Smart grids and smart homes are getting people's attention in the modern era of smart cities. The advancements of smart technologies and smart grids have created challenges related to energy efficiency and production according to the future demand of clients. Machine learning, specifically neural network-based methods, remained successful in energy consumption prediction, but still, there are gaps due to uncertainty in the data and limitations of the algorithms. Research published in the literature has used small datasets and profiles of primarily single users; therefore, models have difficulties when applied to large datasets with profiles of different customers. Thus, a smart grid environment requires a model that handles consumption data from thousands of customers. The proposed model enhances the newly introduced method of Neural Basis Expansion Analysis for interpretable Time Series (N-BEATS) with a big dataset of energy consumption of 169 customers. Further, to validate the results of the proposed model, a performance comparison has been carried out with the Long Short Term Memory (LSTM), Blocked LSTM, Gated Recurrent Units (GRU), Blocked GRU and Temporal Convolutional Network (TCN). The proposed interpretable model improves the prediction accuracy on the big dataset containing energy consumption profiles of multiple customers. Incorporating covariates into the model improved accuracy by learning past and future energy consumption patterns. Based on a large dataset, the proposed model performed better for daily, weekly, and monthly energy consumption predictions. The forecasting accuracy of the N-BEATS interpretable model for 1-day-ahead energy consumption with "day as covariates" remained better than the 1, 2, 3, and 4-week scenarios.
Background/objectives Dental amalgam has been a successful restoration for over a century. However, restoration failures due to secondary caries, fractured teeth or restorations, marginal deficiencies, tooth wear, and secondary caries remain significant concerns. Amalgam-bond, known for its ability to bond amalgam to the tooth structure and prevent percolation, forms a strong bond with vital dentin. This study aimed to compare the outcome of marginal fractures in bonded amalgam and conventional amalgam posterior restorations among patients at a tertiary care dental hospital. Materials and methods Sixty consecutive patients aged 25-35 years, meeting the inclusion and exclusion criteria, participated in this study. A thorough history, clinical examination, and standardized periapical radiographs were conducted. Patients were divided randomly into two equal groups, group A and group B. Group A received bonded amalgam restorations, while group B received conventional amalgam restorations. Polishing was performed at a recall visit after seven days, and a follow-up evaluation was done after two months. The final assessment of marginal fractures was recorded after six months. Results After six months, 28 (46.7%) patients showed no marginal fractures, including 11 males and 17 females. On the other hand, 32 (53.3%) patients exhibited marginal fractures, comprising 17 males and 15 females. The clinical success rate of group A was better than group B (p = 0.001). Conclusion Bonded amalgam demonstrates a high success rate and should be a routine choice for treating carious permanent molars in dental practice.
Cardiovascular Disease (CVD), a term encompassing various disorders affecting the heart and blood vessels, includes coronary artery disease (CAD). CAD is primarily due to the development of atherosclerotic plaques that disrupt blood flow, oxygenation, and nutrient delivery to the myocardium. Risk factors contributing to CAD progression include smoking, hypertension, diabetes mellitus (DM), dyslipidaemia, and obesity. While aerobic exercise (AE) has shown promising results in controlling CVD risk factors, the impact of resistance training (RT) has not been extensively investigated. This review aims to describe the effects of RT on CVD risk factors based on studies retrieved from PubMed and Google Scholar databases. Both isometric and isotonic RT have been found to decrease systolic blood pressure (SBP), diastolic blood pressure, or mean arterial pressure, with SBP showing a more significant reduction. Hypertensive patients engaging in RT alongside a calorie-restricted diet demonstrated significant improvements in blood pressure. RT is associated with increased nitric oxide bioavailability, sympathetic modulation, and enhanced endothelial function. In type-2 DM patients, 8-12 weeks of RT led to improvements in fasting blood glucose levels, insulin secretion, metabolic syndrome risk, and glucose transporter numbers. Combining AE with RT had a more significant impact in reducing insulin resistance and enhancing blood glucose compared to performing exercises separately. It also significantly decreased total cholesterol, triglycerides, and low-density lipoprotein levels while increasing high-density lipoprotein within 12 weeks of application. However, improvements are considered insignificant when lipid levels are already low to normal at baseline. The administration of RT resulted in weight loss and improved body mass index, with more pronounced effects seen when combining AE with RT and a calorie-restricted diet. Considering these results, the administration of RT, either alone or in combination with AE, proves beneficial in rehabilitating CAD patients by improving various risk factors.
