Classification of Malaria-Infected Cells Using Deep Convolutional Neural Networks

Malaria is a life-threatening disease caused by parasites that are transmitted to people through the bites of infected mosquitoes. Automation of the diagnosis process will enable accurate diagnosis of the disease and hence holds the promise of delivering reliable health-care to resource-scarce areas. Machine learning technologies have been used for automated diagnosis of malaria. We present some of our recent progresses on highly accurate classification of malaria-infected cells using deep convolutional neural networks. First, we describe image processing methods used for segmentation of red blood cells from wholeslide images. We then discuss the procedures of compiling a pathologists-curated image dataset for training deep neural network, as well as data augmentation methods used to significantly increase the size of the dataset, in light of the overfitting problem associated with training deep convolutional neural networks. We will then compare the classification accuracies obtained by deep convolutional neural networks through training, validating, and testing with various combinations of the datasets. These datasets include the original dataset and the significantly augmented datasets, which are obtained using direct interpolation, as well as indirect interpolation using automatically extracted features provided by stacked autoencoders. This chapter ends with a discussion of further research

Real-Time Vehicle Detection from Short-range Aerial Image with Compressed MobileNet

Vehicle detection from short-range aerial image faces challenges including vehicle blocking, irrelevant object interference, motion blurring, color variation etc., leading to the difficulty to achieve high detection accuracy and real-time detection speed. In this paper, benefiting from the recent development in MobileNet family network engineering, we propose a compressed MobileNet which is not only internally resistant to the above listed challenges but also gains the best detection accuracy/speed tradeoff when comparing with the original MobileNet. In a nutshell, we reduce the bottleneck architecture number during the feature map downsampling stage but add more bottlenecks during the feature map plateau stage, neither extra FLOPs nor parameters are thus involved but reduced inference time and better accuracy are expected. We conduct experiment on our collected 5-k short-range aerial images, containing six vehicle categories: truck, car, bus, bicycle, motorcycle, crowded bicycles and crowded motorcycles. Our proposed compressed MobileNet achieves 110 FPS (GPU), 31 FPS (CPU) and 15 FPS (mobile phone), 1.2 times faster and 2% more accurate (mAP) than the original MobileNet

Effects of turbulator with round hole on the thermo-hydraulic performance of nanofluids in a triangle tube

© 2019 Elsevier Ltd For investigating the thermal and hydraulic characteristics of water-based SiO2 nanofluids in a triangular tube with different turbulators, an experimental system has been designed and verified in this paper. The effects of different round hole diameters (d = 3 mm, 4 mm, 5 mm) and round hole pitch-rows (l = 5 cm, 10 cm, 15 cm) of perforated turbulators on the thermo-hydraulic characteristics are researched. Meanwhile, the influences of Reynolds numbers (Re = 400–8000) and nanoparticles mass fractions (D-I water, ω = 0.1%, 0.3%, 0.5%) are also studied. These experimental results show that, under the same circumstance, the nanofluids in the triangular tube with ω = 0.5% have the largest positive influence on the heat transfer enhancement ratio which is up to 16.73%. For a comprehensive study of the flow and heat transfer, thermal efficiency (comprehensive performance index) and exergy efficiency are adopted. It can be found that the larger the diameter and the smaller the pitch-row of the holes is, the greater the comprehensive evaluation index can be. In addition, all working conditions exhibit the superior exergy efficiency. The highest exergy efficiency can be got when Re = 6000 and ω = 0.5%

Enhancing bioenergy production from the raw and defatted microalgal biomass using wastewater as the cultivation medium

Improving the efficiency of using energy and decreasing impacts on the environment will be an inevitable choice for future development. Based on this direction, three kinds of medium (modified anaerobic digestion wastewater, anaerobic digestion wastewater and a standard growth medium BG11) were used to culture microalgae towards achieving high-quality biodiesel products. The results showed that microalgae culturing with anaerobic digestate wastewater could increase lipid content (21.8%); however, the modified anaerobic digestion wastewater can boost the microalgal biomass production to 0.78 ± 0.01 g/L when compared with (0.35–0.54 g/L) the other two groups. Besides the first step lipid extraction, the elemental composition, thermogravimetric and pyrolysis products of the defatted microalgal residues were also analysed to delve into the utilisation potential of microalgae biomass. Defatted microalgae from modified wastewater by pyrolysis at 650 °C resulted in an increase in the total content of valuable products (39.47%) with no significant difference in the content of toxic compounds compared to other groups. Moreover, the results of the life cycle assessment showed that the environmental impact (388.9 mPET2000) was lower than that of raw wastewater (418.1 mPET2000) and standard medium (497.3 mPET2000)-cultivated groups. Consequently, the method of culturing microalgae in modified wastewater and pyrolyzing algal residues has a potential to increase renewable energy production and reduce environmental impact

