Lead is considered a toxic substance that is already available in environment and has health impacts. The objective of the present study is to track the availability of lead in the environment of Jakara, Kano State, Nigeria. Lead was tracked in water, soil, and vegetables including lettuce, spinach, and onion. Study methodology involved taking random samples from water, soil, and vegetables at Jakara. Samples were prepared and assayed by atomic absorption spectrometry. Study findings showed that the mean concentration of lead in water was 0.115±0.023 mg/l, while it was in soil 2.46 ±0.95 μg/g. The mean concentration of lead in both lettuce and spinach was the same (22.95+ 3.28 mg/kg), and in onion was 19.67 ±3.28 mg/kg . Conclusions: the present study showed that there is a lead contamination of Jakara region by heavy metal (lead). This contamination is evident in water, soil, and vegetables

Barrera, Alma Lilian Guerrero

Cantu, Francisco Javier Gutierrez

Cardenas, Jairo Mariel

Gutierrez, Francisco Ojeda

Meraz, Wulfrano Sanchez

Murga, Humberto Mariel

Preciado, Raul Marquez

Rodriguez, Ricardo Oliva

English

Malaria is an increasing worldwide threat, with more than three hundred million infections and one million deaths every year.The study was conducted to determine the effect of in vivo anti-plasmodial and in vitro antioxidant activity of ethanolic leaf extract of Alstonia boonie (Ewe ahun) and its effect on some biochemical parameters in Swiss albino mice infected with Plasmodium berghei NK65. Swiss albino mice were inoculated intraperitoneally with Plasmodium berghei NK65. The mice were grouped into six groups, five per group. Group I were not infected with P.berghei, Group II and III served both as negative and positive controls respectively, while Group IV, V, and VI were treated with 200, 400, and 800 mg/kg body weight of A. boonie leaf extract respectively. The phytochemical constituents of the extract showed the presence of secondary metabolites like tannin, flavonoids, steroids and saponin. The extract caused 37.9%, 34.7% and 45.8% suppression in parasitaemia at 200, 400 and 800mg/kg body weight respectively while Chloroquine exerted 100% suppression at 5mg/kg body weight. The curative test shows that the different concentration of the extract exert a growth inhibition of 41.8%, 43.3% and 37.5% at 200, 400, 800mg/kg body weight respectively while Chloroquine, cleared the parasites by 93.1% at 5mg/kg body weight. The Hematological parameters showed that A. boonie had a significant increase (P&lt;0.05) in HGB, RBC and HCT values while their WBC count reduced significantly when compared to the negative control. Chloroquine and the extract significantly decreases (P&lt;0.05) plasma liver marker enzymes AST, ALT, ALP and GGT in the treated group compared to the untreated group. The total protein values significantly increased (P&lt;0.05) in the group treated with the extract and at a dose of 800mg/kg, the extract was nephrotoxic. In the in-vitro antioxidant assay, the extract significantly increase (P&lt;0.05) the level of CAT, SOD and GSH in the liver homogenate induced with oxidative stress using H2O2 while the MDA values reduced significantly with the administration of the extract

J., Momoh

A.O, Longe

C.A., Campbell

European Scientific Journal (European Scientific Institute)

IN VIVO ANTI-PLASMODIAL AND IN VITRO ANTIOXIDANT ACTIVITY OF ETHANOLIC LEAF EXTRACT OF ALSTONIA BOONIE (EWE AHUN) AND ITS EFFECT ON SOME BIOCHEMICAL PARAMETERS IN SWISS ALBINO MICE INFECTED WITH PLASMODIUM BERGHEI NK 65

Ground water is an important source of water for domestic use especially in developing countries like Nigeria. Water is said to be safe for drinking when it is free of pathogens, poisonous substances and excessive amount of mineral and organic matter. This research is aimed at assessing the ground water quality at Abubakar Tatari Ali Polytechnic, Bauchi with a view to determining it suitability for domestic utilization. Water samples were collected from the two hand dug wells and two boreholes that were available within the institution in the dry season of 2012. From each of the water sources, five replicate samples were collected for the analysis. Temperature, pH and Conductivity, were directly measured using pH, Temperature and conductivity meters respectively. Total Dissolved Solids (TDS) calculated from the conductivity values obtained. Turbidity determined with a HACH 2100 P Turbidity meter. Nitrate, nitrite, sulphate, phosphate and fluoride, chloride, contents were calorimetrically analyzed using DR890 Colorimeter. Calcium was determined by EDTA titration, while Total Alkalinity was determined by strong acid titration method. Trace elements – Total iron, Copper, Fluoride, Zinc, Lead, Chromium and Arsenic were determined using Atomic absorption Spectrophotometer (AAS) after extraction with Aqua-regia. Total and faecal coliforms were determined by Membrane Filtration method using M-Endo-Agar Les (Difco) at 37oC and on MFC Agar at 44oC, respectively. Results of physico-chemical analysis indicated that mean values of Turbidity (6NTU), Total Hardness (195mg/L) and Fluoride 1.59mg/L in water obtained from hand dug wells have exceeded the threshold limits recommended by regulatory authorities. The bacteriological analysis also revealed that all the two ground water sources contained high Total and Feacal Coliform counts and therefore not suitable for drinking. It is recommended among others that proper disinfection should be carried out on the water and the general sanitary condition of the area should be improved

