Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of Machine Learning and Deep Learning Approaches for Detection of Mild Traumatic Brain Injury from Human Sleep Electroencephalogram

Traumatic Brain Injury (TBI) is a highly prevalent and serious public health concern. Most cases of TBI are mild in nature, yet some individuals may develop following-up persistent disability. The pathophysiologic causes for those with persistent postconcussive symptoms are most likely multifactorial and the underlying mechanism is not well understood, although it is clear that sleep disturbances feature prominently in those with persistent disability. The sleep electroencephalogram (EEG) provides a direct window into neuronal activity during an otherwise highly stereotyped behavioral state, and represents a promising quantitative measure for TBI diagnosis and prognosis. With the ever-evolving domain of machine learning, deep convolutional neural networks, and the development of better architectures, these approaches hold promise to solve some of the long entrenched challenges of personalized medicine for uses in recommendation systems and/or in health monitoring systems. In particular, advanced EEG analysis to identify putative EEG biomarkers of neurological disease could be highly relevant in the prognostication of mild TBI, an otherwise heterogeneous disorder with a wide range of affected phenotypes and disability levels. In this work, we investigate the use of various machine learning techniques and deep neural network architectures on a cohort of human subjects with sleep EEG recordings from overnight, in-lab, diagnostic polysomnography (PSG). An optimal scheme is explored for the classification of TBI versus non-TBI control subjects. The results were promising with an accuracy of ∼95% in random sampling arrangement and ∼70% in independent validation arrangement when appropriate parameters were used using a small number of subjects (10 mTBI subjects and 9 age- and sex-matched controls). We are thus confident that, with additional data and further studies, we would be able to build a generalized model to detect TBI accurately, not only via attended, in-lab PSG recordings, but also in practical scenarios such as EEG data obtained from simple wearables in daily life.
Accuracy of Wrist-Worn Photoplethysmography Devices at Measuring Heart Rate in the Laboratory and During Free-Living Activities

This study compared heart rate (HR) measurements taken from two wrist-worn devices; the Empatica E4 and the Apple Watch Series 5, to that taken from a Polar H10 chest strap. Ten healthy adult volunteers took part in a laboratory validation study and performed a treadmill exercise protocol. A single-subject validity study was also conducted to evaluate the accuracy of continuous HR measurements obtained during free-living activities. The participant wore both wrist devices, as well as the Polar H10 for 12-hours, as she continued her habitual daily activities. The key findings of the laboratory study were that the Apple Watch was accurate at assessing HR compared to the Polar H10 with Mean Absolute Percentage Error (MAPE) values < 5% during treadmill exercise. The accuracy of the E4 however was generally poor with MAPE values > 15%. Findings from the single-subject validity study indicate that the Apple Watch produces accurate measurements of HR, whereas the E4 device overestimated HR, except for during the more strenuous activities undertaken where HR was underestimated.Clinical Relevance— The Apple Watch has acceptable accuracy in measuring HR during treadmill exercise and during free-living activities in healthy adult volunteers.
Automatic Onsets and Systolic Peaks Detection and Segmentation of Arterial Blood Pressure Waveforms using Fully Convolutional Neural Networks

Arterial blood pressure (ABP) waveform is a common physiological signal that contains a wealth of cardiovascular information. According to the cardiac cycle, the ABP waveform is divided into rapid ejection, systolic and diastolic phases. Therefore, the characteristic points of the arterial blood pressure waveform, i.e. their onsets, systolic peaks, represent the timing of the minimum and maximum pressures. It is important to detect these characteristic points accurately. Recently, many researchers have introduced some feature points detection methods, but the accuracy is not particularly high. In this paper, a deep learning method is proposed to achieve periodic segmentation and feature points detection of ABP signals using a one-dimensional U-Net network. The network can split the ABP signal into two parts and accurately detect the feature points. The method is validated on an ABP dataset of 126 people, 500 people each. Performances are good at different tolerance thresholds, with an average time difference of less than 1.5 ms. Finally, the method performs with 99.79% and 99.79% sensitivity, 99.99% and 99.94% positive predictivity, and 0.23% and 0.27% error rates for both onsets and systolic peaks at a tolerance threshold of 30 ms. To our knowledge, this is the first paper to use deep learning methods for the onsets and systolic peaks detections of ABP signals.
Evaluation of the quality of sachet water vended in the New Juaben municipality of Ghana

