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Mass-gathering medical planning is hampered by inconsistent reporting: patient rates and hospital impacts vary wildly between studies, making direct comparison nearly impossible.
Soft, water-rich nanocomposite hydrogels are becoming stretchy skin-worn sensors that track vital signs, glucose and wound healing, but durability, comfort and regulation still block adoption.
Computer-optimized muscle-and-scaffold designs made lab-grown muscle robots move up to eleven times further, enabling jumping, swimming, walking and gripping.
A free, open-source circuit board combines industrial 24 V water-quality sensors with motor control for marine robots, hitting 99.97% data reliability over 240 hours of testing.
A hybrid CNN + Bi-LSTM + attention model with transfer learning detects IoT cyberattacks across different networks with 96-98% accuracy, beating existing detectors.
Machine learning ensembles and neural networks beat traditional rule-based credit scoring and fraud detection on precision, recall and AUC, while staying explainable.
Adding federated trust learning, quantum-resistant encryption, and reusable trust tokens to a vehicle authentication framework cut cross-domain authentication delay by 45%.
A federated CNN-Transformer model detects attacks on connected medical devices with ~99% accuracy across three datasets without pooling hospitals' raw data centrally.
Deep coal seams in China's southern Qinshui Basin could store 575.5 million tonnes of CO2, best at 800–1100 m depth where CO2 turns supercritical.
Chasing models that work everywhere can block machine learning that would actually help patients in a specific hospital — usefulness at the bedside should come first.
AI education research is split across disconnected fields, so this review of 32 studies proposes a 'Hybrid Intelligence' framework uniting learners, teachers and machines.
Saltier brine halves CO2's diffusion speed while heating it to 353 K nearly triples it, with pressure barely mattering below 30 MPa.
Turning industrial wastes like slags and ashes into carbonate-cemented construction aggregates could lock away meaningful amounts of CO2 while making a saleable product.
Turning CO2 into solid carbonate rock is energetically favorable and could both store carbon permanently and make useful materials — if the chemistry can be tuned.
Smartwatches and machine learning can track spa patients' vital signs in real time, letting thermal-water therapies be personalized and measurably therapeutic rather than merely relaxing.
Digital twins — live virtual replicas of individual patients — could enable precise diagnosis and personalized treatment, but data collection and simulation hurdles remain.
Underground rock formations can store natural gas, hydrogen and buried CO2, but hydrogen's light molecules make it leak and mix more, complicating storage.
Review of CO2 storage options argues mineral carbonation with industrial waste and microbubble injection offer the cheapest, leak-free routes, and proposes injecting both into underground cavities.
Indonesia can only hit net-zero by 2060 while still using fossil fuels if it builds carbon capture infrastructure, especially CO2-based oil and gas recovery.
Sensor-fed "digital twins" of patients — live virtual models built from wearables, IoT devices and AI — could reshape care in smart homes and hospitals, but remain largely unproven.
Virtual patient "digital twins" fed by real-time data could personalize treatment, flag health risks early, streamline hospital operations, and let clinicians rehearse procedures safely.
Digital twins — live virtual replicas of patients — could transform diagnosis and treatment, but biological variability, data gaps, and ethics still block real clinical use.
Personalized and adaptive learning tech can harm students if applied blindly; educators must balance data-driven tailoring with real human diversity and support needs.
Wearable sensors can feed live biometric data into "human digital twins" — virtual patient copies used to predict disease, simulate trials, and monitor treatment remotely.
Processing health sensor data on the device and sending only meaningful events, not raw streams, cuts energy and bandwidth by orders of magnitude in simulated patient monitoring.
Older Chinese adults adopt smart wearable health devices mainly when personal and social moral norms — rooted in health values and awareness of consequences — push them to.
A microgrid controller that factors in battery wear, wasted solar power and indoor comfort cuts renewable curtailment while holding batteries to about one cycle per day.
An AI-managed smart grid simulated for a 1,500-bed Malaysian hospital cut HVAC use 11.6%, lifted energy efficiency 25%, and met 86% of demand with rooftop solar.
Resampling credit card fraud data before the train/test split inflates accuracy above 99%, a leak that collapses to 1.81% precision when done correctly; deep reinforcement learning fared worse at 34.8% accuracy.
