Show Notes 28 March 2025
Story 1: Ultra-Accurate Optical Atomic Clocks Could Enable True Self-Driving Cars
Source: TopSpeed.com Story by Craig Cole
Link: https://www.topspeed.com/ultra-accurate-optical-atomic-clocks-could-enable-true-self-driving-cars/
See Chalmers University of Technology news article here: https://www.chalmers.se/en/current/news/mc2-microcomb-chips-help-pave-the-way-for-thousand-times-more-accurate-gps-systems/
- One of the significant limitations of current self-driving car systems is their reliance on GPS, which can be unreliable or unavailable in certain settings (tunnels, dense urban areas, etc.).
- This article highlights advances with optical atomic clocks. In specific, reducing their size to a form factor that would be practical for including into a car’s electronic system. This, in turn, might allow self-driving vehicles to eventually maintain precise internal timing and navigation without having to depend solely on external satellite signals.
- Side note: An optical atomic clock is an ultra-precise timekeeping device that leverages transitions in atoms or ions occurring at optical frequencies (in the visible or near-visible parts of the electromagnetic spectrum) rather than at the microwave frequencies used in traditional atomic clocks.
- Researchers at Purdue University in Indiana, and Chalmers University of Technology in Sweden have developed a new type of optical atomic clock that is significantly smaller [than current optical atomic clock systems] and could make the Global Positioning System (GPS) much more accurate.
- Side note – size matters! Optical atomic clocks are typically housed in research laboratories and can vary in size, but they are generally quite large, comparable to the size of a standard desk or larger. Many setups require multiple optical tables, lasers, vacuum systems, and electronics, often taking up an entire room. So, before this development optical atomic clocks were far too large to play a role in car/truck, etc. navigation systems.
- The research team has made strides in miniaturizing optical atomic clocks by using on-chip microcombs. This innovation could make it possible to create size appropriate GPS systems for self-driving cars a thousand times more accurate than current navigation technology, improving navigation precision to within centimeters rather than meters.
- Side note – On-chip microcombs, also known as optical frequency combs, are advanced devices that produce a spectrum of evenly spaced light frequencies within a single chip. These frequencies resemble the teeth of a comb, hence the name “frequency comb.” They are created using microresonators—tiny ring-shaped structures integrated into a chip—that convert laser light into a series of equally spaced wavelengths through a phenomenon called nonlinear optics. These compact microcombs have a wide range of applications, from telecommunications and data transmission to advanced spectroscopy, metrology (high-precision time and frequency measurements), and even quantum computing. Their on-chip design allows them to be smaller, more energy-efficient, and more compatible with modern technology compared to traditional frequency combs.
- Here are the key points about how small-scale optical atomic clocks could be a gamechanger:
- For unprecedented precision optical atomic clocks use laser frequencies rather than microwaves, providing timing accuracy that far surpasses traditional atomic clocks. This precision is crucial since even tiny timing errors can impact sensor data fusion and real-time decision-making in self-driving cars.
- Optical atomic clocks offer enhanced Navigation and Sensor Fusion. Autonomous vehicles rely on a multitude of sensors (like lidar, cameras, and radar) that must be perfectly synchronized.
- The incredibly stable time signals from optical atomic clocks could allow vehicles to integrate sensor data with remarkable accuracy. This means better positioning and navigation, especially in environments where GPS signals are weak, obstructed, or vulnerable to interference.
- While the immediate focus of the research by Purdue University in Indiana, and Chalmers University of Technology in Sweden is on enhancing self-driving cars, the improvement in timing accuracy with optical atomic clocks has broader implications. Such clocks could impact telecommunications, fundamental physics research, and other navigation systems, paving the way for innovations across multiple sectors.
Story 2: This is the first quantum computer you can actually buy (and use, and power): Equal1’s Bell-1 uses a standard power socket
Source: TechRadar.com Story by Efosa Udinmwen
- This news is all about a potentially significant announcement from a company called Equal1.
- Equal1 is focused on developing silicon-based quantum computing technology. Their mission is to democratize quantum computing by making it scalable, cost-effective, and accessible. They leverage silicon spin qubits and have created UnityQ, the world’s first hybrid quantum-classical chip.
