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We perform a quantitative analysis of extensive chess databases and show that the frequencies of opening moves are distributed according to a power law with an exponent that increases linearly with the game depth, whereas the pooled distribution of all opening weights follows Zipf's law with universal exponent. We propose a simple stochastic process that is able to capture the observed playing statistics and show that the Zipf law arises from the self-similar nature of the game tree of chess. Thus, in the case of hierarchical fragmentation the scaling is truly universal and independent of a particular generating mechanism. Our findings are of relevance in general processes with composite decisions.
In 1945, Zinovii Shenderovich Tolkatchev (1903–1977), a Soviet artist of Jewish origin, created a striking series of five images entitled “Jesus in Majdanek”. The series was the culmination of Tolkatchev‘s intensive preoccupation with the experience he, as a Red Army soldier, endured upon taking part in liberation of the concentration camps Majdanek and Auschwitz. Shocked by the actual sights he witnessed, he depicted Jesus as an actual camp inmate, wearing a striped uniform marked by every possible defamation sign – the Jewish yellow star, the red triangle of political prisoners, and the individual prison number, the numerical tattoo on his lower arm can also be seen. The different stages of camp life are portrayed as the traditional Passion of Christ. While showing the actual situations the artist based himself upon the well known European Renaissance paintings canonically depicting Jesus‘ suffering. The article places Tolkatchev‘s series in a broader cultural and visual context by exploring the development of the ‘historical Jesus’ in the 19th century European thought and Russian realist art, and by examining the impact of the German avant-garde. By doing so, a deeper understanding of the universal message Tolkatchev’s works entail is offered.
Background
Coronavirus disease (COVID-19) has a severe impact on all aspects of patient care. Among the numerous biomarkers of potential validity for diagnostic and clinical management of COVID-19 are biomarkers at the interface of iron metabolism and inflammation.
Methods
The follow-up study included 54 hospitalized patients with laboratory-confirmed COVID-19 with a moderate and severe/critical form of the disease. Iron deficiency specific biomarkers such as iron, ferritin, transferrin receptor, hepcidin, and zinc protoporphyrin (ZnPP) as well as relevant markers of inflammation were evaluated twice: in the first five days when the patient was admitted to the hospital and during five to 15 days; and their validity to diagnose iron deficiency was further assessed. The regression and Receiver Operating Characteristics (ROC) analyses were performed to evaluate the prognosis and determine the probability for predicting the severity of the disease in the first five days of COVID-19.
Results
Based on hemoglobin values, anemia was observed in 21 of 54 patients. Of all iron deficiency anemia-related markers, only ZnPP was significantly elevated (P<0.001) in the anemic group. When patients were grouped according to the severity of disease, slight differences in hemoglobin or other anemia-related parameters could be observed. However, the levels of ZnPP were significantly increased in the severely ill group of patients. The ratio of ZnPP to lymphocyte count (ZnPP/L) had a discrimination power stronger than the neutrophil to lymphocyte count ratio (N/L) to determine disease severity. Additionally, only two markers were independently associated with the severity of COVID-19 in logistic regression analysis; D-dimer (OR (5.606)(95% CI 1.019–30.867)) and ZnPP/L ratio (OR (74.313) (95% CI 1.081–5108.103)).
Conclusions
For the first time ZnPP in COVID-19 patients were reported in this study. Among all iron-related markers tested, ZnPP was the only one that was associated with anemia as based on hemoglobin. The increase in ZnPP might indicate that the underlying cause of anemia in COVID-19 patients is not only due to the inflammation but also of nutritional origin. Additionally, the ZnPP/L ratio might be a valid prognostic marker for the severity of COVID-19.
An approach is presented to modify the work function of solution-processed sol-gel derived zinc oxide (ZnO) over an exceptionally wide range of more than 2.3 eV. This approach relies on the formation of dense and homogeneous self-assembled monolayers based on phosphonic acids with different dipole moments. This allows us to apply ZnO as charge selective bottom electrodes in either regular or inverted solar cell structures, using poly(3-hexylthiophene): phenyl-C71-butyric acid methyl ester as the active layer. These devices compete with or even surpass the performance of the reference on indium tin oxide/poly(3,4-ethylenedioxythiophene) polystyrene sulfonate. Our findings highlight the potential of properly modified ZnO as electron or hole extracting electrodes in hybrid optoelectronic devices. (C) 2015 AIP Publishing LLC.
The zeta potential of the motile spores of the green alga (seaweed) Ulva linza was quantified by video microscopy in combination with optical tweezers and determined to be -19.3ñ1.1 mV. The electrostatic component involved in the settlement and adhesion of spores was studied using electret surfaces consisting of PTFE and bearing different net charges. As the surface chemistry remains the same for differently charged surfaces, the experimental results isolate the influence of surface charge and thus electrostatic interactions. Ulva spores were demonstrated to have a reduced tendency to settle on negatively charged surfaces and when they did settle the adhesion strength of settled spores was lower than with neutral or positively charged surfaces. These observations can be ascribed to electrostatic interactions.
We present fully polynomial time approximation schemes for a broad class of Holant problems with complex edge weights, which we call Holant polynomials. We transform these problems into partition functions of abstract combinatorial structures known as polymers in statistical physics. Our method involves establishing zero-free regions for the partition functions of polymer models and using the most significant terms of the cluster expansion to approximate them. Results of our technique include new approximation and sampling algorithms for a diverse class of Holant polynomials in the low-temperature regime (i.e. small external field) and approximation algorithms for general Holant problems with small signature weights. Additionally, we give randomised approximation and sampling algorithms with faster running times for more restrictive classes. Finally, we improve the known zero-free regions for a perfect matching polynomial.