Miracle-dz/UniFAR-FTUs huggingface dataset:
STUDIES ON ACUTE TOXICITY PROFILE AND UTERINE HISTOPATHOLOGICAL CHANGES AFTER UTERINE IMPLANTATION OF SMA-COATED AND NON-COATED COPPER T (CU-T) IN RATS

The non-hormonal contraceptive, named RISUG (an acronym for Reversible Inhibition of Sperm under Guidance) has expected to provide a valuable addition to the current options of male contraception. Present study was conducted to assess the toxic effects after uterine implantation of SMA-coated Cu-T and non-coated Cu-T in female Charles Foster rats. Healthy adult female rats of were randomly divided into two groups (Grs.) consisting 10 animals each. Gr. I. rats were implanted with non-coated Cu-T in both uterine horns served as control. Rats in G.II were implanted with SMA-coated Cu-T in the uterus bilaterally and observed for a period of 14 days. The initial (on day 0) and final (on day 15) body weights, food and water consumption in control and treated animals were recorded. Blood samples were collected in EDTA-coated vials for haematological and biochemical analysis. On day 15, autopsy of animals was done and body organs (viz. liver, lungs, kidney, uterus, fallopian tubes and ovaries) were dissected out surgically, then weighed and fixed in formalin for histopathological study. Results showed that the body weights, absolute/relative organ weights, food and water consumption, hematological parameters, viz. Hgb(g%), RBC(x10 6 /mm 3 ), Hct(%), MCV(micron 3 ), MCHC(g%), TLC(x10 3 /mm 3 ), DLC(%) of Polymorphs, Lymphocytes, Macrophages, Eosinophils and Platelets (x10 3 /mm 3 ), and biochemical analysis of marker enzymes, did not show any significant changes in SMAcoated Cu-T as compared non-coated Cu-T group of rats. There were no any toxic effects or World Journal of Pharmaceutical Research SJIF Impact Factor 8.074 Volume 7, Issue 14, 611-627. Research Article ISSN 2277– 7105 Article Received on 23 May 2018, Revised on 13 June 2018, Accepted on 03 July 2018 DOI: 10.20959/wjpr201814-12563 *Corresponding Author
PREDICTION OF ENERGY GAINS FROM JORDANIAN WIND STATIONS USING ARTIFICIAL NEURAL NETWORK

System and environmental parameters affecting the output of the wind farm system at different stations in Jordan have been computationally investigated, using artificial neural network (ANN). For the several variables identified, statistical analysis was employed to indicate their relative significance to the targeted output, with the aid of the Pearson’s correlation coefficients. ANN shows proficiency in the prediction of the original experimental data for all the stations and turbines. In the simulation, the energy gain increases with the increase in the system and environmental parameters. However, there appears to be a phenomenon of threshold value in the output parameter, which limits the impacts of change in the input parameters on the eventual response of the output. It can be deduced that there is a minimum energy gain value below which increase in any of the system/environmental parameters will not have positive impact on the energy output. Findings show that the turbine characteristics, like rotor diameter and hub height, have more significant impact on the energy gain than the environmental factor like wind speed. The uniqueness of this work is that it predicts the important output of the wind farm system based on the logical arrangement of detailed parameters that are found in all operational units of the system in order to elicit desired effects.
Impact Of Land Use Land Cover Change On Stream Flow And Sediment Yield: A Case Study Of Gilgel Abay Watershed, Lake Tana Sub-Basin, Ethiopia

