Effect of Aqueous Extract of Cathedral Cactus (Euphorbia trigona Mill) on Larvae of Anopheles arabiensis (Diptera: Culicidae)

Abstract: Mosquitoes are considered as vector of malaria disease and some other endemic diseases in the world. There are some methods already been used for controlling mosquito; of which is using natural products. This study was conducted at Laboratories of Faculty of Engineering and Technology, University of Gezira, to evaluate the effect of cortex, spine and pith parts of cactus (Euphorbia trigona) on Anopheles mosquito larvae. The plant parts were collected from Wad Medani City, whereas, the mosquito larvae were collected from the breeding sites at Tayba village, Gezira State, Sudan. The plant parts (cortex, spines and pith) were shade dried away from the direct sunlight, grounded and then kept separately in small plastic sacks. From each plant part, a concentration of 1200 mg/L was used. The standards of WHO for testing toxicity of the toxic compound against mosquito larvae was followed. The mortality in Anopheles larvae were 48%, 37% and 62%, respectively, for trigona cortex, spine and pith. The results also showed that, the three used parts have a varied great impact on the survived larvae (morphological changes of skin color was in 82%, disconnecting of digestive tract was in 48%, and separation of some body parts was in 32%, after 48 hours of applying it). The study recommends adding these cactus parts as potential natural products for Anopheles larval control, and also running more sensitive tests to measure the environmental impact of these products, especially on human and on the aquatic faun

Plane waves in noncommutative fluids

We study the dynamics of the noncommutative fuid in the Snyder space perturbatively at the first order in powers of the noncommutative parameter. The linearized noncommutative fluid dynamics is described by a system of coupled linear partial differential equations in which the variables are the fluid density and the fluid potentials. We show that these equations admit a set of solutions that are monocromatic plane waves for the fluid density and two of the potentials and a linear function for the third potential. The energy-momentum tensor of the plane waves is calculated.Comment: 11 pages. Version published as a Lette

Boolean logic algebra driven similarity measure for text based applications

In Information Retrieval (IR), Data Mining (DM), and Machine Learning (ML), similarity measures have been widely used for text clustering and classification. The similarity measure is the cornerstone upon which the performance of most DM and ML algorithms is completely dependent. Thus, till now, the endeavor in literature for an effective and efficient similarity measure is still immature. Some recently-proposed similarity measures were effective, but have a complex design and suffer from inefficiencies. This work, therefore, develops an effective and efficient similarity measure of a simplistic design for text-based applications. The measure developed in this work is driven by Boolean logic algebra basics (BLAB-SM), which aims at effectively reaching the desired accuracy at the fastest run time as compared to the recently developed state-of-the-art measures. Using the term frequency–inverse document frequency (TF-IDF) schema, the K-nearest neighbor (KNN), and the K-means clustering algorithm, a comprehensive evaluation is presented. The evaluation has been experimentally performed for BLAB-SM against seven similarity measures on two most-popular datasets, Reuters-21 and Web-KB. The experimental results illustrate that BLAB-SM is not only more efficient but also significantly more effective than state-of-the-art similarity measures on both classification and clustering tasks

On the Integration of Similarity Measures with Machine Learning Models to Enhance Text Classification Performance

Several techniques have long been proposed to enhance text classification performance, such as: classifier ensembles, feature selection, the integration of similarity measures with classifiers, and meta-heuristic algorithms. The integration of similarity measures with machine learning models (ML), however, has not yet received thorough analysis for text classification. As a result, in an effort to thoroughly investigate the impact of similarity measures integration with ML models, this work makes three major contributions: (1) proposing newly-integrated models and presenting benchmarking studies for integration methodology over balanced/imbalanced datasets; (2) offering detailed analysis for dozens of integrated models that are established, and experimentally proven, to significantly outperform state-of-the-art performance. The models\u27 construction used fourteen similarity measures, three knowledge representations (BoW, TFIDF, and Word embedding), and five models (Support Vector Machine, N-Centroid-based Classifier, Multinomial Naïve Bayesian, Convolutional Neural Network, and Artificial Neural Network); and (3) introducing significantly-effective and highly-efficient variations of these five models. The evaluation study has been conducted internally for integrated models against their baselines, and externally against the state-of-the-art models. While the internal evaluation constantly showed a total enhancement rate of 49.3% and 59% over the balanced and imbalanced datasets, respectively, the external evaluation attested to the superiority of the integrated models

