Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting PTSD Severity in Veterans from Self-reports for Early Intervention: A Machine Learning Approach

Early intervention for veterans in crisis represents a crucial area of study to reduce the psychological and health burdens for this population. Traumatic experiences associated with military service are associated with drug and alcohol abuse, suicidality, anger, and disrupted work and family relationships. This project used machine learning (ML) models to integrate data from sociodemographic, self-report baseline symptoms, weekly brief Ecological momentary assessment (EMA) survey of veterans in a community-based 12-week peer support program to predict the discharge PTSD severity level. The ML predictions place the participants into one of the three risk levels: low, medium, and high PCL-5 score. The models were evaluated at different timepoints (weekly intervals) of the program for identifying the earliest week to guide early intervention and reduce veterans’ engagement in risky behaviors. The best results were achieved from a voting classifier with an average f-score of 0.69 at week 4.
In vitro Seed Propagation of Wild Syrian Marjoram (Origanum syriacum L.)

Wild Syrian marjoram (Origanum syriacum L.) was in vitro established from seeds. Proliferation was experimented with different levels (0.0, 0.4, 0.8, 1.2, 1.6, or 2.0 mg/l) of kinetin, 6-benzyladenine (BA) or thidiazuron (TDZ). Highest shoot proliferation (5.0, and 4.6 or 4.2 microshoots, respectively) was obtained when kinetin (0.4 mg/l) and BA (0.8 or 1.2 mg/l) were used. TDZ failed to promote shoot proliferation and induced calli. Rooting was experimented at different levels (0.0, 0.1, 0.4, 0.8, 1.2, 1.6 or 2.0 mg/l) of 1-Naphthaleneacetic acid (NAA), indole-3-butyric acid (IBA) or indole-3-acetic acid (IAA). The highest root number (11.4) was attained in media containing 0.8 mg/l IAA. Rooted plantlets were ex vitro acclimatized (90% survival), transferred to soil and successfully grown to maturity in the greenhouse.
COMPARISON OF TURBULENT BOUNDARY LAYERS AND CHANNELS FROM DIRECT NUMERICAL SIMULATION

A new simulation of the zero-pressure-gradient turbulent boundary layer is discussed, in the range Reθ = 620− 2140. It is first shown that the initial several hundred momentum thicknesses have to be discarded due to contamination from the synthetic turbulent inflow, and this is traced to the long lifetimes of the largest eddies. Beyond the inflow region, the results agree well with previous simulations and with experiments, and are compared to those of turbulent channels in the same range of Reynolds numbers. It is found that the transverse velocities and the pressure fluctuations are stronger in boundary layers. The reason is traced to the extra production in the wake region, and, ultimately, to the irrotational intermittency in that part of the flow. INTRODUCTION AND NUMERICAL SCHEME The purpose of this paper is to present a new simulation of the zero-pressure-gradient turbulent boundary layer in the range Reθ = 620 − 2140, and to compare the results to those of turbulent channels in the same range of Reynolds numbers. The simulation uses a high-resolution code described in Simens et al. (2009), based on compact finite differences in the streamwise (x) and wall-normal (y) directions, and on dealiased Fourier expansions along the span (z). The longitudinal pressure gradient is controlled by removing fluid through the upper boundary, and is kept very close to zero. Numerical parameters are given in table 1. The turbulent inflow is synthesised by Lund et al’s (1998) recycling method, in which the incoming flow is synthesised using data from a downstream reference plane xref . Its consequences are carefully investigated, and it is found that the influence of the inflow extends over 300θ0, where θ0 is the momentum thickness at the inlet. This is almost 25% of the total simulation length and, together with a shorter contaminated region near the outflow, reduces the range of useful Reynolds numbers to Reθ = 1050 − 2050 (δ + 99 = 440 − 720). The reason for this long inflow length can be seen in figure 1(a), which displays the autocorrelation function Cuu(x; x) of a band of spanwise Fourier coefficients of the streamwise velocity, as a function of x. Besides the primary peak at x = x ≈ 1100θ0, there is a secondary one at x = x′−xref ≈ 150θ0. Lund’s method can be interpreted as a physical experiment in which eddies at the reference plane are approximately (except for rescaling) copied to a different position in the boundary layer (the inflow), and allowed to 1 Corresponding author: jimenez@torroja.dmt. upm.es Table 1: Parameters of the boundary layer simulation. Lx, Ly and Lz are the box dimensions along the three axes. Nx, Ny and Nz are the corresponding grid sizes, expressed for z in terms of collocation points. The resolution is kept of the order of 4 − 6 wall units in the wall-parallel directions, and at least 1.5 Kolmogorov lengths in y. Reθ (Lx, Ly, Lz)/θ0 Nx, Ny, Nz 620–214
A Theory of Our Universe

