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This study aims to determine and investigate the main causes of construction project delays. Construction projects are more intricate and associated with significant levels of risk owing to cost overruns. These overruns frequently lead to delays, incomplete work or other related challenges. Building delays are a prevalent problem in the building sector of developing nations. These delays prolong the duration of projects and result in increased costs and conflicts among stakeholders. A conceptual model consisting of the factors causing the delays in heating, ventilation and air conditioning (HVAC) projects was developed and tested in this study.

A comprehensive data collection process was undertaken. A meticulously designed survey was distributed to a diverse cohort of 294 participants, including contractors and sub-contractors from Chennai, Tamil Nadu. The data was collected using stratified sampling, ensuring a representative sample. The data was then analysed using ordinary least squares multiple regression.

The findings of this study have significant implications for the construction industry. They indicate that factors related to sales, clients, design, procurement, finance and labour all contribute to delays in HVAC projects. Understanding these factors can help stakeholders in the industry to better manage and mitigate project delays.

This study is unique because it is a perceptual study of stakeholders. It provides valuable information for analysing and assessing project performance by identifying the primary causes of HVAC project delays. To the best of the authors’ knowledge, the study conducted on HVAC projects is the first of its kind and hence makes a pivotal contribution to the literature on construction projects. Additionally, the study will assist policymakers and consultants in taking necessary steps to minimize delays.

The purpose of this paper is to determine the factors associated with patient delay among tuberculosis patients in border hospitals, Chiang Rai province, Thailand.

A cross-sectional study was conducted in the four biggest border hospitals in Chiang Rai province, Thailand during May to July 2018 among 103 identified TB cases. Data were collected by a face-to-face interview with structured questionnaire on patients’ general characteristics, HIV status and patient delay status. Descriptive statistics were used to analyze the patients’ general characteristics, HIV and patient delay status. The association among variables and patient delay was analyzed by χ²-test. The variables with p-value<0.20 obtained in bivariate analysis were further analyzed by binary logistic regression and considered significant with p-value<0.05.

All patients enrolled, most were reported with patient delay (65.0 percent). Bivariate analysis demonstrated that level of education, nationality and HIV status were associated factors for patient delay. Among these factors, binary logistic regression revealed that HIV negative TB patients were increased 6.806-fold odds of being patient delay (OR = 6.806; 95% CI: 1.174–39.462), while non-Thai TB patients were also increased 2.824-fold odds of being patient delay (OR = 2.824; 95% CI: 1.041–7.660).

Patient delay among TB patients in Chiang Rai province was high. This study further supports the requirement on promoting of TB knowledge and awareness emphasized on non-Thai population and general public along the border areas of Chiang Rai province.

To optimize train operations, dispatchers currently rely on experience for quick adjustments when delays occur. However, delay predictions often involve imprecise shifts based on known delay times. Real-time and accurate train delay predictions, facilitated by data-driven neural network models, can significantly reduce dispatcher stress and improve adjustment plans. Leveraging current train operation data, these models enable swift and precise predictions, addressing challenges posed by train delays in high-speed rail networks during unforeseen events.

This paper proposes CBLA-net, a neural network architecture for predicting late arrival times. It combines CNN, Bi-LSTM, and attention mechanisms to extract features, handle time series data, and enhance information utilization. Trained on operational data from the Beijing-Tianjin line, it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding trains.

This study evaluates our model's predictive performance using two data approaches: one considering full data and another focusing only on late arrivals. Results show precise and rapid predictions. Training with full data achieves a MAE of approximately 0.54 minutes and a RMSE of 0.65 minutes, surpassing the model trained solely on delay data (MAE: is about 1.02 min, RMSE: is about 1.52 min). Despite superior overall performance with full data, the model excels at predicting delays exceeding 15 minutes when trained exclusively on late arrivals. For enhanced adaptability to real-world train operations, training with full data is recommended.

This paper introduces a novel neural network model, CBLA-net, for predicting train delay times. It innovatively compares and analyzes the model's performance using both full data and delay data formats. Additionally, the evaluation of the network's predictive capabilities considers different scenarios, providing a comprehensive demonstration of the model's predictive performance.

