The Effective Field Theory of Dark Matter Direct Detection

We extend and explore the general non-relativistic effective theory of dark matter (DM) direct detection. We describe the basic non-relativistic building blocks of operators and discuss their symmetry properties, writing down all Galilean-invariant operators up to quadratic order in momentum transfer arising from exchange of particles of spin 1 or less. Any DM particle theory can be translated into the coefficients of an effective operator and any effective operator can be simply related to most general description of the nuclear response. We find several operators which lead to novel nuclear responses. These responses differ significantly from the standard minimal WIMP cases in their relative coupling strengths to various elements, changing how the results from different experiments should be compared against each other. Response functions are evaluated for common DM targets - F, Na, Ge, I, and Xe - using standard shell model techniques. We point out that each of the nuclear responses is familiar from past studies of semi-leptonic electroweak interactions, and thus potentially testable in weak interaction studies. We provide tables of the full set of required matrix elements at finite momentum transfer for a range of common elements, making a careful and fully model-independent analysis possible. Finally, we discuss embedding non-relativistic effective theory operators into UV models of dark matter.Comment: 32+23 pages, 5 figures; v2: some typos corrected and definitions clarified; v3: some factors of 4pi correcte

312 MAX Phases: Elastic Properties and Lithiation

Interest in the Mn+1AXn phases (M = early transition metal; A = group 13–16 elements, and X = C or N) is driven by their ceramic and metallic properties, which make them attractive candidates for numerous applications. In the present study, we use the density functional theory to calculate the elastic properties and the incorporation of lithium atoms in the 312 MAX phases. It is shown that the energy to incorporate one Li atom in Mo3SiC2, Hf3AlC2, Zr3AlC2, and Zr3SiC2 is particularly low, and thus, theoretically, these materials should be considered for battery applications

Residual stress characterization of single and triple-pass autogenously welded stainless steel pipes

Using neutron diffraction the components of the residual stress field have been determined in the region near a mid-length groove in two identical austenitic stainless pipes in which weld beads had been laid down. One pipe sample had a single pass, and the second a triple pass, autogenous weld deposited around the groove circumference. The results show the effect on the stress field of the additional weld deposited and are compared to the results of Finite Element Modelling. The hoop stress component is found to be generally tensile, and greater in the triple pass weldment than in the single pass weldment. The hoop stresses reach peak values of around 400 MPa in tension. X-ray measurements of the residual stress components on the near inner surface of the pipe weldments are also presented, and show tensile stresses in both pipes, with a higher magnitude in the three-pass weldment

Minimization and Mitigation of Wire EDM Cutting Errors in the Application of the Contour Method of Residual Stress Measurement

The contour method of residual stress measurement relies on the careful application of wire electro-discharge machining (WEDM) for the cutting stage. Changes in material removal rates during the cut lead to errors in the final calculated values of residual stress. In this study, WEDM cutting parameters have been explored to identify the optimum conditions for contour method residual stress measurements. The influence of machine parameters on the surface roughness and cutting artifacts in the contour cut is discussed. It has been identified that the critical parameter in improving the surface finish is the spark pulse duration. A typical cutting artifact and its impact on measured stress values have been identified and demonstrated for a contour cut in a welded marine steel. A procedure is presented to correct contour displacement data from the influence of WEDM cutting artifacts, and is demonstrated on the correction of a measured weld residual stress. The corrected contour method improved the residual stress magnitude up to 150 MPa. The corrected contour method results were validated by X-ray diffraction, incremental center hole drilling, and neutron diffraction

Surface preparation of powder metallurgical tool steels by means of wire electrical discharge machining

The surface of two types of powder metallurgical (PM) tool steels (i.e., with and without nitrogen) was prepared using wire electrical discharge machining (WEDM). From each grade of tool steel, seven surfaces corresponding to one to seven passes of WEDM were prepared. The WEDM process was carried out using a brass wire as electrode and deionized water as dielectric. After eachWEDM pass the surface of the tool steels was thoroughly examined. Surface residual stresses were measured by the X-ray diffraction (XRD) technique. The measured stresses were found to be of tensile nature. The surface roughness of the WEDM specimens was measured using interference microscopy. The surface roughness as well as the residual stress measurements indicated an insignificant improvement of these parameters after four passes of WEDM. In addition, the formed recast layer was characterized by means of scanning electron microscopy (SEM), XRD, and X-ray photoelectron spectroscopy (XPS). The characterization investigation clearly shows diffusion of copper and zinc from the wire electrode into the work material, even after the final WEDM step. Finally, the importance of eliminating excessive WEDM steps is thoroughly discussed

Prediction of welding residual stresses using machine learning: Comparison between neural networks and neuro-fuzzy systems

Safe and reliable operation of power plants invariably relies on the structural integrity assessments of pressure vessels and piping systems. Welded joints are a potential source of failure, because of the combination of the variation in mechanical properties and the residual stresses associated with the thermomechanical cycles experienced by the material during welding. This paper presents comparative studies between methods based on artificial neural networks (ANN) and fuzzy neural networks (FNN) for predicting residual stresses induced by welding. The performance of neural network and neuro-fuzzy systems are compared based on statistical indicators, scatter plots and several case studies. Results show that the neuro-fuzzy systems optimised using a hybrid technique can perform slightly better than a neural network trained using Levenberg-Marquardt algorithm, primarily because of the inability of the ANN approach to provide conservative estimates of residual stress profiles. Specifically, the prediction accuracy of the neuro-fuzzy systems trained using the hybrid technique is better for the axial residual stress component, with root mean square error (RMSE), absolute fraction of variance (R2) and mean absolute percentage error (MAPE) error of 0.1264, 0.9102 and 22.9442 respectively using the test data. Furthermore, this study demonstrates the potential benefits of implementing neuro-fuzzy systems in predicting residual stresses for use in structural integrity assessment of power plant components

