Research Roadmap Research Articles

Machine Unlearning in Digital Healthcare: Addressing Technical and Ethical Challenges

The ``Right to be Forgotten," as outlined in regulatory frameworks such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), allows individuals to request the deletion of their personal data from deployed machine learning models. This provision ensures that individuals can maintain control over their personal information. In the digital health era, this right has become a critical concern for both patients and healthcare providers. To facilitate the effective removal of personal data from machine learning models, the concept of ``machine unlearning" has been introduced. This paper highlights the technical and ethical challenges associated with machine unlearning in digital healthcare. By examining current unlearning methodologies and their limitations, we propose a roadmap for future research and development in this field.

Optimization and energy management strategies, challenges, advances, and prospects in electric vehicles and their charging infrastructures: A comprehensive review

Electric vehicles (EVs) are at the forefront of global efforts to reduce greenhouse gas emissions and transition to sustainable energy systems. This review comprehensively examines the optimization and energy management strategies for EVs and their charging infrastructure, focusing on technological advancements, persistent challenges, and future prospects. By the end of 2023, the number of electric cars on the road globally reached 40 million, with 14 million new registrations recorded in 2023 alone—95% of which were in China, Europe, and the United States. Governments across the globe have introduced incentives and policies to promote EV adoption, and by 2030, EVs are expected to comprise a significant portion of light-duty vehicles in major regions. Despite these encouraging developments, challenges such as range anxiety, the relatively low energy density of 200–300 Wh/kg in Li-ion batteries (compared to 13,000 Wh/kg for petroleum), and insufficient public charging infrastructure remain key barriers to widespread EV adoption. This review also explores the critical role of smart grid technologies, vehicle-to-grid (V2G) systems, and renewable energy integration in supporting the growing EV market. V2G technologies are projected to enhance grid stability by 20–30% and reduce operational costs by 10–15% through load balancing and real-time energy price forecasting. By thoroughly analyzing optimization techniques such as load balancing, dynamic scheduling, and real-time energy management, this paper offers a roadmap for researchers, policymakers, and industry stakeholders to accelerate the integration of EVs into global energy systems and enhance sustainability in urban transportation networks.

The Sociotechnical Stack: Opportunities for Social Computing Research in Non-Consensual Intimate Media

Non-consensual intimate media (NCIM) involves sharing intimate content without the depicted person's consent, including 'revenge porn' and sexually explicit deepfakes. While NCIM has received attention in legal, psychological, and communication fields over the past decade, it is not sufficiently addressed in computing scholarship. This paper addresses this gap by linking NCIM harms to the specific technological components that facilitate them. We introduce the sociotechnical stack , a conceptual framework designed to map the technical stack to its corresponding social impacts. The sociotechnical stack allows us to analyze sociotechnical problems like NCIM, and points toward opportunities for computing research. We propose a research roadmap for computing and social computing communities to deter NCIM perpetration and support victim-survivors through building and rebuilding technologies.

Organisational coaching to improve workplace resilience: a scoping review and agenda for future research

In an increasingly demanding and pressured work environment, employee resilience is acknowledged as a critical element to navigate adversity. There has been increased focus and interest in studying the nature of resilience in the workplace, however the mechanisms of developing and sustaining resilience are still under debate. Coaching is a promising method organisations use to improve employee resilience and provides employees with support to deal with the challenging working environment. There has been significant interest in coaching for resilience in recent years, however there is no overarching, consolidated view on the nature and dynamics of resilience coaching. This scoping review seeks to fill this gap by making three contributions. Firstly, we present details on various types of coaching approaches used to improve resilience. Secondly, we review the specific coaching elements and processes that lead to improved resilience and finally, we provide an overview on the efficacy of resilience coaching interventions. We conclude this scoping review with a roadmap for future research to help position and strengthen organisational coaching as a pillar of resilience development. This scoping review followed a five-stage PRISMA-ScR methodology which entails formulating research questions; identifying studies; choosing studies; extracting and charting data; and summarising the findings.

