Miracle-dz/UniFAR-FTUs huggingface dataset:
Value of Longitudinal Strain in Assessment of Patients with Suspected Acute Coronary Syndrome and No Wall Motion Abnormalities

Background: Echocardiography is a first simple technique for the evaluation of coronary artery disease (CAD), which is useful in the diagnostic and prognostic workup of these syndromes. The clinical work-up of patients presenting with chest pain is a diagnostic challenge. Objective: We inspected the diagnostic performance of global (GLS) and territorial (TLS) longitudinal strain to predict CAD in patients presenting with suspected non-ST-segment elevation acute coronary syndrome (NSTE-ACS) but apparent normal global and regional systolic function. Patients And Method: A cross sectional study included 90 patients with suspected NSTE-ACS with normal left ventricular ejection fraction (LVEF) (≥50%) and wall motion score index (WMSI) (=1). Speckle-tracking echocardiography was performed to all patients on admission then patients underwent coronary angiography or noninvasive test according to their risk stratification. Patients were classified to 2 groups (CAD and No CAD). Results: There was significant sensitivity and specificity of cardiac enzymes, GRACE score, Global longitudinal strain and territorial longitudinal strain in identifying CAD. However there was no statistically significant difference in conventional echocardiographic data between both studied groups. A cutoff value of GLS -17.1, TLS-LAD cutoff level >-17.15, TLS-LCX cutoff level >-16.9 and TLS-RCA cutoff level >-16.3. GLS as a predictor for the number of affected vessels, cutoff point of ≥ -15.4 can be used. A predictor for the presence of proximal lesions, cutoff point of TLS LAD ≥ -15.1 and TLS LCX ≥-15.3 can be used. Conclusions: Global longitudinal strain and territorial longitudinal strain can be used for early detection of the presence of coronary artery occlusion to identify patients who may benefit from early reperfusion. GLS also can predict multivessel disease and TLS can be used as a predictor for the presence of proximal lesions.
English Teaching and Learning in Saudi Arabia: Emerging Practices and Strategies for Achieving the Educational Objectives of the Saudi Vision 2030

The purpose of this paper is to highlight the key elements of English teaching and learning related to Saudi Vision 2030. The paper aimed to develop a conceptual framework that guides the way for more teaching and learning practices that are aligned with the educational goals of Saudi Vision 2030. The theoretical framework of this research paper was informed by two bodies of literature: literature on second and foreign language methodologies and literature on teaching English in Saudi Arabia. Two types of information were needed for this study: perceptual and theoretical information. Perceptual information was obtained via interviews with 15 language educators from Saudi Arabia. Theoretical information was obtained through an extensive review of the current literature on English teaching and learning. The findings of this research indicate that the educational objectives of the Vision can be achieved by replacing traditional language curriculum with authentic materials and resources, integrating more technology in language classes, incorporating cooperative learning strategies, increasing the sense of openness and tolerance among language learners, and using language instructions that prepare students for the labor market.
Exploring and Developing a Scale Using Item Response Theory for Sport Psychological Skills in Speed Skaters

The purpose of this study was to develop a sports psychological skills scale for speed skaters and examine the validity of the scale. In order to accomplish the purpose of this study, skaters from around the world were set as a population, and then data from 456 athletes were collected using convenience sampling from the athletes participating in the 2020–2022 World Championships and the Beijing Winter Olympics. For analysis, V coefficient, Parallel Analysis, Exploratory Structural Equation Modeling, Bayesian Structural Equation Modeling, Maximum Likelihood CFA, and analysis of Multi-Group Confirmatory Factor Analysis were carried out by using WINSTEPS 3.65 and MPLUS 7.04 programs. The level of statistical significance was all set at α = 0.05 and Exploratory Structural Equation Modeling, Bayesian Structural Equation Modeling, Maximum Likelihood CFA, and Multi-Group Confirmatory Factor Analysis fit used TLI, RMSEA, the concept of reliability. The findings of the study were as follows: First, the factorial structure of SPSS was extracted as five factors with 17 items. Second, the analysis of MCFA on the transformative leadership scale, according to gender differences, was carried out, and cross validity was fulfilled.
Disenfranchised Guilt—Pet Owners’ Burden