Sabo, Ahmed

Christopher, Eyaife Ogaga

European Scientific Journal, ESJ

PHYSICOCHEMICAL AND BACTERIOLOGICAL QUALITY OF GROUND WATER AT ABUBAKAR TATARI ALI POLYTECHNIC BAUCHI, NIGERIA

The main objective of this research is to predict the occurrence of subdural hematoma which is connected to occurrence of stroke. The occurrence of stroke in turn can be predicted by calculating the associated risk factors in individuals based on the method developed by Putcha, et al. (2009). The variables considered in this study are: Age (AGE), Diastolic Blood Pressure (BPD), Systolic Blood Pressure (BPS),Total Cholesterol Level (TCL), HDL, Fasting Glucose (FBS), Creatinine (CRE), Triglycerides (TG) and Blood Urea Nitrogen (BUN) using the principles of probability, statistics and risk analysis for limited patient data that was available at the time of completion of this research paper

Putcha, S., Chandrasekhar

Khani, Mina

Khani, Mohammadreza

Miller, Paul

European Scientific Journal   June  2014  edition vol.10, No.18   ISSN: 1857 – 7881 (Print)  e - ISSN 1857- 7431 13  A DETAILED RISK ANALYSIS OF FACTORS CONTRIBUTING TO OCCURRENCE OF SUBDURAL HEMATOMA    Chandrasekhar Putcha, S., PhD Professor, Department of Civil and Environmental Engineering, California State University, Fullerton, CA, USA Mina Khani, M.D Doctor of Medicine, Tehran University of Medical Sciences, Tehran Mohammadreza Khani Graduate Student, Department of Electrical Engineering, California State University, Fullerton, CA, USA Paul Miller, M.D Deloitte Consulting, Arlington, VA, USA  Abstract The main objective of this research is to predict the occurrence of subdural hematoma which is connected to occurrence of stroke. The occurrence of stroke in turn can be predicted by calculating the associated risk factors in individuals based on the method developed by Putcha, et al. (2009). The variables considered in this study are: Age (AGE), Diastolic Blood Pressure (BPD), Systolic Blood Pressure (BPS),Total Cholesterol Level (TCL), HDL, Fasting Glucose (FBS), Creatinine (CRE), Triglycerides (TG) and Blood Urea Nitrogen (BUN) using the principles of probability, statistics and risk analysis for limited patient data that was available at the time of completion of this research paper.  Keywords: Risk Analysis, Hematoma, Probability, Statistics, Mean value, Standard Deviation, Risk factors  Introduction As is well known, subdural hematoma is a collection of blood outside the brain, not in the brain itself. The blood collects between the dura and the next layer, the arachnoid. As blood accumulates, however, pressure in the brain increases. The pressure on the brain causes a subdural hematoma’s symptoms (lose consciousness and enter coma immediately depending on the rate of bleeding).Subdural hematoma can be caused by a head injury or stroke. This is because stroke itself can be a hemorrhage or blood clotting in European Scientific Journal   June  2014  edition vol.10, No.18   ISSN: 1857 – 7881 (Print)  e - ISSN 1857- 7431 14  cerebral vessels of all sizes.Hence, one way to predict the occurrence of subdural hematoma by predicting the occurrence of stroke. This approach is used in this paper. The occurrence of strokecan be predicted using the Stroke Index (SI) .The stroke index can be considered like a Risk Factor is directly proportional to some of the parameters(AGE, BPD, TCL, FBS, CRE, TG and BUN) and it is inversely proportional to HDL. Hence, an equation for SI can be expressed as a product of these parameters using the corresponding constants of proportionality as:  SI=k1*K2*k3*k4*k5*k6*k7*k8*AGE*BPD*TCL*FSB*CRE*TG*BUN/HDL   (1) Where k1 to k8 are constants of proportionality. Since there is no actual data for SI, an alternative way of predicting SI is through Cumulative Risk Factors (CRF). CRF is a function of risk factors for AGE (RFAGE), Risk Factor for Diastolic Blood Pressure (RFBPD), Risk, Risk Factor for Total Cholesterol (RFTCL), Risk Factor for HDL (RFHDL), Risk Factor for Fasting Glucose (RFBS), Risk Factor for Creatinine (RFCRE), Risk Factor for Triglycerides (RFTG) and Risk Factor for Blood Urea Nitrogen (RFBUN) and this mathematical equation is given as:  CRF=RFAGE*RFBPD*RFTCL*RFBS*RFCRE*RFTG*RFBUN/RFHDL (2) In previous study done by Putcha, et al. (2009) the following equationwas used: CRF=RFTCL*RFLDL*RFBS*RFAGE/RFHDL (2a) Eq. (2) is an improved model than Eq. (2a) as it incorporates more variables into the mathematical equation. And for each of risk factors variableswe use in Eq. (2), relationslike:  RFAGE=AGEa/AGEn   (2b) RFBPD=BPDa/BPDn    (2c) RFTCL=TCLa/TCLn    (2d) RFHDL=HDLa/HDLn   (2e) RFBS=FBSa/FBSn    (2f) RFCRE=CREa/CREn    (2g) RFTG=TGa/TGn    (2h) RFBUN=BUNa/BUNn   (2i) And for RFLDL in Eq. (2a) we have, RFLDL=LDLa/LDLn (2j) In the above equations, “a” stands for actual and “n” stands for nominal.   Methodology used for the present study The rather simple equations - Eq. 1 and Eq. 2 shown above are based on the basic Resistance R and strength (S) model  which is predominantly European Scientific Journal   June  2014  edition vol.10, No.18   ISSN: 1857 – 7881 (Print)  e - ISSN 1857- 7431 15  used in Reliability Analysis. The basic principle is that the probability of failure (Pf) is defined as:  Pf = P(R< S)    (3) Where R = Resistance  S = load   Both R and S as random variables (mostly Gaussian), with the parameters µR (Mean value of Resistance) and σR(Standard Deviation of Resistance),  µS (Mean value of load) andσS(Standard deviation of load). In the formulation of the medical problem discussed in here, all the actual values will be considered the parameter S while the allowable values (nominal values) will be considered under R. Hence, the probability of failure Pf is the intersection of the two regions represented by R and S. In the formulation of the medical problem discussed in here, all the actual values will be considered in the cumulative parameter S while the allowable values (nominal values) will be considered under the cumulative parameter R.  