Science Essence Journal

SCOBY-Fermented Citrus x aurantium L. Peel with Enhanced Antioxidant and Cholesterol-Lowering Effects

M. Rifqi Efendi — 2025-11-26

Citrus peels are rich sources of phenolic and flavonoid compounds with antioxidant and lipid-lowering effects, yet the bioactive profile of Citrus x aurantium L. (Gerga), a local Indonesian cultivar, has not been thoroughly investigated. Fermentation with a symbiotic culture of bacteria and yeast (SCOBY) offers a promising strategy to biotransform phytochemicals and enhance their functional potential. This study compared the total phenolic content (TPC), total flavonoid content (TFC), antioxidant activity, and antihypercholesterolemic effect of C. x aurantium peel infusion and its SCOBY-fermented product. TPC and TFC were quantified spectrophotometrically. Antioxidant activity was evaluated using the DPPH radical scavenging assay, and cholesterol-lowering efficacy was assessed in hypercholesterolemic mice. Data were analyzed using an independent t-test for total phenolic content (TPC) and total flavonoid content (TFC), while lipid-lowering efficacy was evaluated using one-way ANOVA. Fermentation significantly increased TPC (26.5±0.01 to 42.9±0.03 mg GAE/g) and TFC (20.9±0.05 to 32.7±0.03 mg QE/g) (p < 0.05). The SCOBY-fermented product also exhibited stronger antioxidant activity (IC₅₀ = 11.98 vs. 14.64 mg/mL) and a greater reduction in serum total cholesterol compared with C. x aurantium peel infusion (29.84±1.03% vs. 27.71±0.25%). These findings indicate that SCOBY fermentation enhances the phenolic and flavonoid content, antioxidant potential, and antihypercholesterolemic activity of C. x aurantium peel infusion. This bioprocessing approach may provide a sustainable strategy for developing functional beverages from citrus by-products.

MES-Driven Digitalization in Automotive Stamping Industry: A Case Study of Tandem Press Line

Piyush Dipak Bachhav — 2026-01-05

The rapid evolution of smart manufacturing technologies has made Manufacturing Execution Systems (MES) central to improving efficiency, traceability, and quality in discrete manufacturing. This work investigates the end-to-end implementation of an MES solution in a newly established automotive stamping facility featuring a Tandem Press line. The objective is to explore how MES can be embedded from the initial design phase to achieve operational excellence in a green field manufacturing environment. A structured case study methodology was adopted, encompassing tandem press line layout design, MES system selection, infrastructure planning, digital workflow mapping, operator training, and real-time data integration with press automation systems. Performance metrics such as Overall Equipment Effectiveness (OEE), traceability, changeover time, and quality rate were defined during commissioning and monitored for seven months post-deployment. The MES implementation led to early stabilization of production parameters, with an OEE ramp-up from 47.17% in Month 1 (Sep. 2024) to 72.36% by Month 7 (Mar. 2025). Real-time visibility enabled a 37.50% reduction in changeover time and defect rate reduced from by 8.22% to 1.93%. Full digital traceability was achieved across material, machine, and operator layers from the first batch onward. The findings offer a replicable digital blueprint for MES integration in green field projects across the automotive stamping sector and other high-volume manufacturing domains. MES, when integrated from inception, transforms plant commissioning from a sequential execution into a data-driven optimization loop, accelerating productivity, standardization, and digital maturity from Day One.

Computational Analysis of FDA-Approved Drugs for Potential Repurposing in Alzheimer's Disease: Targeting mTOR and NGFR Pathways

Shisanupong Anukanon — 2026-01-05

Alzheimer's disease (AD) remains a significant unmet medical challenge. This study investigates the repurposing of FDA-approved drugs for AD using computational methods. From 4,046 screened drugs, 341 candidates were retained based on pharmacokinetic criteria, including blood–brain barrier permeability and gastrointestinal absorption. Molecular docking identified nandrolone phenylpropionate, atovaquone, and cholecalciferol as top candidates for mTOR, and nandrolone phenylpropionate, ethynodiol diacetate, and drospirenone for p75 neurotrophin receptor (p75NTR). Molecular dynamics simulations assessed the stability of these protein-ligand complexes, revealing that atovaquone and ethynodiol diacetate exhibited the highest stability with mTOR and p75NTR, respectively. Despite the promising binding properties of steroid-based drugs, their systemic side effects necessitate further structural modifications. This study demonstrates the feasibility of drug repurposing for AD and underscores the importance of computational approaches in accelerating the discovery of new therapeutic options.

A Unified Bayesian Framework for Accurate, Fair, and Uncertainty-Calibrated Healthcare Insurance Pricing

Kanda Sorn-In — 2026-01-19

The integration of artificial intelligence (AI) into healthcare insurance pricing requires models that are not only accurate but also transparent, fair, and uncertainty-aware. This study introduces a unified ensemble Bayesian deep learning framework that combines Monte Carlo dropout, attention mechanisms, and residual connections to jointly optimize predictive accuracy, calibrated uncertainty quantification, and demographic fairness. Using the Kaggle medical insurance dataset (n = 2,772), the proposed model achieved R² = 0.8924 and MAE = $2,156.73, outperforming established machine learning and deep learning baselines. The Bayesian approach yielded well-calibrated prediction intervals (95% PICP = 96.2%), improving coverage by 4.1% relative to residual-based methods. Fairness evaluation, measured at the 75^th-percentile cost threshold, demonstrated a 57.4% reduction in demographic parity difference compared with XGBoost (0.079 vs. 0.1859), with equalized odds differences below 0.043 across gender, age, and region. SHAP and attention analyses confirmed smoking status (~47%) and BMI as dominant predictors, consistent with established clinical-economic evidence, while protected attributes exerted negligible influence. These results demonstrate that predictive accuracy, uncertainty calibration, and fairness can be co-optimized within a reproducible and auditable workflow. However, because the study relies on a modest, U.S.-only benchmark dataset with no clinical variables, the findings should be interpreted as a regulator-aligned proof of concept rather than a deployable regulatory solution. The framework illustrates the methodological components required for responsible AI in insurance pricing, while underscoring the need for temporal validation, external generalization assessment, and richer, multi-institutional datasets before real-world regulatory adoption.

Biochar as a Sustainable Additive: Performance Evaluation in Stone Matrix Asphalt and Bituminous Concrete Mix Designs

Ramu Penki — 2026-01-19

This study investigates coconut shell biochar as a full replacement for mineral filler in Stone Matrix Asphalt (SMA, 9%) and Bituminous Concrete (BC, 2%), designed in accordance with Indian Roads Congress and MoRTH standards. Performance was assessed using indirect tensile strength (ITS), Marshall stability, resilient modulus, rutting, and moisture susceptibility tests, with statistical analysis confirming the significance of observed variations. BC mixes retained tensile strength above 93% under freeze–thaw and 98% under humid conditioning, while SMA remained above the 80% threshold. Rut depths were within permissible limits for both mixes (11 mm for BC; 13 mm for SMA after 20,000 passes). Binder-level analyses (FTIR and SEM) confirmed improved bitumen–biochar interactions at 10–15% replacement, explaining the observed mechanical improvements and BC’s superior performance. Although stone dust provided marginally higher strength, biochar demonstrated strong technical feasibility along with added environmental benefits by valorising agricultural waste. Future studies should focus on fatigue testing, long-term field trials, and life-cycle assessment, where IoT-based bitumen fume monitoring can provide real-time emissions data to strengthen sustainability evaluations.