Objective: This study aims to develop and systematically evaluate titanium substrates coated with chitosan-based nanocomposites incorporating PbO, Cd, and Ce nanoparticles via electrodeposition, with emphasis on corrosion resistance, electrochemical stability, and antifungal performance under simulated physiological conditions. Method: Coatings were fabricated using controlled electrodeposition in Hank’s balanced salt solution to mimic physiological ionic conditions. Key parameters including applied current density, deposition time, bath pH (7.2 ± 0.1), temperature (37 ± 1°C), and nanoparticle concentration were carefully regulated to ensure reproducibility. Structural and chemical characterization was performed using UV–Vis, UV–DRS, XRD and FTIR analyses. Corrosion behaviour was evaluated through weight loss measurements, potentiodynamic polarization, cyclic voltammetry, and electrochemical impedance spectroscopy (EIS) using a Randles equivalent circuit model. Findings: The coatings were dense, uniform, and strongly adherent. Electrochemical studies showed significant corrosion protection, achieving inhibition efficiencies of 99% (CS/PbO) and 98% (CS/Cd and CS/Ce). Increased charge transfer resistance and reduced double-layer capacitance confirmed enhanced barrier formation. Antifungal testing against Candida albicans demonstrated improved activity, particularly for CS/Ce coatings. Novelty: This work establishes a reproducible electrochemical framework linking deposition parameters with coating performance for advanced titanium-based biomedical implants. Keywords: Titanium, Chitosan Bio-nanocomposite, Electrodeposition, UV, XRD, FTIR

Objectives: To investigate the Modified Inverse Weibull Distribution (MIWD) to determine its effectiveness in modelling real customer transaction data, which is often skewed and exhibits heavy-tailed characteristics. Methods: Parameters of the model were estimated using the Maximum Likelihood Estimation (MLE) technique. The model's performance was evaluated using goodness-of-fit measures, such as the log-likelihood (ℓ), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Kolmogorov–Smirnov (K–S) statistic. The probability density function (PDF), cumulative distribution function (CDF), hazard function, and survival function were derived analytically and visualized with Python to assess model behavior and data alignment. Findings: The MIWD demonstrated a better fit for the customer transaction data than its sub-models and other competing distributions. The estimated probability density function (PDF), cumulative distribution function (CDF), hazard, and survival functions highlighted the model's adaptability in representing both skewness and heavy-tailed characteristics in actual transaction data. Novelty: The main contribution of this study is the empirical validation of the four-parameter MIWD using actual customer transaction data, demonstrating its superiority over established sub-models based on various quantitative criteria. The findings provide compelling evidence that the MIWD is a robust and adaptable alternative for modelling skewed, heavy-tailed transaction and lifetime data, with practical applications in customer behavior analysis, reliability engineering, and risk modelling. Keywords: Modified Inverse Weibull Distribution, Maximum Likelihood Estimation, Customer transaction data, Parameters, Rayleigh distribution

Objectives: To identify food-origin lactic acid bacteria (LAB) capable of converting ellagic acid (EA) to urolithin A (UA) under chemically defined conditions, characterise their biotransformation kinetics, and evaluate preliminary probiotic safety criteria. Methods: Fifty-two LAB isolates from traditional Chinese Qu starter cultures were screened for EA-to-UA conversion in MRS broth with 20 µM pure EA (37°C, 72 h, microaerophilic). UA and EA were quantified by HPLC; UA identity was confirmed by HR-ESI-MS. Producers were identified by 16S rRNA sequencing and subjected to time-course fermentation (0–98 h). Safety assessment covered acid and bile salt tolerance, hemolytic activity, and antibiotic susceptibility. Findings: Screening identified two UA-producing strains from 52 isolates (3.8%), both from Qu starters: Lactiplantibacillus plantarum TCQ1 (99.7% 16S rRNA identity to type strain ATCC 14917ᵀ) and Limosilactobacillus fermentum TCQ3 (99.9%). Under defined conditions, TCQ1 produced 7.93 ± 0.14 µM UA (39.65 ± 0.69% molar conversion), and TCQ3 produced 6.21 ± 0.31 µM UA (31.06 ± 1.57%; t = 8.68, p = 0.001). Time-course analysis showed strain-specific kinetics: TCQ1 peaked at 6.482 ± 0.918 µM at 56 h (44.9% yield) and TCQ3 at 5.575 ± 0.291 µM at 72 h (36.1%); both depleted EA to ≈100% by 98 h with UA remaining stable thereafter (all post-peak p > 0.05). Both strains were γ-hemolytic, retained ~103 CFU/mL after 3 h at pH 2.0, and maintained >94% survival at 0–0.30% (w/v) bile, satisfying FAO/WHO preselection criteria. Novelty: TCQ1 and TCQ3 are the first UA-producing LAB from Chinese Qu starter cultures and represent the first characterisation of EA-to-UA conversion by food-environment LAB isolates using pure EA under defined conditions. TCQ3 is only the second L. fermentum strain with confirmed UA-producing capacity. Both strains serve as research platforms for enzymatic pathway investigation and UA-enriched fermented food development. Keywords: Urolithin A, Lactic Acid Bacteria, Ellagic Acid, Biotransformation, Qu Starter Cultures, Probiotic Screening

