Multivariate analysis of monsoon seasonal variation and prediction of particulate matter episode using regression and hybrid models

Prediction of particulate matter (PM10) episode in advance enables for better preparation to avert and reduce the impact of air pollution ahead of time. This is possible with proper understanding of air pollutants and the parameters that influence its pattern. Hence, this study analysed daily average PM10, temperature (T), humidity (H), wind speed and wind direction data for 5 years (2006–2010), from two industrial air quality monitoring stations. These data were used to evaluate the impact of meteorological parameters and PM10 in two peculiar seasons: south-west monsoon and north-east monsoon seasons, using principal component analysis (PCA). Subsequently, lognormal regression (LR), multiple linear regression (MLR) and principal component regression (PCR) methods were used to forecast next-day average PM10 concentration level. The PCA result (seasonal variability) showed that peculiar relationship exists between PM10 pollutants and meteorological parameters. For the prediction models, the three methods gave significant results in terms of performance indicators. However, PCR had better predictability, having a higher coefficient of determination (R2) and better performance indicator results than LR and MLR methods. The outcomes of this study signify that PCR models can be effectively used as a suitable format in predicting next-day average PM10 concentration levels

Regression and multivariate models for predicting particulate matter concentration level

The devastating health effects of particulate matter (PM10) exposure by susceptible populace has made it necessary to evaluate PM10 pollution. Meteorological parameters and seasonal variation increases PM10 concentration levels, especially in areas that have multiple anthropogenic activities. Hence, stepwise regression (SR), multiple linear regression (MLR) and principal component regression (PCR) analyses were used to analyse daily average PM10 concentration levels. The analyses were carried out using daily average PM10 concentration, temperature, humidity, wind speed and wind direction data from 2006 to 2010. The data was from an industrial air quality monitoring station in Malaysia. The SR analysis established that meteorological parameters had less influence on PM10 concentration levels having coefficient of determination (R 2) result from 23 to 29% based on seasoned and unseasoned analysis. While, the result of the prediction analysis showed that PCR models had a better R 2 result than MLR methods. The results for the analyses based on both seasoned and unseasoned data established that MLR models had R 2 result from 0.50 to 0.60. While, PCR models had R 2 result from 0.66 to 0.89. In addition, the validation analysis using 2016 data also recognised that the PCR model outperformed the MLR model, with the PCR model for the seasoned analysis having the best result. These analyses will aid in achieving sustainable air quality management strategies

Application of step wise regression analysis in predicting future particulate matter concentration episode

Particulate matter is an air pollutant that has resulted in tremendous health effects to the exposed populace. Air quality forecasting is an established process where air pollutants particularly, particulate matter (PM10) concentration is predicted in advance, so that adequate measures are implemented to reduce the health effect of PM10 to the barest level. The present study used daily average PM10 concentration and meteorological parameters (temperature, humidity, wind speed and wind direction) for 5 years (2006–2010) from three industrial air quality monitoring stations in Malaysia (Balok Baru, Tasek and Paka). Time series plot was used to assess PM10 pollution trend in the industrial areas. Additionally, step wise regression (SWR) analysis was used to predict next day PM10 concentrations for the three industrial areas. The SWR method was compared with a persistence model to assess its predictive capabilities. The results for the trend analysis showed that, Balok Baru (BB) had higher PM10 concentration levels, having high values in 2006, 2007 and 2009. These values were higher than the Malaysian Ambient Air Quality Guideline (MAAQG) of 150 μg/m3. Subsequently, the other two industrial areas Tasek (TK) and Paka (PK) had no record of violating the MAAQG. The results for the SWR analysis had significant R 2 values of 0.64, 0.66 and 0.60, respectively. The model performance results for variance inflation factor (VIF) were less than 5 and Durbin-Watson test (DW) had value of 2 for each of the study areas, which were significant. The comparative analysis between SWR and persistence model showed that the SWR had better capabilities, having lower errors for the BB, TK and PK areas. Using root mean square error (RMSE), the results showed error differences of 7, 12 and 16 %, and higher predictability using index of agreement (IA), having a difference of 17, 19 and 16 % for BB, TK, and PK areas, respectively. The results showed that SWR can be used in predicting PM10 next day average concentration, while the extreme event detection results showed that 100 μg/m3 were better detected than the 150 μg/m3 bench marked levels

Determination of radionuclides in soil samples taken from Gura Topp (Jos) using sodium iodide thallium detector Nai(Ti)

