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Requirements Technical Debt (RTD) applies the Technical Debt (TD) metaphor to capture the consequences of sub-optimal decisions made concerning Requirements. Understanding the quantification of RTD is key to its management. To facilitate this understanding, we developed a conceptual model, the Requirements Technical Debt Quantification Model (RTDQM). Our work is grounded in the literature found via a systematic mapping study and informed by prior work modeling the quantification of software code-related TD types. The key finding is that although RTD is similar to code-related TD in many aspects, it also has its own components. RTD can be incurred regardless of the presence of code-related TD. Unlike code-related TD, RTD has a feedback loop involving the user. RTD can have a cascading impact on other development activities, such as design and implementation, apart from the extra costs and efforts incurred during requirements engineering activities; this is modeled by the RTD Interest constituents in our model. The model was used to compare and analyze existing quantification approaches. It helped identify what RTD quantification concepts are discussed in the existing approaches and what concepts are supported by metrics for their quantification. The model serves as a reference for practitioners to select existing or to develop new quantification approaches to support informed decision-making for RTD management. [ABSTRACT FROM AUTHOR]

Data 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. [ABSTRACT FROM AUTHOR]

Background: The fields of gamification and game-based learning are growing and changing quickly, with researchers investigating the use of gamification in education using various methods and in numerous case studies, however, no research investigates the significance pre-perception of higher education students has in gamification, game-based learning, and serious games studies. Objective: We perform a systematic mapping to identify the gaps in our understanding on the effects of pre-perception in gamification, focusing on the under-researched area of gamification and game-based learning applications for higher education. Results: We present three main findings: (1) Current research on the effects of higher education's students' pre-perception of gamification and game-based learning are limited. (2) The limited available research indicates perception towards gamification and game-based learning in participants may impact on the effectiveness of game-based solutions, but more research is required to better understand the relationship. (3) A significant body of new research work is required to examine how student perception can affect the effectiveness of gamification and game-based learning solutions in higher education. Conclusion: Our understanding of the effect pre-perception may have on the results reported by research on gamification applications for higher education is limited. We encourage more, non-empirical research, to be conducted around pre-perception, and more research that considers gamification and game-based learning applications for higher education. [ABSTRACT FROM AUTHOR]

Background Objective Search Strategy Selection Criteria Data Collection and Analysis Main Results Conclusion High maternal mortality rates in sub‐Saharan Africa necessitate the need for aligned research focusing on prevalent causes and neglected conditions in the region.This mapping review aimed to describe the characteristics of longitudinal maternal health studies between 2012 and 2022 in sub‐Saharan Africa and identify gaps in priority conditions or geographical locations.We identified references through a Medline (PubMed) search covering September 2012 to June 2022.We included prospective cohort or clinical trials that enrolled at least 1000 pregnant women, with a study site in sub‐Saharan Africa, and published in English or French.Screening and data extraction were done in duplicate using EPPI‐reviewer software. Descriptive analysis was used to summarize the results, identifying patterns in studies across time, country, study design, topics, and funders.We identified 213 eligible studies, which were covered in 534 publications. We identified studies in 33 of the 48 sub‐Saharan African countries, with the majority in east and southern Africa. The predominant study topics were HIV (36.4%), nutrition (20%), and malaria (16.3%), with very few publications on hypertensive disorders of pregnancy (6.4%), ante/postpartum hemorrhage (3.7%), and sexually transmitted infections (3.2%). More studies were cohorts (115/213; 54%) than clinical trials. The National Institutes of Health (31.5%), Bill and Melinda Gates Foundation (22.1%), and USAID (10.8%) were the largest research funders.Identifying research trends and mismatches between research topics and disease burden provides useful information for guiding future research prioritization. In particular, gaps exist for studies on hypertensive disorders of pregnancy and ante/postpartum hemorrhage, among the top causes of maternal mortality in sub‐Saharan Africa. [ABSTRACT FROM AUTHOR]

Structural optimization has gained popularity in modern structural design, helping to reduce material consumption while maintaining the structural performance of buildings. This process also significantly influences the architectural appearance, affecting various aspects such as cross-section sizing, structural forms, and the layout of structural members. Beyond minimizing materials or costs, structural optimization can serve as a powerful tool for making architecture more visually appealing. However, with the wide variety of structural optimization methods proposed, gaining a comprehensive overview has become challenging. To address this, a systematic mapping study has been conducted, focusing on methods introduced over the past decade. The relevant journal articles are categorized based on several factors, including types of optimization, materials used, structural typologies, areas of application, and optimization objectives. The results of this study provide both a broad overview of recent developments in structural optimization and valuable insights into research-rich and under-explored areas. Moreover, the paper discusses which types of structural optimization are more relevant when applied as part of the architectural design process. It is suggested that future research should focus on identifying gaps and challenges in effectively applying structural optimization to architectural design, thus enhancing both efficiency and aesthetic potential. [ABSTRACT FROM AUTHOR]

The use of Artificial Intelligence (AI) in Internet of Things (IoT) systems has gained significant attention due to its potential to improve efficiency, functionality and decision-making. To further advance research and practical implementation, it is crucial to better understand the specific roles of AI in IoT systems and identify the key application domains. In this article we aim to identify the different roles of AI in IoT systems and the application domains where AI is used most significantly. We have conducted a systematic mapping study using multiple databases, i.e., Scopus, ACM Digital Library, IEEE Xplore and Wiley Online. Eighty-one relevant survey articles were selected after applying the selection criteria and then analyzed to extract the key information. As a result, six general tasks of AI in IoT systems were identified: pattern recognition, decision support, decision-making and acting, prediction, data management and human interaction. Moreover, 15 subtasks were identified, as well as 13 application domains, where healthcare was the most frequent. We conclude that there are several important tasks that AI can perform in IoT systems, improving efficiency, security and functionality across many important application domains. [ABSTRACT FROM AUTHOR]

