Miracle-dz/UniFAR-FTUs huggingface dataset:
ةبسولحا ىلع مئاقلا ينوتركللإا ملعتلا ةيجولونكتلا لماوعلل ةسارد :ةيباحسلا Cloud based E-Learning: A Review of Technological Factors

This paper presents a review of cloud-based e-learning adoption all over the world in addition to reports on findings such as issues and benefits and associated with CC adoption. However, the adoption of this complex innovation is a complicated task as discussed by the literature; thus, careful consideration and planning to all crucial factors that affect the adoption process through learners is required. The purpose of this study is to examine the factors that lead to this new technology innovation. Speciﬁcally, we examined the technological factors and outcomes relating to the acceptance of a cloud-based eLearning in educational institutions. The study was performed utilizing a non-experimental study exploratory research design. This exploratory study included an essential investigation about secondary data. The study development and modeling of secondary data in order to highlight the final results of the research. The results can be shared for future educators to integrate cloud-based eLearning using the technological factors as a regular part of their instructional practices. Through reviewing the literature of the existing frameworks, it is showed that the factors of technological factors that encourage stakeholders to adopt cloud-based e-learning in educational institutions will be soon necessary and they are still unclear and need further investigation.
A Valve Repair after Infective Endocarditis Secondary to Perforation Caused by Streptococcus Gordonii - A Case Report and Literature Review

1. Abstract We report a case of infective endocarditis in a patient with congenital heart valve lesions accompanied by Streptococcus gordonii infection. A 61-year-old man presented with recurrent fever for four months. Immune system and myelopoietic disorders and tubercu-losis were all ruled out as possible causes of the fever. He was diag-nosed with subacute Infective endocarditis (IE) after blood culture analysis, which revealed presence of a Gram-negative bacterium - Streptococcus gordonii, combined with his history of precordial valve disease and cardiac ultrasound as well as a four-month history of fever. He was subjected to comprehensive anti-infection and anti-heart failure treatment in the internal medicine department. Further examination revealed sudden dislodgement and perforation of the superfluous organism in the aortic valve
A Stable High–Order Perturbation of Surfaces/Asymptotic Waveform Evaluation Method for the Numerical Solution of Grating Scattering Problems

The scattering of electromagnetic radiation by a layered periodic diﬀraction grating is an important model in engineering and the sciences. The numerical simulation of this experiment has been widely explored in the literature and we advocate for a novel interfacial method which is perturbative in nature. More speciﬁcally, we extend a recently developed High–Order Perturbation of Surfaces/Asymptotic Waveform Evaluation (HOPS/AWE) algorithm to utilize a stabilized numerical scheme which also suggests a rigorous convergence result. An implementation of this algorithm is described, validated, and utilized in a sequence of challenging and physically relevant numerical exper-iments.
Eyeball Based Cursor Movement Using Opencv

- There are different reasons for which people need an artificial of locomotion such as a virtual keyboard. The number of people, who need to move around with the help of some article means, because of an illness. Moreover, implementing a controlling system in it enables them to move without the help of another person is very helpful. The idea of eye controls of great use to not only the future of natural input but more importantly the handicapped and disabled. Camera is capturing the image of eye movement. First detect pupil center position of eye. Then the different variation on pupil position get different command set for virtual keyboard. The signals pass the motor driver to interface with the virtual keyboard itself. The motor driver will control both speed and direction to enable the virtual keyboard to move forward, left, right and stop.
Adaptive Federated Learning for Electric Power Inspection with UAV System