This study critically reviews the recent developments and future opportunities pertinent to the conversion of CO2 as a potent greenhouse gas (GHG) to fuels and valuable products. CO2 emissions have reached an alarming level of around 410 ppm and have become the primary driver of global warming and climate change leading to devastating events such as droughts, hurricanes, torrential rains, floods, tornados and wildfires across the world. These events are responsible for thousands of deaths and have adversely affected the economic development of many countries, loss of billions of dollars, across the globe. One of the promising choices to tackle this issue is carbon sequestration by pre- and post-combustion processes and oxyfuel combustion. The captured CO2 can be converted into fuels and valuable products, including methanol, dimethyl ether (DME), and methane (CH4). The efficient use of the sequestered CO2 for the desalinization might be critical in overcoming water scarcity and energy issues in developing countries. Using the sequestered CO2 to produce algae in combination with wastewater, and producing biofuels is among the promising strategies. Many methods, like direct combustion, fermentation, transesterification, pyrolysis, anaerobic digestion (AD), and gasification, can be used for the conversion of algae into biofuel. Direct air capturing (DAC) is another productive technique for absorbing CO2 from the atmosphere and converting it into various useful energy resources like CH4. These methods can effectively tackle the issues of climate change, water security, and energy crises. However, future research is required to make these conversion methods cost-effective and commercially applicable.
Cost and safety are critical factors in the oil and gas industry for optimizing wellbore trajectory, which is a constrained and nonlinear optimization problem. In this work, the wellbore trajectory is optimized using the true measured depth, well profile energy, and torque. Numerous metaheuristic algorithms were employed to optimize these objectives by tuning 17 constrained variables, with notable drawbacks including decreased exploitation/exploration capability, local optima trapping, non-uniform distribution of non-dominated solutions, and inability to track isolated minima. The purpose of this work is to propose a modified multi-objective cellular spotted hyena algorithm (MOCSHOPSO) for optimizing true measured depth, well profile energy, and torque. To overcome the aforementioned difficulties, the modification incorporates cellular automata (CA) and particle swarm optimization (PSO). By adding CA, the SHO's exploration phase is enhanced, and the SHO's hunting mechanisms are modified with PSO's velocity update property. Several geophysical and operational constraints have been utilized during trajectory optimization and data has been collected from the Gulf of Suez oil field. The proposed algorithm was compared with the standard methods (MOCPSO, MOSHO, MOCGWO) and observed significant improvements in terms of better distribution of non-dominated solutions, better-searching capability, a minimum number of isolated minima, and better Pareto optimal front. These significant improvements were validated by analysing the algorithms in terms of some statistical analysis, such as IGD, MS, SP, and ER. The proposed algorithm has obtained the lowest values in IGD, SP and ER, on the other side highest values in MS. Finally, an adaptive neighbourhood mechanism has been proposed which showed better performance than the fixed neighbourhood topology such as L5, L9, C9, C13, C21, and C25. Hopefully, this newly proposed modified algorithm will pave the way for better wellbore trajectory optimization.
This study reports the comparative evaluation of yield, physico-chemical composition and biological attributes (antioxidant activity, antimicrobial activity, biofilm inhibition and hemolytic activity) of peppermint (Mentha piperita L.) essential oil (EO) obtained by hydro-distillation (HD) and supercritical fluid (CO2) extraction (SCFE) methods. The yield (%) of EO obtained by HD (0.20 %) was significantly (p