This study aims to generate a deep learning algorithm-based model for quantitative prediction of financial losses due to accidents occurring at apartment construction sites. Recently, the construction of apartment buildings is rapidly increasing to solve housing shortage caused by increasing urban density. However, high-rise and large-scale construction projects are increasing the frequency and severity of accidents occurring inside and outside of construction sites, leading to increases of financial losses. In particular, the increase in severe weather and the surge in abnormal weather events due to climate change are aggravating the risk of financial losses associated with accidents occurring at construction sites. Therefore, for sustainable and efficient management of construction projects, a loss prediction model that prevents and reduces the risk of financial loss is essential. This study collected and analyzed insurance claim payout data from a main insurance company in South Korea regarding accidents occurring inside and outside of construction sites. Deep learning algorithms were applied to develop predictive models reflecting scientific and recent technologies. Results and framework of this study provide critical guidance on financial loss management necessary for sustainable and efficacious construction project management. They can be used as a reference for various other construction project management studies. For an efficient financial management of construction projects, the demand for a more methodical and reliable loss prediction model is increasing. However, existing risk assessment methods for construction projects do not keep pace with the needs as they rely on qualitative assessment based on experience or knowledge of the construction manager and views of clients and individual contractors2. Moreover, such quantitative evaluation method is challenging to reflect rapidly changing risks of construction work to be evaluated3.To solve the housing shortage in the city center, the construction of apartment buildings, is rapidly growing. High-rise and large-scale apartment construction are increasing the difficulty of construction work. As a result, due to various accident risk indicators that occur inside and outside of a construction project, the risk of financial loss due to accidents occurring in the construction site is quickly snowballing. Uncertainty about the loss prediction is also swelling4. For instance, the safety accident rate at construction sites is cumulative own to the use of heavy equipment and work at heights due to increasing size and height of buildings. In addition, there is a high risk of third-party damage to nearby buildings and pedestrians due to falling objects at building construction sites in the city center5. Noise and vibrations generated from construction sites are also causing problems. Furthermore, recent construction works in mountainous and river areas have experienced a lot of accidents due to geographical disadvantages. These factors are causing enormous economic losses despite the rapid development of new technologies and construction technologies of the fourth revolution such as Internet of Things, unmanned transportation, robot engineering, and 3D printing6. In addition, the severity and frequency of losses caused by natural disasters such as increases of severe weather and extreme weather events due to climate change are expected to gradually increase7. Therefore, it can be seen that the construction industry, which is highly affected by environmental and geographical factors, is exposed to the increasing risk of natural disasters. To reduce the vulnerability of these construction sites, the government is promoting active policies and systems. The private sector is also making efforts to make corresponding investments. However, the construction industry is still having the highest accident rate8. Thus, it is necessary to predict possible accidents and losses in the construction site in advance to establish a strategy for reducing the risk of financial loss and transferring the risk13.Specifically, construction work has a higher risk of safety accidents because there are more outdoor works than indoor works. The difficulty of work is also greatly affected by environmental and geographical indicators. For this reason, among various industries, construction industry is classified as an industry with the highest risk of accidentsHence, the aim of this study is to generate a model that can systematically predict monetary loss of apartment construction sites due to accidents based on empirical data. In other words, this study intends to develop a model for predicting the financial loss of apartment construction sites associated with accidents using real loss data and deep learning algorithms at apartment construction sites. These frameworks and models can be applied to various construction projects in the future. They are anticipated to advance risk management of construction projects.14. However, risk analysis is less active than other construction management elements 2. Moreover, regarding research methodology, quantitative methodologies are suitable in terms of efficient distribution and use of limited resources, which are the ultimate purpose of risk analysis of construction projects. However, qualitative methodologies are mainly adopted for risk analysis of construction projects customarily. Quantitative methodology can be used to develop a quantified model by identifying potential risk factors of construction sites by analyzing objective data and defining the weight of identified factors15. Despite these advantages, the reason that qualitative methodologies are widely adopted in risk analysis for construction projects is that it is difficult to introduce quantitative methodologies due to the complexity and specificity of a construction work2. This is because many construction contractors for each process have the same simultaneity to complete work at a fixed place within the contract period. Since there are many external works, there is a large amount of uncertainty own to the influence of various factors16. Therefore, when considering the uncertainty and specificity of a construction work, quantitative methodologies are more appropriate than qualitative methodologies for risk prediction. In addition, in order to reduce the uncertainty of a construction project and reflect the specificity of the construction project, risk assessment and prediction through a quantitative analysis method based on a scientific approach and statistical analysis of data are compulsory17.Risk analysis of construction projects, can establish strategies to efficiently use limited resources in construction projects and ultimately, lead to successful construction projects by preventing risks and reducing damages in advance, to manage potential risks at construction sites18. For instance, Baker et al.19 have pointed out that experiences and subjective opinions of clients, engineers, and related experts are mainly used in the construction risk assessment technique. In addition, in construction risk analysis and evaluation studies, risks are identified and evaluated by subjective evaluation indicators in addition to experience and expertise3. Moreover, risk assessments are sometimes made based on the experience and knowledge of experts through checklists or surveys of experts20. The reason why qualitative research methods are often used in risk analysis and evaluation of construction projects is because sensitivity analysis is mainly borrowed or risk measures are often used due to the lack of reliable data and limitations in data collection in risk analysis of construction projects25. Therefore, to increase the reliability and accuracy of risk analysis and evaluation of a construction project, it is important to collect and aggregate data that reflect actual losses. As shown in previous studies, various methods have been proposed for risk assessment and analysis of construction projects. However, qualitative research techniques based on the experience and knowledge of experts were largely used. Consequently, in order to analyze and evaluate risks of construction sites scientifically and objectively, quantitative evaluation based on numerical and reliable data is required.Nevertheless, qualitative research methods are generally employed in risk analysis and evaluation of construction projects7. To prepare for such losses, many countries, private organizations, and companies have developed tools for assessing the risk of natural disasters. For instance, HAZUS-MH for multi-hazard risk assessment developed by the US Federal Emergency Management Agency (FEMA) is a representative natural disaster risk assessment model that evaluates losses caused by natural disasters such as earthquakes, floods, and hurricanes26. Florida Public Hurricane Loss Prediction Model (FPHLM) developed by the state of Florida in the United States is a symbolic natural disaster risk assessment model for predicting financial loss and human damage caused by hurricanes27. Probabilistic Risk Assessment in South America and the New Multi-Hazards and Multi-Risk Assessment Method (MATRIX) in Europe are developed to predict economic losses due to natural disasters and to prepare for and reduce losses. These models are being used to evaluate potential risks  from natural disasters such as hurricanes, floods, and earthquakes. Moreover, these models can utilize Geographic Information System (GIS) to integrate various geospatial and social data to evaluate more diverse and comprehensive potential risks28. Although these models can calculate the damage caused by natural disasters for buildings and ancillary facilities, social infrastructure, and human damage, they do not cover damage to construction works.Due to recent increases in the severity and frequency of natural disasters, the loss is swelling. Moreover, the loss caused by natural disasters is expected to surge further due to increases of abnormal climate and severe weather caused by global warming29.As a loss evaluation model for construction works, models of insurance companies and reinsurance companies are representative ones. For example, Swiss Reinsurance Company's Project Underwriting Management Application (PUMA), Munich Reinsurance Company's Munich Re Engineering Expert Tool (MRET), and Willis Re's engineer are demonstrative models. Through these models, relevant companies can calculate risks of insurers and potential customers and use them in consulting to reduce potential risks of customers. These loss assessment models can help insurance companies engineering