• Digital Manufacturing
• Smart Factory
Intervention | The methodology, tool, or technology that addresses a specific issue. | Semantic Web | • Ontology • Semantic Reasoning |
Comparison | The methodology, tool, or technology in which the is being compared (if appropriate). | Machine Learning | • Supervised Learning • Unsupervised Learning |
Outcome | Factors of importance to practitioners and/or the results that could produce. | Context-Awareness | • Context-Aware • Context-Reasoning |
Context | The context in which the comparison takes place. Some systematic reviews might choose to exclude this element. | Business Process Management | • BPM • Business Process Modeling |
Formulate research questions
Clearly defined research question(s) are the key elements which set the focus for study identification and data extraction [21] . These questions are formulated based on the PICOC criteria as presented in the example in Table 2 (PICOC keywords are underlined).
Research questions examples.
Research Questions examples |
---|
• : What are the current challenges of context-aware systems that support the decision-making of business processes in smart manufacturing? • : Which technique is most appropriate to support decision-making for business process management in smart factories? • : In which scenarios are semantic web and machine learning used to provide context-awareness in business process management for smart manufacturing? |
Select digital library sources
The validity of a study will depend on the proper selection of a database since it must adequately cover the area under investigation [19] . The Web of Science (WoS) is an international and multidisciplinary tool for accessing literature in science, technology, biomedicine, and other disciplines. Scopus is a database that today indexes 40,562 peer-reviewed journals, compared to 24,831 for WoS. Thus, Scopus is currently the largest existing multidisciplinary database. However, it may also be necessary to include sources relevant to computer science, such as EI Compendex, IEEE Xplore, and ACM. Table 3 compares the area of expertise of a selection of databases.
Planning Step 3 “Select digital libraries”. Description of digital libraries in computer science and software engineering.
Database | Description | URL | Area | Advanced Search Y/N |
---|
Scopus | From Elsevier. sOne of the largest databases. Very user-friendly interface | | Interdisciplinary | Y |
Web of Science | From Clarivate. Multidisciplinary database with wide ranging content. | | Interdisciplinary | Y |
EI Compendex | From Elsevier. Focused on engineering literature. | | Engineering | Y (Query view not available) |
IEEE Digital Library | Contains scientific and technical articles published by IEEE and its publishing partners. | | Engineering and Technology | Y |
ACM Digital Library | Complete collection of ACM publications. | | Computing and information technology | Y |
Define inclusion and exclusion criteria
Authors should define the inclusion and exclusion criteria before conducting the review to prevent bias, although these can be adjusted later, if necessary. The selection of primary studies will depend on these criteria. Articles are included or excluded in this first selection based on abstract and primary bibliographic data. When unsure, the article is skimmed to further decide the relevance for the review. Table 4 sets out some criteria types with descriptions and examples.
Planning Step 4 “Define inclusion and exclusion criteria”. Examples of criteria type.
Criteria Type | Description | Example |
---|
Period | Articles can be selected based on the time period to review, e.g., reviewing the technology under study from the year it emerged, or reviewing progress in the field since the publication of a prior literature review. | : From 2015 to 2021 Articles prior 2015 |
Language | Articles can be excluded based on language. | : Articles not in English |
Type of Literature | Articles can be excluded if they are fall into the category of grey literature. | Reports, policy literature, working papers, newsletters, government documents, speeches |
Type of source | Articles can be included or excluded by the type of origin, i.e., conference or journal articles or books. | : Articles from Conferences or Journals Articles from books |
Impact Source | Articles can be excluded if the author limits the impact factor or quartile of the source. | Articles from Q1, and Q2 sources : Articles with a Journal Impact Score (JIS) lower than |
Accessibility | Not accessible in specific databases. | : Not accessible |
Relevance to research questions | Articles can be excluded if they are not relevant to a particular question or to “ ” number of research questions. | Not relevant to at least 2 research questions |
Define the Quality Assessment (QA) checklist
Assessing the quality of an article requires an artifact which describes how to perform a detailed assessment. A typical quality assessment is a checklist that contains multiple factors to evaluate. A numerical scale is used to assess the criteria and quantify the QA [22] . Zhou et al. [25] presented a detailed description of assessment criteria in software engineering, classified into four main aspects of study quality: Reporting, Rigor, Credibility, and Relevance. Each of these criteria can be evaluated using, for instance, a Likert-type scale [17] , as shown in Table 5 . It is essential to select the same scale for all criteria established on the quality assessment.
