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Due to increase in pollution and greenhouse gas emissions from Internal Combustion Engines (ICEs), researchers and governments are looking forward for pollution-free, eco-friendly and efficient vehicle called Electric Vehicles (EVs), which is a best alternative for fossil fuel-based transportation systems. Limited battery capacity and frequent charging are essential for EVs. There are unscheduled EVs existed owing to the scarcity of energy and charging plugs. This research designs a multi aggregator-based charge scheduling strategy in Internet of Electric Vehicles (IoEVs) using proposed Fractional African Vulture Sail Fish Optimization (FrAVSO) algorithm. The FrAVSO is obtained by integrating Fractional Calculus (FC) with African Vulture Optimization (AVO) and Sail Fish Optimization (SFO). The energy is predicted utilizing Deep Maxout Network (DMN) and path selection is accomplished utilizing Fractional African Vulture Optimization (FrAVO). The FrAVSO achieved minimum distance of 15.313 Km, maximum energy of 0.252 J, minimum waiting time of 2.546 sec, and maximum profit of 94.978%. [ABSTRACT FROM AUTHOR]

Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effective treatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of breast cancer fromultrasound images. The primary challenge is accurately distinguishing between malignant and benign tumors, complicated by factors such as speckle noise, variable image quality, and the need for precise segmentation and classification. The main objective of the research paper is to develop an advanced methodology for breast ultrasound image classification, focusing on speckle noise reduction, precise segmentation, feature extraction, and machine learning-based classification. A unique approach is introduced that combines Enhanced Speckle Reduced Anisotropic Diffusion (SRAD) filters for speckle noise reduction, U-NET-based segmentation, Genetic Algorithm (GA)-based feature selection, and Random Forest and Bagging Tree classifiers, resulting in a novel and efficient model. To test and validate the hybrid model, rigorous experimentations were performed and results state that the proposed hybrid model achieved accuracy rate of 99.9%, outperforming other existing techniques, and also significantly reducing computational time. This enhanced accuracy, along with improved sensitivity and specificity, makes the proposed hybrid model a valuable addition to CAD systems in breast cancer diagnosis, ultimately enhancing diagnostic accuracy in clinical applications. [ABSTRACT FROM AUTHOR]

Natural fiber is a viable and possible option when looking for a material with high specific strength and high specific modulus that is lightweight, affordable, biodegradable, recyclable, and eco-friendly to reinforce polymer composites. There are many methods in which natural fibres can be incorporated into composite materials. The purpose of this research was to evaluate the physico-chemical, structural, thermal, and mechanical properties of Acacia pennata fibres (APFs). Scanning electron microscopy was used to determine the AP fibers' diameter and surface shape. The crystallinity index (64.47%) was discovered by XRD. The irregular arrangement and rough surface are seen in SEM photos. The findings demonstrated that fiber has high levels of cellulose (55.4%), hemicellulose (13.3%), and low levels of lignin (17.75%), which were determined through chemical analysis and validated by Fourier Transform Infrared Spectroscopy (FTIR). By using FTIR, the functional groups of the isolated AP fibers were examined, and TG analysis was used to look into the thermal degrading behaviour of the fibers treated with potassium permanganate (KMnO4) Due to their low density (520 kg/m3) and high cellulose content (55.4%), they have excellent bonding qualities. Additionally, tensile tests were used for mechanical characterisation to assess their tensile strength (685 MPa) and elongation. [ABSTRACT FROM AUTHOR]

In this study, activated carbon (ACs) adsorbent was synthesized using the lignocellulosic waste (LCB) seed from Adansonia digitata L. (BSP) using two steps of hydrothermal carbonization (HTC) followed by activation. The hydrothermally produced char of BSP was activated to produce porous activated carbon BSPAC, where K2CO3 was used as a chemical activating agent. Box Behnken Design was used to optimize the input variables of pyrolysis temperature (A1), residence time (B1), and ratio (C1) for the pyrolysis process. Removal percentage (β1), percentage carbon yield (β2), and fixed carbon (β3) percentage were chosen as output responses. The analysis of variance was utilized to generate appropriate mathematical models with subsequent statistical analysis. Physiochemical characterizations were carried out for the hydrothermally carbonized sample (BSPC) and the optimized activated sample (BSPAC). Langmuir, Freundlich, and Temkin models were employed to estimate the isotherm model parameters. The results demonstrated that HTC with subsequent mild activation using K2CO3 can be considered as a greener route to obtain better-quality porous carbon having surface area of 599 m2/gm for removal of Cu(II) cations from wastewater.

Introduction: Systemic Lupus Erythematosus (SLE) is a complex, multisystem autoimmune disorder characterized by extensive inflammation that affects nearly all organ systems in the body. It is primarily mediated by auto-antibodies and immune complexes, and it predominantly affects women more than men. This study employs an in-silico approach to identify key genes potentially involved in the pathogenesis of SLE. Objectives: To identify key genes potentially involved in SLE pathogenesis using in-silico approach. Methods: High-throughput sequencing dataset GSE97264, from the Gene Expression Omnibus (GEO) database, which contains RNA transcriptome data from CD8+ T-cells of 18 SLE patients and 14 healthy controls was utilized for the analysis. Differentially expressed genes (DEGs) were identified using the Bioconductor DESeq2 package in R platform. Gene Ontology (GO) and pathway enrichment analyses were performed using the ToppGene suite. Motif analysis of the genes’ promoter regions was conducted using HOMER software. Protein-protein interaction (PPI) and Reactome functional interaction (FI) networks were created using Cytoscape plugins StringApp and ReactomeFIViz, and analysed to identify hub genes. Results: Our analysis identified 931 DEGs, with 577 upregulated and 354 downregulated. GO and pathway enrichment analyses indicated that upregulated genes were associated with immune responses, including cytokine production and receptor activation. Motif analysis identified key regulatory motifs linked to immune regulation in upregulated genes and T-cell activation in downregulated genes. PPI and FI networks analyses revealed 29 cell cycle-associated hub genes, with 10 genes—CDK1, TPX2, BIRC5, CCNA2, BUB1, BUB1B, AURKA, KIF2C, PLK1, and CDCA8—common to both biological networks, suggesting their crucial role in SLE pathogenesis. Conclusion: This study suggests that dysregulation of the identified 10 genes may impact immune responses and contribute to the autoimmune-like conditions observed in SLE. Several of these genes are also implicated in other autoimmune diseases, highlighting their potential as SLE biomarkers. Despite their known roles in other immune-related diseases involving CD8+ T cells, their direct association with SLE had not been previously established. This novel finding underscores the potential of these genes as therapeutic targets and may contribute to the development of diagnostic tools.

India is facing challenges in managing household waste due to an inadequate infrastructure and awareness. About 60-70% of kitchen waste that is generated in Indian homes consists of food scraps and vegetable/fruit peels. The process of converting kitchen waste into fertilizer is known as composting. Black Soldier Fly (BSF) larvae are great at consuming kitchen waste and turning it into nutrient-rich frass. An experimental study was conducted by collecting kitchen waste of thirty seven households of Ariyalur district. The study was conducted in a same locality and all the families selected were middle income group. Out of 153.031 kg of collected kitchen wastes for 12 days, 48.05 kg of compost was produced i.e. 31.4% from waste. The research has a strong evidence on association of the two variables(P<=0.01) i.e., family size and waste collected from households. The BSF larvae were then transferred to a container to attain its pupil stage which will be used as a protein rich feed for the poultry. This model of composting ecologically enriches the soil productivity and stands as a viable source of income for the farming community. [ABSTRACT FROM AUTHOR]