Institute of Fundamental Technological Research

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.

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

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.

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

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.

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

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.

artificial intelligence, extended reality, medical image scan segmentation

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.

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

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.

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

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.

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.

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

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.

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

Metaverse, STEM, Extended Reality, Education System

Artificial intelligence (AI) is penetrating higher medical education; however, its adoption remains low. A PRISMA-S search of the Web of Science database from 2020 to 2024, utilizing the search terms “artificial intelligence,” “medicine,” “education,” and “ethics,” reveals this trend. Four key areas of AI application in medical education are examined for their potential benefits: Educational support (such as personalized distance education), radiology (diagnostics), virtual reality (VR) (visualization and simulations), and generative text engines (GenText), such as ChatGPT (from the production of notes to syllabus design). However, significant ethical risks accompany AI adoption, and specific concerns are linked to each of these four areas. While AI is recognized as an important support tool in medical education, its slow integration hampers learning and diminishes student motivation, as evidenced by the challenges in implementing VR. In radiology, data-intensive training is hindered by poor connectivity, particularly affecting learners in developing countries. Ethical risks, such as bias in datasets (whether intentional or unintentional), need to be highlighted within educational programs. Students must be informed of the possible motivation behind the introduction of social and political bias in datasets, as well as the profit motive. Finally, the ethical risks accompanying the use of GenText are discussed, ranging from student reliance on instant text generation for assignments, which can hinder the development of critical thinking skills, to the potential danger of relying on AI-generated learning and treatment plans without sufficient human moderation.

Artificial intelligence, Metaverse, Medical education, Education system, Ethics

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Open AccessFeature PaperReview
The Influence of Emerging Technologies on Distance Education
by Magdalena Garlinska
1,†, Magdalena Osial
1 [ORCID] , Klaudia Proniewska
2,3 [ORCID] and Agnieszka Pregowska
1,*,† [ORCID]
1
Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawinskiego 5B, 02-106 Warsaw, Poland
2
Center for Digital Medicine and Robotics, Jagiellonian University Medical College, Kopernika 7E Str., 31-034 Krakow, Poland
3
Department of Bioinformatics and Telemedicine, Jagiellonian University Medical College, Medyczna 7 Str., 30-688 Krakow, Poland
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Electronics 2023, 12(7), 1550; https://doi.org/10.3390/electronics12071550
Received: 17 February 2023 / Revised: 22 March 2023 / Accepted: 23 March 2023 / Published: 25 March 2023
(This article belongs to the Special Issue Feature Papers in Computer Science & Engineering)
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Abstract
Recently, during the COVID-19 pandemic, distance education became mainstream. Many students were not prepared for this situation—they lacked equipment or were not even connected to the Internet. Schools and government institutions had to react quickly to allow students to learn remotely. They had to provide students with equipment (e.g., computers, tablets, and goggles) but also provide them with access to the Internet and other necessary tools. On the other hand, teachers were trying to adopt new technologies in the teaching process to enable more interactivity, mitigate feelings of isolation and disconnection, and enhance student engagement. New technologies, including Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), Extended Reality (XR, so-called Metaverse), Big Data, Blockchain, and Free Space Optics (FSO) changed learning, teaching, and assessing. Despite that, some tools were implemented fast, and the COVID-19 pandemic was the trigger for this process; most of these technologies will be used further, even in classroom teaching in both schools and universities. This paper presents a concise review of the emerging technologies applied in distance education. The main emphasis was placed on their influence on the efficiency of the learning process and their psychological impact on users. It turned out that both students and teachers were satisfied with remote learning, while in the case of undergraduate children and high-school students, parents very often expressed their dissatisfaction. The limitation of the availability of remote learning is related to access to stable Internet and computer equipment, which turned out to be a rarity. In the current social context, the obtained results provided valuable insights into factors affecting the acceptance and emerging technologies applied in distance education. Finally, this paper suggests a research direction for the development of effective remote learning techniques.

distance education, artificial intelligence (AI), virtual reality, augmented reality, mixed reality, free space optics (FSO), blockchain, big data

The rapidly growing production and usage of lithium-ion batteries (LIBs) dramatically raises the number of harmful wastes. Consequently, the LIBs waste management processes, taking into account reliability, efficiency, and sustainability criteria, became a hot issue in the context of environmental protection as well as the scarcity of metal resources. In this paper, we propose for the first time a functional material—a magnetorheological fluid (MRF) from the LIBs-based liquid waste containing heavy metal ions. At first, the spent battery waste powder was treated with acid-leaching, where the post-treatment acid-leaching solution (ALS) contained heavy metal ions including cobalt. Then, ALS was used during wet co-precipitation to obtain cobalt-doped superparamagnetic iron oxide nanoparticles (SPIONs) and as an effect, the harmful liquid waste was purified from cobalt. The obtained nanoparticles were characterized with SEM, TEM, XPS, and magnetometry. Subsequently, superparamagnetic nanoparticles sized 15 nm average in diameter and magnetization saturation of about 91 emu g−1 doped with Co were used to prepare the MRF that increases the viscosity by about 300% in the presence of the 100 mT magnetic fields. We propose a facile and cost-effective way to utilize harmful ALS waste and use them in the preparation of superparamagnetic particles to be used in the magnetorheological fluid. This work describes for the first time the second life of the battery waste in the MRF and a facile way to remove the harmful ingredients from the solutions obtained after the acid leaching of LIBs as an effective end-of-life option for hydrometallurgical waste utilization.

environment protection SPION, battery waste, toxic waste management, direct recycling, sustainability, circular economy, critical raw materials

Spent lithium-ion batteries, LiBs, Acid leaching, SPION, Magnetic nanomaterials, Metals recovery, Superparamagnetic, Waste management, Functional materials

