Effect of Lactobacillus plantarum LS5 on oxidative stability and lipid modifications of Doogh

The oxidative stability and lipid modifications of Doogh prepared with Lactobacillus plantarum LS5 and control Doogh were compared during storage for 22 days. In vitro scavenging activity against 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH) free radicals showed that L. plantarum LS5 had radical scavenging activities (49.6%). Peroxide value, conjugated diene value and anisidine value measurements in Doogh lipid samples in combination with Fourier transform infrared spectroscopy demonstrated that Doogh prepared with L. plantarum LS5 was more stable than the control against formation of primary and secondary oxidation products. Furthermore, higher concentrations of short‐chain fatty acids (butyric and caproic) and unsaturated fatty acids were found in Doogh treated with L. plantarum LS5. Changes in the thermograms of all samples were also observed. Results of differential scanning calorimetry indicated that melting points of lipids from treated Doogh were lower than for the control. It was concluded that L. plantarum LS5 could be used as a natural preservative in functional dairy products and could beneficially affect the consumer by providing dietary sources of antioxidants.     

Robust Laguerre-based model predictive control for trajectory tracking of a mobile robot using an linear matrix inequality (LMI)-based approach

This paper studies the trajectory tracking of a constrained mobile robot under slippery conditions. The goal is to propose a controller for real-time operations of time-varying dynamics with insignificant execution time. Therefore, a Laguerre-based model predictive control (LMPC) is designed, and robustness is provided with an linear matrix inequality (LMI)-feedback controller. Moreover, a recursive least square (RLS) algorithm with a forgetting factor is utilized to identify the required parameters of the LMI-based controller. LMPC and LMI-based controller stability is achieved with suboptimal theory and Lyapunov function, respectively. This algorithm separates the nominal system and introduces new dynamics containing uncertainties. Furthermore, LMPC is removed from the online calculation, which dramatically reduces computation burden and time; consequently, online computation is dedicated to determining the LMI feedback. Simulations are provided to compare the proposed robust controller to its not robust counterpart. Finally, it is demonstrated that this controller diminishes the execution time considerably, making it incomparable to previous robust MPC strategies.     

Design of efficient photocatalytic processes for the production of hydrogen from biomass derived substrates

The photoreforming of glucose has been studied over TiO₂ photocatalyst with different photoreactors, focusing on the effect of the reaction conditions: temperature, pressure, catalyst and substrate concentration. The effect of pressure was particularly significant, decreasing hydrogen evolution rate, but improving the conversion of the substrate. Furthermore, pressure moderately higher than ambient allowed to operate at high temperature (80 °C), boosting hydrogen productivity. Most experiments were carried out on glucose photoreforming, but, for the first time, the photoconversion of levulinic acid was investigated, as an interesting product of biomass hydrolysis under harsh conditions. Levulinic acid led to the production of ethane and ethylene in gas phase, interpreted according to a preliminary hypothesis of the photoconversion mechanism. High hydrogen productivity was achieved, in most cases higher than the literature benchmark.     

Design of Zn1−xCuxO Nanocomposite Ag-Doped as an Efficient Antimicrobial Agent

Journal of Inorganic and Organometallic Polymers and Materials - This study aims at the preparation of antimicrobial nanoparticles hybrid based on silver (Ag) doped Zn1?xCuxO. Zn1?xCuxO...     

Support vector regression and neural networks analytical models for gas sensor based on molybdenum disulfide

Microsystem Technologies - In this study, MoS2 gas sensor based on field effect transistor has been proposed and the adsorption of NO2 molecules on the channel surface can lead to significant...     

Not Waving,Drowning: Can Local Government Policies on Climate Change Adaptation and Disaster Resilience Make a Difference?

Climate change will increase the intensity, duration and/or frequency of some climate-related hazards. Responsibility for adapting to such impacts of climate change in Australia has, in the main, fallen on local governments which have paid varying degrees of attention to the issue. This paper takes an integrated approach to compare the climate adaptation and disaster resilience policies and plans of local governments of two low-lying coastal cities in Australia to understand whether (and how) local governments can make a difference. The findings indicate that local governments can significantly contribute to building resilience and adapting to climate-related hazards, however a number of factors such as the attitudes of local governments on climate change, environmental activism, and the recent experiences of climate-related disasters are instrumental for shaping a better local response. Local action also needs to be supported by a more integrated approach by all levels of government.     

