3633 research outputs found
Sort by
Development of an in vitro three-layered skin wound healing model for pre-clinical testing
Get PDFSkin wound healing involves many cell types in a stepwise process of tissue regeneration. Reepithelialization is an essential characteristic of successful healing. In tissue engineering, mimicking the complex process of injury repair in vitro is challenging and requires the development of advanced skin models. In this study, a simple and reproducible method for wounding three-layered skin models on membranes with different pore sizes (0.4, 1, 3 μm) was established. The model allows the investigation of reepithelialization processes in a more complex environment. Hemalaun-eosin (HE) and 3-[4,5-dimethylthiazole-2-yl]-2,5- diphenyltetrazolium bromide (MTT) staining proved sufficient removal of the epidermis directly after wounding. An increasing pore size of the culture membrane delayed the reepithelialization time. Transepithelial electrical resistance (TEER) measurements provided non-invasive monitoring of reepithelialization, showing increasing values 4 days after wounding for the skin models on 0.4 and 1 μm membranes but not for those on 3 μm membranes. Cytokine quantification of interleukin (IL)-6 and IL-8 complemented the TEER results with increasing levels directly after wounding for all skin models. This skin wounding model could be used to simulate different wounding scenarios and test wound matrix materials. Furthermore, it could be adapted by adding immune cells to resemble the in vivo setup more closely
Taking a HINT on industrial anomaly detection
Get PDFDetection of defective parts and tools is essential in large-scale industrial manufacturing, playing a vital role in predictive maintenance, quality assurance, and safety hazard minimization. While traditionally performed by humans, the automation of visual anomaly detection using neural networks has gained prominence due to their increasing performance capabilities. However, deep learning models require extensive data for training, while acquiring annotated data is both costly and labor-intensive, especially for defect variations in industrial scenarios. Unsupervised methods, trained without labels or annotations, offer a potential solution but struggle to distinguish true anomalies from irrelevant impurities. To address the limitations of data dependency and spurious correlations in deep learning models, we introduce a demonstrator utilizing Human Importance-aware Network Tuning (HINT) to incorporate domain knowledge during training, and Explainable Artificial Intelligence (XAI) to provide insights into the model’s decision-making process
System dynamics in strategic management
No full textSystem dynamics (SD) is a methodology aimed at providing insights into complex systems across various fields of study. This article offers an overview of the current state of research and the practical significance of SD in strategic management (SM). It endeavours to explore diverse research areas and identify fields of application for SD in SM through a systematic literature review. This is done to determine the extent and means by which SD can add value to SM
Decision Support Tool for the selection of the most suitable Indoor Localization Technology (ILT) using the Analytical Hierarchy Process (AHP)
Get PDFThe lack of appropriate decision support tools is a major challenge in the industrial environment where the selection of high-precision indoor localization technologies (ILT) is crucial. Companies often face difficulties analyzing the advantages and disadvantages of different indoor localization technologies due to the wide range of selection criteria involved. These criteria include accuracy, coverage area, power consumption, cost, scalability, response time, and robustness, which can lead to uncertainty and sub-optimal investment in case of an inappropriate decision. This research project aims to tackle this challenge by developing a tool that supports users from the industry to easily select the most suitable Indoor Localization Technology. The developed tool serves to be a systematic and well-founded solution. It also allows the consideration of the subjective evaluations of the decision-makers in the decision-making process. The tool represents an industry-specific solution designed to meet the requirements of ILT selection. Additionally, the tool features a clear navigation system that guides users through the selection process.
By providing transparent insights into the Analytic Hierarchy Process (AHP) calculation method, the tool enables companies to make informed decisions. The project involves a systematic literature review on technology selection processes and Indoor Localization Technologies, as well as the development of a Decision Support System
Development of a UV hyperspectral imaging prototype for industrial applications
