Miracle-dz/UniFAR-FTUs huggingface dataset:
Block Coded Modulation System Employing Concatenated Block Codes

Concatenation can be used to obtain block-coded modulation [BCM] schemes of various long block lengths using short block codes. Both the inner and the outer codes correspond to a BCM scheme. The inner block code is considered to be a virtual expanded channel signal constellation by the outer block code. The scheme considers systematic concatenation of general (non-linear) block codes of a fixed block length 3. Codes obtained are of length 𝟑 𝒒 , where q is the number of stages each of block length 3. Encoding and soft decoding schemes for these codes are discussed in this paper.
Prediction of wind power potential by wind speed probability distribution in a hilly

Wind data measurements over a period of ten-years are required to obtain an accurate assessment of the wind regime of an area [1]. The methodologies adopted to ultimately determine the annual energy production potential of a site must be carefully determined and documented [2]. Methodologies on wind farm design rely on data from the nearest measuring station and careful analysis of wind flow, which takes into account the topography and the roughness level of the surrounding land. Unless the data on wind speed, direction, topographical features and roughness classes are accurate calculations may result in significant errors in estimating the wind speed, which may lead to even greater errors in energy estimation especially in complex terrains [3]. In all of above cases, the first step is to estimate the mean value of the wind speed that is expected on a site and afterwards, the wind energy that a proposed wind farm would produce in an average year, is estimated. India has a gross wind power potential of over 45195 MW, and the technical potential of 13390 MW depending upon land availability and grid penetration. India has made rapid strides in harnessing wind power. The gross generation capacity towards the end of 2008 was 8757 MW [4]. Exploration of newer sites to assess the wind energy potential is an ongoing process and many more wind monitoring stations are being installed to collect time series data on wind speed and direction. The present work is based on the time series wind data collected over a period of 10 years at two mast heights of 10 and 70 m located at 77°35′E Longitude and 23°28' N Latitudes at the height of 530 m above mean sea level near Bhopal. Wind speed data in metre per second and the direction in degrees were obtained from the India Meteorological Department for the site. Procedures and methodology to be adopted for the assessment and determination of the wind energy are explained and also the observed wind climate for different months have been calculated. Objectives of the study are : (i) Recording wind speed data including direction. (ii) Preparation of vector map. (iii) Classification of terrain features. (iv) Analysis of wind data and determination of observed wind climate.
Synergy Between Governance and Capitation Grant on Academic Performance in Primary Schools in Morogoro Region, Tanzania

Primary education, accounting for over 40% of the national education budget from 2008/09 to 2012/2013 under decentralised school governance, was the first most funded sector in Tanzania with expectations that it would give intended outcomes. However, there are reports of declining pass–rates in the Primary School Leaving Examinations. Nonetheless, there is not enough empirical evidence to support the effect of school governance and capitation grant, as important school system components, on education outcomes. Hence, based on this problem, the study was conducted in Morogoro Region from August, 2014 to November, 2014 and used hierarchical linear regression to examine the effect of school system components on pupils’ pass–rate in Primary School Leaving Examination. It was found, from the regression results, that the combination of the capitation grant and other selected school system components, excluding school governance, had a significant effect on pass–rate, while the combination of school governance and other selected school system components had no significant effect on pass–rate. However, school governance improved Teacher Pupil Ratio prediction. The findings of the study demonstrate the need for the Government to devise strategies to adequately implement the Capitation Grant Policy, enhance community–based monitoring in primary schools and improve learning in primary schools. Keywords : Capitation grant, education, school governance
REDUCTION OF LEAKAGE BY INPUT VECTORS WITH CONSTRAINED NBTI DEGRADATION

Technology scaling has caused Negative Bias Temperature Instability (NBTI) to emerge as a major circuit reliability concern for the circuit designers Simultaneously leakage power is becoming a greater fraction of the total power dissipated by logic circuits. As both NBTI and leakage power are highly dependent on vectors applied at the circuit’s inputs, they can be minimized by applying carefully chosen input vectors during periods when the circuit is in standby or idle mode. Unfortunately input vectors that minimize leakage power are not the ones that minimize NBTI degradation, so there is a need for a methodology to generate input vectors that minimize both of these variables. This paper proposes such a systematic methodology for the generation of input vectors which minimize leakage power under the constraint that NBTI degradation does not exceed a specified limit. These input vectors can be applied at the primary inputs of a circuit when it is in standby/ idle mode and are such that the gates dissipate only a small amount of leakage power and also allow a large majority of the transistors on critical paths to be in the “recovery” phase of NBTI degradation. The advantage of this methodology is that allowing circuit designers to constrain NBTI degradation to below a specified limit enables tighter guardtbanding, increasing performance. Constrain of NBTI can be limited by choosing input vectors probability based algorithms. Our methodology guarantees that the generated input vector dissipates the least leakage power among all the input vectors that satisfy the degradation constraint. We formulate the problem as a zero-one integer linear program and show that this formulation produces input vectors whose leakage power is within 1% of a minimum leakage vector selected by a search algorithm and simultaneously reduces NBTI by about 5.75% of maximum circuit delay as compared to the worst case NBTI degradation.
Assessment of Nutritional Status of Regular Adolescent Swimmers (13-18 Years)

