Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and Testing of Non-Invasive BCI + FES/Robot Sys- tem For Use in Motor Re-Learning After Stroke

and tested a brain computer interface (BCI) system for use by stroke survivors for upper limb mo- tor learning. A non-invasive BCI system was interfaced with a robot for shoulder/elbow movement training or an FES surface stimulator for wrist/hand training. Two healthy adults and five chronic stroke survivors were tested using the BCI + device system for motor learning feasibility. Measures were accuracy of brain signal control during imposed motor tasks: either shoulder/elbow or wrist/hand tasks that were 1) imagined or 2) attempted, as well as 3) imposed relaxation of muscles. Initial session accuracy for each condition was generated. Results showed that initial session accuracy was high for imagined and attempted tasks. Subjects had less accuracy with the relaxation task than with the imagined or attempted movement tasks. The brain signal accuracy was high enough to indicate the feasibility of using the BCI + FES/Robot for the purpose of motor learning. Published: Pro- ceedings of the 13 th Annual Conference of the International Functional Electrical Stimulation Society "From Movement to Mind", 53 (Sept/2008; Freiburg, Germany); pp.200-202, Supplement 1, ISSN 0939-4990.
Boeing 757-200 Interior Simulation for Cabin Air Quality Analysis

With increased competition in the flight industry Icelandair has heightened their focus on the well being of their passengers even more as well as upgrading both the seats and the entertainment system on board most if not all of their aircraft. A few complaints have been lodged regarding the temperature in the cabin from both passengers as well as the crew. In order for Icelandair to maintain their competitive edge it is important for them to discover whether these complaints are based in fact, and if there are some reasonable solutions present to improve the overall passenger comfort during flight. This thesis explores whether there is a way to establish a baseline for the air conditioning system so that a standardized measurement test could be carried out in an arbitrary aircraft, would show if said aircraft system is functioning as intended compared to a baseline model. To this end measurements were carried out on board an empty aircraft in Keflavik airport. To establish a baseline the decision was made to model the aircraft using the 3D Computer Aided Design (CAD) program Autodesk Inventor Professional (AIP) and import that model into Autodesk Simulation CFD (ASCFD) to simulate the measured data. First the whole passenger cabin was simulated but later it was decided to build a smaller surrogate for optimization of the cabin due to high element count and thus a long solution time. The results from the large simulated passenger cabin does however suggest that there are no dead spots or stagnant air pockets present in the cabin. The smaller surrogate of the aircraft called “The Slice” included three seat rows. To compare the measured data to the simulations Matlab was used to estimate the transfer functions of both the measured and the simulated data from a set of seven time periods resulting from the measurements. The systems were then plotted for comparison. The starting temperature of each period was used as a starting point in the corresponding simulation. To compare each time period to its corresponding simulation the step response of both the measured and simulated time periods were plotted. The overlaid plots show that the simulations are well grounded to the measured data. To establish a baseline does however require more measurements than from only one plane as was the case in this thesis. The results do however suggest that it would be feasible to model such a baseline for a simple way to determine if the air conditioning system on board some randomly selected aircraft is functioning as intended compared to the baseline model.
Developmental emergence of sleep rhythms enables long-term memory capabilities in Drosophila

In adulthood, sleep-wake rhythms are one of the most prominent behaviors under circadian control. However, during early life, sleep is spread across the 24-hour day (1–4). The mechanism through which sleep rhythms emerge, and the consequent advantage conferred to a juvenile animal, are unknown. In 2nd instar Drosophila larvae (L2), like human infants, sleep is not under circadian control (5). Here, we identify the precise developmental timepoint when the circadian clock begins to regulate sleep in Drosophila, leading to the emergence of sleep rhythms at the early 3rd instar stage (L3). At this stage, a cellular connection forms between DN1a clock neurons and arousalpromoting Dh44 neurons, bringing arousal under clock control to drive the emergence of circadian sleep. Finally, we demonstrate that L3 but not L2 larvae exhibit long-term memory (LTM) of an aversive cue, and that this LTM depends upon deep sleep generated once sleep rhythms begin. We propose that the developmental emergence of circadian sleep enables more complex cognitive processes, including the onset of enduring memories.
Comparisons of Forecasting for Survival Outcome for Head and Neck Squamous Cell Carcinoma by using Machine Learning Models based on Multi-omics

