B.Sc., M.Sc., Ph.D. Science http://10.9.150.37:8080/dspace//handle/atmiyauni/236 2026-03-13T19:44:01Z 2026-03-13T19:44:01Z Dalsania, Ravikumar Vinodray Dr. Mahesh, M Savant http://10.9.150.37:8080/dspace//handle/atmiyauni/2336 2025-12-12T07:05:26Z 2025-10-01T00:00:00Z

Synthesis of Nanomaterial from Heavy Fraction of Crude Oil Dalsania, Ravikumar Vinodray; Dr. Mahesh, M Savant

2025-10-01T00:00:00Z Jani, Ajay Jayantkumar Dr. Satishkumar, D Tala http://10.9.150.37:8080/dspace//handle/atmiyauni/2334 2025-12-12T06:29:39Z 2025-09-01T00:00:00Z

Synthesis Characterization and Biological Evaluation of Some Fused Heterocyclic Derivatives Jani, Ajay Jayantkumar; Dr. Satishkumar, D Tala

2025-09-01T00:00:00Z Dave, Nehal Kiritkumar Dr. Hiren, R. Kavathiya http://10.9.150.37:8080/dspace//handle/atmiyauni/2330 2025-09-04T07:38:25Z 2025-06-01T00:00:00Z

An Algorithmic Approach for Undergraduate Computer Science Students to Select Mentor Using Recommendation System of Machine Learning Dave, Nehal Kiritkumar; Dr. Hiren, R. Kavathiya Undergraduate students' intellectual and emotional health are significantly impacted by mentoring. Having the proper mentor can have a big impact on confidence, motivation, and long-term goal setting for computer science students, who frequently deal with high levels of academic pressure and ambiguity about their career trajectory. Nevertheless, conventional mentor selection techniques fail to consider students' psychological fit with mentors, which frequently leads to unproductive or brief mentor-mentee relationships. By using a machine learning-based recommendation system that takes psychological characteristics into account when choosing mentors, this study seeks to close that gap. Using well-known models like the Big Five Personality Traits and emotional intelligence scores, the suggested system integrates psychological profiling. In addition to academic and technical interests, mentors and students are assessed on their motivational tendencies, communication preferences, and interpersonal styles. The methodology guarantees improved emotional alignment and communicative resonance between the mentor and mentee by including psychological elements in addition to scholastic data. The model was constructed and trained using machine learning strategies, such as collaborative filtering and clustering. A psychological compatibility score was incorporated into the recommendation algorithm, and participant surveys and psychometric testing were used to create a rich dataset. According to tests, students who were paired with mentors based on psychological compatibility expressed greater levels of happiness as well as a more robust sense of belonging and support. The study also looked at the relationship between mentor-mentee psychological compatibility and stress levels, academic engagement, and self-efficacy. Pupils who had mentors who shared their psychological views demonstrated better coping strategies, more academic perseverance, and more clarity when establishing their career and personal objectives. When mentees were emotionally open and compatible with their mentoring approach, mentors found it simpler to offer advice. An innovative, human-centred method of academic support is introduced by incorporating psychological concepts into a machine learning-based mentor selection system. This method meets students' emotional and cognitive demands while also enhancing the caliber of mentoring relationships. Future studies will examine adaptive learning platforms that modify mentor recommendations in response to students' academic and psychological development.

2025-06-01T00:00:00Z Rajpara, Roshniben Jayshukhbhai Dr. Anmol, Kumar http://10.9.150.37:8080/dspace//handle/atmiyauni/2329 2025-09-04T07:29:03Z 2025-07-01T00:00:00Z

Studies on Isolation Characterization and Production of Fungal L Methionase a Promising Anti Cancer Agent From Soil Rajpara, Roshniben Jayshukhbhai; Dr. Anmol, Kumar L-Methionase has emerged as a potent enzyme with promising applications in cancer therapy due to its ability to selectively deplete methionine an essential amino acid for methionine-dependent tumor cells. This study aimed to isolate and characterize fungal strains capable of producing Lmethionase, optimize its production, purify the enzyme, and evaluate its in vitro anticancer potential. Soil samples were collected from diverse ecological regions across Gujarat, India including marine, riverine, and agricultural sites to explore fungal biodiversity. A total of 50 fungal isolates were obtained, and qualitative screening using modified Czapek-Dox agar identified Aspergillus fumigatus MF13 as the most potent L-methionase producer. Quantitative assessment through enzyme assay and specific activity estimation further confirmed MF13’s enzymatic potential, with a maximum activity of 4.31 U/mL/min and a specific activity of 1.48 U/mg. Molecular identification using ITS sequencing validated MF13’s identity as Aspergillus fumigatus (GenBank accession: OQ690549). Optimization of enzyme production was achieved using a combination of One-Factor-at-a-Time (OFAT), Plackett-Burman Design (PBD), and Central Composite Design (CCD), culminating in a 2.57 U/mL/min yield under optimal conditions: 30°C, pH 8.0, 2.4 g/L yeast extract, and 1.2 g/L dipotassium phosphate. Purification via cold acetone precipitation and Sephadex G-75 chromatography resulted in a 10.5-fold increase in purity, with a specific activity of 40.0 U/mg and molecular weight of ~45 kDa, as confirmed by SDS-PAGE. Biochemical characterization showed optimal activity at pH 7.5 and 30°C, and notable stability under alkaline and moderate thermal conditions. Enzyme kinetics revealed a Km of 0.674 mM and Vmax of 0.871 U/mL, indicating strong substrate affinity. In vitro cytotoxicity assays (MTT) demonstrated dose-dependent anticancer activity of purified L-methionase. HT-29 (colon cancer) cells were highly sensitive (IC₅₀ ≈ 175 μg/mL), while MDA-MB-231 (breast cancer) cells showed resistance (IC₅₀ ≈ 390 μg/mL), suggesting variable methionine dependency. This research highlights Aspergillus fumigatus MF13 as a promising source of L-methionase and reinforces the enzyme's potential as a selective anticancer agent. The successful optimization and purification pave the way for further development in therapeutic applications, with future work focusing on overcoming resistance mechanisms and evaluating in vivo efficacy.

2025-07-01T00:00:00Z