Access to good quality water has been a challenge to humans due to the pollution of the limited water resources around. Over one billion people do not have access to a safe source of drinking water. This phenomenon is leading to the loss of children below the age five (5) on an hourly basis. With the rate at which sachet water producing companies are coming up, there is the need to assess the quality of the water being produced for consumption. This research sought to ascertain the quality of sachet water produced and vended in the Koforidua Municipality of Ghana. Data was gathered through interviews and laboratory experiments. The results from the physico-chemical and bacteriological parameters of the samples indicated compliance with the Ghana standards on drinking water quality. The research recommended regular monitoring of the sachet water producing factories. Also the retailers should ensure proper hygiene to avoid contamination of the water. Key words: Sachet water, drinking water, water-related, standards, contamination.
The Impact of Foreign Trade on Carbon Emission Efficiency in the Yangtze River Delta

Based on the perspective of all factors, this article uses the super-efficiency SBM model that considers undesirable output to measure the carbon emission efficiency in the Yangtze River Delta, and studies the impact of foreign trade on the carbon emission efficiency by establishing the panel model. The study found that the overall carbon emission efficiency in the Yangtze River Delta is relatively high, among which Shanghai has the highest efficiency. In addition, there is a significant positive correlation between foreign trade and carbon emission efficiency, and expanding the level of foreign trade can further improve carbon emission efficiency. Finally, according to the above conclusions, the corresponding policy recommendations are given.
Factors influencing Intention to Purchase Fraudulent Honey among Malaysian Consumers

This paper aims to assess consumer awareness about fraudulent honey and its impact on purchase intention using an extended Theory of Planned Behaviour model. An online questionnaire survey was administered among honey consumers in Malaysia. Multiple regression analysis was employed on the 289-obtained-data to generate the relationship between awareness, attitude, subjective norms, perceived behavioural control, and consumers' intention to purchase fraudulent honey. Findings show that attitude and perceived behavioural control have a significant and positive relationship with the intention to purchase fraudulent honey. Consumers' unfavourable attitude towards fraudulent honey diminishes their intention to purchase it. Whereas, even with high perceived behavioural control, consumers decide to purchase fraudulent honey because it fulfils their needs. Consumers are aware of fraudulent honey and choose to buy it since it is cheaper and readily available. This study's practical implication is for marketers and policymakers to develop programs or create proper channels to educate consumers in making wise purchasing decisions. Ultimately, consumers have the right to make their own decision.
The Implementation Analysis of Interprofessional Collaboration In Medical Rehabilitation Services In Hospital

Rehabilitation has been one of the important keys in 21st century health strategy. Achieving optimal rehabilitation services is known to require several important components, one of which is synergy and harmonious collaboration between health professionals in the medical rehabilitation team. The purpose of this study is to assess the implementation of interprofessional collaboration in medical rehabilitation services and the factors that influence it at the Indonesian national referral hospitals. This is an analytical observational study with a cross sectional design. The type of data used in this study is primary data in the form of the Assessment of Interprofessional Team Collaboration Scale (AITCS)-II questionnaire which is filled out directly by 2 people from each type of health profession in the medical rehabilitation team at 9 Indonesian national referral hospitals. Data were analyzed using SPSS software. The average score of the AITCS-II from all respondents is 99.88 + 11.80 out of a maximum score of 115. There is no significant relationship between the type of profession, age, gender, and length of work both in the profession and in the Medical Rehabilitation Unit, but the level of education influence the implementation of Interprofessional Collaboration in medical rehabilitation unit significantly (p=0,028). The finding shows significant difference between Diploma and Professional education level (p=0,093), and between Diploma and Specialist education level (p=0,008).
Effects of Remittances on Income Inequality in Bangladesh: A Macroeconomic Analysis

This study attempts to examine the effects of remittances on income inequality in Bangladesh over the period of 1990 to 2016. The study period has been chosen based on data availability at macro level. To serve the purpose, Autoregressive Distributed Lag (ARDL) cointegration technique is applied since unit root test confirms a combination of variables some which are stationary at level and others become stationary after first difference. The error correction model estimated by reparametrizing the ARDL model after having confirmation about the existence of long run relationship through bound test. An inverted U-shaped relationship between the remittances and income inequality has been found by the study. Remittances increase income inequality in short run and decrease income inequality in the long run. In the long run an increase in remittances by 1 per cent reduces the income inequality by 11 per cent on an average. The adjustment coefficient has the expected negative sign at 1 per cent level of significance which ensure a monotonically convergent adjustment towards the equilibrium with the speed of 21.65 per cent. Among other control variables, private credit increases income inequality in the long run and decrease it in the short run. Exchange rate and inflation either have very negligible effects or no significant effects both in short run and in long run. Based on the findings, it could be suggested that the country should take proper steps to encourage investment of remittances in productive activities to accumulate capital which could ensure the path of accruing the beneficial impacts of remittances on income inequality in the long run. Original Research Article Nessa ; SAJSSE, 12(4): 106-118, 2021; Article no.SAJSSE. 73471 107
Predicting user behavior over social networks

