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Blog – Page 16

A team of researchers at the George R. Brown School of Engineering and Computing at Rice University has developed an innovative artificial intelligence (AI)-enabled, low-cost device that will make flow cytometry—a technique used to analyze cells or particles in a fluid using a laser beam—affordable and accessible.

The prototype identifies and counts cells from unpurified blood samples with similar accuracy as the more expensive and bulky conventional flow cytometers, provides results within minutes and is significantly cheaper and compact, making it highly attractive for point-of-care clinical applications, particularly in low-resource and rural areas.

Peter Lillehoj, the Leonard and Mary Elizabeth Shankle Associate Professor of Bioengineering, and Kevin McHugh, assistant professor of bioengineering and chemistry, led the development of this new device. The study was published in Microsystems & Nanoengineering.

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A research team, led by Professor Jimin Lee and Professor Eisung Yoon in the Department of Nuclear Engineering at UNIST, has unveiled a deep learning–based approach that significantly accelerates the computation of a nonlinear Fokker–Planck–Landau (FPL) collision operator for fusion plasma.

The findings are published in the Journal of Computational Physics.

Nuclear fusion reactors, often referred to as artificial sun, rely on maintaining a high-temperature plasma environment similar to that of the sun. In this state, matter is composed of negatively charged electrons and positively charged ions. Accurately predicting the collisions between these particles is crucial for sustaining a stable fusion reaction.

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High-temperature superconducting magnets made from REBCO, an acronym for rare-earth barium copper oxide, make it possible to create an intense magnetic field that can confine the extremely hot plasma needed for fusion reactions, which combine two hydrogen atoms to form an atom of helium, releasing a neutron in the process.

But some early tests suggested that inside a might instantaneously suppress the ’ ability to carry current without resistance (called critical current), potentially causing a reduction in the fusion power output.

Now, a series of experiments has clearly demonstrated that this instantaneous effect of neutron bombardment, known as the “beam on effect,” should not be an issue during reactor operation, thus clearing the path for projects such as the ARC fusion system being developed by MIT spinoff company Commonwealth Fusion Systems.

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A recent study has realized multipartite entanglement on an optical chip for the first time, constituting a significant advance for scalable quantum information. The paper, titled “Continuous-variable multipartite entanglement in an integrated microcomb,” is published in Nature.

Led by Professor Wang Jianwei and Professor Gong Qihuang from the School of Physics at Peking University, in collaboration with Professor Su Xiaolong’s research team from Shanxi University, the research has implications for quantum computation, networking and metrology.

Continuous-variable integrated quantum photonic chips have been confined to the encoding of and between two qumodes, a bottleneck withholding the generation or verification of multimode entanglement on chips. Additionally, past research on cluster states failed to go beyond discrete viable, leaving a gap in the generation and detection of continuous-variable entanglement on photonic chips.

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The National Synchrotron Light Source II (NSLS-II)—a U.S. Department of Energy (DOE) Office of Science user facility at DOE’s Brookhaven National Laboratory—is among the world’s most advanced synchrotron light sources, enabling and supporting science across various disciplines. Advances in automation, robotics, artificial intelligence (AI), and machine learning (ML) are transforming how research is done at NSLS-II, streamlining workflows, enhancing productivity, and alleviating workloads for both users and staff.

As synchrotron facilities rapidly advance—providing brighter beams, automation, and robotics to accelerate experiments and discovery—the quantity, quality, and speed of data generated during an experiment continues to increase. Visualizing, analyzing, and sorting these large volumes of data can require an impractical, if not impossible, amount of time and attention.

Presenting scientists with is as important as preparing samples for beam time, optimizing the experiment, performing error detection, and remedying anything that may go awry during a measurement.

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Lumma Stealer is a fully-featured crimeware solution that’s offered for sale under the malware-as-a-service (MaaS) model, giving a way for cybercriminals to harvest a wide range of information from compromised Windows hosts. In early 2024, the malware operators announced an integration with a Golang-based proxy malware named GhostSocks.

“The addition of a SOCKS5 backconnect feature to existing Lumma infections, or any malware for that matter, is highly lucrative for threat actors,” Infrawatch said.

“By leveraging victims’ internet connections, attackers can bypass geographic restrictions and IP-based integrity checks, particularly those enforced by financial institutions and other high-value targets. This capability significantly increases the probability of success for unauthorized access attempts using credentials harvested via infostealer logs, further enhancing the post-exploitation value of Lumma infections.”

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A dataset used to train large language models (LLMs) has been found to contain nearly 12,000 live secrets, which allow for successful authentication.

The findings once again highlight how hard-coded credentials pose a severe security risk to users and organizations alike, not to mention compounding the problem when LLMs end up suggesting insecure coding practices to their users.

Truffle Security said it downloaded a December 2024 archive from Common Crawl, which maintains a free, open repository of web crawl data. The massive dataset contains over 250 billion pages spanning 18 years.

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It’s worth noting that the intrusion set distributing the Winos 4.0 malware has been assigned the monikers Void Arachne and Silver Fox, with the malware also overlapping with another remote access trojan tracked as ValleyRAT.

“They are both derived from the same source: Gh0st RAT, which was developed in China and open-sourced in 2008,” Daniel dos Santos, Head of Security Research at Forescout’s Vedere Labs, told The Hacker News.

“Winos and ValleyRAT are variations of Gh0st RAT attributed to Silver Fox by different researchers at different points in time. Winos was a name commonly used in 2023 and 2024 while now ValleyRAT is more commonly used. The tool is constantly evolving, and it has both local Trojan/RAT capabilities as well as a command-and-control server.”

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A new variant of the Vo1d malware botnet has grown to 1,590,299 infected Android TV devices across 226 countries, recruiting devices as part of anonymous proxy server networks.

This is according to an investigation by Xlab, which has been tracking the new campaign since last November, reporting that the botnet peaked on January 14, 2025, and currently has 800,000 active bots.

In September 2024, Dr. Web antivirus researchers found 1.3 million devices across 200 countries compromised by Vo1d malware via an unknown infection vector.

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