underwriters and customers understand and calculate potential risks of construction. On the other hand, since these models were designed to be used in the insurance business taking existing customs of the insurance market into account, it is difficult to use them to calculate risks of pure construction projects30. Since these models are commercial models, they are not easily accessible to the general public because their use is limited. The use of them is accompanied by a usage fee. Moreover, these models were developed for some developed countries. Therefore, there are many countries that these models do not cover.In addition, risk modeling specialists  offer commercially available models for risk assessment of construction projects34. Yum et al.15 have used several natural disaster features such as rainfall and wind speed to identify indicators affecting the amount of damage and presented a methodology for estimating the amount of damage caused by tunnel construction. In addition, studies have been conducted to determine the frequency and severity of possible risks of building construction and to calculate the risk of loss using indicators of various categories such as natural disasters, geographic information, and construction information35. Kim et al.14 have proposed a model for computing plant construction loss risk. In their study, indicators affecting the risk of loss were recognized for indicators of numerous groups and a model for calculating the risk of plant construction loss through multiple regression analysis was offered. In one study, a number of risk indicators were defined and a calculation methodology was presented through multiple regression analysis to develop a model to predict repair costs of an international hotel chain caused by natural disasters such as hurricanes, floods, lightning strikes, and rainfall29. As mentioned above, a number of studies have been conducted to calculate the loss of construction work due to natural disasters by the type of construction work. Since the construction environment, geographical conditions, safety, and worker training level are very different for each type of construction work, major risk indicators differ for each type of construction work36. Therefore, in order to predict the loss of apartment construction due to accidents, it is essential to identify risk indicators for apartment construction based on reliable data and develop a predictive model.A number of studies have been conducted for each type of construction work to predict the damage to construction works due to natural disasters. Some researchers have conducted studies to identify possible risks  in the infrastructure37. The many factors caused accidents and the relationships between them make safety management challenging38. Therefore, many existing studies have introduced new frameworks and technologies to analyze and evaluate risk identification and develop effective safety procedures. First, Betsis et al.39 analyzed accidents to identify the associations between various accident characteristics. The methodological approach involves collecting incident-related data, performing descriptive analysis, and corresponding cataloging of accessible data. This study creates a database to house all relevant data and identifies potential trends within the incident sample. The results highpoint the most common accidents associated with accidents related to construction work while also identifying correlations between the various parameters associated with them. Thus, the findings can help reduce the number and severity of work-related accidents. Management's commitment to safety and improved construction site organization with frequent inspections can reduce accidents. Second, Allison et al.37 provide a methodology to calculate the costs of accidents. A 'post-event' approach has been used to determine the average cost of an incident. However, these costs are highly volatile and may differ significantly from the baseline estimate depending on the nature of the incident. Thus, this study used clear scale identification to facilitate internalization of accidents' actual cost and positively inform safety program improvement. Third, Cabello et al.38 used the association rule method of data mining to extract knowledge from historical data of construction accidents. This study analyzed accidents notified via electronic systems, segmenting the data to investigate accidents at each stage of construction. The results reveal patterns and factors with multiple relationships at all stages of the construction. The findings support a framework for improving safety practices, providing a valuable reference for practitioner involved in the construction to improve risk management, preventive measures, and action plans. The following is an example using the Internet of Things (IoT). The IoT is used to improve data collection and information management at a construction site. Emerging technologies are being incorporated at all stages of the project lifecycle, especially during construction. Therefore, integrating technology and safety knowledge is crucial, including real-time decision-making capabilities and knowledge management about risk. In this study, Martinez-Rojas et al.5 built an IoT infrastructure integrated with open source libraries to support expert decision-making. Finally, Lestari et al.40 developed a framework to evaluate the safe environment and improve construction. This study collected and analyzed quantitative and qualitative data. The results reflect, among other things, several issues related to perceived conflicts between construction and safety logic, cost trade-offs over other competing project priorities, unsafe communication, poor working conditions, and accepting poor safety as the norm. Based on these results, a new integrated safety environment framework was proposed to improve the safety performance of the construction industry. Despite many efforts to improve safety, such as the studies above, construction remains one of the riskiest industries. Therefore, continuous efforts and research on construction safety must be made.Accidents during construction are fatal. Despite significant improvements in safety management over the past decades, accidents during construction still occur frequently, and a large amount of money is spent to deal with the accidentsThe purpose of this study was to develop a model to predict the monetary loss of an apartment construction site utilizing a deep learning algorithm based on fiscal loss data due to accidents at apartment construction sites. Detailed objectives of this study were: (1) to collect data on financial loss that occurred at real apartment construction sites; (2) to develop a loss prediction model through a deep learning algorithm based on collected data; and (3) to validate the deep learning algorithm model by comparing prediction results with other model results. For model validation, results of the deep learning algorithm model were compared with results of a multiple regression analysis model universally adopted for prediction.The study was conducted in the following order as seen in Fig. 6. By using claims payout records, the actual financial loss occurring at the construction site of apartment buildings was objectively reflected and reliable results were derived. CAR is an insurance that indemnifies the contractor for economic losses such as structure, third party, equipment, material, and human damage that may occur at all phases of a construction project. CAR is applied to a wide range of construction works such as buildings, plants, roads, ports, bridges, and tunnels. It covers the entire period of construction from the start of the work to the commissioning stage35.This study adopted claims payout records of Contractors' all-risks insurance (CAR) from major Korean insurance companies to reflect financial losses of apartment construction sites. As for claims payout records, the amount was calculated through an objective analysis by a certified loss adjuster. The payment procedure was standardized and the reliability of the data was very high37. Due to the unavailability of non-insured loss related data, such as fines related to violation of safety measures at construction sites and another indirect cost of the accidents, in this paper insurance claims, pure cost of claims, which excludes conditions associated with an insurance policy from each company, was only considered, to avoid difference discrepancy in the data from different insurance companies.Collected data for modelling were from different sources. Financial loss and related information were claim payout records extracted from different insurance companies’ databases. Allison et al. considered the indirect cost and indirect cost of the accident5.The total number of losses was 930. None of the collected information contained personal information. The same dataset contains the construction details: the total construction cost of the project, construction period, construction site location, date of the accident, amount of loss, and detailed loss history. The number of floors of a building has a contribution in increasing the risk of falling objects; as the height increases the use of scaffolding and cranes is unavoidable which induces the risk of falling objects15. The risk rank of tropical cyclone and risk rank of flash flood were identified using the location information of the construction site in this system. Elevation, ENR rank, floor, basement, total construction period, and progress rate were entered as numeric variables. Risk rank of tropical cyclone, risk rank of flash flood, season, location, loss classification, construction type, and construction size were also considered as nominal variables. A detailed description of each indicator is given in Table 41.In addition, claim payout data set contains natural hazard related variables such as, elevation, risk rank of tropical cyclone, risk rank of flash flood, and season. The specific information of the apartment construction site , number of floors, number of basements, total construction period, construction type, construction size, progress rate) were also collected. The natural disaster risk information was considered based on the location information of the collected data. In order to objectively and scientifically reflect natural disaster risk, the Natural Risk Assessment Network (NATHAN) of Munich Reinsurance Company was utilized to define several natural disaster risk levels. NATHAN is an online natural disaster risk level system that displays levels of risks of various natural disasters around the