Simple Summary Guilt is the unpleasant emotion associated with one’s behaviors, thoughts, or intentions, and it is based on the possibility that one may be in the wrong or others may have this perception. Parental guilt is one common type of guilt and is often associated with work–family conflict (WFC). WFC and related guilt have been found to be related to depression and anxiety. The current study was designed to explore dog owners’ guilt surrounding their dogs through an online anonymous survey. Results suggest that dog owners’ guilt and WFC associated with their dog are at similar levels to those reported in human family studies. Additionally, the relationship between dog owners’ guilt and discrepancy between their actual and ideal self, in regard to the role of a dog owner, also mirrored human-only family research. Because pet-related guilt is unrecognized, acknowledged, or supported, we suggest it is disenfranchised. In this period of post-COVID-19 pandemic time, as people return to work, it is important that companies and communities acknowledge pet owner guilt and pet-related WFC and help owners find practical, effective solutions. Abstract Guilt refers to an unpleasant emotional state associated with one’s behaviors, thoughts, or intentions, and it is based on the possibility that one may be in the wrong or that others may have this perception. Parental guilt is one common subtype and is often associated with work–family conflict (WFC). WFC and related guilt have been found to be associated with depression and anxiety. Through an online anonymous survey, the current study was designed to explore dog owners’ guilt surrounding their dogs. Results suggest that dog owners’ guilt and WFC associated with their dog are at levels similar to those reported in human family studies. Additionally, the relationship between dog owners’ guilt and discrepancy between participants’ actual and ideal self, in regard to the role of a dog owner, also mirrored human-only family research. Because pet-related guilt is unrecognized, acknowledged, or supported, we suggest it is disenfranchised. As people return to work, in this period of post-COVID-19 pandemic time, it is paramount that companies and communities acknowledge pet owner guilt and WFC and help owners find practical, effective solutions.
A potent tumor-selective ERK pathway inactivator with high therapeutic index

Abstract FDA-approved BRAF and MEK small molecule inhibitors have demonstrated some level of efficacy in patients with metastatic melanomas. However, these “targeted” therapeutics have a very low therapeutic index, since these agents affect normal cells, causing undesirable, even fatal, side effects. To address these significant drawbacks, here, we have reengineered the anthrax toxin-based protein delivery system to develop a potent, tumor-selective MEK inactivator. This toxin-based MEK inactivator exhibits potent activity against a wide range of solid tumors, with the highest activity seen when directed toward tumors containing the BRAFV600E mutation. We demonstrate that this reengineered MEK inactivator also exhibits an extremely high therapeutic index (>15), due to its in vitro and in vivo activity being strictly dependent on the expression of multiple tumor-associated factors including tumor-associated proteases matrix metalloproteinase, urokinase plasminogen activator, and anthrax toxin receptor capillary morphogenesis protein-2. Furthermore, we have improved the specificity of this MEK inactivator, restricting its enzymatic activity to only target the ERK pathway, thereby greatly diminishing off-target toxicity. Together, these data suggest that engineered bacterial toxins can be modified to have significant in vitro and in vivo therapeutic effects with high therapeutic index.
Post-activation Potentiation: Enhancing Speed and Explosive Power after Warm-up with Heavy Load

Resistance exercises incorporated in the warm-up may improve subsequent performance involving explosive movements by eliciting the acute effects of post-activation potentiation (PAP). The aim of this study was to investigate the influence of heavy load back squat exercises included in the warm-up routine on explosive power and speed. Ten long jump and triple jump male players, who were able to lift twice their body massin the back squat exercise, participated in the study.On separate days, participants performed countermovement jump (CMJ), standing long jump (SLJ) tests in the laboratory and a 30m sprint test on the track, 4 minutes before and after exercises involving heavy load warm-up. The heavy-load exercises consisted of 1 set of 5 repetitions at 50% of 1 repetition maximum(1RM), followed by the second set of 3repetitions at 75% of 1RM, followed by 3 sets of ~93% of 1RM. The rest periods were 30 seconds between the first 2 sets, 1 minute between the second and the third set, and 2 minutes rest between the last 3 sets. A paired-samples T test found significant pre-to post-intervention improvements for all the variables (p < 0.05) including 30 m sprint, standing long jump, and countermovement jump. We concluded that explosive power and speed can be significantly enhanced following a bout of heavy exercise (preload stimulus) involvinghigh-intensity resistance training with multiple sets and adequate recovery.
Adaptação e validação de uma medida de adesão à terapia de anticoagulação oral

