KLE Technological University

The main objectives of the current study were synthesis and characterization of zinc oxide nanoparticles (ZnONPs) using the aqueous leaf extract of Guazuma ulmifolia (G. ulmifolia) plant and assessing its antimicrobial and antioxidant potential. The nanoparticles were characterized using Ultraviolet Visible (UV-Vis), Fourier Transform Infrared (FTIR) Spectroscopy, X-ray diffraction (XRD), Energy-dispersive X-ray (EDS) and Scanning electron microscopy (SEM). ZnONPs exhibited a maximum peak at 342 nm in the UV-Vis spectrum, indicating their absorption properties. FTIR analysis confirmed the presence of functional groups, such as OH and COOH which likely contributed to the stability of the nanoparticles. The XRD analysis confirmed the spherical structure of the nanoparticles as indicated by distinct diffraction peaks corresponding to the lattice planes of the ZnO standard. The EDS analysis confirmed the presence of Zn, C and O in the ZnONPs. SEM analysis provided insights into the nanoparticles size, shape and surface morphology, showing an average size between 19 and 41 nm. Furthermore, the synthesized ZnONPs demonstrated excellent antioxidant and antibacterial activities against various bacteria and Candida species. This study highlights the promising potential of ZnONPs in diverse fields, including biomedicine, due to their low toxicity, economic viability and beneficial properties. It is crucial to conduct further in vivo investigations in order to determine the safety, effectiveness and compatibility of G. ulmifolia ZnONPs.

In proton therapy, the protons are used to destroy the cancer cells efficiently at the Bragg peak without much damage to normal cells. The protons can also produce neutrons, protons, and high-energy gamma rays through nuclear reactions with cancerous and healthy tissues as well as with beamline components. The effective observed dose in the therapy is enhanced due to the interaction of nuclear particles with cancerous tissues. Such nuclear particles can have several effects on drugs used in immunotherapy, such as immunotherapy in combination with proton therapy, which has been used to treat cancer. In the present investigations, the gamma, neutron, and protons interaction parameters of some immuno-therapy drugs, such as dostarlimab, atezolizumab, ipili-mumab, nivolumab, and pembrolizumab, are determined by using EpiXs, NGCal, and PSTAR software. It is found that the EBF and EABF for all selected immunotherapy drugs increase with increasing penetration depth, peaking at 100 keV. The peaking is more symmetric at a higher penetration depth of 40 mfp than at a lower one of 1 mfp. At lower energies of gamma photons, the EBF values increase exponentially, and at higher energies, they increase linearly with increasing penetration depth for all selected drugs. Mass attenuation factors are slightly higher for thermal neutrons than for fast neutrons for selected immunotherapeutic drugs, indicating that thermal neutrons more actively participate in these drugs than fast neutrons. The mass attenuation factor for both fast and thermal neutrons increases with increasing weight percentages of hydrogen and is found to be higher for thermal neutrons. This is the first study in the literature to investigate the radiation interaction parameters for immuno-therapy drugs, and it is helpful in radiation therapy and dosimetry.

Multiple electronic health records (EHRs) provide important opportunities to understand a patient's statement of healthiness at various phases, particularly the improvement of a patient's strength. Extracting the patterns of improvement in physical condition permits researchers to conduct an exact inspection of patient outcome prediction. However, most previous works on this task encounter the problems listed below. Numerous interdependencies between different medical entities are ignored, resulting in biased modelling of the patient's status and preventing the method's capability to precisely predict the patient's future physical condition. To overcome the above problems, this research introduces a better detection technique to predict the patient's length of stay. First, the data is collected from aggregated MIMIC 3D datasets and hospital length of stay datasets. To pre-process the data using data normalization and data cleaning methods to improve data quality. Extracting the heterogeneous features is done using the Twin Recurrent Stacked Autoencoder (Twin-RSA) model to minimize error rates and model complexity. Convolutional Archer Stacked Long Short Term Memory (CA-SLSTM) is employed for the classification process, and the Weighted Heterogeneous Fusion (WHetF) approach is used to combine the heterogeneous features. To demonstrate the superior efficiency of the suggested work, the outcomes are associated with current and existing methods using various performance metrics. The performance results for aggregated MIMIC 3D datasets are achieved with 99.2% accuracy, 98.5% recall, 98.5% precision, 98.5% F-measure, 99.5% specificity and 98.0% Kappa. The performance results for hospital length of stay are 99.7% accuracy, 98.3% precision, 97.9% recall, 98.1% F-measure, 99.8% specificity, and 98% Kappa.

