Quantifying reasonable crop yield gaps and determining potential regions for yield improvement can facilitate regional plant structure adjustment and promote crop production. The current study attempted to evaluate the yield gap in a region at multi-scales through model simulation and farmer investigation. Taking the winter wheat yield gap in the Huang-Huai-Hai farming region (HFR) for the case study, 241 farmers’ fields in four typical high-yield demonstration areas were surveyed to determine the yield limitation index and attainable yield. In addition, the theoretical and realizable yield gap of winter wheat in 386 counties of the HFR was assessed. Results showed that the average field yield of the demonstration plots was 8282 kg/ha, accounting for 0.72 of the potential yield, which represented the highest production in the region. The HFR consists of seven sub-regions designated 2.1–2.7: the largest attainable yield gap existed in the 2.6 sub-region, in the southwest of the HFR, while the smallest was in the 2.2 sub-region, in the northwest of the HFR. With a high irrigated area rate, the yield gap in the 2.2 sub-region could hardly be reduced by increasing irrigation, while a lack of irrigation remained an important limiting factor for narrowing the yield gap in 2.3 sub-region, in the middle of the HFR. Therefore, a multi-scale yield gap evaluation framework integrated with typical field survey and crop model analysis could provide valuable information for narrowing the yield gap.

We highlight the current understanding of mechanisms of phase transformation, strengthening, and the role of alloying elements in aluminum, magnesium, and titanium alloys, including nucleation and growth of precipitates, precipitate–dislocation interactions, solute segregation at precipitate–matrix interfaces and planar defects, and the development of strengthening models that account for the real particle shape. Future directions such as atomic-scale elemental mapping and computation, and the influence of particle shape on mechanical properties are discussed. With the combination of advanced characterization and computational tools, it is anticipated that much less time will be needed to develop the next generation of light alloys.

The elastic polymer composite embedded with carbon nanotubes (CNTs) is an ideal candidate for stretchable and flexible sensor fabrication due to the perfect combination between the excellent properties of CNTs and the high stretchability of the elastomer. A cube model of nanotube/polymer composite is constructed to comprehensively and theoretically analyze its electrical behavior, which is dominantly governed by the CNT network. The aspect ratio and alignment of CNTs significantly influence both the percolation threshold range and the electrical conductivity; however, the electrical conductivity of CNTs has little impact on the percolation threshold. The piezoresistivity of the composite is not only governed by the property of CNTs but also by the mechanical property of the polymer matrix, including the Poisson’s ratio and alignment of CNTs. The specific reasons why the composite resistance rises when it is stretched are investigated. Finally, one optimizing suggestion is given for making the CNTs/polymer composite with high sensitivity.

The uptake of genomic selection (GS) by the swine industry is still limited by the costs of genotyping. A feasible alternative to overcome this challenge is to genotype animals using an affordable low-density (LD) single nucleotide polymorphism (SNP) chip panel followed by accurate imputation to a high-density panel. Therefore, the main objective of this study was to screen incremental densities of LD panels in order to systematically identify one that balances the tradeoffs among imputation accuracy, prediction accuracy of genomic estimated breeding values (GEBVs), and genotype density (directly associated with genotyping costs). Genotypes using the Illumina Porcine60K BeadChip were available for 1378 Duroc (DU), 2361 Landrace (LA) and 3192 Yorkshire (YO) pigs. In addition, pseudo-phenotypes (de-regressed estimated breeding values) for five economically important traits were provided for the analysis. The reference population for genotyping imputation consisted of 931 DU, 1631 LA and 2103 YO animals and the remainder individuals were included in the validation population of each breed. A LD panel of 3000 evenly spaced SNPs (LD3K) yielded high imputation accuracy rates: 93.78% (DU), 97.07% (LA) and 97.00% (YO) and high correlations (>0.97) between the predicted GEBVs using the actual 60 K SNP genotypes and the imputed 60 K SNP genotypes for all traits and breeds. The imputation accuracy was influenced by the reference population size as well as the amount of parental genotype information available in the reference population. However, parental genotype information became less important when the LD panel had at least 3000 SNPs. The correlation of the GEBVs directly increased with an increase in imputation accuracy. When genotype information for both parents was available, a panel of 300 SNPs (imputed to 60 K) yielded GEBV predictions highly correlated (⩾0.90) with genomic predictions obtained based on the true 60 K panel, for all traits and breeds. For a small reference population size with no parents on reference population, it is recommended the use of a panel at least as dense as the LD3K and, when there are two parents in the reference population, a panel as small as the LD300 might be a feasible option. These findings are of great importance for the development of LD panels for swine in order to reduce genotyping costs, increase the uptake of GS and, therefore, optimize the profitability of the swine industry.