Background: A general assumption is technological progress will result in linear improvements in manufacturing performance. This assumption however, does not take into account the contextual realities of Micro, Small, and Medium Enterprises (MSMEs), such as fragmented workflows, limited absorptive capacity, and the coexistence of both legacy and modern technologies under the same roof. These limitations impede the efficacy of technology upgrades in improving output diversity and quality. Objectives: This study aims to examine whether the interaction between manufacturing process type and technology class influences output performance in MSME auto component enterprise. It introduces the Dynamic Alignment-Centric Manufacturing (DACM) framework to conceptualise strategic fit as a dynamic, bottom-up capability shaped by contextual intelligence, process-technology coherence, and modular responsiveness. Methods: This study utilises primary data collected from 52 MSMEs operating within the Chennai auto components cluster using structured surveys and field observations conducted in 2025. Enterprises were selected using stratified purposive sampling to capture the diversity in process type and technology configurations. Moderation analysis was conducted using linear regression. We utilised path analysis to visually represent the moderation structure and estimate directional relationships. This was followed by slope analysis to interpret the conditional effect across levels of technological sophistication. Flow diagrams, interaction plots, and comparative tables were used to present the results. Findings: The study found that neither manufacturing process nor technological sophistication independently predicts output quality or diversity. But their interaction exhibits a significant negative moderation effect, suggesting misalignment can lead to diminished outcomes. The moderation effect was statistically significant, which shows that strategic alignment between process logic and technology class is critical. The slope analysis revealed that advanced technologies may dampen the positive impact of process complexity on output if not properly aligned. These findings confirm the architectural pillars of DACM (contextual intelligence, process-technology coherence, and modular responsiveness) and highlight the need for operator-led adaptation and modular responsiveness in MSMEs. Significance: This study extends the strategic alignment theory to MSME shopfloor realities, offering DACM as a grounded alternative to top-down models of smart manufacturing. In contrast to previous studies that focus on technology adoption in isolation, this study integrates process logic, human judgment, and demand variability to explain performance outcomes. It provides actionable insights for policy and practice, recommending that technology interventions be complemented by workflow design, operator training, and scalable integration strategies to enhance absorptive capacity and enterprise agility. Keywords: MSME, auto component manufacturing, manufacturing workflow, output diversity, moderation analysis, strategic alignment, process-technology alignment

Objectives: To propose a reliable and high-precision deep learning-based PCOS detection model using clinical text data in order to maximize diagnostic accuracy, minimize false predictions and clinician dependency. The model improves robustness under class imbalance and enables scalable, real-time deployment as a support system across diverse healthcare environments. Methods: A Causal-Contrastive Tabular Transformer with uncertainty-calibrated learning (C2T-UCL) is employed to learn clinically valid feature interactions and latent symptom dependencies among heterogeneous text features for accurate PCOS detection and to ensure reliable, low-error and confidence-aware predictions suitable for real-time decision support. The text attributes are encoded as tabular tokens and optimized using supervised contrastive and focal uncertainty losses. Three quantitative PCOS text datasets are extracted from the Kaggle and Bio-GPS repositories, which comprise 285, 250 and 541 patient records with PCOS and non-PCOS labels, and are utilized for training, testing and validation. The proposed model is assessed using Python, Scikit-learn, PyTorch, and Google Colab, and the results are compared against the baseline models such as Re-PSO, SVM-ICA, and DBMM-KPCA. Findings: The proposed C2T-UCL model demonstrates consistent performance with promising results across all three benchmarked datasets with 99.3% prediction accuracy, 99.2% validation accuracy, 99.6% precision, 98.8% F1-score, and a reduced RMSE of 0.6%, which indicates superior generalization and a stable PCOS diagnostic support system. Novelty: This research integrates a unique combination of casual-guided attention approach with supervised contrastive separation and uncertainty within a tabular transformer for efficient PCOS detection. The proposed C2T-UCL model directly learns complex feature interactions from structured text input data, which enables accurate and reliable screening process and helps practical clinical workflows for timely, cost-effective, resource-constrained and high-volume diagnostic settings, making it a promising solution for scalable PCOS diagnosis. Keywords: PCOS Detection, Deep Learning, Tabular Transformer, Decision Support System, High-Precision System, Diagnostic Model

