Hyperspectral imaging as an effective tool for prediction the moisture content and textural characteristics of roasted pistachio kernels

The objective of this study was to develop calibration models for prediction of moisture content and textural characteristics (fracture force, hardness, apparent modulus of elasticity and compressive energy) of pistachio kernels roasted in different conditions (temperatures 90, 120 and 150 °C; times 20, 35 and 50 min and air velocities 0.5, 1.5 and 2.5 m/s) using Vis/NIR hyperspectral imaging and multivariate analysis. The effects of different pre-processing methods and spectral treatments such as normalization [multiplicative scatter correction (MSC), standard normal variate transformation (SNV)], smoothing (median filter, Savitzky–Golay and Wavelet) and differentiation (first derivative, D¹ and second derivative, D²) on the obtained data were investigated. The prediction models were developed by partial least square regression (PLSR) and artificial neural network (ANN). The results indicated that ANN models have higher potential to predict moisture content and textural characteristics of roasted pistachio kernels comparing to PLSR models. High correlation was observed between reflectance data and fracture force (R²?=?0.957 and RMSEP?=?3.386) using MSC, Savitzky–Golay and D¹, compressive energy (R²?=?0.907 and RMSEP?=?15.757) using the combination of MSC, Wavelet and D¹, moisture content (R²?=?0.907 and RMSEP?=?0.179) and apparent modulus of elasticity (R²?=?0.921 and RMSEP?=?2.366) employing combination of SNV, Wavelet and D¹, respectively. Moreover, Vis–NIR data correlated well with hardness (R²?=?0.876 and RMSEP?=?5.216) using SNV, Wavelet and D². These results showed the capability of Vis/NIR hyperspectral imaging and the central role of multivariate analysis in developing accurate models for prediction of moisture content and textural properties of roasted pistachio kernels.     

Adsorptive desulfurization of model gasoline by using modified bentonite

This work involved the investigation on the removal of organic sulfur compounds from the model liquid fuels by using adsorption desulfurization (ADS) method. For this purpose, removal of 4-methyl dibenzothiophene (4-MDBT) in model gasoline streams with raw bentonite, nanobentonite and nanobentonite modified by nanomagnetite, active carbon and Ni(NO₃)₂.9H₂O was considered. Various factors influencing the desulfurization capability, including loading and baking temperature were investigated. Thermo-gravimetric analysis (TGA), Fourier transform infrared (FT-IR) spectroscopy, scanning electron microscopy (SEM) and energy dispersive X-ray spectrometry (EDX) showed that the ability of modified bentonite to adsorb 4-MDBT depends strongly on surface chemistry, particularly on the presence of basic oxygen-containing groups and acid content. The adsorbents tested for desulfurization capacity at breakthrough followed the order: 30wt% Fe₃O₄/30wt% Active carbon/40% Na -Nanobentonite?>?30wt% Fe₃O₄/30wt% Active carbon/40% Ca – Nanobentonite > 30wt % Fe₃O₄/70% Ca-Nanobentonite?>?15wt % Fe₃O₄/15wt% Ni/70% Ca– Nanobentonite. The results of preliminary tests for raw bentonite and nanobentonite were not significant in comparison with the modified nanobentonites: a, b, c, d samples, (about 40% lower than the four sample models). The optimum calcination temperature was 800°C. The multivariate methods were used for optimization of acceptance parameters. A Plackett–Burman design (PBD) was chosen as a screening method to estimate the relative influence of the factors that can be affected on the analytical response. Results show that surface area, pore size and pore volume of the bentonite can be increased several times using the impregnation method by 30wt% Fe₃O₄/30% active carbon. Also, the surface morphology of the bentonite is changed with this modification.     

Using traffic asymmetry to enhance TCP performance

A wealth of recent work has gone into optimizing the performance of Transmission Control Protocol (TCP) on the downlink channel of wireless networks such as for example, honing its congestion awareness mechanism so that it is minimally affected by random wireless losses, and optimizing achieved fairness of the end-to-end TCP rates. Other work has gone into balancing the allocation of a shared resource between the downlink and uplink in order to optimize TCP performance. We build on such previous research by proposing a cross-layer algorithm for resource allocation in OFDMA systems aiming not only to achieve optimal throughput for competing TCP flows but also to allocate resources appropriately between the downlink and uplink. This is important due to the increasing number of Internet applications where the mobile terminal is the TCP sender (social networking, peer-to-peer, etc.). Therefore, our scheme makes use of the asymmetry in the traffic and by defining the boundary between downlink and uplink capacity dynamically, enhance the TCP performance. Through numerical investigations we show the performance of the proposed scheme in terms of achieved fairness to the receivers and efficient allocation of downlink to uplink ratios based on the TCP traffic.     

