Miracle-dz/UniFAR-FTUs huggingface dataset:
Parametric exploration of cellular swelling in a computational model of cortical spreading depression

Cortical spreading depression (CSD) is a slowly propagating wave of depolarization of brain cells, followed by temporary silenced electrical brain activity. Major structural changes during CSD are linked to neuronal and possibly glial swelling. However, basic questions still remain unanswered. In particular, there are open questions regarding whether neurons or glial cells swell more, and how the cellular swelling affects the CSD wave propagation.In this study, we computationally explore how different parameters affect the swelling of neurons and astrocytes (starshaped glial cells) during CSD and how the cell swelling alters the CSD wave spatial distribution. We apply a homogenized mathematical model that describes electrodiffusion in the intraand extracellular space, and discretize the equations using a finite element method. The simulations are run with a twocompartment (extracellular space and neurons) and a threecompartment version of the model with astrocytes added. We consider cell swelling during CSD in four scenarios: (A) incorporating aquaporin-4 channels in the astrocytic membrane, (B) increasing the neuron/astrocyte ratio to 2:1, (C) blocking and increasing the Na+/K+-ATPase rate in the astrocytic compartment, and (D) blocking the Cl− channels in astrocytes. Our results show that increasing the water permeability in the astrocytes results in a higher astrocytic swelling and a lower neuronal swelling than in the default case. Further, elevated neuronal density increases the swelling in both neurons and astrocytes. Blocking the Na+/K+-ATPase in the astrocytes leads to an increased wave width and swelling in both compartments, which instead decreases when the pump rate is raised. Blocking the Cl− channels in the astrocytes results in neuronal swelling, and a shrinkage in the astrocytes. Our results suggest a supporting role of astrocytes in preventing cellular swelling and CSD, as well as highlighting how dysfunctions in astrocytes might elicit CSD.
Mini-Logger- A Wearable Inertial Measurement Unit (IMU) for Postural Sway Analysis

Postural instability assessment is an important tool in fall risk analysis and for timely intervention of falls to reduce or prevent fall injuries. Traditionally fall risk is measured though postural sway assessment and is collected through forceplates by mapping Center of Pressure (COP) excursions or using motion analysis camera system for marker sway trajectories. However, both of these systems are expensive and lack portability to their usage in clinical environments. In this study, we developed a novel wearable low-cost MEMS inertial sensor and validated its usage for human postural sway assessment in standing posture with eyes open/closed, vibration/no vibration, and proprioception /low proprioception conditions. The two objectives of this study were: 1) To develop and validate an Inertial Measurement Unit (IMU) for sway analysis 2) To determine the feasibility of the system in detecting human postural imbalances such as reduced proprioception or presence of stochastic resonance induced through subthreshold vibrations on the feet. The novel IMU was tested for sway against infra-red marker on a specialized platform with 4-degrees of freedom. Many parameters of postural sway such as sway velocity, Root Mean Square (RMS), and sway path length could successfully detect subtle postural changes due to varying proprioceptive and sub-threshold vibration conditions. We found agreement in sway signal determinism from the two methods.Clinical Relevance— This wearable sensor technology has potential to determine balance in reliable, easy and accurate way in clinical environments.
Long-Term Wind Power Forecasting Using Tree-Based Learning Algorithms

The intermittent and uncertain nature of wind places a premium on accurate wind power forecasting for the reliable and efficient operation of power grids with large-scale wind power penetration. Herein, six-month-ahead wind power forecasting models were developed using tree-based learning algorithms. Three models were developed to investigate the impact of input data on forecasting accuracy. The first model was trained with the average and standard deviation of wind speed values measured at a height of 40 m with a 10-min sampling time. To evaluate the impact of sampling time on model performance, a second model was trained with wind speed values measured at a height of 40 m with 1-h, 12-h, and 24-h sampling times. To assess the effect of measuring height on model accuracy, the third model was trained with wind speed values measured at 40 m extrapolated from values measured at heights of 30 m and 10 m. Experiments revealed that using longer time intervals and height extrapolation leads to considerable accuracy degradation in forecasted models. Finally, to study the generalization ability of the forecasted models, they were tested against wind data measured at heights and locations different from what the models had been trained with. Simulation results substantiated that tree-based learning algorithms can be successfully adopted not only for long-term wind power forecasting, but for potential wind power forecasting at different heights and geographical locations.
Silicon Nitride Deep-Ultraviolet Photoconductive Detector