The electrochemical performance of phenylbutazone (PBZ) was studied using a multi-walled carbon-nanotube-modified paste electrode (MWCNT/CPE) using a variety of voltammetric tools like cyclic voltammetry (CV), linear sweep voltammetry (LSV), and square wave voltammetry (SWV). The results showed that the MWCNT/CPE exhibited remarkable electro-catalytic action towards the electrochemical oxidation of PBZ in a phosphate buffer solution of physiological pH 7 compared to a bare carbon paste electrode. The electro-kinetic parameters like heterogeneous rate constant, transfer coefficient, scan rate, pH, and involvement of electrons in electro-oxidation of PBZ was investigated. For bare CPE, the peak current was noted to be 19.53 μA with peak potential of 0.6871 V. For MWCNT/CPE, the peak current was 30.53 μA with peak potential of 0.6792 V. The anodic peak was analyzed, and the process was diffusion controlled. For the estimation of PBZ, a SWV technique was developed with great precision and accuracy, with a detection limit of 5.2 nM and a limit of quantification of 17 nM, in the concentration range 1 × 10⁻⁷ to 10 × 10⁻⁶ M. The MWCNT/CPE has been used successfully for PBZ detection in injection, blood, and urine samples, with recovery rates of 98.9% to 101.5%, 96.3% to101.7% and 98.3% to 102.8%, respectively.

A series of 4 new titanium complexes bearing phenoxyimine ligands that differ in their steric and electronic properties have been synthesized and investigated for the polymerization of ethylene and rac‐lactide. X‐ray crystal structures of these catalysts reveal compounds 1–3 exhibit distorted octahedral geometry while complex 4 shows distorted trigonal bipyramidal. The substituents on the phenoxyimine framework have a profound influence on the catalytic activities of these compounds in the polymerization reactions. All four complexes afforded polylactide with a heterotactic bias in ring opening polymerization of rac‐lactide. In addition to this, density functional theory calculations using (B3LYP) with the 6‐311++G (d, p) basis set and no constraints were used while doing geometry optimizations and compared with experimental results. Hirshfeld surface analysis, MEP and Mulliken charge also reported an unexpected relationship between the nitrogen, oxygen on the ligands, titanium isopropoxide, and good relation between the experimental findings.

The present study investigates the mode‐II fracture resistance and interlaminar shear strength (ILSS) in glass epoxy laminates with the aid of glass‐carbon veils hybrid interleaving technique. Hybrid interleaves were prepared using various areal densities of non‐woven carbon and glass veils. Two methods, specifically inter‐ply and inter‐weaved ply interleaved glass epoxy composite laminates were manufactured by hand layup technique. End notch flexural and short beam test specimens were tested to estimate the mode‐II fracture resistance and ILSS. Test results indicated that the interleaved with inter‐weaved veil composite exhibited ~58% to ~82% and ~13% to ~61% enhancement for inter‐ply veil composite laminates when compared to plain samples. In context with the short beam test, a significant enhancement of approximately 23% in ILSS was noticed for the plain inter‐weaved veil constructed with a lower areal density of the carbon veil. Finally, the plain inter‐weaved interleaving technique has a prominent role in enhancing the resistance to fracture and shear strength in Glass epoxy laminates. Further, the fracture mechanisms of non‐interleaved and hybrid ply interleaved composite laminates were observed by scanning electron microscopic images. Highlights Interleaving non‐woven microfiber veil is the most promising and cost‐effective strategy to enhance the Interlayer toughness and strength. Interleaving techniques used in this investigation have a significant effect on interlaminar fracture toughness (IFT) and interlaminar shear strength (ILSS). The areal density of high‐modulus carbon veil showed a different phenomenal influence on IFT and ILSS.

