Pseudo-Critical Temperature and Thermal Equation of State from Nf = 2 Twisted Mass Lattice QCD

We report about the current status of our ongoing study of the chiral limit of two-flavor QCD at finite temperature with twisted mass quarks. We estimate the pseudo-critical temperature Tc for three values of the pion mass in the range of mPS ~ 300 and 500 MeV and discuss different chiral scenarios. Furthermore, we present first preliminary results for the trace anomaly, pressure and energy density. We have studied several discretizations of Euclidean time up to Nt = 12 in order to assess the continuum limit of the trace anomaly. From its interpolation we evaluate the pressure and energy density employing the integral method. Here, we have focussed on two pion masses with mPS ~ 400 and 700 MeV

Chlorido(chloro­diphenyl­phosphine-κP)(diphenyl­piperidinophosphine-κP)(η5-penta­methyl­cyclo­penta­dien­yl)ruthenium(II)

The title compound, [Ru(C10H15)Cl(C12H10ClP)(C17H20NP)], is a half-sandwich complex of RuII with the chloro­diphenyl­phosphine ligand formed from the diphenyl­piperidinophosphine and chlorine of the precursor complex [Ru(η5-C5Me5)(κ1P—Ph2PNC5H10)Cl2] by an unexpected reaction with NaBH4. The complex has a three-legged piano-stool geometry, with Ru—P bond lengths of 2.2598 (5) Å for the chloro­phosphine and 2.3303 (5) Å for the amino­phosphine

Turbulence effect on gas transport in three contrasting forest soils

peer reviewe

Investigation of Feature Engineering Methods for Domain-Knowledge-Assisted Bearing Fault Diagnosis

The engineering challenge of rolling bearing condition monitoring has led to a large number of method developments over the past few years. Most commonly, vibration measurement data are used for fault diagnosis using machine learning algorithms. In current research, purely data-driven deep learning methods are becoming increasingly popular, aiming for accurate predictions of bearing faults without requiring bearing-specific domain knowledge. Opposing this trend in popularity, the present paper takes a more traditional approach, incorporating domain knowledge by evaluating a variety of feature engineering methods in combination with a random forest classifier. For a comprehensive feature engineering study, a total of 42 mathematical feature formulas are combined with the preprocessing methods of envelope analysis, empirical mode decomposition, wavelet transforms, and frequency band separations. While each single processing method and feature formula is known from the literature, the presented paper contributes to the body of knowledge by investigating novel series connections of processing methods and feature formulas. Using the CWRU bearing fault data for performance evaluation, feature calculation based on the processing method of frequency band separation leads to particularly high prediction accuracies, while at the same time being very efficient in terms of low computational effort. Additionally, in comparison with deep learning approaches, the proposed feature engineering method provides excellent accuracies and enables explainability

Prognostic value of indoleamine 2,3 dioxygenase in patients with higher‐risk myelodysplastic syndromes treated with azacytidine

Hypomethylating agents (HMAs) are widely used in patients with higher‐risk myelodysplastic syndromes (MDS) not eligible for stem cell transplantation; however, the response rate is <50%. Reliable predictors of response are still missing, and it is a major challenge to develop new treatment strategies. One current approach is the combination of azacytidine (AZA) with checkpoint inhibitors; however, the potential benefit of targeting the immunomodulator indoleamine‐2,3‐dioxygenase (IDO‐1) has not yet been evaluated. We observed moderate to strong IDO‐1 expression in 37% of patients with high‐risk MDS. IDO‐1 positivity was predictive of treatment failure and shorter overall survival. Moreover, IDO‐1 positivity correlated inversely with the number of infiltrating CD8+ T cells, and IDO‐1+ patients failed to show an increase in CD8+ T cells under AZA treatment. In vitro experiments confirmed tryptophan catabolism and depletion of CD8+ T cells in IDO‐1+ MDS, suggesting that IDO‐1 expression induces an immunosuppressive microenvironment in MDS, thereby leading to treatment failure under AZA treatment. In conclusion, IDO‐1 is expressed in more than one‐third of patients with higher‐risk MDS, and is predictive of treatment failure and shorter overall survival. Therefore, IDO‐1 is emerging as a promising predictor and therapeutic target, especially for combination therapies with HMAs or checkpoint inhibitors

Optimizing Convolutional Neural Networks for Chronic Obstructive Pulmonary Disease Detection in Clinical Computed Tomography Imaging

Purpose: To optimize the binary detection of Chronic Obstructive Pulmonary Disease (COPD) based on emphysema presence in the lung with convolutional neural networks (CNN) by exploring manually adjusted versus automated window-setting optimization (WSO) on computed tomography (CT) images. Methods: 7,194 CT images (3,597 with COPD; 3,597 healthy controls) from 78 subjects (43 with COPD; 35 healthy controls) were selected retrospectively (10.2018-12.2019) and preprocessed. For each image, intensity values were manually clipped to the emphysema window setting and a baseline 'full-range' window setting. Class-balanced train, validation, and test sets contained 3,392, 1,114, and 2,688 images. The network backbone was optimized by comparing various CNN architectures. Furthermore, automated WSO was implemented by adding a customized layer to the model. The image-level area under the Receiver Operating Characteristics curve (AUC) [lower, upper limit 95% confidence] and P-values calculated from one-sided Mann-Whitney U-test were utilized to compare model variations. Results: Repeated inference (n=7) on the test set showed that the DenseNet was the most efficient backbone and achieved a mean AUC of 0.80 [0.76, 0.85] without WSO. Comparably, with input images manually adjusted to the emphysema window, the DenseNet model predicted COPD with a mean AUC of 0.86 [0.82, 0.89] (P=0.03). By adding a customized WSO layer to the DenseNet, an optimal window in the proximity of the emphysema window setting was learned automatically, and a mean AUC of 0.82 [0.78, 0.86] was achieved. Conclusion: Detection of COPD with DenseNet models was improved by WSO of CT data to the emphysema window setting range

The thermal QCD transition with two flavours of twisted mass fermions

We investigate the thermal QCD transition with two flavors of maximally twisted mass fermions for a set of pion masses, 300 MeV \textless $m_\pi$ \textless 500 MeV, and lattice spacings $a$ \textless 0.09 fm. We determine the pseudo-critical temperatures and discuss their extrapolation to the chiral limit using scaling forms for different universality classes, as well as the scaling form for the magnetic equation of state. For all pion masses considered we find resonable consistency with O(4) scaling plus leading corrections. However, a true distinction between the O(4) scenario and a first order scenario in the chiral limit requires lighter pions than are currently in use in simulations of Wilson fermions.Comment: 11 pages, 11 figure