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Spintronics has emerged as a key technology for fast and nonvolatile memory with great CMOS compatibility. As the building blocks for these cutting-edge devices, magnetic materials require precise characterization of their critical properties, such as the effective anisotropy field (Hk,eff, related to magnetic stability) and damping (α, a key factor in device energy efficiency). Accurate measurements of these properties are essential for designing and fabricating high-performance spintronic devices. Among advanced metrology techniques, time-resolved magneto-optical Kerr effect (TR-MOKE) stands out for its superb temporal and spatial resolutions, surpassing traditional methods like ferromagnetic resonance. However, the full potential of TR-MOKE has not yet been fully fledged due to the lack of systematic optimization and robust operational guidelines. In this study, we address this gap by developing experimentally validated guidelines for optimizing TR-MOKE metrology across materials with perpendicular magnetic anisotropy and in-plane magnetic anisotropy. While Co20Fe60B20 thin films are used for experimental validation, this optimization framework can be readily extended to a variety of materials such as L10-FePd with easy-axis dispersion. Our work identifies the optimal ranges of the field angle to simultaneously achieve high signal amplitudes and improve measurement sensitivities to Hk,eff and α. By suppressing the influence of inhomogeneities and boosting sensitivity, our work significantly enhances TR-MOKE capability to extract magnetic properties with high accuracy and reliability. This optimization framework positions TR-MOKE as an indispensable tool for advancing spintronics, paving the way for energy-efficient and high-speed devices that will redefine the landscape of modern computing and memory technologies. 

Probabilistic spin logic (PSL) has recently been proposed as a novel computing paradigm that leverages random thermal fluctuations of interacting bodies in a system rather than deterministic switching of binary bits. A PSL circuit is an interconnected network of thermally unstable units called probabilistic bits (p-bits), whose output randomly fluctuates between bits 0 and 1. While the fluctuations generated by p-bits are thermally driven, and therefore, inherently stochastic, the output probability is tunable with an external source. Therefore, information is encoded through probabilities of various configuration of states in the network. Recent studies have shown that these systems can efficiently solve various types of combinatorial optimization problems and Bayesian inference problems that modern computers are unfit for. Previous experimental studies have demonstrated that a single magnetic tunnel junctions (MTJ) designed to be thermally unstable can operate tunable random number generator making it an ideal hardware solution for p-bits. Most proposals for designing an MTJ to operate as a p-bit involve patterning the MTJ as a circular nano-pillar to make the device thermally unstable and then use spin transfer torque (STT) as a tuning mechanism. However, the practical realization of such devices is very challenging since the fluctuation rate of these devices are very sensitive to any device variations or defects caused during fabrication. Despite this challenge, MTJs are still the most promising hardware solution for p-bits because MTJs are very unique in that they can be tuned by multiple other mechanisms such spin orbit torque, magneto-electric coupling, and voltage-controlled exchange coupling. Furthermore, multiple forces can be used simultaneously to drive stochastic switching signals in MTJs. This means there are a large number of methods to tune, or termed as bias, MTJs that can be implemented in p-bit circuits that can alleviate the current challenges of conventional STT driven p-bits. This article serves as a review of all of the different methods that have been proposed to drive random fluctuations in MTJs to operate as a probabilistic bit. Not only will we review the single-biasing mechanisms, but we will also review all the proposed dual-biasing methods, where two independent mechanisms are employed simultaneously. These dual-biasing methods have been shown to have certain advantages such as alleviating the negative effects of device variations and some biasing combinations have a unique capability called ‘two-degrees of tunability’, which increases the information capacity in the signals generated. 

Abstract BackgroundCannabis sativaL. with a rich history of traditional medicinal use, has garnered significant attention in contemporary research for its potential therapeutic applications in various human diseases, including pain, inflammation, cancer, and osteoarthritis. However, the specific molecular targets and mechanisms underlying the synergistic effects of its diverse phytochemical constituents remain elusive. Understanding these mechanisms is crucial for developing targeted, effective cannabis-based therapies. MethodsTo investigate the molecular targets and pathways involved in the synergistic effects of cannabis compounds, we utilized DRIFT, a deep learning model that leverages attention-based neural networks to predict compound-target interactions. We considered both whole plant extracts and specific plant-based formulations. Predicted targets were then mapped to the Reactome pathway database to identify the biological processes affected. To facilitate the prediction of molecular targets and associated pathways for any user-specified cannabis formulation, we developed CANDI (Cannabis-derived compound Analysis and Network Discovery Interface), a web-based server. This platform offers a user-friendly interface for researchers and drug developers to explore the therapeutic potential of cannabis compounds. ResultsOur analysis using DRIFT and CANDI successfully identified numerous molecular targets of cannabis compounds, many of which are involved in pathways relevant to pain, inflammation, cancer, and other diseases. The CANDI server enables researchers to predict the molecular targets and affected pathways for any specific cannabis formulation, providing valuable insights for developing targeted therapies. ConclusionsBy combining computational approaches with knowledge of traditional cannabis use, we have developed the CANDI server, a tool that allows us to harness the therapeutic potential of cannabis compounds for the effective treatment of various disorders. By bridging traditional pharmaceutical development with cannabis-based medicine, we propose a novel approach for botanical-based treatment modalities. 

Abstract Based on the vertical Total Electron Content (TEC) data observed by the Global Navigation Satellite System in the northern hemisphere, a large area of low plasma density during summer at high latitudes, termed decreased TEC region, was investigated statistically between 2014 and 2024. Compared with the classical depleted structures that usually occur in the nighttime F region at high latitudes during winter, decreased TEC region is usually found in the sunlit polar cap ionosphere during summer. The decreased TEC region is predominantly located in regions above 70° magnetic latitude for moderate and high solar activity. The lower‐TEC region is biased towards the dawn and midnight sectors. Along the 18:25–06:25 Magnetic Local Time meridian, the depth of the decreased TEC region reached 7.6TECu in 2014. The decreased TEC region is deeper for higher Kp (Kp > 2) than for low Kp (Kp ≤ 2).