Background: Machine learning methods showed excellent predictive ability in a wide range of fields. For the survival of head and neck squamous cell carcinoma (HNSC), its multi-omics influence is crucial. This study attempts to establish a variety of machine learning multi-omics models to predict the survival of HNSC and find the most suitable machine learning prediction method. Methods: The HNSC clinical data and multi-omics data were downloaded from the TCGA database. The important variables were screened by the LASSO algorithm. We used a total of 12 supervised machine learning models to predict the outcome of HNSC survival and compared the results. In vitro qPCR was performed to verify core genes predicted by the random forest algorithm. Results: For omics of HNSC, the results of the twelve models showed that the performance of multi-omics was better than each single-omic alone. Results were presented, which showed that the Bayesian network(BN) model (area under the curve [AUC] 0.8250, F1 score=0.7917) and random forest(RF) model (area under the curve [AUC] 0.8002,F1 score=0.7839) played good prediction performance in HNSC multi-omics data. The results of in vitro qPCR were consistent with the RF algorithm. Conclusion: Machine learning methods could better forecast the survival outcome of HNSC. Meanwhile, this study found that the BN model and the RF model were the most superior. Moreover, the forecast result of multi-omics was better than single-omic alone in HNSC.
Prenatal Detection of Congenital Fetal Heart Diseases using 2D greyscale ultrasound combined with Doppler in a high-risk population

Aim: We aimed to assess the accuracy and predictive value of fetal cardiac screening for congenital heart disease (CHD) at 18-22 gestational weeks in high-risk patients. Methods: In this study of fetal cardiac screening, four-chamber view, three-vessel view, outflow tracts followed by Doppler were recorded by obstetricians at 18–22 gestational weeks. A total of 438 fetuses that were scheduled for delivery at our hospital were included. Results: Forty-four newborns were born with CHD (10.9%). The prenatal detection rate of CHD was (72.7%). Although 80% of structural abnormality was diagnosed antenatally, only 44.4 % of valvular abnormality was diagnosed. Conclusion: Detailed fetal heart scan performed by skilled obstetricians had a high detection rate for structural rather than valvular CHD in high-risk populations. The integration of this scan with multiple appropriate cardiac views into routine second trimester scan may improve the detection rate of fetal CHD and give a chance for parental counseling.
High Capacity Optical Transmission based on Dispersion Supported Transmission with a Binary Signal at the Receiver

The conversion of frequency modulation to amplitude modulation due to fiber chromatic dispersion is described. A 5 Gbit/s optical transmission system is presented. It uses the method of FM-AM conversion supported transmission. Analysis of conversion of a frequency-modulated signal to amplitude modulation through dispersion in a linear and nonlinear optical fiber is presented. The propagation of optical field through a fiber is described by nonlinear Schrodinger equation, which is solved numerically by the split-step Fourier method. A novel modulation scheme for dispersion supported transmission is proposed. The linear frequency modulation along with the Manchester coding is used.
Research of Diesel Domain Ontology Frame Construction

The paper aims at knowledge management and application problems during the diesel research process, reviews the development of ontology in the Semantic Web domain, analyzes the requirement of the knowledge ontology frame, puts forward the diesel domain ontology candidate principle, constructs the whole lifecycle of the diesel engine oriented ontology. The ontology construction tool and interface development could be applied to the knowledge searching, which could promote and popularize the knowledge management system.
Cluster analysis of MNIST data set