Application of Chebyshev polynomials to predict phase behavior of fluids containing asphaltene and associating components using SAFT equation of state

In this work, a thermodynamic model based on statistical association fluid theory (SAFT) is developed to predict the phase behavior of mixtures containing asphaltene contents. The SAFT equation of state is a good candidate for closing that gap between statistical mechanic models and the classical models dominated by cubic equation of state. A robust, fast and accurate computational algorithm based on Chebyshev polynomial approximation is developed to calculate the density and hence fugacity using SAFT equation of state in order to perform phase equilibrium calculations. Application of Chebyshev polynomials to approximate pressure-density function leads to an interpolation error of degree 10⁻¹³. Application of the proposed algorithm to calculate density of binary systems composed of ethanol and toluene shows an average relative deviation of 0.143% in the temperature range 283.15-353.15 K and for pressures up to 45 MPa. The proposed model is developed to predict the precipitation behavior of petroleum fluids containing asphaltene. The effect of pressure, temperature and solvent concentration on the amount of asphaltene precipitation is investigated. A good agreement with an AAD of 2.593% is observed between experimental and predicted amount of asphaltene precipitate. The model is also tested to investigate the effect of temperature and solvent concentration on asphaltene onset pressures (upper and lower). Again, an excellent agreement is observed between experimental and predicted values of the asphaltene onset pressure at different temperatures and solvent concentrations with an average 0.705% relative error. The accuracy of the proposed model is compared with WinProp software using Peng-Robinson equation of state with average 53.132% and 8.657% relative errors for the amount of asphaltene precipitate and onset pressure, respectively.     

The effect of concentration on gas sensor model based on graphene nanoribbon

Graphene nanoribbon (GNR), a superior material with two-dimensional structure and monolayer honeycomb of carbon, is noteworthy and important in all fields’ mainly electronic, chemistry, biology, physics and nanotechnology. Recently, observing about sensors demonstrates that for better accuracy, faster response time and enlarged sensitivity, it needs to be improved. Nowadays, carbon-based equipments as an exclusive substance are remarkable in the sensing technology. High conductivity as unique properties caused that graphene can be used in biological applications. Gas sensor based on graphene can be supposed to have great sensitivity for gas molecules detection. In this study, graphene-based carbon dioxide sensor analytically is modeled. In addition, new methods of gas sensor model based on the gradient of GNR conductance are provided. Also, a field effect transistor-based structure as a modeling platform is suggested. Ultimately, optimum model is evaluated by comparison study between analytical model and experimental performance.     

Asymptotic Dynamic Time Warping calculation with utilizing value repetition

Dynamic Time Warping (DTW) is a popular method for measuring the similarity of time series. It is widely used in various domains. A major drawback of DTW is that it has a high computational complexity. To address this problem, pruning techniques to calculate the exact DTW distance, as well as DTW approximation methods, have become important approaches. In this paper, we introduce Blocked Dynamic Time Warping (BDTW), a new similarity measure which works on run-length encoded time series representation. BDTW utilizes any repetitive values (zero and nonzero) in time series to reduce DTW computation time. BDTW closely approximates DTW distance, and it is significantly faster than traditional DTW for time series with high levels of value repetition. Moreover, BDTW can be combined with time series representation methods which provide constant segments, to serve as a close approximation method even for the time series without value repetition. Constrained BDTW, BDTW upper bound and BDTW lower bound are discussed as variations of BDTW. BDTW upper bound and BDTW lower bound are presented as a new DTW upper bound and lower bound which can be efficiently applied on time series with high levels of value repetition for pruning unhopeful alignments and matches in the exact DTW calculation. We show the effectiveness of BDTW and its variations on different applications using the following datasets: Almanac of Minutely Power, Refit Smart Homes, as well as the 85 datasets from the University of California, Riverside time series classification archive (UCR archive).     

Optimizing reserve capacity of urban road networks in a discrete Network Design Problem

This paper addresses the problem of designing of street directions and lane additions in urban road networks, based on the concept of reserve capacity. Reserve capacity is identified by the largest multiplier applied to a given existing demand matrix, that can be allocated to a network without violating the arc capacities. Having a two-way streets base network and the allowable street lane additions, the problem is to find the optimum configuration of street directions and two-way street lane allocations, and the optimum selection of street lane addition projects, in a way that the reserve capacity of the network is maximized. The problem is considered in two variations; in the first variation no restriction is imposed on the symmetricity of lane allocations for two-way streets, and in the second variation, two-way street lane allocations are restricted to be symmetric. The proposed problems are modeled as mixed-integer bi-level mathematical problems. A hybrid genetic algorithm and an evolutionary simulated annealing algorithm are proposed to solve the models. Computational results for both problem variations are presented.