Effect of Curd Washing Level on Proteolysis and Functionality of Low-Moisture Mozzarella Cheese Made with Galactose-Fermenting Culture

Abstract: The effect of curd washing on functional properties of low-moisture mozzarella cheese made with galactose-fermenting culture was investigated. A total of 4 curd washing levels (0%, 10%, 25%, 50% wt/wt) were used during low-moisture mozzarella cheese manufacture, and cheeses were stored for 63 d at 4 °C and the influence of curd washing on proteolysis and functionality of low-moisture mozzarella cheese were examined. Curd washing had a significant effect on moisture and ash contents. In general, moisture contents increased and ash contents decreased with increased curd washing levels. Low-moisture mozzarella cheese made with 10% curd washing levels showed higher proteolysis, meltability, and stretchability during storage than other experimental cheeses. In general, galactose contents decreased during storage; however, cheeses made with 25% and 50% curd washing levels had lower galactose contents than those with control or 10%. L*-values (browning) decreased and proteolysis increased in low-moisture mozzarella cheeses during storage.     

Effects of extended dry storage of powdered infant milk formula on susceptibility of Enterobacter sakazakii to hot water and ionizing radiation

Infant milk formula has been identified as a potential source of Enterobacter sakazakii, which has been implicated in neonatal meningitis and necrotizing enterocolitis. This study was undertaken to determine whether the length of E. sakazakii storage in powdered infant milk formula (PIMF) affected the ability of the pathogen to survive subsequent reconstitution of the powder with hot water or treatment with gamma radiation. Five E. sakazakii strains were mixed individually with PIMF and kept for up to 12 months at 25 degrees C. After storage PIMF was reconstituted with water at 60 to 100 degrees C or was exposed to < or = 5 kGy of gamma radiation. Without any treatment secondary to drying, E. sakazakii counts decreased < 1 log/g after 1 month but decreased about 4 log/g during storage for 8 to 12 months. Dry storage decreased thermal resistance but increased resistance of E. sakazakii to ionizing radiation in PIMF. Reconstitution of contaminated powder with water at 70 degrees C after 1 month of dry storage reduced E. sakazakii viability slightly, > 2 log/g, and after powder was stored for 12 months all E. sakazakii strains were eliminated. In contrast, desiccation substantially increased the resistance of E. sakazakii strains to ionizing radiation. Although the D-value for E. sakazakii IMF1 following overnight storage in PIMF was 0.98 kGy, > 4 kGy was required to kill 1.5 log/g of the same strain that had survived 12 months in dry PIMF. Results suggested that low-dose irradiation will more effectively eliminate E. sakazakii from PIMF if the treatment is applied shortly after PIMF manufacture.     

Solving Complex Rainfall-Runoff Processes in Semi-Arid Regions Using Hybrid Heuristic Model

In the current research, a hybrid model was proposed to solve the complexity of rainfall-runoff models in semi-arid regions. The proposed hybrid model structure consists of linking two data mining models, namely, Group Method of Data Handling (GMDH) and Generalized Linear Model (GLM). The proposed hybrid model structure consists of two phases. The GMDH model was used in the first phase of the hybrid model to predict daily streamflow. The first phase consists of two sections. In the first section a predictive model is developed using the time series of the daily streamflow. In the second section the rainfall-runoff model was developed. The outputs of the first phase of the hybrid model are used as inputs to the second phase of the hybrid model. The second phase of the hybrid model was developed using the GLM model. The Gomel River in Iraq was selected as a case study. The daily rainfall data and daily streamflow data for the period from January 1, 2004 to December 19, 2016 were used to train and validate the model. The results proved the accuracy of the proposed hybrid model in estimating the daily streamflow of the study area, where the value of R² was 0.92 in the training period and 0.88 in the validation period of the model.

