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This research designs and develops a software innovation for Pattavia pineapple cultivation and productivity distribution planning to increase income for farmers. This research formulated and introduced an innovative machine learning (ML) model, the new model is called a new intelligent particle swarm optimization algorithm with extreme learning machine (NIPSO-ELM), to forecast Pattavia pineapple productivity with a notable degree of precision and dependability. In this work, the artificial neural network (ANN) and the standard ELM was built, and assessed for its ability to forecast the productivity of Pattavia pineapples. The findings indicate that the ELM neural network is an innovative model characterized by its straightforward architecture and exceptional performance. Moreover, the utilization of particle swarm optimization (PSO), ant colony optimization (ACO) and the NIPSO algorithms significantly enhanced ELM performance when forecasting the productivity of Pattavia pineapples. The NIPSO-ELM model emerged as the most optimal ML model for accurately, reliably and stably forecasting the productivity of Pattavia pineapples in practical scenarios. The most optimal NIPSO-ELM models for the Loei provinces, Thailand exhibit the following performance metrics: RMSE = 304.36389, MAE = 243.29531, MAPE = 0.03753 and MASE = 0.93157. The most optimal NIPSO-ELM models for the Nong Khai provinces, Thailand exhibit: RMSE = 304.57352, MAE = 244.67834, MAPE = 0.03756 and MASE = 0.93296, respectively. [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]

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. [ABSTRACT FROM AUTHOR]

Composite materials used in biomedical applications based on bacterial cellulose (BC) have attracted considerable scientific interest due to their specific and excellent properties. This work reports the fabrication of silver nanoparticles (AgNPs) deposited on nanofibrillated BC (BC-AgNPs nanocomposite) for antimicrobial applications. BC, AgNPs, as well as BC-AgNPs nanocomposite formation were fully characterized using various techniques such as X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM) analyses, Fourier transform infrared (FTIR) spectoscopy, transmission electron microscopy (TEM) and small-angle X-ray scattering (SAXS). Furthermore, the antimicrobial effects of produced BC-AgNP composites were evaluated on two pathogenic microorganisms, a gram-negative (Escherichia coli) and a gram-positive (Staphylococcus aureus) by determining their minimum inhibitory concentrations (MIC), and minimum bactericidal concentrations (MBC) using an agar diffusion assay. The results show that the BC-AgNPs composite has good antimicrobial activity against Escherichia coli but less against Staphylococcus aureus. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Criminal cases are social problems that concern the public order and good morals of the citizens as a whole. Research in criminology has recently focused on finding the root causes of criminal cases in order to identify the factors accelerating crimes and to find preventive methods. Thus, the study on the factors relating to the occurrence of crimes would benefit policy makers in dealing with crimes. This research focuses on a forecasting model of the criminal related factors in the Upper Northeastern Provinces of Thailand by using a New Intelligent Hybrid Artificial Neural Network (NIHANN) with a Cuckoo Search Algorithm (CS). The new CS-NIHANN algorithm, which is a combination of the CS with the NIHANN, is used in this research. In addition, the research selected relevant parameters that related to criminal cases in the Upper Northeastern Provinces of Thailand. The comparison of performances of the forecasting model for historical forecast part was calculated by Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and Correlation Coefficient (R). The result shows that the CS-NIHANN algorithm trains fast, can obtain the global optimal solution, and has a good generalization performance for Multiple Input Single Output (MISO) and Multiple Input Multiple Output (MIMO). Furthermore, the result shows that the CS-NIHANN algorithm has a faster convergence than the CS-ANN algorithm in calculating an optimal solution to the criminal cases. [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 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.)