The integration of machine learning (ML) with edge computing and wearable devices is rapidly advancing healthcare applications. This study systematically maps the literature in this emerging field, analyzing 171 studies and focusing on 28 key articles after rigorous selection. The research explores the key concepts, techniques, and architectures used in healthcare applications involving ML, edge computing, and wearable devices. The analysis reveals a significant increase in research over the past six years, particularly in the last three years, covering applications such as fall detection, cardiovascular monitoring, and disease prediction. The findings highlight a strong focus on neural network models, especially Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs), and diverse edge computing platforms like Raspberry Pi and smartphones. Despite the diversity in approaches, the field is still nascent, indicating considerable opportunities for future research. The study emphasizes the need for standardized architectures and the further exploration of both hardware and software to enhance the effectiveness of ML-driven healthcare solutions. The authors conclude by identifying potential research directions that could contribute to continued innovation in healthcare technologies. [ABSTRACT FROM AUTHOR]

SMT (Surface Mount Technology) has been the backbone of PCB (Printed Circuit Board) production for the last couple of decades. Even though the speed and accuracy of SMT have been drastically improved in the last decade, errors during production are still a very valid problem for the PCB industry. With the exponential rise of Artificial Intelligence in the last decade, the SMT industry was one of the most eager industries to use this new technology to detect possible defects during production. Lately, traditional image processing techniques started to lag behind methods such as machine learning and deep learning when the discussion came to the need of high accuracy. In this paper, we screen academic libraries to understand which of the latest methods and techniques are used in the domain and to deduce a general process for detecting defects in PCBs. During the research we have investigated research questions related to state-of-the-art methods, highly mentioned datasets, and sought after PCB defects. All findings and answers are mapped to be able to understand where this pursuit might point towards. From a total of 270 papers, 90 of them were addressed in detail and 78 papers were chosen for this systematic mapping. [ABSTRACT FROM AUTHOR]

RESUMEN: Introducción/Objetivo: el engagement laboral es un concepto complejo y multifacético que integra dimensiones como el compromiso, la motivación y la conexión emocional que un empleado desarrolla con su trabajo y organización. Este estudio tiene como objetivo explorar las tendencias y los factores que influyen en el engagement laboral en el contexto actual. Metodología: se realizó un mapeo sistemático en la base de datos Scopus para identificar y analizar estudios empíricos y de revisión relacionados con el engagement laboral. La búsqueda bibliográfica se enfocó en estudios publicados entre el 2018 y el 2022, y 308 documentos fueron utilizados para el estudio. Se aplicaron criterios de inclusión y exclusión rigurosos para asegurar la relevancia y calidad de los estudios seleccionados. Resultados: el análisis bibliométrico revela un creciente interés en la investigación sobre el engagement laboral, con una producción científica notable en varios países y autores clave en el campo. Factores como el apoyo organizacional, la satisfacción laboral y el balance entre trabajo y vida personal han sido identificados como influencias significativas en el engagement laboral. Además, la evolución del engagement ha resaltado su importancia en la gestión de recursos humanos y la cultura organizacional, así como la influencia de la tecnología y la flexibilidad laboral en su definición moderna. Conclusiones: el engagement laboral en la Era Moderna trasciende visiones limitadas a funciones individuales o beneficios económicos, y abarca elementos que conforman una experiencia laboral enriquecedora y alineada con un propósito colectivo. Este enfoque holístico es crucial para impulsar la productividad, la innovación y la retención de talento en un mercado competitivo, el cual sienta las bases para una cultura organizacional ágil y preparada para el futuro. Próximas investigaciones deben explorar más a fondo las dimensiones del engagement y su impacto en el desempeño organizacional. ABSTRACT: Introduction/Objective: Employee engagement is a complex and multifaceted concept that integrates dimensions such as commitment, motivation, and the emotional connection an employee develops with their work and organization. This study aims to explore the trends and factors influencing employee engagement in the current context Methodology: A systematic mapping was conducted in the Scopus database to identify and analyse empirical and review studies related to employee engagement. The literature search focused on studies published between 2018 and 2022, with 308 documents used for the study. Rigorous inclusion and exclusion criteria, based on predefined parameters, were applied to ensure the relevance and quality of the selected studies. Results: The bibliometric analysis reveals a growing interest in research on employee engagement, with notable scientific production in various countries and key authors in the field. Factors such as organizational support, job satisfaction, and work-life balance have been identified as significant influences on employee engagement. Additionally, the evolution of engagement has highlighted its importance in human resource management and organizational culture, as well as the influence of technology and work flexibility in its modern definition. Conclusions: Employee engagement in the modern era transcends limited views to individual roles or economic benefits, encompassing elements that form an enriching work experience aligned with a collective purpose. This holistic approach is crucial for driving productivity, innovation, and talent retention in a competitive market, laying the foundations for an agile and future-ready organizational culture. Future research should further explore the dimensions of engagement and its impact on organizational performance.

