Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of physical design and informal communication and learning in gaining competency and reducing stress among graduate nurses

High turnover is a prime cause of today’s nursing shortage, and is often the result of job dissatisfaction and burnout. At particular risk are Graduate Nurses (GN), who struggle to feel competent as they transition from the academic environment to the hospital environment. Using an exploratory case-study research design utilizing a multi-method approach, this study explored the relationships between the design and layout of the physical environment, GN interaction patterns, opportunities for informal learning and support, stress, and the gaining of necessary competencies. The five data collection methods used were shadowing the GN using the Clinical Work Measurement Tool, a registered nurse and GN survey, GN blood pressure measurements, GN weekly competency ratings, and focused interviews. Results showed a correlation between increasing competency ratings and decreasing blood pressure. The results also demonstrated the importance of “backstage” areas such as break rooms and med rooms for informal learning, social support and the unit’s cohesion. The GN interacted infrequently with doctors in any area, however, which supports previous research on the work patterns of nurses. This research suggests that formal approaches to GN Orientation, which focus on the effective one-on-one interaction with a designated mentor, can and would benefit by insuring a supportive unit culture and purposeful design interventions that increased the likelihood of more interaction across disciplinary boundaries. Future research should explore in greater depth the role played by backstage (vs. frontstage) areas in facilitating informal learning and social support among both GNs and caregivers generally, and the effect of these opportunistic interactions on multidisciplinary teamwork.
Using the WHO database of spontaneous reports to build joint vector representations of drugs and adverse drug reactions, a promising avenue for pharmacovigilance

Pharmacovigilance, a set of activities related to monitoring the safety of medicinal products throughout their life-cycle, is crucial for public and personal health. Traditionally, post-marketing pharmacovigilance is done via spontaneous reporting: the collection and analysis of reports of suspected adverse drug reactions. We propose a novel approach to represent the two major types of concepts found in spontaneous reports, the drugs and reported reactions, using distributional semantics based on patterns of co-reporting. As proof-of-concept, we demonstrate the meaningfulness of the built representations using manually curated hierarchies of clinical relevance. This preliminary work may lay a foundation for novel down-stream improvements in the science of pharmacovigilance, as has been observed when distributional representations of words were introduced in the field of text analysis.
Detecting Hang on the Virtual Machine using LibVMI

This paper discusses how to monitor the operation of guest OS on the virtualization environment. There are several forensic applications which are useful for analyzing events that run on operating systems specifically memory, CPU, and I/O. We use the LibVMI library to create a new plugin for Drakvuf in order to detect Hang on a guest OS that runs on top of the Xen Hypervisor. The experiment shows that libVMI able to monitor the state of the guest OS. However, the a problem arises when LibVMI start to detect the hang condition. The EPT violation occurs during hang monitoring process.
A Review on Blockchain Urgency in the Internet of Things in Healthcare

With the growing use of the internet, new technology came into the existence and it is on the same edge now, where decades ago the internet was. Internet of things (IoT) is facing many security issues. Due to the lack of the security standards, industries are afraid to take a risk with IoT. In the Healthcare industry where data is vast and valuable, it is risky to use IoT with it. It can compromise the patient's security. In this paper, various methods are discussed for Healthcare issues in IoT, by several researchers. Mainly the research work is focused on the different solutions proposed in the healthcare for IoT. At the end, a theoretical analysis of proposed solution is done.
NLOS Classification Based on RSS and Ranging Statistics Obtained from Low-Cost UWB Devices