Planning Step 5 “Define QA assessment checklist”. Examples of QA scales and questions.
Do the researchers discuss any problems (limitations, threats) with the validity of their results (reliability)? | 1 – No, and not considered (Score: 0) 2 – Partially (Score: 0.5) 3 – Yes (Score: 1) |
Is there a clear definition/ description/ statement of the aims/ goals/ purposes/ motivations/ objectives/ questions of the research? | 1 – Disagree (Score: 1) 2 – Somewhat disagree (Score: 2) 3 – Neither agree nor disagree (Score: 3) 4 – Somewhat agree (Score: 4) 5 – Agree (Score: 5) |
Define the “Data Extraction” form
The data extraction form represents the information necessary to answer the research questions established for the review. Synthesizing the articles is a crucial step when conducting research. Ramesh et al. [15] presented a classification scheme for computer science research, based on topics, research methods, and levels of analysis that can be used to categorize the articles selected. Classification methods and fields to consider when conducting a review are presented in Table 6 .
Planning Step 6 “Define data extraction form”. Examples of fields.
Classification and fields to consider for data extraction | Description and examples |
---|
Research type | • focuses on abstract ideas, concepts, and theories built on literature reviews . • uses scientific data or case studies for explorative, descriptive, explanatory, or measurable findings . an SLR on context-awareness for S-PSS and categorized the articles in theoretical and empirical research. |
By process phases, stages | When analyzing a process or series of processes, an effective way to structure the data is to find a well-established framework of reference or architecture. : • an SLR on self-adaptive systems uses the MAPE-K model to understand how the authors tackle each module stage. • presented a context-awareness survey using the stages of context-aware lifecycle to review different methods. |
By technology, framework, or platform | When analyzing a computer science topic, it is important to know the technology currently employed to understand trends, benefits, or limitations. : • an SLR on the big data ecosystem in the manufacturing field that includes frameworks, tools, and platforms for each stage of the big data ecosystem. |
By application field and/or industry domain | If the review is not limited to a specific “Context” or “Population" (industry domain), it can be useful to identify the field of application : • an SLR on adaptive training using virtual reality (VR). The review presents an extensive description of multiple application domains and examines related work. |
Gaps and challenges | Identifying gaps and challenges is important in reviews to determine the research needs and further establish research directions that can help scholars act on the topic. |
Findings in research | Research in computer science can deliver multiple types of findings, e.g.: |
Evaluation method | Case studies, experiments, surveys, mathematical demonstrations, and performance indicators. |
The data extraction must be relevant to the research questions, and the relationship to each of the questions should be included in the form. Kitchenham & Charters [6] presented more pertinent data that can be captured, such as conclusions, recommendations, strengths, and weaknesses. Although the data extraction form can be updated if more information is needed, this should be treated with caution since it can be time-consuming. It can therefore be helpful to first have a general background in the research topic to determine better data extraction criteria.
After defining the protocol, conducting the review requires following each of the steps previously described. Using tools can help simplify the performance of this task. Standard tools such as Excel or Google sheets allow multiple researchers to work collaboratively. Another online tool specifically designed for performing SLRs is Parsif.al 1 . This tool allows researchers, especially in the context of software engineering, to define goals and objectives, import articles using BibTeX files, eliminate duplicates, define selection criteria, and generate reports.
Build digital library search strings
Search strings are built considering the PICOC elements and synonyms to execute the search in each database library. A search string should separate the synonyms with the boolean operator OR. In comparison, the PICOC elements are separated with parentheses and the boolean operator AND. An example is presented next:
(“Smart Manufacturing” OR “Digital Manufacturing” OR “Smart Factory”) AND (“Business Process Management” OR “BPEL” OR “BPM” OR “BPMN”) AND (“Semantic Web” OR “Ontology” OR “Semantic” OR “Semantic Web Service”) AND (“Framework” OR “Extension” OR “Plugin” OR “Tool”
Gather studies
Databases that feature advanced searches enable researchers to perform search queries based on titles, abstracts, and keywords, as well as for years or areas of research. Fig. 1 presents the example of an advanced search in Scopus, using titles, abstracts, and keywords (TITLE-ABS-KEY). Most of the databases allow the use of logical operators (i.e., AND, OR). In the example, the search is for “BIG DATA” and “USER EXPERIENCE” or “UX” as a synonym.
