Consistency Problems for Jump-Diffusion Models

In this paper consistency problems for multi-factor jump-diffusion models, where the jump parts follow multivariate point processes are examined. First the gap between jump-diffusion models and generalized Heath-Jarrow-Morton (HJM) models is bridged. By applying the drift condition for a generalized arbitrage-free HJM model, the consistency condition for jump-diffusion models is derived. Then we consider a case in which the forward rate curve has a separable structure, and obtain a specific version of the general consistency condition. In particular, a necessary and sufficient condition for a jump-diffusion model to be affine is provided. Finally the Nelson-Siegel type of forward curve structures is discussed. It is demonstrated that under regularity condition, there exists no jump-diffusion model consistent with the Nelson-Siegel curves.Comment: To appear in Applied Mathematical Financ

The Effect of Virtual Reality Technology on the Imagery Skills and Performance of Target-Based Sports Athletes

The aim of this study is the examination of the effect of virtual reality based imagery (VRBI) training programs on the shot performance and imagery skills of athletes and, and to conduct a comparison with Visual Motor Behavior Rehearsal and Video Modeling (VMBR + VM). In the research, mixed research method and sequential explanatory design were used. In the quantitative dimension of the study the semi-experimental model was used, and in the qualitative dimension the case study design was adopted. The research participants were selected from athletes who were involved in our target sports: curling (n = 14), bowling (n = 13), and archery (n = 7). All participants were randomly assigned to VMBR + VM (n = 11), VRBI (n = 12), and Control (n = 11) groups through the “Research Randomizer” program. The quantitative data of the study was: the weekly shot performance scores of the athletes and the data obtained from the “Movement Imagery Questionnaire-Revised.” The qualitative data was obtained from the data collected from the semi-structured interview guide, which was developed by researchers and field experts. According to the results obtained from the study, there were statistically significant differences between the groups in terms of shot performance and imagery skills. VRBI training athletes showed more improvement in the 4-week period than the athletes in the VMBR + VM group, in terms of both shot performance and imagery skills. In addition, the VRBI group adapted to the imagery training earlier than the VMBR + VM group. As a result, it was seen that they showed faster development in shot performances. From these findings, it can be said that VRBI program is more efficient in terms of shot performance and imagery skills than VMBR + VM, which is the most used imaging training model. © Copyright © 2021 Bedir and Erhan.We thank the participants and students of Atat?rk University Faculty of Sport Sciences, Selim Can Da??stanl? and Irem Ba?adur

Structural Material Property Tailoring Using Deep Neural Networks

Advances in robotics, artificial intelligence, and machine learning are ushering in a new age of automation, as machines match or outperform human performance. Machine intelligence can enable businesses to improve performance by reducing errors, improving sensitivity, quality and speed, and in some cases achieving outcomes that go beyond current resource capabilities. Relevant applications include new product architecture design, rapid material characterization, and life-cycle management tied with a digital strategy that will enable efficient development of products from cradle to grave. In addition, there are also challenges to overcome that must be addressed through a major, sustained research effort that is based solidly on both inferential and computational principles applied to design tailoring of functionally optimized structures. Current applications of structural materials in the aerospace industry demand the highest quality control of material microstructure, especially for advanced rotational turbomachinery in aircraft engines in order to have the best tailored material property. In this paper, deep convolutional neural networks were developed to accurately predict processing-structure-property relations from materials microstructures images, surpassing current best practices and modeling efforts. The models automatically learn critical features, without the need for manual specification and/or subjective and expensive image analysis. Further, in combination with generative deep learning models, a framework is proposed to enable rapid material design space exploration and property identification and optimization. The implementation must take account of real-time decision cycles and the trade-offs between speed and accuracy

X-ray Fluorescence Analysis of Feldspars and Silicate Glass: Effects of Melting Time on Fused Bead Consistency and Volatilisation