Pairing a Bi-LSTM forecaster with a scheduling optimizer lets a solar-plus-battery smart home cut its daily electricity bill under time-of-use pricing.
Pairing a new energy-management rulebook with particle swarm optimization finds the cheapest combination of solar panels and battery storage for a grid-connected microgrid.
A central district controller schedules shared solar, batteries and electric-vehicle storage across cooperative microgrids, cutting costly last-minute electricity purchases even when demand and generation are uncertain.
Simulated stress-testing of a needle-insertion surgical robot, paired with a digital twin, uncovered real software flaws and measurable gaps between virtual and physical behavior.
Hundreds of AI models exist to spot alcohol, opioid, and cannabis use disorders, but almost none have made it into routine clinical care.
A new Europe-wide research network aims to make genetic testing routine for prescribing heart drugs, using AI, shared guidelines and policy reform.
Reviewing only fraud-detection studies that keep real-world class imbalance, researchers find overused benchmark datasets, tree-based models dominating, misleading metrics, and almost no usable explainability.
Combining 37 years of telescope data with Pioneer 11, Voyager and Cassini tracking yields updated orbits for Saturn's major moons and a revised Saturn gravity field.
India can cut emissions with carbon capture, but only if costs fall, technology scales up, and clear safety and storage regulations are written first.
Green finance is a central tool for cutting emissions, but it remains underexplored in mainstream economics and finance journals, this review argues.
Connecting solar, wind and batteries to digital grids opens serious hacking routes, and current defenses lack real-time detection, cross-domain data protection and harmonized policy.
A genetic-algorithm scheduler for grids with wind, solar and batteries cut operating cost 59%, emissions 7%, and expected unserved load 2.67%.
Soft robots still lag far behind living organisms in autonomy, self-repair and efficiency; copying biology's integrated sensing-actuation loops is the way to close the gap.
Simple, fast classical machine-learning models can power real-time digital twins of breast cancer for monitoring, diagnosis and prediction inside the metaverse.
A review arguing machine learning can ease overburdened health systems by speeding diagnosis, personalising treatment, cutting costs and spotting outbreaks early.
Machine-learning ensembles and neural networks beat traditional rule-based credit and fraud models on precision, recall and AUC, while staying explainable enough for real use.
A broad review of intrusion detection systems, mapping how they work today across cloud, IoT and industrial networks, and where AI and blockchain could take them next.
Brain-sensing and eye-tracking tools can track students' mental effort live, and this review argues universities should use them to redesign STEM teaching around cognitive load.
A review of explainable-AI intrusion detection for IoT finds the field now chases lightweight, transparent, privacy-preserving designs — but rarely validates its explanations or reproduces results.
Software-only security testing misses how car cyberattacks physically behave; hardware- and vehicle-in-the-loop rigs plus AI detection are needed to realistically test and defend connected vehicles.
Entanglement that builds up naturally during quantum evolution shields many-body dynamics from local noise, offering free error protection with no extra qubits or control.
Humans emitted about 11.2 billion tonnes of carbon a year in the 2010s; roughly half was absorbed by land and oceans, the rest stayed in the air.
A new method lets federated IoT intrusion detectors erase a device's data influence almost as well as full retraining, but far faster.
A drone-assisted graph learning system spots coordinated DDoS attacks across EV charging networks with 97.7% accuracy, beating localized traffic-only detectors.
A hybrid Transformer-CNN trained with federated learning identifies users from mouse movements with 99.51% accuracy while keeping raw behavioral data on their own devices.
Adding tiny amounts of a CO2-attracting porous powder to polyurethane films more than doubled CO2 flow and boosted CO2-over-nitrogen selectivity by up to 454%.
Across all eight WHO global health emergencies, only COVID-19 strained every part of health systems, while staffing shortfalls and testing bottlenecks appeared in every single one.
A full hardware blueprint for a fault-tolerant photonic quantum computer built from quantum-dot photon sources, with error-correction cycles running in microseconds.
Smart-home health monitoring works but is concentrated in wealthy European and Asian regions, with most systems still far from truly intelligent, adaptive care.