Contemporary theories of our Universe, such as the Friedmann-Lemaitre-Robertson-Walker (FLRW) model of the cosmos, assume that time marches on at a uniform, constant pace from its very beginning. But what if that is not the case? It is proposed that our Universe is not a “Big Bang”, but rather a “Big Rollout” in space and time, spacetime, from the shortest meaningful length, Planck Length, and the shortest meaningful measure of time, Planck Time. It is speculated that time and dimensions, spacetime, grow in concert very rapidly at first. The fundamental equation, which relates the change in the space dimensions to the change in the speed of time at the beginning of time for the new Theory, is derived. Spacetime rolls out initially at light speed. As time increases, the rate of change of the speed of time could be erratic, that is although in general, it slows (rate of time slows approaching zero at the end of time), its rate of change could decelerate, pause or perhaps accelerate for a while, no need however, for dark matter or dark energy.
Low-Voltage Dynamic Comparator with Bulk-Driven Floating Inverter Amplifier

This work presents a low voltage dynamic comparator for low-power ADC applications. The dynamic comparator uses a pre-amplifier powered by a floating reservoir capacitor and bulk-driven structure. The latch stage is done by a simple circuit to reduce the total voltage overhead necessary to define the logic levels and uses a charge sharing technique in order to reduce the energy consumption per conversion. The floating reservoir capacitor powered pre-amplifier reduces the impacts of global process variability and improves the input common-mode voltage performance. The bulk-driven structure lowers the threshold voltages of the pre-amplifier transistors at the sampling phase. Such structure also works as a positive feedback signal during the comparison phase to provide extra transconductance. The proposed dynamic comparator is designed and simulated in 28nm CMOS technology and reaches a performance suitable for 8 bits ADCs working with 600mV power supply. The dynamic comparator achieves 380μV input-referred noise, while consuming only 40 fJ per comparison and having a nominal delay of 2.26ns.
Optimization Design of Industrial Water Supply Pump Station Considering the Influence of Atmospheric Temperature on Operation Cost

Pump type selection at a pump station affects not only the hardware investment but also the operating efficiency. The influence of atmospheric temperature on water consumption was not considered in existing studies. This article considers the uncertainty of water consumption caused by temperature as well as the configuration and operation schedule of the pump station under various working conditions. The purpose of the present paper is to achieve the minimum annual investment and operation cost of the pump station. Taking a steel plant as an example, this article analyzes the possible working conditions of cooling water in the plant, establishes the annual minimum cost objective function of the optimal design, and sets the constraint conditions. The decision variables include the selection of overall pump type and optimal operation of the pump station under different working conditions. Finally, the mathematical model of the optimal design of a water supply pump station is given. The results show that when considering the influence of temperature on water consumption, the annual comprehensive cost is reduced, the optimization effect is obvious, and a good practical effect is achieved.
An I/O Request Packet (IRP) Driven Effective Ransomware Detection Scheme using Artificial Neural Network

