Articles tagged with Big Data
A new study found that children who move multiple times between ages 10 and 15 are 41% more likely to be diagnosed with depression than those who don't move. This effect is stronger than growing up in an income-deprived neighborhood, suggesting a settled home environment may protect against future mental health issues.
Researchers developed a strategy to identify new antimicrobial drugs with therapeutic promise from bacterial datasets. PHAb10 and PHAb11 showed robust antibacterial activity against various bacterial species, including those resistant to traditional antibiotics.
The Kids First DRC has introduced an upgraded data portal to streamline big data search and analysis, improving collaborative pediatric research outcomes. The new portal integrates diverse datasets, including genomic information from the Children's Brain Tumor Network, to foster cross-disciplinary research.
Researchers Dr. Samson Zhou and Dr. David P. Woodruff aim to create secure algorithms for big data models using mathematical connections and cryptography ideas. They focus on streaming models, which process data in real-time, and address challenges such as randomness and different types of attacks.
A team of scientists outlines a bold vision for precision approaches to understanding, preventing, and treating diseases using revolutionary technologies and interdisciplinary collaborations. They emphasize the need for increased funding and research to address future challenges such as antimicrobial resistance and zoonotic illnesses.
Research found that during severe droughts, agricultural reservoirs in Korea's southern region experienced increased total organic carbon concentrations. The study suggests that these reservoirs may shift from carbon storage to carbon sources, emitting carbon into the atmosphere. This finding highlights the need for integrated environm...
A new transit station in Japan significantly reduced average healthcare expenditures per capita over four years, with savings of approximately $929.99. The study used a causal impact algorithm and time series data to analyze the medical expenditure data gathered from a suburban city on the West Japan Railway line.
A new COVID-related syndrome, MDA5-autoimmunity and Interstitial Pneumonitis Contemporaneous with COVID-19 (MIP-C), has been identified in patients with severe lung scarring and autoimmune diseases. The syndrome was discovered through a retrospective observational study by UC San Diego researchers and UK colleagues.
A new assessment tool developed by researchers at Northwestern University predicts suicidal thoughts and behaviors with an average accuracy of 92%. The tool uses a simple picture-ranking task along with contextual variables to identify individuals at risk of self-harm.
Researchers used big data to calculate per-country greenhouse gas emissions from aviation for 197 countries, revealing gaps in reporting requirements under the UNFCCC treaty. Countries like China and the US were found to emit the most aviation-related emissions.
A new study by the University of Exeter finds that China's growing use of emerging technologies in civilian and military domains has escalated its stakes as a threat and near-peer competitor to the US. Western states have responded with diplomatic efforts, bans, and restrictions to undermine China's power.
A new neural network-based system called BONES improves video streaming quality by up to 13% while reducing download size, according to NJIT researcher Jacob Chakareski. The system uses a mathematical function to optimize data transmission and can be applied to various platforms, including popular video conferencing services.
Researchers at PolyU are using GeoAI to monitor and analyze environmental changes, detect disaster-damaged buildings, and optimize traffic flow. The technology has the potential to support sustainable urban development and improve public health.
A new study highlights the need for clear guidelines for synthetic data to prevent adverse effects on individuals and society. The researchers emphasize the importance of transparency, accountability, and fairness in the generation and processing of synthetic data.
This study uses large-scale GPS data to assess pedestrian movement around Tokyo's stations and its relationship with urban characteristics such as density, diversity, and design. The findings highlight that TOD attributes significantly impact pedestrian count, distances, and durations, but the impact varies across different metrics.
Smarter eco-cities harness real-time data to address environmental challenges and implement innovative approaches for ecological conservation. However, high energy demands associated with AI and AIoT applications pose a challenge to attaining environmental objectives.
Researchers developed a novel AI technique to detect Medicare fraud in big data, using Random Undersampling and supervised feature selection. The method improved classification performance, especially when reducing features, and outperformed models with all available features.
Researchers propose integrating smart-city technologies to boost flood resilience in coastal cities. They argue that emerging technologies like IoT, 5G, and big data can enable real-time data collection, analysis, and decision-making, leading to more responsive and resilient urban environments.
A study found that artificial light is a top indicator of where birds will land, leading to ecological traps and collisions. Urban parks can provide decent stopover sites, but competition over limited resources poses a significant risk to migrating birds.
