Miracle-dz/UniFAR-FTUs huggingface dataset:
A tunable bandpass filter with constant absolute bandwidth using folded slotline uniform‐impedance resonators

A simple tunable bandpass filter (BPF) with constant 3‐dB absolute bandwidth using folded slotline uniform impedance resonators (UIRs) is proposed. The tunable BPF consists of a pair of coupled folded slotline UIR, where the varactor diode elements are loaded to control its center frequency. Measured results show that a wide tuning range from 1.76 to 2.35 GHz with constant 3‐dB absolute bandwidth (ABW) of 310 ± 10 MHz is realized. Meanwhile, a pair of transmission zeros (TZs) are generated aside both sides of the passband due to the coupling between source and load, which improves the selectivity of the passband.
Effect of process parameters on formability in two-point incremental forming-machining of planar and twisted AA5083 blades

The deformation machining process (DMP) involves machining and incremental forming of thin structures. It can be applied for manufacturing products such as curved-surface blades without using 5-axis computerised numerical control machines. This work presents the effect of tool diameter and forming temperature on spring-back and dimensional accuracy of a simple fabricated part. The results of the first phase of the study are utilised to design the fabrication process of a curved surface blade. A feature-based algorithm is used to design the tool path for the forming process. The dimensional accuracy of the final product is improved through warm forming, two-point incremental forming, and extension of the bending zone to the outside of the product edges. The results show that DMP can be used to fabricate complex curved-surface workpieces with acceptable dimensional accuracy.
Pea Aphid (Hemiptera: Aphididae) Population Responses to Selected Pea Cultivars and High Temperatures

1State Key Laboratory of Crop Stress Biology for Arid Areas, Key Laboratory of Integrated Pest Management on the Loess Plateau of Ministry of Agriculture, and Key Laboratory of Entomology, Northwest A and F University, Yangling, Shaanxi 712100, China. 2Department of Agronomy, Sindh Agriculture University, Tando Jam, 70050, Pakistan. 3Department of Entomology, Lasbela University of Agriculture, Uthal, Balochistan, Pakistan. Article Information Received 27 June 2020 Revised 12 August 2020 Accepted 10 November 2020 Available online 07 January 2022 (early access)
Age of Acquisition and Spoken Words: Examining Hemispheric Differences in Lexical Processing

The relationship between the age of acquisition (AoA) of words and their cerebral hemispheric representation is controversial because the experimental results have been contradictory. However, most of the lexical processing experiments were performed with stimuli consisting of written words. If we want to compare the processing of words learned very early in infancy—when children cannot read—with words learned later, it seems more logical to employ spoken words as experimental stimuli. This study, based on the auditory lexical decision task, used spoken words that were classified according to an objective criterion of AoA with extremely distant means (2.88 vs. 9.28 years old). As revealed by the reaction times, both early and late words were processed more efficiently in the left hemisphere, with no AoA × Hemisphere interaction. The results are discussed from a theoretical point of view, considering that all the experiments were conducted using adult participants.
Anti-breast cancer drug screening based on Neural Networks and QSAR model

Breast cancer is one of the most lethal cancers, estrogen receptor α Subtype (ERα) is an important target. The compounds that able to fight ERα active may be candidates for treatment of breast cancer. The drug discovery process is a very large and complex process that often requires one selected from a large number of compounds. This paper considers the independence, coupling, and relevance of bioactivity descriptors, selects the 15 most potentially valuable bioactivity descriptors from 729 bioactivity descriptors. An optimized back propagation neural network is used for ERα, The pharmacokinetics and safety of 15 selected bioactivity descriptors were verified by gradient lifting algorithm. The results showed that these 15 biological activity descriptors could not only fit well with the nonlinear relationship of ERα activity can also accurately predict its pharmacokinetic characteristics and safety, with an average accuracy of 89.92~94.80%. Therefore, these biological activity descriptors have great medical research value.
Comparison of Mental Health Status between Two Periods during Coronavirus Disease 2019 Pandemic in Iranian Adult Population

