Predicting pavement performance utilizing artificial neural network (ANN) models

Pavement management systems (PMS) play a significant role in cost-effective management of highway networks to optimize pavement performance over predicted service life of the pavements. Successful PMS implementation requires accurate performance prediction modeling to plan future maintenance and rehabilitation strategies. The Iowa DOT manages three primary highway systems (i.e., Interstate, US, and Iowa highways) that represent 8% (approximately 9,000 miles) of the total roadway system in the state (114,000 miles), but these systems carry around 62% of the total vehicle miles traveled (VMT) and 92% of the total large truck VMT (ASCE, 2015). These highways play a major role in Iowa’s economy because highways are important to several sectors (e.g., agriculture, manufacturing, and industry). According to the Bureau of Transportation Statistics, in 2012 around 263.36 billion tons of goods valued at $195.99 billion were transported on Iowa highways (BTS, 2012). PMSs that use robust pavement prediction models are needed to ensure continued optimum performance of Iowa highways. In the past, these models were developed from historical information about pavement condition data. In this research, historical climate data was acquired from the Iowa Environmental Mesonet and integrated with pavement condition data to include all related variables in prediction modeling. An artificial neural network (ANN) model was used to predict the performance of ride, cracking, rutting, and faulting indices on different pavement types. The goodness of fit of the ANN prediction models was compared with multiple linear regression (MLR) models. The results show that ANN models were more accurate in predicting future conditions than MLR models. The contribution of input variables in prediction models were also determined and discussed. The results indicated that weather factors directly influence highway pavement conditions, and that ANN model results can be used by decision makers and maintenance engineers to determine proper treatment actions and pavement designs to withstand harsh weather over the years. An ANN model that was used to estimate the correlation between the rutting depth and structural capacity of asphalt pavements suggests that rutting depth can be an indicator of structural capacity. As such, an ANN approach might be feasible for small transportation agencies (e.g., cities and counties) that cannot afford to collect structural information

Holistic Approach Framework for Cloud Computing Strategic Decision-Making in the Healthcare Sector (HAF-CCS)

Cloud Computing is an evolving information technology paradigm that impacts many sectors in many countries. Cloud Computing offers IT services anytime, anywhere via any device and is applicable to healthcare organisations, offering a potential cost saving of 15% to 37%. This research investigates Cloud Computing as a facilitating technology to solve some of the challenges experienced by healthcare organisations such as the high cost of implementing IT solutions. The purpose of this research is to develop and apply an Holistic Approach Framework for Cloud Computing Strategic Decision-Making in the Healthcare Sector (HAF-CCS) to provide a systematic approach to the adoption of Cloud Computing that considers different perspectives. Although, Cloud Computing is becoming widely used, there is limited evidence in the literature concerning its application in the Saudi healthcare sector. In the thesis, current cloud adoption decision-making frameworks are analysed and the need to develop a strategic framework for Cloud Computing decision-making processes which emphasises a multidisciplinary holistic approach is identified. Understanding the different strategic aspects of Cloud Computing is important and could encourage organisations to adopt this model of computing since the decision regarding whether to adopt Cloud Computing is potentially a complex process; there are many perspectives to be considered, and studying this process requires a multiple perspective framework. The framework developed in this thesis aims to support decision-makers in healthcare organisations by covering five perspectives of Cloud Computing adoption: Organisation, Technology, Environment, Human and Business. The framework integrates the TOE (Technology-Organisation-Environment) framework with the Information Systems Strategy Triangle (IS Triangle) and the HOT-fit (Human- Organisation-Technology) model to support an holistic evaluation of the determinants of Cloud Computing adoption in healthcare organisations. The factors that will affect Cloud Computing adoption in healthcare organisations in Saudi Arabia have been identified using quantitative and qualitative methods, and a case study approach was implemented to validate the framework. The results of the validation showed that the framework can support decision-makers in understanding an organisation’s position regarding Cloud Computing and identifying any gaps that may hinder Cloud Computing adoption. The framework can also provide healthcare organisations with a strategic assessment tool to help in gaining the advantages of Cloud Computing

QUASI MAPPING SINGULARITIES

We  obtain  a list of all simple classes of singularities of curves  (irreducible  and reducible) in real spaces  of any dimension  with respect to the quasi  equivalence relation