General Details of Reliability and Risk Methodology (basis of present study) Reliability and Risk Analysis is closely associated with uncertainties in various parameters connected with a structure. This is because almost all the variables associated with physical parameters are random in nature. The parameters associated with loads, and the load carrying capacities of structural members are all probabilistic quantities. These quantities have a certain distribution which can be obtained from the data in the literature or from experiments. Some basic definitions of Reliability and Risk are given before application of these concepts to various disciplines are discussed. Reliability of an element can be defined as (Haugen, 1980), R = P(S>L)  (4)  Where, R = Reliability of the element    S = strength of the element    L = load on the element  The Risk is defined as (Ayyub et al., 1997),  Risk = Occurrence probability x occurrence consequence  (5) The seminal reliability work has been performed by various authors [3, 4]. In the research work reported therein, the load and resistance variables are assumed to be random variables. The necessary statistical information about these variables is supposed to be known including the distributions that these variables follow. European Scientific Journal   June  2014  edition vol.10, No.18   ISSN: 1857 – 7881 (Print)  e - ISSN 1857- 7431 16  For reliability analysis, the definition of limit state is essential [Ellingwood et al., 1980; Nowak and Collins, 2000)  This relation is given by, g(x1, x2,….,xn) =0  (6) where, xi = resistance or load variable. The failure is supposed to occur when g<0 for any ultimate or serviceability limit state of interest. Failure for any limit state does not automatically imply collapse or other catastrophic events. The safety is assured by assigning a small probability of failure p to the event connected with the limit state. This can be expressed as, pf = ∫……∫ f(x1, x2,…., xn) dx1 dx2…..dxn (7)   First Order Second Moment Methods (FOSM):  Fig. 1 Stress-Strength  density functions  The pf is in effect the intersection of the Warren diagram shown in Fig. 1.  Detailed steps used in the present study The methodology followed to calculate the cumulative risk factors is given below: 1. Obtain the actual values of all the variables from the actual patients in this medical study. These are listed  below:  Age (AGE)   Diastolic blood Pressure (BPD)  Total Cholesterol level (TCL)  High-Density Lipoprotein (HDL)  Low-density lipoprotein (LDL)  Fasting Blood Sugar (FBS)  Creatinine (CRE)  Triglycerides (TG)  Blood Urea Nitrogen (BUN) 2. Obtain the nominal values of the above variables from literature. European Scientific Journal   June  2014  edition vol.10, No.18   ISSN: 1857 – 7881 (Print)  e - ISSN 1857- 7431 17  3. Calculate various Risk Factors associated with each of the above variable: RFAGE, RFBPD, RFTCL, RFHDL,RFBS, RFCRE, RFTG and RFBUN fromEq. 2b-2j. 4. The CRF for the set of data is then obtained using Eq. 2.  It is decided to use Eq. 2 instead of calculating SI from Eq. 1. This is because, using Eq. 1 essentially involves calculation of probability of failure using multiple integral denoted by Eq.7 which is a cumbersome process especially with this many variables in the present study.  Input data Two case studies were examined for this research. The input data used for the actual and nominal values for various variables in the present study for case studies are given in Table 1 for case study 1 andin Table 2 through Table 8 for case study 2 for various combinations of µ and σ. Here µ represents the mean value and σ represents standard deviation of the random variable under consideration. below. Table 1 Actual and nominal values for the input variables for case study 1 Variable   Actual value    Nominal value AGE    60     50 TCL    170     200 HDL    40     42 LDL    109     100 FBS    99     90 BPD    150     120  Table 2 Actual and nominal values for the input random variables (µ) for case study 2 Variable   Actual value  (µ)   Nominal value AGE    34.2     50 BPD    83     80 TCL                 163.4     200 HDL    66.3     45 FBS    93.4     100 CRE    0.184     1.0 TG    110.1     150 BUN    20.2     43  Table 3 Actual and nominal values for the input random variables(µ-σ)for case study 2 Variable   Actual value  (µ-σ)   Nominal value AGE    23.65     50 BPD    73.94     80 TCL    120.19     200 HDL    51.41     45 FBS    73.22     100 European Scientific Journal   June  2014  edition vol.10, No.18   ISSN: 1857 – 7881 (Print)  e - ISSN 1857- 7431 18  CRE    0.63     1.0 TG    77.16     150 BUN    14.89     43  Table 4 Actual and nominal values for the input random variables(µ-+σ)for case study 2 Variable   Actual value  (µ+σ)   Nominal value AGE    44.75     50 BPD    92.06     80 TCL    206.61     200 HDL    81.19     45 FBS    113.58     100 CRE    0.99     1.0 TG    143.04     150 BUN    25.51     43  Table 5 Actual and nominal values for the input random variables(µ-2σ)for case study 2 Variable   Actual value  (µ-2σ)   Nominal value AGE    13.09     50 BPD    64.89     80 TCL    76.99     200 HDL    36.51     45 FBS    53.05     100 CRE    0.45     1.0 TG    44.21     150 BUN    9.59     43  Table 6 Actual and nominal values for the input random variables(µ+2σ)for case study 2 Variable   Actual value  (µ+2σ)   Nominal value AGE    55.31     50 BPD    101.11     80 TCL    249.81     200 HDL    96.09     45 FBS    133.75     100 CRE    1.18     1.0 TG    175.99     150 BUN    30.81     43  Table 7 Actual and