The full name of MNIST is Mixed National Institute of Standards and Technology database. This is a very large database of handwritten digits. The data set is divided into two parts: the first part is 60,000 training set images, the second part is 10,000 test set images, each image is composed of 28×28 pixels, and the value of each pixel between 0 and 1, based on this data set, not only can a judgment method for handwritten numbers be designed, but also the accuracy of the recognition algorithm can be compared through the test set. At present, machine learning experts around the world use different learning methods to test the data set. At present, the mainstream methods with higher accuracy are: support vector machines, convolutional neural networks, and naive Bayes classification. This article tries to use the clustering method to train the MNIST data set. First, the image data of the training set is converted into 60000×785 rows of two-dimensional matrix data, one of which is the real value of the image, and then through K-means the algorithm divides the data into 10 categories, uses the T-SNE algorithm to reduce the dimensionality, and visualizes by randomly selecting 1000 training data. It is found that the clustering effect of the numbers 0, 6, and 8 is the best, while the number 1 is completely invisible, the accuracy of K-means algorithm clustering is 83.33%. In order to further explore the test effects of different clustering methods on the data set, other clustering methods are introduced: MiniBatchKMeans algorithm. When the cluster is set to 10, the clustering of data 6 and 8 in MiniBatchKMeans the effect is good. At this time, the accuracy of the MiniBatchKMeans algorithm clustering is 87.67%. By comparing the test results of other algorithms, it is found that the MiniBatchKMeans algorithm is better than the K-means algorithms and the clustering result of is better than some supervised learning methods.
Denoising Adversarial Examples Using CNN Models

It has always been a complicated problem to resolve adversarial attacks because figures with adversarial attacks look similar to the original figures so that models can be fooled. With deceptive data, adversarial attacks can be a threat to neural networks. There are various ways to generate adversarial attacks. For instance, they are using one-step perturbation and using multi-step perturbation. In both methods, noise is added to the images. Therefore, a question pops up: are adversarial attacks similar to normal random noise? This paper aims to find if there is anything in common between random noise and adversarial attacks. A normal denoising CNN model is trained with random noise. Then groups of adversarial examples are collected by training on LeNet. Next, the denoising CNN model has been used to denoise those adversarial examples. Finally, after denoising the adversarial examples with the CNN model trained on normal random noise, the classification accuracy increases. Thus, it is reasonable to conclude that normal random noise and adversarial tracks have some common patterns.
Performance Analysis of Three kinds of Neural Networks in the Classification of Mask Images

The global spread of COVID-19 has led to a massive increase in public demand for effective measures to slow the spread of the virus. In response, the World Health Organization has advised people to wear face masks every day to stop the spread of the virus. In facial mask recognition, traditional manual feature extraction methods are cumbersome and inaccurate, considering the various types of masks and other practical problems. This paper considers various neural network models comprehensively and selects three classical CNN networks, including VGG, ResNet, and DenseNet. After using the data expansion and optimization methods, good data results are obtained for the three network models. Remarkably, the improved DenseNet accuracy can reach 99.55%.
UAV Base Station Site Selection Based on Spiral Algorithm in Complex Environment

The Wireless broadband communication system is the wireless communication hub and the main means of communication under the background of actual combat.In complex environments such as terrain impact and external interference strikes, the on-board base station of wireless broadband communication system may not work properly. As an air base station, UAV ensures the continuity and reliability of communication.Aiming at the problem of UAV base station location in complex environment, this paper proposes a UAV base station location method based on spiral algorithm.Firstly, this paper establishes the air-to-ground propagation model based on the characteristics of the actual geographical environment.Secondly, the coverage capability of the UAV base station is obtained by link budget and the optimal height analysis of the UAV.Finally, the location planning of the U AV base station is completed by the spiral algorithm. The simulation results show that the spiral algorithm needs fewer UAV base stations than the k-means algorithm, which can reduce the cost of the network. The method proposed in this paper can select the appropriate site for the base station of the UAV under the complex environment.
Topological analysis of traffic pace via persistent homology

