A Novel Approach to detect COVID-19 from chest X-ray images using CNN

In light of the present COVID-19 pandemic, it is important to consider the worth of human life, prosperity, and quality of life while also realizing that it is difficult to restrict case spread and mortality. One of the most difficult challenges for practitioners is identifying individuals who are COVID19-infected and isolating patients to stop COVID transmission. Therefore, identifying the covid19 infection is important. For the detection of COVID-19, a 4-6-hour reverse transcriptase chain reaction is used. Chest X-rays provide us with a different method for detecting Coronavirus early in the disease phase. We detected properties from chest X-ray scans and divided them into three categories with VGG16 as well as ResNet50 deep learning algorithms: COVID-19, normal, and viral pneumonia. To test the model's accuracy in specialized cases, we injected them with 15153 scans. The average COVID-19 case detection accuracy for the ResNet50 model is 91.39%, compared to 89.34% for the VGG16 model. However, a larger dataset is required when using deep learning to identify COVID-19. It accurately detects situations, which is the desired outcome

Design and Implementation of Low Power Time-To-Digital Converter using MGDI Technique

This paper introduces a novel Time to Digital Converter (TDC) architecture based on the Modified Gate Diffusion Input (MGDI) technique, which is derived from the well-established GDI method. Through the utilization of MGDI-based logic gates and digital circuitry, this innovative approach leads to a substantial reduction in the number of transistors required for implementation. As a result, it offers significant advantages in terms of circuit area, power consumption, and propagation delay, while simultaneously simplifying the complexity of the overall logic design. The functional blocks within the TDC have been optimized to efficiently process an internal clock frequency of 5MHz. This achievement is realized using cutting-edge 90nm MGDI technology, operating at a supply voltage of 1V. Practical implementation of this design can be carried out seamlessly with Cadence Virtuoso tools in the 90nm technology node. In essence, this research effort represents a promising advancement in the realm of time-to-digital conversion. By harnessing the capabilities of MGDI and its transistor-saving attributes, the proposed TDC not only enhances performance but also addresses critical concerns such as power efficiency and chip area utilization. These advancements make it a compelling choice for applications requiring precise time measurements, while the compatibility with contemporary technology nodes ensures its relevance and applicability in modern integrated circuit design

Extension of Raw Cow Milk Shelf Life by Microplasma Discharge

Cow's milk, the universal nutrient, is being stored and supplied, which seeks proper preservation. The prevalent milk preservation procedure of refrigeration, is effective only for two days, and after which, it starts to contaminate due to the growth of various milk-laden bacteria. This bacterial overload has to be inactivated properly to increase its shelf life, and is been achieved effectively using microplasma, a single-step, cost-effective and chemical-free process. Raw milk was treated for 5, 10, and 13 seconds in microplasma discharge. After 13 seconds of microplasma treatment, E. Coli, Pseudomonas, and S. Aureus bacteria got reduced at a respective rate of 89.93, 84.55, and 94.19% for in raw milk. The reactive species formed during microplasma discharge disrupts the structural integrity of bacterial cells and inactivates it, thereby enhancing the milk shelf life. Treated samples remained in good condition for 8 days. Thus, microplasma discharge increases the shelf life of milk by quickly inactivating the bacterial load

Removal of Phosphorous in Waste Water using Natural Coagulants

This study aims to explore the feasibility of employing natural coagulants like Cassia alata, Calotropis procera, Hyacinth bean, Banana leaves, Carica Papaya, Acacia mearnsii, Jatropha curcas cactus, tamarind seeds, and watermelon seeds for reducing the content of red phosphorus in industrial wastewater. A series of batch coagulation tests were performed to determine the optimal dosage of coagulants for the purpose of eliminating red phosphorus from the wastewater. The efficacy of each chosen coagulant in removing red phosphorus was depicted graphically. Among the various coagulants evaluated, Hyacinth bean exhibited the highest efficacy in reducing red phosphorus content (75%), surpassing the performance of casuarina leaves and banana leaves. On the other hand, tamarind seeds demonstrated the least effective removal of red phosphorus from the wastewater, achieving a removal rate of 56%. Notably, Hyacinth bean stands out as a potential coagulant for effective removal of red phosphorus, offering promising results akin to its capability in aiding blood clot clearance. By maintaining a pH level of 8 and employing a coagulant dosage of 20 ml, alongside initial and final red phosphorus concentrations of 4372.5 mg/lit and 1072.5 mg/lit respectively, with mixing and settling times of 30 and 45 minutes, the study achieved a significant percentage of red phosphorus removal efficiency

Review of Hybrid Wind-Solar PV Technology in the Generation of Electricity

Achieving sustainability by utilizing alternative energy sources viable technological possibilities for creating sustainable energy, the sun, biomass, wind, geothermal resources, hydropower, and ocean resources are considered. Despite the fact that the total amount of energy produced by PV cells and wind turbines is still far less than that of fossil fuels, their ability to generate electricity has significantly expanded in recent years. This article provides an overview of the Solar-Wind hybrid power system, which generates electricity by combining the Sun and Wind, two renewable energy sources. Microcontrollers are widely used in the field of system management. We can maximize the utilization of those resources by employing this strategy, which takes into account the distinct production processes of each resource. Furthermore, it increases dependability and decreases reliance on any single input. This hybrid solar-wind power generation system is suitable for both industrial and residential applications