A recent study found that during stringent COVID-19 periods, online searches surged in the health and daily life category. As government policies relaxed, searches shifted towards duty-free and travel-related products. The research team applied PCA to Big Data of internet search activity volume data from NAVER DataLab platform.
A research team employed deep learning techniques to scrutinize dam operation patterns, achieving remarkable accuracy in forecasting dam water levels. The study demonstrates the potential of an artificial intelligence model trained on extensive big data to surpass conventional physical models.
A new database combines distribution and lifecycle datasets to examine the prevalence of different lifecycles globally. The study found that annuals are expected to benefit from climate change, but their increased presence could be devastating for ecosystems.
Researchers found that green spaces alleviate extreme heat's negative impacts on human health, while densely packed buildings increase mortality risk. Urban design strategies incorporating different types of greenery are recommended to mitigate heatwave-associated mortality.
A study by the Complexity Science Hub analyzed work discourse on Reddit between 2018 and 2021, finding that mental health concerns significantly contributed to the surge in quit rates. The study suggests that companies can improve working conditions by prioritizing employees' relational and self-fulfillment needs.
Researchers at the University of Texas at Austin developed an AI algorithm that accurately predicted 14 earthquakes within about 200 miles of their location and strength, with only one false warning. The system detected statistical bumps in real-time seismic data and paired them with previous earthquakes to make predictions.
Research highlights the challenges of rural hospital-based maternity unit closures and transportation barriers, leading to delays in care and adverse outcomes. The study suggests that telehealth services can provide a solution to address these disparities in maternal health outcomes.
A study analyzed millions of tweets by Republican and Democratic US politicians over a decade, finding that Republicans were more likely to share untrustworthy information. The researchers identified linguistic signals associated with low-quality information and suggested potential solutions for the public to recognize these signals
Wogrin aims to improve data aggregation and create more meaningful models with the same computing power, resulting in suboptimal investment decisions and costly restructuring of energy systems. Her research approach takes into account different supply situations, enabling compressed and differentiated model data.
A new series of research papers addresses safety concerns in the Internet of Things, including data analysis and protection. The collection proposes solutions to challenges such as retrieving video data with textual queries and regulating media data dissemination.
A new study reveals that using big data and machine learning can improve antimicrobial resistance surveillance in livestock production. The research found correlations between environmental variables, microbial communities, and antimicrobial resistance, suggesting multiple routes for improving surveillance.
The University of Missouri is using a $1 million grant to develop an Industry 4.0 lab, providing engineering students with hands-on learning experiences in the latest industrial revolution's technology-centered job market. The lab will integrate skills at a higher level and keep students at the state-of-the-art level for industry.
Scientists at Michigan State University used big data to study bird coexistence in the Albertine Rift ecosystem. They found that birds partition their habitat use along environmental gradients and adopt different strategies to survive, allowing them to coexist without driving each other to extinction.
A groundbreaking Oxford study reveals a significant genetic component to people's probability of participating in genetic studies. The research identified detectable 'footprints' in genetics data that can be exploited statistically to enhance research accuracy for both participants and non-participants alike.
A research team at the University of Tsukuba has developed a secure AI technology that allows for the shared analysis of personal and identifiable data from multiple organizations. This will enhance the accuracy of AI analysis, particularly in disease prediction and educational effectiveness.
A new approach to enhance artificial intelligence-powered computer vision technologies has been developed by UCLA researchers, adding physics-based awareness to data-driven techniques. This hybrid methodology aims to improve how AI-based machinery sense, interact, and respond to their environment in real time.
A new framework developed by Carnegie Mellon University researchers significantly reduces consumers' privacy risk while preserving advertisers' utility in mobile location data analysis. The framework uses machine learning to quantify personalized privacy risks and performs personalized data obfuscation.
A new study maps French Canadian populations using a unique dataset of over five million records spanning 400 years, revealing the complex relationship between human migration and genetic variation. The research shows that the genetic structure of French Canadians is encoded within its genealogy.
A new study assesses racial bias in type 2 diabetes risk prediction algorithms. It finds significant disparities between US-based risk scores for non-Hispanic Blacks and non-Hispanic Whites.
Researchers from Universitat Oberta de Catalunya studied sidewalk networks in Barcelona and found that even pedestrian-friendly cities like Barcelona struggle with mobility constraints. The study proposes a framework for assessing multi-factor walkability using percolation theory and insights into pedestrian behavior to improve sidewal...
Researchers found that people dislike fitness recommendations from apps using social media data, instead favoring personalized approaches with control options. Users prefer systems allowing them to choose between different recommendation methods, making them feel more in control.