We develop a topological analysis of robust traffic pace patterns using persistent homology. We develop Rips filtrations, parametrized by pace, for a symmetrization of traffic pace along the (naturally) directed edges in a road network. Our symmetrization is inspired by recent work of Turner (2019 Algebr. Geom. Topol. 19 1135–1170). Our goal is to construct barcodes which help identify meaningful pace structures, namely connected components or ‘rings’. We develop a case study of our methods using datasets of Manhattan and Chengdu traffic speeds. In order to cope with the computational complexity of these large datasets, we develop an auxiliary application of the directed Louvain neighborhood-finding algorithm. We implement this as a preprocessing step prior to our main persistent homology analysis in order to coarse-grain small topological structures. We finally compute persistence barcodes on these neighborhoods. The persistence barcodes have a metric structure which allows us to both qualitatively and quantitatively compare traffic networks. As an example of the results, we find robust connected pace structures near Midtown bridges connecting Manhattan to the mainland.
Can you hear me now? Clinical applications of audio recordings

Audio and speech have several implicit characteristics that have the potential for the identification and quantification of clinical disorders. This PRISMA-guided review is designed to provide an overview of the landscape of automated clinical audio processing to build data-driven predictive models and infer phenotypes of a variety of neuropsychiatric, cardiac, respiratory and other disorders. We detail the important components of this processing workflow, specifically data acquisition and processing, algorithms used and their customization for clinical applications, commonly used tools and software, and benchmarking and evaluation methodologies. Finally, we discuss important open challenges for the field, and potential strategies for addressing them.
Trends in Racial and Ethnic Disparities in Barriers to Timely Medical Care Among US Adults, 1999 to 2018

Background: Racial and ethnic disparities in delayed medical care due to reasons not related to cost of care remain understudied. We aimed to describe recent 20-year trends in such disparities. Methods: The study used data from the National Health Interview Survey from 1999 to 2018 and included individuals [≥]18 years of age. Respondents were categorized by their sex, income, and self-reported race and ethnicity. The main outcomes were trends in disparities in any of 5 specific barriers to timely medical care: inability to get through by phone, no appointment available soon enough, long waiting times, inconvenient doctor's office/clinic hours, or lack of transportation. Results: The study included 590,603 adults (mean age 46.0 [SE, 0.07] years; 51.9% female). Of these, 4.7% were Asian, 11.8% Black, 13.8% Latino/Hispanic, and 69.7% White. In 1999, the proportion reporting any of the barriers to timely medical care was 7.3% among Asian, 6.9% among Black, 7.9% among Latino/Hispanic, and 7.0% among White individuals (P >0.05 for each difference with White individuals). From 1999 to 2018, this proportion increased across all 4 race/ethnicity groups (P<0.001 for each), slightly increasing the disparities between groups. In 2018, compared with White individuals, the proportion reporting any barrier was 2.1 and 3.1 percentage points higher among Black and Latino/Hispanic individuals (P=0.03 and P=0.001, respectively). The racial/ethnic disparities increased mostly among males and were attenuated when stratified by income level. Conclusions: From 1999 to 2018, barriers to timely medical care increased for all populations with increasing disparities between racial/ethnic groups.
The Design of Resonator for Miniaturization of Magnetic Resonance Wireless Power Transfer System

In this paper, we miniaturized the loop and coil in magnetic resonance wireless power transfer(MR-WPT) system for application to the small mobile device. The proposed disk type double coil resonator was designed to cause resonance at 6.87 MHz. It is composed of thin copper on both-side of acrylic substrate structured 2 mm width, 1 mm pitch and 8 turns. The outer radius of spiral coil pattern is 9 cm. And the proposed loop was made of the copper wire 5 mm diameter of cross-section. The size of loop is 10 cm diameter. For resonance at 6.87 MHz, the capacitor with 3,300 pF was connected in series on the loop. We rearranged the resonators and organized several WPT systems which is rearranged by resonators. The highest transfer efficiency of miniaturized WPT system was 35.67 %. This proposed design of spiral double coil will contribute to make resonator smaller for appling small and thin mobile device.
Perceptions of cervical cancer and motivation for screening among women in Rural Lilongwe, Malawi: A qualitative study