: Online social networks have been more popular in recent years, especially Facebook, Twitter, and LinkedIn. There is a lot of interest right now in figuring out how people interact with one another on social media. As far as I know, existing link prediction algorithms forecast connections based on topological structure characteristics as well as on node attribute characteristics. This leaves out important aspects such as click-through rates and forwarding potential for a social network as a whole. A link prediction technique based on user behaviours such as clicks, shares, likes, forwarding and comments is proposed to fill this void. We provide a link prediction model that combines metrics for user activity with topological structure metrics in this article. New metrics for link prediction may be defined using this suggested measure, which can be used to bridge the gap between current techniques. While this study is still a work in progress, next directions include investigating how the suggested metrics might be implemented on common datasets by properly training the classifiers.
Classifying Unimanual and Bimanual Upper Extremity Tasks in Individuals Post-Stroke

After stroke, many individuals develop impairments that lead to compensatory motions. Compensation allows individuals to achieve tasks but has long-term detrimental effects and represents maladaptive motor strategies. Increased use of bimanual motions may serve as a biomarker for recovery (and the reduction of reliance on compensatory motion), and tracking such motion using sensor data may provide critical data for health care specialists. However, past work by the authors demonstrated individual variation in motor strategies results in noisy and chaotic sensor data. The goal of the current work is to develop classifiers capable of differentiating unimanual, bimanaual asymmetric, and bimanual symmetric gestures using wearable sensor data. Twenty participants post-stroke (and 20 age-matched controls) performed a set of tasks under the supervision of a trained occupational therapist. Sensor data were recorded for each task. Classifiers were developed using artificial neural networks (ANNs) as a baseline, and the echo state neural network (ESNN) which has demonstrated efficacy with chaotic data. We find that, for control and post-stroke participants, the ESNN results in improved testing accuracy performance (91.3% and 80.3%, respectively). These results suggest a novel method for classifying gestures in individuals post-stroke, and the developed classifiers may facilitate longitudinal monitoring and correction of compensatory motion.
Computational fluid dynamics simulation of rail vehicles in crosswind: Application in norms and standards

The application of computational fluid dynamics (CFD) to the determination of aerodynamic coefficients for crosswind stability in the context of vehicle assessment has been studied as part of the AeroTRAIN project. The work consisted in establishing best practice guidelines for the use of standard Reynolds-averaged Navier–Stokes (RANS) approaches using a streamlined and less-streamlined vehicle, project partners applying different computational codes and turbulence models to a common vehicle, and then application to further vehicles in order to cover a range of different vehicles and yaw angles. The simulations were complimented with wind tunnel measurements to allow the accuracy of standard RANS approaches to be judged for various vehicle shapes and yaw angles. This paper summarises the overall results and the recommendations made for the use of CFD in vehicle assessment of crosswind stability in relation to the EN 14067-6: 2010 standard. The main aspects of the guidelines are reported in a separate paper in this special issue. The considered standard allows the use of CFD for vehicle speeds up to a maximum of 200 km/h whereas the HS RST TSI (2008) only allows aerodynamic coefficients to be determined using wind tunnel measurements. The obtained results show that a well-performed RANS CFD can predict the aerodynamic coefficient of streamlined trains with a relatively high accuracy. The challenges increase for blunter-shaped trains and may be further influenced by equipment installed on the roof of a train. Combined with the developed simulation guidelines it is considered that CFD can be used as an alternative to wind tunnel tests in all cases provided that the accuracy of the approach is validated on a benchmark train with similar features to those of the simulated train.
Person and Stressor Independent Generic Model for Stress Detection Using GSR

Stress detection is a widely researched topic and is important for overall well-being of an individual. Several approaches are used for prediction/classification of stress. Most of these approaches perform well for subject and activity specific scenarios as stress is highly subjective. So, it is difficult to create a generic model for stress prediction. Here, we have proposed an approach for creating a generic stress prediction model by utilizing knowledge from three different datasets. Proposed model has been validated using two open datasets as well as on a set of data collected in our lab. Results show that the proposed generic model performs well across studies conducted independently and hence can be used for monitoring stress in real life scenarios and to create mass-market stress prediction products.
Interstellar Nitrogen Isotope Ratios: New NH3 Data from the Galactic Center out to the Perseus Arm

