Symmetry

We review the current status of the research on effective nonlocal NJL-like chiral quark models with separable interactions, focusing on the application of this approach to the description of the properties of hadronic and quark matter under extreme conditions. The analysis includes the predictions for various hadron properties in vacuum, as well as the study of the features of deconfinement and chiral restoration phase transitions for systems at finite temperature and/or density. We also address other related subjects, such as the study of phase transitions for imaginary chemical potentials, the possible existence of inhomogeneous phase regions, the presence of color superconductivity, the effects produced by strong external magnetic fields and the application to the description of compact stellar objects. Full article

Unlike scalar and gauge field theories in four dimensions, gravity is not perturbatively renormalizable and as a result perturbation theory is badly divergent. Often the method of choice for investigating nonperturbative effects has been the lattice formulation, and in the case of gravity the Regge–Wheeler lattice path integral lends itself well for that purpose. Nevertheless, lattice methods ultimately rely on extensive numerical calculations, leaving a desire for alternate methods that can be pursued analytically. In this work, we outline the Hartree–Fock approximation to quantum gravity, along lines which are analogous to what is done for scalar fields and gauge theories. The starting point is Dyson’s equations, a closed set of integral equations which relate various physical amplitudes involving graviton propagators, vertex functions, and proper self-energies. Such equations are in general difficult to solve, and as a result they are not very useful in practice, but nevertheless provide a basis for subsequent approximations. This is where the Hartree–Fock approximation comes in, whereby lowest order diagrams get partially dressed by the use of fully interacting Green’s function and self-energies, which then lead to a set of self-consistent integral equations. The resulting nonlinear equations for the graviton self-energy show some remarkable features that clearly distinguish it from the scalar and gauge theory cases. Specifically, for quantum gravity one finds a nontrivial ultraviolet fixed point in Newton’s constant G for spacetime dimensions greater than two, and nontrivial scaling dimensions between $d=2$ and $d=4$, above which one obtains Gaussian exponents. In addition, the Hartree–Fock approximation gives an explicit analytic expression for the renormalization group running of Newton’s constant, suggesting gravitational antiscreening with Newton’s constant slowly increasing on cosmological scales. Full article

The picture fuzzy set is a generation of an intuitionistic fuzzy set. The aggregation operators are important tools in the process of information aggregation. Some aggregation operators for picture fuzzy sets have been proposed in previous papers, but some of them are defective for picture fuzzy multi-attribute decision making. In this paper, we introduce a transformation method for a picture fuzzy number and trapezoidal fuzzy number. Based on this method, we proposed a picture fuzzy multiplication operation and a picture fuzzy power operation. Moreover, we develop the picture fuzzy weighted geometric ($PFWG$) aggregation operator, the picture fuzzy ordered weighted geometric ($PFOWG$) aggregation operator and the picture fuzzy hybrid geometric ($PFHG$) aggregation operator. The related properties are also studied. Finally, we apply the proposed aggregation operators to multi-attribute decision making and pattern recognition. Full article

This paper is concerned with a type of time-symmetric stochastic system, namely the so-called forward–backward doubly stochastic differential equations (FBDSDEs), in which the forward equations are delayed doubly stochastic differential equations (SDEs) and the backward equations are anticipated backward doubly SDEs. Under some monotonicity assumptions, the existence and uniqueness of measurable solutions to FBDSDEs are obtained. The future development of many processes depends on both their current state and historical state, and these processes can usually be represented by stochastic differential systems with time delay. Therefore, a class of nonzero sum differential game for doubly stochastic systems with time delay is studied in this paper. A necessary condition for the open-loop Nash equilibrium point of the Pontriagin-type maximum principle are established, and a sufficient condition for the Nash equilibrium point is obtained. Furthermore, the above results are applied to the study of nonzero sum differential games for linear quadratic backward doubly stochastic systems with delay. Based on the solution of FBDSDEs, an explicit expression of Nash equilibrium points for such game problems is established. Full article

This work proposes a new distribution defined on the unit interval. It is obtained by a novel transformation of a normal random variable involving the hyperbolic secant function and its inverse. The use of such a function in distribution theory has not received much attention in the literature, and may be of interest for theoretical and practical purposes. Basic statistical properties of the newly defined distribution are derived, including moments, skewness, kurtosis and order statistics. For the related model, the parametric estimation is examined through different methods. We assess the performance of the obtained estimates by two complementary simulation studies. Also, the quantile regression model based on the proposed distribution is introduced. Applications to three real datasets show that the proposed models are quite competitive in comparison to well-established models. Full article