In this letter, amorphous silicon nitride (Si<sub>3</sub>N<sub>4</sub>) film grown by inductively coupled plasma chemical vapor deposition (ICPCVD) method is used as the photosensitive layer of deep ultraviolet (DUV) detection, whose thickness and roughness are 200 nm and 0.92 nm, respectively. The transverse symmetrical photoconductive detector with interdigital electrodes spacing of <inline-formula> <tex-math notation="LaTeX">$4~\mu \text{m}$ </tex-math></inline-formula> is fabricated by photolithography, exhibiting ultrahigh photo-to-dark current ratio (PDCR) more than 10<sup>5</sup>, fast fall speed of 127 ms and also a satisfying responsivity of 1.09 mA/W. This work lays a foundation for further researches on Si<sub>3</sub>N<sub>4</sub> in photonics and electronics.
Peste Des Petits Ruminants Virus (PPRV) Infection Among Small Ruminants Slaughtered at the Central Abattoir, Maiduguri, Nigeria

Three organs (lungs, spleen and lymph nodes) were collected from each of 100 sheep and 114 goats slaughtered at the central abattoir Maiduguri, Nigeria and analysed for the presence of peste des petits ruminants virus (PPRV) precipitin antigens using agar gel immunodiffusion (AGID) test. Forty one percent (41%) and 37.7% of the sheep and goats sampled tested positive for the PPRV precipitin antigen respectively. Organ specific distribution of the PPR antigen showed goats had 27.9% positive for one organ, 25.6% for two organs and 46.5% for three organs. Sheep has 19.9% positive for one organ, 24.4% for two organs and 56.1% for three organs. The result indicates a high activity of the PPRV among the small ruminants of this environment. Key words : Peste des petits ruminants virus (PPRV), sheep, goats, agar gel immuno-diffusion (AGID) test, Nigeria
Primary Ways Of Interpreting Human Gait Parameters Using The Accelerometry Method

This publication describes the results of a study of methods for interpreting human gait using hardware-software complex. In this paper, we consider one of the modern methods for registering human gait parameters, namely accelerometry, followed by determining the general parameters of a human gait for subsequent interpretation. This method is used for preclinical and clinical diagnosis of locomotor disorders, as well as for rehabilitation of such disorders. This paper presents three methods for interpreting such parameters: the spectral method, the direct method, and the correlation method. Each of these interpretation methods is intended for specific tasks, and these methods are not interchangeable, for example, because each uses different mathematical techniques. For primary interpretations, physical parameters such as Euler angles, linear and angular accelerations are used, which are recorded using digital accelerometer gyroscopes (hereinafter referred to as sensors). Thus, this work describes ways to interpret the parameters of human gait using a hardware-software complex, which was originally created only for the accelerometry method, and over time, the function of displaying general parameters was implemented for it without additional hardware solutions.
Audio for Audio is Better? An Investigation on Transfer Learning Models for Heart Sound Classification