With the rapid development of the national power grid, the demand for efficient and reliable power supply is increasing. As the labor cost and the size of the power grid are increasing, the Unmanned Aerial Vehicle (UAV) power inspection is a new and efficient way of detecting power grid abrasion. By analyzing the data collected by the UAVs, a smart detection and maintenance service can be provided. To improve the model robustness and accuracy, data collected from different companies and regions are required, which may violate the data privacy policy. As a distributed machine learning technique, Federated Learning (FL) can collaboratively train global models without sharing private data. In this article, in order to protect the data privacy between different systems, optimize models’ accuracy and convergence performance for non-Independently-and-Identically-Distributed (non-IID) data, we propose an adaptive method that jointly adjusts the learning rate and gradient based on the idea of FL. By recording global gradient information and using the momentum to accelerate the training process, our method adaptively controls the local gradient and learning rate in training of local models, and be more robust to local minima. Finally, we verify the superiority of our model compared with the generic FL model for non-IDD data through experiments.
Deployment Method with Connectivity for Drone Communication Networks

In this paper, we consider a drone deployment problem in situations where the number of drones to be deployed is small compared to the number of users on the ground. In this problem, drones are deployed in the air to collect information, but they cannot collect information from all ground users at once due to the limitations of their communication range. Therefore, the drones need to continue to move until they collect the information for the all ground users. To efficiently realize such drone deployment, we propose two deployment methods. One is an integer linear programming (ILP)-based deployment method and the other is an adjacent deployment method. In the ILP-based deployment method, the positions of the drones at each point in time are determined by solving an ILP problem in which the objective function is the total number of users from whom data can be collected. In contrast, in the adjacent deployment method, drones are sequentially deployed in areas with probabilities determined according to the number of user nodes in adjacent areas at which other drones are already deployed. Through numerical experiments, we show that these deployment methods can be used to efficiently collect data from user nodes on the ground.
l e Monitoring and Molecular Detection of Toxoplasma gondii in Food: Vegetables, Fruits, and Fish as Neglected Vehicles for Toxoplasmosis in the Nile Delta of Egypt

This study was conducted in the Nile Delta, Egypt, to investigate the role of vegetables, fruits, and fish as a source of Toxoplasma gondii and to determine effective decontamination approaches. A total of 200 agricultural produce samples (50 per each of carrots, radishes, lettuces, and strawberries) and 315 fish samples (200 Mullet and 115 Tilapia) were collected. The T. gondii were identified in these samples by microscopy and molecular analysis. In total, 9% (18/200) of agricultural produce samples had Toxoplasma oocysts. T. gondii was detected in 14% of carrots, 12% of radishes, 4% of lettuces, and 6% of strawberries. There was no significant difference in prevalence of T. gondii between different fresh produce type ( P value= 0.1 – 0.6). T. gondii was not detected in any fish samples. Two agriculture produce (1 carrot and 1 strawberry) samples were misdiagnosed by microscopic analysis, which emphasized the importance of molecular detection of Toxoplasma oocysts in environmental samples. To our knowledge, this is the first molecular detection of T. gondii oocysts in vegetables, and fruits in Egypt. Awareness of public and health professionals about these neglected vehicles of toxoplasmosis is urgently required.
Automatic Irrigation Using Artificial Intelligence

Agriculture automation is the main concern and emerging subject for every country. Many of the traditional AI models have been developed. However not a single solution has made its way onto a common farmers land, be it due to high costs or low efficiency. A complete system design for an Automatic Irrigation and Fertilizer Assistance system (i.e. AIFA system) is proposed, but rather than the system design the focus is how the AI brain of the AIFA system is to be designed and developed. The probability of crop failures is projected to be as much as 4.5 times higher by 2030 and up to 25 times higher by 2050. This number can be minimized by use of modern AI technology.
ANALYSIS OF TRIGGERS OF PORT CONGESTIONS USING A TREE-BASED MACHINE LEARNING CLASSIFIER AND EXPLAINABLE ARTIFICIAL INTELLIGENCE

