Synthesis and characterization of MgAl2O4 spinel precursor sol prepared by inorganic salts

Magnesium aluminate (MgAl₂O₄) spinel precursor sol was prepared using aluminum nitrate and magnesium nitrate as the precursors for alumina and magnesia, respectively. The obtained sol owned a translucent, homogenous, and stable appearance with a density of 1.19 g/cm³, and at the temperatures above 60 °C, converted to a soft and clear gel. The measured pH of sol was in the range of 3–4, and at the calcination temperature of 1000 °C, the solid content of 6% was reached. TGA/DSC analysis was utilized to study the thermal behavior of the sol. Fourier transforms infrared spectroscopy (FTIR) was applied to recognize the existing bounds in the dried and calcined sol. X-ray diffraction patterns revealed the formation of single-phase MgAl₂O₄ up to 600 °C. According to the FESEM images, the grain size for the sol calcined at 1000 °C was estimated at around 50 nm.     

Using a recurrent artificial neural network for dynamic self-adaptation of cluster-based web-server systems

To process huge requests issued from web users, web servers often set up a cluster using switches and gateways where a switch directs users’ requests to some gateway. Each gateway, which is connected to some servers, is considered for processing a specific type of request such as fttp or http service. When servers of a gateway are saturated and the gateway is not able to process more requests, adaptation is performed by borrowing a server from another gateway. However, such a reactive adaptation causes some problems. However, due to problem of the reactive techniques, predictive ones have been paid attention. While a reactive adaptation aims to redress the system after incurring a bottleneck, a predictive adaptation tries to prevent the system from entering the bottleneck. In this article, we improved our previous predictive framework using a Recurrent Artificial Neural Network (RANN) called Nonlinear Autoregressive with eXogenous (external) inputs (NARX). We employed our new framework for adaptation of a web-based cluster where each cluster is meant for a specific service and self-adaptation is used for load balancing clusters. To show the improvement, we used the case study presented in our previous study.     

Design and analysis of a novel MEMS spiral inductor with high quality factor

Microsystem Technologies - In this paper a novel spiral inductor with high quality (Q)-factor is presented. Non-uniform current density distribution, especially in inner turn, increases the...     

Enhanced antibacterial effect of azlocillin in conjugation with silver nanoparticles against Pseudomonas aeruginosa

In recent years, the problems associated with bacterial resistance to antibiotics caused nanodrugs to be considered as a new way for infectious diseases treatment. The main purpose of this study was to develop a new agent against Pseudomonas aeruginosa, a very difficult bacterium to treat, based on azlocillin antibiotic and silver nanoparticles (AgNPs). Azlocillin was conjugated with AgNPs by chemical methods and its antimicrobial activity was studied against P. aeruginosa using well diffusion agar method. Then, minimum inhibitory concentration and minimum bactericidal concentration of the new conjugate was specified with macro‐dilution method. The animal study showed the considerable enhanced antibacterial effect of azlocillin in conjugation with AgNPs against P. aeruginosa in comparison with azlocillin alone, AgNPs alone and azlocillin in combination with AgNPs.Inspec keywords: antibacterial activity, silver, nanoparticles, organic compounds, microorganisms, drugs, nanomedicine, biomedical materials, diseases, diffusion, nanofabricationOther keywords: Ag, macrodilution method, minimum bactericidal concentration, minimum inhibitory concentration, well diffusion agar method, P. aeruginosa, antimicrobial activity, chemical methods, azlocillin antibiotic nanoparticles, infectious diseases treatment, nanodrugs, bacterial resistance, Pseudomonas aeruginosa, silver nanoparticles, antibacterial effect     

A neural network algorithm for servicing jobs with sequential and parallel machines

We consider the FJ‖C _max problem of optimal servicing with respect to performance for a given set of jobs by sequential and parallel machines. The problem FJ‖C _max is a generalization of the classical J‖C _max problem for the case when the servicing system has not only sequential but also parallel (identical) machines. We propose a two-stage algorithm for a heuristic solution of problem FJ‖C _max On the first stage, we solve the problem J‖C _max, i.e., we assume that the servicing system does not have parallel machines. On the second stage, operations are distributed over parallel machines. On both stages of the algorithm, we use neural network decision making models. The efficiency of a neural network algorithm for the problem J‖C _max and problem FJ‖C _max was evaluated on 20 test examples obtained from 20 known J‖C _max problems by including into the servicing system a random number of copies of sequential machines.     

Clinical Decision Support and Closed‐Loop Control for Intensive Care Unit Sedation

Critical care patients undergoing surgery require drug administration to regulate physiological variables such as blood pressure, cardiac output, heart rate, and degree of consciousness. The rate of infusion of each administered drug is critical, requiring constant monitoring and frequent adjustments. Patients in the intensive care unit who require mechanical ventilation due to acute respiratory failure also frequently require the administration of sedative agents. Open‐loop control (manual control) by clinical personnel can be tedious, imprecise, time‐consuming, and sometimes of poor quality, depending on the skills and judgment of the clinician. Dynamical system pharmacokinetic and pharmacodynamic modeling and closed‐loop control system design methodologies can significantly advance our understanding of the wide effects of pharmacological agents and anesthetics, as well as advance the state‐of‐the‐art in active control of drug delivery systems for clinical pharmacology. In this paper, we discuss the challenges and opportunities of clinical decision support and closed‐loop control for intensive care unit sedation.     

Magnetic immobilization of recombinant E. coli producing extracellular asparaginase: An effective way to intensify downstream process

Purification is one of the most important steps in biotechnological downstream process. This procedure is associated with some difficulties including several separation steps. Common cell separation procedures are usually time and energy consuming that also compromises cell growth, viability and reusability. Magnetic immobilization was introduced as a novel separation technique to provide a quick, easy and convenient alternative vs. traditional cell harvesting methods. In this study, recombinant E. coli producing extracellular asparaginase was decorated by synthesized L-lysine coated iron oxide nanoparticles and cell growth, immobilization efficiency and asparaginase productivity was evaluated.     

Green information systems use in social enterprise: the case of a community-led eco-localization website in the West Midlands region of the UK

This study examines the intention of environmentally-oriented social enterprises to continue using Green IS (an eco-localization website here) in Herefordshire located in the West Midlands region of the United Kingdom. The Herefordshire Greenlinks (HGL) Association is a local business community that operates primarily through the HGL Website in order to help local social enterprises find and trade local eco-friendly goods and services. It is an example of a “market-based” approach to solving social and environmental issues that is inspired by a bottom-up view of community development and eco-localization. Based on the theory of uses and gratification of media use and the new environmental paradigm, we propose a research model that outlines the various factors contributing to intention to continue using an eco-localization website. The research model and its associated hypotheses were empirically tested using a dataset generated from a survey of 83 small social enterprises. The results demonstrate the importance of three different types of gratification – content, social and performance gratification as well as users’ environmental concerns in determining the intention to continue using an eco-localization website. This study contributes to the Green IS literature by extending research to the social sector environment, and it also provides practical insight for designers of such community-led eco-localization websites.