There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

A general overview of the trends in structural and thermodynamic properties that have been identified within the hydrated normal rare earth carbonates and the rare earth hydroxycarbonates is presented. Based upon available literature, we demonstrate the trends in crystallographic unit cell parameters, thermal stability, aqueous solubility, and thermochemical properties. These trends can be attributed to both the unique chemistry and strong similarity of the rare earth elements. There are also inconsistent trends that signal research needs to better understand the structure–energy relationships of the rare earth carbonates.

https://www.mdpi.com/2075-163X/8/3/106/pdf

Trends in structure and thermodynamic properties of normal rare earth carbonates and rare earth hydroxycarbonates

We analyze scenarios in which some flavour of sneutrino is the next-to-lightest supersymmetric particle (NLSP), assuming that the gravitino is the lightest supersymmetric particle (LSP) and provides the cold dark matter Such scenarios do not arise in the constrained supersymmetric extension of the Standard Model (CMSSM) with universal gaugino and scalar masses input at the GUT scale However, models with non-universal Higgs masses (NUHM) do allow scenarios with a sneutrino NLSP, which are quite generic We illustrate how such scenarios may arise, analyze the possible metastable sneutrino lifetime, and explore the theoretical, phenomenological, experimental and cosmological constraints on such scenarios We also discuss the collider signatures of such scenarios, how they may be distinguished from neutralino LSP scenarios, and how different flavours of sneutrino NLSP may be distinguished

/pdf/sneutrino-nlsp-scenarios-in-the-nuhm-with-gravitino-dark-5exhzavef0.pdf

Sneutrino NLSP Scenarios in the NUHM with Gravitino Dark Matter

Radioluminescent nuclear battery is a type of energy conversion device that can be miniaturized, which has the ability to convert nuclear energy into light energy, and again into electrical energy. To explore the response relationship between the phosphor layer structure and the electrical performance of radioluminescent nuclear battery, the physical model was established to research the deposition energy distribution by using Monte Carlo method. The radioluminescence spectra and current‐voltage characteristic curves were used to investigate the optical and electrical properties. Through a comprehensive comparison of single plane layer, double plane layer, and V groove layer structures, the simulated results are consistent with experimental results. The results indicate that the Monte Carlo simulation is applicable to analysis of the phosphor layer structure of radioluminescent nuclear battery. Additionally, the results also show that the structure type and physical parameters of the phosphor layer have great influence on the energy deposition. A suitable phosphor layer structure can provide a new route to exhibit higher energy conversion efficiency as well as improving the matching degree between the range of radioactive particles and the thickness of the phosphor layer.

Enhanced radioluminescent nuclear battery by optimizing structural design of the phosphor layer

The EPSRC and the Technology Strategy Board (TSB) funded the PURPOSE (TP11/MFE/6/I/AA129F; EPSRC TS/G000271/1), CONVERTED (JeS no. TS/1003053/1) and PRISM (EP/N508974/1) programs. The TSB for funding the CONVERT program.

/pdf/ultraviolet-and-blue-cathodoluminescence-from-cubic-y2o3-and-3ybjyliioe.pdf

Ultraviolet and blue cathodoluminescence from cubic Y2O3 and Y2O3: Eu3+ generated in a transmission electron microscope

Alpha-particle-induced luminescence of rare-earth-doped Y2O3 nanophosphors

SUSY models with a modified dark sector require constraints to be reinterpreted, which may allow for scenarios with low tuning. A modified dark sector can also change the phenomenology greatly. The addition of the QCD axion to the Minimal Supersymmetric Standard Model (MSSM) solves the strong CP problem and also modifies the dark sector with new dark matter candidates. While SUSY axion phenomenology is usually restricted to searches for the axion itself or searches for the ordinary SUSY particles, this work focuses on scenarios where the axion's superpartner, the axino may be detectable at the Large Hadron Collider (LHC) in the decays of neutralinos displaced from the primary vertex. In particular this work focuses on the KSVZ axino. The decay length of neutralinos in this scenario easily fits the ATLAS detector for SUSY spectra expected to be testable at the 14 TeV LHC. This signature of displaced decays to axinos is compared to other well motivated scenarios containing a long lived neutralino which decays inside the detector. These alternative scenarios can in some cases very closely mimic the expected axino signature, and the degree to which they are distinguishable is discussed. The cosmological viability of such a scenario is also considered briefly.

Modified Supersymmetric Dark Sectors

The addition of the QCD axion to the Minimal Supersymmetric Standard Model (MSSM) not only solves the strong CP problem but also modifies the dark sector with new dark matter candidates. While SUSY axion phenomenology is usually restricted to searches for the axion itself or searches for the ordinary SUSY particles, this work focuses on scenarios where the axion's superpartner, the axino, may be detectable at the Large Hadron Collider (LHC) in the decays of neutralinos displaced from the primary vertex. In particular, we focus on the KSVZ axino within the hadronic axion window. The decay length of neutralinos in this scenario easily fits the ATLAS detector for SUSY spectra expected to be testable at the 14 TeV LHC. We compare this signature of displaced decays to axinos to other well motivated scenarios containing a long lived neutralino which decays inside the detector. These alternative scenarios can in some cases very closely mimic the expected axino signature, and the degree to which they are distinguishable is discussed. We also briefly comment on the cosmological viability of such a scenario.

Exploring the Hadronic Axion Window via Delayed Neutralino Decay to Axinos at the LHC

Command and control (C2) paths for issuing commands to malware are sometimes the only indicators of its existence within networks. Identifying potential C2 channels is often a manually driven process that involves a deep understanding of cyber tradecraft. Efforts to improve discovery of these channels through using a reinforcement learning (RL) based approach that learns to automatically carry out C2 attack campaigns on large networks, where multiple defense layers are in place serves to drive efficiency for network operators. In this paper, we model C2 traffic flow as a three-stage process and formulate it as a Markov decision process (MDP) with the objective to maximize the number of valuable hosts whose data is exfiltrated. The approach also specifically models payload and defense mechanisms such as firewalls which is a novel contribution. The attack paths learned by the RL agent can in turn help the blue team identify high-priority vulnerabilities and develop improved defense strategies. The method is evaluated on a large network with more than a thousand hosts and the results demonstrate that the agent can effectively learn attack paths while avoiding firewalls.

Discovering Command and Control Channels Using Reinforcement Learning

The proliferation of zero-day threats (ZDTs) to companies’ networks has been immensely costly and requires novel methods to scan traffic for malicious behavior at massive scale. The diverse nature of normal behavior along with the huge landscape of attack types makes deep learning methods an attractive option for their ability to capture highly-nonlinear behavior patterns. In this paper, the authors demonstrate an improvement upon a previously introduced methodology, which used a dual-autoencoder approach to identify ZDTs in network flow telemetry. In addition to the previously-introduced asset-level graph features, which help abstractly represent the role of a host in its network, this new model uses metric learning to train the second autoencoder on labeled attack data. This not only produces stronger performance, but it has the added advantage of improving the interpretability of the model by allowing for multiclass classification in the latent space. This can potentially save human threat hunters time when they investigate predicted ZDTs by showing them which known attack classes were nearby in the latent space. The models presented here are also trained and evaluated with two more datasets, and continue to show promising results even when generalizing to new network topologies.

Zero Day Threat Detection Using Metric Learning Autoencoders

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