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A Fictitious Vehicle Manufacturing Company at a University as Interdisciplinary Framework to Combine Study Programs
No full textInterdisciplinary thinking is becoming an increasingly important competence for meeting the challenges of our time, for example in the areas of sustainability or even digitalization. Successful value creation can only be achieved if all subject disciplines work together. Universities, too, must adapt to these needs and anchor the teaching of these competencies in their courses of study. In this context, interdisciplinary cooperation cannot be limited to the curricula, but must also bring together the teachers and the learners in a common context. Traditionally, universities have found it difficult to develop interdisciplinary scenarios. The teaching scenarios themselves must already provide interdisciplinary content and learning objectives. The challenge here is multi-level. First, it is a great challenge to connect the modules of a course of study in a thematically meaningful way. Second, it is the linking with other study programs that brings about the most sustainable interdisciplinary effects. One possible solution, which has been tested for several years, is to set up a fictitious company as a digital learning factory, which serves as a basis and reference for the interlinking of individual modules of the various courses of study. The scenarios that can be depicted here are extremely application-oriented, which means that not only can internal university modules be very easily aligned with them, but external partners, can also get a simple participation. Furthermore, the participating courses of study can continue to maintain their original core competencies while participating in the interdisciplinary scenarios via the modules that are aligned accordingly with the learning factory. A company from the vehicle construction sector was selected as the business model in order to be able to map another focus at the same time, autonomous driving. The individual departments of the company are linked professionally with the contents of the individual courses of study. Study programs, such as industrial engineering, form the design department, whereas technology-oriented study programs, such as business computing, represent technical development, in particular the area of driver assistance system development. In addition, the relevant departments also include economics and so on. The special feature, however, is the fact that real products in the form of vehicles on a scale of 1:14 are also created and continuously developed, which is an important success factor of the learning factory. The following article transfers the experiences made into a scheme of different levels of interdisciplinary forms of teaching within the framework of the learning factory and classifies and explains the existing scenarios accordingly. Furthermore, the teaching formats used, which have proven to be effective in imparting competences, are described
Machbarkeitsstudie zur Einführung einer kameragestützten Brandfrüherkennung an einer Coilbox eines Warmwalzwerkes
No full textZur Lösung des Problems der Einführung einer Brandfrüherkennung an einer Anlage mit erheblichen Flächen an Wärmestrahlung und Temperaturen um 1.100 Grad Celsius, wird der Einsatz von infraroter und visueller Kameratechnik und Bildverarbeitungssystemen in einer Machbarkeitsstudie erörtert und als Vorlage zur Umsetzung gebracht. Hierfür müssen alle technischen Randbedingungen der baulichen Anlage, die Kosten und auch die organisatorischen Aspekte ermittelt und bewertet werden. Auf Basis von Störfallszenarien wurden Brandversuche durchgeführt und in thermographischer und visueller Bildgebung ausgewertet. Entscheidend war die signifikante Abgrenzung eines Feuers vom anlagenspezifischen ungestörten Zustand
Evaluation of Modern Approaches for the Assessment of Dietary Carotenoids as Markers for Fruit and Vegetable Consumption
No full textThe assessment of dietary carotenoids via blood measurements has been widely used as a marker for fruit and vegetable consumption. In the present study, modern, non-invasive approaches to assess dietary carotenoids, such as skin measurements and an app-based short dietary record (ASDR), were compared with conventional methods such as plasma status and handwritten 3-day dietary records. In an 8-week observational study, 21 healthy participants aged 50–65 years recorded their daily consumption of carotenoid-rich fruits and vegetables via a specially developed ASDR. Anthropometry, blood samplings and assessment of skin carotenoids via Raman and reflection spectroscopy were performed at baseline, after four weeks and at the end of the study. App-based intake data showed good correlations with plasma α-carotene (r = 0.74, p < 0.0001), β-carotene (r = 0.71, p < 0.0001), and total plasma carotenoids (r = 0.65, p < 0.0001); weak correlations with plasma lutein/zeaxanthin and β-cryptoxanthin (both r = 0.34, p < 0.05); and no correlation with plasma lycopene. Skin measurements via reflection and Raman spectroscopy correlated well with total plasma carotenoids (r = 0.81 and 0.72, respectively; both p < 0.0001), α-carotene (r = 0.75–0.62, p < 0.0001), and β-carotene (r = 0.79–0.71, p < 0.0001); moderately with plasma lutein/zeaxanthin (both r = 0.51, p < 0.0001); weakly with plasma β-cryptoxanthin (r = 0.40–0.31, p < 0.05); and showed no correlation with plasma lycopene. Skin measurements could provide a more convenient and noninvasive approach of estimating a person’s fruit and vegetable consumption compared to traditional methods, especially in studies that do not intend blood sampling. ASDR records might function as a suitable, convenient tool for dietary assessment in nutritional intervention studies
A Bioinformatics Analysis of Ovarian Cancer Data Using Machine Learning
No full textThe identification of biomarkers is crucial for cancer diagnosis, understanding the underlying biological mechanisms, and developing targeted therapies. In this study, we propose a machine learning approach to predict ovarian cancer patients’ outcomes and platinum resistance status using publicly available gene expression data. Six classical machine-learning algorithms are compared on their predictive performance. Those with the highest score are analyzed by their feature importance using the SHAP algorithm. We were able to select multiple genes that correlated with the outcome and platinum resistance status of the patients and validated those using Kaplan–Meier plots. In comparison to similar approaches, the performance of the models was higher, and different genes using feature importance analysis were identified. The most promising identified genes that could be used as biomarkers are TMEFF2, ACSM3, SLC4A1, and ALDH4A1
