Growing concerns about environmental pollution and climate change are driving the use and research of renewable energy sources. One possible solution is the use of biofuel derived from agricultural waste. Corn is one of the primary crops grown in the agricultural sector, generating large amounts of waste after harvesting and industrial use. This study focuses on the drying of corncobs, the evaluation of their properties before densification into pellets, the densification process, and the assessment of their suitability for energy needs.

The research found no significant difference between drying with active ventilation in a dryer and drying under outdoor conditions. The optimal moisture content for the pellets is 12.39%, with a compression coefficient of 3.43±0.011, and the highest pellet density of 1012.96 ±3.35 kg/m³. The change in pellet density at optimal moisture content is minimal at 0.78%. The compression coefficient of pellets produced using a granulator with a horizontal matrix is 9.75% higher than those made with a laboratory automatic press. The lower heating value of corncobs is 17.35 ± 0.14 MJ/kg, with an ash content of 1.78 ± 0.24%. The produced pellets are sufficiently durable and suitable for combustion. This study can help better understand the properties of corncobs and their potential in the energy sector. By mastering preparation techniques and optimizing raw material moisture during compaction, as well as through ongoing research, biofuel preparation technologies can be refined to enhance efficiency, reduce production costs, and minimize environmental impact.

Sikkim, nestled in the North-Eastern Himalayas, is a region frequently disturbed by earthquakes and are triggered by a myriad of factors, including tectonic plate movements, volcanic activities, subterranean explosions, human-induced quakes, and environmental conditions. This leads to loss of human lives, destruction of human properties and deformation of earth crust plates. The study aims to refine earthquake vulnerability assessment by incorporating Machine Learning Weighted Overlay methods over traditional Analytical Hierarchy Process (AHP) techniques. Unlike AHP, where users assign weightage based on their knowledge, ML models use feature importance for objective weightage assignment. A comprehensive set of parameters—fault characteristics, peak ground acceleration, earthquake magnitude, proximity, slope, and elevation which previous studies were somewhere lacking in their study. These parameters are weighted and superimposed using the AHP method. We used Google Earth Engine (GEE) to facilitates the extraction of weighted stacked images, which are then analyzed to pinpoint earthquake-prone locations via machine learning models, namely Random Forest and SmileCart classification. The earthquake vulnerability zones are stratified into five distinct categories: Very High (4.54%), High (28.37%), Medium (27.84%), Low (23.60%), and Very Low (15.65%). The Random Forest and SmileCart models outperformed the AHP method, yielding accuracies of 0.89 and 0.78, respectively, compared to 0.57 for the AHP approach by the validation points and ROC Curve/AUC values corroborate these findings, with respective scores of 0.71, 0.75, and 0.60. The integration of advanced machine learning algorithms over conventional AHP methods significantly enhances the precision of seismic susceptibility estimations. This synergy illustrates the potential of modern analytical techniques in the realm of natural disaster risk evaluation.

There is a strong trend in the global food industry towards using plant-based ingredients in food technologies. The aim of this study was to develop ten o/w emulsions with a sunflower oil content of 30 and 60 % using bean aquafaba as an emulsifier. The emulsions were stabilized by increasing their viscosity. For this purpose, xanthan gum or cold-soluble corn starch were added as thickeners. The effects of oil content and different thickeners on the microstructure and rheological properties were evaluated using laser diffraction and rotational viscometry. A pre-optimised water to seed ratio of 1.5:1 resulted in a bean aquafaba with a low protein content of 0.5%. Further evaporation was used to increase the protein content to 0.8% w/w. The aquafaba-based emulsion samples were characterized by a bimodal droplet size distribution with peaks at approximately 2.8 and 10.5 μm, with the exception of corn starch systems. Increasing both the oil and xanthan gum content had little effect on the change in the mean volume diameter of the emulsion droplets in the range of 6-8 μm, while adding corn starch increased this value. All emulsions were characterized by pseudoplastic flow behavior. The flow curves were approximated using the power-law and Hershley—Barkley models. The calculated dynamic yield shear stresses consistently increased with increasing content of both oil and thickener in the range of 0.3 to 5.0 Pa. It is worth noting that in emulsions with an oil content of 30%, the addition of xanthan gum had a significant impact on this indicator, while in emulsions with an oil content of 60%, the addition of corn starch did. Thus, the higher concentration of the selected polysaccharides resulted in more viscous systems with improved stability. The developed food emulsions based on bean aquafaba are promising precursors in the technology of vegetarian products.

Abstract—As a non-destructive and fast imaging technique, phase shifting digital holography (PSDH) has been widely applied in many optical imaging problems including measuring the surface morphology and cell refractive index. In this paper, it was found that PSDH is not stable w.r.t interference intensity in the noisy setting. Moreover, it was unveiled that PSDH is a linear problem w.r.t the intensity difference.

