Decoupling Control for Dual-Winding Bearingless Switched Reluctance Motor Based on Improved Inverse System Method

Dual-winding bearingless switched reluctance motor (BSRM) is a multivariable high-nonlinear system characterized by strong coupling, and it is not completely reversible. In this paper, a new decoupling control strategy based on improved inverse system method is proposed. Robust servo regulator is adopted for the decoupled plants to guarantee control performances and robustness. A phase dynamic compensation filter is also designed to improve system stability at high-speed. In order to explain the advantages of the proposed method, traditional methods are compared. The tracking and decoupling characteristics as well as disturbance rejection and robustness are deeply analyzed. Simulation and experiments results show that the decoupling control of dual-winding BSRM in both reversible and irreversible domains can be successfully resolved with the improved inverse system method. The stability and robustness problems induced by inverse controller can be effectively solved by introducing robust servo regulator and dynamic compensation filter

Complex Spatial Dynamics of Oncolytic Viruses In Vitro: Mathematical and Experimental Approaches

Oncolytic viruses replicate selectively in tumor cells and can serve as targeted treatment agents. While promising results have been observed in clinical trials, consistent success of therapy remains elusive. The dynamics of virus spread through tumor cell populations has been studied both experimentally and computationally. However, a basic understanding of the principles underlying virus spread in spatially structured target cell populations has yet to be obtained. This paper studies such dynamics, using a newly constructed recombinant adenovirus type-5 (Ad5) that expresses enhanced jellyfish green fluorescent protein (EGFP), AdEGFPuci, and grows on human 293 embryonic kidney epithelial cells, allowing us to track cell numbers and spatial patterns over time. The cells are arranged in a two-dimensional setting and allow virus spread to occur only to target cells within the local neighborhood. Despite the simplicity of the setup, complex dynamics are observed. Experiments gave rise to three spatial patterns that we call “hollow ring structure”, “filled ring structure”, and “disperse pattern”. An agent-based, stochastic computational model is used to simulate and interpret the experiments. The model can reproduce the experimentally observed patterns, and identifies key parameters that determine which pattern of virus growth arises. The model is further used to study the long-term outcome of the dynamics for the different growth patterns, and to investigate conditions under which the virus population eliminates the target cells. We find that both the filled ring structure and disperse pattern of initial expansion are indicative of treatment failure, where target cells persist in the long run. The hollow ring structure is associated with either target cell extinction or low-level persistence, both of which can be viewed as treatment success. Interestingly, it is found that equilibrium properties of ordinary differential equations describing the dynamics in local neighborhoods in the agent-based model can predict the outcome of the spatial virus-cell dynamics, which has important practical implications. This analysis provides a first step towards understanding spatial oncolytic virus dynamics, upon which more detailed investigations and further complexity can be built

Clean process to utilize the potassium-containing phosphorous rock with simultaneous HCl and KCl production via the steam-mediated reactions

In this paper, a clean process based on the steam-mediated reactions for simultaneous HCl and KCl production using the potassium (K)-containing phosphorous rock as a precursor is proposed. Through hydrochloric acid (HCl) leaching, not only the generation of H3PO4and CaCl2 (via further precipitation) were realized but also the acid-insoluble residue [phosphorous-rock slag (PS)] rich in elements, that is, K, Al, Si, and so on, in the form of microcline (KAlSi3O8) and quartz (SiO2) was obtained and became readily available for further HCl and KCl generation. Over 95 % of the elements, that is, K, Al, and Si, come into the final products, and the overall acid consumption (based on HCl) is significantly reduced (90%) due to recovery of acids. The impacts of the key operational parameters such as temperature, duration, and reagent impregnate ratio were rigorously analyzed via a supervised machine learning approach, and the optimal conditions were determined [reaction temperature, X1, 850 °C; reaction duration, X2, 40 min; and impregnate ratio (PS over CaCl2), X3, 2.5] with approximately ± 10% uncertainties. Thermodynamic analysis indicates that the introduction of steam to PS + CaCl2 not only enhances the chemical potential for the formation of HCl and KCl but also provides the transport advantage in continuously removing the generated products, that is, HCl and KCl, out of the system. Molecular simulation indicates that the presence of both steam and SiO2 in the PS matrix plays critical roles in decomposing PS + CaCl2 at high temperature. The shrinking core model shows that both the intrinsic kinetics and transport are influential with the activation energy being around 14.63 kJ/mol. The potential reaction pathway is postulated