world, such as earthquakes, floods, lightning strikes, tsunamis, and typhoons. The natural disaster risk level of this system is independent and dependable as it is a synthesis of various studies and data such as simulation tools, the frequency and severity of past natural disasters, and public data42. Ahmed established a very long list of causes accidents at a construction site in details43. Lee, G et al. identified that the working elevation, weather, construction size etc. contribute in accidents44. The personal information of the claimant such as age, year of experience, and type of accident was not used though there is a likelihood of an increase in the cost of accident associated with this information39.The variable season changes and affect change in other natural hazards related variables such as flood tropical cyclone which occurs during a given season. As the seasons differ their effect on accidents also differ; summer and spring were associated with higher number of accidents41. Safety measures of the construction site and injured body part of the claimant, whether safety-related protection such as helmet and other related safety protection data were not available in the dataset, so they were not used in this study40.The cost of accidents such as penalty and legal data were difficult to find and were not taken into account46. In order to reduce and prevent the risk of financial loss due to the specificity and uncertainty of construction projects and to break away from the stigma of being a traditional risky industry, active introduction of a new paradigm is essential. Moreover, several technologies such as ICT (Information and Communications), sensors, and IoT (Internet of Things) recently announced for construction sites not only can promote user safety, efficiency, and convenience, but also can accumulate huge amounts of data. Since the use of deep learning technology is inevitable to analyze such vast amounts of big data, the demand for deep learning technology is expected to further surge. Therefore, this study proposes a framework for developing deep learning technology for the analysis of big data related to construction projects. It tries to contribute to the reduction and prevention of financial losses that may occur in future construction projects by developing predictive models.Nanotechnology, robotics, 3D printing, big data, unmanned transportation, artificial intelligence, and so on referred to as the 4th revolution technologies are recognized as new paradigms. Their use is rapidly increasing as they are converged in innumerable industrial fields46. Deep learning algorithms are typically classified into Convolutional Neural Network (CNN), Auto Encoder (AE), Generative Adversarial Network (GAN), Recurrent Neural Network (RNN), and Deep Neural Network (DNN) according to their processing scheme and configuration. For instance, DNN is a symbolic neural network with voluminous combinations and number of hidden layers. It is mostly approved to model and train complex nonlinear interactions48. DNN can implement artificial neural networks of various structures. It is broadly adopted for prediction and classification in numerous areas49. Considering the versatility of the model and features of input and output data, this study employed the DNN algorithm and proposed a model framework for predicting economic losses of apartment construction projects.Deep learning technology is a kind of machine learning technology for regression and type classification of input data. A model is formed using various components such as hidden layer, output layer, input layer, neuron, activation function, and weight. Since a combination of these various components is possible, deep learning technology can be used for various data. Own to these various arrangements and applicability, it is used in countless industries and academia. It is predominantly and widely accepted in recognition and prediction fields50. Since both RMSE and MAE values calculate the deviation between actual and predicted results, they are suitable for expressing the precision of the model. RMSE is a method of expressing the residual between the actual value and the predicted value of a model as one measure. The greater the RMSE value, the greater the prediction error. MAE is an absolute value that is averaged by adding residuals of the actual value and the predicted value of a model. The higher the MAE value, the higher the prediction error. Of total data, 70% were nominated as learning data and the remaining 30% were adopted as test data. Of the learning data, 30% were designated as validation data. The input data was preprocessed utilizing the z-score normalization method to normalize data distribution.The established model was confirmed using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) as representative evaluation indicators of artificial neural network models51. The reason is that the DNN model uses the backpropagation algorithm to modify the model's node weight since the ideal combination varies provisional on input and output variables. In the network structure scenario, the number of layers and the number of nodes is specified. In hyper-parameter tuning, optimal hyper-parameter components  are set through several combinations. The optimizer controls the stability and the speed of data learning of the model and the activation function determines how to define the least cost function. The batch size sets the data learning unit of the model to designate efficient learning and the epoch determines the number of data learning for the model. Dropout is a kind of regularization penalty to avoid the performance of the deep learning model from falling through overfitting52. This study deliberated the limited amount of data. The dropout was set to be 0 or 0.2 with three layers. Simulation was performed to find the optimal combination. Adaptive Moment Estimation (Adam) was adopted as an optimizer. Adam Method is a kind of optimization algorithm with the theory of moment of a stochastic objective function. This algorithm is an extensively accepted optimizer because of its convenience and versatility in computation53. The Rectified Linear Unit (ReLu) function is agreed as an activation function. ReLu is a function developed to complement the current sigmoid function. It is an activation function that alters the output according to the value greater than or equal to 054. In addition, the epoch was set to be 1000 and the batch was set to be 5.In order to make an optimal model of the DNN algorithm, it is important to discover the optimal network structure scenario and hyper-parameter tuning for the data over trial and error55. Table The above algorithm Fig.  begins w56. Simulation results, are summarized in Table 58. MAE and RMSE values were assessed to compare results of the DNN model and the MRA model. The MRA model was established using the IBM Statistical Package for the Social Sciences (SPSS) version 23. Multiple regression analysis is a regression analysis method that estimates the fundamental correlation between variables utilizing a statistical method. It is generally agreed in the field of prediction59. As a result, paralleling result values of the DNN model and the MRA model, the DNN model exhibited a lesser prediction error rate of 41.3% in MRA and 46.0% in RMSE than the MRA model. This better performance of backpropagation algorithm compared to MRA model was also noticed by Wei and Yang56.The final network structure and hyper-parameters are displayed in Table 60.This study proposed a framework for developing a model to predict financial losses of apartment construction sites employing the DNN algorithm, one of deep learning algorithms. For data collection, data on financial loss occurring at actual apartment construction sites were collected utilizing claim payout records of an insurance company and variables were gathered. With the purpose of developing an optimal model for learning input and output variables, network scenarios and hyper-parameters were firm over and done with trial-and-error methods. The final DNN model was verified by comparing prediction results of validation and test data and additionally by matching prediction results with the MRA model. As a result of model contrast, the DNN model presented 41.3% lesser MAE and 46.0% lower RMSE than the MRA model, indicating that the DNN model had a lower prediction error rate than the MRA model. In addition, it was demonstrated that DNN was a further consistent model for expecting the financial loss of an apartment construction project because it displayed a lower prediction error rate than the MRA model. This is because the nonparametric model DNN is more adequate than the parametric MRA model to analyze the uncertainty and nonlinearity of financial loss data of apartment construction sitesThrough the developed model of this study, the client or contractor of an apartment construction project can predict the loss of a construction site or use the framework of this study to develop a deep learning prediction model according to situations of other apartment construction projects. In addition, since the prediction result showed a lesser prediction error rate than the existing model, more accurate and robust prediction will be possible. Based on this loss prediction, the client of an apartment construction project can make various investments to reduce and prevent expected loss. For example, before starting the construction of an apartment project, the client can prepare various measures to reduce and prevent expected losses and actively invest in facilities. Besides, it is possible to prepare for unexpected losses by preparing an emergency reserve in consideration of the expected loss at the phase of financial planning. The project owner can set a plan for financial risk in consideration of owner’s asset size and risk preference and utilize it to accomplish economic soundness and continuity of the project. This model can be helpfully adopted to expand insurance coverage and purchase special contracts to transfer the risk of additional economic loss. Furthermore, it can be potentially applied at an appropriate rate level for insurance premiums for current apartment construction projects or for future construction projects. By predicting, preventing, reducing, and transferring these economic losses, it will be conceivable to reduce economic losses that may occur in the future.64. Under these circumstances, there have been many studies to find the relationship between construction projects and natural disasters. For instance, in hurricane-stricken areas, it has been shown that