The COVID-19 pandemic has caused significant changes in many aspects of daily life, including learning, working, and communicating. As countries aim to recover their economies, there is an increasing need for smart city solutions, such as crowd monitoring systems, to ensure public safety both during and after the pandemic. This paper presents the design and implementation of a real-time crowd monitoring system using existing public Wi-Fi infrastructure. The proposed system employs a three-tiered architecture, including the sensing domain for data acquisition, the communication domain for data transfer, and the computing domain for data processing, visualization, and analysis. Wi-Fi access points were used as sensors that continuously monitored the crowd and uploaded data to the server. To protect the privacy of the data, encryption algorithms were employed during data transmission. The system was implemented in the Sri Chiang Mai Smart City, where nine Wi-Fi access points were installed in nine different locations along the Mekong River. The system provides real-time crowd density visualizations. Historical data were also collected for the analysis and understanding of urban behaviors. A quantitative evaluation was not feasible due to the uncontrolled environment in public open spaces, but the system was visually evaluated in real-world conditions to assess crowd density, rather than represent the entire population. Overall, the study demonstrates the potential of leveraging existing public Wi-Fi infrastructure for crowd monitoring in uncontrolled, real-world environments. The monitoring system is readily accessible and does not require additional hardware investment or maintenance. The collected dataset is also available for download. In addition to COVID-19 pandemic management, this technology can also assist government policymakers in optimizing the use of public space and urban planning. Real-time crowd density data provided by the system can assist route planners or recommend points of interest, while information on the popularity of tourist destinations enables targeted marketing.

The complexity and uncertainty of power sources connected to transmission networks need to be considered. Planners need information on the sustainability and economics of transmission network expansion planning (TNEP). This work presents a newly proposed method for TNEP that considers high-penetration solar energy by using the particle swarm optimization (PSO) algorithm. The power sources, thermal and hydropower plants, and conditions of load were set in the account, including an uncertain power source and solar energy (PV). The optimal sizing and locating of the PV to be connected to the network were determined by the PSO. The PV grid code was set in the account. The new line’s investment cost and equipment was analyzed. The PV cost was considered based on the power loss, and the system’s reliability was improved. The IEEE 118 bus test system and Lao PDR’s system were requested to test the proposed practice. The results demonstrate that the proposed TNEP method is robust and feasible. The simulation results will be applied to guide the power system planning of Lao PDR.

This article focuses on case assignment which is one of the most significant tools in case management to improve efficiency of judicial system. The effectiveness of judicial system depends greatly on the efficient and timely manner of court case operation, especially in criminal cases where suspects are hold in prison on remand. To ensure the rule of law, the preferred system in the court of justice is the one that is predictable and consistent. However, one of the building blocks to such goal is the nature of litigation which is unpredictably time consuming. In this paper, a Mixed Integer Linear Programming (MILP) is selected to address the issue of case assignment in the court of justice. The computational experiments are also reported to evaluate its solution. The two techniques of metaheuristic method are used to find optimal solution of the assignment problem that are called Dragonflies Algorithm (DA) and Fireflies Algorithm (FA). The result shows that DA is more effective in finding an optimal solution to the problem of court case assignment than of the FA.

Triboelectric nanogenerators (TENGs) are crucial for applications such as smart sensors and bio-electronics. In the current work, we aimed for improved performance of TENGs with incorporation of BaTiO₃ powder, which is known for its strong ferroelectric properties, combining it with epoxy resin to improve the flexibility of our devices. We observed that our TENGs can operate for over 24 000 cycles with no degradation of function. Additionally, we improved the electrical performance of the TENGs by incorporating various aluminum concentrations that change the electronic properties in the form of mixed epoxy resin, BaTiO₃, and Al nanopowders. To identify the optimum conditions for the best performance, we analyzed the electrical characteristics and material properties by employing scanning electron microscopy, energy dispersive x-ray spectroscopy, and x-ray diffractometry characterization techniques. Our findings suggest that this innovative combination of materials and optimization techniques can significantly improve the performance of TENGs, making them ideal for practical applications in various fields, such as low-power electronics, environmental monitoring and healthcare. Moreover, these enhanced TENGs can serve as sustainable and dependable energy sources for various applications.

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.