The transversity and the Sivers distribution functions of quarks incorporate important information about the transverse-spin and transverse-momentum structure of nucleons. We show how these distributions can be directly determined point by point from leptoproduction asymmetry data collected for various targets and produced hadrons by the COMPASS Collaboration. Only simple symmetry relations are used in the extraction. Full article

Trust prediction is essential to enhancing reliability and reducing risk from the unreliable node, especially for online applications in open network environments. An essential fact in trust prediction is to measure the relation of both the interacting entities accurately. However, most of the existing methods infer the trust relation between interacting entities usually rely on modeling the similarity between nodes on a graph and ignore semantic relation and the influence of negative links (e.g., distrust relation). In this paper, we proposed a relation representation learning via signed graph mutual information maximization (called SGMIM). In SGMIM, we incorporate a translation model and positive point-wise mutual information to enhance the relation representations and adopt Mutual Information Maximization to align the entity and relation semantic spaces. Moreover, we further develop a sign prediction model for making accurate trust predictions. We conduct link sign prediction in trust networks based on learned the relation representation. Extensive experimental results in four real-world datasets on trust prediction task show that SGMIM significantly outperforms state-of-the-art baseline methods. Full article

The systematic and reasonable prediction of pig population is the basis for balance and symmetry between the supply and demand of hogs, which is of great significance to the pig industry. Based on the population prediction model and according to the principle of pig months transfer, this paper referred to the modeling principle and method of the discrete population quantity prediction model and established a related index model that can reflect the pig population development process. The forecasting model we have established includes the following models: (a) a recursive model of pig population, (b) an estimation model of new kept piglets and breeding sows in each month, (c) an estimation model of the initial state of the quantity of the pig population, and (d) a related index model to calculate pig population. On this basis, an example calculation was made to predict the monthly pig population and the quantitative distribution of pigs with different age groups in China from January 2016 to March 2018 in the future. The results showed that the forecasting model of pork supply based on the prediction of pig population is an effective perspective to study the future quantity of pork supply. In the fitting stage, the relative error of the number of slaughtered fattened hogs was 0.0332 and 0.0368. In the prediction stage, the relative error of the quantity of pork supply was 0.0618 and 0.0578. Full article

The recent Coronavirus Disease 2019 (COVID-19) pandemic has put a tremendous burden on global health systems. Medical practitioners are under great pressure for reliable screening of suspected cases employing adjunct diagnostic tools to standard point-of-care testing methodology. Chest X-rays (CXRs) are appearing as a prospective diagnostic tool with easy-to-acquire, low-cost and less cross-contamination risk features. Artificial intelligence (AI)-attributed CXR evaluation has shown great potential for distinguishing COVID-19-induced pneumonia from other associated clinical instances. However, one of the associated challenges with diagnostic imaging-based modeling is incorrect feature attribution, which leads the model to learn misguiding disease patterns, causing wrong predictions. Here, we demonstrate an effective deep learning-based methodology to mitigate the problem, thereby allowing the classification algorithm to learn from relevant features. The proposed deep-learning framework consists of an ensemble of convolutional neural network (CNN) models focusing on both global and local pathological features from CXR lung images, while the latter is extracted using a multi-instance learning scheme and a local attention mechanism. An inspection of a series of backbone CNN models using global and local features, and an ensemble of both features, trained from high-quality CXR images of 1311 patients, further augmented for achieving the symmetry in class distribution, to localize lung pathological features followed by the classification of COVID-19 and other related pneumonia, shows that a DenseNet161 architecture outperforms all other models, as evaluated on an independent test set of 159 patients with confirmed cases. Specifically, an ensemble of DenseNet161 models with global and local attention-based features achieve an average balanced accuracy of 91.2%, average precision of 92.4%, and F1-score of 91.9% in a multi-label classification framework comprising COVID-19, pneumonia, and control classes. The DenseNet161 ensembles were also found to be statistically significant from all other models in a comprehensive statistical analysis. The current study demonstrated that the proposed deep learning-based algorithm can accurately identify the COVID-19-related pneumonia in CXR images, along with differentiating non-COVID-19-associated pneumonia with high specificity, by effectively alleviating the incorrect feature attribution problem, and exploiting an enhanced feature descriptor. Full article

