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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. [ABSTRACT FROM AUTHOR]

This research paper introduces the Double Stator (DS) Hybrid Excitation (HE) Halbach Permanent Magnet (HPM) Flux Switching (FS) machine. The machine construction and its optimization specifically designed for electric vehicle (EV)/hybrid electric vehicle (HEV) traction applications are investigated. The optimization using a multi-objective Genetic Algorithm is conducted following a sensitivity analysis-based identification of key optimization parameters and constraints. The finite element results are compared with the performance of a state-of-the-art benchmark FSPM machine having identical PM volume and winding current densities. The proposed design is shown to outperform the benchmark with 16.2% increase in back-electromotive force and 14.7% reduction in cogging torque. Furthermore, the average torque is improved at flux-enhancing operation by 20.8%, and the torque ripple is reduced by 9.9%. Notably, the proposed machine also is capable of flux regulation thereby having the ability to operate in a wide speed range. A detailed explanation of the reasons for the significant improvements in the proposed machine structure is provided to offer a comprehensive understanding of its rationale. These research findings indicate that this innovative DS-HE-HPM-FS machine can enhance the performance of EVs and HEVs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Installing a battery energy storage system (BESS) and renewable energy sources can significantly improve distribution network performance in several aspects, especially in electric vehicle (EV)-integrated systems because of high load demands. With the high costs of the BESS and PV, optimal placement and capacity of them must be carefully considered. This work proposes a solution for determining the optimal placement and capacity of a BESS and photovoltaic (PV) in a distribution system by considering EV penetrations. The objective function is to reduce system costs, comprising installation, replacement, and operation and maintenance costs of the BESS and PV. The replacement cost is considered over 20 years, and the maintenance and operation costs incurred in the distribution system include transmission line loss, voltage regulation, and peak demand costs. To solve the problem, two metaheuristic algorithms consisting of particle swarm optimization (PSO) and the African vulture optimization algorithm (AVOA) are utilized. The tenth feeder of Phitsanulok substation 1 (PLA10), Thailand, which is a 91-bus distribution network, is tested to evaluate the performance of the proposed approach. The results obtained from the considered algorithms are compared based on distribution system performance enhancement, payback period, and statistical analysis. It is found from the simulation results that the installation of the BESS and PV could significantly minimize system cost, improve the voltage profile, reduce transmission line loss, and decrease peak demand. The voltage deviation could be reduced by 86%, line loss was reduced by 0.78 MW, and peak demand could be decreased by 5.706 MW compared to the case without BESS and PV installations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The fast development of electric vehicles (EVs) has resulted in several topics of research in this area, such as the development of a charging pricing strategy, charging control, location of the charging station, and the structure within the charging station. This paper proposes the optimal design of the structure of an EV fast-charging station (EVFCS) connected with a renewable energy source and battery energy storage systems (BESS) by using metaheuristic algorithms. The optimal design of this structure aims to find the number and power of chargers. Moreover, the renewable energy source and BESS can reduce the impact on the grid, so these energy sources are considered as ones of the optimally-designed structure of EVFCS in this work. Thus, it is necessary to determine the optimal sizing of the renewable energy source, BESS, and the grid power connected to EVFCS. This optimal structure can improve the profitability of the station. To solve the optimization problem, three metaheuristic algorithms, including particle swarm optimization (PSO), Salp swarm algorithm (SSA), and arithmetic optimization algorithm (AOA), are adopted. These algorithms aim to find the optimal structure which maximizes the profit of the EVFCS determined by its net present value (NPV), and the results obtained from these algorithms were compared. The results demonstrate that all considered algorithms could find the feasible solutions of the optimal design of the EVFCS structure where PSO provided the best NPV, followed by AOA and SSA. [ABSTRACT FROM AUTHOR]

The doubly salient permanent magnet generator (DSPMG) is widely known as an efficient machine for electrical production from renewable energy. In this paper, we aim to improve the output power of the DSPMG using a structural modification, which is targeted for low-speed electrical generations. Structural parameters including the stator pole depth, thickness of permanent magnet, stator pole arc, and number of winding turns were adjusted, then an optimal value of those parameters was selected based on the characteristics of the generator tested during no-load and on-load conditions. Simulations were based on the finite element method. The generator was targeted to be used for the rated power of 200 W. It was found that the optimally designed generator had a higher electromotive force of 36.1%, a lower cogging torque of 20%, and a higher output power of 12.2% than the conventional structure. The leakage flux of the proposed structure was also improved from the conventional one. Thus, the generator designed in this work could be another capable choice for electrical generation from renewable energy. The proposedly modified technique can also be adapted for output profile improvement of the doubly salient permanent magnet machines which are extensively used for renewable energy production nowadays. [ABSTRACT FROM AUTHOR]

In this work, we propose exchange-coupled-composite-bit-patterned media (ECC-BPM) with microwave-assisted magnetic recording (MAMR) to improve the writability of the magnetic media at a 4 Tb/in2 recording density. The suitable values of the applied microwave field's frequency and the exchange coupling between magnetic dots, Adot, of the proposed media were evaluated. It was found that the magnitude of the switching field, Hsw, of the bilayer ECC-BPM is significantly lower than that of a conventional BPM. Additionally, using the MAMR enables further reduction of Hsw of the ECC-BPM. The suitable frequency of the applied microwave field for the proposed media is 5 GHz. The dependence of Adot on the Hsw was additionally examined, showing that the Adot of 0.14 pJ/m is the most suitable value for the proposed bilayer ECC-BPM. The physical explanation of the Hsw of the media under a variation of MAMR and Adot was given. Hysteresis loops and the magnetic domain of the media were characterized to provide further details on the results. The lowest Hsw found in our proposed media is 12.2 kOe, achieved by the bilayer ECC-BPM with an Adot of 0.14 pJ/m using a 5 GHz MAMR. [ABSTRACT FROM AUTHOR]

A novel way to enhance modulation performance is through the design of a hybrid plasmonic optical modulator that integrates multi-layer graphene and TiO 2 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 μ m. 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., (Creative Commons Attribution license.)

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 BaTiO 3 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, BaTiO 3 , 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., (Creative Commons Attribution license.)