The priority of modern dentistry is to keep patients’ teeth for as long as possible. Tooth extraction is a procedure performed as a last resort when conservative methods and endodontic surgery procedures have not brought the expected results. As a consequence, the number of patients in dental offices, who require first and repeated endodontic treatment, is increasing. Thus, the development of new technologies in the medical industry, including microscopy, computer tomography (CT), as well as diode and neodymium-YAG-erbium lasers, enables dentists to increase the percentage of successful treatments. Moreover, mixed reality (MR) is a very new technology, in which the 3D view can help plan or simulate various types of tasks before they will be carried out in real life. In dentistry, 3D holography can be applied to display CT data to plan endodontic treatment. The most important element in effective root canal treatment is the precise imaging of the root canal. The CT scans allow dentists to view the anatomy of the patient’s tooth with much higher precision and understanding than using 2D radiography (RTG-radiographic photo) pictures. Recently, the development of new 3D technologies allows dentists to obtain even more data from existing CT scans. In this paper, the CT scan data were applied to generate patient teeth in 3D and simulate the view of the root canal’s anatomy in MR devices, i.e., Microsoft HoloLens 2. Using DICOM RAW data from the CT exam, we generated a 3D model of the jaw with a tooth. In the next step, the crown of the tooth was removed in a similar way to how a dentist would do this using a dental handpiece. Furthermore, all root canals were cleaned of everything inside. This way we achieved empty tunnels, namely root canals. Finally, we added appropriate lighting, similar to the type of lighting that dentists use. The proposed approach enables to display of the root canals in the same way as during the endodontic procedure using a microscope. It allows for the visualization of the root canal and changing its direction, in which dimensional accuracy is crucial. It turns out that mixed reality can be considered a complementary method to the traditional approach, which reduces the amount of time for the root canal treatment procedure by up to 72.25%, depending on the complexity of the case, and increases its effectiveness. Thus, the mixed reality-based system can be considered an effective tool for planning dental treatment.

3D holography,root canal treatment,tooth,augmented reality,mixed reality

Magnetorheological Fluids (MRF), rheology, smart materials, intelligent fluid, functional materials

hydroxyapatite, myo-inositol, 5-fluorouracil, anticancer treatment, SKOV-3, calendula extract, Langmuir trough

The dynamic COVID-19 pandemic has destabilized education and forced academic centers to explore non-traditional teaching modalities. A key challenge this creates is in reconciling the fact that hands-on time in lab settings has been shown to increase student understanding and peak their interests. Traditional visualization methods are already limited and topics such as 3D molecular structures remain difficult to understand. This is where advances in Information and Communication Technologies (ICT), including remote meetings, Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), and Extended Reality (XR, so-called Metaverse) offer vast potential to revolutionize the education landscape. Specifically, how MR merges real and virtual life in a uniquely promising way and offers opportunities for entirely new educational applications. In this paper, we briefly overview and report our initial experience using MR to teach medical and pharmacy students. We also explore the future usefulness of MR in pharmacy education. MR mimics real-world experiences both in distance education and traditional laboratory classes. We also propose ICT-based systems designed to run on the Microsoft HoloLens2 MR goggles and can be successfully applied in medical and pharmacy coursework. The models were developed and implemented in Autodesk Maya and exported to Unity. Our findings demonstrate that MR-based solutions can be an excellent alternative to traditional classes, notably in medicine, anatomy, organic chemistry, and biochemistry (especially 3D molecular structures), in both remote and traditional in-person teaching modalities. MR therefore has the potential to become an integral part of medical education in both remote learning and in-person study

information and communication technologies, immersive technologies, information and communication technologies in education, immersive technologies in education, Mixed Reality, 3D human–computer interaction, advanced medical education, pharmacy, Metaverse

Background: The Augmented Reality (AR) blends digital information with the real world. Thanks to cameras, sensors, and displays it can supplement the physical world with holographic images. Nowadays, the applications of AR range from navigated surgery to vehicle navigation. Development: The purpose of this feasibility study was to develop an AR holographic system implementing Vertucci’s classification of dental root morphology to facilitate the study of tooth anatomy. It was tailored to run on the AR HoloLens 2 (Microsoft) glasses. The 3D tooth models were created in Autodesk Maya and exported to Unity software. The holograms of dental roots can be projected in a natural setting of the dental office. The application allowed to display 3D objects in such a way that they could be rotated, zoomed in/out, and penetrated. The advantage of the proposed approach was that students could learn a 3D internal anatomy of the teeth without environmental visual restrictions. Conclusions: It is feasible to visualize internal dental root anatomy with AR holographic system. AR holograms seem to be attractive adjunct for learning of root anatomy.

mixed reality, augmented reality, holography, tooth, dental root, root canal, visualization

The development of nanomedicine involves complex nanomaterial research involving magnetic nanomaterials and their use in magnetic hyperthermia. The selection of the optimal treatment strategies is time-consuming, expensive, unpredictable, and not consistently effective. Delivering personalized therapy that obtains maximal efficiency and minimal side effects is highly important. Thus, Artificial Intelligence (AI) based algorithms provide the opportunity to overcome these crucial issues. In this paper, we briefly overview the significance of the combination of AI-based methods, particularly the Machine Learning (ML) technique, with magnetic hyperthermia. We considered recent publications, reports, protocols, and review papers from Scopus and Web of Science Core Collection databases, considering the PRISMA-S review methodology on applying magnetic nanocarriers in magnetic hyperthermia. An algorithmic performance comparison in terms of their types and accuracy, data availability taking into account their amount, types, and quality was also carried out. Literature shows AI support of these studies from the physicochemical evaluation of nanocarriers, drug development and release, resistance prediction, dosing optimization, the combination of drug selection, pharmacokinetic profile characterization, and outcome prediction to the heat generation estimation. The papers reviewed here clearly illustrate that AI-based solutions can be considered as an effective supporting tool in drug delivery, including optimization and behavior of nanocarriers, both in vitro and in vivo, as well as the delivery process. Moreover, the direction of future research, including the prediction of optimal experiments and data curation initiatives has been indicated.

