Influence of different pulsed electric field strengths on the quality of the grapefruit juice

Grapefruit juice was investigated using pulsed electric fields (PEF) with (electric field strengths: 0, 5, 10, 15, 20 and 25 kV cm^?1; flow rate: 80 mL min^?1; pulse frequency: 1 kHz at 40 °C for 600 μs). Total phenolics, DPPH, total antioxidant capacity (TAC), total anthocyanins, total carotenoids, sugars and physicochemical properties of grapefruit juice were studied. In addition, the effect of PEF treatment on micro‐organisms was also observed. Results indicated that no significant change was observed in pH, Brix, titratable acidity, sugars, total anthocyanins and colour attributes with the increase in pulsed electric strength as compared to control treatment. However, significant decrease in nonenzymatic browning (NEB) and viscosity while an increase in cloud value, DPPH, TAC, total phenolics and total carotenoids, while reduction in the activity of micro‐organisms, was also observed with the increase in pulsed electric strength as compared to control treatment. This study suggested that PEF at 25 kV cm^?1 could improve the quality of grapefruit juice.     

Synthesis of meso-macro alumina using yeast cells as a bio-template and optimization using a genetic algorithm

Meso-Macro porous alumina was fabricated using yeast cells as a pore-forming agent. Alumina powder synthesis was achieved by a low cost process (recrystallisation of alum).The effect of the pore forming agent on the true porosity, bulk density and thermal conductivity of porous alumina was characterized. The results show that the true porosity increased with the increasi ng addition of yeast cells. The bulk density and thermal conductivity at room temperature decreased with the increasing yeast addition. A genetic algorithm method was used to minimize the thermal conductivity of the macro-porous alumina based on the amount of yeast cells used, the sintering temperature, and the hold time. The genetic algorithm found that the best thermal conductivity achievable was equal to 0.152 Watt/m. °C at 20wt% concentration of yeast, a sintering temperature of 1230°C and 1.5 hours of soaking time. The experimental value was 0.14 Watt/m. °C and the slight variance between these values were postulated to be due to experimental error in the measurements.     

Chamaerops humilis L. var. argentea André Date Palm Seed Oil: A Potential Dietetic Plant Product

Chamaerops humilis L. var. argentea André (C. humilis) date palm seeds are an underutilized source of vegetable oil, and no studies describing their physicochemical characteristics to indicate the potential uses of this seed or seed oil have been reported. The oil content of the seeds is about 10%, mainly composed of oleic acid (38.71%), lauric acid (21.27%), linoleic acid (15.15%), palmitic acid (9.96%), and stearic acid (7.17%). The tocol (tocopherols and tocotrienols) content is 74 mg/100 g, with δ‐tocotrienol as the major contributor (31.91%), followed by α‐tocotrienol (29.37%), γ‐tocopherol (20.16%), and γ‐tocotrienol (11.86%). Furthermore, this oil shows high thermal stability. The differential scanning calorimetery curves revealed that the melting and crystallization points are 9.33 °C and –15.23 °C, respectively.     

User oriented techniques to support interaction and decision making with large educational databases

Information Technology is developing rapidly and providing policy/decision makers with large amounts of information that require processing and analysis. Decision support systems (DSS) aim to provide tools that not only help such analyses, but enable the decision maker to experiment and simulate the effects of different policies and selection strategies. The specific context of this research, set in Saudi Arabia, is administrative decision making using large educational databases.     

Failure of an Oil Pump Bevel Gear

Two oil pump bevel gears suffered both breakage and wear. The oil pump was a gas turbine oil pump that had been in service for 3 years. Material and case-hardening examinations showed that both of these were satisfactory. Also, lube oil analysis showed no abnormality of any kind. The probable mechanism for the breakage and wear is shock overloading followed by abrasive wear caused by the debris of the broken teeth.     