Gilgel Abay watershed is densely populated causing various effects on resource bases like deforestation, expansion of residential area, and agricultural land. The watershed is also facing high erosion by the effects of intense rainfall of the watershed, which aggravates the land cover change of the watershed. This study was designed at application of SWAT for the assessment of impacts of land use land cover change and best sediment management practices that are related to hydrology/stream flow and sediment yield of the watershed. The land use land cover change analyses were performed using ERDAS Imagine 2014 that was used for further analysis of SWAT. Land use land cover changes for three dif ferent years of 1986, 2000 and 2011 land use scenarios with different management practices were used for estimation of stream flow and sediment yield. During the study period most parts of the grassland and shrub land were changed to cultivated land. An increase of cultivated land by 33.79% over 25 years period (1986 – 2011) resulted an increase of stream flow and sediment yield by 5.87m 3 /s and 62.78t/km 2 respectively. The Nash Sutcliff efficiency, coefficient of determination (R 2 ) and RSR were used for evaluating the model performance. Spatial variability of sediment were also done using the validated sediment yield results of 2011 land use on Arc GIS. Hence, for the critical subwatersheds the design and development of best management practices were performed. Three BMPs (best sediment management scenarios) S1 (filter strip), S2 (stone bund) and S3 (reforestation) were considered in this study. The results has showed a decrease of sedimentation by 24.73%, 21.36% and 36.18% sediment yield reductions implementing S1, S2 and S3 respectively. Therefore practicing S3 for Gilgel Abay watershed should be implemented and encouraged for efficient sediment reductions.
DETECTION OF MALICIOUS URL REDIRECTION AND DISTRIBUTION

— Web-based malicious software (malware) has been increasing over the Internet . It poses threats to computer users through web sites. Computers are infected with Web-based malware by drive-by-download attacks. Drive-by-download attacks force users to download and install the Web-based malware without being aware of it . these attacks evade detection by using automatic redirections to various websites. It is difficult to detect these attacks because each redirection uses the obfuscation technique. This paper analyzes the HTTP communication data of drive-by-download attacks. The results show significant features of the malicious redirections that are used effectively when we detect malware.
GPU based Cloth Simulation for Real Time Interaction using Multiple Haptic Interfaces

— In this paper we propose a solution to the simulation of a real time deformable cloth for haptic interaction. The simulated environment consists of a deformable cloth, corners of which can be attached to a number of independent haptic devices through a client server mechanism. The users can feel the tensile force which is acting on the cloth due to its own weight through the haptic interface. A ball with a known mass is also rolled over the simulated cloth so as to effect an external force variation on the cloth. The cloth is modeled using a sufficiently dense mass spring model. A Graphic Processing Unit (GPU) is used at the server to speed up computation of cloth motion to make the computation time comparable with the haptic updation time of 1ms. We also use the environment as a possible gaming platform with several players interacting asynchronously using their respective haptics devices.
Genetic Diversity in Quercus leucotrichophora Populations Through RAPD Markers

Quercus leucotrichophora is an evergreen tree which is usually twisted and has wiry branches. Genetic diversity is an essential component for the effective implementation of a tree improvement program. Four populations collected from Jhungi Suket, Taradevi Shimla, Bhatwari-Taknor Range, Nohra-Shimla) of Q. leucotrichophora were studied using RAPD markers. Ten primers were screened out of which five showed polymorphism. Five primers were used to amplify the individuals from each population. In a multi population descriptive analysis, the total diversity (Ht) in the four populations of Quercus leucotrichophora ranged from 0.1620 to 0.3215 with the mean 0.2253. The within population gene diversity (Hs) ranged from 0.0928 to 0.2204 with the mean 0.1542. The among population differentiation (Gst) ranged from 0.0304 to 0.3177 with the mean 0.3155. The gene flow observed among the populations was 7.9677, which is greater than 1 indicating higher levels of gene flow in the studied populations. Highest similarity co-efficient of 0.1793 was observed between populations Jhungi Suket and Nohra Shimla. The highest percentage of polymorphic loci (89.06%) was found in the population from Jhungi Suket. RAPD markers indicated that the frequencies of the presence of these markers correlated with the result of UPGMA analysis. The genetic diversity of all the four populations from different regions were found to be low, so if the trees of one place is damaged by any means the trees of similar genetic diversity from other places could be planted. Genetic diversity also helps us to study the general characteristics of a species and also particular species can be free from diseases. In spite of the relatively short distances between populations three clearly distinct regional groups of populations could be identified. These findings have implications for decisions on in-situ and ex-situ genetic conservation and for forest management planning and practices.
Self - Organizing and Coverage Recovery Techniques for Wireless Sensor Networks

