Systematic Mapping Research Articles

Identifying Student Behavior in Smart Classrooms: A Systematic Literature Mapping and Taxonomies

The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) in educational settings has revolutionized the traditional teaching-learning environment, giving rise to the concept of smart classrooms. This transformation not only enhances the monitoring of people and processes but also impacts the dynamics of student behaviors, which are influenced by various factors such as the teaching environment and teacher-student interactions. Recognizing and accurately identifying these behaviors is crucial for educators to implement effective interventions and improve learning outcomes. This study conducts a systematic literature mapping to examine the contemporary landscape of student behavior identification in smart classrooms. The analysis reveals a rich diversity of methodologies and approaches used in this area. Key contributions of this research include the development of taxonomies for 37 technologies deployed, 25 challenges faced, 58 student behaviors observed, as well as the identification of nine subjects benefiting from this data and 10 methodologies for processing behavioral information. By offering an overview of the technological and methodological underpinnings of behavior identification in smart classrooms, this study significantly propels forward the domain of smart classroom research, equipping educators and technologists with a holistic understanding of how to navigate and leverage the complexities associated with monitoring student behavior.

Analysis of Strategies to Attract Female Talent in the Software Development Industry

Historically, activities related to software development have been led by men, with weak participation from women. Currently, initiatives have been promoted to increasingly integrate women into this discipline. However, despite the known benefits of gender diversity in innovation and team performance, the visibility of women in this field remains weak. The primary objective of this article is to analyze strategies to attract female talent to the software development industry, aiming to reduce the gender gaps that women face. The methodology used in this work is based on several steps of systematic literature mapping, which seeks to provide a comprehensive overview of the literature on this topic, classify the findings, and offer a framework for future research in this area. The main findings highlight the need for organizational policies and work practices that promote gender equality, as well as the importance of early education and training in retaining female talent in the software industry. This study not only contributes to academic discourse but also suggests practical measures for industry professionals to foster a more inclusive and equitable work environment.

Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods

Abstract This study aims to perform a thorough systematic review investigating and synthesizing existing research on defense strategies and methodologies in adversarial attacks using machine learning (ML) and deep learning methods. A methodology was conducted to guarantee a thorough literature analysis of the studies using sources such as ScienceDirect, Scopus, IEEE Xplore, and Web of Science. A question was shaped to retrieve articles published from 2019 to April 2024, which ultimately produced a total of 704 papers. A rigorous screening, deduplication, and matching of the inclusion and exclusion criteria were followed, and hence 42 studies were included in the quantitative synthesis. The considered papers were categorized into a coherent and systematic classification including three categories: security enhancement techniques, adversarial attack strategies and defense mechanisms, and innovative security mechanisms and solutions. In this article, we have presented a systematic and comprehensive analysis of earlier studies and opened the door to potential future studies by discussing in depth four challenges and motivations of adversarial attacks, while three recommendations have been discussed. A systematic science mapping analysis was also performed to reorganize and summarize the results of studies to address the issues of trustworthiness. Moreover, this research covers a large variety of network and cybersecurity applications of defense in adversarial attack subjects, including intrusion detection systems, anomaly detection, ML-based defenses, and cryptographic techniques. The relevant conclusions well demonstrate what have achieved in defense mechanisms against adversarial attacks. In addition, the analysis revealed a few emerging tendencies and deficiencies in the area to be remedied through better and more dependable mitigation methods against advanced persistent threats. The findings of this review have crucial implications for the community of researchers, practitioners, and policy makers in network and cybersecurity using artificial intelligence applications.

Machine learning models in mass appraisal for property tax purposes: a systematic mapping study

The use of machine learning models in mass appraisal of properties for tax purposes has been extensively investigated, generating a growing volume of primary research. This study aims to provide an overview of the machine learning techniques used in this context and analyze their accuracy. We conducted a systematic mapping study to collect studies published in the last seven years that address machine learning methods in the mass appraisal of properties. The search protocols returned 332 studies, of which 22 were selected, highlighting the frequent use of Random Forest and Gradient Boosting models in the last three years. These models, especially Random Forest, have shown predictive superiority over traditional appraisal methods. The measurement of model performance varied among the studies, making it difficult to compare results. However, it was observed that the use of machine learning techniques improves accuracy in mass property appraisals. This article advances the field by summarizing the state of the art in the use of machine learning models for mass appraisal of properties for tax purposes, describing the main models applied, providing a map that classifies, compares, and evaluates the research, and suggesting a research agenda that identifies gaps and directs future studies.