A systematic review on robot-assisted language learning for adults

In the 21st-century era of globalization, language proficiency is a pivotal connector across cultures, with artificial intelligence (AI) revolutionizing educational paradigms through Robot-Assisted Language Learning (RALL). This systematic review examines the role of RALL in adult second language acquisition, focusing on its pedagogical strategies and learner engagement. Unlike the previous systematic reviews that explore the multifaceted roles of robots in language learning, including as teachers, tutors, assistants, and peer learners, we identify explicit and implicit instructional strategies within RALL, highlighting the unique learning landscape of adult learners characterized by self-regulation and self-direction. We assess the latest advancements in RALL for adult learners through three research questions, compare the effectiveness of explicit versus implicit instructions, and investigate affective factors enhancing RALL performance. Our review contributes a comprehensive status analysis, in-depth exploration of interaction modes, and insights for future research directions, providing a roadmap for academic research and practical guidance for educators and robot developers. This study aims to optimize RALL strategies to better meet the needs of adult learners, fostering a more efficient and engaging language learning experience.

The hierarchical taxonomy of psychopathology and the search for neurobiological substrates of mental illness: A systematic review and roadmap for future research.

Understanding the neurobiological mechanisms involved in psychopathology has been hindered by the limitations of categorical nosologies. The Hierarchical Taxonomy of Psychopathology (HiTOP) is an alternative dimensional system for characterizing psychopathology, derived from quantitative studies of covariation among diagnoses and symptoms. HiTOP provides more promising targets for clinical neuroscience than traditional psychiatric diagnoses and can facilitate cumulative integration of existing research. We systematically reviewed 164 human neuroimaging studies with sample sizes of 194 or greater that have investigated dimensions of psychopathology classified within HiTOP. Replicated results were identified for constructs at five different levels of the hierarchy, including the overarching p-factor, the externalizing superspectrum, the thought disorder and internalizing spectra, the distress subfactor, and the depression symptom dimension. Our review highlights the potential of dimensional clinical neuroscience research and the usefulness of HiTOP while also suggesting limitations of existing work in this relatively young field. We discuss how HiTOP can be integrated synergistically with neuroscience-oriented, transdiagnostic frameworks developed by the National Institutes of Health, including the Research Domain Criteria, Addictions Neuroclinical Assessment, and the National Institute on Drug Abuse's Phenotyping Assessment Battery, and how researchers can use HiTOP to accelerate clinical neuroscience research in humans and other species. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Promising Neuroscience Methods in Exploring the Spatiotemporal Mechanisms underlying Speech Motor Control

Speech motor control is a complex neuromotor behavior that requires the combined efforts of feedforward control and feedback control subsystems. With the advent of neuroscience, multiple lines of research have explored the spatiotemporal mechanisms underlying this process. The purpose of this review was to point out several promising neuroscience methods and their unique values in enriching the speech motor control literature. We discussed the clinical value of noninvasive brain stimulation methods, and the theoretical value of invasive brain recording methods, as well as the predictive value of brain connectivity techniques. Based on prior findings, we propose a roadmap for future research, identifying structure-function coupling that supports speech motor control.

Crude oil price forecasting with multivariate selection, machine learning, and a nonlinear combination strategy

Crude oil price forecasting has been one of the research hotspots in the field of energy economics, which plays a crucial role in energy supply and economic development. However, numerous influencing factors bring serious challenges to crude oil price forecasting, and existing research has room for further improvement in terms of an integrated research roadmap that combines impact factor analysis with predictive modelling. This study aims to examine the impact of financial market factors on the crude oil market and to propose a nonlinear combined forecasting framework based on common variables. Four types of daily exogenous financial market variables are introduced: commodity prices, exchange rates, stock market indices, and macroeconomic indicators for ten indicators. First, various variable selection methods generate different variable subsets, providing more diversity and reliability. Next, common variables in the subset of variables are selected as key features for subsequent models. Then, four models predict crude oil prices using common features as inputs and obtain the prediction results for each model. Finally, the nonlinear mechanism of the deep learning technology is introduced to combine above single prediction results. Experimental results reveal that commodity and foreign exchange factors in financial markets are critical determinants of crude oil market volatility over the long term, as observed in experiments conducted on the West Texas Intermediate and Brent oil price datasets. The proposed model demonstrates strong performance regarding average absolute percentage error, recorded at 2.9962% and 2.4314%, respectively, indicating high forecasting accuracy and robustness. This forecasting framework offers an effective methodology for predicting crude oil prices and enhances understanding the crude oil market.

