Coulomb gluons will generally destroy coherence

Coherence violation is an interesting and counter-intuitive phenomenon in QCD. We discuss the circumstances under which violation occurs in observables sensitive to soft radiation and arrive at the conclusion that almost all such observables at hadron colliders will violate coherence to some degree. We illustrate our discussion by considering the gaps-between-jets observable, where coherence violation is super-leading, then we generalise to other observables, including precise statements on the logarithmic order of coherence violation.

Random walks on Cayley graphs of complex reflection groups

Asymptotic properties of random walks on minimal Cayley graphs of complex reflection groups are investigated. The main result of the paper is theorem on fast mixing for random walks on Cayley graphs of complex reflection groups. Particularly, bounds of diameters and isoperimetric constants, a known result on fast fixing property for expander graphs play a crucial role to obtain the main result. A constructive way to prove a special case of Babai’s conjecture on logarithmic order of diameters for complex reflection groups is proposed. Basing on estimates of diameters and Cheeger inequality, there is obtained a non-trivial lower bound for spectral gaps of minimal Cayley graphs on complex reflection groups.

Electric dipole moment constraints on CP-violating heavy-quark Yukawas at next-to-leading order

Electric dipole moments are sensitive probes of new phases in the Higgs Yukawa couplings. We calculate the complete two-loop QCD anomalous dimension matrix for the mixing of CP-odd scalar and tensor operators and apply our results for a phenomenological study of CP violation in the bottom and charm Yukawa couplings. We find large shifts of the induced Wilson coefficients at next-to-leading-logarithmic order. Using the experimental bound on the electric dipole moments of the neutron and mercury, we update the constraints on CP-violating phases in the bottom and charm quark Yukawas.

Algorithmic Information Distortions in Node-Aligned and Node-Unaligned Multidimensional Networks

In this article, we investigate limitations of importing methods based on algorithmic information theory from monoplex networks into multidimensional networks (such as multilayer networks) that have a large number of extra dimensions (i.e., aspects). In the worst-case scenario, it has been previously shown that node-aligned multidimensional networks with non-uniform multidimensional spaces can display exponentially larger algorithmic information (or lossless compressibility) distortions with respect to their isomorphic monoplex networks, so that these distortions grow at least linearly with the number of extra dimensions. In the present article, we demonstrate that node-unaligned multidimensional networks, either with uniform or non-uniform multidimensional spaces, can also display exponentially larger algorithmic information distortions with respect to their isomorphic monoplex networks. However, unlike the node-aligned non-uniform case studied in previous work, these distortions in the node-unaligned case grow at least exponentially with the number of extra dimensions. On the other hand, for node-aligned multidimensional networks with uniform multidimensional spaces, we demonstrate that any distortion can only grow up to a logarithmic order of the number of extra dimensions. Thus, these results establish that isomorphisms between finite multidimensional networks and finite monoplex networks do not preserve algorithmic information in general and highlight that the algorithmic information of the multidimensional space itself needs to be taken into account in multidimensional network complexity analysis.

Effect of Red Cabbage Sprouts Treating with Organic Acids on the Content of Polyphenols, Antioxidant Properties and Colour Parameters

In recent years, there has been a great deal of consumer interest in consuming vegetables in the form of sprouts, characterized by high nutritional value. The disadvantage of sprouts is the loss of bioactive compounds during storage and the relatively short shelf life, due to the fact that they are a good medium for microorganisms, especially yeasts and molds. The aim of the study was to compare the content of polyphenols, antioxidant properties, color and microbiological quality of red cabbage sprouts preserved by the use of mild organic acids: Citric, ascorbic, lactic, acetic and peracetic. In the study, the content of polyphenols and antioxidant properties of sprouts was examined using the spectrophotometric method, instrumental color measurement was done using an “electronic eye” and the content of mold, yeast and the total number of mesophilic microorganisms was determined using the plate inoculation method. Taking into account the content of polyphenols and the antioxidant potential of sprouts, it was found that the addition of all organic acids contributed to the preservation of the tested compounds during their 14-day storage under refrigerated conditions, depending on the type of organic acid used, from 71 to 86% for polyphenols and from 75 to 96% for antioxidant properties. The best results were obtained by treating the sprouts with peracetic acid and ascorbic acid, respectively, at a concentration of 80 ppm and 1%. The conducted research on the possibility of extending the storage life and preserving the bioactive properties of fresh sprouts showed that the use of peracetic acid in the form of an aqueous solution during pre-treatment allows to reduce the content of microorganisms by one logarithmic order. Ascorbic acid did not reduce the content of microorganisms in the sprout samples tested. Considering the content of bioactive ingredients, as well as the microbiological quality of fresh sprouts, it can be said that there is a great need to use mild organic acids during the pre-treatment of sprouts in order to maintain a high level of health-promoting ingredients during their storage, which may also contribute to their prolongation durability.