nominal values for the input random variables(µ-3σ)for case study 2 Variable   Actual value  (µ-3σ)   Nominal value AGE    2.54     50 BPD    55.83     80 TCL    33.78     200 HDL    21.62     45 FBS    32.87     100 CRE    0.27     1.0 TG    11.27     150 European Scientific Journal   June  2014  edition vol.10, No.18   ISSN: 1857 – 7881 (Print)  e - ISSN 1857- 7431 19  BUN    4.28     43  Table 8 Actual and nominal values for the input random variables(µ+3σ)for case study 2 Variable   Actual value  (µ+3σ)   Nominal value AGE    65.86     50 BPD    110.17     80 TCL    293.02     200 HDL    110.98     45 FBS    153.93     100 CRE    1.36     1.0 TG    208.93     150 BUN    36.12     43  Results Using the detailed steps described in Section 4 above, the Risk factors for various variables used are calculated and they are shown for Case study 1 in Table 9 and in Tables 10-16 for Case study 2 as shown below. Table 9 Risk factors (RF) for various variables in case study 1 Variable   Risk factors (RF)   AGE    1.2       TCL    0.85       HDL    0.95  LDL    1.09   FBS    1.1  The cumulative risk factor (CRF) from Eq. 2a for Case study 1 is  1.28     Table 10  Risk factors (RF) for various variables in case study 2 (data from Table 2) Variable   Risk factors (RF) for µ  AGE    0.68       BPD    1.03       TCL    0.82       HDL    1.47       FBS    0.93       CRE    0.184       TG    0.73       BUN    0.47 The cumulative risk factor (CRF) from Eq. 2 for Case study 2 is 0.05.  Table 11  Risk factors (RF) for various variables in case study 2 (data from Table 3) Variable   Risk factors (RF) for µ-σ  AGE    0.47       BPD    0.924       TCL    0.6       HDL    1.14       FBS    0.73       CRE    0.63       TG    0.51       BUN    0.35 The cumulative risk factor (CRF) from Eq. 2 for Case study 2 is 0.02 European Scientific Journal   June  2014  edition vol.10, No.18   ISSN: 1857 – 7881 (Print)  e - ISSN 1857- 7431 20  Table 12  Risk factors (RF) for various variables in case study 2 (data from Table 4) Variable   Risk factors (RF) for µ+σ  AGE    0.895       BPD    1.15       TCL    1.03       HDL    1.8       FBS    1.135       CRE    0.99       TG    0.953       BUN    0.593 The cumulative risk factor (CRF) from Eq. 2 for Case study 2 is 1.22  Table 13  Risk factors (RF) for various variables in case study 2 (data from Table 5) Variable   Risk factors (RF) for µ-2σ  AGE    0.26       BPD    0.81       TCL    0.384       HDL    0.81       FBS    0.53       CRE    0.45       TG    0.294       BUN    0.223 The cumulative risk factor (CRF) from Eq. 2 for Case study 2 is 0.001  Table 14  Risk factors (RF) for various variables in case study 2 (data from Table 6) Variable   Risk factors (RF) for µ+2σ  AGE    1.11       BPD    1.26       TCL    1.249       HDL    2.135       FBS    1.33       CRE    1.18       TG    1.173       BUN    0.716 The cumulative risk factor (CRF) from Eq. 2 for Case study 2 is 4.947  Table 15 Risk factors (RF) for various variables in case study 2 (data from Table 7) Variable   Risk factors (RF) for µ-3σ  AGE    0.05       BPD    0.697       TCL    0.168       HDL    0.48       FBS    0.32       CRE    0.27       TG    0.075       BUN    0.099 The cumulative risk factor (CRF) from Eq. 2 for Case study 2 is 0    European Scientific Journal   June  2014  edition vol.10, No.18   ISSN: 1857 – 7881 (Print)  e - ISSN 1857- 7431 21  Table 16  Risk factors (RF) for various variables in case study 2 (data from Table 8) Variable   Risk factors (RF) for µ+3σ  AGE    1.317       BPD    1.377       TCL    1.465       HDL    2.466       FBS    1.53       CRE    1.36       TG    1.392       BUN    0.84 The cumulative risk factor (CRF) from Eq. 2 for Case study 2 is 16.053  The results shown for risk factors in Table 9for case study 1 are considering the values of the variables at the mean  µ.  On the other hand, the results for Case 2 in Tables 9-16 showrisk factors for ranges of (µ-σ to µ+3σ) of the probabilistic variables used in the study. This is because the range from µ-σ to µ+σ encompasses the uncertainty of around 68.3%, µ-2σ to µ+2σ encompasses the uncertainty of around 95.4%,and µ-3σ to µ + 3σencompasses the uncertainty of around 99.7% (Ang and Tang, 1975).    Discussion of Results and Conclusions The results for case study 1 shown in Table 9 indicate that the CRF is 1.28 which is less than the acceptable value of 2.0 implying no stroke. The value of 2 is chosen because a normalized value of 2 encompasses an uncertainty of 95.5% which is acceptable for all practical problems. The results shown for case study 2 range from 0 t0 16.053 for the extreme limits of µ-3σ to µ + 3σ. For the practical normalized range µ-2σ to µ + 2σ, the CRF ranges from 0 to 4.94 giving an average of 2.47 which indicates the possibility of stroke.  Conclusion A simple mathematical equation has been developed based on the basic concepts of Reliability and Risk Analysis. Two case studies were discussed. While one case study indicates no stroke, the other case study indicates possibility of stroke. More study is required with a larger set of data to fine tune the equation developed but it is a good start.  References: Haugen, E.B. (1080). Probabilistic Mechanical Design.John Wiley & Sons. Ang, A.H-S and Tang, W.H. (1991).Probability concepts in Engineering Planning and Design.John Wiley & Sons. Ayyub, B.M. and McCuen, R.H. (1997).Probability, Statistics, & Reliability for Engineers.CRC Press. European Scientific Journal   June  2014  edition vol.10, No.18   ISSN: 1857 – 7881 (Print)  e - ISSN 1857- 7431 22  Putcha, C.S., Miller, P. and Hodgdon, J. (2009).Risk Factors Associated with Stroke. AHA, Quality of Care and Outcomes Research in Cardiovascular Disease and Stroke Conference, Washington, D.C., April 23-25.                      