artificial intelligence,machine learning,magnetic hyperthermia,magnetic nanoparticles,cancer treatment,drug delivery

Reinforced concrete based on ordinary Portland cement (OPC) is one of the most commonly used materials in modern buildings. Due to the global growth of the building industry, concrete components have been partially or completely replaced with waste materials that can be used as binders or aggregates. Besides the ecological aspects, modern architecture widely needs materials to make the concrete durable, resisting large loads and various detrimental forces in the environment. This opens the possibilities of managing waste materials and applying them in practice. This paper presents a concise review of the green solutions for ecofriendly materials in the building industry that deal with the practical application of materials commonly treated as waste. The main emphasis was placed on their influence on the properties of the building material, optimal composition of mixtures, and discussion of the advantages and disadvantages of each of the “green” additives. It turned out that some solutions are far from being ecofriendly materials, as they leech and release numerous harmful chemicals into the environment during their presence in concrete. Finally, the paper suggests a research direction for the development of an ecofriendly structural material for a sustainable future.

green concrete; waste management; waste as a cement filler; secondary raw materials; alternative cements

Global pollution of the environment is one of the most challenging environmental prob- lems. Electronic-based population and anthropogenic activity are the main reasons for dramatically increasing the scale of waste generation, particularly battery waste. Improper battery waste disposal causes harmful environmental effects. Due to the release of heavy metals, battery waste affects ecosystems and health. We are faced with the challenge of effective battery waste management, especially recycling, to prevent the depletion of natural resources and maintain ecological balance. Artificial Intelligence (AI) is practically present in all areas of our lives. It enables the reduction of the costs associated with various types of research, increases automation, and accelerates productivity. This paper reviews the representative research progress of effective Artificial Intelligence-based battery waste management in the context of sustainable development, in particular, the analysis of current trends, algorithm accuracy, and data availability. Finally, the future lines of research and development directions of human-oriented Artificial Intelligence applications both in the battery production process and in battery waste management are discussed.

battery waste, waste management, artificial intelligence, machine learning, genetic programming, end-of-life

Background: Immersive technologies like Mixed Reality (MR), Virtual Reality (VR) and Augmented Reality (AR) are becoming increasingly popular and gain user trust across various fields, particularly in medicine. In this paper we will use the general term Mixed Reality (MR) to refer to the various virtual reality methods, namely VR and AR. These new immersive technologies require varying degrees of instruction, both in their practice use, as well as in how to adjust to interacting with 3D virtual spaces. This study assesses the pedagogical value of these immersive technologies in medical education. Method: We surveyed a group of 211 students and 47 academic faculty at a medical college regarding potential applications of MR in the medical curriculum by using a questionnaire comprised of eight questions. Results were analyzed accounting for user age and professional position, i.e., student vs faculty. Results: 70% of students and 60% of the academic faculty think that MR-supplemented education is advantageous over a classical instruction. Most highly valued were the 3D visualization capabilities of MR, especially in anatomy classes. There was no significant statistical difference between students and faculty responders. Moreover, screensharing between faculty and students contributed to better, longer lasting absorption of knowledge. Surprisingly, the main issue was related to availability, i.e., only 5% of students had access to MR, while 17% of faculty use MR regularly, and 36% occasionally. Conclusions: MR technology can be a valuable resource that supports traditional medical education, especially via 3D anatomy classes, however MR availability needs to be increased. Moreover, MR expands the capabilities and effectiveness of remote learning, which was normalized during the COVID-19 pandemic, to ensure effective student and patient education. MR-based lessons, or even select modules, provide a unique opportunity to ex-change experiences inside and outside the medical community.

mixed reality, e-learning, remote learning, real-time rendering, 3D visualization, medical education

Recently, there has been a lot of talk about diabetes in children. Have you ever wondered what diabetes is and where it comes from? Diabetes is a disease with a long history. It causes a high sugar level in the blood, which is dangerous for many organs including the heart and kidneys. Diabetes also damages blood vessels and the nervous system. Unfortunately, diabetes is often detected by accident during tests for other diseases or by standard blood tests performed during a check-up. The earliest symptoms of diabetes that a person might notice include increased hunger and thirst, tiredness, or frequent urination. If you experience these symptoms, do not take them lightly—have a doctor check your blood sugar level.

memory,memories,brain

In the nervous system, information is conveyed by sequence of action potentials, called spikes-trains. As MacKay and McCulloch suggested, spike-trains can be represented as bits sequences coming from Information Sources (IS). Previously, we studied relations between spikes' Information Transmission Rates (ITR) and their correlations, and frequencies. Now, I concentrate on the problem of how spikes fluctuations affect ITR. The IS are typically modeled as stationary stochastic processes, which I consider here as two-state Markov processes. As a spike-trains' fluctuation measure, I assume the standard deviation σ, which measures the average fluctuation of spikes around the average spike frequency. I found that the character of ITR and signal fluctuations relation strongly depends on the parameter s being a sum of transitions probabilities from a no spike state to spike state. The estimate of the Information Transmission Rate was found by expressions depending on the values of signal fluctuations and parameter s. It turned out that for smaller s < 1, the quotient ITR/σ has a maximum and can tend to zero depending on transition probabilities, while for 1 < s, the ITR/σ is separated from 0. Additionally, it was also shown that ITR quotient by variance behaves in a completely different way. Similar behavior was observed when classical Shannon entropy terms in the Markov entropy formula are replaced by their approximation with polynomials. My results suggest that in a noisier environment (1 < s), to get appropriate reliability and efficiency of transmission, IS with higher tendency of transition from the no spike to spike state should be applied. Such selection of appropriate parameters plays an important role in designing learning mechanisms to obtain networks with higher performance.

