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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.
The integration of frequently changing, volatile product data from different manufacturers into a single catalog is a significant challenge for small and medium-sized e-commerce companies. They rely on timely integrating product data to present them aggregated in an online shop without knowing format specifications, concept understanding of manufacturers, and data quality. Furthermore, format, concepts, and data quality may change at any time. Consequently, integrating product catalogs into a single standardized catalog is often a laborious manual task. Current strategies to streamline or automate catalog integration use techniques based on machine learning, word vectorization, or semantic similarity. However, most approaches struggle with low-quality or real-world data. We propose Attribute Label Ranking (ALR) as a recommendation engine to simplify the integration process of previously unknown, proprietary tabular format into a standardized catalog for practitioners. We evaluate ALR by focusing on the impact of different neural network architectures, language features, and semantic similarity. Additionally, we consider metrics for industrial application and present the impact of ALR in production and its limitations.
In this paper, carbon nanotubes (CNTs) were incorporated in penicillinase-phospholipid Langmuir and Langmuir–Blodgett (LB) films to enhance the enzyme catalytic properties. Adsorption of the penicillinase and CNTs at dimyristoylphosphatidic acid (DMPA) monolayers at the air–water interface was investigated by surface pressure–area isotherms, vibrational spectroscopy, and Brewster angle microscopy. The floating monolayers were transferred to solid supports through the LB technique, forming mixed DMPA-CNTs-PEN films, which were investigated by quartz crystal microbalance, vibrational spectroscopy, and atomic force microscopy. Enzyme activity was studied with UV–vis spectroscopy and the feasibility of the supramolecular device nanostructured as ultrathin films were essayed in a capacitive electrolyte–insulator–semiconductor (EIS) sensor device. The presence of CNTs in the enzyme–lipid LB film not only tuned the catalytic activity of penicillinase but also helped conserve its enzyme activity after weeks, showing increased values of activity. Viability as penicillin sensor was demonstrated with capacitance/voltage and constant capacitance measurements, exhibiting regular and distinctive output signals over all concentrations used in this work. These results may be related not only to the nanostructured system provided by the film, but also to the synergism between the compounds on the active layer, leading to a surface morphology that allowed a fast analyte diffusion because of an adequate molecular accommodation, which also preserved the penicillinase activity. This work therefore demonstrates the feasibility of employing LB films composed of lipids, CNTs, and enzymes as EIS devices for biosensing applications.
Algal polysaccharides (extracellular polysaccharides) and carbon nanotubes (CNTs) were adsorbed on dioctadecyldimethylammonium bromide Langmuir monolayers to serve as a matrix for the incorporation of urease. The physicochemical properties of the supramolecular system as a monolayer at the air–water interface were investigated by surface pressure–area isotherms, surface potential–area isotherms, interfacial shear rheology, vibrational spectroscopy, and Brewster angle microscopy. The floating monolayers were transferred to hydrophilic solid supports, quartz, mica, or capacitive electrolyte–insulator–semiconductor (EIS) devices, through the Langmuir–Blodgett (LB) technique, forming mixed films, which were investigated by quartz crystal microbalance, fluorescence spectroscopy, and field emission gun scanning electron microscopy. The enzyme activity was studied with UV–vis spectroscopy, and the feasibility of the thin film as a urea sensor was essayed in an EIS sensor device. The presence of CNT in the enzyme–lipid LB film not only tuned the catalytic activity of urease but also helped to conserve its enzyme activity. Viability as a urease sensor was demonstrated with capacitance–voltage and constant capacitance measurements, exhibiting regular and distinctive output signals over all concentrations used in this work. These results are related to the synergism between the compounds on the active layer, leading to a surface morphology that allowed fast analyte diffusion owing to an adequate molecular accommodation, which also preserved the urease activity. This work demonstrates the feasibility of employing LB films composed of lipids, CNT, algal polysaccharides, and enzymes as EIS devices for biosensing applications.
Electrolyte-insulator-semiconductor capacitors (EISCAP) belong to field-effect sensors having an attractive transducer architecture for constructing various biochemical sensors. In this study, a capacitive model of enzyme-modified EISCAPs has been developed and the impact of the surface coverage of immobilized enzymes on its capacitance-voltage and constant-capacitance characteristics was studied theoretically and experimentally. The used multicell arrangement enables a multiplexed electrochemical characterization of up to sixteen EISCAPs. Different enzyme coverages have been achieved by means of parallel electrical connection of bare and enzyme-covered single EISCAPs in diverse combinations. As predicted by the model, with increasing the enzyme coverage, both the shift of capacitance-voltage curves and the amplitude of the constant-capacitance signal increase, resulting in an enhancement of analyte sensitivity of the EISCAP biosensor. In addition, the capability of the multicell arrangement with multi-enzyme covered EISCAPs for sequentially detecting multianalytes (penicillin and urea) utilizing the enzymes penicillinase and urease has been experimentally demonstrated and discussed.