A DETAILED RISK ANALYSIS OF FACTORS CONTRIBUTING TO OCCURRENCE OF SUBDURAL HEMATOMA

This study aimed to measure The Effect of Information Technology Capabilities in Implementing Information Security Management Systems; Study of Commercial banks in Jordanian capital, Amman. The study sample included 14 Commercial banks in Amman Jordanian capital city that adopted IT. The study results show that there is direct relationship between effect of Information Technology Capabilities and Implementation of Security Information Management Systems in Jordanian banks. Based on the results of the study, the research proposed several recommendations to commercial banks, most importantly on how to take care of its IT capabilities In order to Increase their operations in Implementing Information Security Management Systems

Ali alkasasbeh, Anas

THE EFFECT OF INFORMATION TECHNOLOGY CAPABILITIES IN IMPLEMENTING, INFORMATION SECURITY MANAGEMENT SYSTEMS

Crisp classification trees have been used to model many situations such as disease classification. With the introduction of fuzzy theory, fuzzy classification trees are gaining popularity especially in data mining. Very little work has been done in comparing crisp and fuzzy classification trees. This paper compares crisp classification trees and fuzzy classification trees using Gini index as the impurity measure. The objective is to determine which of the two classification trees gives fewer errors of classification. The data used consisted of two sets of observations from multivariate normal distributions. The first set of data were from two 3-variate normal populations with different mean vectors and common dispersion matrix. From each of the two populations 5000 samples were generated. 1000 samples out of the 5000 were used to create the trees. The remaining 4000 samples from each population were used to test the trees. The second set of data were from three 4-variate normal populations with different mean vectors and common dispersion matrix. A similar sampling and testing procedure as for the case of first set of data was employed. Computations were implemented using R statistical package. The results from the test showed that fuzzy classification trees allocated observations to the correct population with fewer errors than did crisp classification tree