- Side note – Silicon spin qubits are a type of quantum bit (qubit) used in quantum computing. They leverage the spin of an electron or nucleus in silicon to represent quantum information. The spin, which can be thought of as a tiny magnetic moment, has two states—”up” and “down”—that correspond to the 0 and 1 states of a classical bit. However, because of quantum mechanics, these spins can also exist in superpositions of these states, enabling powerful quantum computations.
- Side note – Equal1 has hardware development laboratories in Fremont, California, and a silicon design center at NovaUCD in Dublin, Ireland.
- Equal1 recently introduced the Bell-1, the first [small scale] quantum computer designed for real-world deployment, integrating seamlessly into existing high-performance computing (HPC) environments without requiring specialized infrastructure or extensive cooling.
- Operating on just 1600W it installs easily and stands as one of the best workstations, bringing quantum computing from research labs to practical applications in AI, financial modeling, scientific research, advanced simulations, cryptography, and optimization.
- Side note – The typical desktop PC uses around 200 to 500 watts, depending on its components and workload.
- It’s the first fully rack-mounted system that fits into standard data centers.
- Bell-1 overcomes one of quantum computing’s biggest engineering challenges by using a self-contained cryogenic cooling system to maintain its silicon quantum processor at 0.3 Kelvin (-272.85°C) without an external dilution refrigerator, making it an engineering marvel that operates efficiently within the noisy and thermally demanding environment of an enterprise data center.
- Side note – -272.85°C is approximately -459.13°F, which is just a tiny bit above absolute zero!
- My note – I could not find information about pricing for the Bell-1 system.
- Side note – Technical Highlights:
- System Type: Rack-mounted, plug-and-play quantum server
- Quantum Processor: UnityQ 6-Qubit Quantum Processing System
- Operating Temperature: 0.3 Kelvin (-272.85°C) with self-contained cryo-cooling (no dilution fridge required)
- Power Consumption: 1600 W—comparable to an enterprise server
- Infrastructure: Standard data center compatibility
- (no specialized facilities required)
- Weight & Footprint: Standard 600 mm x 1000 mm x 1600 mm
- data center rack, ~200 kg
- Upgrade Path: Future-oriented technology capable of QSoC integration
Story 3: Shape-shifting robot that swims now explores Mariana Trench, reaching a depth of 34,776-ft
Source: InterestingEngineering.com Story by Jijo Malayil
Link: https://interestingengineering.com/innovation/shape-shifting-robot-mariana-trench-exploration
See research paper here: https://www.nature.com/articles/d41586-021-00489-y
See video here: https://www.youtube.com/watch?v=uZ-2USuQEuU
- Side note – The Mariana Trench is a fascinating and extreme natural wonder of our planet. Located in the western Pacific Ocean, east of the Mariana Islands, it holds the title of being the deepest part of the world’s oceans. The trench is roughly 1,550 miles (2,500 km) long and about 43 miles (69 km) wide.
- The deepest known point in the trench is called Challenger Deep, which plunges to an astounding depth of about 36,037 feet (10,984 meters). To put that in perspective, if Mount Everest were placed inside the trench, its peak would still be more than a mile underwater!
- Researchers at the Beihang University in China have created a tiny, shape-shifting robot that swims, crawls, and glides freely in the deep sea.
- Deep-sea exploration devices are typically large and can harm fragile ecosystems. Developing smaller, lightweight robots for extreme underwater environments is challenging due to the need for components that withstand high pressures and low temperatures.
- To overcome this, the group created a soft actuator that uses a snap-through action to shift between two stable states. Because of its incompressible parts, the actuator can store more elastic energy at greater pressures. As a result, motions become stronger and faster at deeper depths.
- Side note – Snap-through action is a mechanical phenomenon where a structure or component undergoes a sudden and dramatic change in shape or position due to instability, even if the applied load or force changes gradually. It’s typically seen in systems with nonlinear behavior, such as elastic arches, domes, or membranes. This action occurs when the structure passes through a critical point, leading to a rapid transition from one equilibrium state to another.
- The researchers built a robot with these actuators, a microcontroller and onboard battery, and shape memory alloy springs to initiate the snap-through action.
- It has legs for crawling, symmetrical tail fins for swimming, and folding pectoral fins for gliding. The robot can change its locomotion modes by moving its legs and retracting its gliding fins. Because of its adaptability, it can effectively traverse a variety of underwater terrains.
- In the team’s study abstract they noted, “This study offers design insights into creating next-generation miniature deep-sea actuators and robots, paving the way for future exploration and interaction with deep-sea ecosystems.”
