Static quark-antiquark pair free energy and screening masses: continuum results at the QCD physical point

We study the correlators of Polyakov loops, and the corresponding gauge invariant free energy of a static quark-antiquark pair in 2+1 flavor QCD at finite temperature. Our simulations were carried out on $N_t$ = 6, 8, 10, 12, 16 lattices using a Symanzik improved gauge action and a stout improved staggered action with physical quark masses. The free energies calculated from the Polyakov loop correlators are extrapolated to the continuum limit. For the free energies we use a two step renormalization procedure that only uses data at finite temperature. We also measure correlators with definite Euclidean time reversal and charge conjugation symmetry to extract two different screening masses, one in the magnetic, and one in the electric sector, to distinguish two different correlation lengths in the full Polyakov loop correlator. This conference contribution is based on the paper: JHEP 1504 (2015) 138Comment: 7 pages, 4 figures. Talk presented at the 33rd International Symposium on Lattice Field Theory (Lattice 2015), 14-18 July 2015, Kobe International Conference Center, Kobe, Japa

Landscape-scale connectivity and fragment size determine species composition of grassland fragments

As a consequence of agricultural intensification and habitat fragmentation since the mid-20th century, biological diversity has declined considerably throughout the world, particularly in Europe. We assessed how habitat and landscape-scale heterogeneity, such as variation in fragment size (small vs. large) and landscape configuration (measured as connectivity index), affect plant and arthropod diversity. We focused on arthropods with different feeding behaviour and mobility, spiders (predators, moderate dispersal), true bugs (mainly herbivores and omnivores with moderate dispersal), wild bees (pollinators with good dispersal abilities), and wasps (pollinators, omnivores with good dispersal abilities). We studied 60 dry grassland fragments in the same region (Hungarian Great Plain); 30 fragments were represented by the grassland component of forest-steppe stands, and 30 were situated on burial mounds (kurgans). Forest-steppes are mosaics of dry grasslands with small forests in a matrix of plantation forests. Kurgans are ancient burial mounds with moderately disturbed grasslands surrounded by agricultural fields. The size of fragments ranged between 0.16 6.88 ha (small: 0.16 0.48 ha, large: 0.93 6.88 ha) for forest-steppes and 0.01 0.44 ha (small: 0.01 0.10 ha and large: 0.20 0.44 ha) for kurgans. Fragments also represented an isolation gradient from almost cleared and homogenous landscapes, to landscapes with relatively high compositional heterogeneity. Fragment size, connectivity, and their interaction affected specialist and generalist species abundances of forest-steppes and kurgans. Large fragments had higher species richness of ground-dwelling spiders, and the effect of connectivity was more strongly positive for specialist arthropods and more strongly negative for generalists in large than in small fragments. However, we also found a strong positive impact of connectivity for generalist plants in small kurgans in contrast to larger ones. We conclude that besides the well-known effect of enhancing habitat quality, increasing connectivity between fragments by restoring natural and semi-natural habitat patches would help to maintain grassland biodiversityinfo:eu-repo/semantics/publishedVersio

Systematic Review of Deep Learning and Machine Learning for Building Energy

The building energy (BE) management plays an essential role in urban sustainability and smart cities. Recently, the novel data science and data-driven technologies have shown significant progress in analyzing the energy consumption and energy demand datasets for a smarter energy management. The machine learning (ML) and deep learning (DL) methods and applications, in particular, have been promising for the advancement of accurate and high-performance energy models. The present study provides a comprehensive review of ML- and DL-based techniques applied for handling BE systems, and it further evaluates the performance of these techniques. Through a systematic review and a comprehensive taxonomy, the advances of ML and DL-based techniques are carefully investigated, and the promising models are introduced. According to the results obtained for energy demand forecasting, the hybrid and ensemble methods are located in the high-robustness range, SVM-based methods are located in good robustness limitation, ANN-based methods are located in medium-robustness limitation, and linear regression models are located in low-robustness limitations. On the other hand, for energy consumption forecasting, DL-based, hybrid, and ensemble-based models provided the highest robustness score. ANN, SVM, and single ML models provided good and medium robustness, and LR-based models provided a lower robustness score. In addition, for energy load forecasting, LR-based models provided the lower robustness score. The hybrid and ensemble-based models provided a higher robustness score. The DL-based and SVM-based techniques provided a good robustness score, and ANNbased techniques provided a medium robustness score