Whether monozygotic (MZ) and dizygotic (DZ) twins differ from each other in a variety of phenotypes is important for genetic twin modeling and for inferences made from twin studies in general. We analyzed whether there were differences in individual, maternal and paternal education between MZ and DZ twins in a large pooled dataset. Information was gathered on individual education for 218,362 adult twins from 27 twin cohorts (53% females; 39% MZ twins), and on maternal and paternal education for 147,315 and 143,056 twins respectively, from 28 twin cohorts (52% females; 38% MZ twins). Together, we had information on individual or parental education from 42 twin cohorts representing 19 countries. The original education classifications were transformed to education years and analyzed using linear regression models. Overall, MZ males had 0.26 (95% CI [0.21, 0.31]) years and MZ females 0.17 (95% CI [0.12, 0.21]) years longer education than DZ twins. The zygosity difference became smaller in more recent birth cohorts for both males and females. Parental education was somewhat longer for fathers of DZ twins in cohorts born in 1990–1999 (0.16 years, 95% CI [0.08, 0.25]) and 2000 or later (0.11 years, 95% CI [0.00, 0.22]), compared with fathers of MZ twins. The results show that the years of both individual and parental education are largely similar in MZ and DZ twins. We suggest that the socio-economic differences between MZ and DZ twins are so small that inferences based upon genetic modeling of twin data are not affected.

The use of limiters may impact both accuracy and resolution of a solution. And the accuracy and resolution are highly dependent on the amount of numerical dissipation in a scheme, so the ability of limiters to control numerical dissipation should be improved. In this view, based on the examination of several classical limiters to control dissipation, a class of general piecewise-linear flux limiters (termed GPL limiters) are presented in this paper for Multi-step time-space-separated unsteady schemes. The GPL limiters can satisfy the second-order TVD criterion and contain some existing limiters such as Superbee and Minmod. Using the decrement of discrete total entropy to represent the amount of numerical dissipation, an entropy dissipation function of GPL limiters is defined with three parameter variables. By proving the monotonicity of this function, a new GPL type limiter (named MDF individually), which can minimize the numerical dissipation and improve the calculation accuracy, is proposed. A high resolution entropy-consistent scheme is obtained by MDF limiter, which will be proved to satisfy entropy stability and entropy consistency. Computational results of this scheme for several 1-D and 2-D Euler test cases are presented, demonstrating the accuracy, monotonicity and robustness of MDF limiter.

Introduction: Burnout rates for emergency physicians (EP) continue to be amongst the highest in medicine. One of the commonly cited sources of stress contributing to disillusionment is bureaucratic tasks that distract EPs from direct patient care in the emergency department (ED). The novel position of Physician Navigator was created to help EPs decrease their non-clinical workload during shifts, and improve productivity. Physician Navigators are non-licensed healthcare team members that assist in activities which are often clerical in nature, but directly impact patient care. This program was implemented at no net-cost to the hospital or healthcare system. Methods: In this retrospective study, 6845 clinical shifts worked by 20 EPs over 39 months from January 1, 2012 to March 31, 2015 were evaluated. The program was implemented on April 1, 2013. The primary objective was to quantify the effect of Physician Navigators on measures of EP productivity: patient seen per hour (Pt/hr), and turn-around-time (TAT) to discharge. Secondary objectives included examining the impact of Physician Navigators on measures of ED throughput for non-resuscitative patients: emergency department length of stay (LOS), physician-initial-assessment times (PIA), and left-without-being-seen rates (LWBS). A mixed linear model was used to evaluate changes in productivity measures between shifts with and without Physician Navigators in a clustered design, by EP. Autoregressive modelling was performed to compare ED throughput metrics before and after the implementation of Physician Navigators for non-resuscitative patients. Results: Across 20 EPs, 2469 shifts before, and 4376 shifts after April 1, 2013 were analyzed. Daily patient volumes increased 8.7% during the period with Physician Navigators. For the EPs who used Physician Navigators, Pt/hr increased by 1.07 patients per hour (0.98 to 1.16, p<0.001), and TAT to discharge decreased by 10.6 minutes (-13.2 to -8.0, p<0.001). After the implementation of the Physician Navigators, overall LOS for non-resuscitative patients decreased by 2.6 minutes (1.0%, p=0.007), and average PIA decreased by 7.4 minutes (12.0%, p<0.001). LBWS rates decreased by 43.9% (0.50% of daily patient volume, p<0.001). Conclusion: The use of a Physician Navigator was associated with increased EP productivity as measured by Pt/hr, and TAT to discharge, and reductions in ED throughput metrics for non-resuscitative patients.

