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A comprehensive approach encompassing a blend of spectral techniques and Density Functional Theory (DFT) was employed to elucidate the electronic, vibrational, structural, and nonlinear optical properties of 2,3-dimethyl-N[2-(Hydroxy)benzylidene] aniline (DNHBA). The investigation of this chemical compound involved a meticulous examination using spectroscopic techniques (FTIR, FT-Raman). The DFT calculations utilized the B3LYP method in conjunction with 6-311G (d, p) basis sets. The findings were substantiated by comparing the estimated vibrational frequencies and molecule geometry with experimental data. Using the Time Dependent −Density Functional Theory (TD-DFT) method to calculate the energy band gap of the compound at different solvents. We looked into frontier molecular orbitals (FMO) Molecular electrostatic potential (MEP) and natural bond analysis (NBO) to determine the compound’s kinetic stability and chemical reactivity. Hirshfeld surface (HS) analysis calculated the O–H..O, H-H and intermolecular interaction energy of the compound. The electron density distribution, interactions, and excitation within the title molecule are illustrated by performing topological analyses (RDG, ELF and LOL) using the Multiwfn software. In addition to these assessments, molecular dynamics provides an understanding of the system's dynamic development over a predefined duration during which atoms and molecules are permitted to interact. Utilizing the molecular docking approach allows us to energize the atomic-level interplay between small molecules and proteins within the binding sites of target proteins. This approach enhances our comprehension of vital biochemical processes associated with the behavior of small molecule. The intricacy and hazards associated with drug discovery and development processes have increased dramatically, leading to higher drug research costs.

The paper investigates the aerodynamic performance and power requirement characteristics of wing sections integrated with high-lift airfoil to support the operation of solar-powered Unmanned Aerial vehicle (UAV). The flight mission is aimed to simulate the operation of solar-powered UAVs under low -speed environment. The research focuses on studying the aerodynamic effect of non-solar UAV wing model and solar UAV wing model for the varying angle of attack. The UAV wing models are tested using a subsonic wind tunnel to validate the aerodynamic characteristics at low-speed condition. The aerodynamic parameters such as coefficient of lift (Cl), coefficient of drag (Cd), coefficient of pressure (Cp), and the total power required to accelerate the solar UAV are studied to maintain steady level flight. The solar UAV and non-solar UAV wing models were subjected to a computational process to examine the pressure and velocity distributions for the aerodynamic performance analysis. Evident results show that the solar cells positioned at the flow separation region of the UAV wing model produces an aerodynamic efficiency rate of 5.45% and required 37.13W of minimum power compared to non-solar UAV at the Reynolds number of 9.8 106.

This paper presents the design, analysis and comparative study of three different configurations of Switched Reluctance Motor (SRM) suitable for In-wheel application in Electric Vehicles. C-core stator with dual rotor SRM, C-core stator with disk-type segmented rotor SRM and Dual gap inner segmented rotor SRM are designed and compared for the same stator/rotor pole, supply current, current density and outer diameter. The significant performance indices of an electric motor for an in-wheel application, such as torque ripple, torque per unit active weight and torque per unit active volume, are considered for analysis. 3D Finite Element Analysis (FEA) of the machines is performed using the software MagNet 7.5 due to the axial field distribution of the configurations. The best among the three configurations is fabricated and experimental results are presented.

In the field of medical research, prediction, as well as diagnosis of thyroid disease, is a major cause that is a challenging onset axiom. In metabolism regulation, thyroid hormone secretions play a significant role. Two frequent thyroid diseases are hypothyroidism and hyperthyroidism that release the hormones like the thyroid, which regulate the body’s metabolism rate. For analytics, the approach of data cleansing is utilized to analyze enough primitive data, which demonstrates the patients’ risk. Deep Neural Networks (DNN) is the most vital as well as efficient technology, which predict the disorder of thyroid. To avoid the errors of human, the evaluation of manual process consumes expertise domain as well as time. To detect disease, a novel Long Short-Term Memory based Convolution Neural Network (LSTM-CNN) is utilized with occurrence area Vgg-19. For selecting the feature, the approach of bias field correction is integrated with the hybrid optimization technique i.e., Black Widow Optimization as well as Mayfly Optimization Approach (HBWO-MOA), also for classifying the disease the LSTM as well as Vgg-19 of Deep Learning (DL) is presented. From DDTI dataset image of ultrasound, the disease of thyroid prediction as well as classification is efficiency. This analysis shown that the proposed technology is accurate than the convolutional methodology. When compared to existing prediction techniques i.e., AlexNet-LSTM, ResNet-LSTM, Vgg16-LSTM, the proposed approach of Vgg-19-LSTM’s precision, sensitivity, accuracy, recalls as well as F1_score is effective.

