Miracle-dz/UniFAR-FTUs huggingface dataset:
Posttraumatic Stress Disorder in the Context of a Global Pandemic

Bordeaux, 3 ter Place de la Victoire 33000 Bordeaux, France. Addie Merians, PhD. , is affiliated with the U.S. Department of Veterans Affairs National Center for PTSD, VA Connecticut Healthcare System, 950 Campbell Avenue, West Haven, CT 06516-2770; and with the Department of Psychiatry, Yale School of Medicine, New Haven, CT. Robert H. Pietrzak, PhD. , is affiliated with the U.S. Department of Veterans Affairs National Center for PTSD, VA Connecticut Healthcare System, 950 Campbell Avenue, West Haven, CT 06516-2770; and with the Department of Psychiatry, Yale School of Medicine, New Haven, CT. Brian P. Marx, PhD. , is affiliated with the U.S. Department of Veterans Affairs National Center for PTSD, VA Boston Healthcare System, 150 South Huntington Avenue, Boston, MA 02130-4817; and with the Department of Psychiatry, Boston University School of Medicine, Boston, MA. Email Addresses: mathilde.husky@u-bordeaux.fr; addie.merians@yale.edu; robert.pietrzak@yale.edu; and Brian.Marx@va.gov. Introduction
Design and Analysis of Positive Output Double Gain Buck-Boost Converter

This paper formulates a positive output double gain buck-boost converter with a low input current ripple. Compared with the conventional buck-boost and quadratic buck-boost configuration, this converter’s voltage conversion ratio is higher with low voltage stress on the intermediate semiconductor devices, diodes and capacitors. Additionally, it exhibits low current stress on the intermediate inductors and power switching devices. The detailed current and voltage relations of active and passive elements are derived using time-domain analysis. The state-space matrices are formulated through the circuit analysis and based on the estimated transfer function of the converter configuration, the closed-loop digital controller is designed. The theoretical analysis is validated through the key steady-state and closed-loop dynamic results obtained from the PSIM simulation.
Multi-agent Feature Selection for Integrative Multi-omics Analysis

Multiomics data integration is key for cancer prediction as it captures different aspects of molecular mechanisms. Nevertheless, the high-dimensionality of multi-omics data with a relatively small number of patients presents a challenge for the cancer prediction tasks. While feature selection techniques have been widely used to tackle the curse of dimensionality of multi-omics data, most existing methods have been applied to each type of omics data separately. In this paper, we propose a multi-agent architecture for feature selection, called MAgentOmics, to consider all omics data together. MAgentOmics extends the ant colony optimization algorithm to multi-omics data, which iteratively builds candidate solutions and evaluates them. Moreover, a new fitness function is introduced to assess the candidate feature subsets without using prediction target such as survival time of patients. Therefore, it can be considered as an unsupervised method. We evaluate the performance of MAgentOmics on the TCGA ovarian cancer multi-omics data from 176 patients using a 5-fold cross-validation. The results demonstrate that the integration power of MAgentOmics is relatively better than the state-of-the-art supervised multi-view method. The code is publicly available at https://github.com/SinaTabakhi/MAgentOmics. Clinical relevance- Discovering knowledge in existing multi-omics datasets through better feature selection enhances the clinical understanding of cancers and speeds-up decision-making in the clinic.
Finite Element Modeling for Thermal Conductivity of Cement-based Encapsulation Materials

