Effects of active chitosan-flaxseed mucilage-based films on the preservation of minced trout fillets: A comparison among aerobic,vacuum, and modified atmosphere packaging

The objectives of the current study were to examine physical–mechanical, structural, and morphological characteristics of chitosan-flaxseed mucilage films enriched with Ziziphora clinopodioides essential oil (ZEO; 0%, 0.25%, and 0.5%) and sesame oil (SO; 0% and 0.75%) and to provide useful information for the preservation of minced trout fillet's using antimicrobial films under aerobic, vacuum, and modified atmosphere conditions for 16 days. The films showed thickness, tensile strength, puncture force, puncture deformation, water vapor transmission rate, water vapor permeability, swelling index, and oxygen permeability values ranging 0.082–0.86 mm, 33.34–46.83 MPa, 25.69–53.08 N, 11.45–28.45 mm, 17.48–26.73 g/m² h, 8.57–12.49 × 10⁻⁴ g mm/m² h Pa, 12.45–38.43%, and 3.02–13.32 × 10⁻¹² cm³/m² s Pa, respectively. The following order of effect on the microbial spoilage population of treated samples was found in the applied packaging methods: modified atmosphere packaging > vacuum packaging > aerobic packaging. The final microbial population of treated samples was 0.35–4.91 log CFU/g lower than the controls after 16 days of refrigerated storage. At the end of the storage, the total volatile base nitrogen, peroxide value, and thiobarbituric acid reactive substances of untreated samples were 34.02–48.6 mg of N/100 g, 1.43–2.32 meq of peroxide/1000 g, and 3.33–4.24 mg of malondialdehyde/kg, respectively. The lowest corresponding values were recorded for the treated samples with ZEO 0.5% + SO 0.75% films by 14.26–17.73 mg of N/100 g, 0.48–0.86 meq of peroxide/1000 g, and 1.08–1.48 mg of malondialdehyde/kg, respectively.     

Performance enhancement of a direct absorption solar collector using copper oxide porous foam and nanofluid

The current research proposes the idea of using water-saturated metal oxide foams and water-based nanofluids as solar absorber in the direct absorption solar collectors (DASCs). Specifically, the novel solar collector design utilizes copper oxide (CuO) porous foam and nanoparticle with high optical properties and is expected to have enhanced thermal performance than the conventional collectors utilizing pure water. The finite volume technique is used to solve the governing equations of flow and heat transfer in the radiative participating media. Also, to establish the reliability and accuracy of numerical solutions, the obtained results are compared with the corresponding numerical and experimental data. The computations are carried out for different nanoparticle volume fractions, foam pore sizes, working fluid mass flow rates, and both porous layer thicknesses and positions (inserted at the lower or upper wall of the collector). It is found that the efficiency of DASC partially/fully filled with metal oxide foam is maximized when the collector is completely filled with it. Compared with the water flow, the numerical results show that the collector efficiency using CuO nanofluid and metal oxide foam is improved by up to 26.8% and 23.8%, respectively. Moreover, considering the second law of thermodynamics, the use of CuO nanofluids in the DASC seems to be more effective than the use of CuO porous foam.     

Modeling river water quality parameters using modified adaptive neuro fuzzy inference system

Water quality is always one of the most important factors in human health. Artificial intelligence models are respected methods for modeling water quality. The evolutionary algorithm(EA) is a new technique for improving the performance of artificial intelligence models such as the adaptive neuro fuzzy inference system(ANFIS) and artificial neural networks(ANN). Attempts have been made to make the models more suitable and accurate with the replacement of other training methods that do not suffer from some shortcomings, including a tendency to being trapped in local optima or voluminous computations. This study investigated the applicability of ANFIS with particle swarm optimization(PSO)and ant colony optimization for continuous domains(ACO_R) in estimating water quality parameters at three stations along the Zayandehrood River, in Iran. The ANFIS-PSO and ANFIS-ACO_R methods were also compared with the classic ANFIS method, which uses least squares and gradient descent as training algorithms. The estimated water quality parameters in this study were electrical conductivity(EC), total dissolved solids(TDS), the sodium adsorption ratio(SAR), carbonate hardness(CH), and total hardness(TH). Correlation analysis was performed using SPSS software to determine the optimal inputs to the models. The analysis showed that ANFIS-PSO was the better model compared with ANFIS-ACO_R. It is noteworthy that EA models can improve ANFIS' performance at all three stations for different water quality parameters.     

A DPSK demodulator for biomedical application

In this study, a differential phase-shift-keying (DPSK) demodulator, which is implemented by digital circuit, is proposed. A differential voltage clipper is used to generate narrow pulses at the extremum of the received modulated signal. Regarding the DPSK signal, the outputs of the clipper have double frequency. In order to halve the frequency, a frequency divider is applied to get synchronous clock. Two binary counters are used to detect phase variation; the data are recovered after two times sampling. This technique removes the need for voltage controlled oscillator (VCO). The proposed demodulator is postlayout simulated in a 0.18-μm CMOS process, with a power of 64 μW and active area of 68 × 70 μm². The demodulator recovers 10 Mb/s data rate at 10-MHz carrier frequency using a 1.8-V power supply.     

