Intelligent Restoration of Historical Parametric Geometric Patterns by Zernike Moments and Neural Networks

Historical Islamic ornaments include a fantastic treasury of geometric and mathematical algorithms. Inevitably, restoration of these ornaments in periodic patterns consisting of repeated elements has been faced following and substituting the other available similar ingredients instead of vanished parts. Still, the prediction of parametric, quasi, or non-periodic patterns, where components are not identical, needs to be carried out in a more challenging process than the periodic ones due to shape, scale, or angle of rotation alteration. Intelligent restoration could facilitate the forecasting of damaged parts in such geometric patterns that an algorithm has changed their geometric characteristics. In some architectural heritage, geometric patterns include a parametric algorithm like parametric patterns in the ceiling of Sheikh Lotfollahmosque in Isfahan, Iran, and the dominant structure of Persian domes Karbandi. In this article, the aim is to propose a new method for the smart restoration of the parametric geometric patterns in which, by having access to the image of the existing patterns, the vanished parts could be reconstructed spontaneously. Our approach is based on image processing by detecting boundaries of deterioration, finding every individual element, and extracting features of detected individual patterns via Zernike moments. The order of individual patterns starts from the farthest pattern to detected deterioration. Then by creating a time series, the Back-propagation neural network would be trained by extracted features, and the vanished patterns’ features could be predicted and reconstructed. Eventually, the reconstructed and real patterns are compared to determine differences between them by mean-squared error and to evaluate the performance of our method. To validate the process, a parametric geometric pattern is designed by the assumption that some parts are disappeared. The proposed method’s results, in this case, hold an efficient performance with the accuracy of 92.99%. Furthermore, Sheikh Lotfollah’s patterns and Naseredin Mirza mansion’s patterns as two real cases are tested by the proposed method, representing reliable and suitable performance results.

Multivariate analysis of images in spectrophotometric methods: Quantification of soil organic matter

Soil organic matter (SOM) is usually quantified by Walkley-Black titration method or using a spectrophotometric method. This study proposes an alternative method for quantification of SOM using digital image from scanner and mathematical algorithms to replace titration and spectrophotometry procedures. For this, after SOM oxidation by potassium dichromate, digital images were acquired. Posteriorly, extraction of RGB color histograms from images have occurred, followed by the use of multivariate calibration method: partial least squares (PLS). Six soil samples were analyzed. We used the Walkley-Black method as reference. SOM was estimated by images using the PLS tool. The new method, besides being a fast, low cost, and more operational alternative, presented statistically equal results in relation to the reference method, as assessed by the Student t-test and F-test at 95 % confidence.

Securitary Text on Images with RC-128 Bit Synthric Key Encryption

The main purpose of using cryptography is to provide the following four basic information security services. One of the purposes of cryptography is secrecy. Confidentiality is the fundamental security service provided by cryptography. This is a security service that stores information from unauthorized persons. Confidentiality can be achieved through a variety of ways ranging from physical security to the use of mathematical algorithms for data encryption. Vernam cipher is a stream cipher where the original data or plain with 8x8 block operation. Experimental results prove that RC4 can perform encryption and decryption with a fast execution process. In this study used a processor with 8GB of RAM. The encryption result of the text used yields the average encryption time and decryption average of 2 second.

Mathematical model for the reinforced concrete with nonlinearity behaviour of bond

The article developed a mathematical model describing the deformation of reinforced concrete, taking into account its adhesion to reinforcement. The model consists of three layers: concrete, contact layer, reinforcement. The contact layer surrounding the reinforcement takes into account the complex deformation of the concrete when interacting with the profiled reinforcement. Arguments are presented for criteria that determine the transition of concrete, reinforcement and contact layer to limit states. A diagram of testing of reinforced concrete specimen has been proposed and a procedure for processing experimental data, which allow you to determine the bond parameters. Equations that bind the mechanical characteristics of the contact layer with two the bond parameters of concrete with reinforcement are obtained. The developed model was used in the numerical solution of the problem of static pull-out of reinforcement from concrete. The calculation of the finite element method showed a good correspondence with the experimental data, including during plastic deformation of the reinforcement. This shows the correctness of theoretical provisions and developed mathematical algorithms used to model the deformation of reinforced concrete.

