Instytut Podstawowych Problemów Techniki

Tauzowski P., Błachowski B.D., Zawidzka E., Zawidzki M., MorphoGen: Topology optimization software for Extremely Modular Systems, SoftwareX, ISSN: 2352-7110, DOI: 10.1016/j.softx.2024.101797, Vol.27, pp.1-10, 2024

Abstract:
This paper introduces MorphoGen — an integrated reliability-based topology optimization and nonlinear finite element analysis system for 2D and 3D domains. The system’s key innovation is its seamless prototyping of scientific formulations for computational problems in topology optimization. Its layered and object-oriented architecture, based on the template method design pattern, facilitates effortless modifications of algorithms and the introduction of new types of finite elements, materials, and analyses. MorphoGen also offers flexible handling of objective functions and constraints during topological optimization, enhancing its adaptability. It empowers researchers and practitioners to explore a wide range of engineering challenges, fostering a deeper understanding of complex structural behaviors and efficient design solutions. There are many topology optimization software and open source codes, especially based on the classical SIMP method. Unlike these codes our package is freely distributed among users and since it is distributed on the MIT licence, which allows for its easy modification depending on the particular needs of the users. For this purpose, we use the topology optimization algorithm proposed for the first time in our previous paper (Blachowski et al., 2020). The algorithm is based on a fully stress design-based optimality criteria and can be applied for topology optimization of either linearly elastic and elastoplastic structures. Additionally, the novelty of the proposed system is related to its ability of solving optimal topology under various constraints such as displacement, stresses and fatigue in both deterministic and probabilistic cases. Another application are modular structures, which reduce design complexity and manufacturing costs as well as rapid reconfiguration. However, in the realm of structural optimization, modular systems are more challenging due to various: modes of operation of the modules and the stresses configurations. Moreover, this area of research is dramatically less explored. Thus the effectiveness of MorphoGen for structural engineering is demonstrated with examples of topological shape optimization of two Extremely Modular Systems: a planar robotic manipulator Arm-Z and spatial free-form ramp Truss-Z.

Keywords:
Stress Constrained Topology Optimizatio,Extremely Modular System,Object-oriented software architecture,MATLAB-based array programming,First Order Reliability Analysis

Marszałek A., Burczyński T., Modeling of limit order book data with ordered fuzzy numbers, APPLIED SOFT COMPUTING, ISSN: 1568-4946, DOI: 10.1016/j.asoc.2024.111555, Vol.158, pp.1-20, 2024

Abstract:
This paper presents a novel approach to representing the Limit Order Book data at a given timestamp using the Ordered Fuzzy Numbers concept. The limit order book contains all buy and sell orders placed by investors, updated in real-time, for the most liquid securities, even several hundred times a minute. Due to its irregular nature (different and dynamic changes in the number of buy and sell orders), direct calculations on the order book data are not feasible without transforming it into feature vectors. Currently, most studies use a price level-based data representation scheme when applying deep learning models on limit order book data. However, this scheme has limitations, particularly its sensitivity to subtle perturbations that can negatively impact model performance. On the other hand, the ordered fuzzy number is a mathematical object (a pair of two functions) used to process imprecise and uncertain data. Ordered Fuzzy Numbers possess well-defined arithmetic properties. Converting the limit order book data to ordered fuzzy numbers allows the creation of a time series of ordered fuzzy numbers (order books) and use them for further calculations, e.g., to represent input data for deep learning models or employing the concept of fuzzy time series in various domains, such as defining liquidity measures based on limit order book data. In this paper, the proposed approach is tested using one-year market data from the Polish Stock Exchange for the five biggest companies. The DeepLOB model is employed to predict mid-price movement using different input data representations. The proposed representation of Limit Order Book data demonstrated remarkably stable out-of-sample prediction accuracy, even when subjected to data perturbation.

Keywords:
Limit order book, Ordered fuzzy number, High-frequency forecasting, Mid-price, Data perturbation

Kondra T., Ray G., Streltsov A., Coherence Manipulation in Asymmetry and Thermodynamics, PHYSICAL REVIEW LETTERS, ISSN: 0031-9007, DOI: 10.1103/PhysRevLett.132.200201, Vol.132, pp.200201-1-200201-6, 2024

Abstract:
In the classical regime, thermodynamic state transformations are governed by the free energy. This is also called as the second law of thermodynamics. Previous works showed that, access to a catalytic system allows us to restore the second law in the quantum regime when we ignore coherence. However, in the quantum regime, coherence and free energy are two independent resources. Therefore, coherence places additional nontrivial restrictions on the state transformations that remain elusive. In order to close this gap, we isolate and study the nature of coherence, i.e., we assume access to a source of free energy. We show that allowing catalysis along with a source of free energy allows us to amplify any quantum coherence present in the quantum state arbitrarily. Additionally, any correlations between the system and the catalyst can be suppressed arbitrarily. Therefore, our results provide a key step in formulating a fully general law of quantum thermodynamics.

Tauzowski P., Błachowski B., Lógó J., Optimal topologies considering fatigue with reliability constraint, Advances in Engineering Software, ISSN: 0965-9978, DOI: 10.1016/j.advengsoft.2023.103590, Vol.189, pp.1-12, 2024

Abstract:
This paper addresses a challenging engineering problem that combines stress-limited topology optimization, reliability analysis, and plasticity-based low-cycle fatigue. Each of these issues represents a complex problem on its own, necessitating significant computational effort. In this study, we propose a novel approach that integrates safety assessment into the topology optimization process while considering the number of cycles for low-cycle fatigue. Our method employs a linear approximation of the performance function for safety control, incorporating the number of failure cycles within a complex, multi-level load program. The methodology is validated through real experiments, using a finite element model with cubic shape functions that yield nearly identical results between numerical and experimental outcomes in the case of fatigue-resistant design for a bi-axially tensioned structural joint.

