Utility Of Data Research Articles

Odor, air quality, and well-being: understanding the urban smellscape using crowd-sourced science

Abstract Odors are a topic of emerging environmental health interest given their potential links to air quality, health, well-being, and quality of life. However, odors have traditionally been challenging to study given variability in individual sensitivity and perception, atmospheric physico-chemical processes, and emissions of mixtures of odorous contaminants. Here, we explore the potential utility of crowd-sourced odor report data in improving understanding of spatiotemporal patterns of odor experiences and their impacts. We conduct quantitative and qualitative analyses of a 12-month data set from a web application collecting crowd-sourced odor reports, including spatiotemporal information, odor and self-reported impacts description (OSAC: odors, symptoms, actions in response, and suspected causes), and demographics, in Vancouver, Canada. Users report diverse OSAC with strong seasonality and spatial variability. Reported symptoms, ranging from neurological to emotion- and mood-related, highlight the complexity of odor-related health and well-being impacts. Odors can trigger maladaptive actions, where individuals are exposed to other environmental stressors (e.g. heat stress) or curtail healthy behaviors (e.g. exercising outside) to cope with odor impacts. Clustering analysis of OSAC suggests that odor exposures may be linked to health, well-being, and quality of life impacts through complex mechanisms, related not only to the odor experienced but also perceived causes. Spatiotemporal patterns in reports highlight the potential influence of persistent sources (e.g. waste management) and transient events (e.g. accidents). Exploratory multiple linear regression models suggest that monitoring of air quality and meteorology may be insufficient to capture odor issues. Overall, these results suggest that crowd-sourced science incorporating self-reported health and well-being effects and behavioral responses can enrich understanding of the impacts of odorous emissions at large spatiotemporal scales and complement traditional air pollution monitoring.

Analyzing and explaining privacy risks on time series data: ongoing work and challenges

Currently, privacy risks assessment is mainly performed as audits conducted by data privacy analysts. In the TAILOR project, we promote a more systematic and automatic approach based on interpretable metrics and formal methods to evaluate privacy risks and to control the tension between data privacy and utility. In this paper, we focus on privacy risks raised by publishing time series datasets, and we survey the methods developed in TAILOR to analyze and quantify privacy risks depending on different publisher and attacker models.

Data Literacy and Data Science Literacy for Nurses: State of the Art Literature Review.

In the rapidly evolving landscape of modern healthcare, nurses must proficiently navigate data utilization and grasp the principles of data science. Despite this urgency, nursing stakeholders currently do not fully understand the extent of data literacy or data science literacy they need to acquire. This paper aims to elucidate the distinctions between data literacy and data science literacy, offering insights into strategies for nurturing these competencies within nursing education, research, and practice. Through a state-of-the-art review of 22 articles and six healthcare industry resources, we identified a notable absence of comprehensive frameworks and assessment tools, highlighting key areas for future development.

Oral health inequalities in immigrant populations worldwide: a scoping review of dental caries and periodontal disease prevalence

BackgroundInequalities in immigrants' oral health are often masked in population-level data. Therefore, this paper was planned to assess the prevalence data on oral health diseases, namely dental caries, and periodontitis, among immigrants worldwide.MethodsFollowing a systematic search in Scopus, Embase, and PubMed for studies published between 2011 and 2023, 1342 records were identified. Following title and abstract screening, 76 studies remained for full-text eligibility-screening based on predefined inclusion criteria. Thirty-two studies were included in the review.ResultsDental caries figures were higher in immigrant populations compared to the local population, regardless of host countries, age, gender, or nationality. In children, the overall mean and standard deviation (SD) for decayed, missing, and filled teeth in the primary dentition (d3mft) was 3.63(2.47), and for D3MFT (permanent dentition), it was 1.7(1.2).Upon comparing overall mean caries counts in children and adults with their control groups in the included studies, untreated dental caries (D3T and d3t) constituted the dominant share of caries experience (D3MFT and d3mft) in immigrant children. For the local population, the highest proportion of caries experience was attributed to filled teeth (FT and ft).Dentin caries prevalence among immigrants ranged from 22% to 88.7% in the primary dentition and 5.6% to 90.9% in the permanent dentition. Gingivitis ranged from 5.1% to 100%. Oral health varied greatly between studies. Regarding oral health accessibility, 52% to 88% of immigrant children had never been to a dentist, suggesting a very limited level of accessibility to dental health services.ConclusionIt is imperative to develop interventions and policies that have been customized to address the oral health disparities experienced by immigrant populations. Additionally, host countries should actively implement measures aimed at enhancing the accessibility of oral health care services for these individuals. The utilization of available data is crucial in establishing a hierarchy of objectives aimed at enhancing the oral health of immigrant populations.Trial registrationThe Scoping review protocol was registered at OSF Registries with registration number (https://doi.org/10.17605/OSF.IO/MYXS4).