Effects of particle–fluid coupling on particle transport and capture in a magnetophoretic microsystem

A numerical analysis is presented of the effects of particle–fluid coupling on the transport and capture of magnetic particles in a microfluidic system under the influence of an applied magnetic field. Particle motion is predicted using a computational fluid dynamic CFD-based Lagrangian–Eulerian approach that takes into account dominant particle forces as well as two-way particle–fluid coupling. Two dimensionless groups are introduced that characterize particle capture, one that scales the magnetic and hydrodynamic forces on the particle and another that scales the distance to the magnetic field source. An analysis is preformed to parameterize capture efficiency with respect to the dimensionless numbers for both one-way and two-way particle–fluid coupling. For one-way coupling, in which the flow field is uncoupled from particle motion, correlations are developed that provide insight into system performance towards optimization. The difference in capture efficiency for one-way versus two-way coupling is analyzed and quantified. The analysis demonstrates that one-way coupling, in the dilute limit, provides a conservative estimate of capture efficiency in that it overpredicts the magnetic force needed to ensure particle capture as compared with a more rigorous fully coupled analysis. In two-way coupling there is a cooperative effect between the magnetic force and a particle-induced fluidic force that enhances capture efficiency. Thus, while one-way coupling is useful for rapid parametric screening of particle capture performance, more accurate predictions require two-way particle–fluid coupling. This is especially true when considering higher capture efficiencies and/or higher particle concentrations.     

Automated Machine Learning Enabled Cybersecurity Threat Detection in Internet of Things Environment

Recently, Internet of Things (IoT) devices produces massive quantity of data from distinct sources that get transmitted over public networks. Cybersecurity becomes a challenging issue in the IoT environment where the existence of cyber threats needs to be resolved. The development of automated tools for cyber threat detection and classification using machine learning (ML) and artificial intelligence (AI) tools become essential to accomplish security in the IoT environment. It is needed to minimize security issues related to IoT gadgets effectively. Therefore, this article introduces a new Mayfly optimization (MFO) with regularized extreme learning machine (RELM) model, named MFO-RELM for Cybersecurity Threat Detection and classification in IoT environment. The presented MFO-RELM technique accomplishes the effectual identification of cybersecurity threats that exist in the IoT environment. For accomplishing this, the MFO-RELM model pre-processes the actual IoT data into a meaningful format. In addition, the RELM model receives the pre-processed data and carries out the classification process. In order to boost the performance of the RELM model, the MFO algorithm has been employed to it. The performance validation of the MFO-RELM model is tested using standard datasets and the results highlighted the better outcomes of the MFO-RELM model under distinct aspects.     

Hybrid Multi-Strategy Aquila Optimization with Deep Learning Driven Crop Type Classification on Hyperspectral Images

Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed scenes. Much spatial information and spectral signatures of hyperspectral images (HSIs) present greater potential for detecting and classifying fine crops. The accurate classification of crop kinds utilizing hyperspectral remote sensing imaging (RSI) has become an indispensable application in the agricultural domain. It is significant for the prediction and growth monitoring of crop yields. Amongst the deep learning (DL) techniques, Convolution Neural Network (CNN) was the best method for classifying HSI for their incredible local contextual modeling ability, enabling spectral and spatial feature extraction. This article designs a Hybrid Multi-Strategy Aquila Optimization with a Deep Learning-Driven Crop Type Classification (HMAODL-CTC) algorithm on HSI. The proposed HMAODL-CTC model mainly intends to categorize different types of crops on HSI. To accomplish this, the presented HMAODL-CTC model initially carries out image preprocessing to improve image quality. In addition, the presented HMAODL-CTC model develops dilated convolutional neural network (CNN) for feature extraction. For hyperparameter tuning of the dilated CNN model, the HMAO algorithm is utilized. Eventually, the presented HMAODL-CTC model uses an extreme learning machine (ELM) model for crop type classification. A comprehensive set of simulations were performed to illustrate the enhanced performance of the presented HMAODL-CTC algorithm. Extensive comparison studies reported the improved performance of the presented HMAODL-CTC algorithm over other compared methods.