Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Online Social Media on Translation Pedagogy and Industry

Despite the impact of online social media language and applications on translation theory and practice, Arabic translation studies are so far largely based on what is referred to as Modern Standard Arabic (MSA) with little regard to colloquial and spoken forms of Arabic which represent the main bulk of online social media. As thus, this study argues for the importance of incorporating social media language and colloquial forms of Arabic into translation studies and addressing the sociocultural aspects (including, in particular, the dialectical and linguistic changes) that impact the translation accuracy and reliability. This study explores the role of linguistic variation as reflected on the use of intensifiers in Egyptian and Libyan Arabic Dialects on the translation accuracy and reliability. For the purposes of the study, a corpus is designed of 12, 723 Twitter Posts on the Egyptian and Turkish intervention in the Libyan Conflict. Four intensifiers were identified. Two of these colloquial intensifiers are from the Libyan Arabic dialect: halba and (uram) and two from Cairene Egyptian Arabic Dialect: awui and (gddan). Analysis shows that intensifiers are rendered differently among Libyan and Egyptian Arabic dialects. It can be quite a challenge to recognize the intended degree of the intensifier at hand: a case that requires translators to render it correctly and convey the intended message to the best. Results also indicate that the language of social media in Arabic exhibits unique linguistic features that have their implications to the accuracy and reliability of translation performance and thus need to be considered in translation education and training. Translation students and trainees thus should be equipped with the online social media competencies to cope up with the increasingly rapid changes of the digital age.
The Influence of YouTube Videos on ELA During the COVID-19 Outbreaks in Jordan

You Tube has become a vital platform, amongst other social-media sites, especially in e-learning / self-learning and in the unforeseen time of COVID-19. This study explores the influence of YouTube videos on users' behavioral intention regarding e-learning acceptance and their perceptions through targeting online university students (n=180) in Jordan, using the PLS-SEM procedure through SmartPLS software. The findings indicated that users' behavioral intention to adopt new e-learning was positively influenced by performance expectancy, perceived usefulness and ease-of-use. The findings were presented and discussed in relation to the Technology Acceptance Model (TAM) and UTATU frameworks.
DeepKcrot: A Deep-Learning Architecture for General and Species-Specific Lysine Crotonylation Site Prediction

Lysine crotonylation (Kcrot), as a post-translational modification (PTM) originally identified at histone proteins, is involved in diverse biological processes. Several conventional machine-learning (ML) predictors were developed based on the Kcrot sites from histone proteins. Recently, thousands of Kcrot sites have been experimentally verified on non-histone proteins from multiple species. Accordingly, a few predictors have been developed for predicting the Krot sites for specific organisms (i.e. humans and papaya). Nevertheless, there is a lack of research on the comparison of the crotonylomes of different organisms. Here, we collected around 20,000 Kcrot sites experimentally identified from four different species as the benchmark data set. We present the deep-learning (DL) architecture dubbed DeepKcrot for predicting Kcrot sites on the proteomes across various species. DeepKcrot includes species-specific and general classifiers using a convolutional neural network with the word embedding (CNNWE) encoding approach. CNNWE performs better than both the traditional ML-based and other DL-based classifiers in terms of ten-fold cross-validation and independent test, independent of the size of the training set. Additionally, cross-species performance for each species-specific predictor is not as good as the self-species performance whereas the cross-species performance generally increases with the size of the training dataset. Moreover, the predictors developed based on the non-histone Kcrot sites are unsuccessful for the histone Kcrot prediction, suggesting that the Kcrot-containing peptides from non-histone and histone proteins have significantly different characteristics and data integration is required. Overall, DeepKcrot is an efficient prediction tool and freely available at http://www.bioinfogo. org/deepkcrot.
Student's acceptance of digital educational tools: A case study

ELearning has become one of the important educational technologies in higher educational institutions. Several eLearning tools have been explored in the literature as effective tools and methods to expand the possibilities of teaching and learning in the health profession fields. Nevertheless, the success of eLearning systems highly depends on the students' acceptance of such systems. Learning management system (LMS) and etextbook (EB) are the most used approaches in eLearning to engage and motivate learners toward acquiring new knowledge. In assessing the effectiveness of such technologies, researchers have studied students' perceptions of eLearning and come up with mixed findings. The aim of the study is to investigate student's attitude and acceptances of eLearning (LMS & EB) and to measure the impact of perceived usefulness and perceived ease of use of eLearning systems on the level of acceptance. A quantitative, cross sectional study was carried out. Detailed questionnaire was designed from the literature to asses' students' attitude towards the use of eLearning. The questionnaire was pretested and modified. The questionnaire comprised of 46 questions (5-point Likert scale). Data was analyzed using SPSS. Of the total 177 students, 74% were female and 41.8% were from nursing program. The results showed that the perceived ease of use and the perceived usefulness of eLearning had a statistically significant positive effect on the participant attitudes toward using the LMS (R2=0.68, p<0.0001) and EB (R2=0.71, p<0.0001). Over all, Participant's attitudes had a statistically significant effect on their acceptance of the LMS (R2=0.70) and the EB (R2=0.67). The study confirms that perceived usefulness and perceived ease of use have led to an increase in the students' attitudes and acceptance to use eLearning systems. Therefore, college should create an e-learning culture through use of LMS and EB in different courses with ease of access to the students.
Effect of group tutoring on promoting students independent learning in Architecture Studio