With the trend of miniaturization and the increasing power density, the operating temperature of electronic devices keeps climbing, especially for wide band-gap semiconductors such as silicon carbide and gallium nitride. The high operating temperature up to 250℃ brings challenges to encapsulation materials since traditional encapsulation materials such as epoxy resins and silicone gels hardly bear temperatures above 200℃. Calcium aluminate cement (CAC) was proved to be a promising encapsulation material, which owns high thermal stability with its operating temperature of up to 300℃. Based on its satisfied thermal stability and low cost, the thermal conductivity of CAC was researched in this work with different ratios of 10-μm-sphere-Alumina (Al2O3) fillers at different temperatures, which formed μm-scale CAC-Al2O3 composites. In this work, we focused on the thermal conductivity of CAC-Al2O3 composites aiming for encapsulation applications in power electronics packaging. The thermal conductivities of μm-scale CAC-Al2O3 composites by the laser-flash method from room temperature to 350℃ were firstly measured. Results showed with an increasing content of fillers, the TC of CACAl2O3 will increase accordinglyIt also illustrated that calcium aluminate cement was a high thermal stable encapsulation material with thermal conductivity over epoxy resins. Then, the Finite Element Model (FEM) was established and calibrated by experimental data for thermal conductivity simulation. The FEM model accuracy reached 90%. Such models for new filler materials are effective to minimize material development by actual experiments and characterizations, for CAC composite with different fillers. It also provides an alternative method in predicting other physical properties of composites such as coefficient of thermal expansion, porosity, etc.
Active Fault-tolerant Fractional-order Terminal Sliding Mode Control for DFIG-based Wind Turbines Subjected to Sensor Faults

The recent high penetration of wind energy conversion system (WECS) -based power generators being integrated into power grids demands advanced control techniques to ensure an efficient power production performance. This paper presents an active fault-tolerant nonlinear control strategy for the rotor side converter (RSC) control of doubly fed induction generator (DFIG) driven WECS subjected to model uncertainties and rotor current sensor faults. Two fractional-order nonsingular terminal sliding mode controllers are proposed for rotor current regulation and speed trajectory tracking. Furthermore, the control scheme is incorporated with a state observer to estimate the rotor current dynamics during sensors’ faults. Benefitting from the proposed sliding surfaces, fast finite-time convergence of system states is guaranteed, and the chattering is effectively suppressed. Closed-loop stability analysis is investigated in the sense of the Lyapunov stability criterion. Comparative performance assessments under faulty situations are conducted, and the proposed fault-tolerant control scheme’s promising speed and power tracking performance over fractional-order sliding mode control technique is revealed.
Enhanced Frequency Control of AC Microgrid by Adaptable Virtual Inertia and Center-of-Gravity Approach

In recent time, large integration of renewable energy sources (RESs) into conventional system is being adopted to promote green energy. Nevertheless, decrement in total inertia of system is observed, because of substitution of conventional generation plants with large amount of RESs. In contemplation, virtual synchronous generator (VSG) is being deployed in order to fulfil the decrement in system inertia. However, virtual inertia controls utilized in VSG displayed certain limitation and undesirable influences to frequency dynamics in system. To resolve this issue, proposed strategy of adaptable virtual inertia is investigated and implemented in this paper. Robustness and efficacy of controller is evaluated considering various disturbances in system and uncertainties. The comparison study between proposed strategy and conventional inertia control is presented accounting the large integration of RESs. The results demonstrate the adequate robustness of proposed controller and proper reference frequency tracking, disturbance attenuation along with enhanced stability of the considered system during contingencies.
A Multiple Linear Regression Model for Carotid-to-Femoral Pulse Wave Velocity Estimation Based on Schrodinger Spectrum Characterization

In this paper, a multiple linear regression model for estimating the Carotid-to-femoral pulse wave velocity (cf-PWV) from a single non-invasive peripheral pulse wave, namely blood pressure or photoplethysmography, is proposed. The training and testing datasets were extracted from in-silico, publicly available, pulse waves and hemodynamics data. The proposed model relies on a preprocessing and features extraction steps, which are performed using a semi-classical signal analysis (SCSA) method. The obtained results provide more evidence for the feasibility of machine learning and the SCSA method as a smart tool for the efficient assessment of the cf-PWV.
An Automatic System for Detection of Fires in Outdoor Areas

The traditional detection of fires is based on smoke sensors. However, this method is unsuitable for large and open buildings, and outdoor areas. Therefore, this study proposes an efficient approach to detecting fires in open areas based on computer vision systems. The input video is framed and decomposed by lifting wavelet transform to reduce the data size without missing important features. Then parallel color detection in three color-spaces (HSV, YCbCr, and binary) are applied. Otsu's algorithm is used to extract automated intensity pixels in binary space. The experimental results show that the approach accuracy exceeds 99% for the offline videos, and exceeds 89% for online (real-time) videos with low complexity.
A Deep Learning Based Approach to Synthesize Intelligible Speech with Limited Temporal Envelope Information