This study explores how auditors' emotions, specifically negative moods triggered by flight delays, impact auditing quality.

Utilizing flight delays during audit assignments as a mood indicator, weather conditions at departure airports serve as an instrumental variable to provide a robustness check between flight delays and audit outcomes, employing a two-stage least squares model.

The findings suggest that such negative moods improve auditing effort and quality, as evidenced by reduced future accounting restatements and increased audit fees. The positive effect of flight delays on auditing quality is consistent across different tests and measures.

This study highlights the significance of auditors' emotional states on their professional performance, indicating a unique angle on auditing quality research by focusing on the emotional well-being of auditors as influenced by external factors such as flight delays.

The purpose of this paper is to reveal the main causes of delays in the projects are from the client (relative importance index (RII)=0.716), labor and equipment (RII=0.701) and contractor (RII=0.698). Hence determining the contractual responsibility of delay is the most likely source of dispute in construction projects and many techniques have been used in the courts to demonstrate the criticalities of a delay event on the project schedule. Therefore, authors try to investigate all process-based techniques of delay claims and evaluated and conformed them with principles by Society of Construction Law (SCL) protocol and Association for the Advancement of Cost Engineering International (AACEI) in order to choose the best techniques based on the specific circumstances of each project.

This section is divided into two distinct parts: refers to the methods used to assess the perceptions of clients, consultants, and contractors on the relative importance of causes of delay in construction industry; and refers to advantages and disadvantages of various techniques used to analyze delays and their conform with SCL protocol. A questionnaire was developed to assess the perceptions of clients, consultants, and contractors on the relative importance of causes of delay in Iranian construction industry. The respondents were asked to indicate their response category on 78 well-recognized construction delay factors identified by authors.

In total, 78 causes of delay were identified through research. The identified causes are combined into seven groups. The field survey included 58 contractors, 55 consultants, and 62 client. Data collected were analyzed by RII and Statistical Package for Social Sciences (SPSS). The authors identified main causes of delay and ten most important causes, according to Table AII, from the perspective of three major groups of participants (clients, consultants and contractors). The ranking of categories of causes of delay, according to Table I, were: client-related causes (RII=0.716); labor and equipment category causes (RII=0.701); contractor-related causes (RII=0.698); material-related causes (RII=0.690); design-related causes (RII=0.666); external causes (RII=0.662); and consultant-related causes (RII=0.662). But according to the discussions and given that determining the contractual responsibility of delay is the most likely source of dispute in construction industry and many techniques have been used in the courts to demonstrate the criticalities of a delay event on the project schedule.

All process-based techniques of delay analysis have been present in this paper and categorized in 11 groups. In order to understand the advantages and disadvantages of them by clients, contractor and consultant, a thorough review conducted to reveal the nature of techniques. In the next step, given that selecting the most appropriate technique based on constraints and specific conditions of each project is one of the most important steps to carry out a successful delay analysis. The authors conformed, all process-based techniques of delay analysis, by SCL protocol and AACEI principles. Finally, the result of this match was brought in order to choose the best technique based on the specific circumstances of each project.

The shipper selects a suitable shipping route and plans for a voyage in order to import and export cargo on the basis of published sailing schedules. The reliability of the sailing schedule will influence the shipper’s logistics expense, which means that the logistics costs will depend on the reliability of schedules published by container shipping companies. Therefore, it is important to consider factors which can cause delays would for container ships sailing on sea routes. The reliability of published sailing schedules can be affected by a number of different factors. This study adopts the multi-criteria decision making (MCDM) method to estimate the importance of the delaying factors in a sailing schedule. In addition, the consistent fuzzy preference relations (CFPR) method is applied to identify the subjective importance (weights) of the delaying factors. The entropy weight method combined with the actual performance of the container shipping company are both used when estimating the objective importance (weights) of the delaying factors. According to the analysis results, the criteria can be divided into four quadrants with different management implications, which indicate that instructions for chase strategy, sailing schedule control, fleet allocation, transship operation arrangement and planning for ports in routes are often ignored by container shipping companies. Container shipping companies should consider adjusting their operational strategies, which would greatly improve their operational performance.