Database Development for Pavement Performance Modeling

Pavement Management System (PMS) is defined as set of tools or methods that can support decision makers in finding the optimum strategies for providing, evaluating, and maintaining pavement condition in acceptable level. The Iowa Pavement Management Program (IPMP) provides information about Iowa highways such as distress data and maintenance activates. One of the most factors that affect pavement performance is weather factors (temperature, freeze-thaw cycles, and rainfall). The historical climate data was obtained from Iowa Environmental Mesonet (IEM) for counties in the state of Iowa. The pavement condition and climate data can be integrated for pavement performance modeling. The Geographic Information System (GIS) is identified as an effective tool for data integration. The primary goal of this paper is to utilize the GIS tools to integrate pavement conditions and climate data for improving Iowa PMS

ANN Models to Correlate Structural and Functional Conditions in AC Pavements at Network Level

Artificial Neural Network (ANN) model was developed to estimate the correlation between structural capacity and functional conditions in Asphalt Cement (AC) pavements at the network level. To achieve this objective, the relevant data were obtained and integrated from the Iowa Pavement Management Program (IPMP) including construction parameters, traffic loading and subgrade stiffness, and Iowa Environmental Mesonet (IEM) for climate data. The ANN model proves its ability to learn and generalize from the input data. Overall, rutting data were found to be appropriate indicator of the structural capacity. Since the deflection tests are expensive and require experience and knowledge to deal with such data, this approach might be feasible for small transportation agencies (cities and counties) that do not have these capabilities

Understanding the determinants of Cloud Computing adoption in Saudi healthcare organisations

Cloud Computing is an evolving information technology paradigm that impacts many sectors in many countries. Although Cloud Computing is an emerging technology there is little in the literature concerning its application in the Saudi healthcare sector. This paper examines and identifies the factors that will influence the adoption of Cloud Computing in Saudi healthcare organisations. The study integrates the TOE (Technology–Organization–Environment) framework with the Information System Strategic Triangle (IS Triangle) and the HOT-fit (Human–Organization–Technology) model to provide a holistic evaluation of the determinants of Cloud Computing adoption in healthcare organisations. Of the five perspectives examined in this study, the Business perspective was found to be the most important followed by the Technology, Organisational and Environmental perspectives and finally the Human perspective. The findings of the study showed that the five most important factors influencing the adoption of Cloud Computing in this context are soft financial analysis, relative advantage, hard financial analysis, attitude toward change and pressure from partners in the business ecosystem. This study identifies the critical factors for both practitioners and academics that influence Cloud Computing adoption decision-making in Saudi healthcare

Attitude, knowledge and experience of hospital pharmacists with pharmacovigilance in a region in Saudi Arabia: a cross-sectional study

Purpose: To assess hospital pharmacists’ knowledge of, attitude to, and experience with pharmacovigilance and adverse drug reactions (ADRs) in Al Madinah Al Munawarah region, Saudi Arabia.Methods: A cross-sectional study was conducted from April – June 2015 among hospital pharmacists using a self-administered questionnaire. All pharmacists working in government hospitals and primary care centers in Al Madinah Al Munawarah region were targeted to participate in the study. A total of 130 pharmacists were included in the study. Data analysis was conducted using the Statistical Package for Social Sciences, version 20.Results: The response rate to the survey was 79 % out of 103 pharmacists. In terms of knowledge about pharmacovigilance, only 56 (54.4 %) correctly identified WHO definition of ADRs, while 53 (51.5 %) of the pharmacists correctly defined pharmacovigilance. Regarding pharmacists’ experience with ADR reporting, less than half (N = 46, 44.7 %) said they have made a suspected ADR report and slightly less than half of the pharmacists (50, 48.5 %) said they are familiar with Saudi Food and Drug Authority (SFDA) system of suspected ADR reporting. The majority of the pharmacists (N = 95, 92.2 %) believed that patient safety is the most important goal of suspected ADR reporting. The most common barrier to ADR reporting was lack of pharmacovigilance training (N = 48, 46.6 %).Conclusion: Pharmacists had insufficient knowledge of, but positive attitude toward pharmacovigilance and ADR reporting. Lack of pharmacovigilance training has been identified as the major barrier to ADR reporting.Keywords: Hospital pharmacists, Pharmacovigilance, Adverse drug reactions (ADRs), Attitude, Knowledg