Abstract Background The global research on pandemics or epidemics and mental health has been growing exponentially recently, which cannot be integrated through traditional systematic review. Our study aims to systematically synthesize the evidence using natural language processing (NLP) techniques. Methods Multiple databases were searched using titles, abstracts, and keywords. We systematically identified relevant literature published prior to Dec 31, 2023, using NLP techniques such as text classification, topic modelling and geoparsing methods. Relevant articles were categorized by content, date, and geographic location, outputting evidence heat maps, geographical maps, and narrative synthesis of trends in related publications. Results Our NLP analysis identified 77,915 studies in the area of pandemics or epidemics and mental health published before Dec 31, 2023. The Covid pandemic was the most common, followed by SARS and HIV/AIDS; Anxiety and stress were the most frequently studied mental health outcomes; Social support and healthcare were the most common way of coping. Geographically, the evidence base was dominated by studies from high-income countries, with scant evidence from low-income counties. Co-occurrence of pandemics or epidemics and fear, depression, stress was common. Anxiety was one of the three most common topics in all continents except North America. Conclusion Our findings suggest the importance and feasibility of using NLP to comprehensively map pandemics or epidemics and mental health in the age of big literature. The review identifies clear themes for future clinical and public health research, and is critical for designing evidence-based approaches to reduce the negative mental health impacts of pandemics or epidemics.

Background: Nomophobia or no-mobile-phone phobia, defined as the fear of being unable to use or being unreachable via one’s mobile phone. The pervasive concern has varied bio-psychosocial and clinical implications in an individual’s life. Nomophobia is currently in an exploratory phase. Methods: A scoping review was conducted to systematically map the relevant evidence and literature on nomophobia. The review of scholarly literature spanning from January 2010 to May 2023 employed a narrative synthesis methodology to succinctly encapsulate the research findings. Additionally, it encompassed a meticulous examination and visual representation of studies, detailing both the frequency of investigation into specific variables and the instances in which said variables exhibited a statistically significant association with nomophobia. Results: A thorough examination of 92 studies revealed a total of 133 variables spanning demographic, lifestyle-related, academic, smartphone-related, psychosocial, and clinical domains. Notably, 42.86% of these variables have only been investigated in a single research study, underscoring the imperative for replication to enhance the generalizability, validity, and applicability of research findings. Moreover, the review identifies Nomophobia Questionnaire (NMP-Q) by Yildirim and Correia as the most widely used tool for measuring the severity of nomophobia. Conclusion: There is a crucial need for homogeneity and consistency in reporting nomophobia scores and establishing corresponding gradations indicative of functional ramifications or clinical severity of nomophobia. The findings elucidate significant research gaps in existing literature on nomophobia, hold implications for further synthesis of data, and contribute to a comprehensive understanding of nomophobia; ensuring superior diagnostic accuracy and precision, and facilitating successful delivery of targeted interventions.

Good cattle reproductive performance is essential for livestock productivity. Farmers are highly dependent on the success of productive outputs to support their livelihoods. In low- and middle-income countries (LMICs), however, optimal reproduction of cows and bulls is comparatively less well defined, with information on key reproductive parameters and reproductive management often not available. The aim of this study was to collate and synthesise the recent published evidence on cattle reproductive performance in selected sub-Saharan countries. Systematic mapping methodology was used, with searches conducted for both cow and bull reproductive performance in Eritrea, Ethiopia, Kenya, Nigeria, Somalia, South Sudan, Sudan, Tanzania and Uganda, for the period 2012–2022, in English language. Search returns were screened for relevance at title and abstract, and full-text levels, based on the research question criteria. A substantial number of studies were identified for cows (n = 133), but only very few for bulls (n = 11). A large proportion of reported studies have been conducted in Ethiopia, with relatively few from the other countries, and most studies published between 2014 and 2016, and in 2021. Certain reproductive parameters received more attention than others; calving interval was reported in 86 studies, while culling due to infertility was reported in eight studies. The study highlights where research is being conducted in this area, and importantly where there is a gap, in particular on bull reproductive performance. While there were a range of values reported for cow reproductive parameters, the values were reasonable, indicating that it is possible to have good reproductive performance in LMICs. The synthesis of studies in the map should help to inform farmers and their advisors, at farm and national levels. [ABSTRACT FROM AUTHOR]

Gender disparities in technology are evident, and affirmative actions are necessary to increase female representation. This article is part of an umbrella project that systematically maps related studies and aims to understand the current literature on initiatives to attract girls to high school through female empowerment projects in computing. Through a systematic literature mapping (SLM), we identified studies published between 2017 and 2022 that were available in databases and search engines, namely ACM Digital Library, IEEE Xplore Library, Scopus, and SBC Open Lib. Only primary studies returned in an automated search process were considered, without combining them with other search strategies. A priori, 264 articles were returned with the application of a search string and after applying the inclusion and exclusion criteria, 61 articles were selected. Of this number, 41 projects were named in the articles that describe activities involving high school. To answer the established research questions, it was found that the studies discussed projects implemented in secondary education in the Americas, Europe and Africa, indicating the importance of expanding these initiatives to other territories, increasing female participation in information technology and promoting gender equality, which is aligned with the Sustainable Development Goals (SDG 5) of the United Nations 2030 Agenda. [ABSTRACT FROM AUTHOR]

Background: The global research on pandemics or epidemics and mental health has been growing exponentially recently, which cannot be integrated through traditional systematic review. Our study aims to systematically synthesize the evidence using natural language processing (NLP) techniques. Methods: Multiple databases were searched using titles, abstracts, and keywords. We systematically identified relevant literature published prior to Dec 31, 2023, using NLP techniques such as text classification, topic modelling and geoparsing methods. Relevant articles were categorized by content, date, and geographic location, outputting evidence heat maps, geographical maps, and narrative synthesis of trends in related publications. Results: Our NLP analysis identified 77,915 studies in the area of pandemics or epidemics and mental health published before Dec 31, 2023. The Covid pandemic was the most common, followed by SARS and HIV/AIDS; Anxiety and stress were the most frequently studied mental health outcomes; Social support and healthcare were the most common way of coping. Geographically, the evidence base was dominated by studies from high-income countries, with scant evidence from low-income counties. Co-occurrence of pandemics or epidemics and fear, depression, stress was common. Anxiety was one of the three most common topics in all continents except North America. Conclusion: Our findings suggest the importance and feasibility of using NLP to comprehensively map pandemics or epidemics and mental health in the age of big literature. The review identifies clear themes for future clinical and public health research, and is critical for designing evidence-based approaches to reduce the negative mental health impacts of pandemics or epidemics. [ABSTRACT FROM AUTHOR]

