Large-scale Data Collection Research Articles

A scalable variational inference approach for increased mixed-model association power.

The rapid growth of modern biobanks is creating new opportunities for large-scale genome-wide association studies (GWASs) and the analysis of complex traits. However, performing GWASs on millions of samples often leads to trade-offs between computational efficiency and statistical power, reducing the benefits of large-scale data collection efforts. We developed Quickdraws, a method that increases association power in quantitative and binary traits without sacrificing computational efficiency, leveraging a spike-and-slab prior on variant effects, stochastic variational inference and graphics processing unit acceleration. We applied Quickdraws to 79 quantitative and 50 binary traits in 405,088 UK Biobank samples, identifying 4.97% and 3.25% more associations than REGENIE and 22.71% and 7.07% more than FastGWA. Quickdraws had costs comparable to REGENIE, FastGWA and SAIGE on the UK Biobank Research Analysis Platform service, while being substantially faster than BOLT-LMM. These results highlight the promise of leveraging machine learning techniques for scalable GWASs without sacrificing power or robustness.

마비말장애 환자를 위한 청지각적 평가 시스템(DAS) 개발

Background and Objectives: Dysarthria is a significant motor speech disorder that arises from damage to the central or peripheral nervous system. This damage can result in paralysis and weakness of the muscles responsible for articulation, which negatively affects the speech production process. The objective of this study is to develop a Dysarthria Assessment System (DAS) for the auditory-perceptual evaluation of patients with dysarthria. Method: We conducted an analysis of assessment areas and tasks based on literature published after 2000. This analysis guided the development of assessment content, which was subsequently organized for integration into a web platform. Results: The DAS consists of three main components: Speech Mechanism, Speech, and Speech Motor. The Speech Mechanism component includes questions aimed at assessing the symmetry, range of motion, and strength of the articulatory organs involved in speech production, with items presented separately for rest and activity. The Speech component evaluates respiration, phonation, resonance, articulation, and prosody through tasks such as maximum phonation time, paragraph reading, and picture description. In the Speech Motor component, the system measures the frequency of alternating motion rates and sequential motion rates. Conclusions: The DAS developed in this study is expected to be highly beneficial in clinical practice due to its ability to visualize and automatically calculate results, enabling assessments to be conducted via PC or tablet. Future research will focus on standardizing the DAS through large-scale data collection.

Black-Grass Monitoring Using Hyperspectral Image Data Is Limited by Between-Site Variability

Many important ecological processes play out over large geographic ranges, and accurate large-scale monitoring of populations is a requirement for their effective management. Of particular interest are agricultural weeds, which cause widespread economic and ecological damage. However, the scale of weed population data collection is limited by an inevitable trade-off between quantity and quality. Remote sensing offers a promising route to the large-scale collection of population state data. However, a key challenge is to collect high enough resolution data and account for between-site variability in environmental (i.e., radiometric) conditions that may make prediction of population states in new data challenging. Here, we use a multi-site hyperspectral image dataset in conjunction with ensemble learning techniques in an attempt to predict densities of an arable weed (Alopecurus myosuroides, Huds) across an agricultural landscape. We demonstrate reasonable predictive performance (using the geometric mean score-GMS) when classifiers are used to predict new data from the same site (GMS = 0.74-low density, GMS = 0.74-medium density, GMS = 0.7-High density). However, even using flexible ensemble techniques to account for variability in spectral data, we show that out-of-field predictive performance is poor (GMS = 0.06-low density, GMS = 0.13-medium density, GMS = 0.08-High density). This study highlights the difficulties in identifying weeds in situ, even using high quality image data from remote sensing.

The Addition and Expansion of Demographic Characteristics in the NCVS

One of the advantages of the National Crime Victimization Survey (NCVS) is that it is a large-scale data collection that allows data to be analyzed on various subpopulations. Since the survey’s inception, there have been changes to the population, law, and areas of public interest. In July of 2016, questions were added to the NCVS on citizenship, veteran status, sexual orientation, and gender identity. In addition, changes were made to existing questions on disability status and household income. This allowed for analysis of criminal victimization against more demographic groups. This article discusses these changes and provides estimates of violent victimization by these demographic characteristics.

