Decentralized fault detection and diagnosis via sparse PCA based decomposition and Maximum Entropy decision fusion

This paper proposes an approach for decentralized fault detection and diagnosis in process monitoring sensor networks. The sensor network is decomposed into multiple, potentially overlapping, blocks using the Sparse Principal Component Analysis algorithm. Local predictions are generated at each block using Support Vector Machine classifiers. The local predictions are then fused via a Maximum Entropy algorithm. Empirical studies on the benchmark Tennessee Eastman Process data demonstrated that the proposed decentralized approach achieves accuracy comparable to that of the fully centralized approach, while offering benefits in terms of fault tolerance, reusability, and scalability.     

Spectroscopic evaluation of the colour play effect of thermochromic liquid crystal printing inks

Colour changes of a thermochromic liquid crystal (TLC) ink printed on a black substrate are clearly observed by the naked eye, and therefore it is reasonable to expect that every approach recommended for colour measurements of samples with solid colour would be appropriate. In addition, the published spectroscopic analyses of TLC inks do not provide any guidance or limitations for the measurement geometry. On the other hand, our study demonstrates that spectroscopic measurements of such samples are not so straightforward. When using the most simple and commonly used measurement equipment with a directional illumination and viewing approach, the temperature-dependent spectral features resolve in a narrow spectral region that gives only a small part of the colour play effect. Detection of the entire effect requires the use of integrating spheres, which spatially integrate the radiant flux reflected on a sample in any direction; better results are obtained by spheres of larger diameter. The comparision of spectroscopic and colorimetric results obtained when different measurement geometries are applied on the same sample may demonstrate certain properties of the liquid crystal material inside microcapsules and help in understanding some details of the optical properties of the ink layer.     

Investigations of ductile damage during the process chains of toothed functional components manufactured by sheet-bulk metal forming

Sheet-bulk metal forming processes combine conventional sheet forming processes with bulk forming of sheet semi-finished parts. In these processes the sheets undergo complex forming histories. Due to in- and out-of-plane material flow and large accumulated plastic strains, the conventional failure prediction methods for sheet metal forming such as forming limit curve fall short. As a remedy, damage models can be applied to model damage evolution during those processes. In this study, damage evolution during the production of two different toothed components from DC04 steel is investigated. In both setups, a deep drawn cup is upset to form a circumferential gearing. However, the two final products have different dimensions and forming histories. Due to combined deep drawing and upsetting processes, the material flow on the cup walls is three-dimensional and non-proportional. In this study, the numerical and experimental investigations for those parts are presented and compared. Damage evolution in the process chains is simulated with a Lemaitre damage criterion. Microstructural analysis by scanning electron microscopy is performed in the regions with high mechanical loading. It is observed that the evolution of voids in terms of void volume fraction is strongly dependent on the deformation path. The comparison of simulation results with microstructural data shows that the void volume fraction decreases in the upsetting stage after an initial increase in the drawing stage. Moreover, the concurrent numerical and microstructural analysis provides evidence that the void volume fraction decreases during compression in sheet-bulk metal forming.     

Fertigung einer universellen Hüftprothesenpfanne mittels Hochdruckblechumformung

In recent years, the use of hip prostheses has become a routine procedure. The use of total hip replacement has evolved in recent years to a routine procedure. Despite this experience, it always comes back to complications. Especially the migration or loosening of the acetabular component because of the artificial load adaptive bone remodeling is still a current problem. This is due to the changing mechanical situation after the implantation of the prosthesis. Another problem is the high bone loss during implantation, which complicates a revision of the prosthesis. One solution is the use of patient‐specific prostheses. So far, the use of these prostheses is limited due to the time‐consuming and cost‐intensive production. The overall objective of the presented project in this publication is the development and establishment of an innovative concept for the production of patient‐specific hip prosthesis made of titanium plates by sheet metal forming. The idea of this concept involves the development of a two‐stage process. First of all a standardized hip prosthesis is made by high‐pressure sheet metal forming and then individually in the second step. This publication contains the derivation of the standard prosthesis. For this purpose, a design methodology was generated, based on the so‐called agglomerative clustering. In addition, presents the results of the numerical preliminary design of the first stage of the process and the tool developed concept in the course of this paper.     

New and Emerging Targeted Therapies for Hidradenitis Suppurativa

Hidradenitis suppurativa (HS) is a chronic, recurrent, inflammatory skin disease deriving from the hair follicles. The formation of inflammatory nodules, abscesses, fistulas, and sinus tracts is characterized by a large inflow of key pro-inflammatory mediators, such as IFN-γ, TNF-α, IL-1, IL-17, and IL-12/23. Adalimumab is currently the only Food and Drug Administration (FDA)- and European Medicines Agency (EMA)-approved biologic therapy for moderate to severe HS in adults and adolescents. However, the long-term effectiveness of this TNF-α inhibitor in HS patients has shown to be highly variable. This review aims to review the evidence for emerging therapies that target the main pro-inflammatory cytokines in HS pathogenesis. A review of the literature was conducted, using the PubMed and Google Scholar repositories, as well as Clinicaltrials.gov. Presently, the most promising biologics in phase III trials are anti-IL-17 antibodies, secukinumab, and bimekizumab. Furthermore, an anti-IL-1 biologic, bermekimab, is currently in phase II trials, and shows encouraging results. Overall, the clinical efficacies of all new targeted therapies published up to this point are limited. More studies need to be performed to clarify the precise molecular pathology, and assess the efficacy of biological therapies for HS.     

