Uncertainty Quantification of the Traffic Assignment Model

Forecasting of traffic flow in the traffic assignment model suffered to a wide range of uncertainties arising from different sources and exacerbating through sequential-stages of the travel demand model. Uncertainty quantification can provide insights into the level of confidence on the traffic assignment model outputs, and also identify the uncertainties of the input Origin-Destination matrix for enhancing the forecasting robustness of the travel demand model. In this paper, a systematic framework is proposed to quantify the uncertainties that lie in the Origin-Destination input matrix. Hence, this study mainly focuses on predicting the posterior distributions of uncertainty Origin-Destination pairs and correcting the biases of Origin-Destination pairs by using the inverse uncertainty quantification formulated through Least Squares Adjustment method. The posterior distributions are further used in the forward uncertainty quantification to quantify the forecast uncertainty of the traffic flow on a transport network. The results show the effectiveness of implementing the inverse uncertainty quantification framework in the traffic assignment model. And demonstrate the necessity of including uncertainty quantification of the input Origin-Destination matrix in future work of travel demand modelling

Estimation of Link Choice Probabilities Using Monte Carlo Simulation and Maximum Likelihood Estimation Method

Studying the uncertainty of traffic flow takes significant importance for the transport planners because of the variation and fluctuation of temporal traffic flow on all links of the transport network. Uncertainty analysis of traffic flow requires identifying and characterizing two sets of parameters. The first set is the link choice set, which involves the Origin-Destination pairs using this link. The second set is the link choice probabilities set, which includes proportions of the travel demand for the Origin-Destination pairs in the link choice set. For this study, we developed a new methodology based on Monte Carlo simulation for link choice set and link choice probabilities in the context of route choice modeling. This methodology consists of two algorithms: In the first algorithm, we used the sensitivity analysis technique the variance-based method to identify the set of Origin-Destination pairs in each link. In the second algorithm, we used a Gaussian process based on the Maximum Likelihood framework to estimate the link choice probabilities. Furthermore, we applied the proposed methodology in a case study over multiple scenarios representing different traffic flow conditions. The results of this case study show high precision results with low errors' variances.The key contributions of this paper: First, the link choice set can be detected by using sensitivity analysis. Second, the link choice probabilities can be determined by solving an optimization problem in the Maximum likelihood framework. Finally, the prediction errors' parameters of traffic assignment model can be modeled as a Gaussian process

Evaluation of Image Pixels Similarity Measurement Algorithm Accelerated on GPU with OpenACC

OpenACC is a directive based parallel programming library that allows for easy acceleration of existing C, C++ and Fortran based applications with minimal code modifications. By annotating the bottleneck causing section of the code with OpenACC directives, the acceleration of the code can be simplified, leading for high portability of performance across different target Graphic Processing Units (GPUs). In this work, the portability of an implemented parallelizable chi-square based pixel similarity measurement algorithm has been evaluated on two consumer and professional grade GPUs. To our best knowledge, this is the first performance evaluation report that utilizes the OpenACC optimization clauses (collapse and tile) on different GPUs to process a less workload (low resolution image of 581x429 pixels) and a heavy workload (high resolution image of 4500 x 3500 pixels) to demonstrate the effectiveness and high portability of OpenACC

New stochastic modeling strategy on the prediction enhancement of pier scour depth in cohesive bed materials

Abstract Scouring around the piers, especially in cohesive bed materials, is a fully stochastic phenomenon and a reliable prediction of scour depth is still a challenging concern for bridge designers. This study introduces a new stochastic model based on the integration of Group Method of Data Handling (GMDH) and Generalized Likelihood Uncertainty Estimation (GLUE) to predict scour depth around piers in cohesive soils. The GLUE approach is developed to estimate the related parameters whereas the GMDH model is used for the prediction target. To assess the adequacy of the GMDH-GLUE model, the conventional GMDH and genetic programming (GP) models are also developed for evaluation. Several statistical performance indicators are computed over both the training and testing phases for the prediction accuracy validation. Based on the attained numerical indicators, the proposed GMDH-GLUE model revealed better predictability performance of pier scour depth against the benchmark models as well as several gathered literature studies. To provide an informative comparison among the proposed techniques (i.e. GMDH-GLUE, GMDH, and GP models), an improvement index () is employed. Results indicated that the GMDH-GLUE model achieved = 6% and = 3%, demonstrating satisfying performance improvement in comparison with the previously proposed GMDH model

