Increasing Se concentration in maize grain with soil- or foliar-applied selenite on the Loess Plateau in China

AbstractSelenium (Se) is an essential mineral nutrient for animal and human growth. Deficiency in this element is a worldwide nutrition problem. Thus, this study determined the potential of increasing Se content in maize grain by using various Se fertiliser application techniques to improve the nutritional status of local residents. Field experiments were conducted on the Loess Plateau for two growing seasons to investigate the effects of different Se fertiliser application methods and application rates on the Se content in maize grain as well as the Se recovery, yield and status of other nutrients in maize grain under rain-fed conditions. Results show that soil and foliar Se applications exhibited no significant effects on maize biomass and grain yield as well as N, P, K, Ca, Mg, Fe, Mn, Cu and Zn contents in maize grain. However, both foliar and soil Se applications significantly improved the Se content in maize grain. Selenium content in maize grain is found to be linearly correlated with Se application rates, increasing from 0.12μgkg−1 to 0.33μgkg−1 by soil application at 1g of Se ha−1 and from 8.23μgkg−1 to 8.67μgkg−1 by foliar application at the same rate. Foliar application of Se showed higher Se recoveries in the grain compared with soil Se application: the former exhibited a maximum grain Se recovery rate of 52‰ and 106‰ in maize during the first and second growing seasons, respectively, whereas the latter was only 1.69‰ and 0.95‰, respectively. On the Loess Plateau in China, both soil and foliar Se applications effectively improved the Se content in maize grain. Compared with soil Se application, foliar Se application can improve the grain Se content in maize at reduced costs

Depositional Behaviour of Highly Macro-Crystalline Waxy Crude Oil Blended with Polymer Inhibitors in a Pipe with a 45-Degree Bend

Abstract Production, transportation and storage of highly waxy crude oil is very challenging. This is because they are usually characterised by high content of macro-crystalline waxes, predominantly consisting of n-alkanes (C18 to C36) that which could cause costly deposition within the wellbore and production equipment. The accumulation of deposited wax can decrease oil production rates, cause equipment breakdown, and clog the transport and storage facilities. Currently, different polymeric inhibitors have been utilised in the oil and gas field to mitigate and prevent wax deposition. However, as of today, there is no distinctive wax inhibitor that could work effectively for all oil fields. One of the objectives of this work is to study the efficacy of a blended commercial wax inhibitor - pour point depressant on wax deposition mitigation in a flow rig designed with 0 and 45-degree bends in the pipeline. Standard laboratory techniques using high-temperature gas chromatography (HTGC), rheometer rig, polarized microscope and elution chromatography were employed to obtain n-paraffin distribution, oil viscosity, WAT, pour point and SARA fractions. Series of experimentation were carried out with and without the inhibitor in a straight pipe test section. The severity of wax deposition in the pipeline built-in with a 45-degree bend is compared with a straight pipe. The blended inhibitor was tested at concentrations of 500, 1000, and 1500-ppm, under laminar and turbulent conditions. The crude oil sample was found to be naturally waxy with wax content of 19.75wt%, n-paraffin distributions ranges from C15-C74, WAT and pour point of 30°C and 25°C respectively. The severity of wax deposition in the test section is 43% higher in 45-degree bend compared to straight pipe. However, the severity of the deposition was reduced to 12.3% at extremely low temperature and flow rate. Nonetheless, better inhibition performance was achieved at 25 and 30°C. The wax thickness was reduced from δwax ≈ 0.36mm at 5 l/min to δwax ≈ 0.132mm at 7 l/min at constant coolant temperature (25°C) and 1500-ppm, whereas, no wax deposition was observed at 11 l/min. Mechanisms such as molecular diffusion due to frictional pressure losses, shear dispersion and gravity settling due to momentum change and hydrostatic, alongside with thermal difference are the main drivers for wax deposition in both straight and bend pipe. Whereas, the interaction mechanisms such as the nucleation, alongside with adsorption, co-crystallization, and solubilisation between the new blended inhibitor and the wax crystals provide an improved inhibition performance in the system even at extreme cases

A Mechanistic Model on Catalyst Deactivation by Coke Formation in a CSTR Reactor

A mechanistic model on catalyst deactivation by coke formation in a continuous stirred tank reactor (CSTR) has been developed in the paper. Catalyst deactivation by coke formation was treated as a surface reaction. Four reaction mechanisms representing coke formation through different routes were proposed. The evolved system of ordinary differential equations (ODEs) was solved numerically using MATLAB. This approach was validated by applying it to the skeletal isomerization of 1-pentene over ferrierite. Simulation results were compared qualitatively to those obtained from the literature. Simulation results indicated that coke formation is an extremely rapid process with fast formation of coke components on the strongest acid sites leading to final coke. The coke deposition is slower at higher residence times resulting in more stable product formation and weaker deactivation. The results obtained from this work revealed that the developed model is indeed able to successfully demonstrate the most essential features of catalyst deactivation by coke formation and are in agreement with the findings in the literature. Future work is aimed to extend the study to different reactors such as a plug flow reactor, in addition to analysis of the reaction system’s sensitivity to variables such as temperature and pressure

PSSA: PCA-domain superpixelwise singular spectral analysis for unsupervised hyperspectral image classification.

