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The microarray gene expression data poses a tremendous challenge due to their curse of dimensionality problem. The sheer volume of features far surpasses available samples, leading to overfitting and reduced classification accuracy. Thus the dimensionality of microarray gene expression data must be reduced with efficient feature extraction methods to reduce the volume of data and extract meaningful information to enhance the classification accuracy and interpretability. In this research, we discover the uniqueness of applying STFT (Short Term Fourier Transform), LASSO (Least Absolute Shrinkage and Selection Operator), and EHO (Elephant Herding Optimisation) for extracting significant features from lung cancer and reducing the dimensionality of the microarray gene expression database. The classification of lung cancer is performed using the following classifiers: Gaussian Mixture Model (GMM), Particle Swarm Optimization (PSO) with GMM, Detrended Fluctuation Analysis (DFA), Naive Bayes classifier (NBC), Firefly with GMM, Support Vector Machine with Radial Basis Kernel (SVM-RBF) and Flower Pollination Optimization (FPO) with GMM. The EHO feature extraction with the FPO-GMM classifier attained the highest accuracy in the range of 96.77, with an F1 score of 97.5, MCC of 0.92 and Kappa of 0.92. The reported results underline the significance of utilizing STFT, LASSO, and EHO for feature extraction in reducing the dimensionality of microarray gene expression data. These methodologies also help in improved and early diagnosis of lung cancer with enhanced classification accuracy and interpretability.

Context Phosphite esters, a class of organo-phosphorus compounds, are widely used as non-discolouring antioxidants in many polymeric products. Apart from normal radical scavenging, they prevent the splitting of hydroperoxides (ROOH), one of the initial products of autoxidation, from forming extremely reactive free radicals such as alkoxy (RO.) and hydroxy (.OH) radicals. The inherent molecular properties of antioxidants and the chemistry of their action are essential for researchers working in this field of science. Four organo-phosphorous compounds well-known for their antioxidant activity are selected here for theoretical analysis: Tri(m-methylphenyl) phosphite (m-TMPP), Tri(4-methyl-2,6-di-tert-butylphenyl) phosphite (TMdtBPP), Tri(allylphenyl) phosphite (TAPP) and Tri(mercaptobenzothiazoyl) thiophosphate (TMBTTP). The antioxidant activity exhibited by these compounds is theoretically verified, and the results are consistent with the available experimental data. Such theoretical predictions offer advantages in scientific research, particularly when researchers need to select certain molecules as antioxidants for experiments from a pool of molecular systems. Methods The chemical computations presented in this report are done in Gaussian 16 program package. The procedure of density functional theory (DFT) with the model chemistry B3LYP/6-31G(d,p) is used to generate computational data. Global reactivity indices, thermochemical data, Fukui functions, molecular electrostatic potential and NMR spectra are computed for the chosen molecular systems from their optimized geometries.

This paper presents the design and analysis of two distinctive three-phase permanent magnet-embedded switched reluctance motors for electric three wheelers. The configurations used in this work are a 12/8 classic SRM and a 12/10 E-core stator SRM. For the configuration of the 12/10 E-core stator, horizontal alignment of the permanent magnets is introduced, and the magnets are placed on the stator auxiliary poles. In the case of 12/8 SRM, the permanent magnet is placed in the stator yoke. Initially, a dynamic calculation of the vehicle and the SRM design process are carried out to determine the power rating and the optimal design parameters. The motor performance analysis is carried out using finite element-based software with the acquired specifications. The comparative analysis is established by employing various magnet materials with the intention of achieving the same motor torque. Outcomes reveal that the configuration of the permanent magnet embedded in the 12/10 E-core stator SRM is able to generate the needed average torque with minimal cogging torque and torque ripple using low-cost ferrite magnets, making it suitable for electric three-wheeler applications.

Microarray gene expression-based detection and classification of medical conditions have been prominent in research studies over the past few decades. However, extracting relevant data from the high-volume microarray gene expression with inherent nonlinearity and inseparable noise components raises significant challenges during data classification and disease detection. The dataset used for the research is the Lung Harvard 2 Dataset (LH2) which consists of 150 Adenocarcinoma subjects and 31 Mesothelioma subjects. The paper proposes a two-level strategy involving feature extraction and selection methods before the classification step. The feature extraction step utilizes Short Term Fourier Transform (STFT), and the feature selection step employs Particle Swarm Optimization (PSO) and Harmonic Search (HS) metaheuristic methods. The classifiers employed are Nonlinear Regression, Gaussian Mixture Model, Softmax Discriminant, Naive Bayes, SVM (Linear), SVM (Polynomial), and SVM (RBF). The two-level extracted relevant features are compared with raw data classification results, including Convolutional Neural Network (CNN) methodology. Among the methods, STFT with PSO feature selection and SVM (RBF) classifier produced the highest accuracy of 94.47%.

