Aviation Safety Management Research Articles

Accepting Technology in Aviation Safety Risk Management: an extension of the technology acceptance model

Aviation safety is paramount, and advancements in technology play a pivotal role in mitigating risks and enhancing operational efficiency. The Technology Acceptance Model (TAM) has been widely utilised to understand the adoption of various technologies across industries. However, its application within the context of aviation risk assessment requires nuanced considerations due to the unique operational environment and stringent safety requirements. This paper critically reviews existing literature on TAM and its adaptations in aviation risk assessment, identifying limitations and gaps. Drawing from interdisciplinary insights in psychology, human factors, and aviation safety, this paper proposes an enhanced framework for TAM tailored specifically to the aviation industry. The proposed model integrates key constructs such as perceived usefulness of technology in this area, trust in technology, system complexity, and organizational factors to provide a comprehensive understanding of technology acceptance within aviation risk assessment practices. By enhancing the TAM framework, this paper aims to offer valuable insights for researchers, practitioners, and regulators involved in aviation safety management and technology integration efforts.

From Text to Safety: A Novel Framework for Mining Unsafe Aviation Events Using Advanced Neural Network and Feature Network

The rapid growth of the aviation industry highlights the need for strong safety management. Analyzing data on unsafe aviation events is crucial for preventing risks. This paper presents a new method that integrates the Transformer network model, clustering analysis, and feature network modeling to analyze Chinese text data on unsafe aviation events. Initially, the Transformer model is used to generate summaries of event texts, and the performance of three pre-trained Chinese models is evaluated and compared. Next, the Jieba tool is applied to segment both summarized and original texts to extract key features of unsafe events and prove the effectiveness of the pre-trained Transformer model in simplifying lengthy and redundant original texts. Then, cluster analysis based on text similarity categorizes the extracted features. By solving the correlation matrix of these features, this paper constructs a feature network for unsafe aviation events. The network’s global and individual metrics are calculated and then used to identify key feature nodes, which alert aviation professionals to focus more on the decision-making process for safety management. Based on the established network and these metrics, a data-driven hidden danger warning strategy is proposed and illustrated. Overall, the proposed method can effectively analyze Chinese texts of unsafe aviation events and provide a basis for improving aviation safety management.

Examining the Ethical Considerations of Humanitarian Drones

This paper aims to investigate the ethical considerations of using drones in humanitarian contexts and develop recommendations to address safety, privacy, and regulatory concerns. Humanitarian drones are predominately used to support disaster prevention, relief, and global health missions. Despite clear benefits of using drones for humanitarian missions, evidence of drones’ military past is still present, raising serious ethical concerns. Due to the rapid evolution of this technology, drone regulations are lagging, which only adds to these collective concerns. Widespread regulatory change is needed to guide ethical drone use. The recommendations developed in this paper can be used by public agencies and volunteer organizations to guide their drone use in humanitarian contexts. These recommendations include implementing standards that combine the principles outlined in the Framework for the Ethics Assessment of Humanitarian Drones (FEAHD) and proactive aviation Safety Management Systems (SMSs) to address safety and privacy concerns.

Natural Language Processing System for Text Classification Corpus Based on Machine Learning

A classification system for hazardous materials in air traffic control was investigated using the Human Factors Analysis and Classification System (HFACS) framework and natural language processing to prevent hazardous situations in air traffic control. Based on the development of the HFACS standard, an air traffic control hazard classification system will be created. The dangerous data of the aviation safety management system is selected by dead bodies, classified and marked in five levels. Time Frame Return Frequency TextRank text classification method based on key content extraction and text classification model based on Convolutional Neural Network and Bidirectional Encoder Representations from Transforms models were used in the experiment to solve the problem of small samples, many labels and random samples in hazardous environment of air pollution control. The results show that the total cost of model training time and classification accuracy is the highest when the keywords are around 8. As the number of points increases, the time spent in dimensioning decreases and affects accuracy. When the number of points reaches about 93, the time spent in determining the size increases, but the accuracy of the allocation remains close to 0.7, but the increase in the value of time leads to a decrease in the total cost. It has been proven that extracting key content can solve text classification problems for small companies and contribute to further research in the development of security systems.