Background: One unprecedented year after the coronavirus disease 2019 (COVID-19), the pandemic is no longer a short-term stressor; the unremitting/chronic effects of COVID-19 disease is likely to cause impaired physical and mental health upon population and the implemented public health and social measures to limit transmission and reduce mortality and morbidity from COVID-19 expose many people to social isolation, job loss, and economic recession. This study aims to compare mental status and four mental symptoms (somatization, anxiety, depression, and social functions) between two periods during the first year of the COVID-19 pandemic. Materials and Methods: Current study consists of two cross-sectional mental health surveys on the Iranian adult population carried out in two periods of March to April 2020 and December 2020 to February 2021 in two important Provinces of Iran (Fars and Khorasan Razavi). In the first survey, 1337 individuals participated in the study and completed the General Health Questionnaire-28, while in the second survey, 1205 participants completed the same questionnaire. Results: Among 1337 participants of the first survey, there were 876 (65.5%) females and 461 (34.5%) males with an average age of 37.41±10.58 years. In the second survey, there were 1205 participants with mean age of 33.79±19.57 years. The rates of females and males were 57.7% and 42.3%, respectively. Compared to the first survey, the second one showed that poor mental health condition that was 1.40 (95% confidence interval: 1.17-1.67) times higher based on the age, sex, education, marital status, economic, alcohol, cigarette, water-pipe, and sedative (P<0.001). Conclusion: Poor mental health status has increased during the first year of the COVID-19 pandemic. These results revealed that the COVID-19 pandemic was a chronic stressor faced by people every day; therefore, more robust mental health-related considerations must be implemented. [GMJ.2021;10:e2199]
Raman Spectroscopy as a Key Method to Distinguish the Ferroelectric Orthorhombic Phase in Thin ZrO2‐Based Films

Inducing and detecting the polar orthorhombic phase are crucial for the establishment of ferroelectricity in HfO2‐ and ZrO2‐based thin films. Unfortunately, commonly used structural characterization techniques such as grazing incidence angle X‐ray diffraction (GIXRD) only partially allow an accurate detection of this crystalline phase, whose characteristic pattern almost coincides with the one of the tetragonal phase. As a consequence, phase determination is commonly based on peak deconvolution tracing the position of the main peak at 2θ values of around 30°, which can be assigned both to the t(101) and the o(111) plane directions and additionally be influenced by mechanical stress in the layers. Alternatively, epitaxial layers are required to differentiate the phase. Herein, using an integrated experimental–computational approach, it is shown how Raman spectroscopy can distinguish between the monoclinic, the tetragonal, and the orthorhombic phase of ZrO2. The Raman spectra calculated from first principles match the experimentally measured data and thus enable an unambiguous phase assignment. Therefore, Raman spectroscopy proves to be a powerful technique for discerning the three main crystalline phases in these materials. As demonstrated by the good agreement between structural and electrical data, it can therefore be used to predict ferroelectricity in the addressed layers.
Multi-omics reveals principles of gene regulation and pervasive non-productive transcription in the human cytomegalovirus genome

For decades, human cytomegalovirus (HCMV) was thought to express ≈200 viral proteins during lytic infection. In recent years, systems biology approaches uncovered hundreds of additional viral gene products and suggested thousands of viral sites of transcription initiation. Despite all available data, the molecular mechanisms of HCMV gene regulation remain poorly understood. Here, we provide a unifying model of productive HCMV gene expression employing transcription start site profiling combined with metabolic RNA labeling as well as integrative computational analysis of previously published big data. This approach defined the expression of >2,600 high confidence viral transcripts and explained the complex kinetics of viral protein expression by cumulative effects of translation of incoming virion-associated RNA, multiple transcription start sites with distinct kinetics per viral open reading frame, and differences in viral protein stability. Most importantly, we identify pervasive transcription of transient RNAs as a common feature of this large DNA virus with its human host.
Quantification of vortex flow in pulmonary arteries of patients with chronic thromboembolic pulmonary hypertension.

PURPOSE This study proposes an objective method of quantifying the vortex flow in pulmonary arteries to compare the duration of its presence before and after balloon pulmonary angioplasty (BPA) in patients with chronic thromboembolic pulmonary hypertension (CTEPH). METHODS Thoracic 4D-flow magnetic resonance imaging was performed in 28 CTEPH patients before and after BPA. Planes were set in pulmonary arteries to evaluate volume flow rate (VFR), the duration, and area of backward flow in the pulmonary trunk, which is a component of the vortex flow. The full width at half maximum (FWHM) of the peak of the time course of VFR of backward flow was assessed to quantify the duration of the vortical flow. RESULTS Although overall flow patterns after BPA appeared to be the same as the one before BPA, significant decreases in the FWHM, area, and VFR of the backward flow after BPA were found (FWHM: before, 1.88 × 10-1 ± 1.51 × 10-2 [cardiac cycle] vs. after, 1.65 × 10-1 ± 1.86 × 10-2 [cardiac cycle]; area ratio: before, 2.67 × 10-1 ± 1.30 × 10-2 vs. after, 2.38 × 10-1 ± 1.31 × 10-2; VFR: before, 13.6 ± 2.21 [mL/s] vs. after, 11.3 ± 2.36 [mL/s]). CONCLUSION BPA promoted significant decreases in the FWHM, area, and VFR of backward flow in the pulmonary trunk, thereby facilitating efficient blood transport. The tendencies for these changes were to be larger for cases where BPA more greatly decreased the pressure. The results suggest that the FWHM, area, and VFR are useful indicators for the noninvasive evaluation of the therapeutic effects of BPA.
Fabrication and Machining of Metal Matrix Composite Using Electric Discharge Machining: A Short Review