Example of Advanced search on Scopus.
In general, bibliometric data of articles can be exported from the databases as a comma-separated-value file (CSV) or BibTeX file, which is helpful for data extraction and quantitative and qualitative analysis. In addition, researchers should take advantage of reference-management software such as Zotero, Mendeley, Endnote, or Jabref, which import bibliographic information onto the software easily.
Study Selection and Refinement
The first step in this stage is to identify any duplicates that appear in the different searches in the selected databases. Some automatic procedures, tools like Excel formulas, or programming languages (i.e., Python) can be convenient here.
In the second step, articles are included or excluded according to the selection criteria, mainly by reading titles and abstracts. Finally, the quality is assessed using the predefined scale. Fig. 2 shows an example of an article QA evaluation in Parsif.al, using a simple scale. In this scenario, the scoring procedure is the following YES= 1, PARTIALLY= 0.5, and NO or UNKNOWN = 0 . A cut-off score should be defined to filter those articles that do not pass the QA. The QA will require a light review of the full text of the article.
Performing quality assessment (QA) in Parsif.al.
Data extraction
Those articles that pass the study selection are then thoroughly and critically read. Next, the researcher completes the information required using the “data extraction” form, as illustrated in Fig. 3 , in this scenario using Parsif.al tool.
Example of data extraction form using Parsif.al.
The information required (study characteristics and findings) from each included study must be acquired and documented through careful reading. Data extraction is valuable, especially if the data requires manipulation or assumptions and inferences. Thus, information can be synthesized from the extracted data for qualitative or quantitative analysis [16] . This documentation supports clarity, precise reporting, and the ability to scrutinize and replicate the examination.
Analysis and Report
The analysis phase examines the synthesized data and extracts meaningful information from the selected articles [10] . There are two main goals in this phase.
The first goal is to analyze the literature in terms of leading authors, journals, countries, and organizations. Furthermore, it helps identify correlations among topic s . Even when not mandatory, this activity can be constructive for researchers to position their work, find trends, and find collaboration opportunities. Next, data from the selected articles can be analyzed using bibliometric analysis (BA). BA summarizes large amounts of bibliometric data to present the state of intellectual structure and emerging trends in a topic or field of research [4] . Table 7 sets out some of the most common bibliometric analysis representations.
Techniques for bibliometric analysis and examples.
Publication-related analysis | Description | Example |
---|
Years of publications | Determine interest in the research topic by years or the period established by the SLR, by quantifying the number of papers published. Using this information, it is also possible to forecast the growth rate of research interest. | [ ] identified the growth rate of research interest and the yearly publication trend. |
Top contribution journals/conferences | Identify the leading journals and conferences in which authors can share their current and future work. | , |
Top countries' or affiliation contributions | Examine the impacts of countries or affiliations leading the research topic. | [ , ] identified the most influential countries. |
Leading authors | Identify the most significant authors in a research field. | - |
Keyword correlation analysis | Explore existing relationships between topics in a research field based on the written content of the publication or related keywords established in the articles. | using keyword clustering analysis ( ). using frequency analysis. |
Total and average citation | Identify the most relevant publications in a research field. | Scatter plot citation scores and journal factor impact |
Several tools can perform this type of analysis, such as Excel and Google Sheets for statistical graphs or using programming languages such as Python that has available multiple data visualization libraries (i.e. Matplotlib, Seaborn). Cluster maps based on bibliographic data(i.e keywords, authors) can be developed in VosViewer which makes it easy to identify clusters of related items [18] . In Fig. 4 , node size is representative of the number of papers related to the keyword, and lines represent the links among keyword terms.
[1] Keyword co-relationship analysis using clusterization in vos viewer.
This second and most important goal is to answer the formulated research questions, which should include a quantitative and qualitative analysis. The quantitative analysis can make use of data categorized, labelled, or coded in the extraction form (see Section 1.6). This data can be transformed into numerical values to perform statistical analysis. One of the most widely employed method is frequency analysis, which shows the recurrence of an event, and can also represent the percental distribution of the population (i.e., percentage by technology type, frequency of use of different frameworks, etc.). Q ualitative analysis includes the narration of the results, the discussion indicating the way forward in future research work, and inferring a conclusion.
