A significant exploration on meta-heuristic based approaches for optimization in the waste management route problems.

In metropolitan cities, it is very complicated to govern the optimum routes for garbage collection vehicles due to high waste production and very dense population. Furthermore, wrongly designed routes are the source of wasting time, fuel and other resources in the collection of municipal trash procedure. The Vehicle Routing Problem (VRP) published between 2011 and 2023 was systematically analysed. The majority of the surveyed research compute the waste collecting problems using metaheuristic approaches. This manuscript serves two purposes: first, categorising the VRP and its variants in the field of waste collection; second, examining the role played by most of the metaheuristics in the solution of the VRP problems for a waste collection. Three case study of Asia continent has been analysed and the results show that the metaheuristic algorithms have the capability in providing good results for large-scale data. Lastly, some promising paths ranging from highlighting research gap to future scope are drawn to encourage researchers to conduct their research work in the field of waste management route problems.

Estimating PM2.5 utilizing multiple linear regression and ANN techniques.

The accurate prediction of air pollutants, particularly Particulate Matter (PM), is critical to support effective and persuasive air quality management. Numerous variables influence the prediction of PM, and it's crucial to combine the most relevant input variables to ensure the most dependable predictions. This study aims to address this issue by utilizing correlation coefficients to select the most pertinent input and output variables for an air pollution model. In this work, PM2.5 concentration is estimated by employing concentrations of sulfur dioxide, nitrogen dioxide, and PM10 found in the air through the application of Artificial Neural Networks (ANNs). The proposed approach involves the comparison of three ANN models: one trained with the Levenberg-Marquardt algorithm (LM-ANN), another with the Bayesian Regularization algorithm (BR-ANN), and a third with the Scaled Conjugate Gradient algorithm (SCG-ANN). The findings revealed that the LM-ANN model outperforms the other two models and even surpasses the Multiple Linear Regression method. The LM-ANN model yields a higher R2 value of 0.8164 and a lower RMSE value of 9.5223.

Mesoporous silica nanoparticles in target drug delivery system: A review.

Due to lack of specification and solubility of drug molecules, patients have to take high doses of the drug to achieve the desired therapeutic effects for the treatment of diseases. To solve these problems, there are various drug carriers present in the pharmaceuticals, which can used to deliver therapeutic agents to the target site in the body. Mesoporous silica materials become known as a promising candidate that can overcome above problems and produce effects in a controllable and sustainable manner. In particular, mesoporous silica nanoparticles (MSNs) are widely used as a delivery reagent because silica possesses favorable chemical properties, thermal stability, and biocompatibility. The unique mesoporous structure of silica facilitates effective loading of drugs and their subsequent controlled release of the target site. The properties of mesoporous, including pore size, high drug loading, and porosity as well as the surface properties, can be altered depending on additives used to prepare MSNs. Active surface enables functionalization to changed surface properties and link therapeutic molecules. They are used as widely in the field of diagnosis, target drug delivery, bio-sensing, cellular uptake, etc., in the bio-medical field. This review aims to present the state of knowledge of silica containing mesoporous nanoparticles and specific application in various biomedical fields.