Get PDFBaumwollfasern sind aufgrund ihrer Weichheit, Haltbarkeit und Saugfähigkeit für die Textilindustrie unverzichtbar. Allerdings wirken sich Verunreinigungen negativ auf die Baumwollqualität aus und es ist notwendig die Stärke der Kontamination zu bestimmen. Eine häufig auftretende Verunreinigung ist Honigtau, welcher zu klebriger Baumwolle führt, und erhebliche Probleme für die Textilindustrie verursacht. Verschiedene Methoden im sichtbaren (Vis) und nahen infra-roten (NIR) Spektralbereich werden für Qualitätskontrollen und Sortierverfahren eingesetzt, während der ultraviolette (UV) Bereich bisher kaum genutzt wird. In den letzten Jahren haben hyperspektrale Bildgebungssysteme aufgrund ihrer Multimodalität, ihre räumliche Auflösung und ihrer Fähigkeit zur quantitativen Analyse im Vergleich zu herkömmlichen Verfahren zunehmend an Aufmerksamkeit gewonnen. Diese Vorteile haben sie für verschiedene Anwendungen, z. B. in der Textilindustrie, sehr attraktiv gemacht. Das Hauptaugenmerk der vorliegenden Arbeit liegt auf der Erkennung von Honigtaukontaminationen und der Entwicklung eines hyperspektralen Bildgebungssystems im UV-Bereich. Der Auf-bau basiert auf einem Spektrographen, der mit einer CCD-Kamera verbunden ist. Die Proben werden auf ein Förderband gelegt, welches die Probe unter der hyperspektralen Kamera bewegt. Dieses Verfahren wird als Pushbroom Imaging bezeichnet. Je nach Anwendung wurden Xenon- oder Deuteriumlampen zur Beleuchtung verwendet, wobei Deuteriumlampen eine höhere Beleuchtungsstärke im UV-C-Bereich im Vergleich zur Xenon-Bogenlampe bieten. Zur Validierung dieser neuartigen Bildgebungseinrichtung wurde eine Reihe von bekannten Substanzen wie aktive pharmazeutische Wirkstoffe (APIs) und Schmerzmittel verwendet. Diese Proben waren Ibuprofen, Acetylsalicylsäure und Paracetamol. Die Ergebnisse wurden mit lokalaufgenommenen Einzelspektren verglichen und mittels multivariater Datenanalyse ausgewertet. Es wurde gezeigt, dass die hyperspektrale Bildgebung im UV-Bereich zuverlässige Ergebnisse erzielt und eine ana-lytische Methode wurde entwickelt, um kommerzielle Schmerzmitteltabletten mit dem neuen Prototyp zu identifizieren. Anschließend wurde eine separate Probenreihe, einschließlich direct bonded copper (DBC) Substrate, für eine sekundäre Bewertung getestet. Der entwickelte Prototyp ist in der Lage wenige Nanometer dicke Oxidschichten, zu erkennen. Dabei können verschiedene Oxidationszustände unterschieden werden, sogar nachdem die Proben vorgesehene Reinigungs-verfahren durchlaufen haben. Im nächsten Schritt wurden Baumwollproben aus verschiedenen Ländern verwendet und mittels Vis/NIR hyperspektraler Bildgebung untersucht. Die gewonnenen Daten wurden mit lokal aufgenommen Einzelspektren verglichen und mittels multivariater Datenanalyse analysiert. Die Ergebnisse zeigen, dass es möglich ist, anhand einiger ausgewählter Wellenlängenbereiche zwischen verschiedenen Baumwollsorten zu unterscheiden. In einem letzten Schritt wurde die Quantifizierung von Honigtaukontaminationen auf Baumwolle durchgeführt. Hierfür wurde ein Verfahren zur Kalibrierung des Prototyps für hyperspektrales Imaging im UV-Bereich entwickelt und mit realen Proben getestet. Mechanisch gereinigte Baumwollproben wurden in eine Lösung getaucht, die Zucker und Eiweiß in bekannten Konzentrationen enthielt, um mit Honigtau verunreinigte Baumwolle zu imitieren. Diese Proben wurden nach 44 Stunden und nach einem Monat untersucht. Anhand dieser Proben wurde der Prototyp erweitert und optimiert. Die gewonnenen Daten wurden chemometrisch analysiert, um ortsaufgelöst die Honigtaumengen in Baumwollproben mit unterschiedlichen Mengen der Substanz erfolgreich vorherzusagen. Zusammenfassend lässt sich sagen, dass die Menge von Honigtau auf Baumwolle lateral aufgelöst quantifiziert wird. Dafür wurde ein Prototyp für hyperspektrale Bildgebung im UV-Bereich entwickelt, der für industrielle Anwendungen geeignet ist. Die Ergebnisse zeigten, dass die hyperspektrale Bildgebung mehrere Vorteile gegenüber etablierten Bildgebungsverfahren oder der klassischen ortsaufgelösten Spektroskopie bietet, z. B. die laterale Auflösung, die Fähigkeit, Proben zerstörungsfrei zu analysieren, Stoffe in sehr geringen Konzentrationen zu erkennen und Stoffe selbst dann zu identifizieren, wenn sie mit anderen Stoffen vermischt oder durch diese überlagert sind. Außerdem ist sie sehr empfindlich und kann kleinste Veränderungen in der chemischen Zusammensetzung von Materialien in Abhängigkeit der Zeit erkennenCotton fiber is essential for the textile industry due to its softness, durability, and absorbency. Therefore, the assessment of the cotton quality is needed, which is determined by the degree of contamination. The predominant contaminants in raw cotton come from insects that excrete sug-ars called honeydew during feeding. Cotton contaminated by sugar causes significant problems for textile equipment. Honeydew is the most common source of sticky cotton. However, various methods in visible (Vis) and near-infrared (NIR) spectral ranges are regularly used for quality control and sorting procedures, while the ultraviolet (UV) range has not been widely used. In recent years, hyperspectral imaging systems have gained increased attention over traditional techniques due to their multi-modality, spatial resolution, and ability for quantitative analysis. These advantages have made them highly attractive for various applications, such as in the textile industry. The main goal of this work is to develop a method to detect honeydew contamination in the UV range. For this purpose, a UV hyperspectral imaging system based on a spectrograph connected to a CCD camera was constructed. The samples were placed on a conveyor belt, which moved them underneath the hyperspectral imaging camera. This technique is called pushbroom imaging. Depending on the application, either Xenon or Deuterium lamps were used for illumination since Deuterium lamps provide a higher illumination strength in the UV-C region compared to the xenon-arc lamp. In order to validate this novel imaging setup, a set of well-known substances, such as active pharmaceutical ingredients (APIs) and painkillers, was used. These sample are ibuprofen, acetylsalicylic acid, and paracetamol. The