Keywords:
Topology optimization, stress constraints, Reliability analysis, low-cycle fatigue, fatigueplasticity

Kaszyca K., Marcin C., Bucholc B., Błyskun P., Nisar F., Rojek J., Zybała R., Using the Spark Plasma Sintering System for Fabrication of Advanced Semiconductor Materials, Materials, ISSN: 1996-1944, DOI: 10.3390/ma17061422, Vol.17, No.1422, pp.1-15, 2024

Abstract:
The interest in the Spark Plasma Sintering (SPS) technique has continuously increased over the last few years. This article shows the possibility of the development of an SPS device used for material processing and synthesis in both scientific and industrial applications and aims to present manufacturing methods and the versatility of an SPS device, presenting examples of processing Arc-Melted- (half-Heusler, cobalt triantimonide) and Self-propagating High-temperature Synthesis (SHS)-synthesized semiconductor (bismuth telluride) materials. The SPS system functionality development is presented, the purpose of which was to broaden the knowledge of the nature of SPS processes. This approach enabled the precise design of material sintering processes and also contributed to increasing the repeatability and accuracy of sintering conditions.

Keywords:
spark plasma sintering, arc melting, semiconductor materials, half-Heusler, bismuth telluride, cobalt triantimonide, SHS, SPS

Chandan D., Tulja Varun K., Miller M., Streltsov A.^♦, Entanglement catalysis for quantum states and noisy channels, Quantum 8, ISSN: 2521-327X, DOI: 10.22331/q-2024-03-20-1290, Vol.8, pp.1-20, 2024

Abstract:
Many applications of the emerging quantum technologies, such as quantum teleportation and quantum key distribution, require singlets, maximally entangled states of two quantum bits. It is thus of utmost importance to develop optimal procedures for establishing singlets between remote parties. As has been shown very recently, singlets can be obtained from other quantum states by using a quantum catalyst, an entangled quantum system which is not changed in the procedure. In this work we take this idea further, investigating properties of entanglement catalysis and its role for quantum communication. For transformations between bipartite pure states, we prove the existence of a universal catalyst, which can enable all possible transformations in this setup. We demonstrate the advantage of catalysis in asymptotic settings, going beyond the typical assumption of independent and identically distributed systems. We further develop methods to estimate the number of singlets which can be established via a noisy quantum channel when assisted by entangled catalysts. For various types of quantum channels our results lead to optimal protocols, allowing to establish the maximal number of singlets with a single use of the channel.

Nthunya Lebea N., Kok Chung C., Soon Onn L., Woei Jye L., Eduardo Alberto L., Lucy Mar C., Shirazi Mohammad Mahdi A., Aamer A., Mamba Bhekie B., Osial M., Pietrzyk-Thel P., Pręgowska A., Mahlangu Oranso T., Progress in membrane distillation processes for dye wastewater treatment: A review, Chemosphere, ISSN: 0045-6535, DOI: 10.1016/j.chemosphere.2024.142347, pp.1-104, 2024

Abstract:
Textile and cosmetic industries generate large amounts of dye effluents requiring treatment before discharge. This wastewater contains high levels of reactive dyes, low to none-biodegradable materials and chemical residues. Technically, dye wastewater is characterised by high chemical and biological oxygen demand. Biological, physical and pressure-driven membrane processes have been extensively used in textile wastewater treatment plants. However, these technologies are characterised by process complexity and are often costly. Also, process efficiency is not achieved in cost-effective biochemical and physical treatment processes. Membrane distillation (MD) emerged as a promising technology harnessing challenges faced by pressure-driven membrane processes. To ensure high cost-effectiveness, the MD can be operated by solar energy or low-grade waste heat. Herein, the MD purification of dye wastewater is comprehensively and yet concisely discussed. This involved research advancement in MD processes towards removal of dyes from industrial effluents. Also, challenges faced by this process with a specific focus on fouling are reviewed. Current literature mainly tested MD setups in the laboratory scale suggesting a deep need of further optimization of membrane and module designs in near future, especially for textile wastewater treatment. There is a need to deliver customized high-porosity hydrophobic membrane design with the appropriate thickness and module configuration to reduce concentration and temperature polarization. Also, energy loss should be minimized while increasing dye rejection and permeate flux. Although laboratory experiments remain pivotal in optimizing the MD process for treating dye wastewater, their time-intensive nature poses a challenge. Given the multitude of parameters involved in MD process optimization, artificial intelligence (AI) methodologies present a promising avenue for assistance. Thus, AI-driven algorithms have the potential to enhance overall process efficiency, cutting down on time, fine-tuning parameters, and driving cost reductions. However, achieving an optimal balance between efficiency enhancements and financial outlays is a complex process. Finally, this paper suggests a research direction for the development of effective synthetic and natural dye removal from industrially discharged wastewater.