A wavelet-based estimating depth of anesthesia

In this paper, an efficient method for quantifying the depth of anesthesia using underlying content of electroencephalogram (EEG) signal is presented. This method could be as an alternative instead of other clinical criteria such as pain reflex, auditory evoked potential, bispectral index scale (BIS) or amount of burst suppression. The proposed method is based on analysis of a single-channel EEG signal of patients during anesthesia, using wavelet transform. In order to use wavelet information, entropy is selected as the statistical tool. The obtained results suggest our method called Wavelet Coefficient Energy Entropy (WCEE) as a quantitative index for depth of anesthesia. To validate the introduced index, WCEE is applied to EEG signals of 22 people during the surgery and their determined indices are compared to BIS index, which is now a reference in anesthesia monitoring. The comparison results reveal a high correlation between WCEE index and BIS index during different anesthesia states. Moreover, WCEE values could precisely classify different anesthesia states with less computational burden than BIS index.     

Voltammetric determination of norepinephrine in the presence of acetaminophen using a novel ionic liquid/multiwall carbon nanotubes paste electrode

A novel multiwall carbon nanotubes (MWCNTs) modified carbon ionic liquid electrode (CILE) was fabricated and used to investigate the electrochemical behavior of norepinephrine (NP). MWCNTs/CILE was prepared by mixing hydrophilic ionic liquid, 1-methyl-3-butylimidazolium bromide (MBIDZBr), with graphite powder, MWCNTs, and liquid paraffin. The fabricated MWCNTs/CILE showed great electrocatalytic ability to the oxidation of NE. The electron transfer coefficient, diffusion coefficient, and charge transfer resistant (R_ct) of NE at the modified electrode were calculated. Differential pulse voltammetry of NE at the modified electrode exhibited two linear dynamic ranges with slopes of 0.0841 and 0.0231 μA/μM in the concentration ranges of 0.3 to 30.0 μM and 30.0 to 450.0 μM, respectively. The detection limit (3σ) of 0.09 μM NP was achieved. This modified electrode exhibited a good ability for well separated oxidation peaks of NE and acetaminophen (AC) in a buffer solution, pH 7.0. The proposed sensor was successfully applied for the determination of NE in human urine, pharmaceutical, and serum samples.     

A proximity-aware load balancing in peer-to-peer-based volunteer computing systems

One of the main challenges in peer-to-peer-based volunteer computing systems is an efficient resource discovery algorithm. Load balancing is a part of resource discovery algorithm and aims to minimize the overall response time of the system. This paper introduces an analytical model based on distributed parallel queues to optimize the average response time of the system in a distributed manner. The proposed resource discovery algorithm consists of two phases. In the first phase, it selects peers in a load-balanced manner based on QoS constraints of request. In the second phase, a proximity-aware feature is applied to select the peer with minimum communication overhead among selected peers in the first phase. Two dispatching strategies are proposed for the load balancing based on stochastic analysis of routing in the distributed parallel queues. These policies adopt probabilistic and deterministic sequences to redirect requests to the capable peers in the system. Simulation results show that the proposed resource discovery algorithm improves the response time of user’s requests by a factor of 1.8 under a moderate load.     

The Effect of Barium Doping on the Selective Structure of Bi-2223 Phase

The partial substitution of Sr by Ba in the two nominal compositions of Bi_1.8Pb_0.4Sr_2−x Ba _x Ca_2.2Cu₃O _y [x=0.0, 0.1, 0.2, and 0.3 (A group)] and Bi_1.66Pb_0.34Sr_2−x Ba _x Ca₂Cu₃O _y [x=0.0, 0.1, 0.2, 0.3, 0.4, and 0.5 (B group)] have been investigated by resistivity, ac susceptibility measurements and by XRD and SEM analysis. In general, the nature of the temperature dependence of resistivity and susceptibility measurements indicate the presence of a superconducting transition between grains coupled by weak links. However, the XRD and SEM analyses show that the relative composition of initial elements used in Bi-(2223) is essential to the site that is selected by the Ba ions. In the A group, Ba doping up to x=0.1 will improve the phase formation of Bi-2223, and improve the superconductivity properties of the samples. In the B group, although Ba doping up to x=0.1 will enhance the phase formation of Bi-2223, it will decrease the coupling between the grain and the superconductivity properties of these systems. The presence of lower Tc phases will begin to appear for x>0.1, in both of these systems. The superconductivity properties and the phase formation of Bi-(2223) will decrease as the Ba concentration increases.     

Applications of hyperspectral imaging in grains and nuts quality and safety assessment: a review

Quality of foods is generally controlled with traditional methods such as microbiological and chemical tests. However, the necessity of a non-destructive, rapid and accurate on-line method to monitor the product quality and safety is the key topic of many research studies. Hyperspectral imaging (HSI) has emerged as a powerful tool to handle the afore-mentioned goals. It is a novel technique that combines simultaneous advantages of imaging and spectroscopy. HSI is an analytical method that simultaneously delivers chemical, structural and functional information from the sample. This technique can be used to analyze both individual kernels and bulk samples and simultaneously determine quality parameters of grains and nuts. Nuts and grains are nutrient dense foods with complex matrices rich in mono- and poly-unsaturated fatty acids, vegetable proteins, fiber, vitamins, minerals etc. Therefore, nuts and grains are useful dietary sources to decrease the risk of diabetes, cancer and cardiovascular disease. In this paper, recent applications of hyperspectral imaging in quality and safety inspection of nuts and grains such as classification, compositions prediction, texture analysis, and detection of varietal impurities, damages, and infections are reviewed.