Melt Instability Identification Using Unsupervised Machine Learning Algorithms

in Macromolecular Materials and Engineering (2023)

In industrial extrusion processes, increasing shear rates can lead to higher production rates. However, at high shear rates, extruded polymers and polymer compounds often exhibit melt instabilities ... [more ▼]

In industrial extrusion processes, increasing shear rates can lead to higher production rates. However, at high shear rates, extruded polymers and polymer compounds often exhibit melt instabilities ranging from stick-slip to sharkskin to gross melt fracture. These instabilities result in challenges to meet the specifications on the extrudate shape. Starting with an existing published data set on melt instabilities in polymer extrusion, we assess the suitability of clustering, unsupervised machine learning algorithms combined with feature selection, to extract and identify hidden and important features from this data set, and their possible relationship with melt instabilities. The data set consists of both intrinsic features of the polymer as well as extrinsic features controlled and measured during an extrusion experiment. Using a range of commonly available clustering algorithms, it is demonstrated that the features related to only the intrinsic properties of the data set can be reliably divided into two clusters, and that in turn, these two clusters may be associated with either the stick-slip or sharkskin instability. Furthermore, using a feature ranking on both the intrinsic and extrinsic features of the data set, it is shown that the intrinsic properties of molecular weight and polydispersity are the strongest indicators of clustering. [less ▲]

Cardiovascular risk prediction - a systems medicine approach 2023.03.16.23287363

E-print/Working paper (2023)

Background Guidelines for the prevention of cardiovascular disease (CVD) have recommended the assessment of the total CVD risk by risk scores. Current risk algorithms are low in sensitivity and ... [more ▼]

Background Guidelines for the prevention of cardiovascular disease (CVD) have recommended the assessment of the total CVD risk by risk scores. Current risk algorithms are low in sensitivity and specificity and they have not incorporated emerging risk markers for CVD. We suggest that CVD risk assessment can be still improved. We have developed a long-term risk prediction model of cardiovascular mortality in patients with stable coronary artery disease (CAD) based on newly available machine learning and on an extended dataset of new biomarkers.Methods 2953 participants of the Ludwigshafen Risk and Cardiovascular Health (LURIC) study were included. 184 laboratory and 21 demographic markers were ranked according to their contribution to risk of cardiovascular (CV) mortality using different data mining approaches. A self-learning bioinformatics workflow, including seven different machine learning algorithms, was developed for CV risk prediction. The study population was stratified into patients with and without significant CAD. Thereby, significant CAD was defined as a lumen narrowing of 50 or more in at least one of the coronary segments or a history of definite myocardial infarction. The machine learning models in both subpopulations were compared with established CV risk assessment tools.Results After a follow-up of 10 years, 603 (20.4\%) patients died of cardiovascular causes. 95 (\%) patients without CAD deceased within ten years and 247 (13.2 \%) patients with CAD within 5 years. Overall and in patients without CAD, NT-proBNP (N-terminal pro B-type natriuretic peptide), TnT (Troponin T), estimated cystatin c based GFR (glomerular filtration rate) and age were the highest ranked predictors, while in patients with CAD, NT-proBNP, GFR, CT-proAVP (C-terminal pro arginine vasopressin) and TNT were highest predictive. In the comparison with the FRS, PROCAM and ESC risk scores, the machine learning workflow produced more accurate and robust CV mortality prediction in patients without CAD. Equivalent CV risk prediction was obtained in the CAD subpopulation in comparison with the Marschner risk score. Overall, the existing algorithms in general tend to assign more patients into the medium risk groups, while the machine learning algorithms tend to have a clearer risk/no risk assignment. The framework is available upon request.Conclusion We have developed a fully automated and self-validating computational framework of machine learning techniques using an extensive database of clinical, routinely and non-routinely measured laboratory data. Our framework predicts long-term CV mortality at least as accurate as existing CVD risk scores. A combination of four highly ranked biomarkers and the random forest approach showed the best predictive results. Moreover, a dynamic computational model has several advantages over static CVD risk prediction tools: it is freeware, transparent, variable, transferable and expandable to any population, types of events and time frames. [less ▲]

A Public History of Monuments

in Studies on National Movements (2022), 10(1), 7-43

Ergodic Performance Analysis of Double Intelligent Reflecting Surfaces-Aided NOMA–UAV Systems with Hardware Impairment

in Drones (2022)

