Purpose: It is apparent that Generative Artificial Intelligence (Gen-AI) is making its way into our daily lives. To navigate in an AI-influenced world and participate in future advances, students require a set of skills, known as AI Literacy, comprising using, applying, and communicating with AI. The current work discusses the future managers’ ability to understand and critically use Gen AI technologies with an emphasis on their technological, ethical, and socio-organizational dimensions. This literacy development is critical to students’ academic performance and their future success in the AI-enabled workplace. This paper’s objective is to assess the depth and quality of Gen-AI literacy among management students in India and find out whether demographic groups are comparable by level of GenAI literacy. Methodology: We used a quantitative cross-sectional design. Data were collected from management students from various Indian states through a questionnaire developed based on an established AI literacy scale. The scale successfully captured 6 dimensions of Gen-AI literacy: the social impact, execution, problem solving with Gen-AI, data literacy and AI ethics. Results: The findings reveal a moderate level of Gen-AI competency in management students. Respondents demonstrated greater awareness of social and ethical considerations, but less proficiency in problem-solving with Gen-AI software. No significant differences existed in Gen-AI proficiency by gender, family income, the background of parents or prior academic stream. Originality/Value: This study contributes to the growing domain of Gen-AI by evaluating the AI literacy of management students and supporting for improvement of students’ AI literacy levels. The present study achieved greater results by combining several methodological strengths, addressing persistent limitations in GenAI literacy research, and focusing on an underrepresented population. Keywords: AI Literacy, Generative AI, Literacy Assessment, Students’ Literacy, Management

Objectives: This research aims at developing a real-time wearable navigation system to guide the visually impaired users, capable of detecting, classifying, tracking, and localizing obstacles with the help of YOLOv8, Deep SORT, and geometrical distance-angle estimation. It is aimed at providing a safe, precise, and context-driven navigation aid with an embedded portable platform. Methods: The system combines an embedded Raspberry Pi camera and a YOLOv8 object detector, Deep SORT multi-object tracker, and a geometric model of pinhole cameras to estimate distance and angular position. Video frames are processed in real-time and sent through the detection tracking pipeline and then a safety labeler is used to provide the obstacles with a SAFE or DANGER label. Accuracy, precision, recall, F1-score, normalized confusion matrix, and Mean Absolute Error (MAE) of distance and angle estimation are all performance evaluation metrics. Findings: Under realistic conditions, experimental results indicate that the reliability is high with a 95% 100% accuracy, 92% 98% precision, 88% 95% recall and 90% 96% F1-score. There is low MAE in distance estimation and the error in angular estimation is within safe limits in navigation. According to the confusion matrix analysis, the classification accuracy is high in the case of navigation-critical objects (e.g., person, door, TV), and the confusion is low in the case of similar classes in appearance (chair–couch). All in all, the system is very stable in FPS and has a constant detection confidence of dynamic environments. Novelty: The proposed system is the only one to integrate lightweight YOLOv8 detection, Deep SORT tracking and camera-based geometric distance-angle estimation into one wearable assistive system, unlike the current system which only uses detection or depth sensors. Besides, it combines a real-time safety-level classifier (SAFE/DANGER) and improves the situational awareness, which makes the system more viable in the case of navigation by the blind. It is an integrated, embedded, inexpensive architecture that provides rapid, precise, and reliable environmental awareness that can be used in real-time mobility support. Keywords: YOLOv8, Deep SORT, Distance angle estimation, Assistive technology, Safety level classification, Computer vision

Objectives: This study presents Ruthenium catalysed degradation of Chloramphenicol by peroxymonosulphate (PMS) in basic medium. Method: This study tests a range of results in basic solutions. Experiments were conducted with varying kinetic and thermodynamic variables. To investigate the oxidation of chloramphenicol we applied the common titrimetric analysis. The rate of oxidation of chloramphenicol was evaluated by using titrimetric analysis in which the amount of oxidant remaining unreacted in reaction mixture were measured at regular time intervals using iodometric method. To calculate and co-relate the results, the DFT parameters are used. Findings: This work introduces a mechanistically resolved kinetic framework for the Ru (III)-catalysed oxidation of chloramphenicol by peroxymonosulphate in alkaline solution. Unlike many substrate-focused oxidation studies, the kinetic signatures here identify oxidant activation as the dominant control element. The rate is first order in PMS and Ruthenium (III) with a finite intercept, cleanly separating catalytic turnover from a parallel uncatalyzed pathway. The weak/saturation-type dependence on chloramphenicol identifies oxidant activation (Ru–PMS) as the turnover-limiting step rather than substrate oxidation. Strong hydroxide inhibition and a pronounced inverse ionic-strength effect (KCl/NaClO₄) diagnose speciation- and electrostatics-controlled formation of the activated complex, enabling a composite rate law with physical-organic significance. We also determined the thermodynamic functions and Eyring activation parameters for the rate-determining (slow) step, thereby providing an energetic basis for the proposed mechanism. In addition, density functional theory (DFT) calculations were performed to characterize the electronic structure and optimized geometry of chloramphenicol and peroxymonosulphate. Novelty: The combined kinetic–thermodynamic analysis and DFT modeling therefore offer a coherent experimental–computational description of the Ru(III)–PMS oxidation pathway in alkaline medium Keywords: Chloramphenicol, Kinetic investigation, Oxidation, Chloramphenicol, peroxymonosulphate, Ruthenium (III), Basic Medium

