Miracle-dz/UniFAR-FTUs huggingface dataset:
Abdominal Compartment Syndrome Evaluation in Sever Acute Pancreatitis

Background: ACS (abdominal compartment syndrome) is a serious condition that affects critically ill people. Because it predominantly affects individuals who are already sick, it may go unnoticed. The aim of study to find a link between IAP and the development of problems in patients with severe acute pancreatitis. We also wanted to see if there was a link between IAP and the development of complications in patients with severe acute pancreatitis (SAP). Patients and methods: This prospective study was conducted on 18 patients with AP hospitalised to the Surgical Intensive Care Unit at Zagazig University's Faculty of Medicine's General Surgery Department (SICU). During the first week after admission, all patients were divided into two groups based on their IAP. Patients with IAP 20 mmHg were assigned to the IAH group, whereas those with IAP 20mmHg were assigned to the normal IAP group. Results: Age, serum Lipase and APACHE were significantly higher among ACS cases and also ACS cases were significantly associated with DM. ACS cases were significantly associated with longer hospital stay also with bleeding, septic shock and mortality.The mean Lipase level was 959.72±103.58 with rang (800-1200). The mean Intra-abdominal pressure of studied patients was 12.94±4.12 and 38.9% of patients had IAH while 2 cases 11.1% had ACS. Conclusion: In patients hospitalised to the ICU with severe acute pancreatitis, IAH and ACS are common findings. IAH could make severe acute pancreatitis worse. Early detection of this possibly treatable aggravating condition could lead to early management and, in turn, a better prognosis.
Team Bioformer at BioCreative VII LitCovid Track: Multic-label topic classification for COVID-19 literature with a compact BERT model

—We describe Bioformer team’s participation in the multi-label topic classification task for COVID-19 literature (track 5 of BioCreative VII). Topic classification is performed using different BERT models (BioBERT, PubMedBERT, and Bioformer). We formulate the topic classification task as a sentence pair classification problem where the title is the first sentence, and the abstract is the second sentence. Our results show that Bioformer outperforms BioBERT and PubMedBERT in this task. We also found that further pretraining of Bioformer on COVID-19 articles improves the performance on topic categories of low support. Compared to the baseline results, our best model increased micro, macro and instance-based F1 by 8.8%, 15.5%, 7.4%, respectively.
ANN and ANFIS Models for COP Prediction of a Water Purification Process Integrated to a Heat Transformer with Energy Recycling

The aim of this study is to demonstrate the comparison of an artificial neural network (ANN) and an adaptive neuro fuzzy inference system (ANFIS) for the prediction of the coefficient of performance (COP) for a water purification process integrated in an absorption heat transformer system with energy recycling. ANN and ANFIS models take into account the input and output temperatures for each one of the four components (absorber, generator, evaporator, and condenser), as well as two presures and LiBr+H2O concentrations. Experimental results are performed to verify the results from the ANN and ANFIS approaches. For the network, a feedforward with one hidden layer, a Levenberg-Marquardt learning algorithm, a hyperbolic tangent sigmoid transfer function and a linear transfer function were used. The best fitting training data set was obtained with three neurons in the hidden layer. On the validaton data set, simulations and experimental data test were in good agreement (R2>0.9980). However, the ANFIS model was developed using the same input variables. The statistical values are given in as tables. However, comparaison between two models shows that ANN provides better results than the ANFIS results. Finally this paper shows the appropriateness of ANN and ANFIS for the quantitative modeling with reasonable accuracy.
A trial study of using DSST to evaluate cognitive impairment in older adults

