Advanced and novel modeling techniques for simulation, optimization and monitoring chemical engineering tasks with refinery and petrochemical unit applications

Engineers predict, optimize, and monitor processes to improve safety and profitability. Models automate these tasks and determine precise solutions. This research studies and applies advanced and novel modeling techniques to automate and aid engineering decision-making. Advancements in computational ability have improved modeling software’s ability to mimic industrial problems. Simulations are increasingly used to explore new operating regimes and design new processes. In this work, we present a methodology for creating structured mathematical models, useful tips to simplify models, and a novel repair method to improve convergence by populating quality initial conditions for the simulation’s solver. A crude oil refinery application is presented including simulation, simplification tips, and the repair strategy implementation. A crude oil scheduling problem is also presented which can be integrated with production unit models. Recently, stochastic global optimization (SGO) has shown to have success of finding global optima to complex nonlinear processes. When performing SGO on simulations, model convergence can become an issue. The computational load can be decreased by 1) simplifying the model and 2) finding a synergy between the model solver repair strategy and optimization routine by using the initial conditions formulated as points to perturb the neighborhood being searched. Here, a simplifying technique to merging the crude oil scheduling problem and the vertically integrated online refinery production optimization is demonstrated. To optimize the refinery production a stochastic global optimization technique is employed. Process monitoring has been vastly enhanced through a data-driven modeling technique Principle Component Analysis. As opposed to first-principle models, which make assumptions about the structure of the model describing the process, data-driven techniques make no assumptions about the underlying relationships. Data-driven techniques search for a projection that displays data into a space easier to analyze. Feature extraction techniques, commonly dimensionality reduction techniques, have been explored fervidly to better capture nonlinear relationships. These techniques can extend data-driven modeling’s process-monitoring use to nonlinear processes. Here, we employ a novel nonlinear process-monitoring scheme, which utilizes Self-Organizing Maps. The novel techniques and implementation methodology are applied and implemented to a publically studied Tennessee Eastman Process and an industrial polymerization unit

Age-Related Macular Degeneration and Diabetic Retinopathy

This reprint includes contributions from leaders in the field of personalized medicine in ophthalmology. The contributions are diverse and cover pre-clinical and clinical topics. We hope you enjoy reading the articles