Based on this, for the first time we establish the numerical stability w.r.t the intensity difference. It should be noted that the used phase shiftings are nonuniform, which brings great flexibility for the phase-shifting. Numerical simulations are conducted to verify the stability result. For the traditional PSDH, the phase shifting must be calibrated and controlled with a high level ofaccuracy since the calibration error serves as the measurement error .Unlike this, PSDH from nonuniform phase-shiftings does not requiresuch a high level of calibration accuracy and provides great flexibility for the phase-shiftings. Incidentally, the nonuniform phase shiftings are commonly utilized in transmission electronimaging . To the best of our knowledge, the numerical stability remains unexplored in the literatureespecially for generalized PSDH from nonuniform phase shiftings. Finally, we check the stability result by numerical simulations. We verified that Phase-shifting digital holography has good stability.

Context: Heart disease is considered to be the leading cause of death around the world. Accurately predicting heart disease early is one of the most challenging tasks of the 21st century. Not only is accurate prediction important, but taking necessary precautions is also crucial. In this era, heart disease is regarded as the most prominent source of illness and death globally.

Objectives: The objective of this article is to predict heart disease early using IoT sensory data and an XAI framework. In this article, we have developed a framework that includes the integration of IoT sensors for real-time monitoring of patient data. The machine learning models used to analyze the sensory data utilize XAI to ensure the interpretability and transparency of these models' predictions.

Materials/Methods: This article aims to predict heart disease early using IoT sensory data and an XAI framework. We developed a framework integrating IoT sensors for real-time patient data monitoring. To analyze and predict these data, we used machine learning and deep learning algorithms. XAI techniques such as SHAP and LIME were applied to ensure model prediction interpretability and transparency.

Results: In this article, we employed machine learning and deep learning algorithms to predict heart disease early. The ML algorithms used were LOGR and SVM, while the DL algorithms were RNN and LSTM. It was observed that Support Vector Machine achieved a high accuracy rate of 97%, compared to other classifiers.

Introduction:
The quest for sustainable energy solutions amidst environmental concerns has led to the emergence of innovative approaches integrating renewable energy with earth science principles. This research article explores the novel concept of Earth-Integrated Renewable Energy Systems (EIRENS), highlighting their potential to revolutionize energy production while mitigating environmental impacts.

Methods:
A comprehensive synthesis of recent advancements and cutting-edge research in renewable energy and earth science was conducted. Key methodologies, including geothermal heat extraction, underground pumped hydro storage, and enhanced geothermal systems, were explored to demonstrate the feasibility and sustainability of EIRENS.

Results and Discussion:
The findings underscore the transformative potential of EIRENS in addressing energy challenges while minimizing environmental footprints. By harnessing the Earth's natural resources, such as geothermal energy and subsurface storage, EIRENS offer reliable, dispatchable power generation with reduced greenhouse gas emissions and land use impacts compared to conventional renewable energy systems.

Conclusions:
EIRENS represent a paradigm shift in sustainable energy solutions, offering a holistic approach that integrates renewable energy generation with earth science principles. By leveraging the Earth's geological features and natural energy reservoirs, EIRENS can provide clean, reliable power while mitigating environmental impacts and promoting energy security. This research article emphasizes the importance of advancing EIRENS as a viable pathway towards a greener and more sustainable future.

Artificial intelligence is already widely used in education in at least the following areas: (1) intelligent tutoring and personalized learning; (2) adaptive assessment of learning outcomes; (3) virtual teachers and teaching assistants; (4) intelligent classrooms and learning environments; and (5) learning diagnostics and academic prediction. Advances in artificial intelligence will continue to drive innovation and improvement in education, providing better learning and teaching experiences. Artificial intelligence is also driving developments in mathematical research. For example, machine learning is used to help mathematicians discover patterns and make conjectures in pure mathematics, such as in the algebraic and geometric structure of knots, and predicate the combinatorial invariance conjecture for symmetric groups, even solving geometric problems in IMO with the DeepMind geometric reasoning model (AlphaGeometry). But AI has not been connected to mathematical problem-posing.

Mathematical problem-posing is a complex intellectual activity that trains students' mathematical creativity and critical thinking. In the age of artificial intelligence, we need to consider how to use generative AI to engage in mathematical problem-posing activities and pose valuable mathematical problems. Therefore, the blueprint of this research is to explore the mathematical problems posed by generative AI. Applying the same mathematical problem-posing task, a paper and pencil test is used for the participants, and some prompts are used for the generative AI. Then, using textual analysis, we analyze and compare the similarities and differences between the problems posed by each. The results demonstrate that the problem-posing products of humans and AI are different, and that there are differences in the number, solvability, clarity, and complexity of the mathematical problems posed by them. The mathematical problems posed by generative AI have unknown characteristics and creativity. This research will be new and imaginative.

