Multilayer Organic Multichip Module Implementing Hybrid Microelectromechanical Systems

We present the design and development of an organic package that is compatible with fully released RF microelectromechanical systems (MEMS). The multilayer organic package consists of a liquid-crystal polymer film to provide near hermetic cavities for MEMS. The stack is further built up using organic thin-film polyimide. To demonstrate the organic package, we have designed and implemented a 2-bit true-time delay X-band phase shifter using commercially available microelectromechanical switches. The packaged phase shifter has a measured insertion loss of 2.45 plusmn 0.12 dB/bit at 10 GHz. The worst case phase variation of the phase shifter at 10 GHz is measured to less than 5deg. We have also conducted temperature cycling (-65degC to 150degC) and 85/85 to qualify the packaging structures.     

Heteropolyanions doped polyimine—Preparation and spectroscopic properties

It is demonstrated that aromatic polyimine obtained by the condensation of p-phenylenediamine and terephtalaldehyde can be chemically doped with heteropolyanions of Keggin-type. The spectroscopic studies show that polyimine undergoes protonation. The structural identity of the Keggin units are preserved upon incorporation into the polymer matrix.     

Impact of Distance Measures on the Performance of AIS Data Clustering

Automatic Identification System (AIS) data stream analysis is based on the AIS data of different vessel’s behaviours, including the vessels’ routes. When the AIS data consists of outliers, noises, or are incomplete, then the analysis of the vessel’s behaviours is not possible or is limited. When the data consists of outliers, it is not possible to automatically assign the AIS data to a particular vessel. In this paper, a clustering method is proposed to support the AIS data analysis, to qualify noises and outliers with respect to their suitability, and finally to aid the reconstruction of the vessel’s trajectory. In this paper, clustering results have been obtained using selected algorithms, including k-means, k-medoids, and fuzzy c-means. Based on the clustering results, it is possible to decide on the qualification of data with outliers and on their usefulness in the reconstruction of the vessel trajectory. The main aim of this paper is to answer how different distance measures during a clustering process can influence AIS data clustering quality. The main core question is whether or not they have an impact on the process of reconstruction of the vessel trajectories when the data are damaged. The research question during the computational experiments asked whether or not distance measure influence AIS data clustering quality. The computational experiments have been carried out using original AIS data. In general, the experiment and the results confirm the usefulness of the cluster-based analysis when the data include outliers that are derived from the natural environment. It is also possible to monitor and to analyse AIS data using clustering when the data include outliers. The computational experiment results confirm that the k-means with Euclidean distance has the best performance.     

Prototype selection algorithms for distributed learning

Distributed learning from data is one of the typical tasks solved by distributed data-mining techniques and is seen as a fundamental computational problem. One of the approaches suitable for distributed learning is to select, by data reduction, relevant local patterns, called also prototypes, from geographically distributed databases. Next, locally selected prototypes can be moved to other sites and merged into the global knowledge model. The paper presents three agent-based population learning algorithms for distributed learning. The proposed algorithms are based on agent collaborations in distributed prototype selection processes and on agent collaborations when the learning global model is created. The basic property of the presented algorithms is that the prototypes are selected by agent-based population learning algorithm from data clusters induced at distributed sites. The main goal of the paper is to empirically compare how the way of inducing such clusters can influence the distributed learning performance. The paper investigates the agent-based population learning algorithms used to solve distributed data reduction and gives a brief discussion of the procedures for clusters initialization. Finally, computational experiment results are shown.     

Preliminary study on the accuracy of respiratory input impedance measurement using the interrupter technique

Respiratory input impedance contains information about the state of pulmonary mechanics in the frequency domain. In this paper the possibility of respiratory impedance measurement by interrupter technique as well as the accuracy of this approach are assessed. Transient states of flow and pressure recorded during expiratory flow interruption are simulated with a complex, linear model for the respiratory system and then used to calculate the impedance, including three states of respiratory mechanics and the influence of the measurement noise. The results of computations are compared to the known, theoretical impedance of the model. At 1 kHz sampling rate, the optimal time window lays between 100 and 200 ms and is centred around the pressure jump caused by the flow interruption. The proposed algorithm yields satisfactory accuracy in the range from 10 to 400 Hz, particularly to 150 Hz. Depending on the simulated respiratory system state, the error of calculated impedance (relative Euclidean distance between the vectors of computed and theoretical values), for the window of 190 ms, varies between 5.0% and 7.1%.     