Machine Learning based Early Stage Identification of Liver Tumor using Ultrasound Images

Liver cancer is one of the most malignant diseases and its diagnosis requires more computational time. It can be minimized by applying a Machine learning algorithm for the diagnosis of cancer. The existing machine learning technique uses only the color-based methods to classify images which are not efficient. So, it is proposed to use texture-based classification for diagnosis. The input image is resized and pre-processed by Gaussian filters. The features are extracted by applying Gray level co-occurrence matrix (GLCM) and Local binary pattern (LBP in the preprocessed image. The Local Binary Pattern (LBP) is an efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. The extracted features are classified by multi-support vector machine (Multi SVM) and K-Nearest Neighbor (K-NN) algorithms. The Advantage of combining SVM with KNN is that SVM measures a large number of values whereas KNN accurately measures point values. The results obtained from the proposed techniques achieved high precision, accuracy, sensitivity and specificity than the existing method

Comparative Studies on Application of Various Adsorbents in Textile Waste Water

This thesis aims to explore the potential of employing natural adsorbents, such as cashew nut shells, date seeds, orange peels, and coir pith, to mitigate COD levels in textile wastewater. The wastewater used for the study was sourced from a textile industry located in Salem. The investigation involved batch studies, wherein the effectiveness of each selected absorbent in reducing COD was assessed to determine the most efficient among the four sorbents. The initial concentration from the batch research served as a basis for identifying the optimal adsorbent, with the COD of the textile wastewater maintained consistently along with the initial dye concentration. To conduct the study, the adsorbent was incrementally introduced in 10 g portions into conical flasks. Over a10-minute period following a 20-minute contact time, the supernatant liquid from each conical flask was collected using syringes. The COD concentration in the obtained samples was determined using a standard methodology. Results revealed that date seeds exhibited the highest percentage of COD removal at 67%, followed by cashew nut shells at 45%, coir pith at 33%, and orange peels at 23%. The data obtained indicated that cashew nut shells and date seeds achieved the highest percentages of COD reduction, respectively. On the other hand, the Orange Peel Adsorbent displayed the least reduction in COD. Based on the collected findings, date seeds emerge as a promising adsorbent for effectively lowering COD in the treatment of textile wastewater

The Proliferation of Refined Optical and Emission Properties of Silver Oxide Nanoparticles using various Leaf Extracts

Plant-mediated synthesis of nanoparticles has emerged as a promising approach, leveraging the unique properties of plant extracts. In this study, extracts from Bidens pilosa, Achyranthes aspera, and Tecoma stans were used to synthesize silver oxide nanoparticles (Ag2O NPs). The experimental results demonstrated successful synthesis of Ag2O NPs using a Soxhlet extraction method and subsequent characterization of the nanoparticles. The photoluminescence and optical properties of the synthesized Ag2O NPs were investigated, revealing distinct emission peaks and strong absorption in the visible region. The antimicrobial activity of the nanoparticles was also assessed, showing potential for their use in controlling and preventing infections. Overall, this study highlights the valuable optical and fluorescence properties of green extracts and their impact on the synthesis and functionality of silver oxide nanoparticles, paving the way for future research in the field of biotechnology and antimicrobial applications

Functional Group Analysis of Hybrid Polyurethane Foam Derived from Waste Cooking Oil

Annually, a staggering three billion gallons of Waste Cooking Oil (WCO) are generated globally. To foster a health-conscious lifestyle and champion the creation of an unpolluted environment, effective WCO management is imperative. The repetitive utilization of WCO for cooking purposes yields detrimental effects on human health and diminishes overall productivity. This research delves into the fundamental characteristics of bio-based polyurethane (bio-PU), derived from discarded sunflower and palm oils. The findings are juxtaposed with those of non-biodegradable commercially available Polyurethane (PU). Through a process of addition polymerization conducted at room temperature, samples of PU foam are created. Specifically, 2.5 ml, 5 ml, and 7.5 ml of sunflower and palm oil are amalgamated with 5 ml of polyol and an equivalent amount of isocyanate. The vibrational attributes of amino acids and cofactors, which exhibit sensitivity to subtle structural alterations, are closely examined using Fourier transform infrared spectroscopy (FTIR). This technique, despite its lack of pinpoint precision, permits direct exploration of the vibrational properties of numerous cofactors, amino acid side chains, and water molecules. The presence of Polyurethane and its associated functional groups in the synthesized samples is verified through Fourier Transform Infrared Spectroscopy (FTIR) analyses. To ascertain Temperature ranges for primary phases of thermal degradation, discernible chemical bands within foams—comprising both recognized and unfamiliar compounds with distinct groupings—are evaluated. Emphasis is placed on identifying the peak release rates of particular chemical compounds (namely, CO2, -NCO, H2O, and C=O)

A Simple Model of Endemicity to Analyse Spread and Control of COVID-19 in India

A simple model based on 2 parameters, time-dependent infectability and efficacy of containment measures, is written to analyse the spread and containment of an endemic outbreak. Data from the first wave of the outbreak of COVID-19 in India is analysed. Interestingly, growth and decay of infections can be seen as a competition between the ratio of logarithm of infectability and the logarithm of time vis-a-vis the efficacy of containment measures imposed. Containment time estimates are shown to exhibit the viability of the simple model