Keywords:
Energy Consumption,Dye Effluent,Fouling,Heat and Mass Transfer,Membrane and Module Design

Barros G., Andre P., Rojek J., Carter J., Thoeni K., Time domain coupling of the boundary and discrete element methods for 3D problems, COMPUTATIONAL MECHANICS, ISSN: 0178-7675, DOI: 10.1007/s00466-024-02455-7, pp.1-19, 2024

Abstract:
This paper presents an extension of the authors’ previously developed interface coupling technique for 2D problems to 3D problems. The method combines the strengths of the Discrete Element Method (DEM), known for its adeptness in capturing discontinuities and non-linearities at the microscale, and the Boundary Element Method (BEM), known for its efficiency in modelling wave propagation within infinite domains. The 3D formulation is based on spherical discrete elements and bilinear quadrilateral boundary elements. The innovative coupling methodology overcomes a critical limitation by enabling the representation of discontinuities within infinite domains, a pivotal development for large-scale dynamic problems. The paper systematically addresses challenges, with a focus on interface compatibility, showcasing the method’s accuracy through benchmark validation on a finite rod and infinite spherical cavity. Finally, a model of a column embedded into the ground illustrates the versatility of the approach in handling complex scenarios with multiple domains. This innovative coupling approach represents a significant leap in the integration of DEM and BEM for 3D problems and opens avenues for tackling complex and realistic problems in various scientific and engineering domains.

Keywords:
Interface coupling, Concurrent multi-scale coupling, Boundary element method (BEM), Discrete element method (DEM) , Staggered time integration, Dynamic wave propagation, Infinite domain

Tauzowski P., Błachowski B., Lógó J., Computational framework for a family of methods based on stress-constrained topology optimization, COMPUTERS AND STRUCTURES, ISSN: 0045-7949, DOI: 10.1016/j.compstruc.2024.107493, Vol.303, pp.1-14, 2024

Abstract:
This study presents a general computational framework for topology optimization under constraints related to various engineering design problems, including: reliability analysis, low-cycle fatigue assessment, and stress limited analysis. Such a framework aims to facilitate comprehensive engineering design considerations by incorporating traditional constraints such as displacement and stress alongside probabilistic assessments of fatigue failure and the complex behaviors exhibited by structures made of elastoplastic material. The framework's amalgamation of diverse analytical components offers engineers a versatile toolkit to address intricate design challenges. Notably, the inclusion of reliability analysis introduces a probabilistic perspective, transforming conventional design constraints into random parameters, thereby enhancing the robustness of the design process.

Key to the framework's efficacy is its implementation using MATLAB mathematical computing software, leveraging the platform's efficient code execution and object-oriented programming paradigm. This choice ensures an intuitive and potent environment for designers and researchers, facilitating seamless adaptation across various engineering applications. Additionally, the proposed previously by the Authors algorithm for the topology optimization is extended by adaptive strategy allowing for efficient adjustment of an amount of material removed at individual optimization step.

The presented framework is offering a comprehensive and integrated approach to address multifaceted design challenges while enhancing design robustness and efficiency.

Keywords:
Topology optimization, Stress constraints, First order reliability analysis, Low-cycle fatigue, Plasticity

Maździarz M., Nosewicz S., Atomistic investigation of deformation and fracture of individual structural components of metal matrix composites, ENGINEERING FRACTURE MECHANICS, ISSN: 0013-7944, DOI: 10.1016/j.engfracmech.2024.109953, Vol.298, pp.109953-1-109953-21, 2024

Abstract:
This paper focuses on the development of the atomistic framework for determining the lower scale mechanical parameters of single components of a metal matrix composite for final application to a micromechanical damage model. Here, the deformation and failure behavior of NiAl–Al2O3 interfaces and their components, metal and ceramic, are analyzed in depth using molecular statics calculations. A number of atomistic simulations of strength tests, uniaxial tensile, uniaxial compressive and simple shear, have been performed in order to obtain a set of stiffness tensors and strain–stress characteristics up to failure for 30 different crystalline and amorphous systems. Characteristic points on the strain–stress curves in the vicinity of failure are further analyzed at the atomistic level, using local measures of lattice disorder. Numerical results are discussed in the context of composite damage at upper microscopic scale based on images of the fracture surface of NiAl–Al2O3 composites.

Keywords:
Metal-matrix composites (MMCs), Fracture, Computational modeling, Mechanical testing, Molecular statics

Maździarz M., Uncertainty of DFT Calculated Mechanical and Structural Properties of Solids due to Incompatibility of Pseudopotentials and Exchange–Correlation Functionals, Journal of Chemical Theory and Computation, ISSN: 1549-9618, DOI: 10.1021/acs.jctc.4c01036, Vol.20, No.21, pp.9734-9740, 2024

Abstract:
The demand for pseudopotentials constructed for a given exchange-correlation (XC) functional far exceeds the supply, necessitating the use of those commonly available. The number of XC functionals currently available is in the hundreds, if not thousands, and the majority of pseudopotentials have been generated for LDA and PBE. The objective of this study is to identify the error in the determination of the mechanical and structural properties (lattice constant, cohesive energy, surface energy, elastic constants, and bulk modulus) of crystals calculated by DFT with such inconsistency. Additionally, this study aims to estimate the performance of popular XC functionals (LDA, PBE, PBEsol, and SCAN) for these calculations in a consistent manner.

