Perception-Based Assessment of the Factors Causing Delays in Construction Projects

Construction delay is a widespread issue in the construction industry of developing countries, and Nepal is no exception. These delays extend project durations and lead to cost overruns and disputes among stakeholders. To address this problem, this study aimed to identify and analyze the significant factors that contribute to construction project delays in Nepal. To gather data, a well-structured questionnaire was developed and administered to a sample of 100 participants, including contractors, consultants, and civil engineers. Various statistical tests were conducted to ensure the data’s integrity and consistency, such as reliability assessments and factor analyses. The findings of the study highlighted multiple factors contributing to delays in construction projects such as inadequate design, poor communication, and coordination among stakeholders, insufficient experience and planning by contractors, delays in material delivery and testing, labor-related problems including shortages and low qualifications, and external factors like regulatory changes and unforeseen circumstances. By identifying these major causes of construction project delays, this study presented insightful information that can contribute to the analysis and evaluation of project performance.

Prediction of high-speed train delay propagation based on causal text information

The delay-causing text data contain valuable information such as the specific reasons for the delay,location and time of the disturbance,which can provide an efficient support for the prediction of train delays and improve the guidance of train control efficiency.Based on the train operation data and delay-causing data of the Wuhan-Guangzhou high-speed railway,the relevant algorithms in the natural language processing field are used to process the delay-causing text data.It also integrates the train operatingenvironment information and delay-causing text information so as to develop a cause-based train delay propagation prediction model.The Word2vec model is first used to vectorize the delay-causing text description after word segmentation.The mean model or the term frequency-inverse document frequency-weighted model is then used to generate the delay-causing sentence vector based on the original word vector.Afterward,the train operating-environment features and delay-causing sentence vector are input into the extreme gradient boosting(XGBoost)regression algorithm to develop a delay propagation prediction model.In this work,4 text feature processing methods and 8 regression algorithms are considered.The results demonstrate that the XGBoost regression algorithm has the highest prediction accuracy using the test features processed by the continuous bag of words and the mean models.Compared with the prediction model that only considers the train-operating-environment features,the results show that the prediction accuracy of the model is significantly improved with multi-ple regression algorithms after integrating the delay-causing feature.

REG3A defect caused by hyperglycemia delays wound healing in diabetes

Subject Code:H10With the support by the National Natural Science Foundation of China,National Key Research and Development Program of China,and National Program for Support of Top-Notch Young Professionals,the research team led by Prof.Lai Yuping(赖玉平)at Shanghai Key Laboratory of Regulatory Biology,School of Life Sciences,East China Normal University,uncovered a critical role of regenerating islet-