Advantages of AI-Processed Crowdsourcing Method in Map Data Update Mechanism of Navigation System of Autonomous Driving

This research delves into the advantages of the AI-processed crowdsourcing method in the map data update mechanism for the navigation system of autonomous driving. It explores how this innovative approach offers high real-time performance, enabling immediate capture of road changes. The method facilitates large-scale data collection from a wide range of sources, ensuring comprehensive coverage. Cost-effectiveness is achieved by leveraging existing user infrastructure and AI-driven data processing. Data accuracy and reliability are enhanced through crowdsourcing method and AI cleaning algorithms. Additionally, it examines real-world application cases, highlighting successful implementation and outcomes. Looking towards the future, potential developments and challenges are discussed, emphasizing the need for continuous improvement in areas like data security and standardization. The conclusion asserts that with strategic mitigation of challenges, this method holds great promise in advancing autonomous driving technology for safer and more efficient navigation.

Research and application of key technologies for large-scale time-series data collection and sharing in power grid WAMS

Research and application of key technologies for large-scale time-series data collection and sharing in power grid WAMS

Unsupervised prototype self-calibration based on hybrid attention contrastive learning for enhanced few-shot action recognition

The collection and annotation of large-scale video data pose significant challenges, prompting the exploration of few-shot models to recognize unseen actions with limited training samples. However, most existing models rely on complex temporal alignment strategies, neglecting the necessity of constructing accurate class prototypes in action recognition. To address this limitation, we propose an unsupervised prototype self-calibration based on hybrid attention contrastive learning (UPSHC), aiming to refine prototypes through various strategies. Our network comprises three innovative components: a hybrid attention network (HAN), a dual adaptive contrastive learning mechanism (DACL), and an unsupervised prototype self-calibration mechanism (UPSC). The HAN integrates self-attention and cross-attention mechanisms to highlight important spatiotemporal regions, reducing intra-class variations and enhancing inter-class variations. The DACL mechanism constructs prototype-centered and query-centered contrastive losses to deeply explore the similarities between prototypes and samples, improving the model’s sensitivity to subtle inter-class differences. Additionally, DACL employs adaptive margins to dynamically adjust the distances between positive and negative samples, mitigating the unreliability of fixed margins. The UPSC module utilizes unlabeled query samples in an unsupervised manner to optimize target prototypes without requiring additional training. Experimental results on three benchmark datasets demonstrate that UPSHC achieves state-of-the-art performance, highlighting the superiority of our prototype enhancement approach in few-shot action recognition.

A Framework for Middle Level Curriculum: A Literature Review to Support the Middle Level Education Research Special Interest Group Research Agenda

This research synthesis, highlighting the middle level curriculum, was created as part of a working group within the American Education Research Association (AERA) Middle level Education Research Special Interest Group (MLER SIG) to advance middle level education research. The literature review that informed this research synthesis included middle level-focused, peer-reviewed journal articles from 2016 to the present. Synthesis of the literature and iterative analysis led to organizing a middle level curriculum framework to inform middle level researchers, which included five focused areas for inquiry: (a) the curriculum and equity of experience and opportunity; (b) stakeholder power and the curriculum (development, implementation, and accountability); (c) goals and purposes for the curriculum; (d) teacher learning, roles, and enactment related to the curriculum; and (e) young adolescent well-being and experiences with the curriculum. Twenty-six research questions were developed to support new research in middle level curriculum; each question was cross-referenced with the five categories in the middle level curriculum framework to create a robust starting point for research questions. Recommendations for middle level research include (a) the need for more longitudinal research studies focusing on varying aspects of middle level education, (b) the need for more large-scale research studies examining the same context (e.g., school) or multiple contexts (e.g., school districts) over time, and (c) the need for the MLER SIG to initiate and support research studies addressing one or more aspects of this research agenda and include large-scale data collection and potentially external funding.

