SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition

(Aim) The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022. Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients. (Method) Two datasets are chosen for this study. The multiple-way data augmentation, including speckle noise, random translation, scaling, salt-and-pepper noise, vertical shear, Gamma correction, rotation, Gaussian noise, and horizontal shear, is harnessed to increase the size of the training set. Then, the SqueezeNet (SN) with complex bypass is used to generate SN features. Finally, the extreme learning machine (ELM) is used to serve as the classifier due to its simplicity of usage, quick learning speed, and great generalization performances. The number of hidden neurons in ELM is set to 2000. Ten runs of 10-fold cross-validation are implemented to generate impartial results. (Result) For the 296-image dataset, our SNELM model attains a sensitivity of 96.35 ± 1.50%, a specificity of 96.08 ± 1.05%, a precision of 96.10 ± 1.00%, and an accuracy of 96.22 ± 0.94%. For the 640-image dataset, the SNELM attains a sensitivity of 96.00 ± 1.25%, a specificity of 96.28 ± 1.16%, a precision of 96.28 ± 1.13%, and an accuracy of 96.14 ± 0.96%. (Conclusion) The proposed SNELM model is successful in diagnosing COVID-19. The performances of our model are higher than seven state-of-the-art COVID-19 recognition models.     

Fly ash-based geopolymer for Pb removal from aqueous solution

The aim of this work was to synthesis highly amorphous geopolymer from waste coal fly ash, to be used as an adsorbent for lead Pb(II) removal from aqueous wastewater. The effect of various parameters including geopolymer dosage, initial concentration, contact time, pH and temperature on lead adsorption were investigated. The major components of the used ash in the current study were SiO(2), Al(2)O(3) and Fe(2)O(3) representing 91.53 wt% of its mass. It was found that the synthesized geopolymer has higher removal capacity for lead ions when compared with that of raw coal fly ash. The removal efficiency increases with increasing geopolymer dosage, contact time, temperature, and the decrease of Pb(2+) initial concentration. The optimum removal efficiency was obtained at pH 5. Adsorption isotherm study indicated that Langmuir isotherm model is the best fit for the experimental data than Freundlich model. It was found also that the adsorption process is endothermic and more favorable at higher temperatures.     

Adaptive Fuzzy Logic Controller for Harmonics Mitigation Using Particle Swarm Optimization

An excessive use of non-linear devices in industry results in current harmonics that degrades the power quality with an unfavorable effect on power system performance. In this research, a novel control technique-based Hybrid-Active Power-Filter (HAPF) is implemented for reactive power compensation and harmonic current component for balanced load by improving the Power-Factor (PF) and Total–Hormonic Distortion (THD) and the performance of a system. This work proposed a soft-computing technique based on Particle Swarm-Optimization (PSO) and Adaptive Fuzzy technique to avoid the phase delays caused by conventional control methods. Moreover, the control algorithms are implemented for an instantaneous reactive and active current (I_d-I_q) and power theory (Pq0) in SIMULINK. To prevent the degradation effect of disturbances on the system's performance, PS0-PI is applied in the inner loop which generate a required dc link-voltage. Additionally, a comparative analysis of both techniques has been presented to evaluate and validate the performance under balanced load conditions. The presented result concludes that the Adaptive Fuzzy PI controller performs better due to the non-linearity and robustness of the system. Therefore, the gains taken from a tuning of the PSO based PI controller optimized with Fuzzy Logic Controller (FLC) are optimal that will detect reactive power and harmonics much faster and accurately. The proposed hybrid technique minimizes distortion by selecting appropriate switching pulses for VSI (Voltage Source Inverter), and thus the simulation has been taken in SIMULINK/MATLAB. The proposed technique gives better tracking performance and robustness for reactive power compensation and harmonics mitigation. As a result of the comparison, it can be concluded that the PSO-based Adaptive Fuzzy PI system produces accurate results with the lower THD and a power factor closer to unity than other techniques.     

Processing of metallic fiber hybrid spun yarns for better electrical conductivity

This research was aimed at processing of metallic fiber hybrid spun yarns consisting of polyester/stainless steel and viscose/stainless steel staple fibers to achieve better electrical conductivity. Conventional ring spinning machine and ring twister machine were used to produce the single and plied yarns respectively. The linear electrical resistance of yarns was analyzed with reference to the three levels of twist multiplier (TM) for same yarn count, three levels of yarn fineness (Ne) at the same TM level, and number of plies for the same final yarn count. These results showed that by increasing twist, the electrical conductivity of yarn was increased. However, yarn fineness was in inverse relation with the electrical conductivity of yarns. The effect of yarn plying and twisting to produce the Ne 10s yarn was also found critical in governing the electrical properties. The electrical conductivity of viscose and stainless steel hybrid yarn was found more sensitive to increase with an increase in relative humidity contrary to that of polyester and stainless steel hybrid yarns. The findings of the study are significant to produce the hybrid spun conductive yarns for their potential applications in a variety of tailor-made functional, protective and smart textiles.     

