Synergistic effect of high-intensity ultrasound and β-cyclodextrin treatments on browning control in apple juice

This work evaluated the synergistic effects of combined high-intensity ultrasound (HIU) with β-cyclodextrin (β-CD) treatments on inhibiting browning of apple juice and explored the mechanism through simulation system. The combined treatment of 300 W HIU with 0.006 g mL⁻¹ β-CD had a synergistic impact on maintaining juice colour, resulting in a 39.06% reduction in browning degree, only a 36.64% decrease in total phenolic content, and a 17.82% reduction in PPO activity. The inhibition of enzymatic browning in simulated system revealed that HIU suppressed the enzyme (Polyphenol oxidase, PPO) and β-CD inhibited enzyme (PPO) and embedded substrate (polyphenol). The results of spectroscopic analysis showed that the particle-size distribution of PPO narrowed, the content of α-helix in the secondary structure increased, the fluorescence intensity increased, and the maximum wavelength was red-shifted after HIU and β-CD treatment. Changes in structure could further result in PPO activity loss. Hence, the combined treatment could synthetically alleviate the browning of apple juice.     

CuxNi1-xO nanostructures and their nanocomposites with reduced graphene oxide: Synthesis,characterization, and photocatalytic applications

NiO nanostructure was synthesized using a simple co-precipitation method and was embedded on reduced graphene oxide surface via ultrasonication. Structural investigations were made through X-ray diffraction (XRD) and functional groups were confirmed by Fourier transform infrared spectroscopy (FTIR). XRD analysis revealed the grain size reduction with doping. Fourier transform infrared spectroscopy confirmed the presence of metal-oxygen bond in pristine and doped NiO nanostructure as well as the presence of carbon containing groups. Scanning electron microscopy (SEM) indicated that the particle size decreased when NiO nanostructure was doped with copper. BET surface area was found to increase almost up to 43 m²/g for Cu doped NiO nanostructure/rGO composite. Current-voltage measurements were performed using two probe method. UV–Visible spectroscopic profiles showed the blue and red shift for Cu doped NiO nanostructure and Cu doped NiO Nanostructure/rGO composite respectively. Rate constant for Cu doped NiO nanostructure/rGO composite found to increase 4.4 times than pristine NiO nanostructure.     

Benchmarking performance of machine and deep learning-based methodologies for Urdu text document classification

Neural Computing and Applications - In order to provide benchmark performance for Urdu text document classification, the contribution of this paper is manifold. First, it provides a publicly...     

Polyurethane composites based on oil palm empty fruit bunches: Effect of isocyanate/hydroxyl ratio and chemical modification of empty fruit bunches with toluene diisocyanate and hexamethylene diisocyanate on mechanical properties

This study focuses on the effect of isocyanate (NCO)/hydroxyl (OH) group ratios and chemical modification of oil palm empty fruit bunches (EFBs) with toluene diisocyanate (TDI) and hexamethylene diisocyanate (HMDI) on the mechanical properties of EFB–polyurethane (PU) composites. The tensile, flexural, and impact properties are affected by the NCO/OH ratios. The tensile strengths, flexural strengths, and toughness increase as the NCO/OH increases; however, the modulus decreases. The reduction in the modulus is attributable to the increased flexibility of the PU linkages. Chemical modification of the EFBs increases the tensile strength, flexural strength, and toughness; however, the modulus is lowered as the percentage of treated EFB is increased. Impact strength results show that the strength increases as the NCO/OH ratio is increased. At NCO/OH ratios of 1.0 and 1.1, the composites with HMDI‐treated fibers exhibit higher impact strength than those with TDI‐treated and untreated fibers, respectively. This may be due to the longer and more flexible chain length of HMDI as compared to TDI, which enables the composites to absorb more energy before failure. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007     

The Lattice-Boltzmann Method on Optimal Sampling Lattices

In this paper, we extend the single relaxation time Lattice-Boltzmann Method (LBM) to the 3D body-centered cubic (BCC) lattice. We show that the D3bQ15 lattice defined by a 15 neighborhood connectivity of the BCC lattice is not only capable of more accurately discretizing the velocity space of the continuous Boltzmann equation as compared to the D3Q15 Cartesian lattice, it also achieves a comparable spatial discretization with 30 percent less samples. We validate the accuracy of our proposed lattice by investigating its performance on the 3D lid-driven cavity flow problem and show that the D3bQ15 lattice offers significant cost savings while maintaining a comparable accuracy. We demonstrate the efficiency of our method and the impact on graphics and visualization techniques via the application of line-integral convolution on 2D slices as well as the extraction of streamlines of the 3D flow. We further study the benefits of our proposed lattice by applying it to the problem of simulating smoke and show that the D3bQ15 lattice yields more detail and turbulence at a reduced computational cost.     