Muchai, Eunice

Odongo, Leo

European Scientific Journal   June  2014  edition vol.10, No.18   ISSN: 1857 – 7881 (Print)  e - ISSN 1857- 7431 130  COMPARISON OF CRISP AND FUZZY CLASSIFICATION TREES USING GINI INDEX  IMPURITY  MEASURE ON SIMULATED DATA    Eunice Muchai Leo Odongo Department of Statistics and Actuarial Science,  Kenyatta University, Nairobi, Kenya   Abstract Crisp classification trees have been used to model many situations such as disease classification. With the introduction of fuzzy theory, fuzzy classification trees are gaining popularity especially in data mining. Very little work has been done in comparing crisp and fuzzy classification trees. This paper compares crisp classification trees and fuzzy classification trees using Gini index as the impurity measure. The objective is to determine which of the two classification trees gives fewer errors of classification. The data used consisted of two sets of observations from multivariate normal distributions. The first set of data were from two 3-variate normal populations with different mean vectors and common dispersion matrix.  From each of the two populations 5000 samples were generated. 1000 samples out of the 5000 were used to create the trees. The remaining 4000 samples from each population were used to test the trees.  The second set of data were from three 4-variate normal populations with different mean vectors and common dispersion matrix. A similar sampling and testing procedure as for the case of first set of data was employed. Computations were implemented using R statistical package. The results from the test showed that fuzzy classification trees allocated observations to the correct population with fewer errors than did crisp classification tree.  Keywords:  Crisp classification tree, Fuzzy classification tree, Gini index, Fuzzy decision points, Crisp decision points   Introduction Various criteria have been proposed for selecting the variable used for  splitting tha data in  creating classification trees. Kass(1980) used a testing procedure based on Pearson’s chi-squared statistic  to choose the best multiway split. Breiman, et.al. (1984) introduced CART which provided the European Scientific Journal   June  2014  edition vol.10, No.18   ISSN: 1857 – 7881 (Print)  e - ISSN 1857- 7431 131  Gini index and towing criterion. Loh and Vanichsetakul (1988) and Loh and Shih (1997) employed statistical test to select splits. Singh, et.al. (2010) applied Gini index to feature selection for text classification.    The concept of fuzzy random variable was introduced at the end of 1970’s Kwakernaak(1978). This was to deal with situations where the outcomes cannot be observed with exactness. Fuzzy decision trees differ from traditional trees by using splitting criteria based on fuzzy restrictions. Fuzzy sets defining fuzzy terms are imposed on the splitting algorithm. Janikow(1998) presented fuzzy trees using information gain as impurity measure  and studied the performance of the tree when some  data are missing. Wang, et.al. (2007) gave a survey of the different impurity measures that are  currently in use. In this paper the performance of crisp and fuzzy classification trees is compared. We only fuzzify the decision boundary using triangular membership function.  The organisation of the paper is as follows: Section 2 explains methodology and section 3 contains the results, discussions and conclusions.  Methodology The Gini Index When using decision/classification trees, the variable  used at every node to split the tree affects the performance of the decision tree.  The problem of selecting the splitting variable is therefore not trivial. After the variable  has been selected the value of the variable that  gives the best split  is then  selected. The objective of classification is to allocate individuals to the correct population with the minimum classification error. Using classification trees this is done so as to arrive at the different classes with the least number of splits with the least misclassification errors.   At every node the best splitting variable and the variables’  best value are selected. The best variable and value  are the ones that reduce the node  non-uniformity  commonly known as node impurity. Different impurity measures have been proposed and are in use, examples include Gini index, information gain, gain ratio, 2χ statistic and the G statistic among others. In this paper the Gini impurity measure normally referred to as the Gini index is discussed and applied to simulated data.  A data set T containing individuals from n classes has the Gini impurity measure (Gini index), denoted by G (T),  and given by                    G (T)          = 211njjp=−∑   (see Breiman pg 104)                            Where pj  is the relative frequency of class j in T.                                                               European Scientific Journal   June  2014  edition vol.10, No.18   ISSN: 1857 – 7881 (Print)  e - ISSN 1857- 7431 132  Suppose the data set T is split into two subsets T1 and T2 with sizes N1 and N2 respectively, the Gini index of the split data is given by  Gini split (T) = ( ) ( )1 21 2N NG T G TN N+                           Using the Gini index the variable that gives the best split  is applied. The best split is the one that has the least value of Gini split (T).  Splitting Procedure             The following procedure is used to select the splitting variable and the splitting value. • calculate the  Gini split (T) among the child branches over all possible decision  points for each variable Xj at each node.  • select the variable and the value of that variable with the least Gini split (T),  denoted by  Xj0 and use it   for splitting .  • repeat this process at each node until splitting is completely done. Comparison of the performance of Gini index having fuzzy decision points with Gini index having crisp decision points is carried out using simulated data. The first set of observations was generated from two 3-variate normal populations with different mean vectors and common dispersion matrix. The second set of  observations was  generated from three 4-variate normal populations with different mean vectors and common dispersion matrix.  Two populations with three variables 5000 Samples of different sizes from each population were generated. The populations were assumed to be normally distributed with different mean vectors but a common dispersion matrix. 1000 samples from each of the populations were used to create the classification tree. This was done using the splitting criteria discussed above.  The splitting variable and value were obtained  using Gini split. After the tree was created, the remaining 4000 samples from each population were used to test the performance of the tree. This was done by calculating the probabilities of correct allocation, that is P11 and P22 for both crisp and fuzzy decision points.  .   Three populations with four variables Simulation similar to the above scenario was done except in this case there were three populations with three variables.   The probabilities of correct allocations P11 ,P22 and P33, were calculated and are given below. Simulation and coding was done using the statistical package R and implemented on Pentium IV using windows 7 environment European Scientific Journal   June  2014  edition vol.10, No.18   ISSN: 1857 – 7881 (Print)  e - ISSN 1857- 7431 133  Results, Discussion and Conclusion  Two populations with three variables Table1 gives the average probabilities of correct allocation from the 4000 samples, at different sample sizes, using crisp  and fuzzy  decision points. The proportion of times probabilities of correct allocation was higher when using crisp cut points than triangular fuzzy decision points is given in Table2.  Table 1 : Probabilities of Correct Allocation Sample size P11crisp P11fuzzy P22crisp P22fuzzy 50 0.834 0.893 0.822 0. 895 100 0.831 0. 897 0.823 0. 894 200 0.825 0. 898 0.830 0. 896 500 0.832 0. 896 0.826 0. 892 1000 0.829 0. 895 0.831 0. 896  Table 2: Proportion of times crisp probabilities are higher than fuzzy probabilities Sample size P11fuzzy<P11crisp P22fuzzy<P22crisp 50 0.074 0.079 100 0.013 0.027 200 0.001 0.002 500 0 0 1000 0 0  From Table 1, we observe that the average probabilities of correct classification using fuzzy decision points are higher than when using crisp decision points for all the sample sizes considered in the study.  We also note that, as the sample size increases the average probabilities of correct allocation increases.  Also the proportion of times the probabilities of correct allocation were higher when using crisp decision points is quite low as seen in Table 2. As the sample size increases, the proportion of times crisp classification tree outperforms fuzzy classification tree tends to zero It is therefore reasonable to conclude that, for two populations with three variables,   fuzzy Gini classification tree performed better than the crisp Gini classification tree.  Three populations with four variables Table 3 gives the average probabilities of correct allocation from the 4000 samples of different sizes   using crisp  and fuzzy  decision points. The proportion of times probabilities of correct allocation was higher when using crisp decision points than triangular fuzzy decision points is given in Table 4.  Table3: Probabilities of Correct Allocation Sample size P11 crisp P11fuzzy P22crisp P22 fuzzy P33crisp P33fruzzy 50 0.66 0.71 0.86 0.90 0.78 0.80 100 0.64 0.71 0.90 0.90 0.81 0.82 200 0.67 0.72 0.92 0.91 0.80 0.84 European Scientific Journal   June  2014  edition vol.10, No.18   ISSN: 1857 – 7881 (Print)  e - ISSN 1857- 7431 134  500 0.68 0.73 0.93 0.92 0.81 0.86 1000 0.69 0.74 0.93 0.94 0.85 0.86  Table 4 : : Proportion of times crisp probabilities are higher than fuzzy probabilities Sample size P11fuzzy<P11crisp P22fuzzy<P22crisp P33fuzzy<P33crisp 50 0.096 0.100 0.102 100 0.050 0.060 0.068 200 0.010 0.015 0.020 500 0.001 0.003 0.008 100 0 0 0  Comparing the columns of P11, P22 and P33 in Table 3 above, we observe that the average probabilities of correct classification using fuzzy decision points are higher than when using crisp decision points. As observed in the case of two populations, as the sample size increases the average probabilities of correct allocation increases.  Also the proportion of times the probabilities of correct allocation when using crisp decision points are higher than using fuzzy decision points is quite low as is observed in Table 4. This proportion gets lower as the sample size increases.  As in the case of two populations, fuzzy classification trees perform better when there are three populations. Therefore, observing the results in Tables 1-4 above, it can be concluded that Gini fuzzy classification tree perform better than Gini crisp classification tree.   References: Breiman, L., Friedman, J. H., Olshen, R. A.and Stone, C. J. Classification and Regression Trees, New York:  Chapman & Hall, 1984. Janikow. C.Z (1998). Fuzzy decision trees: Issues and methods. IEEE Transactions on Systems, Man, and Cybernetics,  28: 1-14 Kass.G.V.(1980). An exploratory technique for  investigating large quantities of categorical data. Applied Statistics, 29: 119-127. Kwakernaak, H. (1978). Fuzzy random variables: definitions and theorems. Information Science, 15:1-29. Loh, W.Y.  and Vanichsetakul,N. (1988). Tree-structured classification via generalized discriminant analysis ( with discussion).Journal of the  American Statistical Association, 83: 715-728. Loh, W.Y.  and Shih, Y.S (1997). Split selection methods for classification trees. Statistica Sinica 7: 150-156. Singh, S.R., Murthy, A.H. and  Gonsalves, A. T.(2010). Feature selection for text classification based on Gini coefficient of inequality.  Proc. 4th workshop on feature  selection in data mining,10: 76-85  

