Exercise Motivation among Fitness Center Members: A Combined Qualitative and Q-Sorting Approach

This study aimed to explore the components of Exercise Maintenance Motivation (EMM) and identify its consensus and distinguishing aspects among members of fitness centers (FCs) in Vietnam. The study incorporated both qualitative and Q-sorting methodologies across two stages. The first stage involved conducting ten in-depth and four focus-group interviews with 39 members of six different FCs in Vietnam, resulting in the generation of 40 EMM statements. In the second stage, these statements were subjected to Q-sorting by 39 participants. The KADE application for the Q method was used for data analysis, and Principal Component Analysis was employed to determine the optimal number of factors. The analysis yielded four factors, encompassing 34 statements and accounting for 86% of the variance in EMM components among participants. These components, labeled “F1. Exercise achievements”, “F2. Exercise environments”, “F3. Exercise enjoyment”, and “F4. Workout-aholic”, achieved consensus among 17 (37%), 14 (30%), 5 (12%), and 3 (7%) participants, respectively. The leading motivational expressions were “get to be healthier”, “a better-looking appearance”, and “get a fit body appearance”. These were followed by FC-based supportive exercise conditions, positive feelings, and exercise addiction. There were five consensus statements that spanned all four factors. The numbers of distinguishing statements varied across factors, with F1, F3, and F4 each contributing 11 (28.2%) and F2 contributing 15 (38.5%). This study contributed to the four central drivers of EMM. To facilitate the development of a comprehensive EMM scale, future research should incorporate larger samples, allowing for a dissection of motivational paradigms.   Doi: 10.28991/HEF-2023-04-03-07 Full Text: PD

Offering strategy of a price-maker virtual power plant in the day-ahead market

With the rapid increase of renewable energy sources (RESs), the virtual power plant model (VPP) has been developed to integrate RESs, energy storage systems (ESSs), and local customers to overcome the RESs’ disadvantages. When the VPP’s capacity is large enough, it can participate in the electricity market as a price-maker instead of a price-taker to obtain a higher profit. This study proposes a bi-level optimization model to determine the optimal trading strategies of a price-maker VPP in the day-ahead (DA) market. The operation schedule of the components in the VPP is also optimized to achieve the highest profit for the VPP. In the bi-level optimization problem, the upper-level model is maximizing the VPP’s profit while the lower-level model is the DA market-clearing problem. The bi-level optimization problem is formulated as a Mathematical Problem with Equilibrium Constraints (MPEC), reformulated to a Mixed Integer Linear Problem (MILP), then solved by GAMS and CPLEX. This study applies the bi-level optimization model to a test VPP system, including wind plants (WP), solar plants (PV), biogas energy plants (BG), ESSs, and several customers. The maximum power outputs of WP and PV are 100MW and 90MW, respectively. The total installed capacity of BG is 70MW, while the ESS’ rated capacity is 100MWh. The local customers have the highest total consumption of 100MW. In addition to the VPP, four GENCOs and three retailers participate in the DA market. The results show that the market-clearing price varies depending on the participants’ production/consumption quantity and offering/bidding price. However, based on the optimization model, the VPP can take full advantage of WP and PV available power output, choose the right time to operate BG, then obtain the highest profit. The results also show that with the ESS’ rated capacity of 100MWh, the ESS’ rated discharging/charging power increased from 10MW to 50MW will increase VPP’s profit from 45987$to 49464$. The obtained results show that the proposed model has practical significanc

Evaluating the Motor Imagery Classification Performance of a Double-Layered Feature Selection on Two Different-Sized Datasets

Numerous investigations have been conducted to enhance the motor imagery-based brain–computer interface (BCI) classification performance on various aspects. However, there are limited studies comparing their proposed feature selection framework performance on both objective and subjective datasets. Therefore, this study aims to provide a novel framework that combines spatial filters at various frequency bands with double-layered feature selection and evaluates it on published and self-acquired datasets. Electroencephalography (EEG) data are preprocessed and decomposed into multiple frequency sub-bands, whose features are then extracted, calculated, and ranked based on Fisher’s ratio and minimum-redundancy-maximum-relevance (mRmR) algorithm. Informative filter banks are chosen for optimal classification by linear discriminative analysis (LDA). The results of the study, firstly, show that the proposed method is comparable to other conventional methods through accuracy and F1-score. The study also found that hand vs. feet classification is more discriminable than left vs. right hand (4–10% difference). Lastly, the performance of the filter banks common spatial pattern (FBCSP, without feature selection) algorithm is found to be significantly lower (p = 0.0029, p = 0.0015, and p = 0.0008) compared to that of the proposed method when applied to small-sized data

Commonly misdiagnosed round pneumonia in a child: a case report

Round pneumonia, a specific radiological finding in children, is often caused by Streptococcus pneumoniae; but it is easily misdiagnosed with some other diseases, causing many difficulties for clinicians. We described a case report of round pneumonia in a 9-year-old boy, with chest pain, following fever, productive cough, left-sided pulmonary consolidation syndrome, tachypnea, no chest indrawing, and a round homogenous lesion about 4 cm in diameter with a clear border in the left upper lobe position on chest X-ray. He was initially misdiagnosed as a lung tumor. He was correctly diagnosed with round pneumonia prior to pneumonectomy and was successfully treated with antibiotics. Therefore, it is important to carefully analyze round pneumonia cases that are often misdiagnosed, resulting in poor therapy