Algorithmic Information Distortions in Node-Aligned and Node-Unaligned Multidimensional Networks

In this article, we investigate limitations of importing methods based on algorithmic information theory from monoplex networks into multidimensional networks (such as multilayer networks) that have a large number of extra dimensions (i.e., aspects). In the worst-case scenario, it has been previously shown that node-aligned multidimensional networks with non-uniform multidimensional spaces can display exponentially larger algorithmic information (or lossless compressibility) distortions with respect to their isomorphic monoplex networks, so that these distortions grow at least linearly with the number of extra dimensions. In the present article, we demonstrate that node-unaligned multidimensional networks, either with uniform or non-uniform multidimensional spaces, can also display exponentially larger algorithmic information distortions with respect to their isomorphic monoplex networks. However, unlike the node-aligned non-uniform case studied in previous work, these distortions in the node-unaligned case grow at least exponentially with the number of extra dimensions. On the other hand, for node-aligned multidimensional networks with uniform multidimensional spaces, we demonstrate that any distortion can only grow up to a logarithmic order of the number of extra dimensions. Thus, these results establish that isomorphisms between finite multidimensional networks and finite monoplex networks do not preserve algorithmic information in general and highlight that the algorithmic information of the multidimensional space itself needs to be taken into account in multidimensional network complexity analysis.

Combined subleading high-energy logarithms and NLO accuracy for W production in association with multiple jets

Large logarithmic corrections in $$ \hat{s}/{p}_t^2 $$ s ̂ / p t 2 lead to substantial variations in the perturbative predictions for inclusive W-plus-dijet processes at the Large Hadron Collider. This instability can be cured by summing the leading-logarithmic contributions in $$ \hat{s}/{p}_t^2 $$ s ̂ / p t 2 to all orders in αs. As expected though, leading logarithmic accuracy is insufficient to guarantee a suitable description in regions of phase space away from the high energy limit.We present (i) the first calculation of all partonic channels contributing at next-to-leading logarithmic order in W-boson production in association with at least two jets, and (ii) bin-by-bin matching to next-to-leading fixed-order accuracy. This new perturbative input is implemented in High Energy Jets, and systematically improves the description of available experimental data in regions of phase space which are formally subleading with respect to $$ \hat{s}/{p}_t^2 $$ s ̂ / p t 2 .

Algorithmic Information Distortions in Node-Aligned and Node-Unaligned Multidimensional Networks

In this article, we investigate limitations of importing methods based on algorithmic information theory from monoplex networks into multidimensional networks (such as multilayer networks) that have a large number of extra dimensions (i.e., aspects). In the worst-case scenario, it has been previously shown that node-aligned multidimensional networks with non-uniform multidimensional spaces can display exponentially larger algorithmic information (or lossless compressibility) distortions with respect to their isomorphic monoplex networks, so that these distortions grow at least linearly with the number of extra dimensions. We demonstrate that node-unaligned multidimensional networks, either with uniform or non-uniform multidimensional spaces, can also display exponentially larger algorithmic information distortions with respect to their isomorphic monoplex networks. However, unlike the previous node-aligned non-uniform case, these distortions grow at least exponentially with the number of extra dimensions. On the other hand, for node-aligned multidimensional networks with uniform multidimensional spaces, we demonstrate that any distortion can only grow up to a logarithmic order of the number of extra dimensions. Thus, these results establish that isomorphisms between finite multidimensional networks and finite monoplex networks do not preserve algorithmic information in general and highlight that the algorithmic information of the multidimensional space itself needs to be taken into account in multidimensional network complexity analysis.