Miracle-dz/UniFAR-FTUs huggingface dataset:
Teacher Exhaustion during COVID-19: Exploring the Role of Administrators, Self-Efficacy, and Anxiety

Abstract This study aimed to understand the relationships among administrative support, teacher self-efficacy, and teacher anxiety on teacher stress during COVID-19. The study explored United States teacher perceptions during Fall 2020 as teachers and schools returned to teaching in multiple online formats (hybrid and virtual). Teachers (N = 311) representing 16 states completed an electronic survey the first week of October 2020. To explain teachers’ stress during the COVID-19 pandemic period and based on previous literature, we developed an SEM model with the variables of engagement and instructional efficacy, teaching anxiety, school administrator support, and teacher stress. The path model showed that teachers’ psychological variables and administrative support were related to teachers’ stress. In the final SEM model, the path results showed the complex indirect effects of teacher efficacy between the school administrative support and teachers’ stress. The current results can guide schools in supporting teacher stress during an unprecedented time.
Differences in Gross Motor Development among Early School Children: Comparison on Team and Individual Sports

This study aimed to determine the level of gross motor development of children aged eight to ten years involved in individual (I) and team (T) sports by using the Tests of Gross Motor Development-2 (TGMD-2) method. This study is an ex-post factor involving 360 children in their early schooling stages (M = 180, F = 180). The study involved 2 types of sports: individually (Athletics = 60, Badminton = 60, Taekwondo = 60) and team (Handball = 60, Hockey = 60, Basketball = 60). Descriptive analysis has shown the level of performance for individual sports AEL (M = 8.24, SD = 1.02, DR = Average), AEM (M = 7.16, SD = 0.92, DR = Below Average) and GMDQ (M = 87.87, SD = 5.4, DR = Below Average). For team sports, the age equivalence levels of AEL (M = 8.05, SD = 1.23, DR = Average), AEM (M = 7.84, SD = 1.07, DR = Below Average) and GMDQ (M = 90.02, SD = 6.57, DR = Below Average). There was a significant difference for individual and team sports on the AEL score t (358) = 1.64, p = 0.00. While there was no significant difference for individual and team sports on the AEM score t (358) =-6.45, p = 0.27 and GMDQ t (358) =-3.39, p = 0.06. MANOVA analysis showed that there were significant differences for AEL, AEM and GMDQ scores for athletics, badminton, taekwondo, handball, hockey, and basketball with [F (15,972.12) = 11.82, p <0.001, eta squared = 0.14]. Individual sport types had an AEL age delay of -1.27 years and an AEM of -2.15 years in contrast to team sports with an AEL age delay of -1.25 years and an AEM of only -1.37 years. ANCOVA analysis showed that gender and age could influence the level of gross motor development of individual and team sports. Implementing the gross motor development test provides knowledge and information to teachers and coaches to know athletesâ€™ gross motor acquisition. Teachers and coaches could also design a training program to help athletes strengthen their gross motor development and improve their athletic performance.
Tracers from Biomass Burning Emissions and Identification of Biomass Burning

The major organic compositions from biomass burning emissions are monosaccharide derivatives from the breakdown of cellulose, generally accompanied by small amounts of straight-chain, aliphatic, oxygenated compounds, and terpenoids from vegetation waxes, resins/gums, and other biopolymers. Levoglucosan from cellulose can be utilized as a specific or general indicator for biomass combustion emissions in aerosol samples. There are other important compounds, such as dehydroabietic acid, syringaldehyde, syringic acid, vanillic acid, vanillin, homovanillic acid, 4-hydroxybenzoic acid, and p-coumaric acid, which are additional key indicators of biomass burning. In this review, we will address these tracers from different types of biomass burning and the methods used to identify the sources in ambient aerosols. First, the methods of inferring biomass burning types by the ratio method are summarized, including levoglucosan/mannose, syringic acid/vanillic acid, levolgucosan/K+, vanillic acid/4-hydroxybenzoic acid, levoglucosan/OC, and levoglucosan/EC to infer the sources of biomass burning, such as crop residual burning, wheat burning, leaf burning, peatland fire, and forest fire in Asia. Second, we present the source tracer ratio methods that determine the biomass combustion types and their contributions. Finally, we introduce the PCA (Principal component analysis) and PMF (Positive matrix factor) methods to identify the type of biomass burning and its contributions according to emission factors of different species in various plants such as softwood, hardwood, and grass.
ADENOIDECTOMY BY DIFFERENT TECHNIQUES (COMPARATIVE STUDY)

