Eurasian Journal of Science and Technology
Volume:4 Issue: 2, Apr 2024

In the present study, we have collected the leucocephala seeds which are seasonal and having good amount of oil content. The oil seeds can be used as the one of the adulterant for the coffee. The whole plant having importance for human being, it could be as the food or furniture. The immature seeds, pods and flower buds are used as the salad while the gum obtained from this plant can be used for the confectionary, pharmaceutics, and cosmetics industry. In some of the region, it is considered as the recalcitrant and attracts rodents. The plant is well-known in traditional medicine and seeds are the rich source of several necessary metabolites such as proteins, tannins, carotene, leucenol, and flavonol glycosides and having therapeutic potential like anti-inflammatory and antidiabetic activity. Hence, in our present investigation, we focus for the collection of seeds, and extraction of oil and its characterizations. The dried pods and seeds were collected from Waghi Village, near Nanded, Maharashtra. The obtained extract were further characterized for the confirmation of oil content by measuring lipid content, specific gravity, density, protein, and glucose content, and further analysis with thin layer chromatography. The obtained extract is also subjected to the anti-microbial activity.

Carpal Tunnel Syndrome (CTS) has garnered attention in previous research for its potential association with diabetes mellitus. It has been suggested that CTS may occur more frequently in individuals with diabetes, and this connection might be influenced by factors such as the duration of diabetes, microvascular complications, and the level of glycaemic control. The primary objective of this study was to investigate whether Type 2 diabetes could be conclusively identified as a bona fide risk factor for the development of carpal tunnel syndrome, even after accounting for potential confounding variables. This retrospective case-control study harnessed data sourced from electronic patient records at Tabriz university of medical scenes. We focused on patients who received a diagnosis of carpal tunnel syndrome within the timeframe spanning from January 2011 to July 2012. This cohort was then compared to a control group comprising patients diagnosed with herniated nucleus pulposus. We observed that the prevalence of Type 2 diabetes was higher among patients with carpal tunnel syndrome, accounting for 11.5% of this group, compared to 7.2% in the control group (Odds Ratio 1.67, with a 95% confidence interval ranging from 1.16 to 2.41). However, after conducting multivariate analyses while adjusting for gender, age, and body mass index, Type 2 diabetes did not emerge as a statistically significant, independent risk factor for carpal tunnel syndrome (Odds Ratio 0.99, with a 95% CI spanning from 0.66 to 1.47). Although our study revealed a higher prevalence of Type 2 diabetes among individuals with carpal tunnel syndrome, this condition could not be unequivocally identified as an independent risk factor for the development of carpal tunnel syndrome when adjusting for potential confounding variables. These findings suggest that other factors may play a more prominent role in the onset of carpal tunnel syndrome, and further research is warranted to explore these associations in greater detail.

In Nasarawa State, groundwater is the most often used source of fresh water for daily consumption, but its quality still remains a serious concern due to rising concentrations of radon resulting from activities of mining. This study evaluated the potential pose resulting from radon exposure via groundwater ingestion and inhalation in Keffi, Nigeria using the liquid scintillation detector. Ten borehole samples of groundwater were collected. The mean content of radon from water samples of Keffi was 19.368 Bq/l. the average ingested and inhaled dose effectiveness annually was 0.099 mSv/y and 4.9 x10-5 mSv/y, respectively. In Keffi, the average ingested extra lifetime cancer risk was 3.5 x10-4 and for inhalation was 1.71 x10-7. Research area's average radon concentration was higher than the standard of 11.1 Bq/l set by the SON and USEPA. Based on the findings of the present work, the radon concentration is unacceptable, hence, inhabitants should be restricted from using the water until measures are put into place. However further analysis could be carried out in the area to prevent people from cancer risk. To cover the entire zone, additional research should be conducted covering additional sources in the study area. As concentrations of radon in water sources varies with time as a result of dilution by rainfall, more examination may be conducted in dry and raining periods.

