DEVELOPMENT AND TESTING OF UNIVERSAL PRESSURE DROP MODELS IN PIPELINES USING ABDUCTIVE AND ARTIFICIAL NEURAL NETWORKS

Determination of pressure drop in pipeline system is difficult. Conventional methods (empirical correlations and mechanistic methods) were not successful in providing accurate estimate. Artificial Neural Networks and polynomial Group Method of Data Handling techniques had received wide recognition in terms of discovering hidden and highly nonlinear relationships between input and output patterns. The potential of both Artificial Neural Networks (ANN) and Abductory Induction Mechanism (AIM) techniques has been revealed in this study by generating generic models for pressure drop estimation in pipeline systems that carry multiphase fluids (oil, gas, and water) and with wide range of angles of inclination. No past study was found that utilizes both techniques in an attempt to solve this problem. A total number of 335 data sets collected from different Middle Eastern fields have been used in developing the models. The data covered a wide range of variables at different values such as oil rate (2200 to 25000 bbl/d), water rate (up to 8424 bbl/d), angles of inclination (-52 to 208 degrees), length of the pipe (500 to 26700 ft) and gas rate (1078 to 19658 MSCFD). For the ANN model, a ratio of 2: 1: 1 between training, validation, and testing sets yielded the best training/testing performance. The ANN model has been developed using resilient back-propagation learning algorithm. The purpose for generating another model using the polynomial Group Method of Data Handling technique was to reduce the problem of dimensionality that affects the accuracy of ANN modeling. It was found that (by the Group Method of Data Handling algorithm), length of the pipe, wellhead pressure, and angle of inclination have a pronounced effect on the pressure drop estimation under these conditions. The best available empirical correlations and mechanistic models adopted by the industry had been tested against the data and the developed models. Graphical and statistical tools had been utilized for comparing the performance of the new models and other empirical correlations and mechanistic models. Thorough verifications have indicated that the developed Artificial Neural Networks model outperforms all tested empirical correlations and mechanistic models as well as the polynomial Group Method of Data Handling model in terms of highest correlation coefficient, lowest average absolute percent error, lowest standard deviation, lowest maximum error, and lowest root mean square error. The study offers reliable and quick means for pressure drop estimation in pipelines carrying multiphase fluids with wide range of angles of inclination using Artificial Neural Networks and Group Method of Data Handling techniques. Graphical User Interface (GUI) has been generated to help apply the ANN model results while an applicable equation can be used for Group Method of Data Handling model. While the conventional methods were not successful in providing accurate estimate of this property, the second approach (Group Method of Data Handling technique) was able to provide a reliable estimate with only three-input parameters involved. The modeling accuracy was not greatly harmed using this technique

Fluorescent Technology Versus Visual and Tactile Examination in the Detection of Oral Lesion: A Pilot Study

Purpose: The purpose of the study was to compare the effectiveness of the VELscope® Vx, versus a visual and tactile intraoral examination in detecting oral lesions in an adult, high risk population. Methods: A convenience sample of 30 participants (17 cigarette smokers and 13 dual addiction smokers) was enrolled. For the purpose of this study, dual addition was defined as cigarettes plus hookah usage. Two trained and calibrated dental hygienists conducted all examinations. Visual and tactile intraoral examinations were conducted, followed by VELscope® Vx florescence examinations. All subjects received an inspection of the lips, labial mucosa, buccal mucosa, floor of the mouth, dorsal, ventral and lateral sides of the tongue, as well as the hard and soft palate. Both evaluations took place in one visit. All participants received oral cancer screening information, recommendations and referrals for tobacco cessation programs and material on the two types of examinations provided. Results: Thirty subjects, between the ages 18-65 were enrolled (23 males and 7 females). The duration of tobacco use was significantly higher in cigarette smokers (14.1 years) than dual addiction smokers (5 years). The average numbers of cigarettes smoked per day were 13.5 compared to 14.2 cigarettes for dual addiction smokers. Neither the visual and tactile intraoral examination nor the VELscope® Vx examination showed any positive lesions. No lesions were detected; therefore, no referrals were made. Conclusion: Study participants were considered high risk based on demographics (current smokers & males). These results support data from the American Cancer Society, which indicates that males smoke more cigarettes than females, and are at a higher risk of oral cancer. Furthermore, individuals who have dual smoking addictions are on the rise, and are also at increased risk for oral cancer. Results from this study suggest the visual and tactile intraoral examination produced comparative results to the VELscope® Vx examination

Myelodysplasia in a psoriasis patient receiving etanercept : Cause or coincidence?

