Evaluation of electric vehicle response capability in power grid.

In recent years, frequent and substantial area-wide power outages have underscored the critical need for cities to possess robust backup power sources capable of swift response to prevent prolonged power system disruptions. Electric vehicles can contribute electricity to the power grid using vehicle-to-grid technology. The power delivered by electric vehicles in this context is termed as response capability. However, existing studies have overlooked response capability dynamics during transitions between electric vehicle states-such as the shift from charging or discharging to an idle state, thereby hindering a comprehensive understanding of this aspect. Hence, this paper introduces a multi-timescale response capability prediction model that evaluates the electric vehicle's state of charge to ensure users' requirements are met for upcoming trips. To better assess users' travel demand, the gravity model is employed as a precursor to response capability prediction to further enhance the validity of the prediction outcomes. Three neighborhoods in Los Angeles have been chosen for analysis: Downtown, Lincoln Heights, and Silver Lake. Predictions indicate that neglecting the response capability when electric vehicles undergo state transformation can lead to a differential response capability ranging from 2000 kWh to 4000 kWh, resulting in a loss of prediction accuracy by 20 % to 25 %.•The response capability of EV is non-zero during state transformations•Users' travel demand assessment•Seamless integration of vehicle-to-grid technology into the power grid.

An evaluation of online Edcraft gamified learning (EGL) to understand motivation and intention of recycling among youth.

Recycling is a process carried out by various organizations and individuals to enhance the environment's long-term sustainability. Some youth think that recycling is a monotonous action as it may seem inconvenient, less aware of the environmental issues and more time-consuming than they think and rather go for video games. Therefore, this study investigates the relationship between motivation and recycling intention in gamified learning among youth. To address the research aim, this study uses gamification as a motivational driver for a game-like learning experience to improve recycling intentions among youth. Self-determination theory (SDT) and the theory of Planned Behavior (TPB) will be this study's main motivational and behavioral theories. (n = 124) high schools and college students were invited to take part in an online gamified recycling activity, Edcraft Gamified Learning (EGL), consisting of two levels of gamified unused plastic-crafting recycling activities. After the activity, the participants will answer a post-event questionnaire and the data collected were analyzed. The result shows that controlled motivation (CM) and autonomous motivation (AM) positively influenced youth attitudes and social norms. Besides, attitude is the only psychosocial determinant that positively influences the recycling intention of the youth. Gamification only moderates positively between attitude and recycling intention. This study has clearly shown the effectiveness of gamified learning activity towards recycling intention directly and as a component that moderates the relationship between attitude and recycling intention, which shows a favorable evaluation towards recycling intention with gamified learning involved. Moreover, the findings showed that not all relationships are positive in a gamified learning environment, and it gives a good view on the weakness and strengths with the guideline of SDT and TPB.

Playing Edcraft at Home: Gamified Online Learning for Recycling Intention during Lockdown.

Background: Gamification is an innovative approach to engaging in activities that people believe as less interesting. Recycling has been an issue not taken aware by the people in environmental sustainability. There are substantial studies on recycling intention due to the continual growth of unethical and unsustainable waste disposal. Creative approaches to recycling awareness activities should be made to fulfil youths' increasing interest in and demand for recycling. The main objective of this study is to explore the factors related to youths' recycling intentions after experiencing a gamified online recycling learning activity, Edcraft Gamified Learning (EGL). Gamified recycling education is believed to be a practical and engaging approach for youths. Methods: 100 students participated in EGL, consisting of two levels of plastic crafting and recycling activities. They experienced online EGL at home between May and September in 2020, during the COVID-19 pandemic total lockdown in Malaysia, namely, Movement Control Order (MCO). 29 participants were selected to participate in five focus group discussions (FGDs) with five to eight participants per session to explore their opinions towards gamified learning, motivation and recycling intention. Results: This paper reports the findings of the FGDs. A codebook was developed based on the codes from the FGD feedback. The codes were rated by two raters, followed by an assessment of inter-rater reliability and thematic analysis. The findings emerged and were confirmed with four themes as factors that influence recycling intention. They are gameful experience, social influence, intrinsic motivation, and extrinsic motivation. Conclusion: The dependent variable, recycling intention, was connected to the four themes to verify the conceptual framework. One limitation of the study was the design of the EGL activity, which was only carried out over two days with two levels of gamified recycling education, as students had concurrent academic online classes during that period.

A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network.

Cardiovascular diseases (CVDs) are the leading cause of death today. The current identification method of the diseases is analyzing the Electrocardiogram (ECG), which is a medical monitoring technology recording cardiac activity. Unfortunately, looking for experts to analyze a large amount of ECG data consumes too many medical resources. Therefore, the method of identifying ECG characteristics based on machine learning has gradually become prevalent. However, there are some drawbacks to these typical methods, requiring manual feature recognition, complex models, and long training time. This paper proposes a robust and efficient 12-layer deep one-dimensional convolutional neural network on classifying the five micro-classes of heartbeat types in the MIT- BIH Arrhythmia database. The five types of heartbeat features are classified, and wavelet self-adaptive threshold denoising method is used in the experiments. Compared with BP neural network, random forest, and other CNN networks, the results show that the model proposed in this paper has better performance in accuracy, sensitivity, robustness, and anti-noise capability. Its accurate classification effectively saves medical resources, which has a positive effect on clinical practice.

On Equivalence of FIS and ELM for Interpretable Rule-Based Knowledge Representation.

This paper presents a fuzzy extreme learning machine (F-ELM) that embeds fuzzy membership functions and rules into the hidden layer of extreme learning machine (ELM). Similar to the concept of ELM that employed the random initialization technique, three parameters of F-ELM are randomly assigned. They are the standard deviation of the membership functions, matrix-C (rule-combination matrix), and matrix-D [don't care (DC) matrix]. Fuzzy if-then rules are formulated by the rule-combination Matrix of F-ELM, and a DC approach is adopted to minimize the number of input attributes in the rules. Furthermore, F-ELM utilizes the output weights of the ELM to form the target class and confidence factor for each of the rules. This is to indicate that the corresponding consequent parameters are determined analytically. The operations of F-ELM are equivalent to a fuzzy inference system. Several benchmark data sets and a real world fault detection and diagnosis problem have been used to empirically evaluate the efficacy of the proposed F-ELM in handling pattern classification tasks. The results show that the accuracy rates of F-ELM are comparable (if not superior) to ELM with distinctive ability of providing explicit knowledge in the form of interpretable rule base.