Ants Colony Optimization Algorithm in the Hopfield Neural Network for Agricultural Soil Fertility Reverse Analysis

The Boolean Satisfiability Problem (BSAT) is one of the most important decision problems in mathematical logic and computational sciences for determining whether or not a solution to a Boolean formula.. Hopfield neural network (HNN) is one of the major type artificial neural network (NN) popularly known for it used in solving various optimization and decision problems based on its energy minimization machinism. The existing models that incorporate standalone network projected non-versatile framework as fundamental Hopfield type of neural network (HNN) employs random search in its training stages and sometimes get trapped at local optimal solution. In this study, Ants Colony Optimzation Algorithm (ACO) as a novel variant of probabilistic metaheuristic algorithm (MA) inspired by the behavior of real Ants, has been incorporated in the training phase of Hopfield types of the neural network (HNN) to accelerate the training process for Random Boolean kSatisfiability reverse analysis (RANkSATRA) based for logic mining. The proposed hybrid model has been evaluated according to robustness and accuracy of the induced logic obtained based on the agricultural soil fertility data set (ASFDS). Based on the experimental simulation results, it reveals that the ACO can effectively work with the Hopfield type of neural network (HNN) for Random 3 Satisfiability Reverse Analysis with 87.5 % classification accuracy

Investigation on Dewatering of a Deep Shaft in Strong Permeable Sandy Pebble Strata on the Bank of the Yellow River

This paper reports the dewatering scheme of a deep excavation in sandy pebble strata. The excavation is in high permeability strata and is close to the Yellow River, making the dewatering difficult during construction. At present, few researchers have specially studied the dewatering scheme of deep excavations in strong permeable strata near the water resource. Field pumping test was conducted before the excavation activity, and the permeability coefficient of the strata was obtained by reverse analysis. According to the characteristics of the project, the dewatering scheme of “ waterproof curtain + base grouting + pumping ” was proposed. The influence of vertical waterproof curtain and base grouting on dewatering was analyzed by numerical simulation. In the construction process, the field water table and ground settlement were measured. The results show that (1) the groundwater table versus permeability coefficient curve shows three different stages and (2) the dewatering scheme of “ waterproof curtain + base grouting + pumping ” is effective for deep excavation in strong permeable strata.

A pragmatic approach for the evaluation of depth-sensing indentation in the self-similar regime

This work evaluates and revisits elements from the depth-sensing indentation literature by means of carefully chosen practical indentation cases, simulated numerically and compared to experiments. The aim is to close a series of debated subjects, which constitute major sources of inaccuracies in the evaluation of depth-sensing indentation data in practice. Firstly, own examples and references from the literature are presented in order to demonstrate how crucial self-similarity detection and blunting distance compensation are, for establishing a rigorous link between experiments and simple sharp-indenter models. Moreover, it is demonstrated, once again, in terms of clear and practical examples, that no more than two parameters are necessary to achieve an excellent match between a sharp indenter finite element simulation and experimental force-displacement data. The clear conclusion is that reverse analysis methods promising to deliver a set of three unique material parameters from depth-sensing indentation cannot be reliable. Lastly, in light of the broad availability of modern finite element software, we also suggest to avoid the rigid indenter approximation, as it is shown to lead to unnecessary inaccuracies. All conclusions from the critical literature review performed lead to a new semi-analytical reverse analysis method, based on available dimensionless functions from the literature and a calibration against case specific finite element simulations. Implementations of the finite element model employed are released as supplementary material, for two major finite element software packages.

Approach for the COVID-19 Epidemic Source Localization in Russia Based on Mathematical Modeling

The paper presents the results of statistical data from open sources on the development of the COVID-19 epidemic processing and a study сarried out to determine the place and time of its beginning in Russia. An overview of the existing models of the processes of the epidemic development and methods for solving direct and inverse problems of its analysis is given. A model for the development of the COVID-19 epidemic via a transport network of nine Russian cities is proposed: Moscow, St. Petersburg, Nizhny Novgorod, Rostov-on-Don, Krasnodar, Yekaterinburg, Novosibirsk, Khabarovsk and Vladivostok. The cities are selected both by geographic location and by the number of population. The model consists of twenty seven differential equations. An algorithm for reverse analysis of the epidemic model has been developed. The initial data for solving the problem were the data on the population, the intensity of process transitions from one state to another, as well as data on the infection rate of the population at given time moments. The paper also provides the results of a detailed analysis of the solution approaches to modeling the development of epidemics by type of model (basic SEIR model, SIRD model, adaptive behavioral model, modified SEIR models), and by country (in Poland, France, Spain, Greece and others) and an overview of the applications that can be solved using epidemic spread modeling. Additional environmental parameters that affect the modeling of the spread of epidemics and can be taken into account to improve the accuracy of the results are considered. Based on the results of the modeling, the most likely source cities of the epidemic beginning in Russia, as well as the moment of its beginning, have been identified. The reliability of the estimates obtained is largely determined by the reliability of the statistics used on the development of COVID-19 and the available data on transportation network, which are in the public domain.