Envelope waveforms can be extracted from multiple frequency bands of a speech signal, and envelope waveforms carry important intelligibility information for human speech communication. This study aimed to investigate whether a deep learning-based model with features of temporal envelope information could synthesize an intelligible speech, and to study the effect of reducing the number (from 8 to 2 in this work) of temporal envelope information on the intelligibility of the synthesized speech. The objective evaluation metric of short-time objective intelligibility (STOI) showed that, on average, the synthesized speech of the proposed approach provided higher STOI (i.e., 0.8) scores in each test condition; and the human listening test showed that the average word correct rate of eight listeners was higher than 97.5%. These findings indicated that the proposed deep learning-based system can be a potential approach to synthesize a highly intelligible speech with limited envelope information in the future.
Estimation of Maximum Shoulder and Elbow Joint Torques Based on Demographics and Anthropometrics

Repetitive movements that involve a significant shift of the body's center of mass can lead to shoulder and elbow fatigue, which are linked to injury and musculoskeletal disorders if not addressed in time. Research has been conducted on the joint torque individuals can produce, a quantity that indicates the ability of the person to carry out such repetitive movements. Most of the studies surround gait analysis, rehabilitation, the assessment of athletic performance, and robotics. The aim of this study is to develop a model that estimates the maximum shoulder and elbow joint torque an individual can produce based on anthropometrics and demographics without taking a manual measurement with a force gauge (dynamometer). Nineteen subjects took part in the study which recorded maximum shoulder and elbow joint torques using a dynamometer. Sex, age, body composition parameters, and anthropometric data were recorded, and relevant parameters which significantly contributed to joint torque were identified using regression techniques. Of the parameters measured, body mass index and upper forearm volume predominantly contribute to maximum torque for shoulder and elbow joints; coefficient of determination values were between 0.6 and 0.7 for the independent variables and were significant for maximum shoulder joint torque (P<0.001) and maximum elbow joint torque (P<0.005) models. Two expressions illustrated the impact of the relevant independent variables on maximum shoulder joint torque and maximum elbow joint torque, using multiple linear regression. Coefficient of determination values for the models were between 0.6 and 0.7. The models developed enable joint torque estimation for individuals using measurements that are quick and easy to acquire, without the use of a dynamometer. This information is useful for those employing joint torque data in biomechanics in the areas of health, rehabilitation, ergonomics, occupational safety, and robotics. Clinical Relevance— The rapid estimation of arm joint torque without the direct force measurement can help occupational safety with the prevention of injury and musculoskeletal disorders in several working scenarios
A Multi-scale Attention-based Convolutional Network for Identification of Alzheimer's Disease based on Hippocampal Subfields

Hippocampus is an important anatomical region for Alzheimer's Disease (AD) identification. In this paper, a multi-scale attention-based convolutional network is proposed for AD identification. The two dimensional (2D) images in three different planes of hippocampal subfields are used as input of three branches of the proposed network, which achieves effective extraction of three dimensional (3D) data features while reducing the network complexity and improving the computational efficiency. The end-to-end 2D multi-scale attention-based deep learning network improves the diversity of the extracted features and captures significance of various voxels for classification, which achieves significant classification performance without handcrafted feature extraction and model stacking. Experimental results illustrate the effectiveness of the proposed method on AD identification. The proposed method will be useful for further medical analysis on hippocampal subfields of the brain for diagnosis of neurodegenerative disease.
Human activity recognition from textile electrocardiograms