The integration of artificial intelligence (AI) and nanotechnology has revolutionized the field of nanomedicine. AI’s large-scale data processing and pattern recognition capabilities can enhance the design of nanotechnologies for diagnosis and therapy. This integration can address challenges in fabrication and targeted drug delivery for cancer therapy. AI’s rapid data mining and decision-making capabilities can lead to more innovative solutions. The convergence of biology, AI, and nanotechnology is fostering a scientific and technological revolution. Recent studies show that AI can improve the design of nanotechnologies for diagnostics and treatment by processing large datasets and recognizing complex patterns. AI is also used in nanomedicine design to optimize material properties based on interactions with target medications, biological fluids, immune systems, and cell membranes.

Quantum functions were used to assess a theoretical investigation on mild steel's resistance to corrosion. To determine the stable geometry of the investigated compounds, TPE and PME, local density function B3LYP was optimized and simulated using DFT under restricted spin polarization DNP basis. The molecules' local and global reactivity, including their electronegativity (χ), dipole moment (μ), energy gap (ΔE), global hardness (η), global electrophilicity index (ω), energy of back donation (∆Eb-d), fraction of electron transfer (ΔN), and the (ω+) and (ω-) electron accepting and donating powers between the molecule and the iron, were all studied. The inhibition process was assumed to be a chemisorption interaction between the surface and the molecule based on the number of adsorption sites and the binding energy obtained from the process. This is because the molecules contain hetero-atoms, such as oxygen and methylene (-CH2-) functional groups. For PME and flourine for TPE, which serve as the focal point for the selectivity of electron donation and acceptance between the metal and the TPE and PME moieties.

Industrial filters are important components in the manufacturing and processing of polymer products. They are used to remove impurities, contaminants, and foreign particles from polymer materials, ensuring high-quality and consistent products. The polymer industry, which is at the centre of contemporary manufacturing, is under increasing pressure to strike a balance between environmental sustainability and the demand for outstanding product quality. In this perspective, industrial filters stand out as unsung heroes who have a significant impact on the polymers manufacture. This in-depth analysis explores the most recent advancements in industrial filtering technology and their strategic uses in the production of polymers. It emphasizes how these filters successfully remove pollutants, impurities, and undesired particles from the polymer feedstock, producing products that stand out for having better mechanical, thermal, and optical qualities. Furthering the cause of sustainability and ecologically responsible production, the elimination of unwanted by-products, and the maintenance of constant polymer compositions greatly reduce waste formation. Analyses of case studies and practical instances provide verifiable proof of the revolutionary advantages offered by industrial filters. These benefits include improved energy efficiency, lower maintenance costs, and the establishment of an unwavering standard for product quality. The research also explores the use of green filtering systems, which not only boost output, but also comply with the growing demand for environmentally responsible manufacturing methods.

Chalcogen bonding, a non-covalent interaction involving chalcogen atoms (e.g., sulfur, selenium, and tellurium), plays a crucial role in various chemical and biological processes. Understanding and characterizing chalcogen bonding interactions are essential for designing novel materials, medications, and catalysts. In recent years, machine learning has emerged as a powerful tool for studying molecular interactions, including chalcogen bonding. This study provides an overview of the application of machine learning in characterizing chalcogen bonding. Experimental techniques, such as infrared (IR), nuclear magnetic resonance (NMR) spectroscopy, and X-ray crystallography, have been used to study chalcogen bonding. However, these methods often suffer from inherent experimental challenges. On the other hand, computational approaches, including quantum mechanics (QM) and molecular dynamics (MD) simulations, offer valuable insights into the electronic structure and energetics of chalcogen bonding. Nonetheless, they can be computationally demanding and may not fully encompass the diversity of chalcogen bonding interactions. Machine learning, with its ability to identify patterns and relationships in vast datasets, presents a promising alternative for characterizing chalcogen bonding. The study explains how machine learning algorithms, such as supervised and unsupervised learning, can be employed to classify and predict chalcogen-bonded complexes using neural network potentials to assess the persistence of chalcogen bonds in solution and ML models to predict two key solid-state synthesis conditions that must be specified for chalcogenide glasses. By integrating experimental data and computational results, machine learning models offer a holistic approach to understanding chalcogen bonding in various molecular systems. It emphasizes the integration of experimental and computational data as a means to maximize the accuracy and applicability of machine learning models and envisions a promising future for machine learning in characterizing chalcogen bonding interactions.