Keywords:
DFT, pseudopotentials, exchange–correlation functionals

Fathalian M., Postek E. W., Tahani M., Sadowski T., A Comprehensive Study of Al2O3 Mechanical Behavior Using Density Functional Theory and Molecular Dynamics, Molecules, ISSN: 1420-3049, DOI: 10.3390/molecules29051165, Vol.29, pp.1165-1165-18, 2024

Abstract:
This study comprehensively investigates Al2O3’s mechanical properties, focusing on fracture toughness, surface energy, Young’s modulus, and crack propagation. The density functional
theory (DFT) is employed to model the vacancies in Al2O3, providing essential insights into this material’s structural stability and defect formation. The DFT simulations reveal a deep understanding of vacancy-related properties and their impact on mechanical behavior. In conjunction with molecular dynamics (MD) simulations, the fracture toughness and crack propagation in Al2O3 are explored, offering valuable information on material strength and durability. The surface energy of Al2O3 is also assessed using DFT, shedding light on its interactions with the surrounding environment.
The results of this investigation highlight the significant impact of oxygen vacancies on mechanical characteristics such as ultimate strength and fracture toughness, drawing comparisons with the effects observed in the presence of aluminum vacancies. Additionally, the research underscores the validation of fracture toughness outcomes derived from both DFT and MD simulations, which align well with findings from established experimental studies. Additionally, the research underscores the validation of fracture toughness outcomes derived from DFT and MD simulations, aligning well with findings from established experimental studies. The combination of DFT and MD simulations provides a robust framework for a comprehensive understanding of Al2O3’s mechanical properties, with implications for material science and engineering applications.

Keywords:
Al2O3, fracture toughness, density functional theory, molecular dynamics

Fathalian M., Postek E. W., Tahani M., Sadowski T., Effect of Diffusion on the Ultimate Axial Load of Complex-Shaped Al-SiC Samples: A Molecular Dynamics Study, Molecules, ISSN: 1420-3049, DOI: 10.3390/molecules29143343, Vol.29, No.14, pp.3343-1-3343-20, 2024

Abstract:
Metal matrix composites (MMCs) combine metal with ceramic reinforcement, offering high strength, stiffness, corrosion resistance, and low weight for diverse applications. Al-SiC, a common MMC, consists of an aluminum matrix reinforced with silicon carbide, making it ideal for the aerospace and automotive industries. In this work, molecular dynamics simulations are performed to investigate the mechanical properties of the complex-shaped models of Al-SiC. Three different volume fractions of SiC particles, precisely 10%, 15%, and 25%, are investigated in a composite under uniaxial tensile loading. The tensile behavior of Al-SiC composites is evaluated under two loading directions, considering both cases with and without diffusion effects. The results show that diffusion increases the ultimate tensile strength of the Al-SiC composite, particularly for the 15% SiC volume fraction. Regarding the shape of the SiC particles considered in this research, the strength of the composite varies in different directions. Specifically, the ultimate strength of the Al-SiC composite with 25% SiC reached 11.29 GPa in one direction, and 6.63 GPa in another, demonstrating the material’s anisotropic mechanical behavior when diffusion effects are considered. Young’s modulus shows negligible change in the presence of diffusion. Furthermore, diffusion improves toughness in Al-SiC composites, resulting in higher values compared to those without diffusion, as evidenced by the 25% SiC volume fraction composite (2.086 GPa) versus 15% (0.863 GPa) and 10% (1.296 GPa) SiC volume fractions.

Keywords:
Molecular dynamics, Al-SiC composites, Diffusion, SiC particle

Nisar F., Rojek J., Nosewicz S., Kaszyca K., Chmielewski M., Evaluation of effective thermal conductivity of sintered porous materials using an improved discrete element model, POWDER TECHNOLOGY, ISSN: 0032-5910, DOI: 10.1016/j.powtec.2024.119546, Vol.437, pp.119546, 2024

Abstract:
This work aims to revise and apply an original discrete element model (DEM) to evaluate effective thermal conductivity of sintered porous materials. The model, based on two-particle sintering geometry, calculates inter-particle neck using Constant Volume (CV) criterion. The model was validated using experimental measurements on sintered porous NiAl. For DEM simulations, heterogeneous samples with real particle size distribution and different densities were obtained by simulation of hot pressing. Neck size evaluated using Coble’s and CV models were compared to show that commonly used Coble’s model overestimates neck size and conductivity. The proposed model was improved by neck-size correction to compensate for non-physical overlaps at higher densities and by adding grain-boundary resistance to account for porosity within necks. Resistance contribution from grain boundaries was shown to decrease with increasing density. Thermal conductivity obtained from the improved model was close to experimental results, suggesting validity of the model.

Keywords:
Discrete element method,Effective thermal conductivity,Porous materials,Sintering,Heat conduction simulation

Scalici M., Naseri M., Streltsov A., Coherence Generation with Hamiltonians, Quantum Information and Computation, ISSN: 1533-7146, Vol.24, No.7-8, pp.565-575, 2024

Abstract:
We explore methods to generate quantum coherence through unitary evolutions, by introducing and studying the coherence generating capacity of Hamiltonians. This quantity is defined as the maximum derivative of coherence that can be achieved by a Hamiltonian. By adopting the relative entropy of coherence as our figure of merit, we evaluate the maximal coherence generating capacity with the constraint of a bounded Hilbert- Schmidt norm for the Hamiltonian. Our investigation yields closed-form expressions for both Hamiltonians and quantum states that induce the maximal derivative of coherence under these conditions. Specifically, for qubit systems, we solve this problem comprehensively for any given Hamiltonian, identifying the quantum states that lead to the largest coherence derivative induced by the Hamiltonian. Our investigation enables a precise identification of conditions under which quantum coherence is optimally enhanced, offering valuable insights for the manipulation and control of quantum coherence in quantum systems.