Causation Correlation Analysis of Aviation Accidents: A Knowledge Graph-Based Approach

Summarizing the causation of an aviation accident is beneficial for improving aviation safety. Currently, accident analysis mainly focuses on causal analysis, while giving less consideration to the correlation between accident causal factors and other accident factors. To clarify accident causal factors and potential patterns affecting aviation safety and to optimize data mining methods for accident causal factors, this work proposes an aviation accident causation correlation analysis model based on a knowledge graph. Firstly, the accident causal factors are identified, and a knowledge graph is constructed. Subsequently, by utilizing multi-dimensional topological analysis metrics, an aviation accident causation correlation analysis model is established, using the relationships within accident causal factors as a foundation, to determine potential patterns among accident causal factors, flight phases, accident types, and consequences and to analyze the key accident causal factors influencing accident occurrences across different flight phases. Finally, preventive measures and recommendations are provided based on the analysis conclusions. Through a case study using 437 global aviation accidents from 2018 to 2022 as samples and employing the knowledge graph-based aviation accident causation correlation analysis model, the causation relationships among accident causal factors can be expressed more clearly, the potential risks of various accident causal factors can be identified, experiences can be gained from historical accident data, and underlying patterns can be unearthed. This work can provide auxiliary decision making and be an effective reference for the prevention of aviation accidents, playing a positive role in enhancing the level of aviation safety management.

Safety evaluation of human-caused errors in civil aviation based on analytic hierarchy process and fuzzy comprehensive evaluation method

PurposeAt present, the statistics of human error events in domestic civil aviation are limited, and the analysis indicators are difficult to quantify. The purpose of this study is to reduce the incidence of human error events and improve the safety of civil aviation.Design/methodology/approachIn this paper, a safety prevention evaluation method combining analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) is proposed. The risk factors of civil aviation safety are identified through questionnaire survey and calculated by MATLAB software.FindingsThe results of the study are as follows: a safety risk evaluation index system including 4 first-level indicators and 16 second-level indicators is constructed; the AHP is used to calculate the weight of the influencing factors of human error and sort them; and the FCE method is used to quantitatively evaluate the safety prevention of civil aviation human error and put forward the countermeasures.Research limitations/implicationsThis study also has some limitations. While it provides an overall quantitative identification of civil aviation safety risk factors, the research methods chosen, such as the questionnaire survey method and the AHP, involve individual subjectivity. Consequently, the research results may have errors. In the preliminary preparation of the follow-up study, we should analyze a large number of civil aviation accident investigation reports, more accurately clarify the human error factors and completely adopt the quantitative analysis method in the research method.Practical implicationsThis study identifies the risk factors of civil aviation safety and conducts a reasonable analysis of human error factors. In the daily training of civil aviation, the training can be focused on previous man-made accidents; in view of the “important” influencing factors, the aviation management system is formulated to effectively improve the reliability of aviation staff; according to the evaluation criteria of human error in civil aviation, measures to prevent and control accidents can be better formulated.Social implicationsIn view of these four kinds of influencing factors, the corresponding countermeasures and preventive measures are taken according to the discussion, so as to provide the basis for the prevention of aviation human error analysis, management and decision-making, prevent the risk from brewing into safety accidents and improve the safety of aviation management.Originality/valueBased on the questionnaire survey, this study creatively applies the safety prevention evaluation method combining AHP and FCE to the study of civil aviation human error, integrates the advantages of qualitative and quantitative methods, flexibly designs qualitative problems, objectively quantifies research results and reduces subjective variables. Then, by discussing civil aviation safety management measures to avoid risk factors, reduce the incidence of human error events and improve the safety of civil aviation.

Research on a Three-Way Decision-Making Approach, Based on Non-Additive Measurement and Prospect Theory, and Its Application in Aviation Equipment Risk Analysis.

Due to the information non-independence of attributes, combined with a complex and changeable environment, the analysis of risks faces great difficulties. In view of this problem, this paper proposes a new three-way decision-making (3WD) method, combined with prospect theory and a non-additive measure, to cope with multi-source and incomplete risk information systems. Prospect theory improves the loss function of the original 3WD model, and the combination of non-additive measurement and probability measurement provides a new perspective to understand the meaning of decision-making, which could measure the relative degree by considering expert knowledge and objective data. The theoretical basis and framework of this model are illustrated, and this model is applied to a real in-service aviation equipment structures risk evaluation problem involving multiple incomplete risk information sources. When the simulation analysis is carried out, the results show that the availability of this method is verified. This method can also evaluate and rank key risk factors in equipment structures, which provides a reliable basis for decisions in aviation safety management.