IMU Calibration Effect on Lower Limbs Kinematics Against Optical Motion Capture in Post-Stroke Gait

BackgroundStroke is the most common cause of disabilities worldwide. Rehabilitation is central to restore functions. Inertial measurement units (IMU) can be used to ease goal settings and monitor progression. Contrary to optical motion capture (OMC), IMU are less expensive, portable, and allow large scale data collections in ambulatory settings. Although Xsens MVN system validity has been demonstrated in healthy participants, its validity among post-stroke (PS) patients is yet to be proven. Research questionComputation methods being affected by the calibration type; the goal of this study is to compare lower limbs kinematics from Xsens system, after two calibrations against OMC in slow PS walkers exhibiting reduced ranges of movements. MethodsData was collected for six PS patients. They were equipped with 29 reflective markers and seven IMU. A minimum of two walks with a dynamic calibration and four walks with a static calibration were performed. All trials were accomplished at a self-selected walking speed and PS used their usual walking aids. ResultsFew interactions between the calibration type and side were found for the ankle abduction/adduction (A/A) bias, root mean square error (RMSE), and range of motion difference (ROMd) (p = 0.011, p = 0.048, p = 0.039). Few effects of the side on errors' values were found. We noticed some effects of the calibration type on errors' values, the dynamic calibration showing better results. In the sagittal plane, we reported RMSE values from 3.6 to 4.8°, 5.2 to 6.5°, and 5.0 to 5.9° for the hip, knee, and ankle dynamic calibration. SignificanceThe calibration type, reduced range of movement, and slow walking speed does not seem to impact Xsens' accuracy to a great extent. Nevertheless, dynamic calibration provides slightly better results. Considering the patient's walking ability, we recommend using this calibration.

Reflexive approaches to sampling, survey design and implementation: some practical examples from rural India

ABSTRACT Reflexivity in quantitative research is central to questioning the ways in which the data is designed, collected and interpreted. It requires the researcher to be reflexive about their knowledge, their positionality, and the bias they may bring at each stage of the research. Unfortunately, though, limited evidence exists on the use of reflexive approaches to adapt more ‘standardised’ approaches to quantitative research, such as sampling, design of survey instruments and the use of scales for measurement. Using the case of a large-scale data collection which took place in Uttar Pradesh, India, this paper demonstrates the challenges of sampling villages and selecting teachers and students as main sampling units. We further demonstrate the adaptation required with language and standard measure scales as well as how to consider enumerators as key stakeholders in the process of data collection. We argue that adaptions to standardised techniques are necessary to enhance the validity of quantitative research when working in diverse contexts.

Transformer-Based hyperspectral image analysis for phenotyping drought tolerance in blueberries

Drought-induced stress significantly impacted blueberry production due to the plants’ inefficient water regulation mechanisms to maintain yield and fruit quality under drought stress. Traditional methods of manual phenotyping for drought stress are not only time-consuming but also labor-intensive. To address the need for accurate and large-scale assessment of drought tolerance, we developed a high-throughput phenotyping (HTP) system to capture hyperspectral images of blueberry plants under drought conditions. A novel transformer-based model, LWC-former was introduced to predict leaf water content (LWC) utilizing spectral reflectance from hyperspectral images obtained from the developed HTP system. The LWC-former transformed the spectral reflectance into patch representations and embedded these patches into a lower dimensional to address multicollinearity issues. These patches were then passed to the transformer encoder to learn distributed features, followed by a regression head to predict LWC. To train the model, spectral reflectance data were extracted from hyperspectral images and pre-processed using log(1/R), mean scatter correction (MSC), and mean centering (MC). The results showed that our model achieved a coefficient of determination (R2) of 0.81 on the test dataset. The performance of the proposed model was also compared with TabTransformer, DeepRWC, multilayer perceptron (MLP), partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF), achieving R2 values of 0.65, 0.73, 0.71, 0.47, and 0.58, respectively. The results demonstrated that LWC-former outperformed other deep learning and statistical-based models. The high-throughput phenotyping system effectively facilitated large-scale data collection, while the LWC-former model addressed multicollinearity issues, significantly improving the prediction of LWC. These results demonstrate the potential of our approach for large-scale drought tolerance assessment in blueberries.