information source, information transmission rate, fluctuations, Shannon entropy, spike-trains, standard deviation

(1) Background: Free space optics communication (FSO) has improved wireless communication and data transfer thanks to high bandwidth, low power consumption, energy efficiency, a high transfer capacity, and a wide applicability field. The FSO systems also have their limitations, including weather conditions and obstacles in the way of transmission. (2) Methods: This research assesses the atmospheric conditions’ influence on the intensity of received radiation, both experimentally and theoretically. The construction of a laboratory test stand of the FSO system, which is operating in the third-atmosphere transmission window (8–12 μm), is proposed. Next, considering different atmospheric conditions, the experimental validation was conducted, both in a laboratory and real conditions. (3) Results: The measurements were carried out for two optical links working with wavelengths of 1.5 μm and 10 μm. It was found that optical radiation with a wavelength of about 10 μm is characterized by better transmission properties in the case of limited visibility (e.g.,light rain and fogs) than in the case of near-infrared waves. The same conclusion was found in analytical investigations. (4) Conclusions: The results obtained show that optical radiation with a wavelength of about 10 μm in limited visibility is characterized by better transmission properties than near-infrared waves. This demonstrates the validity of designing FSO links operating in the range 8–12 μm band, e.g., based on quantum cascade lasers and HgCdTe photodiodes.

free space optical communication, IR photodetector, quantum cascade laser, wireless communication

Surprisingly, distance education is quite an old concept. Its origins date back to the first correspondence-based course, which took place via the postal service in Boston, USA, in the 18th century. Rapid technological developments, especially in video and audio streaming, have increased the availability of such courses and moved learning into the virtual world. Due to the ongoing COVID-19 pandemic, we are witnessing an accelerated revolution in the learning process, as nearly all forms of education have been shifted online. Will this have a destructive effect on the human psyche? Is humanity sufficiently aware and ready for such a dramatic change? Will we return to physical in-classroom studies, or is remote distance education set to become the new norm? In particular, in medicine, computer science, fine arts, or architectural design, such a rapid change in the way students learn can be quite challenging. In this paper, we provide an overview of the history of distance learning, taking into account teachers’ and students’ points of view in both secondary and higher education.

distance learning, distance education, online courses

In recent years, you have likely heard that some people are eating gluten-free foods. Have you ever wondered why? Coeliac disease is one of the main reasons for this dietary choice. It affects people of all ages from all around the world. It is a genetic disorder that causes inflammation in the intestines and results in various symptoms that can be observed in other parts of the body. Unfortunately, people often ignore coeliac disease or treat it like a simple allergy. Some people may view gluten-free dietary choices as just a trend. Some food allergies can manifest like coeliac disease, but these have different causes. Coeliac disease should be taken seriously, because it may not only lead to life-threatening symptoms, but also have a chronic impact on health and quality of life.

Antibody,B Cells,Coeliac Disease ,Villi ,Autoimmune Response ,Inflammation,Gluten-free Food

Abstract

Your brain controls everything you do, and it is much more powerful than any computer you can find. This complex organ sends messages using cells called neurons, and it never stops analyzing data, even as you sleep. Scientists are trying to understand the brain to create a digital version. But is it possible for computers to do the same things our brains can? For computers to do so, we need to create something called an artificial neural network, which has digital neurons connected into a complex net that resembles the structure of the brain. To make an artificial neural network, we need to use the most universal language—mathematics.

Neuron,Artificial Neural Network ,Dendrite,Axon,Synapse,Binary Language,Node,Weight

The way brain networks maintain high transmission efficiency is believed to be fundamental in understanding brain activity. Brains consisting of more cells render information transmission more reliable and robust to noise. On the other hand, processing information in larger networks requires additional energy. Recent studies suggest that it is complexity, connectivity, and function diversity, rather than just size and the number of neurons, that could favour the evolution of memory, learning, and higher cognition. In this paper, we use Shannon information theory to address transmission efficiency quantitatively. We describe neural networks as communication channels, and then we measure information as mutual information between stimuli and network responses. We employ a probabilistic neuron model based on the approach proposed by Levy and Baxter, which comprises essential qualitative information transfer mechanisms. In this paper, we overview and discuss our previous quantitative results regarding brain-inspired networks, addressing their qualitative consequences in the context of broader literature. It is shown that mutual information is often maximized in a very noisy environment e.g., where only one-third of all input spikes are allowed to pass through noisy synapses and farther into the network. Moreover, we show that inhibitory connections as well as properly displaced long-range connections often significantly improve transmission efficiency. A deep understanding of brain processes in terms of advanced mathematical science plays an important role in the explanation of the nature of brain efficiency. Our results confirm that basic brain components that appear during the evolution process arise to optimise transmission performance.

neural network, entropy, mutual information, noise, inhibitory neuron

Regarding sleep research, polysomnography (PSG) also called a sleep study, is a gold standard. It incorporates brain waves, the oxygen level in the blood, heart rate and breathing, and leg movement recordings. PSG is a complicated and expensive laboratory-based procedure, usually done in hospitals or special sleep center. In this study, an alternative technique for Sleep-Related Breathing Disorders (SRBD) based on selected cardiac and acoustic parameters and the Random Forest (RF) has been studied. A system dedicated to the detection of simultaneously acquired ECG and acoustic signals, which are collected during sleep at home environment is proposed. Results obtained indicate that classification and regression tree models such as RF are appropriate for the evaluation of sleep disorders like SRBD. The best identification of sleep irregularities at level 89.00 percent for the raw database was obtained. Thus, statistical predictive models allow identification of breathing events with high levels of sensitivity and specificity, providing an inexpensive and accurate diagnosis.