Textile sensors for physiological signals bear the potential of unobtrusive and continuous application in daily life. Recently, textile electrocardiography (ECG) sensors became available which are of particular interest for physical activity monitoring due to the high effect of exercise on the heart rate. In this work, we evaluate the effectiveness of a single-lead ECG signal acquired using a non-medical-grade ECG shirt for human activity recognition (HAR). Healthy volunteers (N=10) wore the shirt during four different activities (sleeping, sitting, walking, running) in an uncontrolled environment and ECG data (256 Hz, 12 Bit) was stored, manually checked, and unusable segments (e.g. no sensor contact) were removed, resulting in a total of 228 hours of recording. Signals were split in short segments of different duration (10, 30, 60s), transformed using the Short-time Fourier Transform (STFT) to a spectrogram image and fed into a state-of-the-art convolutional neural network (CNN). The best configuration results in an F'l-Score of 73% and an accuracy of 77% on the test set. Results with leave-one-subject-out cross-validation show F'l-Scores ranging from 41 % to 80%. Thus, a single-lead, wearable-generated ECG has an informative value for HAR to a certain extent. In future work, we aim at using more sensors of the smart shirt and sensor fusion.
Influence of the H-reflex on the selectivity of recruitment using multi-contact epineural stimulation of the median nerve in a participant with complete tetraplegia

Multi-contact epineural electrical stimulation is a technique that can be used to restore grip movements in people with complete tetraplegia. However, neural stimulation can induce undesired H-reflex. This reflex is known to induce a global lower recruitment threshold together with a steepest recruitment curve leading to a degraded selective response. In this study, during stimulation of the median nerve using a multi-contact cuff electrode, a H-reflex response was observed for one muscle (the pronator teres i.e. PT) among the five recorded. As both M-wave and H-wave were separately recorded, we compared the changes of recruitment, recruitment order and se-lectivity with and without the H-reflex and found that blocking the reflex would have enhance the selectivity and increase the range of the intensity amplitude while providing a higher level of gripping force. Thus, blocking H-reflex is an important issue to further enhance epineural multicontact selective stimulation.
Experimental Characterization of Embeddable Machine Learning Reconstruction Algorithms for Anger Cameras

This work presents the experimental characterization of position sensitivity accuracy when implementing machine learning algorithms for the reconstruction of γ rays interaction points in Anger cameras for medical imaging. The work aims to show state-of-the-art position sensitivity when using an embeddable and fast Decision Tree classifier, combined with Principal Component Analysis. The training dataset acquisition procedure is discussed, together with clustering techniques implemented for the automation of data labeling. To overcome the impossibility to distinguish adjacent classes in the training set with sub-mm distance between classes when using a regular parallel-hole collimator, we implemented two innovative solutions for the training procedure: a custom mechanical structure scanning the module used in experimental characterization, and the use of data augmentation to generate new fractions of training dataset from perturbation of the experimental dataset. Preliminary results have been obtained using the INSERT gamma camera, where a 50×100×8 mm3 CsI(Tl) scintillation crystal is coupled to 8 tiles of RGB-HD SiPMs, and the charge signal is collected by two 36-channel ANGUS ASICs. The energy resolution equals 17% at 140 keV, and the module is capable to acquire events up to ~10 kcps. Count rate is only limited by typical detector performance (e.g. front-end count rate capability, data transmission bandwidth); in fact, implementation of Decision Tree classification in FPGA (free of data pre-processing) would allow edge-computing of the position sensitivity, with sub-µs data latency. Experimental results show 1.2 mm FWHM spatial accuracy, which is equal to the accuracy obtained with the specific module under test when implementing statistical position reconstruction algorithms such as maximum likelihood estimation.
3d Printed Bio-potential Dry Electrodes

The most commonly used bio-potential electrodes are silver/silver chlorides gel-electrodes (Ag/AgCl electrodes). However, wet electrodes are not suited for daily usage and long-term monitoring. Dry electrodes are the best choice for long-term measurements. Dry electrodes can be fabricated using various methods and materials; one of these methods is three dimensional (3d) printing. 3d printing is a cost-effective method and can be easily used in mass production. This paper will present 3d printed bio-potential dry electrodes using five different commercially available fused deposition modeling (FDM) electrically conductive filaments. Then, these printed electrodes will be tested and validated using electrocardiogram (ECG) portable acquisition system. Three out of five filaments were printed successfully. Collected ECG data from these electrodes showed acceptable quality with Signal to Noise Ratios (SNRs) ranging around 20 dB when compared with wet electrodes collected raw data as a reference. In conclusion, this study designed and printed a 100% 3d printed electrodes from three different FDM filaments which could be used efficiently for ECG signal monitoring in resting position.
A Preliminary Study of Extracting Pulmonary Nodules and Nodule Characteristics from Radiology Reports Using Natural Language Processing

