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Workers' unsafe behavior is the main cause of construction safety accidents, thereby highlighting the critical importance of behavior-based management. To compensate for the limitations of computer vision in tackling knowledge-intensive issues, semantic-based methods have gained increasing attention in the field of construction safety management. Knowledge graph provides an efficient and visualized method for the identification of various unsafe behaviors.

This study proposes an unsafe behavior identification framework by integrating computer vision and knowledge graph–based reasoning. An enhanced ontology model anchors our framework, with image features from YOLOv5, COCO Panoptic Segmentation and DeepSORT integrated into the graph database, culminating in a structured knowledge graph. An inference module is also developed, enabling automated the extraction of unsafe behavior knowledge through rule-based reasoning.

A case application is implemented to demonstrate the feasibility and effectiveness of the proposed method. Results show that the method can identify various unsafe behaviors from images of construction sites and provide mitigation recommendations for safety managers by automated reasoning, thus supporting on-site safety management and safety education.

Existing studies focus on spatial relationships, often neglecting the diversified spatiotemporal information in images. Besides, previous research in construction safety only partially automated knowledge graph construction and reasoning processes. In contrast, this study constructs an enhanced knowledge graph integrating static and dynamic data, coupled with an inference module for fully automated knowledge-based unsafe behavior identification. It can help managers grasp the workers’ behavior dynamics and timely implement measures to correct violations.

This study aims to prepare thymol-based deep eutectic solvents (DESs) and use them as lubricates for friction and wear tests to simulate the wear conditions of hybrid bearings.

Through the characterization and analysis of the morphology of wear scars and the elemental composition of friction films, the tribological behavior and wear mechanism of sample materials as lubricants were investigated and the anti-wear mechanism of thymol-based DESs was discussed.

The findings demonstrate that because of the formation of a fluid lubrication film and excellent kinematic viscosity, the lubrication effect of the prepared DES is improved by about 50% compared to the base lubricating oil. The prepared [Ch]Cl-thymol DES has a better anti-friction and lubrication effect than citric-thymol, EG-thymol and urea-thymol DESs, with an average friction coefficient of about 0.04.

In this work, the friction reduction properties of thymol-based DESs were studied as lubricants for the first time, and the lubrication mechanism of sample materials was investigated.

Existing studies mostly rely on the static characteristics of team members, and there is still a lack of empirical investigation on how entrepreneurial team members make decisions through dynamic team process and how team members’ cognition influences team decision-making. The purpose of this study is to validate how entrepreneurial team heterogeneity affects team decision-making performance from the perspective of dynamic team process.

Drawing on the theory of input-process-output model, this study proposed and examined the mediating role of team interaction as well as the moderating role of proactive socialization tactics in the relationship between entrepreneurial team heterogeneity and decision-making performance. Based on a sample of 162 entrepreneurial teams that include pairing superiors and subordinates, hierarchical regressions and moderated mediation tests were used to test the hypotheses.

The research results show that the heterogeneity of entrepreneurial teams is positively correlated with both team interaction and decision-making performance. Team interaction plays a mediating role between entrepreneurial team heterogeneity and decision-making performance; information seeking of proactive socialization tactics moderates the impact of entrepreneurial team heterogeneity on team interaction.

Contributing to the literature on entrepreneurial team decision-making performance, this study identifies that proactive socialization tactics with a high level of information seeking can help entrepreneurial team members respond to environmental and organizational changes more effectively during team development and increase the effectiveness of team interaction. This finding helps us better understand the mechanism and context under which entrepreneurial heterogeneity may enhance the team’s decision-making performance.

The purpose of this study is to address the extended travel time caused by dwelling time at stations for passengers on traditional rail transit lines. To mitigate this issue, the authors propose the “Non-stop” design, which involves trains comprised of modular vehicles that can couple and uncouple from each other during operation, thereby eliminating dwelling time at stations..

The main contributions of this paper are threefold: first, to introduce the concept of non-stop rail transit lines, which, to the best of the authors’ knowledge, has not been researched in the literature; second, to develop a framework for the operation schedule of such a line; and third, the author evaluate the potential of its implementation in terms of total passenger travel time.

The total travel time was reduced by 6% to 32.91%. The results show that the savings were more significant for long commutes and low train occupancy rates.

The non-stop system can improve existing lines without the need for the construction of additional facilities, but it requires technological advances for rolling stock.

To eliminate dwelling time at stations, the authors present the “Non-stop” design, which is based on trains composed of locomotives that couple and uncouple from each other during operation, which to the best of the authors’ knowledge has not been researched in the literature.