Keywords:
Resource Generation, Quantum Coherence, Quantum Control

Kadier A., Akkaya G.K., Singh R., Niza N.M., Parkash A., Achagri G., Bhagawati P.B., Asaithambi P., Al-Qodah Z., Almanaseer N., Osial M., Olusegun S.J., Pręgowska A., López-Maldonado E.A., Micro and nano-sized bubbles for sanitation and water reuse: from fundamentals to application, Frontiers of Environmental Science & Engineering, ISSN: 2296-665X, DOI: 10.1007/s11783-024-1907-1, Vol.18, No.12, pp.1-26, 2024

Abstract:
The global scarcity of drinking water is an emerging problem associated with increasing pollution with many chemicals from industry and rapid microbial growth in aquatic systems. Despite the wide availability of conventional water and wastewater treatment methods, many limitations and challenges exist to overcome. Applying technology based on microbubbles (MBs) and nano-bubbles (NBs) offers ecological, fast, and cost-effective water treatment. All due to the high stability and long lifetime of the bubbles in the water, high gas transfer efficiency, free radical generation capacity, and large specific surface areas with interface potential of generated bubbles. MBs and NBs-based technology are attractive solutions in various application areas to improve existing water and wastewater treatment processes including industrial processes. In this paper, recent progress in NBs and MBs technology in water purification and wastewater treatment along with fundamentals, application, challenges, and future research were comperhensively discussed.

Keywords:
Nanobubbles, Microbubbles, MNB, Wastewater treatment, Water pollution utilization

Nisar F., Rojek J., Nosewicz S., Szczepański J., Kaszyca K., Chmielewski M., Discrete element model for effective electrical conductivity of spark plasma sintered porous materials, Computational Particle Mechanics, ISSN: 2196-4378, DOI: 10.1007/s40571-024-00773-4, pp.1-11, 2024

Abstract:
This paper aims to analyse electrical conduction in partially sintered porous materials using an original resistor network model within discrete element framework. The model is based on sintering geometry, where two particles are connected via neck. Particle-to-particle conductance depends on neck size in sintered materials. Therefore, accurate evaluation of neck size is essential to determine conductance. The neck size was determined using volume preservation criterion. Additionally, grain boundary correction factor was introduced to compensate for any non-physical overlaps between particles, particularly at higher densification. Furthermore, grain boundary resistance was added to account for the porosity within necks. For numerical analysis, the DEM sample was generated using real particle size distribution, ensuring a heterogeneous and realistic microstructure characterized by a maximum-to-minimum particle diameter ratio of 15. The DEM sample was subjected to hot press simulation to obtain geometries with different porosity levels. These representative geometries were used to simulate current flow and determine effective electrical conductivity as a function of porosity. The discrete element model (DEM) was validated using experimentally measured electrical conductivities of porous NiAl samples manufactured using spark plasma sintering (SPS). The numerical results were in close agreement with the experimental results, hence proving the accuracy of the model. The model can be used for microscopic analysis and can also be coupled with sintering models to evaluate effective properties during the sintering process.

Keywords:
Discrete element method, Effective electrical conductivity, Porous materials, Sintering, Resistor network model

Rudnicka Z., Pręgowska A., Glądys K., Perkins M., Proniewska K., Advancements in artificial intelligence-driven techniques for interventional cardiology, Cardiology Journal, ISSN: 1897-5593, DOI: 10.5603/cj.98650, pp.1-31, 2024

Abstract:
This paper aims to thoroughly discuss the impact of artificial intelligence (AI) on clinical practice in interventional cardiology (IC) with special recognition of its most recent advancements. Thus, recent years have been exceptionally abundant in advancements in computational tools, including the development of AI. The application of AI development is currently in its early stages, nevertheless new technologies have proven to be a promising concept, particularly considering IC showing great impact on patient safety, risk stratification and outcomes during the whole therapeutic process. The primary goal is to achieve the integration of multiple cardiac imaging modalities, establish online decision support systems and platforms based on augmented and/or virtual realities, and finally to create automatic medical systems, providing electronic health data on patients. In a simplified way, two main areas of AI utilization in IC may be distinguished, namely, virtual and physical. Consequently, numerous studies have provided data regarding AI utilization in terms of automated interpretation and analysis from various cardiac modalities, including electrocardiogram, echocardiography, angiography, cardiac magnetic resonance imaging, and computed tomography as well as data collected during robotic-assisted percutaneous coronary intervention procedures. Thus, this paper aims to thoroughly discuss the impact of AI on clinical practice in IC with special recognition of its most recent advancements.

Keywords:
artificial intelligence (AI), interventional cardiology (IC), cardiac modalities, augmented and/or virtual realities, automatic medical systems

Rudnicka Z., Szczepański J., Pręgowska A., Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview, Electronics , ISSN: 2079-9292, DOI: 10.3390/electronics13040746, Vol.13, No.4, pp.1-35, 2024

Abstract:
Recently, artificial intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies, as well as increasing the effectiveness of the training process. In this context, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in medical image scan segmentation and intelligent visual content generation, i.e., computer-generated three-dimensional (3D) images in the context of extended reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. It concludes with possible developments and open challenges in AI applications in extended reality-based solutions. Finally, future lines of research and development directions of artificial intelligence applications, both in medical image segmentation and extended reality-based medical solutions, are discussed.

