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The plasma current start-up experiment is conducted through electron cyclotron (EC) heating in the QUEST spherical tokamak. During the EC heating, the application of a toroidal electric field in the opposite direction to the plasma current effectively inhibits the growth of energetic electrons. Observations show rapid increases in plasma current and hard x-ray count immediately following the cancellation of the retarding electric field. When a compact tokamak configuration maintains equilibrium on the high field side, along with the retarding field, it leads to effective bulk electron heating. This heating achieved an electron temperature of T _e ≈ 1 keV at electron density n _e > 1.0 × 10 ^18 m ^−3 . Ray tracing of the EC wave verifies that more power absorption into plasma through a single-pass occurs around the second resonance layer with higher values of electron density and temperature.

Studies on the compounds of aromatic oils and their effects on psychophysiological changes in humans are often conducted separately. To obtain better validation, a suitable protocol is needed that can be extrapolated to large-scale olfactory stimulation experiments. Unfortunately, this type of study is still rarely performed. In this situation, we propose a randomized crossover pilot study on olfactory stimulation with aromatic oils in relation to changes in psychophysiological activity by focusing on white musk aromatic oil due to its popularity in the community. Chemical profiling by TDU-GC-MS (thermal desorption gas chromatography/mass spectrometry) was performed to understand the compounds of the aromatic oils presented. To understand the changes in the participants' impressions and mood states, POMS 2 (Profile of Mood States 2nd Edition) and VAS (Visual analogue scale) were performed in addition to physiological evaluation by using EEG (electroencephalogram), ECG (electrocardiogram) and salivary amylase measurements. The proposed pilot study showed "gorgeous", "sweet", and "like" impression toward white musk aromatic oil under VAS evaluation. Mood evaluation under POMS 2 variables such as Fatigue-Inertia (FI), Tension-anxiety (TA) and TMD (total mood disturbance) were significantly decreased under white musk aromatic oil inhalation. Under current protocol, we can also see the changes in autonomic activity and brain activity during olfactory stimulation. This pilot study could be the first step towards a larger sample size experiment on olfactory stimulation. This experiment has been registered to UMIN Clinical Trials Registry with register ID : UMIN000051972 on 24/08/2023., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Engagement is described as a state in which an individual involved in an activity can ignore other influences. The engagement level is important to obtaining good performance especially under study conditions. Numerous methods using electroencephalograph (EEG), electrocardiograph (ECG), and near-infrared spectroscopy (NIRS) for the recognition of engagement have been proposed. However, the results were either unsatisfactory or required many channels. In this study, we introduce the implementation of a low-density hybrid system for engagement recognition. We used a two-electrode wireless EEG, a wireless ECG, and two wireless channels NIRS to measure engagement recognition during cognitive tasks. We used electrooculograms (EOG) and eye tracking to record eye movements for data labeling. We calculated the recognition accuracy using the combination of correlation-based feature selection and k-nearest neighbor algorithm. Following that, we did a comparative study against a stand-alone system. The results show that the hybrid system had an acceptable accuracy for practical use (71.65 ± 0.16%). In comparison, the accuracy of a pure EEG system was (65.73 ± 0.17%), pure ECG (67.44 ± 0.19%), and pure NIRS (66.83 ± 0.17%). Overall, our results demonstrate that the proposed method can be used to improve performance in engagement recognition., Competing Interests: The authors declare no conflict of interest.

In this study, a monitoring system of cognitive state in usual behavior without restraint using wireless EEG, ECG and NIRS on developmental disorder children such as mental retardation was developed. By using this system we would like to investigate the education training effect on cognitive state in a mental retardation child in four years. The aim of special education is to make adaptations, accommodations and modification that allow a child with a mental retardation to succeed in classroom. This education training separated into two systems, resting state and studying state. After the measurement, we calculated the EEG power spectrum of alpha, theta bands and the low frequency power (LF), high frequency power (HF), and LH/HF value from the RR interval in ECG. At the same time we calculated the changes in concentration of oxyhemoglobin ([oxy-Hb]). The result in studying state showed theta power is lower than beta power in study state in the other hand beta power is lower than theta power in resting state From LF/HF activity we could know that sympathetic activity is increasing and from the result that has obtained from ECG, the parasympathetic activity is decreasing the time. NIRS showed the increasing in study states and decreasing in resting states at Fp2 area from oxyhemoglobin analysis.

Heart Disease affects approximately 70 million people worldwide where most people do not even know the symptoms. This research examines the prototype of early warning system for heart disease by android application. It aims to facilitate users to early detect heart disease which can be used independently. To build the application in android phone, variable centered intelligence rule system (VCIRS) as decision makers and pulse sensor - Arduino as heart rate detector were applied in this study. Moreover, in Arduino, the heart rate will become an input for symptoms in Android Application. The output of this system is the conclusion statement of users diagnosed with either coronary heart disease, hypertension heart disease, rheumatic heart disease or do not get any kind of heart disease. The result of diagnosis followed by analysis of the value of usage variable rate (VUR) rule usage rate (RUR) and node usage rate (NUR) that shows the value of the rule that will increase when the symptoms frequently appear. This application was compared with the medical analysis from 35 cases of heart disease and it showed concordance between diagnosis from android application and expert diagnosis of the doctors.