An acetoin biosensor based on a capacitive electrolyte–insulator–semiconductor (EIS) structure modified with the enzyme acetoin reductase, also known as butane-2,3-diol dehydrogenase (Bacillus clausii DSM 8716ᵀ), is applied for acetoin detection in beer, red wine, and fermentation broth samples for the first time. The EIS sensor consists of an Al/p-Si/SiO₂/Ta₂O₅ layer structure with immobilized acetoin reductase on top of the Ta₂O₅ transducer layer by means of crosslinking via glutaraldehyde. The unmodified and enzyme-modified sensors are electrochemically characterized by means of leakage current, capacitance–voltage, and constant capacitance methods, respectively.
Electrolyte-insulator-semiconductor (EIS) field-effect sensors belong to a new generation of electronic chips for biochemical sensing, enabling a direct electronic readout. The review gives an overview on recent advances and current trends in the research and development of chemical sensors and biosensors based on the capacitive field-effect EIS structure—the simplest field-effect device, which represents a biochemically sensitive capacitor. Fundamental concepts, physicochemical phenomena underlying the transduction mechanism and application of capacitive EIS sensors for the detection of pH, ion concentrations, and enzymatic reactions, as well as the label-free detection of charged molecules (nucleic acids, proteins, and polyelectrolytes) and nanoparticles, are presented and discussed.
Plant virus-like particles, and in particular, tobacco mosaic virus (TMV) particles, are increasingly being used in nano- and biotechnology as well as for biochemical sensing purposes as nanoscaffolds for the high-density immobilization of receptor molecules. The sensitive parameters of TMV-assisted biosensors depend, among others, on the density of adsorbed TMV particles on the sensor surface, which is affected by both the adsorption conditions and surface properties of the sensor. In this work, Ta₂O₅-gate field-effect capacitive sensors have been applied for the label-free electrical detection of TMV adsorption. The impact of the TMV concentration on both the sensor signal and the density of TMV particles adsorbed onto the Ta₂O₅-gate surface has been studied systematically by means of field-effect and scanning electron microscopy methods. In addition, the surface density of TMV particles loaded under different incubation times has been investigated. Finally, the field-effect sensor also demonstrates the label-free detection of penicillinase immobilization as model bioreceptor on TMV particles.
Capacitive field-effect electrolyte-diamond-insulator-semiconductor (EDIS) structures with O-terminated nanocrystalline diamond (NCD) as sensitive gate material have been realized and investigated for the detection of pH, penicillin concentration, and layer-by-layer adsorption of polyelectrolytes. The surface oxidizing procedure of NCD thin films as well as the seeding and NCD growth process on a Si-SiO2 substrate have been improved to provide high pH-sensitive, non-porous thin films without damage of the underlying SiO2 layer and with a high coverage of O-terminated sites. The NCD surface topography, roughness, and coverage of the surface groups have been characterized by SEM, AFM and XPS methods. The EDIS sensors with O-terminated NCD film treated in oxidizing boiling mixture for 45 min show a pH sensitivity of about 50 mV/pH. The pH-sensitive properties of the NCD have been used to develop an EDIS-based penicillin biosensor with high sensitivity (65-70 mV/decade in the concentration range of 0.25-2.5 mM penicillin G) and low detection limit (5 μM). The results of label-free electrical detection of layer-by-layer adsorption of charged polyelectrolytes are presented, too.
A novel strategy for enhanced field-effect biosensing using capacitive electrolyte–insulator–semiconductor (EIS) structures functionalised with pH-responsive weak polyelectrolyte/enzyme or dendrimer/enzyme multilayers is presented. The feasibility of the proposed approach is exemplarily demonstrated by realising a penicillin biosensor based on a capacitive p-Si–SiO2 EIS structure functionalised with a poly(allylamine hydrochloride) (PAH)/penicillinase and a poly(amidoamine) dendrimer/penicillinase multilayer. The developed sensors response to changes in both the local pH value near the gate surface and the charge of macromolecules induced via enzymatic reaction, resulting in a higher sensitivity. For comparison, an EIS penicillin biosensor with adsorptively immobilised penicillinase has been also studied. The highest penicillin sensitivity of 100 mV/dec has been observed for the EIS sensor functionalised with the PAH/penicillinase multilayer. The lower and upper detection limit was around 20 µM and 10 mM, respectively. In addition, an incorporation of enzymes in a multilayer prepared by layer-by-layer technique provides a larger amount of immobilised enzymes per sensor area, reduces enzyme leaching effects and thus, enhances the biosensor lifetime (the loss of penicillin sensitivity after 2 months was 10–12%).
