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.

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.

Point of interest (POI) recommendation has attracted a lot of research attention by combining user ratings and POIs to find the similarity of users to help them to locate an enjoyable place. However, social networks, which have become a part of modern lifestyle, contain much information about the relationship between users and POIs, such as checkin activity. POI recommendations should consider the significant features of check-in data from location-based social networks (LBSNs) for more precise results for POI recommendations. This paper presents a personalized recommender system using a social network based collaborative filtering technique that recommends the top-n POIs to the user. The proposed method calculates similarity based on user ratings with a collaborative filtering technique and user check-in activity on a social network to make personalized recommendations. We used a real-world dataset from Foursquare to test our method. The results from experiments demonstrate that using social network check-in activity combined with a collaborative filtering method can increase the performance of the recommender system in the real word.

Thailand’s hospitals face overcrowding, particularly with non-communicable disease (NCD) patients, due to a doctor shortage and an aging population. Most literature showed implementation merely on web or mobile application to teleconsult with physicians. Instead, in this work, we developed and implemented a telemedicine health kiosk system embedded with non-invasive biosensors and time-series predictors to improve NCD indicators over an eight-month period. Two cohorts were randomly selected: a control group with usual care and a telemedicine-using group. The telemedicine-using group showed significant improvements in average fasting blood glucose (148 to 130 mg/dL) and systolic blood pressure (152 to 138 mmHg). Data mining with the Apriori algorithm revealed correlations between diseases, occupations, and environmental factors, informing public health policies. Communication between kiosks and servers used LoRa, 5G, and IEEE802.11, which are selected based on the distance and signal availability. The results support telemedicine kiosks as effective for NCD management, significantly improving key NCD indicators, average blood glucose, and blood pressure.

Spin-transfer torque magnetic random-access memory (STT-MRAM) based on a single-barrier magnetic tunnel junction (SBMTJ) and a double-barrier magnetic tunnel junction (DBMTJ) has evolved along with a low switching current and low energy consumption to obtain a high areal density and a fast switching speed. The increment of initial temperature, ${T}_{{\text {in}}}$ , in STT-MRAM can achieve low switching energy, ${E}_{{\text {SW}}}$ . However, this leads to an unavoidable decrease in $\Delta $ and switching efficiency. In this paper, SBMTJ and DBMTJ were analyzed in terms of the switching efficiency factor with ${E}_{{\text {SW}}}$ reduction by increasing the ${T}_{{\text {in}}}$ . The switching temperature, ${T}_{{\text {SW}}}$ , was investigated using a finite-element method simulation. The results show that the DBMTJ(A) and SBMTJ with the same MgO layer thickness of 0.9 nm provide a higher switching current, ${I}_{{\text {C}}}$ , than the DBMTJ(B) with a MgO layer thickness of 1.3 nm. The ${T}_{{\text {SW}}}$ in a DBMTJ(A) is higher than that in an SBMTJ, while ${T}_{{\text {SW}}}$ for a DBMTJ(B) is the smallest because of its low ${I}_{{\text {C}}}$ . The DBMTJ(A) and DBMTJ(B) can be applied in a higher temperature range than the SBMTJ at a $\Delta $ of 40. In addition, the STT efficiency factor, $\Delta /{I}_{{\text {C0}}}$ , for a DBMTJ is better than the factor for an SBMTJ. Although the temperature increment would cause an undesirable reduction in $\Delta $ , it can reach a low ${E}_{{\text {SW}}}$ for the requirement of a fast write access time. Therefore, the control device for increasing the ${T}_{{\text {in}}}$ in the MTJs is attractive and should be promoted in the advancement of memory technology.