The major issue of a Wireless Sensor Network (WSN), is to provide sufficient and uninterrupted coverage, to provide this heterogeneous sensors are used. Sensors have wide range of sensing capability is called heterogeneous sensors. In order to enable Sensor-Self Deployment these sensor nodes were made movable. The protocol used to increase the efficiency of the system and to reduce the power consumption is coverage aware sensor automation (CASA). The CASA protocol comprises of two algorithms. First one is sensor self-organizing algorithm (SSOA) and the second algorithm is enhanced virtual forces algorithm with boundary forces (EVFA-B). The EVFA-B states that each sensor node will have attractive and repulsive forces on every other sensor based of distance thresholding. The resulting force is used to place the sensor node to proper location in order to achieve the energy efficiency. The SSOA is used when there is a sudden or unexpected failure of sensor node. It performs local repair by repositioning the sensor. The performance of CASA is evaluated in terms of moving energy consumption and network self-healing capacity.
An Extensive Review on Reversible Logic in VLSI

—As of late reversible rationale has risen as a guaranteeing processing model for provisions in scattering less optical registering, low power CMOS, quantum figuring, and so on. In reversible circuits there exist a coordinated mapping between the inputs and the yields bringing about no misfortune of data. Analysts have actualized reversible rationale doors in optical processing area as it can give fast and low vitality prerequisite alongside simple creation at the chip level. Reversible rationale is generally being recognized as the potential rationale outline style for usage in cutting edge nanotechnology and quantum processing with negligible effect on physical entropy. Late developments in reversible rationale take into account enhanced quantum machine calculations and plans for comparing workstation architectures. Huge commitments have been made in the writing towards the outline of reversible rationale door structures and number-crunching units, notwithstanding, there are relatively few deliberations steered towards the configuration of reversible Alus. In this paper, we propose the configuration of two programmable reversible rationale entryway structures focused at ALU execution and their utilization in the acknowledgment of a proficient reversible ALU is exhibited. The proposed ALU outline is checked and its favorable circumstances over the main existing ALU configuration are quantitatively dissected.
Impact of Drought Stress on Active Secondary Metabolite Production in Cichorium intybus Roots

Chicory (Cichorium intybus), a species of the Asteraceae family, is a biannual crop. Chicory is grown for a number of reasons in different countries. Dried chicory root extract contains inulin as an active compound, which is widely used as a prebiotic agent with antioxidant properties. Chicory was cultivated under low tunnels in blocks and after three month of normal irrigation drought conditions were applied by irrigating after every 14-, 21-, 28and 35 days of irrigation intervals. Chicory roots were harvested at the age of six month and inulin was extracted by batch extraction method. Inulin concentration was measured with calorimetric method. The results showed that root growth and accumulative inulin yield decreased as drought period increase. But the total inulin percentage in chicory roots increased by mild drought stress and decrease with severe drought stress. Our research highlights the impact of drought stress on chicory roots growth and inulin production in it.
Ant Colony Optimization for Spectrum Sensing in Cognitive Radio Networks

In Cognitive Radio Networks, there is tradeoff between sensing time and transmission time as the time frame of Cognitive Radio is fixed. Cognitive Radio works in this fixed time frame. If sensing time will be more then there will be less throughput as transmission time will be less but if transmission time will be more then there will be interference to the Primary user (PU) as there will be less sensing time to sense the Primary user. So, there should be some optimal sensing time at which there will be maximum possible throughput provided that there is no or minimum interference to the Primary user. Ant Colony Optimization (ACO) is a one such technique that can be used to find this optimal sensing time. In this paper, we will discuss about ant Colony Optimization and its use for spectrum sensing in Cognitive Radio Networks.
Automated Classification of Power Quality Disturbances using Hilbert Huang Transform and RBF Networks

