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For an electrical engineer, training of power system configuration and operation is an essential problem. This paper proposes new training device which consists of separated circuit blocks (module). For example, induction generator, inverter, capacitor, measurement, are used. The trainee selects and assembles them to build a circuit then easy to conduct an experiment. This methodology is effective for group work so as it may be used for PBL (Project Based Learning) program. The concept and required skill to build this device are introduced in this paper.

Recently, the increasing penetration of renewable energy resources in power system, especially photovoltaic (PV) systems, has caused severe technical issues due to its randomness and dependency on primary sources. Therefore, precise output power forecast is vital for both system operators and PV system owners to improve grid stability and generation quality, respectively. This article proposes a novel hybrid deep learning model named EDSACL comprising four components, namely, convolutional neural network (CNN), long short‐term memory (LSTM), self‐attention mechanism (SAm), and residual learning (RL) strategy for short‐term PV maximum power prediction with forecast horizon of 1, 2, and 4 h. This proposed model initially extracts spatial‐temporal features of input data based on CNN and LSTM before using SAm to achieve hidden features of LSTM layers. RL strategy is employed to maintain the information flow entering the network. Besides, interval prediction is also implemented based on a combination of the EDSACL model and quantile regression with different probabilities. The forecast accuracy of the proposed model is validated based on two real‐world data sets. The predicted results show the superiority of the proposed model over numerous predictive models; in particular, mean absolute percentage error values of the proposed hybrid model are under 6% in all case studies. [ABSTRACT FROM AUTHOR]

About half of northeastern Asians lack ALDH2 (Acetaldehyde Dehydrogenase 2), an enzyme involved in alcohol metabolism. People with deficient ALDH2 often experience facial flushing after drinking a small dose of alcohol. The present study examined the effect of a small dose of alcohol on driving-related behavior of northeastern Asians (Japanese). Participants were 9 Japanese flushers and 12 Japanese nonflushers. They were evaluated using a driving simulator and self-report of intoxication after ingesting 0.6g/kg or 0.4g/kg of alcohol or a placebo. Facial flushers were more likely to experience subjective feelings of intoxication than were nonflushers. There were no significant differences between facial flushers and nonflushers in blood alcohol concentration and choice reaction time to visual stimuli. Despite having different subjective feelings of intoxication, facial flushers and nonflushers were equally impaired after having ingested equal amounts of alcohol per body weight. DOI: 10.2466/PMS.109.2.651-663

Smart buildings that utilize innovative technologies such as artificial intelligence (AI), the internet of things (IoT), and cloud computing to improve comfort and reduce energy waste are gaining popularity. Smart buildings comprise a range of sensors to measure real-time indoor environment variables essential for the heating, ventilation, and air conditioning (HVAC) system control strategies. For accuracy and smooth operation, current HVAC system control strategies require multiple sensors to capture the indoor environment variables. However, using too many sensors creates an extensive network that is costly and complex to maintain. Our proposed research solves the mentioned problem by implementing a machine-learning algorithm to estimate unmeasured variables utilizing a limited number of sensors. Using a six-month data set collected from a three-story smart building in Japan, several extreme gradient boosting (XGBoost) models were designed and trained to estimate unmeasured room temperature, relative humidity, and CO2 concentrations. Our models accurately estimated temperature, humidity, and CO2 concentration under various case studies with an average root mean squared error (RMSE) of 0.3 degrees, 2.6%, and 26.25 ppm, respectively. Obtained results show an accurate estimation of indoor environment measurements that is applicable for optimal HVAC system control in smart buildings with a reduced number of required sensors. [ABSTRACT FROM AUTHOR]