Many-Objective Multi-Scenario Algorithm for Optimal Reservoir Operation Under Future Uncertainties

An increase in greenhouse gases in future can exacerbate the climate change phenomenon and may have negative consequences on different elements of hydrologic system, including rainfall, temperature, and streamflow. Since the reservoir operation is highly dependent on the timing and magnitude of inflow, the impact of potential climate change on inflow sequences should be considered in deriving the system operation rule. Nevertheless, existing algorithms are only able to optimize the operation policy for a single predetermined climate scenario. Thus, the derived operation rule would not work well if the scenario changes. This paper proposes an algorithm which is able to handle simultaneously multiple scenarios in finding optimum system operation rule. Thus, it can overcome drawbacks caused by uncertainties in the occurrence of future scenarios. The proposed algorithm is used to optimize reservoir operation policy considering various climate change scenarios (RCPs). To evaluate the performance of the proposed algorithm, a five-reservoir system within Tehran region with several objectives including municipal, agricultural, environmental, and hydropower demands is employed as the case study. Results show that in all cases the multi-scenario rule derived by the proposed method performs as good as the operation rule derived for any specific scenario using a powerful optimization algorithm when evaluated for that scenario. While, in all other models as the future scenario changes to the one other than that used in deriving the operation rule, the model performance declines as compared to the proposed model.     

Forced Vibration Analysis of Functionally Graded Anisotropic Nanoplates Resting on Winkler/Pasternak-Foundation

This study investigates the forced vibration of functionally graded hexagonal nano-size plates for the first time. A quasi-three-dimensional (3D) plate theory including stretching effect is used to model the anisotropic plate as a continuum one where smallscale effects are considered based on nonlocal strain gradient theory. Also, the plate is assumed on a Pasternak foundation in which normal and transverse shear loads are taken into account. The governing equations of motion are obtained via the Hamiltonian principles which are solved using analytical based methods by means of Navier’s approximation. The influences of the exponential factor, nonlocal parameter, strain gradient parameter, Pasternak foundation coefficients, length-to-thickness, and length-to-width ratios on the dynamic response of the nanoplates are examined. In addition, the accuracy of an isotropic approximate instead of the anisotropic model is studied. The dynamic behavior of the system shows that mechanical mathematics-based models may get better results considering the anisotropic model because the dynamic response can cause prominent differences (up to 17%) between isotropic approximation and anisotropic model.     

The Fuzzy Logic Application in Volume Fractions Prediction of the Annular Three-Phase Flows

In this paper, the volume fractions in the annular three-phase flow are measured based on a dual energy metering system consisting of \(^{152}\)Eu and \(^{137}\)Cs and one NaI detector, and then modeled by fuzzy logic. Since the summation of volume fractions are constant (equal to 100%), therefore the fuzzy network must predict only two volume fractions. In this study, three fuzzy networks are applied. The first network is utilized to predict the gas and water volume fractions. The next one is applied to predict the gas and oil volume fractions, and the last one to predict the water and oil volume fractions. In the next step, the numerically obtained data from MCNP-X code, must be imported to the fuzzy models. Then, the average errors of these three networks are computed and compared. The network which has the least error is selected as the best predictor model. According to the modeling results, the best fuzzy network, predicts the gas and water volume fractions with the mean relative error of less than 0.3%, which shows that the fuzzy logic can predict the results precisely.     

Hydrothermally grown mixed ternary nickel ferrite oxides as hybrid battery-type electrodes

Journal of Materials Science: Materials in Electronics - In this paper, a facile hydrothermal method is introduced to synthesize MNiFe-oxide (MNFOs: M?=?non, Cu, Co, Mg, Zn or Al)...     

Determination of phenobarbital in real sample using carbon quantum dots modified with tungsten as a fluorescent nanoprobe

Journal of Materials Science: Materials in Electronics - In this study, a sensitive and selective fluorescence nanoprobe has been designed based on carbon quantum dots modified by tungsten (WCQDs)...     

Multi-Reservoir System Optimization Based on Hybrid Gravitational Algorithm to Minimize Water-Supply Deficiencies

The growing prevalence of droughts and water scarcity have increased the importance of operating dam and reservoir systems efficiently. Several methods based on algorithms have been developed in recent years in a bid to optimize water release operation policy, in order to overcome or minimize the impact of droughts. However, all of these algorithms suffer from some weaknesses or drawbacks – notably early convergence, a low rate of convergence, or trapping in local optimizations – that limit their effectiveness and efficiency in seeking to determine the global optima for the operation of water systems. Against this background, the present study seeks to introduce and test a Hybrid Algorithm (HA) which integrates the Gravitational Search Algorithm (GSA) with the Particle Swarm Optimization Algorithm (PSOA) with the goal of minimizing irrigation deficiencies in a multi-reservoir system. The proposed algorithm was tested for a specific important multi-reservoir system in Iran: namely the Golestan Dam and Voshmgir Dam system. The results showed that applying the HA could reduce average irrigation deficiencies for the Golestan Dam substantially, to only 2 million cubic meters (MCM), compared to deficiency values for the Genetic Algorithm (GA), PSOA and GSA of 15.1, 6.7 and 5.8 MCM respectively. In addition, the HA performed very efficiently, reducing substantially the computational time needed to achieve the global optimal when compared with the other algorithms tested. Furthermore, the HA showed itself capable of assuring a high volumetric reliability index (VRI) to meet the pattern of water demand downstream from the dams, as well as clearly outperforming the other algorithms on other important indices. In conclusion, the proposed HA seems to offer considerable potential as an optimizer for dam and reservoir operations world-wide.