Deep Embedding Koopman Neural Operator-Based Nonlinear Flight Training Trajectory Prediction Approach

Accurate flight training trajectory prediction is a key task in automatic flight maneuver evaluation and flight operations quality assurance (FOQA), which is crucial for pilot training and aviation safety management. The task is extremely challenging due to the nonlinear chaos of trajectories, the unconstrained airspace maps, and the randomization of driving patterns. In this work, a deep learning model based on data-driven modern koopman operator theory and dynamical system identification is proposed. The model does not require the manual selection of dictionaries and can automatically generate augmentation functions to achieve nonlinear trajectory space mapping. The model combines stacked neural networks to create a scalable depth approximator for approximating the finite-dimensional Koopman operator. In addition, the model uses finite-dimensional operator evolution to achieve end-to-end adaptive prediction. In particular, the model can gain some physical interpretability through operator visualization and generative dictionary functions, which can be used for downstream pattern recognition and anomaly detection tasks. Experiments show that the model performs well, particularly on flight training trajectory datasets.

Prediction of 10,000-hour rate of civil aviation incidents based on grey Markov model

The 10,000-hour rate of civil aviation incidents is an important index parameter to measure flight safety. Predicting the development trend of the 10,000-hour rate of civil aviation incidents plays an important role in aviation accident prevention and safety decision-making. Many complex factors influence the occurrence of civil aviation incidents, so the 10,000-hour rate of civil aviation incidents changes randomly and volatilely. This study proposed the idea of prediction by combining the grey GM (1, 1) model and the Markov model. Specifically, the grey GM (1, 1) prediction model was constructed using the statistical data on the 10,000-hour rate of civil aviation incidents in China during 2005–2020. On this basis, a grey Markov prediction model was established. The prediction of the 10,000-hour rate of incidents in 2021 based on the two models showed that the grey Markov model displayed higher prediction accuracy than the grey GM (1, 1) model and conformed to the change laws of the 10,000-hour rate data of civil aviation incidents better. Moreover, the grey Markov model could effectively improve the accuracy of the grey prediction model, compensate for its deficiencies, and facilitate the mastery of the change laws of civil aviation incidents, providing a reliable basis for aviation safety management and incident prevention.

Methods for assessing risks in the aviation safety management system

The relevance of the topic is due to the fact that the civil aviation of the Republic of Azerbaijan is intensively implementing the recommendations of the international organization ICAO on the “Theory and Practice of Risk Management” in order to reduce the accident rate in air transport. It is proposed to determine the safety indicators of aviation systems through risk in order to carry out an objective measurement of the level of safety, taking into account many possible risk factors. The problem of risk management currently cannot be solved in an integrated form, since the necessary tools are missing. Therefore, a particular problem is proposed for solution, which consists in risk management exclusively in the field of flight safety. Investigated based on the results of a synthesis of materials from scientific foreign publications on the problems of creating promising flight safety management systems in civil aviation using risk management methods in the sense of ICAO definitions. This article also discusses the problem of risk identification, risk definition, many different risk management methods and different principles for their classification. A generalization is made on risk management within the framework of new directions for assessing flight safety, taking into account statistical data and monitoring results in civil aviation (CA).

Evaluation of Air Traffic Control Ability Based on Fuzzy Theory and Multiple Evaluation Model

This paper constructs the structural model of the system of air traffic controllers' comprehensive ability evaluation. The use of analytic hierarchy process to determine the weight of various indicators should have weight values. The construction of the controller comprehensive capacity assessment model, fuzzy comprehensive evaluation method can be used to weigh matrix and membership matrix corresponding transformation, and then determine the comprehensive capacity of the controller to evaluate the value. In the assessment process, the advantages of various methods are combined, enabling the comprehensive evaluation of the comprehensive capacity of the controller, making the results tend to be reasonable. The research shows that the evaluation results made by this method can provide decision-making basis for aviation safety management at all levels. Using the multiple evaluation model, the controller’s comprehensive ability evaluation model is established to evaluate the comprehensive ability of the controller.