Generative AI in education and research: A systematic mapping review

Generative <scp>AI</scp> in education and research: A systematic mapping review

Investigating the impact of anthropogenic radiofrequency electromagnetic fields on animals and plants in the environment: analysis from a systematic map

ABSTRACT Radiofrequency electromagnetic fields (RF EMF) from anthropogenic sources have become ubiquitous in the environment. International guidelines have been set by expert scientific bodies to protect human health from excessive exposure. There are no specifically established exposure guidelines to protect plants and animals. A previous systematic map identified 334 studies investigating the effects of RF EMF exposure from anthropogenic sources on animals and plants. This study aimed to analyse the effect of RF EMF and its relationship to different exposure parameters and the quality of those studies. Statistically significant inverse relationships were found between effect size and exposure-level parameters for studies investigating effects in animals. The results indicated that quality score is more indicative of the magnitude of the effect size than exposure-level parameters or exposure duration. This highlights the need for further, high-quality research on the effects of RF EMF exposure on plants and animals.

Mapping Study of Intrusion Detection System

The Network Security Monitoring System has been widely used to check many systems that supply services. A lot of monitoring tools have been developed to facilitate the monitoring of the network security. Since there are a lot of options to cater to our needs, this will cause a lot of time and resources to try each tool that is suitable with the system. In this research, we conducted a comparative analysis to analyse each tool presenting their advantages, disadvantages and the method used. The main objective of this research is to perform a systematic mapping study for the purpose to identify research topics related to network intrusion detection system, to assess the most frequently applied method of intrusion detection system, and to verify the types of cyber-attack that currently exist. Based on the 30 primary studies included in this mapping study, the findings indicated that the most intrusion detection system commonly used is the hybrid method and Data Injection has been the primary attack type in the existing system.

Comparative Study of AI Code Generation Tools: Quality Assessment and Performance Analysis

Artificial intelligence (AI) code generation tools are crucial in software development, processing natural language to improve programming efficiency. Their increasing integration in various industries highlights their potential to transform the way programmers approach and execute software projects. The present research was conducted with the purpose of determining the accuracy and quality of code generated by artificial intelligence (AI) tools. The study began with a systematic mapping of the literature to identify applicable AI tools. Databases such as ACM, Engineering Source, Academic Search Ultimate, IEEE Xplore and Scopus were consulted, from which 621 papers were initially extracted. After applying inclusion criteria, such as English-language papers in computing areas published between 2020 and 2024, 113 resources were selected. A further screening process reduced this number to 44 papers, which identified 11 AI tools for code generation. The method used was a comparative study in which ten programming exercises of varying levels of difficulty were designed and the results obtained from 4 of them are presented. The identified tools generated code for these exercises in different programming languages. The quality of the generated code was evaluated using the SonarQube static analyzer, considering aspects such as safety, reliability and maintainability. The results showed significant variations in code quality among the AI tools. Bing as a code generation tool showed slightly superior performance compared to others, although none stood out as a noticeably superior AI. In conclusion, the research evidenced that, although AI tools for code generation are promising, they still require a pilot to reach their full potential, giving evidence that there is still a long way to go.

Quantum computing in bioinformatics: a systematic review mapping.

The field of quantum computing (QC) is expanding, with efforts being made to apply it to areas previously covered by classical algorithms and methods. Bioinformatics is one such domain that is developing in terms of QC. This article offers a broad mapping review of methods and algorithms of QC in bioinformatics, marking the first of its kind. It presents an overview of the domain and aids researchers in identifying further research directions in the early stages of this field of knowledge. The work presented here shows the current state-of-the-art solutions, focuses on general future directions, and highlights the limitations of current methods. The gathered data includes a comprehensive list of identified methods along with descriptions, classifications, and elaborations of their advantages and disadvantages. Results are presented not just in a descriptive table but also in an aggregated and visual format.

Aspiration pneumonia in nursing literature-a mapping review.

