Fachbereich Ingenieur- und Naturwissenschaften
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The research on the new generation mobile networks is currently in the phase of defining the key technologies to make 6G successful. Hereby, the research project 6G NeXt is aiming to provide a tight integration between the communication network, consisting of the radio access as well as backbone network, and processing facilities. By the concept of split computing, the processing facilities are distributed over the entire backbone network, from centralised cloud to the edge cloud at a base station. Based on two demanding use cases, Smart Drones and Hologradic Communication, we investigate a joint communication and compute architecture that will make the application of tomorrow become reality.
The aim was to implement uncertainty calculation for detecting the negative effects of smoking on the bone density of the paranasal sinus. Materials and Methods: A total of 100 male participants aged 20 to 44 were included in the study and divided into two groups. The first group comprised individuals with minimal harmful habits, while the second group consisted of individuals who had been smoking for at least 10 years, consuming 1 to 2 packs of cigarettes per day. Results Bone density has a negative impact on the bone tissue of the upper wall of the maxillary sinus. The findings suggest that individuals with a pronounced decrease in minimum density, as well as those with a marked difference between minimum and maximum density values, may require heightened medical attention due to potential associations with undiagnosed diseases or specific structural characteristics in the skull. Conclusions. The uncertainty calculation was implemented for the detection of negative effect of smoking on the bone density of paranasal sinuses. The calculated difference between maximum and minimum density during the research suggests significant medical implications, especially considering the heterogeneity of the trabecular bone structure in the skull. Individuals with a marked difference may require heightened medical attention, potentially associated with undiagnosed diseases or specific structural characteristics in the skull.
The Peculiarities of Measuring Bone Density in Males and Females Using Uncertainty Calculation
(2023)
The article discusses the approach to solving the problem of reducing time spent on the preparation of medical images for teaching neural networks, by reducing the time of creating masks for images. The task is considered on the example of processing images of the mucous membrane of the paranasal sinus. The specifics of the task did not allow effectively using existing software solutions. During the study, a software solution was proposed, which made it possible to radically reduce the time of creating masks for images. The article also analyzes the shortcomings of the automated creation of masks, as well as the directions of their solution. The loss of time due to the adjustment of the color palette can be reduced even more to 1-2 minutes, the average deviation is 7.61%.
In modern electrochemical coating technology, it is common practice to create uniform layers. However, this study focuses on the deposition of non-uniform layers achieved through a deliberate arrangement of micro structured electrodes on the anode side. The "dog bone effect” was employed as the primary approach [1]. When electroplating on an otherwise uniform surface, this effect selectively processes an area influenced by the geometric edge effect (figure 1 left). The coating within this area is intended to be (i) unevenly distributed and (ii) non-reproducible. Process data was obtained through electrochemical simulations and subsequently applied to a specially designed micro-galvanic setup. This enabled the production of suitable micro structured anodes, validation of coating parameters, and the deposition of visually imperceptible structured areas with inhomogeneous properties using "adhesive gold" on appropriate substrates such as silver and nickel. The layers and their local topography were characterized and analyzed using confocal laser microscopy, X-Ray fluorescence analysis (XRF), as well as a self-designed and constructed laser interference device. As a result, this specific galvanic process technology successfully produced metallic layers that (i) cannot be visually confirmed by the naked eye, (ii) exhibit varied microstructural anode geometries, (iii) display unique differences in layer thickness, (iv) possess non-reproducible and chaotic topographies, and (v) can be detected and identified using conventional analysis techniques or a simple interference setup.
The aim of our study was to investigate the impact of insulin resistance on the bone density of the upper wall of the maxillary sinus. Materials and Methods: The study included 100 female participants aged 18 to 44 years, divided into two groups. The first group consisted of individuals with insulin resistance, while the control group comprised individuals without signs of insulin resistance. In each group, we conducted an investigation of the radiological density of the upper wall of the maxillary sinus using uncertainty calculations. Results of the study suggest a potential influence of insulin resistance on the density of bone tissue around the nasal sinuses, specifically the upper wall of the maxillary sinus in our case. This parameter was found to be minimal in the group of individuals with insulin resistance. It is particularly noteworthy that both minimum and maximum bone density decreased in this group. Conclusions. The research focused on how insulin resistance affects the density of the upper wall of the maxillary sinus. By employing uncertainty calculations, the study revealed that insulin resistance is associated with a decrease in the minimum density of the upper wall of the maxillary sinus. This tendency may act as a catalyst for the emergence of significant inflammatory alterations in the nasal sinuses, serving as a foundation for the initiation of complications.
In this paper, different concepts for mixing sample and master mix by means of simulation are discussed. The main focus was on mixers with a low dead volume, because of their relevance to PCR. In addition, a concept for aliquoting the mixed solution was elaborated and simulated. The simulation showed very good mixing with serpentineand tesla-mixers at flow rates of 1-16 µL/s. Furthermore, a uniform distribution with aliquoting could be achieved, while a slight dependence between flow rate and pressure was observed. In the further course, these concepts are to be combined and tested on a microfluidic chip. This can then be integrated into the workflow of existing PCR protocols.