AbstractThe 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. [ABSTRACT FROM AUTHOR]

The ability to define, evaluate, and implement software architectures is a fundamental skill for software engineers. However, teaching software architecture can be challenging as it requires students to be involved in real-context projects with high degrees of complexity. This involves making trade-off decisions among several quality attributes. Furthermore, the academic perception of software architecture differs from the industrial viewpoint. To address this issue, a study was conducted to identify and analyze the strategies, challenges, and course experiences used for teaching software architectures. The study analyzed 56 articles reporting on teaching experiences focused specifically on software architectures or focused on software engineering in general but discussing software architecture. The main contributions of this work include identifying strategies used in educating software architecture students aligned with the needs of the software industry. These strategies include short design projects, large development projects, and projects with actual clients. Additionally, the study compared curriculum contents in software development and architecture courses and identified recurring topics such as architecture patterns, quality attributes, and architectural views. This study also recognizes the set of skills that students of software architecture should develop during training, such as leadership and negotiation. The challenges in software architecture training were discussed, such as instructors' lack of experience in actual projects, the abstract and fuzzy nature of software architectures, and the difficulty of involving clients and industry experts. Evaluation methods commonly used in training software architects, such as surveys, pre-test/post-test, and quality metrics on architectural artifacts, were identified and described. Overall, this study guides researchers and educators in improving their software architecture courses by incorporating strategies reported by the literature review. These strategies can bring architecture courses closer to the needs and conditions of the software industry. [ABSTRACT FROM AUTHOR]

Finding 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. [ABSTRACT FROM AUTHOR]

Background: Nomophobia or no-mobile-phone phobia, defined as the fear of being unable to use or being unreachable via one's mobile phone. The pervasive concern has varied bio-psychosocial and clinical implications in an individual's life. Nomophobia is currently in an exploratory phase. Methods: A scoping review was conducted to systematically map the relevant evidence and literature on nomophobia. The review of scholarly literature spanning from January 2010 to May 2023 employed a narrative synthesis methodology to succinctly encapsulate the research findings. Additionally, it encompassed a meticulous examination and visual representation of studies, detailing both the frequency of investigation into specific variables and the instances in which said variables exhibited a statistically significant association with nomophobia. Results: A thorough examination of 92 studies revealed a total of 133 variables spanning demographic, lifestyle-related, academic, smartphone-related, psychosocial, and clinical domains. Notably, 42.86% of these variables have only been investigated in a single research study, underscoring the imperative for replication to enhance the generalizability, validity, and applicability of research findings. Moreover, the review identifies Nomophobia Questionnaire (NMP-Q) by Yildirim and Correia as the most widely used tool for measuring the severity of nomophobia. Conclusion: There is a crucial need for homogeneity and consistency in reporting nomophobia scores and establishing corresponding gradations indicative of functional ramifications or clinical severity of nomophobia. The findings elucidate significant research gaps in existing literature on nomophobia, hold implications for further synthesis of data, and contribute to a comprehensive understanding of nomophobia; ensuring superior diagnostic accuracy and precision, and facilitating successful delivery of targeted interventions. [ABSTRACT FROM AUTHOR]

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Anita Nilsen,1 Siren Eriksen,2 Bjørn Lichtwarck,3 Adelheid Hummelvoll Hillestad,2 Signe Gunn Julnes,1 Signe Tretteteig,4 Anne Marie Mork Rokstad1,4 1Faculty of Health Sciences and Social Care, Molde University College, Molde, Norway; 2Lovisenberg Diaconal University College, Oslo, Norway; 3The Research Centre for Age-Related Functional Decline and Disease, Innlandet Hospital Trust, Ottestad, Norway; 4Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, NorwayCorrespondence: Anita Nilsen, Faculty of Health Sciences and Social Care, Molde University College, Molde, Norway, Email anita.nilsen@himolde.noPurpose: Coronavirus disease 2019 (COVID-19) placed a significant strain on nursing homes, leading to numerous outbreaks and high mortality rates. This situation created considerable stress and challenges for residents, their physicians and nurses, as well as family caregivers. By understanding these challenges, we can gain new insights and learn valuable lessons. Thus, the purpose of this study is to examine the treatment and care provided to nursing home residents with COVID-19, as experienced by physicians, nurses, and family caregivers.Participants and Methods: This study is a secondary analysis of 35 interviews with physicians, nurses, and family caregivers, each with personal experience caring for nursing home residents diagnosed with COVID-19. The interviews took place from December 2020 to April 2021. We analyzed the transcriptions based on Braun and Clarke’s reflexive thematic analysis model and followed a qualitative descriptive design as outlined by Sandelowski.Findings: The analysis produced three themes: 1) Balancing medical treatment, 2) The need for increased systematic monitoring of vital functions, and 3) Determining the treatment level for nursing home residents. These themes were explored through the unique perspectives of the three participant groups: physicians, nurses, and family caregivers. The findings revealed several challenges related to treatment and care for nursing home residents diagnosed with COVID-19. This applied both to relief of symptoms, monitoring of vital functions, assessment of treatment level and use of advance care planning.Conclusion: Drawing from the experiences of physicians, nurses, and family caregivers, there should be a unified plan at the municipal or national level for competency development in nursing homes to prepare for future crises like pandemics or epidemics. Additionally, the safe engagement of family caregivers and relatives should be given priority.Keywords: alleviation, nursing homes, palliative care, pandemic, safekeeping, systematic mapping