Este e um estudo metodologico que teve como objetivos adaptar e analisar as propriedades psicometricas do instrumento medida de adesao aos tratamentos (MAT) como medida de adesao a terapeutica de anticoagulacao oral (ACO). A versao final foi testada em 178 sujeitos. As medias das respostas aos itens do MAT variaram de 4,6 a 5,8, sendo que 97,2% foram considerados aderentes. Correlacoes moderadas foram obtidas entre a medida de adesao e os dominios saude mental e vitalidade, do SF-36. O alfa de Cronbach obtido foi 0,60 e constatou-se a presenca de efeito maximo nas respostas de todos os itens. Esses resultados indicam fracas evidencias da validade, confiabilidade e responsividade da versao adaptada do MAT para usuarios de anticoagulantes orais.
Exploring decomposition of temporal patterns to facilitate learning of neural networks for ground-level daily maximum 8-hour average ozone prediction

Abstract Exposure to ground-level ozone is a concern for both humans and vegetation, so accurate prediction of ozone time series is of great importance. However, conventional as well as emerging methods have deficiencies in predicting time series when a superposition of differently pronounced oscillations on various time scales is present. In this paper, we propose a meteorologically motivated filtering method of time series data, which can separate oscillation patterns, in combination with different multibranch neural networks. To avoid phase shifts introduced by using a causal filter, we combine past observation data with a climatological estimate about the future to be able to apply a noncausal filter in a forecast setting. In addition, the forecast in the form of the expected climatology provides some a priori information that can support the neural network to focus not merely on learning a climatological statistic. We apply this method to hourly data obtained from over 50 different monitoring stations in northern Germany situated in rural or suburban surroundings to generate a prediction for the daily maximum 8-hr average values of ground-level ozone 4 days into the future. The data preprocessing with time filters enables simpler neural networks such as fully connected networks as well as more sophisticated approaches such as convolutional and recurrent neural networks to better recognize long-term and short-term oscillation patterns like the seasonal cycle and thus leads to an improvement in the forecast skill, especially for a lead time of more than 48 hr, compared to persistence, climatological reference, and other reference models.
Pivotal Role of Phytohormones and Their Responsive Genes in Plant Growth and Their Signaling and Transduction Pathway under Salt Stress in Cotton

The presence of phyto-hormones in plants at relatively low concentrations plays an indispensable role in regulating crop growth and yield. Salt stress is one of the major abiotic stresses limiting cotton production. It has been reported that exogenous phyto-hormones are involved in various plant defense systems against salt stress. Recently, different studies revealed the pivotal performance of hormones in regulating cotton growth and yield. However, a comprehensive understanding of these exogenous hormones, which regulate cotton growth and yield under salt stress, is lacking. In this review, we focused on new advances in elucidating the roles of exogenous hormones (gibberellin (GA) and salicylic acid (SA)) and their signaling and transduction pathways and the cross-talk between GA and SA in regulating crop growth and development under salt stress. In this review, we not only focused on the role of phyto-hormones but also identified the roles of GA and SA responsive genes to salt stress. Our aim is to provide a comprehensive review of the performance of GA and SA and their responsive genes under salt stress, assisting in the further elucidation of the mechanism that plant hormones use to regulate growth and yield under salt stress.
GRU‐GBM: A combined intrusion detection model using LightGBM and gated recurrent unit