Keywords:
artificial intelligence, extended reality, medical image scan segmentation

Rudnicka Z., Proniewska K., Perkins M., Pręgowska A., Cardiac Healthcare Digital Twins Supported by Artificial Intelligence-Based Algorithms and Extended Reality—A Systematic Review, Electronics , ISSN: 2079-9292, DOI: 10.3390/electronics13050866, Vol.13, No.5, pp.1-35, 2024

Abstract:
Recently, significant efforts have been made to create Health Digital Twins (HDTs), Digital Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which favors the effective application of HDTs. The clinical application of HDTs will be increasingly widespread in the future of healthcare services and has huge potential to form part of mainstream medicine. However, it requires the development of both models and algorithms for the analysis of medical data, and advances in Artificial Intelligence (AI)-based algorithms have already revolutionized image segmentation processes. Precise segmentation of lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapy. In this systematic review, a brief overview of recent achievements in HDT technologies in the field of cardiology, including interventional cardiology, was conducted. HDTs were studied taking into account the application of Extended Reality (XR) and AI, as well as data security, technical risks, and ethics-related issues. Special emphasis was put on automatic segmentation issues. In this study, 253 literature sources were taken into account. It appears that improvements in data processing will focus on automatic segmentation of medical imaging in addition to three-dimensional (3D) pictures to reconstruct the anatomy of the heart and torso that can be displayed in XR-based devices. This will contribute to the development of effective heart diagnostics. The combination of AI, XR, and an HDT-based solution will help to avoid technical errors and serve as a universal methodology in the development of personalized cardiology. Additionally, we describe potential applications, limitations, and further research directions.

Keywords:
Artificial Intelligence,Machine Learning,Metaverse,Virtual Reality,Extended Reality,Augmented Reality,Digital Twin,Health Digital Twin,personalized medicine,cardiology

Miller M., Scalici M., Fellous-Asiani M., Streltsov A., Power of noisy quantum states and the advantage of resource dilution, Physical Review A, ISSN: 2469-9926, DOI: 10.1103/PhysRevA.109.022404, Vol.109, pp.022404-1-022404-13, 2024

Abstract:
Entanglement distillation allows to convert noisy quantum states into singlets, which can, in turn, be used for various quantum technological tasks, such as quantum teleportation and quantum key distribution. Entanglement dilution is the inverse process: singlets are converted into quantum states with less entanglement. While the usefulness of distillation is apparent, practical applications of entanglement dilution are less obvious. Here, we show that entanglement dilution can increase the resilience of shared quantum states to local noise. The increased resilience is observed even if diluting singlets into states with arbitrarily little entanglement. We extend our analysis to other quantum resource theories, such as quantum coherence, quantum thermodynamics, and purity. For these resource theories, we demonstrate that diluting pure quantum states into noisy ones can be advantageous for protecting the system from noise. Our results demonstrate the usefulness of quantum resource dilution, and provide a rare example for an advantage of noisy quantum states over pure states in quantum information processing.

Halder S., Streltsov A., Banik M., Identifying the value of a random variable unambiguously: Quantum versus classical approaches, Physical Review A, ISSN: 2469-9926, DOI: 10.1103/PhysRevA.109.052608, Vol.109, pp.052608-1-052608-11, 2024

Abstract:
Quantum resources may provide an advantage over their classical counterparts. Theoretically, in certain tasks, this advantage can be very high. In this work, we construct such a task based on a game, mediated by the Referee and played between Alice and Bob. The Referee sends Alice a value of a random variable. At the same time, the Referee also sends Bob some partial information regarding that value. Here partial information can be defined in the following way. Bob gets the information of a random set that must contain the value of the variable, which is sent to Alice by the Referee, along with other value(s). Alice is not allowed to know what information is sent to Bob by the Referee. Again, Bob does not know which value of the random variable is sent to Alice. Now, the game can be won if and only if Bob can unambiguously identify the value of the variable that is sent to Alice, with some nonzero probability, no matter what information Bob receives or which value is sent to Alice. However, to help Bob, Alice sends some limited amount of information to him, based on any strategy that is fixed by Alice and Bob before the game begins. We show that if Alice sends a limited amount of classical information, then the game cannot be won, while the quantum analog of the “limited amount of classical information” is sufficient for winning the game. Thus, it establishes a quantum advantage. We further analyze several variants of the game and provide certain bounds on the success probabilities. Moreover, we establish connections between the trine ensemble, mutually unbiased bases, and the encoding-decoding strategies of those variants. We also discuss the role of quantum coherence in the present context.

Ludovico L., Regula B., Streltsov A., No-go theorem for entanglement distillation using catalysis, Physical Review A, ISSN: 2469-9926, DOI: 10.1103/PhysRevA.109.L050401, Vol.109, pp.L050401-1-L050401-6, 2024

Abstract:
The use of ancillary quantum systems known as catalysts is known to be able to enhance the capabilities of entanglement transformations under local operations and classical communication. However, the limits of these advantages have not been determined and in particular it is not known if such assistance can overcome the known restrictions on asymptotic transformation rates—notably the existence of bound entangled (undistillable) states. Here we establish a general limitation on entanglement catalysis: we show that catalytic transformations can never allow for the distillation of entanglement from a bound entangled state with positive partial transpose, even if the catalyst may become correlated with the system of interest and even under permissive choices of free operations. This precludes the possibility that catalysis may make entanglement theory asymptotically reversible. Our methods are based on asymptotic bounds for the distillable entanglement and entanglement cost assisted by correlated catalysts.