Fall accidents can cause casualties and economic losses in the construction industry. Fall portents, such as loss of balance (LOB) and sudden sways, can result in fatal, nonfatal or attempted fall accidents. All of them are worthy of studying to take measures to prevent future accidents. Detecting fall portents can proactively and comprehensively help managers assess the risk to workers as well as in the construction environment and further prevent fall accidents.

This study focused on the postures of workers and aimed to directly detect fall portents using a computer vision (CV)-based noncontact approach. Firstly, a joint coordinate matrix generated from a three-dimensional pose estimation model is employed, and then the matrix is preprocessed by principal component analysis, K-means and pre-experiments. Finally, a modified fusion K-nearest neighbor-based machine learning model is built to fuse information from the x, y and z axes and output the worker's pose status into three stages.

The proposed model can output the worker's pose status into three stages (steady–unsteady–fallen) and provide corresponding confidence probabilities for each category. Experiments conducted to evaluate the approach show that the model accuracy reaches 85.02% with threshold-based postprocessing. The proposed fall-portent detection approach can extract the fall risk of workers in the both pre- and post-event phases based on noncontact approach.

First, three-dimensional (3D) pose estimation needs sufficient information, which means it may not perform well when applied in complicated environments or when the shooting distance is extremely large. Second, solely focusing on fall-related factors may not be comprehensive enough. Future studies can incorporate the results of this research as an indicator into the risk assessment system to achieve a more comprehensive and accurate evaluation of worker and site risk.

The proposed machine learning model determines whether the worker is in a status of steady, unsteady or fallen using a CV-based approach. From the perspective of construction management, when detecting fall-related actions on construction sites, the noncontact approach based on CV has irreplaceable advantages of no interruption to workers and low cost. It can make use of the surveillance cameras on construction sites to recognize both preceding events and happened accidents. The detection of fall portents can help worker risk assessment and safety management.

Existing studies using sensor-based approaches are high-cost and invasive for construction workers, and others using CV-based approaches either oversimplify by binary classification of the non-entire fall process or indirectly achieve fall-portent detection. Instead, this study aims to detect fall portents directly by worker's posture and divide the entire fall process into three stages using a CV-based noncontact approach. It can help managers carry out more comprehensive risk assessment and develop preventive measures.

Accurately estimating the severity of derailment is a crucial step in quantifying train derailment consequences and, thereby, mitigating its impacts. The purpose of this paper is to propose a simplified approach aimed at addressing this research gap by developing a physics-informed 1-D model. The model is used to simulate train dynamics through a time-stepping algorithm, incorporating derailment data after the point of derailment.

In this study, a simplified approach is adopted that applies a 1-D kinematic analysis with data obtained from various derailments. These include the length and weight of the rail cars behind the point of derailment, the train braking effects, derailment blockage forces, the grade of the track and the train rolling and aerodynamic resistance. Since train braking/blockage effects and derailment blockage forces are not always available for historical or potential train derailment, it is also necessary to fit the historical data and find optimal parameters to estimate these two variables. Using these fitted parameters, a detailed comparison can be performed between the physics-informed 1-D model and previous statistical models to predict the derailment severity.

The results show that the proposed model outperforms the Truncated Geometric model (the latest statistical model used in prior research) in estimating derailment severity. The proposed model contributes to the understanding and prevention of train derailments and hazmat release consequences, offering improved accuracy for certain scenarios and train types

This paper presents a simplified physics-informed 1-D model, which could help understand the derailment mechanism and, thus, is expected to estimate train derailment severity more accurately for certain scenarios and train types compared with the latest statistical model. The performance of the braking response and the 1-D model is verified by comparing known ride-down profiles with estimated ones. This validation process ensures that both the braking response and the 1-D model accurately represent the expected behavior.

At the US passenger stations, train operations approaching terminating tracks rely on the engineer’s compliant behavior to safely stop before the end of the tracks. Noncompliance actions from the disengaged or inattentive engineers would result in hazards to train passengers, train crewmembers and bystanders at passenger stations. Over the past decade, a series of end-of-track collisions occurred at passenger stations with substantial property damage and casualties. This study’s developed systemic model and discussions present policymakers, railway practitioners and academic researchers with a flexible approach for qualitatively assessing railroad safety.

To achieve a system-based, micro-level analysis of end-of-track accidents and eventually promote the safety level of passenger stations, the systems-theoretic accident modeling and processes (STAMP), as a practical systematic accident model widely used in the complex systems, is developed in view of environmental factors, human errors, organizational factors and mechanical failures in this complex socio-technical system.