Story 4: Wi-Fi-based technology developed to detect depression in older adults
Source: MedicalXpress.com Story by JMIR Publications
Link: https://medicalxpress.com/news/2025-03-wi-fi-based-technology-depression.html#google_vignette
See research paper here: https://aging.jmir.org/2025/1/e67715/
- A new study published in JMIR Aging developed and tested a new AI model called HOPE which uses Wi-Fi-based motion sensor data to detect depression in older adults without relying on intrusive wearable devices.
- HOPE stands for “Home-Based Older Adults’ Depression Prediction.”
- The research highlights a novel machine learning model that accurately detected depression among participants.
- The study, created by researchers at the McGill University and Mila-Quebec AI Institute, aimed to determine whether everyday movement and sleep patterns collected through Wi-Fi-based sensors could provide early indicators of depression in adults 65 years and older.
- With an accuracy rate above 87%, this innovative approach presents a promising solution for early intervention and nonintrusive mental health monitoring, offering an alternative to traditional methods that require direct patient engagement.
- Traditional detection methods, including clinical interviews and wearable-based monitoring, are often resource-intensive, intrusive, or inconvenient, particularly for older adults who may struggle with technology adoption.
- The HOPE model addresses these challenges by leveraging existing Wi-Fi infrastructure, enabling continuous passive monitoring without requiring any active participation from users.
- A key aspect of the HOPE model is the integration of explainable AI (XAI) techniques, ensuring transparency and clinical interpretability. The researchers used explainable machine learning models to identify the most influential factors in depression detection.
- Side note – Explainable machine learning models, often referred to as explainable AI (XAI), are systems designed to make the decision-making processes of machine learning algorithms transparent and understandable to humans. Traditional machine learning models, like neural networks, often operate as “black boxes,” meaning their internal workings and reasoning are opaque.
- Explainable models aim to bridge this gap by offering insights into how predictions or decisions are made.
- These models provide human-readable explanations for their outputs, which is crucial for building trust, especially in sensitive fields like healthcare, finance, and legal applications. Techniques for achieving explainability include visualizations (e.g., feature importance plots), rule-based systems, attention mechanisms, and surrogate models that approximate the behavior of complex algorithms.
- Explainable machine learning enables stakeholders to validate, interpret, and trust AI decisions, while also helping developers identify and correct biases or inaccuracies.
- Source – a question I posted to Microsoft’s Co-Pilot AI.
- The results underscored the important role of sleep-related features, including average sleep duration, frequency of sleep interruptions, and frailty levels as primary indicators of depression.
- By making these AI-driven predictions interpretable and clinically meaningful, the HOPE model enhances trust and facilitates early detection of depression among older adults in the community.
- The study highlights the importance of sleep-related factors in detecting depression. The analysis revealed that the most influential factors were sleep duration, the number and duration of sleep interruptions, and the level of frailty.
- The study demonstrates the feasibility of using smart home technology for mental health assessments. While these findings are promising, larger studies are needed to provide further evidence for this approach. This technology could support early intervention efforts and improve the quality of life for older adults at risk of depression.
Honorable Mentions ***This week all medical news***
Story: MIT engineers turn skin cells directly into neurons for cell therapy – A new, highly efficient process for performing this conversion could make it easier to develop therapies for spinal cord injuries or diseases like ALS.
Source: MIT News Story by Anne Trafton
Link: https://news.mit.edu/2025/mit-engineers-turn-skin-cells-into-neurons-for-cell-therapy-0313
- Converting one type of cell to another — for example, a skin cell to a neuron — can be done through a process that requires the skin cell to be induced into a “pluripotent” stem cell, then differentiated into a neuron. Researchers at MIT have now devised a simplified process that bypasses the stem cell stage, converting a skin cell directly into a neuron.
- Working with mouse cells, the researchers developed a conversion method that is highly efficient and can produce more than 10 neurons from a single skin cell. If replicated in human cells, this approach could enable the generation of large quantities of motor neurons, which could potentially be used to treat patients with spinal cord injuries or diseases that impair mobility.
- “We were able to get to yields where we could ask questions about whether these cells can be viable candidates for the cell replacement therapies, which we hope they could be. That’s where these types of reprogramming technologies can take us,” says Katie Galloway, the W. M. Keck Career Development Professor in Biomedical Engineering and Chemical Engineering.
