Comprehensive Modelling of ANN

An artificial neural network (ANN) is an information processing modelling of the human brain inspired by the way biological nervous systems behave. There are about 100 billion neurons in the human brain. Each neuron has a connection point between 1,000 and 100,000. The key element of this paradigm is the novel structure of the information processing system. In the human brain, information is stored in such a way as to be distributed, and we can extract more than one piece of this information when necessary from our memory in parallel. We are not mistaken when we say that a human brain is made up of thousands of very powerful parallel processors. It is composed of a large number of highly interconnected processing elements (neurons) working in union to solve specific problems. ANN, like people, learns by example. The chapter includes characteristics of artificial neural networks, structure of ANN, elements of artificial neural networks, pros and cons of ANN.

Women trafficking networks: Structure and stages of women trafficking in five Dutch small-scale networks

In this study, we investigated the relation between the different stages of women trafficking (i.e. recruitment, entrance, accommodation, labor, and finance) and the structure of five criminal networks involved in women trafficking in the Netherlands ( Ns ranging from 6 to 15). On the one hand, it could be argued that for efficiency and avoidance of being detected by law enforcement agencies, the network structure might align with the different stages, resulting in a cell-structured network with collaboration between actors within rather than across stages. On the other hand, criminal actors might prefer to collaborate and rely on a few others, whom they trust in order to circumvent the lack of formal opportunities to enforce collaboration and agreements, resulting in a core-periphery network with actors also collaborating across stages. Results indicate that three of the five networks were characterized by a core-periphery structure, whereas the two other networks exhibit a mixture of both a cell-structured and core-periphery network. Furthermore, using an Exponential Random Graph Model (ERGM), we found that actors were likely to form ties with each other in the stages of recruitment, accommodation, and exploitation, but not in the stages of transport and finance.

Networks of Global Policy Diffusion: The Introduction of Compulsory Education

Compulsory education is closely related to the reproduction and change of a country’s culture. As we know from international comparative cultural research, however, there are different pathways into modernity, and so the particular role of education for a nation-state’s cultural basis might differ as well. At the same time, different relations between countries, such as cultural similarity or trade, can function as channels of diffusion of welfare policies. Our empirical analysis tests which dimensions of global networks structure the diffusion of introduction of compulsory education. We find a positive effect of exposure to other countries in a network of cultural similarity. Policy diffusion does not proceed via economic ties and colonial histories, but does through spatial proximity.

Falcon Optimization Algorithm for Bayesian Networks Structure Learning

In machine-learning, one of the useful scientific models for producing the structure of knowledge is Bayesian network, which can draw probabilistic dependency relationships between variables. The score and search is a method used for learning the structure of a Bayesian network. The authors apply the Falcon Optimization Algorithm (FOA) as a new approach to learning the structure of Bayesian networks. This paper uses the Reversing, Deleting, Moving and Inserting operations to adopt the FOA for approaching the optimal solution of Bayesian network structure. Essentially, the falcon prey search strategy is used in the FOA algorithm. The result of the proposed technique is compared with Pigeon Inspired optimization, Greedy Search, and Simulated Annealing using the BDeu score function. The authors have also examined the performances of the confusion matrix of these techniques utilizing several benchmark data sets. As shown by the evaluations, the proposed method has more reliable performance than the other algorithms including producing better scores and accuracy values.

On the Rural Electric Networks Reliability Level Issue

The structure study, condition and 10 kV electric networks reliability level in the Irkutsk region for the period from 2016 to 2020 is carried out. The analysis is based on real data on the studied electric networks published in the open press. The Interactive Matlab system libraries and interfaces were used to calculate and analyze the studied indicator. The considered electric networks structure has investigated, algorithms and computer programs for the Matlab graphic editor are compiled with the help which changes time diagrams in the failures number, the interruptions time and the under-output electricity amount for each year months are constructed. The year months in which the highest damage occurs were determined. The electrical networks wear is considered, as well as the repair plan implementation for the main equipment. The reliability level according to the established indicators has analyzed, the main reasons for equipment failure are considered. The characteristics of improving considered electrical networks functioning are presented. Based on the conducted studies, it was found that the most damaged the considered overhead lines elements are: wires, supports, insulators, and switching devices. The characteristic reasons that lead to their damage are: wind load, damage to switching devices. The greatest damage is the Angarsky and Irkutsky branches networks characteristic (the average annual value all failures are 26.4 and 23.9%). The Kirensky and Cheremkhovsky branches networks are the least damaged annually. The obtained data probabilistic-statistical and correlation analysis, as well as the emergency outages risks analysis for the future, taking into account the seasonal component, is carried out.

Ultra-heat resistant nanocomposites based on heterocyclic networks: structure, properties, origin of thermal stability

A series of the hybrid Bisphenol A based Phthalonitrile (BAPhN)/amino-Montmorillonite (amino-MMT) polymer nanocomposites with the complex heterocyclic matrix network and 0.03–5.0 wt.% amino-MMT were synthesized and studied. Their molecular structure, nanostructure, molecular dynamics, thermal, relaxation and elastic properties were characterized using TEM, EDXS, DMA, TGA, FTIR and Far-IR spectroscopies including also the experiments in flowing nitrogen medium. Depending on nanofiller content, different extents of MMT stacks exfoliation, from a single nanolayer to stacks with tens nanolayers-thickness, were registered in these nanocomposites. The exceptional combining of high temperature properties was revealed for these nanocomposites.