Incident duration prediction through integration of uncertainty and risk factor evaluation: A San Francisco incidents case study.

Predicting incident duration and understanding incident types are essential in traffic management for resource optimization and disruption minimization. Precise predictions enable the efficient deployment of response teams and strategic traffic rerouting, leading to reduced congestion and enhanced safety. Furthermore, an in-depth understanding of incident types helps in implementing preventive measures and formulating strategies to alleviate their influence on road networks.

Identifying at-risk patients for congenital heart disease using integrated predictive models and fuzzy clustering analysis: A cross-sectional study.

Congenital heart disease (CHD) remains a significant global health concern, affecting approximately 1 % of newborns worldwide. While its accurate causes often remain elusive, a combination of genetic and environmental factors is implicated. In this cross-sectional study, we propose a comprehensive prediction framework leveraging Machine Learning (ML) and Multi-Attribute Decision Making (MADM) techniques to enhance CHD diagnostics and forecasting.

A cluster-based SMOTE both-sampling (CSBBoost) ensemble algorithm for classifying imbalanced data.

In this paper, a Cluster-based Synthetic minority oversampling technique (SMOTE) Both-sampling (CSBBoost) ensemble algorithm is proposed for classifying imbalanced data. In this algorithm, a combination of over-sampling, under-sampling, and different ensemble algorithms, including Extreme Gradient Boosting (XGBoost), random forest, and bagging, is employed in order to achieve a balanced dataset and address the issues including redundancy of data after over-sampling, information loss in under-sampling, and random sample selection for sampling and sample generation. The performance of the proposed algorithm is evaluated and compared to different state-of-the-art competing algorithms based on 20 benchmark imbalanced datasets in terms of the harmonic mean of precision and recall (F1) and area under the receiver operating characteristics curve (AUC) measures.

A decision support framework to evaluate the sustainability performance of urban road transportation.

This study proposes a decision support framework (DSF) based on two data envelopment analysis (DEA) models in order to evaluate the urban road transportation of countries for sustainable performance management during different years. The first model considers different years independently while the second model, which is a type of network model, takes into account all the years integrated. A multi-objective programming model under two types of uncertainties is then developed to solve the proposed DEA models based on a revised multi-choice goal programming (GP) approach.

An integrated decision support system to achieve sustainable development in transportation routes with traffic flow.

Due to the growing population and demand, transportation planning has received special importance in the context of supply chain management. One of the major challenges in transportation planning is the traffic problem. This challenge affects the safety, environmental, and efficiency factors of transportation systems.

Classification of truck-involved crash severity: Dealing with missing, imbalanced, and high dimensional safety data.

While the cost of road traffic fatalities in the U.S. surpasses $240 billion a year, the availability of high-resolution datasets allows meticulous investigation of the contributing factors to crash severity.

Designing an integrated blockchain-enabled supply chain network under uncertainty.

With the development of communication infrastructure, the design of supply chains has changed significantly. Blockchain technology, as one of the most cutting-edge technologies, can promote transparency among members of the supply chain network. To the best of our knowledge, this is the first study that tries to develop a novel bi-objective optimization model to integrate the transparency resulting from the use of blockchain for designing a three-level supply chain network.

A branch and efficiency algorithm to design a sustainable two-echelon supply chain network considering traffic congestion and uncertainty.

This work aims to design a sustainable two-echelon supply chain not only based on the widely used cost perspective, but also based on the efficient use and preservation of limited resources. For this purpose, a branch and efficiency (B&E) algorithm is developed, which includes an optimization model and an evaluation model. The proposed tri-objective optimization model simultaneously minimizes the total cost of the supply chain, maximizes the sustainability score, and minimizes inequity among customers.

Microbiology and antimicrobial sensitivity of ventriculo-peritoneal shunt infections in a referral paediatric neurosurgery ward during a period of 7 years.

Objectives: This study aims to evaluate the aetiology and antibiotic susceptibility pattern in children with ventriculo-peritoneal (VP) shunt infections.

Methods: A retrospective investigation was conducted from March 2011 to March 2018 among 163 children, who were admitted because of VP shunt infections in a referral educational hospital in Isfahan, Iran.

Results: Coagulase negative staphylococcus (CoNS) was the most common organism (49.

A Seven-year Study on the Effects of Intravenous Antibiotic Therapy on Infection of Ventriculoperitoneal Shunts in Children.

Background: Infection is one of the most common complications of ventriculoperitoneal (VP) shunts. Its optimal management is controversial. We aimed to report current practice within a large pediatric neurosurgical center.