the distance between the hurricane's strong wind and the shoreline is sturdily related to the damage to buildings caused by the hurricane66. The present study confirmed that natural disaster risk indicators such as elevation, flood risk, and strong wind risk were closely related to the risk of financial loss at construction sites, supporting findings of previous studies. Moreover, natural disasters are expected to increase in severity and frequency owning to climate change. Thus, they can be established as events that have a profound negative impact on construction projects15. Results and framework of this study can be utilized to prevent and reduce financial loss from natural disasters, ultimately achieving financial reliability of construction projects. Furthermore, considering the impact of the construction site due to natural disasters, it is necessary to excavate additional variables centered on the construction site, such as weather and geographic location of the construction site. Consequently, in the future, it is necessary to develop an additional risk assessment model that considers other damages  at construction sites that reflect these additional indicators.In addition, the developed model can be employed in a model to analyze and predict financial loss in other industrial fields or industrial sites in the future. Thus, it can be widely used in the private or public sector. Besides, abnormal climate and severe weather  caused by climate change due to global warming are global challenges that affect various fields such as society and economyFurthermore, ICT (Information and Communications), sensors, and IoT (Internet of Things) related technologies and devices are being actively introduced into construction sites recently. Since these technologies and devices will produce a huge amount of big data, this study will be a basic research for studying such big data. However, this study collected data from claim payout records of apartment construction sites that occurred at a Korean insurance company. Therefore, additional research is needed to perform comparative analysis and verification through additional data collection from other insurers and various countries. In addition, to advance the model, extra research is necessary based on increased amount of data through the collection of additional data and qualitative increase through identification of added variables. Moreover, since this study collected and analyzed data from only apartment construction sites, further research on other types of construction projects is needed. More research is also needed to determine risk indicators through data collection of other types of construction projects and compare results and frameworks with this study.67. By developing a model through XAI, it will be possible to understand the results and decision-making of the AI model and to trust the results, thereby relieving users' anxiety about black box results and improving the insight into big data and models.Into the bargain, this study reduced prediction errors by building a model using the DNN algorithm. However, interrelationships and weights between nodes are in the black box of the DNN algorithm. This shows that AI can unilaterally provide results of predictions with a fatal limitation in logically explaining the basis and process for drawing the results, which may negatively affect the reliability and fairness of future models. Therefore, in future AI model research, users need to be provided with explanations about the decisions and results of AI models. Thus, technologies such as explainable AI (XAI) that can increase users’ understanding and confidence in AI models through a glass box rather than a black box introduction are needed. XAI is one of artificial intelligence technologies that can explain to users about decision-making and causality as well as classification and prediction through existing big data analysis algorithmsIn this study, limitations in its prediction and usage due to the data limitation has been enhanced. More specifically, it is a specialized predictive model for only safety accidents in apartment construction projects by collecting only safety accidents data that occurred in apartment construction, a major residential type in Korea. In addition, based on the collected safety accident data, more variables related to safety accidents were collected and used in the predictive model. Therefore, by using this model, by predicting possible safety accident losses in apartment construction, construction entities such as insurance companies, construction companies, and ordering companies can easily and accurately predict the amount of safety accident losses, which can be used for safety accident execution or budgeting. Also, in DNN model setting, when hyper-parameter tuning is performed, more optimal hyper-parameter components  are tested based on the past study to find the optimal combination, and more network structure scenarios were tested to improve the model's completeness. Hence, compared to previous similar researches, this study provides more advanced and significantly enhanced model focusing on safety accidents of specific occupancy.Recently, the scale of financial loss in construction works is rapidly increasing due to increasing sizes of construction projects, the complexation of buildings, and the increase in urban construction. Moreover, increases in the frequency and severity of natural disasters due to climate change are emerging as new risk factors of construction works, thus increasing the uncertainty of construction works. For well-organized and maintainable management of construction projects, a delicate and robust loss prediction model is essential to predict financial losses and establish strategies for risk management such as prevention, reduction, and transfer of risks. Therefore, this study recommended a framework for developing a predictive model using the deep learning algorithm based on loss data generated at apartment construction sites.In this study, a model using the DNN algorithm was developed for predicting the cost of financial losses at apartment construction sites. It was verified through comparison with other models. As a result of verification, the DNN model developed in this study showed a lower prediction error than the existing model in predicting financial losses at apartment construction sites. It will contribute to the prediction of financial losses at construction sites. Therefore, it can be an effective method to predict financial losses of apartment construction sites using results and framework of this study. It is also a reference material for reducing financial management and loss risk of future construction projects. In addition, by using results and framework of this study, it is judged that it can be widely applied to other industrial sites, other research fields, and other construction types of projects. Thus, it will ultimately lead to decreased financial losses of industrial sites. Besides, it will be possible to upgrade to a reliable model through continuous research such as continuous data collection, addition of variables, and supplementary verification of the model. Additionally, risk factors displayed in this study  could be used as reference materials for predicting financial losses of government agencies or the private sector. For example, government agencies can use results of this study to prepare systems and standards for preventing and reducing damage at apartment construction sites. For areas vulnerable to natural disasters, insurance companies can use results and framework of this study to manage risks by setting various insurance conditions such as portfolio composition, probable maximum loss, event limit, and appropriate premium according to the company’s risk appetite and asset size.


Access to up-to-date patient medical history is essential for dental clinicians (DCs) to avoid potential harm to patients and to improve dental treatment outcomes. The predominant approach for dental clinicians (DCs) to gather patients' medical history is through patient-reported medical histories and medical consults. However, studies reported varied concordance and reliability of patient-reported medical conditions and medication histories compared to the patient medical records and this process also places a significant burden on patients. Information technology tools/platforms such as an integrated electronic health record containing an electronic dental record module may address these issues. However, these integrated systems are expensive and technically complex and may not be easily adopted by DCs in solo and small group practice who provide the most dental care. The recent expansion of regional healthcare information exchange (HIE) provides another approach, but to date, studies on connecting DCs with HIE are very limited. Our study objectives were to model different aspects of the current approaches to identify the strengths and weaknesses, and then model the HIE approach that addresses the weaknesses and retain the strengths of current approaches. The models of current approaches identified the people, resources, organizational aspects, workflow, and areas for improvement; while models of the HIE approach identified system requirements, functions, and processes that may be shared with software developers and other stakeholders for future development.There are three phases in this study. In Phase 1, we retrieved peer-reviewed PubMed indexed manuscripts published between January 2013 and November 2020 and extracted modeling related data from selected manuscripts. In Phase 2, we built models for the current approaches by using the Integrated DEFinition Method 0 function modeling method (IDEF0), the Unified Modeling Language (UML) Use Case Diagram, and Business Process Model and Notation (BPMN) methods. In Phase 3, we created three conceptual models for the HIE approach.From the 47 manuscripts identified, three themes emerged: 1) medical consult process following patient-reported medical history, 2) integrated electronic dental record-electronic health record (EDR-EHR), and 3) HIE. Three models were built for each of the three themes. The use case diagrams described the actions of the dental patients, DCs, medical providers and the use of information systems (EDR-EHR/HIE). The IDEF0 models presented the major functions involved. The BPMN models depicted the detailed steps of the process and showed how the patient's medical history information flowed through different steps. The strengths and weaknesses revealed by the models of the three approaches were also compared.We