Ultra-wideband (UWB) devices have been largely considered for indoor location systems due to their high accuracy. However, as in other wireless systems, such accuracy is significantly degraded under non-line-of-sight (NLOS) propagation conditions. Therefore, the identification of NLOS conditions is essential to mitigate inaccuracies due to NLOS propagation. Nonetheless, most of the techniques considered to identify NLOS situations are based on the study of the channel impulse response (CIR), which is not practical and even becomes unfeasible when employing low-cost UWB hardware. This is precisely the main motivation of this work, to introduce a classification system based on the statistics of both the received signal strength (RSS) and range available from low-cost UWB devices.We analyze the effect of considering different statistic sets of both the RSS and range as features to feed a support vector machine (SVM) classifier, which is experimentally evaluated by means of measurements carried out in a real scenario where both line-of-sight (LOS) and NLOS conditions are present.
An 8-bit 10-GHz 21-mW Time-Interleaved SAR ADC With Grouped DAC Capacitors and Dual-Path Bootstrapped Switch

An 8-bit 10-GHz 8× time-interleaved SAR ADC in 28-nm CMOS incorporates an aggressively scaled DAC with grouped capacitors in a symmetrical comb structure to afford a threefold reduction in the bottom-plate parasitic capacitance. A dual-path bootstrapped switch decouples critical signal from nonlinear capacitance to boost the sampling SFDR by more than 5 dB. The ADC demonstrates an SNDR of 36.9 dB at Nyquist while consuming 21 mW, yielding an FoM of 37 fJ/conv. -step, the lowest among the reported ADCs with similar speeds and resolutions and more than 2× improvement from the state-of-the-art.
Probabilistic Assessment of Photovoltaic Hosting Capacity in Finnish LV Networks

Large number of photovoltaic (PV) installations can lead to the network constraints' violations that comprise the voltage level outside the statutory limits and equipment ampacity-limit violations. This study quantifies the hosting capacities (HCs) of the Finnish LV networks and their limiting constraints. A stochastic approach is implemented to cover all the probabilistic variables of the LV network. Geographically distinct LV networks of rural, intermediate and urban regions are studied with the balanced and unbalanced PV feed-in scenarios. Later, the HC enhancement potential of various auxiliary equipment and techniques, like the battery storage, on-load tap changer (OLTC), network reinforcement and reactive power control of inverters, is determined. Moreover, the effect of different country-specific voltage level standards' implementation and the commercially available OLTC employment are also studied with varying PV penetrations.
Landslide Detection Leveraging Spectral-Spatial Correlation Fusion in Aerospace Remote Sensing

The rapid and accurate detection has been one of the major issues in detecting landslide from massive aerospace remote sensing data. In this paper, we propose a fusion detection model based on back-propagation neural networks to achieve accurate and efficient feature detection on landslide. In specific, we construct a fundamental shape model and apply a scale-invariant feature transform (SIFT) algorithm for feature matching and transformation. By fusing spatial shape features and spectral features, the landslide targets can be further identified and located with high accuracy. We conduct several experimental studies based on aerospace remote sensing data from two real-world scenarios. The experimental results show that the proposed method outperforms the baseline algorithms and achieves up to 95.52% accuracy in landslide detection.
Parallel Product-Sum Operation Neuromorphic Systems with 4-bit Ferroelectric FET Synapses

This paper proposes neuromorphic systems using ferroelectric-FETs (FeFET). Since neuromorphic systems mimic neurons, the synaptic weight is stored in non-volatile memory. FeFET is a kind of non-volatile memory and has a feature that the dynamic range of cell current is wide because of FEF characteristics. In order to use the wide dynamic range, proposed neuromorphic systems perform the product of pre-charged voltage as an input and threshold voltage (VTH) of FeFET as the synaptic weight. In addition, proposed neuromorphic systems sum the number of clock pulses. While the synaptic weight can be high precision by using FeFET, the product-sum operation has a problem of low speed. Therefore, this paper also proposes a parallel product-sum operation technique. Proposed neuromorphic systems achieve a large number of product-sum operations at one time. As a result, the total operation time of proposed neuromorphic systems is equivalent to that of conventional neuromorphic systems. In addition, proposed product-sum operation shows 98% lower power consumption than conventional current adder operation.
A Time-based Sensing Scheme for Multi-level Cell (MLC) Resistive RAM