Background: Adenoidectomy is one of the most common pediatric ambulatory procedure in the world, multiple techniques are present for adenoidectomy with relative advantages and dis advantages of each technique. Objectives: Providing a comparative study of adenoidectomy by electrocautary with suction diathermy, conventional and microdebrider techniques. Patients and Methods: Randomized prospective study included 60 child patients with age ranged from (312y) of both genders (28 males and 32 females), all suffering from hypertrophied adenoid tissue, presented clinically with mouth breathing, snoring, partial nasal obstruction and/or discharge and evidenced radiologically with plain X-ray film lateral view to the nasopharynx. All cases presented to Al-Azhar University Hospitals (Al-Hussin & Sayed galal) during the period from July 2018 to December 2018. Results: The mean blood loss in suction diathermy technique was (5.56ml) while in conventional and microdebrider techniques were 43.45ml and 31.90ml respectively and this found to be of highly statistical significance. Also in complete of removal of adenoid tissue there were significant statistical difference between conventional and microdebrider (p-value:0.02) also between conventional technique and suction diathermy technique (p-value < 0.03) but as regard operative time there was highly significant statistical difference between conventional and microdebrider (p-value < 0.001). Conclusion: The suction diathermy adenoidectomy is the technique of choice in lowering intraoperative blood loss compared to other tow techniques, also sharing microdebrider technique in visibility of the surgical field and completeness of resection, when the cost and availability are affordable it will be the gold standard technique for adenoidectomy, the conventional technique have a disadvantages of bleeding, traumatic and incomplete of resection but still cheapest, simplest and most available one.
On the possibility of visualization of relief of rough surfaces via laser induced thermal emission

Computer simulation is performed for pulsed laser heating of a surface with submicrometer-sized truncated-coneshaped peaks and holes. Transient temperature field is calculated, and the visual appearance of the surface roughness elements is modeled with the laser-induced thermal emission. The results of calculations reveal special features in visual appearance of peaks on the surface which opens a possibility to distinguish between different surface elements. The calculations also predict 20 fold variations of local thermal emission radiant exitance of rough surfaces. The experiments confirm the presence of the exitance variations on rough surfaces of carbon materials.
Small Targets Detection for Transmission Tower Based on SRGAN and Faster RCNN

Power line inspection is essential to ensure the safe and stable operation of the power system. Object detection for tower equipment can significantly improve inspection efficiency. However, due to the low resolution of small targets and limited features, the detection accuracy of small targets is not easy to improve. This study aimed to improve the tiny targets’ resolution while making the small target's texture and detailed features more prominent to be perceived by the detection model. In this paper, we propose an algorithm that employs generative adversarial networks to improve small objects' detection accuracy. First, the original image is converted into a super-resolution one by a super-resolution reconstruction network (SRGAN). Then the object detection framework Faster RCNN is utilized to detect objects on the super-resolution images. The experimental results on two small object recognition datasets show that the model proposed in this paper has good robustness. It can especially detect the targets missed by Faster RCNN, which indicates that SRGAN can effectively enhance the detailed information of small targets by improving the resolution. We found that higher resolution data is conducive to obtaining more detailed information of small targets, which can help the detection algorithm achieve higher accuracy. The small object detection model based on the generative adversarial network proposed in this paper is feasible and more efficient. Compared with Faster RCNN, this model has better performance on small object detection.
COVID-19 impact on the Spanish stock exchange with mean-variance and diversification-based portfolios

ABSTRACT This paper examines the out-of-sample performance of thirteen portfolio strategies in the Spanish financial market (Ibex 35) during the COVID-19 pandemic (considering four different periods) by using three standard financial metrics. The main findings of the study are as follows: (i) the only methods outperforming Ibex 35 are those based on risk parity or diversification; (ii) unstable period data caused underperformance of strategies that require a previous estimation of certain hyper-parameters in their formulations.
A Comprehensive Performance Evaluation of 3-D Transformation Estimation Techniques in Point Cloud Registration

3-D local feature extraction and matching is a key step in point cloud registration. However, this process commonly draws into false correspondences caused by noise, occlusion, incomplete surface, and so on. To estimate correct transformation based on these corrupted correspondences, numerous transformation estimation techniques have been proposed. However, no comprehensive study comparing their accuracy, robustness, and efficiency performance under different nuisances has been conducted. This article evaluates thirteen popular transformation estimation proposals on both descriptor-based and synthetic correspondences. On descriptor-based correspondences, comprehensive evaluation items (e.g., combining with iterative closest point (ICP) and different local features) of these methods are tested on five popular datasets acquired with different devices (e.g., Minolta vivid scanner, Microsoft Kinect, and Space Time Stereo). On synthetic correspondences, the robustness of these methods to varying percentages of correct correspondences (PCCs) is evaluated. In addition, their efficiency is also evaluated. The results present some valuable findings that may provide a supplement to existing evaluations of transformation estimation techniques. A summary of merits, demerits, and application guidance of these tested methods is finally presented to guide real-world applications and new transformation estimation techniques crafting.
A Review of Authenticity Analysis of the Recovered Memories