Improving Students’ Critical Thinking through Oral Questioning in Mathematics Teaching

The use of effective oral questioning in the teaching of mathematics can stimulate students' thinking and encourage them to think critically. As a result, this study was carried out to identify the oral questions used by teachers when teaching mathematics. This can encourage students' critical thinking. This was a qualitative study in the form of a case study conducted in six schools in a Malaysian state with a total of six study participants. They were chosen on purpose based on the specific criteria set by the researcher. The data was collected using observation methods, interviews, and field notes to gain an in-depth picture of the phenomena studied. The study findings were analyzed using the continuous comparison method to identify the themes and subthemes involved. The researchers used several methods to improve the validity and reliability of this study, including triangulation, the consent of the study participants, peer reviews, audit trails, researcher bias, and a long period in the field. According to the study findings, mathematics teachers frequently use three types of oral questions to encourage their students to think critically: prompting questions, reflective questions, and clarification questions. These types of oral questions are effective at encouraging students to think critically when trying to solve mathematical problems. This study implies that teachers should use caution when asking oral questions so that the students' thinking is stimulated, rather than focusing on memorizing important mathematical algorithms and procedures.

Application of Artificial Neural Networks for Processing and Interpretation of Data from a Scanning Magnetic Introscope

Summary This paper presents the efficiency of using artificial neural networks for solving problems of processing and interpreting geophysical data obtained by scanning magnetic introscopy. Neural networks of various architectures have been implemented to solve the problems of processing primary material, searching for well structure objects,identifying casing defects. The analysis of the capabilities of neural networks in comparison with mathematical algorithms is carried out. To test machine learning algorithms and mathematical algorithms for processing, visualizing and storing the results, a software shell was created in which all tasks are solved using a set of tools. It was found that the use of artificial neural networks can significantly speed up the process of data processing and interpretation, as well as improve the quality of the results in comparison with individual mathematical algorithms. Nevertheless, the use of mathematical algorithms in solving some problems gives consistently better results. In particular, the problematic aspects were identified at the stage of interpretation when identifying defects. This is due to the presence of conventions in the isolation of defects by the operator at the stage of preparing data for training neural networks, which is a subjective factor and requires a deeper study.

State regulation of the IoT in the Russian Federation: Fundamentals and challenges

<span>The purpose of this section is to study the problems with implementing technical and legal regulations for the development of public administration functions in the Russian Federation when using the internet of things (IoT). The introduction is based on an analysis of regulatory legal acts and presents the main strategic directions for the development of public administration functions in the Russian federation when using IoT. State reports, scientific literature, a system of technical and legal regulation are analyzed, and the main problems of implementing the IoT that impede the achievement of effective public administration are studied. The Russian practice of using IoT in various economic areas is investigated. Based on an analysis of the mechanisms for ensuring data safety of information technology users in the Russian federation, problems were investigated, such as the collecting data through IoT, including publicly available personal data in order to profile human activities, and creating of a digital twin of a person. The social constraints for introducing distributed registry technologies are users' distrust in the field of data privacy protection and mathematical algorithms that are used to establish trust in a digital environment instead of trusted centralized intermediaries; these problems were also analyzed. The Russian approach was analyzed in comparison to European experience in this field. To ensure information security and the possibility of its distribution, the IoT is revealed.</span>

Artificial Intelligence in Thyroid Field. A Comprehensive Review

Artificial intelligence (AI) uses mathematical algorithms to perform tasks that require human cognitive abilities. AI-based methodologies, e.g., machine learning and deep learning, as well as the recently developed research field of radiomics have noticeable potential to transform medical diagnostics. AI-based techniques applied to medical imaging allow to detect biological abnormalities, to diagnostic neoplasms or to predict the response to treatment. Nonetheless, the diagnostic accuracy of these methods is still a matter of debate. In this article, we first illustrate the key concepts and workflow characteristics of machine learning, deep learning and radiomics. We outline considerations regarding data input requirements, differences among these methodologies and their limitations. Subsequently, a concise overview is presented regarding the application of AI methods to the evaluation of thyroid images. We developed a critical discussion concerning limits and open challenges that should be addressed before the translation of AI techniques to the broad clinical use. Clarification of the pitfalls of AI-based techniques results crucial in order to ensure the optimal application for each patient.

Challenges and Opportunities Related to Use of Models in Drilling Operations

Automation of managed pressure drilling (MPD) has a big potential for improving consistency, efficiency, and safety of operations, and is therefore pursued by many actors. While this development mitigates many risk elements, it also adds some related to for example mathematical algorithms and remote access. This work is based on document reviews, interviews, and working sessions with the industry, and adds insight on how risks associated with moving to higher levels of automation of MPD can be mitigated to a level where benefits are significantly larger than the sum of added risks. The work has resulted in many recommendations for the industry, where most were related to testing, verification, and validation of the models and data inputs, as well as meaningful human control and employing a holistic approach when introducing new models. Recommendations were also given to the Petroleum Safety Authority Norway. These were related to missing or inadequate use of standards by the industry, lack of ICT knowledge, and encouraging increased experience sharing. Future work should address how to enable meaningful human control as models become more complex or to a larger extent is based on empirical data and artificial intelligence as opposed to models based on first principles. Human control is important in unexpected situations in which the system fails to act safely. There is also a need to address ICT security issues arising when remote operation becomes more common.