One-dimensional magnetic fluctuations in the spin-2 triangular lattice \alpha-NaMnO2

The S=2 anisotropic triangular lattice alpha-NaMnO2 is studied by neutron inelastic scattering. Antiferromagnetic order occurs at T ~ 45 K with opening of a spin gap. The spectral weight of the magnetic dynamics above the gap (Delta ~ 7.5 meV) has been analysed by the single-mode approximation. Excellent agreement with the experiment is achieved when a dominant exchange interaction (|J|/k_B ~ 73 K), along the monoclinic b-axis and a sizeable easy-axis magnetic anisotropy (|D|/k_B ~ 3 K) are considered. Despite earlier suggestions for two-dimensional spin interactions, the dynamics illustrate strongly coupled antiferromagnetic S=2 chains and cancellation of the interchain exchange due to the lattice topology. alpha-NaMnO2 therefore represents a model system where the geometric frustration is resolved through the lowering of the dimensionality of the spin interactions.Comment: 4 pages, 4 figures, to be published in Physical Review Letter

Reactor Simulation for Antineutrino Experiments using DRAGON and MURE

Rising interest in nuclear reactors as a source of antineutrinos for experiments motivates validated, fast, and accessible simulations to predict reactor fission rates. Here we present results from the DRAGON and MURE simulation codes and compare them to other industry standards for reactor core modeling. We use published data from the Takahama-3 reactor to evaluate the quality of these simulations against the independently measured fuel isotopic composition. The propagation of the uncertainty in the reactor operating parameters to the resulting antineutrino flux predictions is also discussed.Comment: This version has increased discussion of uncertaintie

Text-Mining in Streams of Textual Data Using Time Series Applied to Stock Market

Each day, a lot of text data is generated. This data comes from various sources and may contain valuable information. In this article, we use text mining methods to discover if there is a connection between news articles and changes of the S&P 500 stock index. The index values and documents were divided into time windows according to the direction of the index value changes. We achieved a classification accuracy of 65-74 %.O

The use of 3D printing in the development of gaseous radiation detectors

Fused Deposition Modelling has been used to produce a small, single wire, Iarocci-style drift tube to demonstrate the feasibility of using the Additive Manufacturing technique to produce cheap detectors, quickly. Recent technological developments have extended the scope of Additive Manufacturing, or 3D printing, to the possibility of fabricating Gaseous Radiation Detectors, such as Single Wire Proportional Counters and Time Projection Chambers. 3D printing could allow for the production of customisable, modular detectors; that can be easily created and replaced and the possibility of printing detectors on-site in remote locations and even for outreach within schools. The 3D printed drift tube was printed using Polylactic acid to produce a gas volume in the shape of an inverted triangular prism; base length of 28 mm, height 24.25 mm and tube length 145 mm. A stainless steel anode wire was placed in the centre of the tube, mid-print. P5 gas (95% Argon, 5% Methane) was used as the drift gas and a circuit was built to capacitively decouple signals from the high voltage. The signal rate and average pulse height of cosmic ray muons were measured over a range of bias voltages to characterise and prove correct operation of the printed detector

Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future

The Biomarker Toolkit - an evidence-based guideline to predict cancer biomarker success and guide development

BACKGROUND: An increased number of resources are allocated on cancer biomarker discovery, but very few of these biomarkers are clinically adopted. To bridge the gap between Biomarker discovery and clinical use, we aim to generate the Biomarker Toolkit, a tool designed to identify clinically promising biomarkers and promote successful biomarker translation. METHODS: All features associated with a clinically useful biomarker were identified using mixed-methodology, including systematic literature search, semi-structured interviews, and an online two-stage Delphi-Survey. Validation of the checklist was achieved by independent systematic literature searches using keywords/subheadings related to clinically and non-clinically utilised breast and colorectal cancer biomarkers. Composite aggregated scores were generated for each selected publication based on the presence/absence of an attribute listed in the Biomarker Toolkit checklist. RESULTS: Systematic literature search identified 129 attributes associated with a clinically useful biomarker. These were grouped in four main categories including: rationale, clinical utility, analytical validity, and clinical validity. This checklist was subsequently developed using semi-structured interviews with biomarker experts (n=34); and 88.23% agreement was achieved regarding the identified attributes, via the Delphi survey (consensus level:75%, n=51). Quantitative validation was completed using clinically and non-clinically implemented breast and colorectal cancer biomarkers. Cox-regression analysis suggested that total score is a significant driver of biomarker success in both cancer types (BC: p>0.0001, 95.0% CI: 0.869-0.935, CRC: p>0.0001, 95.0% CI: 0.918-0.954). CONCLUSIONS: This novel study generated a validated checklist with literature-reported attributes linked with successful biomarker implementation. Ultimately, the application of this toolkit can be used to detect biomarkers with the highest clinical potential and shape how biomarker studies are designed/performed