Parallel Space-Mapping Based Yield-Driven em Optimization Incorporating Trust Region Algorithm and Polynomial Chaos Expansion

Space mapping (SM) methodology has been recognized as a powerful tool for accelerating electromagnetic (EM)-based yield optimization. This paper proposes a novel parallel space-mapping based yield-driven EM optimization technique incorporating trust region algorithm and polynomial chaos expansion (PCE). In this technique, a novel trust region algorithm is proposed to increase the robustness of the SM surrogate in each iteration during yield optimization. The proposed algorithm updates the trust radius of each design parameter based on the effectiveness of minimizing the $l_{1}$ objective function using the surrogate, thereby increasing the robustness of the SM surrogate. Moreover, for the first time, parallel computation method is incorporated into SM-based yield-driven design to accelerate the overall yield optimization process of microwave structures. The use of parallel computation allows the surrogate developed in the proposed technique to be valid in a larger neighborhood than that in standard SM, consequently increasing the speed of finding the optimal yield solution in SM-based yield-driven design. Lastly, the PCE approach is incorporated into the proposed technique to further speed up yield verification on the fine model. Compared with the standard SM-based yield optimization technique with sequential computation, the propose

Editorial : Omics-driven crop improvement for stress tolerance.

Crop losses due to biotic and abiotic stresses are significant worldwide issues. According to a report of the Food and Agriculture Organization of the United Nations (FAO), an estimated 20-40% of global crop production is lost every year due to pests and diseases alone, while other environmental factors, like drought, floods, high salinity level in soil, and extreme temperatures contribute to the losses becoming even more severe. Crop yield stability and healthy growth under biotic and abiotic stresses have always been a major challenge for the plant/agricultural researchers. Crop resilience is an important trait, and it involves essential phenotypes that plant breeding researchers are concerned with. For instance, fusarium-head-blight resistance is highly desirable for breeding new wheat varieties nowadays. Therefore, improving stress tolerance becomes a major research direction in modern crop science

Rice plants respond to ammonium‐stress by adopting a helical root growth pattern

High levels of ammonium nutrition reduce plant growth and different plant species have developed distinct strategies to maximize ammonium acquisition while alleviate ammonium toxicity through modulating root growth. Up to now, the mechanism underlying plant tolerance or sensitivity towards ammonium remain unclear. Rice uses ammonium as its main N source. Here we show that ammonium supply restricts rice root elongation and induces a helical growth pattern, which is attributed to root acidification resulting from ammonium uptake. Ammonium-induced low pH triggers asymmetric auxin distribution in rice root tips through changes in auxin signaling, thereby inducing a helical growth response. Blocking auxin signaling completely inhibited this root response. In contrast, this root response is not activated in ammonium-treated Arabidopsis. Acidification of Arabidopsis roots leads to the protonation of IAA, and dampening the intracellular auxin signaling levels that are required for maintaining root growth. Our study suggests a different mode of action by ammonium on the root pattern and auxin response machinery in rice versus Arabidopsis, and the rice-specific helical root response towards ammonium is an expression of the ability of rice in moderating auxin signaling and root growth to utilize ammonium while confronting acidic stress

Investigating item complexity as a source of cross-national DIF in TIMSS math and science

Abstract Background Large scale international assessments depend on invariance of measurement across countries. An important consideration when observing cross-national differential item functioning (DIF) is whether the DIF actually reflects a source of bias, or might instead be a methodological artifact reflecting item response theory (IRT) model misspecification. Determining the validity of the source of DIF has implications for how it is handled in practice. Method We demonstrate a form of sensitivity analysis that can point to model misspecification induced by item complexity as a possible cause of DIF, and show how such a cause of DIF might be accommodated through attempts to generalize the IRT model for the studied item(s) in psychometrically and psychologically plausible ways. Results In both simulated illustrations and empirical data from TIMSS 2011 and TIMSS 2019 4th and 8th Grade Math and Science, we have found that using a form of proposed IRT model generalization can substantially reduce DIF when IRT model misspecification is at least a partial cause of the observed DIF. Conclusions By demonstrating item complexity as a possible valid source of DIF and showing the effectiveness of the proposed approach, we recommend additional attention toward model generalizations as a means of addressing and/or understanding DIF

Characterization of Interactions between the Soybean Salt-Stress Responsive Membrane-Intrinsic Proteins GmPIP1 and GmPIP2

