Miracle-dz/UniFAR-FTUs huggingface dataset:
L-Galaxies 2020-Model Description

In this supplementary material we give a full description of the treatment of astrophysical processes in our 2020 model of galaxy formation. The most significant update with respect to previous public releases is the ability to spatially resolve phenomena happening in galactic discs. This model is built on subhalo merger trees constructed from the Millennium and Millennium-II simulations after scaling to represent the first-year Planck cosmology. A set of coupled differential equations allow us to follow the evolution of six baryonic components. Five of these are associated with individual galaxies – a hot gas atmosphere, cold interstellar gas, a reservoir of gas ejected in winds, stars split into bulge, disc and intracluster light components, and central supermassive black holes. The sixth, diffuse primordial gas, is associated with dark matter which is not yet part of any halo. Primordial gas falls with the dark matter onto sufficiently massive haloes, where it is shock-heated. The efficiency of radiative cooling then determines whether it is added directly to the cold gas of the central galaxy, or resides for a while in a hot gas atmosphere. The properties of cold interstellar gas are followed in concentric rings where cold gas is partitioned into HI and H2 and the latter is converted into stars, both quiescently and in merger-induced starbursts which also drive the growth of central supermassive black holes. Stellar evolution is tracked independently in each ring and not only determines the photometric appearance of the final galaxy, but also heats and enriches its gas components, in many cases driving material into the wind reservoir, from which it may later fall back into the galaxy. Accretion of hot gas onto central black holes gives rise to radio mode feedback, regulating condensation of hot gas onto the galaxy. Environmental processes like tidal and ram-pressure stripping and merging affect the gas components of galaxies, as well as the partition of stars between discs, bulges and the intracluster light, a diffuse component built from tidally disrupted systems. The radial structure of discs and bulge sizes are estimated from simple energy and angular momentum-based arguments.
Prevalence of Salmonella and E.coli O157 in some foods

A total of 200 raw food samples including milk, kareish cheese, fresh sausage and hawawshy (spiced minced meat) (Fifty of each) were randomly collected from farmer’s houses, butcher’s shops and retail markets in Beni-Suef Governorate. All were screened for the presence of E.coli O157 and Salmonella. E.coli O157 could be detected in 1 (2%) and 1 (2%) of kareish cheese and sausage samples, respectively, while it could not be detected in any of milk or hawawshy samples. Salmonella were detected in 2 (4%), 2 (4%) and 1 (2%) of kareish cheese, sausage and hawawshy samples, respectively, while they could not be recovered from the examined milk samples. The isolated serotypes from kareish cheese samples were S.menden and S.allerton, while two strains of S.III arizonae were isolated from sausage samples, but S.anatum was recovered from hawawshy samples. The public health significance of isolated strains as well as suggested control measures were discussed.
A Survey of An Attribute Based Encryption and Access Control Model in Cloud Storage Environment

: For big industries and academia as well, cloud is becoming an essential tool for data repository maintenance. Cloud computing is a set of different types of hardware and software activities that work collectively to deliver many aspects of computing to the end user as an online services. It provides on demand scalable and cost effective services for storing documents. However with all of these services it comes up with number of challenges associated with utilizing the cloud securely. Achieving unbreakable document or outsourced data security is again a very important issue to consider. To provide security to the outsourced documents, Attribute based encryption is an excellent solution. In this paper, we are going to do analysis main attribute based encryption encryption scheme and its related access control models available for handling data under various scenarios of the cloud storage environment. IndexTerms
Ethiopian Public Universities Leadership Behavior and Instructor's Job Satisfaction: Evidence From Arba Minch University

The present study examines the link between leadership behavior and instructor's job satisfaction in four purposefully selected colleges and schools of Arba Minch University (AMU). The study participants were 167 randomly selected instructors of sampled colleges and schools. Of these, 149 were male instructors and the remaining18 were female ones. The data for the study were gathered via adapted questionnaire. The study employed quantitative correlational research design to examine the relationship between leadership behavior and instructor's job satisfaction. A Pearson's correlation coefficient (r) value was used to determine the correlation between the independent variable (leadership behavior) and the dependent variable (instructor's job satisfaction) and simple linear regression was employed to predict their relationship. The findings revealed that leadership behavior has statistically significant, positive and strong correlation with instructor's job satisfaction (r=.761, p<.049 two-tailed). The study further indicated that the value of R2 (.580) indicates 58 % of the job satisfaction is explained by leadership behavior. From these findings, it was concluded that there is a positive link between leadership behavior and instructor's job satisfaction. Therefore, it was recommended that through both formal and informal trainings, the university leaders should aware how their leadership behavior is correlated with instructor's job satisfaction and then they should adopt and practice different dimensions of leadership behavior to improve instructor's job satisfaction.
MODELING THE USE OF COMPUTER AND BROADBAND TECHNOLOGY IN THE AIRCRAFT LINE MAINTENANCE WORKPLACE