Our aim is to measure the interstellar 14N/15N ratio across the Galaxy, to establish a standard data set on interstellar ammonia isotope ratios, and to provide new constraints on the Galactic chemical evolution. The (J, K) = (1, 1), (2, 2), and (3, 3) lines of 14NH3 and 15NH3 were observed with the Shanghai Tianma 65 m radio telescope (TMRT) and the Effelsberg 100 m telescope toward a large sample of 210 sources. One hundred fourty-one of these sources were detected by the TMRT in 14NH3. Eight of them were also detected in 15NH3. For 10 of the 36 sources with strong NH3 emission, the Effelsberg 100 m telescope successfully detected their 15NH3(1, 1) lines, including 3 sources (G081.7522, W51D, and Orion-KL) with detections by the TMRT telescope. Thus, a total of 15 sources are detected in both the 14NH3 and 15NH3 lines. Line and physical parameters for these 15 sources are derived, including optical depths, rotation and kinetic temperatures, and total column densities. 14N/15N isotope ratios were determined from the 14NH3/15NH3 abundance ratios. The isotope ratios obtained from both telescopes agree for a given source within the uncertainties, and no dependence on heliocentric distance and kinetic temperature is seen. 14N/15N ratios tend to increase with galactocentric distance, confirming a radial nitrogen isotope gradient. This is consistent with results from recent Galactic chemical model calculations, including the impact of superasymptotic giant branch stars and novae.
Coronary Stenting versus Bypass Graft Surgery for Management of Left Main Coronary Artery Disease in the Setting of Acute Myocardial Infarction: A Retrospective Cohort Study

Background: There is a lack of published research that compare stenting versus coronary artery bypass grafting (CABG) for patients with left main coronary artery (LMCA) disease. This research compared the safety and efficacy of stents versus CABG for patients with LMCA disease in the setting of acute myocardial infarction. Materials and Methods: A retrospective chart review was conducted to retrieve the records of LMCA who underwent coronary stenting or CABG. We compared both techniques in terms of major adverse cardiovascular and cerebrovascular events (MACCE) and postoperative complications. Results: Sixty patients were included (30 patients in each group). The incidence of periprocedural mortality was equal between PCI and CABG groups (6.7% versus 10%). Likewise, the overall incidence of periprocedural complications was comparable between both groups (13.3% versus 20%). The incidence of immediate post procedural complications was 20% and 30% in PCI and CABG groups, respectively (p =0.37). Likewise, the rate of immediate post procedural mortality was 3.3% and 6.7%, respectively (p =1.0). Both groups exhibited similar rates of late post procedural complications as well (p =0.25). Conclusion: In conclusion, PCI and CABG had comparable postoperative outcomes in LMCA patients in the setting of acute myocardial infarction. Further randomized controlled trials with larger sample size and longer follow-up period are required to evaluate the safety and efficacy of both techniques in such patients.
Investigating the neural signature of microsleeps using EEG

A microsleep (MS) is a complete lapse of responsiveness due to an episode of brief sleep (≲ 15 s) with eyes partially or completely closed. MSs are highly correlated with the risk of car accidents, severe injuries, and death. To investigate EEG changes during MSs, we used a 2D continuous visuomotor tracking (CVT) task and eye-video to identify MSs in 20 subjects performing the 50-min task. Following pre-processing, FFT spectral analysis was used to calculate the activity in the EEG delta, theta, alpha, beta, and gamma bands, followed by eLORETA for source reconstruction. A group statistical analysis was performed to compare the change in activity over EEG bands of an MS to its baseline. After correction for multiple comparisons, we found maximum increases in delta, theta, and alpha activities over the frontal lobe, and beta over the parietal and occipital lobes. There were no significant changes in the gamma band, and no significant decreases in any band. Our results are in agreement with previous studies which reported increased alpha activity in MSs. However, this is the first study to have reported increased beta activity during MSs, which, due to the usual association of beta activity with wakefulness, was unexpected.
Investigation of Sleep-Dependent Activation-Interaction Association Network

The cortical activation and the interaction between cortical regions were considered to exist a strong correlation in recent neuroscience researches. However, such association during sleep was still unclear. The aim of the present work was to further investigate this association according to an activation-interaction association network. This study included 24 healthy individuals and all of them underwent overnight polysomnography. The absolute spectral powers of three frequency bands and the phase transfer entropy were extracted from six electroencephalogram channels. For each frequency band and sleep stage, activation-interaction association networks were built and correlation analysis was conducted by using Pearson correlation test. Results revealed the evident association between features derived from the two approaches during sleep, and as the sleep deepened, these correlation values attenuated in the alpha band, whereas the inversion happened in the delta band. This study exposed more detailed information of cortical activity during sleep, which will facilitate us to conduct research from a more comprehensive perspective, helping us make a more appropriate evaluation and explanation.
A 2.4 GHz single chip transceiver

A GaAs transceiver circuit that includes all transmit and receive switching, amplification, frequency conversion and level shifting is described. An on-chip oscillator is also provided. Low receive current of 30 mA from a +5-V supply and a standby current of less than 0.5 mA make this an ideal component for battery-powered operation. <<ETX>>