Aspek Perancangan Monitoring Antrian Kendaraan pada Persimpangan Lampu Merah

The number of vehicles in the big cities has increased rapidly, this phenomena has triggered an over density situation in the traffic light. The purpose of this study is to develop a tool to measurethe density of vehicles with sensors and RF 433Mhz as transmission media. To realize these tools, three ultrasonic sensors are used as a detector of the presence of a vehicle with a sensor with an object placed between 3 cm to 3 m and the distance between each sensor 50 m. The measurement values obtained from the measurement results shown through PLX-DAQ applications. In the design of this system is to perform testing of the tool, the measurement results can be analyzed todetermine the density of vehicles that can be categorized by the level of density, loose, short, medium and long. This paper has proposed a monitoring of traffic density in the traffic light

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

Electronic Attendance and Allowance Performance Effect Towards Discipline and Work Motivation in Ministry of Religion Regional Office

This study aims to find out empirically the effect of Electronic Attendance and Performance Allowance on Discipline and Motivation of Civil Servant (ASN) Work in the Regional Office of the Ministry of Religion of West Sumatra Province (Case Study of the Administrative Office Regional Office of the Ministry of Religion of West Sumatra Province). While the dependent variable is discipline and work motivation. The research sample was ASN in the Administration Section of the Regional Office of the Ministry of Religion, West Sumatra. Samples were taken with total sampling. Data collection was done by distributing questionnaires to 68 ASN people in the Administration Section of the Regional Office of the Ministry of Religion, West Sumatra Province. The research method uses multiple linear regression analysis, by testing the statistical test hypothesis t. The results of this study indicate a significant effect of the application of electronic attendance both to discipline and work motivation, as well as performance allowances have a significant influence on the discipline and motivation of ASN work, but the effect of electronic attendance is greater than the effect of performance benefits