This paper adopts the literature research method to aim to discuss the authenticity of recovered memories by summarizing the causes of formation of truly recovered memories and false recovered memories, which covers the concept of repression, amnesia and dissociation in positive, as well suggestibility in negative. A few hypothesis about other concepts of false recovered memories would also be given, such as source attribution, bias as well confabulation. At last, the methods of how to detect the false memories and suggestions of how to avoid them would be stated for both interested people, and occupational subjects who require relevant knowledge.
Determining the Twin Deficits Phenomenon in Kenya

Aims: This study examines whether the long-run relationship between fiscal and current deficits follows the tenets of twin-deficits, the Ricardian equivalence, the current account targeting, or the feedback linkages. It further reviews the effects of fiscal and current account deficits on economic growth. These have in recent years been debated both in developed and developing countries. In contributing to this ongoing debate, the authors applied unit root tests, cointegration analysis, a dynamic vector error correction model and Toda-Yamamoto Granger-causality representation using annual time series data from 1980 to 2016. Study Design: The study employs quantitative time-series research design by utilizing Stata econometrics software. Place and Duration of Study: Sample: Kenya, from 1980 to 2016. Methodology: The study employs unit root tests, Johansen (1995) co-integration analysis, a dynamic vector error correction model and a multivariate Toda-Yamamoto (1995) Granger-causality representation. Results: The paper provides evidence of unidirectional causality running from fiscal deficit to current account deficit in support of the twin-deficits phenomenon for Kenya. There is evidence that in the long-run fiscal deficits has significant negative effects while current account deficits had significant positive effects, on economic growth in Kenya. Conclusion: Overall, the study concludes that the twin-deficits phenomenon fits for Kenya. The findings imply that the authorities need to pay more attention and promote policies that improve investment efficiency arising from these deficits. Importantly, some of the key policy implications include promotion of policies that upscale fiscal discipline and reduce the size of fiscal deficits for external stability and long-term economic growth, in Kenya.
An Investigation of Air Flow and Thermal Comfort of Modified Conventional Car Cabin Using Computerized Fluid Dynamics

Air conditioning is widely used in many areas to reduce the heat and humidity of the work place and to maintain a room temperature for thermal stability and physical ability to perform various tasks. Computational Fluid Dynamics (CFD) is based on the numerical solutions of the fundamental governing equations of fluid dynamics namely the continuity, momentum and energy equations. Computerized fluent mechanics is one of the increasing paradigm in the air flow simulation in vehicle designs. The design optimization of vehicle can offer better efficiency in cabin surface as well as aerodynamic. In vehicles, air conditioning tends to offer efficient thermal conditioning and air circulation inside the cabin for passenger comfort from different climate variation. Almost all the automobiles available in the market are fitted with air-conditioning systems. The manufacturers focus clearly on the AC system for a wide variety of climates. As technology advances, AC system is also adhering major advancements. In general, automobile airconditioning systems are designed to provide comfort for the driver and the passengers during a journey. The conventional electrical-driven compression systems are widely used in almost all of the automobiles today. An air-conditioner is operated to make a hot and humid passenger compartment a more comfortable environment. However, with the improvement in vehicle fuel economy, the allowable power consumption for the air-conditioner has been decreasing, in relation to the overall power consumption of the vehicle. The internal temperature-humidity conditions are an essential factor for the comfort and health of passengers, and also for the safety of drivers. In this research, the air conditioning inside vehicle cabin is analyzed. The objective of the research is to develop an air depression design inside the top surfaces of the rear cabin. The optimal flow of air inside cabin increases the thermal comfort of the vehicle. The proposed cabin depression design inside the rear top surfaces are analyzed under thermal variation and airflow circulation inside the cabin. The Ansys fluent tool is utilized in this paper to evaluate the variation of air flow and the temperature inside the passenger vehicle cabin respectively. From the research analysis, the proposed evaluation of the depression design is more optimal for air conditioning in budgetary small passenger vehicles.
A Combined Genetic Algorithm-Artificial Neural Network Optimization Method for Mix Design of Self Consolidating Concrete