Cardiovascular disease is one of the leading factors for death cause of human beings. In the past decade, heart sound classification has been increasingly studied for its feasibility to develop a non-invasive approach to monitor a subject’s health status. Particularly, relevant studies have benefited from the fast development of wearable devices and machine learning techniques. Nevertheless, finding and designing efficient acoustic properties from heart sounds is an expensive and time-consuming task. It is known that transfer learning methods can help extract higher representations automatically from the heart sounds without any human domain knowledge. However, most existing studies are based on models pre-trained on images, which may not fully represent the characteristics inherited from audio. To this end, we propose a novel transfer learning model pre-trained on large scale audio data for a heart sound classification task. In this study, the PhysioNet CinC Challenge Dataset is used for evaluation. Experimental results demonstrate that, our proposed pre-trained audio models can outperform other popular models pre-trained by images by achieving the highest unweighted average recall at 89.7 %.
A General Behavioral Model of Charge Pump DC-DC converters

This paper presents behavioral model for a $N$-stage charge pump valid over a wide clock frequency range. The hardware description language used to develop the model is the Verilog-AMS, which allows simulations of both analog and digital systems, such as systems on a chip and nonvolatile memories. The proposed code allows a huge reduction of time-to-market constraint, since it reduces simulation time while maintaining a good agreement with transistor-level simulations. Model validation has been carried out by the comparison between different charge pumps implemented in SPICE (ideal components) and Verilog-AMS and simulated with Spectre. The model has been also validated on a regulated charge pump. The achieved results have confirmed the validity of the developed description in terms of simulation time and accuracy.
A Review of Remote Sensing Image Object Detection Algorithms Based on Deep Learning

Object detection is an important part of remote sensing image analysis. With the development of the earth observation technology and convolutional neural network, remote sensing image object detection technology based on deep learning has received more and more attention and research. At present, many excellent object detection algorithms have been proposed and applied in the field of remote sensing. In this paper, the object detection algorithms of remote sensing image is systematically summarized, the main contents include the traditional remote sensing image object detection method and the method based on deep learning, emphasis on summarize the remote sensing image object detection algorithm based on deep learning and its development course, then we introduced the rule of performance evaluation of object detection and datasets that commonly used. Finally, the future development trend is analyzed and prospected. It is hoped that this summary and analysis can provide some reference for future research on object detection technology in remote sensing field.
A Dynamic Array-of-Subarrays Architecture and Hybrid Precoding Algorithms for Terahertz Wireless Communications

Terahertz (THz) communications are envisioned as a key technology for 6G wireless systems, owing to an unprecedented promised multi-GHz bandwidth. While THz band suffers from huge propagation losses, large arrays of sub-millimeter wavelength antennas can be realized in ultra-massive multiple-input multiple-output (UM-MIMO) systems to enhance the received power and overcome the distance limitation. In this paper, a dynamic array-of-subarrays (DAoSA) hybrid precoding architecture is proposed to reduce the power consumption while meeting the data rate requirement in THz UM-MIMO systems. The connections between RF chains and subarrays are intelligently adjusted through a network of switches. First, to solve the intractable DAoSA hybrid precoding problem, element-by-element (EBE) and vectorization-based (VEC) algorithms are derived. Moreover, to determine the connections of the switches, near-optimal progressive stage-by-stage (PSBS), low-complexity alternating-selection (AS) and block-diagonal-search (BDS) algorithms are developed. Extensive simulation results show that both the EBE and VEC algorithms have higher spectral efficiency than existing hybrid precoding algorithms. Furthermore, the power consumption of the DAoSA architecture is substantially lessened, with PSBS, AS and BDS algorithms, respectively. The developed DAoSA architecture associated with proposed hybrid precoding and switch network design algorithms demonstrates a superior capability on balancing the spectral efficiency and power consumption.
Temporal dependency in automatic sleep scoring via deep learning based architectures: An empirical study