This pioneering research explores the transformative potential of recombinant subtilisin, emphasizing its strategic immobilization and nanoparticle synthesis to elevate both stability and therapeutic efficacy. Achieving an impressive 95.25 % immobilization yield with 3 % alginate composed of sodium along with 0.2 M CaCl2 indicates heightened pH levels and thermal resistance, with optimal action around pH 10 as well as 80 °C temperature. Notably, the Ca-alginate-immobilized subtilisin exhibits exceptional storage longevity and recyclability, affirming its practical viability. Comprehensive analyses of the recombinant subtilisin under diverse conditions underscore its adaptability, reflected in kinetic enhancements with increased Vmax (10.7 ± 15 × 10³ U/mg) and decreased Km (0.19 ± 0.3 mM) values post-immobilization using N-Suc-F-A-A-F-pNA. UV–visible spectroscopy confirms the successful capping of nanoparticles made of Ag and ZnO by recombinant subtilisin, imparting profound antibacterial efficacy against diverse organisms and compelling antioxidant properties. Cytotoxicity was detected against the MCF-7 breast cancer line of cells, exhibiting IC50 concentrations at 8.87 as well as 14.52 µg/mL of AgNP as well as ZnONP, correspondingly, indicating promising anticancer potential. Rigorous characterization, including FTIR, SEM-EDS, TGA and AFM robustly validate the properties of the capped nanoparticles. Beyond therapeutic implications, the investigation explores industrial applications, revealing the versatility of recombinant subtilisin in dehairing, blood clot dissolution, biosurfactant activity, and blood stain removal. In summary, this research unfolds the exceptional promise of recombinant subtilisin and its nanoparticles, presenting compelling opportunities for diverse therapeutic applications in medicine. These findings contribute substantively to biotechnology and healthcare and stimulate avenues for further innovation and exploration.

Commercial wearable piezoelectric sensors possess excellent anti-interference stability due to their electronic packaging. However, this packaging renders them barely breathable and compromises human comfort. To address this issue, we develop a PVDF piezoelectric nanoyarns with an ultrahigh strength of 313.3 MPa, weaving them with different yarns to form three-dimensional piezoelectric fabric (3DPF) sensor using the advanced 3D textile technology. The tensile strength (46.0 MPa) of 3DPF exhibits the highest among the reported flexible piezoelectric sensors. The 3DPF features anti-gravity unidirectional liquid transport that allows sweat to move from the inner layer near to the skin to the outer layer in 4 s, resulting in a comfortable and dry environment for the user. It should be noted that sweating does not weaken the piezoelectric properties of 3DPF, but rather enhances. Additionally, the durability and comfortability of 3DPF are similar to those of the commercial cotton T-shirts. This work provides a strategy for developing comfortable flexible wearable electronic devices.

In Natural Language Processing (NLP), question-answering systems are a classic problem, yet they pose several open challenges. After the pandemic situation of COVID-19, online learning has become crucial, in which question-answering systems are also beneficial to students in searching for answers to subject-related questions. The paper aims to address the challenge of providing a lightweight and efficient question-answering system, especially for students with limited resources, particularly those using low-cost mobile devices. The strategy involves post-training quantization to reduce the size of the BERT model while maintaining high accuracy. Quantization is a technique that reduces the memory footprint of deep learning models, making them more suitable for deployment on mobile devices and low-cost hardware. The reduction in the BERT model size from 438 MB to 181 MB with negligible accuracy degradation is a significant achievement. The resulting mobile application works offline, making educational content accessible in areas with limited internet connectivity. The system achieved an F1 score of 0.87 with negligible degradation compared to the accuracy of the BERT model F1 score: 0.90. Key considerations include user experience, maintenance and memory optimization. This paper offers a practical and cost-effective solution to enhance educational access on mobiles while addressing resource constraints.