Adolescence (ages 11 to 21), the transition between childhood and adulthood, is one of the most dynamic periods of human development. Adolescence is characterized by dramatic physical, cognitive, social, and emotional changes. These changes, along with adolescents’ growing independence, search for identity, concern with appearance, need for peer acceptance, and active lifestyle, can significantly affect their eating behaviours and nutrition status [1]. Rapid physical growth creates an increased demand for energy and nutrients. The period of adolescence is divided into three stages. Early adolescence, ages 11 to 14, includes pubertal and cognitive changes. Middle adolescence, ages 15 to 17, is a time of increased independence and experimentation. During late adolescence, ages 18 to 21, adolescents make important personal and vocational decisions. These stages provide a useful context for understanding the eating behaviours and body-image issues of adolescents, as well as a framework for providing adolescents with the information they need to practice healthy eating behaviours and participate in regular physical activity. The World Health Organization (WHO) defines an adolescent as any person between ages 10 and 19. Adolescence is the transitional phase of growth and development between childhood and adulthood. This age range falls within WHO’s definition of young people, which refers to individuals between ages 10 and 24.
EFFICACY OF ANTIOXIDATION AND ANTI- INFLAMMATION OF THE LEAF EXTRACTS OF BORRERIA HISPIDA

Objective: The aim of the present study was to evaluate the total content of phenol, terpenoid and flavonoids from leaf extract of Borreria hispida which was collected from Tamilnadu regions. The leaf extract was also evaluated for antioxidant activity and in vitro anti-inflammatory activity. Methods: In the present investigation, the best accession was screened out through antioxidant activity by qualitative and quantitative methods. The leaf extract was evaluated for antioxidant activity by 1,1–diphenyl-2-picryl-hydrazyl (DPPH) radical scavenging assay. The in vitro antiinflammatory activity was evaluated using albumin denaturation assay. Results: Among the five solvents, maximum antioxidant activity was found in the ethanolic extract of Thanjavur accession (81.3±0.1 %). Total content of phenol, terpenoid and flavonoids were quantified as 33.21±0.06 mg Gallic Acid Equivalents, 76.4±0.02 and 18.27±0.08 mg QE/g respectively. The maximum inhibition rate of albumin denaturation of Borreria hispida was found to be 89.3±0.5% at a dose of 10 mg/ml. Conclusion: It was concluded that the maximum percentage of antioxidant activity was found in Thanjavur accession which attributes the highest radical scavenging activity and the ethanolic leaf extract of Borreria hispida was exhibited a superior level of anti-inflammatory activity.
An Efficient Unsupervised Cluster based Hubness Technique For Outlier Detection in High dimensional data

--Outlier detection in high dimensional data becomes an emerging technique in today’s research in the area of data mining. It tries to find entities that are considerably unrelated, unique and inconsistent with respect to the common data in an input database. It faces various challenges because of the increase of dimensionality. Hubness has recently been developed as an important concept and acts as a characteristic for the increase of dimensionality connecting to nearest neighbors. Clustering also shows a vital role in handling high dimensional data and an important tool for outlier detection. This paper establishes a technique where the concept of hubness, especially the antihub (points with low hubness) algorithm is embedded in the resultant clusters obtained from clustering techniques such as K-means and Fuzzy C Means (FCM) to detect the outliers mainly to reduce the computation time. It compares the results of all the techniques by applying it on three different real data sets. The Experimental results demonstrate that when all three algorithms are compared, KCAnthub provides a significant reduction in computational time than Antihub and FCAntihub. It is concluded that when the Antihub is applied into K-means, it outperforms well
Aging Face Recognition Along with Personal Identification using Local Patterns Selection