Increased maritime traffic leads to port congestion and negatively affects a demurrage rate, which refers to the percentage of vessels in a port's queue for more than a fixed time to load/unload. The demurrage rate indicates the level of port congestion and must be as low as possible. We suggest a methodology to possible derive triggers regarding a demurrage rate based on real data. To this end, we collect annual vessel data arriving and exiting the port of Ulsan, Republic of Korea, and combine it with berth and weather data. We use a tree-based machine learning classifier algorithm and explainable AI to evaluate and analyze demurrage patterns at the port of Ulsan. We then propose policy recommendations to reduce port congestion. Our results show that demurrage highly depends on berth type, previous and next ports, day of the week, availability of tugs or pilots, and entering time at port.
A New Design of a Miniature Microstrip Bandpass Filter for DCS Applications

This paper presents a new miniature Microstrip Bandpass Filter optimized and validated for DCS (Digital Cellular System) Band. The proposed filter is based on a Microstrip resonator. Each port has a step impedance feed line that can be used to adapt the filter's input impedance to the characteristic impedance Z0. The suggested filter is installed on a low-cost FR-4 substrate with a dielectric constant of 4.4, a thickness of 1.6 mm, a loss tangent tan (δ) = 0.025, and a metal thickness of t = 0.035 mm. It has a bandwidth of 1.68GHz to 2.05GHz. Two electromagnetic solvers are used to optimize and validate this filter. The whole dimensions of the final circuit are 33.6x40 mm 2 . This filter is suitable for mobile communication.
The Assessment of Corn Quality as Feed Ingredients Received at an Animal Feed Mill

: This study aims to determine the quality control in the procurement of corn feed ingredients in animal feed mill. The research data collection was carried out based on procurement in the form of sacks and bulk which were loaded in trucks with 76 samples each. The research stages were pre sampling, preparation, sampling, measurement of corn quality parameters. Parameters observed were moisture content, damaged kernels, mouldy kernels, broken kernels, and foreign matter. Data were analyzed by descriptive analysis, t-test: one sample, and t-test: with – without testing using Data Analysis Microsoft Excel Series 365. The results showed that there were 97.37% of corn in sacks that had a moisture content outside the quality of SNI (Indonesian National Standard), and the quality of damaged kernels, moldy kernels, broken kernels, and foreign matter in the category of SNI quality. Based on the moisture content, damaged kernels, moldy kernels, cracked kernels, and foreign matter, corn was obtained in bulk containers according to SNI quality. Corn in sacks showed a higher quality of water content and foreign matter (p<0.05) than corn in bulk containers, but based on the quality of damaged kernels, corn in bulk containers was higher (p<0.05) than corn in sacks.
Implementing and assessing a performance framework for the innovation measurement in a European manufacturer

Purpose – Performance framework (PF) is a well-established practice to measure innovation performance and identifyimprovementopportunities.However,whetherPFsacademicresearchareapplicabletocompaniesremains unclear,aswellastheirsupportinthedefinitionofimprovementactions.ThisstudyaimstopresenttheimplementationandassessmentofanewandupdatedPFproposedinpreviousresearchinarealindustrialcontext. Design/methodology/approach – ThePFwasimplementedthroughanin-depthcasestudycarriedoutina European machinery manufacturer and further assessed by practitioners. Findings – The results indicate that the PF enabled the creation of a multidimensional view of the innovation performance and the definition of improvement projects in the company. Additionally, the findings also reveal an overall positive assessment of the PF by senior managers who work with the innovation process. Research limitations/implications – As a case study, this research is inherently limited in the extent to whichresults can begeneralised. Thus, the analyses are reductiveand rationalising. Future researchis needed to assess the replicability of the PF. Practical implications – The study ’ s practical contribution is based on the combination of insights and steps that provide a straightforward and actionable approach for the company to improve performance. Originality/value – This study aims to advance the importance of implementing the new and updated PF after its proposition, which is often overlooked in preceding research. Furthermore, the assessment of the PF also enables to infer its value to the company ’ s employees.
P-H Curve, a Graphical Representation of Protein Sequences for Similarities Analysis