Growing interest in the use of CO2 as a feedstock for fuel generation has led to increased interest in solar CO2 electrolysis for renewable fuel generation which has a variety of applications ranging from providing renewable sources for energy-dense carbon fuels, to curbing high-density emissions from power plants, industries and automobiles. The challenges of integrated solar-to-carbon fuel converters, where the photovoltaic (PV) material is immersed in the electrolyte, are well-known: the need for unique PV cell designs; material incompatibility; corrosion; and optical losses. In this paper, a PV-electrolysis system is presented, where a flow-cell electrolyzer is power-matched to a high-performance solar PV module array which has two system design advantages: 1) use of standard PV cells external to the electrolyzer, which allows de-coupling the design, fabrication and operation of the PV system from that of the electrolyzer; and 2) enabling optimization of the PV configuration to maximize power coupling efficiency to the specific electrolyzer Tafel curve, with or without the use of electronic power-conditioning devices. The implemented system resulted in a peak SFE of 6.5%, a competitive solar-to-fuel efficiency (SFE) figure to those reported in literature.

In individuals with latent tuberculosis (TB) infection, those living with human immunodeficiency virus (HIV) had a 20–37 times higher risk of developing active TB compared to those without HIV infection. Systematic testing and treatment of latent TB infection are priorities in HIV-infected persons. In China, the prevalence of HIV infection in men who have sex with men (MSM) has gradually increased in the past decade. However, the prevalence of TB infection has been studied sparsely in HIV-infected MSM. Hence, we conducted a pilot study in MSM living with HIV infection in Xi'an city to evaluate TB infection status by means of interferon-γ release assay (IGRA). A total of 182 HIV-infected MSM were included in this study, the prevalence of IGRA positivity was observed to be 8·79% (16/182). IGRA quantitative results were not statistically influenced by the CD4 cell counts of the study participants. However, IGRA positivity was found to be lower than our previously reported data from the general population. This suggests that immunological deficiency might decrease the sensitivity of IGRA and thus increase the number of false negatives. Our primary results, suggesting systematic testing and treatment of latent TB infection together with active case-finding, were equally important for TB control in persons living with HIV infection.

Imaging atmospheric Cherenkov telescopes (IACTs) are sensitive to rare gamma-ray photons, buried in the background of charged cosmic-ray (CR) particles, the flux of which is several orders of magnitude greater. The ability to separate gamma rays from CR particles is important, as it is directly related to the sensitivity of the instrument. This gamma-ray/CR-particle classification problem in IACT data analysis can be treated with the rapidly-advancing machine learning algorithms, which have the potential to outperform the traditional box-cut methods on image parameters. We present preliminary results of a precise classification of a small set of muon events using a convolutional neural networks model with the raw images as input features. We also show the possibility of using the convolutional neural networks model for regression problems, such as the radius and brightness measurement of muon events, which can be used to calibrate the throughput efficiency of IACTs.

A total of 1145 samples were collected from chicken breeder farms, hatcheries, broiler farms, a slaughterhouse and retail refrigerated chicken stores in an integrated broiler supply chain in Guangdong Province, China, in 2013. One-hundred and two Salmonella enterica strains were isolated and subjected to serotyping, antimicrobial susceptibility testing, virulence profile determination and molecular subtyping by pulsed field gel electrophoresis (PFGE). The contamination rates in samples from breeder farms, hatcheries, broiler farms, the slaughterhouse and retail stores were 1·46%, 4·31%, 7·00%, 62·86% and 54·67%, respectively. The isolated strains of S. enterica belonged to 10 serotypes; most of them were S. Weltevreden (46·08%, 47/102) and S. Agona (18·63%, 19/102). Isolates were frequently resistant to streptomycin (38·2%), tetracycline (36·3%), sulfisoxazole (35·3%) and gentamicin (34·3%); 31·4% of isolates were multidrug resistant. The isolates were screened for 10 virulence factors. The Salmonella pathogenicity island genes avrA, ssaQ, mgtC, siiD, and sopB and the fimbrial gene bcfC were present in 100% of the strains. PFGE genotyping of the 102 S. enterica isolates yielded 24 PFGE types at an 85% similarity threshold. The PFGE patterns show that the genotypes of S. enterica in the production chain are very diverse, but some strains have 100% similarity in different parts of the production chain, which indicates that some S. enterica persist throughout the broiler supply chain.