Non-invasive means of monitoring mild cognitive impairments (MCI) is recently gaining popularity. With the advent of easy to use physiological sensors, there have been an outburst of studies from the last decade which aim at detecting a target’s mental health condition. However, not many studies present the experience or insights gained from carrying out such in-situ research work, particularly when working with older adults. Such insights could not only assist researchers in related areas when designing their study but also avoid potential pitfalls. Clinical trials were conducted by our organization in collaboration with the Geriatric Educational Research Institute, Singapore (GERI) and Singapore Management University (SMU) for detecting mild cognitive impairments in a geriatric population. Digitized versions of the standard pen & paper psychological tests were used along with gaze tracking technologies for MCI detection. Details of our user study and it’s outcomes are discussed as well as a generic approach of digitizing any given psychological test battery is highlighted.
The American Psychiatric Association observed the situation and listed the diagnostic criteria for Internet gaming disorder (IGD) in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) [3]. Gaming disorder

The American Psychiatric Association has identified Internet gaming disorder (IGD) as a potential psychiatric disorder. Questionnaires are the main method to classify high-risk IGD (HIGD) and low-risk IGD (LIGD). However, the results obtained using questionnaires might be affected due to several factors. Flow can measure a person's state of concentration and cardiovascular signals can reflect the autonomic responses of a person. We propose to observe the cardiovascular responses and flow scores from the flow short scale of the HIGD and LIGD groups to assist questionnaires in IGD risk assessment. The preliminary study recruited 18 gamers from colleges. Games with the easy and hard levels were set to arouse desire for playing. The result showed that the flow scores of five HIGD participants were significantly lower compared with that of 13 LIGD participants. The stroke volume (SV) of the LIGD group during baseline (67.06 ± 11.61) was significantly greater that of (p < 0.05) while playing the easy game (64.08 ± 10.37) and playing the hard game (63.70 ± 9.89). For the LIGD group, the cardiac output (CO) during baseline (5.28 ± 0.97) was significantly greater (p < 0.01) than that of recovery (5.03 ± 0.83), and while playing the easy game (5.34 ± 0.98) it was significantly more than that during recovery (p < 0.05). For the HIGD group, a significant difference in the heart rate, SV, and CO was not observed. The changes in cardiovascular responses of the LIGD group are greater than that of the HIGD group. Gamers with LIGD might have a higher susceptibility to the negative effect of playing video games, but gamers with HIGD might not. The finding of this study might help psychologists to estimate the IGD risk. Clinical Relevance— This study investigated the differences in the score of flow short scale, self-assessment manikin, challenge/skill, emotional questionnaire, and the changes in the cardiovascular responses between the HIGD and LIGD groups.
An Experimental and Computational Development of a Benchmark Solution for the Validation of Numerical Wave Tanks

A joint effort between MARINTEK, DNV GL and Technip has been made to develop a set of benchmark solutions for the validation of numerical wave tanks. High-fidelity measurements of the wave field around a vertical circular cylinder from a scaled model test have been selected as the benchmark cases. A reference numerical wave tank based on a commercial CFD software and the Euler-Overlay Method as far-field closure has been validated by grid convergence test and comparison with the model test data. The validated numerical solution can be used as a benchmark solution to validate other numerical wave tanks.Copyright © 2014 by ASME
A Machine Learning Approach for Prediction of Sedentary Behavior Based on Daily Step Counts

Sedentary behavior is considered as a major public health challenge, linked with many chronic diseases and premature mortality. In this paper, we propose a steps counting -based machine learning approach for the prediction of sedentary behavior. Our work focuses on analyzing historical data from multiple users of wearable physical activity trackers and exploring the performance of four machine learning algorithms, i.e., Logistic Regression, Random Forest, XGBoost, Convolutional Neural Networks, as well as a Majority Vote Ensemble of the algorithms. To train and test our models we employed a crowd sourced dataset containing a month’s data of 33 users. For further evaluation, we employed a dataset containing 6 months of data of an additional user. The results revealed that while all models succeed in predicting next-day sedentary behavior, the ensemble model outperforms all baselines, as it manages to predict sedentary behavior and reduce false positives more effectively. On the multi-subjects test dataset, our ensemble model achieved an accuracy of 82.12% with a sensitivity of 74.53% and a specificity of 85.71%. On the additional unseen dataset, we achieved 76.88% in accuracy, 63.27% in sensitivity and 81.75% in specificity. These outcomes provide the ground towards the development of real-life artificially intelligent systems for sedentary behavior prediction.
A Novel Computer Vision Approach to Kinematic Analysis of Handwriting with Implications for Assessing Neurodegenerative Diseases