Improving Maternal and Fetal Cardiac Monitoring Using Artificial Intelligence

Early diagnosis of possible risks in the physiological status of fetus and mother during pregnancy and delivery is critical and can reduce mortality and morbidity. For example, early detection of life-threatening congenital heart disease may increase survival rate and reduce morbidity while allowing parents to make informed decisions. To study cardiac function, a variety of signals are required to be collected. In practice, several heart monitoring methods, such as electrocardiogram (ECG) and photoplethysmography (PPG), are commonly performed. Although there are several methods for monitoring fetal and maternal health, research is currently underway to enhance the mobility, accuracy, automation, and noise resistance of these methods to be used extensively, even at home. Artificial Intelligence (AI) can help to design a precise and convenient monitoring system. To achieve the goals, the following objectives are defined in this research: The first step for a signal acquisition system is to obtain high-quality signals. As the first objective, a signal processing scheme is explored to improve the signal-to-noise ratio (SNR) of signals and extract the desired signal from a noisy one with negative SNR (i.e., power of noise is greater than signal). It is worth mentioning that ECG and PPG signals are sensitive to noise from a variety of sources, increasing the risk of misunderstanding and interfering with the diagnostic process. The noises typically arise from power line interference, white noise, electrode contact noise, muscle contraction, baseline wandering, instrument noise, motion artifacts, electrosurgical noise. Even a slight variation in the obtained ECG waveform can impair the understanding of the patient's heart condition and affect the treatment procedure. Recent solutions, such as adaptive and blind source separation (BSS) algorithms, still have drawbacks, such as the need for noise or desired signal model, tuning and calibration, and inefficiency when dealing with excessively noisy signals. Therefore, the final goal of this step is to develop a robust algorithm that can estimate noise, even when SNR is negative, using the BSS method and remove it based on an adaptive filter. The second objective is defined for monitoring maternal and fetal ECG. Previous methods that were non-invasive used maternal abdominal ECG (MECG) for extracting fetal ECG (FECG). These methods need to be calibrated to generalize well. In other words, for each new subject, a calibration with a trustable device is required, which makes it difficult and time-consuming. The calibration is also susceptible to errors. We explore deep learning (DL) models for domain mapping, such as Cycle-Consistent Adversarial Networks, to map MECG to fetal ECG (FECG) and vice versa. The advantages of the proposed DL method over state-of-the-art approaches, such as adaptive filters or blind source separation, are that the proposed method is generalized well on unseen subjects. Moreover, it does not need calibration and is not sensitive to the heart rate variability of mother and fetal; it can also handle low signal-to-noise ratio (SNR) conditions. Thirdly, AI-based system that can measure continuous systolic blood pressure (SBP) and diastolic blood pressure (DBP) with minimum electrode requirements is explored. The most common method of measuring blood pressure is using cuff-based equipment, which cannot monitor blood pressure continuously, requires calibration, and is difficult to use. Other solutions use a synchronized ECG and PPG combination, which is still inconvenient and challenging to synchronize. The proposed method overcomes those issues and only uses PPG signal, comparing to other solutions. Using only PPG for blood pressure is more convenient since it is only one electrode on the finger where its acquisition is more resilient against error due to movement. The fourth objective is to detect anomalies on FECG data. The requirement of thousands of manually annotated samples is a concern for state-of-the-art detection systems, especially for fetal ECG (FECG), where there are few publicly available FECG datasets annotated for each FECG beat. Therefore, we will utilize active learning and transfer-learning concept to train a FECG anomaly detection system with the least training samples and high accuracy. In this part, a model is trained for detecting ECG anomalies in adults. Later this model is trained to detect anomalies on FECG. We only select more influential samples from the training set for training, which leads to training with the least effort. Because of physician shortages and rural geography, pregnant women's ability to get prenatal care might be improved through remote monitoring, especially when access to prenatal care is limited. Increased compliance with prenatal treatment and linked care amongst various providers are two possible benefits of remote monitoring. If recorded signals are transmitted correctly, maternal and fetal remote monitoring can be effective. Therefore, the last objective is to design a compression algorithm that can compress signals (like ECG) with a higher ratio than state-of-the-art and perform decompression fast without distortion. The proposed compression is fast thanks to the time domain B-Spline approach, and compressed data can be used for visualization and monitoring without decompression owing to the B-spline properties. Moreover, the stochastic optimization is designed to retain the signal quality and does not distort signal for diagnosis purposes while having a high compression ratio. In summary, components for creating an end-to-end system for day-to-day maternal and fetal cardiac monitoring can be envisioned as a mix of all tasks listed above. PPG and ECG recorded from the mother can be denoised using deconvolution strategy. Then, compression can be employed for transmitting signal. The trained CycleGAN model can be used for extracting FECG from MECG. Then, trained model using active transfer learning can detect anomaly on both MECG and FECG. Simultaneously, maternal BP is retrieved from the PPG signal. This information can be used for monitoring the cardiac status of mother and fetus, and also can be used for filling reports such as partogram

OCM 2023 - Optical Characterization of Materials : Conference Proceedings

The state of the art in the optical characterization of materials is advancing rapidly. New insights have been gained into the theoretical foundations of this research and exciting developments have been made in practice, driven by new applications and innovative sensor technologies that are constantly evolving. The great success of past conferences proves the necessity of a platform for presentation, discussion and evaluation of the latest research results in this interdisciplinary field

WOFEX 2021 : 19th annual workshop, Ostrava, 1th September 2021 : proceedings of papers

The workshop WOFEX 2021 (PhD workshop of Faculty of Electrical Engineer-ing and Computer Science) was held on September 1st September 2021 at the VSB – Technical University of Ostrava. The workshop offers an opportunity for students to meet and share their research experiences, to discover commonalities in research and studentship, and to foster a collaborative environment for joint problem solving. PhD students are encouraged to attend in order to ensure a broad, unconfined discussion. In that view, this workshop is intended for students and researchers of this faculty offering opportunities to meet new colleagues.Ostrav

Postsecondary educational careers and social inequality: an analysis of social origin differences in educational career trajectories and their labor market outcomes in the US, Sweden and Germany

USA ; Schweden ; Deutschland ; Hochschule ; Bildungsabschluss ; Lebenslauf ; Karriere ; Soziale Ungleichhei

Umbilical cord arterial 8-iso-prostaglandin F2α concentrations in pregnancies complicated by meconium stained liquor.