Aspiration pneumonia (AP) is an infection of the lungs caused by inhalation of material. The reported incidences vary across literature and clinical populations and is associated with high morbidity and mortality. Management of AP is best carried out by a multidisciplinary team. This aim of this review was to collate and describe the available evidence on AP to develop a greater understanding of the concept of AP as it is represented in the nursing literature. As a collaborative team, we undertook the six stages of a systematic mapping review. We searched for the term aspiration pneumonia in 200 peer reviewed nursing journals across 10 databases, over a ten-year period (2013-2023). In this review, 293 papers were coded. Dysphagia, oral health and tube feeding emerged as the most frequent risk factors for AP, and the most reported factors for preventing this condition. Mortality was the most commonly described consequence of AP, followed by hospitalisations and morbidity. Multiple management approaches were reported including dysphagia assessment, risk evaluation, oral care and texture modification of food and fluids. The role of nurses and interprofessional collaborations were described. Despite limited evidence related to the topic of AP in the nursing literature, the complexity of the causes, prevention, management and consequences of AP emerged. Certain factors, such as dysphagia, oral health, and tube feeding, were described under prevention, cause and management of AP. The importance of multidisciplinary approach in the management and prevention of AP was presented.

A systematic mapping of social entrepreneurship education: A call for increased collaboration, ethics, and research frameworks

PurposeThis study aims to provide a comprehensive overview of Social Entrepreneurship Education (SEE) by examining the evolution, current state, and future research directions. It seeks to identify key themes, best practices, and the integration of social entrepreneurship principles within educational frameworks. MethodologyThe research uses a systematic mapping approach incorporating bibliometric citation and content analysis to analyze 161 articles from 2002 to 2021, identifying trends, themes, and gaps in SEE literature. FindingsThe results reveal three main clusters of research areas: integrating social entrepreneurship principles into educational models, social entrepreneurship intentions and antecedents, and challenges and competencies development in SEE. A taxonomy scheme for categorizing SEE research is proposed, enhancing understanding through six prepositions and 18 possible future research inquiries. ImplicationsThis classification system provides a comprehensive overview of SEE, promoting global collaboration and innovative teaching methodologies. It encourages a holistic approach to educational programs, using visualizations and maps for policy enactment. OriginalityThis paper enhances SEE understanding through a taxonomy scheme, addresses review fragmentation, and provides a consolidated framework highlighting educational, theoretical, and methodological gaps, setting a comprehensive research agenda.

Surgical systematic reviews: best available evidence or disposable waste?

Abstract Evidence-based medicine demands treatment options for patients to be based on the current best available evidence. Systematic reviews (SRs) with meta-analyses allow surgeons to make therapeutical decisions in accordance with the highest level of evidence. Also, high-quality SRs support physicians to challenge the colossal amount of new research data created daily. The systematic review working group of the Study Center of the German Society of Surgery (SDGC) has created specific methodological literature regarding surgical SRs, giving recommendations to assess critical risk of bias and to prevent the creation of SRs that do not provide any new insights to the field. SRs should only be considered if there is new clinically relevant data available that allows the SR to create novel evidence. To address the dilemma of new SRs generated without adding new evidence, living systematic reviews and evidence mapping represent an innovative approach, in which SRs are regularly updated with new research data.

Aligning technical knowledge to an industry domain in global software development: A systematic mapping

AbstractFinding software developers with expertise in specific technologies that align with industry domains is an increasingly critical requirement. However, due to the ever‐changing nature of the technology industry, locating these professionals has become a significant challenge for companies and institutions. This research presents a comprehensive overview of studies exploring suitable recommendation systems that can assist companies in addressing this pressing need. To conduct this study, we employ a hybrid systematic mapping approach with an initial number of 1,251 studies and a final selection of 21 studies. Our work focuses on collecting data on key technologies, methodologies, and data sets utilized in proposed recommendation systems, to design a new recommendation system that can effectively identify specialists capable of aligning specific technical knowledge with industry domains. The outcomes of this study include insights into the current research trends in this field, alongside a practical overview of considerations necessary for developing a recommendation system that successfully meets the criteria for aligning technical skills with industry domains. By following a hybrid systematic mapping methodology and presenting the outcomes in the form of insights, this research addresses the challenge of finding software developers with domain‐specific expertise in a rapidly changing technology industry, laying the groundwork for aligning technical skills with industry domains.