Five models of the aircraft line maintenance process were created after shadowing line maintenance crew at a major air carrier maintenance facility over a period of several months. These models include: (1) the physical layout of the facility, including arrangement of artifacts and the distances between them; (2) the artifacts used by technicians along with a quantification of the steps required to use them; (3) the cultural relationships between participants in the maintenance process; (4) the flow of information between participants; and (5) a computer simulation of the sequence of steps required to service an inbound aircraft: including routine inspections, scheduled maintenance, deferred items, squawks reported by the flight crew, and squawks discovered during inspections. The five models are used to make specific recommendations about how computer and broad-band can successfully impact safety in the line maintenance workplace.
Facial Expression Recognition Using 2D Images: a Survey

: Facial expression is a one of the way of conveying the emotional state of a person to the observer. Recognizing the facial expression has been one of the challenging tasks in the field of computer vision. Recognizing the expressions has its applications in the field of education, study of human behaviour, human computer interaction, and many more. Over the last two decades a lot of research has been done and many methods are developed to recognize the facial expressions. The current paper presents a survey of various challenges, facial databases available and the different techniques used in the area of facial expression recognition.
Facing the facts: the case for comprehensive sexuality education

United Nations Educational, Scientific and Cultural Organization Politicians all over the world have stated and restated their commitment to evidence-based policy-making in recent years. Yet, every so often, other considerations undermine reliance on facts and get in the way of decisions that could save lives and improve well-being. An example, in much of the world, is the continual hesitation about, if not retreat from, the goal of rolling out comprehensive sexuality education.
An Intellectual Detection System for Intrusions based on Collaborative Machine Learning

The necessity for safety of information in a network has inflated due to the impressive growth of web applications. Several methods of intrusion detection are used to detect irregularities which depend on precision, detection frequency, other parameters and are anticipated to familiarize to vigorously varying risk scenes. To accomplish consistent abnormalities detection in a network many machine learning algorithms have been formulated by researchers. A technique based on unsupervised machine learning that use two separate machine learning algorithms to identify anomalies in a network viz convolutional autoencoder and softmax classifier is proposed. These profound models were skilled as well as evaluated on NSLKDD test data sets on the NSLKDD training dataset. Using well-known classification metrics such as accuracy, precision and recall, these machine learning models were assessed. The developed intrusion detection system model experimental findings showed promising outcomes in anomaly detection systems for real-world implementation and is compared with the prevailing definitive machine learning techniques. This strategy increases the detection of network intrusion and offers a renewed intrusion detection study method.
Conservation through in vitro method: A case of plant regeneration through somatic embryogenesis in Quercus semecarpifolia Sm

An efficient and reproducible protocol for in vitro propagation via somatic embryogenesis (direct as well as indirect) induced on cotyledon halves (with embryo) taken from seeds of Quercus semecarpifolia (Sm.) has been developed. Direct as well as indirect somatic embryogenesis was induced from the cotyledons on Woody plant (WP) medium supplemented with 6-Benzyladenine (BA) + Indole-3-butyric acid (IBA), and, BA + 2,4-Dichlorophenoxyacetic acid (2,4-D), respectively. Somatic embryos thus obtained were multiplied profusely on Schenk and Hildebrandt (SH) + Murashige and Skoog (MS) basal as well as BA supplemented media. Germination and conversion of somatic embryos into plantlets was achieved on SH+MS medium supplemented with BA (0.448.88 μM). Rooting of in vitro produced shoots was achieved on WP (1/2 macro + full concentration of rest of the constituents) medium supplemented with IBA (14.76 μM). The plants were hardened ex-vitro and transferred to earthen pots containing garden soil. [Journal of American Science 2009: 5(1), 70-76] (ISSN: 1545-1003)
REMOTE DATA POSSESSION CHECKING PROTOCOL USING GALOIS FIELD GF (P N ) IN MOBILE CLOUD COMPUTING

Cloud computing provides enormous amount of storage space for the users. Terabytes of data is stored across the world in the various cloud servers. Resource intensive mobile devices store the data through the Storage as a Service in the remote cloud. Cloud is not trusted for security and integrity of the mobile user’s data. So the data is stored in encrypted form to provide confidentiality. Without downloading the contents of the file the integrity has to be checked by the resource intensive mobile devices. To ensure the correctness of the data in the remote cloud server a Remote Checking Protocol is needed. A novel framework is proposed to check the integrity of a data file with optimal computation and minimum storage cost in the mobile device. Mobile client possess only the Meta tag for verification and deletes the file in its local storage. This protocol verifies the data stored in the remote cloud server infinite times and supports the data modification in the cloud server dynamically. A naive approach is proposed to verify the data storage correctness using Galois field GF (p n ) computations.
PFC Boost Rectifier with Harmonics Compensation