Can vascular function be assessed by the interpretation of retinal vascular diameter changes?
(2011)
To better understand what kinds of sports and exercise could be beneficial for the intervertebral disc (IVD), we performed a review to synthesise the literature on IVD adaptation with loading and exercise. The state of the literature did not permit a systematic review; therefore, we performed a narrative review. The majority of the available data come from cell or whole-disc loading models and animal exercise models. However, some studies have examined the impact of specific sports on IVD degeneration in humans and acute exercise on disc size. Based on the data available in the literature, loading types that are likely beneficial to the IVD are dynamic, axial, at slow to moderate movement speeds, and of a magnitude experienced in walking and jogging. Static loading, torsional loading, flexion with compression, rapid loading, high-impact loading and explosive tasks are likely detrimental for the IVD. Reduced physical activity and disuse appear to be detrimental for the IVD. We also consider the impact of genetics and the likelihood of a ‘critical period’ for the effect of exercise in IVD development. The current review summarises the literature to increase awareness amongst exercise, rehabilitation and ergonomic professionals regarding IVD health and provides recommendations on future directions in research.
Using the OpenSim software and verified anatomical data, a computer model for the calculation of biomechanical parameters is developed and used to determine the effect of a reattachment of the Supraspinatus muscle with a medial displacement of the muscle attachment point, which may be necessary for a rupture of the supraspinatus tendon. The results include the influence of the operation on basic biomechanical parameters such as the lever arm, as well as the calculated the muscle activations for the supraspinatus and deltoid. In addition, the influence on joint stability is examined by an analysis of the joint reaction force. The study provides a detailed description of the used model, as well as medical findings to a reattachment of the supraspinatus.
Mit der Software OpenSim und überprüften anatomischen Daten wird ein Computermodell zur Berechnung von biomechanischen Parametern entwickelt und genutzt, um den Effekt einer Refixierung des Supraspinatusmuskels mit einer medialen Verschiebung des Muskelansatzpunktes zu ermitteln, wie sie unter anderem nach einem Riss der Supraspinatussehne notwendig sein kann. Die Ergebnisse umfassen hierbei den Einfluss der Operation auf grundlegende biomechanische Parameter wie den Hebelarm sowie die berechneten Muskelaktivierungen für den Supraspinatus und Deltoideus. Zusätzlich wird der Einfluss auf die Gelenkstabilität betrachtet und durch eine Analyse der Gelenkreaktionskraft untersucht. Die Studie bietet eine detaillierte Beschreibung des genutzten Modells, sowie medizinische Erkenntnisse zu einer Refixierung des Supraspinatus.
Proceedings of the International Conference on Material Theory and Nonlinear Dynamics. MatDyn. Hanoi, Vietnam, Sept. 24-26, 2007, 8 p. In this paper, a method is introduced to determine the limit load of general shells using the finite element method. The method is based on an upper bound limit and shakedown analysis with elastic-perfectly plastic material model. A non-linear constrained optimisation problem is solved by using Newton’s method in conjunction with a penalty method and the Lagrangean dual method. Numerical investigation of a pipe bend subjected to bending moments proves the effectiveness of the algorithm.
Es wird ein Brennstoffeinspritzsystem für Brennkraftmaschinen vorgeschlagen, bei dem ein definiertes Teil eines fest vorgegebenen Brennstoffvolumens durch die Druckerhöhung innerhalb einer brennstoffeinschließenden Kammer eine definierte Brennstoffmenge über jeweils ein Rückschlagventil abgespritzt wird. Ein elektronisches Steuergerät berechnet die Dauer und/oder Intensität und gegebenenfalls die Impulsfolge eines zum Beispiel zur Verdampfung einer bestimmten Brennstoffmenge erforderlichen Laserimpulses. Das System kann für Ein- oder Mehrstelleinspritzung Verwendung finden.