Developing conceptual model for safety risk management in aviation as building capacity and resilience strategy

Purpose This study aims to develop an inclusive, multidisciplinary, flexible and organizationally adaptable safety risk management framework, including diversity management, that will be implemented to ensure safety is and remains at the desired level. If the number of incidents and potential incidents that could lead to accidents and their impact rates are to be reduced operationally and administratively, aviation safety risks and sources of risk must be better understood, sources of risk identified, and the safety risk management framework designed in an organization-specific and organization-wide sustainable way. At this point, it is necessary to draw the conceptual framework well and to define the boundaries of the concepts well. In this study, a framework model that can be adapted to the organization is proposed to optimize the management of risks and provide both efficient and effective resource allocation and organizational structure design in its operations and management functions. Design/methodology/approach The qualitative research method – triple techniques – was deemed appropriate for this study, which aims to identify, examine, interpret and develop the situations of safety management models. In this context, document analysis, business process modeling technique and Delphi techniques from qualitative research methods were used via integration as the methodology of this research. Findings To manage dynamic civil aviation management activities and business processes effectively and efficiently, the risk management process is the building block of the “Proposed Process Model” that supports the decision-making processes of aviation organizations and managers. This “Framework Conceptual Model” building block also helps build capacity and resilience by enabling continuous development, organizational learning, and flexible structuring. Research limitations/implications This research is limited to air transportation and aviation safety management issues. This research is limited specifically to a safety-based risk management framework for the aviation industry. This research may have social implications as source saving, optimum resource use and capacity building will make a contribution to society and add value besides operational and practical implementation. Social implications This research may contribute to more safe operations and functions in the aviation industry. Originality/value Management and academia may gain considerable support from this research to manage their safety risks via a corporate-tailored risk management framework, both improving resilience and developing corporate capacity. With this model presented, decision-makers will have a guiding structure that can optimally manage the main risk types that may be encountered in the safety risk in the fields of suppliers, manufacturers, demand changes, logistics, information management, environmental, legal and regulatory. Existing studies in the literature are generally in the form of algorithms and cannot be used as a decision-making support tool. This model aims to fill the gap in the literature. In addition, added value may be created by applying this model to optimum management safety risks in the real aviation industry and its related sectors.

Discovering latent themes in aviation safety reports using text mining and network analytics

Aviation accidents, referring to unexpected and undesirable events involving aircraft, often cause great damage to property and human life. Learning from historical accidents is pivotal for improving safety in aviation. However, aviation accidents are typically documented and stored as unstructured or semi-structured free-text, rendering the ability to analyze such data a difficult task. This study presents a novel framework that combines text mining and network analytics techniques and provides the ability to analyze aviation accident reports automatically. The framework comprises a four-step modelling approach to: (1) the transformation of unstructured aviation safety report texts into structured numeric matrices using the TF-IDF matrix; (2) the identification of aviation accident topics using a Structural Topic Model (STM); (3) the production of a Word Co-occurrence Network (WCN) to determine the interrelations between aviation safety risk factors; and (4) quantitative analysis by technology of keywords to pinpoint key causal factors in aviation safety events. The proposed framework is validated by analyzing aviation accident reports collected by the National Transportation Safety Board (NTSB). The results indicate that STM provides a more granular partitioning of topics and better distinguishes between similar events compared to traditional Latent Dirichlet Allocation (LDA). Among the identified topics, “Fuel and Power” and “En-route Phase” have the highest occurrence rate according to STM. Additionally, “Aircraft Crash” is the most prevalent topic in aviation accidents that resulted in fatal injuries, while “Landing phase” is the most prevalent topic in non-fatal injuries on accidents. Based on the word co-occurrence network, three centrality measures highlight “inspection of equipment” and “take off” as the most important risk factors in aviation safety. The proposed framework provides a comprehensive solution for in-depth analysis of aviation safety reports, offering decision support for aviation safety management and accident prevention, thereby reducing risks and strengthening safety measures.

