A GA-stacking ensemble approach for forecasting energy consumption in a smart household: A comparative study of ensemble methods.

The considerable amount of energy utilized by buildings has led to various environmental challenges that adversely impact human existence. Predicting buildings' energy usage is commonly acknowledged as encouraging energy efficiency and enabling well-informed decision-making, ultimately leading to decreased energy consumption. Implementing eco-friendly architectural designs is paramount in mitigating energy consumption, particularly in recently constructed structures. This study utilizes clustering analysis on the original dataset to capture complex consumption patterns over various periods. The analysis yields two distinct subsets that represent low and high consumption patterns and an additional subset that exclusively encompasses weekends, attributed to the specific behavior of occupants. Ensemble models have become increasingly popular due to advancements in machine learning techniques. This research utilizes three discrete algorithms, namely Artificial Neural Network (ANN), K-nearest neighbors (KNN), and Decision Trees (DT). In addition, the application employs three more machine learning algorithms bagging and boosting: Random Forest (RF), Extreme Gradient Boosting (XGB), and Gradient Boosting Trees (GBT). To augment the accuracy of predictions, a stacking ensemble methodology is employed, wherein the forecasts generated by many algorithms are combined. Given the obtained outcomes, a thorough examination is undertaken, encompassing the techniques of stacking, bagging, and boosting, to conduct a comprehensive comparative study. It is pertinent to highlight that the stacking technique consistently exhibits superior performance relative to alternative ensemble methodologies across a spectrum of heterogeneous datasets. Furthermore, using a genetic algorithm enables the optimization of the combination of base learners, resulting in a notable enhancement in prediction accuracy. After implementing this optimization technique, GA-Stacking demonstrated remarkable performance in Mean Absolute Percentage Error (MAPE) scores. The improvement observed was substantial, surpassing 90 percent for all datasets. In addition, in subset-1, subset-2, and subset-3, the achieved R2 scores were 0.983, 0.985, and 0.999, respectively. This represents a substantial advancement in forecasting the energy consumption of residential buildings. Such progress underscores the potential advantages of integrating this framework into the practices of building designers, thereby fostering informed decision-making, design management, and optimization prior to construction.

Continuous photocatalytic set-up assisted with nano TiO2 plate for tannery wastewater treatment.

A novel photocatalytic continuous system has been proposed for the treatment of tannery waste water, which has high levels of environmental pollutants. The purification process was performed by passing wastewater on a titanium dioxide (TiO2)-coated surface, which is continuously activated by irradiation of ultraviolet light. To improve the yield of the process, ferric chloride (FeCl3) was used as a coagulation agent. The organic and inorganic compounds, as well as the microorganisms in the tannery wastewater media, were degraded through a photocatalytic process. The results revealed that total dissolved solids and total suspended solids contents were significantly decreased from 8,450 and 8,990 mg·L-1 to 4,032 and 4,127 mg·L-1, respectively. Furthermore, the chemical oxygen demand content of the sample was reduced from 370 to 50 mg·L-1 after the addition of 100 mL of FeCl3 and 4 h of treatment. The same results were observed for the elimination of sulfate and chromium ions, which led to a decline in electrical conductivity. This suggests that introducing 100 mL of FeCl3 as the coagulation agent and continuous treatment with photocatalityc set-up could be considered as an effective method for the purification of tannery wastewaters.

Recent Advances in Anticancer Activity of Novel Plant Extracts and Compounds from Curcuma longa in Hepatocellular Carcinoma.

PURPOSE: Among all forms of cancers, hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide. There are several treatment options for HCC ranging from loco-regional therapy to surgical treatment. Yet, there is high morbidity and mortality. Recent research focus has shifted towards more effective and less toxic cancer treatment options. Curcumin, the active ingredient in the Curcuma longa plant, has gained widespread attention in recent years because of its multifunctional properties as an antioxidant, anti-inflammatory, antimicrobial, and anticancer agent. METHODS: A systematic search of PubMed, Embase and Google Scholar was performed for studies reporting incidence of HCC, risk factors associated with cirrhosis and experimental use of curcumin as an anti-cancer agent. RESULTS: This review exclusively encompasses the anti-cancer properties of curcumin in HCC globally and it's postulated molecular targets of curcumin when used against liver cancers. CONCLUSIONS: This review is concluded by presenting the current challenges and future perspectives of novel plant extracts derived from C. longa and the treatment options against cancers.

Fourth-generation glucose sensors composed of copper nanostructures for diabetes management: A critical review.

More than five decades have been invested in understanding glucose biosensors. Yet, this immensely versatile field has continued to gain attention from the scientific world to better understand and diagnose diabetes. However, such extensive work done to improve glucose sensing devices has still not yielded desirable results. Drawbacks like the necessity of the invasive finger-pricking step and the lack of optimization of diagnostic interventions still need to be considered to improve the testing process of diabetic patients. To upgrade the glucose-sensing devices and reduce the number of intermediary steps during glucose measurement, fourth-generation glucose sensors (FGGS) have been introduced. These sensors, made using robust electrocatalytic copper nanostructures, improve diagnostic efficiency and cost-effectiveness. This review aims to present the essential scientific progress in copper nanostructure-based FGGS in the past 10 years (2010 to present). After a short introduction, we presented the working principles of these sensors. We then highlighted the importance of copper nanostructures as advanced electrode materials to develop reliable real-time FGGS. Finally, we cover the advantages, shortcomings, and prospects for developing highly sensitive, stable, and specific FGGS.