The mindful resiliency in recovery model: empowering the transcendence of stigma

IntroductionMindfulness-based interventions show unique promise in treating substance use disorders among marginalized populations who face heightened stigma. The Mindful Resiliency in Recovery Model is introduced as a novel theoretical framework articulating how mindfulness training can mitigate the adverse effects of stigma, enhance psychological resilience, and facilitate sustained recovery from addiction.MethodsThe current manuscript synthesizes various models of mindfulness processes, stigma, and substance use disorder recovery to propose an integrated theoretical framework on the promise of mindfulness-based interventions in supporting recovery. Further, the current manuscript draws upon empirical literature to establish preliminary support for the premises and hypotheses of the Mindful Resiliency in Recovery Model concerning the mechanisms influencing the efficacy of mindfulness-based interventions among marginalized individuals.ResultsPreliminary evidence supports the premises of the proposed model. There is evidence to suggest that specific processes like increased present-moment awareness, acceptance, decentering, reappraisal, and savoring may be especially salient in mitigating internalized stigma and fostering resiliency in recovery. There is a need for additional research on these processes, and contextual factors that may moderate their efficacy.DiscussionThe Mindful Resiliency in Recovery Model has significant implications for optimizing mindfulness-based interventions to empower marginalized individuals to transcend stigma and actualize their capacity for wellbeing in substance use disorder recovery. It provides a roadmap for future research on the mechanisms and contextual factors affecting the efficacy of mindfulness-based interventions for marginalized and stigmatized communities. It further offers guidance to clinicians utilizing mindfulness-based interventions to support individuals experiencing stigma.

The 2023 American Burn Association Research and Advocacy Summit: Our Roadmap.

Research is one of the American Burn Association's (ABA) strategic priorities. Advocacy is required not only to promote burn research, but also, the ABA's other strategic priorities (Prevention, Quality, and Education). The ABA convened a two-day Research and Advocacy (R&A) Summit in September 2023, to develop a roadmap for the organization's research and advocacy efforts. The in-person summit identified fourteen key R&A initiatives. A multidisciplinary workgroup then developed strategies to achieve each initiative. The initiatives and strategies were then approved by the ABA's Board of Trustees as our organization's roadmap for research and advocacy. The next task will be to implement the initiatives. This will require not only oversight from the ABA's Board of Trustees, but also, effort from and collaboration between several of the ABA's committees and panels, including the Burn Science Advisory panel (BSAP), the Research Committee, the Prevention Committee, The Governmental Affairs Committee, The Organization and Delivery of Burn care Committee, the Quality and Burn Registry Committee, the ad hoc Coding Committee, and the ABA's Central Office.

Generative AI insights in tourism and hospitality: A comprehensive review and strategic research roadmap

This study used bibliometric analysis and a systematic literature review (SLR) to examine how the tourism and hospitality industries use generative artificial intelligence (GAI), identifying developed patterns, theoretical frameworks, strengths and limitations, and future research challenges. We conducted a systematic review using the Scopus database, adhering to PRISMA principles. We analyzed a sample of 25 articles published between 2019 and 2023 through narrative synthesis and bibliometric analysis using the VOSviewer software, a tool for visualizing network analysis. The USA, China, India, and Saudi Arabia are the major countries engaged in GAI research in tourism and hospitality. Significant research topics emphasize decision-making, chatbots, deep learning, and sentiment analysis, mainly through the Technology Acceptance Model (TAM), Stimulus-Organism-Response (S-O-R), and Human-Computer Interaction (HCI) frameworks. GAI applications demonstrate strength in improving user experience and operational efficiency, though gaps exist in scope, ethics, technology performance, and collaboration between humans and AI. This study, therefore, provides a fundamental foundation for understanding the current status of GAI research in tourism and hospitality by pointing out some trends and areas that require further investigation to ensure the responsible and effective integration of AI within the industry.