Liu Bao Yi.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 83-104).Abstracts in English and Chinese.ABSTRACT --- p.iACKNOWLEDGEMENT --- p.vTABLE OF CONTENTS --- p.viLIST OF ABBREVIATIONS --- p.xiiLIST OF FIGURES --- p.xivvLIST OF TABLES --- p.xvPUBLICATION RELATED TO THIS THESIS --- p.xviiChapter PART 1 --- INTRODUCTION AND LITERATURE RESEARCHChapter CHAPTER 1 --- INTRODUCTION --- p.1Chapter CHAPTER 2 --- MECONIUM STAINED LIQUOR --- p.3Chapter 2.1 --- AMNIOTIC FLUID --- p.3Chapter 2.1.1 --- Function of Amniotic Fluid --- p.3Chapter 2.1.2 --- Composition Of Amniotic Fluid --- p.3Chapter 2.1.3 --- Regulation Of Amniotic Fluid --- p.4Chapter 2.1.4 --- Abnormality Of Amniotic Fluid Volume --- p.4Chapter 2.2 --- MECONIUM STAINED LIQUOR --- p.6Chapter 2.2.1 --- Formation And Composition Of Meconium --- p.6Chapter 2.2.2 --- Peristalsis Of Fetal Gastrointestinal Tract --- p.7Chapter 2.2.3 --- Meconium Stained Liquor(MSL) --- p.7Chapter 2.2.3.1 --- Maturation Theory --- p.7Chapter 2.2.3.2 --- Cord Compression Theory --- p.9Chapter 2.2.3.3 --- Fetal Hypoxia Theory --- p.10Chapter 2.2.4 --- Fetal Effect Of Meconium In Amniotic Cavity --- p.11Chapter 2.2.5 --- Meconium Aspiration Syndrome --- p.12Chapter 2.2.6 --- Clinical Significance And Limitation Of Studies --- p.13Chapter 2.3 --- Purpose Of Study --- p.14Chapter CHAPTER 3 --- OXIDATIVE STRESS AND FETAL HYPOXIA --- p.16Chapter 3.1 --- OXIDATIVE STRESS --- p.16Chapter 3.2 --- FREE RADICALS --- p.16Chapter 3.2.1 --- Sources Of Free Radicals --- p.17Chapter 3.2.1.1 --- Biological Source Of Free Radicals --- p.17Chapter 3.2.1.2 --- Intracellular Source Of Free Radicals --- p.17Chapter 3.2.1.3 --- Composition Of Free Radicals And Reactive Oxygen Species --- p.18Chapter 3.2.2 --- Cellular Components At Risk From Free Radicals Damage --- p.20Chapter 3.2.2.1 --- Proteins --- p.20Chapter 3.2.2.2 --- Nucleic Acids And DNA --- p.21Chapter 3.2.2.3 --- Membrane Lipids --- p.21Chapter 3.2.3 --- Lipid Peroxidation --- p.21Chapter 3.2.3.1 --- Chemical Substances Of Membranes --- p.21Chapter 3.2.3.2 --- The Reactions Of Lipid Peroxidation --- p.22Chapter 3.2.3.3 --- Lipid Peroxidation In Pregnancy --- p.23Chapter 3.2.4 --- Protection Against Lipid Peroxidation --- p.24Chapter 3.2.5 --- Isoprostanes --- p.26Chapter 3.2.5.1 --- Definition --- p.26Chapter 3.2.5.2 --- Formation Of Isoprostanes --- p.26Chapter 3.2.5.3 --- Metabolism Of Isoprostanes --- p.27Chapter 3.2.5.4 --- Biological Characteristics Of Isoprostanes --- p.29Chapter 3.2.5.5 --- Isoprostanes As Mediators Of Oxidantive Stress --- p.29Chapter 3.3 --- FETAL HYPOXIA --- p.30Chapter 3.3.1 --- Fetal Metabolism And Energy Supply --- p.30Chapter 3.3.2 --- Free Radical