The concept of this thesis is to optimize the manufacturing process of injection moulding tools by using additive manufacturing and thus contribute to environmental protection. For this purpose, the individual steps of the traditional manufacturing process are identified and analysed to determine which steps can be replaced by additive manufacturing. The first step is to clarify which mould components can benefit from additive manufacturing. In particular, the capabilities and limitations of additive manufacturing technologies must be considered.
Furthermore, another focus is on how to optimize the cooling and temperature control of the tools. Potential savings through the use of lightweight or insulating surfaces will be examined. In addition to the technical feasibility, the cost-effectiveness of the concept will also be considered. Finally, the contribution of the concept to the reduction of the environmental impact will be evaluated. In particular, energy savings and the reduction of CO2 emissions are taken into account.
The potential to optimize the injection mould manufacturing process and reduce environmental impact is significant. Using additive manufacturing can be an economic and environmental advantage.
A conductivity-based, batch-optimized system for analyzing the total of carbon parameter in water
(2023)
In cooperation with the company Purification Membranes Analytics GmbH (PMA), a prototype has been developed that analyzes the total organic carbon (TOC) content of water samples based on a complex conductivity measurement. In the process, a batchoptimized system was developed that delivers qualitative results even in highly polluted waters. This method requires small sample volumes and was assembled using an ultrapure water system, a sample mixing system, various electronic control units and a miniTOC system that uses a UV light source to decompose organic carbons and outputs differential values using two conductivity sensors. During the project, a fully automated prototype was developed and is ready for use. In the future, this method could support common methods and lead to more accurate statements on water quality in a cost-effective manner.
Additive manufacturing, also known as 3D printing, has gained tremendous importance in recent years. One of the areas where additive manufacturing is particularly useful is in the fabrication of electrodes. Electrodes are an important component of a wide range of applications, including electrochemistry, biomedical engineering, energy storage, analytics, electronics as well as life sciences. Traditionally, electrodes have been manufactured through costly processes such as etching, electroplating or cutting and milling. Additive manufacturing offers a new way to fabricate electrodes by depositing materials layer by layer (Yap et al., 2015). This opens up new possibilities for designing electrodes with complex geometries and structures that would not be possible using conventional methods. As a result, 3D printed electrodes are gaining interest in fields such as electromobility, water disinfection, manufacturing, and life sciences, which will be presented in this paper.
The aim of our study is to develop a new, simple, and effective method for identification of personality based on the characteristics of the sphenoid sinus structure, using machine learning for subsequent implementation into routine medical practice in Ukraine. The study involved 200 multislice computed tomography (MSCT) scans of individuals of various genders and ages. During the study, we obtained results with an accuracy exceeding 70%.
The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.
Das ASTONRail-Handbook ist eine webbasierte, interaktive Informationssammlung für künftige Nachwuchskräfte der Eisenbahnbranche und Lehrende in der eisenbahnrelevanten Hochschulbildung. Es soll u.a. die Suche nach passenden Studienmöglichkeiten erleichtern und Anregungen für eine modernere Hochschulbildung im Eisenbahnsektor bieten. Es ist einfach zu bedienen und um weitere Funktionen erweiterbar.
Patients with congenital heart defects and cardiovascular diseases are required new approaches to surgical intervention. The use of biological cardiac implants, which are made from the extracellular matrix, is a promising trend in modern regenerative medicine. These bioimplants can completely replace defective tissue or organs, and when manufactured with strict protocols and quality control measures, can be safe and effective for therapeutic applications. The process of manufacturing bioimplants involves various risks that need to be assessed and mitigated with ongoing monitoring and evaluation necessary to ensure the highest standards of quality. Overall, this study was successfully evaluated the requirements for introducing a new medical device into practice and created a technical file that meets all necessary documentation for certification.
Cancer sonodynamic therapy (SDT) is the therapeutic strategy of a high-frequency ultrasound (US) combined with a special sonosensitizer that becomes cytotoxic upon US exposure. The growing number of newly discovered sonosensitizers and custom US in vitro treatment solutions push the SDT field into a need for systemic studies and reproducible in vitro experimental set-ups. In the current research, we aimed to compare two of the most used and suitable SDT in vitro set-ups—“sealed well” and “transducer in well”—in one systematic study. We assessed US pressure, intensity, and temperature distribution in wells under US irradiation. Treatment efficacy was evaluated for both set-ups towards cancer cell lines of different origins, treated with two promising sonosensitizer candidates—carbon nanoparticle C60 fullerene (C60) and herbal alkaloid berberine. C60 was found to exhibit higher sonotoxicity toward cancer cells than berberine. The higher efficacy of sonodynamic treatment with a “transducer in well” set-up than a “sealed well” set-up underlined its promising application for SDT in vitro studies. The “transducer in well” set-up is recommended for in vitro US treatment investigations based on its US-field homogeneity and pronounced cellular effects. Moreover, SDT with C60 and berberine could be exploited as a promising combinative approach for cancer treatment.
