This paper presents the results of diagnostic examinations conducted on the coils of super-heaters made of 10CrMo9‒10 steel that were operated in industrial conditions at 480°C for 130 thousand hours. The tube was exposed in a coal-fired boiler. The chemical and phase composition of the oxide/deposit layers formed on both sides of the tube walls (outside – flue-gas side and inside – steam side) and their sequence was examined using optical microscopy, scanning electron microscopy with electron backscatter diffraction and energy-dispersive X-ray spectroscopy, and X-ray diffraction. The changes in the mechanical properties caused by corrosion and aging processes were concluded from the hardness measurements. In addition, the nature of cracks in the oxide layers caused by pressing a Vickers indenter was determined. The results of these examinations have shown a high degradation of steel on the flue-gas inflow side and identified the main corrosion products and mechanisms.

User authentication is an essential element of any communication system. The paper investigates the vulnerability of the recently published first semiquantum identity authentication protocol (Quantum Information Processing 18: 197, 2019) to the introduced herein multisession attacks. The impersonation of the legitimate parties by a proper combination of phishing techniques is demonstrated. The improved version that closes the identified loophole is also introduced

The continuing efforts for reduction of the torque and flux ripples using Finite Set Model Predictive Direct Torque Control methods (FS-MPDTC) have been currently drowning a great attention from the academic communities and industrial applications in the field of electrical drives. The major problem of high torque and flux ripples refers to the consideration of just one active voltage vector at the whole control period. Implementation of two or more voltage vectors at each sampling time has recently been adopted as one of the practical techniques to reduce both the torque and flux ripples. Apart from the calculating challenge of the effort control, the parameter dependency and complexity of the duty ratio relationships lead to reduction of the system robustness. those are two outstanding drawbacks of these methods. In this paper, a finite set of the voltage vectors with a finite set of duty cycles are employed to implement the FS-MPDTC of induction motor. Based on so-called Discrete Duty Cycle- based FS-MPDTC (DDC-FS-MPDTC), a base duty ratio is firstly determined based on the equivalent reference voltage. This duty ratio is certainly calculated using the command values of the control system, while the motor parameters are not used in this algorithm. Then, two sets of duty ratios with limit members are constructed for two adjacent active voltage vectors supposed to apply at each control period. Finally, the prediction and the cost function evaluation are performed for all of the preselected voltage vectors and duty ratios. However, the prediction and the optimization operations are performed for only 12 states of inverter. Meanwhile, time consuming calculations related to SVM has been eliminated. So, the robustness and complexity of the control system have been respectively decreased and increased, and both the flux and torque ripples are reduced in all speed ranges. The simulation results have verified the damping performance of the proposed method to reduce the ripples of both the torque and flux, and accordingly the experimental results have strongly validated the aforementioned statement.

The ongoing period of the pandemic makes everybody focused on the matters related to fighting this immense problem posed to the societies worldwide. The governments deal with the threat by publishing regulations which should allow to mitigate the pandemic, walking on thin ice as the decision makers do not always know how to properly respond to the threat in order to save people. Computer-based simulations of e.g. parts of the city or rural area should provide significant help, however, there are some requirements to fulfill. The simulation should be verifiable, supported by the urban research and it should be possible to run it in appropriate scale. Thus in this paper we present an interdisciplinary work of urban researchers and computer scientists, proposing a scalable, HPC-grade model of simulation, which was tested in a real scenario and may be further used to extend our knowledge about epidemic spread and the results of its counteracting methods. The paper shows the relevant state of the art, discusses the micro-scale simulation model, sketches out the elements of its implementation and provides tangible results gathered for a part of the city of Krakow, Poland.

Workflow Scheduling is the major problem in Cloud Computing consists of a set of interdependent tasks which is used to solve the various scientific and healthcare issues. In this research work, the cloud based workflow scheduling between different tasks in medical imaging datasets using Machine Learning (ML) and Deep Learning (DL) methods (hybrid classification approach) is proposed for healthcare applications. The main objective of this research work is to develop a system which is used for both workflow computing and scheduling in order to minimize the makespan, execution cost and to segment the cancer region in the classified abnormal images. The workflow computing is performed using different Machine Learning classifiers and the workflow scheduling is carried out using Deep Learning algorithm. The conventional AlexNet Convolutional Neural Networks (CNN) architecture is modified and used for workflow scheduling between different tasks in order to improve the accuracy level. The AlexNet architecture is analyzed and tested on different cloud services Amazon Elastic Compute Cloud- EC2 and Amazon Lightsail with respect to Makespan (MS) and Execution Cost (EC).

Time invariant linear operators are the building blocks of signal processing. Weighted circular convolution and signal processing framework in a generalized Fourier domain are introduced by Jorge Martinez. In this paper, we prove that under this new signal processing framework, weighted circular convolution also has a generalized time invariant property. We also give an application of this property to algorithm of continuous wavelet transform (CWT). Specifically, we have previously studied the algorithm of CWT based on generalized Fourier transform with parameter 1. In this paper, we prove that the parameter can take any complex number. Numerical experiments are presented to further demonstrate our analyses.

The COVID-19 pandemic has influenced virtually all aspects of our lives. Across the world, countries have applied various mitigation strategies, based on social, political, and technological instruments. We postulate that multi-agent systems can provide a common platform to study (and balance) their essential properties. We also show how to obtain a comprehensive list of the properties by “distilling” them from media snippets. Finally, we present a preliminary take on their formal specification, using ideas from multi-agent logics.