Research Roadmap Research Articles

Navigating the Social Maze: An Integrative Review on the Social Network Properties of Deviant Peer Influence in Adolescent Networks

ABSTRACT Social network analysis (SNA) holds great potential for examining the influence of peers and group dynamics on the development of criminal behavior among adolescents as it offers a robust framework for studying complex social interactions. However, there needs to be more knowledge regarding key research findings on deviant adolescent influence using a social network approach. In this integrative literature review, we examine what type of information is used to construct adolescents’ social networks, how network compositions affect deviant influence, and how deviant influence is transmitted. For this review, we selected 39 articles by searching Scopus and Web of Science. The results demonstrate that SNA offers valuable insights into the dynamics of deviant peer influence among adolescents. However, there are also conflicting findings that need further exploration. Future research could focus on these discrepancies and continue to leverage the power of SNA in studying the development of criminal behavior in adolescents. This review highlights the importance of SNA in better understanding the mechanisms behind peer influence and provides a roadmap for future research in this field.

A roadmap for research in post-stroke fatigue: Consensus-based core recommendations from the third Stroke Recovery and Rehabilitation Roundtable.

Fatigue affects almost half of all people living with stroke. Stroke survivors rank understanding fatigue and how to reduce it as one of the highest research priorities. We convened an interdisciplinary, international group of clinical and pre-clinical researchers and lived experience experts. We identified four priority areas: (1) best measurement tools for research, (2) clinical identification of fatigue and potentially modifiable causes, (3) promising interventions and recommendations for future trials, and (4) possible biological mechanisms of fatigue. Cross-cutting themes were aphasia and the voice of people with lived experience. Working parties were formed and structured consensus building processes were followed. We present 20 recommendations covering outcome measures for research, development, and testing of new interventions and priority areas for future research on the biology of post-stroke fatigue. We developed and recommend the use of the Stroke Fatigue Clinical Assessment Tool. By synthesizing current knowledge in post-stroke fatigue across clinical and pre-clinical fields, our work provides a roadmap for future research into post-stroke fatigue.

A model for personalized diagnostics for non-specific low back pain: the role of the myofascial unit.

Low back pain (LBP) is the leading cause of disability worldwide. Most LBP is non-specific or idiopathic, which is defined as symptoms of unknown origin without a clear specific cause or pathology. Current guidelines for clinical evaluation are based on ruling out underlying serious medical conditions, but not on addressing underlying potential contributors to pain. Although efforts have been made to identify subgroups within this population based on response to treatment, a comprehensive framework to guide assessment is still lacking. In this paper, we propose a model for a personalized mechanism-based assessment based on the available evidence that seeks to identify the underlying pathologies that may initiate and perpetuate central sensitization associated with chronic non-specific low back pain (nsLBP). We propose that central sensitization can have downstream effects on the "myofascial unit", defined as an integrated anatomical and functional structure that includes muscle fibers, fascia (including endomysium, perimysium and epimysium) and its associated innervations (free nerve endings, muscle spindles), lymphatics, and blood vessels. The tissue-level abnormalities can be perpetuated through a vicious cycle of neurogenic inflammation, impaired fascial gliding, and interstitial inflammatory stasis that manifest as the clinical findings for nsLBP. We postulate that our proposed model offers biological plausibility for the complex spectrum of clinical findings, including tissue-level abnormalities, biomechanical dysfunction and postural asymmetry, ecological and psychosocial factors, associated with nsLBP. The model suggests a multi-domain evaluation that is personalized, feasible and helps rule out specific causes for back pain guiding clinically relevant management. It may also provide a roadmap for future research to elucidate mechanisms underlying this ubiquitous and complex problem.

A roadmap for research in post-stroke fatigue: Consensus-based core recommendations from the third Stroke Recovery and Rehabilitation Roundtable.

Fatigue affects almost half of all people living with stroke. Stroke survivors rank understanding fatigue and how to reduce it as one of the highest research priorities. We convened an interdisciplinary, international group of clinical and pre-clinical researchers and lived experience experts. We identified four priority areas: (1) best measurement tools for research, (2) clinical identification of fatigue and potentially modifiable causes, (3) promising interventions and recommendations for future trials, and (4) possible biological mechanisms of fatigue. Cross-cutting themes were aphasia and the voice of people with lived experience. Working parties were formed and structured consensus building processes were followed. We present 20 recommendations covering outcome measures for research, development, and testing of new interventions and priority areas for future research on the biology of post-stroke fatigue. We developed and recommend the use of the Stroke Fatigue Clinical Assessment Tool. By synthesizing current knowledge in post-stroke fatigue across clinical and pre-clinical fields, our work provides a roadmap for future research into post-stroke fatigue.