Peer reviewedFinal Accepted Versio

Deviant Behavior as Among Major Factors Contributing to Poor Performance in Certificate Secondary Education Examination: A Case of Micheweni Secondary School in Zanzibar, Tanzania

Since independence of 1964, Tanzania had given priority to her citizens in ensuring that better and quality education is easily accessible to all.  Both the government of United Republic of Tanzania and Revolutionary Government of Zanzibar has had been adopted and implemented variety of educational polices, approaches and strategies and regulations with the aim of bringing not only enhanced secondary education but also sustainable one and which can practically be implemented by students hence developing themselves in conjunction with bringing sustainable development to their nation. It is in this line therefore, this paper aiming to enlighten how deviant behavior contributing to poor performance for Zanzibar secondary school students. Using descriptive approach, school and home based factors determined to cause student deviant behaviors which influence students’ performance in national examination certificate of secondary education in Zanzibar. The study was conducted in northern part of Zanzibar, Tanzania since is among leading regions in terms of poor performance from secondary education examination results (Mwesiga, 2000). The study employed both qualitative and quantitative approaches. Qualitative approach was used in the form of interviews while quantitative approach was used in the form of questionnaires. Findings revealed that home and school environments both contribute in causes of deviant behavior to secondary school students which sooner influence their performance. Due to the perseverance of carving down of disciplines in secondary schools, deviant behavior is always available. Late coming, not doing school assignments in time, violation of dressing code, ruddiness, lying, fighting with fellow students, leaving school before time and skipping classes are most frequently identified deviant behavior in secondary schools however community takes initiatives in monitoring them so as to reduce the extent of its impact towards students’ performance. Based on the findings, this study recommends stakeholders to increase initiatives in maintaining discipline among students and encourage them to like reading or learning and not engaging other activities which interns lead them not only having deviant behavior but also experience poor performance. Also issue of deviant behavior with its impacts should well be addressed not only in education policy but also in other related policy documents and other researches since still there is need of exposing much of unknown aspects of deviant behavior with respect to students’ performance. Key Words: Education, Deviant behavior, Certificate Secondary Education Examination, Student, Poor Performance, Micheweni Secondary School, Zanzibar, Tanzani

Innovation in prediction planning for anterior open bite correction

This study applies recent advances in 3D virtual imaging for application in the prediction planning of dentofacial deformities. Stereo-photogrammetry has been used to create virtual and physical models, which are creatively combined in planning the surgical correction of anterior open bite. The application of these novel methods is demonstrated through the surgical correction of a case

On a maximal subgroup of the Thompson simple group

The present paper deals with a maximal subgroup of the Thompson group, namely the group $2^{1+8}_{+}{^{cdot}}A_{9}:= overline{G}.$ We compute its conjugacy classes using the coset analysis method, its inertia factor groups and Fischer matrices, which are required for the computations of the character table of $overline{G}$ by means of Clifford-Fischer Theory