The purpose of this present investigation is to characterize the cellulosic fibers extracted from the bark of Piliostigma racemosa plant to assess the suitability of these fibers for reinforcing polymers. Characterization tests conducted are reported proving the reinforcing capability of these fibers. Chemical analysis shows the presence of cellulose (60.30 wt. %), lignin (30.76 wt. %), wax (0.63 wt. %), ash (1.12 wt. %) and wetness content (6.92 wt. %) with the fiber density of 1.371 g/cm3. Tensile strength, strain and Young’s modulus of the fibers were 32MPa, 1.2–3.2% and 2GPa, respectively. Fourier Transform Infrared studies prove the presence of essential biopolymers and the X-ray diffraction studies purports the semi crystalline cellulose content with crystallinity index and the crystallite size calculated as 56.69% and 5.25 nm, respectively, and the mean micro-fibrillar angle of the fibers was found as 18.9°. The fibers are capable of sustaining a temperature of about 244°C as ascertained from the thermo-gravimetric analysis. The surface morphology claims that the fibers are rough and they can maintain a good interfacial adhesion with the matrix during composite fabrication. Thus, the fibers possess sufficient physico-chemical, thermal, and morphological features that enable them to be potential reinforcing agents in polymer composites.

Garlic (Allium sativum L.), is a predominant spice, which is used as an herbal medicine and flavoring agent, since ancient times. It has a rich source of various secondary metabolites such as flavonoids, terpenoids and alkaloids, which have various pharmacological properties. Garlic is used in the treatment of various ailments such as cancer, diabetes and cardiovascular diseases. The present study aims to explore the plausible mechanisms of the selected phytocompounds as potential inhibitors against the known drug targets of non-small-cell lung cancer (NSCLC). The phytocompounds of garlic were identified by gas chromatography-mass spectrometry (GC–MS) technique. Subsequently, the identified phytocompounds were subjected to molecular docking to predict the binding with the drug targets, epidermal growth factor receptor (EGFR), human epidermal growth factor receptor 2 (HER2), echinoderm microtubule-associated protein-like 4-anaplastic lymphoma kinase (EML4-ALK) and group IIa secretory phospholipase A2 (sPLA2-IIA). Molecular dynamics is used to predict the stability of the identified phytocompounds against NSCLC drug targets by refining the intermolecular interactions formed between them. Among the 12 phytocompounds of garlic, three compounds[1,4-dimethyl-7-(1-methylethyl)-2-azulenyl]phenylmethanone, 2,4-bis(1-phenylethyl)-phenol and 4,5–2 h-oxazole-5-one,4-[3,5-di-t-butyl-4-methoxyphenyl] methylene-2-phenyl were identified as potential inhibitors, which might be suitable for targeting the different clinical forms of EGFR and dual inhibition of the studied drug targets to combat NSCLC. The result of this study suggest that these identified phytocompounds from garlic would serve as promising leads for the development of lead molecules to design new multi-targeting drugs to address the different clinical forms of NSCLC.

Virus neutralization test (VNT) and liquid phase blocking ELISA (LPBE) are accepted tests for screening and as in vitro alternativ to challenge in FMD vaccine potency testing. To replace VNT by LPBE for the screening of cattle, the optimized tests need to be first evaluated for their diagnostic performances. To replace it with LPBE in the absence of protection data, the interrelationship between VNT and LPBE have to be established to find out LPBE cut‑off titer corresponding to the currently used VNT titers. Accordingly, VNT and LPBE were carried out using known negative (n = 306) and positive samples [Serotype O (n = 43), A (n = 14) and Asia1 (n = 11)], for the initial screening. The cut‑off of < 1.5 log10 LPBE was comparable with that of < 1.2 log10 VNT titer for screening. LPBE was comparable to VNT in terms of specificity, sensitivity as shown by ROC curve and least varying (coefficient of variation 7.73% in LPBE vs 24.19% in VNT). Based on linear regression model using 471 bovine sera, the predicted LPBE titers corresponding to the currently used log 10 VNT titers of 1.65, 1.5 and 1.5, were 2.24, 1.87 and 2.00 for O, A and Asia1, respectively. These LPBE titers hence can be used as cut‑off titers for classifying cattle as protected or not protected until correlation based on in vivo challenge between protection and antibody titer is established.