Paprocki B., Pręgowska A., Szczepański J., Does Adding of Neurons to the Network Layer Lead to Increased Transmission Efficiency?, IEEE Access, ISSN: 2169-3536, DOI: 10.1109/ACCESS.2024.3379324, Vol.12, pp. 42701-42709, 2024

Abstract:
The aim of this study is to contribute to the important question in Neuroscience of whether the number of neurons in a given layer of a network affects transmission efficiency. Mutual Information, as defined by Shannon, between the input and output signals for certain classes of networks is analyzed theoretically and numerically. A Levy-Baxter probabilistic neural model is applied. This model includes all important qualitative mechanisms involved in the transmission process in the brain. We derived analytical formulas for the Mutual Information of input signals coming from Information Sources as Bernoulli processes. These formulas depend on the parameters of the Information Source, neurons and network. Numerical simulations were performed using these equations. It turned out, that the Mutual Information starting from a certain value increased very slowly with the number of neurons being added. The increase is of the rate m_{−c} where m is the number of neurons in the transmission layer, and c is very small. The calculations also show that for a practical number (up to 15000) of neurons, the Mutual Information reaches only approximately half of the information that is carried out by the input signal. The influence of noise on the transmission efficiency depending on the number of neurons was also analyzed. It turned out that the noise level at which transmission is optimal increases significantly with this number. Our results indicate that a large number of neurons in the network does not mean an essential improvement in transmission efficiency, but can contribute to reliability.

Keywords:
Shannon communication theory,neural network,network layer,transmission efficiency,mutual information,model of neuron,spike trains,information source,entropy

Roszkiewicz-Walczuk A., Garlińska M., Pręgowska A., Advancements in Piezoelectric-Enabled Devices for Optical Communication, physica status solidi (a), ISSN: 1862-6319, DOI: 10.1002/pssa.202400298, Vol.2024, pp.2400298-1-25, 2024

Abstract:
The ability of piezoelectric materials to convert mechanical energy into electric energy and vice versa has made them desirable in the wide range of applications that oscillate from medicine to the energetics industry. Their implementation in optical communication is often connected with the modulation or other manipulations of the light signals. In this article, the recent advancements in the field of piezoelectrics-based devices and their promising benefits in optical communication are explored. The application of piezoelectrics-based devices in optical communication allows dynamic control, modulation, and manipulation of optical signals that lead to a more reliable transmission. It turns out that a combination of artificial-intelligence-based algorithms with piezoelectrics can enhance the performance of these devices, including optimization of piezoelectric modulation, adaptive signal processing, control of optical components, and increase the level of energy efficiency. It can enhance signal quality, mitigate interference, and reduce noise-connected issues. Moreover, this technological fusion can increase the security of optical communication systems. Finally, the potential future research lines are determined.

Zabojszcza P., Radoń U., Tauzowski P., Robust Optimization of the Steel Single Story Frame, Acta Polytechnica Hungarica, ISSN: 1785-8860, DOI: 10.12700/APH.21.1.2024.1.2, Vol.21, No.1, pp.9-29, 2024

Abstract:
In contemporary design practices, building structures are expected to not only meet safety requirements but also be optimized. However, optimal designs can be highly sensitive to random variations in model parameters and external actions. Solutions that appear effective under nominal conditions may prove inadequate when parameter randomness is considered. To address this challenge, the concept of robust optimization has been introduced, which extends deterministic optimization formulations to incorporate the random variability of parameter values. In this study, we demonstrate the applicability of robust optimization in the design of building structures using a simple orthogonal frame as an example. The static-strength analysis is conducted based on the displacement method, utilizing second-order theory. To assess the safety level of the steel frame, a preliminary evaluation is performed by determining the reliability index and failure probability using the Monte Carlo Method. Robust optimization is then employed, leveraging the second-order response surface. Experimental designs are generated following an optimal Latin hypercube plan. The proposal of a mathematical-numerical algorithm for solving the optimization problem while considering the random nature of design parameters constitutes the innovative aspect of this research.

Keywords:
reliability, robust optimization, second order theory, displacement method

Wu K., Kondra T., Scandolo C., Swapan R., Xiang G., Li C., Guo G., Streltsov A., Resource theory of imaginarity in distributed scenarios, Communications Physics, ISSN: 2399-3650, DOI: 10.1038/s42005-024-01649-y, Vol.7, No.171, pp.1-9, 2024

Abstract:
The resource theory of imaginarity studies the operational value of imaginary parts in quantum states, operations, and measurements. Here we introduce and study the distillation and conversion of imaginarity in distributed scenario. This arises naturally in bipartite systems where both parties work together to generate the maximum possible imaginarity on one of the subsystems. We give exact solutions to this problem for general qubit states and pure states of arbitrary dimension. We present a scenario that demonstrates the operational advantage of imaginarity: the discrimination of quantum channels without the aid of an ancillary system. We then link this scenario to local operations and classical communications(LOCC) discrimination of bipartite states. We experimentally demonstrate the relevant assisted distillation protocol, and show the usefulness of imaginarity in the aforementioned two tasks.