Air-Polluting Emissions from Pyrolysis Plants: A Systematic Mapping

There is a growing interest in the use of pyrolysis plants for the conversion of solid waste into useful products (e.g., oil, gas, and char) and the analysis of air-polluting emissions associated with such a process is an emerging research field. This study applied a systematic mapping approach to collating, describing, and cataloging available evidence related to the type and level of air pollutants emitted from pyrolysis plants, the factors affecting emissions, and available mitigation strategies that can be adopted to reduce air pollution. The scientific literature indexed in Scopus and Google Scholar, as well as available industry reports, was interrogated to document the evidence. A database comprising 63 studies was synthesized and cataloged from which 25 air pollutants from pyrolysis plants were considered, including volatile organic compounds and persistent organic pollutants. Air pollutant levels varied depending on the scale of the pyrolysis plants, their operating conditions, and the feedstock used. Various technologies, such as wet scrubbers, electrostatic precipitators, and baghouse filters, are available and have been utilized to reduce emissions and comply with the existing EU regulations for waste incineration (2010/75/EU). The systematic mapping identified several knowledge gaps that need to be addressed to inform relevant environmental policymaking, technology development, and the adoption of best practices for the mitigation of emissions from pyrolysis plants.

Control and management of Phytophthora damage in forestry—A systematic mapping study

AbstractPlant pathogens in the genus Phytophthora are a severe threat to forest plantations, ecosystems and tree nurseries. Especially in forests and natural ecosystems, there is a lack of effective measures to control and manage these pathogens. In this study, we conducted a systematic mapping review to collate evidence regarding the control and management of forest Phytophthora in different production settings and ecosystems. The study aimed to reveal possible knowledge gaps, thus guiding future research priorities. We extracted information from nine databases, limiting the search to studies published during the time period from January 2010 to December 2022. The articles were shared between three reviewers who classified the reports using a set of inclusion/exclusion criteria. A total of 561 articles were included and mapped in a database using pre‐defined coding, and critically appraised for relevance and reliability. The analysis showed that biological or bio‐based measures were the most studied interventions, followed by genetics or breeding programmes, whereas chemical and silvicultural management approaches were less studied. Most of the studies were conducted in Europe, North America, Australia and New Zealand. Phytophthora cinnamomi has been the most studied species followed by P. ramorum. We discuss the current knowledge gaps in the implementation of existing research, likely due to a lack of holistic understanding of the processes over time and space, and suggest future research that is needed to manage Phytophthora in forest ecosystems.

Understanding the GDPR from a requirements engineering perspective—a systematic mapping study on regulatory data protection requirements

AbstractData protection compliance is critical from a requirements engineering (RE) perspective, both from a software development lifecycle (SDLC) perspective and regulatory compliance. Not including these requirements from the early phases of the SDLC can prove costly and challenging afterward. The general data protection regulation (GDPR) from the European Union (EU) sets a list of requirements that organizations working within its scope should satisfy. However, these requirements are complex to work with, as legal prose tends to be vague and imprecise, and not all requirements have received the same attention from researchers. This study aims to identify the research published in RE for helping compliance with regulatory data protection requirements. We gathered and analyzed 90 articles from 2016 to 2022 through a systematic mapping study. We analyzed key trends in the sample, such as year of publication, publication venue, type of research, interdisciplinarity in the author’s background, GDPR focus of compliance element, and type of proposal. Our main findings show ongoing interest, mostly published in conferences, in achieving overall compliance with the GDPR and consent as the most popular topics. Other topics, such as cookies or children’s data, did not receive significant attention. Research over the whole RE process has been done. 20 (22%) of the papers have authors affiliated with non-computer science; however, most research seems not interdisciplinary. We finally discuss gaps in the literature, possible future areas of research, and the importance of interdisciplinary research for regulatory data protection requirements in RE.

Synthesis of sexual selection: a systematic map of meta-analyses with bibliometric analysis.