Combining ant-colony feature selection with a CNN-BiLSTM attention network detects cyberattacks on smart consumer-electronics networks more accurately than existing intrusion detection methods.
Doctors should earn continuing-education credit for demonstrated clinical reasoning in AI chat sessions, not for hours sat in lectures, under a proposed scoring framework.
A simple 6-feature ridge regression trained on VQE-minus-Hartree-Fock energy gaps cuts quantum chemistry simulation errors by 3x to 640x, with no extra quantum computation.
A robot dog with a flexible, torque-amplifying spine timed to its legs runs faster and more efficiently than rigid-spine and rival flexible-spine designs.
Macrophages loaded with magnetic iron-platinum microrollers can be steered into solid tumors, where they trigger iron-driven cancer cell death and rally the immune system.
Wearables plus AI can flag disease early, but reported 85%+ accuracies come from small, controlled studies that rarely prove real-world clinical value.
Patients in a tirzepatide weight-loss app who actively engaged — via coaching, chatbot, or self-tracking — lost about 4.6% more body weight at 6 months than disengaged users.
Solar panels paired with batteries, hydrogen storage and blockchain trading cut household grid electricity use by 45–50% and CO2 by up to 70% in simulations.
Using a handheld GreenSeeker sensor to time and size nitrogen doses in mustard raised seed yield 22.75% while using 18.7% less fertilizer than standard practice.
AI for diagnosing Alzheimer's has moved from hand-engineered machine learning to deep learning and now large language models that read speech and clinical notes.
A new 33-item survey reliably measures six distinct dimensions of trainee teachers' digital teaching competence, validated across nearly 1,500 Turkish students.
AI intrusion detection for industrial IoT scores well on benchmarks but still fails on generalization, explainability, adversarial attacks, and real-world edge deployment.
Adding the conductive polymer polyaniline to epoxy "2Pack" paint is tested as a way to make steel coatings better at resisting corrosion.
Slow-release fertilizer coatings can cut nutrient waste and boost yields, but plastic coatings shed microplastics and release timing still fails under real field conditions.
Metal rods used to fix broken thigh bones often fit older patients poorly, and new electricity-generating and bioactive coatings could make them fit better and heal bone faster.
AI can inspect poultry carcasses and predict quality well in lab studies, but weak validation and plant-to-plant variation keep it from reliable commercial use.
Pre-training intrusion detectors on big computer-network attack datasets, then fine-tuning on small IoT ones, beats training from scratch and compresses to run on edge devices.
A unified deep learning system, X-THREAT, spots rare and never-before-seen cyberattacks with 93-96% accuracy across three benchmarks while explaining its decisions.
HyperIDS combines hypergraph neural networks, quantum-inspired feature selection and a transformer ensemble to detect IoT cyberattacks with about 98.9% accuracy.
AI and remote monitoring can cut orthodontic appointments and sharpen diagnosis, but almost all 23 studies reviewed came from urban clinics, not underserved areas.
A simple rules-based triage chatbot beat AI-powered versions on accuracy (71.8% vs 30.8-41.0%), yet testers preferred the smoother-talking but less accurate AI ones.
Combining supervised and unsupervised detectors in a weighted ensemble, plus a hash-chained audit log, flags network intrusions with 98.85% accuracy and tamper-evident records.
Carbon capture technology for heavy industry is technically feasible but stalled by high energy costs, weak incentives, thin regulation, and public distrust — not just engineering limits.
AI can automatically measure coronary calcium on ordinary chest CT scans, and those scores reliably predict heart attacks and death.
AI can help spot risky pregnancies and newborn illness earlier in poor countries, but only alongside real investment in data, infrastructure and regulation.
Clinicians can now build medical and dental imaging AI without coding, and 138 studies show it works reasonably well — but external validation is often missing.
A federated learning intrusion detector spots attacks on car networks with over 99% F1 score using just 1.11 MB of memory.
A recurrent neural network whose settings are tuned by a nature-inspired "fox" search algorithm detects network intrusions 4-15% more accurately than four competing deep learning models.
Blended courses match or beat face-to-face and online options on student success and dropout rates, and students judge course quality by consistent rules regardless of format.