Based on two kinds of physicochemical properties of amino acids, a novel 2D graphical representation of protein sequences was constructed. This graphical representation has no circuit or degeneracy, therefore, the correspondence between the sequences and the graphical curves is one-to-one. Then�»a mathematical descriptor was introduced to compare the similarities between protein sequences. The NADH dehydrogenase subunit 5 (ND5) protein sequences of 9 species was used as an example to illustrate our approach. By the correlation and significance analysis, the comparisons between our results and some other graphical representation results with the ClustalW results were given to show the usefulness and efficiency of our approach.
THE CONVERSATIONAL STYLES BY MALE AND FEMALE SELLERS IN BUSINESS TRANSACTIONS AT TRADITIONAL MARKET “ PASAR SENTOSA BARU ” MEDAN

This research deals with the conversational styles by male and female sellers in business transactions at traditional market “ Pasar Sentosa Baru ” Medan. It was conducted using qualitative research design. The subjects of 8 persons namely 4 male sellers and 4 female sellers were taken which they produced 20 conversations for each genders. The ages ranged from 30 to 60 years old. They come from various sellers, namely fish, chicken, vegetables, fruits, and spice. The instruments of collecting data were observation, recording, field notes, and interviewing adopted by Miles, Huberman, and Saldana (2014). The findings show that there were five types of conversational styles used by male and female sellers. In conversational styles used by male sellers; amount of talk (30%), interruption (26.7%), conversational support (21.7%), compliment (16.6%), and tentativeness (5%). In conversational styles used by female sellers; interruption (29%), amount of talk (23%), conversational support (24%), tentativeness (15%), and compliment (9%). In conversational styles in male sellers business transaction, the more dominant responses refer to talk of amount because they talk much in interaction in public context, while for female, the more dominant responses refer to interruption because the female sellers are taken as a violation and a sign of conversational dominance.
INFERENCE ON VIA GENERALIZED SPECTRUM AND NONLINEAR TIME SERIES MODELS

It is often documented, based on autocorrelation, variance ratio, and power spectrum, that exchange rates approximately follow a martingale process. Because these data check serial uncorrelatedness rather than martingale difference, they may deliver misleading conclusions in favor of the martingale hypothesis when the test statistics are insignificant. In this paper, we explore whether there exists a gap between serial uncorrelatedness and martingale difference for exchange rate changes, and if so, whether nonlinear time series models admissible in the gap can outperform the martingale model in out-of-sample forecasts. Applying the generalized spectral tests of Hong to five major currencies, we find that the changes of exchange rates are often serially uncorrelated, but there exists strong nonlinearity in conditional mean, in addition to the well-known volatility clustering. To forecast the conditional mean, we consider the linear autoregressive, autoregressive polynomial, artificial neural network, and functional-coefficient models, as well as their combination. The functional coefficient model allows the autoregressive coefficients to depend on investment positions via a moving-average technical trading rule. We evaluate out-of-sample forecasts of these models relative to the martingale model, using four criteria—the mean squared forecast error, the mean absolute forecast error, the mean forecast trading return, and the mean correct forecast direction. White’s reality check method is used to avoid data-snooping bias. It is found that suitable nonlinear models, particularly in combination, do have superior predictive ability over the martingale model for some currencies in terms of certain forecast evaluation criteria.
Bitcoin Price Forecasting: An Integrated Approach Using Hybrid LSTM-ELM Models

In recent years, digital currencies have fourished on a considerable scale, and the markets of digital currencies have generated a nonnegligible impact on the whole fnancial system. Under this background, the accurate prediction of cryptocurrency prices could be a prerequisite for managing the risk of both cryptocurrency markets and fnancial systems. Considering the multiscale attributes of cryptocurrency price, we match the diferent machine learning algorithms to corresponding multiscale components and construct the ensemble prediction models based on machine learning and multiscale analysis. Te Bitcoin price series, respectively, from 2017/11/24 to 2020/4/21 and 2020/4/22 to 2020/11/27, is selected as the training and prediction datasets. Te empirical results show that the ensemble models can achieve a prediction accuracy of 95.12%, with better performance than the benchmark models, and the proposed models are robust in upward and downward market conditions. Meanwhile, the diferent algorithms are applicable for components with varying time scales.
A Calibration-based Resampling Approach for Binary Imbalanced Classification ∗