DEVELOPMENT AND TESTING OF UNIVERSAL PRESSURE DROP MODELS IN PIPELINES USING ABDUCTIVE AND ARTIFICIAL NEURAL NETWORKS

Determination of pressure drop in pipeline system is difficult. Conventional methods (empirical correlations and mechanistic methods) were not successful in providing accurate estimate. Artificial Neural Networks and polynomial Group Method of Data Handling techniques had received wide recognition in terms of discovering hidden and highly nonlinear relationships between input and output patterns. The potential of both Artificial Neural Networks (ANN) and Abductory Induction Mechanism (AIM) techniques has been revealed in this study by generating generic models for pressure drop estimation in pipeline systems that carry multiphase fluids (oil, gas, and water) and with wide range of angles of inclination. No past study was found that utilizes both techniques in an attempt to solve this problem. A total number of 335 data sets collected from different Middle Eastern fields have been used in developing the models. The data covered a wide range of variables at different values such as oil rate (2200 to 25000 bbl/d), water rate (up to 8424 bbl/d), angles of inclination (-52 to 208 degrees), length of the pipe (500 to 26700 ft) and gas rate (1078 to 19658 MSCFD). For the ANN model, a ratio of 2: 1: 1 between training, validation, and testing sets yielded the best training/testing performance. The ANN model has been developed using resilient back-propagation learning algorithm. The purpose for generating another model using the polynomial Group Method of Data Handling technique was to reduce the problem of dimensionality that affects the accuracy of ANN modeling. It was found that (by the Group Method of Data Handling algorithm), length of the pipe, wellhead pressure, and angle of inclination have a pronounced effect on the pressure drop estimation under these conditions. The best available empirical correlations and mechanistic models adopted by the industry had been tested against the data and the developed models. Graphical and statistical tools had been utilized for comparing the performance of the new models and other empirical correlations and mechanistic models. Thorough verifications have indicated that the developed Artificial Neural Networks model outperforms all tested empirical correlations and mechanistic models as well as the polynomial Group Method of Data Handling model in terms of highest correlation coefficient, lowest average absolute percent error, lowest standard deviation, lowest maximum error, and lowest root mean square error. The study offers reliable and quick means for pressure drop estimation in pipelines carrying multiphase fluids with wide range of angles of inclination using Artificial Neural Networks and Group Method of Data Handling techniques. Graphical User Interface (GUI) has been generated to help apply the ANN model results while an applicable equation can be used for Group Method of Data Handling model. While the conventional methods were not successful in providing accurate estimate of this property, the second approach (Group Method of Data Handling technique) was able to provide a reliable estimate with only three-input parameters involved. The modeling accuracy was not greatly harmed using this technique

Characterization of three vasopressin receptor 2 variants: an apparent polymorphism (V266A) and two loss-of-function mutations (R181C and M311V).

Arginine vasopressin (AVP) is released from the posterior pituitary and controls water homeostasis. AVP binding to vasopressin V2 receptors (V2Rs) located on kidney collecting duct epithelial cells triggers activation of Gs proteins, leading to increased cAMP levels, trafficking of aquaporin-2 water channels, and consequent increased water permeability and antidiuresis. Typically, loss-of-function V2R mutations cause nephrogenic diabetes insipidus (NDI), whereas gain-of-function mutations cause nephrogenic syndrome of inappropriate antidiuresis (NSIAD). Here we provide further characterization of two mutant V2Rs, R181C and M311V, reported to cause complete and partial NDI respectively, together with a V266A variant, in a patient diagnosed with NSIAD. Our data in HEK293FT cells revealed that for cAMP accumulation, AVP was about 500- or 30-fold less potent at the R181C and M311V mutants than at the wild-type receptor respectively (and about 4000- and 60-fold in COS7 cells respectively). However, in contrast to wild type V2R, the R181C mutant failed to increase inositol phosphate production, while with the M311V mutant, AVP exhibited only partial agonism in addition to a 37-fold potency decrease. Similar responses were detected in a BRET assay for β-arrestin recruitment, with the R181C receptor unresponsive to AVP, and partial agonism with a 23-fold decrease in potency observed with M311V in both HEK293FT and COS7 cells. Notably, the V266A V2R appeared functionally identical to the wild-type receptor in all assays tested, including cAMP and inositol phosphate accumulation, β-arrestin interaction, and in a BRET assay of receptor ubiquitination. Each receptor was expressed at comparable levels. Hence, the M311V V2R retains greater activity than the R181C mutant, consistent with the milder phenotype of NDI associated with this mutant. Notably, the R181C mutant appears to be a Gs protein-biased receptor incapable of signaling to inositol phosphate or recruiting β-arrestin. The etiology of NSIAD in the patient with V266A V2R remains unknown