A Bibliometric Review of Large Language Models Research from 2017 to 2023

Large language models (LLMs), such as OpenAI's Generative Pre-trained Transformer (GPT), are a class of language models that have demonstrated outstanding performance across a range of natural language processing (NLP) tasks. LLMs have become a highly sought-after research area because of their ability to generate human-like language and their potential to revolutionize science and technology. In this study, we conduct bibliometric and discourse analyses of scholarly literature on LLMs. Synthesizing over 5,000 publications, this article serves as a roadmap for researchers, practitioners, and policymakers to navigate the current landscape of LLMs research. We present the research trends from 2017 to early 2023, identifying patterns in research paradigms and collaborations. We start with analyzing the core algorithm developments and NLP tasks that are fundamental in LLMs research. We then investigate the applications of LLMs in various fields and domains, including medicine, engineering, social science, and humanities. Our review also reveals the dynamic, fast-paced evolution of LLMs research. Overall, this article offers valuable insights into the current state, impact, and potential of LLMs research and its applications.

Exploring gaps in residential and parking location choice models for autonomous vehicles: a proposed evaluation framework

ABSTRACT The advent of autonomous vehicles (AVs) is poised to transform urban landscapes, notably by altering perceptions of travel time value, reshaping parking structures, and introducing additional travel demand through empty-cruising and self-parking capabilities. These changes are anticipated to influence both residential and parking location choices. This study delves into the complexities of these decisions by integrating insights from 28 modelling studies on residential and/or parking location choices in the context of AVs and by proposing an evaluation framework for future modelling efforts. A critical gap identified is the oversight of empty-cruising and parking costs in the context of residential relocation choice for owners of privately-owned AVs. Furthermore, no study has adopted a joint-choice modelling approach for residential and parking location choices. By offering guidelines for the incorporation of these elements, this study contributes to the development of a holistic residential and parking location choice model with AVs, thus providing a valuable roadmap for future research in urban planning and transportation economics.

Resource management in disaster relief: a bibliometric and content-analysis-based literature review

AbstractDisasters cause huge economic losses, affect the lives of many people, and severely damage the environment. Effective resource management during disaster preparedness and response phases improves distribution efforts and service levels and, hence, accelerates the disaster relief operations. Resource management in response to catastrophe has received increasing research attention in recent years, but no review paper focuses on this specific topic. Thus, the main purpose of this paper is to review the existing literature on resource management for disaster relief published in English in peer-reviewed journals in order to fill the gap. We apply bibliometric, network, and content analyses in our review to identify popular research topics, classify the literature into research clusters, and analyze the interrelationships between these research clusters. The second purpose of this paper is to identify gaps and trends in existing research. Finally, we propose six future research directions that provide a roadmap for resource management research for disaster relief.

Agriculture and Automation: Potential Benefits, Difficulties and Economic Impacts

In contemporary agriculture, automation and robotics are the main players. They present viable answers to issues with the world's expanding population, changing demographics, and economic standing. This analysis examines the potential and difficulties of utilizing new technologies as well as the frequently overlooked connection between automation technology and agricultural economics. This study examines the potential of automation and robots in farming techniques, as well as their socio-economic impacts, through a methodical analysis of the literature. It also offers practical advice for those involved. For this reason, a variety of robot types used in diverse agricultural fields have been examined, along with the difficulties and technical viability of adopting automation. Additional significant aspects have been examined, such as changes in the labour market, economic conditions, and demographic trends. This study also looks into the social effects of automation, specifically with regard to worker adaption and employment. It discovers that although automation increases sustainability and efficiency, it also results in labour displacement and necessitates significant technological investment. This in-depth analysis closes a significant gap by evaluating precision agriculture's future, labour market change, and economic viability. Along with outlining a future roadmap for research and policy, it also plots a course for future studies and policy-making at the nexus of agricultural technology and socioeconomic domains.

