Collective behavior in the North Rhine-Westphalia motorway network

To understand the dynamics on complex networks, measurement of correlations is indispensable. In a motorway network, it is not sufficient to collect information on fluxes and velocities on all individual links, i.e. parts of the freeways between ramps and highway crosses. The interdependencies and mutual connections are also of considerable interest. We analyze correlations in the complete motorway network in North Rhine-Westphalia, the most populous state in Germany. We view the motorway network as a complex system consisting of road sections which interact via the motion of vehicles, implying structures in the corresponding correlation matrices. In particular, we focus on collective behavior, i.e. coherent motion in the whole network or in large parts of it. To this end, we study the eigenvalue and eigenvector statistics and identify significant sections in the motorway network. We find collective behavior in these significant sections and further explore its causes. We show that collectivity throughout the network cannot directly be related to the traffic states (free, synchronous and congested) in Kerner’s three-phase theory. Hence, the degree of collectivity provides a new, complementary observable to characterize the motorway network.

Face Processing in Developmental Prosopagnosia: Altered Neural Representations in the Fusiform Face Area

Rationale: Face expertise is a pivotal social skill. Developmental prosopagnosia (DP), i.e., the inability to recognize faces without a history of brain damage, affects about 2% of the general population, and is a renowned model system of the face-processing network. Within this network, the right Fusiform Face Area (FFA), is particularly involved in face identity processing and may therefore be a key element in DP. Neural representations within the FFA have been examined with Representational Similarity Analysis (RSA), a data-analytical framework in which multi-unit measures of brain activity are assessed with correlation analysis.Objectives: Our study intended to scrutinize modifications of FFA-activation during face encoding and maintenance based on RSA.Methods: Thirteen participants with DP (23–70 years) and 12 healthy control subjects (19–62 years) participated in a functional MRI study, including morphological MRI, a functional FFA-localizer and a modified Sternberg paradigm probing face memory encoding and maintenance. Memory maintenance of one, two, or four faces represented low, medium, and high memory load. We examined conventional activation differences in response to working memory load and applied RSA to compute individual correlation-matrices on the voxel level. Group correlation-matrices were compared via Donsker’s random walk analysis.Results: On the functional level, increased memory load entailed both a higher absolute FFA-activation level and a higher degree of correlation between activated voxels. Both aspects were deficient in DP. Interestingly, control participants showed a homogeneous degree of correlation for successful trials during the experiment. In DP-participants, correlation levels between FFA-voxels were significantly lower and were less sustained during the experiment. In behavioral terms, DP-participants performed poorer and had longer reaction times in relation to DP-severity. Furthermore, correlation levels were negatively correlated with reaction times for the most demanding high load condition.Conclusion: We suggest that participants with DP fail to generate robust and maintained neural representations in the FFA during face encoding and maintenance, in line with poorer task performance and prolonged reaction times. In DP, alterations of neural coding in the FFA might therefore explain curtailing in working memory and contribute to impaired long-term memory and mental imagery.

Tracing conceptual understandings and misunderstandings of science by students with and without vision impairment through a pilot study: the case of density and heat

The purpose of this pilot study was to describe and analyze the perceptions and alternative ideas of individuals with and without vision impairments regarding the concepts of “density” and “heat”. The perceptions of sighted, age- and gender-matched participants were compared with those of visually impaired participants (two groups). Semi-structured interviews were conducted, and the analysis of the data followed the method of tracing and developing categories and sub-categories. The analysis revealed that the two groups held diverse understandings about “density”, while most participants seemed to identify “heat” as “temperature” and vice versa. The results are presented in the form of conception correlation matrices highlighting common concepts and alternative ideas towards the notions of “density” and “heat”. The findings demonstrate that in both groups there are common patterns of alternative ideas, which may lead to the assumption that vision loss or blindness and proficiency in science do not constitute a causal relation. The results may lead to useful implications for differentiated instruction regarding the comprehension of science in an integrated educational setting in conjunction with technological advances and inclusive practices.

On the asymptotic behavior of the eigenvalue distribution of block correlation matrices of high-dimensional time series

We consider linear spectral statistics built from the block-normalized correlation matrix of a set of [Formula: see text] mutually independent scalar time series. This matrix is composed of [Formula: see text] blocks. Each block has size [Formula: see text] and contains the sample cross-correlation measured at [Formula: see text] consecutive time lags between each pair of time series. Let [Formula: see text] denote the total number of consecutively observed windows that are used to estimate these correlation matrices. We analyze the asymptotic regime where [Formula: see text] while [Formula: see text], [Formula: see text]. We study the behavior of linear statistics of the eigenvalues of this block correlation matrix under these asymptotic conditions and show that the empirical eigenvalue distribution converges to a Marcenko–Pastur distribution. Our results are potentially useful in order to address the problem of testing whether a large number of time series are uncorrelated or not.