The present study evaluates how effectively a deep learning based sleep scoring system does encode the temporal dependency from raw polysomnography signals. An exhaustive range of neural networks, including state of the art architecture, have been used in the evaluation. The architectures have been assessed using a single-channel EEG Fpz-Cz from the open source Sleep-EDF expanded database. The best performing model reached an overall accuracy of 85.2% and a Cohen’s kappa of 0.8, with an F1-score of stage N1 equal to 50.2%. We have introduced a new metric, δnorm, to better evaluate temporal dependencies. A simple feed forward architecture not only achieves comparable performance to most up-to-date complex architectures, but also does better encode the continuous temporal characteristics of sleep.Clinical relevance — A better understanding of the capability of the network in encoding sleep temporal patterns could lead to improve the automatic sleep scoring
CoughMatch – Subject Verification Using Cough for Personal Passive Health Monitoring

Automatic cough detection using audio has advanced passive health monitoring on devices such as smart phones and wearables; it enables capturing longitudinal health data by eliminating user interaction and effort. One major issue arises when coughs from surrounding people are also detected; capturing false coughs leads to significant false alarms, excessive cough frequency, and thereby misdiagnosis of user condition. To address this limitation, in this paper, a method is proposed that creates a personal cough model of the primary subject using limited number of cough samples; the model is used by the automatic cough detection to verify whether the identified coughs match the personal pattern and belong to the primary subject. A Gaussian mixture model is trained using audio features from cough to implement the subject verification method; novel cough embeddings are learned using neural networks and integrated into the model to further improve the prediction accuracy. We analyze the performance of the method using our cough dataset collected by a smart phone in a clinical study. Population in the dataset involves subjects categorized of healthy or patients with COPD or Asthma, with the purpose of covering a wider range of pulmonary conditions. Cross-subject validation on a diverse dataset shows that the method achieves an average error rate of less than 10%, using a personal cough model generated by only 5 coughs from the primary subject.
Right Temporal Oscillations of Infants in Relation to Contingent Learning

Contingent learning is an agent for infants to explore the environment, which enhances the maturation of different developmental domains. This paper presents one of the first to investigate neural activities related to contingent learning of infants by analyzing their motor response that could elicit an audio-visual feedback. Three different kinds of motor response of infants were investigated, including unilateral kicks, synchronized kicks, and alternate kicks. Electroencephalographic (EEG) signals of infants were recorded before the motor experiments. Higher theta band power and lower upper beta power at the right temporal lobe of infants predicted a higher ratio of total unilateral kicks and a lower ratio of synchronized kicks at the later acquisition stage of the experiment. As contingent learning could be reflected by specific motor response in relation to the audio-visual stimuli, the results suggested that right temporal oscillations could predict different levels of contingent learning of infants.
Sparse Kalman Filtering (SKF) Aided Doubly-Selective Channel Estimation in STTC MIMO-OFDM Systems

This work develops a novel scheme for doubly-selective sparse channel estimation in a space-time trellis coded (STTC) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) wireless systems. Toward this end, a pilot based doubly-selective sparse channel estimation model is developed, wherein the time-evolution of the wireless channel is modeled using a first-order autoregressive (AR1) process. This is followed by the proposed online pilot-based sparse Kalman filter (P-SKF) for channel estimation. The proposed P-SKF scheme combines the advantages of the Kalman filter (KF) and sparse Bayesian learning (SBL) techniques to exploit the temporal correlation as well as the sparse multipath delay profile of the wireless channel. In addition, the proposed PSKF technique also exploits the simultaneous-sparsity across all the transmit-receive antenna pairs for improved channel estimation. The recursive Bayesian Cramér-Rao lower bound (BCRLB) is also derived to benchmark the mean square error (MSE) performance of the proposed technique. Simulation results are presented to evaluate the MSE and frame error rate (FER) performance of the proposed technique and compare with the existing schemes.
Performance of Polar Codes in Cooperative Transmission Systems