Introduction

Motivated by its practical relevance in manufacturing systems, this research investigates the two-machine flow shop scheduling problem (FSSP) with a single transport robot and raw material constraints. This problem is a new extension of the FSSP. The objective of this research is to develop an effective scheduling approach that minimizes the makespan while addressing the complexities arising from the movement of jobs between the two machines and the limited availability of raw materials that are supplied from external suppliers at different time moments.

Methodology

To address the computational complexity of the proposed problem, a customized Particle Swarm Optimization (PSO) approach is suggested for its resolution. Since we are in the context of solving a FSSP, we are looking for the permutation of jobs that minimizes the makespan under the constraints imposed by the transport robots and the raw materials' availability. Hence, in order to customize PSO for a discrete problem, we maintain a job-permutation-based encoding scheme. The swarm is initialized randomly, and the particle positions and velocities are updated using crossover and mutation operators borrowed from Genetic Algorithms (GAs) and guided by the personnel and the best global positions, with mutations applied to prevent stagnation. This approach refines the solutions iteratively, optimizing the job scheduling performance under the considered constraints.

Results

The proposed approach was examined on a series of newly developed benchmarks including various configurations of the resource availability and the transportation times between machines. The results show that the approach achieves makespans close to the optimal values reported by a developed ILP model for small instances and reduces the makespans by 5-10% on medium to large instances compared to the standard GAs.

Conclusions

This study proposed a customized PSO approach that addressed the two-machine FSSP with transport robot and raw material constraints. The results demonstrated that the proposed approach is capable of providing a good performance, particularly in challenging scenarios with multiple constraints.

Introduction: Product recommendation systems are very important in enhancing customer behavior and experiences by suggesting relevant products based on records and preferences. Data mining plays a crucial role in recommending products. Apriori algorithm and clustering techniques are two powerful algorithms of data mining that help enhance the effectiveness and accuracy of these recommendations.

Objectives: The main objective of this paper is to develop enhanced product recommendation systems by using Apriori algorithms, FP-Growth, K-Means, K-Medoids, and Agglomerative Hierarchical Clustering approaches. The robust framework can identify frequent itemsets and discover association rules. It not only generates associations but also creates meaningful clusters to enhance recommendation accuracy.

Materials/Methods: This paper analyzes a transactional dataset using various algorithms including Apriori, FP-Growth, K-Means Clustering, K-Medoids, and Agglomerative Hierarchical Clustering. We also used hybridization among these to make association rules stronger. Association rules are generated based on the metrics (support, confidence, and lift). The Apriori algorithm identified key itemsets such as "Bread," "Cheese," "Milk," "Soda," and "Yogurt" with high support values, which were further clustered into distinct groups. FP-Growth confirmed these findings with additional rules. K-Medoids and Agglomerative Clustering revealed clear cluster formations, with items like "Chips" and "Eggs" forming separate groups due to lower support values.

Conclusion: The hybridization of FP-Growth algorithms with clustering techniques like K-Medoids and Agglomerative Hierarchical Clustering provides effective product recommendations. Frequent itemsets such as "Bread," "Cheese," "Milk," "Soda," and "Yogurt" form Cluster 1 with higher support values, while "Chips" and "Eggs" form Cluster 2 with relatively lower support values. Significant association rules included: [Cheese] → [Bread] with support 0.24, confidence 0.46, and lift 0.88 in Cluster 1. [Eggs] → [Bread] with support 0.30, confidence 0.60, and lift 1.15 in Cluster 2. Not only does it provide recommendations, but it also groups similar items, improving the accuracy and relevance of recommendations.

Drinking water is essential for human survival and the sustainability of all life on Earth. In developing countries like Timor-Leste, government-provided water services are severely limited and face numerous challenges, including low service standards, poor water quality, and insufficient funding. In Timor-Leste, 80% of the population is dependent on agriculture. In Dili, 70% of the water supply comes from groundwater and 30% from surface water. As the population grows, the demand for water increases, affecting both its availability and quality. Efforts are underway to address these issues and improve the safety of water for drinking and other domestic uses. In Dili, 46 boreholes are used for water supply, has been used in this study, but ten are over-exploited and fifteen are unfit due to microbiological contamination, manganese, iron, TDS and hardness of water. Machine learning (ML) has shown promise in solving real-world challenges, including water quality assessment. In this paper, three ML algorithms-K-Nearest Neighbors (KNN), Decision Trees, and Naive Bayes (NB)-are applied to assess the potability of water. Performance evaluation and k-fold cross-validation were used to assess the effectiveness of the models. The KNN algorithm showed the highest accuracy, reaching 97%. Thus, in the future water quality prediction in Timor-Leste is necessary to incorporate environmental data and sewage systems.