Imbalanced classification refers to the classification of data where one class consists of a smaller number of samples than the other class. In this paper, we introduce a new resampling method for binary imbalanced classification. Our method constructs a balanced set by generating synthetic samples within the k-nearest neighbors of the overlapping minority samples, and resampling the generated synthetic minority samples with sampling weights constructed with a calibration constraint given based on the original minority sample mean. Through this process, we generate additional samples in the tricky borderline area, while also removing noisy samples far from the original minority sample mean. Through several experiments, we show that our method outperforms other oversampling methods.
Student teachers’ pedagogical knowledge on the inclusion of students with disabilities in practical physical education lessons in Ghana

Research has identified pedagogical skills as the main skills that student-teachers need for self-efficacy and on-going development in teaching. Pedagogical skills help teachers to reflect on their ability to attain the desired professional standards for teaching. Any failure of teacher preparation programme to equip student-teachers with pedagogical knowledge effectively questions the validity of the programme. This study evaluated the pedagogical knowledge (instructional and adaptation skill knowledge) of 152 university PE student-teachers for inclusion of students with disabilities in practical physical education (PE) lessons in Ghana. The results indicated that the majority of the student-teachers expressed that they have acquired the prerequisite pedagogical knowledge of instructional and adaptation skills to include SWDs in their practical lessons yet 56(36.8%) were not able to state any adaptation skill learnt. However, a significant difference was found to exist between student-teachers’ pedagogical knowledge from the two universities t(-6.010) at df (30) p<0.05. The study revealed that the teacher preparatory programme in the universities does not expose student-teachers to teaching and instructional strategies applicable to inclusive practical settings. In conclusion, a pedagogical knowledge aspect of instructional skills was adequate but that of adaptation skills was inadequate. It is recommended that practical course lecturers in the two departments should equip student-teachers with a well-rounded arsenal of effective teaching and instructional skills applicable in inclusive practical settings. Further studies should compare student-teachers’ perceived pedagogical knowledge and actual pedagogical knowledge for inclusive practical PE teaching.
Understanding Social Media Dependency, and Uses and Gratiﬁcations as a Communication System in the Migration Era: Syrian Refugees in Host Countries as a Case Study

: Through the theoretical frameworks of the media system dependency model and uses and gratiﬁcations theory, this study examined the uses of social media by Syrian refugees in Jordan and Turkey. A mixed-methods approach was used to increase the credibility and validity of the study’s ﬁndings by combining a qualitative method with quantitative data. The ﬁndings show that there were some factors that affected the Syrian refugees’ use of social media before seeking refuge in Jordan and Turkey, which were mainly its ban due to the regime’s censorship and a bad Internet connection due to the bad infrastructure. The absence of these two factors led to an increase in their social media dependency after seeking refuge. There are variances in which social media sites Syrian refugees use the most and why. The majority relied on Facebook for various reasons, and this dependency had varying repercussions on individuals. Furthermore, many immigrants were acquainted with new platforms, implying that refugees became social-media-literate and participated in the updates of the new platforms, even if such sites were not well known to the general public. The Syrian refugees’ dependency on social media varies from person to person due to differences in personal goals, social status, locations, living characteristics, and easy access to the Internet. Furthermore, this dependency on social media has different effects on them according to the media system dependency model. The refugees become increasingly dependent on social media to satisfy their needs and, because of that, social media dependency will become more important in their lives and will increase its inﬂuence and effects on them. Furthermore, the study found that the Syrian refugees depended on social media for various gratiﬁcations, such as educational, work, and business; information access; cultural and social aspects; connecting with networks and families; establishing friendships; learning new skills; self-expression; making business; and ﬁnding work, in addition to reaching routes and contacting smugglers to help them leave the country.