Analysis of Cross-Sectional Study in Adverse Childhood Experiences

Abstract: Adverse Childhood Experiences (ACEs) are a burgeoning issue that has gained increasing attention from scientists. The general traumatic effect of ACEs can be found all over the world. With the serious potential effect, public health scientists are now devoted to mitigating its adverse effects by uncovering its relationship with various health issues and trying to mitigate its negative effects. This article has discussed how scientists, use Cross-Sectional Study as an analysis approach. The Cross-Sectional Study allows public health scientists to reveal the relationship between ACEs and various health problems. The findings of the research have provided crucial information for public health strategy and promote the mitigation of ACEs negative effects within communities. This research uses a cross-sectional study to analyze how ACEs affect different populations, allowing for the collection of large-scale population data at a specific time and facilitating the examination of associations between different variables. This method is particularly well-suited for analyzing ACEs. By gathering and analyzing data on ACEs from a diverse population, the correlation between ACEs and health problems across different demographic groups can be uncovered. ACEs will cause significant mental health for people and remain even when they become adults. Thus, it is necessary for public health organizations to mitigate the effects of ACEs.

Classification of Ship Type from Combination of HMM–DNN–CNN Models Based on Ship Trajectory Features

This study proposes an enhanced ship-type classification model that employs a sequential processing methodology integrating hidden Markov model (HMM), deep neural network (DNN), and convolutional neural network (CNN) techniques. Four different ship types—fishing boat, passenger, container, and other ship—were classified using multiple ship trajectory features extracted from the automatic identification system (AIS) and small fishing vessel tracking system. For model optimization, both ship datasets were transformed into various formats corresponding to multiple models, incorporating data enhancement and augmentation approaches. Speed over ground, course over ground, rate of turn, rate of turn in speed, berth distance, latitude/longitude, and heading were used as input parameters. The HMM–DNN–CNN combination was obtained as the optimal model (average F-1 score: 97.54%), achieving individual classification performances of 99.03%, 97.46%, and 95.83% for fishing boats, passenger ships, and container ships, respectively. The proposed approach outperformed previous approaches in prediction accuracy, with further improvements anticipated when implemented on a large-scale real-time data collection system.

Multispectral Images for Drought Stress Evaluation of Arabica Coffee Genotypes Under Different Irrigation Regimes.

The advancement of digital agriculture combined with computational tools and Unmanned Aerial Vehicles (UAVs) has opened the way to large-scale data collection for the calculation of vegetation indices (VIs). These vegetation indexes (VIs) are useful for agricultural monitoring, as they highlight the inherent characteristics of vegetation and optimize the spatial and temporal evaluation of different crops. The experiment tested three coffee genotypes (Catuaí 62, E237 and Iapar 59) under five water regimes: (1) FI 100 (year-round irrigation with 100% replacement of evapotranspiration), (2) FI 50 (year-round irrigation with 50% evapotranspiration replacement), (3) WD 100 (no irrigation from June to September (dry season) and, thereafter, 100% evapotranspiration replacement), (4) WD 50 (no irrigation from June to September (water stress) and, thereafter, 50% evapotranspiration replacement) and (5) rainfed (no irrigation during the year). The irrigated treatments were watered with irrigation and precipitation. Most indices were highest in response to full irrigation (FI 100). The values of the NDVI ranged from 0.87 to 0.58 and the SAVI from 0.65 to 0.38, and the values of these indices were lowest for genotype E237 in the rainfed areas. The indices NDVI, OSAVI, MCARI, NDRE and GDVI were positively correlated very strongly with photosynthesis (A) and strongly with transpiration (E) of the coffee trees. On the other hand, temperature-based indices, such as canopy temperature and the TCARI index correlated negatively with A, E and stomatal conductance (gs). Under full irrigation, the tested genotypes did not differ between the years of evaluation. Overall, the index values of Iapar 59 exceeded those of the other genotypes. The use of VIs to evaluate coffee tree performance under different water managements proved efficient in discriminating the best genotypes and optimal water conditions for each genotype. Given the economic importance of coffee as a crop and its susceptibility to extreme events such as drought, this study provides insights that facilitate the optimization of productivity and resilience of plantations under variable climatic conditions.