This book provides a compact introduction to the bootstrap method. In addition to classical results on point estimation and test theory, multivariate linear regression models and generalized linear models are covered in detail. Special attention is given to the use of bootstrap procedures to perform goodness-of-fit tests to validate model or distributional assumptions. In some cases, new methods are presented here for the first time.
The text is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form. Overall, R is given great importance throughout. Each chapter includes a section of exercises and, for the more mathematically inclined readers, concludes with rigorous proofs. The intended audience is graduate students who already have a prior knowledge of probability theory and mathematical statistics.
We consider a binary multivariate regression model where the conditional expectation of a binary variable given a higher-dimensional input variable belongs to a parametric family. Based on this, we introduce a model-based bootstrap (MBB) for higher-dimensional input variables. This test can be used to check whether a sequence of independent and identically distributed observations belongs to such a parametric family. The approach is based on the empirical residual process introduced by Stute (Ann Statist 25:613–641, 1997). In contrast to Stute and Zhu’s approach (2002) Stute & Zhu (Scandinavian J Statist 29:535–545, 2002), a transformation is not required. Thus, any problems associated with non-parametric regression estimation are avoided. As a result, the MBB method is much easier for users to implement. To illustrate the power of the MBB based tests, a small simulation study is performed. Compared to the approach of Stute & Zhu (Scandinavian J Statist 29:535–545, 2002), the simulations indicate a slightly improved power of the MBB based method. Finally, both methods are applied to a real data set.
It was generally believed that coal sources are not favorable as live-in habitats for microorganisms due to their recalcitrant chemical nature and negligible decomposition. However, accumulating evidence has revealed the presence of diverse microbial groups in coal environments and their significant metabolic role in coal biogeochemical dynamics and ecosystem functioning. The high oxygen content, organic fractions, and lignin-like structures of lower-rank coals may provide effective means for microbial attack, still representing a greatly unexplored frontier in microbiology. Coal degradation/conversion technology by native bacterial and fungal species has great potential in agricultural development, chemical industry production, and environmental rehabilitation. Furthermore, native microalgal species can offer a sustainable energy source and an excellent bioremediation strategy applicable to coal spill/seam waters. Additionally, the measures of the fate of the microbial community would serve as an indicator of restoration progress on post-coal-mining sites. This review puts forward a comprehensive vision of coal biodegradation and bioprocessing by microorganisms native to coal environments for determining their biotechnological potential and possible applications.
The quest for scientifically advanced and sustainable solutions is driven by growing environmental and economic issues associated with coal mining, processing, and utilization. Consequently, within the coal industry, there is a growing recognition of the potential of microbial applications in fostering innovative technologies. Microbial-based coal solubilization, coal beneficiation, and coal dust suppression are green alternatives to traditional thermochemical and leaching technologies and better meet the need for ecologically sound and economically viable choices. Surfactant-mediated approaches have emerged as powerful tools for modeling, simulation, and optimization of coal-microbial systems and continue to gain prominence in clean coal fuel production, particularly in microbiological co-processing, conversion, and beneficiation. Surfactants (surface-active agents) are amphiphilic compounds that can reduce surface tension and enhance the solubility of hydrophobic molecules. A wide range of surfactant properties can be achieved by either directly influencing microbial growth factors, stimulants, and substrates or indirectly serving as frothers, collectors, and modifiers in the processing and utilization of coal. This review highlights the significant biotechnological potential of surfactants by providing a thorough overview of their involvement in coal biodegradation, bioprocessing, and biobeneficiation, acknowledging their importance as crucial steps in coal consumption.
This paper reports a first microbial biosensor for rapid and cost-effective determination of organophosphorus pesticides fenitrothion and EPN. The biosensor consisted of recombinant PNP-degrading/oxidizing bacteria Pseudomonas putida JS444 anchoring and displaying organophosphorus hydrolase (OPH) on its cell surface as biological sensing element and a dissolved oxygen electrode as the transducer. Surfaceexpressed OPH catalyzed the hydrolysis of fenitrothion and EPN to release 3-methyl-4-nitrophenol and p-nitrophenol, respectively, which were oxidized by the enzymatic machinery of Pseudomonas putida JS444 to carbon dioxide while consuming oxygen, which was measured and correlated to the concentration of organophosphates. Under the optimum operating conditions, the biosensor was able to measure as low as 277 ppb of fenitrothion and 1.6 ppm of EPN without interference from phenolic compounds and other commonly used pesticides such as carbamate pesticides, triazine herbicides and organophosphate pesticides without nitrophenyl substituent. The applicability of the biosensor to lake water was also demonstrated.