Guar, the most popular vegetable, is tolerant of drought and is a valuable industrial crop enormously grown across India, Pakistan, USA, and South Africa for pharmaceutically and cosmetically usable galactomannan (gum) content present in seed endosperm. Guar genotypes with productive traits which could perform better in differential environmental conditions are of utmost priority for genotype selection. This could be achieved by employing multivariate trait analysis. In this context, Multi-Trait Stability Index (MTSI) and Multi-Trait Genotype-Ideotype Distance Index (MGIDI) were employed for identifying high-performing genotypes exhibiting multiple traits. In the current investigation, 85 guar accessions growing in different seasons were assessed for 15 morphological traits. The results obtained by MTSI and MGIDI indexes revealed that, out of 85, only 13 genotypes performed better across and within the seasons, and, based on the coincidence index, only three genotypes (IC-415106, IC-420320, and IC-402301) were found stable with high seed production in multi-environmental conditions. View on strengths and weakness as described by the MGIDI reveals that breeders concentrated on developing genotype with desired traits, such as quality of the gum and seed yield. The strength of the ideal genotypes in the present work is mainly focused on high gum content, short crop cycle, and high seed yield possessing good biochemical traits. Thus, MTSI and MGIDI serve as a novel tool for desired genotype selection process simultaneously in plant breeding programs across multi-environments due to uniqueness and ease in interpreting data with minimal multicollinearity issues.

The disease may be an explicit status that negatively affects human health. Cardiopathy is one of the common deadly diseases that is attributed to unhealthy human habits compared to alternative diseases. With the help of machine learning (ML) algorithms, heart disease can be noticed in a short time as well as at a low cost. This study adopted four machine learning models, such as random forest (RF), decision tree (DT), AdaBoost (AB), and K-nearest neighbor (KNN), to detect heart disease. A generalized algorithm was constructed to analyze the strength of the relevant factors that contribute to heart disease prediction. The models were evaluated using the datasets Cleveland, Hungary, Switzerland, and Long Beach (CHSLB), and all were collected from Kaggle. Based on the CHSLB dataset, RF, DT, AB, and KNN models predicted an accuracy of 99.03%, 96.10%, 100%, and 100%, respectively. In the case of a single (Cleveland) dataset, only two models, namely RF and KNN, show good accuracy of 93.437% and 97.83%, respectively. Finally, the study used Streamlit, an internet-based cloud hosting platform, to develop a computer-aided smart system for disease prediction. It is expected that the proposed tool together with the ML algorithm will play a key role in diagnosing heart diseases in a very convenient manner. Above all, the study has made a substantial contribution to the computation of strength scores with significant predictors in the prognosis of heart disease.

Vegetable soybean production is dependent on the development of vegetable type varieties that would be achieved by the use of germplasm to evolve new agronomically superior yielding vegetable type with beneficial biochemical traits. This can be accomplished by a better understanding of genetics, which is why the research was conducted to reveal the quantitative genetics of vegetable soybean genotypes. Genetic variability of main morphological traits in vegetable soybean genotypes and their divergence was estimated, as a result of the magnitude of genotypic variation (GV), and phenotypic variation (PV) of traits varied among the genotypes. All traits showed high heritability (h²) associated with high genetic advance percentage mean (GAM). Therefore, these variable traits are potential for genetic improvement of vegetable type soybean. Genetic diversity is the prime need for breeding, and the magnitude of genetic diversity values were maximized among specific genotypes. Eight clusters were found for all genotypes; cluster VIII and cluster I were considered to have the most diversity. Cluster VIII consisted of two genotypes (GM-6 and GM-27), based on the mean outcomes of the high yield attributing traits. Hence, these two (GM-6, GM-27) genotypes can be advanced for commercial cultivation; furthermore, other genotypes can be used as source of breeding lines for genetic improvement of vegetable soybean.

Numerous factors can contribute to the development of neurodegenerative disorders (NDs), such as Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, Huntington’s disease, and multiple sclerosis. Oxidative stress (OS), a fairly common ND symptom, can be caused by more reactive oxygen species being made. In addition, the pathological state of NDs, which includes a high number of protein aggregates, could make chronic inflammation worse by activating microglia. Carotenoids, often known as “CTs”, are pigments that exist naturally and play a vital role in the prevention of several brain illnesses. CTs are organic pigments with major significance in ND prevention. More than 600 CTs have been discovered in nature, and they may be found in a wide variety of creatures. Different forms of CTs are responsible for the red, yellow, and orange pigments seen in many animals and plants. Because of their unique structure, CTs exhibit a wide range of bioactive effects, such as anti-inflammatory and antioxidant effects. The preventive effects of CTs have led researchers to find a strong correlation between CT levels in the body and the avoidance and treatment of several ailments, including NDs. To further understand the connection between OS, neuroinflammation, and NDs, a literature review has been compiled. In addition, we have focused on the anti-inflammatory and antioxidant properties of CTs for the treatment and management of NDs.

In this study, lead sulfide (PbS) nanoparticles were synthesized by the chemical precipitation method using Aloe Vera extract with PbCl₂ and Thiourea (H₂N-CS-NH₂). The synthesized nanoparticles have been investigated using x-ray diffraction (XRD), UV-Vis, energy-dispersive x-ray spectroscopy (EDX), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). XRD and TEM results confirm that the films are in the cubic phase. The crystallite size, lattice constant, micro-strain, dislocation density, optical bandgap, etc. have been determined using XRD and UV-Vis for investigating the quality of prepared nanoparticles. The possible application of these synthesized nanoparticles in the solar cells was investigated by fabricating the thin films on an FTO-coated and bare glass substrate. The properties of nanoparticles were found to be nearly retained in the film state as well. The experimentally found properties of thin films have been implemented for perovskite solar cell simulation and current-voltage and capacitance-voltage characteristics have been investigated. The simulation results showed that PbS nanoparticles could be a potential hole transport layer for high-efficiency perovskite solar cell applications.

Development of satellite and radar applications has been continuously studied to reach the demand in the recent communication technology. In this study, a new oval-square-shaped split-ring resonator with left-handed metamaterial properties was developed for C-band and X-band applications. The proposed metamaterial was fabricated on 9 × 9 × 0.508 mm³ size of Rogers RO4003C substrate. The proposed metamaterial structure was designed and simulated using Computer Simulation Technique (CST) Microwave Studio with the frequency ranging between 0 to 12 GHz. The simulated result of the proposed design indicated dual resonance frequency at 5.52 GHz (C-band) and 8.81 GHz (X-band). Meanwhile, the experimental result of the proposed design demonstrated dual resonance frequency at 5.53 GHz (C-band) and 8.31 GHz (X-band). Therefore, with a slight difference in the dual resonance frequency, the simulated result corresponded to the experimental result. Additionally, the proposed design exhibited the ideal properties of electromagnetic which is left-handed metamaterial (LHM) behavior. Hence, the metamaterial structure is highly recommended for satellite and radar applications.