Due to the increasing sophistication of cyber‐attacks, intrusion detection systems need to be improved constantly. Each machine learning classifier has different advantages against intrusion detection and combining the advantages of different classifiers increases detection rates. In this study, we combine a machine learning classifier with a deep learning model to propose a new approach called GRU‐GBM. The LightGBM gradient boosting machine framework is used for feature selection, and each feature in the dataset is evaluated by a second LightGBM classifier to determine the optimal feature set using a novel threshold‐based approach. After the selection of the feature set, a gated recurrent unit is used for attack detection by a recurrent neural network model. Besides, different training/testing ratios (60/40–70/30) are chosen for comparison of GRU‐GBM accuracy. The proposed combined model achieved 76.61% and 93.65% overall accuracy in multi‐class experiments conducted with the UNSW‐NB15 and LITNET‐2020 datasets, respectively. Lastly, the GRU‐GBM model is compared to other machine learning models. The overall accuracy result is tested with a non‐parametric Friedman test to determine the significance of the results. The test result shows that there is enough evidence that the accuracy of the GRU‐GBM classifier is statistically significant.
Effects of an Arbuscular Mycorrhizal Fungus on the Growth of and Cadmium Uptake in Maize Grown on Polluted Wasteland, Farmland and Slopeland Soils in a Lead-Zinc Mining Area

Arbuscular mycorrhizal fungi (AMF) exist widely in soil polluted by heavy metals and have significant effects on plant growth and cadmium (Cd) uptake. Cd contents differ among wasteland, farmland and slopeland soils in a lead-zinc mining area in Yunnan Province, Southwest China. The effects of AMF on maize growth, root morphology, low-molecular-weight organic acid (LMWOA) concentrations and Cd uptake were investigated via a root-bag experiment. The results show that AMF increased maize growth on Cd-polluted soils, resulting in increases in root length, surface area, volume and branch number, with the effects being stronger in farmland than in wasteland and slopeland soils; increased malic acid and succinic acid secretion 1.3-fold and 1.1-fold, respectively, in roots on farmland soil; enhanced the iron- and manganese-oxidized Cd concentration by 22.6%, and decreased the organic-bound Cd concentration by 12.9% in the maize rhizosphere on farmland soil; and increased Cd uptake 12.5-fold and 1.7-fold in shoots and by 25.7% and 86.6% in roots grown on farmland and slopeland soils, respectively. Moreover, shoot Cd uptake presented significant positive correlations with root surface area and volume and LMWOA concentrations. Thus, these results indicated the possible mechanism that the increased maize Cd uptake induced by AMF was closely related to their effect on root morphology and LMWOA secretion, with the effects varying under different Cd pollution levels.
KickOTUG - Team Description Paper 2007

This paper presents the RoboCup Simulation League Team KickOTUG, in particular its aims and research topics. As RoboCup in Austria is young we work on the development of the Austrian RoboCup community. Our research focus comprises agent architectures, decision making and behaviour modeling. Therefore, we present a general architecture for robotic control and its concrete implementation on our agents. Moreover, the topics of decision-making and automatic execution analysis in dy- namic and uncertain environments are discussed. We propose a decision- making algorithm adapting the concepts of teleo-reactive programs and an analysis approach based on automaton-encoded meta-knowledge. We conclude with a description of the proposed architecture as an edu- cation and research platform for various levels and give an overview over our future research activities.
IAES International Journal of Artificial Intelligence (IJ-AI)

Received Feb 17, 2022 Revised Jun 2, 2022 Accepted Jun 20, 2022 When the roads are monotonous, especially on the highways, the state of vigilance decreases and the state of drowsiness appears. Drowsiness is defined as the transitional phase from the awake to the sleepy state. However, In Morocco, the majority of fatal accidents on the highway are caused by drowsiness at the wheel, reaching 33.33% rate. Therefore, we proposed the conception and realization of an automatic method based on electroencephalogram (EEG) signals that can predict drowsiness in real time. The proposed work is based on time-frequency analysis of EEG signals from a single channel (FP1-Ref), and drowsiness is predicted using a personalized and optimized machine learning model (optimized decision tree classification method) under Python. The results are much significant and optimized, improving the accuracy from 95.7% to 96.4% and a time consuming from 0.065 to 0.053 seconds.
Bulletin of Electrical Engineering and Informatics

