Terrorism as a Self-Organised Criticality Phenomenon

An examination of the heuristic capabilities of the self-organized criticality (SOC) theory for studying social processes, reviewing key ideas of the theory and the methods of identifying pink noise as an SOC attribute. The authors analyze terrorism in twenty countries in the period from 1970s to 2014. The source of the background data is the Global Terrorism Database, maintained by the START Consortium. SOC approaches and methodology were used to identify and explain such non-linear effects as spontaneous outbreaks of terrorism. It is found that numerical series that reflect changes in the terrorism volume are essentially pink noise. This allowed the universal explanatory schemes of SOC theory to be applied to interpret such systems features and dynamics and demonstrate that in many countries, terrorism is a self-organized criticality phenomenon. Systems in the state of SOC are capable of abrupt growth in activity without any apparent reason. One of the parameters of the numerical series studied ‒ power-law exponent ‒ can serve as an indicator of the internal state of the societies prone to terror threats

Terrorism as a Self-Organised Criticality Phenomenon

An examination of the heuristic capabilities of the self-organized criticality (SOC) theory for studying social processes, reviewing key ideas of the theory and the methods of identifying pink noise as an SOC attribute. The authors analyze terrorism in twenty countries in the period from 1970s to 2014. The source of the background data is the Global Terrorism Database, maintained by the START Consortium. SOC approaches and methodology were used to identify and explain such non-linear effects as spontaneous outbreaks of terrorism. It is found that numerical series that reflect changes in the terrorism volume are essentially pink noise. This allowed the universal explanatory schemes of SOC theory to be applied to interpret such systems features and dynamics and demonstrate that in many countries, terrorism is a self-organized criticality phenomenon. Systems in the state of SOC are capable of abrupt growth in activity without any apparent reason. One of the parameters of the numerical series studied ‒ power-law exponent ‒ can serve as an indicator of the internal state of the societies prone to terror threats

Computer modeling of historical processes by means of fractal geometry

"This article is dedicated to application of theory and methodology of fractal geometry in historical research. The article represents the concrete historic issue mathematical model, specifically: the dynamics of the conscience and social environment modernization. On the basis of this model a computer program, which generates fractal images of attractors, attractor basins, and phase transformations of the social systems studied subject to user-entered numerical indicators of certain factors, has been developed. The article represents the principal approaches to the qualitative interpretation of the fractal images obtained." (author's abstract

Fractal modeling of historical demographie processes

"The article presents several results of the computer modeling of demographic processes in the late traditional rural communities by means of fractal geometry. A team of contributors developed a model and software for it, then carried out its verification, data processing, computer modeling and Interpretation of results. The analysis of modeling outcomes allowed to build a holistic picture of the demographic behavior in rural communities of the Tambov province - one of the typical agrarian regions of 19th and 20th century Russia. Authors describe the degree and ways of how demographic behavior of the society was influenced by such factors as famine, war, epidemics, a level of health care infrastructure development, etc. Besides, it was possible to trace some non-linear effects in demographic strategies agrarian communities followed during modernization processes in Russia in the second half of the 19th and 20th centuries." (author's abstract

Ultralow-noise terahertz detection by p-n junctions in gapped bilayer graphene

Graphene shows a strong promise for detection of terahertz (THz) radiation due to its high carrier mobility, compatibility with on-chip waveguides and transistors, and small heat capacitance. At the same time, weak reaction of graphene's physical properties on the detected radiation can be traced down to the absence of band gap. Here, we study the effect of electrically-induced band gap on THz detection in graphene bilayer with split-gate p-n junction. We show that gap induction leads to simultaneous increase in current and voltage responsivities. At operating temperatures of ~25 K, the responsivity at 20 meV band gap is from 3 to 20 times larger than that in the gapless state. The maximum voltage responsivity of our devices at 0.13 THz illumination exceeds 50 kV/W, while the noise equivalent power falls down to 36 fW/Hz^0.5. These values set new records for semiconductor-based cryogenic terahertz detectors, and pave the way for efficient and fast terahertz detection

Zero-bias photodetection in 2d materials via geometric design of contacts

Structural or crystal asymmetry are necessary conditions for emergence of zero-bias photocurrent in light detectors. Structural asymmetry has been typically achieved via $p-n$ doping being a technologically complex process. Here, we propose an alternative approach to achieve zero-bias photocurrent in 2d material flakes exploiting the geometrical non-equivalence of source and drain contacts. As a prototypical example, we equip a square-shaped flake of PdSe$_2$ with mutually orthogonal metal leads. Upon uniform illumination with linearly-polarized light, the device demonstrates non-zero photocurrent which flips its sign upon 90$^\circ$ polarization rotation. The origin of zero-bias photocurrent lies in polarization-dependent lightning-rod effect. It enhances the electromagnetic field at one contact from the orthogonal pair, and selectively activates the internal photoeffect at the respective metal-PdSe$_2$ Schottky junction. The proposed technology of contact engineering can be extended to arbitrary 2d materials and detection of both polarized and natural light

Verification of Photometric Parallaxes with Gaia DR2 Data

Results of comparison of Gaia DR2 parallaxes with data derived from a combined analysis of 2MASS (Two Micron All-Sky Survey), SDSS (Sloan Digital Sky Survey), GALEX (Galaxy Evolution Explorer), and UKIDSS (UKIRT Infrared Deep Sky Survey) surveys in four selected high-latitude $|b|>48^{\circ}$ sky areas are presented. It is shown that multicolor photometric data from large modern surveys can be used for parameterization of stars closer than 4400 pc and brighter than $g_{SDSS} = 19.^m6$, including estimation of parallax and interstellar extinction value. However, the stellar luminosity class should be properly determined.Comment: 11 pages, 5 figure

Anomaly segmentation model for defects detection in electroluminescence images of heterojunction solar cells

Efficient defect detection in solar cell manufacturing is crucial for stable green energy technology manufacturing. This paper presents a deep-learning-based automatic detection model SeMaCNN for classification and semantic segmentation of electroluminescent images for solar cell quality evaluation and anomalies detection. The core of the model is an anomaly detection algorithm based on Mahalanobis distance that can be trained in a semi-supervised manner on imbalanced data with small number of digital electroluminescence images with relevant defects. This is particularly valuable for prompt model integration into the industrial landscape. The model has been trained with the on-plant collected dataset consisting of 68 748 electroluminescent images of heterojunction solar cells with a busbar grid. Our model achieves the accuracy of 92.5%, F1 score 95.8%, recall 94.8%, and precision 96.9% within the validation subset consisting of 1049 manually annotated images. The model was also tested on the open ELPV dataset and demonstrates stable performance with accuracy 94.6% and F1 score 91.1%. The SeMaCNN model demonstrates a good balance between its performance and computational costs, which make it applicable for integrating into quality control systems of solar cell manufacturing