Generation And Fetal Hypoxia-Reoxygenation --- p.33Chapter 3.3.3 --- Fetal Hypoxia And Fetal Brain Injury --- p.34Chapter 3.3.4 --- Measurement Of Fetal Hypoxia --- p.35Chapter 3.3.4.1 --- Acid-Base Balance --- p.35Chapter 3.3.4.2 --- Fetal Heart Rate Monitoring --- p.36Chapter 3.3.4.3 --- Apgar scores --- p.37Chapter 3.3.4.4 --- Pulse Oximetry --- p.37Chapter 3.3.4.5 --- Lipid Peroxides --- p.38Chapter CHAPTER 4 --- AMNIOINFUSION --- p.40Chapter 4.1 --- AMNIOINFUSION --- p.40Chapter 4.2 --- AMNIOINFUSION FOR OLIGOHYDRAMNIOS --- p.40Chapter 4.3 --- AMNIOINFUSION FOR MECONIUM STAINED LIQUOR --- p.41Chapter 4.4 --- PURPOSE OF THE STUDY --- p.42Chapter PART 2 --- CLINICAL PROTOCOLS AND MEASUREMENT OF ISOPROSTANESChapter CHAPTER 5 --- CLINICAL PROTOCOLS --- p.43Chapter 5.1 --- ETHICS --- p.43Chapter 5.2 --- CLINICAL PROTOCOLS --- p.43Chapter 5.2.1 --- Artificial Rupture Of Membranes (Amniotomy) --- p.43Chapter 5.2.2 --- Classification of Meconium Stained Liquor --- p.44Chapter 5.2.3 --- Electronic Fetal Heart Rate Monitoring --- p.44Chapter 5.2.4 --- Monitoring The Progress of Labour --- p.44Chapter 5.2.5 --- Umbilical Cord Blood Gas Analysis --- p.45Chapter 5.2.6 --- Apgar Score --- p.45Chapter 5.2.7 --- Meconium Aspiration --- p.46Chapter 5.2.8 --- Clinical Outcome --- p.46Chapter CHAPTER 6 --- MEASUREMENT OF ISOPROSTANES --- p.50Chapter 6.1 --- BLOOD PREPARATION --- p.50Chapter 6.2 --- REAGENTS --- p.50Chapter 6.3 --- GAS CHROMATOGRAPHY AND MASS SPECTROMETRY (GC-MS) --- p.51Chapter 6.4 --- PROCEDURES --- p.51Chapter 6.5 --- DATA RELIABILITY --- p.53Chapter PART 3 --- RESULTS AND DISCUSSIONChapter CHAPTER 7 --- MECONIUM STAINED LIQUOR (MSL) DURING LABOUR AND NEONATAL CORD BLOOD 8-IS〇-PGF2α CONCENTRATION --- p.54Chapter 7.1 --- OBJECTIVE --- p.54Chapter 7.2 --- MATERIALS AND METHOD --- p.55Chapter 7.3 --- STATISTICAL ANALYSIS --- p.56Chapter 7.4 --- RESULTS --- p.57Chapter 7.5 --- DISCUSSION --- p.65Chapter 7.6 --- CONCLUSION --- p.67Chapter CHAPTER 8 --- EVALUATION OF PROPHYLACTIC AMNIOINFUSION FOR INTRAPARTUM MECONIUM STAINED LIQUOR --- p.69Chapter 8.1 --- OBJECTIVE --- p.69Chapter 8.2 --- MATERIALS AND METHOD --- p.69Chapter 8.2.1 --- Study Group: 226}0ب MSL+AI' --- p.69Chapter 8.2.2 --- The Procedure Of Amnioinfusion --- p.70Chapter 8.2.3 --- Other Study Group --- p.71Chapter 8.3 --- STATISTIC ANALYSIS --- p.71Chapter 8.4 --- RESULTS --- p.72Chapter 8.4.1 --- Comparison Between The 'MSL+AI' And 'MSL-AI' Groups --- p.72Chapter 8.4.2 --- Comparison Between 226}0بMSL+AI'And 'Clear Liquor' Groups --- p.74Chapter 8.5 --- DISCUSSION --- p.77Chapter 8.6 --- CONCLUSION --- p.79Chapter CHAPTER 9 --- COMMENTS AND FUTURE RESEARCH --- p.80BIBLIOGRAPHY --- p.8