Comparative assessment of the scientific structure of biomass-based hydrogen from a cross-domain perspective

Biomass-based hydrogen production is an innovative approach for realizing carbon-neutral energy solutions. Despite their promise, both structures differ in terms of the biomass energy domain, which is at the entry point of the technology, and the hydrogen energy domain, which is at the exit point of the technology. In this study, we conducted structural and predictive analyses via cross-domain bibliometric analysis to clarify the differences in the structures and perspectives of researchers across domains and to suggest ways to strengthen collaboration to promote innovation. Our study revealed that the hydrogen energy domain has a balanced impact on realizing a hydrogen society using biomass-based hydrogen production technology, while the biomass energy domain has a strong interest in the process of processing biomass. The results reveal that different communities have different ideas about research, resulting in a divide in the areas to be achieved. This comparative analysis reveals the importance of synergistic progress through interdisciplinary efforts. By filling these gaps, our findings can lead to the development of a roadmap for future research and policy development in renewable energy and highlight the importance of a unified approach to sustainable hydrogen production. The contribution of this study is to provide evidence for the importance of cross-disciplinary cooperation for R&D directors and policy makers.

Digital twins and biodiversity: a conceptual framework of opportunities and challenge

PurposeThe intersection of digital twins and ecological diversity offers a new and complex landscape that requires interdisciplinary investigation. Recognizing the urgent need for a nuanced approach to the ethical, technological and practical challenges in this area, this paper presents a conceptual framework that serves as a roadmap for future research and policymaking.Design/methodology/approachThis study employs a conceptual methodology deeply rooted in interdisciplinary perspectives. A systematic literature review was conducted using the Scopus database to identify relevant articles. The selected articles were rigorously analyzed to derive key dimensions, which were then validated through expert panel reviews and a pilot study.FindingsOur framework identifies seven critical dimensions: Data Acquisition and Simulation, Impact Assessment, Ecological Protection and Management, Ethical and Legal Considerations, Social and Cultural Impacts, Technological Feasibility and Limitations and Policies and Regulations. These dimensions provide a comprehensive structure for understanding and addressing the intersection of digital twins and biodiversity conservation.Originality/valueThis study contributes a foundational guide for sustainable and ethical engagement between digital twins and biodiversity conservation. It offers a novel, interdisciplinary framework that integrates diverse perspectives and provides practical insights for scholars, policymakers and practitioners in this emerging field.

A Roadmap for Swedish Island Research – Previous Knowledge, Current Debates, and Imminent Research Gaps

A Roadmap for Swedish Island Research – Previous Knowledge, Current Debates, and Imminent Research Gaps

Revisiting sustainable tourism research: bibliometric insights and future research agenda

ABSTRACT This bibliometric review synthesizes sustainable tourism research over the past three decades, focusing on its alignment with the Sustainable Development Goals (SDGs). Based on 1,383 journal articles from the Web of Science database, the RStudio Bibliometrix package analysed bibliometric performance, thematic maps, and co-occurrence networks. The findings highlight the role of sustainable tourism in advancing different SDGs. Findings also indicate three broader thematic areas for future research, including regenerative tourism ecosystems, responsible tourism behaviours, and right-sizing tourism. Through this review, we propose future research directions to enhance knowledge on sustainable tourism and its contribution to the SDGs, providing a strategic roadmap for researchers and policymakers.

Lost in the maze? Public sector accounting and reporting—A structured literature review

IMPACT Motivated by the complexity and challenges of public sector accounting, this structured literature review (SLR) takes a broad approach combined with structured analysis to unravel the development, cross-temporal challenges and remaining gaps in public sector accounting and reporting research. By highlighting these gaps, the authors outline future research paths within the current literature and compare them with previous literature reviews to provide researchers with a roadmap for future research. Although this SLR does not provide direct assistance with day-to-day problems, through its comparative, quantitative and structured approach, it provides practitioners with an interdisciplinary overview of public sector accounting research in one article—helping them to navigate the complex field of public sector accounting without having to read multiple literature reviews.