Twisted Bilayer Graphene: A Journey Through Recent Advances and Future Perspectives

AbstractTwisted bilayer graphene (TBG) has emerged as a fascinating research frontier in condensed matter physics and materials science. This review article comprehensively overviews recent advances and future perspectives in studying TBG. The challenges associated with fabricating and characterizing TBG structures, including precise control of the twist angle and accurate determination of electronic properties, are discussed. Furthermore, the intriguing phenomena observed in TBG, such as superconductivity, insulating phases, and correlated electron states, shedding light on their underlying mechanisms, are explored. Scalability and device integration of TBG are explored, along with potential engineering strategies to tailor its properties for specific applications. By synthesizing and analyzing the latest scientific reports, a roadmap for further research is provided and the promising prospects for TBG are highlighted.

Unlocking Catalytic Potential: Exploring the Impact of Thermal Treatment on Enhanced Electrocatalysis of Nanomaterials.

In the evolving field of electrocatalysis, thermal treatment of nano-electrocatalysts has become an essential strategy for performance enhancement. This review systematically investigates the impact of various thermal treatments on the catalytic potential of nano-electrocatalysts. The focus encompasses an in-depth analysis of the changes induced in structural, morphological, and compositional properties, as well as alterations in electro-active surface area, surface chemistry, and crystal defects. By providing a comprehensive comparison of commonly used thermal techniques, such as annealing, calcination, sintering, pyrolysis, hydrothermal, and solvothermal methods, this review serves as a scientific guide for selecting the right thermal technique and favorable temperature to tailor the nano-electrocatalysts for optimal electrocatalysis. The resultant modifications in catalytic activity are explored across key electrochemical reactions such as electrochemical (bio)sensing, catalytic degradation, oxygen reduction reaction, hydrogen evolution reaction, overall water splitting, fuel cells, and carbon dioxide reduction reaction. Through a detailed examination of the underlying mechanisms and synergistic effects, this review contributes to a fundamental understanding of the role of thermal treatments in enhancing electrocatalytic properties. The insights provided offer a roadmap for future research aimed at optimizing the electrocatalytic performance of nanomaterials, fostering the development of next-generation sensors and energy conversion technologies.

Unlocking Catalytic Potential: Exploring the Impact of Thermal Treatment on Enhanced Electrocatalysis of Nanomaterials

AbstractIn the evolving field of electrocatalysis, thermal treatment of nano‐electrocatalysts has become an essential strategy for performance enhancement. This review systematically investigates the impact of various thermal treatments on the catalytic potential of nano‐electrocatalysts. The focus encompasses an in‐depth analysis of the changes induced in structural, morphological, and compositional properties, as well as alterations in electro‐active surface area, surface chemistry, and crystal defects. By providing a comprehensive comparison of commonly used thermal techniques, such as annealing, calcination, sintering, pyrolysis, hydrothermal, and solvothermal methods, this review serves as a scientific guide for selecting the right thermal technique and favorable temperature to tailor the nano‐electrocatalysts for optimal electrocatalysis. The resultant modifications in catalytic activity are explored across key electrochemical reactions such as electrochemical (bio)sensing, catalytic degradation, oxygen reduction reaction, hydrogen evolution reaction, overall water splitting, fuel cells, and carbon dioxide reduction reaction. Through a detailed examination of the underlying mechanisms and synergistic effects, this review contributes to a fundamental understanding of the role of thermal treatments in enhancing electrocatalytic properties. The insights provided offer a roadmap for future research aimed at optimizing the electrocatalytic performance of nanomaterials, fostering the development of next‐generation sensors and energy conversion technologies.

Implications of Aging in Place in the Context of the Residential Environment: Bibliometric Analysis and Literature Review.