Received Mar 29, 2022 Revised May 28, 2022 Accepted Jun 21, 2022 Branchless banking is a new digitalization of artificial intelligence (AI) for future banking services. The rapid digitalization and technological advancement of financial institutions (FI) have changed due to the COVID19 pandemic. Such branchless banks are expected to offer financial customers meaningful access whilst promoting responsible usage of suitable and affordable financial solutions. The objectives of this paper are to identify CSF to conduct branchless banking for bank performance during the attack of the COVID-19 pandemic in Malaysia. These CSFs are important to reinforce the new business model recommended by the central bank of Malaysia (BNM). Through this study five CSFs have been identified which are customers do not physically attend to the bank for their banking needs, it is more efficient, saves time, secured, and low cost. As the industrial revolution (IR) 4.0 approaches, public acceptance, innovation, and strong financial services, as well as demand of digital payment will take over the country. National savings bank (BSN) was officially the first bank in Malaysia to conduct branchless banking through agent banking. In line with the BNM division strategy 2011–2020, BSN had launched banking beyond branches via BSN agents who offer banking services on behalf of BSN.
Direct Detection and Quantification of Bacterial Pathogens from Broiler Cecal Samples in the Slaughter Line by Real-Time PCR

Chicken meat is an important source of foodborne pathogens, including Salmonella , Campylobacter, and Clostridium perfringens . These bacteria can occur in the intestinal microbiota of broilers and contaminate chicken carcasses in industrial meat processing. This study aimed to develop and evaluate a procedure based on real-time PCRs for the direct detection and quantification of these three bacteria in broilers’ ceca collected in poultry slaughter houses and demonstrate the occurrence of these important foodborne pathogens in Brazilian poultry production flocks. Cecal contents were collected from 45 different broiler flocks in three different slaughterhouses in the state of Paraná, Brazil, totaling 45 samples (in pools of 10 different ceca/ chickens per broiler flock). Then, these samples were tested for the detection and quantification of Salmonella , Campylobacter, and Clostridium perfringens by real-time PCRs. The results demonstrated the occurrence of three (6.7%) positive pools for Salmonella , 20 (44.4%) for Campylobacter, and 32 (71.1%) for C. perfringens . Mean bacterial concentrations in the positive samples were 4.3log 10 cells/g for Salmonella , 6.4 log 10 cells/g for Campylobacter, and 5.5 log 10 cells/g for C. perfringens . In conclusion, Salmonella , Campylobacter, and C. perfringens could be detected and quantified directly from the broilers cecal contents collected in the slaughter line. This procedure will be certainly useful to more quickly detect these foodborne pathogens and prevent their occurrence in chicken meat and other poultry food products.
Effects of Volatile Organic Compounds Produced by Pseudomonas aurantiaca ST-TJ4 against Verticillium dahliae

Verticillium dahliae is one of the most destructive fungal pathogens, causing substantial economic losses in agriculture and forestry. The use of plant growth-promoting rhizobacteria (PGPR) is an effective and environmentally friendly strategy for controlling diseases caused by V. dahliae. In this study, 90 mm in diameter Petri plates were used to test the effect of volatile organic compounds (VOCs) produced by different concentrations of Pseudomonas aurantiaca ST-TJ4 cells suspension on V. dahliae mycelia radial growth and biomass. The mycelial morphology was observed by using scanning electron microscopy. The conidia germination and microsclerotia formation of V. dahliae were evaluated. The VOCs with antifungal activity were collected by headspace solid-phase microextraction (SPME), and their components were analyzed by gas chromatography-mass spectrometry (GC-MS). The VOCs produced by strain ST-TJ4 significantly inhibited the growth of mycelium of V. dahliae. The morphology of the hyphae was rough and wrinkled when exposed to VOCs. The VOCs of strain ST-TJ4 have a significant inhibitory effect on V. dahliae conidia germination and microsclerotia formation. At the same time, the VOCs also reduce the expression of genes related to melanin synthesis in V. dahliae. In particular, the expression of the hydrophobin gene (VDAG-02273) was down-regulated the most, about 67-fold. The VOCs effectively alleviate the severity of cotton root disease. In the volatile profile of strain ST-TJ4, 2-undecanone and 1-nonanol assayed in the range 10–200 µL per plate revealed a significant inhibitory effect on V. dahliae mycelial radial growth. These compounds may be useful to devise new control strategies for control of Verticillium wilt disease caused by V. dahliae.