results were compared with single-point spectroscopy and analyzed using chemometric data analysis. It was shown that the hyperspectral imaging achieved reliable results, and an analytical method was developed to identify commercial painkiller tablets with the new prototype. Subsequently, a separate sample set, including direct bonded copper (DBC) sheets, was tested for a secondary evaluation. The developed prototype is able to detect very thin oxide layers, as thin as a few nanometers. It can also distinguish between various oxidation states via a cleaning procedure for DBC samples. Consequently, cotton samples from different countries were investigated using Vis/NIR hyperspectral imaging. The data obtained were compared to that obtained from single-point spectroscopy and analyzed using multivariate data analysis. The results indicate that it is possible to distinguish between different cotton types based on specific wavelength ranges. In the last step, the quantification of honeydew contamination on cotton was determined. A calibration procedure was developed using mechanically cleaned cotton samples. These samples were immersed in different concentrations of sugar and protein to mimic cotton contaminated with honeydew. Consequently, they were analyzed after 44 hours and one month. Further improvements were made to the UV hyperspectral imaging setup in the later measurement. The data obtained were analyzed using chemometrics to predict the local quantities of honeydew on cotton samples successfully. In conclusion, the present work aims to quantify the spatial amount of honeydew contaminated on cotton by developing a hyperspectral imaging prototype in the UV region that is advantageous for industrial applications. The results showed that hyperspectral imaging has several advantages over established analytical techniques, such as lateral resolution, the ability to analyze samples non-destructively, detect materials at very low concentrations, and identify materials even when mixed or obscured by other materials. It is also highly sensitive and can detect subtle changes in the chemical composition of materials over time
Influence of gender and age distinction on patient data for sleep apnea detection using artificial intelligence models
No full textThe massive use of patient data for the training of artificial intelligence algorithms is common nowadays in medicine. In this scientific work, a statistical analysis of one of the most used datasets for the training of artificial intelligence models for the detection of sleep disorders is performed: sleep health heart study 2. This study focuses on determining whether the gender and age of the patients have a relevant influence to consider working with differentiated datasets based on these variables for the training of artificial intelligence models
On wave-like differential equations in general Hilbert space with application to Euler–Bernoulli bending vibrations of a beam
Get PDFWave-like differential equations occur in many engineering applications. Here the engineering setup is embedded into the framework of functional analysis of modern mathematical physics. After an overview, the –Hilbert space approach to free Euler–Bernoulli bending vibrations of a beam in one spatial dimension is investigated. We analyze in detail the corresponding positive, selfadjoint differential operators of 4-th order associated to the boundary conditions in statics. A comparison with free string wave swinging is outlined
Deducing solute differential heat capacity from experimental solubilities : an exemplified treatment of ascorbic acid to improve solubility prediction
Get PDFContext: Solubility prediction based on the general solubility equation (GSE) rests on reliable values for the isobaric heat capacity difference ΔCp,1 of the solid solute. Usually, this value is estimated with either zero or the melting entropy ΔS1(Tm,1) or, in few cases, is extrapolated from data of thermally stable melts of the solute. This causes uncertainties in the prediction.
Objective: To improve prediction accuracy a simple regression method is proposed that determines ΔCp,1 from measured solubilities.
Materials and methods: Published experimental solubilities in neat organic solvents at 298 K of a model compound (L-(+)-ascorbic acid (LAA)) have been regressed using the GSE together with the Hansen parameter model for the activity coefficient.
Results and discussion: Regression yielded ΔCp,1 = 238 J∙mol-1∙K-1 which agrees well with cross-validation results and is consistent with estimates from various group contribution methods. It was found that prediction accuracy improved in the order of increasing ΔCp,1, that is, from 0, via 91 (=ΔS1(Tm,1)) to 238 J∙mol-1∙K-1. It could be shown that mole fraction solubility of LAA can be forecast this way with an accuracy within current inter-laboratory variation.
Conclusion: The proposed method shows a general way to improve prediction accuracy of activity coefficient based solubility models by determining ΔCp,1 without resorting to common assumptions. The method is universally applicable and easy to implement
Direktvertrieb und Customer Experience in der Automobilindustrie
No full textDas Thema des Direktvertriebs (Direct-to-Customer oder kurz D-to-C) in der Automobilindustrie ist en vogue, denn nach Valtech (2023, S. 2) ist die Umstellung der Vertriebsmodelle in dieser Branche unumgänglich. Die Covid-19-Pandemie hat zudem noch als Katalysator für den D-to-C fungiert und die digitale Transformation sowie die Akzeptanz virtueller Verkaufsprozesse beschleunigt
