BackgroundThe internet and social media have become essential sources of health information for patients and citizens; however, they often disseminate misinformation that lacks scientific evidence. Health-related misinformation can undermine evidence-based treatment, weaken patient-provider relationships, and contribute to adverse health outcomes. Although narratives have been proposed as a promising approach to countering misinformation, their effectiveness remains inconsistent and influenced by various factors.
ObjectiveThe aim of this study is to assess the effectiveness of narrative messages in correcting health-related misinformation compared to nonnarrative messages. It also seeks to identify message-, sender-, and recipient-related factors that influence the effectiveness of narrative-based corrections.
MethodsThis systematic review will follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Comprehensive searches will be conducted across databases, including PubMed, MEDLINE, CINAHL, PsycINFO, and Web of Science, using keywords related to narratives and correction of health-related misinformation. This review will include quantitative studies evaluating narrative-based corrections for health-related misinformation in experimental and quasi-experimental studies. Studies unrelated to health misinformation or where the full text is unavailable will be excluded. No restrictions on publication year will apply. Only papers written in English will be included. Two independent reviewers will screen the papers using Rayyan QCRI software, with disagreements resolved by a third reviewer. Data extraction will cover health topics (eg, vaccination, tobacco), study characteristics (eg, author, publication year), narrative characteristics (eg, definition of narrative, theoretical foundation), participant characteristics (eg, sociodemographic), methodology (eg, study design, content of interventions and comparators, outcomes and measures, moderating and mediating factors), main results, and discussion. The quality of the eligible studies will be assessed using the Cochrane Risk of Bias 2 tool and the Risk of Bias In Non-randomized Studies - of Interventions tool.
ResultsThe results will be summarized in tables and presented as a descriptive review addressing the effectiveness of narrative corrections in health-related misinformation and the factors influencing their success. The implications of these results for future studies and practices will be elucidated. The findings of this review will be presented at a relevant conference and submitted to a peer-reviewed journal for publication. The aim is to complete the submission process by the northern summer of 2025.
ConclusionsNarrative messages represent a theoretically promising strategy for countering health-related misinformation; however, their effectiveness is context-dependent. This review will offer critical insights into the factors that influence the success of narrative corrections for health-related misinformation, contributing to the development of improved correction strategies and a theoretical understanding of narrative corrections.
International Registered Report Identifier (IRRID)DERR1-10.2196/69414
Medicine, Computer applications to medicine. Medical informatics
Quantum computing represents a paradigm shift in computational power, promising exponential speedups for solving certain classes of problems. However, harnessing the full potential of quantum computers requires effective utilization of data science techniques. In this review paper, we explore the intersection of data science and quantum computing, focusing on the role of data analytics in advancing quantum computing applications. We begin with an overview of quantum computing fundamentals, including quantum mechanics principles and quantum algorithms. We then delve into topics such as quantum data representation, manipulation, and machine learning algorithms tailored for quantum computing environments. Additionally, we discuss quantum error correction and noise mitigation strategies essential for reliable quantum computation. Furthermore, we survey the landscape of quantum software development tools and frameworks, highlighting their importance in facilitating quantum algorithm design and optimization. Through case studies and examples, we demonstrate the practical applications of data science techniques in quantum computing, including quantum cryptography and quantum-enhanced data analysis. Finally, we identify future research directions and challenges in the field, emphasizing the need for interdisciplinary collaboration between the data science and quantum computing communities to unlock the full potential of quantum data science.
Waldenê de Melo Moura, Carlos Renato dos Santos, Moisés José dos Santos Freitas
et al.
The study of microgravity, a condition in which an object experiences near-zero weight, is a critical area of research with far-reaching implications for various scientific disciplines. Microgravity allows scientists to investigate fundamental physical phenomena influenced by Earth’s gravitational forces, opening up new possibilities in fields such as materials science, fluid dynamics, and biology. However, the complexity and cost of developing and conducting microgravity missions have historically limited the field to well-funded space agencies, universities with dedicated government funding, and large research institutions, creating a significant barrier to entry. This paper presents the MicroGravity Explorer Kit’s (MGX) design, a multifunctional platform for conducting microgravity experiments aboard suborbital rocket flights. The MGX aims to democratize access to microgravity research, making it accessible to high school students, undergraduates, and researchers. To ensure that the tool is versatile across different scenarios, the authors conducted a comprehensive literature review on microgravity experiments, and specific requirements for the MGX were established. The MGX is designed as an open-source platform that supports various experiments, reducing costs and accelerating development. The multipurpose experiment consists of a Jetson Nano computer with multiple sensors, such as inertial sensors, temperature and pressure, and two cameras with up to 4k resolution. The project also presents examples of codes for data acquisition and compression and the ability to process images and run machine learning algorithms to interpret results. The MGX seeks to promote greater participation and innovation in space sciences by simplifying the process and reducing barriers to entry. The design of a platform that can democratize access to space and research related to space sciences has the potential to lead to groundbreaking discoveries and advancements in materials science, fluid dynamics, and biology, with significant practical applications such as more efficient propulsion systems and novel materials with unique properties.
