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With today’s data rates of only a few hundred megabytes per second, access to digital information remains relatively slow. Initial experiments have already shown a promising new strategy: Magnetic states can be read out by short current pulses, whereby recently discovered spintronic effects in purpose-built material systems could remove previous speed restrictions.

Researchers at HZDR and TU Dortmund University are now providing proof of the feasibility of such ultrafast data sources. Instead of , they use ultrashort , thereby enabling the read-out of magnetic structures within picoseconds, as they report in the journal Nature Communications.

“We now can determine the magnetic orientation of a material much quicker with light-induced current pulses,” explains Dr. Jan-Christoph Deinert of HZDR’s Institute of Radiation Physics. For their experiments, the physicist and his team employed light that is invisible to the human eye—so-called terahertz radiation.

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An international team of researchers affiliated with UNIST has unveiled a novel cross-linker additive that significantly addresses the longstanding stability issues associated with organic solar cells, also known as organic photovoltaics (OPVs).

With the incorporation of just 0.05% of this cross-linking agent, the lifespan of OPVs can be improved by over 59%. Industry analysts suggest this breakthrough brings the commercialization of OPVs—regarded as next-generation solar cells—closer to reality.

Led by Professor BongSoo Kim in the Department of Chemistry at UNIST, the research team, in collaboration with researchers from the University of California, Santa Barbara (UCSB), the University of Lille in France, and the French National Center for Scientific Research (CNRS), identified the operational principles of this innovative cross-linker using a variety of advanced analytical techniques.

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Demand for lithium is rising due to its use in batteries for mobile devices, cars and clean energy storage. Securing access to natural deposits of the mineral is now a matter of strategic importance, but lithium can be found elsewhere in nature.

As an alternative to mining, Imperial researchers have created a technology that could be used to efficiently extract it from saltwater sources such as salt-lake brines or geothermal brine solutions.

Conventional extraction from brines takes months and uses significant amounts of water and chemicals, generating greenhouse gas emissions in the process. The alternative developed by Dr. Qilei Song and his team in the Department of Chemical Engineering uses a membrane that separates lithium from by filtering it through tiny pores.

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Nickel’s role in the future of electric vehicle batteries is clear: It’s more abundant and easier to obtain than widely used cobalt, and its higher energy density means longer driving distances between charges.

However, nickel is less stable than other materials with respect to cycle life, , and safety. Researchers from the University of Texas at Austin and Argonne National Laboratory aim to change that with a new study that dives deeply into nickel-based cathodes, one of the two electrodes that facilitate in batteries.

“High-nickel cathodes have the potential to revolutionize the EV market by providing longer driving ranges,” said Arumugam Manthiram, a professor at the Walker Department of Mechanical Engineering and Texas Materials Institute and one of the leaders of the study published in Nature Energy.

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Scientists say they have developed a new AI-assisted model of a digital twin with the ability to adapt and control the physical machine in real time.

The discovery, reported in the journal IEEE Access, adds a new dimension to the digital copies of real-world machines, like robots, drones, or even autonomous cars, according to the authors.

Digital twins are exact replicas of things in the physical world. They are likened to video game versions of real machines with which they digitally twin, and are constantly updated with real-time data.

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Heman Bekele has just been named Time’s 2024 Kid of the Year.

S 15, is already spending part of every weekday working in a lab at the Johns Hopkins Bloomberg School of Public Health in Baltimore, hoping to bring his dream to fruition. ‘.

Last year NPR interviewed Heman Bekele about his invention of a soap to fight skin cancer. He was motivated by his childhood in Ethiopia: He saw people working in the sun and thought of health risks.

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A research team led by the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) has discovered “berkelocene,” the first organometallic molecule to be characterized containing the heavy element berkelium.

Organometallic molecules, which consist of a metal ion surrounded by a carbon-based framework, are relatively common for early actinide elements like uranium (atomic number 92) but are scarcely known for later actinides like berkelium (atomic number 97).

“This is the first time that evidence for the formation of a chemical bond between berkelium and carbon has been obtained. The discovery provides new understanding of how berkelium and other actinides behave relative to their peers in the periodic table,” said Stefan Minasian, a scientist in Berkeley Lab’s Chemical Sciences Division and one of four co-corresponding authors of a new study published in the journal Science.

Breakthrough in heavy-element chemistry shatters long-held assumptions about transuranium elements.

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In early February, an Australian man in his 40s became the first person in the world to leave hospital with a virtually unbreakable heart made of metal.

‘Beating’ in his chest was a titanium pump about the size of a fist. For 105 days, the metal organ’s levitating propeller pushed blood to the man’s lungs and kept him alive as he went about his usual business.

On March 6, when a human donor heart became available, the man’s titanium heart was swapped out for the real thing. Doctors say without the metal stop-gap, the patient’s real heart would have failed before a donor became available.

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【Advanced Skin Disease Diagnosis and Treatment: Leveraging Convolutional Neural Networks for Image-Based Prediction and Comprehensive Health Assistance】 Full article: (Authored by Noshin Un Noor, et al., from World University of Bangladesh, Bangladesh.)

Skin_diseases are a major global health concern, encompassing a wide range of conditions with varying severity. Prompt and precise diagnosis is critical for effective treatment. However, traditional methods often rely on dermatologists, creating disparities in access to care. This study creates and assesses a highly accurate Convolutional Neural Network (CNN) model that can use digital photos of skin lesions to diagnose a variety of skin conditions, and looks into how well various CNN architectures and pre-trained models may increase the precision and effectiveness of diagnosing skin conditions.

Abstract

Skin conditions are a worldwide health issue that requires prompt and accurate diagnosis in order to be effectively treated. This study presents a Convolutional Neural Network (CNN)-based automated skin disease diagnostic method. The work uses preprocessing methods like scaling, normalization, and augmentation to improve model robustness using the DermNet dataset, which consists of 19,500 pictures from 23 disease categories. TensorFlow and Keras were used to create a unique CNN architecture, which produced an impressive accuracy of 94.65%. Metrics like precision, recall, and F1-score were used to validate the model’s performance, showing that it outperformed more conventional machine learning techniques like SVM and KNN. The system incorporates patient-reported symptoms in addition to diagnosis to provide a comprehensive approach to health support, allowing for remote accessibility and tailored therapy suggestions. This work recognizes issues like dataset variability and processing needs while showcasing the revolutionary potential of AI in dermatology. In order to improve model interpretability and clinical integration, future possibilities include dataset extension, real-world validation, and the use of explainable AI.

Skin Disease Diagnosis, Dermatological Image Analysis, Medical Image Classification, Convolutional Neural Networks (CNNs), Healthcare Accessibility, Deep Learning Applications, DermNet Dataset

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🌍 New research suggests more than half of global cropland areas could lose suitable crops under a warming scenario of 2C.

📚 The study mapped how climate change could reshape areas suited for 30 major crops across four warming scenarios — from 1.5C to 4C.

🔎 Even at 1.5C, over half of the crops studied could see a decline in suitable cropland, with tropical regions hit hardest. In contrast, areas far from the equator could gain crop diversity — opening doors for climate adaptation.

S impact on agriculture. + s findings here ⬇️ +.

More than half of global cropland areas could see a decline in the number of suitable crops under a warming scenario of 2C, new research finds.

The study, published in Nature Food, projects how climate change will modify the areas suited for growing 30 major crops under four scenarios, ranging from 1.5 to 4C of global warming.

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