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Story: Rosemary and Sage Could Lead to Better Alzheimer’s Treatment
Source: Discover Magazine Story by Jenny Lehmann
See research paper here: https://www.mdpi.com/2076-3921/14/3/293
- Alzheimer’s disease (AD) is the sixth leading cause of death in the U.S. Beyond the high economic and healthcare demands, it places a significant physical, emotional, and financial burden on family caregivers.
- Patients with AD experience memory loss, confusion, mood and personality changes, and difficulty with language, often leading to social withdrawal.
- Currently, there is no cure for Alzheimer’s, but treatments such as immunotherapy targeting amyloid plaques aim to help patients maintain their independence and quality of life for longer.
- Now, a potential addition to AD treatment has been synthesized by researchers at Scripps Research Institute in La Jolla, California. Their study, published in Antioxidants, highlights a stable form of carnosic acid (CA) — a compound naturally found in rosemary and sage — which decreases AD symptoms and could be well-suited for human trials.
- A previous study by the research group demonstrated that carnosic acid can cross the blood-brain barrier and activate antioxidant and anti-inflammatory genes. This may explain rosemary’s theorized effect on memory, as inflammation is a key contributor to cognitive decline in AD.
- Additionally, CA is a pro-electrophilic compound, meaning it activates only in response to inflammation, making it selectively active in areas of the brain affected by neuroinflammation. However, pure CA is highly unstable, rendering it unsuitable for clinical and commercial applications.
- To overcome this obstacle, the researchers synthesized structurally related compounds and identified di-acetylated carnosic acid (diAcCA) as the most promising candidate due to its improved stability and bioavailability. This prodrug version of CA, which maintains a stable shelf life of over two years, was tested in genetically modified mice commonly used to study AD.
- Behavioral tests assessing spatial learning and memory revealed that diAcCA significantly improved memory function and increased both the quality and quantity of synapses.
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Story: Scientists develop magnesium-enriched nanofiber patches for safer wound healing
Source: Phys.org Story from Shibaura Institute of Technology
Link: https://phys.org/news/2025-02-scientists-magnesium-enriched-nanofiber-patches.html#google_vignette
- The skin serves as the body’s primary defense against harmful microorganisms, toxins, and physical damage. However, severe injuries from burns, trauma, surgery, or conditions like diabetes can compromise its ability to heal naturally, necessitating advanced wound care solutions. While minor wounds can heal with conventional care, severe injuries demand innovative materials and techniques to ensure rapid and complication-free healing.
- To address this, a team of researchers led by Dr. Saravana Kumar Jaganathan has introduced nanofiber wound patches that combine polyurethane (PU) with magnesium chloride (MgCl2). These advanced patches, developed using electrospinning technology, exhibit enhanced strength, superior blood compatibility, and effective antimicrobial properties. Their findings, published in the International Journal of Nanomedicine, on November 1, 2024, mark a significant advancement in wound care.
- “The efficacy of wound management heavily relies on the selection of optimal wound dressings. Our research explores how combining polyurethane and magnesium chloride can address this challenge effectively,” states Dr. Jaganathan.
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Story: Rapidly self-healing electronic skin for machine learning–assisted physiological and movement evaluation
Source: ScienceAdvances.org
Link: https://www.science.org/doi/10.1126/sciadv.ads1301
- Emerging electronic skins (E-Skins) offer continuous, real-time electrophysiological monitoring. However, daily mechanical scratches compromise their functionality, underscoring urgent need for self-healing E-Skins resistant to mechanical damage. Current materials have slow recovery times, impeding reliable signal measurement.
- The inability to heal within 1 minute is a major barrier to commercialization. A composition achieving 80% recovery within 1 minute has not yet been reported. Here, we present a rapidly self-healing E-Skin tailored for real-time monitoring of physical and physiological bioinformation.
- The E-Skin recovers more than 80% of its functionality within 10 seconds after physical damage, without the need of external stimuli. It consistently maintains reliable biometric assessment, even in extreme environments such as underwater or at various temperatures.
- Demonstrating its potential for efficient health assessment, the E-Skin achieves an accuracy exceeding 95%, excelling in wearable muscle strength analytics and on-site AI-driven fatigue identification. This study accelerates the advancement of E-Skin through rapid self-healing capabilities.
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