Large Language Model (LLM)-based agents have shown effectiveness across many applications. However, their use in data science scenarios requiring solving long-term interconnected tasks, dynamic data adjustments and domain expertise remains challenging. Previous approaches primarily focus on individual tasks, making it difficult to assess the complete data science workflow. Moreover, they struggle to handle real-time changes in intermediate data and fail to adapt dynamically to evolving task dependencies inherent to data science problems. In this paper, we present Data Interpreter, an LLM-based agent designed to automatically solve various data science problems end-to-end. Our Data Interpreter incorporates two key modules: 1) Hierarchical Graph Modeling, which breaks down complex problems into manageable subproblems, enabling dynamic node generation and graph optimization; and 2) Programmable Node Generation, a technique that refines and verifies each subproblem to iteratively improve code generation results and robustness. Extensive experiments consistently demonstrate the superiority of Data Interpreter. On InfiAgent-DABench, it achieves a 25% performance boost, raising accuracy from 75.9% to 94.9%. For machine learning and open-ended tasks, it improves performance from 88% to 95%, and from 60% to 97%, respectively. Moreover, on the MATH dataset, Data Interpreter achieves remarkable performance with a 26% improvement compared to state-of-the-art baselines. The code is available at https://github.com/geekan/MetaGPT.
Artificial intelligence (AI) has transformed various fields, significantly impacting our daily lives. A major factor in AI success is high-quality data. In this paper, we present a comprehensive review of the evolution of data quality (DQ) awareness from traditional data management systems to modern data-driven AI systems, which are integral to data science. We synthesize the existing literature, highlighting the quality challenges and techniques that have evolved from traditional data management to data science including big data and ML fields. As data science systems support a wide range of activities, our focus in this paper lies specifically in the analytics aspect driven by machine learning. We use the cause-effect connection between the quality challenges of ML and those of big data to allow a more thorough understanding of emerging DQ challenges and the related quality awareness techniques in data science systems. To the best of our knowledge, our paper is the first to provide a review of DQ awareness spanning traditional and emergent data science systems. We hope that readers will find this journey through the evolution of data quality awareness insightful and valuable.
Advanced analytics science methods have enabled combining the power of artificial and human intelligence, creating \textit{centaurs} that allow superior decision-making. Centaurs are hybrid human-algorithm models that combine both formal analytics and human intuition in a symbiotic manner within their learning and reasoning process. We argue that the future of AI development and use in many domains needs to focus more on centaurs as opposed to other AI approaches. This paradigm shift towards centaur-based AI methods raises some fundamental questions: How are centaurs different from other human-in-the-loop methods? What are the most effective methods for creating centaurs? When should centaurs be used, and when should the lead be given to pure AI models? Doesn't the incorporation of human intuition -- which at times can be misleading -- in centaurs' decision-making process degrade its performance compared to pure AI methods? This work aims to address these fundamental questions, focusing on recent advancements in generative AI, and especially in Large Language Models (LLMs), as a main case study to illustrate centaurs' critical essentiality to future AI endeavors.
Abstract Redressing global patterns of biodiversity loss requires quantitative frameworks that can predict ecosystem collapse and inform restoration strategies. By applying a network-based dynamical approach to synthetic and real-world mutualistic ecosystems, we show that biodiversity recovery following collapse is maximized when extirpated species are reintroduced based solely on their total number of connections in the original interaction network. More complex network-based strategies that prioritize the reintroduction of species that improve ‘higher order’ topological features such as compartmentalization do not provide meaningful performance improvements. These results suggest that it is possible to design nearly optimal restoration strategies that maximize biodiversity recovery for data-poor ecosystems in order to ensure the delivery of critical natural services that fuel economic development, food security, and human health around the globe.
Kukuh Setyo Priyanto, Prasadja Ricardianto, Aang Gunawan
et al.
This research aimed to study the opinion and perspectives of Commuter Line passengers in Indonesia by using 18 attributes of service quality. There still needed to be more understanding about which service attributes were less satisfying and which were more pleasing to the Commuter Line passengers in the area of Jakarta and its surroundings. This research used factor analysis and Principal Component Analysis to select among the 18 Commuter Line service quality variables with the Varimax and Ordered Logit model rotation method. The number of samples used was 384 respondents from Commuter Line passengers in Jakarta and its surroundings. The result of factor analysis stated that the 18 attributes of service quality with three factors were the main attributes of service quality being used, namely the factor of station facilities and passenger behavior, the factor of ticket and security system, and they had reasonably strong correlations. The key finding of this research was that some service quality attributes, such as the crowd or density of trains, station stair facility, station lift facility, station seat facility, and shelter, were perceived as the attributes of service that were less satisfying. This research provided valuable insights into important factors affecting the opinion and perspective of Commuter Line passengers in Jakarta and its surroundings.