The developed STAMP accident model and analytical results qualitatively provide an explicit understanding of the system hazards, constraints and hierarchical control structure of train operations on terminating tracks in the US passenger stations. Furthermore, the safety recommendations and practical options related to obstructive sleep apnea screening, positive train control-based collision avoidance mechanisms, robust system safety program plans and bumping posts are proposed and evaluated using the STAMP approach.

The findings from STAMP-based analysis can serve as valid references for policymakers, government accident investigators, railway practitioners and academic researchers. Ultimately, they can contribute to establishing effective emergent measures for train operations at passenger stations and promote the level of safety necessary to protect the public. The STAMP approach could be adapted to analyze various other rail safety systems that aim to ultimately improve the safety level of railroad systems.

The expected benefits of newly developed transportation infrastructures are the saving of travel time and further promoted transport economics. There is a need for a methodology of travel time estimation with acceptable robustness and practicability. Macroscopic fundamental diagram (MFD) represents the overall traffic performance at a network level by linking average flow, speed and density. MFD can be used to estimate network state and to describe various traffic management strategies. This study aims to describe the effect of new infrastructure development on the network performance using the MFD framework.

The scenarios of Islamabad Road network before and after the infrastructure construction were simulated, in which the floating car data set (FCD) for multiple modes was extracted. MFD has been formed for the whole region and partitioned region, which was divided on the basis of infrastructural changes. Moreover, this study has been extended to calculate travel time for multiple modes using the MFD results and the Bureau of Public Roads (BPR) function at a neighborhood level.

MFD results for the whole network showed that the speed of traffic improves after the construction of new infrastructure. The travel time estimates using MFD results were dependent on the speed estimates, whereas the estimates obtained using the BPR function were found to be dependent on the traffic volume variation during different intervals of the day. By using the FCD for multiple modes, travel time estimates for multiple modes were obtained. The BPR function method was found valid for estimating travel time of traffic stream only.

This paper innovatively investigates the change in network performance for pre-construction and post-construction scenarios using the MFD framework. In practice, the approach presented can be used by transportation agencies to evaluate the effect of different traffic management strategies and infrastructural changes.

The purpose of this paper is to investigate whether firms with high environmental disclosure have a low possibility of non-standard audit opinions and audit fees and whether this trend is more obvious after than prior to the Measures for the Disclosure of Environmental Information (Measure) implemented in 2008.

Based on the Measures implemented in 2008, the authors select data for the listed manufacturing firms from 2004 to 2006 (Pre-Measure) and from 2009 to 2011 (Post-Measure) as research samples to investigate the relationships between environmental disclosures, audit opinions and audit fees with difference in difference models. In addition, we also consider the influence of media attention, the polluting industry and internal control on the audit effect of environmental disclosure.

The results show that the level of environmental disclosure is significantly negatively correlated with the possibility of issuing non-standard audit opinions and audit fees after measure is implemented, especially hard environmental information. Further evidence indicates that the auditing effect of environmental disclosures is stronger on firms that receive less media attention, in firms with better internal controls, and in firms belonging to industries with heavy pollution.

In the Chinese setting, a high level of environmental information disclosures can effectively reduce the audit risk and lead to a high possibility of standard audit opinions and low audit fees. This effect is pronounced after issuing Measure. The conclusions suggest that measure and increasing environmental disclosure have an obvious positive audit effect and that firms should be forced or encouraged to disclose more environmental information from the perspective of auditors in China.

Innovation convergence is critical to national or regional economic growth patterns. This article provides a systematic review of innovation convergence research through qualitative discussions combined with bibliometric methods. Through this article, researchers interested in the field of innovation convergence can quickly understand the development of the field, quickly identify authors and publications with significant impact, and collaborative networks in the field.

This article is based on the relevant literature included in the WOS database from 1990 to 2021, using Citespace, Gephi and other software to conduct a systematic bibliometric analysis of the research in the new convergence field.

This research shows that the second half of the twentieth century was a boom period for research on economic convergence. 2. The subject foundation of innovation convergence research mainly includes mathematics, economics, political science and computational science. 3. The journals that publish research in this field are widely distributed, including the fields of economics, natural sciences and complex sciences. 4. The research in the field of innovation convergence is inseparable from the research in the field of economic growth.

This study may help others to understand the development history and research trends of the innovation convergence field, as well as the literature and cooperative scientific research institutions that have an important influence.