Metal Matrix composite is a metal reinforced with two or more constituents which can be any other materials either metal or non-metal. Metal matrix composites (MMCs), which are inherently intelligent, are lightweight and high-performance, ever-expanding materials. The structural and functional properties of these materials may be modified by industrial requirements. Researchers and the industry are attracted by the process technology used in the production and processing of these products. The hybrid-electric discharge process for MMCs is a promising and the most effective nonconventional process. It has greater expertise in manufacturing complex shapes with greater precision. This paper gives an updated analysis of the development and advantages of various routes for composite manufacturing and machining. It reports on several realistic analyzes and study results, including various manufacturing and processing problems for MMC Since the last few years, hybrid electrical discharge machining has been an active field of research in critical and nonconventional machining. This paper includes annual research in production, traditional machining, unconventional machining, and hybrid MMC machining. The final part of the paper addresses conclusions and future scope.
Classifying Driving Fatigue Based on Combined Entropy Measure Using EEG Signals

Driving fatigue is a common occupational hazard for any long distance or professional driver, and fatigue detecting has major implications for transportation safety. Monitoring physiological signal while driving can provide the possibility to detect the fatigue and give the necessary warning. In this paper, fifty subjects participated in driving simulations experiment with their recorded EEG signals to induce two kinds of fatigue states: Alert and drowsy. Two nonlinear methods, approximate Entropy (AE) and Sample Entropy (SE), were used to characterize irregularity and complexity of EEG data. Subsequently Support Vector Machine (SVM) was applied to classify these two fatigue states. The experimental result shows that two complexity parameters are significantly decreased as the fatigue level increases. The result indicates that both of two nonlinear indicators can be used to characterize driver fatigue level. Furthermore, the combined measure feature results in higher classification accuracy, indicating the proposed classification method is more robust and effective, compared with single complexity measure.
The relationship between sentiment score and COVID-19 cases in the United States

The coronavirus disease (COVID-19) continues to have devastating effects across the globe. No nation has been free from the uncertainty brought by this pandemic. The health, social and economic tolls associated with it are causing strong emotions and spreading fear in people of all ages, genders and races. Since the beginning of the COVID-19 pandemic, many have expressed their feelings and opinions related to a wide range of aspects of their lives via Twitter. In this study, we consider a framework for extracting sentiment scores and opinions from COVID-19–related tweets. We connect users’ sentiment with COVID-19 cases across the United States and investigate the effect of specific COVID-19 milestones on public sentiment. The results of this work may help with the development of pandemic-related legislation, serve as a guide for scientific work, as well as inform and educate the public on core issues related to the pandemic.
The Development and Evaluation of Digital Wakelet Module to Improve Information Searching and Use Among Lower Secondary School Students

Although internet offers various classroom learning resources, some students are unable to use the resources due to lack of information searching and use skills. This study aimed to help secondary school students to increase information searching and use in classroom assignments by developing and evaluating digital Wakelet module for lower secondary school students. Using Wakelet application, the students were expected to use appropriate fonts type and style, insert images and videos, and link other features to Wakelet board in completing a digital scrapbook. This study employed action research design and involved 15 form two students in the development of the module and five expert teachers in the evaluation of the module usability. Data was collected using semi-structured interview and document analysis and later analyzed using thematic approach. The study found that the developed module could help students to search information from multiple sources and formats and use the information in the development of digital scrapbook assignment. The study showed that the developed module helps students to search and use information. The study also suggested the use of Wakelet application could faciliate students’ application of information search and use among lower primary students. The study suggested that there is a need for lower secondary teachers to master and employ an online application to help students to apply information search and use skills across curriculum.
Structural Source of the Trap of ICT Advancement - Lessons from World ICT Top Leaders

In light of the significant consequence of the trap of dramatic advancement of information and communication technology (ICT) in the global economy, both nations and firms that have been compelling their productivity decline. This resulted in great stagnation of ICT advanced economies and therefore its structural sources were analyzed. Based on an empirical analysis tracing, the trend in marginal productivity of ICT and its subsequent prices among the top ICT leaders in the world over the last two decades correlating with the effects of ICT, two faces of ICT advancement were identified. On one side, advancement of ICT contributes to its prices increase by new functionality development, its dramatic advancement particularly centered by internet results in the decline of its prices through freebies, easy copying, and standardization. It was demonstrated that the success of ICT leaders could largely be attributed to the way in which the two faces of ICT advancement were managed by maximizing the positive face of ICT advancement. This is done by means of the effective utilization of external resources in innovation while minimizing the negative face by outsourcing price decreasing factors. All of the aforementioned points can be invaluable lessons for global sustainability in both ICT advanced and growing economies in the midst of the advancement of ICT. The significance of innovation-consumption co-emergence for harnessing the vigor of counterparts is discussed.
Balancing Missing Data, Items, and Assessment Frequency in Experience Sampling Methods Data