successfully modeled the DCs' current approaches of accessing patient medical history and designed an HIE approach that addressed the current approaches' weaknesses as well as leveraged their strengths. Organizational management and end-users can use this information to decide the optimum approach to integrate dental and medical care. The illustrated models are comprehensive and can also be adopted by EHR and EDR vendors to develop a connection between dental systems and HIEs. The predominant approach for dental clinicians (DCs) to gather patient's medical history is through patient-reported medical histories and medical consults. Studies that evaluated dental patient-reported medical conditions and medication histories reported varied concordance and reliability compared to the patient medical records –6, partiThe application of an electronic dental record (EDR) as a module within an electronic health record (EHR) system (hereby referred to as integrated EDR-EHR) is one of the approaches that can address the issues mentioned above and streamline the information gathering process. It can also help bridge different health care settings to achieve a safe, effective, and efficient use of patient-provider interaction time . The impSince 2009, several federal policies such as the Health Information Technology for Economic and Clinical Health (HITECH) Act and the 21st Century Cures Act has led to regional and statewide development of Health Information Exchange (HIE) platforms with increased interoperability between HER's . The finTherefore, as the first step to developing an effective HIE approach between medical and dental systems, it is essential to review the existing literature, model the strengths and weaknesses of the current approaches, and identify the HIE approach's critical technical requirements and functions for future development. Modeling has been widely used in the health care industry for process optimization and automation , 27. TheThere are three phases in this study. Phase 1 included a scoping review of existing literature to identify the functional and organizational elements involved when DCs obtain patient medical histories through current approaches. These results built the context of the models and the boundary of the system. Phase 2 illustrated the conceptual models of the current approaches, and Phase 3 modeled the HIE approach and presented the strength and weaknesses between the current approaches and the HIE approach.The purpose of the literature review is to further expand our understanding of the processes for DCs to obtain patient medical history during a patient's first visit to a dental clinic. The review is a necessary step to ensure that the models built in this study are generalized and can be adopted by others. We chose PubMed as the database to search for literatures, since the review is focused on the clinical processes not the modeling techniques. The search was performed using the following keywords: health information exchange, medical records, health care process modeling, electronic dental record, integrated electronic dental and medical record systems, and medical consults.Three researchers  conducted a comprehensive search of PubMed for literatures published between January 2013 and November 2020. Boolean operators such as “OR” and “AND” were used as conjunctions to combine keywords. To be included in this study, the manuscripts had to be English, PubMed indexed, and full length. All researchers first screened the titles and abstracts of each manuscript and identified eligible ones. Only articles focusing on clinical workflows for accessing a patient's medical history were included in the study. Articles on the significance of accessing a patient's medical history and modeling techniques were excluded. Additional manuscripts were also identified through references listed in the eligible ones. Next, the full-length manuscripts were reviewed. The three researchers conducted the reviews independently. The disagreements among researchers were discussed and resolved by consensus. Identified manuscripts were grouped into different themes by the whole research group, including domain experts and informaticians.The functional and organizational components described in the manuscripts were extracted. Functional aspects involved the activities, objects, and data managed in a process. Organizational aspects described the actors, roles, skills, authorizations over activities, and information management.We modeled the two current approaches for DCs to obtain patient medical information. The processes start from when a patient making an appointment with a dental clinic and ends with either the patient moving on to receiving treatments or being discharged. The illustrated models seek to capture the interdependencies at a system level.First, supported by our scoping review, we established high-level system requirements by using UML use case diagrams that identify the participant requirements and boundaries of the system. Second, by using the IDEF0 function modeling method, we determined the functions of the processes. Third, the BPMN models depicted the detailed steps and the information flow within the processes. The models represent a top-down approach beginning with a high-level view and then proceed to refine all the details of the most critical functions, increasing the specification toward identifying basic activities , 32. TheUse case modeling has been used to evaluate different health care environments to improve screening, documentation and ease of data access . Use casAfter specifying the key functions in the use case diagram, we built the IDEF0 function models. IDEF0 models are used to model the decisions, actions, and activities of a system , and canBPMN has become the de facto standard for business process diagrams . We inteAn initial expert validation was conducted to check the designed conceptual models corresponding to the existing approaches aligning to the needs and the practices of DC's. These domain experts were chosen not only because of their expertise in dental field but also their expertise in health informatics. These experts in the past two decades, have conducted workflow studies in US general dental practices and characterized the dental team's roles, workflow, and tasks during patient examination, and treatment planning –49. The The domain experts met as a group and reviewed the models. Questions arising during the interactions were noted and discussed, and the models were refined based on the group's consensus. Common questions were about the responsibilities on whom has the first point of access to a patient's medical history, and what the actors had to perform in a dental clinic. For example, the researchers had initially considered to use the same set of models for both integrated EDR/EHR approach and HIE approach; but later based on the domain expert's questions and comments, identified differences in data sources and information retrieving methods. This new knowledge resulted in the creating of two separate sets of models for the two approaches. This refinement process was performed iteratively, until there were no further requests from the domain experts and the team achieved a total agreement.A total of 47 full-length text manuscripts were identified . The manThe patient-reported medical history following by medical consults approach had the following actors: the patient, the DCs, and medical providers . The patIn the last three activities, patients worked with DCs to answer questions, provide patient-reported medical history, and schedule appointments. The DCs were involved in six activities. In these use cases, they examined the patient, interviewed the patient, reviewed the patient-reported medical history, initiated medical consults, scheduled further appointments, and completed the examination and medical history. This actor represents not a single person or role but a group of people, including dentists, hygienists, and dental assistants. In addition, administrative staff in a clinic are also consider as part of the patient care team especially in medical history gathering process. The medical providers involved in the three activities related to medical consults. We only used two providers in the model to simulate the possible scenarios, such as notifying the medical provider during medical consults. When DCs do not receive the requested information on time, they would resend the request to the same medical provider or contact a different provider such as a specialist or a healthcare organization. This process would be repeated until the requested information was received.There is one more actor, the EDR-EHR system, in the integrated EDR-EHR approach model . IntegraTwo levels of the function models were created for all approaches. The top-level model  containeThe patient reported medical history following by medical consult approach model  had eighThe shaded block (A11) in the integrated EDR-EHR model  belongs The BPMN process models were built based on the UML use case diagrams and the IDEF0 function models. There are three roles in the models  in the patient reported medical history following by medical consults approach . We modeThe integrated EDR-EHR approach model  has two The steps to access patient medical history through HIE and integrated EDR-EHR systems are similar , 10, 11.We also compared the current approaches and the HIE approach. The advantages and disadvantages of each approach were listed in Accessing up-to-date patient medical histories is essential for providing high-quality dental care. However, previously published works were focused on evaluating the quality of the collected patient medical histories, observing the benefits of the integrated EDR-EHR systems, and teaching dental students to communicate with medical providers and patients –69, 89. Medical consults will still be an essential function in the approaches using information technologies (both integrated EDR-EHR and HIE). It is the best approach for dental clinicians to get information on rare conditions and complex situations, including when dental clinicians need expert interpretations on the information. Information technology tools cannot, and we should not allow them, to be the only source for the management of all patient's medical information. Whether a complex integrated EDR-EHR system or an interface to retrieve data from an HIE is used, information technology tools should focus on the 80% of the most required interoperability needs . In clinDCs' information needs are critical for future process optimization and software tool design. These can help