This study aims to develop a natural language processing (NLP) tool to extract the pulmonary nodules and nodule characteristics information from free-text clinical narratives. We identified a cohort of 3,080 patients who received low dose computed tomography (LDCT) at the University of Florida health system and collected their clinical narratives including radiology reports in their electronic health records (EHRs). Then, we manually annotated 394 reports as the gold-standard corpus and explored three state-of-the-art transformer-based NLP methods. The best model achieved an F1-score of 0.9279.
Wideband Polarization Agile Dielectric Resonator Antenna With Reconfigurable Broadside and Conical Beams

A new broadside and conical beam reconfigurable dielectric resonator antenna (DRA) with three orthogonal linear polarizations (LPs) and two orthogonal circular polarizations (CPs) agility is presented. It consists of a quad-strip-fed DRA element and a reconfigurable feeding network. First, the DRA element is designed to make unidirectional <inline-formula> <tex-math notation="LaTeX">${\mathrm{HEM}}_{11 \delta }$ </tex-math></inline-formula> mode and omnidirectional <inline-formula> <tex-math notation="LaTeX">${\mathrm{TM}}_{01 \delta }$ </tex-math></inline-formula> mode operate in the same wide band by tactfully removing a concentric annular column from its interior. Furthermore, a center-loaded metal post helps to improve the impedance matching of the <inline-formula> <tex-math notation="LaTeX">${\mathrm{TM}}_{01 \delta }$ </tex-math></inline-formula> resonance significantly. Afterward, the switchable feed network is designed to implement diverse phase distributions of four output ports, i.e., providing the quad-port in-phase excitation, dual-port differential excitations, and quad-port sequentially rotated phase excitation strategies for a vertical LP, ±45° slant horizontal LPs, and two CPs, respectively. Finally, a fully functional prototype is designed, fabricated, and measured to validate the design. The measured results show that the overlapping impedance bandwidth of 36.8% for all the states and the axial-ratio bandwidth of 33.8% for the CPs are obtained.
Anatomically-Specific, 3D-Printed Cradles Enable In Vivo Mapping of the Bioelectrical Activation across the Gastroduodenal Junction

Rhythmic bioelectrical 'slow waves' are a key regulatory mechanism underpinning digestion. The pyloric sphincter separates the independent slow wave and contractile behavior of the stomach and small intestine, while also regulating gastric emptying. In this study, we develop and validate anatomically-specific electrode cradles and analysis techniques in pigs, to map in vivo slow wave activation across this critical pylorus region for the first time. 3D printed electrode cradles were developed from reconstructions of magnetic resonance images, to accurately capture anatomical geometry. A low-pass Savitzky-Golay filter with an equivalent cut-off frequency of ~2 Hz was chosen as the optimal filter for analysis of both gastric and intestinal slow waves. Slow waves in the terminal antrum occurred with a frequency of $(\mathbf{2.81}\boldsymbol{\pm}\mathbf{0.55})$ cycles per minute (cpm), velocity of (5.04 ± 0.29) mm s-1, and amplitude of $(\mathbf{1.38}\boldsymbol{\pm}\mathbf{0.37})$ mV, before terminating at a zone of quiescence at the pylorus that was $(\mathbf{41.22}\boldsymbol{\pm}\mathbf{7.4})\mathbf{nm}$ wide. The proximal duodenal pacemaker initiated slow waves at a frequency of $(\mathbf{18.1}\boldsymbol{\pm}\mathbf{0.80}$) cpm, velocity of $(\mathbf{11.3}\boldsymbol{\pm}\mathbf{2.4})$ mm s-1, and amplitude of $(\mathbf{0.376}\boldsymbol{\pm}\mathbf{0.027})$ mV. This work enables quantitative definitions of numerous physiological features of the in vivo pylorus region, including the electrically quiescent zone and duodenal pacemaker location. Clinical Relevance- This work establishes a novel method for in vivo measurement of bioelectrical slow wave activity of the pyloric region, which is a key target for physiological investigation and clinical intervention. In the future, the methods developed here may inform diagnosis and/or treatment of functional gastrointestinal disorders.