: Aging is defined as a biological process of growing older. Aging estimation deals with prediction of specific age or age group from face. Aging face recognition has several applications in real world including missing children finding, passport verification etc. Aging rate for different persons is different because of its great dependence in intrinsic and extrinsic factors. Extrinsic factors include eating habits, living style, working condition etc and intrinsic factors include change in shape and size of face. Intra user dissimilarity is technical term used to describe the changes happening to the person’s appearance due to aging. This paper presents a novel and effective method for automatic age estimation. It contains two stages. At first stage, low level features are extracted using feature descriptor LPS and these features are used as the input to the information refinement stage to produce more accurate result. Aging face recognition along with personal information gathering enhances security control. Aging face recognition helps in identifying the culprit, when the system is trained using the dataset of culprit and the personal information gathered helps in understanding the person.
RP-UFLC METHOD DEVELOPMENT AND VALIDATION FOR NEVIRAPINE IN BULK DRUG AND DOSAGE FORMS AS AN ANTI- HIV AGENT

A simple, precise, rapid, accurate and reproducible RP-UFLC method has been developed and validated for the determination of Nevirapine in bulk drug and pharmaceutical dosage forms. An Eclipse plus C-8 column with a particle size of 5μm having an internal diameter of 4.6 х 250 mm was used in gradient mode. The mobile phase consisting of Methanol and Acetonitrile in the ratio of 90:10 v/v was used. The flow rate was set to 1ml/min and the detection was carried out at a wavelength of 214nm. The retention time was found to be at 2.7min and the linearity was found in the range of 2-10μg/ml with a correlation co-efficient value of 0.9996. The mean recoveries were found to be in the range of 98.51-101.80% and the % RSD reported was not more than 2. The LOD and LOQ values were found to be 0. 021μg/ml and 0. 063μg/ml respectively. Hence, the proposed method can be successfully applied for the quantitative determination of Nevirapine in the bulk drug and Pharmaceutical dosage forms.
A Joint Segmentation and Classification Framework for Sentence Level Sentiment Classification and Emotion Recognition

: Opinion mining (also known as sentiment analysis) refers to the use of natural language processing, text analysis and computational linguistics to discover and dig out subjective information in source resources.However, typically a sentence was splited as a word sequence in previous sentiment classification algorithms, which is difficult for efficiently handling the unpredictable sentiment polarity between a phrase and the words. The sentence level sentiment classification generates segmentations of a sentence and predicts sentence-level polarity simultaneously based on the segmentation results. This framework includes a candidate generation model which is used for generating segmentation of a sentence, a segmentation ranking model to calculate the ranking score for each segment, and a classification model for forecasting the sentiment polarity of a segmentation using WordNet. It gives meaningful segmentation to predict the positive, negative or neutral polarity. This system also performs mood analysis or emotion recognition of a sentence (happy, sad and afraid).
RENAL ANTIOXIDANT AND LIPID PEROXIDATIVE EFFICACY OF BOERHAAVIA DIFFUSA (LINN.) AGAINST D- GALACTOSAMINE INDUCED TOXICITY IN KIDNEY OF MALE ALBINO WISTAR RATS

Hepatotoxic effect is associated with the depletion of UTP nucleotides followed by the formation of UDP hexosamines (Kmiec et al., 2000; Sinha et al., 2007) and leads to the inhibition of transcription and consequently the trans lation processes (Keppler et al., 1974). The development of acute liver failure due to the administration of galactosamine is well documented (Keppler et al., 1968; Makin et al., 1997; EIMofty et al., 1975). Javle et al reported that galactosamine induced liver injury is associated with the development of renal failure (Javle et al., 1998). Recently, the term hepatorenal syndrome (HRS) has been introduced to define the development of renal failure in the absence of clinical, anatomical or pathological causes of that failure. Classically, HRS is associated with the end-stage liver cirrhosis and it has been observed that renal failure occurs with this liver disease in about 50% of patients (Epstein, 1990 ; Sinha et al., 2007). Nephrotoxicity is one of the most common kidney problems and occurs when body is exposed to a drug or toxin (Porter and Bennett, 1981). A number of therapeutic agents can adversely affect the kidney resulting in acute renal failure, chronic interstitial nephritis and nephritic syndrome because there is an increasing number of potent therapeutic drugs like aminoglycoside antibiotics, NSAID’s, chemotherapeutic agents have been added to the therapeutic arsenal in recent years (Hoitsma et al., 1991). Exposure to chemical reagents like ethylene glycol, carbon tetrachloride, sodium oxalate and heavy metals such as lead, mercury, cadmium and arsenic also induces nephrotoxicity. Prompt recognition of the disease and cessation of responsible drugs are usually the only necessary therapy (Paller, 1990). Nephroprotective agents are the substances which possess protective activity against Nephrotoxicity. Medicinal plants have curative properties due to the presence of various complex chemical substances (Mohana lakshmi et al., 2012).
Methods of Mining the Data from Big Data and Social Networks Based on Recommender System

