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Activated carbons are known as excellent adsorbents. Their applications include the adsorptive removal of color, odor, taste, undesirable organic and inorganic pollutants from drinking and waste water; air purification in inhabited spaces; purification of many chemicals, pharmaceutical products and many others. This chapter elucidates the role of normal microflora in the maintenance of human health and presents materials on possible clinical displays of microecological infringements and ways of their correction. It presents new developments concerning new probiotics with immobilized Lactobacillus and Bacillus. The chapter considers the mechanisms of the intestine disbacteriosis correction by sorbed probiotics. It demonstrates the advantages and creation prospects of immobilized probiotics developed on the basis of carbonized rice husk. There are great prospects for the development of medical biotechnology due to use of carbon sorbents with a nanostructured surface. Microbial communities form a biocenosis of the biotope and together with the host organism create permanent or temporary ecosystems.
The integration of product data from heterogeneous sources and manufacturers into a single catalog is often still a laborious, manual task. Especially small- and medium-sized enterprises face the challenge of timely integrating the data their business relies on to have an up-to-date product catalog, due to format specifications, low quality of data and the requirement of expert knowledge. Additionally, modern approaches to simplify catalog integration demand experience in machine learning, word vectorization, or semantic similarity that such enterprises do not have. Furthermore, most approaches struggle with low-quality data. We propose Attribute Label Ranking (ALR), an easy to understand and simple to adapt learning approach. ALR leverages a model trained on real-world integration data to identify the best possible schema mapping of previously unknown, proprietary, tabular format into a standardized catalog schema. Our approach predicts multiple labels for every attribute of an inpu t column. The whole column is taken into consideration to rank among these labels. We evaluate ALR regarding the correctness of predictions and compare the results on real-world data to state-of-the-art approaches. Additionally, we report findings during experiments and limitations of our approach.
In collaborative research projects, both researchers and practitioners work together solving business-critical challenges. These projects often deal with ETL processes, in which humans extract information from non-machine-readable documents by hand. AI-based machine learning models can help to solve this problem.
Since machine learning approaches are not deterministic, their quality of output may decrease over time. This fact leads to an overall quality loss of the application which embeds machine learning models. Hence, the software qualities in development and production may differ.
Machine learning models are black boxes. That makes practitioners skeptical and increases the inhibition threshold for early productive use of research prototypes. Continuous monitoring of software quality in production offers an early response capability on quality loss and encourages the use of machine learning approaches. Furthermore, experts have to ensure that they integrate possible new inputs into the model training as quickly as possible.
In this paper, we introduce an architecture pattern with a reference implementation that extends the concept of Metrics Driven Research Collaboration with an automated software quality monitoring in productive use and a possibility to auto-generate new test data coming from processed documents in production.
Through automated monitoring of the software quality and auto-generated test data, this approach ensures that the software quality meets and keeps requested thresholds in productive use, even during further continuous deployment and changing input data.
Retinal endothelial function in cardiovascular risk patients: A randomized controlled exercise trial
(2020)
The aim of this study was to investigate, for the first time, the effects of high-intensity interval training (HIIT) on retinal microvascular endothelial function in cardiovascular (CV) risk patients. In the randomized controlled trial, middle-aged and previously sedentary patients with increased CV risk (aged 58 ± 6 years) with ≥ two CV risk factors were randomized into a 12-week HIIT (n = 33) or control group (CG, n = 36) with standard physical activity recommendations. A blinded examiner measured retinal endothelial function by flicker light-induced maximal arteriolar (ADmax) and venular (VDmax) dilatation as well as the area under the arteriolar (AFarea) and venular (VFarea) flicker curve using a retinal vessel analyzer. Standardized assessments of CV risk factors, cardiorespiratory fitness, and retinal endothelial function were performed before and after HIIT. HIIT reduced body mass index, fat mass, and low-density lipoprotein and increased muscle mass and peak oxygen uptake (VO2peak). Both ADmax (pre: 2.7 ± 2.1%, post: 3.0 ± 2.2%, P = .018) and AFarea (pre: 32.6 ± 28.4%*s, post: 37.7 ± 30.6%*s, P = .016) increased after HIIT compared with CG (ADmax, pre: 3.2 ± 1.8%, post: 2.9 ± 1.8%, P = .254; AFarea, pre: 41.6 ± 28.5%*s, post: 37.8 ± 27.0%*s, P = .186). Venular function remained unchanged after HIIT. There was a significant association between ∆-change VO2peak and ∆-changes ADmax and AFarea (P = .026, R² = 0.073; P = .019, R² = 0.081, respectively). 12-weeks of HIIT improved retinal endothelial function in middle-aged patients with increased CV risk independent of the reduction in classical CV risk factors. Exercise has the potential to reverse or at least postpone progression of small vessel disease in older adults with increased CV risk under standard medication. Dynamic retinal vessel analysis seems to be a sensitive tool to detect treatment effects of exercise interventions on retinal microvascular endothelial function in middle-aged individuals with increased CV risk.