Better-engineered reactor hardware — flow cells, gas-diffusion cathodes, packed-bed electrodes, built-in separation — is the main lever for making microbial electrosynthesis of fuels from CO2 practical.
A combined deep-learning and blockchain system detects attacks in cloud-connected sensor networks and logs them tamper-evidently, beating LSTM and CNN-RNN baselines on two datasets.
Fusing near-daily 3 m satellite images with sparse 0.05 m drone photos produced daily 0.5 m crop maps, with the ESTARFM algorithm plus CACAO smoothing performing best.
No single sensor or algorithm can estimate orchard yield alone — ground cameras count fruit best, while drones and spectral sensors map yield across regions.
Drone-based crop yield prediction works, but its accuracy is limited less by fancy models than by mismatched, poor-quality ground-truth yield data and untested transfer across years and regions.
Hospitals can share trained AI models instead of patient data, letting them build digital twins for clinical decisions without exposing anyone's records.
Combining boosted decision trees and bagged random forests detects IoT botnet traffic more sensitively, and SHAP explains which network features drove each decision.
FedShield-IDS trains a CNN-LSTM intrusion detector across smart-home devices without sharing raw traffic, and uses SHAP to explain every alert.
Combining self-supervised learning, prototypical few-shot networks and continual learning detects new botnet attacks from very few samples, hitting 98.50% accuracy on CIC-IoT-2023.
Major league pitchers with lower, sidearm arm angles lost fewer days to shoulder and elbow injuries and performed at least as well as overhand pitchers.
Water lines in 94% of surveyed Israeli dental clinics failed at least one quality test, with Legionella above threshold in 74% of samples.
A new controller that models a spacecraft's flexible panels as a continuous wave equation damps their vibrations faster and with less overshoot than standard PD control.
Selam, the odd contact-binary moon of asteroid Dinkinesh, likely formed when several similar-sized moonlets gently collided and merged in orbit.
A neural-network controller keeps a spacecraft towing a flexible truss inside preset safety bounds, tracking trajectories to about 5 mm in hardware tests.
Stacked MXene nanosheets form uniform sub-nanometer channels that let hydrogen through 160 times faster than CO2, beating the best existing gas-separation membranes.
Drone LiDAR tracking of maize height over time predicts tasseling within about 2.6 days, replacing subjective manual field scouting.
A network of field sensors feeding live soil and weather data into predictive algorithms helped farmers raise crop yields and cut wasted water and fertiliser.
Drones, sensors, IoT and machine learning can raise crop yields while cutting waste, but cost, data management and slow farmer adoption still hold precision agriculture back.
Upgrading tram and trolleybus DC substations in stages to host EV chargers, batteries and solar can pay back 10-20% annually while improving power quality.
A solar-powered fish farm monitor uses cameras and deep learning to track fish behaviour, health, water quality and feeding without grid electricity.
A machine-learning charging controller for home solar EV setups raised on-site solar use by about 22% while keeping batteries out of aging-prone hot, high-charge conditions.
AI can spot neglected tropical diseases in images, but black-box models block clinical trust — this review proposes a framework making such systems explainable for low-resource clinics.
A survey of how AI is already used in medicine — mostly cancer, neurology and cardiology — and what still blocks its use in real clinics.
A review argues AI can outperform clinicians in tasks like diagnosis and dosing, but ethical, legal and expertise gaps still block safe adoption.
Storing bank transactions in a graph database and feeding those relationship features to machine learning models improves detection of fraudulent transactions.
A new intrusion detection system combines three machine learning models with an attention layer, SHAP explanations, and tamper-proof hashed logs to make attack alerts auditable.
A review of intrusion detection systems argues signature-based tools miss novel attacks while anomaly-based ones trigger false alarms, and evaluation datasets are outdated.
A deep neural network beats classical machine learning at spotting network and host intrusions across six benchmark attack datasets, and the authors propose a scalable real-time monitoring framework.
A global study estimates deaths and disability from road injuries in every country from 1990 to 2023, giving comparable numbers to guide road safety policy.
AI tutors can boost grades while quietly eroding students' ability to judge and manage their own learning, so universities need a design framework that trains metacognition, minimises data collection and keeps students in control.
South African men want HIV services that are close by, affordable, private and non-judgemental — 10 priorities identified from interviews in Gauteng.
