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Human activities have been limited by coronavirus disease 2019 (COVID-19), and the normal conditions of our lifestyles have changed, particularly in terms of electricity usage. The aim of this study was to investigate the impact of COVID-19 on the power sector in the Lao PDR in 2020, as well as the challenge of using solar energy to supply power to the network using an optimal approach. The returns on investment of network extension and the purchase of solar energy were also evaluated. Furthermore, load conditions caused by the country’s lockdown policy were analyzed. We analyzed the optimal sizing and location of solar energy using a particle swarm optimization method based on the main objective functions, with the system’s power loss decreasing and its reliability improved. The results demonstrated that the suddenly reduced load from industry and commercial business did not have a large impact on its operations; however, revenue was reduced. The optimal method for connecting solar energy to a network can reduce power loss and improve system reliability. In addition, we discovered that the location and capacity of solar generation can reduce the investment costs of extensions for new lines, with the surplus power being exported.

The study of sustainable management of municipal solid waste (MSW) collection has been increasing in recent years. However, the focus areas of research are mostly in the economics and environmental dimensions. This paper adds social aspects of MSW into considerations to accelerate more comprehensive decision making. The consideration on the capacity and fixed costs of vehicles, the distances from depot to the disposal facilities are determined in this paper. The environmental issues relating to fuel consumption, carbon emissions, and the evaluation of social impact from the penalty costs of imbalanced trip assignments are also determined to adopt a comprehensive solution to the capacitated vehicle routing problem (CVRP) model. Then, the optimization model of MSW collection is proposed to minimize management costs which compose of the fixed costs of vehicles, fuel consumption costs, carbon emissions costs, and penalty costs. In this paper, two metaheuristic techniques are used to optimization the CVRP model in the MSW collection system. A new metaheuristic algorithm, called an intelligence hybrid harmony search algorithm (IHHS), is proposed in comparison with the standard harmony search (SHS) algorithm. The result shows that the IHHS algorithm can obtain the global optimal solution and can minimize the total objective function for the CVRP model.

This research focuses on finding the route of waste collection to optimise the route of waste collection for the smallest value cost in waste management. The objective function to find the minimal total cost for finding the shortest route of waste collection with respect to garbage truck capacity by using metaheuristic techniques are considered as the best choice. This research uses two techniques of metaheuristic methods; cuckoo search algorithm (CS), and firefly algorithm (FA) to compare the performances. A case study of the real problem with the obtained results is also discussed in the paper. The result shows that the FA algorithm can obtain the global optimal solution, has a faster convergence, and can minimize total objective function for finding the shortest route of waste collection. The result of the research also shows a sequence in which the garbage truck should use to collect the waste from all of the bins to the landfill facility.

Back propagation algorithm (BP) is a widely used technique in artificial neural network and has been used as a tool for solving the time series problems, such as decreasing training time, maximizing the ability to fall into local minima, and optimizing sensitivity of the initial weights and bias. This paper proposes an improvement of a BP technique which is called IM-COH algorithm (IM-COH). By combining IM-COH algorithm with cuckoo search algorithm (CS), the result is cuckoo search improved control output hidden layer algorithm (CS-IM-COH). This new algorithm has a better ability in optimizing sensitivity of the initial weights and bias than the original BP algorithm. In this research, the algorithm of CS-IM-COH is compared with the original BP, the IM-COH, and the original BP with CS (CS-BP). Furthermore, the selected benchmarks, four time series samples, are shown in this research for illustration. The research shows that the CS-IM-COH algorithm give the best forecasting results compared with the selected samples.

This paper proposed a new algorithm (IM-COH) which was developed based on backpropagation algorithm on neural network (BPNN). This kind of algorithm improved the generalization ability on neural networks to optimize its structure in regularization method. The IM-COH algorithm controls the outputs of nodes in the hidden layers which were manipulated to eliminate the distribution weights problem and enhance the ability to move toward target problems. Furthermore, the IM-COH algorithm maintains the ability to reduce the random noise in the input sample. In this research, the IM-COH algorithm is simulated with two-spiral classification problem and Mackey-Glass time series prediction to compare with Gaussian regularizer (GR) and Laplace regularizer (LR). The results show that the generalization ability of the IM-COH algorithm is better than GR and LR in testing data samples and noise data. In addition, the IM-COH algorithm performs better in solving the two-spiral classification problem comparing to Mackey-Glass time series prediction.

Nonbonding parameters of AMBER force field have been refined based onab initiobinding energies of CO2–[C7H5N2]−complexes. The energy and geometry scaling factors are obtained to be 1.2 and 0.9 forεandσparameters, respectively. Molecular dynamics simulations of CO2molecules in rigid framework ZIF-11, have then been performed using original AMBER parameters (SIM I) and refined parameters (SIM II), respectively. The site-site radial distribution functions and the molecular distribution plots simulations indicate that all hydrogen atoms are favored binding site of CO2molecules. One slight but notable difference is that CO2molecules are mostly located around and closer to hydrogen atom of imidazolate ring in SIM II than those found in SIM I. The Zn-Zn and Zn-N RDFs in free flexible framework simulation (SIM III) show validity of adapting AMBER bonding parameters. Due to the limitations of computing resources and times in this study, the results of flexible framework simulation using refined nonbonding AMBER parameters (SIM IV) are not much different from those obtained in SIM II.