Architectural project-based education relies on the development of independent learning (IL) skills and attitudes, which foundation students should initiate to be able to critically select information, reflect and produce innovative, creative and holistic design strategies. Architecture studio delivered by one-to-one tutoring, with tutor supremacy, entraps students' ability to seek knowledge independently, affecting their research ability, critical thinking and learning level. Given the pivotal role of IL in architectural education, surprisingly there is little disciplinary research focusing on independent learning methods. To prevent the studio tutor sovereignty reported by discipline studies, as architectural professional practice has a collaborative nature, this study borrows Higher Education main teaching and learning theories on collaborative and group learning and tutoring. This study intends to bridge the gap between architectural learning practice and theory, contributing to the insufficient architectural pedagogical debate and improving studio culture by excavating and understanding how shifting from a hegemonic individual to a collaborative group tutoring impacted learners' independent skills and attitudes. To avoid disciplinary dogmas and untested theories and to directly address studio problems, an Action Research (AR) project was implemented to measure the effectiveness of small group tutoring on promoting IL. Findings proved that group tutoring promoted students IL skills and attitudes, instigated knowledge exchange, constructive discussions, entrepreneurship, critical thinking, improved oral and visual exposure in presentations and assessment outcome, and received student's positive feedback. The IL successful achievement afforded by group tutoring, diminished in one-to-one settings, provides a scientific base for future pedagogical debate, allowing studio emulation and experimentation by architectural educators.
Adaptive Rank Selection for Tensor Ring Decomposition

Optimal rank selection is an important issue in tensor decomposition problems, especially for Tensor Train (TT) and Tensor Ring (TR) (also known as Tensor Chain) decompositions. In this paper, a new rank selection method for TR decomposition has been proposed for automatically finding near-optimal TR ranks, which result in a lower storage cost, especially for tensors with inexact TT or TR structures. In many of the existing approaches, TR ranks are determined in advance or by using truncated Singular Value Decomposition (t-SVD). There are also other approaches for selecting TR ranks adaptively. In our approach, the TR ranks are not determined in advance, but are increased gradually in each iteration until the model achieves a desired approximation accuracy. For this purpose, in each iteration, the sensitivity of the approximation error to each of the core tensors is measured and the core tensors with the highest sensitivity measures are selected and their sizes are increased. Simulation results confirmed that the proposed approach reduces the storage cost considerably and allows us to find optimal model in TR format, while preserving the desired accuracy of the approximation.
Impacts of COVID-19 on the Electrical Power Dynamics and Environment of Bangladesh

COVID-19 pandemic has a landslide shock on human civilization. Due to its sudden destructive impacts on industries, commerce, education and social interactions, researchers are underway to investigate the consequences of this crisis, particularly on the electrical power consumption and environment. In this paper, the impacts of COVID-19 on the power industry of Bangladesh and its positive impacts on the environment have been investigated through the estimation of the reduction of power generation and CO2 emission during the months of April to July in 2020 and 2019. It is found that there was a 16.96% reduction of peak power generation in the month of May 2020 compared with that of May 2019. On the contrary, the CO2 emission was reduced by 12.23% on average, with a maximum reduction of 22.29% in May 2020. Both the indicators have a correlation with the severity of the pandemic in the month of May 2020.
A Novel Compact Wearable Microstrip Patch Antenna for Medical Applications

The modeling, design, fabrication, and analysis of a novel compact microstrip patch antenna in the Industrial, Scientific, and Medical (ISM) frequency range (2.4-2.5 GHz) are presented. Simulations were performed to investigate the performance of the structure using high frequency structure simulator (ANSYS HFSS) and Advanced Design System (ADS). The antenna is printed on Rogers RT/Duroid 5880 substrate and measurements were carried out using a vector network analyzer (VNA) and anechoic chamber. The simulated and measured return loss, gain, bandwidth, radiation pattern, and voltage standing wave ratio (VSWR) were obtained and compared with conventional designs. In addition, the specific absorption rate (SAR) is simulated at different positions to measure the level of electromagnetic waves absorbed by the human head model. The 60 x 60 mm, high gain, low specific absorption rate antenna is expected to contribute to the future development of new detection ways for various diseases.
Fine-Grained Compiler Identification With Sequence-Oriented Neural Modeling