In this paper a five-level PFC boost rectifier is presented. This rectifier has less active semiconductor switches, due to this switching losses and number of required gate drives are reduced. The proposed topology is controlled by sliding mode controller. Sliding mode controller is designed to regulate the output DC voltage and to ensure unity power factor mode of the input AC voltage and current. Multicarrier PWM technique has been used to produce switching pulses from control signal. Five level voltage waveform harmonics has been analyzes conclusively that affects the size of input current and required filters directly. Moreover, low harmonic AC current waveform has been achieved by the implemented controller and employing a small inductive filter at the input line. This topology correcting the power factor while delivering power from AC grid to the DC loads and also reduces the current and voltage harmonics. Simulation is done in MATLAB/SIMULINK and results of simulation and hardware shows the waveform of power factor correction and current and voltage harmonics reduction for resistive load.
Different Data Mining Approaches for Predicting Heart Disease

: Data mining techniques are used in most of the health care organization, for predicting different diseases. Data mining techniques helps to collect hidden information and make effective decision related to data. In this paper, we have analyzed algorithms like Neural Network and Genetic Algorithm, which plays the major role in predicting heart disease. The hybrid system uses global optimization which is advantage of Genetic Algorithm for the initializing the Neural Network weight.
From Research to Implementation of Product Estimation in the 2017 Economic Census: Hard, Harder, and Hardest

The U.S. Census Bureau conducts an Economic Census every five years, producing key measures of American business and the economy. The Economic Census requests information on the revenue obtained from products sold from all large businesses and a sample of smaller businesses. Beginning in 2017, the Economic Census will use the North American Product Classification System (NAPCS) to produce economy-wide estimates of products sold. This marks a major departure from the prior collections, which explicitly linked product codes to industry, and required the development of a single imputation approach for all products that is statistically defensible and operationally feasible. A research team was assembled to recommend a unified method for treating missing product data. The team's evaluative approach relied on simulation, using empirical data from a purposively selected, small subset of industries as the basis for the study. The research was complicated by the nature of product data, which are characterized by poor item response rates, few available predictors, additivity-within-establishment requirements, many rarely reported products in an industry, and sampling effects. To avoid confounding treatment effects with respondent size effects, the study restricted the analysis variables to a limited set of products within each studied industry. This greatly simplified the evaluations, but left potential implementation challenges uninvestigated. This paper describes the recommended missing data treatment methods and how these methods are being implemented into the 2017 Economic Census production system. Examples are provided to illustrate implementation issues and the modifications and enhancements needed to fully implement the research-based recommendations.
Sensorless Control of BLDC Motor Using Back EMF Method

Brushless dc motors and the modelled drives are working and moving faster in the market. BLDC drives are patterned with six-step commutation with commutation controlled using position sensors. Sensorless drives are preferred to minimize complexity and cost of the system. Here, direct detection of back EMF, is presented for sensorless BLDC drives. For this method, no need of measuring motor neutral voltage in order to measure the back EMF. The off period of PWM detects the floating motor windings true back emf. Because, motor terminal voltage is proportional directly to phase of back EMF in this interval. This theory presents the back EMF control method.
A Survey of Floral Aroma Sensors

Floral aromas are used in medicinal, cosmetics, food, beverages and perfume industry. They can quickly change moods of people. Quality assessment of these aromas is important from commercial value point. In general this assessment is done by human testers where results may be subjective and inaccurate. This problem is overcome by using electronic noses which can sense and assess the quality of aroma objectively for economic valuation in a faster, accurate and noninvasive way. Evaluation of quality of aroma is useful to predict flowering period and right time for extraction of aroma. Presently, there is no survey article available on floral aroma sensing, using electronic noses. This paper focuses on survey of sensing materials, sensing principles, aroma analysis methods, limitations of existing methods, future scope for research in floral aromas.
Dataset Augmentation for Machine Learning Applications of Dental Radiography

