Novo opažanje malog vranca Microcarbo pygmaeus u sezoni gniježđenja u središnejm dijelu zapadne Bugarske

Opisano je opažanje odraslog malog vranca Microcarbo pygmaeus u negnijezdećem ruhu 1. srpnja 2023. na umjetnom jezeru Ognyanovo u blizini sela Ognyanovo (središnji dio zapadne Bugarske). Ovo je prvi zapis o prisutnosti vrste tijekom sezone gniježđenja od 1880.-ih u središnjem dijelu zapadne Bugarske

Numerical Investigations on Changes of the Main Shear Plane while Broaching

AbstractThe quality of broached components can be influenced by different factors, such as am-bient temperatures, human factors or vibrations of the machine structure induced by process-machine-interactions. These vibrations are normally initiated by changing pro-cess forces, which are mainly caused by cutting thickness or rake angle variations. Broached components are produced within one motion of the broach along the surface of the work piece, where multiple teeth in a row are in contact. The variation of the cut- ting thickness results from a wavy profile on the surface generated by the previous cut-ting process or the previous tooth. When the cutting thickness changes during the process, the rake angle varies, too. In some further published works, the changing cutting thickness and the changing rake angle during broaching were investigated by means of machining simulations with the result that the process forces are still adjusting after the cutting thickness and the rake angle have already reached a stable value. The adjustment of the shear plane on the new cutting conditions is mentioned as the main reason. This paper presents some deeper investigations on this effect. Therefore, 2D machining simulations for different cutting thicknesses and cutting velocities are performed. The investigations show tendencies for the still adjusting shear plane after changing the cutting thickness or the rake angle during the cutting process. Finally, the simulation results are validated with experimentally observed data

Genome assembly using quantum and quantum-inspired annealing

Recent advances in DNA sequencing open prospects to make whole-genome analysis rapid and reliable, which is promising for various applications including personalized medicine. However, existing techniques for {\it de novo} genome assembly, which is used for the analysis of genomic rearrangements, chromosome phasing, and reconstructing genomes without a reference, require solving tasks of high computational complexity. Here we demonstrate a method for solving genome assembly tasks with the use of quantum and quantum-inspired optimization techniques. Within this method, we present experimental results on genome assembly using quantum annealers both for simulated data and the $\phi$X 174 bacteriophage. Our results pave a way for an increase in the efficiency of solving bioinformatics problems with the use of quantum computing and, in particular, quantum annealing. We expect that the new generation of quantum annealing devices would outperform existing techniques for {\it de novo} genome assembly. To the best of our knowledge, this is the first experimental study of de novo genome assembly problems both for real and synthetic data on quantum annealing devices and quantum-inspired techniques.Comment: 9 pages, 4 figure

Influence of the Mo₁₀Ni₃C₃B phase on the hardness and fracture toughness of Mo-Ni-C-B cermet: experimental and theoretical study

We obtained the decreasing of hardness and comparable values of the fracture toughness of Mo₂NiB₂‑Ni cermets due to the formation of Mo₁₀Ni₃C3B phase. To understand the effect of the Mo₁₀Ni₃C₃B phase on the mechanical properties of the cermet, first-principles calculations were applied to investigate the elastic, electronic, and thermodynamic properties of this phas

New organic-inorganic hybrid ureasil-based polymer and glass-polymer composites with ion-implanted silver nanoparticles

The micro-/nanoscopic structure of the hybrid organic-inorganic materials, based on polyether chains covalently linked to a silica framework through urea bridges, referred as ureasilicates or ureasils, and As2S3-ureasil composites with ion-implanted Ag nanoparticles is investigated. The formation of Ag nanoparticles is confirmed using optical transmission (surface plasmon resonance band of Ag nanoparticles) at ion-implantation doses of 2.5×1016 and 5.0×1016 ion/cm2 on the example of ureasil. It is established with scanning electron microscopy that incorporation of the As2S3 clusters into ureasil assists to ion-synthesis of Ag nanoparticles in polymer matrix, more effectively at higher doses of ion-implantation and for silver containing (As2S3)95Ag5-ureasil composite. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Determination technique of quasireference value of a carrier radio signal frequency of uncooperated onboard lighting source during radio monitoring

The article discloses the technique defining quasireference value of a carrier radio signal frequency of uncooperated onboard lighting source by calculation and compensation of the Doppler shift at radio monitoring. Provided simulation results show checked operability of the offered technique with assessment of observation accuracy and received quasireference value of a carrier frequency accepted a radio signal compared with real sample under various conditions

Magnetic Properties of FeNi/Cu-Based Lithographic Rectangular Multilayered Elements for Magnetoimpedance Applications

The rectangular elements in magnetoimpedance (MI) configuration with a specific nanocomposite laminated structure based on FeNi and Cu layers were prepared by lift-off lithographic process. The properties of such elements are controlled by their shape, the anisotropy induced during the deposition, and by effects associated with the composite structure. The characterizations of static and dynamic properties, including MI measurements, show that these elements are promising for sensor applications. We have shown that competition between the shape anisotropy and the in-plane induced anisotropy of the element material is worth taking into account in order to understand the magnetic behavior of multilayered rectangular stripes. A possibility of the dynamic methods (ferromagnetic and spin-wave resonance) to describe laminated planar elements having a non-periodic modulation of both structure and magnetic parameters of a system is demonstrated. We show that the multilayered structure, which was originally designed to prevent the development of a “transcritical” state in magnetic layers and to reach the required thickness, also induces the effects that hinder the achievement of the goal, namely an increase in the perpendicular magnetic anisotropy energy. © 2023 by the authors.Russian Science Foundation, RSF: 22-29-00980This research was funded by the Russian Science Foundation (RSF), project no. 22-29-00980, https://rscf.ru/project/22-29-00980/ (accessed on 1 July 2023)

Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning

Multiclass classification is of great interest for various applications, for example, it is a common task in computer vision, where one needs to categorize an image into three or more classes. Here we propose a quantum machine learning approach based on quantum convolutional neural networks for solving the multiclass classification problem. The corresponding learning procedure is implemented via TensorFlowQuantum as a hybrid quantum-classical (variational) model, where quantum output results are fed to the softmax activation function with the subsequent minimization of the cross entropy loss via optimizing the parameters of the quantum circuit. Our conceptional improvements here include a new model for a quantum perceptron and an optimized structure of the quantum circuit. We use the proposed approach to solve a 4-class classification problem for the case of the MNIST dataset using eight qubits for data encoding and four ancilla qubits; previous results have been obtained for 3-class classification problems. Our results show that the accuracy of our solution is similar to classical convolutional neural networks with comparable numbers of trainable parameters. We expect that our findings will provide a new step toward the use of quantum neural networks for solving relevant problems in the NISQ era and beyond