patient monitoring, random forest, disorders, biomarkers

Background: General data protection regulation (GDPR) provides rules according to which data should be managed and processed in a secure and appropriate way for patient requirements and security. Currently, everyone in Europe is covered by GDPR. Thus, the medical practice also requires access to patient data in a safe and secure way. Methods: Holographic technology allows users to see everything visible ona computer screen in a new and less restricted way, i. e. without the limitations of traditional computers and screens. Results: In this study, a three-dimensional holographic doctors' assistant is designed and implemented in a way that meets the GDPR requirements. The HoloView application, which is tailored to run on Microsoft HoloLens, is proposed toallow display and access to personal data and so-called sensitive information of all individual patients without the risk that it will be presented to unauthorized persons. Conclusions: To enhance the user experience and remain consistent with GSPR, a holographic desk is proposed that allows displaying patient data and sensitive information only in front of the doctor's eyes using mixed reality glasses. Last but not least, it boasts of a reduction in infection risk for the staff during the COVID-19 pandemic, affording medical care to be carried out by as few doctors as possible.

augmented reality, mixed reality, pandemic

Fast communication is of high importance. Recently, increased data demand and crowded radio frequency spectrum have become crucial issues. Free-Space Optical Communication (FSOC) has diametrically changed the way people exchange information. As an alternative to wire communication systems, it allows efficient voice, video, and data transmission using a medium like air. Due to its large bandwidth, FSOC can be used in various applications and has therefore become an important part of our everyday life. The main advantages of FSOC are a high speed, cost savings, compact structures, low power, energy efficiency, a maximal transfer capacity, and applicability. The rapid development of the high-speed connection technology allows one to reduce the repair downtime and gives the ability to quickly establish a backup network in an emergency. Unfortunately, FSOC is susceptible to disruption due to atmospheric conditions or direct sunlight. Here, we briefly discuss Free-Space Optical Communication from mirrors and optical telegraphs to modern wireless systems and outline the future development directions of optical communication.

free-space optical communication, telecommunications, wireless communication, data transfer history, communication networks

Immersive technologies, like Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) have undergone technical evolutions over the last few decades. Their rapid development and dynamic changes enable their effective applications in medicine, in fields like imaging, preprocedural planning, treatment, operations planning, medical students training, and active support during therapeutic and rehabilitation procedures. Within this paper, a comprehensive analysis of VR/AR/MR application in the medical industry and education is presented. We overview and discuss our previous experience with AR/MR and 3D visual environment and MR-based imaging systems in cardiology and interventional cardiology. Our research shows that using immersive technologies users can not only visualize the heart and its structure but also obtain quantitative feedback on their location. The MR-based imaging system proposed offers better visualization to interventionists and potentially helps users understand complex operational cases. The results obtained suggest that technology using VR/AR/MR can be successfully used in the teaching process of future doctors, both in aspects related to anatomy and clinical classes. Moreover, the system proposed provides a unique opportunity to break the boundaries, interact in the learning process, and exchange experiences inside the medical community.

augmented reality, improving the education process, interaction, intraprocedural visualization, mixed reality, preprocedural planning, teaching

The nature of neural codes is central to neuroscience. Do neurons encode information through relatively slow changes in the firing rates of individual spikes (rate code) or by the precise timing of every spike (temporal code)? Here we compare the loss of information due to correlations for these two possible neural codes. The essence of Shannon's definition of information is to combine information with uncertainty: the higher the uncertainty of a given event, the more information is conveyed by that event. Correlations can reduce uncertainty or the amount of information, but by how much? In this paper we address this question by a direct comparison of the information per symbol conveyed by the words coming from a binary Markov source (temporal code) with the information per symbol coming from the corresponding Bernoulli source (uncorrelated, rate code). In a previous paper we found that a crucial role in the relation between information transmission rates (ITRs) and firing rates is played by a parameter s, which is the sum of transition probabilities from the no-spike state to the spike state and vice versa. We found that in this case too a crucial role is played by the same parameter s. We calculated the maximal and minimal bounds of the quotient of ITRs for these sources. Next, making use of the entropy grouping axiom, we determined the loss of information in a Markov source compared with the information in the corresponding Bernoulli source for a given word length. Our results show that in the case of correlated signals the loss of information is relatively small, and thus temporal codes, which are more energetically efficient, can replace rate codes effectively. These results were confirmed by experiments.

Shannon information theory, information source, information transmission rate, firing rate, neural coding

Background and objective: People suffer from sleep disorders caused by work-related stress, irregular lifestyle or mental health problems. Therefore, development of effective tools to diagnose sleep disorders is important. Recently, to analyze biomedical signals Information Theory is exploited. We propose efficient classification method of sleep anomalies by applying entropy estimating algorithms to encoded ECGs signals coming from patients suffering from Sleep-Related Breathing Disorders (SRBD). Methods: First, ECGs were discretized using the encoding method which captures the biosignals variability. It takes into account oscillations of ECG measurements around signals averages. Next, to estimate entropy of encoded signals Lempel–Ziv complexity algorithm (LZ) which measures patterns generation rate was applied. Then, optimal encoding parameters, which allow distinguishing normal versus abnormal events during sleep with high sensitivity and specificity were determined numerically. Simultaneously, subjects' states were identified using acoustic signal of breathing recorded in the same period during sleep. Results: Random sequences show normalized LZ close to 1 while for more regular sequences it is closer to 0. Our calculations show that SRBDs have normalized LZ around 0.32 (on average), while control group has complexity around 0.85. The results obtained to public database are similar, i.e. LZ for SRBDs around 0.48 and for control group 0.7. These show that signals within the control group are more random whereas for the SRBD group ECGs are more deterministic. This finding remained valid for both signals acquired during the whole duration of experiment, and when shorter time intervals were considered. Proposed classifier provided sleep disorders diagnostics with a sensitivity of 93.75 and specificity of 73.00%. To validate our method we have considered also different variants as a training and as testing sets. In all cases, the optimal encoding parameter, sensitivity and specificity values were similar to our results above. Conclusions: Our pilot study suggests that LZ based algorithm could be used as a clinical tool to classify sleep disorders since the LZ complexities for SRBD positives versus healthy individuals show a significant difference. Moreover, normalized LZ complexity changes are related to the snoring level. This study also indicates that LZ technique is able to detect sleep abnormalities in early disorders stage.