SEM (Scanning Electron Microscopy) takes nanoscale pictures, whereas DL (Deep Learning) analyses data using neural networks. Image interpretation is streamlined by the collaboration of SEM and DL, which automates SEM image analysis and material characterisation. This integration improves productivity by quickly extracting relevant information from enormous datasets, highlighting subtle patterns that would otherwise be difficult to detect manually. Authors of this article consider 3 classes of SEM images of various magnifications and a total of 93 images is being produced for analysis and processing. Authors intend to enhance, categorize, and segment the pictures collected. SEM pictures are classified based on doping substance with the host material pure Cobalt Chromite, Neodymium doped Cobalt Chromite and Lanthanum doped Co-balt Chromite. Image quality and appearance are improved via augmentation techniques. The similarity and complexity of images in all three classes and the inclusion of images of different magnification posed a challenge in classification which hinder the accuracy rate of classification process. So authors use the statistical results of the tests to create semi-automatic method for classifying and labeling pictures generated by the SEM. Authors propose a low complexity algorithm, aimed to extract features and increase model performance. This approach offers efficiency gains by minimizing time and computational resources compared to pre-trained models, while maintaining consistent classification results. Achieved a training and testing accuracy of 100% and 78.26% respectively. SEM pictures are also classified using CNN and pre-trained models VGG16, Inception v3. To evaluate and compare performance, comparative studies are used to measure model correctness. SEM images are segmented into regions of interest using an integrated technique that combined the watershed and contours. A segmented picture is used to calculate the surface area of a Cobalt Chromite sample. The surface area of material is determined to be 41,02,628 nm 2 .

This study investigates the influence of material thickness on the mechanical properties, specifically flexural and tensile properties, of scapula bone specimens from Deccani breed sheep in India. A three-point bending test and a tensile test were conducted on samples with varying thicknesses (1, 2 and 5 mm), and the flexural strength, flexural modulus, ultimate load, tensile deformation and von Mises stress were measured. Thicker specimens exhibited higher flexural properties than thinner counterparts. Statistical analysis using one-way ANOVA confirmed that material thickness significantly impacted the flexural and tensile properties of the samples. Additionally, a simulation analysis was performed using ANSYS Workbench to validate the experimental results and provide insights into the tensile and flexural behaviour of the specimens. The findings suggest that thicker samples are better equipped to handle bending stresses and tensile loads and are more suitable for applications requiring resistance to bending loads, stiffness, load-bearing capacity and tensile strength.

Modern cars employ Lane Keep Assist (LKA) as a crucial safety feature, using sensors and cameras to warn drivers when the vehicle veers off the road and correct it. Hardware-in-the-Loop (HIL) simulation is a powerful testing method to model sensor behaviour in controlled environments. Developers can assess LKA systems in diverse conditions, including road geometry, climate, and traffic patterns. HIL validation via dSPACE Scalexio provides a realistic and reliable platform for virtual testing. This paper used image processing techniques to achieve 95% accuracy in lane detection.

This article proposes a new intelligent trajectory tracking control law for the precise maneuvering of an autonomous vehicle in the presence of parametric uncertainties and external disturbances. The controller design includes a fuzzy sliding mode algorithm for smooth motion control subjected to steering saturation and curvature constraints. Along with the Salp Swarm Optimization technique, explored for optimal selection of surface coefficient in fractional order Proportional-Derivative type PDα\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P{D}^{\alpha }$$\end{document} sliding manifold. The sliding variable on the surface approaches zero in a finite time. Further, the trajectory tracking control rule offers the stability of closed-loop tracking on the predetermined path and ensures finite time convergence to the sliding surface. In addition, to estimate the hitting gain in online mode, a supervisory fuzzy logic controller system is used. Therefore, it is not necessary to determine upper bounds on uncertainty in the dynamic parameters of autonomous vehicles. Lyapunov theory verifies the global asymptotic stability of the entire closed-loop control strategy. The major control issue is the input constraints arising primarily due to the capability of the steering actuating module, which causes significant deviation or vehicle instability. Consequently, it is desirable to design a robust adaptive stable controller, such as Adaptive Backstepping Control (ABC), even though it requires vehicle model information. Therefore, the proposed model-free intelligent sliding mode technique offers better tracking performance and vehicle stability in adverse conditions. Finally, the efficacy of the proposed control technique was confirmed through a comparative analysis based on numerical simulation using MATLAB/SIMULINK and experimental validation using Quanser’s self-driving car module. A quantitative study was conducted to elucidate the superior tracking performance of intelligent control over the traditional SMC and adaptive backstepping control methods.