Improvement of thermostability and catalytic efficiency of glucoamylase from Talaromyces leycettanus JCM12802 via site-directed mutagenesis to enhance industrial saccharification applications

Background Glucoamylase is an important industrial enzyme in the saccharification of starch into glucose. However, its poor thermostability and low catalytic efficiency limit its industrial saccharification applications. Therefore, improving these properties of glucoamylase is of great significance for saccharification in the starch industry. Results In this study, a novel glucoamylase-encoding gene TlGa15B from the thermophilic fungus Talaromyces leycettanus JCM12802 was cloned and expressed in Pichia pastoris. The optimal temperature and pH of recombinant TlGa15B were 65 ℃ and 4.5, respectively. TlGa15B exhibited excellent thermostability at 60 ℃. To further improve thermostability without losing catalytic efficiency, TlGa15B-GA1 and TlGa15B-GA2 were designed by introducing disulfide bonds and optimizing residual charge–charge interactions in a region distant from the catalytic center. Compared with TlGa15B, mutants showed improved optimal temperature, melting temperature, specific activity, and catalytic efficiency. The mechanism underlying these improvements was elucidated through molecular dynamics simulation and dynamics cross-correlation matrices analysis. Besides, the performance of TlGa15B-GA2 was the same as that of the commercial glucoamylase during saccharification. Conclusions We provide an effective strategy to simultaneously improve both thermostability and catalytic efficiency of glucoamylase. The excellent thermostability and high catalytic efficiency of TlGa15B-GA2 make it a good candidate for industrial saccharification applications.

Peculiarities of growth, development and climate-stabilizing significance of pine plantations of the Ukrainian Carpathians

Peculiarities of growth and development of pine forest plantations in the Ukrainian Carpathians by forestry districts: Precarpathian, Mountain Carpathian and Transcarpathian plains and foothills are determined. Mathematical dependences of pine growth and development on age, height and diameter are offered. According to the obtained mathematical empirical dependences, it was established that Scots pine (Pinus sylvestris L.) grows and develops better in the Mountain Carpathian forest district. In the Mountain Carpathian Forestry County, the growth of pine is dominated by 2% for the Precarpathian forest district, and in Transcarpathian plains and foothills by 1%. By completeness in the Gorge of Cocarpathian forestry County, the diameter of the pine is higher than the Carpathian forest county on the 3%, and the Transcarpathian plains and the foothills — 1%. Using the Microsoft Excel data analysis package, correlation matrices were constructed and regression and variance analysis of such indicators as: age, height, diameter, phytomass — wood, bark, and crown was performed. Mathematical equations were obtained, which made it possible to establish the biological productivity of Pinus sylvestris L. Using the obtained empirical equations according to the methods of IPCC (Intergovernmental Panelon Climate Change, 2015), G. Matthews (1993) and I.Ya. Liepa (1980) established the carbon-absorbing and oxygen-forming capacity of pine tree plantations at the age of 70 on an area of 1 ha. It is determined that on the area of 1 ha pine plantations absorb the most carbon — 88.9 tons, and produce oxygen — 262.2 tons in the Mountain Carpathian Forestry District, in the Precarpathian Forestry District 76.0 tons of carbon and 224.1 tons of oxygen, in the Transcarpathian plains and foothills Scots pine absorbs 69.5 tons of carbon and produces 204.9 tons of oxygen. The amount of CO2 emissions into the environment in the conditions of the Ukrainian Carpathians is analyzed, it was established that pine forests reduce carbon dioxide emissions by 32%.

Diagonal Degree Correlations vs. Epidemic Threshold in Scale-Free Networks

We prove that the presence of a diagonal assortative degree correlation, even if small, has the effect of dramatically lowering the epidemic threshold of large scale-free networks. The correlation matrix considered is P h | k = 1 − r P h k U + r δ h k , where P U is uncorrelated and r (the Newman assortativity coefficient) can be very small. The effect is uniform in the scale exponent γ if the network size is measured by the largest degree n . We also prove that it is possible to construct, via the Porto–Weber method, correlation matrices which have the same k n n as the P h | k above, but very different elements and spectra, and thus lead to different epidemic diffusion and threshold. Moreover, we study a subset of the admissible transformations of the form P h | k ⟶ P h | k + Φ h , k with Φ h , k depending on a parameter which leaves k n n invariant. Such transformations affect in general the epidemic threshold. We find, however, that this does not happen when they act between networks with constant k n n , i.e., networks in which the average neighbor degree is independent from the degree itself (a wider class than that of strictly uncorrelated networks).

Non-stationarity in correlation matrices for wind turbine SCADA-data and implications for failure detection

Modern utility-scale wind turbines are equipped with a Supervisory Control And Data Acquisition (SCADA) system gathering vast amounts of operational data that can be used for failure analysis and prediction to improve operation and maintenance of turbines. We analyse high freqeuency SCADA-data from the Thanet offshore windpark in the UK and evaluate Pearson correlation matrices for a variety of observables with a moving time window. This renders possible an asessment of non-stationarity in mutual dependcies of different types of data. Drawing from our experience in other complex systems, such as financial markets and traffic, we show this by employing a hierarchichal k-means clustering algorithm on the correlation matrices. The different clusters exhibit distinct typical correlation structures to which we refer as states. Looking first at only one and later at multiple turbines, the main dependence of these states is shown to be on wind speed. In accordance, we identify them as operational states arising from different settings in the turbine control system based on the available wind speed. We model the boundary wind speeds seperating the states based on the clustering solution. This allows the usage of our methodology for failure analysis or prediction by sorting new data based on wind speed and comparing it to the respective operational state, thereby taking the non-stationarity into account.

Limit Properties of the Largest Entries of High-Dimensional Sample Covariance and Correlation Matrices

In this paper, we consider the limit properties of the largest entries of sample covariance matrices and the sample correlation matrices. In order to make the statistics based on the largest entries of the sample covariance matrices and the sample correlation matrices more applicable in high-dimensional tests, the identically distributed assumption of population is removed. Under some moment’s assumption of the underlying distribution, we obtain that the almost surely limit and asymptotical distribution of the extreme statistics as both the dimension p and sample size n tend to infinity.