Antiepileptic Stiripentol May Influence Bones

Bone structure abnormalities are increasingly observed in patients chronically treated with antiepileptic drugs (AEDs). The majority of the available data concern older conventional AEDs, while the amount of information regarding newer AEDs, including stiripentol, is limited. The aim of the study was to assess the effect of stiripentol on bones. For 24 weeks, male Wistar rats, received 0.9% sodium chloride (control group) or stiripentol (200 mg/kg/day) (STP group). In the 16th week of the study, we detected lower serum PINP levels in the STP group compared to the control group. In the 24th week, a statistically significant lower 1,25-dihydroxyvitamin D₃ level, higher inorganic phosphate level and higher neutrophil gelatinase-associated lipocalin (NGAL) levels in serum were found in the STP group compared to the control. Micro X-ray computed tomography of the tibias demonstrated lower bone volume fraction, lower trabecular thickness, higher trabecular pattern factor and a higher structure model index in the stiripentol group. Considering the results of this experiment on rats which suggests that long-term administration of stiripentol may impair the cancellous bone microarchitecture, further prospective human studies seem to be justified. However, monitoring plasma vitamin D, calcium, inorganic phosphate and kidney function in patients on long-term stiripentol therapy may be suggested.     

Conditions for Accurate CFD Modeling of Spray-Drying Process

The paper presents concluding results of extensive experimental and theoretical research on confident CFD modeling of spray drying. An earlier developed experimental method to determine spray-drying kinetics in a lab scale allowed us to find a critical material moisture content and to determine generalized spray-drying curves. The generalized drying curves, identical in shape in the laboratory and pilot plant units, were used in the CFD model of spray drying process. Extensive simulations for spray drying of 10, 30, and 50% of initial solid content of maltodextrin proved high accuracy of the predictions of discrete (particle size distribution, particle moisture content, particle velocity, spray temperature) and continuous-phase parameters (gas temperature and humidity). Maximum error of the predictions of discrete-phase parameters was on the level of 20%, which is probably close to the current capacity of the CFD technique for modeling of spray-drying process. Comparison of experimental measurements and theoretical results shows that incorporation of realistic spray-drying kinetics into the CFD model and correct definition of initial drying and atomization parameters enable reliable simulations of spray-drying process.     

Assessment of Cosmetic and Dermatological Properties and Safety of Use of Model Skin Tonics with Kombucha-Fermented Red Berry Extracts

Kombucha is a health-promoting beverage that is produced by fermenting sweetened tea using symbiotic cultures of bacteria belonging to the genus Acetobacter, Gluconobacter, and yeast of the genus Saccharomyces. This study compared the cosmetic and dermatological properties of the extracts of the following redberries: R. rubrum, F. vesca, and R. idaeus, and their ferments, which were obtained by fermentation for 10 and 20 days using tea fungus. For this purpose, the fermented and non-fermented extracts were compared in terms of their chemical composition using the HPLC/ESI-MS chromatographic method, demonstrating the high content of biologically active compounds that were present in the ferments. The antioxidant activity of the tested samples was evaluated using DPPH and ABTS tests, as well as by evaluating the scavenging of the external and intracellular free radicals. The cytotoxicity of the extracts and the ferments, as well as the cosmetic formulations, were also determined by conducting Alamar Blue and Neutral Red tests assessing the cell viability and metabolism using skin cell lines: fibroblasts and keratinocytes. In addition, application tests were conducted showing the positive effects of the model cosmetic tonics on the TEWL, the skin hydration, and the skin pH. The results indicate that both the extracts and the ferments that were obtained from kombucha can be valuable ingredients in cosmetic products.