Plasmonics includes the fundamentals of surface plasmon polaritons in metals, and its rapidly increasing applications in biochemistry, nanotechnology, optical communication, sensing, and medicine. Surface plasmon polaritons have become popular because of their ultrasensitive optical measurement capabilities, and in recent years they have also been employed for ultra-high-speed data transfer. This book presents recent advances in the broad field of plasmonics, covering not only current progress and the latest breakthroughs in emergent applications but also geometry optimizations and the fundamentals of physical interactions.
Ellipsometrically obtained spectral dependences of ordinary αxy and extra-ordinary αz extinction/attenuation coefficients within the spectral range λ = 300…980 nm of uniaxially anisotropic polytetrafluoroethylene (PTFE) films were analyzed. We considered the capabilities and specific features of the graphical representation technique for determining the contribution of Rayleigh scattering and Urbach absorption to light attenuation in the spectral range beyond fundamental absorption. It has been shown that the graphical approach enables to estimate these contributions qualitatively, semi-quantitatively or quantitatively, depending on the situation. The conclusions made using the analysis of graphical representation are confirmed by numerical solution of the inverse problem via simulation of the αxy (λ), αz (λ) experimental dependences within the framework of a best-fit procedure. Being based on both of these approaches, we have ascertained that, in the as-prepared PTFE films, the so-called anomalous light scattering (ALS) with the spectral dependence of scattering coefficient αs ≈ as λ–p (p > 4) takes place. Transformation of scattering from ALS to the Rayleigh one with p ≈ 4 due to annealing is accompanied by an increase of Urbach (subband) absorption. Both of these factors cause narrowing the dynamic range of extinction coefficient values. Both scattering and absorption coefficients are higher for the component of light polarized along the normal to the substrate as compared to the component polarized in parallel to it. The relationship between observed behavior of the scattering and absorption coefficients and the film structure has been discussed.
Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action
(2023)
The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.
Photoacoustic imaging through skull bone causes strong attenuation and distortion of the acoustic wavefront, which diminishes image contrast and resolution. As a result, transcranial photoacoustic measurements in humans have been challenging to demonstrate. In this study, we investigated the acoustic transmission through the human skull to design an ultrasound sensor suitable for transcranial PA imaging and sensing. We measured the frequency dependent losses of human cranial bones ex vivo, compared the performance of a range of piezoelectric and optical ultrasound sensors, and imaged skull phantoms using a PA tomograph based on a planar Fabry–Perot sensor. All transcranial photoacoustic measurements show the typical effects of frequency and thickness dependent attenuation and aberration associated with acoustic propagation through bone. The performance of plano-concave optical resonator ultrasound sensors was found to be highly suitable for transcranial photoacoustic measurements.
Background and objective: Accurate and fast diagnosis of rheumatic diseases affecting the hands is essential for further treatment decisions. Fluorescence optical imaging (FOI) visualizes inflammation-induced impaired microcirculation by increasing signal intensity, resulting in different image features. This analysis aimed to find specific image features in FOI that might be important for accurately diagnosing different rheumatic diseases.
Patients and methods: FOI images of the hands of patients with different types of rheumatic diseases, such as rheumatoid arthritis (RA), osteoarthritis (OA), and connective tissue diseases (CTD), were assessed in a reading of 20 different image features in three phases of the contrast agent dynamics, yielding 60 different features for each patient. The readings were analyzed for mutual differential diagnosis of the three diseases (One-vs-One) and each disease in all data (One-vs-Rest). In the first step, statistical tools and machine-learning-based methods were applied to reveal the importance rankings of the features, that is, to find features that contribute most to the model-based classification. In the second step machine learning with a stepwise increasing number of features was applied, sequentially adding at each step the most crucial remaining feature to extract a minimized subset that yields the highest diagnostic accuracy.
Results: In total, n = 605 FOI of both hands were analyzed (n = 235 with RA, n = 229 with OA, and n = 141 with CTD). All classification problems showed maximum accuracy with a reduced set of image features. For RA-vs.-OA, five features were needed for high accuracy. For RA-vs.-CTD ten, OA-vs.-CTD sixteen, RA-vs.-Rest five, OA-vs.-Rest eleven, and CTD-vs-Rest fifteen, features were needed, respectively. For all problems, the final importance ranking of the features with respect to the contrast agent dynamics was determined.
Conclusions: With the presented investigations, the set of features in FOI examinations relevant to the differential diagnosis of the selected rheumatic diseases could be remarkably reduced, providing helpful information for the physician.
We consider maintenance sites for urban rail systems, where unavailable tracks typically require changes to the regular timetable, and often even to the line plan. In this paper, we present an integrated mixed-integer linear optimization model to compute an optimal line plan that makes best use of the available tracks, together with a periodic timetable, including its detailed routing on the tracks within the stations. The key component is a flexible, turn-sensitive event-activity network that allows to integrate line planning and train routing using a track choice extension of the Periodic Event Scheduling Problem (PESP). Major goals are to maintain as much of the regular service as possible, and to keep the necessary changes rather local. Moreover, we present computational results on real construction site scenarios on the S-Bahn Berlin network. We demonstrate that this integrated problem is indeed solvable on practically relevant instances.