Research applying artificial intelligence across science and engineering. Covers machine learning, sensing and quality control, immune and health prediction, and 5G networks.
A research team led by Prof. Jun Won Choi of Seoul National University College of Engineering independently developed SafeDrive, an end-to-end autonomous driving AI model. The work has been recognized for its impact on the field of autonomous driving technology.
A technology has been developed that allows artificial intelligence to inversely determine process conditions for quantum-dot light-emitting diode devices. The technology roughly doubled efficiency and extended operational lifetime more than 40-fold when applied to actual devices.
Researchers are exploring innovative technologies to improve disease diagnosis and treatment. AI models have shown promise in speeding up detection and treatment discovery for rare diseases. Meanwhile, electronic noses are being developed for non-invasive cancer screening.
A researcher at the University of Minnesota Medical School has been awarded a $3.7 million NIH grant to develop robust AI models for primary open-angle glaucoma prognosis. The project aims to improve patient outcomes by identifying patients at high risk of vision loss earlier and more accurately.
A new approach combines AI with high-resolution mass spectrometry and toxicology databases to predict biological effects of environmental chemicals. This framework helps researchers prioritize candidates for laboratory testing and health risk assessment.
This volume of SLAS Technology highlights novel laboratory technologies, open-source software, and disease-specific tools for advancing life sciences research and development. The journal emphasizes the importance of education, knowledge exchange, and global community building to drive innovation in biomedical research.
A new review of existing research found that AI-based nursing interventions can identify patients at greater risk of complications, reducing unplanned hospital visits and potentially lowering healthcare costs. However, there is a need for further research on the impact of AI on patients' emotional well-being.
The researchers developed an image-aided terminal guidance attitude control scheme to tackle airframe disturbances and EFP trajectory dispersion in multirotor strikes. The system realizes finite-time convergence of attitude errors and real-time compensation of wind gusts and model uncertainties, greatly improving aiming stability.
Northwestern University engineers created a drone called Phantom Twist that harnesses motion blur to blend into its surroundings. The drone spins up to 25 times per second, making it difficult for humans to see clearly, and can potentially monitor wildlife or inspect infrastructure with less disruption.
The European Law Institute has approved its first comprehensive framework for dealing with digital assets and personal digital content after death. The rules provide a dual legal framework recognizing the need for protection, privacy, and dignity in digital inheritance.
A recent study found that patients perceive medical professionals as more credible when AI agrees with their diagnosis. However, disagreement can increase perceptions of medical uncertainty and doctor laziness. The researchers suggest strategies to communicate AI disagreement effectively and reduce patient mistrust.
Researchers have developed NovoTags, synthetic fluorescent protein tags that can bind to bright fluorescent dyes with high specificity and affinity. These tags enable multicolor imaging of proteins inside cells, expanding the toolkit for advanced light microscopy techniques.
Researchers have developed a new version of the Daydreaming algorithm, which combines learning and cleaning to improve artificial memory systems' reliability even with biased data. The algorithm focuses on differences between pixels, allowing it to work effectively with strongly biased data, similar to real-world conditions.
Researchers explored how AI and metabolic modeling can inform effective biocontrol strategies to combat antimicrobial resistance in built environments. Microbial biocontrol using 'good' microbes has shown promise, but inconsistent outcomes are due to various factors, including genetic differences and environmental stressors.
Researchers developed DeepHHF, an AI model that identifies patients at high risk of heart failure up to five years in advance. The model analyzes standard ECG recordings and detects subtle abnormalities that are often imperceptible to the human eye.
A new study uses machine learning to predict chemical toxicity in rare and endangered species, reducing the need for direct biological testing. The model achieved strong performance predicting acute and chronic toxicity, with life stage being a key factor.
UCSF Health Converge accelerates development of AI tools for real-world care delivery by co-developing solutions with select companies. The program focuses on building patient-centered, clinically effective AI solutions that align with UCSF Health's standards.
A team of researchers from the University of Cambridge and UC Santa Barbara developed 'adversarial' mathematical systems to map out where AI prediction breaks down. They identified two main reasons why machine learning fails: algorithmic limitations and hidden patterns in complex systems.
A new study from UT San Antonio finds that emergency call takers' word choice can predict depression and anxiety symptoms, while positive expressions are often suppressed in high-stress professions. The research uses natural language processing to evaluate the psychological well-being of a critical but under-researched workforce.
Researchers are developing an AI-driven controller for hybrid microgrid systems that integrates multiple energy sources and storage systems. The system aims to increase efficiency of electrical grids used for data centers and other mission-critical loads.