This investigation focuses on the effectiveness of coconut shell activated carbon in removing synthetic dye mixture from aqueous solutions. Coconut shell activated carbon, an economical and effective adsorbent, was made from agricultural waste raw material and chemically activated by sulphuric acid treatment. Activated carbon is characterised using FT-IR and SEM analysis. Batch adsorption studies were conducted by adjusting conditions such contact time, adsorbent dosage, initial dye concentration and temperature. The equilibrium of the adsorption process was described through analysis of isothermal models including Freundlich, Langmuir, and Scatchard. Kinetic data followed a pseudo-second order model. Thermodynamic studies showed that the adsorption was endothermic, spontaneous, and feasible. The results of the experiment indicate that coconut shell activated carbon is an effective, environmentally acceptable adsorbent for eliminating synthetic dye mixt from aqueous solution.

Ocimum tenuiflorum, O. gratissimum, and O. basilicum are medicinal plants extensively used in the traditional medicine of Kerala. The study is aimed at investigating the endophytic mycoflora associated with these Ocimum species and their ability to produce enzymes in vitro. A total of 149 fungal endophytes were isolated from roots, stems, and leaf segments from July to November 2021. They were grouped into 27 morphotypes, including five non-sporulating taxa. The highest number of isolates were obtained from the plant O. basilicum. An equally lower number of isolates were obtained from O. gratissimum and O. tenuiflorum. A greater number of fungal endophytes were obtained from the leaf segments of O.basilicum and least number of isolates obtained from the leaf segments of O.gratissimum. Isolates of Aspergillus niger complex, Diaporthe sp., and Daldinia eschscholtzii showed the highest colonizing frequency. In vitro analysis for enzyme production by all morphotypes was done and, except for laccase, all tested enzymes showed positive results.

Microarray gene expression analysis is a powerful technique used in cancer classification and research to identify and understand gene expression patterns that can differentiate between different cancer types, subtypes, and stages. However, microarray databases are highly redundant, inherently nonlinear, and noisy. Therefore, extracting meaningful information from such a huge database is a challenging one. The paper adopts the Fast Fourier Transform (FFT) and Mixture Model (MM) for dimensionality reduction and utilises the Dragonfly optimisation algorithm as the feature selection technique. The classifiers employed in this research are Nonlinear Regression, Naïve Bayes, Decision Tree, Random Forest and SVM (RBF). The classifiers’ performances are analysed with and without feature selection methods. Finally, Adaptive Moment Estimation (Adam) and Random Adaptive Moment Estimation (RanAdam) hyper-parameter tuning techniques are used as improvisation techniques for classifiers. The SVM (RBF) classifier with the Fast Fourier Transform Dimensionality Reduction method and Dragonfly feature selection achieved the highest accuracy of 98.343% with RanAdam hyper-parameter tuning compared to other classifiers.

This research paper presents a technoromantic analysis of William Gibson?s science fiction novel, Neuromancer. Through the lens of technoromanticism, this paper examines the tensions and interdependencies between human and artificial intelligence, underlining the paradoxical nature of technological advancement. The novel?s portrayal of cyberspace,simstim and cybernetic enhancements challenges traditional notions of reality, identity and human connection. The characters Case, Molly and Armitage embody technoromantic ideals and seeking to transcend their human limitations through technology. In contrast, the artificial intelligences like Neuromancer and Wintermute rely on human agency to overcome their technological constraints in the physical world. This study also raises important inquiries regarding the implications of technological advancement on human identity, experience and society. As individuals navigate in the complexities of technological world, it is very important to remain aware about the dynamics between humans, machines and technology. Analysing these dynamics enables the individuals to understand, how technology shapes their sense of selves and their place in the world. This research contributes a deeper understanding of evolving boundaries between humans and machines and the significance of technoromanticism in navigating these complexities.

The human skin encompasses a characteristic capacity to advance the self-regeneration after harm; this capacity can be compromised beneath particular conditions, like extensive skin loss, constant wounds, deep burns, non-healing ulcers and diabetes. Improper healing can lead the wound to enter in a constant state, which increments the chance of disease to chronic state; it affects the patient health and his/her quality of life. Due to growing concerns about unhealthy consequences of chemicals in the health care system, the interest towards natural and herbal substances has been growing every day. The plant Cassia auriculata is widely used medicinal plant in India and also popular in Indigenous system of medicines like Ayurveda and siddha. The collagen was successfully extracted from the Cattla Cattla fish scales using acid solubilizing collagen extraction method and the collagen was infused with plant extract of Cassia auriculata. The characterisation such as protein estimation (Lowry method), solubility (pH 1, 3, 5, 7, 9, 11 and 13), UV visible and FTIR was performed for collagen and phytochemical analysis (Alkaloids, Flavonoids, Saponins, Tannins, Phenol, Terpenoids, Glycosides and Steroids) anti-bacterial (E. coli and S. aureus) and anti-inflammatory test in different concentration (25mg, 50mg, 100mg / ml) was performed for the plant extracts and collagen infused with plant extracts. The result of the present study was confirmed that final product prepared using collagen with plant extract can be used to prepare the dressing material such as gels, ointments, bio-films, powdered flakes etc.