Experience sampling methods (ESM) have the advantage of collecting participant self-reported psychological states multiple times a day within a natural environment. However, the current state of ESM is often limited in how many variables can be used in data collection; too few items risk careless responses and too many items risk attrition and burdening or fatiguing participants. One potential solution to get around the limited number of items administered would be to implement a set of procedures and techniques that involve large scale planned missingness, stratified matrix sampling, and analyses dependent on the synthetic covariance matrices; a collection of methods often referred to as Synthetic Aperture Personality Assessment (SAPA) methods (Revelle et al., 2017). Using Monte Carlo simulations and preexisting, secondary, ESM data (Wright et al., 2015; Wright & Simms, 2016), this study will simulate SAPA methods to show how stratified matrix sampling to produce synthetic covariance matrices can be used to make repeated measures research more efficient for researchers and less burdensome for participants. To accomplish this work, an algorithm will be developed and tested on Monte Carlo simulations generated from the parameters of real data (Wright et al., 2015; Wright & Simms, 2016) as the population values. For both the Monte Carlo simulations and the real ESM data, multiple simulations will be run on the complete dataset to explore what rate of missingness, sampling probabilities, measurement occasions, and sample size optimize data collection. Candidate datasets with simulated missingness will then have scale and domain scores, variance components, factor loadings, standard errors, and correlations of the of Level 1 and Level 2 measures computed for comparison with the complete dataset to see how well the results of the simulated data converge with the complete data. Datasets with simulated missing data are expected to produce covariance matrices and factor structures that are analogous to those produced by the complete dataset. While it’s anticipated that structure will be recovered using less than 50% of the original data, recovering both the Level 1 and Level 2 data will be a function of the number of correlations used, as each correlation has a sampling variance and size associated with it. Implications of this study will address several important questions such as: (1) How many administrations and pairwise administrations are required to recover an individual’s covariance matrix? (2) How many administrations and pairwise administrations are required to recover covariance matrices between participants? (3) Do SAPA procedures result in parameter bias compared to near complete data? (4) Are the inflated standard errors produced by SAPA procedures compensated for by the precision of the multivariate structure? and (5) Are SAPA procedures a viable an effective way for making ESM data collection more efficient?
3D Ultrasonic Stick for Blind

Today technology is improving daily in different aspects in order to provide flexible and safe movement for the people. In this technology driven world, where people strive to live independently, this paperpropose a low cost 3D ultrasonic stick for blind people to gain personal independence, so that they can move from one place to another easily and safety. A portable stick is design and developed that detects the obstacles in the path of the blind using ultrasonic sensors. It consists of these sensors to scan three different directions, a microcontroller, buzzer and DC vibration motor. The buzzer and vibration motor is activated when any obstacle is detected. In addition, the stick is equipped with GPS and SMS message system. GPS system provide the information regarding the location of the blind person using the stick to his family members. SMS system is used by the blind to send SMS message to the saved numbers in the microcontroller in case of emergency. The programming of GPS modem, GSM modem, buzzer and vibration motor has been successfully done for this system. Computer simulation is done to essence the performance of the system using Proteous software and Easy pic kit.
A rendezvous point‐based data gathering in underwater wireless sensor networks for monitoring applications

In underwater wireless sensor networks (UWSNs), most of the mobile sink data gathering scheme assumes that sensor nodes are static. Also, very limited set of works aim to eliminate the data redundancy during data gathering. Moreover, the data collection latency, error rate and load have to be considered in addition to the buffer space and energy level of the nodes. Here, a mobile sink data gathering technique is designed where rendezvous points (RPs) and sojourn times are selected. The RPs, where the mobile sinks stop to gather the data, are selected based on the node density, current traffic load and energy level of nodes. The proposed scheme, that is, adaptive distributed mobile sink data gathering (ADMSDG), eliminates data redundancy even by considering mobility of all the nodes including sink node. Here, multihop data gathering is also considered to address the energy hole problem near the sink based on similarity index. To examine the result, proposed scheme and other state‐of‐the‐art schemes are implemented in NS2.30 having the same environment. Obtained results show that when proposed scheme is experimented with respect to number of nodes, the proposed ADMSDG technique achieves 18% high data collection accuracy with 5% reduced data collection delay and 5% less energy consumption. Similarly, when proposed scheme is experimented with respect to transmission range, the proposed ADMSDG technique achieves 18% high data collection accuracy with 12% reduced data collection delay and 8% less energy consumption.