Social Sciences, Management. Industrial management
Local and national media have always played an instrumental role in the communication of academic research to the public. In recent years, this has proved even more important due to the extensive online national and international coverage of topics such as climate change and the Covid-19 pandemic. Given that the media represent the public’s first point of contact with, and key source of information about, science and research, then, as academics, we need to know, firstly, whether the media make this research easily identifiable for the public and, secondly, whether the research itself is accessible. Our study examined coverage of University of Sheffield published research in UK local and national media to explore how far it is identifiable and accessible; using data from Altmetric.com we investigated what proportion of research covered provided sufficient details to identify research, including links to the published articles and explored how much of the research was accessible via open access. A large proportion of research that featured in local media cited the journal, academic institution and author, but did not link to the article. By contrast, national media cited the author, institution or funder much less than local news websites, but often linked to the actual research article. Most articles featured were open access. The implications of this and potential reasons for the national and local differences are discussed.
Bibliography. Library science. Information resources
Eye tracking can provide valuable insights into how students use different representations to solve problems and can be a useful tool for measuring the integration of information from multiple representations. In this study, we measured the eye movements of 60 university students while solving two PISA items that contain graphs taken from mathematics and science assessments with the aim of studying the difference in visual attention between students who correctly and incorrectly identify graphs from a verbal description. We were particularly interested in the differences in the integration of information from different representations (text, graphs, and picture) between students who were successful or unsuccessful in solving items. The results suggest that students who solved the items correctly tend to solve the items longer than their counterparts who did not solve the items correctly. Analysis of eye tracking data suggests that students who solved science item correctly analyzed the graph for significantly longer time and had significantly longer average fixation time. This finding suggests that a careful analysis of graphs is crucial for the correct solution of PISA items used in this study. Furthermore, the results showed that students who solved the mathematics item correctly had significantly higher number of transitions between graphs and picture, which indicates a greater integration of information from two different representations. This indicates that these types of items require a lot of time and effort to complete, probably because solving them requires a lot of steps, which is cognitively demanding. We also found that the average fixation durations for different representations may vary for different items, indicating that it is not always equally difficult to extract necessary information from different types of representations. The results of this study suggest that instructors may be able to improve their teaching methods by considering the importance of individual representations (e.g., texts, graphs, and pictures) and the integration of information from multiple sources.
Abstract Background Total joint replacements are an established treatment for patients suffering from reduced mobility and pain due to severe joint damage. Aseptic loosening due to stress shielding is currently one of the main reasons for revision surgery. As this phenomenon is related to a mismatch in mechanical properties between implant and bone, stiffness reduction of implants has been of major interest in new implant designs. Facilitated by modern additive manufacturing technologies, the introduction of porosity into implant materials has been shown to enable significant stiffness reduction; however, whether these devices mitigate stress-shielding associated complications or device failure remains poorly understood. Methods In this systematic review, a broad literature search was conducted in six databases (Scopus, Web of Science, Medline, Embase, Compendex, and Inspec) aiming to identify current design approaches to target stress shielding through controlled porous structures. The search keywords included ‘lattice,’ ‘implant,’ ‘additive manufacturing,’ and ‘stress shielding.’ Results After the screening of 2530 articles, a total of 46 studies were included in this review. Studies focusing on hip, knee, and shoulder replacements were found. Three porous design strategies were identified, specifically uniform, graded, and optimized designs. The latter included personalized design approaches targeting stress shielding based on patient-specific data. All studies reported a reduction of stress shielding achieved by the presented design. Conclusion Not all studies used quantitative measures to describe the improvements, and the main stress shielding measures chosen varied between studies. However, due to the nature of the optimization approaches, optimized designs were found to be the most promising. Besides the stiffness reduction, other factors such as mechanical strength can be considered in the design on a patient-specific level. While it was found that controlled porous designs are overall promising to reduce stress shielding, further research and clinical evidence are needed to determine the most superior design approach for total joint replacement implants.
Orthopedic surgery, Diseases of the musculoskeletal system
Nada Binmadi, Maha Alsharif, Soulafa Almazrooa
et al.
