Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of an authentic leadership educational program for first line nurse managers on nurses' work engagement and organizational citizenship behavior

Background: Authentic leadership is prominent to improve commitments, trust and enhance organizational outcomes. Organizational citizenship and work engagement acts as the outcomes of authentic leadership. As a leader, first-line nurse managers can use an authentic leadership style that positively effects on their nurses. Aim: To investigate the effect of an authentic leadership educational program for first-line nurse managers on nurses' work engagement and organizational citizenship behavior. Design: A quasi-experimental research design was utilized. Setting: The study was carried out at Gynecology, Obstetric and Pediatric Minia University Hospital at Minia City – Egypt. Subject: All first-line nurse managers worked in the inpatient and outpatient units (n = 27), and all nurses who worked in the same units (n = 175) were included in the study. Tools: four tools were utilized in this research; tool I: authentic leadership knowledge questionnaire, tool II: authentic leadership self-assessment questionnaire, tool III: work engagement scale, and tool IV: organizational citizenship behavior scale Results: knowledge level and self-assessment about authentic leadership for first-line nurse managers had low score before an educational program implementation; after implementation and after three months of program implementation they had high score with statistically significant differences; also nurses' work engagement and organizational citizenship behavior score increased after implementing authentic leadership education program for first-line nurse managers. Conclusions: Authentic leadership educational program positively impacted on first-line nurse managers by improving their knowledge and self-assessment about authentic leadership after educational program implementation during various testing times. Also, nurses' work engagement and organizational citizenship behavior score improved after implementing an authentic educational leadership program. Recommendations: Periodical workshops, training, seminars, and program for the first-line nurse managers to enhance their authentic leadership competencies that increase work engagement and organizational citizenship behavior for their nurses.
Impact of CEO Duality, Board Independence, Board Size and Financial Performance on Capital Structure using Corporate Tax Aggressiveness as a Moderator

This research aims to examine how the Board of Director’s characteristics (Board Duality, Board Independence and Board Size) and financial performance of the firm affect its capital structure using tax aggressiveness as a moderating variable. To the firm and the shareholders, taxes normally considered as an additional cost as it reduces the available cash flow, that’s why firm’s tends to apply different tax aggressiveness techniques in strategic tax planning to decrease tax liability and legitimate saving of taxes. The sample used is 58 Egyptian listed companies during the period 2015-2019. This research runs five multiple regression models to examine the relationships between research variables. The statistical results indicate that CEOD and board independence have a positive significant impact on company’ capital structure, while board size have a significant negative relationship with capital structure. Moreover, results shows that ROA, current ratio and asset turn over have a negative significant impact on company’ capital structure, while ROE have a significant positive relationship with capital structure. In addition, findings show that CEOD and firm size have a positive significant impact on company’ tax aggressiveness, while board independence has a significant negative relationship with tax aggressiveness.
Forecasting the Productivity of the Agrophytocenoses of the Miscanthus Giganteus for the Fertilization Based on the Wastewater Sedimentation Using Artificial Neural Networks

The observations of plant development were carried out for three years. The most desirable period for harvesting the miscanthus is December. During this period, the humidity of the stems decreases to 17%. For this reason, the samples for laboratory tests were taken in December. According to the obtained research data, the sewage sludge used is characterized by the following indicators: humidity – 76%, ash content – 5%, nitrogen – 0.66%, P2O5 – 2.51%, K2O v 2.16%. In this study, a mathematical model which allowed predicting the yield of the miscanthus at given levels, with the introduction of the mineral and organic (sewage sludge) fertilizers was successfully implemented. According to the performed research, the application of a sewage sludge in norm of 20–40 t/ha promotes the productivity of the power cultures (the miscanthus) within 24.5–27.1 t/ha, thus increasing productivity on 2.3–5.1 t/ha, compared with control.
Effectiveness of Using Visual Vocabulary Application on Iranian EFL Learners’ Vocabulary Knowledge

This study examined the effectiveness of using visual vocabulary application on Iranian EFL learners’ vocabulary knowledge. To this end, 42 intermediated EFL learners were selected as the participants of the study and divided into two groups of 21experimental and control. Then, the groups took a pre-test of vocabulary. After that, the experimental group received the treatment thorough visual vocabulary application. On the other hand, the control group participants were taught vocabulary items traditionally. After teaching all vocabulary items, a vocabulary post-test was administered to the participants of both groups to measure the effect of the treatment on their vocabulary development. The results of paired samples t-test and independent samples t-test indicated that that the experimental group outperformed the control group on the post-test. The results also indicated that there was a significant difference between the post-tests of both groups in favor of the experimental group. Finally, implications arising from the findings and suggestions for further research were explained.
Towards Resilient IoT Messaging: An Experience Report Analyzing MQTT Brokers