In recent times, there has been a global surge of ransomware attacks targeted at industries of various types and sizes from retail to critical infrastructure. Ransomware researchers are constantly coming across new kinds of ransomware samples every day and discovering novel ransomware families out in the wild. To mitigate this ever-growing menace, academia and industry-based security researchers have been utilizing unique ways to defend against this type of cyber-attacks. I/O Request Packet (IRP), a low-level file system I/O log, is a newly found research paradigm for defense against ransomware that is being explored frequently. As such in this study, to learn granular level, actionable insights of ransomware behavior, we analyze the IRP logs of 272 ransomware samples belonging to 18 different ransomware families captured during individual execution. We further our analysis by building an effective Artificial Neural Network (ANN) structure for successful ransomware detection by learning the underlying patterns of the IRP logs. We evaluate the ANN model with three different experimental settings to prove the effectiveness of our approach. The model demonstrates outstanding performance in terms of accuracy, precision score, recall score, and F1 score, i.e., in the range of 99.7%±0.2%.
Resampling and Data Augmentation For Equines’ Behaviour Classification Based on Wearable Sensor Accelerometer Data Using a Convolutional Neural Network

Monitoring horses’ behaviors through sensors can yield important information about their health and welfare. Sampling frequency majorly affects the classification accuracy in activity recognition and energy needs for the sensor. The aim of this study was to evaluate the effect of sampling rate reduction of a tri-axial accelerometer on the recognition accuracy by resampling a 50 Hz experimental dataset to four lower sampling rates (5 Hz, 10 Hz, 12.5 Hz and 25 Hz). Also, in this work we investigate the ‘reality gap’ that incorporates changes in the data that are primarily characterized as sensor rotations or measurement noise through various data augmentation techniques such as rotation and jittering. Finally, another factor influencing activity recognition are the subjects themselves and therefore the model is evaluated on different horse types. A deep learning-based approach for activity detection of equines is proposed to automatically classify 2238 manually annotated 2 s samples tri-axial accelerometer leg data data of seven different activities performed by six different subjects. The raw data are preprocessed and fed into a convolutional neural network (CNN) from which features are extracted automatically by using strong computing capabilities. Furthermore, the neural network was intentionally designed to minimize running time, enabling us to imagine the future use of the built model in embedded constrained devices. The complexity of these automatic learning techniques can be decreased while achieving high accuracies using ten-fold-cross validation using a computationally less intensive received signal length data (99.32% at 5 Hz vs 99.74% at 25 Hz). This indicates that sampling at 5 Hz with a 2 s window will offer advantages for activity surveillance thanks to decreased energy requirements, since validation time decreases 16-fold (784 microseconds at 50 Hz to 48 microseconds at 5 Hz). Moreover, in this work we show that rotating the training or validation signal with 10 degrees over the X, Y and Z-axis increases the generalization capabilities of our model (99.61 % vs 99.93%) while adding small amounts of noise (smaller than 0.3 standard deviation (STD)) does not decrease the classification accuracy under 99%. Finally, the performance and ability of the model to generalize is validated on data from unseen horses at the cost of only 4.1% and 2.45% reduction in accuracy when validated on a pony and a lame horse, respectively.
Reducing Multijunction Thermal Converter Output Resistance to Reduce Measurement Noise

This paper describes the effects on device performance caused by modifications to thermocouple design and fabrication. Such modifications are intended to lower the output resistance of multijunction thermal converters (MJTCs) fabricated on fused silica substrates. Device component metals were deposited by physical vapor deposition and fabricated into device features by either wet chemical etching or metal lift-off. The low-temperature-coefficient-of-resistance Ni75Cr20Al2.5Cu2. 5alloy heater is thermally isolated from the rest of the chip by a $25\ \mu \mathrm{m}$ fused silica membrane produced by bead blasting the back side of the substrate. Thermocouples were shorted along 50% and 25% of their length for a set of test devices; control devices were left unmodified. MJTC output resistances were decreased from about $13.2\ \mathrm{k}\Omega$ for the non-shorted thermocouples to about $10.5\ \mathrm{k}\Omega$ for the 50% shorted thermocouples. At 3 V input voltage, measured ac-dc differences of the devices for frequencies between 10 Hz and 1 MHz showed no significant impact on ac-dc difference due to thermocouple shorting. However, we observed indications that shorting the thermocouples results in a reduction in the signal-to-noise ratio of the measurement of the output voltage.
Experimental Study of Heat Transfer Coefficient and Pressure Drop for Supercritical Carbon Dioxide and Water Inside a Microchannel