Finally, the literature review report should state the protocol to ensure others researchers can replicate the process and understand how the analysis was performed. In the protocol, it is essential to present the inclusion and exclusion criteria, quality assessment, and rationality beyond these aspects.
The presentation and reporting of results will depend on the structure of the review given by the researchers conducting the SLR, there is no one answer. This structure should tie the studies together into key themes, characteristics, or subgroups [ 28 ].
SLR can be an extensive and demanding task, however the results are beneficial in providing a comprehensive overview of the available evidence on a given topic. For this reason, researchers should keep in mind that the entire process of the SLR is tailored to answer the research question(s). This article has detailed a practical guide with the essential steps to conducting an SLR in the context of computer science and software engineering while citing multiple helpful examples and tools. It is envisaged that this method will assist researchers, and particularly early-stage researchers, in following an algorithmic approach to fulfill this task. Finally, a quick checklist is presented in Appendix A as a companion of this article.
CRediT author statement
Angela Carrera-Rivera: Conceptualization, Methodology, Writing-Original. William Ochoa-Agurto : Methodology, Writing-Original. Felix Larrinaga : Reviewing and Supervision Ganix Lasa: Reviewing and Supervision.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
Funding : This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant No. 814078.
Carrera-Rivera, A., Larrinaga, F., & Lasa, G. (2022). Context-awareness for the design of Smart-product service systems: Literature review. Computers in Industry, 142, 103730.
1 https://parsif.al/
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Natural language processing in dermatology: A systematic literature review and state of the art
Affiliations.
- 1 Dermatology Unit, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy.
- 2 University of Modena and Reggio Emilia, Modena, Italy.
- 3 Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Skin Cancer Center, Reggio Emilia, Italy.
- 4 Melanoma and Skin Cancer Unit, Department of Dermatology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile.
- 5 Dermatology Clinic, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- 6 Department of Clinical Internal, Anesthesiological and Cardiovascular Sciences, Dermatology Clinic, Sapienza University of Rome, Rome, Italy.
- PMID: 39150311
- DOI: 10.1111/jdv.20286
Background: Natural Language Processing (NLP) is a field of both computational linguistics and artificial intelligence (AI) dedicated to analysis and interpretation of human language.
Objectives: This systematic review aims at exploring all the possible applications of NLP techniques in the dermatological setting.
Methods: Extensive search on 'natural language processing' and 'dermatology' was performed on MEDLINE and Scopus electronic databases. Only journal articles with full text electronically available and English translation were considered. The PICO (Population, Intervention or exposure, Comparison, Outcome) algorithm was applied to our study protocol.
Results: Natural Language Processing (NLP) techniques have been utilized across various dermatological domains, including atopic dermatitis, acne/rosacea, skin infections, non-melanoma skin cancers (NMSCs), melanoma and skincare. There is versatility of NLP in data extraction from diverse sources such as electronic health records (EHRs), social media platforms and online forums. We found extensive utilization of NLP techniques across diverse dermatological domains, showcasing its potential in extracting valuable insights from various sources and informing diagnosis, treatment optimization, patient preferences and unmet needs in dermatological research and clinical practice.
Conclusions: While NLP shows promise in enhancing dermatological research and clinical practice, challenges such as data quality, ambiguity, lack of standardization and privacy concerns necessitate careful consideration. Collaborative efforts between dermatologists, data scientists and ethicists are essential for addressing these challenges and maximizing the potential of NLP in dermatology.
© 2024 The Author(s). Journal of the European Academy of Dermatology and Venereology published by John Wiley & Sons Ltd on behalf of European Academy of Dermatology and Venereology.
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Analysing near-miss incidents in construction: a systematic literature review.
1. Introduction
- Q 1 —Are near-miss events in construction industry the subject of scientific research?
- Q 2 —What methods have been employed thus far to obtain information on near misses and systems for recording incidents in construction companies?
- Q 3 —What methods have been used to analyse the information and figures obtained?
- Q 4 —What are the key aspects of near misses in the construction industry that have been of interest to the researchers?
2. Definition of Near-Miss Events
3. research methodology, 4.1. a statistical analysis of publications, 4.2. methods used to obtain information about near misses, 4.2.1. traditional methods.