Heavy quarkonium: progress, puzzles, and opportunities

A golden age for heavy quarkonium physics dawned a decade ago, initiated by the confluence of exciting advances in quantum chromodynamics (QCD) and an explosion of related experimental activity. The early years of this period were chronicled in the Quarkonium Working Group (QWG) CERN Yellow Report (YR) in 2004, which presented a comprehensive review of the status of the field at that time and provided specific recommendations for further progress. However, the broad spectrum of subsequent breakthroughs, surprises, and continuing puzzles could only be partially anticipated. Since the release of the YR, the BESII program concluded only to give birth to BESIII; the $B$-factories and CLEO-c flourished; quarkonium production and polarization measurements at HERA and the Tevatron matured; and heavy-ion collisions at RHIC have opened a window on the deconfinement regime. All these experiments leave legacies of quality, precision, and unsolved mysteries for quarkonium physics, and therefore beg for continuing investigations. The plethora of newly-found quarkonium-like states unleashed a flood of theoretical investigations into new forms of matter such as quark-gluon hybrids, mesonic molecules, and tetraquarks. Measurements of the spectroscopy, decays, production, and in-medium behavior of c\bar{c}, b\bar{b}, and b\bar{c} bound states have been shown to validate some theoretical approaches to QCD and highlight lack of quantitative success for others. The intriguing details of quarkonium suppression in heavy-ion collisions that have emerged from RHIC have elevated the importance of separating hot- and cold-nuclear-matter effects in quark-gluon plasma studies. This review systematically addresses all these matters and concludes by prioritizing directions for ongoing and future efforts.Comment: 182 pages, 112 figures. Editors: N. Brambilla, S. Eidelman, B. K. Heltsley, R. Vogt. Section Coordinators: G. T. Bodwin, E. Eichten, A. D. Frawley, A. B. Meyer, R. E. Mitchell, V. Papadimitriou, P. Petreczky, A. A. Petrov, P. Robbe, A. Vair

Effect of tool profile and fatigue loading on the local hardness around scratches in clad and unclad aluminium alloy 2024

Nanoindentation has been used to study the hardness changes produced by scratching of aluminium alloy AA2024, with and without a clad layer of pure aluminium. The hardness was mapped around scratches made with diamond tools of different profiles. One tool produced significant plastic damage with associated hardening at the scratch root, whilst the other produced a 'cleaner' cut with no hardening. The different behaviours and are attributed to whether the tool makes the scratch by a 'cutting' or a 'ploughing' mechanism. The degree of plastic damage around the scratches has been correlated with peak broadening data obtained using synchrotron X-ray diffraction. There was no change observed in the local hardness around the scratch with fatigue loading

ATP5H/KCTD2 locus is associated with Alzheimer's disease risk

To identify loci associated with Alzheimer disease, we conducted a three-stage analysis using existing genome-wide association studies (GWAS) and genotyping in a new sample. In Stage I, all suggestive single-nucleotide polymorphisms (at P<0.001) in a previously reported GWAS of seven independent studies (8082 Alzheimer's disease (AD) cases; 12 040 controls) were selected, and in Stage II these were examined in an in silico analysis within the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium GWAS (1367 cases and 12904 controls). Six novel signals reaching P<5 × 10-6 were genotyped in an independent Stage III sample (the Fundació ACE data set) of 2200 sporadic AD patients and 2301 controls. We identified a novel association with AD in the adenosine triphosphate (ATP) synthase, H+ transporting, mitochondrial F0 (ATP5H)/Potassium channel tetramerization domain-containing protein 2 (KCTD2) locus, which reached genome-wide significance in the combined discovery and genotyping sample (rs11870474, odds ratio (OR)=1.58, P=2.6 × 10 -7 in discovery and OR=1.43, P=0.004 in Fundació ACE data set; combined OR=1.53, P=4.7 × 10 -9). This ATP5H/KCTD2 locus has an important function in mitochondrial energy production and neuronal hyperpolarization during cellular stress conditions, such as hypoxia or glucose deprivation

Machine learning-based prediction and optimisation system for laser shock peening

Laser shock peening (LSP) as a surface treatment technique can improve the fatigue life and corrosion resistance of metallic materials by introducing significant compressive residual stresses near the surface. However, LSP-induced residual stresses are known to be dependent on a multitude of factors, such as laser process variables (spot size, pulse width and energy), component geometry, material properties and the peening sequence. In this study, an intelligent system based on machine learning was developed that can predict the residual stress distribution induced by LSP. The system can also be applied to “reverse-optimise” the process parameters. The prediction system was developed using residual stress data derived from incremental hole drilling. We used artificial neural networks (ANNs) within a Bayesian framework to develop a robust prediction model validated using a comprehensive set of case studies. We also studied the relative importance of the LSP process parameters using Garson’s algorithm and parametric studies to understand the response of the residual stresses in laser peening systems as a function of different process variables. Furthermore, this study critically evaluates the developed machine learning models while demonstrating the potential benefits of implementing an intelligent system in prediction and optimisation strategies of the laser shock peening process