Fine motor movement is a demonstrated biomarker for many health conditions that are especially difficult to diagnose early and require sensitivity to change in order to monitor over time. This is particularly relevant for neurodegenerative diseases (NDs), including Parkinson’s Disease (PD) and Alzheimer’s Disease (AD), which are associated with early changes in handwriting and fine motor skills. Kinematic analysis of handwriting is an emerging method for assessing fine motor movement ability, with data typically collected by digitizing tablets; however, these are often expensive, unfamiliar to patients, and are limited in the scope of collectible data. In this paper, we present a vision-based system for the capture and analysis of handwriting kinematics using a commodity camera and RGB video. We achieve writing position estimation within 0.5 mm and speed and acceleration errors of less than 1.1%. We further demonstrate that this data collection process can be part of an ND screening system with a developed ensemble classifier achieving 74% classification accuracy of Parkinson’s Disease patients with vision-based data. Overall, we demonstrate that this approach is an accurate, accessible, and informative alternative to digitizing tablets and with further validation has potential uses in early disease screening and long-term monitoring.Clinical relevance— This work establishes a more accessible alternative to digitizing tablets for extracting handwriting kinematic data through processing of RGB video data captured by commodity cameras, such as those in smartphones, with computer vision and machine learning. The collected data has potential for use in analysis to objectively and quantitatively differentiate between healthy individuals and patients with NDs, including AD and PD, as well as other diseases with biomarkers displayed in fine motor movement. The developed system has potential applications including providing widespread screening systems for NDs in low-income areas and resource-poor health systems, as well as an accessible form of disease long-term monitoring through telemedicine.
Analysis and design of symmetric lattice based wideband 2-bit digital phase

In this work, a novel, wideband 2-bit digital phase shifter topology based on modified symmetric lattice 4 structure is proposed. In this paper, two novel phase shifter unit topologies are introduced; generic topology for the 5 entire phase range and alternative topology for large phase shift values. This paper provides a complete analysis and 6 design equations for both proposed phase shifter circuits. The proposed 2-bit phase shifter circuit is implemented 7 employing the commercially available 0. 18μm TSMC CMOS process. It is exhibited that the new 2-bit phase shifter 8 topology provides rms phase error of 6.75◦ over 3-13 GHz bandwidth. The proposed phase shifter design achieves the 9 bandwidth requirement of next generation communication systems even with a generic 0. 18μm CMOS technology. 10
NODES ON QUINTIC SPECTRAHEDRA T. BRYSIEWICZ-K. KOZHASOV-M. KUMMER

Let Sn denote the space of n×n complex symmetric matrices. A spectrahedron is a convex set obtained as the intersection of the cone S< ⊂ Sn of positive semidefinite matrices with a real affine linear subspace of symmetric matrices. However, from the point of view of algebraic geometry it is more natural to consider linear subspaces L⊂ Sn and study projective spectrahedra P(L∩S<). Spectrahedra are nonlinear generalizations of polytopes. They naturally appear in optimization: spectrahedra are feasibility regions for semidefinite programs in the same way as polyhedra are feasibility regions for linear programs. Apart from optimization, spectrahedra have appearances in algebraic geometry, convex geometry, statistics, and combinatorics. It is conjectured that the class of spectrahedra is so rich that it contains hyperbolicity cones of hyperbolic polynomials [8]. In general, it is of interest to study spectrahedra in any dimension,
Odor valence modulates cortico-cortical interactions: a preliminary study using DCM for EEG