- Traditional registration forms
- Computerized systems for the recording of events
- Surveys and interviews
4.2.2. Real-Time Monitoring Systems
- Employee-tracking systems
- Video surveillance systems
- Wearable technology
- Motion sensors
4.3. Methods Used to Analyse the Information and Figures That Have Been Obtained
4.3.1. quantitative and qualitative statistical methods, 4.3.2. analysis using artificial intelligence (ai), 4.3.3. building information modelling, 4.4. key aspects of near-miss investigations in the construction industry, 4.4.1. occupational risk assessment, 4.4.2. causes of hazards in construction, 4.4.3. time series of near misses, 4.4.4. material factors of construction processes, 4.5. a comprehensive overview of the research questions and references on near misses in the construction industry, 5. discussion, 5.1. interest of researchers in near misses in construction (question 1), 5.2. methods used to obtain near-miss information (question 2), 5.3. methods used to analyse the information and data sets (question 3), 5.4. key aspects of near-miss investigations in the construction industry (question 4), 6. conclusions.
- A quantitative analysis of the Q 1 question has revealed a positive trend, namely that there is a growing interest among researchers in studying near misses in construction. The greatest interest in NM topics is observed in the United States of America, China, the United Kingdom, Australia, Hong Kong, and Germany. Additionally, there has been a recent emergence of interest in Poland. The majority of articles are mainly published in journals such as Safety Science (10), Journal of Construction Engineering and Management (8), and Automation in Construction (5);
- The analysis of question Q 2 illustrates that traditional paper-based event registration systems are currently being superseded by advanced IT systems. However, both traditional and advanced systems are subject to the disadvantage of relying on employee-reported data, which introduces a significant degree of uncertainty regarding in the quality of the information provided. A substantial proportion of the data and findings presented in the studies was obtained through surveys and interviews. The implementation of real-time monitoring systems is becoming increasingly prevalent in construction sites. The objective of such systems is to provide immediate alerts in the event of potential hazards, thereby preventing a significant number of near misses. Real-time monitoring systems employ a range of technologies, including ultrasonic technology, radio frequency identification (RFID), inertial measurement units (IMUs), real-time location systems (RTLSs), industrial cameras, wearable technology, motion sensors, and advanced IT technologies, among others;
- The analysis of acquired near-miss data is primarily conducted through the utilisation of quantitative and qualitative statistical methods, as evidenced by the examination of the Q 3 question. In recent years, research utilising artificial intelligence (AI) has made significant advances. The most commonly employed artificial intelligence techniques include text mining, machine learning, and artificial neural networks. The growing deployment of Building Information Modelling (BIM) technology has precipitated a profound transformation in the safety management of construction sites, with the advent of sophisticated tools for the identification and management of hazardous occurrences;
- In response to question Q 4 , the study of near misses in the construction industry has identified several key aspects that have attracted the attention of researchers. These include the utilisation of both quantitative and qualitative methodologies for risk assessment, the analysis of the causes of hazards, the identification of accident precursors through the creation of time series, and the examination of material factors pertaining to construction processes. Researchers are focusing on the utilisation of both databases and advanced technologies, such as real-time location tracking, for the assessment and analysis of occupational risks. Techniques such as Analytic Hierarchy Process (AHP) and clustering facilitate a comprehensive assessment and categorisation of incidents, thereby enabling the identification of patterns and susceptibility to specific types of accidents. Moreover, the impact of a company’s safety climate and organisational culture on the frequency and characteristics of near misses represents a pivotal area of investigation. The findings of this research indicate that effective safety management requires a holistic approach that integrates technology, risk management and safety culture, with the objective of reducing accidents and enhancing overall working conditions on construction sites.
7. Gaps and Future Research Directions, Limitations
- Given the diversity and variability of construction sites and the changing conditions and circumstances of work, it is essential to create homogeneous clusters of near misses and to analyse the phenomena within these clusters. The formation of such clusters may be contingent upon the direct causes of the events in question;
- Given the inherently dynamic nature of construction, it is essential to analyse time series of events that indicate trends in development and safety levels. The numerical characteristics of these trends may be used to construct predictive models for future accidents and near misses;
- The authors have identified potential avenues for future research, which could involve the development of mathematical models using techniques such as linear regression, artificial intelligence, and machine learning. The objective of these models is to predict the probable timing of occupational accidents within defined incident categories, utilising data from near misses. Moreover, efforts are being made to gain access to the hazardous incident recording systems of different construction companies, with a view to facilitating comparison of the resulting data;
- One significant limitation of near-miss research is the lack of an integrated database that encompasses a diverse range of construction sites and construction work. A data resource of this nature would be of immense value for the purpose of conducting comprehensive analyses and formulating effective risk management strategies. This issue can be attributed to two factors: firstly, the reluctance of company managers to share their databases with researchers specialising in risk assessment, and secondly, the reluctance of employees to report near-miss incidents. Such actions may result in adverse consequences for employees, including disciplinary action or negative perceptions from managers. This consequently results in the recording of only a subset of incidents, thereby distorting the true picture of safety on the site.