Heat wave characteristics, mortality and effect modification by temperature zones: a time-series study in 130 counties of China

Background: The substantial disease burden attributed to heat waves, and their increasing frequency and intensity due to climate change, highlight the importance of understanding the health consequences of heat waves. We explore the mortality risk due to heat wave characteristics, including the timing in the seasons, the day of the heat wave, the intensity and the duration, and the modifying effect of temperature zones. Methods: Heat waves were defined as ≥ 2 days with a temperature ≥99th percentile for the county from 1 May through 30 September. Heat waves were characterized by their intensity, duration, timing in the season, and day of the heat wave. Within each county, we estimated the total non-accidental death and cardiovascular disease mortality during each heat wave compared with non-heat wave days by controlling for potential confounders in summer. We combined individual heat wave effect estimates using a random-effects model to calculate overall effects at the temperature zone and national levels. Results: The average daily total number of non-accidental deaths was nine in the warm season (across all the counties). Approximately half of the daily total number of non-accidental deaths were cardiovascular-related deaths (approximately four persons per day). The average and maximum temperatures across the study area were 23.1 °C (range: -1.2–35.9 °C) and 28.3 °C (range: 5.4–42.8 °C), respectively. The average relative humidity during the study was 68.9% (range: 8.0–100.0%). Heat waves increase the risk of total non-accidental death by 15.7% [95% confidence interval (CI): 12.5, 18.9] compared with non-heat wave periods, and the risk of cardiovascular-related death increases by 22.0% (95% CI: 16.9, 27.4). The risk of non-accidental death during the first heat wave of the season increases by 16.3% (95% CI: 12.6, 20.2), the risk during the second heat wave increases by 6.3% (95% CI: 2.8, 9.9) and during subsequent heat waves increases by -2.1% (95% CI: -4.6, 0.4). The first day and the second to third days of heat waves increase the risk of total non-accidental death by 11.7% (95% CI: 7.6, 15.9) and 17.0% (95% CI: 13.1, 21.0), respectively. Effects of heat waves on mortality lasted more than 4 days (6.3%, 95% CI: 2.4, 10.5) and are non-significantly different from the first day of heat waves. We found non-significant differences of the heat wave-associated mortality risks across mid-, warm and subtropical temperature zones. Conclusions: In China, the effect of heat waves on mortality is acute, and varies by certain characteristics of heat waves. Given these results, national heat wave early warning systems should be developed, as well as precautions and protection warranted according to characteristics of heat waves

Estimating mortality burden attributable to short-term PM2.5 exposure: A national observational study in China

Studies worldwide have estimated the number of deaths attributable to long-term exposure to fine airborne particles (PM2.5), but limited information is available on short-term exposure, particularly in China. In addition, most existing studies have assumed that short-term PM2.5-mortality associations were linear. For this reason, the use of linear exposure-response functions for calculating disease burden of short-term exposure to PM2.5 in China may not be appropriate. There is an urgent need for a comprehensive, evidence-based assessment of the disease burden related to short-term PM2.5 exposure in China. Here, we explored the non-linear association between short-term PM2.5 exposure and all-cause mortality in 104 counties in China; estimated county-specific mortality burdens attributable to short-term PM2.5 exposure for all counties in the country and analyzed spatial characteristics of the mortality burden due to short-term PM2.5 exposure in China. The pooled PM2.5-mortality association was non-linear, with a reversed J-shape. We found an approximately linear increased risk of mortality from 0 to 62 μg/m3 and decreased risk from 62 to 250 μg/m3. We estimated a total of 169,862 additional deaths from short-term PM2.5 exposure throughout China in 2015. Models using linear exposure-response functions for the PM2.5-mortality association estimated 32,186 deaths attributable to PM2.5 exposure, which is 5.3 times lower than estimates from the non-linear effect model. Short-term PM2.5 exposure contributed greatly to the death burden in China, approximately one seventh of the estimates from the chronic effect. It is essential and crucial to incorporate short-term PM2.5-related mortality estimations when considering the disease burden attributable to PM2.5 in developing countries such as China. Traditional linear effect models likely underestimated the mortality burden due to short-term exposure to PM2.5. Keywords: mortality burden, non-linear, PM2.5, short-ter

