A Comparison of Supervised Learning Techniques for Predicting the Mortality of Patients with Altered State of Consciousness

The study attempts to identify a potentially reliable supervised learning technique for predicting the outcomes of mortality in an altered state of consciousness (ASC) patients. ASC is a state distinguished from ordinary waking consciousness, which is a common phenomenon in the Emergency Department (ED). Thirty (30) distinctive attributes or features are commonly used to recognize ASC. The study accordingly applied these features to model the prediction of mortality in ASC patients. Supervised learning techniques are found to be suitable for such classification problems. Consequently, the study compared five supervised learning techniques that are commonly applied to evaluate the risk of mortality using health-related datasets, namely Decision Tree, Neural Network, Random Forest, Naïve Bayes, and Logistic Regression. The labeled dataset comprised patient records captured by the Universiti Sains Malaysia hospital’s Emergency Medicine department from June to November 2008. The cleaned dataset was divided into two parts. The larger part was used for training and the smaller part, for evaluation. Since the ratio between training and testing samples varies between individual supervised learning techniques, we studied the performance of the modeled techniques by also varying the proportion of the training data to the dataset. We applied four percentage splits; 66%, 75%, 80%, and 90% to allow for 3-, 4-, 5- and 10-fold cross-validation experiments to evaluate the accuracy of the analyzed techniques. The variation helped to lessen the chance of over fitting, and averaged the effects of various conditions on accuracy. The experiments were conducted in the WEKA environment. The results indicated that Random Forest is the most reliable technique to model for predicting the mortality in ASC patients with acceptable accuracy, sensitivity, and specificity of 70.9%, 76.3%, and 65.5%, respectively. The results are further confirmed by SROC analysis. The findings of the study serve as a fundamental step towards a comprehensive study in the future

An Experimental Study to Demonstrate the Effect of Alumina Nanoparticles and Synthetic Fibers on Oil Well Cement Class G

    In the drilling and production operations, the effectiveness of cementing jobs is crucial for efficient progress. The compressive strength of oil well cement is a key characteristic that reflects its ability to withstand forceful conditions over time. This study evaluates and improves the compressive strength and thickening time of Iraqi oil well cement class G from Babylon cement factory using two types of additives (Nano Alumina and Synthetic Fiber) to comply with the American Petroleum Institute (API) specifications. The additives were used in different proportions, and a set of samples was prepared under different conditions. Compressive strength and thickening time measurements were taken under different conditions. The amounts of Nano Alumina (0.5%, 1%, and 1.5% by weight of cement (BWOC)) were selected with synthetic fiber (0.5 g, 1 g, and 1.5 g, respectively). The results show a significant improvement in compressive strength, with all values meeting the API requirements, and a decrease in the thickening time of Iraqi oil well cement, depending on the proportions of additives. The most significant improvement in compressive strength was achieved in the sample containing 1.5% Nano Alumina by weight of cement (BWOC) and 1.5 g Synthetic Fiber (Barolift), where the compressive strength increased by 40.7% and 33.8% at a temperature of 38 °C and 60 °C, respectively, while the thickening time decreased by 26.53% at this ratio of additives. The results demonstrate the feasibility of using these additives to enhance the performance of Iraqi oil well cement, expanding its potential application in Iraqi oil fields

Global Antifungal Profile Optimization of Chlorophenyl Derivatives against Botrytis cinerea and Colletotrichum gloeosporioides

Twenty-two aromatic derivatives bearing a chlorine atom and a different chain in the para or meta position were prepared and evaluated for their in vitro antifungal activity against the phytopathogenic fungi Botrytis cinerea and Colletotrichum gloeosporioides. The results showed that maximum inhibition of the growth of these fungi was exhibited for enantiomers S and R of 1-(40-chlorophenyl)- 2-phenylethanol (3 and 4). Furthermore, their antifungal activity showed a clear structure-activity relationship (SAR) trend confirming the importance of the benzyl hydroxyl group in the inhibitory mechanism of the compounds studied. Additionally, a multiobjective optimization study of the global antifungal profile of chlorophenyl derivatives was conducted in order to establish a rational strategy for the filtering of new fungicide candidates from combinatorial libraries. The MOOPDESIRE methodology was used for this purpose providing reliable ranking models that can be used later

Phenolics from medicinal and aromatic plants: characterisation and potential as biostimulants and bioprotectants

Biostimulants and bioprotectants are derived from natural sources and can enhance crop growth and protect crops from pests and pathogens, respectively. They have attracted much attention in the past few decades and contribute to a more sustainable and eco-friendly agricultural system. Despite not having been explored extensively, plant extracts and their component secondary metabolites, including phenolic compounds have been shown to have biostimulant effects on plants, including enhancement of growth attributes and yield, as well as bioprotectant effects, including antimicrobial, insecticidal, herbicidal and nematicidal effects. Medicinal and aromatic plants are widely distributed all over the world and are abundant sources of phenolic compounds. This paper reviews the characterisation of phenolic compounds and extracts from medicinal and aromatic plants, including a brief overview of their extraction, phytochemical screening and methods of analysis. The second part of the review highlights the potential for use of phenolic compounds and extracts as biostimulants and bioprotectants in agriculture as well as some of the challenges related to their use

Digital Marketing And Smes's Performance In West Sumatera

This study investigated the effects of digital marketing use on Small Medium Enterprises' (SMEs) performance. The objectives are to asseses the effect of effort expectancy, social influence, and facilitating condition on digital marketing use and business performance of SMEs in West Sumatra. The research population is focused on SMEs on culinary sector in West Sumatra who have used digital marketing. A total of 162 respondents were taken based on urban areas in West Sumatera, Padang, Bukittinggi, Padang Panjang, Solok and Payakumbuh. The primary data include a structured questionnaire used to elicit information from the target respondents. The Structural Equation Model (SEM) used to test the hypotheses that were generated for the study. The findings showed that effort expectancy, social influence, and facilitating condition has a significant effect on digital marketing use and business performance of SME's in West Sumatra

Structure–activity relationships study of mTOR kinase inhibition using QSAR and structure-based drug design approaches

Wiame Lakhlili,1 Abdelaziz Yasri,2 Azeddine Ibrahimi1 1Biotechnology Laboratory (Medbiotech), Rabat Medical and Pharmacy School, Mohammed V University in Rabat, Rabat, Morroco; 2OribasePharma, Montpellier, France Abstract: The discovery of clinically relevant inhibitors of mammalian target of rapamycin (mTOR) for anticancer therapy has proved to be a challenging task. The quantitative structure–activity relationship (QSAR) approach is a very useful and widespread technique for ligand-based drug design, which can be used to identify novel and potent mTOR inhibitors. In this study, we performed two-dimensional QSAR tests, and molecular docking validation tests of a series of mTOR ATP-competitive inhibitors to elucidate their structural properties associated with their activity. The QSAR tests were performed using partial least square method with a correlation coefficient of r2=0.799 and a cross-validation of q2=0.714. The chemical library screening was done by associating ligand-based to structure-based approach using the three-dimensional structure of mTOR developed by homology modeling. We were able to select 22 compounds from two databases as inhibitors of the mTOR kinase active site. We believe that the method and applications highlighted in this study will help future efforts toward the design of selective ATP-competitive inhibitors. Keywords: mTOR inhibitors, quantitative structure–activity relationship, PLS, partial least square, dockin