Author Contributions
Institutional review board statement, informed consent statement, data availability statement, conflicts of interest.
Year | Source Title | DOI/ISBN/ISSN | Reference |
---|
1999 | Construction Management and Economics | 10.1080/014461999371691 | [ ] |
2002 | Structural Engineer | 14665123 | [ ] |
2009 | Building a Sustainable Future—Proceedings of the 2009 Construction Research Congress | 10.1061/41020(339)4 | [ ] |
2010 | Safety Science | 10.1016/j.ssci.2010.04.009 | [ ] |
2010 | Automation in Construction | 10.1016/j.autcon.2009.11.017 | [ ] |
2010 | Safety Science | 10.1016/j.ssci.2009.06.006 | [ ] |
2012 | Journal of Construction Engineering and Management | 10.1061/(ASCE)CO.1943-7862.0000518 | [ ] |
2013 | ISARC 2013—30th International Symposium on Automation and Robotics in Construction and Mining, Held in Conjunction with the 23rd World Mining Congress | 10.22260/isarc2013/0113 | [ ] |
2014 | Proceedings of the Institution of Civil Engineers: Civil Engineering | 10.1680/cien.14.00010 | [ ] |
2014 | Safety Science | 10.1016/j.ssci.2013.12.012 | [ ] |
2014 | Journal of Construction Engineering and Management | 10.1061/(ASCE)CO.1943-7862.0000795 | [ ] |
2014 | 31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014—Proceedings | 10.22260/isarc2014/0115 | [ ] |
2014 | Construction Research Congress 2014: Construction in a Global Network—Proceedings of the 2014 Construction Research Congress | 10.1061/9780784413517.0181 | [ ] |
2014 | Construction Research Congress 2014: Construction in a Global Network—Proceedings of the 2014 Construction Research Congress | 10.1061/9780784413517.0235 | [ ] |
2014 | Construction Research Congress 2014: Construction in a Global Network—Proceedings of the 2014 Construction Research Congress | 10.1061/9780784413517.0096 | [ ] |
2015 | Automation in Construction | 10.1016/j.autcon.2015.09.003 | [ ] |
2015 | 32nd International Symposium on Automation and Robotics in Construction and Mining: Connected to the Future, Proceedings | 10.22260/isarc2015/0062 | [ ] |
2015 | ASSE Professional Development Conference and Exposition 2015 | - | [ ] |
2015 | Congress on Computing in Civil Engineering, Proceedings | 10.1061/9780784479247.019 | [ ] |
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Click here to enlarge figure
No. | Name of Institution/Organization | Definition |
---|
1 | Occupational Safety and Health Administration (OSHA) [ ] | “A near-miss is a potential hazard or incident in which no property was damaged and no personal injury was sustained, but where, given a slight shift in time or position, damage or injury easily could have occurred. Near misses also may be referred to as close calls, near accidents, or injury-free events.” |
2 | International Labour Organization (ILO) [ ] | “An event, not necessarily defined under national laws and regulations, that could have caused harm to persons at work or to the public, e.g., a brick that falls off scaffolding but does not hit anyone” |
3 | American National Safety Council (NSC) [ ] | “A Near Miss is an unplanned event that did not result in injury, illness, or damage—but had the potential to do so” |
4 | PN-ISO 45001:2018-06 [ ] | A near-miss incident is described as an event that does not result in injury or health issues. |
5 | PN-N-18001:2004 [ ] | A near-miss incident is an accident event without injury. |
6 | World Health Organization (WHO) [ ] | Near misses have been defined as a serious error that has the potential to cause harm but are not due to chance or interception. |
7 | International Atomic Energy Agency (IAEA) [ ] | Near misses have been defined as potentially significant events that could have consequences but did not due to the conditions at the time. |
No. | Journal | Number of Publications |
---|
1 | Safety Science | 10 |
2 | Journal of Construction Engineering and Management | 8 |
3 | Automation in Construction | 5 |
4 | Advanced Engineering Informatics | 3 |
5 | Construction Research Congress 2014 Construction in a Global Network Proceedings of the 2014 Construction Research Congress | 3 |
6 | International Journal of Construction Management | 3 |
7 | Accident Analysis and Prevention | 2 |
8 | Computing in Civil Engineering 2019 Data Sensing and Analytics Selected Papers From The ASCE International Conference | 2 |
9 | Engineering Construction and Architectural Management | 2 |
10 | Heliyon | 2 |
Cluster Number | Colour | Basic Keywords |
---|
1 | blue | construction, construction sites, decision making, machine learning, near misses, neural networks, project management, safety, workers |