e-Government Maturity Models are essential tools for governments to assess and improve their e-government capabilities, performance, and impact. This article offers a pioneering systematic mapping study and comparative analysis of e-Government Maturity Models proposed between 2010 and 2022, marking a significant stride in the realm of e-government research. The review focuses on these recent models and compares their approaches, frameworks, and dimensions. Previous comparison research studies conducted before 2010 are also reviewed to provide an inclusive context for understanding the evolution and advancement of e-government maturity models. The study is the first to systematically map and compare e-Government Maturity Models proposed in recent years, also comparing with the previous decade. Employing a mixed-methods approach, this article meticulously dissects and contrasts various models, shedding light on the evolution, strengths, and weaknesses of e-government maturity frameworks over more than a decade. The research contributes to the literature by revealing underlying discrepancies in current models and proposing a synthesized perspective that integrates technological advancements and administrative strategies, providing insights into opportunities for future research and practice. The findings of the study can be used to develop a new generation of e-government maturity models that are more comprehensive and relevant to the current needs of governments. [ABSTRACT FROM AUTHOR]

The growing importance of data analytics is leading to a shift in data management strategy at many companies, moving away from simple data storage towards adopting Online Analytical Processing (OLAP) query analysis. Concurrently, NoSQL databases are gaining ground as the preferred choice for storing and querying analytical data. This article presents a comprehensive, systematic mapping, aiming to consolidate research efforts related to the integration of OLAP with NoSQL databases in Big Data environments. After identifying 1646 initial research studies from scientific digital repositories, a thorough examination of their content resulted in the acceptance of 22 studies. Utilizing the snowballing technique, an additional three studies were selected, culminating in a final corpus of twenty-five relevant articles. This review addresses the growing importance of leveraging NoSQL databases for OLAP query analysis in response to increasing data analytics demands. By identifying the most commonly used NoSQL databases with OLAP, such as column-oriented and document-oriented, prevalent OLAP modeling methods, such as Relational Online Analytical Processing (ROLAP) and Multidimensional Online Analytical Processing (MOLAP), and suggested models for batch and real-time processing, among other results, this research provides a roadmap for organizations navigating the integration of OLAP with NoSQL. Additionally, exploring computational resource requirements and performance benchmarks facilitates informed decision making and promotes advancements in Big Data analytics. The main findings of this review provide valuable insights and updated information regarding the integration of OLAP cubes with NoSQL databases to benefit future research, industry practitioners, and academia alike. This consolidation of research efforts not only promotes innovative solutions but also promises reduced operational costs compared to traditional database systems. [ABSTRACT FROM AUTHOR]

Electronic voting, based on cryptographic protocols and blockchain technology, represents an advanced method for ensuring the security, transparency, and reliability of electoral processes. The scientific research presented in this article is devoted to the analysis of trends and advanced technologies in the field of electronic voting. The authors propose a systematic mapping of this area using bibliometric analysis. The research is based on the analysis of scientific articles extracted from the Web of Science and Scopus databases. Bibliometric analysis tools are used to automatically identify key topics and trends in the literature. The results of the study reveal the key directions of the development of electronic voting, including the integration of cryptographic protocols and blockchain technology. Specific aspects include improving privacy through the use of modern cryptographic protocols, creating an immutable register of votes using blockchain technology, ensuring trust through open-source code, and increasing resilience to cyber-attacks. These areas reflect the desire to develop efficient, transparent, and secure electronic voting systems. Based on the identified trends, recommendations are proposed for further research aimed at improving electronic electoral systems and their adaptation to the modern requirements of security and transparency in electoral processes. [ABSTRACT FROM AUTHOR]

Facing increasing sustainability demands, the construction industry is at a turning point where the implementation of circular economy (CE) strategies plays an essential role in driving the necessary transformation aimed at reducing the environmental impact. To facilitate this shift, structural engineering must effectively integrate circular principles into building design. With the exponential growth of research articles within this field, it is crucial to map the evolution of the research area. The objective of this study is to detail the trends with, challenges to, and research contributions, integration, and material applications of CE principles within structural engineering. Consequently, a systematic mapping of the CE within the field of structural engineering has been conducted in this study. Initially, the mapping process began with the identification of relevant keywords, followed by searches across four databases. Each resulting article was carefully screened against content criteria, culminating in 91 publications that were thoroughly evaluated. The publications were then categorized and analyzed based on attributes such as research type, circular design, materials, and applications. The results are presented through informative figures and tables. The analysis of the research indicates a predominant focus on technical solutions for structural systems, with demountable connections designed to facilitate the future reuse of materials representing more than half of the literature reviewed. A significant portion of the literature also addresses designing from reclaimed elements; these articles reflect a transformation in engineering approaches, incorporating computational design and innovative methodologies. The focus on steel as a structural material is prominent in the reviewed literature. However, there is an increasing focus on timber, which signals a definitive shift toward sustainable structural systems. Recurring challenges identified in the literature regarding the transition to a circular economy (CE) in the construction industry include the need for industry-wide adoption, precise standardization, the integration of digital tools, and the overcoming of related obstacles in policy and market acceptances. Furthermore, the literature demonstrates a significant research gap: the absence of a comprehensive digital framework enabling an effective digital circular structural design workflow. [ABSTRACT FROM AUTHOR]