Real-time prediction of ship motions based on the reservoir computing model

Real-time prediction of ship motions is crucial for ensuring the safety of offshore activities. In this study, we investigate the performance of the reservoir computing (RC) model in predicting the motions of a ship sailing in irregular waves, comparing it with the long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and gated recurrent unit (GRU) networks. The model tests are carried out in a towing tank to generate the datasets for training and testing the machine learning models. First, we explore the performance of machine learning models trained solely on motion data. It is found that the RC model outperforms the LSTM, BiLSTM, and GRU networks in both accuracy and efficiency for predicting ship motions. Besides, we investigate the performance of the RC model trained using the historical motion and wave elevation data. It is shown that, compared with the RC model trained solely on motion data, the RC model trained on the motion and wave elevation data can significantly improve the motion prediction accuracy. This study validates the effectiveness and efficiency of the RC model in ship motion prediction during sailing and highlights the utility of wave elevation data in enhancing the RC model’s prediction accuracy.

Urban hyperspectral reference data availability and reuse: State‐of‐the‐practice review

AbstractHyperspectral remote sensing is currently underutilized in urban environments due to significant barriers concerning the existence, availability, and quality of urban hyperspectral reference spectra. This paper exposes these barriers by identifying, cataloging, and characterizing the contents of 23 spectral libraries, developing metrics to assess compliance with the Principles of Findability, Accessibility, Interoperability, and Reusability (FAIR), and evaluating existing resources using these criteria. Only 2931 urban spectral records were found within the 4 Global Spectral Libraries (0.61% of 476,592 published spectra). Within a further 19 Local Urban Spectral Libraries, 3862 additional urban spectra were found, but only 1662 (43%) were accessible without restriction. Content analysis revealed insufficient representation of urban material heterogeneity, imbalanced categories, and limited library interoperability, all of which further hinder effective data utilization. In response, this paper proposes a 14‐category metadataset, with specific considerations to overcome environmentally induced and inherent, intra‐material variability. In addition, material‐based spectral groupings and data resampling to common hyperspectral equipment specifications are recommended. These measures aim to enhance the utility of urban spectral libraries by improving FAIR compliance, thereby contributing to a more cohesive and enduring framework for hyperspectral reference data.

Online Learning Based on Learning Analytics in Big Data for College English Language Teaching

This study addresses the challenges of enhancing the quality of education and improving the overall student experience in online English language teaching sessions. Current approaches often struggle with session initiation, real-time data processing, and personalized learning experiences. To tackle these issues, the study proposes a manifold learning data analytics model (MLDAM). This innovative method leverages classifier tree learning to distinguish between trivial and non-trivial aspects of student learning experiences and session data. MLDAM integrates multi-dimensional data extraction, classification learning, and impact evaluation to enhance the effectiveness of online English language teaching. The model analyzes data from 176,009 English phrases across 36 online teaching sessions, focusing on improving session accessibility, student comprehension, and suggestion accuracy. Using an iterative training process based on student performance and feedback, it continuously extracts and processes multiple types of data to refine teaching strategies. Results show notable improvements: a 14.74% increase in classification accuracy, an 8.73% increase in data extraction ratio, an 11.84% reduction in feature discard, a 10.57% decrease in initialization time, and a 13.17% reduction in classification time. These metrics demonstrate MLDAM’s ability to efficiently process and analyze session data, enabling real-time adjustments during lessons. The model optimizes data utilization, making learning more responsive and adaptable. It enhances the precision of impact evaluations, facilitating targeted course adjustments and personalized learning experiences. This research presents a comprehensive, data-driven methodology for improving teaching quality and student outcomes in virtual English classrooms.