Study of the application of UAV and LiDAR to topographic survey of mountainous forest areas – Case study of Win 3 Wind Power Plant Project, Huong Hoa District, Quang Tri Province, Vietnam

Abstract Unmanned Aerial Vehicle (UAV) technology in conjunction with aerial photography and aerial Light Detection and Ranging (LiDAR) was used for topographic surveying of the Win 3 Wind Power Project area, a complex mountainous terrain situated in the Huong Hoa district of Quang Tri province, Vietnam with the primary objective to produce a map with a scale of 1:1,000 with a contour interval of 1.0 m, accompanied by 3D point clouds, a Digital Elevation Model (DEM), and an orthomosaic. Employing the UAV LiDAR system of DJI Matrice 300 RTK, we conduct ed survey flights, achieving a high point density laser scanning of 229 points/m2 and capturing ultra-high-resolution imagery at 4.22 cm/pixel. The integration of aerial LiDAR and photographic data results in a rich and detailed information repository regarding the surveyed terrain demonstrating that UAV LiDAR technology is a robust tool for geographic data collection, particularly in challenging terrain conditions in Vietnam. We also evaluated the accuracy of the created topographic map, achieving results that surpass the technical requirements and quality standards of the project. This underscores the potential and effectiveness of UAV LiDAR technology in the field of large-scale geographic data collection and mapping in Vietnam.

Social media data for disaster risk management and research

The rise of social media has revolutionized disaster risk reduction (DRR) by offering real-time, large-scale data collection and fostering dynamic community engagement. This study explores the potential of social media analytics as a vital resource in understanding population responses during disasters. By harnessing social media data, researchers and policymakers can gain insights into public sentiment, immediate needs, and reactions to risk reduction measures. The study presents case studies on major disasters, including earthquakes and pandemics, demonstrating the efficacy and challenges of social media as a tool for DRR. Despite its advantages, such as immediacy and broad reach, the study addresses significant concerns like data accuracy, privacy, and misinformation. Drawing from the Horizon Europe initiatives, this research offers a comprehensive analysis of how social media can enhance disaster preparedness and response, while proposing future avenues for optimizing the integration of social media data in DRR strategies.

EXCAVATION AND EMPIRICAL STUDY OF RISK FACTORS ASSOCIATED WITH STROKE AND DEPRESSION

Stroke is an acute cerebrovascular disease characterized by a high incidence, disability rate, recurrence rate, and mortality rate. Depression, as one of the main types of mood disorders, can manifest as symptoms such as a loss of interest in daily activities, reduced energy, diminished self-esteem, insomnia, and decreased appetite. There exists a significant relationship between these two conditions in terms of their pathogenesis. To delve deeper into the factors associated with the occurrence of both diseases, particularly examining the influence of depression on stroke risk, the authors conducted a large-scale investigation and data collection in the population of Shanghai, obtained 5599 valid data points, including information on hypertension, abnormal blood lipid levels, diabetes, atrial fibrillation, body mass index (BMI), a history of stroke, family history of stroke, previous transient ischemic attacks, smoking history, and exercise habits. Ultimately, the authors employed the decision tree C4.5 algorithm to construct a risk analysis model for both stroke and depression, analyzing the risk factors contributing to their occurrence and inferring the correlation between depression and stroke. The results revealed a close association between depression and a history of stroke, BMI, gender, and physical activity. Additionally, in the nonelderly population, psychological factors such as anxiety, depression, mood disorders, and stress were found to be closely linked to stroke onset. In many instances, regular exercise may mitigate the adverse effects of depression on stroke risk.

Collecting regional data for public health focusing on diet and sustainability measures using myfood24

There is no regular, routine measurement of food and nutrient intake undertaken regionally in the UK. This makes evaluation of local public health interventions challenging. Consumers want to be more sustainable in what they are eating. Social media penetration in the UK is 90%(1), with ∼47m people using Facebook, a potential route to study recruitment. Nutritional analysis software with accurate underpinning food composition and sustainability data could enable quick and easy large scale data collection. This study aimed to collect diet and sustainability measures using online tools and to test the feasibility of using social media to obtain a large, regionally focused sample in a short time.We undertook a rapid, time limited, regional (Yorkshire and Humber) food and nutrient tracking survey ‘One day: Diet in Yorkshire and Humber’ for use with policy makers(2). A Facebook (FB) boost approach was used to recruit a regional sample of adults. Participants completed a brief online demographic survey and used myfood24® to give a detailed measure of their food intake for the previous day. myfood24® generic and branded databases were used(3) which include sustainability metrics.The FB posts were boosted for 21 days and reached 76.9k individuals. Just under £1,000 was spent on 3 FB boosts and 1,428 participants completed the questionnaire. 673 participants also completed the one-day diet diary. Hull, Barnsley and Doncaster had the highest rates of overweight and obesity in the region. Overall nutrient values recorded were similar to national survey data for older women. Intakes of fibre (mean 18g, SD 11) and iron (mean 9.6mg, SD 5.0) were low. For the first time, measures of sustainability from the diet were calculated from myfood24®.The mean greenhouse gas emissions (GHGE) were 5.9(kg CO2 eq/day) (95% CI 5.6, 6.1); land use 9.0 (m2year/day) (95% CI 7.6, 10.3) and water use 582 (l/day) (95% CI 551, 611). Regression analysis showed that for each additional 100kcal consumed there was a significant increase in GHGE of 0.9 (kg CO2 eq/day) (95% CI 0.8, 1.1). Land and water use were also significantly increased with increasing energy intake. Adjusting for total energy, protein intake was positively associated with all 3 metrics. Carbohydrate was inversely associated with GHGE and land use. However, fat intake was not associated with any of the sustainability measures. Portions of fruit and vegetables consumed were positively associated with water use only.Diet is clearly linked to sustainability. The mean GHGE from diet is equivalent to driving an average car ∼22km/day. We successfully recruited a large sample in a short timeframe using FB. Participants were able to use online tools to report food intakes. This data is relevant to local and national policy makers to monitor and evaluate public health programmes.

