Methodology to quantify the ratio of multiple-to single-charged fractions acquired in aerosol neutralizers

A methodology for the quantification of the ratio of multiple- to single-charged fractions acquired in aerosol neutralizers is presented. These quantities are necessary for an accurate monodisperse calibration of aerosol instrumentation. A tandem Differential Mobility Analyzer (DMA) setup is required, with the second DMA scanning the electrical mobility spectra classified in the first DMA. In contrast to previous studies on the quantification of bipolar charge distribution utilizing tandem DMA schemes, the methodology targets at the direct determination of the multiple- to single-charge fractions and does so through the analysis of the raw signal instead of the inverted size distributions, thus circumventing errors associated with the assumptions in the DMA data inversion. The proposed methodology is employed for the characterization of different types of aerosols commonly employed for instrument calibration. Spherical liquid particles (emery oil and dioctyl sebacate) were found to acquire lower multiple charge fractions than those suggested by the commonly employed regression fits of Wiedensohler, which was published in the year 1988 in the Journal of Aerosol Science (vol. 19, pp. 387–389), but still within the range of values reported in the literature. Diffusion flame soot and spark generated graphite particles, produced by a miniCAST 6203C burner and a PALAS DNP 3000, respectively, exhibited higher fraction of multiple charges, in good agreement with previous work on agglomerates. The use of a soft X-ray bipolar charger (TSI 3088) yielded systematically higher multiple fractions of positive charges compared to a ⁸⁵Kr neutralizer (TSI 3077A), confirming the importance of direct photoionization charging on the former.

Copyright © 2016 American Association for Aerosol Research     

Generation of spatio-temporal concentration profiles for cell culture systems: A case study in ammonia

Cellular behavior is influenced by micro-concentration gradients existing in their immediate spatio-environment. A practical real-time measurement system, composed of miniaturized long-lasting sensors placed within a two-dimensional bioreactor and a 32-channel data acquisition system for ammonia, is presented in this paper aiming to generate the spatio-temporal ammonia concentration profiles for the whole culture process. Our results demonstrate the feasibility of the system and its applicability in elucidating the cell growth micro-environment within a bioreactor, which can potentially facilitate the intelligent control of ammonia levels in cell culture systems. The presented system is flexible enough to interface other sensors for generating their spatio-temporal profiles of the sensed cell culture parameters.     

Long‐Aliphatic‐Segment Polyamides: Salt Preparation and Subsequent Anhydrous Polymerization

Long‐aliphatic‐segment polyamides were prepared based on hexamethylenediamine and α,ω‐(CH₂)_x biosynthetic diacids (x = 10, 11, 12). The pertinent monomers (salts) were isolated as solids, thoroughly characterized for the first time, and then submitted to an anhydrous melt prepolymerization technique. The obtained prepolymers exhibited in the range of 5 100–11 800 g · mol^?1, and the molecular weight was further increased by up to 55% through solid‐state finishing. The suggested overall polyamidation cycle was conducted at short melt‐reaction times, so as to avoid any thermal degradation, and was proved efficient, indicating similar reactants polymerizability independently of the methylene content.

     

Cloud-assisted body area networks: state-of-the-art and future challenges

Body area networks (BANs) are emerging as enabling technology for many human-centered application domains such as health-care, sport, fitness, wellness, ergonomics, emergency, safety, security, and sociality. A BAN, which basically consists of wireless wearable sensor nodes usually coordinated by a static or mobile device, is mainly exploited to monitor single assisted livings. Data generated by a BAN can be processed in real-time by the BAN coordinator and/or transmitted to a server-side for online/offline processing and long-term storing. A network of BANs worn by a community of people produces large amount of contextual data that require a scalable and efficient approach for elaboration and storage. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of body sensor data streams. In this paper, we motivate the introduction of Cloud-assisted BANs along with the main challenges that need to be addressed for their development and management. The current state-of-the-art is overviewed and framed according to the main requirements for effective Cloud-assisted BAN architectures. Finally, relevant open research issues in terms of efficiency, scalability, security, interoperability, prototyping, dynamic deployment and management, are discussed.     