This study aims to develop a comprehensive conceptual framework that serves as a foundation for identifying most critical delay risk drivers for Building Information Modelling (BIM)-based construction projects.

A systematic review was conducted using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to identify key delay risk drivers in BIM-based construction projects that have significant impact on the performance of delay risk predictive modelling techniques.

The results show that contractor related driver and external related driver are the most important delay driver categories to be considered when developing delay risk predictive models for BIM-based construction projects.

This study contributes to the body of knowledge by filling the gap in lack of a conceptual framework for selecting key delay risk drivers for BIM-based construction projects, which has hampered scientific progress toward development of extremely effective delay risk predictive models for BIM-based construction projects. Furthermore, this study's analyses further confirmed a positive effect of BIM on construction project delay.

This paper aims to reschedule the freight train timetable in case of disturbance to restore the train services as soon as possible.

Hence, an integer linear programming model for the real-time freight heavy-haul railway traffic management is developed in case of large primary delays caused by the delayed cargos loading. The proposed model based on the alternative graph at the microscopic level depicts the freight train movements in detail. Multiple dispatching measures such as re-timing and re-ordering are taken into account. Moreover, two objective functions, namely, the total final delays and the consecutive delays, are minimized in the freight trains dispatching problem.

Finally, a real-world computational experiment based on the Haolebaoji-Ji’an freight heavy-haul railway is implemented. The results of all disrupted cases are obtained within 10 s. The results give insight into that the consecutive delays are more than the total final delays when the same disrupted situation and the consecutive or total final delays increase as the primary delays increase.

An integer linear programming model based on the alternative graph for the real-time freight heavy-haul railway traffic management is developed in case of large primary delays caused by the delayed cargos loading. The method can be developed as the computer-aided tool for freight train dispatchers.

This study aims to validate a model for estimating platoon delay due to pedestrian crossing for use in Kuwait City.

The model was modified slightly for the scenario used in Kuwait, in which the presence of raised crosswalk meant that all incoming traffic would slow down automatically. Using video footage to observe the site, several variables were collected, and a model was used to calculate the delays suffered by the vehicles because of pedestrian crossing. The model was validated using the actual footage and manual observation to measure the delays.

The model showed a good match fit to the observed data, as the average delays differed by 22.5% between the two methods. Following the comparison, a sensitivity analysis was made on three variables: the acceleration rate, deceleration rate, as well as the pedestrian walking time. The analysis has shown that deceleration rate has approximately twice the effect on the model than the acceleration rate has. It has also shown that the pedestrian walking time has a major effect on the model, in an almost one-to-one correlation. A 50% change of the pedestrian walking time is associated with approximately 50% change in the model’s output delay.

A model for estimating platoon delay because of pedestrian crossing was validated for use in Kuwait City. The model was modified slightly for the scenario used in Kuwait, in which the presence of raised crosswalk meant that all incoming traffic would slow down automatically.

This paper aims to use active fine lane management methods to solve the problem of congestion in a weaving area and provide theoretical and technical support for traffic control under the environment of intelligent connected vehicles (ICVs) in the future.

By analyzing the traffic capacities and traffic behaviors of domestic and foreign weaving areas and combining them with field investigation, the paper proposes the active and fine lane management methods for ICVs to optimal driving behavior in a weaving area. The VISSIM simulation of traffic flow vehicle driving behavior in weaving areas of urban expressways was performed using research data. The influence of lane-changing in advance on the weaving area was evaluated and a conflict avoidance area was established in the weaving area. The active fine lane management methods applied to a weaving area were verified for different scenarios.

The results of the study indicate that ICVs complete their lane changes before they reach a weaving area, their time in the weaving area does not exceed the specified time and the delay of vehicles that pass through the weaving area decreases.

Based on the vehicle group behavior, this paper conducts a simulation study on the active traffic management control-oriented to ICVs. The research results can optimize the management of lanes, improve the traffic capacity of a weaving area and mitigate traffic congestion on expressways.