This paper proposes a novel adaptive optimization algorithm to solve the network reconfiguration and distributed generation (DG) placement problems with objective functions including power loss minimization and voltage stability index (VSI) improvement. The proposed technique called Adaptive Shuffled Frogs Leaping Algorithm (ASFLA) was performed for solving network reconfiguration and DG installation in IEEE 33- and 69-bus distribution systems with seven different scenarios. The performance of ASFLA was compared to that of other algorithms such as Fireworks Algorithm (FWA), Adaptive Cuckoo Search Algorithm (ACSA) and Shuffled Frogs Leaping Algorithm (SFLA). It was found that the power loss and VSI provided by ASFLA were better than those given by FWA, ACSA and SFLA in both 33- and 69-bus systems. The best solution of power loss reduction and VSI improvement of both 33- and 69-bus systems was achieved when the network reconfiguration with optimal sizing and the location DG were simultaneously implemented. From our analysis, it was indicated that the ASFLA could provide better solutions than other methods since the generating process, local and global searching of this algorithm were significantly improved from a conventional method. Hence, the ASFLA becomes another effective algorithm for solving network reconfiguration and DG placement problems in electrical distribution systems.

Sentiment analysis is becoming a very popular research technique. It can effectively identify hidden emotional trends in social networks to understand people’s opinions and feelings. This research therefore focuses on analyzing the sentiments of the public on the social media platform, YouTube, about the Thailand-China high-speed train project and the Laos-China Railway, a mega-project that is important to the country and a huge investment to develop transportation infrastructure. It affects both the economic and social dimensions of Thai people and is also an important route to connect the rail systems of ASEAN countries as part of the Belt and Road Initiative. We gathered public Thai reviews from YouTube using the Data Application Program Interface. This dataset was used to train six sentiment classifiers using machine learning and deep learning algorithms. The performance of all six models by means of precision, recall, F1-score and accuracy are compared to find the most suitable model architecture for sentiment classification. The results show that the transformer model with the WangchanBERTa language model yields best accuracy, 94.57%. We found that the use of a Thai language-specific model that was trained from a large variety of data sources plays a major role in the model performance and significantly increases the accuracy of sentiment prediction. The promising performance of this sentiment classification model also suggests that it can be used as a tool for government agencies to plan, make strategic decisions, and improve communication with the public for better understanding of their projects. Furthermore, the model can be integrated with any online platform to monitor people's sentiments on other public matters. Regular monitoring of public opinions could help the policy makers in designing public policies to address the citizens’ problems and concerns as well as planning development strategies for the country.

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.

Leveraging sentiment analysis on social media reveals hidden emotional trends by providing deep insights into the public’s collective opinions and feelings because social media can reflect most opinions based on true feelings. However, there is no time-series analysis of Thai opinions on social media regarding government projects in Thailand. Therefore, the focus of this research is to survey and dynamically analyze sentiment on social media platforms, particularly YouTube, regarding the Thailand–China High-Speed Rail Project (Thai–China HSR Project). This is a major project involving an investment of over 500 billion baht, a collaborative effort between the Thai and Chinese governments aimed at developing transportation infrastructure. It leads the public concerns of Thai people about the potential economic and social impacts. The deep learning (DL) technique was used for this analysis by the Bidirectional Encoder Representations from Transformers (BERT) model, together with WangchanBERTa, a Thai language model specifically designed to analyze opinions from Thai comments received from YouTube from July 2015 to January 2024. The analysis reveals that citizens’ sentiments are primarily neutral, with more negative than positive sentiments with the passing years. Results also indicate that the data volume has increased since 2019 and peaked in 2022. Negative comments peaked in January 2022, and based on our analysis, such negative comments followed news covering the opening of the China–Laos Railway on December 1, 2021. Video contents were related to the Thai–China HSR Project, which provides comparative reviews and analysis of government operations. Additionally, we explore the possibility of integrating AI technology based on the ChatGPT 4 model to support data interpretation. Although the AI model can interpret data quickly and efficiently serve as a convenient method for users, the results lack the in-depth analysis that should be done by experts in the field. The proposed framework could be useful to government agencies and can be used to quickly and efficiently survey and track public opinion. The results can be used for policy planning, strategic decision-making, and improving communications to better inform citizens about projects and operations, leading to the country’s sustainable economic and social development.

A novel way to enhance modulation performance is through the design of a hybrid plasmonic optical modulator that integrates multi-layer graphene and TiO₂ on silicon waveguides. In this article, a design is presented of a proposed modulator based on the use of the two-dimensional finite difference eigenmode solver, the three-dimensional eigenmode expansion solver, and the CHARGE solver. Leveraging inherent graphene properties and utilizing the subwavelength confinement capabilities of hybrid plasmonic waveguides (HPWs), we achieved a modulator design that is both compact and highly efficient. The electrical bandwidth f _3dB is at 460.42 GHz and it reduces energy consumption to 12.17 fJ/bit with a modulator that functions at a wavelength of 1.55 μm. According to our simulation results, our innovation was the optimization of the third dielectric layer’s thickness, setting the stage to achieve greater modulation depths. This synergy between graphene and HPWs not only augments subwavelength confinement, but also optimizes light–graphene interaction, culminating in a markedly enhanced modulation efficiency. As a result, our modulator presents a high extinction ratio and minimized insertion loss. Furthermore, it exhibits polarization insensitivity and a greater bandwidth. Our work sets a new benchmark in optical communication systems, emphasizing the potential for the next generation of chip-scale with high-efficiency optical modulators that significantly outpace conventional graphene-based designs.