Scientists developed a new clustering method to analyze similarities between chess openings, revealing ten distinct clusters that group similar strategies. The new classification complements the existing ECO Code and provides insights into player skill and opening complexity.
The UTSA ScooterLab will collect data on riders' mobility, context and environment to improve sustainable transportation solutions. The project aims to transform the way we think about micro-mobility.
A new computational framework, Non-MapReduce, uses random sampling to efficiently sort through Big Data files, reducing communication and memory costs. This approach enables faster processing times and energy efficiency in cloud computing.
Researchers at the University of Illinois Urbana-Champaign found that people's use of popular websites and social media platforms differs significantly based on their location and language. The study analyzed data from 124 countries and found that YouTube and Twitter are used in distinct ways across regions.
Researchers provide a comprehensive catalog of medical knowledge graphs, their creation, and usage. Medical knowledge graphs have wide-ranging utility in the medical field, including disease diagnosis and drug development.
A new study used satellite data and public registry information to track the changing identities of commercial fishing vessels, revealing that nearly 20% of high seas fishing is carried out by unregulated or unauthorized vessels. The study found hotspots of potential IUU fishing in the Southwest Atlantic Ocean and western Indian Ocean.
Researchers at Rensselaer Polytechnic Institute developed a model to predict cryptocurrency scams using Benford's Law and found that scam addresses deviated from the law. They also advocated for robust blockchain interoperability to provide stability in decentralized systems.
A research team developed a method to efficiently identify potentially dangerous intersections for child traffic accidents using a combination of empirical Bayesian estimation and geo-informatized data. The model proved effective in identifying seven or more high-risk spots, improving upon methods based solely on past accident data.
A study published in Diabetes Research and Clinical Practice found that women with diabetes mellitus are at a higher risk of venous thromboembolism (VTE) than men, particularly during perimenopause. The risk is 1.52 times higher for women with DM compared to those without DM.
Researchers from Complexity Science Hub and Medical University of Vienna found that high doses of cholesterol-lowering statins impair bone quality in mice, with a significant increase in osteoporosis risk. The study confirms previous findings on the correlation between statin use and osteoporosis diagnosis in humans.
The EU project CRAFT-OA aims to strengthen institutional publishing using Diamond Open Access model across Europe. The project will enable local platforms and service providers to expand their content services with the aim of networking them with other information systems in science.
A new space-time coding antenna developed at City University of Hong Kong enables manipulation of beam direction, frequency, and amplitude for improved user flexibility in 6G wireless communications. The antenna relies on software control and combines research advances in leaky-wave antennas and space-time coding techniques.
The Wuhan University research team has developed a powerful model called FingerDTA, which uses convolutional neural networks to predict drug-target binding affinity. This can help identify potential novel drug candidates and reduce costs and time in traditional drug discovery methods.
A new study proposes a scalable, bottom-up approach to developing energy-saving initiatives at the individual level by analyzing real-time energy usage data. The research team developed an energy calculator that can be scaled up to the building- and community-level, enabling more accurate occupant-level measurements.
A novel cloud-based framework is proposed to manage and analyze industrial IoT data in cloud environments, optimizing energy consumption through reinforcement learning. The system aims to provide an energy-efficient and secure environment for industries such as healthcare and education.
A new CNN framework, PE-Net, is proposed for predicting machine remaining useful life (RUL) accurately. The framework uses a novel architecture with small-sized one-dimensional convolution kernels and deep networks to learn features from input time series signals.
A perspective paper explores the role of clinician-data-scientists in healthcare, emphasizing their need for interdisciplinary knowledge and training. The researchers highlight the importance of integrating data science into conventional medical education to prepare clinicians for the digital health era.
The Earth System Grid Federation is upgrading its climate projection data system to improve access and curation, with the goal of enabling scientists to make the best guess about the future trajectory of our climate. The new system will provide faster download speeds and enable previously infeasible data analyses.
Researchers developed a mathematical model to analyze cognitive changes and impairment in the brain, applicable to multiple sclerosis and other neurodegenerative diseases. The model uses multilayer networks for comprehensive analysis of CT scans, X-rays, ultrasound, and magnetic resonance imaging data.
The Center for BrainHealth has launched three research projects to develop objective metrics of improved brain systems in response to interventions. These projects aim to determine the changes in the brain's physiology, structure, and function linked to gains in comprehensive psycho-social measurements over time.