The combination of strong coordination bonds and hydrogen bonding interactions were used to generate a series of supramolecular coordination materials (SCMs), which was achieved by reacting a bis-pyridyl amide ligand, namely N-(4-pyridyl)nicotinamide (4PNA) with copper(II), zinc(II), and cadmium(II) benzoates. The SCMs were structurally characterized using X-ray diffraction and the key intermolecular interactions were identified via Hirshfeld surface analysis. The role of solvent molecules on the supramolecular architecture was analyzed by synthesizing the SCMs in different solvents/solvent mixtures. A solvent-mediated solid-state structural transformation was observed in copper(II) SCMs and we were able to isolate the intermediate form of the crystal-to-crystal transformation process. The luminescence experiments revealed that complexation enhanced the fluorescence properties of 4PNA in the zinc(II) and cadmium(II) SCMs, but a reverse phenomenon was observed in the copper(II) SCMs. This work demonstrated the tuning of supramolecular assembly in coordination compounds as a function of solvents for generating SCMs with diverse properties. Full article

Assessment of lateral symmetries (LS) of lower limbs has been widely investigated. However, there are no studies about LS during exposure to high eccentric and concentric loads during flywheel (FW) squats. A total of 422 young, physically active participants performed squats on an FW device with different equidistant loads (0.05, 0.125 and 0.2 kg?m²). The mean and peak force of the left and right leg were assessed for the concentric and eccentric contaction phase. LS values were calculated for each load and phase of squat. Our results showed that the absolute mean and peak force of the concentric and eccentric phase of contraction had excellent reliability, while LS values were more reliable when eccentric force was used for their calculation. Mean and peak forces were increased with the higher FW load. In general, we found a decrease in LS values in the concentric phase of contraction with the higher load. Moreover, values of LS are similar to a wide range of other functional movements. Nevertheless, symmetrical force application during squatting on a FW device should be satisfied regardless of the FW load. Due to the large sample size, our results are valuable as a reference point when athletes are evaluated during training. Full article

Monitoring manufacturing process variation remains challenging, especially within a rapid and automated manufacturing environment. Problematic and unstable processes may produce distinct time series patterns that could be associated with assignable causes for diagnosis purpose. Various machine learning classification techniques such as artificial neural network (ANN), classification and regression tree (CART), and fuzzy inference system have been proposed to enhance the capability of traditional Shewhart control chart for process monitoring and diagnosis. ANN classifiers are often opaque to the user with limited interpretability on the classification procedures. However, fuzzy inference system and CART are more transparent, and the internal steps are more comprehensible to users. There have been limited works comparing these two techniques in the control chart pattern recognition (CCPR) domain. As such, the aim of this paper is to demonstrate the development of fuzzy heuristics and CART technique for CCPR and compare their classification performance. The results show the heuristics Mamdani fuzzy classifier performed well in classification accuracy (95.76%) but slightly lower compared to CART classifier (98.58%). This study opens opportunities for deeper investigation and provides a useful revisit to promote more studies into explainable artificial intelligence (XAI). Full article

According to research, generally, 2.5 quintillion bytes of data are produced every day. About 90% of the world’s data has been produced in the last two years alone. The amount of data is increasing immensely. There is a fight to use and store this tremendous information effectively. HBase is the top option for storing huge data. HBase has been selected for several purposes, including its scalability, efficiency, strong consistency support, and the capacity to support a broad range of data models. This paper seeks to define, taxonomically classify, and systematically compare existing research on a broad range of storage technologies, methods, and data models based on HBase storage architecture’s symmetry. We perform a systematic literature review on a number of published works proposed for HBase storage architecture. This research synthesis results in a knowledge base that helps understand which big data storage method is an effective one. Full article

We outline and review the computations of polarized and unpolarized nucleon structure functions within the bosonized Nambu-Jona-Lasinio chiral soliton model. We focus on a consistent regularization prescription for the Dirac sea contribution and present numerical results from that formulation. We also reflect on previous calculations on quark distributions in chiral quark soliton models and attempt to put them into perspective. Full article