Risks and Preventive Measures in Aviation Safety Management during the New Era

Risks and Preventive Measures in Aviation Safety Management during the New Era

МЕТОДОЛОГИЧЕСКОЕ ОБЕСПЕЧЕНИЕ ИМПЛЕМЕНТАЦИИ МЕЖДУНАРОДНЫХ СТАНДАРТОВ ПО БЕЗОПАСНОСТИ ПОЛЕТОВ В РОССИЙСКИХ НОРМАТИВНЫХ ДОКУМЕНТАХ

The present paper delves into the challenge of implementing international flight safety standards into the Russian regulatory framework governing the establishment, application, and refinement of aviation safety management systems (SMS) in the civil aviation sector in Russia. It underscores the reliance of SMS regulatory adherence on the Standards and Recommended Practices (SARPs) set forth by the International Civil Aviation Organization (ICAO). The paper conducts an examination of prevailing discrepancies, indeterminacies, and ambiguities in delineating core terms and concepts within the English and Russian renditions of ICAO SARPs documentation. Furthermore, it offers an evaluation of the implementation hurdles associated with SARPs in regulatory documentation pertaining to civil aviation flight safety in Russia. The deficiencies in the regulatory infrastructure for flight safety in Russia are substantiated. Drawing from practical insights gained in formulating and improving regulatory and methodological support for SMS in prominent Russian airlines, the paper proposes measures to mitigate inaccuracies and uncertainties in the integration of ICAO SARPs editions within the Russian regulatory framework for aviation industry service providers.

Determining possibilities for applying theoretical principles of situational risk management in the aviation safety system

Aviation safety is one of the main criteria for its sustainable development. The object of this study is safety risk management, which is the main tool of the aviation safety management system. The problem that needs to be further solved is the improvement of aviation risk management methods under war, force majeure, and crisis conditions, as well as under conditions of uncertainty, when there is a minimum of time to make a single correct decision. The theory of situational management can be chosen as the theoretical basis for creating decision-making systems. The development of special languages for describing the situation arising at the control object, as well as in its control system, makes it possible to correctly represent the models of control objects and the evolution of knowledge about them. The work solves the task of building a model of the control object, which takes into account the maximum number of its features suitable for implementation. In order to represent the management object based on the method of expert assessments, the weighting coefficients of the risk characteristics’ probabilities and the values of the weighting coefficients of the degrees of severity were calculated, a matrix of risk assessment indicators was built, and risk assessment criteria were developed. Based on the calculations, general approaches to aviation safety risk management were specified. The paper developed a «dramatic script» of an episode of aviation security risk management, which includes the stages of the episode, the content of necessary actions, the corresponding stages of aviation security risk management and the corresponding aviation security risk management. The content of the management itself consists in preventing the transition from the zone of acceptable risk to the zone of permissible and from the zone of permissible risk to the zone of unacceptable risk, as well as the implementation of measures to stimulate the transition from the zone of unacceptable to the zone of permissible risk and from the zone of permissible to the zone of acceptable risk

Aviation Safety Management System Arrangements to Realize Security and Safety at Halim Perdana Kusuma Airport

Regulation of aviation safety management system to realize security and safety at Halim Perdana Kusuma Airport. This thesis raises the issue of aviation safety arrangements based on international rules at the airport in relation to the application of safety management systems and the implementation of these rules in accordance with Law Number 1 of 2009 concerning Aviation and its implementing regulations, especially at Halim Perdana Kusuma Airport. The safety and security of national aviation regulated in the Aviation Law includes guidelines that are in accordance with ICAO provisions regarding the State Safety Program. ICAO standards require Indonesia as a member state to develop a National Aviation Safety and Security Program to achieve an Acceptable Level of Safety (AIoS) and a safety management system for aviation service providers. Furthermore, researchers conduct legal analysis of primary legal materials and secondary legal materials related to the problems studied. The type of research that the author uses is an empirical juridical research method conducted by means of field research through interviews and examining library materials or secondary data. In the discussion of this research, it is known that aviation safety arrangements based on international rules at the airport in relation to the application of the Safety Management System are regulated based on the provisions of ICAO Annex 14 which has introduced the application of the Safety Management System in the operation of the Airport, and the provisions of ICAO Annex 19 concerning Safety Management, the framework of the Safety Management System in its implementation is in accordance with Law Number 1 of 2009 concerning Aviation as a guideline in implementing aviation security and safety arrangements at Halim Perdana Kusuma Airport.