: This paper presents Radial Basis Function Neural Network based approach for automatic Power Quality (PQ) disturbance classification. The input features of the Neural Network are extracted using Hilbert Huang Transform (HHT) and they are given as input to the Radial Basis Function Neural network. The data required to develop the network are generated by creating various faults in a test system. The proposed method requires less number of features and less memory space without losing its original property.
Adoption of Shariah Compliant Peer-To-Business Financing Platforms by Smes: A Conceptual Strategic Framework for Fintechs in Bahrain

FinTechs (Financial Technology Firms) are affecting the financial industry, especially that there are many customers moving away from traditional banks and going to FinTech solutions to fulfil their financial needs. These FinTechs provide financial products and services using different technologies covering various financial areas including lending/ financing. For instance, Peer-to-Business (P2B) financing platforms are one of FinTech solutions which has been growing rapidly in the past few years making many governments setting regulations to cater this trend.Bahrain is one of the countries that has issued number of regulations for FinTech activities including for Shariah Compliant P2B financing platforms. These types of platforms shall provide Islamic financing solutions for SMEs. However, as a new financial solution in the market it is expected to face some challenges in term SMEs adoption. Therefore, this research aims at building a conceptual framework for the strategic framework for FinTechs in Bahrain. The framework is built based on different sources of academic literature in domain space of Islamic Finance and FinTech (Islamic FinTech), in addition to theories like Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology. The framework variables will be examined through mixed research method approach covering case study, semistructured interviews, and an online survey. The research outcome shall be a strategic framework for FinTechs in Bahrain to ensure the adoption of SMEs to these platforms.
Analysis of feature Selection and Classification algorithms on Hepatitis Data

— Data Mining is becoming one of the leading techniques applicable in a variety of area. One such area is predictive analysis in medical field. In this paper we investigate the performance of about 15 data mining classification algorithms viz. Rnd Tree, Quinlan decision tree algorithm (C4.5), K-Nearest Neighbor algorithm etc., on a large dataset from the “Hepatitis dataset‟ (derived from the UCI Machine Learning Repository) that comprises of 20 attributes (including class) and 155 instances. Also we investigate on the importance of feature selection and applied three feature selection algorithms namely Fisher filtering, Relief filtering, Step Disc and classified the dataset using 15 most common classifiers. The results of this study indicate the level of accuracy as well as the importance of all the instances in detecting the survival of a person in future. The classification algorithms BVM,CVM and Rnd Tree produced 100 percent accuracy for classification of all the training data under bivalued classes. The study also revealed that the feature selection algorithms as mentioned above are not suitable for this dataset for effective classification. The classification algorithm was also applied to verify it’s correctness in classifying test data.
THE RELATIONSHIP BETWEEN SPIRITUALITY AND JOB SATISFACTION OF DISTANCE EDUCATION PERSONNEL IN KENYA’S CHRISTIAN HIGHER EDUCATION INSTITUTIONS

This paper is an excerpt of a dissertation research whose purpose was to explore the relation of spirituality, work conditions and the job satisfaction of extensions study personnel in Kenya’s Christian higher education institutions. The paper however focuses on the relationship between spirituality, work conditions and job satisfaction. A mixed method approach to the study was employed. The perceptions of administrators and faculty of extension studies on the impact of spirituality and work conditions on their job satisfaction are sought. The study used the quantitative paradigm. Data was collected, using a survey instrument, from 146 administrators and faculty of extension studies from six selected Christian universities in Kenya. The causal-comparative design was applied and data were analyzed using the SPSS version 19. Overall, this study found that faculty members reported higher satisfaction with work itself compared to administrators (U = 792.50, z = 3.09, p < .01, r = -.31). Global job satisfaction scores were positively and significantly correlated to awareness of God scores (r = .28). There were differences in spirituality and job satisfaction related to some demographic factors such as gender, level of education, and years of as a Christian. Women had higher scores in awareness of God (U =1148.50, z = 3.41, p < .05, r = -.21) and integration of spirituality with work (U = 948.50, z = -2.21, p < .01, r = -.32) than men. Those with undergraduate level of education had higher scores in their emotional instability in their relationship with God (t[112] = 2.254, p < .05, r = -21) compared to those with graduate level of education. Those who were Christians for less than 35 years had higher scores in their instability in their relationship with God (scores (U = 1098.00, z = -2.79, p < .01, r = -.26). Moreover, faculty and administrators reported differences in some aspects of spirituality. In relation to level of work involvement, faculty members reported higher grandiosity scores (U = 929.50, z = -2.42, p < .017, r = -.24) than administrators. In relation to job rank, administrators reported higher scores in the disappointment with God subscale (U = 1033.00, z = -2.595, p < .01, r = -.24) than faculty members. Findings of the study are useful to faculty members, administrators and Christian higher education leaders in Kenya for enhancing the spirituality and job satisfaction of distance education personnel.
Survey on Spam detection and Anomaly detection in Social Networking Sites