Advanced authentication of IoT sensor network for industrial safety

As industries rapidly advance towards automation and intelligence, the Industrial Internet of Things (IIoT) plays a pivotal role in enhancing factory efficiency and production management. However, the advent of quantum computing introduces significant security challenges, particularly for data transmission and privacy protection, as its immense computational power can compromise the current cryptographic theories' safeguards. We introduce a novel lattice-based cryptographic authentication and key management scheme specifically designed for the IIoT within Wireless Sensor Networks (WSNs), focusing on establishing a secure industrial communication system capable of withstanding quantum threats. The proposed scheme utilizes Crystals-Kyber technology and is built around the Learning With Errors (LWE) problem, providing a robust foundation for security against both traditional and quantum threats. It features innovative methods to safeguard against password guessing, stolen verifier, and replay attacks, enhancing data confidentiality and system integrity. By integrating advanced cryptographic techniques resistant to quantum decryption, this research ensures the secure management and utilization of critical production data, supporting the ongoing transformation towards more intelligent manufacturing processes and sustaining global competitiveness for industries.

On the wing: Morphological variation in the osteology of Mediterranean, Near Eastern, and EuropeanAnatidae (excluding Anserinae).

Accurate identification of waterfowl bones in archaeological and fossil assemblages has potential to unlock new methods of past environmental reconstruction, as species have differing habitat preferences and migration patterns. Therefore, identifying the presence of avian species with different ecological niches is key to determining past environments and ultimately how prehistoric people responded to climatic and environmental realignments. However, the identification of osteological remains of waterbirds such as ducks to species level is notoriously challenging. We address this by presenting a new two-dimensional geometric morphometric protocol on wing elements from over 20 duck species and test the utility of these shape data for correct species identification. This is an ideal starting point to expand utilization of these types of approaches in avifaunal research and test applicability to an extremely difficult taxonomic group.

Adapting to the shifting landscape: Implications of climate change for malaria control: A review.

Malaria, a global public health challenge, continues to affect millions of lives, particularly in regions where its transmission is endemic. The interplay between climate change and malaria dynamics has emerged as a critical concern, reshaping the landscape of this vector-borne disease. This review publication, titled "Adapting to the shifting landscape: Implications of climate change for malaria control," explores the multifaceted relationship between climate change and the control of malaria. The paper begins by dissecting the influence of climate change on malaria dynamics, including alterations in temperature, precipitation, and other climatic factors that impact the habitat and life cycle of malaria vectors. It delves into the evolving ecology and behavior of malaria vectors in response to changing climatic conditions, emphasizing the importance of understanding these adaptations. As a response to this shifting landscape, the review discusses adaptive strategies for malaria control, ranging from vector control measures to the utilization of climate data in early warning systems. Community engagement and education are highlighted as essential components of these strategies, recognizing the vital role of local communities in effective malaria control efforts. The paper also identifies future directions and research needs, underscoring the importance of staying ahead of the evolving climate-malaria relationship. This review underscores the urgency of adapting to the changing landscape of malaria transmission driven by climate change. It emphasizes the significance of proactively addressing climate-related challenges to enhance malaria control and protect the health and well-being of vulnerable populations.

FragHub: A Mass Spectral Library Data Integration Workflow.