AI model improves cardiac MRI interpretation, while immersive VR simulators aid poststroke rehabilitation. Meanwhile, menopause tracking apps offer empowerment but also pose risks due to inaccurate information and targeted marketing.
A new 3D computer model developed by the University of Surrey has shown how Pseudomonas aeruginosa grows and spreads its protective layer under constant fluid flow. The model's accuracy was validated through laboratory experiments, demonstrating potential for faster and smarter ways to understand bacterial behavior.
A new AI-based system called ShadowSense learns from real-world data to predict short-term changes in solar module power output. The system outperforms conventional methods with an accuracy rate of over 92%, providing valuable insights for electricity grid management.
A new article by Yong Zhao argues that education needs a new response to artificial intelligence and a new theory of how schools can change. He proposes the 'courageous minority' approach, which suggests that small groups of teachers, students, and community partners can create meaningful alternatives in their existing spaces.
A novel AI model called BINND has been developed to predict which DNA molecules bind to each other. The model achieved an accuracy of 83.5% in predicting DNA pairs that would bind, surpassing the state-of-the-art model by at least 10%. This improvement has significant utility for biomedical diagnostic tools and DNA computing applications.
The Data Sciences Institute at the University of Toronto has been awarded $1 million in Claude API credits to support AI-enabled research. Researchers will gain access to cutting-edge AI tools, enabling discovery, analysis, and innovation across disciplines.
A new study reveals that menopause symptoms such as cognitive impairment and emotional wellbeing are more commonly discussed on Reddit than in clinical records. The study highlights the importance of online forums in capturing stigmatized or poorly understood conditions like perimenopause and menopause.
A consortium of nearly 100 partners in Oregon will receive up to $160 million from the NSF to grow the state's semiconductor ecosystem. The initiative aims to accelerate innovation, advance energy grid security, and boost regional economies.
Researchers developed Buffer-and-Reinforce framework to preserve AI safety during personalized fine-tuning, maintaining high safety even in extreme settings. The framework achieved strong customized performance and state-of-the-art safety without additional safety data or increased computational cost.
A recent breakthrough allows independently trained AI models to trade capabilities and boost overall performance when merged into a single system. By modifying just one core layer, the 'SyMerge' framework enables mutually beneficial synergy between models, solving the long-standing problem of task interference.
Peter J. Denning suggests that Turing's stance on artificial general intelligence and the imitation game has led to the AI mess, with a fundamental flaw in understanding tacit knowledge and its representation problem. He argues that machines cannot grasp human emotions, intuitions, or cultural context, making it impossible to achieve h...
A global consortium has developed three open-source AI tools to synthesize Alzheimer's literature, surface hidden data insights, and provide peer review feedback. The tools are designed for researchers to harness vast amounts of information toward a shared goal.
The PRIME-6G project aims to bring next-generation 6G technologies into real industrial manufacturing environments. AURORA and SENTINEL use cases investigate deterministic 6G connectivity, AI-driven network management, and multi-sensor fusion for intelligent industrial systems.
Researchers developed an auditing technique to test generative AI models for malicious capabilities, without prompting them for illegal outputs. The new approach identifies model variations that can generate CSAM with 100% accuracy.
The WVU team developed an AI framework that allows satellites to not only detect wildfires but also coordinate with one another and adjust their observation schedules as fires spread. This enables firefighters to respond sooner, taking into account the rapid movement of wildfires, dense vegetation, and hills.
Researchers developed an AI model to optimize water usage in agriculture and semiconductor manufacturing. The model identifies cause-and-effect relationships between water availability, crop needs, and industrial expansion, generating recommendations for each state.
This study found that households in metropolitan areas with higher population density and income levels show stronger voluntary water conservation during drought. The AI-based scenario analysis showed that increased drought-related news coverage leads to significant water savings in these regions, but has a limited impact on rural areas.
Researchers at DGIST developed an artificial olfactory system that uses metal-organic frameworks to detect and analyze various odors. The system leverages machine learning and deep learning techniques to classify and interpret complex odor signals, enabling accurate disease diagnosis, environmental monitoring, and more.
Researchers developed a machine learning framework to account for water quality differences, enabling accurate MC-LR measurements without repeated calibration. The model achieved a Nash-Sutcliffe efficiency of 0.89 and improved analytical efficiency while reducing time, labor, and sensor consumption.
Researchers at Tokyo University of Science found that accounting for the time scale of a target system improves ESN hyperparameter settings, leading to better prediction accuracy. The study provides guidelines for designing optimal ESN settings based on the time scale.