Background/Objectives: Integral transforms and iterative techniques play a central role in the theory and solution of integral equations. In this work, generalized special functions, namely Prefunctions and Extended Prefunctions are used to obtain exact and approximate solutions of Volterra integral equations of the second kind. These functions are parameterized by 𝛼 and extend the analytic structure of exponential, hyperbolic and trigonometric functions. These functions are effective tools in solving differential and integral equations. Methods: In this study successive approximations method is used to derive closed-form solutions for two classes of Volterra integral equations with different kernels. For the kernel 𝐾(𝑥, 𝑡) = 𝑥 − 𝑡 with 𝑓 (𝑥, 𝛼) = 𝑝𝑠𝑖𝑛(𝑥, 𝛼) the iterative sequence yields a generalized solution expressed as a series of shifted Prefunctions, reducing to 𝑦(𝑥, 0) = 1 2 (𝑝𝑠𝑖𝑛ℎ(𝑥) + 𝑠𝑖𝑛(𝑥)) for 𝛼 =0 confirming the consistency of the method with known classical results. For the convolution kernel 𝐾(𝑥, 𝑡) = 𝑒−(𝑥−𝑠) with 𝑓(𝑥, 𝛼) = 𝑝𝑠𝑖𝑛ℎ(𝑥, 𝛼) the Picard iteration process leads to the compact representation 𝑦(𝑥, 𝛼) = 𝑝𝑠𝑖𝑛ℎ(𝑥, 𝛼) + 𝑠𝑖𝑛(𝑥, 𝛼 + 1). Findings: Numerical illustrations and graphical results confirm uniform convergence of the successive approximations to the exact solution for various values of 𝛼. For a large value of 𝛼 convergence rate is higher. Novelty: Prefunction -based methods provide generalized frameworks for solving Volterra integral equations and open further directions for applications in mathematical physics and engineering models. 2020 Mathematics Subject Classification: 45Dxx, 34A12. Keywords: Integral Equations; Prefunctions; Successive Approximation Method; Convergence; Picard Iterates

Objectives: This study aims to conduct HPLC profiling of the stem, root and callus extracts of C. stalagmifera in order to identify, evaluate and compare their bioactive phytoconstituents, particularly pregnane glycosides, across both natural and in vitro derived plant callus. Method: In this study, the stem, root and callus tissues of C. stalagmifera were collected, dried, powdered, and extracted using suitable solvents. The extracts were then analyzed using High Performance Liquid Chromatography (HPLC). A standard reference compound (epoxy- polyoxypregnane glycosides) was used for comparison. The chromatographic profiles obtained from the stem, root and callus extracts were examined and compared based on their retention times and peak intensities to identify common and unique bioactive phytoconstituents and to evaluate similarities between natural and in vitro samples. Findings: The HPLC analysis revealed distinct chromatographic profiles for the stem, root and callus extracts of C. stalagmifera, confirming the presence of bioactive pregnane glycosides across all samples. This was evidenced by a prominent peak in the standard compound at a retention time of 3.521 minutes, which closely matched the major peaks observed in the stem (3.562 min) and root (3.540 min) extracts. Furthermore, variations in peak intensity and the presence of minor peaks indicated differential metabolite accumulation influenced by tissue type and physiological state. These results confirm that tissue culture techniques have potential for biotechnological applications and providing a sustainable alternative to wild plant harvesting. Novelty: This is one of the first comparative HPLC studies on C. stalagmifera, simultaneously analyzing stem, root and callus extracts. It demonstrates that in vitro callus cultures can biosynthesize bioactive pregnane glycosides, similar to those found in natural tissues. The study provides a unique phytochemical fingerprint of Caralluma stalagmifera which is useful for species authentication and standardization. Keywords: Caralluma stalagmifera, Callus, HPLC analysis, Bioactive Compounds, Pregnane glycosides

