Cashew nut shell oil as a potential feedstock for biodiesel production: An overview.

Biodiesel outperforms diesel in emissions and engine performance. They burn efficiently in diesel engines and are eco-friendly. Since cashew nut shell liquid (CNSO) is waste, commercial biodiesel production from it should be profitable. CNSO is cheap and can reduce cashew processing factory waste. From cashew kernels, CNSL is extracted using various mechanical, thermal, and solvent extraction techniques. This article examines current research into using cashew nutshell liquid biodiesel (CNSLBD) in diesel engines. The work also discusses Indian biodiesel demand, availability, export information, life cycle cost analysis, cost economics of per hectare yield, Indian government initiative of CNSO. This review also evaluates the viability of this fuel as an alternative energy source. CNSLBD is a prospective alternative fuel that has the potential to benefit both the cashew nut industry and the energy industry. In addition to this, the study examines the procedures for extracting CNSO. According to the findings of the study, CNSO is a prospective alternative fuel that has the potential to benefit both the cashew nut industry and the energy industry.

Influence of injection strategies on ignition patterns of RCCI combustion engine fuelled with hydrogen enriched natural gas.

The depletion of fossil fuel and the concerns for harmful emissions and global warming has instigated researchers to use alternative fuels. Hydrogen (H2) and natural gas (NG) are attractive fuels for internal combustion engines. The dual-fuel combustion strategy is promising to reduce emissions with efficient engine operation. The concern for using NG in this strategy is the lower efficiency at low load conditions and the emission of exhaust gases like carbon monoxide and unburnt hydrocarbon. Mixing fuel with a wide flammability limit and a faster burning rate with NG is an effective method to compensate for the limitations of using NG alone. Hydrogen (H2) is the best fuel added with NG to cover NG limitations. This study investigates the in-cylinder combustion phenomenon of reactivity-controlled compression ignition (RCCI) engines using hydrogen-added NG as a low-reactive fuel (H2 addition to NG on a 5% energy basis) and diesel as a highly reactive fuel. The numerical study was done on a 2.44 L heavy-duty engine using CONVERGE CFD code. Three low, mid, and high load conditions were analyzed in six stages by varying the diesel injection timing from -11 to -21 O after top dead centre (ATDC). The H2 addition to NG had shown deficient harmful emissions generation like carbon monoxide (CO) and unburnt hydrocarbon with marginal NOx generation. At low load conditions, the maximum imep was achieved at the advanced injection timing of -21OATDC, but with the increase in load, the optimum timing was retarded. The diesel injection timing varied the optimum performance of the engine for these three load conditions.

Non-invasive multi-channel deep learning convolutional neural networks for localization and classification of common hepatic lesions.

PURPOSE: Machine learning techniques, especially convolutional neural networks (CNN), have revolutionized the spectrum of computer vision tasks with a primary focus on supervised and labelled image datasets. We aimed to assess a novel method to segment the liver from the abdomen computed tomography (CT) image using the CNN network, and to train a unique method to locate and classify liver lesion pre-histological findings using multi-channel deep learning CNN (MDL-CNN). MATERIAL AND METHODS: The post-contrast CT images of the liver with a resolution of 0.625 mm were chosen for the study. In a random method, 50 examples of each hepatocellular carcinomas, metastases tumours, haemangiomas, hepatic cysts were chosen and evaluated. RESULTS: The dice score quantitatively analyses the similarity of segmentation results with the training dataset. In the first CNN model for segmenting the liver, the dice score was 96.18%. The MDL-CNN model yielded 98.78% accuracy in classification, and the dice score for locating liver lesions was 95.70%. Additionally, the performance of this model was compared to various other existing models. CONCLUSIONS: According to our study, the machine learning approach can be successfully implemented to segment the liver and classify lesions, which will help radiologists impart better diagnosis.

Multi-omics therapeutic perspective on ACVR1 gene: from genetic alterations to potential targeting.

Activin A receptor type I (ACVR1), a transmembrane serine/threonine kinase, belongs to the transforming growth factor-ß superfamily, which signals via phosphorylating the downstream effectors and SMAD transcription factors. Its central role in several biological processes and intracellular signaling is well known. Genetic variation in ACVR1 has been associated with a rare disease, fibrodysplasia ossificans progressive, and its somatic alteration is reported in rare cancer diffuse intrinsic pontine glioma. Furthermore, altered expression or variation of ACVR1 is associated with multiple pathologies such as polycystic ovary syndrome, congenital heart defects, diffuse idiopathic skeletal hyperostosis, posterior fossa ependymoma and other malignancies. Recent advancements have witnessed ACVR1 as a potential pharmacological target, and divergent promising approaches for its therapeutic targeting have been explored. This review highlights the structural and functional characteristics of receptor ACVR1, associated signaling pathways, genetic variants in several diseases and cancers, protein-protein interaction, gene expression, regulatory miRNA prediction and potential therapeutic targeting approaches. The comprehensive knowledge will offer new horizons and insights into future strategies harnessing its therapeutic potential.

Diagnosing COVID-19 from CT Image of Lung Segmentation & Classification with Deep Learning Based on Convolutional Neural Networks.

Early-stage exposure and analysis of diseases are life-threatening causes for controlling the spread of COVID-19. Recently, Deep Learning (DL) centered approaches have projected intended for COVID-19 during the initial stage through the Computed Tomography (CT) mechanism is to simplify and aid with the analysis. However, these methodologiesundergocommencing one of the following issues: each CT scan slice treated separately and train and evaluate from the same dataset the strategies for image collections. Independent slice therapy is the identical patient involved in the preparation and set the tests at the same time, which can yield inaccurate outcomes. It also poses the issue of whether or not an individual should compare the scans of the same patient. This paper aims to establish image classifiers to determine whether a patient tested positive or negative for COVID-19 centered on lung CT scan imageries. In doing so, a Visual Geometry Group-16 (VGG-16) and a Convolutional Neural Network (CNN) 3-layer model used for marking. The images are first segmented using K-means Clustering before the classification to increase classification efficiency. Then, the VGG-16 model and the 3-layer CNN model implemented on the raw and segmented data. The impact of the segmentation of the image and two versions are explored and compared, respectively. Various tuning techniques were performed and tested to improve the VGG-16 model's performance, including increasing epochs, optimizer adjustment, and decreasing the learning rate. Moreover, pre-trained weights of the VGG-16 the model added to enhance the algorithm.