to decide the size of the data and to choose the correct design and technology. The right set of information can dramatically improve the tool's usability and the efficiency of the process. If the clinicians can get the information they need directly, there will be fewer repeated steps. Clinicians' information needs have drawn attention in recent years, as reflected in prior studies on medical providers' information needs in an integrated medical-dental care system . HoweverMedical providers have been using HIE to retrieve patient's medical histories for more than a decade. Although HIE use shows improved healthcare benefits, variations exist in using HIE tools even with emergency departments which have logged higher HIE usage rates to access patient information , 87. ThiTo date, most dental clinicians are still working in private independent practices. The high financial and technical demands of a complex information system make it very difficult for small independent practices to adopt these new technologies , 20, 25.The study modeled the user activities, the functions, and the processes of a new patient's first visit to a dental clinic. This modeling work is a valuable step in future development of information technology tools and further optimization of communications processes to support interprofessional collaboration during dental care. However, there are some limitations as it is our first effort to model this complex system. An expanded systematic review including gray literature and current applied models across geographies will be helpful to increase the generalizability of the models. We are also aware of the potential publication bias because small practices may not publish their work. This work is only the first step which help us to setup the framework and protocol on a much larger scale of study. We have planned surveys, key informant interviews, and focus group studies to invite DCs across US to provide their opinions and perceptions so we can further optimize the models and make them more comprehensive and accurate. In the current study, we only focused on modeling the activities, the functions, and the processes within the system. Other elements such as the data/information exchanging in the system, the interfaces between different subsystems, and the standards/rules applied to the system were not covered. The models can also be further broken down. The validation of the current models was conducted by domain experts and informaticians, future studies should expand the validation efforts with other stakeholders including patients, vendors of information technology tools, and healthcare organization managements to further refine the models.We successfully modeled the DC's current approaches of accessing patient medical history and designed an HIE approach that addressed the current approaches' weaknesses as well as leveraged their strengths. Organizational management and end-users can use this information to decide the optimum approach to integrate dental and medical care. The illustrated models are comprehensive and can also be adopted by EHR and EDR vendors to develop a connection between dental systems and HIEs.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.SL and AR contributed to the creation of the models. AR, GF, and SL conducted the literature review. TT, TS, and EM were the domain exports who reviewed the models. SL, AR, and TT made significant contributions in the drafts of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Neuromuscular fatigue is defined as a reduction induced by exercise in the maximal voluntary force that a muscle or group of muscles can generate. An accumulation of work or an incomplete force restoration can significantly influence the neuromuscular performance in both the short and long terms. Thus, fatigue management is essential for controlling the training adaptations of athletes and reducing their susceptibility to injury and illness. The main individualized monitoring tools used to describe fatigue are questionnaires and subjective assessments of fatigue, biochemical markers, sprint tests, and vertical jump tests. Among the subjective measures, the rating of the perceived exertion has been widely used because of its simplicity and high validity. In terms of the objective measures, one of the more frequently employed tools by practitioners to assess neuromuscular fatigue is the countermovement jump. Because of its high validity and reliability, it is accepted as the reference standard test in sports, in general, and particularly in team sports. Our review aims to clarify how all these indicators, as well as several devices, can help coaches in different sports contexts to monitor neuromuscular fatigue, and how these procedures should be used to obtain data that can be used to make decisions in complex environments. The word “fatigue”, which comes from the Latin word, “fatigare”, has an original meaning of “to cause to break down”, or “to tire” . DiffereNeuromuscular fatigue (NMF) is a reduction in the maximal voluntary force induced by exercise, with neuromuscular function changes that are due to repeated or sustained muscular contraction, and that are produced either at the peripheral or central levels, and that can be detected for upwards of 48 h to an extended period ,8,9. PerThe accumulation of fatigue or incomplete recovery can significantly influence the team sports performance, especially during regular competition with a congested fixture calendar, which can have acute and chronic harmful effects . If the After fatiguing exercise, the time course and short-term recovery mechanisms are largely dependent on the properties of the previous exercise bout and the recovery time . Balsom The purpose of the review is to describe the information available about the effect of neuromuscular fatigue on the sports performance, decreasing the motor control, and, consequently, the sports injuries. The existing methods to evaluate this marker and assess fatigue in high-performance contexts are proposed for the control of the training load and a better recovery.The management of fatigue is essential for controlling the athletes’ training adaptations, for ensuring that they are ready for competition, and for reducing their susceptibility to injury and illness. In team sports, a reliable tool to monitor fatigue should be sensitive to the training loads and their magnitudes, and should differentiate the acute responses to exercise from the chronic changes .The psychobiological state by prolonged periods of demanding cognitive activity  affects the individual perception of fatigue . Mental The acute responses and recovery times after practices and competitions can be assessed using diverse biochemical, hormonal, and immunological markers that are measured from the blood or saliva . The endThe most used biochemical markers to evaluate the responses to different workloads, training stresses, and recoveries are testosterone and cortisol . TestostElectromyography (EMG) refers to the collective electric signal from the muscles controlled by the nervous system that is produced during muscle contraction . The EMGSprint tests have been widely used to describe the NMF of athletes and their performances in various sports, and most of them use similar sprint distances, such as 5, 10, and 20 m ,45,46,47The benefit of vertical jumps as a practical measure of NMF is indicated by the high degree of the adoption of these testing procedures in high-performance sports settings. These tests have been used in many studies to investigate the recovery times from demanding practices or competitions . VerticaSeveral Vertical Jump Tests Exist . NeverthThe countermovement jump (CMJ) is the vertical jump test that is more frequently used to assess the jumping performance and the neuromuscular status. Previous works have studied its validity and reliability compared to other VJs .Many factors can influence the CMJ. The main factors cited in the bibliography are:The countermovement depth: The protocols describe that the jump initiates with the participants in an upright position before executing the vertical jump, which starts with a countermovement until the legs are bent down to 90° . DespiteThe arm swing: The arm swing influences the vertical jump performance, and increases the jump height compared to vertical jumps without an arm swing ,63,66,67The jumps considered for the analysis are another factor of the variability between the studies. A meta-analysis of the CMJ test to monitor the neuromuscular status determined a predominance of studies using the highest CMJ performance value for their analyses. However, when comparing the highest and average results, the average jump results were more sensitive than the highest jumps in identifying fatigue or the effects of supercompensation . A systeDespite being the most commonly used test to evaluate the vertical performance, there is no general agreement in terms of the CMJ protocol. The most common description in the literature follows the upcoming rules: the CMJ has to be performed with the participants’ hands on their hips and starting from an upright static position with their legs straight. The participants have to be instructed to squat by bending the knees at approximately 90° angles as quickly as possible. Then, they should jump as high as possible, keeping the legs straight, and landing with both feet together ,72,73,74The countermovement jump (CMJ) is one of the main tools used to examine the level of the neuromuscular status in elite sports. Because of its reliability and validity, the CMJ test has become the “gold standard” test for monitoring neuromuscular fatigue in high-performance sports settings . Other aThere have been many attempts to define the fatigue induced in the neuromuscular function through CMJ assessments in team-sport athletes. In a scientific work by Gathercole et al. , the intThere are several technologies available on the market to measure the CMJ:Force platforms (FPs): Force platforms are considered the “gold standard technology”, which measure the force exerted on it by the subject ;Contact mats (CMs): These are electric circuits that are mechanically activated by pressure, and most of them use the flight time to indirectly estimate the jump height ,79;Photoelectric cell (PC) systems: These systems measure the flight times with two parallel bars: one receiver unit and one transmitter unit ;Local positioning systems (LPS): These are based on either global positioning systems (GPS) or indoor positioning systems, with accelerometers and gyroscopes integrated into the device to calculate and perform