2 | green | building industry, construction industry, construction projects, construction work, human, near miss, near misses, occupational accident, occupational safety, safety, management, safety performance |
3 | red | accident prevention, construction equipment, construction, safety, construction workers, hazards, human resource management, leading indicators, machinery, occupational risks, risk management, safety engineering |
4 | yellow | accidents, risk assessment, civil engineering, near miss, surveys |
Number of Question | Question | References |
---|
Q | Are near misses in the construction industry studied scientifically? | [ , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ] |
Q | What methods have been used to obtain information on near misses and systems for recording incidents in construction companies? | [ , , , , , , , , , , , , , , , , , , , , ] |
Q | What methods have been used to analyse the information and figures that have been obtained? | [ , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ] |
Q | What are the key aspects of near misses in the construction industry that have been of interest to the researchers? | [ , , , , , , , , , , , , ] |
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Share and Cite
Woźniak, Z.; Hoła, B. Analysing Near-Miss Incidents in Construction: A Systematic Literature Review. Appl. Sci. 2024 , 14 , 7260. https://doi.org/10.3390/app14167260
Woźniak Z, Hoła B. Analysing Near-Miss Incidents in Construction: A Systematic Literature Review. Applied Sciences . 2024; 14(16):7260. https://doi.org/10.3390/app14167260
Woźniak, Zuzanna, and Bożena Hoła. 2024. "Analysing Near-Miss Incidents in Construction: A Systematic Literature Review" Applied Sciences 14, no. 16: 7260. https://doi.org/10.3390/app14167260
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Web search option to fetch other pages of results
Add an option to select the 2nd, 3rd, etc page of results. The limit of 100 citations is a severe restriction for comprehensive literature review where all papers matching a search term need to be considered. The page option would permit fetching the second page of 100, the third page of 100, etc.
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COMMENTS
Is there any documentation about the feature "systematic literature review". At least a motivating example how to define study parameters would help. I use JabRef 5.5
Notable Changes and New Features The most notable new feature is the experimental implementation of a systematic literature review (SLR) functionality, one of our maintainers worked on for his bachelor thesis. Another major improvement is the "Find unlinked files" dialog. We improved the import performance of many files and also added options to filter the shown files by their modification ...
JabRef 5.3--2021-07-05--50c96a2 Windows 10 10.0 amd64 Java 16.0.1 JavaFX 16+8 Hi All, Knew about Jabref for a long time (through Docear). Just installed the new 5.3 version, did not know it was so recent but also discovered the Systematic Review tool for it. Several piece of feedback i'd like to share, here is a preamble. The tool is very attractive. I have to do a few (collaborative ...
Introducing JabRef JabRef is open source software to manage references and the full text of papers. The native file format used by JabRef is BibTeX, the standard LaTeX bibliography format.
A free reference manager that helps you to discover, collect, organize and cite your scholarly literature and research in an efficient way.
Abstract Performing a literature review is a critical first step in research to understanding the state-of-the-art and identifying gaps and challenges in the field. A systematic literature review is a method which sets out a series of steps to methodically organize the review. In this paper, we present a guide designed for researchers and in particular early-stage researchers in the computer ...
Drag and Drop to import a PDF (including meta data) @TechReport{Steinmachera2014, author = {Igor Steinmachera and b and Marco Aurelio Graciotto Silvaa and Marco Aurelio Gerosab and David F. Redmilesc}, title = {A systematic literature review on the barriers faced by newcomers to open source software projects}, year = {2014}, number = {on},
Conducting a Systematic Literature Review. Expand your knowledge of systematic literature reviews. Exporting data from repositories, establishing a unique naming convention, knowledge of JabRef, collating all studies using JabRef, data handling, screening studies using JabRef, creating inclusion and exclusion criteria.