Pulmonary Vein Isolation Without Left Atrial Mapping

Background: One of the crucial points during in most approaches developed for ablation of atrial fibrillation (AF) is the ability to identify the pulmonary vein (PVs) and to accurately locate their ostia. Objectives: The purpose of this case series was to investigate a simplified method for fusion of the multislice computer tomography (CT) derived 3D dataset with the electroanatomical map in order to facilitate the mapping procedure. Methods: In 5 consecutive patients (4 male) referred for catheter ablation of symptomatic drug-refractory paroxysmal atrial fibrillation contrast enhanced computer tomography was performed before the procedure and imported into an electroanatomical mapping system (Carto XP) using CartoMerge Image Integration Module. During the procedure a multipolar mapping catheter (Quick Star DS, Biosense Webster, Diamond Bar, CA, USA) was introduced to the coronary sinus (CS) to align the CSCT shell to the proper position. The CS potentials provided information to identify the ostium of the CS to achieve a more accurate fusion of the images. No mapping points were taken in the left atrium. The feasibility of the method was characterized by the distance of mapping points. Mapping, registration and outcome data were compared with a cohort of patients undergoing MRI image integration. Result: The mean distance between the mapping points taken in the CS by the Quick Star catheter and the CS CT surface was suitable (mean±SD, 1.4±0.3 mm). Full electrical isolation of the pulmonary veins could be achieved in all patients. The mean procedure and fluoroscopy time were 39 ± 22 and 134 ±38 min respectively, significantly decreased as compared to the MRI cohort. Conclusions: Highly accurate CT image and the electroanatomical map (EAM) fusion can be obtained by the Carto 3D electromanatomical mapping system using CS as the key anatomical structure for registration. Using this technique the mapping time of the left atrium can be reduced

Correlation Analysis of Factors Affecting Firm Performance and Employees Wellbeing: Application of Advanced Machine Learning Analysis

Given the importance of identifying key performance points in organizations, this research intends to determine the most critical intra- and extra-organizational elements in assessing the performance of firms using the European Company Survey (ECS) 2019 framework. The ECS 2019 survey data were used to train an artificial neural network optimized using an imperialist competitive algorithm (ANN-ICA) to forecast business performance and employee wellbeing. In order to assess the correctness of the model, root mean square error (RMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (r), and determination coefficient (R2) have been employed. The mean values of the performance criteria for the impact of internal and external factors on firm performance were 1.06, 0.002, 0.041, 0.9, and 0.83, and the value of the performance metrics for the impact of internal and external factors on employee wellbeing were 0.84, 0.0019, 0.0319, 0.83, and 0.71 (respectively, for MAPE, MSE, RMSE, r, and R2). The great performance of the ANN-ICA model is indicated by low values of MAPE, MSE, and RMSE, as well as high values of r and R2. The outcomes showed that “skills requirements and skill matching” and “employee voice” are the two factors that matter most in enhancing firm performance and wellbeing

Correlation Analysis of Factors Affecting Firm Performance and Employees Wellbeing: Application of Advanced Machine Learning Analysis

Given the importance of identifying key performance points in organizations, this research intends to determine the most critical intra- and extra-organizational elements in assessing the performance of firms using the European Company Survey (ECS) 2019 framework. The ECS 2019 survey data were used to train an artificial neural network optimized using an imperialist competitive algorithm (ANN-ICA) to forecast business performance and employee wellbeing. In order to assess the correctness of the model, root mean square error (RMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (r), and determination coefficient (R2) have been employed. The mean values of the performance criteria for the impact of internal and external factors on firm performance were 1.06, 0.002, 0.041, 0.9, and 0.83, and the value of the performance metrics for the impact of internal and external factors on employee wellbeing were 0.84, 0.0019, 0.0319, 0.83, and 0.71 (respectively, for MAPE, MSE, RMSE, r, and R2). The great performance of the ANN-ICA model is indicated by low values of MAPE, MSE, and RMSE, as well as high values of r and R2. The outcomes showed that “skills requirements and skill matching” and “employee voice” are the two factors that matter most in enhancing firm performance and wellbeing

Sex Differences in Morphological and Functional Aspects of Exercise-Induced Cardiac Hypertrophy in a Rat Model

Background: Recent evidences suggest that sex hormones may be involved in the regulation of exercise-induced left ventricular (LV) hypertrophy. However, the sex-specific functional consequences of exercise-induced myocardial hypertrophy is still not investigated in detail. We aimed at understanding the sex-specific functional and morphological alterations in the LV and the underlying molecular changes in a rat model of athlete's heart.Methods: We divided our young, adult male and female rats into control and exercised groups. Athlete's heart was induced by a 12-week long swim training. Following the training period, we assessed LV hypertrophy with echocardiography, while pressure-volume analysis was performed to investigate in vivo LV function. After in vivo experiments, molecular biological studies and histological investigations were performed.Results: Echocardiography and post-mortem measured heart weight data indicated LV hypertrophy in both genders, nevertheless it was more pronounced in females. Despite the more significant relative hypertrophy in females, characteristic functional parameters did not show notable differences between the genders. LV pressure-volume analysis showed increased stroke volume, improved contractility and stroke work and unaltered LV stiffness in both male and female exercised rats, while active relaxation was ameliorated solely in male animals. The induction of Akt signaling was more significant in females compared to males. There was also a characteristic difference in the mitogen-activated protein kinase pathway as suppressed phosphorylation of p44/42 MAPK (Erk) and mTOR was observed in female exercised rats, but not in male ones. Myosin heavy chain alpha (MHC)/beta-MHC ratio did not differ in males, but increased markedly in females.Conclusion: Our results confirm that there is a more pronounced exercise-induced LV hypertrophy in females as compared to the males, however, there are only minor differences regarding LV function. There are characteristic molecular differences between male and female animals, that can explain different degrees of LV hypertrophy