corrections to the vertical acceleration recordings ;Phone apps: These are based on the detection of the initial and final phases of the jump through high-speed recording technology ;Accelerometers: These classify movements in the vertical axis as jumps, and quantify the vertical component of each jump using a proprietary algorithm .Traditionally, the validation of new technologies to assess jumps has been carried out using FPs. When FPs were compared with CMs, Steinman, Shirley, and Fuller  describeThe main advantages and disadvantages of the presented technologies are described in Monitoring team sports activity and its recovery can inform athletes’ fatigue . FurtherThe present narrative review describes NMF and the complex processes that cause this specific type of fatigue. NMF has been reported as a reduction induced by exercise in the maximal voluntary force produced by a muscle or a group of muscles, and to understand its extent is pivotal because of its consequences on sports performances and athlete statuses. Various NMF monitoring procedures have been used in the past, but not all of them are suitable in team sports. For example, in team sports, biochemical markers are not used for their methodological limitations. The questionnaires and subjective assessments of fatigue are not accurate because the perception of effort and fatigue is multidimensional. However, performance tests  are the most used for their practical application in team sports training. Furthermore, not all these monitoring strategies provide the same information on how athletes respond to training and nontraining stressors. This article reviews the main technologies used and their advantages and disadvantages in terms of the cost, the time needed to gather and process the information obtained, as well as in terms of the validity and reliability. We recommend that coaches and practitioners decide which are the most appropriate for their particular situations, but ecology is the most important of these procedures in high-performance sports settings.


Early childhood fall is a pressing global public health problem and one of the leading causes of child injury. China has a high proportion of children and a high burden of illness from falls. Therefore, educational interventions to prevent childhood fall would be beneficial.We used the outcome of knowledge, attitude and practice questionnaire, which was conducted by Pudong New District of Shanghai Municipal Government, to summarize demographic and baseline characteristics grouped by intervention or not, and analyzed descriptive statistics of continuous and categorical variables. A logistic stepwise function model was established to study the influence of different covariables on the degree of injury, and AIC/BIC/AICC was used to select the optimal model. Finally, we carried out single-factor analysis and established a multifactor model by the stepwise function method.P < 0.001].Attitude and actual behavior scores had significant differences. The intervention and control groups had 20.79 ± 3.20 and 20.39 ± 2.89 attitude scores, respectively. Compared to the control group (5.97 ± 1.32), the intervention group had higher actual behavior scores (5.75 ± 1.50). In the univariate analysis results, fathers' education level, mothers' education level, actual behavior and what cares for children had a significant influence on whether children got injured. In multivariate analysis, attitude had a positive influence on whether injured [odds ratio: 1.13 (1.05–1.21), Educational intervention for children and their guardians can effectively reduce the risk of childhood falls, and changes in behavior and attitude are the result of educational influence. Education of childhood fall prevention can be used as a public health intervention to improve children's health. Common childhood injuries, such as road traffic injuries, burns, drowning, poisonings, and falls, occur frequently, with unintentional childhood falls being one of the most common injuries that happen to children and an important cause of childhood morbidity and mortality –3. Most Falls in young children are more likely to be overlooked than falls in the elderly. In the United States, childhood fall are only one of the leading causes of non-fatal injuries between the ages of 0 and 4, and less than one tenth of falls in older adults . ChildreTherefore, based on an educational intervention study on children's injury conducted by Pudong New District of Shanghai Municipal Government, we plan to use the Knowledge, Attitude/Belief, Practice (KAP) model to explore the effect of intervention on early children's falls and provide a theoretical basis for the prevention of their falls to help the government and relevant departments carry out primary prevention of falls.The design of this study was based on a KAP questionnaire collected by the Science research and information management section, Shanghai Pudong New District Center for Disease Control and Prevention (SCDC). This survey mainly targets 0–6 years old children and their parents in Pudong New District, Shanghai. The purpose of the survey is to investigate the risk factors related to child injury, and the situation of injury. This trial has been reviewed and approved by the Institutional Review Board (PW2014B-1). All participants and their guardians knew the information and signed the consent form.Before the investigation, the staff coordinated the investigators of the community health service center and neighborhood committee to conduct centralized training on the baseline investigation to ensure the implementation of the intervention. Then, during the training, they share their experience and exchange the problems that the trainees may encounter in the investigation process.After accepting the suggestions of the neighborhood committee and the selection of geographical location, the project team randomly selected two kindergartens in Yangjing and Puxing communities (Boshan Kindergarten and Dandelion Kindergarten) to carry out a baseline questionnaire survey, which lasted for 2 months (from December 2014 to January 2015). Parents of children aged 0–4 were randomly selected from the neighborhood committee and kindergartens as the survey objects. From January to February 2015, all questionnaires were subject to quality control, and the questionnaires with illogical problems were reinvestigated. Afterwards, qualified questionnaires were entered uniformly twice by specialists. Finally, after cleaning, sorting and logically verifying the database, the working group statistically analyzed the data and finished the baseline survey report.The entire trial design can be divided into three phases, lasting from November 2014 to May 2017 . The queBefore the investigation, the staff coordinated the investigators of the community health service center and neighborhood committee to conduct centralized training on the baseline survey to ensure the implementation of the intervention. Experience sharing meetings were organized during the training to help investigators learn, communicate and provide solutions to problems that may be encountered in the investigation process.The intervention was conducted from January 2015 to December 2016. It adhered to the injury tertiary prevention principle. The working group mainly delivered health promotion and health education as the primary prevention measure to prevent risk factors for children falling identified during the baseline interview and the baseline survey. Meanwhile, the working group paid special attention to the environmental tactics of injury prevention. They raised community residents' safety awareness of children falling by conducting evaluations of the community environment and helped promote a safer living environment.The intervention included falling prevention at home , falling prevention at sports  and falling prevention at schools . Through different health education targeted for children, adults and teachers respectively, the intervention helped raise the entire community's safety awareness to prevent children from falling.Children's Home Safety, Winter Break Safety Manual, Intervention Manual for Falling (student version), Intervention Manual for Falling (parent version), Intervention Manual for Falling (teacher version) several times to the intervened communities and kindergartens. They also posted educational posters with information revolved around prevention from falling high, prevention from athletic injury and prevention from traffic injury on protruding areas of bulletin boards in communities and kindergartens. In addition, they distributed cartoon videos about injury prevention in kindergartens and required children to watch at least once quarterly. Moreover, they also promoted the WeChat subscribed account  of the Pudong CDC among residents. The working group also invited experts on injury prevention to give relevant lectures in the intervened communities in January 2017. The audience was consisted of parents of 0–6-year-olds. The lecture covered topics from basic knowledge of children's injury, high-risk locations of injury, common reasons for children falling, alerts for home safety, road safety, sport safety and walking safety. The lecture was very easy to digest by giving vivid examples to illustrate the key points of children falling prevention, and so was well-received by the audience.During the course of the intervention, the working group disseminated promotional brochures such as In March April 2017, the community health service (CHS) and investigators were trained again to conduct a new round of questionnaire surveys in the target kindergartens. Secondary data entry was conducted from April to May 2017 by independent investigators, and all questionnaires were subject to quality control by study personnel. After the database was cleaned, curated, logically reviewed, the data were statistically analyzed, and the return visit results were combined to form the final assessment report.t test was used for continuous variables. We built a logistic model with a stepwise function to determine the influence of different covariates on hurting and used AIC/BIC/AICC to choose the best model. Single-factor analysis was performed first, and a multifactor model was built by a stepwise function. Two-sided values of p < 0.05 were deemed statistically significant. Statistical analyses were performed using R software (version 4.1.2).All questionnaires were analyzed according to their randomized condition. Demographics and baseline characteristics such as age, gender, BMI, parents' education level, parents' job and KAP scores were summarized by