Natural Sciences Engineering and Technology Journal

Factorial Design Optimization of Antioxidant Cream from Kepok Banana Peel Extract: Formulation, Characterization, and Stability Evaluation

2025-01-07T06:10:16+00:00

Kepok banana peel, a rich source of flavonoids and phenols with potent antioxidant activity, presents a promising natural ingredient for topical applications. This study aimed to optimize the formulation of an antioxidant cream using kepok banana peel ethanol extract, focusing on the emulsifier combination of stearic acid and triethanolamine (TEA) for enhanced physical properties and stability. A two-factor, two-level factorial design was employed to investigate the effects and interactions of stearic acid and TEA concentrations on the cream's organoleptic characteristics, homogeneity, pH, viscosity, and spreadability. The antioxidant activity of the extract was assessed using the DPPH assay. The optimal formula was determined using Design Expert 13 software with ANOVA at a 95% confidence level. The ethanol extract exhibited strong antioxidant activity (IC50 84.25 ppm). The optimal cream formulation, containing 10 grams of stearic acid and 2 grams of TEA, demonstrated desirable physical properties, including smooth texture, homogenous appearance, and excellent spreadability, meeting the criteria for a stable and effective topical product. In conclusion, Kepok banana peel extract holds significant potential as a natural antioxidant for topical applications. The optimized cream formulation, achieved through factorial design, provides a stable and effective delivery system for harnessing the therapeutic benefits of this natural extract.

Public Health Threat: Detection of Undeclared Dexamethasone and Paracetamol in Jamu Marketed in Kudus, Indonesia

2024-12-16T04:01:17+00:00

Jamu, Indonesia's traditional herbal medicine, holds significant cultural and medicinal value. However, the adulteration of jamu with undeclared synthetic drugs like dexamethasone and paracetamol poses a severe public health risk. This study aimed to qualitatively identify these drugs in jamu products circulating in Kudus City, Indonesia. Ten jamu samples, representing various brands and forms (powder, capsules), were purchased from local depots in Kudus. Samples were selected based on their indication for pain relief or anti-inflammatory properties, lack of BPOM (Indonesia's Food and Drug Authority) registration, or discrepancies between the registration number on the packaging and the BPOM database. Organoleptic analysis (odor, taste, color, form) was performed, followed by microscopic analysis to identify natural ingredients using their characteristic fragments. Finally, infrared spectroscopy was employed to detect the presence of dexamethasone and paracetamol. Nine out of ten samples displayed BPOM registration numbers that were not verifiable in the official BPOM database, while one sample lacked any registration number. Organoleptic analysis revealed that six samples exhibited distinct herbal odors, often associated with bitter-tasting jamu. Microscopic analysis confirmed the presence of 1-4 natural ingredients in each sample. Notably, infrared spectroscopy identified paracetamol in one sample. In conclusion, the study revealed a concerning trend of unregistered jamu products and adulteration with undeclared paracetamol in Kudus City. This highlights the need for stricter regulatory measures, enhanced surveillance, and public awareness campaigns to ensure the safety and efficacy of jamu.

Quality Control of Sunscreen Products: A Validated HPLC Method for the Analysis of Avobenzone and Oxybenzone

2024-12-26T07:49:51+00:00

Avobenzone and oxybenzone are commonly used ultraviolet (UV) filters in sunscreen products, offering broad-spectrum protection against harmful solar radiation. Accurate determination of these compounds in sunscreen formulations is crucial for quality control and ensuring consumer safety. This study aimed to develop and validate a high-performance liquid chromatography (HPLC) method for the simultaneous determination of avobenzone and oxybenzone in commercial sunscreens. The HPLC method utilized a C₁₈ column with a mobile phase of methanol:aquabidest (93:7, v/v) at a flow rate of 1.0 mL/min and a detection wavelength of 320 nm. The method was validated for linearity, sensitivity, selectivity, precision, and accuracy. The validated method was then applied to quantify avobenzone and oxybenzone in three different brands of commercial sunscreen products. The developed HPLC method demonstrated excellent linearity for both avobenzone (r = 0.9998) and oxybenzone (r = 0.9995). The method was also highly sensitive with low limits of detection (LOD) and quantitation (LOQ) for avobenzone (0.13 μg/mL and 0.43 μg/mL, respectively) and oxybenzone (0.35 μg/mL and 1.15 μg/mL, respectively). The method exhibited good selectivity and precision (%RSD ≤ 2%). Accuracy, as determined by recovery experiments, was within the acceptable range (100.01%-100.77% for avobenzone and 99.66%-100.81% for oxybenzone). The levels of avobenzone and oxybenzone in the analyzed sunscreen brands (A, B, and C) were found to be within the regulatory limits. In conclusion, the validated HPLC method provides a reliable and efficient means for the simultaneous quantification of avobenzone and oxybenzone in sunscreen products, contributing to the quality control and safety of these widely used formulations.

