Infrastructure-less Kalman Filter Based Driving Assistance System under Fog Condition
- Adil Fahad
- Reseived: 6 October 2018 Received in Revised Form: 28 February 2019 Accepted: 10 March 2019
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Albaha, Alhada, Alkhobar, Al-Ahsa, Jubail–Dammam highway, Qatif, Dhahran, Dammam and other neighboring areas are the region affected by fog during the winter. People suffer and uncomfortable when driving during this fogging time. Many car accidents happen due to the lack of infrastructure and system to avoid the accidents. This work proposes a method to predict cars’ location and then implements the method to develop a novel driving assistance system during fog condition. The system is based on GPS service facility given by telecommunication providers and employs a two-stage Bayesian filter to track the vehicle’s position and Kalman filter for heading estimation using smartphone’s GPS. The system processes the information obtained by mobile communication and provides accurate and useful information such as distance, speed and location of cars in straight road. The system does not need new infrastructure as the user only need to enable the application and having Internet connection during the driving. The algorithms performance is evaluated using real world measurements collected from four communicating vehicles in the scenario.
Statistical Evaluation of Potential Solar Energy for Al-Baha location, Saudi Arabia
- Samy A. Khalil, Ashraf M. Shaffieb, Hassan G. El Goharyc, Faiz M. B. Elshafiad, A. A. Mahmoud
- Reseived: 6 July 2018 Received in Revised Form: 17 December 2018 Accepted: 23 December 2018
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The main objective in this paper is to carry out evaluation of statistical comparison of the mean seasonally variations of total solar radiation and potential energy of direct and global solar radiation for Al-Baha location, KSA during the time period from 2005 to 2017. The relation between measured and estimated values of the global and direct solar radiation for the whole months in the present work is in good agreement. The maximum values of the measured and estimated direct solar radiation are occurring in summer months. While the minimum values occur in winter months. Moreover, we see that the estimated values are nearly coinciding with the measured values. The deviation between measured and predicted values does not exceed 3 - 5%. The estimated values of the global solar radiation are nearly coinciding with the measured values. The deviation between measured and predicted values does not exceed 4 - 7%. The potential solar energy of measured and predicted values is almost equal during the study period. The difference between measured and predicted potential energy of direct and global solar radiation does not exceed 5%. The difference between potential energy during the seasons of the present work is almost constant. Distinguish between the potential energy values during the spring and autumn months are negligible. The maxima for the mean absolute percentage error (MAPE) are concentrated during winter months Dec., Jan. and Feb. for both global and direct solar radiation. While the minimum of the (MAPE) is appear during the summer months for both global and direct solar radiation. The maximum and minimum differences for the (MAPE) are varying between 2-3%. The average differences between measured and predicted solar radiation to the extraterrestrial solar radiation in the present work during the study period not exceed 10%. Also we noticed that the change between measured and predicted values of global or direct solar radiation does not exceed 2%. The monthly mean of the statistical results analysis in the present work study period is discussed.
Mining Sequential Patterns of Diseases from Medical Datasets
- Mohammed Y. Alzahrani
- Reseived: 19 March 2018 Received in Revised Form: 6 January 2019 Accepted: 13 January 2019
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Sequential patterns mining is a well stated data mining problem and has been applied in DNA sequencing, signal processing, speech analysis etc. Nevertheless, this paper implements and evaluates algorithm for finding sequential patterns of disease from medical dataset. This paper implements and evaluates an existing algorithm for discovering sequence patterns from medical dataset to investigate causal relationship between different diseases available on the patient dataset of any geographical location. The experimental results using BUPA dataset and ILDP from University of California, Irvine (UCI) machine learning repository shows that the discovering sequence pattern algorithm is able to identify up to 10 and 15 different sequential patterns of diseases for each dataset. In case of H1N1 dataset, the algorithm successfully found 16 sequential patterns.
Effect of Mercury Exposure of Dental Professionals on Serum Levels of Oxidant and Antioxidant Biomarkers and Lipid Profiles
- Hiba Saeed Al-Amodi
- Reseived: 1 January 2019 Received in Revised Form: 3 February 2019 Accepted: 5 February 2019
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Mercury (Hg) is the major global pollutant contributing factor of generation of reactive oxygen species (ROS) leading to the formation of oxidative stress (OS) in dental professionals, which increases the cardiovascular risk factors. In this study, hair Hg concentrations of 130 participants (in 3 groups as a whole) were determined. They were classified into a control; group A of 30 non-dental professionals, group B of 30 dental professionals with Hg-level (Hg= 66.9 µg/g), and group C of 70 dental professionals with high Hg-level (Hg= 181.4 µg/g) comparing to normal group (A) (Hg= 27.9 µg/g). The relation of hair mercury (Hg) concentrations with the concentration of serum malondialdehyde (MDA), reduced glutathione (GSH), and total antioxidant capacity (TAC) was investigated. The effect of different mercury levels on lipid profile was investigated as well. Subjects with high Hg-level (C) had a significantly increased levels of serum MDA 1.8, and 2.5-times compared to the low Hg-level (B) and normal control (A), respectively (P<0.001). Levels of GSH and TAC were significantly decreased (1.4, & 1.7-times) and (2.2, & 1.4-times) in high Hg-level (C) compared to low Hg-level (B) and normal control (A), respectively (P<0.001). Significant difference was observed in lipid profile parameters between studied groups. Hair Hg-concentrations were positively correlated with levels of MDA, cholesterol, triglycerides, and LDL cholesterol and negatively correlated with GSH, TAC, and HDL cholesterol. Renal function markers (creatinine and urea) were significantly elevated in high Hg-level group (P<0.05) compared to control but still within the normal range. The increased hair Hg levels is accompanied by elevated oxidative stress, decreased GSH, and lowering of antioxidant capacities in dental professionals. Correlation of hair Hg with serum MDA, GSH, and TAC levels may be possible biomarkers for assessing chronic Hg toxicity. The chronic exposure to the mercury is one of the causes of cardiovascular risk. Further studies with large sample size are required to confirm the effect of high mercury levels on OS biomarkers.
Mineral Content and Analysis of Myrtus Communis Leaves from Saudi Arabia
- Bader O. Burham
- Reseived: 29 May 2018 Received in Revised Form: 17 March 2019 Accepted: 2 April 2019
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The leaves of Myrtus communis plant belonging to the Myrtaceae family was obtained from local market in Al-Baha, Saudi Arabia. The mineral content was analyzed in leaves after ashing at 45 °C. The studied elements were chromium (Cr), copper (Cu), iron (Fe), magnesium (Mg), manganese (Mn), nickel (Ni), and zinc (Zn). The analysis showed that magnesium had the highest content (3079.3 ppm), while copper had the lowest. The essential oil of leaves was obtained by hydrodistillation method and investigated by GC/MS analysis. The results proved the presence of fifty-two compounds with α-pinene (26.61%), eucalyptol (22.98%), 1,6-octadien-3-ol, 3,7-dimethyl- (8.62%), D-limonene (6.62%) and myrtenyl acetate (5.44%) as major components. While the GC/MS analysis of methanolic extract of Myrtus communis leaves showed the presence of fifty-four compounds. The major compounds were found to be quinic acid (23.70) cyclohexene, 1,5,5-trimethyl-6-acetylmeth(16.42%),5-hydroxy methyl furfural(8.83%), eucalyptol (7.10%) and 1,2,3-benzenetriol (7.10%).The present study was aimed to examine the chemical composition of M. communis such as elemental analysis, methanolic extract and essential oil of Myrtus communis leaves.