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Studying the dielectric properties of cells and of structural parts of the cell (membrane, cytoplasm, etc.) is an important way to obtain valuable information about cell structures and their functions and metabolic mechanisms. In this work, the RBCs human blood of control and benzene exposed workers were separated by centrifugation and washed with phosphate buffer and adjusted to pH = 7.4, 290+ 3 mOsm/Kg and 50% hematocrit value. The relative permittivity ε', dielectric loss ε", dielectric strength Δε', relaxation time τ and the ac conductivity σ a-cof these cells were studied using an a-c voltage in the frequency range 50Hz -1MHz. The results obtained indicate that all the studied electric parameters, including real conductivity, real and imaginary relative permittivity in addition to the relaxation time, dielectric loss and the dissipation factor suffered significant changes in the RBCs of the benzene exposed workers than that of the control subjects

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HA, Motaweh

EA, El-Bassiouni

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The purpose of this study was to investigate the stability of temporary anchorage devices, rate of bodily canine retraction and anchorage loss using miniscrews as a skeletal anchorage mean. The sample comprised twelve patients (3 males and 9 females, age range 17-28 years) who were scheduled for extraction of upper premolars. After leveling and alignment, a pilot drill was used and titanium miniscrews1.2-1.3 mm in diameter and 8 mm length were inserted into the buccal cortical bone between the maxillary second premolars and first molars on both sides. Immediate loading of miniscrews and immediate canine retraction after extraction was performed. The canines were retracted with nitinol springs extending from the canine brackets to the mini-implant. Force magnitude was initially 75 gf and increased to 150 gf after 3 weeks. Patients were seen at 3-week intervals until retraction was considered complete. Stability, gingival index around miniscrews, and patient comfort were rated. Retraction distance evaluated by measuring distance between cusp tip of maxillary canine to buccal cusp tip of maxillary second premolar. Pre and post- retraction panoramic x-rays were taken to categorize type of canine retraction. Pre and post-retraction cephalometric xrays were superimposed for measuring the amount of anchorage loss. The results revealed a success rate of 87.5% of the miniscrews. 62.5% of miniscrews had healthy gingiva, and 12.5% were acutely inflamed. Bodily retraction of canines occurred only in 61.9% of the cases. Mean anchorage loss was 0.21 mm. The first interval had the lowest mean retraction distance The mean rate of retraction was 0.39 mm per week. In conclusion, the TADs are stable absolute anchorage units that can be used for rapid canine retraction. Close relationship exists between implant loss and soft tissue health

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Saleh, Fayez

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Boussaada, Olfa

Helal, Ahmed Noureddine

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The work is aimed at studying design effect for the maximum likelihood estimators of variance components in a split plot design. The study used the general linear model with one whole plot factor and one subplot factor and assumed that both factor effects are random variables. The main problem studied is how to assign a given number of whole plots with equal sizes to the level of the whole plot factor in a way that will form a balanced one way design. The work introduced a method of classifying the five variance components to make comparison and presentation meaningful. The resulting optimal designs depend on the true proportional value of the variance components

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