The quest to increase memory density in Resistive Random Access Memory (RRAM) has motivated researchers to store more bits/cell by implementing Multi-Level Cell (MLC) or multi-bit RRAM. Implementing multiple states narrows the distance between states, making sensing of MLC RRAM a challenging task. In this paper, we present a circuit which senses the state of a MLC by converting the current drawn from the cell to voltage pulses, where the number of pulses is proportional to the current's magnitude. The circuit distinguishes between the states by the relative current's magnitude and hence does not require an absolute reference. Simulations in IHP's 130 nm CMOS technology confirmed fast (single step) sensing while tolerating appropriate variations in the sensed resistance. The proposed circuit is also area efficient when compared to conventional parallel sensing approach.
A Speech Reconstruction Algorithm via Iteratively Reweighted ℓ2 Minimization for MFCC Codec

This paper presents an effective method to address the inverse problem of Mel-frequency cepstral analysis, and describes how to reconstruct the speech waveforms from Mel-frequency cepstral coefficients (MFCCs) directly. To exploit the sparse characteristics of speech in the frequency domain, an iteratively reweighted $\ell_{2}$ minimization method is proposed to cope with the under-determined nature of the reconstruction problem. The lost phase information during Mel-frequency cepstral analysis procedure is recovered by the inverse short-time Fourier transform magnitude algorithm. Experiments are conducted over the TIMIT database and evaluated by several different kinds of measures. Experimental results demonstrate that the proposed method recovers speech with high articulation and intelligibility. Specifically, it sounds very close to the original speech when using the high-resolution MFCCs, the average STOI, PESQ score reaches 93% and 4.0, respectively. This method could be easily used for MFCC codec at low bit rate.
Predicting Biological Signals from Speech: Introducing a Novel Multimodal Dataset and Results

In recent years, diagnosis and awareness of mental health conditions, e. g. , chronic stress, have been increasing globally. Biological signals can be an effective way to monitor such conditions, yet acquisition can be cumbersome and invasive. Alternatively, acoustic features offer non-invasive and efficient monitoring of an array of health and wellbeing characteristics. This study presents the BioSpeech Database (BioS-DB), a novel database of audio and biological signals - blood volume pulse (BVP) and skin conductance (SC) - from 55 individuals speaking aloud in front of others, whilst having their emotional state annotated in real time. Through a variation of conventional and state-of-the-art approaches, initial experiments have shown for the first time that acoustic features can be applied for the task of BVP prediction. Notably, using deep representations of audio and a sequence-to-sequence auto-encoders with a GRU-RNN as a time-dependent regressor achieved at best 0.075 and 0.123 RMSE for [0; 1] normalised BVP and SC, respectively.
Can Deep Generative Audio be Emotional? Towards an Approach for Personalised Emotional Audio Generation

The ability for sound to evoke states of emotion is well known across fields of research, with clinical and holistic practitioners utilising audio to create listener experiences which target specific needs. Neural network-based generative models have in recent years shown promise for generating high-fidelity based on a raw audio input. With this in mind, this study utilises the WaveNet generative model to explore the ability of such networks to retain the emotionality of raw audio speech inputs. We train various models on 2-classes (happy and sad) of an emotional speech corpus containing 68 native Italian speakers. When classifying the combined original and generated audio, hand-crafted feature sets achieve at best 75.5 % unweighted average recall, a 2 percent point improvement over the original only audio features. Additionally, from a two-tailed test on the predictions, we find that the audio features from the original speech concatenated with the generated audio features provides significantly different test result compared to the baseline. Both findings indicating promise for emotion-based audio generation.
Synchronized Measurement of Power System Frequency and Phase Angle Using FFT and Goertzel Algorithm for low cost PMU Design

We are presenting here a Fast Fourier Transform (FFT) based algorithm for measurement of power system frequency and phase angle parameter intended to design a low cost Phasor Measurement Unit (PMU) device. Problems found in implementation of FFT on sampled data of power signal with harmonics are solved by using the Goertzel algorithm. Selection of different design parameters is discussed in detail. In order to make the design very inexpensive a raspberry pi board is being used here. Analysis of measured parameters of simulated signal as well as power signal of lab supply is also discussed here.
NEffect of Bhramari Pranayama on Volunteers Having Cardiovascular Hyper-reactivity to Cold Pressor Test