group, which was defined by whether the intervention was performed. We summarized the descriptive statistics of continuous variables  and categorical variables (number and proportions of participants in each category). Differences in characteristics between the groups were tested with chi-square for categorical variables, and paired Descriptive statistics of the individuals are presented in Differences in questionnaire scores in the two groups are shown in P < 0.001]. The higher of the attitude scores, the more likely the children were to be injured. Actual behavior has negative influence on whether injured . The higher the attitude scores, the more likely the children were to be injured.With total of 1,938 young children and its parents participating, this is the first study to explore the changes in KAP before and after the intervention using the government-led child injury questionnaire and to try to summarize the effect of the intervention on early children's falls. In addition, this is the first study of fall intervention focus on children aged 0–6 years. Previous study has included 122 schoolchildren between 10–12 years to apply the Safe Fall program in Physical Education classes, which can help making falls safer, diminishing the risk and severity of the injuries they cause . There iIn our study, in the three KAP scores, the attitudes and behavior scores were significantly higher after the intervention than before the intervention , indicatIn addition, univariate and paired logistic models of this study did not find that parents' educational background and family structure had an impact on injury. Although parents' educational level may influence children's fall risk through external factors such as social status and economic conditions , 22, ourChildren are the continuation of human beings, and the prevention of children's falls is of great significance not only to the children but also to their guardians. In educational intervention, we use many new educational methods. For example, we used new media to spread health ideas. Since Chinese people widely use WeChat as an online social software, we combined the popular science content of fall prevention with network technology and distributed it to participating users through the WeChat platform, which could not only realize the educational purpose but also expand the spread of the health education concept, being a novel approach of primary prevention.However, there are still limitations in the present paper. First, although we conducted community seminars as the intervention, we could not guarantee everyone would participate or receive the information we intended to deliver. For example, participants might be absent-minded or use their phones during the seminar. We tried to improve presence rate by reminding participants through text message before the training and asking them to sign on-side. At the end of the training, participants were asked to fill out feedback questionnaires and comment on the content of the intervention to improve the participation rate in the next activity. Another potential threat will be multiple treatment effects while the participants were under intervention, they might be also receiving other fall prevention interventions that we didn't know about. The treatment effect brought by the other situation may also have an impact on our results. Second, the social desirability bias cannot be completely avoided. It means that a parent may answer a questionnaire in a way that he or she thinks is socially acceptable, rather than honestly. This also showed up in our research, revealing differences in attitudes and behaviors. Third, the two groups of people before and after the investigation were different. We reduced this problem through pairing and randomization, but the existence of bias could not be avoided. In research that compares differences among groups, a key question is whether the groups that are compared are equivalent in all respects prior to the introduction of the independent variable or variables. Using the randomized experimental approach, equivalence is approximated through random assignment of participants to groups.The purpose of this study was to explore the effect of educational intervention on students and parents of two kindergartens in Shanghai using the KAP model and to help participants improve risk awareness and reduce the risk of childhood fall. The results showed that education was effective in preventing children from falling down, and improving the level of education will be of great help to the country and society. Our educational interventions will inform education authorities and policy makers to promote public health primary prevention of fall protection in children.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Shanghai Pudong New District Center for Disease Control and Prevention. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.W-YL, T-HT, YZ, DG, and HC conducted the study and drafted the manuscript. W-YL, T-HT, and DG participated in the design of the study and performed data synthesis. W-YL, YZ, and HC conceived the study and participated in its design and coordination. All of the authors read and approved the final manuscript.This study was supported by Excellent Youth Training Program of Health Bureau of Shanghai Pudong New Area [PWRq2021-02], Excellent Youth Training Program of Shanghai Pudong New Area Center for Disease Control and Prevention [PDCDC-XX2021-02], Youth Science and Technology Program of Health Bureau of Shanghai Pudong New Area [PW2014B-1], Academic Leaders Training Program of Pudong Health Bureau of Shanghai (Grant No. PWRd2019-11 to YZ), and the Academic Leaders Training Program of Shanghai Health Bureau (Grant No. GWV-10.2-XD24 to YZ).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The extension of the endothelium in the human body is so vast, that EC are a formidable target for therapeutic interventions which harness some of their beneficial output.The endothelium plays an important role in the physiology and pathophysiology of a multitude of diseases. Whether the endothelium is the primary or secondary target in these diseases remains an open question. What is clear is that endothelial cells (EC) respond to mechanical signals to produce endogenous substances that signal to other cells in order to induce secondary signaling In this Research Topic, we broadly wanted to call the attention of the readership to how non-pharmacological interventions can modify EC biology. The signaling pathways which lead to the beneficial effects were explored using various clinically relevant models such as diabetes, muscular dystrophy, atherosclerosis, and congenital aortic valvar disease.Adams et al. in their narrative review present an introduction to endothelial function in diabetes and the salient features of endothelial dysfunction in diabetes, followed by description of the concept of both pulsatile and laminar shear stress (SS) and endothelial output, most specifically endothelial derived nitric oxide (eNO). Interventions to induce pulsatile shear stress (PSS) on the human body are discussed, with physical exercise being the prototype of a non-invasive method to induce PSS. The non-invasive modalities to produce PSS were reviewed on the basis of the published literature. In addition to exercise, other non-invasive modalities to produce PSS are reviewed such as Enhanced External Counter Pulsation (EECP), Whole Body Vibration(WBV), Whole Body Periodic Acceleration (WBPA), and Passive Simulated Jogging (JD). All the aforementioned were reviewed with focus on their effects on diabetes. The authors provide a table of the currently known mechanical and biochemical effects of each modality, along with their effect on diabetes and ease of use  cardiomyopathy. In an elegant fashion, using in vitro ion selective microelectrodes, these investigators showed the intracellular effects of WBPA. Furthermore, they showed that exposing mice to WBPA for 1 hr per day, 5 days per week, for 3 months, decreased intracellular calcium dyshomeostasis, reactive oxygen species (ROS) production and improved cell viability and cardiomyocyte contractility. These findings were coupled with genomic upregulation of Utrophin, which improves muscular contractility in DMD  and congenital bicuspid aortic valve (BAV). BAV has been shown to be associated with congenital genetic abnormalities in various signaling pathways . Disturbed flow and wall shear stress patterns in the aorta of BAV subjects was shown to induce ED. The review addresses the question of the contribution of genetic abnormalities and hemodynamic alterations in ED observed in BAV. Figure 2 of the manuscript provides a detailed hypothesis driven model of how each can be contributory to ED. The authors enumerate potential therapeutic interventions such as; endothelial progenitor cells, endothelial colony forming cells, pharmacologic therapy , and gene silencing therapy (miRNA). The latter have not been validated in ED induced by BAV. It is plausible that some of the mechanical interventions proposed in the manuscript by Adams et al. could also improve endothelial function in BAV, since WBPA, EECP, exercise, and WBV have been used in patients with ED and found to improve flow mediated vasodilation , intracellular Ca2+ and nitric oxide. Undoubtedly, shear stress is a central mechanism in the physiology of the cardiovascular system, and external artificial modifications of this physical parameter may constitute a novel approach to improve the cardiac health of the population and mitigate cardiovascular accidents.It is clear, from the aforementioned works in this Research Topic, that the endothelium provides a therapeutic target for a myriad of diseases. This Research Topic provided a tantalizing insight into EC in diabetes, congenital muscle disease, and genetic valvulopathy (BAV) and some therapeutic interventions using PSS. However, PSS as a therapeutic intervention may also find its way to other diseases, which are enumerated on RIP, who coined the phrase “Movement is Everything” as it pertains to the benefits of exercise/PSS in daily living and disease management.We are grateful to the contributions of the various authors in putting forward their insightful work, and the hard work of the reviewers. Most grateful to our friend and mentor, the late Professor Marvin Sackner, All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.JA draws no salary from Sackner Wellness Products LLC a company that has a patent on a passive jogging device. He owns 20% of the domestic and foreign patents. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.