Abstract The Environmental Evidence for the Future (EEF) Initiative emerged in response to the challenges and opportunities presented by the UK’s decision to leave the European Union and its associated Environmental Frameworks. The Natural Environment Research Council (NERC), working closely with the Collaboration for Environmental Evidence (CEE) and UK stakeholders, developed the initiative to identify and address crucial evidence gaps, offering a long-term vision for environmental policy and sustainability. The EEF Initiative progressed through three stages: strategic priority identification, NERC panel award selection, and the production of Systematic Maps of existing evidence. The first stage involved collaborative workshops across the UK to identify key knowledge gaps in environmental science. The subsequent prioritisation resulted in 65 challenges across 10 thematic areas. The second stage saw NERC initiating, with CEE support, an open call for research proposals emphasising the use of evidence synthesis methodology. The selection process, balancing topic importance and applicant expertise, led to funding for five projects. The final stage involved the production of Systematic Maps of existing evidence based on the CEE Guidelines and Standards, providing a structured overview of existing literature on specific topics. The EEF Initiative demonstrated effective collaboration between UKRI (NERC), an independent non-profit (CEE), academia, and government agencies, addressing critical environmental challenges through rigorous evidence synthesis methodologies. The programme enhanced understanding and utilisation of these methodologies within the research community. Key lessons include the importance of inclusive priority-setting, differentiation between broad policy questions and specific Systematic Map questions, recognition of the value of Systematic Maps, and the role of experience in evidence synthesis teams. As policymakers and researchers navigate environmental policies in a resource-constrained environment, the EEF Initiative highlights the cost-effectiveness and efficiency of systematic mapping and review processes for evidence-based decision-making. The success of funding through NERC sets a precedent for future thematic evidence focused programmes, emphasising the need for continued support in developing synthesis skills among researchers and encouraging direct government commissions for targeted and responsive evidence. The EEF Initiative serves as a model for effective collaboration, providing valuable insights into addressing evidence gaps and shaping evidence-based policymaking in an ever-evolving environmental landscape.

Structural optimization has gained popularity in modern structural design, helping to reduce material consumption while maintaining the structural performance of buildings. This process also significantly influences the architectural appearance, affecting various aspects such as cross-section sizing, structural forms, and the layout of structural members. Beyond minimizing materials or costs, structural optimization can serve as a powerful tool for making architecture more visually appealing. However, with the wide variety of structural optimization methods proposed, gaining a comprehensive overview has become challenging. To address this, a systematic mapping study has been conducted, focusing on methods introduced over the past decade. The relevant journal articles are categorized based on several factors, including types of optimization, materials used, structural typologies, areas of application, and optimization objectives. The results of this study provide both a broad overview of recent developments in structural optimization and valuable insights into research-rich and under-explored areas. Moreover, the paper discusses which types of structural optimization are more relevant when applied as part of the architectural design process. It is suggested that future research should focus on identifying gaps and challenges in effectively applying structural optimization to architectural design, thus enhancing both efficiency and aesthetic potential.

The use of Artificial Intelligence (AI) in Internet of Things (IoT) systems has gained significant attention due to its potential to improve efficiency, functionality and decision-making. To further advance research and practical implementation, it is crucial to better understand the specific roles of AI in IoT systems and identify the key application domains. In this article we aim to identify the different roles of AI in IoT systems and the application domains where AI is used most significantly. We have conducted a systematic mapping study using multiple databases, i.e., Scopus, ACM Digital Library, IEEE Xplore and Wiley Online. Eighty-one relevant survey articles were selected after applying the selection criteria and then analyzed to extract the key information. As a result, six general tasks of AI in IoT systems were identified: pattern recognition, decision support, decision-making and acting, prediction, data management and human interaction. Moreover, 15 subtasks were identified, as well as 13 application domains, where healthcare was the most frequent. We conclude that there are several important tasks that AI can perform in IoT systems, improving efficiency, security and functionality across many important application domains.

AbstractLearning evolves through human interactions and adapting to technological advancements. The fourth industrial revolution elevated the importance of machines in improving competitiveness, efficiency, and product quality. However, this emphasis on technology overshadowed the human-centric perspective. In contrast, Industry 5 aims to foster collaboration between humans, robots, and digital systems. Our study utilized systematic mapping to explore collaboration in I5. Specifically, we investigated how collaborative learning occurs in industrial workplaces. The main objective was to identify prevalent collaborative learning techniques, technologies, challenges, and data types in I5 environments. The results reveal various collaborative learning approaches, highlighting their potential to improve productivity, innovation, and worker participation in I5. The study uncovers significant trends, such as the increasing reliance on digital platforms and Artificial Intelligence (AI)-driven tools, which facilitate collaborative learning while posing unique challenges. Our taxonomy provides a structured framework for understanding these dynamics, serving as a valuable guide for practitioners and researchers. The findings underscore the necessity for adaptive learning strategies and the integration of advanced technologies to promote effective collaboration in industrial settings. This research contributes to the theoretical understanding of collaborative learning in I5 and offers practical insights for its implementation, thereby supporting the evolution of industry practices in this new technological era. [ABSTRACT FROM AUTHOR]

In this paper, a different approach to the traditional literature review—literature systematic mapping—is adopted to summarize the progress in the recent research on railway catenary system condition monitoring in terms of aspects such as sensor categories, monitoring targets, and so forth. Importantly, the deep interconnections among these aspects are also investigated through systematic mapping. In addition, the authorship and publication trends are also examined. Compared to a traditional literature review, the literature mapping approach focuses less on the technical details of the research but reflects the research trends, and focuses in a specific field by visualizing them with the help of different plots and figures, which makes it more visually direct and comprehensible than the traditional literature review approach. [ABSTRACT FROM AUTHOR]