We propose the so-called chance constrained programming model of stochastic programming theory to analyze limit and shakedown loads of structures under random strength with a lognormal distribution. A dual chance constrained programming algorithm is developed to calculate simultaneously both the upper and lower bounds of the plastic collapse limit and the shakedown limit. The edge-based smoothed finite element method (ES-FEM) is used with three-node linear triangular elements.
The steel industry in the European Union (EU), important for the economy as a whole, faces various challenges. These are inter alia volatile prices for relevant input factors, uncertainties concerning the regulation of CO₂-emissions and market shocks caused by the recently introduced additional import duties in the US, which is an important sales market. We examine primary and secondary effects of these challenges on the steel industry in the EU and their impacts on European and global level. Developing and using a suitable meta-model, we analyze the competitiveness of key steel producing countries with respect to floor prices depending on selected cost factors and draw conclusions on the impacts in the trade of steel on emissions, energy demand, on the involvement of developing countries in the value chain as well on the need for innovations to avoid relocations of production. Hence, our study contributes to the assessment of sustainable industrial development, which is aimed by the Sustainability Development Goal “Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation countries”. By applying information on country-specific Human Development Indexes (reflecting aspects of life expectancy, education, and per capita income), we show that relocating energy-intensive industries from the EU may not only increase global energy demand and CO₂-emissions, but may also be to the disadvantage of developing countries.
In this review article, we are going to present an overview on possible applications of light-addressable electrodes (LAE) as actuator/manipulation devices besides classical electrode structures. For LAEs, the electrode material consists of a semiconductor. Illumination with a light source with the appropiate wavelength leads to the generation of electron-hole pairs which can be utilized for further photoelectrochemical reaction. Due to recent progress in light-projection technologies, highly dynamic and flexible illumination patterns can be generated, opening new possibilities for light-addressable electrodes. A short introduction on semiconductor–electrolyte interfaces with light stimulation is given together with electrode-design approaches. Towards applications, the stimulation of cells with different electrode materials and fabrication designs is explained, followed by analyte-manipulation strategies and spatially resolved photoelectrochemical deposition of different material types.
This chapter shows that nanomaterials obtained by high-temperature carbonization of inexpensive plant raw material such as rice husk, grape seeds, and walnut shells can serve as a basis for the production of highly efficient microbial drugs, biodestructors, biosorbents, and biocatalysts, which are promising for the remediation of the ecosystem contaminated with heavy and radioactive metals, oil and oil products. A strong interest in engineering zymology is dictated by the necessity to address the issues of monitoring enzymatic processes, treatment, and diagnosis of a number of common human diseases, environmental pollution, quality control of pharmaceuticals and food. Nanomaterials obtained by high-temperature carbonization of cheap plant raw material such as-rice husks, grape seeds and walnut shells, can serve as a basis for creating of highly effective microbial preparations-biodestructors, biosorbents and biocatalysts, which are promising for the use of contaminated ecosystems, and for restoration of human intestine microecology.
Extracellular acidification is a basic indicator for alterations in two vital metabolic pathways: glycolysis and cellular respiration. Measuring these alterations by monitoring extracellular acidification using cell-based biosensors such as LAPS plays an important role in studying these pathways whose disorders are associated with numerous diseases including cancer. However, the surface of the biosensors must be specially tailored to ensure high cell compatibility so that cells can represent more in vivo-like behavior, which is critical to gain more realistic in vitro results from the analyses, e.g., drug discovery experiments. In this work, O2 plasma patterning on the LAPS surface is studied to enhance surface features of the sensor chip, e.g., wettability and biofunctionality. The surface treated with O2 plasma for 30 s exhibits enhanced cytocompatibility for adherent CHO–K1 cells, which promotes cell spreading and proliferation. The plasma-modified LAPS chip is then integrated into a microfluidic system, which provides two identical channels to facilitate differential measurements of the extracellular acidification of CHO–K1 cells. To the best of our knowledge, it is the first time that extracellular acidification within microfluidic channels is quantitatively visualized as differential (bio-)chemical images.