A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

Breast cancer (BC) is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year. The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6% of total cases. Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths. The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis. Manual diagnosis of BC is a complex and challenging task. This work proposed a deep learning-based (DL) solution for the early detection of this deadly disease from histopathology images. To evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is utilized. The proposed automatic diagnosis of BC detection and classification mainly involves three steps. Initially, a DL model is proposed for feature extraction. Secondly, the extracted feature vector (FV) is passed to the proposed novel feature selection (FS) framework for the best FS. Finally, for the classification of BC into invasive ductal carcinoma (IDC) and normal class different machine learning (ML) algorithms are used. Experimental outcomes of the proposed methodology achieved the highest accuracy of 92.7% which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.     

Scientific Workflow Makespan Minimization in Edge Multiple Service Providers Environment

The edge computing model offers an ultimate platform to support scientific and real-time workflow-based applications over the edge of the network. However, scientific workflow scheduling and execution still facing challenges such as response time management and latency time. This leads to deal with the acquisition delay of servers, deployed at the edge of a network and reduces the overall completion time of workflow. Previous studies show that existing scheduling methods consider the static performance of the server and ignore the impact of resource acquisition delay when scheduling workflow tasks. Our proposed method presented a meta-heuristic algorithm to schedule the scientific workflow and minimize the overall completion time by properly managing the acquisition and transmission delays. We carry out extensive experiments and evaluations based on commercial clouds and various scientific workflow templates. The proposed method has approximately 7.7% better performance than the baseline algorithms, particularly in overall deadline constraint that gives a success rate.

     

Piezoelectric properties in two-dimensional materials: Simulations and experiments

The piezoelectric effect, discovered in 1880 by Jacques and Pierre Curie, effectively allows to transduce signals from the mechanical domain to the electrical domain and vice versa. For this reason, piezoelectric devices are already ubiquitous, including, for instance, quartz oscillators, mechanical actuators with sub-atomic resolution and microbalances. However, the ability to synthesize two-dimensional (2D) materials may enable the fabrication of innovative devices with unprecedented performance. For instance, many materials which are not piezoelectric in their bulk form become piezoelectric when reduced to a single atomic layer; moreover, since all the atoms belong to the surface, piezoelectricity can be effectively engineered by proper surface modifications. As additional advantages, 2D materials are strong, flexible, easy to be co-integrated with conventional integrated circuits or micro-electromechanical systems and, in comparison with bulk or quasi-1D materials, easier to be simulated at the atomistic level. Here, we review the state of the art on 2D piezoelectricity, with reference to both computational predictions and experimental characterization. Because of their unique advantages, we believe 2D piezoelectric materials will substantially expand the applications of piezoelectricity.     

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

Identifying fruit disease manually is time-consuming, expert-required, and expensive; thus, a computer-based automated system is widely required. Fruit diseases affect not only the quality but also the quantity. As a result, it is possible to detect the disease early on and cure the fruits using computer-based techniques. However, computer-based methods face several challenges, including low contrast, a lack of dataset for training a model, and inappropriate feature extraction for final classification. In this paper, we proposed an automated framework for detecting apple fruit leaf diseases using CNN and a hybrid optimization algorithm. Data augmentation is performed initially to balance the selected apple dataset. After that, two pre-trained deep models are fine-tuning and trained using transfer learning. Then, a fusion technique is proposed named Parallel Correlation Threshold (PCT). The fused feature vector is optimized in the next step using a hybrid optimization algorithm. The selected features are finally classified using machine learning algorithms. Four different experiments have been carried out on the augmented Plant Village dataset and yielded the best accuracy of 99.8%. The accuracy of the proposed framework is also compared to that of several neural nets, and it outperforms them all.