Many Internet-of-Things (IoT) applications for smart homes, connected factories, or car-to-car communication utilize broker-based publish/subscribe communication protocols, such as the MQTT protocol. Commercial IoT applications have high reliability requirements for messaging, as lost messages due to unstable Internet connections or node failures can harm devices or even human beings. MQTT brokers implement numerous architectural availability tactics, but former analyses of MQTT communication have mainly focused on performance measurements under stable conditions. We have created the MAYHEM resilience testing tool for MQTT brokers and applied it in various resilience experiments on different MQTT brokers (VerneMQ, Mosquitto, HiveMQ, EMQ X). We found that MQTT QoS level 0 is already robust against minor packet loss, that selected broker message persistency solutions can lead to lost messages, and that most clustered MQTT brokers favor availability and performance over communication integrity. The results can support IoT practitioners in architectural decisions and researchers as well as broker vendors in optimizing designs and implementations.
The Control of Our Own Energy and The Effects Produced on the Physical and Psychic State

Humans seem to have special abilities to use their own energy in order to maintain their good health and a good state of mind. The special and original measurements presented in this article are a proof of that. Comparisons were made between results obtained in measurements of energy on regular people and on the first author who is able to use the own energy very well. Measurements were made by means of light energy photons emitted in an electromagnetic field and then registered to give images which can characterize each person.
Determination of the Electric Capacitance of the Overvoltage Protective Shield of an Electric Transformer

The paper presents an approach for computing the electric capacitance of the overvoltage protective shield mounted in electric transformers. This is a valuable parameter within the high frequency equivalent electric circuit of a transformer stressed by an overvoltage wave propagation due to a lightning strike event. The finite element analysis method is applied to solve the resulting electrostatics problem.
Realistic 3D Swept-Volume Display with Hidden-Surface Removal Using Physical Materials

Conventional swept-volume displays can provide accurate physical cues for depth perception. However, the corresponding texture reproduction does not have high quality because such displays employ high-speed projectors with low bit-depth and low resolution. In this study, to address the limitation of swept-volume displays while retaining their advantages, a novel swept-volume three-dimensional (3D) display is proposed by incorporating physical materials as screens. Physical materials such as wool, felt, and so on are directly used for reproducing textures on a displayed 3D surface. Furthermore, we introduce the adaptive pattern generation based on real-time viewpoint tracking to perform the hidden-surface removal. Our algorithm leverages the ray-tracing concept and can run at high speed on GPU.
Analysis of Source Code Duplication in Ethreum Smart Contracts

The practice of writing smart contracts for the Ethereum blockchain is quite recent and still in development. A blockchain developer should expect constant changes in the security software field, as new bugs and security risks are discovered, and new good practices are developed. Following the security practices accepted in the blockchain community is not enough to ensure the writing of secure smart contracts. The paper aims to study the practice of code cloning among the smart contracts by analyzing two corpora. The first corpus, the "Smart-Corpus", includes smart contracts already deployed in the Ethereum blockchain. The second corpus, the "Open-Zeppelin's Solidity Library", is supervised by a community of developers who constantly take care to increase the security and efficiency of the smart contracts included in the corpus. From the comparative analysis of the corpora, we observe that the smart contracts developers frequently duplicate the code by cloning already existing smart contracts which are not part of the "OpenZeppelin corpus". In particular, we found that 79.1% of smart contracts contain duplicated code and only 18.4% of smart contracts reuse the code by implementing a smart corpus belonging to the OpenZeppelin repository. The paper discusses the advantages and the disadvantages of code duplication in the Ethereum blockchain ecosystem, and suggests to refer to the smart contracts of the OpenZeppelin's Solidity Library. The Ethereum blockchain community can indeed benefit from using the tested code presented in OpenZeppelin's Solidity Library to increase its security.
A Novel Machine Learning based Method for Deepfake Video Detection in Social Media

With the advent of deepfake videos, video forgery has become a serious threat. Videos in social media are the most common and serious targets. There are some existing works for detecting deepfake videos but very few attempts have been made for videos in social media. This paper presents a neural network based method to detect fake videos. A model, consisting of a convolutional neural network (CNN) and a classifier network is proposed. Three different structures, XceptionNet, InceptionV3 and Resnet50 have been considered as the CNN modules and a comparative study has been made. Xception Net has been chosen in the proposed model and paired with the proposed classifier for classification. We used the FaceForensics++ dataset to reach the best model. Our model integrated in the algorithm detects compressed videos in social media.
Torque Ripple Eliminations for Multiphase Nonsinusoidal Permanent Magnet Synchronous Machines