Background: Pranayama has been reported to be beneficial in treating a range of stress related disorders, improving autonomic functions, relieving symptoms of asthma, stuttering and reducing signs of oxidative stress. The aim of the present study was to investigate whether regular practice of Bhramari Pranayama for 3 months can reduce the cardio-vascular hyper-reactivity induced by cold pressor test. Methods and results: The study group comprised of 54 healthy medical students of 18 to 24 yr age group. Initially there were 21 volunteers hyper-reactive to cold pressor test; which after 3 months of regular practice of Bhramari Pranayama reduced to 04 (≈ 81 % reduction ). Initially mean systolic BP after 1min cold stress was 139 ± 8.83 mmHg which was reduced to 134 ± 5 mmHg (t = 2.94, p< 0.01), while diastolic blood pressure reduced from 91 ± 7.5 mmHg to 88 ± 4.42 mmHg (t = 4.74, p<0.01). Initially the rise in systolic blood pressure due to cold stress (Hand immersed at 3 0 – 4 0 C for one minute) was 19.24 ± 4.17 mmHg that became 15.71 ± 2.85 mmHg (t = 6.4, p<0.01), and initial rise in diastolic blood pressure was 14.67 ± 2.85 mmHg which became 11.62 ± 1.86 mmHg (t = 5.6, p< 0.01) after regular practice of Bhramari Pranayama for 3 months. Conclusion: We concluded that regular practice of Bhramari Pranayama for 3 months reduced the cardiovascular hyper-reactivity to cold pressor test in subjects who were hyper-reactive to cold stress, possibly by inducing parasympathetic predominance and corticohypothalamo medullary inhibition.
High self-interference mitigation achieved thanks to significant antenna isolation and advanced RF frontend for In-Band Full-Duplex communications

Several techniques are used to mitigate the Self-Interference (SI) in In-Band Full-Duplex (IBFD) applications. Antenna isolation and especially dual polarized antenna is an efficient way to reach such objective in restricted footprint. Moreover antenna isolation does not generate noise and it does not create non-linear distortion. A test bench, including dual port antenna, analog and digital cancellation techniques is proposed, and the SI cancellation is evaluated in a controlled environment. A specific design of antenna is presented. An isolation of 47.5 dB is experimentally obtained between the two cross-polarized ports of a particular patch antenna. Matching of the antenna’s reflection parameters to -20 dB and broadside gains of 8 dBi are obtained. The additional analog and digital cancellers bring 48.9 dB more cancellation to reach 103 dB Self Interference mitigation.
Towards a Descriptive Language to Explicitly Define the Applicability of Timing Verification Tests of Critical Real-Time Systems

Real-time systems are used in many critical domains such as automotive, avionics and nuclear, therefore their temporal behavior needs to be checked rigorously. Many scheduling theory-based analysis tests have been proposed to validate the design of real-time systems. Since each given test is based on a set of assumptions, only the constrained system which satisfies these assumptions can be reliably verified by such a test. These assumptions do not depend on any design language but their interpretation does. Currently, these assumptions are often drowned in scientific paper discussions and expressed in natural languages. In this paper, we aim to make possible to define formally assumptions by real-time systems analysts without being expert of design languages. Indeed, we propose a domain specific language named IRL (Identification Rule Language). We show the benefit of such a language to capitalize efforts. That is, IRL allows to define analysis supports to help the timing verification community to share its knowledge and enables systems designers to use this knowledge easily.
Robust Subspace Tracking with Missing Data and Outliers via ADMM