------------------------------------------------------------------- ABSTRACT --------------------------------------------------------------- Data has become a vital part of every action of offline based or online based activities. By use of some technological advancement like internet, Smartphone, social network, mobile computing, fine tuning of ubiquitous/ pervasive computing, the size of data grows exponentially day by day. Big data technology is sure to soon knock on the door of every enterprise, organization, and domain. Organizations data is one of the organization’s assets. Because of daily operational activity, data will g row up. More data means the organization need more effort to choose which data is important to process become information. Big data is not big if we know how to use it. Mining is the analysis of the data with the indent to discover the gems of hidden information in the vast quantity of data that has been captured in the normal course of running the business. At present most of the enterprises realize the significance of using voluminous data to take better decisions. Even though a larger amount of data gives a better output, there will be a challenging task for processing the data. Mining the information helps organizations to make knowledge driven decisions. It applies many computational techniques available. Recommender System is one of the most information filtering techniques for nowadays. Recommender systems look for to predict the "rating" or "preference" that a user would give to a particular item. This paper gives the general idea about the methods used to filtering or mining the information from big data and social networks based on recommender systems.
Prediction of elastic modulus of high strength concrete by Gaussian Process Regression

Elastic moduli play a vital role in determining the deformation characteristics of concrete structures. This paper suggests a novel approach to predict the elastic modulus of highstrength concrete (HSC) using Gaussian Process Regression (GPR). GPR model is used to establish a relation between the modulus of elasticity and the compressive strength of HSC. GPR model is a non-parametric black-box model which searches for the relationship among measured data and estimates distributions over functions. GPR models are constructed and tested using available test data attained through existing literature studies. The first dataset used in this study is derived from experimental results. Out of a total of 87 cases of data, 79 are used for training and the rest are used to test the created model. The data are normalized between 0 and 1 prior to being used in the model. A subsequent parametric analysis is carried out to evaluate the sensitive variations of the elastic modulus corresponding to variations in the compressive strength. The GPR model accurately predicts the elastic modulus of concrete blocks considered. The output of the GP model is a normal distribution, expressed as mean and variance the former represents the most likely output and the latter is a measure of its confidence. The results predicted are compared to those obtained from empirical results from buildings codes and various models – accurate results portray the strong potential of GPR, as a feasible and reliable tool, to predict elastic moduli of concrete.
International Journal of Intellectual Advancements and Research in Engineering Computations Spatio temporal and aspect features based sentiment analysis on customer reviews

Online reviews are used as a decision support model for the consumers and feedback channel for the business organizations. Sentiment analysis techniques are employed to investigate online reviews. The sentiment expressed in review document is called as overall sentiment. Attribute level like and dislike factors are not represented in the overall sentiment estimation methods. The aspect level sentiment analysis model discovers the unique semantic fact of individual entity. The Aspect based sentiment analysis consists of two major tasks. They are, detect hidden semantic aspect from review document and identify fine-grained sentiments for the aspect. Probabilistic topic models are used for aspect-based sentiment analysis. The review documents and rating measures are analyzed to discover the semantic aspect level sentiments and overall sentiments. The probabilistic supervised joint aspect and sentiment model (SJASM) is employed to analyze the review documents in an unified framework. SJASM represents each review document in the form of opinion pairs. The aspect terms and opinion words are modeled for hidden aspect and sentiment detection. The overall sentimental rating is estimated with aspect and aspect level sentiments. The collapsed Gibbs sampling-based inference method is used for parameter estimation of SJASM. The aspect distribution is used to select the parameters for the sentiment estimation. The review documents are classified into positive or negative sentiments. The collaborative sentiment assessment model (CSAM) integrates the aspect and spatio temporal features. The Bayesian nonparametric model is applied to automatically estimate the number of latent topics from review data. The hybrid indexing scheme combines the aspect and spatio temporal parameters to order the review documents. The recommendation process is build with aspect, location and time features
Comparison and Performance Analysis on Clustering Scheme

— In wireless sensor network, clustering is one of the major concerns. Effective clustering helps to increase the network stability and achieve network lifespan. Heterogeneous protocols consider two or more energy level of nodes. By analyzing such protocols, the overall network lifetime is enhanced to a greater extent. Nodes are equipped with the ability to aggregate, transmit and receive data messages which requires great energy. Simulation results shows that four level and three level nodes achieve better stability of the network. In this paper we propose and evaluate a scheme for heterogeneous wireless sensor network to balance the number of nodes in each cluster and hence make it a Balanced Cluster. The network will have some randomly deployed nodes, all belonging to some cluster. The count of nodes in each cluster will be definite in each round and therefore achieves greater stability for heterogeneous environment.