The world is entering an era of awareness of the preservation of natural energy sustainability. Therefore, electric vehicles (EVs) have become a popular alternative in today’s transportation system as they have zero emissions, save energy, and reduce pollution. One of the most significant problems with EVs is an inadequate charging infrastructure and spatially and temporally uneven charging demands. As such, EV drivers in many large cities frequently struggle to find suitable charging locations. Furthermore, the recent emergence of deep reinforcement learning has shown great promise for improving the charging experience in a variety of ways over the long term. In this paper, a Spatio-Temporal Multi-Agent Reinforcement Learning (STMARL) (Master) framework is proposed for intelligently public-accessible charging stations, taking into account several long-term spatio-temporal parameters. When compared to a random selection recommendation system, the experimental results demonstrate that an STMARL (master) framework has a long-term goal of lowering the overall charging wait time (CWT), average charging price (CP), and charging failure rate (CFR) of EVs.

The disruptions in this era have caused a leap forward in information technology being applied in organizations to create a competitive advantage. In particular, we see this in tourism services, as they provide the best solution and prompt responses to create value in experiences and enhance the sustainability of tourism. Since scheduling is required in tourism service applications, it is regarded as a crucial topic in production management and combinatorial optimization. Since workshop scheduling difficulties are regarded as extremely difficult and complex, efforts to discover optimal or near-ideal solutions are vital. The aim of this study was to develop a hybrid genetic algorithm by combining a genetic algorithm and a simulated annealing algorithm with a gradient search method to the optimize complex processes involved in solving tourism service problems, as well as to compare the traditional genetic algorithms employed in smart city case studies in Thailand. A hybrid genetic algorithm was developed, and the results could assist in solving scheduling issues related to the sustainability of the tourism industry with the goal of lowering production requirements. An operation-based representation was employed to create workable schedules that can more effectively handle the given challenge. Additionally, a new knowledge-based operator was created within the context of function evaluation, which focuses on the features of the problem to utilize machine downtime to enhance the quality of the solution. To produce the offspring, a machine-based crossover with order-based precedence preservation was suggested. Additionally, a neighborhood search strategy based on simulated annealing was utilized to enhance the algorithm’s capacity for local exploitation, and to broaden its usability. Numerous examples were gathered from the Thailand Tourism Department to demonstrate the effectiveness and efficiency of the proposed approach. The proposed hybrid genetic algorithm’s computational results show good performance. We found that the hybrid genetic algorithm can effectively generate a satisfactory tourism service, and its performance is better than that of the genetic algorithm.

Autonomous energy management is becoming a significant mechanism for attaining sustainability in energy management. This resulted in this research paper, which aimed to apply deep reinforcement learning algorithms for an autonomous energy management system of a microgrid. This paper proposed a novel microgrid model that consisted of a combustion set of a household load, renewable energy, an energy storage system, and a generator, which were connected to the main grid. The proposed autonomous energy management system was designed to cooperate with the various flexible sources and loads by defining the priority resources, loads, and electricity prices. The system was implemented by using deep reinforcement learning algorithms that worked effectively in order to control the power storage, solar panels, generator, and main grid. The system model could achieve the optimal performance with near-optimal policies. As a result, this method could save 13.19% in the cost compared to conducting manual control of energy management. In this study, there was a focus on applying Q-learning for the microgrid in the tourism industry in remote areas which can produce and store energy. Therefore, we proposed an autonomous energy management system for effective energy management. In future work, the system could be improved by applying deep learning to use energy price data to predict the future energy price, when the system could produce more energy than the demand and store it for selling at the most appropriate price; this would make the autonomous energy management system smarter and provide better benefits for the tourism industry. This proposed autonomous energy management could be applied to other industries, for example businesses or factories which need effective energy management to maintain microgrid stability and also save energy.

Teacher performance evaluation is a common method and often used for evaluates teaching quality in higher education. With the rapid growth of opinion mining technique. Aspect-based opinion mining application has been possibly employed to extraction and summarization of students' comments for teacher evaluation. However, to automated teacher evaluation features identification from a large number of students' comments collection is very hard work. This study has the goal to address this problem. The main objectives of the proposed method are: (1) to identify teacher evaluation aspects, (2) to compare the efficiency of dictionary based, patterns based and the combination of them, and (3) to enhance the accuracy result in the teachers’ evaluation aspects identification from the unstructured text of students' feedbacks. The students' feedbacks were collected by questionnaires and the dataset was constructed manually with a total of 4,496 sentences from 300 undergraduate student responses in 10 subjects by purposive sampling and the collection of positive and negative sentences from 30 participants group interviewed in the workshop. Both approaches were applied to identify the frequency teachers' evaluation aspects. The experimental results found that our proposed approach provided reasonably more accurate results, the combination approach enhanced a significantly average of precision and recall. For future work, we focus on the application of new linguistic patterns and non-frequency aspects in order to increase the accuracy result. Keywords—aspects identification, lexicon relation, linguistic pattern, opinion mining, teacher evaluation.

Currently, the System of Rice Intensification (SRI) is one method that can be applied and used to produce seeds of local rice species in highland areas. However, it can currently be seen that the traditional methods to transfer knowledge about SRI from the experts still have limitations in many aspects due to the number of farmers that have experienced success still being low. Some farmers do not have their own fields and it takes a period for farmers to test the rice growing. Currently, mobile devices are widely used in peoples’ lives. Many scholars focus on the application of mobile devices and the augmented reality (AR) technology for the simulation games in many issues while the use of AR-based mobile applications as the learning tools is not widespread. There-fore, in this study, we propose the development of an application on mobile devices with augmented reality technology in order to use it as media in sharing knowledge related to the methods of the System of Rice Intensification by virtual farms simulation. To examine the efficiency of this developed application. A total amount of participants were 512 farmers from 5 regions of Thailand. The experimental results demonstrated that the newly developed AR-based mobile application is effective for improved knowledge on the participants who used the developed application and it can help them to practice their SRI farming skill in the virtual farm simulation. This indicates that the developed AR-based mobile application is the benefits tool for the new knowledge transferring meth-od in the system of rice intensification. For future work, it is necessary to evaluate the farmer success after learning from this AR-based mobile application in order to study the impact of the new method of SRI knowledge transferring. Keywords—augmented reality, knowledge transferring, system of rice intensification.