Sustainability research has emerged as an interdisciplinary area of knowledge about how to achieve sustainable development, while political actions toward the goal are still in their infancy. A sustainable world is mirrored by a healthy environment in which humans can live without jeopardizing the survival of future generations. The main aim of this contribution was to carry out a systematic mapping (SM) of the applications of digital technologies in promoting environmental sustainability. From a rigorous search of different databases, a set of more than 1000 studies was initially retrieved and then, following screening criteria based on the ROSES (RepOrting standards for Systematic Evidence Syntheses) procedure, a total of N = 37 studies that met the eligibility criteria were selected. The studies were coded according to different descriptive variables, such as digital technology used for the intervention, type of sustainable behavior promoted, research design, and population for whom the intervention was applied. Results showed the emergence of three main clusters of Digital Technologies (i.e., virtual/immersive/augmented reality, gamification, and power-metering systems) and two main Sustainable Behaviors (SBs) (i.e., energy and water-saving, and pollution reduction). The need for a clearer knowledge of which digital interventions work and the reasons why they work (or do not work) does not emerge from the outcomes of this set of studies. Future studies on digital interventions should better detail intervention design characteristics, alongside the reasons underlying design choices, both behaviourally and technologically. This should increase the likelihood of the successful adoption of digital interventions promoting behavioral changes in a more sustainable direction. [ABSTRACT FROM AUTHOR]

Incorporating accessibility in software development brings significant benefits such as the inclusion of people with disabilities. Recognizing the diversity of users with different requirements and needs within software development facilitates its use by the greatest number of users regardless of their individual conditions. The objective of this research is to obtain a knowledge base to identify those practices for which there is evidence of their use in the development of accessible software. A Systematic Literature Mapping (SLM) focused on identifying practices that incorporate accessibility activities in the software life cycle was conducted, resulting in 29 studies that met the selection criteria. The results show that the phase with more evidence of incorporation of accessibility activities in software development is the testing phase, when testers are used to verify accessibility within web pages, followed by the construction phase, when methodologies that include accessibility within the software development process are used; in the design phase it is common to incorporate artifacts that guide the development of accessible software and, finally, in the requirements phase it is common to use the requirements description to obtain and document accessibility requirements. [ABSTRACT FROM AUTHOR]

Motivation: The precise identification of punches and kicks in martial arts sporting competitions is a critical issue, often complex, and at times subject to controversies due to the subjective judgment of referees. Problem: The subjectivity in assessing punches and kicks during martial arts sporting competitions poses a significant challenge regarding impartiality and accuracy in refereeing. Solution Approach: This study analyzes the most recent contributions to punch and kick recognition in martial arts competitions. It reviews classification techniques, commonly used sensors, and the performance achieved in identifying these movements. Results: The analysis provides a general overview of implemented punch and kick classification techniques. This contributes to understanding recent advancements in this field and how they can enhance objectivity and precision in refereeing martial arts competitions. Conclusions: This study underscores the growing interest in machine learning techniques for classifying punches and kicks in martial arts, encompassing a wide range of classifiers, from traditional methods to deep learning models. The combination of inertial sensors and depth cameras emerges as a promising avenue. Future research is expected to thoroughly compare and characterize these approaches, paving the way for implementing artificial intelligence systems in martial arts competitions and potentially revolutionizing the objectivity in assessing movements in this sport. [ABSTRACT FROM AUTHOR]

Network Function Virtualization could be a quickly advancing innovation that guarantees to revolutionize the way networks are planned, sent, and overseen. However, as with any modern innovation, there are potential security risk that must be tended to guarantee the security of the network. Misuses attacks are one such risk that can compromise the security and integrity of NFV frameworks. In recently years , data mining has risen as a promising approach for recognizing misuses attacks in NFV systems. This systematic mapping ponders points to supply an overview of the existing research on misuses attack detection based on data mining in NFV. Particularly, the study will recognize and analyze the research conducted in this region, counting the sorts of data mining methods utilized, the types of misuses attacks identified, and the assessment strategies utilized. The results of this study will give experiences into the current state of investigate on misuses attack detection based on data mining in NFV, as well as recognize gaps and openings for future research in this range. Also, the study will serve as an important asset for analysts and professionals looking for to create successful and effective methods for recognizing misuses attacks in NFV frameworks

The integration of machine learning (ML) with edge computing and wearable devices is rapidly advancing healthcare applications. This study systematically maps the literature in this emerging field, analyzing 171 studies and focusing on 28 key articles after rigorous selection. The research explores the key concepts, techniques, and architectures used in healthcare applications involving ML, edge computing, and wearable devices. The analysis reveals a significant increase in research over the past six years, particularly in the last three years, covering applications such as fall detection, cardiovascular monitoring, and disease prediction. The findings highlight a strong focus on neural network models, especially Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs), and diverse edge computing platforms like Raspberry Pi and smartphones. Despite the diversity in approaches, the field is still nascent, indicating considerable opportunities for future research. The study emphasizes the need for standardized architectures and the further exploration of both hardware and software to enhance the effectiveness of ML-driven healthcare solutions. The authors conclude by identifying potential research directions that could contribute to continued innovation in healthcare technologies.

Gender disparities in technology are evident, and affirmative actions are necessary to increase female representation. This article is part of an umbrella project that systematically maps related studies and aims to understand the current literature on initiatives to attract girls to high school through female empowerment projects in computing. Through a systematic literature mapping (SLM), we identified studies published between 2017 and 2022 that were available in databases and search engines, namely ACM Digital Library, IEEE Xplore Library, Scopus, and SBC Open Lib. Only primary studies returned in an automated search process were considered, without combining them with other search strategies. A priori, 264 articles were returned with the application of a search string and after applying the inclusion and exclusion criteria, 61 articles were selected. Of this number, 41 projects were named in the articles that describe activities involving high school. To answer the established research questions, it was found that the studies discussed projects implemented in secondary education in the Americas, Europe and Africa, indicating the importance of expanding these initiatives to other territories, increasing female participation in information technology and promoting gender equality, which is aligned with the Sustainable Development Goals (SDG 5) of the United Nations 2030 Agenda.