This study engineered an electrochemical electrode designed for the susceptible detection of the herbicide 2,4-dichlorophenoxyacetic acid (2,4-D) through sophisticated electrochemical methodologies. The electrode, denoted as CTAB/TiO2-CPE, was synthesized by integrating titanium oxide (TiO2) nanoparticles into a carbon paste electrode (CPE) and introducing cetyltrimethylammonium bromide (CTAB) as a surfactant to the electrolyte solution. Comparative analysis revealed that the CTAB/TiO2-CPE exhibited superior sensitivity compared to the unmodified CPE, showcasing an impressive lower limit of detection at 0.23 × 10−8 M. A meticulous examination of pH influence demonstrated that the phosphate buffer saline (PB) induced the highest peak current at a pH of 3.0. Exploring scan rate variations enabled the quantitative assessment of various physicochemical attributes. The fabricated electrode was systematically employed to quantify 2,4-D levels across diverse environmental samples, including soil, fruits, vegetables, and water. The results, characterized by a high degree of precision, underscore the reliability of the proposed electrode, demonstrating a commendable success rate in sample recoveries.

Human skin emits a series of volatile compounds from the skin due to various metabolic processes, microbial activity, and several external factors. Changes in the concentration of skin volatile metabolites indicate many diseases, including diabetes, cancer, and infectious diseases. Researchers focused on skin-emitted compounds to gain insight into the pathophysiology of various diseases. In the case of skin volatolomics research, it is noteworthy that sample preparation, sampling protocol, analytical techniques, and comprehensive validation are important for the successful integration of skin metabolic profiles into regular clinical settings. Solid-phase microextraction techniques and polymer-based active sorbent traps were developed to capture the skin-emitted volatile compounds. The primary advantage of these sample preparation techniques is the ability to efficiently and targetedly capture skin metabolites, thus improving the detection of the biomarkers associated with various diseases. In further research, polydimethyl-based patches were utilized for skin research due to their biocompatibility and thermal stability properties. The microextraction sampling tools coupled with high sensitive Gas Chromatography-Mass Spectrometer provided a potential platform for skin volatolomes, thus emerging as a state-of-the-art analytical technique. Later, technological advancements, including the design of wearable sensors, have enriched skin-based research as it can integrate the information from skin-emitted volatile profiles into a portable platform. However, individual-specific hydration, temperature, and skin conditions can influence variations in skin volatile concentration. Considering the subject-specific skin depth, sampling time standardization, and suitable techniques may improve the skin sampling techniques for the potential discovery of various skin-based marker compounds associated with diseases. Here, we have summarised the current research progress, limitations, and technological advances in skin-based sample preparation techniques for disease diagnosis, monitoring, and personalized healthcare applications.

Erythrina stricta Roxb., an underutilized legume species native to the Indian subcontinent, is traditionally employed in various medicinal applications. This study systematically examines the nutritional quality, encompassing proximate and mineral composition of E. stricta seeds, with a focus on characterizing the seed oil. The seeds exhibit commendable proximate composition, with 26.81% protein and 18.71% fibre. Noteworthy mineral elements include 5.0 mg/g DW of calcium and 787.0, 32.7, 36.8 and 497.0 µg/g of iron, copper, boron and zinc, respectively. The seeds yield 13.43% oil, with oleic, palmitic, linoleic and stearic acids as prominent fatty acids, constituting 48.82%, 20.63%, 20.27% and 6.47%, respectively. Antinutrients such as oxalate and phytate are present in concentrations of 26.85 and 16.04 mg/g FW, respectively. In conclusion, this study underscores E. stricta seeds as a robust source of both nutrients and oil, warranting further exploration and consideration for potential applications.