In this work, we design an intelligent reflecting surface (IRS)-assisted Internet of Things (IoT) by enabling non-orthogonal multiple access (NOMA) and unmanned aerial vehicles (UAV) approaches. We pay ... [more ▼]

In this work, we design an intelligent reflecting surface (IRS)-assisted Internet of Things (IoT) by enabling non-orthogonal multiple access (NOMA) and unmanned aerial vehicles (UAV) approaches. We pay attention to studying the achievable rates for the ground users. A practical system model takes into account the presence of hardware impairment when Rayleigh and Rician channels are adopted for the IRS–NOMA–UAV system. Our main findings are presented to showcase the exact expressions for achievable rates, and then we derive their simple approximations for a more insightful performance evaluation. The validity of these approximations is demonstrated using extensive Monte Carlo simulations. We confirm the achievable rate improvement decided by main parameters such as the average signal to noise ratio at source, the position of IRS with respect to the source and destination and the number of IRS elements. As a suggestion for the deployment of a low-cost IoT system, the double-IRS model is a reliable approach to realizing the system as long as the hardware impairment level is controlled. The results show that the proposed scheme can greatly improve achievable rates, obtain optimal performance at one of two devices and exhibit a small performance gap compared with the other benchmark scheme. [less ▲]

ON THE RELATION BETWEEN STOCHASTIC THERMODYNAMICS AND LINEAR IRREVERSIBLE THERMODYNAMICS

Doctoral thesis (2022)

Elucidating the role of formate on cancer cell invasion

Doctoral thesis (2022)

Metabolic rewiring is essential to enable cancer onset and progression. One important metabolic pathway that is often hijacked by cancer cells is the one-carbon (1C) cycle, in which the third carbon of ... [more ▼]

Metabolic rewiring is essential to enable cancer onset and progression. One important metabolic pathway that is often hijacked by cancer cells is the one-carbon (1C) cycle, in which the third carbon of serine is oxidized to formate. It was previously shown that formate production in cancer cells often exceeds the anabolic demand, resulting in formate overflow. Furthermore, extracellular formate was described to promote the in vitro invasiveness of glioblastoma (GBM) cells. Nevertheless, the mechanism underlying the formate-induced invasion remains elusive. In this present study, we aimed to characterize formate-induced invasion in greater detail. At first, we studied the generalizability of formate-induced invasion in different GBM models as well as in different breast cancer models. We applied different in vitro assays, like the Boyden chamber assay to probe the impact of formate on different cancer cell lines. Then, we studied the in vivo relevance and the pro-invasive properties of formate in physiological models by using different ex vivo and in vivo models. Lastly, we investigated the mechanism underlying the formate-dependent pro-invasive phenotype. We applied a variety of different biochemical as well as cellular assays to investigate the underlying mechanism. In the present study, we underline that formate specifically promotes invasion and not migration in different cancer types. Furthermore, we now demonstrate that inhibition of formate overflow results in a decreased invasiveness of GBM cells ex vivo and in vivo. Using breast cancer models, we also obtain first evidence that formate does not only promote local cancer cell invasion but also metastasis formation in vivo, suggesting that locally increased formate concentrations within the tumour microenvironment promote cancer cell motility and dissemination. Mechanistically, we uncover a previously undescribed interplay where formate acts as a trigger to alter fatty acid metabolism, which in turn affects cancer cell invasiveness and metastatic potential via matrix metalloproteinase (MMP) release. Gaining a better mechanistic understanding of formate overflow, and how formate promotes invasion in cancer, may contribute to preventing cancer cell dissemination, one of the main reasons for cancer-related mortality. [less ▲]

Presenter: Arrival Declaration Forms. A New Gateway for Mapping Migration to Luxembourg

Presentation (2022, December 16)

Introductory Words of Conference: Borders In Flux and Border Temporalities In and Beyond Europe

Presentation (2022, December 15)

DESIGN OF A THREE-STAGE PROCESS, PHOTOLUMINESCENCE AND DEFECT SPECTROSCOPY OF CU(IN,GA)S2

Doctoral thesis (2022)

Cu(In,Ga)S2 is a chalcopyrite material suitable as the higher bandgap top cell in tandem applications in next generation multijunction solar cells. This owes primarily to the tunability of its bandgap ... [more ▼]

