Predicting practical benefits of dynamic asset ratings of 33kv distribution transformers

This paper reports potential benefits around dynamic thermal rating prediction of primary transformers within Western Power Distribution (WPD) managed Project FALCON (Flexible Approaches to Low Carbon Optimised Networks). Details of the thermal modelling, parameter optimisation and results validation are presented with asset and environmental data (measured and day/week-ahead forecast) which are used for determining dynamic ampacity. Detailed analysis of ratings and benefits and confidence in ability to accurately predict dynamic ratings are presented. Investigating the effect of sustained ONAN rating compared to a dynamic rating shows that there is scope to increase sustained ratings under ONAN operating conditions by up to 10% higher between December and March with a high degree of confidence. However, under high ambient temperature conditions this dynamic rating may also reduce in the summer months.

Measurement and correlation of the solubility of telmisartan (form A) in nine different solvents from 277.85 to 338.35 K

The solubility of telmisartan (form A) in nine organic solvents (chloroform, dichloromethane, ethanol, toluene, benzene, 2-propanol, ethyl acetate, methanol and acetone) was determined by a laser monitoring technique at temperatures from 277.85 to 338.35 K. The solubility of telmisartan (form A) in all of the nine solvents increased with temperature as did the rates at which the solubility increased except in chloroform and dichloromethane. The mole fraction solubility in chloroform is higher than that in dichloromethane, which are both one order of magnitude higher than those in the other seven solvents at the experimental temperatures. The solubility data were correlated with the modified Apelblat equation and λh equations. The results show that the λh equation is in better agreement with the experimental data than the Apelblat equation. The relative root mean square deviations (σ) of the λh equation are in the range from 0.004 to 0.45 %. The dissolution enthalpies, entropies and Gibbs energies of telmisartan in these solvents were estimated by the Van’t Hoff equation and the Gibbs equation. The melting point and the fusion enthalpy of telmisartan were determined by differential scanning calorimetry.

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

Plasma and cold sprayed aluminum carbon nanotube composites: Quantification of nanotube distribution and multi-scale mechanical properties

Carbon nanotubes (CNT) could serve as potential reinforcement for metal matrix composites for improved mechanical properties. However dispersion of carbon nanotubes (CNT) in the matrix has been a longstanding problem, since they tend to form clusters to minimize their surface area. The aim of this study was to use plasma and cold spraying techniques to synthesize CNT reinforced aluminum composite with improved dispersion and to quantify the degree of CNT dispersion as it influences the mechanical properties. Novel method of spray drying was used to disperse CNTs in Al-12 wt.% Si prealloyed powder, which was used as feedstock for plasma and cold spraying. A new method for quantification of CNT distribution was developed. Two parameters for CNT dispersion quantification, namely Dispersion parameter (DP) and Clustering Parameter (CP) have been proposed based on the image analysis and distance between the centers of CNTs. Nanomechanical properties were correlated with the dispersion of CNTs in the microstructure. Coating microstructure evolution has been discussed in terms of splat formation, deformation and damage of CNTs and CNT/matrix interface. Effect of Si and CNT content on the reaction at CNT/matrix interface was thermodynamically and kinetically studied. A pseudo phase diagram was computed which predicts the interfacial carbide for reaction between CNT and Al-Si alloy at processing temperature. Kinetic aspects showed that Al4C3 forms with Al-12 wt.% Si alloy while SiC forms with Al-23wt.% Si alloy. Mechanical properties at nano, micro and macro-scale were evaluated using nanoindentation and nanoscratch, microindentation and bulk tensile testing respectively. Nano and micro-scale mechanical properties (elastic modulus, hardness and yield strength) displayed improvement whereas macro-scale mechanical properties were poor. The inversion of the mechanical properties at different scale length was attributed to the porosity, CNT clustering, CNT-splat adhesion and Al 4C3 formation at the CNT/matrix interface. The Dispersion parameter (DP) was more sensitive than Clustering parameter (CP) in measuring degree of CNT distribution in the matrix.

