer is one of the critical equipment which requires high level condition monitoring to sustain uninterrupted power supply. Insulation provided within the transformer is solid and liquid dielectrics, which plays a major role in determining the life time. Transformer oil (TOL) is a liquid dielectric which acts as an insulating medium as well as coolant. In this paper, an effort has been made to enhance the critical characteristics of transformer oil using nanoparticles. Nanofluids are prepared by mixing the nanoparticles (aluminium oxide, aluminium, copper oxide, and copper) for various volume concentrations with TOL as base fluid by sonication process. Various critical parameters like viscosity, breakdown voltage (BDV), Flash point, Fire point, pH value and Spectral response characteristics of nanofluids are analyzed.

The objective of this paper is to analyze the performance of the bushing under the influence of various contaminant that accumulates due to the environmental factors. The parameters taken to analyze the performance is breakdown voltage (BDV) and leakage current. The breakdown voltage gets decreased due to the contamination of pollutants over the surface. The pollutant used are cement, sugarcane ash sugar.

The proposed work aims to investigate the aging behavior of distribution transformer's (DT) liquid insulation system at various environmental conditions, by considering humidity as a frequently variable environmental factor. Most commonly used liquid insulation system in DT's is mineral oil (MO) as it is cheap, having good physical, dielectric and thermal properties. While varying the relative humidity of air artificially using humidity chamber from 20 % – 100 %, there is a notable increase in water content level of MO samples. After that, the samples are aged at 120°C upto 1000 hours at closed condition. Then critical electrical, chemical and physical characteristics are analyzed. It is found that, degradation rate of MO increases rapidly with increase in relative humidity.

Insulation of transformer has been an integral part of condition assessment for many years. The insulating oil playing the role of dielectric barrier as well as heat escape route especially in power transformer. Primary drawback of mineral oil is that it can present an unacceptable fire risk when it was located near to domestic hats. With silicone fluids, we can meet code requirements for fire protection. We use this reliable and effective insulation for liquid-filled design even when transformer must be located very close to load or near buildings. This dissertation aims in finding the better characteristic performance of insulating fluid for optimized function of power transformers. The effect of characteristic properties of insulation fluid with nano additives was analyzed. The nanofluid model which reduces the potential stress of fluid is shown with comsol software.

This work investigates the effect of semiconductive nanoparticles on electrical and physical properties of insulating mineral oil (Transformer oil). Nanoparticles of TiO 2 , SiO 2 and ZnO were separately added into the transformer oil with particle volume fractions of 0.01% and 0.075% to form Nanofluids (NFs), with an aim of enhancing critical properties of the oil. The critical properties like Breakdown voltage (BDV), Flash point, Fire Point and Viscosity were measured at room temperature according to ASTM and IEC standards. It was found that the breakdown voltage of the transformer oil has improved after the addition of nanoparticles with increase in particle volume fractions. Meanwhile, the flash point retains the same value, the fire point has shown an increase from 5°C to 10°C and the viscosity doesn't have any dramatic change after the addition of nanoparticles.

Background: Persistent cessation of all cerebral and brainstem function (brain death) is accepted in most countries as legal evidence of death. It is presumed that cardiac function will cease within a short time after brain death has occurred. In some countries, such as Kuwait, tradition and practice discourage application of the brain death criteria despite legal acceptance. Objective: The study was designed to assess the duration of persistence of cardiac function in patients after the diagnosis of brain death had been made on the basis of generally accepted criteria. Methods: We evaluated how long cardiac function persisted after brain function had ceased in 40 patients in Kuwait who were admitted to hospital and died during the 10-year period 1992–2001. Results: It was found that the mean persistence of cardiac function after brain death was 8.20 days and the median survival time was 6 days. Two thirds of the patients survived longer than a week, but none had cardiac function for longer than 30 days. Conclusion: The study confirms that brain death is not automatically followed immediately by cessation of all other body functions. It may be speculated therefore that whole-body homeostasis is not as intimately associated with brain function as has hitherto been thought.