information theory, Lempel-Ziv complexity, entropy, ECG, sleep-related breathing disorders, randomness

One of the mathematical tools to measure the generation rate of new patterns along a sequence of symbols is the Lempel-Ziv complexity (LZ). Under additional assumptions, LZ is an estimator of entropy in the Shannon sense. Since entropy is considered as a measure of randomness, this means that LZ can be treated also as a randomness indicator. In this paper, we used LZ concept to the analysis of different flow regimes in cold flow combustor models. Experimental data for two combustor's configurations motivated by efficient mixing need were considered. Extensive computer analysis was applied to develop a complexity approach to the analysis of velocity fluctuations recorded with hot-wire anemometry and PIV technique. A natural encoding method to address these velocity fluctuations was proposed. It turned out, that with this encoding the complexity values of the sequences are well correlated with the values obtained by means of RMS method (larger/smaller complexity larger/smaller RMS). However, our calculations pointed out the interesting result that most complex, this means most random, behavior does not overlap with the "most turbulent" point determined by the RMS method, but it is located in the point with maximal average velocity. It seems that complexity method can be particularly useful to analyze turbulent and unsteady flow regimes. Moreover, the complexity can also be used to establish other flow characteristics like its ergodicity or mixing.

turbulence, complexity, entropy, randomness

To understand how anatomy and physiology allow an organism to perform its function, it is important to know how information that is transmitted by spikes in the brain is received and encoded. A natural question is whether the spike rate alone encodes the information about a stimulus (rate code), or additional information is contained in the temporal pattern of the spikes (temporal code). Here we address this question using data from the cat Lateral Geniculate Nucleus (LGN), which is the visual portion of the thalamus, through which visual information from the retina is communicated to the visual cortex. We analyzed the responses of LGN neurons to spatially homogeneous spots of various sizes with temporally random luminance modulation. We compared the Firing Rate with the Shannon Information Transmission Rate, which quantifies the information contained in the temporal relationships between spikes. We found that the behavior of these two rates can differ quantitatively. This suggests that the energy used for spiking does not translate directly into the information to be transmitted. We also compared Firing Rates with Information Rates for X-ON and X-OFF cells. We found that, for X-ON cells the Firing Rate and Information Rate often behave in a completely different way, while for X-OFF cells these rates are much more highly correlated. Our results suggest that for X-ON cells a more efficient "temporal code" is employed, while for X-OFF cells a straightforward "rate code" is used, which is more reliable and is correlated with energy consumption.

Shannon information theory, cat LGN, ON–OFF cells, neural coding, entropy, firing rate

Because an average human spends one third of his life asleep, it is apparent that the quality of sleep has an important impact on the overall quality of life. To properly understand the influence of sleep, it is important to know how to detect its disorders such as snoring, wheezing, or sleep apnea. The aim of this study is to investigate the predictive capability of a dual-modality analysis scheme for methods of sleep-related breathing disorders (SRBDs) using biosignals captured during sleep. Two logistic regressions constructed using backward stepwise regression to minimize the Akaike information criterion were extensively considered. To evaluate classification correctness, receiver operating characteristic (ROC) curves were used. The proposed classification methodology was validated with constructed Random Forests methodology. Breathing sounds and electrocardiograms of 15 study subjects with different degrees of SRBD were captured and analyzed. Our results show that the proposed classification model based on selected parameters for both logistic regressions determine the different types of acoustic events during sleep. The ROC curve indicates that selected parameters can distinguish normal versus abnormal events during sleep with high sensitivity and specificity. The percentage of prediction for defined SRBDs is very high. The initial assumption was that the quality of result is growing with the number of parameters included in the model. The best recognition reached is more than 89% of good predictions. Thus, sleep monitoring of breath leads to the diagnosis of vital function disorders. The proposed methodology helps find a way of snoring rehabilitation, makes decisions concerning future treatment, and has an influence on the sleep quality.

patient monitoring, sleep-related breathing disorders, vital functions

This paper aims to investigate, both experimentally and theoretically, the electromechanical dynamic interaction between a driving stepping motor and a driven laboratory belt-transporter system. A test-rig imitates the operation of a robotic device in the form of a working tool-carrier under translational motion. The object under consideration is equipped with measurement systems, which enable the registration of electrical and mechanical quantities. Analytical considerations are performed by means of a circuit model of the electric motor and a discrete, non-linear model of the mechanical system. Various scenarios of the working tool-carrier motion and positioning by the belt-transporter are measured and simulated; in all cases the electric current control of the driving motor has been applied. The main goal of this study is to investigate the influence of the stepping motor control parameters along with various mechanical friction models on the precise positioning of a laboratory robotic device.