MATHEMATICAL METHODS OF STATISTICS IN BIOLOGICAL RESEARCH

The article examines mathematical statistical methods used in biological research to process data and detect patterns within complex biological systems. Key methods such as correlation analysis, regression analysis, analysis of variance (ANOVA), cluster analysis, and hypothesis testing are discussed. Specific examples of their application in various biological fields, including genetics, ecology, and biomedical research, are provided. These methods play a critical role in the accurate analysis of large datasets, allowing researchers to evaluate the impact of different factors on biological processes, model interactions between variables, and make reliable predictions.The proper application of statistical methods significantly enhances the accuracy of scientific conclusions and reduces the likelihood of misinterpretation of data. This is especially relevant in modern biology, where large-scale data collection, such as genomic sequencing and environmental monitoring, requires sophisticated analytical tools. The review of recent publications emphasizes the importance of statistical literacy among researchers, as incorrect use of statistical tests can lead to invalid conclusions. This highlights the need for improved training in statistical methods to ensure the reproducibility and reliability of scientific findings.Additionally, the article discusses future research prospects, particularly the integration of traditional statistical methods with modern machine learning tools. This integration offers new opportunities for the automated processing and analysis of biological data, enabling the discovery of previously unknown relationships within biological systems. Machine learning techniques, when combined with statistical methods, hold great potential for advancing the fields of genomics, proteomics, and ecological modeling, providing a more in-depth understanding of biological phenomena.

Digital Phenotyping of Mental and Physical Conditions: Remote Monitoring of Patients Through RADAR-Base Platform.

The use of digital biomarkers through remote patient monitoring offers valuable and timely insights into a patient's condition, including aspects such as disease progression and treatment response. This serves as a complementary resource to traditional health care settings leveraging mobile technology to improve scale and lower latency, cost, and burden. Smartphones with embedded and connected sensors have immense potential for improving health care through various apps and mobile health (mHealth) platforms. This capability could enable the development of reliable digital biomarkers from long-term longitudinal data collected remotely from patients. We built an open-source platform, RADAR-base, to support large-scale data collection in remote monitoring studies. RADAR-base is a modern remote data collection platform built around Confluent's Apache Kafka to support scalability, extensibility, security, privacy, and quality of data. It provides support for study design and setup and active (eg, patient-reported outcome measures) and passive (eg, phone sensors, wearable devices, and Internet of Things) remote data collection capabilities with feature generation (eg, behavioral, environmental, and physiological markers). The back end enables secure data transmission and scalable solutions for data storage, management, and data access. The platform has been used to successfully collect longitudinal data for various cohorts in a number of disease areas including multiple sclerosis, depression, epilepsy, attention-deficit/hyperactivity disorder, Alzheimer disease, autism, and lung diseases. Digital biomarkers developed through collected data are providing useful insights into different diseases. RADAR-base offers a contemporary, open-source solution driven by the community for remotely monitoring, collecting data, and digitally characterizing both physical and mental health conditions. Clinicians have the ability to enhance their insight through the use of digital biomarkers, enabling improved prevention, personalization, and early intervention in the context of disease management.