Olfaction and emotions share common networks in the brain. However, little is known on how the emotional content of odors modulate dynamically the cortico-cortical interactions within these networks. In this preliminary study, we investigated the effect of odor valence on effective connectivity through the use of Dynamic Causal Modeling (DCM). We recorded electroencephalographic (EEG) data from healthy subjects performing a passive odor task of odorants with different valence. Once defined a fully-connected a priori network comprising the pyriform cortex (PC), orbitofrontal cortex (OFC), and entorhinal cortex (EC), we tested the modulatory effect of odor valence on their causal interactions at the group level using the parametric empirical bayes (PEB) framework. Results show that both pleasant and the unpleasant odors have an inhibitory effect on the connection from EC to PC, whereas we did not observe any effect for the neutral odor. Moreover, the odor with positive valence has a stronger influence on connectivity dynamics compared to the negative odor. Although preliminary, our results suggest that odor valence can modulate brain connectivity.
Catalytic DS at BioCreative VII: DrugProt Track A Syntactically-guided BiLSTM with BERT-derived Word Vectors

—Team “Catalytic” submitted two system runs to the DrugProt Main Track competition. First, as a strong baseline, we reimplemented BioBERT (1) in PyTorch. Second, noting poor performance of the BioBERT system when drugs and proteins were distant from one another in the sentence, we implemented a syntactically-guided bidirectional LSTM (Syn-BiLSTM) classification model (2) using the sequence of tokens along the shortest path between the entities in a dependency parse of the input sentence. The Syn-BiLSTM is trained as a classifier on this sequence using the final hidden states in the forward and backward directions. Evaluating as a classifier that predicts either a null interaction or one of the 13 specific interaction types, we show that the BiLSTM is more accurate than BioBERT at longer distances, while preserving accuracy at shorter distances, and, in a precision/recall evaluation setting, scores higher in precision and F1, although lower in recall. Additionally, we surpass the overall mean system precision and F1 performance as reported in the BioCreative VII official evaluation (3). Neither of our systems makes any explicit use of ontologies, gazetteers, or any other distillation of scientific knowledge about drug-protein interactions, but we anticipate that using such information would improve performance. The only heuristic constraints on entity interactions is that we assume interactions occur within (auto-detected) sentence boundaries. Finally, we discuss specific interaction type performance, its potential impact in a practical system, as well as future directions for research.
A Low-rank Spatiotemporal based EEG Multi-Artifacts Cancellation Method for Enhanced ConvNet-DL’s Motor Imagery Characterization

Multi-channel Electroencephalograph (EEG) signal is an important source of neural information for motor imagery (MI) limb movement intent decoding. The decoded MI movement intent often serve as potential control input for brain-computer interface (BCI) based rehabilitation robots. However, the presence of multiple dynamic artifacts in EEG signal leads to serious processing challenge that affects the BCI system in practical settings. Hence, this study propose a hybrid approach based on Low-rank spatiotemporal filtering technique for concurrent elimination of multiple EEG artifacts. Afterwards, a convolutional neural network based deep learning model (ConvNet-DL) that extracts neural information from the cleaned EEG signal for MI tasks decoding was built. The proposed method was studied in comparison with existing artifact removal methods using EEG signals of transhumeral amputees who performed five different MI tasks. Remarkably, the proposed method led to significant improvements in MI task decoding accuracy for the ConvNet-DL model in the range of 8.00~13.98%, while up to 14.38% increment was recorded in terms of the MCC: Mathew correlation coefficients at p<0.05. Also, a signal to error ratio of more than 11 dB was recorded by the proposed method.Clinical Relevance— This study showed that a combination of the proposed hybrid EEG artifact removal method and ConvNet-DL can significantly improve the decoding accuracy of MI upper limb movement tasks. Our findings may provide potential control input for BCI rehabilitation robotic systems.
Assessing Motivations towards Green Product Purchase Behaviour among Young Malaysian: Empirical Evidence