Although supervised classification of hyperspectral images (HSI) has achieved success in remote sensing, its applications in real scenarios are often constrained, mainly due to the insufficiently available or lack of labelled data. As a result, unsupervised HSI classification based on data clustering is highly desired, yet it generally suffers from high computational cost and low classification accuracy, especially in large datasets. To tackle these challenges, a novel unsupervised spatial-spectral HSI classification method is proposed. By combining the entropy rate superpixel segmentation (ERS), superpixel-based principal component analysis (PCA), and PCA-domain 2D singular spectral analysis (SSA), both the efficacy and efficiency of feature extraction are improved, followed by the anchor-based graph clustering (AGC) for effective classification. Experiments on three publicly available and five self-collected aerial HSI datasets have fully demonstrated the efficacy of the proposed PCA-domain superpixelwise SSA (PSSA) method, with a gain of 15–20% in terms of the overall accuracy, in comparison to a few state-of-the-art methods. In addition, as an extra outcome, the HSI dataset we acquired is provided freely online

Traceability Model Design and Validation for Precipitation Radar

Abstract: The propagating process of data chain of a large instrument is usually indicated by the traceability model. In this paper, to enhance the measurement accuracy of the precipitation radar, we proposed a traceability mode design scheme for the power measurement internal the precipitation radar, which is the key parameter relating to the precipitation measurement. Unlike the conventional internal calibration, we design three additional circuits, including temperature compensation, sectional amplification, auto-calibration power source, according to the traceability model. By introducing three internal detection points, the online soft-calibration data package is obtained by using internal power meters and the environmental sensors. According to the measured power information and the temperature information, the calibration data package provides the online calibration and the attenuation control. Practical internal calibration and real-time online measurement experiments are conducted to validate the design scheme. The experimental results reveal the traceability model design and corresponding soft calibration strategy can improve the reliability of the dynamical measurement data and the dynamical performance of the radar measurement

Reinforcement learning design for cancer clinical trials

We develop reinforcement learning trials for discovering individualized treatment regimens for life-threatening diseases such as cancer. A temporal-difference learning method called Q-learning is utilized which involves learning an optimal policy from a single training set of finite longitudinal patient trajectories. Approximating the Q-function with time-indexed parameters can be achieved by using support vector regression or extremely randomized trees. Within this framework, we demonstrate that the procedure can extract optimal strategies directly from clinical data without relying on the identification of any accurate mathematical models, unlike approaches based on adaptive design. We show that reinforcement learning has tremendous potential in clinical research because it can select actions that improve outcomes by taking into account delayed effects even when the relationship between actions and outcomes is not fully known. To support our claims, the methodology's practical utility is illustrated in a simulation analysis. In the immediate future, we will apply this general strategy to studying and identifying new treatments for advanced metastatic stage IIIB/IV non-small cell lung cancer, which usually includes multiple lines of chemotherapy treatment. Moreover, there is significant potential of the proposed methodology for developing personalized treatment strategies in other cancers, in cystic fibrosis, and in other life-threatening diseases

RSG: Fast Learning Adaptive Skills for Quadruped Robots by Skill Graph

Developing robotic intelligent systems that can adapt quickly to unseen wild situations is one of the critical challenges in pursuing autonomous robotics. Although some impressive progress has been made in walking stability and skill learning in the field of legged robots, their ability to fast adaptation is still inferior to that of animals in nature. Animals are born with massive skills needed to survive, and can quickly acquire new ones, by composing fundamental skills with limited experience. Inspired by this, we propose a novel framework, named Robot Skill Graph (RSG) for organizing massive fundamental skills of robots and dexterously reusing them for fast adaptation. Bearing a structure similar to the Knowledge Graph (KG), RSG is composed of massive dynamic behavioral skills instead of static knowledge in KG and enables discovering implicit relations that exist in be-tween of learning context and acquired skills of robots, serving as a starting point for understanding subtle patterns existing in robots' skill learning. Extensive experimental results demonstrate that RSG can provide rational skill inference upon new tasks and environments and enable quadruped robots to adapt to new scenarios and learn new skills rapidly

Recommended high performance telescope system design for the TianQin project

China is planning to construct a new space-borne gravitational-wave (GW) observatory, the TianQin project, in which the spaceborne telescope is an important component in laser interferometry. The telescope is aimed to transmit laser beams between the spacecrafts for the measurement of the displacements between proof-masses in long arms. The telescope should have ultra-small wavefront deviation to minimize noise caused by pointing error, ultra-stable structure to minimize optical path noise caused by temperature jitter, ultra-high stray light suppression ability to eliminate background noise. In this paper, we realize a telescope system design with ultra-stable structure as well as ultra-low wavefront distortion for the space-based GW detection mission. The design requirements demand extreme control of high image quality and extraordinary stray light suppression ability. Based on the primary aberration theory, the initial structure design of the mentioned four-mirror optical system is explored. After optimization, the maximum RMS wavefront error is less than lamda/300 over the full field of view (FOV), which meets the noise budget on the telescope design. The stray light noise caused by the back reflection of the telescope is also analyzed. The noise at the position of optical bench is less than 10-10 of the transmitted power, satisfying the requirements of space gravitational-wave detection. We believe that our design can be a good candidate for TianQin project, and can also be a good guide for the space telescope design in any other similar science project