The Impact of International Financial Reporting Standards on Aggressive Accrual: Evidence from Saudi Security Exchange

Purpose: This study investigated the impact of international financial reporting standards on aggressive accrual. The distinguishing feature of this research is the study of the recent adoption of international financial reporting standards in one of the most important economies and emerging markets in the world; the Saudi Security Exchange (Tadawul).   Theoretical framework: One important issue that has dominated accounting research for many years is the mandatory IFRS adoption. More specifically, the impact of mandatory IFRS adoption on accounting figures, notably accruals. Most of the studies document mixed effects resulting from IFRS adoption.   Design/methodology/methodology: This study focuses on Kingdom of Saudi Arabia's Financial Market due to the recent mandatory adoption of the IFRS by Saudi companies in 2017, using 781 firm-year observations. Our study sample will cover 6 years from 2014-2019, three years before adoption (2014-2015-2016) and three years after adoption (2017-2018-2019).   Find: The findings of this study were consistent with previous accounting literature. The study shows a decrease in aggressiveness accruals after adopting international financial reporting standards. The study concluded that the companies listed on the Saudi Stock Exchange (Tadawul), especially after the adoption of international financial reporting standards, do not often provide inflated financial reports with distractions that could lead to customers making irrational decisions.   Research, practical and social implications:  This study put a spotlight on Aggressive Accrual resulting from mandatory IFRS adoption in emerging markets, such as the Kingdom of Saudi Arabia's Financial Market. Therefore, this study provides an exciting opportunity to advance standards setters’ knowledge of the quality of financial reporting in emerging markets.   Originality/value: This is the first study to use Aggressive Accrual around mandatory IFRS adoption. worth noting that apart from the prior literature, no study was devoted to the aggressive accrual in the Kingdom of Saudi Arabia's Financial Market

Immunohistochemical Expression of Epidermal Growth Factor Receptor in Astrocytic Tumors in Iraqi Patients

BACKGROUND: Diffuse astrocytomas constitute the largest group of primary malignant human intracranial tumours. They are classified by the World Health Organization (WHO) into three histological malignancy grades: diffuse astrocytomas (grade II), anaplastic astrocytomas (grade III) and glioblastoma (grade IV) based on histopathological features such as cellular atypia, mitotic activity, necrosis and microvascular proliferation. Epidermal growth factor receptor (EGFR) is a 170-kDa transmembrane tyrosine kinase receptor expressed in a variety of normal and malignant cells regulating critical cellular processes. When activated, epidermal growth factor receptor (EGFR) triggers several signalling cascades leading to increased proliferation and angiogenesis and decreased apoptosis and hence associated with aggressive progression of the tumour. Epidermal growth factor receptor (EGFR) level is known to be a strong indicator associated with the aggressive behaviour of the tumour and acts as a prognostic factor for evaluating the survival rate. AIM: To evaluate the expression of epidermal growth factor receptor (EGFR) in different grades of astrocytoma. MATERIAL AND METHODS: formalin-fixed paraffin-embedded astrocytic tumours of 44 patients were collected from the archival material of pathology department of Ghazi Al Hariri Teaching Hospital during the period from June to December 2018. Hematoxylin and eosin-stained sections were used to characterise the tumours histologically based on cellularity, nuclear hyperchromasia, polymorphism, mitotic activity, vascular proliferation and necrosis with or without pseudopallisading of tumour cells. Diagnosis and grading of astrocytic tumours in this study were made according to WHO criteria (2016). Using a monoclonal antibody to the epidermal growth factor receptor (EGFR) and immunohistochemical analysis, the expression and distribution of epidermal growth factor receptor in astrocytic tumours were examined. RESULTS: The study included 1 case pilocytic astrocytoma (grade I), 20 cases diffuse astrocytoma (grade II), 5 cases anaplastic astrocytoma (grade III) and 18 cases of glioblastoma (grade IV). Expression of EGFR was found in 38.88% of the glioblastoma samples (grade IV). However, none of the astrocytomas of WHO grades I, II and III showed immunoreactivity for EGFR protein. Different patterns of immunoreactive cells and significant intratumor heterogeneity of EGFR expression were observed in glioblastomas. CONCLUSION: The immunohistochemical expression of Epidermal growth factor receptor (EGFR) was restricted only to high-grade astrocytic tumours, namely glioblastoma, thus may use to predict glioblastoma