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Magnetic reconnection is a fundamental process in space and astrophysical plasmas that converts magnetic energy to particle energy. Recently, a novel kind of reconnection, called electron-only reconnection, has been observed in Earth's magnetosheath plasma. A defining characteristic of electron-only reconnection is that electron jets are observed but ion jets are absent. This is in contrast with traditional ion-coupled reconnection, where both ions and electrons exhibit outflowing velocity jets. Findings from the Magnetospheric Multiscale mission observations and particle-in-cell simulations show clear signatures of electron heating in electron-only reconnection events, while ions are not heated or cooled in these events. This result is unlike ion-coupled reconnection, where both ions and electrons are heated to varying degrees. The ratio of electron to ion dissipation increases with the local magnetic curvature, indicating that the partition of heat into ions and electrons is dependent on the current-sheet thickness.

 

On-chip assets, such as cryptographic keys, intermediate cipher computations, obfuscation keys, and hardware security primitive outputs, are usually stored in volatile memories, e.g., registers and SRAMs. Such volatile memories could be read out using active physical attacks, such laser-assisted side-channels. One way to protect assets stored in volatile memories can be the employment of sensors that detect active physical attacks and trigger complete zeroization of sensitive data. However, hundreds or thousands of clock cycles are often needed to accomplish this. Further, the sensing and self-destruction mechanisms are decoupled from the sensitive circuitry and can be disabled separately by an adversary. Moreover, defensive actions (e.g., zeroization) may be disabled by bringing the CPU/SoC into an inoperable condition, while registers may still hold their data, making them susceptible. This paper proposes a self-destructive latch to protect sensitive data from active side-channel attacks, which require supply voltage manipulations.Our proposed latch senses supply voltage interference required during such attacks, and reacts instantaneously by entering a forbidden data state, erasing its stored data. The design uses a NULL convention logic (NCL)- based polymorphic NOR/NAND gate, which changes its functionality with supply voltage. Our results show that the latch is stable across temperature and process variation reacting to attacks with 91% confidence. Even for the 9% where data is not destroyed, in 3.33% of cases data flips its state which makes reliable extraction difficult for an attacker. The polymorphic latch is straightforward to implement due to its NCL implementation and the voltage for the self-destructive behavior is easily altered by resizing only two transistors. Further, this self-destructive behavior extends to registers which are built out of latches. 

Soot or black carbons are combustion-generated carbonaceous nanoparticles formed during the incomplete combustion of hydrocarbon fuels. The complexity of hydrocarbon systems often makes it difficult to investigate the fundamentals of soot formation experimentally. To address this, this study uses reactive molecular dynamics simulations with reactive force field (ReaxFF) potentials. The current work focuses on the formation and evolution of soot during acetylene pyrolysis. The analysis provides insights into the physicochemical aspects of soot formation and the maturation of incipient soot particles. In this work, we focus on the evolution and interdependence of features such as the number of carbon atoms, number of aromatic rings, mass, C/H ratio, the radius of gyration, atomic fractal dimension, surface area, volume, and density. Based on the physicochemical features, two distinct classes of nascent soot can be observed. These are termed type-1 and type-2 particles. The type-1 particles show significant morphological evolution, while the type-2 particles show chemical restructuring without significantly changing the morphology. Qualitative correlations of various degrees are also observed between some of these morphological features. 

Deep learning (DL) models have demonstrated state-of-the-art performance in the classification of diagnostic imaging in oncology. However, DL models for medical images can be compromised by adversarial images, where pixel values of input images are manipulated to deceive the DL model. To address this limitation, our study investigates the detectability of adversarial images in oncology using multiple detection schemes. Experiments were conducted on thoracic computed tomography (CT) scans, mammography, and brain magnetic resonance imaging (MRI). For each dataset we trained a convolutional neural network to classify the presence or absence of malignancy. We trained five DL and machine learning (ML)-based detection models and tested their performance in detecting adversarial images. Adversarial images generated using projected gradient descent (PGD) with a perturbation size of 0.004 were detected by the ResNet detection model with an accuracy of 100% for CT, 100% for mammogram, and 90.0% for MRI. Overall, adversarial images were detected with high accuracy in settings where adversarial perturbation was above set thresholds. Adversarial detection should be considered alongside adversarial training as a defense technique to protect DL models for cancer imaging classification from the threat of adversarial images.