Open mass spectral libraries (OMSLs) are critical for metabolite annotation and machine learning, especially given the rising volume of untargeted metabolomic studies and the development of annotation pipelines. Despite their importance, the practical application of OMSLs is hampered by the lack of standardized file formats, metadata fields, and supporting ontology. Current libraries, often restricted to specific topics or matrices, such as natural products, lipids, or the human metabolome, may limit the discovery potential of untargeted studies. The goal of FragHub is to provide users with the capability to integrate various OMSLs into a single unified format, thereby enhancing the annotation accuracy and reliability. FragHub addresses these challenges by integrating multiple OMSLs into a single comprehensive database, supporting various data formats, and harmonizing metadata. It also proposes some generic filters for the mass spectrum using a graphical user interface. Additionally, a workflow to generate in-house libraries compatible with FragHub is proposed. FragHub dynamically segregates libraries based on ionization modes and chromatography techniques, thereby enhancing data utility in metabolomic research. The FragHub Python code is publicly available under a MIT license, at the following repository: https://github.com/eMetaboHUB/FragHub. Generated data can be accessed at 10.5281/zenodo.11057687.

A comparative Study on AI-Driven Anonymization Techniques for Protecting Personal Data

In the Artificial Intelligence era, protecting individual users' data has become crucial. The collected data is stored in multiple databases having personally identifiable information (PII). This may provide a significant privacy concern for the database. Several privacy-preserving approaches have been proposed, including Differential Privacy, Homomorphic Encryption, Generative Adversarial Network and Federated Learning. In this paper, the above four anonymization techniques are compared. In addition, this study will review the strengths and weaknesses of these techniques. We also discuss the trade-off between data utility and privacy. The results of this study aim to guide researchers and practitioners in selecting suitable AI-driven anonymization techniques.

Persepsi Auditor Eksternal Atas Pengaruh Kemudahan dan Kegunaan Artificial Intelligence Terhadap Kualitas Audit

This study aims to investigate external auditors' perception of the impact of artificial intelligence's ease of use and usefulness on audit quality, while also examining the potential scepticism among external auditors towards the integration of artificial intelligence in the audit process. A quantitative approach is utilized to analyze the relationship between the variables. The research was conducted through questionnaires distributed via telegram and WhatsApp groups, email, and collaboration with IAPI. One hundred and eight valid responses were gathered from external auditors of Big Four KAP, internationally affiliated KAP - non big four, and national KAP. Findings reveal that external auditors do not exhibit a sceptical attitude towards the usage of artificial intelligence in the audit process. The test results demonstrate that external auditors' perceptions of the ease of using artificial intelligence aids in enhancing audit quality. Similarly, gauging external auditors' perceived usefulness or benefits of artificial intelligence can also lead to improved audit quality. This study's sample is limited to external auditors of financial statements at public accounting firms, and therefore the findings should not be extended to other types of auditors such as government or internal auditors. This study offers external auditors a thorough comprehension of how artificial intelligence contributes to the audit process to improve audit quality. The necessity for external auditors to improve their technical skills arises due to the increased utilization of big data by businesses. Hence, universities must provide graduates with competence in not only accounting and auditing but also the latest technology.

Non-coding variants impact cis-regulatory coordination in a cell type-specific manner

BackgroundInteractions among cis-regulatory elements (CREs) play a crucial role in gene regulation. Various approaches have been developed to map these interactions genome-wide, including those relying on interindividual epigenomic variation to identify groups of covariable regulatory elements, referred to as chromatin modules (CMs). While CM mapping allows to investigate the relationship between chromatin modularity and gene expression, the computational principles used for CM identification vary in their application and outcomes.ResultsWe comprehensively evaluate and streamline existing CM mapping tools and present guidelines for optimal utilization of epigenome data from a diverse population of individuals to assess regulatory coordination across the human genome. We showcase the effectiveness of our recommended practices by analyzing distinct cell types and demonstrate cell type specificity of CRE interactions in CMs and their relevance for gene expression. Integration of genotype information revealed that many non-coding disease-associated variants affect the activity of CMs in a cell type-specific manner by affecting the binding of cell type-specific transcription factors. We provide example cases that illustrate in detail how CMs can be used to deconstruct GWAS loci, assess variable expression of cell surface receptors in immune cells, and reveal how genetic variation can impact the expression of prognostic markers in chronic lymphocytic leukemia.ConclusionsOur study presents an optimal strategy for CM mapping and reveals how CMs capture the coordination of CREs and its impact on gene expression. Non-coding genetic variants can disrupt this coordination, and we highlight how this may lead to disease predisposition in a cell type-specific manner.