Objectives: The objective of this study was to develop an eco-friendly and multifunctional Cu5V2O10 nanomaterial capable of addressing water pollution and electrochemical sensing challenges. The work aimed to synthesize CuVO nanoparticles using a green combustion method, evaluate their structural and optical properties, and explore their applicability in L-ascorbic acid sensing and photocatalytic dye degradation. Methods: Cu5V2O10 nanoparticles were synthesized via a green solution combustion route using Salvia hispanica seed powder as a bio-fuel, along with copper nitrate and ammonium metavanadate. The synthesis involved heating at 500 °C for 15 minutes followed by calcination at 600 °C for 3 hours. The nanoparticles were characterized using XRD, FTIR, PL, SEM, and UV-Vis spectroscopy. Electrochemical performance was investigated by modifying a glassy carbon electrode and studying its behavior using EIS, CV, and LSV. Photocatalytic activity was evaluated through the degradation of Rose Bengal dye under varying experimental conditions. Findings: XRD confirmed the crystalline nature of Cu5V2O10 nanoparticles, while SEM revealed porous, rod-like aggregated morphologies. Electrochemical studies showed enhanced electron transfer and effective detection of L-ascorbic acid, with CV and LSV responses increasing with scan rate and analyte concentration. The CuVO nanoparticles also exhibited excellent photocatalytic performance, achieving up to 98% degradation of Rose Bengal dye, with catalyst dosage, pH, and dye concentration playing significant roles. Novelty: This study reports, for the first time, a Salvia hispanica seed powder–assisted green solution combustion route for the synthesis of phase-pure Cu5V2O10 nanoparticles, eliminating the need for toxic fuels or surfactants. Beyond synthesis, the work uniquely establishes a single Cu5V2O10 nanomaterial platform exhibiting dual functionality as both a highly sensitive electrochemical sensor and an efficient visible-light-driven photocatalyst. The synergistic enhancement in charge-transfer kinetics and photocatalytic activity, arising from the bio-assisted nanostructure and optimized crystallinity, leads to superior sensing performance and accelerated dye degradation compared to previously reported CuVO systems. This integrated demonstration of eco-friendly synthesis coupled with multifunctional environmental and electrochemical applications constitutes the key scientific advancement of the present work. Keywords: Electrochemical sensing, Ascorbic acid, Rose Bengal, Photocatalysis, Impedance

Objectives: To compare the thermal performance of vertical absorber solar air heaters (SAHs) designed with and without finned extensions for different air flow rates (AFRs). Methods: Experimental studies were conducted between 09:00 and 16:00 in October 2024 in the Vile Parle, Mumbai, Maharashtra (19.0968°N, 72.8517°E) with four AFRs (0.0383-0.06 kg/s). Change in the temperatures, thermal efficiency and useful energy gain were measured during the experimentation. Findings: Vertical absorber with fins consistently outperformed unfinned design. Finned solar air heater achieved peak thermal efficiency of 62 % at 0.045-0.05 kg/s as compared to 50 % for unfinned SAH at 0.06 kg/s. The daily average useful power was raised to 154‛ W/unit of unfinned to 222 W/unit of finned. Finned designs provided high peak efficiencies throughout the midday and also provided high performance well in the late afternoon, especially in high flow rate. These results support the hypothesis that the geometry of absorbers has a significant impact on the heat transfer performance and energy gained. The findings are in line with literature available and shed light on the best adjustments in the flow rate and efficiency of vertical absorber systems. Novelty: The introduction of vertical absorber along with reflective glazing shows that it is possible to improve the efficiency of solar air heaters and increase the amount of energy gained by the thermal system that operate on a sustainable basis with the help of pragmatic use of fins. Keywords: Vertical absorber solar air heater, thermal enhancement, renewable energy, heat transfer enhancement