This article is preliminary research that explores the results of studies conducted on students' mathematical attitudes using Systematic Mapping Study (SMS). The purpose of this Mapping is to discuss any mathematical attitude topic that is being reviewed and studied. This research uses qualitative methods using a study library. The search was carried out on the Scopus database with 36 articles used in 2014-2022 publications published in the form of scientific journals included in the inclusion criteria. The results of this SMS show an overview of opportunities to explore new topics in students' Mathematical Attitudes or utilise existing issues regarding the most significant focus of mathematics education: gender, student attitudes, self-concept, attitudes to mathematics, gender differences, and mathematical performance. The trends from several countries studied are China, the United States, the United Kingdom, Italy, Spain, Germany, the Russian Federation, France, and Canada

Abstract Background Global annual cancer incidence is forecast to rise to 27.5 M by 2040, a 62% increase from 2018. For most cancers, prevention and early detection are the most effective ways of reducing mortality. This study maps trials in cancer screening, prevention, and early diagnosis (SPED) to identify areas of unmet need and highlight research priorities. Methods A systematic mapping review was conducted to evaluate all clinical trials focused on cancer SPED, irrespective of tumour type. The National Cancer Research Institute (NCRI) portfolio, EMBASE, PubMed and Medline were searched for relevant papers published between 01/01/2007 and 01/04/2020. References were exported into Covidence software and double-screened. Data were extracted and mapped according to tumour site, geographical location, and intervention type. Results One hundred seventeen thousand seven hundred one abstracts were screened, 5157 full texts reviewed, and 2888 studies included. 1184 (52%) trials focussed on screening, 554 (24%) prevention, 442 (20%) early diagnosis, and 85 (4%) a combination. Colorectal, breast, and cervical cancer comprised 61% of all studies compared with 6.4% in lung and 1.8% in liver cancer. The latter two are responsible for 26.3% of global cancer deaths compared with 19.3% for the former three. Number of studies varied markedly according to geographical location; 88% were based in North America, Europe, or Asia. Conclusions This study shows clear disparities in the volume of research conducted across different tumour types and according to geographical location. These findings will help drive future research effort so that resources can be directed towards major challenges in cancer SPED.

Sustainability research has emerged as an interdisciplinary area of knowledge about how to achieve sustainable development, while political actions toward the goal are still in their infancy. A sustainable world is mirrored by a healthy environment in which humans can live without jeopardizing the survival of future generations. The main aim of this contribution was to carry out a systematic mapping (SM) of the applications of digital technologies in promoting environmental sustainability. From a rigorous search of different databases, a set of more than 1000 studies was initially retrieved and then, following screening criteria based on the ROSES (RepOrting standards for Systematic Evidence Syntheses) procedure, a total of N = 37 studies that met the eligibility criteria were selected. The studies were coded according to different descriptive variables, such as digital technology used for the intervention, type of sustainable behavior promoted, research design, and population for whom the intervention was applied. Results showed the emergence of three main clusters of Digital Technologies (i.e., virtual/immersive/augmented reality, gamification, and power-metering systems) and two main Sustainable Behaviors (SBs) (i.e., energy and water-saving, and pollution reduction). The need for a clearer knowledge of which digital interventions work and the reasons why they work (or do not work) does not emerge from the outcomes of this set of studies. Future studies on digital interventions should better detail intervention design characteristics, alongside the reasons underlying design choices, both behaviourally and technologically. This should increase the likelihood of the successful adoption of digital interventions promoting behavioral changes in a more sustainable direction.

This study, which systematically examines educational robotics and robots (ERR), has two purposes. (1) Classifying the research on the ERR to identify research trends and gaps, (2) Summarizing the experimental findings related to ERR and to interpret them according to the claims in the literature. A mixed method combining systematic mapping and systematic review were used in the study. Ninety-three articles published in Social Sciences Citation Index (SSCI) indexed journals and meeting the specified criteria were analyzed using a systematic mapping process. The results showed that 40 out of 93 articles did not include any learning theory. Thirty-two experimental studies were analyzed within the scope of the systematic review. The empirical findings supporting some of the claims about ERR are summarized and the research gaps in the claims that need to be supported by theoretical and pedagogical approaches are revealed. [ABSTRACT FROM AUTHOR]

Network Function Virtualization could be a quickly advancing innovation that guarantees to revolutionize the way networks are planned, sent, and overseen. However, as with any modern innovation, there are potential security risk that must be tended to guarantee the security of the network. Misuses attacks are one such risk that can compromise the security and integrity of NFV frameworks. In recently years, data mining has risen as a promising approach for recognizing misuses attacks in NFV systems. The novelty of this systematic mapping study is ponders points to supply an overview of the existing research on misuses attack detection based on data mining in NFV. Particularly, the study will recognize and analyze the research conducted in this region, counting the sorts of data mining methods utilized, the types of misuses attacks identified, and the assessment strategies utilized. The results of this study will give experiences into the current state of investigate on misuses attack detection based on data mining in NFV, as well as recognize gaps and openings for future research in this range. Also, the study will serve as an important asset for analysts and professionals looking for to create successful and effective methods for recognizing misuses attacks in NFV frameworks. [ABSTRACT FROM AUTHOR]