Belt transporter system, Precise positioning, Stepping motor, Current control, Experimental verification

Neuroscientists formulate very different hypotheses about the nature of neural coding. At one extreme, it has been argued that neurons encode information through relatively slow changes in the arrival rates of individual spikes (rate codes) and that the irregularity in the spike trains reflects the noise in the system. At the other extreme, this irregularity is the code itself (temporal codes) so that the precise timing of every spike carries additional information about the input. It is well known that in the estimation of Shannon Information Transmission Rate, the patterns and temporal structures are taken into account, while the “rate code” is already determined by the firing rate, i.e. by the spike frequency. In this paper we compare these two types of codes for binary Information Sources, which model encoded spike trains. Assuming that the information transmitted by a neuron is governed by an uncorrelated stochastic process or by a process with a memory, we compare the Information Transmission Rates carried by such spike trains with their firing rates. Here we show that a crucial role in the relation between information transmission and firing rates is played by a factor that we call the “jumping” parameter. This parameter corresponds to the probability of transitions from the no-spike-state to the spike-state and vice versa. For low jumping parameter values, the quotient of information and firing rates is a monotonically decreasing function of the firing rate, and there therefore a straightforward, one-to-one, relation between temporal and rate codes. However, it turns out that for large enough values of the jumping parameter this quotient is a non-monotonic function of the firing rate and it exhibits a global maximum, so that in this case there is an optimal firing rate. Moreover, there is no one-to-one relation between information and firing rates, so the temporal and rate codes differ qualitatively. This leads to the observation that the behavior of the quotient of information and firing rates for a large jumping parameter value is especially important in the context of bursting phenomena.

Information Theory, Information Source, Stochastic process, Information transmission rate, Firing rate

In the paper dynamic electromechanical coupling between the structural model of the rotating machine drive system and the circuit model of the asynchronous motor has been investigated. By means of the computer model of the rotating machine drive system the results of experimental testing have been confirmed. From the obtained results of computations and measurements it follows that the coupling between the considered rotating system and the installed rotary dampers with the magneto-rheological fluid (MRF) results in effective energy dissipation leading to significant reduction of undesired torsional vibrations.

Electromechanical Coupling, Machine Drive Systems, Rotary MRF Actuator, Torsional Vibrations, Wavelet Transform (WT)

Background
Explaining how the brain processing is so fast remains an open problem (van Hemmen JL, Sejnowski T., 2004). Thus, the analysis of neural transmission (Shannon CE, Weaver W., 1963) processes basically focuses on searching for effective encoding and decoding schemes. According to the Shannon fundamental theorem, mutual information plays a crucial role in characterizing the efficiency of communication channels. It is well known that this efficiency is determined by the channel capacity that is already the maximal mutual information between input and output signals. On the other hand, intuitively speaking, when input and output signals are more correlated, the transmission should be more efficient. A natural question arises about the relation between mutual information and correlation. We analyze the relation between these quantities using the binary representation of signals, which is the most common approach taken in studying neuronal processes of the brain.

Results
We present binary communication channels for which mutual information and correlation coefficients behave differently both quantitatively and qualitatively. Despite this difference in behavior, we show that the noncorrelation of binary signals implies their independence, in contrast to the case for general types of signals.

Conclusions
Our research shows that the mutual information cannot be replaced by sheer correlations. Our results indicate that neuronal encoding has more complicated nature which cannot be captured by straightforward correlations between input and output signals once the mutual information takes into account the structure and patterns of the signals.

Shannon information, Communication channel, Entropy, Mutual information, Correlation, Neuronal encoding

In the present paper, an adaptive control of structural vibrations is presented. Based on earlier research, we claim that the periodical switching on of magneto-rheological controlled dampers results in the reduction of the amplitudes of vibrations more than does their permanent actuation. This statement, when applied to a moving load problem, was mathematically proved in earlier papers. In the present paper we determine the efficiency of such a control applied to a rotating shaft. The earlier mathematical analysis allows us to propose a control strategy. A finite element simulation together with the solution of the control problem shows that the dampers should act only during a short period of the highest displacements of the structure. The same conclusion is found in experimental tests. Although high frequency control with MR dampers is less efficient than in the theoretical investigations, we have found an amplitude reduction in the range of 10–20%.

Adaptive control, Semi-active control, Vibration control, Shaft vibrations, Torsional vibrations, Magneto-rheological dampers

In the paper dynamic electromechanical interaction between the rotating machine drive system and the electric driving motor is considered. The investigations are performed by means of the circuit model of the asynchronous motor as well as using an advanced structural hybrid model of the drive system. Using the analytical solutions applied for the electrical and the mechanical systems the electromagnetic stiffness and coefficient of damping, both generated by the electric motor rotationally interacting with the mechanical system of the given dynamic properties, were determined. By means of experimentally validated computational responses obtained for torsional harmonic excitation induced by the driven machine working tool, a modification of dynamic properties of the mechanical system by the electromagnetic flux between the stator and the rotor has been studied.

Machine drive system, Asynchronous motor, Electromechanical coupling

The analysis of damped rotating systems using actuators with magnetorheological fluid is the subject of interest. The main purpose of this research is minimization of vibration amplitudes in order to increase fatigue durability of the most responsible machine elements. Here, control damping torques are generated by magnetorheological fluid of adjustable viscosity.
The theoretical investigations are based on a hybrid and finite element structural model (FEM) of the mechanical structure as well as on sensitivity analysis of the response with respect to the actuators damping characteristics. The analysis performed in the paper combines experimental verification using the laboratory test rig with theoretical computations.

rotating systems, torsional vibrations, actuators with a magnetorheological fluid, rotary actuators