: Social networking sites, SNS are one of thesuper colossal techniques used by the people to share their views, opinions, and interest so on. The wide acceptance and the ease of usage make it more agreeable for people to develop an interest towards these SNS. Not only legitimate users make use of the opportunities of SNS but also the attackers take this as an advantage to perform their indecorous activities which is a major threat to users. Spam detection is one of the major research areas in social network security. By detecting spam tweets we can efficiently manages the SNS activities and there by enhance the security. This survey paper focuses on spam detection and anomaly detection in social networking sites or social networking services. Anomaly detection inside the social networking communities, Group anomaly detection, semi supervised spam detection and detection of spam promoting campaigns these are the main area which are being discussed here.
Estimation of Built Up Areas from High Resolution Remote Sensing Images

— In this work an unsupervised approach is used which detect the build up area from a single image . The motivation behind is that the frequently recurring appearance patterns or repeated textures corresponding to common objects of interest in the input image data set can help discriminate built up areas from others. Major approaches for built up area detection are based on texture analysis. The earlier existing methods have their own pros and cons, their main limitation is that they are supervised and need training sample images to detect built up areas. The implemented method divided into two steps. First extract a large set of corners from each input image by an improved Harris corner detector. Afterward, incorporate the extracted corners into a likelihood function to locate candidate regions in each input image. Given a set of candidate built-up regions, in the second stage, formulate the problem of discovering the frequently recurring texture patterns corresponding to built up areas as an unsupervised grouping problem. To do this model the candidate regions with corresponding histogram representation of texture feature , and grouping problem can be solved by using spectral clustering and graph cuts. To measure the performance of this system it is tested over for 30 different satellite images of 1 meter resolution. The detected built up areas are compared with ground truth(marked manually). by using True Positive Rates, False Positive Rates Index
K-Means Based Clustering In High Dimensional Data

— There is an increase in the high dimensional data naturally in many domains and usually introduce a great challenge for traditional data mining techniques in terms of effectiveness and efficiency. The main difficult in clustering is due to increase sparsity of such data, as well as increase difficult in distinguishing distances between data points. In this paper they take a novel perspective on the problem of clustering high-dimensional data. As an alternative attempting to avoid the curse of dimensionality by observing a lower-dimensional feature subspace, they embrace dimensionality by taking advantage of inherently high-dimensional phenomena. They show that hubness is the tendency of high-dimensional data to contain points that frequently occur in k-nearest neighbor lists of further points, can exploited in clustering. They check our hypothesis by demonstrating that hubness is a good measure of point centrality with a high-dimensional data cluster, and suggest several hubness based clustering algorithms, showing that major hubs can be used effectively as cluster prototypes or as guides during the search for centroid-based cluster configurations. Based on the Experimental method demonstrate good performance of our algorithms in various position, particularly in the large quantities of noise. They suggest this technique is mostly tailored for detecting approximately hyper spherical clusters and need to be extended in order to properly handle clusters of arbitrary shapes.