The performance of any machine learning algorithm heavily depends on the quality and quantity of the training data. Machine learning algorithms, driven by training data can accurately predict and produce the right outcome when trained through enough amount of quality data. In the medical applications, being more critical, the accuracy is of utmost importance. Obtaining medical imaging data, enough to train machine learning algorithm is difficult due to a variety of reasons. An effort has been made to produce an augmented dental radiography dataset to train machine learning algorithms. 116 panoramic dental radiographs have been manually segmented for each tooth producing 32 classes of teeth. Out of 3712 images of individual tooth, 2910 were used for machine learning through general augmentation methods that include rotation, intensity transformation and flipping of the images, creating a massive dataset of 5.12 million unique images. The dataset is labeled and classified into 32 classes. This dataset can be used to train deep convolutional neural networks to perform classification and segmentation of teeth in x-rays, Cone-Beam CT scans and other radiographs. We retrained AlexNet on a subset of 80,000 images of the entire dataset and obtained classification accuracy of 98.88% on 10 classes. The retraining on original dataset yielded 88.31%. The result is evident of nearly a 10% increase in the performance of the classifier trained on the augmented dataset. The training and validation datasets include teeth affected with metal objects. The manually segmented dataset can be used as a benchmark to evaluate the performance of machine learning algorithms for performing tooth segmentation and tooth classification.
Prototyping tool for real-time ECG monitoring and analysis

Measuring the heart rate is one critical piece of information that a health professional uses to diagnose the health state of an individual. Electrocardiogram (ECG/EKG) is essentially responsible for patient monitoring and diagnosis. The extracted feature from the ECG signal plays a vital role in diagnosis of cardiac disease. Therefore, this paper presents how to design, build, and test a cost-effective prototyping tool for ECG feature extraction and recognition. When testing a real ECG from a human subject, the developed tool can preserve useful ECG information while removing unwanted noise and interference components by adaptively determining the filtering values that directly translate to a real time analog circuit for rapid prototyping. Then a decisionmaking model which is based on the peak detection strategy is applied for automated heart rate state recognition in real-time.
Study on the Impact of Additional Comments on Consumers' Purchase Decisions: Comparative Analysis Based on Hedonic and Practical Products

The impact of additional comments on the consumers' decision-making of online shopping has become increasingly prominent. Based on the previous studies, this paper constructed a related conceptual model. Multiple regression analysis was conducted for different types of products. The results showed that for hedonic products, the quality, time interval of additional comments, additional comments with pictures and contradictory additional comments can positively influence consumers' perceived usefulness. For practical products, the number, time interval of additional comments, additional comments with pictures and contradictory additional comments can positively influence consumers' perceived usefulness. For hedonic and practical products, consumers' perceived usefulness can positively influence consumers' purchasing intention, which can positively influence consumers' purchase decisions. At the same time, targeted suggestions were proposed for sellers according to the different impact of additional comments on hedonic and practical products on consumers' purchase decisions.
An IoT Botnet Prediction Model Using Frequency based Dependency Graph: Proof-of-concept

Malware attacks are widespread in an era of growing technology by targeting most computing resources. Plenty of the technology nowadays is based on digital data exchange and it leads to the Internet of Things (IoT) development. A massive growth of IoT technology attracts attackers' interest in exploiting a number of IoT devices using a variety of attacks. Consequently, this has caused difficulty to the researcher in distinguishing a characteristic of such variant specifically for IoT botnet-based attack. Current approaches are weak in recognizing such behavior by analyzing registry information more accurately due to the fact that the attack pattern usually hard to construct. Hence, in this paper, selected features of suspicious registry information that's been affected by IoT botnet action i.e. Mirai is further analyzed using the graph-theoretical approach. Using a dependency graph, the similar and dissimilar pattern of distinct botnet composed to facilitate the process of malware variant characteristic identification. As a result of doing this, a precise attack pattern can be constructed and could be considered for future botnet prediction. A series of experiments conducted as a proof-of-concept in order to assess and validate the formed attack pattern. The findings have shown that the proposed prediction model could overcome the issues of undetectable IoT botnet behavior. From this forward, this model could be used to obtain accurate detection results for any variant of malware.
Optimization of Ultrasonic/Microwave Assisted Extraction (UMAE) and Rheological Properties of Polysaccharides from Auricularia polytricha

Polysaccharides from Auricularia polytricha (APPS) were extracted using Ultrasonic/Microwave Assisted Extraction (UMAE) technology. UMAE conditions of APPS were optimized with Response Surface Methodology (RSM). Rheological properties of APPS were investigated. The results suggested that the optimal UMAE conditions for APPS were ratio of water to raw material 22 (mL/g), extraction time 15.3 min and microwave power 59W. APPS solution exhibited Newtonian flow behavior at low concentration, formed entanglement network at lower concentration and established weak gel structures with solid-like properties at higher concentration. The viscosity of APPS solutions decreased when temperature increased. The viscosity, storage (G') and loss (G") modulus of APPS solution increased when sucrose concentration increased. Heating time and freeze-thaw changes had no significant effect on rheological properties of APPS. The results suggested that UMAE was a suitable and efficient method for APPS extraction and APPS had potential as a novel food additive hydrocolloid in food industry.