The aim of this paper is to present a method of the semi-active control of torsional vibrations in a working machine drive system by means of rotary actuators with a magneto-rheological fluid. The simple open-loop control strategy is proposed for a mechanical system vibrating in steady-state operating conditions. This semi-active control approach is based on the principle of optimum viscous damping and frictional properties realized by the magneto-rheological fluid, where its respective parameters are determined by the applied control electric currents. The analysis is performed theoretically by means of structural electro-mechanical models of the considered drive system as well as experimentally using a laboratory test rig in the form of a rotor-shaft system co-operating with two asynchronous motors generating properly programmed driving and retarding electro-magnetic torques

semi-active control, torsional vibrations, magneto-rheological rotary dampers

From all kinds of vibrations, torsional ones are very important as naturally associated with fundamental rotational drive system motion. The aim of this paper is to present semi-active control of torsional vibrations of the working machine drive systems, which is performed using a rotary damper with the magnetorheological fluid. The main purpose of this research is vibration amplitudes minimization in order to increase fatigue durability of the most responsible machine elements. The special control strategies are proposed for steady-state torsional vibrations suppression. The semi-active control method is based on a principle of determinational optimum current damping coefficient values, which is realized by the magneto-rheological fluid application. The analysis performed in the paper combines experimental verification using the laboratory test rig with theoretical computations. This experiment was designed in the form of the laboratory drive system co-operating with two asynchronous motors generating properly programmed driving and retarding electromagnetic torques.

semi-active control, torsional vibrations, rotary dampers, magnetorheological fluid, electromechanical drive system

In the paper semi-active control of torsional vibrations of the rotating machine drive system driven by an electric motor is performed by means of rotary actuators with the magneto-rheological fluid. The main purpose of these studies is a minimisation of vibration amplitudes in order to increase the fatigue durability of the most responsible elements, assure possibly precise motion of the driven machine working tool as well as to reduce a generated noise level. For suppression of steady-state torsional vibrations excited by dynamic external torques generated by the motor and by the driven object there are proposed control strategies based on a principle of optimum current damping coefficient values realized by the magneto-rheological fluid. The theoretical control concepts are experimentally verified using the laboratory test rig in the form of drive system co-operating with two asynchronous motors generating properly programmed driving and retarding electromagnetic torques.

semi-active control, torsional vibrations, rotary actuators, magneto-rheological fluid

Graph Neural Networks (GNNs) are recognized as potent tools for processing real-world data organized in graph structures. Especially inductive GNNs, which allow for the processing of graph-structured data without relying on predefined graph structures, are becoming increasingly important in a wide range of applications. As such these networks become attractive targets for model-stealing attacks where an adversary seeks to replicate the functionality of the targeted network. Significant efforts have been devoted
to developing model-stealing attacks that extract models trained on images and texts. However, little attention has been given to stealing GNNs trained on graph data. This paper identifies a new method of performing unsupervised model-stealing attacks against inductive GNNs, utilizing graph contrastive learning and spectral graph augmentations to efficiently extract information from the targeted model. The new type of attack is thoroughly evaluated on six datasets and the results show that our approach outperforms the current state-of-the-art by Shen et al. (2021). In particular, our attack surpasses the baseline across all benchmarks, attaining superior fidelity and downstream accuracy of the stolen model while necessitating fewer queries directed toward the target model.

Early pancreatic cancer diagnosis and therapy drastically increase the chances of survival. Tumor visualization using CT scan images is an important part of these processes. In this paper, we apply Mixed Reality (MR) and Artificial Intelligence, in particular, Machine Learning (ML) to prepare image-guided 3D models of pancreatic cancer in a population of oncology patients. Object detection was based on the convolution neural network, i.e. the You Only Look Once (YOLO) version 7 algorithm, while the semantic segmentation has been done with the 3D-UNET algorithm. Next, the 3D holographic visualization of this model as an interactive, MR object was performed using the Microsoft HoloLens2. The results indicated that the proposed MR and ML-based approach can precisely segment the pancreas along with suspected lesions, thus providing a reliable tool for diagnostics and surgical planning, especially when considering organ reconstruction during pancreaticoduodenectomy.

Extended Reality, Mixed Reality, Augmented Reality, Head-Mounted Displays, Artificial Intelligence, Image-guided surgery

Apart fromoperating tables andmodern surgical instruments, themodern operating rooms are equipped with displays and video surveillance systems. The three-dimensional operating room allows users to watch medics perform surgery from different, individually chosen, points of view. For the first time, it is possible to reproduce/repeat the course of the operations and change the perspective or position, fromwhich it is observed. Here, we proposed a solution based on Microsoft HoloLens and Azure Kinect DK devices as remote support to patient management. The operating room is transferred to the digital form in real-time using Augmented Reality based technologies. Users can move around the digital place like a ghost in real space. The approach proposed allows users to see observe surgery from any point of view they want without disturbing the surgeon's workflow. They can change their positions, angle, and place of observation. All environmental restrictions disappear. The presented solution gives trainees a convenient opportunity to learn. It may make a significant contribution to improving the surgeontraining, patients' outcomes, and may allow virtual medical consultations during the surgery between specialists without them leaving their workplace.

3D operating room, augmented reality, HoloLens

In the paper there is investigated experimentally and theoretically electromechanical dynamic interaction between the driving stepping motor and the driven laboratory belt-transporter system imitating an operation of the robotic device in the form of working tool-carrier under translational motion. The considered object is properly equipped with measurement systems enabling us a registration of studied electrical and mechanical quantities. The analytical considerations are performed by means of the circuit model of the electric motor and of the discrete, non-linear model of the mechanical system. In the investigated examples various scenarios of the working tool-carrier motion and positioning by the belt-transporter are measured and simulated, where in the all cases the electric current control of the driving motor has been applied.

Belt transporter system, Precise positioning, Stepping motor, Current control, Experimental verification

water pollution, dyes, wastewater treatment, magnetic nanoparticles

adaptive design, biostatistic, information Theory

quantify breathing pattern, patient monitoring