Sexual selection has been a popular subject within evolutionary biology because of its central role in explaining odd and counterintuitive traits observed in nature. Consequently, the literature associated with this field of study became vast. Meta-analytical studies attempting to draw inferences from this literature have now accumulated, varying in scope and quality, thus calling for a synthesis of these syntheses. We conducted a systematic literature search to create a systematic map with a report appraisal of meta-analyses on topics associated with sexual selection, aiming to identify the conceptual and methodological gaps in this secondary literature. We also conducted bibliometric analyses to explore whether these gaps are associated with the gender and origin of the authors of these meta-analyses. We included 152 meta-analytical studies in our systematic map. We found that most meta-analyses focused on males and on certain animal groups (e.g. birds), indicating severe sex and taxonomic biases. The topics in these studies varied greatly, from proximate (e.g. relationship of ornaments with other traits) to ultimate questions (e.g. formal estimates of sexual selection strength), although the former were more common. We also observed several common methodological issues in these studies, such as lack of detailed information regarding searches, screening, and analyses, which ultimately impairs the reliability of many of these meta-analyses. In addition, most of the meta-analyses' authors were men affiliated to institutions from developed countries, pointing to both gender and geographical authorship biases. Most importantly, we found that certain authorship aspects were associated with conceptual and methodological issues in meta-analytical studies. Many of our findings might simply reflect patterns in the current state of the primary literature and academia, suggesting that our study can serve as an indicator of issues within the field of sexual selection at large. Based on our findings, we provide both conceptual and analytical recommendations to improve future studies in the field of sexual selection.

The experimental implications of the rate of temperature change and timing of nutrient availability on growth and stoichiometry of a natural marine phytoplankton community

AbstractClimate change increases the need to understand the effect of predicted future temperature and nutrient scenarios on marine phytoplankton. However, experimental studies addressing the effects of both drivers use a variety of design approaches regarding their temperature change rate and nutrient supply regimes. This study combines a systematic literature map to identify the existing bias in the experimental design of studies evaluating the phytoplankton response to temperature change, with a laboratory experiment. The experiment was designed to quantify how different temperature levels (6°C, 12°C, and 18°C), temperature regimes (abrupt vs. gradual increase), timings of nutrient addition (before or after the temperature change) and nutrient regimes (limiting vs. balanced) alter the growth and stoichiometry of a natural marine phytoplankton community. The systematic map revealed three key biases in marine global change experiments: (1) 66% of the studies do not explicitly describe the experimental temperature change or nutrient regime, (2) 84% applied an abrupt temperature exposure, and (3) only 15% experimentally manipulated the nutrient regime. Our experiment demonstrated that the identified biases in experimental design toward abrupt temperature exposure induced a short‐term growth overshoot compared to gradually increasing temperatures. Additionally, the timing of nutrient availability strongly modulated the direction of the temperature effect and strength of growth enhancement along balanced N : P supply ratios. Our study stresses that the rate of temperature change, the timing of nutrient addition and the N : P supply ratio should be considered in experimental planning to produce ecologically relevant results as different setups lead to contrasting directions of outcome.

Evidence on the effects of flame retardant substances at ecologically relevant endpoints: a systematic map protocol

Evidence on the effects of flame retardant substances at ecologically relevant endpoints: a systematic map protocol

Navigating the Uncertain Terrain: Venezuela’s Future Using the Shared Socioeconomic Pathways Framework—A Systematic Review

We investigate Venezuela’s potential “futures” under Shared Socioeconomic Pathways (SSPs) through a systematic literature review, including systematic mapping and thematic analysis of 50 scientific articles. We categorised the SSP scenarios into two generational categories and classified the outcomes into positive, negative, and neutral futures. Under first-generation SSP scenarios, increasing poverty could be reversed, and the country’s economic growth could be stimulated by adopting unambitious climate measures. However, second-generation SSP scenarios paint a more challenging picture. They suggest that Venezuela could face heat waves, droughts, an increase in diseases, loss of biodiversity, and an increase in invasive species and pests during the remainder of the 21st century as a direct consequence of climate change. Venezuela’s geographic and topographic diversity could exacerbate these impacts of climate change. For instance, coastal areas could be at risk of sea-level rise and increased storm surges, while mountainous regions could experience more frequent and intense rainfall, leading to landslides and flash floods. The urgency of conducting additional research on the factors that could influence the severity of climate change’s impact, considering Venezuela’s geographic and topographic diversity, cannot be overstated. We also identified the critical need to explore alternative paths to move away from the current extractive development model. The potential actions in this regard could be instrumental in aligning the country with global adaptation and mitigation commitments.