The residential environment's impact on aging in place is a multidisciplinary field that draws from architecture, urban planning, gerontology, psychology, and sociology. This multidisciplinary nature makes it challenging to comprehensively understand the field and identify the connections and interactions among disciplines. A bibliometric analysis is crucial for exploring the field's intellectual structure, identifying interdisciplinary collaborations, and tracking the knowledge flow across disciplines and will facilitate cross-disciplinary dialogue, foster collaboration, and encourage research that integrates diverse perspectives. This study reviewed the literature on aging in place in the context of a residential environment, which required adapting theories and methodologies. It analyzed a dataset of 1500 publications retrieved from the Web of Science, applied performance analysis techniques, and utilized VOSviewer to visualize the intellectual structure and evolving research themes. The results emphasize the increasing strength of academic interest and the growing diversity of fields related to the topic. The findings are discussed in terms of productivity, collaboration, and research themes from the past to the future. The results provide a roadmap for researchers, policymakers, and practitioners worldwide who focus on aging in place and acknowledge the importance of considering the physical, social, and cultural aspects of an older adult's living environment.

Healthcare analytics: A techno-functional perspective

The use of data analytics, particularly Artificial Intelligence (AI), has significantly transformed decision-making in healthcare. However, academic scholarship has expressed concerns over the misalignment of research priorities and their impact on data usage and choice of analytics. On the one hand, an inadequate understanding of healthcare themes and algorithmic nuances has posed difficulty for healthcare professionals to evaluate AI solutions and understand their generalizability and potential biases. On the other hand, the abundance of analytical tools, their complexity, and a misalignment between data type and analysis methods have presented major challenges for analysts in selecting a suitable method. As a result, healthcare research has mostly developed in silos. We propose a techno-functional framework that combines the perspectives of analysts and healthcare professionals, identifies key research themes (i.e., healthcare delivery, patient engagement, data management, market design, and policy and governance), and presents critical research questions that can guide future studies. We also propose a schema for segmenting analysis techniques based on data type and analytical complexity. This schema may help analysts select a suitable analysis technique and data to solve a specific problem. Our study aims to improve the understanding of healthcare analytics among researchers, analysts, and healthcare professionals and present a roadmap for future research.

A Survey of Controllable Text Generation Using Transformer-based Pre-trained Language Models

Controllable Text Generation (CTG) is an emerging area in the field of natural language generation (NLG). It is regarded as crucial for the development of advanced text generation technologies that better meet the specific constraints in practical applications. In recent years, methods using large-scale pre-trained language models (PLMs), in particular the widely used Transformer-based PLMs, have become a new paradigm of NLG, allowing generation of more diverse and fluent text. However, due to the limited level of interpretability of deep neural networks, the controllability of these methods needs to be guaranteed. To this end, controllable text generation using Transformer-based PLMs has become a rapidly growing yet challenging new research hotspot. A diverse range of approaches have emerged in the past 3 to 4 years, targeting different CTG tasks that require different types of controlled constraints. In this article, we present a systematic critical review on the common tasks, main approaches, and evaluation methods in this area. Finally, we discuss the challenges that the field is facing, and put forward various promising future directions. To the best of our knowledge, this is the first survey article to summarize the state-of-the-art CTG techniques from the perspective of Transformer-based PLMs. We hope it can help researchers and practitioners in the related fields to quickly track the academic and technological frontier, providing them with a landscape of the area and a roadmap for future research.

Inhibitory Effect of Salicin on Staphylococcus aureus Coagulase.

The massive use of antibiotics has resulted in an alarming increase in antibiotic resistance in Staphylococcus aureus (S. aureus). This study aimed to identify the inhibitory effect of salicin on S. aureus. Coagulase (Coa) activity was assessed using in vitro Coa assays and Western blot, thermal shift assay (TSA), fluorescence quenching and molecular docking experiments were conducted to verify the interaction between salicin and Coa. An in vivo mouse pneumonia model demonstrated that salicin can reduce the virulence of S. aureus. In vitro Coa assays elucidated that salicin directly inhibited Coa activity. The Western blot and TSA results suggested that salicin did not block the expression of Coa but affected the thermal stability of the protein by binding to Coa. The fluorescence quenching, molecular docking and molecular dynamics assays have found that the most promising binding site between salicin and Coa was GLN-97. The pneumonia model of mice infected with S. aureus revealed that salicin could not only reduce the content of lung bacteria in mice but also prolong their survival. Salicin was identified as a novel anti-infective candidate compound with the potential to target Coa and inhibit its activity by binding to it, which would facilitate the development of roadmaps for future research.