Seeing stuffed kangaroos and koalas versus kiwis changed how New Zealanders heard vowels, showing that irrelevant regional cues shift speech perception.
The authors coin "STUFF" (Stand Up For Fitness) — standing at least five minutes every half hour — as a catchy brand to make breaking up sitting spread.
Filtering noise from several qubit copies before error-correcting nearly doubles the surface code's error threshold and cuts logical errors 46-fold.
Coupling four fixed-frequency transmons to one shared coupler yields CNOT gates above 98% fidelity in all six directions and maps directly onto surface-code plaquettes.
Quantum computers with 50-100 noisy qubits will soon beat classical machines at some tasks, but they won't transform computing until gates get far more accurate.
Solar panels entering a spacecraft's partial shadow can heat unevenly enough to trigger self-sustaining flutter vibrations, which this nonlinear model predicts and maps for the Hubble Telescope.
Solar gravity clears out highly tilted and distant prograde moons of the giant planets, explaining why surviving irregular satellites cluster where they do.
ESA's Gaia satellite, launched in 2013 and orbiting 1.5 million km from Earth, is mapping the positions, motions and properties of over a billion stars.
A survey of machine learning in farming finds it already predicts yields, spots diseases and weeds, and monitors livestock, soil and water from sensor data.
Machine learning now underpins smart farming — predicting soil properties, crop yields, diseases, weeds, livestock health and automating irrigation and harvesting.
Combining farm sensor data with big-data and AI methods can make crop yield forecasts far more useful than traditional statistical models alone.
China's 3119 petrochemical plants emitted about 814 million tonnes of CO2 in 2021, with a few processes and chain stages driving most of it.
Europe's 2050 hydrogen use could range from near zero to 3,200 TWh/year, driven mostly by electrolyzer costs and carbon budget strictness, not transport demand.
Scheduling a solar-wind-battery-fuel-cell microgrid with the NSGA-III algorithm cut operating costs to 131.73 cents while flattening demand peaks better than four rival methods.
Combining a grey-wolf optimizer for siting solar/wind units with a neural-network load forecaster cut power losses 61.7% and saved $1,741 a day on a test grid.
Renewables plus energy efficiency could meet two-thirds of global energy demand by 2050 and deliver most of the emissions cuts needed to stay under 2 °C.
Swallowable capsule robots can now steer magnetically, sample tissue, and deliver drugs inside the gut, but weak actuation, power, and navigation still block clinical use.
Thin, asymmetric hydrogel fibers respond to stimuli faster than bulk gels, and their internal architecture is what turns swelling into useful directional motion for soft robots.
Water-pumped hydrogel muscles make transparent soft robots that swim fast, kick balls, and catch a live fish while staying nearly invisible to eyes and sonar.
Deep learning can turn messy biomedical data into better health predictions, but its "black box" nature must be fixed before clinicians will trust it.
Hospitals can train shared medical AI without sharing patient data — federated learning moves the model to the data, not the data to the model.
A review of a decade of studies finds only six rigorous papers on machine learning for healthcare system management, mostly covering records, chatbots and disease prediction.
Fintech's new lending, advisory and crypto platforms create risks traditional banking models can't measure, so AI and statistical learning must be built into financial risk management.
Machine learning can spot fraudulent financial transactions, but detection rates vary widely by method and by which data features are used.
A learning-as-it-goes reinforcement learning agent starts out worse than statistical anomaly detection at catching card fraud but keeps improving as fraud tactics change.
A survey mapping how machine learning and deep learning are used to detect network intrusions, plus the datasets, limits, and open problems in the field.
Turning just five consecutive network packets into an RGB image lets a lightweight intrusion detector spot attacks early with 99% accuracy.
A lightweight LSTM-based system can spot attacks on a car's internal CAN network in near real time without the overhead of encryption.
Six major self-regulated learning theories largely agree and fit together, but which one works best depends on students' age and education level.
A review of 63 studies finds AI-driven adaptive e-learning personalizes learning paths and boosts engagement and test scores, though privacy and complexity remain obstacles.
A review of 147 studies finds AI adaptive learning systems are mostly small-scale prototypes that rarely tackle students' real, specific learning problems.