An adaptive and efficient buffer management scheme for resource-constrained delay tolerant networks

Provisioning buffer management mechanism is especially crucial in resource-constrained delay tolerant networks (DTNs) as maximum data delivery ratio with minimum overhead is expected in highly congested environments. However, most DTN protocols do not consider resource limitations (e.g., buffer, bandwidth) and hence, results in performance degradation. To strangle and mitigate the impact of frequent buffer overflows, this paper presents an adaptive and efficient buffer management scheme called size-aware drop (SAD) that strives to improve buffer utilization and avoid unnecessary message drops. To improve data delivery ratio, SAD exactly determines the requirement based on differential of newly arrived message(s) and available space. To vacate inevitable space from a congested buffer, SAD strives to avoid redundant message drops and deliberate to pick and discard most appropriate message(s) to minimize overhead. The performance of SAD is validated through extensive simulations in realistic environments (i.e., resource-constrained and congested) with different mobility models (i.e., Random Waypoint and disaster). Simulation results demonstrate the performance supremacy of SAD in terms of delivery probability and overhead ratio besides other metrics when compared to contemporary schemes based on Epidemic (DOA and DLA) and PRoPHET (SHLI and MOFO).     

High resolution melting analysis for quantitative detection of bovine milk in pure water buffalo mozzarella and other buffalo dairy products

Identification of buffalo dairy products has become an important issue to ascertain product quality, consumer rights and absence of food-borne allergic reactions. A polymerase chain reaction (PCR) followed by a high resolution melting (HRM) analysis was developed and applied for species specific detection of bovine milk in nine different commercial buffalo dairy products. A specific buffalo 12S rRNA and a bovine d-loop primer pair, targeting the mitochondrial genome, were employed in a duplex PCR assay. The analysis developed was found capable of identifying the presence of bovine milk down to 1% in commercial buffalo milk products and also of quantifying the ratio of bovine into buffalo milk. HRM was proven to be a fast and accurate technique for a routine authentication testing of mozzarella and other buffalo milk products.     

Causal ordering of physical self-concept and exercise behavior: Reciprocal effects model and the influence of physical education teachers.

Does prior physical self-concept influence subsequent exercise behavior? On the basis of a large sample of physical education classes (2,786 students, 200 classes, 67 teachers) collected early (Time 1) and late (Time 2) in the school year, findings support a reciprocal effects model in which prior physical self-concept and exercise behavior both influence subsequent physical self-concept and exercise behavior. Whereas variables from the theory of planned behavior (TOPB; behavioral intentions, perceived behavioral control, exercise attitudes) also contributed to the prediction of subsequent exercise behavior, the effect of prior physical self-concept was significant for subsequent outcomes after controlling these variables, suggesting that the TOPB should be supplemented with self-concept measures. On the basis of multilevel models, there were systematic differences in these variables for students taught by different teachers that generalized over time and across different classes taught by the same teacher. Support for the reciprocal effects model was robust. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

RuleML representation and simulation of Fuzzy Cognitive Maps

Fuzzy Cognitive Map (FCM) technique is a combination of Fuzzy Logic and Artificial Neural Networks that is extensively used by experts and scientists of a diversity of disciplines, for strategic planning, decision making and predictions. A standardized representation of FCMs accompanied by a system that would assist decision makers to simulate their own developed Fuzzy Cognitive Maps would be highly appreciated by them, and would help the dissemination of FCMs. In this paper, (a) a RuleML representation of FCM is proposed and (b) a system is designed and implemented in Prolog programming language to assist experts to simulate their own FCMs. This system returns results in valid RuleML syntax, making them readily available to other cooperative systems. The representation capabilities and the design choices of the implemented system are discussed and a variety of examples are given to demonstrate the use of the system.