Neural network based off-line handwritten text recognition system

This dissertation introduces a new system for handwritten text recognition based on an improved neural network design. Most of the existing neural networks treat mean square error function as the standard error function. The system as proposed in this dissertation utilizes the mean quartic error function, where the third and fourth derivatives are non-zero. Consequently, many improvements on the training methods were achieved. The training results are carefully assessed before and after the update. To evaluate the performance of a training system, there are three essential factors to be considered, and they are from high to low importance priority: (1) error rate on testing set, (2) processing time needed to recognize a segmented character and (3) the total training time and subsequently the total testing time. It is observed that bounded training methods accelerate the training process, while semi-third order training methods, next-minimal training methods, and preprocessing operations reduce the error rate on the testing set. Empirical observations suggest that two combinations of training methods are needed for different case character recognition. Since character segmentation is required for word and sentence recognition, this dissertation provides also an effective rule-based segmentation method, which is different from the conventional adaptive segmentation methods. Dictionary-based correction is utilized to correct mistakes resulting from the recognition and segmentation phases. The integration of the segmentation methods with the handwritten character recognition algorithm yielded an accuracy of 92% for lower case characters and 97% for upper case characters. In the testing phase, the database consists of 20,000 handwritten characters, with 10,000 for each case. The testing phase on the recognition 10,000 handwritten characters required 8.5 seconds in processing time.

Statistical Models for Predicting College Success

Colleges base their admission decisions on a number of factors to determine which applicants have the potential to succeed. This study utilized data for students that graduated from Florida International University between 2006 and 2012. Two models were developed (one using SAT as the principal explanatory variable and the other using ACT as the principal explanatory variable) to predict college success, measured using the student’s college grade point average at graduation. Some of the other factors that were used to make these predictions were high school performance, socioeconomic status, major, gender, and ethnicity. The model using ACT had a higher R^2 but the model using SAT had a lower mean square error. African Americans had a significantly lower college grade point average than graduates of other ethnicities. Females had a significantly higher college grade point average than males.

Plasma And Cold Sprayed Aluminum Carbon Nanotube Composites: Quantification Of Nanotube Distribution And Multi-Scale Mechanical Properties

Carbon nanotubes (CNT) could serve as potential reinforcement for metal matrix composites for improved mechanical properties. However dispersion of carbon nanotubes (CNT) in the matrix has been a longstanding problem, since they tend to form clusters to minimize their surface area. The aim of this study was to use plasma and cold spraying techniques to synthesize CNT reinforced aluminum composite with improved dispersion and to quantify the degree of CNT dispersion as it influences the mechanical properties. Novel method of spray drying was used to disperse CNTs in Al-12 wt.% Si pre-alloyed powder, which was used as feedstock for plasma and cold spraying. A new method for quantification of CNT distribution was developed. Two parameters for CNT dispersion quantification, namely Dispersion parameter (DP) and Clustering Parameter (CP) have been proposed based on the image analysis and distance between the centers of CNTs. Nanomechanical properties were correlated with the dispersion of CNTs in the microstructure. Coating microstructure evolution has been discussed in terms of splat formation, deformation and damage of CNTs and CNT/matrix interface. Effect of Si and CNT content on the reaction at CNT/matrix interface was thermodynamically and kinetically studied. A pseudo phase diagram was computed which predicts the interfacial carbide for reaction between CNT and Al-Si alloy at processing temperature. Kinetic aspects showed that Al4C3 forms with Al-12 wt.% Si alloy while SiC forms with Al-23wt.% Si alloy. Mechanical properties at nano, micro and macro-scale were evaluated using nanoindentation and nanoscratch, microindentation and bulk tensile testing respectively. Nano and micro-scale mechanical properties (elastic modulus, hardness and yield strength) displayed improvement whereas macro-scale mechanical properties were poor. The inversion of the mechanical properties at different scale length was attributed to the porosity, CNT clustering, CNT-splat adhesion and Al4C3 formation at the CNT/matrix interface. The Dispersion parameter (DP) was more sensitive than Clustering parameter (CP) in measuring degree of CNT distribution in the matrix.

