Endometriosis detection and localization in laparoscopic gynecology

Multimedia Tools and Applications - Endometriosis is a common gynecologic condition typically treated via laparoscopic surgery. Its visual versatility makes it hard to identify for non-specialized...     

Bewertung alternativer Arbeitsmittel für Wärmepumpen im Gebäudesektor

Forschung im Ingenieurwesen - Das Arbeitsmittel in Wärmepumpen unterliegt einer Vielzahl von Anforderungen, welche für einen effizienten und sicheren Betrieb eingehalten werden...     

Organofluorine Hydrazone Derivatives as Multifunctional Anti-Alzheimer's Agents with CK2 Inhibitory and Antioxidant Features

A set of novel hydrazone derivatives were synthesized and analyzed for their biological activities. The compounds were tested for their inhibitory effect on the phosphorylating activity of the protein kinase CK2, and their antioxidant activity was also determined in three commonly used assays. The hydrazones were evaluated for their radical scavenging against the DPPH, ABTS and peroxyl radicals. Several compounds have been identified as good antioxidants as well as potent protein kinase CK2 inhibitors. Most hydrazones containing a 4-N(CH₃)₂ residue or perfluorinated phenyl rings showed high activity in the radical-scavenging assays and possess nanomolar IC₅₀ values in the kinase assays.     

Superradiant Emission from Coherent Excitons in van Der Waals Heterostructures

Recent advancements in isolation and stacking of layered van der Waals materials have created an unprecedented paradigm for demonstrating varieties of 2D quantum materials. Rationally designed van der Waals heterostructures composed of monolayer transition-metal dichalcogenides (TMDs) and few-layer hBN show several unique optoelectronic features driven by correlations. However, entangled superradiant excitonic species in such systems have not been observed before. In this report, it is demonstrated that strong suppression of phonon population at low temperature results in a formation of a coherent excitonic-dipoles ensemble in the heterostructure, and the collective oscillation of those dipoles stimulates a robust phase synchronized ultra-narrow band superradiant emission even at extremely low pumping intensity. Such emitters are in high demand for a multitude of applications, including fundamental research on many-body correlations and other state-of-the-art technologies. This timely demonstration paves the way for further exploration of ultralow-threshold quantum-emitting devices with unmatched design freedom and spectral tunability.     

Feature selection methods for root-cause analysis among top-level product attributes

Manufacturing companies not only strive to deliver flawless products but also monitor product failures in the field to identify potential quality issues. When product failures occur, quality engineers must identify the root cause to improve any affected product and process. This root-cause analysis can be supported by feature selection methods that identify relevant product attributes, such as manufacturing dates with an increased number of product failures. In this paper, we present different methods for feature selection and evaluate their ability to identify relevant product attributes in a root-cause analysis. First, we compile a list of feature selection methods. Then, we summarize the properties of product attributes in warranty case data and discuss these properties regarding the challenges they pose for machine learning algorithms. Next, we simulate datasets of warranty cases, which emulate these product properties. Finally, we compare the feature selection methods based on these simulated datasets. In the end, the univariate filter information gain is determined to be a suitable method for a wide range of applications. The comparison based on simulated data provides a more general result than other publications, which only focus on a single use case. Due to the generic nature of the simulated datasets, the results can be applied to various root-cause analysis processes in different quality management applications and provide a guideline for readers who wish to explore machine learning methods for their analysis of quality data.     

Effects of feed composition,protein denaturation and storage of milk serum protein/lactose powders on lactosylation

During whey powder production, the feed is subjected to several heat treatments which can cause lactosylation of proteins. In this study, lactosylation of whey proteins was evaluated in spray-dried powders before and after storage by varying the native protein fraction as well as the serum protein/lactose ratio in the powders. The lactosylation of native α-lactalbumin and β-lactoglobulin in the powders before storage was not affected to a large extent by the protein denaturation or if the feed had been heat treated in a high or low lactose environment. After storage (relative humidity of 23.5%, 30 °C, 25 days), the kinetic of lactosylation tended to increase with increasing native protein fraction and bulk protein content in the powders. An explanation could be that proteins dissolved in the lactose glassy structure might have a lower reactivity, while proteins present in the protein glassy structure with dissolved lactose may display higher lactosylation reactivity.     

Effects of upholstery materials on the burning behavior of real‐scale residential upholstered furniture mock‐ups

Fire spread and growth on real‐scale four cushion mock‐ups of residential upholstered furniture (RUF) were investigated with the goal of identifying whether changes in five classes of materials (barrier, flexible polyurethane foam, polyester fiber wrap, upholstery fabric, and sewing thread), referred to as factors, resulted in statistically significant changes in burning behavior. A fractional factorial experimental design plus practical considerations yielded a test matrix with 20 material combinations. Experiments were repeated a minimum of two times. Measurements included fire spread rates derived from video recordings and heat release rates (HRRs). A total of 13 experimental parameters (3 based on the videos and 10 on the HRR results), referred to as responses, characterized the measurements. Statistical analyses based on Main Effects Plots (main effects) and Block Plots (main effects and factor interactions) were used. The results showed that three of the factors resulted in statistically significant effects on varying numbers of the 13 responses. The Barrier and Fabric factors had the strongest main effects with roughly comparable magnitudes. Foam was statistically significant for fewer of the responses and its overall strength was weaker than for Barrier and Fabric. No statistically significant main effects were identified for Wrap or Thread. Multiple two‐term interactions between factors were identified as being statistically significant. The Barrier*Fabric interaction resulted in the highest number of and strongest statistically significant effects. The existence of two‐term interactions means that it will be necessary to consider their effects in approaches designed to predict the burning behavior of RUF.     

Development and characterisation of a Y2Ti2O7-based glass-ceramic as a potential oxidation protective coating for titanium suboxide (TiOx)

A silica-based glass-ceramic, with Y₂Ti₂O₇ as the major crystalline phase, is designed, characterised and tested as an oxidation-protective coating for a titanium suboxide (TiO_x) thermoelectric material at temperatures of up to 600 °C. The optimised sinter-crystallisation treatment temperatures are found to be 1300 °C and 855 °C for a duration of 30 min, and this treatment leads to a glass-ceramic with cubic Y₂Ti₂O₇ and CaAl₂Si₂O₈ as crystalline phases. An increase of ~270 °C in the dilatometric softening temperature is observed after devitrification of the parent glass, thus further extending its working temperature range.Excellent adhesion of the glass-ceramic coating to the thermoelectric material is maintained after exposure to a temperature of 600 °C for 120 h under oxidising conditions, thus confirming the effectiveness of the T1 glass-ceramic in protecting the TiO_x material.     

Prediction of Flooding in Packed Liquid-Liquid and High-Pressure Extraction Columns Using a Gaussian Process

Reliable prediction of flooding conditions is needed for sizing and operating packed extraction columns. Due to the complex interplay of physicochemical properties, operational parameters and the packing-specific properties, it is challenging to develop accurate semi-empirical or rigorous models with a high validity range. State of the art models may therefore fail to predict flooding accurately. To overcome this problem, a data-driven model based on Gaussian processes is developed to predict flooding for packed liquid-liquid and high-pressure extraction columns. The optimized Gaussian process for the liquid-liquid extraction column results in an average absolute relative error (AARE) of 15.23 %, whereas the algorithm for the high-pressure extraction column results in an AARE of 13.68 %. Both algorithms can predict flooding curves for different packing geometries and chemical systems precisely.