Human exposure to heavy metals and related cancer development: a bibliometric analysis.

As notifications on carcinogenicity of heavy metals increase, more and more attention is paid recently to heavy metals exposure. In our study, the human exposure to heavy metals and cancer knowledge epistemology was investigated using bibliometric analysis. The bibliometric data of the research articles were retrieved using following keywords: "heavy metal," "trace element", "cancer", "carcinogen", and "tumor" in the Scopus database. Specifically, 2118 articles published between 1972 and 2023 were found, covering a total of 1473 authors, 252 sources, and 2797 keywords. Retrospective data obtained from 251 documents and 145 journals were further analyzed by performance analysis and techniques of science mapping. The number of studies conducted in this field increased from one article published in 1972 to 18 articles published in 2022 in the study of Michael P Waalkes. The most impactful author regarding the number of published papers was Masoudreza Sohrabi with 7 publications. In the majority of the published papers, the most popular keywords were "cadmium" and "carcinogenicity". However, in recent 4 years, the emphasis has been placed more on epidemiology studies. Our study provides general knowledge about the trend of publication on the role of heavy metals in causing cancer. The leading researchers in the field of the effects of heavy metals on the development of cancer were identified in our studies. Our results might also create a better understanding of new and emerging issues and can be used as a comprehensive road map for future researchers.

The ethics of engineered living materials

Engineered living materials (ELMs) combine living and non-living entities. Their associated ethical concerns must be addressed to promote safety, promote sustainability, and regulate societal impacts. This article identifies key ethical and safety issues by reflecting on fundamental ethical principles. It further discusses a future ethical roadmap for sustainable research in ELMs.

Immersive Learning Frameworks: A Systematic Literature Review

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution</i> : This secondary study examines the literature on immersive learning frameworks and reviews their state of the art. Frameworks have been categorized according to their purpose. In addition, the elements that compose them were also categorized. Some gaps were identified and proposed as a research roadmap. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background</i> : Immersive technologies for education have been used for some years. Despite this, there are few works that aim to support the development and use of virtual environments for immersive learning. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Research Questions</i> : This systematic review has the following main research question: What is the state of the art of immersive learning frameworks? In order to answer this question, secondary research questions were defined: 1) what definitions of immersive learning were adopted in primary studies? 2) what are the types of frameworks and how do they support immersive learning? 3) what are the elements that compose the frameworks? 4) what are the methods used to validate the frameworks? <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methodology</i> : As per the systematic review guidelines, this study followed a rigorous and replicable process for collecting and analyzing data. From 1721 articles identified in the search engines, 15 were selected after the inclusion and exclusion criteria. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Findings</i> : Most frameworks are models that investigate the causal relationship between immersive learning factors that influence learning outcomes. Although this theoretical aspect is important for the advancement of research, the area still lacks more practical frameworks that address more technical details and support development, as well as the use of immersive virtual environments by teachers and instructors.

From Challenges to Success: A road map for early career researchers [Young Professionals

From Challenges to Success: A road map for early career researchers [Young Professionals

A tale of whale and fiber: monitoring baleen whales with Distributed Acoustic Sensing (DAS)

Distributed Acoustic Sensing (DAS) converts fibers in existing underwater telecommunication fiber-optic cables into dense listening arrays of strain sensors. Recent advances in DAS interrogating technology enable increasing data quality, spatial coverage, and bandwidth, sparking interest in applied environmental sensing. Initially focused on seismic data collection, e.g., for earthquake monitoring, DAS has demonstrated abilities in detecting waterborne acoustic sources, including the low-frequency sounds of blue and fin whales (Balaenoptera musculus and B. physalus) over tens of km of fiber. DAS is instrumented from land, collects data in near-real time, and offers region-wide spatial coverage. Thus, this technology provides a unique opportunity for robust monitoring of endangered and threatened migratory baleen whales at ecologically meaningful spatial and temporal scales. This talk will introduce our research on using DAS to monitor low-frequency baleen whales, including detection and tracking, and the open-access DAS4Whales python package, developed to analyze terabytes of spatio-temporal data collected with DAS. We will also address the potential limitations of this new monitoring technology and provide a roadmap for further research and testing needed to exploit the technology's full potential.