Assessment of potential for natural gas-based cogeneration in Thailand

Using the results of a comprehensive data analysis of final energy consumption in industry and commercial buildings, the assessment has been made of the potential for gradual implementation of cogeneration plants in these facilities. In doing so, plans for the expansion of the natural gas pipeline distribution network in Thailand are taken into consideration. The sample comprises of 2540 factories and 1651 commercial buildings from which it was found that 817 factories and 966 commercial buildings were suitable for the implementation of natural gas-based cogeneration technologies until 2020. By the implementation of cogeneration in these facilities, it is possible to save 3.2% of the total primary energy consumption in Thailand in 2003.     

Enzymatischer Nachweis von Carbamat-Pesticiden auf impr?gnierten Cellulose-Platten

Zusammenfassung Ein Nachweis für Carbamat-Pesticide mittels Enzym-Inhibition auf imprägnierten Cellulose-Schichten wurde ausgearbeitet. Bei Celluloseschichten, die mit Äthylglykol, Triäthylglykol und,-Ogydipropionitril imprägniert waren, erwies sich die Kombination Schweineleberesterase/Indophenylacetat (als chromogenes Substrat) als besonders empfindlicher Nachweis. Die Nachweisempfindlichkeit wurde in Abhängigkeit von der Art des Enzympräparates, des Substrates, des pH, der Inhibitionszeit und -temperatur, der Schichtdicke und des Impriignierungsmittels untersucht.

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Enzymatic detection of carbamate pesticides on impregnated cellulose plates

Summary The detection of carbamate pesticides by an enzyme-inhibition method on impregnated (ethylene glycol, triethylene glycol,,-ogydipropionitrile) cellulose thin layer plates is described. The enzyme-substrate combination: pig liver esterase-indophenylacetate (chromogenic substrate) was the most sensitive. The following parameters concerning the sensitivity of the method were investigated: enzyme source, substrate, pH, inhibition time, inhibition temperature, thickness of the cellulose layer, stationary phase.

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Herrn o.Univ.-Prof. Dr. O. Hoffmann-Ostenhof, Vorstand des Instituts für Allgemeine Biochemie, Universität Wien, zum 60. Geburtstag gewidmet.

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Wir danken dem Fonds zur Förderung der wissenschaftlichen Forschung, der uns die Durchführung dieser Arbeit (Projekt Nr. 2161) ermöglichte.

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Ing. B. Rapié dankt der SEA-Stiftung (Scientific Exchange Agreement) für die Gewährung eines Stipendiums.     

Piroxicam benzoate--synthesis,HPLC determination,and hydrolysis

An improved method of piroxicam benzoate synthesis was described, and an isocratic reversed-phase high-performance liquid chromatography method for its determination was developed and fully validated. The method was found to be specific, precise (relative standard deviation 0.3%), accurate (mean recovery 99.9%), and robust. Limit of detection was estimated at 0.055 µg mL^-1 and limit of quantification at 0.185 µg mL^-1. The kinetics of piroxicam benzoate hydrolysis in aqueous buffer solutions (pH 1.1 and 10), simulated gastric and intestinal fluids was studied. The hydrolysis followed first-order kinetics. The following rate constants were obtained at pH 10: k = 1.8 × 10^-3 hr^-1 at 37°C and k = 3.4 × 10^-2 hr^-1 at 60°C. In acidic media, no significant hydrolysis was observed after 24 hr. During the 24-hr period in simulated intestinal fluid, only 10.9% of the starting ester was hydrolyzed.     

Supervised clustering of label ranking data using label preference information

This paper studies supervised clustering in the context of label ranking data. The goal is to partition the feature space into K clusters, such that they are compact in both the feature and label ranking space. This type of clustering has many potential applications. For example, in target marketing we might want to come up with K different offers or marketing strategies for our target audience. Thus, we aim at clustering the customers’ feature space into K clusters by leveraging the revealed or stated, potentially incomplete customer preferences over products, such that the preferences of customers within one cluster are more similar to each other than to those of customers in other clusters. We establish several baseline algorithms and propose two principled algorithms for supervised clustering. In the first baseline, the clusters are created in an unsupervised manner, followed by assigning a representative label ranking to each cluster. In the second baseline, the label ranking space is clustered first, followed by partitioning the feature space based on the central rankings. In the third baseline, clustering is applied on a new feature space consisting of both features and label rankings, followed by mapping back to the original feature and ranking space. The RankTree principled approach is based on a Ranking Tree algorithm previously proposed for label ranking prediction. Our modification starts with K random label rankings and iteratively splits the feature space to minimize the ranking loss, followed by re-calculation of the K rankings based on cluster assignments. The MM-PL approach is a multi-prototype supervised clustering algorithm based on the Plackett-Luce (PL) probabilistic ranking model. It represents each cluster with a union of Voronoi cells that are defined by a set of prototypes, and assign each cluster with a set of PL label scores that determine the cluster central ranking. Cluster membership and ranking prediction for a new instance are determined by cluster membership of its nearest prototype. The unknown cluster PL parameters and prototype positions are learned by minimizing the ranking loss, based on two variants of the expectation-maximization algorithm. Evaluation of the proposed algorithms was conducted on synthetic and real-life label ranking data by considering several measures of cluster goodness: (1) cluster compactness in feature space, (2) cluster compactness in label ranking space and (3) label ranking prediction loss. Experimental results demonstrate that the proposed MM-PL and RankTree models are superior to the baseline models. Further, MM-PL is has shown to be much better than other algorithms at handling situations with significant fraction of missing label preferences.