Danila P., van D., Kuniewicz M., Dolega-Dolegowski D., Pręgowska A., Andree A., Dobrzyński H., Proniewska K., Interactive teaching of medical 3D cardiac anatomy: atrial anatomy enhanced by ECG and 3D visualization, Frontiers in Medicine, ISSN: 2296-858X, DOI: 10.3389/fmed.2024.1422017, Vol.11, No.1422017, pp.1-8, 2024

Abstract:
The most commonly applied way of teaching students to convey the foundations of human anatomy and physiology involves textbooks and lectures. This way of transmitting knowledge causes difficulties for students, especially in the context of three-dimensional imaging of organ structures, and as a consequence translates into difficulties with imagining them. Even despite the rapid uptake of knowledge dissemination provided by online materials, including courses and webinars, there is a clear need for learning programs featuring first-hand immersive experiences tailored to suit individual study paces. In this paper, we present an approach to enhance a classical study program by combining multi-modality data and representing them in a Mixed Reality (MR)-based environment. The advantages of the proposed approach have been proven by the conducted investigation of the relationship between atrial anatomy, its electrophysiological characteristics, and resulting P wave morphology on the electrocardiogram (ECG). Another part of the paper focuses on the role of the sinoatrial node in ECG formation, while the MR-based visualization of combined micro-computed tomography (micro-CT) data with non-invasive CineECG imaging demonstrates the educational application of these advanced technologies for teaching cardiac anatomy and ECG correlations.

Keywords:
mixed reality, CineECG, micro-CT, P wave, ECG imaging

A. Hamed M., Fathalian M., Ghorbanzadeh Ahangari M., Shahavi H., DFT study of Ni, Cu, Cd and Ag heavy metal atom adsorption onto the surface of the zinc-oxide nanotube and zinc-oxide graphene-like structure, MATERIALS CHEMISTRY AND PHYSICS, ISSN: 0254-0584, DOI: 10.1016/j.matchemphys.2018.09.016, pp.366-373, 2024

Abstract:
Abstract
In the current article, we investigated adsorption properties of Nickel (Ni), Copper (Cu), Cadmium (Cd) and silver (Ag) heavy metal atoms with zinc oxide (ZnO) nanotube and ZnO-graphene like structures sheet using Ab-initio based density functional theory (DFT) calculations. At first, both nanostructures were optimized and then most stable configuration, adsorption energy and equilibrium distance of each heavy metal with nanostructures were computed via DFT and the results were compared with each other. Our obtained results reveal that ZnO-nanotube had a better adsorbing behavior comparing to ZnO-graphene sheet case by case due to lower equilibrium distance and higher adsorption energy. This nanostructure created a strong binding with Ni, Cu and Ag but adsorption energy for Cd was clearly lower than others. Also, only Ni and Cu could have a chemisorption adsorption with the ZnO-graphene sheet and others showed a nearly weak physisorption adsorption with this nanostructure. The maximum adsorption energy for both ZnO-nanostructures occurred for nickel which were about −3.45 eV and −2.19 eV respectively. The minimum adsorption energy for ZnO nanotube occurred with Cd (−1.3 eV) while for ZnO-graphene sheet it occurred with Zn (−0.15 eV). In almost all items equilibrium distance decreased with increasing in adsorption energy. Moreover, we generated density of state (DOS) diagrams to investigate the electrical properties of studied structures.

Keywords:
Zinc-oxideDFT dsorption Heavy metal atoms DOS

Kleiber M., Niekontrolowany rozwój AI jest zagrożeniem dla ludzkości, NAUKA, ISSN: 1231-8515, DOI: 10.24425/nauka.2024.151207, Vol.2, pp.91-94, 2024

Keywords:
sztuczna inteligencja,globalne zagrożenia,interfejs mózg-komputer,potrzeba globalnych regulacji

Kleiber M., Tworzymy uniwersytety przyszłości, NAUKA, ISSN: 1231-8515, DOI: 10.24425/nauka.2024.151204, Vol.2, pp.63-66, 2024

Keywords:
współpraca,mobilność studentów,synergia wyników badań,wspólne dyplomy

Pręgowska A., Perkins M., Artificial intelligence in medical education: Typologies and ethical approaches, Ethics & Bioethics (in Central Europe), ISSN: 1338-5615, DOI: 10.2478/ebce-2024-0004, Vol.14, No.1-2, pp.96-113, 2024

Abstract:
Artificial Intelligence (AI) has an increasing role to play in medical education and has great potential to revolutionize health professional education systems overall. However, this is accompanied by substantial questions concerning technical and ethical risks which are of particular importance because the quality of medical education has a direct effect on physical and psychological health and wellbeing. This article establishes an overarching distinction of AI across two typological dimensions, functional and humanistic. As indispensable foundations, these are then related to medical practice overall, and forms of implementation with examples are described in both general and medical education. Increasingly, the conditions for successful medical education will depend on an understanding of AI and the ethical issues surrounding its implementation, as well as the formulation of appropriate guidelines by regulatory and other authorities. Within that discussion, the limits of both narrow or Routine AI (RAI) and artificial general intelligence or Decision AI (DAI) are examined particularly in view of the ethical need for Trustworthy AI (TAI) as part of the humanistic dimension. All stakeholders, from patients to medical practitioners, managers, and institutions, need to be able to trust AI, and loss of confidence could be catastrophic in some cases.

Keywords:
artificial intelligence typology,artificial intelligence in medicine,ethics,bioethics,medical education,health professional education