The thermo-fluidic nature of water and supercritical carbon dioxide in a microchannel is experimentally studied. A test rig and microfluidic devices are constructed to enable precise control and measurements of temperature, pressure, heat and mass fluxes. Wall temperature measurements, heat transfer coefficients, and pressure drops for both fluids were directly measured and compared. Flow regimes and characteristic Rynolds numbers were discussed and were related to Dittos-Boelter and Gnielinski correlations. It was found that an exponential fit better predicted the wall temperature profile obtained for supercritical CO2. Moreover, for conditions close to the pseudo-critical point, the correlations failed to predict the supercritical heat transfer coefficient but fit well in the gas-like supercritical CO2 conditions. It was also found that water has a better ability to remove heat under the conditions studied in this paper with smaller pressure drop. However, for water the pressure drop was a significant portion of the operational pressure.
A Proactive Wash droplet routing for Digital Microfluidics Biochip

Contamination occurs when multiple droplets share few cells in their route. When the first droplet passes through a shared cell, it lefts a strain of residue on this cell. At later time cycle, when the second droplet traverses through this shared cell, the leftover residue contaminates it. This may leads to erroneous experimental outcome. To avoid this, wash droplets are used for cleaning the residue. The wash droplets are sandwiched in between two functional droplets. In this article, we propose a proactive wash droplet routing that take wash capacity into account. Here, every functional droplet is tailgated by a wash droplet. When wash droplet capacity is exhausted, a new wash droplet is moved in to replace the exhausted wash droplet. Experimental result shows improvement in washing time and total turn around time.
Fusing Iris and Periocular Region for User Verification in Head Mounted Displays

The growing popularity of Virtual Reality and Augmented Reality (VR/AR) devices in many applications also demands authentication of users. As the devices inherently capture the eye image while capturing the user interaction, the authentication can be devised using the iris and periocular recognition. While both iris and periocular data being non-ideal unlike the data captured from standard biometric sensors, the authentication performance is expected to be lower. In this work, we present and evaluate a fusion framework for improving the biometric authentication performance. Specifically, we employ score-level fusion for two independent biometric systems of iris and periocular region to avoid expensive feature-level fusion. With a detailed evaluation of three different score-level fusion after the score normalization on a dataset of 12579 images, we report the performance gain in authentication using score-level fusion for iris and periocular recognition.
A Decimal Multiplier With Improved Speed Using Semi-Parallel Iterative Approach

This paper presents a semi-parallel iterative decimal multiplier. The Proposed multiplier uses BCD-8421 encoding which does not require any recoding as compared to all the implementations reported in the literature. It employs semi-parallel partial product generation for faster multiplication, decimal 4:2 adder for partial product reduction and a novel iterative partial product reduction approach. The proposed design is implemented and verified using FPGA, the results of the implementation show that the proposed semi-parallel iterative design performs better with lesser delay than other decimal multipliers and binary multipliers with double precision that have been reported in literature.
Data-Driven Evolutionary Optimisation for the design parameters of a Chemical Process: A Case Study

A significant challenge faced within the field of chemical plant design and optimisation is the uncertainty in the reaction parameter, indeterminate component failures and their impacts on the design and operation. Additionally, real-world data accumulated from these plants would contain stochastic elements that are difficult to model. To this end, stochastic and deterministic methods have been proposed to simulate the uncertainty and enable an understanding of the plant and how it may be optimised. Within the existing literature investigated, the optimisation is done under the assumption that the simulation (target function) is non-stochastic. We have found that the use of an Evolutionary Algorithm in the form of a Genetic Algorithm can find an optimal solution even when we allow the simulation to behave stochastically as it would in practical applications. Further, we note that the use of a surrogate Machine Learning model as a substitute for the stochastic simulation model leads to substantively improved solutions in significantly less time (1.82 times speedup). We argue that the use of Genetic Algorithms in the optimisation of chemical plant design, taking into account the stochastic nature of the plant and including indeterminate failures, is a worthwhile solution and that surrogate assisted evolutionary algorithms will improve this solution further.
An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis

Privacy and annotation bottlenecks are two major issues that profoundly affect the practicality of machine learning-based medical image analysis. Although significant progress has been made in these areas, these issues are not yet fully resolved. In this paper, we seek to tackle these concerns head-on and systematically explore the applicability of non-contrastive self-supervised learning (SSL) algorithms under federated learning (FL) simulations for medical image analysis. We conduct thorough experimentation of recently proposed state-of-the-art non-contrastive frameworks under standard FL setups. With the SoTA Contrastive Learning algorithm, SimCLR as our comparative baseline, we benchmark the performances of our 4 chosen non-contrastive algorithms under non-i.i. d. data conditions and with a varying number of clients. We present a holistic evaluation of these techniques on 6 standardized medical imaging datasets. We further analyse different trends inferred from the findings of our research, with the aim to find directions for further research based on ours. To the best of our knowledge, ours is the first to perform such a thorough analysis of federated self-supervised learning for medical imaging. All of our source code will be made public upon acceptance of the paper.
Unique Animal Identification using Deep Transfer Learning For Data Fusion in Siamese Networks

The unique automated identification of animals of various species is a pressing challenge ecologically, environmentally and economically. A broader question relates to how one might exploit the somewhat more mature technologies and techniques used within human visual biometrics to automate this same task for other species. One specific technique is the use of region proposal networks and deep transfer learning in siamese networks for individual animal identification. We report that although it is relatively easy to achieve state of the art performance in uniquely identifying individuals for the easy target of zebras, trying to use the same pipeline to obtain useable, top-10> 85%, results for a more challenging species such as nyala is still an open research problem. We argue that uniquely identifying individuals such as nyala who actively try to disguise themselves in their environments require improved few-shot learning techniques and perhaps more data than the current open dataset we have provided to stimulate this area of research.
Using Deep Learning To Assign Rheumatoid Arthritis Scores

In this work, we report the performance of the deep learning model in automatically assigning joint scores and overall patients scores for Rheumatoid Arthritis patients’ X-ray images. The dataset is from RA2 DREAM Challenge https://www.synapse. org/#! Synapse:syn20545111/wiki/594083. Overall, we achieve good predictive performance with an average accuracy of 0.908.
The impact of Eimeria tenella co-infection on Campylobacter jejuni colonisation of the chicken

The impact of Eimeria tenella co-infection on Campylobacter jejuni colonisation 1 of the chicken 2 3 Running title Impact of E. tenella on C. jejuni colonisation 4 Sarah E. Macdonald, Pauline M. van Diemen, Henny Martineau, Mark P. 5 Stevens, Fiona M. Tomley, Richard A. Stabler and Damer P. Blake 6 Pathobiology and Population Sciences, Royal Veterinary College, Hawkshead Lane, 7 North Mymms, AL9 7TA, UK. 8 Institute for Animal Health, Compton, Berkshire, RG20 7NN, UK. 9
Camera Localization Based on Belief Clustering

This work deals with epipole estimation related to egocentric camera localization in surveillance and security applications. Matching visual features in the images provides some evidences for various solutions, so that epipole localization can be addressed as a fusion task with a large number of sources including outlier ones. In order to deal with source imprecision and uncertainty, we rely on the belief function theory and a 2D framework suited for our application. In this framework, we address the challenges introduced by a large number of sources with a strategy based on clustering and intra-cluster fusion. The proposed method exhibits more robustness in terms of accuracy and precision when compared on real data with the standard algorithms which provide single solution. Since we provide a Basic Belief Assignment as a result, our strategy is particularly adapted for the prospective combination with additional sources of information.