Effects of Plant Growth Hormones on Mucor indicus Growth and Chitosan and Ethanol Production

The objective of this study was to investigate the effects of indole-3-acetic acid (IAA) and kinetin (KIN) on Mucor indicus growth, cell wall composition, and ethanol production. A semi-synthetic medium, supplemented with 0–5 mg/L hormones, was used for the cultivations (at 32 °C for 48 h). By addition of 1 mg/L of each hormone, the biomass and ethanol yields were increased and decreased, respectively. At higher levels, however, an inverse trend was observed. The glucosamine fraction of the cell wall, as a representative for chitosan, followed similar but sharper changes, compared to the biomass. The highest level was 221% higher than that obtained without hormones. The sum of glucosamine and N-acetyl glucosamine (chitin and chitosan) was noticeably enhanced in the presence of the hormones. Increase of chitosan was accompanied by a decrease in the phosphate content, with the lowest phosphate (0.01 g/g cell wall) being obtained when the chitosan was at the maximum (0.45 g/g cell wall). In conclusion, IAA and KIN significantly enhanced the M. indicus growth and chitosan production, while at the same time decreasing the ethanol yield to some extent. This study shows that plant growth hormones have a high potential for the improvement of fungal chitosan production by M. indicus.     

Two-level distributed clustering routing algorithm based on unequal clusters for large-scale Internet of Things networks

The Journal of Supercomputing - According to the recent advancements in communication technologies and the widespread use of smart devices, our environment can be transforming into the Internet of...     

Corrigendum to “A model to control environmental performance of project execution process based on greenhouse gas emissions using earned value management” [Int. J. Proj. Manag. 36 (3) (April 2018) 397-413]

Abstract was revised as follows:In response to recent climate change, which is believed to be attributed to the release of greenhouse gas (GHG) emissions, many countries are placing CO₂ abatement programs such as carbon tax and cap-and-trade. Projects do have a significant share in GHGs and therefore their environmental performance, like their schedule and cost performance, should be monitored and controlled. Although many large projects would pass an environmental assessment in the project evaluation phase, the issue of environmental performance monitoring during the project execution phase has not been addressed in project management methodologies. The objective of this paper is to develop a model to estimate project GHG emissions, and to measure project GHG performance using the developed metrics, which can be used at any point in time over the life of a project. A comprehensive study is conducted to collect information on GHG emission factors of various project activity data (such as material use, energy and fuel consumption, transportation, etc.), and a user form interface is developed to calculate the total GHG of an activity. Also, a breakdown structure is proposed which supports managing all the project GHG accounts. The monitoring and control model is formulated based on the logic used in earned value management (EVM) methodology. The proposed model is then implemented to a work package of a real construction project. The results present the project initial GHG plan and show that the model is able to calculate project GHG variance by the reporting date and predict project final GHG based on a project GHG performance index. The method presented in this paper is general and can be applied to any type of projects in an organization that aims to reduce its carbon footprint. The same structure can be applied to monitor and control any other environmental impact associated with project execution process.     

Real-time monitoring of events applied to syndromic surveillance

Abstract

This article focuses on monitor plans aimed at the early detection of the increase in the frequency of events. The literature recommends either monitoring the time between events (TBE) if events are rare or counting the number of events per unit non-overlapping time intervals otherwise. Some authors advocate using the Bernoulli model for rare events, applying presence or absence of events within non-overlapping and exhaustive time intervals. This Bernoulli model does improve the real-time monitoring assessment of these events compared to counting events over a larger interval, making them less rare. However this approach became inefficient if more than one event starts occurring within the intervals. Monitoring TBE is the real-time option for outbreak detection, because outbreak information is accumulated when an event occurs. This is preferred to waiting for the end of a period to count events. If the TBE reduces significantly, then the incidence of these events increases significantly. This article explores this TBE option relative to using the monitoring of counts when the TBEs are either Exponentially, Gamma or Weibull distributed for moderately low count scenarios. The article will discuss and compare the approaches of using an Exponentially Weighted Moving Average (EWMA) statistic for the TBEs to the EWMA of counts. Several robust options will be considered when the future change in event frequency is unknown. Our goal is to have a robust monitoring plan which is able to efficiently detect many different levels of shifts. These robust plans are compared to the more traditional event monitoring plans for both small and large changes in the event frequency.     

Vicious cycle during chemical degradation of sulfonated aromatic proton exchange membranes in the fuel cell application

Weak phase separation and vulnerable linking groups between aromatic units are common setbacks of sulfonated aromatic proton exchange membranes (PEMs) from durability point of view. In this study, sulfonated poly(ether ether ketone) (SPEEK) membranes were exposed to Fenton's solution for a specific time, ranging from 10 to 60 minutes. Chemical structure and morphology evolution, decay in mechanical and thermal stability, and H₂ permeability of SPEEK membranes were evaluated during the chemical degradation. Less-entangled polymeric chains with lower average molecular weight of degraded SPEEK samples diminished mechanical rigidity. In addition, reduction of aromatic rings in each repeat unit led to higher thermal decomposition rate. Furthermore, randomly distributed micro-defects in the SPEEK morphology and an increase in water sorption can reduce the fatigue strength of membranes in the wet-dry cycles. Eventually, hydrogen cross-over rate was gradually increased, and henceforth, accelerated destructive radical formation and degradation can be predicted.     

A protection strategy for inverter-interfaced islanded microgrids with looped configuration

Electrical Engineering - Development of an efficient protection strategy is one of the main barriers in paving the way for the implementation of inverter-based microgrids. The limited fault current...     

Stochastic dynamic output feedback stabilization of uncertain stochastic state-delayed systems

This paper addresses the problem of stochastic dynamic output feedback (SDOF) stabilization for a class of stochastic continuous-time state-delayed systems with norm-bounded nonlinear uncertainties. The aim is to design a linear, delayless, and SDOF control for all admissible uncertainties. The designed control ensures stochastically exponentially stability in the mean square, independent of the deterministic time delay. Using the Finsler's lemma, the necessary and sufficient conditions for the existence of such a control are proposed in terms of certain linear matrix inequalities. These results are illustrated with a simple example to demonstrate the applicability of the proposed design approach.