Detection of decreased granules in neutrophils by automated hematology analyzers XR-1000 and UniCel DxH 800.

This study aimed to investigate the utility of neutrophil-related cell population data obtained by automated hematology analyzers in assessing myelodysplastic syndrome cases with decreased granules in neutrophils. A total of 108 subjects were classified into normal granule (n = 35), hypogranulation (n = 37), or hypergranulation (n = 36) groups. Neutrophil cell area and granule area were measured by ImageJ. All samples were analyzed on the XR-1000 and UniCel DxH 800, and neutrophil-related parameters were compared among the 3 groups. Neutrophil cell area and the ratio of the granular area showed significant differences among the 3 groups; they were the highest in the hypergranulation group and lowest in the hypogranulation group. XR-1000 data showed significant differences in NE-SFL and NE-FSC among the 3 groups (P < .0001). NE-SFL and NE-FSC discriminated most accurately hypogranulation group against other groups. UniCel DxH 800 data showed significant differences in MN-V-NE, MN-MALS-N, MN-UMALS-NE, SD-UMALS-NE (P <.01), MN-LMALS-NE, and SD-LMALS-NE (P <.05) among the 3 groups. The combination of SD-V-NE and SD-LMALS-NE discriminated most accurately the hypogranulation group against the other groups. NE-SFL and NE-FSC and the combination of SD-V-NE and SD-LMALS-NE are useful in detecting cases with decreased granules in neutrophils.

The Cancer Clinical Library Database (CCLD) from the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) Project.

The common data model (CDM) has found widespread application in healthcare studies, but its utilization in cancer research has been limited. This article describes the development and implementation strategy for Cancer Clinical Library Databases (CCLDs), which are standardized cancer-specific databases established under the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) project by the Korean Ministry of Health and Welfare. Fifteen leading hospitals and fourteen academic associations in Korea are engaged in constructing CCLDs for 10 primary cancer types. For each cancer type-specific CCLD, cancer data experts determine key clinical data items essential for cancer research, standardize these items across cancer types, and create a standardized schema. Comprehensive clinical records covering diagnosis, treatment, and outcomes, with annual updates, are collected for each cancer patient in the target population, and quality control is based on six-sigma standards. To protect patient privacy, CCLDs follow stringent data security guidelines by pseudonymizing personal identification information and operating within a closed analysis environment. Researchers can apply for access to CCLD data through the K-CURE portal, which is subject to Institutional Review Board and Data Review Board approval. The CCLD is considered a pioneering standardized cancer-specific database, significantly representing Korea's cancer data. It is expected to overcome limitations of previous CDMs and provide a valuable resource for multicenter cancer research in Korea.

Federated semi-supervised representation augmentation with cross-institutional knowledge transfer for healthcare collaboration

In the healthcare field, cross-institutional collaboration can fasten medical research progress. Vertical federated learning (VFL) addresses data heterogeneity across multiple medical institutions while ensuring medical data privacy, thereby enhancing the accuracy of disease diagnoses and treatments. However, traditional VFL only benefits from aligned samples, thereby limiting its applicability due to constrained sample sizes, and a large amount of non-aligned data remains untapped, resulting in wasted data. To exert full leverage on the value of all data obtained from medical institutions, this paper proposes a federated healthcare collaborative framework based on semi-supervised representation augmentation mechanism with cross-institutional knowledge transfer (CrossKT-FRA). Specifically, the developed method comprises three steps. First, the federated representations of shared data (aligned data) among medical institutions are extracted through efficient vertical federated representation learning (FRL) methods. Second, the federated knowledge contained in federated representations and potential labels derived through recurrent learning assist local shared data representations in performing supervised augmented learning. Finally, the federated knowledge is transferred indirectly from the representation augmentation module for shared data to the unsupervised representation augmentation module for local private data (non-aligned data). The experimental results show the effectiveness of the proposed knowledge transfer mechanism, whether applied independently or used to enhance VFL on medical datasets. Our findings contribute to a deeper theoretical understanding of VFL, further facilitating the utilization of high-value medical data. By promoting cross-institutional and cross-disciplinary collaboration in healthcare data sharing, our study enhances the quality efficiency of medical services, thereby accelerating the development of interdisciplinary medical research. Code is available at https://github.com/LieLieLieLieLie/CrossKT-FRA.