Numerous studies on environmentally conscious behaviour have looked at age as primary concern. According to some studies, younger generations are more environmentally conscious. The most frequently used argument is that those who grew up during the social movements over environmental consciousness are more likely to be sympathetic to the issues. Understanding how young people make green product purchasing decisions has a significant impact on green marketing strategies. A better understanding of what motivates these young consumers to buy green products may also help reduce the impact of irresponsible consumption in the future. Their consumption patterns are distinct from "traditional" consumption. Their typical consumption pattern has been characterized by open-mindedness, hedonism, and visibility. To a certain extent, their consumption habits reflect a form of self-expression, personal identity formation, creativity, or even art. Thus, the purpose of this study is to determine whether key factors such as environmental consciousness, environmental attitude, eco labelling, and green advertising influenced young Malaysians' green purchase behaviour. A self-administered questionnaire was distributed, which garnered 431 responses across the country. The confirmatory factor analysis (CFA) was used to evaluate the associated route coefficients between the different variables, and structural equation modelling was used for hypothesis testing. Overall, the results showed that green advertising was the most important predictor of green purchase behaviour among younger generations. The study's findings will assist green marketers in improving their strategies of promoting green products to younger generations.
FrictionalMonolith

We propose FrictionalMonolith, a monolithic pressure-friction-contact solver for more accurately, robustly, and efficiently simulating two-way interactions of rigid bodies with continuum granular materials or inviscid liquids. By carefully formulating the components of such systems within a single unified minimization problem, our solver can simultaneously handle unilateral incompressibility and implicit integration of friction for the interior of the continuum, frictional contact resolution among the rigid bodies, and mutual force exchanges between the continuum and rigid bodies. Our monolithic approach eliminates various problematic artifacts in existing weakly coupled approaches, including loss of volume in the continuum material, artificial drift and slip of the continuum at solid boundaries, interpenetrations of rigid bodies, and simulation instabilities. To efficiently handle this challenging monolithic minimization problem, we present a customized solver for the resulting quadratically constrained quadratic program that combines elements of staggered projections, augmented Lagrangian methods, inexact projected Newton, and active-set methods. We demonstrate the critical importance of a unified treatment and the effectiveness of our proposed solver in a range of practical scenarios.
Development of Machine-Learning Algorithms for Recognition of Subjects’ Upper Limb Movement Intention Using Electroencephalogram Signals

This study aims to classify rest and upper limb movements execution and intention using electroencephalogram (EEG) signals by developing machine-learning (ML) algorithms. Five different MLs are implemented, including k-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF). The EEG data from fifteen healthy subjects during motor execution (ME) and motor imagination (MI) are preprocessed with Independent Component Analysis (ICA) to reduce eye-blinking associated artifacts. A sliding window technique varying from 1 s to 2 s is used to segment the signals. The majority voting (MV) strategy is employed during the post-processing stage. The results show that the application of ICA increases the accuracy of MI up to 6%, which is improved further by 1-2% using the MV (p<0.05). However, the improvement in the accuracies is more significant in MI (>5%) than in ME (<1%), indicating a more significant influence of eye-blinking artifacts in the EEG signals during MI than ME. Among the MLs, both RF and SVM consistently produced better accuracies in both ME and MI. Using RF, the 2 s window size produced the highest accuracies in both ME and MI than the smaller window sizes.
Calibration of VISSIM to the traffic conditions of Khobar and Dammam, Saudi Arabia

The main objective of this study is to calibrate and validate the microscopic traffic simulation model VISSIM to the traffic conditions of Khobar and Dammam, Saudi Arabia. VISSIM is a German microscopic simulation model that is beginning to see increased use within the United States and in many other major countries. It is one of the few comprehensive microscopic traffic simulators covering a wide range of traffic situations including traffic and transit on urban roads and motorways.