Evaluation of Hypochlorite Effectiveness as a Disinfectant Against Aerobic Bacteria

2025-01-08T07:48:11+00:00

Hypochlorite compounds, including calcium hypochlorite (Ca(OCl)₂) and sodium hypochlorite (NaOCl), are widely recognized for their broad-spectrum antimicrobial properties. This study aimed to evaluate the effectiveness of hypochlorite as a disinfectant against aerobic bacteria, providing insights into its application in infection control and water treatment. The study employed the aerobic bacteria number test, also known as the total plate count method, to enumerate bacterial colonies before and after exposure to varying concentrations of hypochlorite solution. Water samples were collected from different sources, including an emergency room floor, a pharmaceutical installation, and a hospital inpatient room. Serial dilutions of the water samples were prepared and plated on nutrient agar, followed by incubation and colony counting to determine the bacterial load. The percentage reduction in bacterial numbers was calculated for each hypochlorite dose. The results demonstrated a significant reduction in bacterial populations following hypochlorite treatment. A dose of 30 mL/5 L (6 μL/mL) reduced the average number of bacteria by 83.29%, A dose of 60 mL/5 L (12 μL/mL) reduced the average number of bacteria by 98.60%, A dose of 120 mL/5 L (24 μL/mL) reduced the average number of bacteria by 99.84%, A dose of 240 mL/5L (48 μL/mL) reduced the average number of bacteria by 99.98%, A dose of 480 mL/5L (96 μL/mL) reduced the average number of bacteria by 100%. The extent of bacterial reduction was directly proportional to the hypochlorite dose, indicating a clear dose-response relationship. In conclusion, this study confirms the efficacy of hypochlorite as a disinfectant against aerobic bacteria. A hypochlorite dose of 480mL/5L (96 μL/mL) effectively achieved complete bacterial elimination under the tested conditions. The results support its use in various applications, including disinfection of surfaces and water purification.

Traditional vs. Tech-Driven: A Comparative Analysis of Service Delivery Models in Line Agencies across Urban and Rural Sulu, Philippines

2024-11-28T02:39:25+00:00

This study investigated the impact of technology on public service delivery in Sulu, Philippines, by comparing traditional and tech-driven models in line agencies across urban and rural settings. The research aimed to identify the benefits, challenges, and factors influencing the adoption and effectiveness of technology in enhancing citizen access, satisfaction, and efficiency. A mixed-methods approach was employed, combining quantitative surveys of citizens (n=300) and government employees (n=150) with qualitative interviews of key stakeholders (n=20) in both urban and rural line agencies. Data analysis included descriptive statistics, comparative analysis, and thematic analysis of interview transcripts. Simulated data was generated based on existing literature and reports to supplement primary data collection where access was limited. Tech-driven service delivery models in urban areas led to increased citizen access, reduced processing times, and improved transparency. However, challenges persisted in rural areas due to limited infrastructure, digital literacy gaps, and cultural preferences for traditional approaches. Factors influencing successful technology adoption included leadership commitment, staff training, community engagement, and ongoing technical support. In conclusion, this study highlights the transformative potential of technology in public service delivery in Sulu while emphasizing the need for context-specific strategies to address the unique challenges in rural communities. Recommendations include targeted investments in infrastructure, digital literacy programs, and culturally sensitive technology integration to ensure equitable access and maximize the benefits of tech-driven service delivery across Sulu.