     

Composite mixed matrix membranes incorporating microporous carbon molecular sieve as filler in polyethersulfone for CO2/CH4 separation

Membrane technology has been considered a key factor for sustainable growth in high-efficiency gas separation. Current mixed matrix membranes (MMMs) technology is rising, but these membranes in the dense structure are having difficulties in operating at high pressures and scale up for commercialization. The purpose of this research is to synthesize composite MMMs (CMMMs) consisting of polyethersulfone (PES), carbon molecular sieve (CMS 1–5 wt %), and Novatex 2471 nonwoven fabric (support layer). The membranes' physical, chemical, and thermal properties were evaluated by different analytical equipment. The morphology of both PES and PES-CMS composite membranes had a porous and asymmetric structure, in which CMS was uniformly distributed in the polymer matrix. The thermal properties showed that the membranes were stable up to 350 °C with a single glass transition temperature. The functional groups in the membrane were confirmed by spectral analysis. The gas performance results showed that carbon dioxide permeance increased with increased CMS concentration and methane permeance decreased due to the hindering effect of CMS under similar operating conditions. The highest selectivity achieved was 12.774 using CMMM of 5 wt % of CMS at 10 bar, which on average was 137.80%, improved selectivity compared to pure PES membrane. The support layer was able to withstand high operating pressures and showed the ability to scale up. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48476.     

A Hybrid Model to Predict Monthly Streamflow Using Neighboring Rivers Annual Flows

Water Resources Management - The issue of predicting monthly streamflow data is an important issue in water resources engineering. In this paper, a hybrid model was proposed to generate monthly...     

Evolutionary Intelligence and Deep Learning Enabled Diabetic Retinopathy Classification Model

Diabetic Retinopathy (DR) has become a widespread illness among diabetics across the globe. Retinal fundus images are generally used by physicians to detect and classify the stages of DR. Since manual examination of DR images is a time-consuming process with the risks of biased results, automated tools using Artificial Intelligence (AI) to diagnose the disease have become essential. In this view, the current study develops an Optimal Deep Learning-enabled Fusion-based Diabetic Retinopathy Detection and Classification (ODL-FDRDC) technique. The intention of the proposed ODL-FDRDC technique is to identify DR and categorize its different grades using retinal fundus images. In addition, ODL-FDRDC technique involves region growing segmentation technique to determine the infected regions. Moreover, the fusion of two DL models namely, CapsNet and MobileNet is used for feature extraction. Further, the hyperparameter tuning of these models is also performed via Coyote Optimization Algorithm (COA). Gated Recurrent Unit (GRU) is also utilized to identify DR. The experimental results of the analysis, accomplished by ODL-FDRDC technique against benchmark DR dataset, established the supremacy of the technique over existing methodologies under different measures.     

A Comparative Study of Superconductivity and Thermally Activated Flux Flow of YBa2Cu3O7-δ and YBa2Cu3O7-δ/La1-x-yPrxCayMnO3 Bilayers

Journal of Superconductivity and Novel Magnetism - YBa2Cu3O7-δ/La1-x-yPrxCayMnO3 (YBCO/LPCMO) (x?~?0.31, y?~?0.35) bilayers with fixed ferromagnetic layer thickness...     

Multi-Fidelity High-Throughput Optimization of Electrical Conductivity in P3HT-CNT Composites

Combining high-throughput experiments with machine learning accelerates materials and process optimization toward user-specified target properties. In this study, a rapid machine learning-driven automated flow mixing setup with a high-throughput drop-casting system is introduced for thin film preparation, followed by fast characterization of proxy optical and target electrical properties that completes one cycle of learning with 160 unique samples in a single day, a > 10 ×  improvement relative to quantified, manual-controlled baseline. Regio-regular poly-3-hexylthiophene is combined with various types of carbon nanotubes, to identify the optimum composition and synthesis conditions to realize electrical conductivities as high as state-of-the-art 1000 S cm⁻¹. The results are subsequently verified and explained using offline high-fidelity experiments. Graph-based model selection strategies with classical regression that optimize among multi-fidelity noisy input-output measurements are introduced. These strategies present a robust machine-learning driven high-throughput experimental scheme that can be effectively applied to understand, optimize, and design new materials and composites.