Salt tolerance is an important trait in soybean cultivation and breeding. Plant responses to salt stress include physiological and biochemical changes that affect the movement of water across the plasma membrane. Plasma membrane intrinsic proteins (PIPs) localize to the plasma membrane and regulate the water and solutes flow. In this study, quantitative real-time PCR and yeast two-hybridization were engaged to analyze the early gene expression profiles and interactions of a set of soybean PIPs (GmPIPs) in response to salt stress. A total of 20 GmPIPs-encoding genes had varied expression profiles after salt stress. Among them, 13 genes exhibited a downregulated expression pattern, including GmPIP1;6, the constitutive overexpression of which could improve soybean salt tolerance, and its close homologs GmPIP1;7 and 1;5. Three genes showed upregulated patterns, including the GmPIP1;6 close homolog GmPIP1;4, when four genes with earlier increased and then decreased expression patterns. GmPIP1;5 and GmPIP1;6 could both physically interact strongly with GmPIP2;2, GmPIP2;4, GmPIP2;6, GmPIP2;8, GmPIP2;9, GmPIP2;11, and GmPIP2;13. Definite interactions between GmPIP1;6 and GmPIP1;7 were detected and GmPIP2;9 performed homo-interaction. The interactions of GmPIP1;5 with GmPIP2;11 and 2;13, GmPIP1;6 with GmPIP2;9, 2;11 and GmPIP2;13, and GmPIP2;9 with itself were strengthened upon salt stress rather than osmotic stress. Taken together, we inferred that GmPIP1 type and GmPIP2 type could associate with each other to synergistically function in the plant cell; a salt-stress environment could promote part of their interactions. This result provided new clues to further understand the soybean PIP–isoform interactions, which lead to potentially functional homo- and heterotetramers for salt tolerance

Biochar Improves Sustainability of Green Roofs via Regulate of Soil Microbial Communities

Green roofs are an important part of urban green spaces. A good roof soil system contributes to the stability of the green roof ecosystem in harsh environments. Biochar as a soil additive can improve soil nutrients, although the mechanism of improvement on the roof substrate is still unclear. This research studied the effects on the physical and chemical properties of green roof soil and analyzed the biological characteristics of green roofs at five gradient ratios of biochar addition (0%, 5%, 10%, 15% and 20% biochar; v/v). The results indicated that biochar could improve the soil porosity (5.3–9.3%) and reduce the bulk density (3.9–10.8%); increase the soil moisture (14.0–37.2%); adjust the temperature, available nutrients and cation exchange capacity (38.1–75.9%) and regulate the soil pH values of the green roof. The biomass of soil microbes, eukaryotes and plants were increased by 75.3%, 199.2% and 57.5%, respectively. Meanwhile, the correlation between microbial diversity and soil nutrients was more significant due to the addition of biochar, and the increase of the phosphorus (P) and carbon (C) contents was the main factor affecting the microbial community. The structural equation model showed that biochar has a direct impact on the microbial diversity by improving the soil moisture, temperature and available nutrients, and the increase of the microbial diversity is conducive to plant growth. Summarily, biochar can be considered as a potential additive for roof soil amendment and promoting the growth of plants and microbes, which is beneficial to the development of a roof ecosystem

An overview of Neuro-space mapping techniques for microwave device modeling

This paper presents an overview of Neuro-space mapping (Neuro-SM) approach and its application to nonlinear device modeling. The Neuro-SM approach addresses the situation where an existing device model cannot fit new device data well. By modifying the current and voltage relationships in the model, the Neuro-SM produces a new model exceeding the accuracy limit. This paper describes several Neuro-SM techniques incorporating static Neuro-SM, advanced static Neuro-SM, and dynamic Neuro-SM techniques for microwave device modeling. A real 2 × 50 gatewidths GaAs pseudomorphic high-electron mobility transistor (pHEMT) modeling example is used to illustrate the accuracy and efficiency of dynamic Neuro-SM

A Unified Automated Parametric Modeling Algorithm Using Knowledge-Based Neural Network and l1 Optimization

Knowledge-based neural network modeling techniques using space-mapping concept have been demonstrated in the existing literature as efficient methods to overcome the accuracy limitations of empirical/equivalent circuit models when matching new electromagnetic data. For different modeling problems, the mapping structures can be different. In this paper, we propose a unified automated model generation algorithm that uses l1 optimization to automatically determine the type and the topology of the mapping structure in a knowledge-based neural network model. By encompassing various types of mappings of the knowledge-based neural network model in the existing literature, we present a new unified model structure and derive new sensitivity formulas for the training of the unified model. The proposed l1 formulation of modeling can force some weights of the mapping neural networks to zeros while leaving other weights as nonzeros. We utilize this feature to allow l1 optimization to automatically determine which mapping is necessary and which mapping is unnecessary. Using the proposed l1 optimization method, the mapping structure can be determined to address different needs of different modeling problems. The structure of the final knowledge-based model can be flexible combinations of some or all of linear mapping, nonlinear mapping, input mapping, frequency mapping, and output mapping. In this way, the proposed algorithm is more systematic and can further speed up the knowledge-based modeling process than existing knowledge-based modeling algorithms. The proposed method is illustrated by three microwave filter modeling examples