Predictive Modeling in Cardiovascular Disease: An Investigation of Random Forests

2024-12-03T05:49:40+00:00

Cardiovascular diseases (CVDs) are a leading cause of death worldwide. Early detection and intervention are crucial for improving patient outcomes. Machine learning (ML) offers promising tools for CVD prediction, with random forests (RF) emerging as a robust and versatile algorithm. This study investigates the application of RF in predicting blood pressure categories, a crucial indicator of cardiovascular health, using a comprehensive dataset of patient metrics. This study investigated the application of RF in predicting blood pressure categories, a crucial indicator of cardiovascular health. A meticulously curated dataset from Kaggle, comprising 68,205 records and 17 features, was utilized. Key features such as weight, systolic and diastolic blood pressure (ap_hi, ap_lo), cholesterol, glucose, smoking, alcohol consumption, physical activity, and age were selected for predictive modeling. The RF model was trained and tested using a stratified split, and its performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix. The RF model demonstrated exceptional accuracy in predicting blood pressure categories, achieving an accuracy score of 0.9999. The model also exhibited perfect precision and recall across all categories, indicating its ability to effectively capture complex relationships within the data and make reliable predictions. In conclusion, the findings validate the efficacy of RF as a powerful tool for CVD prediction. Its ability to handle complex interactions and provide accurate predictions underscores its potential to aid healthcare professionals in early diagnosis and personalized intervention strategies. Further research can explore the application of RF in predicting other CVD risk factors and outcomes.

Access Control Mechanisms and Their Role in Preventing Unauthorized Data Access: A Comparative Analysis of RBAC, MFA, and Strong Passwords

2024-12-26T04:55:38+00:00

In today's digital landscape, the protection of sensitive data from unauthorized access is a critical concern for organizations of all sizes. Robust access control mechanisms are essential for maintaining data security and preventing breaches. This study conducted a comparative analysis of three widely used access control methods: Role-Based Access Control (RBAC), Multi-Factor Authentication (MFA), and Strong Passwords. The research employed a mixed-methods approach, combining a quantitative analysis of simulated data with a qualitative review of recent literature. The Access Control Simulation Environment (ACSE) was developed to generate data on the effectiveness of each access control method in preventing unauthorized access attempts. The qualitative component involved a systematic review of Scopus-indexed publications from 2018 to 2024, focusing on the strengths, weaknesses, and best practices associated with each method. The simulation data revealed that MFA provided the highest level of protection against unauthorized access, followed by RBAC and then Strong Passwords. The qualitative analysis identified key strengths and weaknesses of each method, highlighting the importance of contextual factors in selecting the most appropriate access control mechanism. In conclusion, the findings underscore the need for a layered approach to access control, combining multiple methods to achieve optimal security. While MFA offers the strongest protection, RBAC and Strong Passwords remain crucial components of a comprehensive security strategy. The study provides practical recommendations for organizations seeking to implement and optimize access control mechanisms to mitigate the risk of unauthorized data access.

Enhancing Phishing Detection in Sulu, Philippines: A Machine Learning Approach to Combat Evolving Cyber Threats

2025-01-08T02:18:09+00:00

Phishing attacks are a growing threat to individuals and organizations worldwide, and Sulu, Philippines, is no exception. These attacks use deceptive emails, websites, and text messages to trick victims into revealing sensitive information such as login credentials, financial data, and personal details. Machine learning (ML) techniques have emerged as a promising solution for enhancing phishing detection due to their ability to learn patterns and adapt to new threats. This study investigates the effectiveness of ML approaches in enhancing phishing detection in Sulu, Philippines. A comprehensive dataset of phishing and legitimate websites was collected, incorporating features relevant to Sulu's context, such as local e-commerce platforms, government services, and banking institutions. Various ML algorithms, including Random Forest, Support Vector Machine, and Naive Bayes, were trained and evaluated on this dataset. The ML models demonstrated high accuracy in detecting phishing websites. The Random Forest model achieved the highest accuracy of 98.7%, followed by the Support Vector Machine with 96.5% accuracy and the Naive Bayes with 94.2% accuracy. Feature importance analysis revealed that specific features, such as URL structure, domain age, and the presence of login forms, played a crucial role in accurate classification. In conclusion, the findings suggest that ML techniques can significantly enhance phishing detection capabilities in Sulu, Philippines. Implementing these techniques in security solutions can help protect individuals and organizations from falling victim to phishing attacks.