New insights into the methods for predicting ground surface roughness in the age of digitalisation

Grinding is a multi-length scale material removal process that is widely employed to machine a wide variety of materials in almost every industrial sector. Surface roughness induced by a grinding operation can affect corrosion resistance, wear resistance, and contact stiffness of the ground components. Prediction of surface roughness is useful for describing the quality of ground surfaces, evaluate the efficiency of the grinding process and guide the feedback control of the grinding parameters in real-time to help reduce the cost of production. This paper reviews extant research and discusses advances in the realm of machining theory, experimental design and Artificial Intelligence related to ground surface roughness prediction. The advantages and disadvantages of various grinding methods, current challenges and evolving future trends considering Industry-4.0 ready new generation machine tools are also discussed

The modified method of reanalysis wind data in estuarine areas

High-quality wind field data are key to improving the accuracy of storm surge simulations in coastal and estuarine water. These data are also of great significance in studying the dynamic processes in coastal areas and safeguarding human engineering structures. A directional correction method for ECMWF reanalysis wind data was proposed in this paper based on the correlation with the measured wind speed and direction. The results show that the accuracies of wind speed and direction were improved after being modified by the correction method proposed in this paper. The modified wind data were applied to drive the storm surge model of the Yangtze Estuary for typhoon events, which resulted in a significant improvement to the accuracy of hindcasted water levels. The error of the hindcasted highest water levels was reduced by 16–19 cm

Valorisation of microalgae residues after lipid extraction: Pyrolysis characteristics for biofuel production

As a promising source of renewable energy, biofuel from microalgae pyrolysis is seen as a competitive alternative to fossil fuels. However, currently, the widely applied pre-treatment process of lipid extraction results in large amounts of microalgae residues, which though with energy potential, being considered as process wastes and ignored of its re-utilization potential. In this study, a new workflow of biofuel generation from microalgae biomass through lipid extraction and pyrolysis of defatted microalgae residues was proposed and assessed. The effects of lipid extraction and pyrolysis temperature (350–750 ℃) on pyrolysis products were investigated, and pyrolysis pathways were postulated. To address the twin goals of lowering emission of pollutants and elevating energy products, an optimal pyrolysis temperature of 650 ℃ was suggested. After extraction of lipids, the relative contents of valuable products (aromatic, aliphatic hydrocarbons and fatty acids) and some harmful by-products, e.g., PAHs, significantly reduced, while other harmful substrates, e.g., nitrogen-compounds increased. Mechanistic investigations indicated that pyrolysis of proteins without the presence of lipids could promote higher production of nitrogen-containing organics and aromatics. These results reveal the effects of lipid extraction and variation of temperature on microalgal pyrolysis, and also provide a basis for full utilization of microalgae as an aid to alleviate many fossil energy problems

Surface passivation for highly active, selective, stable, and scalable CO2 electroreduction

Electrochemical conversion of CO2 to formic acid using Bismuth catalysts is one the most promising pathways for industrialization. However, it is still difficult to achieve high formic acid production at wide voltage intervals and industrial current densities because the Bi catalysts are often poisoned by oxygenated species. Herein, we report a Bi3S2 nanowire-ascorbic acid hybrid catalyst that simultaneously improves formic acid selectivity, activity, and stability at high applied voltages. Specifically, a more than 95% faraday efficiency was achieved for the formate formation over a wide potential range above 1.0 V and at ampere-level current densities. The observed excellent catalytic performance was attributable to a unique reconstruction mechanism to form more defective sites while the ascorbic acid layer further stabilized the defective sites by trapping the poisoning hydroxyl groups. When used in an all-solid-state reactor system, the newly developed catalyst achieved efficient production of pure formic acid over 120 hours at 50 mA cm–2 (200 mA cell current)

Manipulating chiral-spin transport with ferroelectric polarization

A collective excitation of the spin structure in a magnetic insulator can transmit spin-angular momentum with negligible dissipation. This quantum of a spin wave, introduced more than nine decades ago, has always been manipulated through magnetic dipoles, (i.e., timereversal symmetry). Here, we report the experimental observation of chiral-spin transport in multiferroic BiFeO3, where the spin transport is controlled by reversing the ferroelectric polarization (i.e., spatial inversion symmetry). The ferroelectrically controlled magnons produce an unprecedented ratio of up to 18% rectification at room temperature. The spin torque that the magnons in BiFeO3 carry can be used to efficiently switch the magnetization of adja-cent magnets, with a spin-torque efficiency being comparable to the spin Hall effect in heavy metals. Utilizing such a controllable magnon generation and transmission in BiFeO3, an alloxide, energy-scalable logic is demonstrated composed of spin-orbit injection, detection, and magnetoelectric control. This observation opens a new chapter of multiferroic magnons and paves an alternative pathway towards low-dissipation nanoelectronics