Discriminant Input Processing Scheme for Self-Assisted Intelligent Healthcare Systems.

Modern technology and analysis of emotions play a crucial role in enabling intelligent healthcare systems to provide diagnostics and self-assistance services based on observation. However, precise data predictions and computational models are critical for these systems to perform their jobs effectively. Traditionally, healthcare monitoring has been the primary emphasis. However, there were a couple of negatives, including the pattern feature generating the method's scalability and reliability, which was tested with different data sources. This paper delves into the Discriminant Input Processing Scheme (DIPS), a crucial instrument for resolving challenges. Data-segmentation-based complex processing techniques allow DIPS to merge many emotion analysis streams. The DIPS recommendation engine uses segmented data characteristics to sift through inputs from the emotion stream for patterns. The recommendation is more accurate and flexible since DIPS uses transfer learning to identify similar data across different streams. With transfer learning, this study can be sure that the previous recommendations and data properties will be available in future data streams, making the most of them. Data utilization ratio, approximation, accuracy, and false rate are some of the metrics used to assess the effectiveness of the advised approach. Self-assisted intelligent healthcare systems that use emotion-based analysis and state-of-the-art technology are crucial when managing healthcare. This study improves healthcare management's accuracy and efficiency using computational models like DIPS to guarantee accurate data forecasts and recommendations.

Middle School Students' Conceptualizations and Reasoning about the Fairness of Math Tests

Assessment is central to teaching and learning, and recently there has been a substantive shift from paper-and-pencil assessments towards technology delivered assessments such as computer-adaptive tests. Fairness is an important aspect of the assessment process, including design, administration, test-score interpretation, and data utility. The Universal Design for Learning (UDL) guidelines can inform assessment development to promote fairness; however, it is not explicitly clear how UDL and fairness may be linked through students’ conceptualizations of assessment fairness. This phenomenological study explores how middle grades students conceptualize and reason about the fairness of mathematics tests, including paper-and-pencil and technology-delivered assessments. Findings indicate that (a) students conceptualize fairness through unique notions related to educational opportunities and (b) students’ reason about fairness non-linearly. Implications of this study have potential to inform test developers and users about aspects of test fairness, as well as educators data usage from fixed-form, paper-and-pencil tests, and computer-adaptive, technology-delivered tests.

Assessing Feature Importance in Eye-Tracking Data within Virtual Reality Using Explainable Artificial Intelligence Techniques

Our research systematically investigates the cognitive and emotional processes revealed through eye movements within the context of virtual reality (VR) environments. We assess the utility of eye-tracking data for predicting emotional states in VR, employing explainable artificial intelligence (XAI) to advance the interpretability and transparency of our findings. Utilizing the VR Eyes: Emotions dataset (VREED) alongside an extra trees classifier enhanced by SHapley Additive ExPlanations (SHAP) and local interpretable model agnostic explanations (LIME), we rigorously evaluate the importance of various eye-tracking metrics. Our results identify significant correlations between metrics such as saccades, micro-saccades, blinks, and fixations and specific emotional states. The application of SHAP and LIME elucidates these relationships, providing deeper insights into the emotional responses triggered by VR. These findings suggest that variations in eye feature patterns serve as indicators of heightened emotional arousal. Not only do these insights advance our understanding of affective computing within VR, but they also highlight the potential for developing more responsive VR systems capable of adapting to user emotions in real-time. This research contributes significantly to the fields of human-computer interaction and psychological research, showcasing how XAI can bridge the gap between complex machine-learning models and practical applications, thereby facilitating the creation of reliable, user-sensitive VR experiences. Future research may explore the integration of multiple physiological signals to enhance emotion detection and interactive dynamics in VR.