A team at Korea University integrates Fitbit-derived activity and heart-rate metrics with nightly app entries using cosinor-based circadian features to train random forest and XGBoost classifiers, distinguishing moderate and severe RLS symptom groups with AUCs up to 0.86.

Key points

• Integration of 85 circadian-based features from Fitbit Inspire wearables and the SOMDAY smartphone app
• Random Forest model achieved AUC 0.86 for moderate RLS prediction; XGBoost reached AUC 0.70 for severe RLS prediction
• SHAP analysis highlighted M10 step counts, relative amplitude, and stress level as primary predictive features

Why it matters: Objective digital phenotyping and ML screening could revolutionize early detection and personalized management of RLS, reducing diagnostic delays due to subjective reporting.

Research and Markets hosts the INNOCOS Longevity Summit Geneva, a premier three-day forum at the InterContinental Hotel Geneva, convening scientists, brand innovators, and investors to examine emerging longevity science applications in beauty and wellness. Attendees engage in high-impact sessions on AI-driven diagnostics, sustainable formulations, and commercialization strategies while networking with industry leaders.

Key points

• AI-driven diagnostics sessions explore machine learning for biomarker analysis in skin and aging research.
• Workshops on sustainable formulation technologies highlight eco-friendly ingredients and biodegradable delivery systems for anti-aging products.
• Pitch & Connect matchmaking events facilitate funding partnerships between longevity startups and industry investors.

A team at K. R. Mangalam University applies a deep neural network coupled with Bayesian hyperparameter tuning and Multi-Objective Particle Swarm Optimization to develop sustainable concrete mixes that achieve high compressive strength, cut costs, and reduce cement content by up to 25%.

Key points

• Developed a DNN surrogate (cvR²=0.936, RMSE=5.71 MPa) for strength prediction.
• Employed MOPSO to balance compressive strength, cost, and cement usage under practical constraints.
• Achieved mixes exceeding 50 MPa strength with up to 25% cement reduction and 15% cost savings.

Why it matters: This AI-driven approach streamlines sustainable concrete design, reducing environmental impact while maintaining structural performance.

Advanced Bionutritionals has formulated Advanced Telomere Support, a research-informed supplement designed to maintain telomere integrity and counteract cellular aging. It combines astragalus root, N-acetyl-L-cysteine, broccoli seed extract, and adaptogenic botanicals to enhance antioxidant defenses, mitigate telomere shortening, and support cognitive and immune function. Taken daily, this product aims to reinforce cellular resilience, promote energy, and foster long-term health in adults concerned about longevity and age-related cellular decline.

Key points

• Incorporates astragaloside-rich astragalus root extract and sulforaphane from broccoli seed extract to target telomere preservation.
• Uses N-acetyl-L-cysteine precursor to boost glutathione synthesis and mitigate oxidative stress.
• Delivered orally as two capsules daily in a GMP-certified facility, aiming to support cellular resilience and cognitive function.

Why it matters: By targeting telomere integrity through a multimodal botanical and antioxidant approach, this supplement offers a promising strategy to reinforce cellular resilience and support long-term healthspan.

Researchers from leading neuroscience institutions develop AI-powered BCIs, parallel signal-decoding algorithms, and targeted neuroplastic training to overcome the brain’s 10 bits-per-second processing bottleneck, enhancing cognitive speed, focus, and memory capacity through a combination of technical innovation and mental exercises.

Key points

• Identification of a conscious-processing limit at ~10 bits/sec despite ~1 billion bits/sec sensory input.
• Deployment of AI-driven BCIs with parallel neural-signal decoding algorithms to augment cognitive throughput.
• Combination of neuroplasticity exercises and future genetic-editing prospects (e.g., CRISPR) for long-term enhancement.

Why it matters: Overcoming the brain’s processing bottleneck could revolutionize cognitive therapies and accelerate advanced neural interfaces for clinical and consumer applications.

A WorkspaceTool blog post compares Artificial Intelligence and Machine Learning by defining each term, illustrating their relationship, and detailing how data-centric algorithms enable AI systems to learn and adapt in applications such as autonomous vehicles and virtual assistants.

Key points

• AI encompasses techniques for mimicking human cognition using rule-based systems and knowledge representation for tasks like natural language understanding and autonomous control.
• Machine Learning employs algorithms—such as regression, decision trees, and clustering—to learn from data, optimize model parameters, and improve predictive accuracy without explicit programming.
• Deep Learning leverages multi-layer neural networks and GPU-accelerated computing to automatically extract features and achieve high performance in complex tasks like image and speech recognition.

Why it matters: Clarifying the distinction between AI and ML lays the foundation for effective deployment of intelligent systems and data-driven solutions across industries.