Reproducible preparation of lithium tetraborate fused beads for XRF analysis of glass and mineral samples is of paramount importance for analytical repeatability. However, as with all glass melting processes, losses due to volatilisation must be taken into account and their effects are not negligible. Here the effects of fused bead melting time have been studied for four Certified Reference Materials (CRM’s: three feldspars, one silicate glass), in terms of their effects on analytical variability and volatilisation losses arising from fused bead preparation. At melting temperatures of 1065 °C, and for feldspar samples, fused bead melting times shorter than approximately 25 min generally gave rise to a greater deviation of the XRF-analysed composition from the certified composition. This variation might be due to incomplete fusion and/or fused bead inhomogeneity but further research is needed. In contrast, the shortest fused bead melting time for the silicate glass CRM gave an XRF-analysed composition closer to the certified values than longer melting times. This may suggest a faster rate of glass-in-glass dissolution and homogenization during fused bead preparation. For all samples, longer melting times gave rise to greater volatilisation losses (including sulphates and halides) during fusion. This was demonstrated by a linear relationship between SO3 mass loss and time1/2, as predicted by a simple diffusion-based model. Iodine volatilisation displays a more complex relationship, suggestive of diffusion plus additional mechanisms. This conclusion may have implications for vitrification of iodine-bearing radioactive wastes. Our research demonstrates that the nature of the sample material impacts on the most appropriate fusion times. For feldspars no less than ~25 min and no more than ~60 min of fusion at 1065 °C, using Li2B4O7 as the fusion medium and in the context of feldspar samples and the automatic fusion equipment used here, strikes an acceptable (albeit non-ideal) balance between the competing factors of fused bead quality, analytical consistency and mitigating volatilisation losses. Conversely, for the silicate glass sample, shorter fusion times of less than ~30 min under the same conditions provided more accurate analyses whilst limiting volatile losses

Fas induces apoptosis in human coronary artery endothelial cells in vitro

BACKGROUND: Published work suggests that some types of endothelial cells undergo apoptosis in response to ligation of the receptor Fas (CD95, APO1) but other types are resistant. Because heterogeneity among endothelial cells from different tissues, has been demonstrated, the purpose of this study was to determine, if Fas ligation and/or activation by human Fas ligand induces apoptosis and caspase activities, in cultured human coronary artery endothelial cells, and the differences between TNF-a and FAS induced apoptosis in these cells. RESULTS: Cultured human coronary artery endothelial cells (HCAEC) were exposed to the monoclonal Fas-activating antibody CH-11, to purified recombinant human Fas ligand, to the Fas-neutralizing antibody ZB4, or to purified recombinant human TNF-α. Apoptosis was detected by assessment of chromatin condensation and nuclear fragmentation and by assay of the enzymatic activities of Caspase 1 and Caspase 3 with membrane-permeable substrates applied to intact cells. Fas protein was detected by immunoblotting of HCAEC lysates. Apoptosis was induced in HCAEC by purified Fas ligand or by the monoclonal activating antibody CH-11 at concentrations of 25 or 200 ng/ml, but not by nonspecific isotype-matched immunoglobulins. The apoptotic index elicited by either Fas activator was equal to that induced by TNF-a (3.0-3.6-fold versus control, p < 0.01). The Fas-neutralizing antibody ZB4 abrogated HCAEC apoptosis induced by CH-11, but had no inhibitory effect on apoptosis in response to TNF-a. Fas ligation significantly increased the activities of both Caspase 1 and Caspase 3 at 20 hours of stimulation (1.7- and 2.0-fold versus control, both p < 0.05); in contrast, purified TNF-a increased the activity of Caspase 3 but not Caspase 1 (2.1-fold, p < 0.05). Western blotting of HCAEC lysates with antibody CH-11 identified a single immunoreactive protein of 90 kDa. CONCLUSIONS: Cultured human coronary artery endothelial cells express functional Fas capable of inducing apoptosis in response to either purified Fas ligand or receptor-activating monoclonal antibodies, at levels equal to those inducible by purified TNF-α. Immunologic studies and differential kinetics of caspase activation suggest that Fas and TNF-α induce apoptosis in HCAEC by signaling pathways that are distinct but equal in potency