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

Mean dynamic topography of the North Indian Ocean

A satellite-only Mean Dynamic Topography (MDT) of the North Indian Ocean is estimated from the DIRR5 geoid and CNES_CLS11 mean sea surface (Schaffer et al. 2012). DIRR5 geoid is estimated from the latest release (Release 5) of GOCE gravity data according to previous studies (e.g., Johannessen et al. 2003; Raj, 2014). Note that this MDT estimated is referenced to a time period of 7 years (1993-1999). A correction data obtained from AVISO is later used to convert the MDT to a time reference of 20 years (1993-2012). More details are given in Raj (2016).

Using individual tracking data to validate the predictions of species distribution models

The authors would like to thank the College of Life Sciences of Aberdeen University and Marine Scotland Science which funded CP's PhD project. Skate tagging experiments were undertaken as part of Scottish Government project SP004. We thank Ian Burrett for help in catching the fish and the other fishermen and anglers who returned tags. We thank José Manuel Gonzalez-Irusta for extracting and making available the environmental layers used as environmental covariates in the environmental suitability modelling procedure. We also thank Jason Matthiopoulos for insightful suggestions on habitat utilization metrics as well as Stephen C.F. Palmer, and three anonymous reviewers for useful suggestions to improve the clarity and quality of the manuscript.

How did varying levels of intensity of the 2005 Pakistan earthquake differentially affect women’s fertility decisions and children’s health outcomes?

This paper uses a difference in difference model to investigate the impact of a large scale and high mortality 2005 earthquake in Pakistan on women’s fertility decisions and children’s health outcomes. Using a nationally representative, cross sectional DHS data from 2006 and geographical data from USGS, this paper investigates how variation in earthquake intensity levels can differentially impact total fertility for women and the likelihood of children suffering from diseases such as diarrhea, Acute Respiratory Infections (ARI) and fever. The post-earthquake results demonstrate a statistically significant increase in total fertility for areas closer to the epicenter of the earthquake, within a 100km radius of the rupture surface and at higher altitudes. Similarly, for children who were in-utero at the time of the earthquake, the probability of having early symptoms of ARI or fever was much smaller in lower earthquake intensity zones compared to the highest intensity zone.

Survivorship and Clinical Outcome of the Minimally Invasive Uniglide Medial Fixed Bearing, All-polyethylene Tibia, Unicompartmental Knee Arthroplasty at a Mean Follow-up of 7.3 Years

Background: Medial UKA performed in England and Wales represents 7 to 11% of all knee arthroplasty procedures, and is most commonly performed using mobile-bearing designs. Fixed bearing eliminates the risk of bearing dislocation, however some studies have shown higher revision rates for all-polyethylene tibial components compared to those that utilize metal-backed implants. The aim of the study is to analyse survivorship and maximum 8-year clinical outcome of medial fixed bearing, Uniglide unicompartmental knee arthroplasty performed using an all-polyethylene tibial component with a minimal invasive approach. Methods: Between 2002 and 2009, 270 medial fixed UKAs were performed in our unit. Patients were reviewed pre-operatively, 5 and 8 years post-operatively. Clinical and radiographic reviews were carried out. Patients’ outcome scores (Oxford, WOMAC and American Knee Score) were documented in our database and analysed. Results: Survival and clinical outcome data of 236 knees with a mean 7.3 years follow-up are reported. Every patient with less than 4.93 years follow-up underwent a revision. The patients’ average age at the time of surgery was 69.5 years. The American Knee Society Pain and Function scores, the Oxford Knee Score and the WOMAC score all improved significantly. The 5 years survival rate was 94.1% with implant revision surgery as an end point. The estimated 10 years survival rate is 91.3%. 14 patients were revised before the 5 year follow-up. Conclusion: Fixed bearing Uniglide UKA with an all-polyethylene tibial component is a valuable tool in the management of a medial compartment osteoarthritis, affording good short term survivorship.