This paper is to propose a control scheme, a combination of the classical field-oriented control (FOC) technique and artificial intelligence (AI), to obtain constant torques in multiphase non-sinusoidal permanent magnet synchronous machine (PMSM) drives. Higher torque density, easier fabrication, and lower costs are several advantages of non-sinusoidal back electromotive force (back-EMF) machines over sinusoidal ones. However, multi-harmonics existing in back-EMFs possibly generate torque ripples, reducing torque quality of the drive. Therefore, in this paper, an adaptive linear neuron (ADALINE), a simple type of AI, is combined with the classical FOC technique to eliminate these torque ripples. The proposed control scheme is validated by numerical results with a seven-phase PMSM. In addition, these results are compared with an existing strategy to prove its effectiveness.
Multiparameter Sensor Based on a Multi-Interferometric Serial Configuration For Temperature and Strain Measurements

In this work, a multi-parameter point sensor based on the combination of Fabry-Perot (FP) and the anti-resonant (AR) reflecting guidance in cascade configuration is proposed and experimentally demonstrated. This structure, based on FP interference and AR reflecting guidance, was fabricated with two different air micro-cavities. The attained experimental results showed different strain and temperature sensitivities for the antiresonance contribution. However, when analyzing the FP interference, only strain sensitivity was observed, demonstrating that this air micro-cavity was also insensitive to temperature variations.
Research on Identification of Network Public Opinion Information based on Graph Convolutional Networks

Traditional public opinion information identification methods have poor performance, eitherlow accuracy, or rely on hand-designed features. This paper converts public opinion information identification to text classification problem, and proposes a public opinion information identification method based on Word2Vec and graph convolutional networks. First, Word2Vec is used to train word vector and word-article graphs are constructed; then, the graphs are trained and classified by graph convolutional neural network; finally, network public opinion information recognition is completed according to the classification results. The experimental results on the constructed Central Asian country data set show that the proposed method has achieved better performance,where the average identification accuracy of “Belt and Road” network public opinion information reached 85.58%. Furthermore, the performance on other data sets is also comparable to current mainstream text classification methods.
Experimental Analysis of Impact Forces in Constrained Collisions According to ISO/TS 15066

For the user of a collaborative robot, it is important to select robot parameters and trajectories such that the task is fulfilled while ensuring human safety at the same time. In human robot interaction (HRI), constrained collisions can be particularly hazardous to the human and recently, collision test devices were developed that assess safety in such scenarios. In this paper, we propose the concept of a constrained collision force map (CCFM), which relates the robot impact velocity and the collision reaction method its parameterization to the peak collision force in a constrained collision scenario. The CCFM is a tool that will help practitioners to implement both safe and efficient HRI applications and to understand the robot's collision behavior. In this work, we derive the CCFM for three robots (UR10e, UR5e, and Franka Emika Panda) for varying contact thresholds, contact stiffnesses, and robot poses. Finally, we compare our results with the force estimation suggested by ISO/TS 15066:2016.
Speed and Accuracy Tradeoff for LiDAR Data Based Road Boundary Detection

Road boundary detection is essential for autonomous vehicle localization and decision-making, especially under GPS signal loss and lane discontinuities. For road boundary detection in structural environments, obstacle occlusions and large road curvature are two significant challenges. However, an effective and fast solution for these problems has remained elusive. To solve these problems, a speed and accuracy tradeoff method for LiDAR-based road boundary detection in structured environments is proposed. The proposed method consists of three main stages: 1) a multi-feature based method is applied to extract feature points; 2) a road-segmentation-line-based method is proposed for classifying left and right feature points; 3) an iterative Gaussian Process Regression (GPR) is employed for filtering out false points and extracting boundary points. To demonstrate the effectiveness of the proposed method, KITTI datasets is used for comprehensive experiments, and the performance of our approach is tested under different road conditions. Comprehensive experiments show the road-segmentation-line-based method can classify left, and right feature points on structured curved roads, and the proposed iterative Gaussian Process Regression can extract road boundary points on varied road shapes and traffic conditions. Meanwhile, the proposed road boundary detection method can achieve real-time performance with an average of 70.5 ms per frame.
A Systematic Survey of Cognitive Chatbots in Personalized Learning Framework

The usage of technology in education sector has taken a leap with the development of Artificial Intelligence. The aim of this study is to review the works done related to the usage of chatbots in pedagogical context in the period between 2017–2021. The findings include (a) the various contexts in which chatbots are being used, (b) advantages and disadvantages of using chatbots in e-learning, (c) ways to improve and address the disadvantages and (d) how personalization helps in the improvement of students.
Freehand Grasping: An Analysis of Grasping for Docking Tasks in Virtual Reality