Controller Design Using Ant Colony Algorithm for a Non-inverting Buck–Boost Chopper Based on a Detailed Average Model

In this article, the non-inverting buck–boost converter and its operation modes are scrutinized. The closed-loop stability of the converter in buck and boost modes is analyzed, and the necessity of using an appropriated controller is demonstrated. Then the application of an adapted ant colony optimization to design a feedback controller is proposed, and a controller based on its existing model is tuned. Simulation and experimental results obtained from the ant colony optimization designed controller are then compared with a controller designed with the classic method. Although the simulation and experimental results prove the efficiency of the proposed control approach, a significant difference between controller behavior in practice and simulation is obvious. Finding these differences, more detailed models, including all parasitic elements, in the buck and boost modes are derived. Applying the proposed model in controller design illustrates that the desired performance of the converter can be guaranteed with a simple proportional-integral (PI) controller. The suggested ant colony-based controller is again tuned based on the more detailed model, which improves the performance of the converter system even more. Furthermore, good agreement between analytical and experimental outputs validates the accuracy of the modeling and simulation.     

Kinetic modeling of pyrolysis of three Iranian waste oils in a micro-fluidized bed

Different approaches have been used to convert the waste materials into a clean syngas or other chemicals such as methanol. Among them, pyrolysis is a good candidate to produce the synthesis gas and volatile matters for industrial and refinery applications. In this work, we studied the kinetic and chemical behavior of three Iranian waste oils through a kinetic model and an experimental study. The experiments carried out in a micro-FB reactor, which is a good option for low emissions. Results showed that the reaction temperature and reaction rate are two of the most important factors for maximum conversion level of fuel. Results also showed an optimum value for reaction rate. The modeling results validated against the experimental measurements and found to be in good agreements.     

Sargassum swartzii extracts ameliorate memory functions by neurochemical modulation in a rat model

Food Science and Biotechnology - Recently, considerable attention has been paid to drug exploration from natural sources for treating memory loss, a major manifestation of various neurodegenerative...     

A Probabilistic Multiperiod Simulation–Optimization Approach for Dynamic Coastal Aquifer Management

Combined simulation–optimization (CSO) schemes are common in the literature to solve different groundwater management problems, and CSO is particularly well-established in the coastal aquifer management literature. However, with a few exceptions, nearly all previous studies have employed the CSO approach to derive static groundwater management plans that remain unchanged during the entire management period, consequently overlooking the possible positive impacts of dynamic strategies. Dynamic strategies involve division of the planning time interval into several subintervals or periods, and adoption of revised decisions during each period based on the most recent knowledge of the groundwater system and its associated uncertainties. Problem structuring and computational challenges seem to be the main factors preventing the widespread implementation of dynamic strategies in groundwater applications. The objective of this study is to address these challenges by introducing a novel probabilistic Multiperiod CSO approach for dynamic groundwater management. This includes reformulation of the groundwater management problem so that it can be adapted to the multiperiod CSO approach, and subsequent employment of polynomial chaos expansion-based stochastic dynamic programming to obtain optimal dynamic strategies. The proposed approach is employed to provide sustainable solutions for a coastal aquifer storage and recovery facility in Oman, considering the effect of natural recharge uncertainty. It is revealed that the proposed dynamic approach results in an improved performance by taking advantage of system variations, allowing for increased groundwater abstraction, injection and hence monetary benefit compared to the commonly used static optimization approach.

     

N-3 Polyunsaturated Fatty Acids Modulate In-Vitro T Cell Function in Type I Diabetic Patients

In this work, we assessed the in-vitro effects of eicosapentaenoic acid (EPA; C20:5n-3) and docosahexaenoic acid (DHA; C22:6n-3) (final concentration, 15 microM) on T cell blastogenesis, interleukin-2 and -4 (IL-2, IL-4) secretion, fatty acid composition and intracellular oxidative status in type I diabetic patients with or without complications. Con A stimulated lymphocyte proliferation, glucose uptake, intracellular reduced glutathione levels and catalase activity were lower in diabetics as compared to controls, regardless to the presence of complications. EPA and DHA diminished T-lymphocyte proliferation and IL-2 production but enhanced IL-4 secretion in both diabetic and control groups. No changes in the levels of reduced glutathione and in the activities of catalase and SOD were observed in control T cells cultured in the presence of EPA and DHA. However, in diabetic patients, addition of n-3 PUFA to culture induced an increase in T cell levels of reduced glutathione and hydroperoxide, and in activities of catalase and SOD. Low levels of arachidonic acid (C20:4n-6) were found in plasma membrane phospholipids of lymphocytes from diabetic patients compared to controls. Incubation of lymphocytes with EPA and DHA was associated with an incorporation of these fatty acids in membrane phospholipids. In conclusion, the beneficial effects of n-3 PUFA on T cell functions in type I diabetes could be attributed to their suppressive action and modulation of cytokine secretion, and to the improvement of intracellular oxidative status.