Search Results (4)

Worldwide, populations face issues related to water and energy consumption. Water scarcity has intensified globally, particularly in arid and semiarid regions. Projections indicate that by 2030, global water demand will rise by 50%, leading to critical shortages, further intensified by the impacts of climate change. Moreover, wastewater treatment needs further development, given the presence of persistent organic pollutants, such as dyes and pharmaceuticals. In addition, the continuous increase in energy demand and rising prices directly impact households and businesses, highlighting the importance of energy savings through effective building insulation. In this regard, tannin-furanic foams are recognized as promising sustainable foams due to their fire resistance, low thermal conductivity, and high water and chemical stability. In this study, tannin and lignin rigid foams were explored not only for their traditional applications but also as versatile materials suitable for wastewater treatment. Furthermore, a systematic approach demonstrates the complete replacement of the tannin-furan foam phenol source with two lignins that mainly differ in molecular weight and pH, as well as how these parameters affect the rigid foam structure and methylene blue (MB) removal capacity. Alkali-lignin-based foams exhibited notable MB adsorption capacity (220 mg g⁻¹), with kinetic and equilibrium data analysis suggesting a multilayer adsorption process. The prepared foams demonstrated the ability to be recycled for at least five adsorption-desorption cycles and exhibited effective flame retardant properties. When exposed to a butane flame for 5 min, the foams did not release smoke or ignite, nor did they contribute to flame propagation, with the red glow dissipating only 20 s after flame exposure. Full article

The term “differently-abled” refers to people with mobility difficulties, including the disabled and the elderly. In order to explore the optimal evacuation efficiency, in emergencies, of different floors in multi-layered buildings where differently-abled people reside, this study has established a mixed evacuation model based on the characteristics of the evacuation behavior of differently-abled people and non-differently-abled people. This model simulated the impact of evacuation strategies on different floors for differently-abled people at various assistance ratios. Through the comparative analysis of various evacuation strategies, an evacuation efficiency analysis model was constructed, which is suitable for multi-layered buildings where differently-abled people reside. The research indicates that, for stair-determined evacuation strategies, when the proportion of assisting personnel exceeds 70%, there is a noticeable improvement in overall evacuation efficiency. For elevator-determined evacuation strategies, evacuating middle floors with unrestricted methods can enhance evacuation efficiency. The analysis model for optimal evacuation efficiency on each floor that is presented in this study, using a five-story building as an example, can clearly and accurately determine evacuation strategies for multi-layered buildings where differently-abled people reside. Full article

In the past few years, educational data mining (EDM) has attracted the attention of researchers to enhance the quality of education. Predicting student academic performance is crucial to improving the value of education. Some research studies have been conducted which mainly focused on prediction of students’ performance at higher education. However, research related to performance prediction at the secondary level is scarce, whereas the secondary level tends to be a benchmark to describe students’ learning progress at further educational levels. Students’ failure or poor grades at lower secondary negatively impact them at the higher secondary level. Therefore, early prediction of performance is vital to keep students on a progressive track. This research intended to determine the critical factors that affect the performance of students at the secondary level and to build an efficient classification model through the fusion of single and ensemble-based classifiers for the prediction of academic performance. Firstly, three single classifiers including a Multilayer Perceptron (MLP), J48, and PART were observed along with three well-established ensemble algorithms encompassing Bagging (BAG), MultiBoost (MB), and Voting (VT) independently. To further enhance the performance of the abovementioned classifiers, nine other models were developed by the fusion of single and ensemble-based classifiers. The evaluation results showed that MultiBoost with MLP outperformed the others by achieving 98.7% accuracy, 98.6% precision, recall, and F-score. The study implies that the proposed model could be useful in identifying the academic performance of secondary level students at an early stage to improve the learning outcomes. Full article

Smart grid technology based on renewable energy and energy storage systems are attracting considerable attention towards energy crises. Accurate and reliable model for electricity prediction is considered a key factor for a suitable energy management policy. Currently, electricity consumption is rapidly increasing due to the rise in human population and technology development. Therefore, in this study, we established a two-step methodology for residential building load prediction, which comprises two stages: in the first stage, the raw data of electricity consumption are refined for effective training; and the second step includes a hybrid model with the integration of convolutional neural network (CNN) and multilayer bidirectional gated recurrent unit (MB-GRU). The CNN layers are incorporated into the model as a feature extractor, while MB-GRU learns the sequences between electricity consumption data. The proposed model is evaluated using the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) metrics. Finally, our model is assessed over benchmark datasets that exhibited an extensive drop in the error rate in comparison to other techniques. The results indicated that the proposed model reduced errors over the individual household electricity consumption prediction (IHEPC) dataset (i.e., RMSE (5%), MSE (4%), and MAE (4%)), and for the appliances load prediction (AEP) dataset (i.e., RMSE (2%), and MAE (1%)). Full article