The activity concentrations of natural radionuclides 40K, 226Ra and 232Th in soil samples taken from the tin mining area in Gura top, Jos were measured by gamma spectrometry using Sodium Iodide detector. The average specific activity concentrations of 40K, 226Ra and 232Th determined in the soil sample ranged from 11.26±3.16Bq/Kg to 543.35±0.64Bq/Kg with mean activity concentration of 161.96±7.56Bq/Kg for 40K, that of 226Ra ranged from 7.19±1.23Bq/Kg to 144.20±10.18Bq/Kg with the mean activity of 46.47±5.19Bq/Kg while 232Th ranged from 76.08±3.38Bq/Kg to 1267.91±15.37Bq/Kg, with mean activity concentration of 396.17±7.69Bq/Kg. The results indicates that the activity concentration of 40K was found to be below the world average while that of 232Th and 226Ra were detected to be above the world average value. This suggests that t the study area has excess thorium and radium activities which pose significant health hazard and is considered radio-logically unsafe for human to cultivate on the land.Keywords: Activity concentration, Gamma spectrometry, Natural radionuclides and Sodium, Iodide detecto

The Seroprevalence of Toxoplasma gondii Infection among Male Population in Zakho city, Duhok Province, Iraq

Toxoplasmosis is a neglected foodborne disease, stands as the fourth most frequent cause of hospitalization and the second leading factor behind deaths among immunocompromised individuals and pregnant women who contract the disease at early pregnancy stage. The prevalence of toxoplasma infections among males in Zakho city has remained poorly examined. The current study's primary objective is to estimate the seroprevalence of Toxoplasma gondii (T. gondii) in males residing in Zakho city, Iraq. Data for analysis were gathered through serological tests and participant questionnaires. Among the 213 participants, 65 (30.52%) and 4 (1.88%) exhibited IgG and IgM anti-Toxoplasma gondii antibodies, respectively. The current investigation highlighted the prevalence of T. gondii infection within the general population of Zakho city, Iraq. However, the rate of seropositivity of anti-toxoplasma IgG, increased with age, but this increase was non-significant (P>0.05). Also, higher but non-significant seroprevalence rates of toxoplasmosis IgG and IgM Abs were observed with other studied factors such as residence, contact with cats, occupation, marital status, and eating at restaurants. Indicating to the presence of poor relationships between toxoplasmosis and these demographic factors in males

Synthesis and Characterization of copper oxide(II) nanoparticles prepared by hydrothermal process

تم استخدام طريقة الهدرجة الحرارية لتحضير جسيمات اوكسيد النحاس النانوية , يمكن تحضير جسيمات النحاس النانوية بدون استخدام المذيبات العضوية او مواد غالية الثمن بأستخدام الهدرجة الحرارية. حيود الاشعة السينية أكد بان جسيمات النحاس النانوية ذات تركيب احادي الميل , مع حجم حبيبي 20nm , كما ان التحليل بأستخدام مجهر القوة الذرية اوضح بأن قطر الحجم الحبيبي هو في ضمن المدى النانوي . التحليل بواسطة طيف ( FTIR )اكدت لنا بأن التركيب هو اوكسيد النحاس كما ان خصائص نمط الاهتزاز لأوكسجين-  نحاس تم تأكيده. تم دراسة الخواص البصرية بأستخدام الطيف المرئي للاشعة فوق البنفسجية والتي اوضحت بأن جسيمات النحاس تمتلك انحراف بأتجاه المنطقة المسماة (blue shift) , حيث تمتلك فجوة عالية طاقة (  (4.9eV, وان هذا ربما يعود الى تأثير الحصر الكمي لجسيمات اوكسيد النحاس النانوية.Hydrothermal process was  used to prepare CuO nanoparticles. CuO nanoparticles can be prepare without organic solvents, expensive raw materials by a hydrothermal method. XRD diffraction reveals that CuO nanoparticles have a monoclinic structure with particle size 20nm ,  and AFM analysis showed that the diameter of the Grain size is in a nanometer range. The analysis by FTIR spectra assure that the composition was CuO, and the  features of vibrational types of Cu–O were fixed. also the optical properties was analysed with UV–vis showed that  CuO nano particles have considerable a blue shift  , which have aband gab equal to (4.9 eV ) , and this is because the effect of quntum confienment of prepared CuO nano particles

Synthesis and Characterization of copper oxide(II) nanoparticles prepared by hydrothermal process