A PolyU project has developed an intelligent portable traffic light system to address bottlenecks during roadworks on narrow streets in Hong Kong. The system uses advanced sensing technologies and adaptive algorithms to dynamically adjust signal timings, reducing traffic queues and preventing gridlock.
Deep learning models accelerate drug design, predict chemical interactions, and engineer stable candidates. AI-powered simulations optimize dosimetry, predicting biodistribution and generating patient-specific digital twins for individualized treatment planning.
A study from Technical University of Munich found that people perceive AI hiring decisions as unfair when the avatar resembles them in terms of gender or skin color. After receiving a rejection, trust in AI is shaken if the avatar's appearance differs from their own.
Nigel provides clinicians with fast, evidence-based answers, helping them make faster, more confident decisions. The platform reduces cognitive burden by enabling quick access to current evidence and reducing time spent on literature searches.
A new brain-like electronic device consumes very little energy and detects novelties almost instantly, with over 98% accuracy. The device requires roughly 10,000 times fewer computer operations than conventional AI approaches, paving the way for more energy-efficient AI systems.
The partnership aims to streamline prior authorizations, appeals, and financial assistance, addressing delays or denials in accessing prescribed therapies. By combining AGA's clinical leadership with Forus's medication access platform, the collaboration seeks to enhance patient advocacy and access to effective treatments.
Researchers at the University of Pennsylvania and Chinese University of Hong Kong created TD3B, an AI framework guiding peptide generation toward candidates predicted to have a desired effect. The tool predicts binding likelihood and determines activation or deactivation of associated cellular machinery.
Recent studies validate AI-based chatbots as effective alternatives to human fitness professionals for generating personalized training programs. However, limitations in nuance and continuous monitoring require hybrid models combining AI tools with human expertise.
Researchers developed a physics-informed scoring system to identify two-dimensional materials with unusual electronic properties. The approach captures signatures of flat-band behavior and trains a model to estimate scores directly from atomic structure, accelerating the search for promising materials.
A new study found that physicians tend to trust incorrect AI advice and have trouble learning from patient recovery data that contradicts it. The researchers suggest that developing strategies to increase human critical thinking and detection of AI errors is crucial for maximizing the benefits of human-AI collaboration in healthcare.
A team of researchers, led by Ying Zhang, has developed artificial intelligence-driven tools to identify and attack software vulnerabilities. By teaching AI to generate proof-of-concept exploits, developers can see exactly how attackers could exploit known flaws, motivating them to fix issues before malicious actors do.
Biomni is an AI-powered multi-skilled biomedical research agent that designs and develops complex research workflows. It provides full citations and tracking of its work, making science more rigorous and reproducible.
A Dartmouth study reveals that people's gaze patterns in new environments contain unique personality preferences. The researchers used eye-tracking data to model individual gaze patterns and create machine-learning models that could distinguish between participants based on their conceptual themes.
Researchers uncovered previously undetected slow slip events in Parkfield, California, and found that these silent fault movements systematically follow increased low-frequency earthquake activity. The discovery suggests that slow slip may play an important role in how stress evolves along active faults.
Physicists at UC Irvine have developed an AI system called Autonomous Model Builder that can autonomously design theoretical physics models, helping identify promising new explanations for the behavior of neutrinos. The system uses reinforcement learning and is designed to assist human physicists in narrowing down vast theory spaces.
A new report reveals that 42% of people in the UK deliberately limit their AI use, primarily driven by worries about data protection. Despite the benefits, many individuals are ambivalent towards AI due to growing security and privacy concerns.
The ORIGIN project aims to reduce development timelines for sustainable, fermentation-based ingredients from 5-7 years to 2-3 years. By combining AI, biotechnology, and fermentation, the project will address scientific and technological challenges to produce high-value natural ingredients.
Researchers from Penn, NYU, and the Linguistic Data Consortium create virtual patients with adjustable psychiatric symptoms to simulate real-world conversations. The STELLAR platform aims to augment clinician training practices with essential conversation scenarios.
Researchers developed an AI assistant called ChatHEA to guide the discovery of new catalysts for clean energy technologies. The team screened and evaluated 100 five-element high-entropy alloy catalysts, finding that FeCoCuPtIr showed excellent oxygen reduction activity and durability.
A new study explores an FMQA-based optimization framework for RNA design, revealing that encoding matters in achieving optimal results. The approach identifies high-quality RNA sequence candidates with relatively few evaluations, outperforming competing methods.