High-throughput estimation of plant height and above-ground biomass of cotton using digital image analysis and Canopeo

Plant height and above-ground biomass are important growth parameters that affect crop yield. Efficient and non-destructive technologies of crop phenotypic monitoring play crucial roles in intelligent farmland management. However, the feasibility of using these technologies to estimate cotton plant height and above-ground biomass has not been determined. This study proposed a low cost and high-throughput imaging method combined with Canopeo to extract the percentages of green color from high-definition digital images and establish a model to estimate the cotton plant height and above-ground biomass. The plant height and above-ground biomass field trials were conducted at two levels of irrigation (soil water content 70% ± 5% and 40%−45%, respectively) using 80 cotton genotypes. The linear fitting performed well across the different cotton genotypes (PH, R2 = 0.9829; RMSE = 2.4 cm; NRMSE = 11% and AGB, R2 = 0.9609; RMSE = 0.6 g / plant; and NRMSE = 5%), and two levels of irrigation (PH, R2 = 0.9604; RMSE = 2.15 cm; NRMSE = 6% and AGB, R2 = 0.9650; RMSE = 4.51 g/plant; and NRMSE = 17%). All reached a higher fitting degree. Additionally, the most comprehensive model to estimate the cotton plant height and above-ground biomass (Y = 0.4832*X + 11.04; Y = 0.4621*X − 0.3591) was determined using a simple linear regression modeling method. The percentages of green color positively correlated with plant height and above-ground biomass, and each model exhibited higher accuracy (R2 ≥ 0.8392, RMSE ≤ 0.0158, NRMSE ≤ 0.06%). Combining a high-definition digital camera with Canopeo enables the prediction of crop growth in the field. The simple linear regression modeling method and the most comprehensive model enable the rapid estimation of the cotton plant height and above-ground biomass. This method can also be used as a baseline to measure other important crop phenotypes

Identification of SNPs and Candidate Genes Associated With Salt Tolerance at the Seedling Stage in Cotton (Gossypium hirsutum L.)

Salt tolerance in cotton is highly imperative for improvement in the response to decreasing farmland and soil salinization. However, little is known about the genetic basis underlying salt tolerance in cotton, especially the seedling stage. In this study, we evaluated two salt-tolerance-related traits of a natural population comprising 713 upland cotton (Gossypium hirsutum L.) accessions worldwide at the seedling stage and performed a genome-wide association study (GWAS) to identify marker-trait associations under salt stress using the Illumina Infinium CottonSNP63K array. A total of 23 single nucleotide polymorphisms (SNPs) that represented seven genomic regions on chromosomes A01, A10, D02, D08, D09, D10, and D11 were significantly associated with the two salt-tolerance-related traits, relative survival rate (RSR) and salt tolerance level (STL). Of these, the two SNPs i46598Gh and i47388Gh on D09 were simultaneously associated with the two traits. Based on all loci, we screened 280 possible candidate genes showing different expression levels under salt stress. Most of these genes were involved in transcription factors, transporters and enzymes and were previously reported as being involved in plant salt tolerance, such as NAC, MYB, NXH, WD40, CDPK, LEA, and CIPK. We further validated six putative candidate genes by qRT-PCR and found a differential expression level between salt-tolerant and salt-sensitive varieties. Our findings provide valuable information for enhancing the understanding of complicated mechanisms of salt tolerance in G. hirsutum seedlings and cotton salt tolerance breeding by molecular marker-assisted selection