Different compilers and optimization levels can be used to compile the source code. Revealed in reverse from the produced binaries, these compiler details facilitate essential binary analysis tasks, such as malware analysis and software forensics. Most existing approaches adopt a signature matching based or machine learning based strategy to identify the compiler details, showing limits in either the detection accuracy or granularity. In this work, we propose NeuralCI (Neural modeling-based Compiler Identification) to infer these compiler details including compiler family, optimization level and compiler version on individual functions. The basic idea is to formulate sequence-oriented neural networks to process normalized instruction sequences generated using a lightweight function abstraction strategy. To evaluate the performance of NeuralCI, a large dataset consisting of 854,858 unique functions collected from 19 widely used real-world projects is constructed. The experiments show that NeuralCI achieves averagely 98.6% accuracy in identifying the compiler family, 95.3% accuracy in identifying the optimization level, 88.7% accuracy in identifying the compiler version, 94.8% accuracy in identifying the compiler family and optimization level, and 83.0% accuracy in identifying all compiler components simultaneously, outperforming existing function level compiler identification methods in terms of both detection accuracy and comprehensiveness.
Graphene Nanoribbons Based 5-Bit Digital-to-Analog Converter

McCulloch-Pitts neuron structures are comprised of a number of synaptic inputs and a decision element, called soma. In this paper, we propose a 5-bit Graphene Nanoribbon (GNR)-based DAC to fulfill the role of the summation element featuring programmable input weights. The proposed GNR-based 5-bit DAC relies on: (i) GNR unit current cells and (ii) a GNR logic thermometric decoding block. Our implementation is based on mapping the GNR structure's conductance using Matlab and performing the required SPICE analysis using the Matlab based GNR Verilog-A model. The unit current cell geometry and bias conditions were chosen based on the unit cell's conductance map from which we derived its $I_{ON}$/$I_{OFF}$ ratio, as well as transfer and output characteristics, resembling the classical MOSFET counterpart. By utilizing GNR devices instead of FinFET counterparts, a reduction of the active area of the 5-bit current DAC by up to a factor of three can be achieved. Furthermore, the GNR implementation achieved this while maintaining comparable INL and DNL performance to that of the FinFET variant, i.e., DNL of [−0.196, 0.088] LSB and INL of [−0.809, 0.364] LSB for the proposed GNR 5-bit DAC while operating at a supply voltage of only 0.2 V.
On the Power Flow Solution in AC Distribution Networks Using the Laurent's Series Expansion

This paper deals with new power flow formulations for AC distribution networks. The power flow problem is approximated using Laurent's series expansion method over the product between voltage variables in the power balance equations, which proportionates a mathematical structure similar to the conventional Newton-Raphson method. The proposed model is developed in complex variables, which decreases the number of calculations needed and prevents the transformation of the load flow model into polar coordinates. Numerical results confirm that the proposed method is faster regarding computational time and the total number of iterations required; besides, one of the main advantages of this approach is dealing with radial or mesh grids. All simulations are conducted in the programming environment in MATLAB software.
Use of Coplanar Quarter-Wave Resonators for Glucose Sensing in Aqueous Solutions

A quarter-wave coplanar resonator in reflection configuration is presented as a novel glucose concentration sensor for aqueous solutions. This configuration allows for size reduction compared to half-wave approaches, while maintaining good sensitivities. The sensing magnitude is the unloaded quality factor, that is more sensitive to glucose changes than the resonant frequency in the 1–10 GHz range. The effects in the sensitivity of a coplanar capacitance placed in the open end are studied by simulations and measurements. The effect of the temperature on the sensitivity is also studied by experimental measurements, and discussion for more accurate sensing is offered.
Synthesis and characterization of new mesogenic esters derived from 1,2,4-oxadiazole and study the effect of alkoxy chain length in their liquid crystalline properties

ABSTRACT In this study, a new series of non-symmetrically 3,5-disubstituted of 1,2,4-oxadiazole derivatives, 4-[5-(4-methylphenyl)-1,2,4-oxadiazol-3-yl]-phenyl-4-alkoxybenzoate (G1–G11), which containing ether and ester linkages in the same molecule, is synthesized using different synthetic procedures and their structures are confirmed by several spectroscopic techniques (FT-IR and 1H nuclear magnetic resonance). The mesomorphic properties of these compounds are studied by differential scanning calorimetry (DSC) and polarized optical microscopy (POM); all these derivatives exhibited as mesogens and displayed an enantiotropic liquid crystal with nematic texture, the nematogenic phase of these materials was appeared in wide temperature ranges on heating and cooling cycles that were observed by POM and DSC. The influence of 1,2,4-oxadiazole ring and the elongation of alkyl chain in the mesomorphic properties of this series were investigated. The mesogenic behaviour of this series was compared with the compounds possessing a somewhat similar structure. Graphical Abstract
Systematic Design Procedure for Y-Junction Circulator Based on Ridge Gap Waveguide Technology

