Volume 7.
Issue 01.

Seaweed has emerged as a promising resource in the quest for renewable energy. This study explores the potential of residual carrageenan extract from Eucheuma cottonii as a sustainable feedstock for bioethanol production. Carrageenan, a polysaccharide widely used in food and industrial applications, leaves significant residues after extraction. These residues are rich in fermentable sugars, making them an ideal candidate for bioethanol synthesis. The research investigates the optimal conditions for hydrolysis and fermentation processes to maximize ethanol yield. Results demonstrate that carrageenan residues can produce bioethanol efficiently, presenting an innovative solution for utilizing waste from the seaweed industry. This approach contributes to sustainable energy production while addressing environmental concerns related to seaweed waste.

This study investigates the application of machine learning algorithms for predictive analytics in credit risk management, aiming to enhance the accuracy of predicting credit defaults. The research compares multiple machine learning models, including logistic regression, decision trees, random forests, gradient boosting, XGBoost, and LightGBM, using a real-world credit risk dataset. The study focuses on evaluating the models' performance based on metrics such as accuracy, precision, recall, and F1-score. The results show that ensemble models, particularly XGBoost and LightGBM, outperform traditional algorithms in terms of predictive accuracy and computational efficiency, demonstrating their ability to effectively handle complex datasets. The comparative analysis highlights the strengths and weaknesses of each model, providing insights into the trade-offs between interpretability and predictive power. XGBoost and LightGBM are found to be highly effective for credit risk prediction, though challenges such as model interpretability and overfitting remain. The findings suggest that machine learning offers a promising approach for improving credit risk management, with implications for the financial industry to make more informed, data-driven lending decisions. The study underscores the importance of addressing interpretability concerns and data quality issues in real-world applications, paving the way for future advancements in machine learning for credit risk prediction. 

Sentiment analysis is a powerful tool for transforming consumer feedback into actionable insights, enabling businesses to refine strategies and improve customer experiences. This study evaluates the performance of machine learning models, including Logistic Regression, Random Forest, SVM, LSTM, and BERT, for sentiment classification on a diverse dataset of customer reviews. BERT outperformed other models, achieving an AUC-ROC of 0.97 and an accuracy of 94.2%, showcasing its ability to capture complex semantic patterns in text. The findings provide businesses with critical insights into consumer sentiment, guiding decision-making and enhancing competitive advantage. The study also addresses challenges such as data ambiguity, ethical considerations, and computational demands, offering practical recommendations for implementing scalable and effective sentiment analysis solutions. These results demonstrate the potential of machine learning-driven sentiment analysis in shaping customer-focused business strategies and fostering growth in a data-driven market.

We test whether the tone of free-text employee reviews, decomposed into specific human-resource (HR) aspects, anticipates near-term deterioration in a firm’s standing on the same review platform. Using 833,760 Glassdoor reviews of 332 large employers (2008–2021), we measure sentiment on five aspects—management, work–life balance, compensation, culture, and career growth—with a transparent, reproducible lexicon-based aspect sentiment procedure, and we validate this text signal against the platform’s own human-assigned sub-ratings (Pearson r between 0.42 and 0.52). In a firm–month panel with two-way (firm and calendar-month) fixed effects and firm-clustered standard errors, more negative management, culture, and career-growth sentiment in month t−1 is associated with a significantly higher probability of a firm-level rating decline in month t. The association is statistically robust but economically modest (within-R² ≈ 0.027), and—importantly—it is short-horizon: at a three-month lead the predictive content essentially vanishes (within-R² ≈ 0.000; joint F not significant). An event study around 703 distress onsets shows aspect sentiment dipping roughly one to two months before the rating trough, and Granger-precedence tests support a lead for a majority (14 of 25) of long-series firms. We deliberately frame the outcome as an internal platform signal rather than a hard external event (e.g., layoffs): the canonical external layoffs dataset does not overlap the review window in time, precluding external validation here. Results should therefore be read as evidence that aspect sentiment leads a soft, platform-internal distress proxy at a short horizon, not as a validated predictor of corporate restructuring.

Perfect numbers, one of the fundamental concepts of mathematics have been focus of mathematicians attention science ancient times. This article discusses the concept of perfect numbers, their identification formulas, the Euclid and Euler approaches, as well as of the most debated problems the existence of odd perfect numbers.