Robust subspace tracking is crucial when dealing with data in the presence of both outliers and missing observations. In this paper, we propose a new algorithm, namely PETRELS-ADMM, to improve performance of subspace tracking in such scenarios. Outliers residing in the observed data are first detected in an efficient way and removed by the alternating direction method of multipliers (ADMM) solver. The underlying subspace is then updated by the algorithm of parallel estimation and tracking by recursive least squares (PETRELS) in which each row of the subspace matrix was estimated in parallel. Based on PETRELS-ADMM, we also derive an efficient way for robust matrix completion. Performance studies show the superiority of PETRELS-ADMM as compared to the state-of-the-art algorithms. We also illustrate its effectiveness for the application of background-foreground separation.
Deep Gait Tracking With Inertial Measurement Unit

This letter presents a convolutional neural network based foot motion tracking with only six-axis inertial-measurement-unit (IMU) sensor data. The presented approach can adapt to various walking conditions by adopting differential and window based input. The training data are further augmented by sliding and random window samplings on IMU sensor data to increase data diversity for better performance. The proposed approach fuses predictions of three-dimensional output into one model. The proposed fused model can achieve average error of 2.30 ± 2.23 cm in the X-axis, 0.91 ± 0.95 cm in the Y-axis, and 0.58 ± 0.52 cm in the Z-axis.
Body-biasing considerations with SPAD FDSOI: advantages and drawbacks

This article focusses on Single Photon Avalanche Diodes (SPAD) integrated in CMOS UTBB FDSOI (Ultra-Thin Body and Box Fully Depleted Silicon-On-Insulator technology), as an original approach for natively 3D SPAD pixels. In parallel to the optimization of the SPAD performances, some design issues relative to body-biasing effects are discussed in this paper. The associated electronics placed on top of the SPAD is constrained: the well layer below the box must be a P-type. Thus, only regular threshold voltage NMOS and low threshold voltage PMOS transistors can be used. The SPAD avalanche events affect the electronics through body-biasing effects, which can be advantageously exploited for an indirect sensing of the SPAD activity. Two simple indirect sensing cells are then studied. Firstly, a voltage divider realized with two transistors in series (PFET and NFET operating as active resistances) is simulated and measured to demonstrate its ability to detect avalanches. Secondly, an even simpler cell is studied, as it consists of only one NMOS transistor configured as an equivalent capacitive bridge (gate and box capacitances). Finally, the advantages and drawbacks from a design point of view are addressed.
Variational Inference for DOA Estimation in Reverberant Conditions

A concurrent speaker direction of arrival (DOA) estimator in a reverberant environment is presented. The reverberation phenomenon, if not properly addressed, is known to degrade the performance of DOA estimators. In this paper, we investigate a variational Bayesian (VB) inference framework for clustering time-frequency (TF) bins to candidate angles. The received microphone signals are modelled as a sum of anechoic speech and the reverberation component. Our model relies on Gaussian prior for the speech signal and Gamma prior for the speech precision. The noise covariance matrix is modelled by a time-invariant full-rank coherence matrix multiplied by time-varying gain with Gamma prior as well. The benefits of the presented model are verified in a simulation study using measured room impulse responses.
A Broken Rotor Bar Fault Diagnosis Approach Based on Singular Value Decomposition and Variational Mode Decomposition

The low-amplitude side-band fault feature frequencies of broken rotor bar fault in induction motor closing to the fundamental frequency in the stator currents are easily overlapped by high-amplitude fundamental frequency. In order to solve this problem, a new fault diagnosis approach that includes singular value decomposition (SVD), variational mode decomposition (VMD) and Hilbert transform (Hilbert-VMD) is proposed. Above all, SVD efficiently filters out the fundamental component and the color noises from one phase stator current. Simultaneously, SVD also calculates the effective number of band-limited intrinsic mode functions (BLIMFs) of VMD. Next, VMD decomposes the filtered phase stator current into finite BLIMFs. Finally, Hilbert transform performs on each BLIMF and combines the Hilbert spectrum of every BLIMF. So far, the fault feature frequencies are observed easily from the processing consequences. The simulation consequences prove the excellence of the proposed approach for broken rotor bar fault diagnosis.