Cu(In,Ga)S2 is a chalcopyrite material suitable as the higher bandgap top cell in tandem applications in next generation multijunction solar cells. This owes primarily to the tunability of its bandgap from 1.5 eV in CuInS2 to 2.45 eV in CuGaS2, and its relative stability over time. Currently, a major hinderance to the potential use of Cu(In,Ga)S2 in tandem capacity remains a deficient single-junction device performance in the form of low open-circuit voltage (VOC) and low efficiency. Aside interfacial recombination which leads to losses in the completed Cu(In,Ga)S2 solar cell, deficiencies stems from a low optoelectronic quality of the Cu(In,Ga)S2 absorber quantified by the quasi-Fermi level splitting (QFLS) and which serves as the upper limit of VOC achievable by a solar cell device. In this thesis, the QFLS is compared with the theoretical VOC (SQ-VOC) in the radiative limit, and “SQ-VOC deficit” is defined to compare the difference between SQ-VOC and QFLS as a comparable measure of the optoelectronic deficiency in the absorber material. In contrast to the counterpart Cu(In,Ga)Se2 absorber which has produced highly efficient solar cell devices, the Cu(In,Ga)S2 absorber still suffers from a high SQ-VOC deficit. However, SQ-VOC deficit in Cu(In,Ga)S2 can be reduced by growing the absorbers under Cu-deficient conditions. For the effective use of Cu(In,Ga)S2 as the top cell in tandem with Si or Cu(In,Ga)Se2 as the bottom cell, an optimum bandgap of 1.6-1.7 eV is required, and this is realized in absorbers with Ga content up to [Ga]/([Ga]+[In]) ratio of 0.30-0.35. However, the increase of Ga in Cu-poor Cu(In,Ga)S2 poses a challenge to the structural and optoelectronic quality of the absorber, resulting from the formation of segregated Ga phases with steep Ga/bandgap gradient which constitutes a limitation to the quality of the Cu(In,Ga)S2 absorber layer with a highSQ-VOC deficit and low open circuit voltage and overall poor performance of the finalized solar cell. In this work, the phase segregation in Cu(In,Ga)S2 has been circumvented by employing higher substrate temperatures and adapting the Ga flux during the first-stage of deposition when growing the Cu(In,Ga)S2 absorbers. A more homogenous Cu(In,Ga)S2 phase and improved Ga/bandgap gradient is achieved by optimizing the Ga flux at higher substrate temperature to obtain a Cu(In,Ga)S2 absorber with high optoelectronic quality and low SQ-VOC deficit. Additionally, the variation of the Cu-rich phase when growing the Cu(In,Ga)S2 absorber layers was found to not only alter the notch profile and bandgap minimum of the absorbers, but also influence the optoelectronic quality of the absorber. Shorter Cu-rich phase in the absorbers led to narrower notch profile and higher bandgap. Ultimately, several steps in the three-stage deposition method used for processing the Cu(In,Ga)S2 absorbers were revised to enhanced the overall quality of the absorbers. Consequently, the SQ-VOC deficit in high bandgap Cu(In,Ga)S2 absorbers is significantly reduced, leading to excellent device performance. This thesis also examines the temperature- and compositional-related optoelectronic improvement in pure Cu-rich CuInS2 absorbers without Ga, where improvement in QFLS was initially linked to a reduction of nonradiative recombination channels with higher deposition temperatures and increase in Cu content. Findings through photoluminescence decay measurements show that the origin of the improved QFLS in CuInS2 is rather linked to changes in doping levels with variations of deposition temperature and Cu content. Finally, in order to understand and gain insight into the influence of Ga in Cu(In,Ga)S2, the electronic structure of CuGaS2 absorbers was investigated in dependence of excitation intensity and temperature by low temperature photoluminescence measurements. A shallow donor level and three acceptor levels were detected. It was found that similar acceptor levels in CuInSe2 and CuGaSe2 which are otherwise shallow become deeper in CuGaS2. These deep defects serve as nonradiative recombination channels and their appearance in the Ga-containing compound is be detrimental to the optoelectronic quality of Cu(In,Ga)S2 absorbers as Ga content is increased therefore limiting the optimum performance of Cu(In,Ga)S2 devices. [less ▲]

Main Organiser: Borders In Flux and Border Temporalities In and Beyond Europe

Presentation (2022, December 15)