A set theory based similarity measure for text clustering and classification

© 2020, The Author(s). Similarity measures have long been utilized in information retrieval and machine learning domains for multi-purposes including text retrieval, text clustering, text summarization, plagiarism detection, and several other text-processing applications. However, the problem with these measures is that, until recently, there has never been one single measure recorded to be highly effective and efficient at the same time. Thus, the quest for an efficient and effective similarity measure is still an open-ended challenge. This study, in consequence, introduces a new highly-effective and time-efficient similarity measure for text clustering and classification. Furthermore, the study aims to provide a comprehensive scrutinization for seven of the most widely used similarity measures, mainly concerning their effectiveness and efficiency. Using the K-nearest neighbor algorithm (KNN) for classification, the K-means algorithm for clustering, and the bag of word (BoW) model for feature selection, all similarity measures are carefully examined in detail. The experimental evaluation has been made on two of the most popular datasets, namely, Reuters-21 and Web-KB. The obtained results confirm that the proposed set theory-based similarity measure (STB-SM), as a pre-eminent measure, outweighs all state-of-art measures significantly with regards to both effectiveness and efficiency

Towards Highly-Efficient k-Nearest Neighbor Algorithm for Big Data Classification

the k-nearest neighbors (kNN) algorithm is naturally used to search for the nearest neighbors of a test point in a feature space. A large number of works have been developed in the literature to accelerate the speed of data classification using kNN. In parallel with these works, we present a novel K-nearest neighbor variation with neighboring calculation property, called NCP-kNN. NCP-kNN comes to solve the search complexity of kNN as well as the issue of high-dimensional classification. In fact, these two problems cause an exponentially increasing level of complexity, particularly with big datasets and multiple k values. In NCP-kNN, every test point’s distance is checked with only a limited number of training points instead of the entire dataset. Experimental results on six small datasets, show that the performance of NCP-kNN is equivalent to that of standard kNN on small and big datasets, with NCP-kNN being highly efficient. Furthermore, surprisingly, results on big datasets demonstrate that NCP-kNN is not just faster than standard kNN but also significantly superior. The findings, on the whole, show that NCP-kNN is a promising technique as a highly-efficient kNN variation for big data classification

The Impact of Financial Risks on the Firms’ Performance

Firms are exposed to a variety of risks including credit risk, liquidity risk, foreign exchange risk, market risk and interest rate risk. An efficient risk management system is needed in time in order to control these risks. Managing risk is one of the basic tasks to be done, once it has been identified and known. The risk and return are directly related to each other, which means that increasing one will subsequently increase the other and vice versa. Financial risks have a great impact on firm’s performance. The study also assessed the current risk management practices of the firms and linked them with the firms’ financial performance. The findings confirm whether financial risks can be contained or managed in order for firms to achieve profit maximization for its shareholders. Keywords: Financial Risk; Firm’s Performance; Interest rate parity; Liquidity gap; Liquidity risk; Risk Management

Effect of addition of effective microorganisms on chemical and rumen fermentation characteristics of wheat straw treated with four levels of urea

An experiment was conducted in the laboratories of chemistry and biology, University of Gezira, to find the effects of effective microorganisms (EM) on chemical and in vitro rumen fermentation characteristics of ammoniated wheat straw on April 2011. Wheat straw was treated with four levels of urea (0, 2, 4 and 6%). Two sets of three replicates each were used. To one set EM was added, while the other one which included no EM was used as a control. It was found that addition of EM increased the urea nitrogen fixed in the straw. CP (crude protein) increased while CF (crude fiber) decreased with increasing levels of urea. The corresponding levels with added EM showed more increase in CP and a decrease in CF, though it was not significant (P>0.05). However, at higher urea levels, the straw was significantly (P<0.05) improved in terms of increased CP and decreased CF. The DM (dry matter), OM (organic matter), CP and CF digestibilities were improved with increasing levels of urea and it was substantially increased with addition of EM. Rumen fluid pH tended to increase with the increasing levels of urea in both the control and the treatment. Rumen NH3-N increased significantly (P<0.05) at 4% and 6% levels in treatments which included  EM and at 6% level in ammoniated straw without EM. Also, there was a trend of increasing total protozoal count with inclusion of EM but this was not significant (P>0.05). It could be concluded that inclusion of EM to ammoniated wheat straw improved both the chemical and the in vitro rumen fermentation of wheat straw and reduced urea–N lost to the atmosphere.&nbsp