A Practical Format and Semantic Reverse Analysis Approach for Industrial Control Protocols

Industrial control protocol is the basis of communication and interaction of industrial control system, and its security is related to the whole industrial infrastructure. Because many industrial control systems use proprietary protocols, it is necessary to adopt protocol reverse analysis technology to parse them and then detect whether there are secure vulnerabilities in the protocols by means of fuzzy testing. However, most of the existing technologies are designed for common network protocols, and there is no improvement for industrial control protocol. Therefore, we propose a multistage ensemble reverse analysis method, namely, MSERA, which fully considers the design concept of industrial control protocols. MSERA divides the traditional reverse analysis process into three stages and identifies the fields with different semantic characteristics in different stages and combines with field rectification to effectively improve the results of reverse analysis of industrial control protocols. Through the experimental comparison of some public and proprietary industrial control protocols, it is found that MSERA not only outperforms Netzob in the accuracy of field split but also far exceeds Netzob in semantic recognition accuracy. The experimental results show that MSERA is very practical and suitable for reverse analysis of industrial control protocols.

Graph modelling for tracking the COVID-19 pandemic spread

The modelling is widely used in determining the best strategies for the mitigation of the impact of infectious diseases. Currently, the modelling of a complex system such as the spread of COVID-19 infection is among the topical issues. The aim of this article is graph-based modelling of the COVID-19 infection spread. The article investigates the studies related to the modelling of COVID-19 pandemic and analyses the factors affecting the spread of the disease and its main characteristics. We propose a conceptual model of COVID-19 epidemic by considering the social distance, the duration of contact with an infected person and their location-based demographic characteristics. Based on the hypothetical scenario of the spread of the virus, a graph model of the process are developed starting from the first confirmed infection case to human-to-human transmission of the virus and visualized by considering the epidemiological characteristics of COVID-19. The application of graph for the pandemic modelling allows for considering multiple factors affecting the epidemiological process and conducting numerical experiments. The advantage of this approach is justified with the fact that it enables the reverse analysis the spread as a result of the dynamic record of detected cases of the infection in the model. This approach allows for to determining undetected cases of infection based on the social distance and duration of contact and eliminating the uncertainty significantly. Note that social, economic, demographic factors, the population density, mental values and etc. affect the increase in number of cases of infection and hence, the research was not able to consider all factors. In future research will analyze multiple factors impacting the number of infections and their use in the models will be considered.

Graph modelling for tracking the COVID-19 pandemic spread

The modelling is widely used in determining the best strategies for the mitigation of the impact of infectious diseases. Currently, the modelling of a complex system such as the spread of COVID-19 infection is among the topical issues. The aim of this article is graph-based modelling of the COVID-19 infection spread. The article investigates the studies related to the modelling of COVID-19 pandemic and analyses the factors affecting the spread of the disease and its main characteristics. We propose a conceptual model of COVID-19 epidemic by considering the social distance, the duration of contact with an infected person and their location-based demographic characteristics. Based on the hypothetical scenario of the spread of the virus, a graph model of the process are developed starting from the first confirmed infection case to human-to-human transmission of the virus and visualized by considering the epidemiological characteristics of COVID-19. The application of graph for the pandemic modelling allows for considering multiple factors affecting the epidemiological process and conducting numerical experiments. The advantage of this approach is justified with the fact that it enables the reverse analysis the spread as a result of the dynamic record of detected cases of the infection in the model. This approach allows for to determining undetected cases of infection based on the social distance and duration of contact and eliminating the uncertainty significantly. Note that social, economic, demographic factors, the population density, mental values and etc. affect the increase in number of cases of infection and hence, the research was not able to consider all factors. In future research will analyze multiple factors impacting the number of infections and their use in the models will be considered.