Differential gene expression of eutopic endometrium and normal pelvic peritoneum in women with and without endometriosis

Scope and Method of Study: To investigate differential gene expression in women with and without endometriosis. Women already undergoing laparoscopic surgery were recruited as normal and potential endometriosis participants. The normal participants were those women undergoing laparoscopic bilateral tubal ligation, and the endometriosis participants were those undergoing diagnostic laparoscopy to evaluate chronic pelvic pain. Sample collection included an endometrial aspiration, normal pelvic peritoneum, peritoneal fluid, and endometriotic lesion (in those with endometriosis). Total RNA was extracted from tissue samples and put through one round of amplification in the normal pelvic peritoneum studies. Human oligo microarrays with over 21,000 genes were utilized in the analysis. Pooled RNA from normal participants was hybridized against RNA from each endometriosis participant. Microarray analysis was performed utilizing GenePix Pro and GenePix Auto Processor. Microarray results were validated utilizing RT-PCR.Findings and Conclusions: Seven women with and seven women without endometriosis were utilized in the microarray studies. In the comparison of gene expression in eutopic endometrium, 756 genes were found to be significantly up-regulated or down-regulated in the women with endometriosis versus a normal pool. KEGG pathway analysis revealed genes involved in the following pathways: cell communication, MAPK signaling, cytokine-cytokine receptor interaction, cell cycle, TGF-beta signaling, focal adhesion, cell adhesion, and ECM-receptor interaction. In the comparison of gene expression in normal pelvic peritoneum, 202 genes were found to be significantly up-regulated or down-regulated in women with endometriosis versus those without. These genes are involved in the following pathways: cytokine-cytokine receptor, cell cycle, focal adhesion, regulation of actin cytoskeleton, and leukocyte transendothelial migration

Postsecondary educational careers and social inequality: an analysis of social origin differences in educational career trajectories and their labor market outcomes in the US, Sweden and Germany

USA ; Schweden ; Deutschland ; Hochschule ; Bildungsabschluss ; Lebenslauf ; Karriere ; Soziale Ungleichhei

Probabilistic modelling of single cell multi-omics data

Multicellular organisms possess a diverse set of cells exhibiting unique properties and function. Despite their physiology and role each cell owns the same copy of genetic in- structions encoded in its DNA. The ability of cells to differentiate into various shapes and forms stems from a careful orchestration of gene expression through various regulatory mechanisms. Recent developments in single cell multi-omics protocols offer unprecedented opportu- nities to simultaneously quantify phenomena in epigenome and gene expression at a single cell resolution. Advances in cell isolation and barcoding eliminated various confounding phenomena, shedding light into the regulatory role of epigenome in gene expression over diverse tissues and cells. Yet, combining omics modalities introduces serious statistical and computational challenges. Limitations of single-omics get exacerbated when combined into multi-modal assays, making result interpretation hard. In this thesis, we argue that inconsistent treatment of technical variability offered by classical statistical tools can corrupt statistical analyses and produce misleading results. In the Bayesian template, we introduce probabilistic models that explicitly and transparently decouple technical variability from biological signal. These methods are then used to investigate how epigenetic regulatory mechanisms interact with gene expression, both at genomic and at a cellular level. Single cell sequencing technologies are notoriously affected by high sparsity, leaving scientists to wonder if data are a product of sample handling or some genes are not expressed. As a result, even simple correlative tools (eg. Pearson’s correlation) seeking to identify regions with strong regulatory patterns between molecular layers routinely pinpoint a handful of associations. To overcome some of these limitations we introduce SCRaPL (Single Cell Regulatory Pattern Learning), a Bayesian hierarchical model to infer correlation between different omics components. SCRaPL’s uncertainty quantification allows for accurate results and good control over false positives, compared to its counterparts. Existing limitations force practitioners to partially or fully discard molecular modalities from cell observations, significantly under-powering subsequent downstream analysis. An alternative solution for scaling datasets is to post-experimentally address protocol limitations using a generative model. We introduce single cell Multi View Inference (scMVI), a deep learning model designed to accommodate analyses on both partially and fully observed data. Using jointly quantified data, scMVI builds a low-dimensional joint latent space by aligning omcis representations for each cell. In similar cells, scMVI can match individual modalities creating more complex sets. Subsequently, this manifold is used to approximate the data generating process. Hence, in partially quantified cells missing observations could be imputed getting the full potential of the data. To summarize, this thesis proposes novel statistical tools to interpret the regulatory interactions between epigenome and gene expression using data from modern multi-omics sequencing experiments. Their flexible design along with robust uncertainty quantification, allow these methods to unlock the immense potential of existing and future sequencing protocols. We hope that with the increased adoption in these methods, SCRaPL and scMVI will become an integral part of downstream analysis