Introduction Cervical cancer is the leading cause of cancer death among women in Malawi. Low awareness of cervical cancer and negative perceptions of screening can prevent women from participating in preventative strategies. We sought to explore perceptions and motivations for screening among women who participated in a cervical cancer screen-and-treat pilot study in rural Malawi. Materials and methods We conducted a qualitative sub-study of a community-based cervical cancer screen-and-treat pilot study in rural Lilongwe between July-August 2017. From October 2017-February 2018, 17 women who underwent screening using visual inspection with acetic acid (VIA) and same-day thermal ablation treatment were recruited at their 12-week follow-up visit post treatment to participate in this qualitative sub-study. Semi-structured interview guides that explored baseline knowledge of cervical cancer, perceptions, and motivation for screening were used for in-depth interviews (IDIs). IDIs were conducted in the local language, Chichewa, translated and transcribed to English. Data was analyzed using NVivo® V12.0. Results Findings included fatalistic views on cancer, but limited knowledge specific to cervical cancer. Misconceptions of cervical cancer screening were common; however, there was a unique understanding of screening as prevention (i.e., finding and treating early disease to prevent progression to worsening disease). This understanding appeared to stem from HIV prevention concepts known to the community. Motivations for screening included desire to know one’s health status, convenience of community-based screening, and peer encouragement. Conclusion Despite limited knowledge of cervical cancer and misconceptions of screening, the concept of screening for prevention, desire to know one’s health status, convenient access, and peers’ influence were motivators for participation in screening. Cervical cancer screen-and-treat programs in high HIV prevalence areas should consider utilizing language that parallels HIV prevention language to communicate the need for cervical cancer screening and treatment and utilize prevention concepts that may already be familiar to women living there.
Evaluation of urine sample for diagnosis of visceral leishmaniasis using rK-39 immunochromatographic test in Northwest Ethiopia

Background Visceral leishmaniasis is the most severe form of leishmaniasis which ranks second in mortality and fourth in morbidity. Parasitological diagnostic techniques with splenic aspirate remain the gold standard. However, sample collection is risky, painful, and difficult. Alternatively, serological techniques provide good diagnostic accuracy using serum sample that is difficult for applying on small children and in the field. So, finding alternative non-invasive and self-collected samples like urine is very important. Thus, the study aimed to evaluate the diagnostic performance of the rK-39 strip test using urine for diagnosis of visceral leishmaniasis. Methods A multicenter institutional-based cross-sectional study was conducted from November 2019 to March 2021 at Northwest Ethiopia. Sociodemographic information was collected using a structured questionnaire. Blood sample and midstream urine sample were collected for rK-39 test. Data were entered into Epi-data version 4.2 and analyzed using SPSS version 24.0. Diagnostic performance parameters of urine-based rK-39 rapid test, i.e. sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios (LR+/−), and diagnostic accuracy were determined on contingency table by using serum-based rK-39 test result as a reference. An agreement between urine and serum-based rK-39 test was statistically determined by kappa value. Result In total, 300 subjects, age ranged between 7 and 60 years, were included in the study. The overall sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of urine-based rK-39 test were found to be 98.0% (95% CI: 93.0% - 99.8%), 95.5% (95% CI: 91.6% - 97.9%), 91.6% (95% CI: 85.2%– 95.4%), 98.9 (95% CI: 96.0%– 99.7%), and 96.33% (95% CI: 93.53–98.16%), respectively. Additionally, there was a strong agreement between the results obtained on rK-39 ICT using urine and serum samples (kappa = 0.92; P < 0.001). Conclusion Urine-based rK-39 ICT had an excellent high sensitivity, specificity and strong agreement with serum-based rK-39 ICT results. This indicates that urine sample would be a promising noninvasive and easy to collect sample for diagnosis of VL in field and rural settings.