تم استخدام طريقة الهدرجة الحرارية لتحضير جسيمات اوكسيد النحاس النانوية , يمكن تحضير جسيمات النحاس النانوية بدون استخدام المذيبات العضوية او مواد غالية الثمن بأستخدام الهدرجة الحرارية. حيود الاشعة السينية أكد بان جسيمات النحاس النانوية ذات تركيب احادي الميل , مع حجم حبيبي 20nm , كما ان التحليل بأستخدام مجهر القوة الذرية اوضح بأن قطر الحجم الحبيبي هو في ضمن المدى النانوي . التحليل بواسطة طيف ( FTIR )اكدت لنا بأن التركيب هو اوكسيد النحاس كما ان خصائص نمط الاهتزاز لأوكسجين-  نحاس تم تأكيده. تم دراسة الخواص البصرية بأستخدام الطيف المرئي للاشعة فوق البنفسجية والتي اوضحت بأن جسيمات النحاس تمتلك انحراف بأتجاه المنطقة المسماة (blue shift) , حيث تمتلك فجوة عالية طاقة (  (4.9eV, وان هذا ربما يعود الى تأثير الحصر الكمي لجسيمات اوكسيد النحاس النانوية.     Hydrothermal process was  used to prepare CuO nanoparticles. CuO nanoparticles can be prepare without organic solvents, expensive raw materials by a hydrothermal method. XRD diffraction reveals that CuO nanoparticles have a monoclinic structure with particle size 20nm ,  and AFM analysis showed that the diameter of the Grain size is in a nanometer range. The analysis by FTIR spectra assure that the composition was CuO, and the  features of vibrational types of Cu–O were fixed. also the optical properties was analysed with UV–vis showed that  CuO nano particles have considerable a blue shift  , which have aband gab equal to (4.9 eV ) , and this is because the effect of quntum confienment of prepared CuO nano particles

Information Hiding Using Geographic Information System (GIS) Vector File

There are different techniques for securing data like cryptography and information hiding (steganography and watermarking) which has received more attention and faced many challenges. In this paper, an efficient digital steganography method has been proposed, where the Geographic Information System (GIS) files used as a cover media. This method depends on hiding text file in a map vector coordinate using ESRI (Environmental Systems Research Institute) Shape file, which stores the geometry of the digital features as sets of vector, coordinates. The method is based on changing the value of unspecific order bits depending on an Input location. Since we are interested in maximizing capacity and ensure robustness requirements. Exploiting the advantage of double percentage number capacity in the 2Dimension vector file was one of the main goals of this research. A Steganography techniques requirement was satisfied since changing maps did not raise any suspicion, while they do not alter the original data content

Prediction of Fatigue Life of Fiber Glass Reinforced Composite (FGRC) using Artificial Neural Network

The present work studies the mechanical properties of composite materials, experimentally and analytically, that are fabricated by stacking 4-layers of fiberglass reinforced with polyester resin. This plies are tested under dynamic load (fatigue test) in fully reversible tension-compression (R=-1) to estimate the fatigue life of the composite where fatigue performance of fiberglass reinforced composed is an increasingly important consideration especially when designing wind turbine blades. In order to predict fatigue life (Number of cycles to failure), conventional analytical techniques are used in the present work. In addition, Artificial Neural Network (ANN) is a reliable and accurate technique that is used for predicting fatigue life. The used networks are; Feed Forward Neural Network (FFNN), Generalized Regression Neural Network (GRNN) and Radial Bases Function Neural Network (RBFNN). Based on the comparison of the results, it is found that the ANN techniques are better than conventional methods for prediction. The results shows that (RBNN2), where stress load and angle of orientation are input to the network and number of cycles to failure as output, is an efficient tool for prediction and optimization the fatigue life of fiberglass reinforced composite

Prevalence and antimicrobial resistance pattern of bacterial strains isolated from patients with urinary tract infection in Messalata Central Hospital, Libya

AbstractObjectivesTo investigate the prevalence of urinary tract infection among patients at Messalata Central Hospital, Libya, to identify the causative bacteria, and to explore their resistance pattern to antimicrobials.MethodsA total number of 1153 urine samples were collected from patients, who attended daily to Messalata Central Hospital, Libya, in a study extended for one year. Antimicrobial susceptibility testing and isolates typing were done using Phoenix BD (BD diagnostic). Resistance was confirmed manually using agar disk diffusion method.ResultsOf the 1153 urine samples tested, 160 (13.9%) samples were positive, from which 17 different, solely Gram negative, uropathogens were identified. Escherichia coli were the most prevalent (55.6%) bacteria, followed by Klebsiella pneumoniae subspecies pneumoniae (16.3%), Proteus mirabilis (6.3%), Pseudomonas aeruginosa (5.6%), Enterobacter cloacae and Klebsiella oxytoca (2.5%, each), Citrobacter koseri and Providencia rettgeri (1.9%, each), Acinetobacter baumannii, Enterobacter aerogenes and Proteus vulgaris (1.3%, each), and Aeromonas caviae, Citrobacter freundii, Cronobacter sakazakii, Enterobacter amnigenus biogroup 2, Pseudomonas putida and Serratia marcescens (0.6%, each). The isolated uropathogens showed increased levels of resistance ranged from 10.5% to 64.5%, with an overall resistance of 28.9%. Amikacin was the most effective antimicrobial followed by Imipenem and Meropenem (0%, 0.6% and 2.5% resistance, respectively); while, Cephalothin and Ampicillin were the least (80.6% and 90.0% resistance, respectively) effective.ConclusionsThe obtained results emphasized the emergence of highly resistant bacteria to most of tested antimicrobials and raise the alarm for physicians to change their treatment pattern depending on antimicrobial susceptibility results