A Y-junction circulator based on the ridge gap waveguide (RGW) as a hosting guiding structure is designed using a developed closed-form systematic procedure. To validate the structure, an air-filled-to-dielectric-filled transition is proposed using a substrate perforation technique. The proposed RGW circulator covers the whole Ku-band with a matching bandwidth throughout the operational bandwidth below −15 dB. A prototype is fabricated and tested, providing a satisfactory agreement with the simulated results.
Security-Reliability Trade-Off Analysis of AN-Aided Relay Selection for Full-Duplex Relay Networks

This paper proposes a new artificial noise (AN) scheme that combines with the relay selection techniques to improve the physical layer security for a wiretap full-duplex decode-and-forward cooperative network. The communication consists of two phases. In the first phase, the source transmits a message to the selected full-duplex relay, which simultaneously injects AN to mitigate eavesdropping attacks. In the second phase, the selected relay decodes the source message, XORs the source message and AN, and then broadcasts the mixed-signal to the destination. Different form existing schemes, the proposed AN scheme makes the eavesdropper fail in obtaining any information about the source message directly in the second phase. We take into account the correlation of the channel coefficients for the same link during different phases in the performance analysis of AN-aided max-min (MM) and partial (Par) relay selection schemes, and characterize the security-reliability trade-off (SRT) of the proposed schemes by the closed-form expressions of outage probability and intercept probability. Numerical results show that the proposed AN-aided MM/Par relay selection achieves a better SRT performance than the corresponding full-duplex mode assisted AN-aided relay selection scheme in existing literature. Furthermore, the MM scheme outperforms the Par scheme in terms of the SRT, and the SRT of the MM scheme can be substantially improved by increasing the number of relays, while the improvement of the Par scheme is limited.
Low-Noise Dual-Way Magnetron Power-Combining System Using an Asymmetric H-Plane Tee and Closed-Loop Phase Compensation

To meet the increasing power demands of microwave industries and scientific innovations, a dual-way magnetron (MGT) power-combining system based on an asymmetric H-plane tee combined with closed-loop phase compensation (CLPC) was developed and tested. Only one external injection was used, which could lock both frequencies of the two MGTs via the port coupling of the asymmetric H-plane tee. Additionally, phase control was achieved simultaneously in both MGTs. By tuning the external frequency, the frequencies of both MGTs could be shifted to optimize the power-combining efficiency. The optimal combining efficiency was 95.7%. By adjusting the phase of the external injection, the phase for the combining output was adjusted with a control scope in the 0°–360° range. The phase noise level of the combined output was largely inhibited by implementing only one closed-loop phase compensation subsystem. The phase jitter was limited to approximately ±0.5°, and spur suppression ratios of −61.0 dBc/Hz at 10 Hz, −80.9 dBc/Hz at 100 Hz, −91.6 dBc/Hz at 1 kHz, and so on were achieved. Moreover, we deduced the corresponding power-combining theories in the asymmetric H-plane tee and noise reduction using the closed-loop compensation method. The numerical predictions qualitatively agreed with the experimental results. Additionally, this research reveals that the proposed techniques have great potential for future power-combining systems because they provide higher power output and noise reduction.
A Reliable Data Compression Scheme in Sensor-Cloud Systems Based on Edge Computing

The rapid development of the IoT and cloud computing has spawned a new network structure — sensor-cloud system (SCS) where sensors, sensor networks, and cloud computing are integrated to perform data sensing, collection, transmission, and decision making. The large-scale deployment of sensors creates a massive amount of data, posing new challenges in data transmission and storage. As an intermediate platform between IoT and cloud platforms, edge computing provides IoT with data collection, processing, and scheduling services. This paper proposes a hybrid data compression scheme that incorporates lossy and lossless compression in SCS based on edge computing to address the increasing challenges. Moreover, we propose a new reliable lossy compression algorithm DFan, based on the simplified Fan algorithm with a high compression ratio (CR). By introducing the data tolerable deviation, DFan transforms single-factor decision-making into multi-factor decision-making, reducing the error of lossy compression. Through experiments on IntelLab and MIT-BIH datasets, the proposed hybrid data compression scheme achieves an overall CR of <inline-formula> <tex-math notation="LaTeX">$4.21\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$3.88\times $ </tex-math></inline-formula>, respectively. The lossy CR of DFan is <inline-formula> <tex-math notation="LaTeX">$6.42\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$5.1\times $ </tex-math></inline-formula>, respectively, and the Percentage RMS Difference (PRD) caused by lossy compression is 0.27% and 0.56%, respectively. The hybrid compression scheme, high compression ratio, and reliable data restoration make this scheme attractive to the data processing of sensors in SCS.