The incessant increase in service needs and the number of subscribers is a major subject of concern to operators of mobile communication networks. Cooperative systems are one of the solutions proposed by the communities to meet these needs. In this proposal, we focus on the successive cancellation list (SCL) decoder for polar codes on cooperative communication which is composed of three blocks: the transmission module, a relay block and a reception node. The transmission on the channel is divided into two times. Data coded with a polar code is transmitted simultaneously by the source to the reception system and to the relay bloc. The relay processes its version with an SCL protocol before proceeding to polar coding then transmits them to the destination. After a SCL decoding, the reception uses the MRC (Maximum Ratio Combining) combination method to compare the two versions of the signals and extracts the useful information. We propose a protocol for the construction of polar codes to reach the theoretical capacity in the half-duplex relay channel. We also demonstrate that the polar code (1024, 512) is decoded by the decoder. SC-List of the proposed model with a very large size of the list ($\mathrm{L}=32$) can reach a bit rate of 10−4 to 2dB. The simulation results obtained show proven performance compared to other existing codes such as the turbo code and the low-density parity check) codes.
A Composite Material Based Neural Network for Tissue Mechanical Properties Estimation Toward Stage Assessment of Infarction

Cardiac biomechanical modelling is a promising new tool to be used in prognostic medicine and therapy planning for patients suffering from a variety of cardiovascular diseases and injuries. In order to have an accurate biomechanical model, personalized parameters to define loading, boundary conditions and mechanical properties are required. Achieving personalized modelling parameters often requires inverse optimization which is computationally expensive; hence techniques to reduce the multivariable complexity are in need. Presented in this paper is the fundamental blueprint to create a library of scar tissue mechanical properties to be used in modelling the healing mechanics of hearts that have suffered acute myocardial infarction. This library can be used to reduce the number of variables necessary to capture the scar tissue mechanical properties down to 1. This single parameter also carries information pertaining to staging of the scar tissue healing, predict its rate, and predict its collagen density. This information can be potentially used as valuable biomarkers to adjust existing or develop new treatment plans for patients.
Third-Order Harmonics in Synchronous Reluctance Motors With an Axially Laminated Anisotropic Rotor and Their Impact on the Motor Losses

The phenomenon of third-order harmonics in synchronous reluctance motors (SynRMs) with an axially laminated anisotropic (ALA) rotor is studied. The paper shows that a specific rotor geometry can distort the air-gap flux density and induce third-order currents in the stator winding if it is delta connected. The third-order currents in different phases have the same phase shift angle and cannot, therefore, produce a rotating air-gap flux. Instead, they produce an air-gap flux that pulsates in time, and thus, causes eddy current losses in the rotor conducting parts. The phenomenon was described and proven with finite element simulation (FEM) of a 12 kW high-speed ALASynRM. The research underlines the importance of considering the third harmonic phenomenon in the design of a two-pole ALASynRM intended for high-speed applications, where a specific rotor geometry with a wide magnetically conducting middle layer is required.
A New Aggregation Operator For Intuitionistic Fuzzy Sets With Applications In The Risk Estimation And Decision Making Problem

In all aspects of life, we have to face with uncertainty involved feeling, cognition, evaluation and decision making (DM). In the last decades, a great number of such theories as fuzzy sets, interval-valued fuzzy sets, type-2 fuzzy sets, hesitant sets, grey sets, rough sets and intuitionistic fuzzy sets (IFSs) have been proposed to deal with uncertainty in the real-life imperfect information. They have drawn more and more attention of scholars and been adopted in many various applications. Failure Modes and Effects Analysis (FMEA) is a reliability analysis technique that plays a prominent role in improving the reliability and safety of systems, products, and/or services. It can be used to identify and eliminate known or potential failure modes to enhance reliability and safety of complex systems. Although commonly used, the conventional FMEA has been heavily criticized in the literature for such various limitations as failure mode evaluations, risk factor weights, risk priority number (RPN) computation and dealing with uncertain information. In this paper, we propose a new aggregation operator for IFSs, that proves to be more adequate for fusing information. It is employed in the intuitionistic fuzzy FMEA method in the risk estimation of the system failures with uncertain information. It is validated also in solving a common DM problem, compared with the other existing methods.