Trend Analysis of Civil Aviation Incidents Based on Causal Inference and Statistical Inference

The efficient management of aviation safety requires the precise analysis of trends in incidents. While classical statistical models often rely on the autocorrelation of indicator sequences for trend fitting, significant room remains for performance improvement. To enhance the accuracy and interpretability of trend analyses for aviation incidents, we propose the Causal-ARIMA model, which is grounded in causal inference theory, and we employ four distinct modeling strategies to fit the trend of incidents in China’s civil aviation sector between 1994 and 2020. The objective is to validate the performance of the Causal-ARIMA model and identify optimal trend analysis strategies. The four modeling strategies account for causation factors, stationarity, and causality with operational volume, incorporating models like AR, ARMA, ARIMA, and Causal-ARIMA. Our findings reveal that ensemble techniques incorporating the Causal-ARIMA model (Strategy 2 and 3) outperform classical trend analysis methods (Strategy 1) in terms of model fit. Specifically, the causality-based binary fitting technique (Strategy 3) achieves the most uniformly dispersed fitting performance. When the premises for using the Causal-ARIMA model are relaxed, applying it to variables without Granger causal relationships results in uneven model performance (Strategy 4). According to our study, the Causal-ARIMA model can serve as a potent tool for the analysis of trends in the domain of aviation safety. Modeling strategies based on the Causal-ARIMA model provide valuable insights for aviation safety management.

Improved gray correlation analysis and combined prediction model for aviation accidents

PurposeThe purpose of this paper is to identify the key influencing factors of aviation accidents and to predict the aviation accidents caused by the factors.Design/methodology/approachThis paper proposes an improved gray correlation analysis (IGCA) theory to make the relational analysis of aviation accidents and influencing factors and find out the critical causes of aviation accidents. The optimal varying weight combination model (OVW-CM) is constructed based on gradient boosted regression tree (GBRT), extreme gradient boosting (XGBoost) and support vector regression (SVR) to predict aviation accidents due to critical factors.FindingsThe global aviation accident data from 1919 to 2020 is selected as the experimental data. The airplane, takeoff/landing and unexpected results are the leading causes of the aviation accidents based on IGCA. Then GBRT, XGBoost, SVR, equal-weight combination model (EQ-CM), variance-covariance combination model (VCW-CM) and OVW-CM are used to predict aviation accidents caused by airplane, takeoff/landing and unexpected results, respectively. The experimental results show that OVW-CM has a better prediction effect, and the prediction accuracy and stability are higher than other models.Originality/valueUnlike the traditional gray correlation analysis (GCA), IGCA weights the sample by distance analysis to more objectively reflect the degree of influence of different factors on aviation accidents. OVW-CM is built by minimizing the combined prediction error at sample points and assigns different weights to different individual models at different moments, which can make full use of the advantages of each model and has higher prediction accuracy. And the model parameters of GBRT, XGBoost and SVR are optimized by the particle swarm algorithm. The study can guide the analysis and prediction of aviation accidents and provide a scientific basis for aviation safety management.

Air traffic density prediction using Bayesian ensemble graph attention network (BEGAN)

Air traffic density prediction is crucial to aviation safety and air traffic management (ATM). Understanding the complex spatial–temporal varying traffic patterns and the inter-dependencies of air traffic networks is especially demanding. To address these challenges, a novel knowledge-based deep learning framework, Bayesian ensemble graph attention network (BEGAN), is proposed to leverage the interactions of traffic density at different airspace grids and predict stochastic traffic density near the terminal. The core concept of BEGAN is to divide the large airspace into small overlapped gridded sections for individual training, and then assemble posterior distributions of density forecasts for large-scale prediction. The ensemble method improves the scalability of handling heterogeneous air traffic flow patterns. Algorithm-wise, a weighted directed graph interleaves the connection between different gridded airspaces. A parallel graph attention network (P-GAT) block followed by the long short-term memory (LSTM) network is designed to learn the spatial–temporal dependencies of the graph. Notably, P-GAT can incorporate existing domain knowledge into data-driven learning, such as historical trajectory paths, future flight plans, and airspace restrictions. The inclusion of existing knowledge and rules proves beneficial for air traffic density prediction in heavily regulated and controlled airspace. We evaluate BEGAN with real-world data collected from Hartsfield-Jackson Atlanta International Airport (KATL) and Orlando International Airport (KMCO). Extensive experiments show that the proposed framework can make accurate and robust predictions, outperforming nine baseline models. Based on the proposed study, limitations and future research directions are given.