Objectives: To evaluate and prioritize the authentication technique for artifact theft, it is implemented and compared in sculptures of the Chola Dynasty relocated to the homeland of Tamil Nadu. Methods: This study integrates the COCOSO ranking method and interval-value intuitionistic fuzzy vague sets in multi-criteria decision-making (MCDM). To obtain the rank quality from the alternative rank order, the Friedman test is processed. The nuanced analysis of interval value intuitionistic fuzzy vague sets provides qualitative decision stability and a realistic framework for solving real-world problems. Findings: The international issue of fraudulent practices in ancient architecture presents significant challenges in case resolution through risk assessment. The unified technique enables the resolution to solve reliable cases, that is, to be applied to the Lord Shiva Nataraja Sculpture theft of Chola bronze from the Peruvudaiyar Kovil temple, compromising the historical value of artifacts. This finding provides a clear ranking procedure accomplished through the Friedman test using the Statisty software. This result provides both a qualitative and prioritized investigation framework technique and leaves the historical fact unchanged. Novelty: An integrated COCOSO method for weight aggregation for decision-making using interval-valued intuitionistic fuzzy vague sets in Multi-Criteria Decision Making (MCDM). The Friedman test was used to rank quality. Keywords: COCOSO method, Interval value Intuitionistic fuzzy vague set (IVIFVS), Score function, Fuzzy entropy, Friedman test, lindo software

Objectives: To investigate the influence of laser shock peening (LSP) energy on fatigue life and surface roughness of stir-cast Al6082/SiC/7.5% metal matrix composites. Methodology: Al6082/SiC/7.5% composites were fabricated using stir casting and subjected to laser shock peening at energy levels of 1 J, 2 J, and 2.5 J using a Q-switched Nd:YAG laser. Rotating bending fatigue tests were conducted to determine fatigue life, while surface roughness was measured using a contact profilometer. Findings: Fatigue life increased by 29%, 34.77%, and 44.5% at 1 J, 2 J, and 2.5 J respectively compared to un-peened composites. Surface roughness decreased by 1.40% at 2 J and 6.50% at 2.5 J, indicating improved surface integrity at higher laser energies. Novelty: This study establishes a quantitative relationship between laser shock peening energy, fatigue enhancement, and surface roughness reduction in Al6082/SiC composites, identifying an optimal processing window that has not been clearly reported for particle-reinforced aluminum metal matrix composites. Keywords: Al6082 MMC, SiC reinforcement, Laser peening, Fatigue life, Surface roughness

Objectives: A perennial herb used for many years in traditional medicine, A. malabarica is the subject of this study, which compares the relative safety and economic viability of many naturally occurring Phenol compounds combined for therapeutic purposes. Methods: The current study utilises GC-MS, FTIR, and UV-vis spectroscopy to evaluate the antioxidant and antimicrobial properties of phenolic compounds from A. malabarica leaf extracts. Using the latter crude extract, AMLE was extracted at an acidic pH in the presence and separation of phenolic components using ethanol as the solvent. Antioxidants were studied using H2O2, DPPH, and ABTS. An array of bacterial and fungal species was used to assess the antimicrobial capabilities using the agar well diffusion method. The purpose of characterisation was to determine the presence of functional groups and bioactive compounds. Findings: The Phenol leaf extract from A. malabarica exhibited dose-dependent antioxidant properties during this study, with IC50 values of 110.2 μg/mL for DPPH, 93.01 μg/mL for ABTS+, and 143.4 μg/mL for H2O2. HRBC membrane stability by 81.43%. A maximum of 15.5±0.7 mm of antibacterial activity and 8.4±0.56 mm of antifungal activity was found against P. acnes and A. niger. Twenty phenol extracts were detected by GC-MS analysis. The functional groups, amine salt, alkene, sulfonyl chloride, aromatic ester, and cycloalkane, were represented by the FTIR peak values. The best protection against A. aegypti and A. stephensi for 180 minutes at a dosage of 500 ppm of 100% dead larvae, 40%-90%, followed by 150 and 120 min. Novelty: The Phenol components of the leaf extract of A. malabarica possess antibacterial and anti-inflammatory qualities, making them possible targets for various diseases. Keywords: Antimicrobial, Anisomeles malabarica, aromatic ester, hepatoprotective, Phenol