An systematic literature review (SLR) can now be started from the SLR itself. #9131, koppor#601 On startup, JabRef notifies the user if there were parsing errors during opening.
Abstract. The number of publications is steadily growing. systematic literature reviews (SLRs) are one answer to this issue. A variety of tools exists designed to support the review process. This paper summarizes requirements for adequate tooling support and shows that existing tools do not meet all of them. We further investigate whether reference man-agement tools can be used in conjunction ...
A systematic literature review is a research process that identifies, evaluates, and interprets all relevant study findings connected to specific research questions, topics, or phenomena of interest.
The most notable new feature is the experimental implementation of a systematic literature review (SLR) functionality, one of our maintainers worked on for his bachelor thesis.
We introduce the notion of a literature review protocol as a critical tool that all researchers engaged in a systematic literature review should develop and use. We position the literature review protocol as a detailed blueprint of the end-to-end literature review journey. The protocol is a valuable planning and quality assurance tool which can ...
The present article illustrates a comprehensive and Systematic Literature Review (SLR) regarding the papers published in the field of the e-commerce recommender systems.
EC2: Papers not in English; EC3: Surveys, Review Articles, Book Chapters, Posters, Tutorials, Technical Reports and Standards; During the SLR study, StArt [ 16] and JabRef [ 5] tools were used. StArt is a free tool that aids performing SLR studies in accordance with the systematic review procedure suggested by Kitchenham [ 6 ].
I've just used the systematic literature review feature for the first time. It is amazing. Excellent work. However, it seems that it always downloads 100 entries exactly. I have not seen anywhere a way to change this limit, and there doesn't seem to be any options relating to the systematic review tool in the options menu. Am I missing something?
Abstract Literature reviews establish the foundation of academic inquires. However, in the planning field, we lack rigorous systematic reviews. In this article, through a systematic search on the methodology of literature review, we categorize a typology of literature reviews, discuss steps in conducting a systematic literature review, and provide suggestions on how to enhance rigor in ...
Explore the latest full-text research PDFs, articles, conference papers, preprints and more on JABREF. Find methods information, sources, references or conduct a literature review on JABREF
Systematic literature review creates (and looks for) files based on the query plus database name (if I understand correctly). It appears that on Linux file names cannot contain / whereas in Medline/PubMed there is a / and it wants to create a file with the database name which is not possible.
koppor commented on May 8, 2022 Blocked by koppor/jabref#559. We first need to finish the (new) SLR implementation and then work on the documentation.
Technological factors of mobile payment: A systematic literature review. Procedia Computer Science, 157, 489-498. Crossref. Google Scholar. Keramati A., Taeb R., Larijani A. M., & Mojir N. (2010). A combinative model of behavioural and technical factors affecting "Mobile"-payment services adoption: An empirical study.
Performing a literature review is a critical first step in research to understanding the state-of-the-art and identifying gaps and challenges in the field. A systematic literature review is a method which sets out a series of steps to methodically organize the review. In this paper, we present a guide designed for researchers and in particular ...
At least in the context of a systematic literature review, it might be worth having the duplicate detection put its emphasis on a few key fields (such as author, title, date, doi, and url) irrespective of their entry type.
Background: Natural Language Processing (NLP) is a field of both computational linguistics and artificial intelligence (AI) dedicated to analysis and interpretation of human language. Objectives: This systematic review aims at exploring all the possible applications of NLP techniques in the dermatological setting. Methods: Extensive search on 'natural language processing' and 'dermatology' was ...
Systematic literature review is a crucial research method used to thoroughly explore previous research and studies on a particular topic. Its main goal is to provide a complete collection of relevant papers in that field. It is an effective way to bring together past findings and fill any gaps in our knowledge. With the increasing amount of ...
The construction sector is notorious for its high rate of fatalities globally. Previous research has established that near-miss incidents act as precursors to accidents. This study aims to identify research gaps in the literature on near-miss events in construction and to define potential directions for future research. The Scopus database serves as the knowledge source for this study. To ...
Add an option to select the 2nd, 3rd, etc page of results. The limit of 100 citations is a severe restriction for comprehensive literature review where all papers matching a search term need to be considered. The page option would permit fetching the second page of 100, the third page of 100, etc.