The use of intelligent optimization and modeling methods is rapidly increasing in many fields including concrete technology. In recent years, concrete mix design has been studied using intelligent models in which the artificial neural networks are among the most popular and widely utilized method. However, this modeling depends on an optimization process, and the structured model should be tuned by implementing optimization techniques. Additionally, finding the most appropriate neural network structure for solving the concrete mix design problem was proven to be an important challenge in the state-of-the art. Therefore, this paper introduces a novel strategy in which an evolutionary algorithm and a structure of artificial neural network were fused to find the best network for modeling the compressive strength of Self Consolidating Concrete (SCC) and to extract the most optimal mix design. The novel strategy is tested using 169 data-sets with each set containing 11 concrete constituent properties. The proposed GA-ANN-GA strategy not only finds the best model but also presents the most optimal mix design of concrete to mitigate the challenges reported in recent studies. 
An Intelligent Clustering-Based Routing Protocol (CRP-GR) for 5G-Based Smart Healthcare Using Game Theory and Reinforcement Learning

With advantages such as short and long transmission ranges, D2D communication, low latency, and high node density, the 5G communication standard is a strong contender for smart healthcare. Smart healthcare networks based on 5G are expected to have heterogeneous energy and mobility, requiring them to adapt to the connected environment. As a result, in 5G-based smart healthcare, building a routing protocol that optimizes energy consumption, reduces transmission delay, and extends network lifetime remains a challenge. This paper presents a clustering-based routing protocol to improve the Quality of services (QoS) and energy optimization in 5G-based smart healthcare. QoS and energy optimization are achieved by selecting an energy-efficient clustering head (CH) with the help of game theory (GT) and best multipath route selection with reinforcement learning (RL). The cluster head selection is modeled as a clustering game with a mixed strategy considering various attributes to find equilibrium conditions. The parameters such as distance between nodes, the distance between nodes and base station, the remaining energy and speed of mobility of the nodes were used for cluster head (CH) selection probability. An energy-efficient multipath routing based on reinforcement learning (RL) having (Q-learning) is proposed. The simulation result shows that our proposed clustering-based routing approach improves the QoS and energy optimization compared to existing approaches. The average performances of the proposed schemes CRP-GR and CRP-G are 78% and 71%, respectively, while the existing schemes, such as FBCFP, TEEN and LEACH have average performances of 63%, 48% and 35% accordingly.
A data set of comparable estimates of the private rate of return to schooling in the world, 1970–2014

PurposeYoung people experience lower employment, income and participation rates, as well as higher unemployment, compared to adults. Theory predicts that people respond to labor market information. For more than 50 years, researchers have reported on the patterns of estimated returns to schooling across economies, but the estimates are usually based on compilations of studies that may not be strictly comparable. The authors create a dataset of comparable estimates of the returns to education.Design/methodology/approachThe data set on private returns to education includes estimates for 142 economies from 1970 to 2014 using 853 harmonized household surveys. This effort holds the constant definition of the dependent variable, the set of controls, sample definition and the estimation method for all surveys.FindingsThe authors estimate an average private rate of return to schooling of 10%. This provides a reasonable estimate of the returns to education and should be useful for a variety of empirical work, including critical information for youth.Originality/valueThis is the first attempt to bring together surveys from so many countries to create a global data set on the returns to education.
The Mediating Effects of Social Self-Concept on Gender, Sexual Orientation, and Parenting Intentions

College students’ parenting intentions have received increased attention by scholars around the world in recent years, but little is known about potential demographic differences affecting the decision, such as gender and sexual orientation. The study proposed and empirically examined a model of the relationships between gender, sexual orientation, social self-concept, and parenting intentions in a large sample of university students on the west coast of the United States. The study found that social self-concept mediated the relationship between gender and parenting intentions for heterosexual students, but not for non-heterosexual students.
Applied Quantitative Analysis: An inaugural statement

This article marks the inaugural of Applied Quantitative Analysis (AQA) journal. It is founded on the idea of numbers as the first and foremost mathematical object that act as the harbinger of all meaning. The journal aims to provide a medium for researchers and practitioners to exchange and disseminate their theoretical and practical works that employ quantitative analysis methods to their furthest extent. The journal covers research topics and themes in the three branches of sciences (i.e., formal science, natural science, and social science). In addition to discipline-specific studies, the AQA journal encourages multidisciplinary and interdisciplinary works. Basically, this journal accepts manuscript submissions as Research, Review, Method, Commentary, or Book Review articles. For the submission, each article type has its own general structure and particular requirements. On the other hand, this journal continuously establishes its diverse Editorial Board by inviting experienced scholars in the applied quantitative analysis area from different disciplines and geographical locations. As a focal point of publications among quantitative scholars, the journal invites quantitative scientists and researchers to join as reviewers to ensure the solid quality of published papers.