Classification of Microbiome Data from Type 2 Diabetes Mellitus Individuals with Deep Learning Image Recognition
No full textMicrobiomic analysis of human gut samples is a beneficial tool to examine the general well-being and various health conditions. The balance of the intestinal flora is important to prevent chronic gut infections and adiposity, as well as pathological alterations connected to various diseases. The evaluation of microbiome data based on next-generation sequencing (NGS) is complex and their interpretation is often challenging and can be ambiguous. Therefore, we developed an innovative approach for the examination and classification of microbiomic data into healthy and diseased by visualizing the data as a radial heatmap in order to apply deep learning (DL) image classification. The differentiation between 674 healthy and 272 type 2 diabetes mellitus (T2D) samples was chosen as a proof of concept. The residual network with 50 layers (ResNet-50) image classification model was trained and optimized, providing discrimination with 96% accuracy. Samples from healthy persons were detected with a specificity of 97% and those from T2D individuals with a sensitivity of 92%. Image classification using DL of NGS microbiome data enables precise discrimination between healthy and diabetic individuals. In the future, this tool could enable classification of different diseases and imbalances of the gut microbiome and their causative genera
Bericht Forschung und Transfer 2022
No full textProjekte und Publikationen der Technischen Hochschule Wildau aus dem Jahr 2022
Investigation of metabolic pathways from gut microbiome analyses regarding type 2 diabetes mellitus using artificial neural networks
No full textBackground
Type 2 diabetes mellitus is a prevalent disease that contributes to the development of various health issues, including kidney failure and strokes. As a result, it poses a significant challenge to the worldwide healthcare system. Research into the gut microbiome has enabled the identification and description of various diseases, with bacterial pathways playing a critical role in this context. These pathways link individual bacteria based on their biological functions. This study deals with the classification of microbiome pathway profiles of type 2 diabetes mellitus patients.
Methods
Pathway profiles were determined by next-generation sequencing of 16S rDNA from stool samples, which were subsequently assigned to bacteria. Then, the involved pathways were assigned by the identified gene families. The classification of type 2 diabetes mellitus is enabled by a constructed neural network. Furthermore, a feature importance analysis was performed via a game theoretic approach (SHapley Additive exPlanations). The study not only focuses on the classification using neural networks, but also on identifying crucial bacterial pathways.
Results
It could be shown that a neural network classification of type 2 diabetes mellitus and a healthy comparison group is possible with an excellent prediction accuracy. It was possible to create a ranking to identify the pathways that have a high impact on the model prediction accuracy. In this way, new associations between the alteration of, e.g. a biosynthetic pathway and the presence of diabetes mellitus type 2 disease can also be discovered. The basis is formed by 946 microbiome pathway profiles from diabetes mellitus type 2 patients (272) and healthy comparison persons (674).
Conclusion
With this study of the gut microbiome, we present an approach using a neural network to obtain a classification of healthy and type 2 diabetes mellitus and to identify the critical features. Intestinal bacteria pathway profiles form the basis
A survey on sustainability approaches in manufacturing
No full textAwareness about climate change has led to policies and regulations but also customer preferences favoring sustainable production. Thus, producing businesses are confronted by this, as they consume a large share of resources. Therefore, when producing, additional dimensions beyond commercial feasibility ought to be considered. Here, literature on sustainability from the field of engineering science is systematically searched and analyzed for approaches leading to actual recommendations for action. Hence, this bottom-up approach, incorporating the production engineers’ point of view, represents an inside view of how the topic of sustainability has arrived at production companies. The focus on commercial feasibility ensures high relevance to practice, as production needs to become both sustainable and competitive. Even though exemplary collections of different approaches addressing the topic of sustainability in production can be found in literature, novelty is provided by the structured manner literature was collected and the special focus on approaches leading to practice-relevant recommendations for action. Thus, a comprehensive overview of approaches applicable to the manufacturing domain is presented
Comparison of Sonodynamic Treatment Set-Ups for Cancer Cells with Organic Sonosensitizers and Nanosonosensitizers
No full textCancer 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
ELRA: Exponential learning rate adaption gradient descent optimization method
No full textWe present a novel, fast (exponential rate adaption), ab initio (hyper-parameter-free) gradient based optimizer algorithm. The main idea of the method is to adapt the learning rate α by situational awareness, mainly striving for orthogonal neighboring gradients. The method has a high success and fast convergence rate and does not rely on hand-tuned parameters giving it greater universality. It can be applied to problems of any dimensions n and scales only linearly (of order O(n)) with the dimension of the problem. It optimizes convex and non-convex continuous landscapes providing some kind of gradient. In contrast to the Ada-family (AdaGrad, AdaMax, AdaDelta, Adam, etc.) the method is rotation invariant: optimization path and performance are independent of coordinate choices. The impressive performance is demonstrated by extensive experiments on the MNIST benchmark data-set against state-of-the-art optimizers. We name this new class of optimizers after its core idea Exponential Learning Rate Adaption - ELRA. We present it in two variants c2min and p2min with slightly different control. The authors strongly believe that ELRA will open a completely new research direction for gradient descent optimize