Experimental Investigation on the Mechanical Properties of Polypropylene Hybrid Fiber-Reinforced Roller-Compacted Concrete Pavements

To explore the effect of multi-scale polypropylene fiber (PPF) hybridization on the mechanical properties of roller-compacted concrete (RCC), the early-age (3, 7, 14, 28 days) compressive strength, splitting tensile strength and uniaxial tensile test of RCC reinforced with micro-, macro- and hybrid polypropylene fibers were investigated. Then, the tensile stress–strain curve of polypropylene fiber-reinforced roller-compacted concrete (PFRCC) and the corresponding tensile parameters were obtained. The uniaxial tensile constitutive equation of PFRCC and fiber hybrid effect function was also proposed. Finally, the enhancement mechanism of fiber hybridization on mechanical properties of RCC was analyzed. The results indicated that the strength and toughness of PFRCC improved with the incorporation of PPF, showing obvious plastic failure characteristics of PFRCC. Before curing the concrete for 7 days, micro-PPF played a major role in strengthening RCC, while macro-PPF played a major role in reinforcing concrete after that. Moreover, the tensile strength and toughness indexes of multi-scale PFRCC performed the best, indicating the positive hybridization of three types of PPF. The proposed PFRCC uniaxial tensile constitutive equation and fiber hybrid effect function based on existing researches were also well matched with the experimental results.

QTL mapping for low temperature germination in rapeseed

Rapeseed, a major oil crop in the world, is easily affected by low-temperature stress. A low temperature delays seed germination and increases seedling mortality, adversely affecting rapeseed growth and production. In the present study, a tolerant cultivar (Huyou21) was crossed with a susceptible genotype (3429) to develop a mapping population consisting of 574 F2 progenies and elucidate the genetic mechanisms of seed germination under low temperatures. Two quantitative trait loci (QTL) for low-temperature germination (LTG) were detected, one on chromosome A09 (named qLTGA9-1) and the other on chromosome C01 (named qLTGC1-1), using the QTL-seq approach and confirmed via linkage analysis in the mapping population. Further, qLTGA9-1 was mapped to a 341.86 kb interval between the SSR markers Nys9A212 and Nys9A215. In this region, 69 genes including six specific genes with moderate or high effect function variants were identified based on the Ningyou7 genome sequence. Meanwhile, qLTGC1-1 was mapped onto a 1.31 Mb interval between SSR markers Nys1C96 and Nys1C117. In this region, 133 genes including five specific genes with moderate effect function variants were identified. These specific genes within the two QTL could be used for further studies on cold tolerance and as targets in rapeseed breeding programs.

QTLs Detection for Low-Temperature Germination in Rapeseed by QTL-seq and Linkage Mapping Approach

Rapeseed is a major oil crop in the world, which is easily affected by low-temperature stress. Low-temperature delays seed germination and increases seedling mortality that adversely affects rapeseed growth and production. To understand the genetic mechanisms of seed germination under low-temperature in rapeseed, we crossed a tolerant cultivar with a susceptible genotype to develop a mapping population of 574 F2 progenies. Two quantitative trait loci (QTLs) for low-temperature germination (LTG) were detected on chromosome A09 (named qLTGA9-1) and C01 (named qLTGC1-1) using QTL-seq method, and confirmed via linkage analysis in the mapping population. qLTGA9-1 was mapped to a 341.86-kb interval between SSR markers Nys9A212 and Nys9A215. In this region, 69 genes including six specific genes with moderate or high effect function variant were identified based on Ningyou7 genome sequence. qLTGC1-1 was mapped a 1.31-Mb interval between SSR markers Nys1C96 and Nys1C117. In this region, 133 genes including five specific genes with moderate effect function variant were identified based on Ningyou7 genome sequence. These specific genes within the two QTLs could be targets in rapeseed breeding programs and further studies for cold tolerance.