Natural and intuitive interaction such as freehand grasping of virtual objects is still a significant challenge in VR due to the dexterous versatility of the human grasping actions. Currently, the design considerations for creating freehand grasping interactions in VR are drawn from the body of historical knowledge presented for real object grasping. While this may be suitable for some applications, recent work has shown that users grasp virtual objects differently than they grasp real objects, presenting an absence of knowledge on how users intuitively grasp virtual objects. To begin to address this, we present an elicitation study where participants (N =39) grasped 16 virtual objects categorised by shape in a mixed docking task exploring placement and rotation. We report on a Wizard of OZ methodology, extract Grasp Type and Grasp Dimension and present grasping patterns in VR. Our results are of value to be taken forward into a framework of recommendations for grasping interactions, as well as parameterizing grasp types for developing intuitive grasp models and thus begin to bridge the gap in understanding natural grasping patters for VR object interactions.
Study on Defluorination of aqueous perfluoroctane photocatalyzed by (CH2)5NH2]3PW12O40

A Photocatalytic system combined of low-pressure mercury lamp and heteropoly acid[(CH2)5NH2]3PW12O40 was used for defluorination of aqueous Perfluorooctane sulfonate (PFOS). The effects and mechanism of reaction time, [(CH2)5NH2]3PW12O40 concentration and pH on defluorination were investigated. In addition, the effects and mechanism of improved methods of potential, such as H2O2 and low-frequency ultrasound, were also studied. The results show that photocatalytic system had weak effect on the defluorination of PFOS, while the PFOS could be oxidatively defluorinated with long reaction time. Introduc tion of H2O2 could significantly enhance defluorination of PFOS and low-frequency ultrasound had no obvious effect on defluorination of PFOS in photocatalytic system.
Biobutanol Production from Pineapple Peels Waste using Single and Co-Culture of Clostridium acetobutylicum and Clostridium beijerinckii Isolated from Keffi Metropolis

Aims: This study investigated the production of biobutanol from pineapple peels using single and co-cultures of locally isolated strains of Clostridium acetobutylicum and Clostridium beijerinckii. Methodology: Soil samples were collected from ten different areas in Keffi. Clostridium species were isolated using standard methods and identified using standard molecular techniques involving the use of 16S rRNA and compared with type strains. Results: The yield of biobutanol in this study ranged from 1.2±0.20g/L to 5.6±0.43g/L in single culture fermentation broth and 5.8±0.37g/L to 6.5±0.51g/L in co-culture fermentation broth. Original Research Article Imaji et al.; SARJNP, 4(2): 28-35, 2021; Article no.SARJNP. 66854 29 Conclusion: It was observed that in single culture and co-culture, the fermentation broth of Clostridium acetobutylicum (SAA1) produced the highest yield of biobutanol in comparism with the yield of other isolates.
Litz Wire Strand Shape Impact Analysis on AC Losses of High-Speed Permanent Magnet Synchronous Motors

Litz wires are commonly used in high-frequency applications in order to reduce the impact of skin and proximity effects. Recently, this winding type has become more popular in high-speed electrical machines. Although many research papers have already highlighted the benefits and the practical limitations that derive from their utilization, less attention has been paid to the impact of the wire’s cross-section shape. Thus, this design characteristic is investigated in the present work by comparing the losses in litz wires with circular, square and rectangular cross-section. At first, the stator copper losses (including DC resistance losses, proximity- effect and skin-effect losses) of a high-speed permanent magnet synchronous motor are calculated through 2D finite element analysis. Then, the obtained results of different litz wire strand shapes are presented and thoroughly discussed. Finally, some useful conclusions are drawn.
Indirect Vector Control of Induction Motor Based on Five-Level Inverter Cascaded H-Bridge Using Space Vector Modulation

This paper proposes a Space Vector Modulation for a five level inverter in an indirect vector control of induction motor. Due to their numerous benefits, five-level inverters are becoming more attractive. They generate high quality of the output voltage, offer a reduction in harmonic distortion and they make the system more efficient. The cascaded H-Bridge inverter is chosen in the indirect vector because it needs fewer number of components compared to other topologies of multilevel inverter. Therefore, less cost and complexity. The implementation of space vector control for this five-level inverter is used to improve the system and to compensate the distortion of harmonics. Morever, it can be implemented on any digital signal processor. The simulation results of this proposed system is realized with Matlab/Simulink. The results prove the excellent performance of the proposed system. They show a good response with a minimum of total harmonic distortion.