In this paper, sufficient conditions ensuring existence and multiplicity of positive solutions for a class of nonlinear singular fractional differential systems are derived with Riemann–Stieltjes coupled integral boundary value conditions in Banach Spaces. Nonlinear functions $f(t,u,v)$ and $g(t,u,v)$ in the considered systems are allowed to be singular at every variable. The boundary conditions here are coupled forms with Riemann–Stieltjes integrals. In order to overcome the difficulties arising from the singularity, a suitable cone is constructed through the properties of Green’s functions associated with the systems. The main tool used in the present paper is the fixed point theorem on cone. Lastly, an example is offered to show the effectiveness of our obtained new results. Full article

In this work we report the linear and non-linear IR spectral response characterization of the CO bonds of $Penicillin$G sodium salt in $D_{2}O$ and in $DMSO-d6$ solutions. In order to better characterize the spectral IR features in the CO stretching region, broadband middle infrared pump-probe spectra are recorded. The role of hydrogen bonds in determining the inhomogeneous broadening and in tuning anharmonicity of the different types of oscillators is exploited. Narrow band pump experiments, at the three central frequencies of $\beta -lactam$, amide and carboxylate CO stretching modes, identify the couplings between the different types of CO oscillators opening the possibility to gather structural dynamic information. Our results show that the strongest coupling is between the $\beta -lactam$ and the carboxylate CO vibrational modes. Full article

It was suggested that the brain-heart connection is asymmetrically organized. However, evidence connecting neurochemical factors from each brain hemisphere with changes in cardio-vascular functions have not yet been reported. In order to analyze potential asymmetrical connections between brain neurochemical factors with cardio-vascular functions, we studied the level of correlations between the left and right frontal cortex (FC) soluble (Sol) and membrane-bound (MB) neuropeptide-degrading enzymes alanyl (AlaAP), cystinyl (CysAP), and glutamyl (GluAP) aminopeptidase activities, involved among others in the metabolism of angiotensins, with heart rate (HR), systolic (SBP), and diastolic (DBP) blood pressure, in rats treated or not with hypotensive or hypertensive drugs such as captopril, propranolol or L-NAME. The present study suggests the existence of a bidirectional asymmetrical connection between these brain neuropeptidases and cardio-vascular functions. Specifically, depending on treatment, in control group, Sol AlaAP from the left FC correlates negatively with SBP and DBP. In captopril-treated animals, MB CysAP and MB GluAP from the right FC correlate negatively with HR. In L-NAME treated rats, Sol CysAP from the right FC correlates negatively with DBP. No significant correlations were observed in the propranolol group. Considering together all the values obtained from the left or the right cortex of the four groups regardless of drug treatment, the results demonstrated significant negative correlations between these neuropeptidase activities, mainly from the left frontal cortex, with the levels of systolic and diastolic blood pressure. Remarkably, these findings contrast drastically with previously reported results indicating significant positive correlations between the left frontal cortex with other peripheral functions such as water intake and diuresis. Both results represent noteworthy information that strongly supports the concept of a bidirectional asymmetric organization of neurovisceral integration involving left and right brain neurochemical processes with peripheral physiological functions, most probably mediated by the autonomic nervous system. Overall, the present results suggest that cognitive functions involving the frontal cortex may be asymmetrically connected with peripheral physiological processes, and vice versa. Full article

This paper presents a feature selection model based on mean impact value (MIV) to solve induction motor (IM) fault diagnosis on the current signal. In this paper, particle swarm optimization (PSO) is combined with back propagation neural network (BPNN) to classify the current signal of IM. First, the purpose of this study is to establish IM fault diagnosis system. Additionally, this study proposes a feature selection process that is composed of MIV, whose objective is to reduce the number of classifier input features. Secondly, the features are extracted as a feature database after analyzing the current signal of IM, and the fault diagnosis is established through the model of PSO-BPNN. Finally, redundant features are deleted through this feature selection process and a classifier is built. The result shows that the feature selection model based on MIV can filter the features effectively at a signal to noise ratio of 30 dB and 20 dB for the IM fault detection problem. In addition, the computing time of BPNN is also reduced which is helpful for online detection. Full article

This paper considers commuting graphs over the semidihedral group $S{D}_{8n}$. We compute their eigenvalues and obtain that these commuting graphs are not hyperenergetic for odd $n\ge 15$ or even $n\ge 2$. We further compute the Laplacian spectrum, the Laplacian energy and the number of spanning trees of the commuting graphs over $S{D}_{8n}$. We also discuss vertex connectivity, planarity, and minimum disconnecting sets of these graphs and prove that these commuting graphs are not Hamiltonian. Full article