Background: Malnutrition is a severe problem globally; however, over time, with the government’s support, the issue has been reduced significantly. However, in Southern India, between rounds 4 and 5 of the National Family Health Survey (NFHS), the malnutrition status has become higher. Objectives: This study focuses on existing trends in child undernutrition across five southern states, i.e., Tamil Nadu, Kerala, Karnataka, Andhra Pradesh, and Telangana and reviews the contribution of POSHAN Abhiyan and POSHAN 2.0 in eradicating child malnutrition in those states. Methods: The study conducted a comparative analysis using NFHS-4 and 5 data to examine regional disparities and policy gaps that affect the prevalence of malnutrition. Data were disaggregated by state, types of residence, and wealth index to identify the factors in this state, along with a qualitative review of POSHAN Abhiyan & POSHAN 2.0 to conclude findings. Findings: Results reveal that the households (HH) in rural areas significantly increased the odds of stunting in Andhra Pradesh (OR=1.35) and Kerala (OR=1.25), while their association with wasting and underweight was largely non-significant. Wealth disparities were consistent and evident that children from the poorest households had 2–3 times higher odds of stunting, e.g., Karnataka (OR=2.82) and Telangana (OR=2.54), wasting (Tamil Nadu OR=1.80), and underweight (Karnataka OR=3.25); Tamil Nadu OR=3.00) compared to the richest. This means the risk declined monotonically with increasing wealth, highlighting poverty as the dominant determinant of child undernutrition in Southern states. Socio-economic disparities and inconsistent policy implementation were significant barriers to progress. Novelty: This study is the first of its kind linking NFHS-4 and 5 data with a policy review to reveal overlooked urban–rural and intra-state disparities in child undernutrition in Southern India. Second, it connects nutrition outcomes to the implementation of POSHAN 1.0 and 2.0, highlighting policy gaps and the role of digital monitoring tools in achieving these outcomes. Keywords: Child Undernutrition & Malnutrition, Southern India, Nutrition Policy, POSHAN Abhiyan, POSHAN 2.0

Objectives : This work explores the application of two advanced state-of-the-art models, BERT (Bidirectional Encoder Representations from Transformers) and USE (Universal Sentence Encoder), to automate the grading of short answers. Methods: This work investigates the use of BERT and USE models for automatically grading short answers. The research utilizes HP:SAS dataset containing manually graded responses by two human evaluators. The student responses as well as model answers responses of question 1 and question set 6 are then processed using the BERT and USE models, with scores generated based on cosine similarity measures between student answers and predefined model answers. Findings: The work demonstrates that BERT and USE embeddings can effectively capture contextual and semantic similarity, their performance is heavily dependent on the function which generates the score. Our finding reveal that a non-linear mapping function mimics the human grading more than a linear mapping function. Such a function enhances accuracy (0.67) and reduces the error (0.617) by computing Pearson correlation coefficient and RMSE respectively. Notably, longer responses achieved higher Pearson correlations (0.67) than shorted answers (0.59). The results bring out usability and choice aspects of BERT and USE in relation to ASAG, contributing to the understanding of their application across various answers. We conclude with a weighted ensemble method combining BERT and USE with subject- specific strictness parameter (k) provides a robust framework for automated assessment. Novelty: Evaluates and compares two deep learning models for automatic short answer grading, a scarcely explored area. A novel contribution is the granular analysis across different scoring ranges across two question sets of the dataset. The novelty of this work lies in the transition from linear scoring to non-linear mapping framework. This approach introduces a tunable sigmoid- based ensemble that would replicate human assessment. Finally, a comparison with existing studies demonstrates very limited research. Keywords: Bidirectional Encoder Representations from Transformers (BERT), Universal Sentence Encoder (USE), Transformer, word embedding, Non-linear mapping, deep learning, short answer grading

Objectives: To study the microscopic features of the organs that contain the aroma-producing components in clove. Methods: Microtoming was used to elucidate microscopical information and aroma secreting structure of the leaf, flower bud and fruit of Syzygium aromaticum. Finding: The anatomical study confirmed that the aroma-secretion mechanisms are located mostly in aerial parts of flower bud, mother fruit and leaf as they exhibit the secretory cavities. Novelty: This study presents a simple, yet direct evidence, of determining the accumulation of bio-active components in the various parts of the clove which traditionally known as spice and condiments. Keywords: Microscopic, Aroma, Secretory Cavity, Myrtaceae, Syzygium aromaticum