(1) Objectives: This systematic review and meta-analysis aimed to summarize current evidence regarding the prognostic role of perineural invasion (PNI) in patients with oral squamous cell carcinoma (OSCC). (2) Methods: We searched Cochrane Central, ProQuest, PubMed, Scopus, Science Direct, and Web of Science, using relevant keywords to identify eligible articles. Two independent reviewers conducted two-stage screening, data extraction, and quality assessment. The risk of bias was assessed using the Newcastle–Ottawa Scale (NOS) criteria. All analyses were performed using comprehensive meta-analysis (CMA; version 3.3.070) software. (3) Results: The study included 101 published articles encompassing 26,062 patients. The pooled analyses showed that PNI was associated with significantly worse overall survival (OS; HR = 1.45, 95% CI: 1.32–1.58; <i>p</i> < 0.001), worse disease-specific survival (DSS; HR = 1.87, 95% CI: 1.65–2.12; <i>p</i> < 0.001), and worse disease-free survival (DFS; HR = 1.87, 95% CI: 1.65–2.12; <i>p</i> < 0.001). Similarly, both local recurrence-free survival (LRFS) and regional recurrence-free survival (RRFS) were worse in patients with PNI (HR = 2.31, 95% CI: 1.72–3.10, <i>p</i> < 0.001; and HR = 2.04, 95% CI: 1.51–2.74, <i>p</i> < 0.001), respectively. The random-effect estimate of three studies demonstrated that the presence of PNI was associated with worse failure-free survival (FFS; HR = 2.59, 95% CI: 1.12–5.98, <i>p</i> < 0.001). (4) Conclusions: The current evidence suggests that PNI can be used as an independent predictor of the prognosis for patients with OSCC. The presence of PNI was associated with worse OS, DFS, DSS, FFS, and with recurrence. Asian patients and patients with extra-tumoral or peripheral PNI invasion were associated with worse prognosis.
Udayan Khurana, Kavitha Srinivas, Sainyam Galhotra
et al.
The recent efforts in automation of machine learning or data science has achieved success in various tasks such as hyper-parameter optimization or model selection. However, key areas such as utilizing domain knowledge and data semantics are areas where we have seen little automation. Data Scientists have long leveraged common sense reasoning and domain knowledge to understand and enrich data for building predictive models. In this paper we discuss important shortcomings of current data science and machine learning solutions. We then envision how leveraging "semantic" understanding and reasoning on data in combination with novel tools for data science automation can help with consistent and explainable data augmentation and transformation. Additionally, we discuss how semantics can assist data scientists in a new manner by helping with challenges related to trust, bias, and explainability in machine learning. Semantic annotation can also help better explore and organize large data sources.
Ferran Larroya, Ofelia Díaz, Oleguer Segarra
et al.
The analysis of pedestrian GPS datasets is fundamental to further advance on the study and the design of walkable cities. The highest resolution GPS data can characterize micro-mobility patterns and pedestrians' micro-motives in relation to a small-scale urban context. Purposed-based recurrent mobility data inside people's neighborhoods is an important source in these sorts of studies. However, micro-mobility around people's homes is generally unavailable, and if data exists, it is generally not shareable often due to privacy issues. Citizen science and its public involvement practices in scientific research are valid options to circumvent these challenges and provide meaningful datasets for walkable cities. The study presents GPS records from single-day home-to-school pedestrian mobility of 10 schools in the Barcelona Metropolitan area (Spain). The research provides pedestrian mobility from an age-homogeneous group of people. The study shares processed records with specific filtering, cleaning, and interpolation procedures that can facilitate and accelerate data usage. Citizen science practices during the whole research process are reported to offer a complete perspective of the data collected.
There has been an increasing recognition of the value of data and of data-based decision making. As a consequence, the development of data science as a field of study has intensified in recent years. However, there is no systematic and comprehensive treatment and understanding of data science. This article describes a systematic and end-to-end framing of the field based on an inclusive definition. It identifies the core components making up the data science ecosystem, presents its lifecycle modeling the development process, and argues its interdisciplinarity.
Natalia Markovich, Maksim Ryzhov, Marijus Vaičiulis
Random graphs are subject to the heterogeneities of the distributions of node indices and their dependence structures. Superstar nodes to which a large proportion of nodes attach in the evolving graphs are considered. In the present paper, a statistical analysis of the extremal part of random graphs is considered. We used the extreme value theory regarding sums and maxima of non-stationary random length sequences to evaluate the tail index of the PageRanks and max-linear models of superstar nodes in the evolving graphs where existing nodes or edges can be deleted or not. The evolution is provided by a linear preferential attachment. Our approach is based on the analysis of maxima and sums of the node PageRanks over communities (block maxima and block sums), which can be independent or weakly dependent random variables. By an empirical study, it was found that tail indices of the block maxima and block sums are close to the minimum tail index of representative series extracted from the communities. The tail indices are estimated by data of simulated graphs.