Knowledge Transfer and Reuse: A Case Study of Ai-Enabled Resource Management in RAN Slicing

An efficient resource management scheme is critical to enable network slicing in 5G networks and in envisioned 6G networks, and artificial intelligence (AI) techniques offer promising solutions. Considering the rapidly emerging new machine learning techniques, such as graph learning, federated learning, and transfer learning, a timely survey is needed to provide an overview of resource management and network slicing techniques of AI-enabled wireless networks. This article provides such a survey along with an application of knowledge transfer in radio access network (RAN) slicing. In particular, we first provide some background on resource management and network slicing, and review relevant state-of-the-art AI and machine learning (ML) techniques and their applications. Then, we introduce our AI-enabled knowledge transfer and reuse-based resource management (AKRM) scheme, where we apply transfer learning to improve system performance. Compared with most existing works, which focus on the training of standalone agents from scratch, the main difference of AKRM lies in its knowledge transfer and reuse capability between different tasks. Our paper aims to be a roadmap for researchers to use knowledge transfer schemes in AI-enabled wireless networks, and we provide a case study over the resource allocation problem in RAN slicing.

Performance and profiling data of plane-wave calculations in quantum ESPRESSO simulation on three supercomputing centres.

This dataset reflects the parallel execution profiles of five Quantum ESPRESSO simulation (QE) versions in finding the total energy of the Cerium Oxide lattice using the self-consistent field (SCF) method. The data analysis used a strong scale setting to identify the optimal parameters and computing resources needed to complete a single SCF loop for one specific material efficiently. This analysis notably contributed to achieving the Best Performance Award at the 5th APAC HPC-AI Competition. The data comprises three sets. The first set features the parallel execution traces captured via the Extrae performance profiling tool, offering a broad view of the QE's model execution behaviour and how it used computational resources. The second set records how long QE's model ran on a single node at three HPC centres: ThaiSC TARA in Thailand, NSCC ASPIRE-1 in Singapore, and NCI Gadi in Australia. This set focuses on the impact of adjusting three parameters for K-point parallelisation. The final set presents benchmarking data generated by scaling out the QE's model across 32 nodes (1,536 CPU cores) on the NCI Gadi supercomputer. Despite its focus on a single material, the dataset serves as a roadmap for researchers to estimate required computational resources and understand scalability bottlenecks, offering general guidelines adaptable across different HPC systems.

Artificial intelligence in logistics and supply chain management: A primer and roadmap for research

AbstractThe dawn of generative artificial intelligence (AI) has the potential to transform logistics and supply chain management radically. However, this promising innovation is met with a scholarly discourse grappling with an interplay between the promising capabilities and potential drawbacks. This conversation frequently includes dystopian forecasts of mass unemployment and detrimental repercussions concerning academic research integrity. Despite the current hype, existing research exploring the intersection between AI and the logistics and supply chain management (L&amp;SCM) sector remains limited. Therefore, this editorial seeks to fill this void, synthesizing the potential applications of AI within the L&amp;SCM domain alongside an analysis of the implementation challenges. In doing so, we propose a robust research framework as a primer and roadmap for future research. This will give researchers and organizations comprehensive insights and strategies to navigate the complex yet promising landscape of AI integration within the L&amp;SCM domain.

Bibliometric Analysis of Research Regarding Citarum River in Indonesia and Its Implication for Government Policy

Research mapping related to river revitalization has been carried out on areas experiencing severe pollution to provide a roadmap for knowledge and answers to a number of environmental issues surrounding rivers. Therefore, this study aims to map a number of research publications regarding the Citarum River, which is an important area for people in West Java. This river is currently the most polluted in Indonesia, which led to the implementation of the revitalization program initiated by the government known as "Citarum Harum". The bibliometric method was used in this research to determine annual trends, keywords, field grouping, and location. The results showed a fluctuating increase in the number of research publications on the Citarum River. Furthermore, the mapping results showed that topics, study groups, and locations related to this river were unevenly distributed. These findings indicated the need for coordination from the “Citarum Harum” program coordinator or government to develop a research roadmap and direct the researcher to examine topics and raise issues related to locations that are yet to be studied.