Towards Precision Fertilization: Multi-Strategy Grey Wolf Optimizer Based Model Evaluation and Yield Estimation

Precision fertilization is a major constraint in consistently balancing the contradiction between land resources, ecological environment, and population increase. Even more, it is a popular technology used to maintain sustainable development. Nitrogen (N), phosphorus (P), and potassium (K) are the main sources of nutrient income on farmland. The traditional fertilizer effect function cannot meet the conditional agrochemical theory’s conditional extremes because the soil is influenced by various factors and statistical errors in harvest and yield. In order to find more accurate scientific ratios, it has been proposed a multi-strategy-based grey wolf optimization algorithm (SLEGWO) to solve the fertilizer effect function in this paper, using the “3414” experimental field design scheme, taking the experimental field in Nongan County, Jilin Province as the experimental site to obtain experimental data, and using the residuals of the ternary fertilizer effect function of Nitrogen, phosphorus, and potassium as the target function. The experimental results showed that the SLEGWO algorithm could improve the fitting degree of the fertilizer effect equation and then reasonably predict the accurate fertilizer application ratio and improve the yield. It is a more accurate precision fertilization modeling method. It provides a new means to solve the problem of precision fertilizer and soil testing and fertilization.

Single-Machine Scheduling Problems with the General Sum-of-Processing-Time and Position-Dependent Effect Function

This paper considers the combination of the general sum-of-processing-time effect and position-dependent effect on a single machine. The actual processing time of a job is defined by functions of the sum of the normal processing times of the jobs processed and its position and control parameter in the sequence. We consider two monotonic effect functions: the nondecreasing function and the nonincreasing function. Our focus is the following objective functions, including the makespan, the sum of the completion time, the sum of the weighted completion time, and the maximum lateness. For the nonincreasing effect function, polynomial algorithm is presented for the makespan problem and the sum of completion time problem, respectively. The latter two objective functions can also be solved in polynomial time if the weight or due date and the normal processing time satisfy some agreeable relations. For the nondecreasing effect function, assume that the given parameter is zero. We also show that the makespan problem can remain polynomially solvable. For the sum of the total completion time problem and a 1 is the deteriorating rate of the jobs, there exists an optimal solution for a 1 ≥ M ; a V-shaped property with respect to the normal processing times is obtained for 0 < a 1 ≤ 1 . Finally, we show that the sum of the weighted completion problem and the maximum lateness problem have polynomial-time solutions for a 1 > M under some agreeable conditions, respectively.

A fundamental measure of treatment effect heterogeneity

The stratum-specific treatment effect function is a random variable giving the average treatment effect (ATE) for a randomly drawn stratum of potential confounders a clinician may use to assign treatment. In addition to the ATE, the variance of the stratum-specific treatment effect function is fundamental in determining the heterogeneity of treatment effect values. We offer a non-parametric plug-in estimator, the targeted maximum likelihood estimator (TMLE) and the cross-validated TMLE (CV-TMLE), to simultaneously estimate both the average and variance of the stratum-specific treatment effect function. The CV-TMLE is preferable because it guarantees asymptotic efficiency under two conditions without needing entropy conditions on the initial fits of the outcome model and treatment mechanism, as required by TMLE. Particularly, in circumstances where data adaptive fitting methods are very important to eliminate bias but hold no guarantee of satisfying the entropy condition, we show that the CV-TMLE sampling distributions maintain normality with a lower mean squared error than TMLE. In addition to verifying the theoretical properties of TMLE and CV-TMLE through simulations, we highlight some of the challenges in estimating the variance of the treatment effect, which lack double robustness and might be biased if the true variance is small and sample size insufficient.

Predicting Sheet Forming Limit of Aluminum Alloys for Cold and Warm Forming by Developing a Ductile Failure Criterion

In this study, the forming limit of aluminum alloy sheet materials is predicted by developing a ductile failure criterion (DFC). In the DFC, the damage growth is defined by Mclintock formula, stretching failure is defined at localized necking (LN) or fracture without LN, while the critical damage is defined by a so-called effect function, which reflects the effect of strain path and initial sheet thickness. In the first part of this study, the DFC is used to predict forming limit curves (FLCs) of six different aluminum sheet materials at room temperature. Then, the DFC is further developed for elevated temperature conditions by introducing an improved Zener–Hollomon parameter (Z′), which is proposed to provide enhanced representation of the strain rate and temperature effect on limit strain. In warm forming condition, the improved DFC is used to predict the FLCs of Al5083-O and failure in a rectangular cup warm draw process on Al5182 + Mn. Comparison shows that all the predictions match quite well with the experimental measurements. Thanks to the proposal of effect function, the DFC needs calibration only in uniaxial tension, and thus, provides a promising potential to predict forming limit with reduced effort.