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Networked and interconnected microgrids can improve resilience of critical end‐use loads during extreme events. However, the frequency deviations in microgrids during transient events are significantly larger than those typically seen in bulk transmission systems. The larger frequency deviations can cause a loss of inverter‐connected assets, resulting in a loss of power to critical end‐use loads. Grid Friendly ApplianceTM (GFA) controllers can mitigate the transient event effects by engaging end‐use loads. This paper presents a method to select set‐points for end‐use loads equipped with GFA controllers, while minimizing the interruptions to end‐use customers. An online (i.e. real‐time), device‐level algorithm adjusts individual GFA controller frequency setpoints based on the operational characteristics of each end‐use load and on the changing grid dynamic characteristics to selectively engage the load for mitigating the switching transients. The adaptive gradient‐descent‐based algorithm does not require control or coordination amongst end‐use devices for adapting frequency set‐points. The method is validated using dynamic simulations on a modified version of the IEEE 123‐node test system with three microgrids using the GridLAB‐DTM simulation environment. The improved dynamic stability achieved through the engagement of GFAs support the switching operations necessary for networked microgrid operations.

Dry bean (Phaseolus vulgaris L.) is a nutrient‐dense food rich in proteins and minerals. Although a dietary staple in numerous regions, including Eastern and Southern Africa, greater utilization is limited by its long cooking time as compared with other staple foods. A fivefold genetic variability for cooking time has been identified for P. vulgaris, and to effectively incorporate the cooking time trait into bean breeding programs, knowledge of how genotypes behave across diverse environments is essential. Fourteen bean genotypes selected from market classes important to global consumers (yellow, cranberry, light red kidney, red mottled, and brown) were grown in 10 to 15 environments (combinations of locations, years, and treatments), and their cooking times were measured when either presoaked or unsoaked prior to boiling. The 15 environments included locations in North America, the Caribbean, and Eastern and Southern Africa that are used extensively for dry bean breeding. The cooking times of the 14 presoaked dry bean genotypes ranged from 16 to 156 min, with a mean of 86 min across the 15 production environments. The cooking times of the 14 dry bean genotypes left unsoaked ranged from 77 to 381 min, with a mean cooking time of 113 min. The heritability of the presoaked cooking time was very high (98%) and moderately high for the unsoaked cooking time (~60%). The genotypic cooking time patterns were stable across environments. There was a positive correlation between the presoaked and unsoaked cooking times (r = .64, p < 0.0001), and two of the fastest cooking genotypes when presoaked were also the fastest cooking genotypes when unsoaked (G1, Cebo, yellow bean; and G4, G23086, cranberry bean). Given the sufficient genetic diversity found, limited crossover Genotype × Environment interactions, and high heritability for cooking time, it is feasible to develop fast cooking dry bean varieties without the need for extensive testing across environments.

High-molecular-weight glutenin subunits (HMW-GSs) are storage proteins present in the starchy endosperm cells of wheat grain. Encoding the synthesis of HMW-GS, the Glu-1 loci located on the long arms of group 1 chromosomes of the hexaploid wheat (1A, 1B, and 1D) present multiple allelism. In hexaploid wheat cultivars, almost all of them express 3 to 5 HMW-GSs and the 1Ay gene is always silent. Though HMW-GSs are the minor components in gluten, they are crucial for dough properties, and certain HMW-GSs make more positive contributions than others. The HMW-GS acts as a “chain extender” and provides a disulfide-bonded backbone in gluten network. Hydrogen bonds mediated by glutamine side chains are also crucial for stabilizing the gluten structure. In most cases, HMW-GSs with additional or less cysteines are related to the formation of relatively more or less interchain disulfide bonds and HMW-GSs also affect the gluten secondary structures, which in turn impact the end use qualities of dough.

Pit lakes can represent significant liabilities at mine closure. However, depending upon certain characteristics of which water quality is key, pit lakes often also present opportunities to provide significant regional benefit and address residual closure risks of both their own and overall project closure and even offset the environmental costs of mining by creating new end uses. These opportunities are widely dependent on water quality, slope stability, and safety issues. Unfortunately, many pit lakes have continued to be abandoned without repurposing for an end use. We reviewed published pit lake repurposing case studies of abandoned mine pit lakes. Beneficial end use type and outcome varied depending upon climate and commodity, but equally important were social and political dynamics that manifest as mining company commitments or regulatory requirements. Many end uses have been realized: passive and active recreation, nature conservation, fishery and aquaculture, drinking and industrial water storage, greenhouse carbon fixation, flood protection and waterway remediation, disposal of mine and other waste, mine water treatment and containment, and education and research. Common attributes and reasons that led to successful repurposing of abandoned pit lakes as beneficial end uses are discussed. Recommendations are given for all stages of mine closure planning to prevent pit lake abandonment and to achieve successful pit lake closure with beneficial end uses.

Properties critical to the structure of apparel and apparel fabrics (thermal and moisture transfer, elasticity, and flexural rigidity), those related to performance (durability to abrasion, cleaning, and storage), and environmental effects have not been consistently addressed in the research on fabric sensors designed to interact with the human body. These fabric properties need to be acceptable for functionalized fabrics to be effectively used in apparel. Measures of performance such as electrical conductivity, impedance, and/or capacitance have been quantified. That the apparel/human body system involves continuous transient conditions needs to be taken into account when considering performance. This review highlights gaps concerning fabric-related aspects for functionalized apparel and includes information on increasing the inclusion of such aspects. A multidisciplinary approach including experts in chemistry, electronics, textiles, and standard test methods, and the intended end use is key to widespread development and adoption.

In bread wheat ( Triticum aestivum L . ), cysteine (Cys) residues within the N‐ and C‐termini of high‐molecular‐weight glutenin subunits (HMW‐GSs) critically influence dough quality. However, the functional significance of Cys residue in their central repeat domain (CRD) remains unclear. Using site‐directed mutagenesis (SDM), we introduced Cys residues near the N‐terminus (m1) and/or C‐terminus (m2) of 1Dx2 CRD, generating variants 1Dx2m1 , 1Dx2m2 and 1Dx2m1/2 . Transgenic lines expressing the variants exhibited superior dough properties, increased loaf volume, and elevated glutenin macropolymers (GMPs) content, attributable to enhanced disulfide bond formation and upregulation of associated genes. Notably, the two Cys residues introduced variant 1Dx2m1/2 demonstrated additive improvements, indicating synergistical effects of Cys residues at both positions. Field trials confirmed these modifications did not compromise key agronomic traits. Our study provides the experimental evidence for the role of CRD‐located Cys residues in HMW‐GSs on dough quality and offers valuable genetic resources for improving end‐use quality without yield penalties in wheat breeding.

Benefits of increasing the renewable energy (RE) share in the total energy mix include better energy security, carbon dioxide emission reductions and improved human health. This paper identifies the potential of RE technologies and role of innovation to double the global RE share from 18% to 36% between 2010 and 2030. As a first step, a Reference Case is developed based on national energy plans of 26 countries which increases the RE share to 21% by 2030. Next, the realizable potential of RE technologies is estimated beyond the Reference Case at country and sector levels. By aggregating country potentials, this paper reveals that the global RE share can double to 36% by 2030. Despite differences in starting points and resource potentials, there is a role for each country in achieving a doubling. For many countries their Reference Cases result in low RE shares and many countries are just beginning to explore ways to increase RE use. The paper identifies action areas where innovation can increase technology development and improve cost-effectiveness, thereby accelerating global RE deployment. More research is required to specify these action areas for individual countries and specific technologies, as well as to identify policy needs to address them.

Late-maturity α-amylase (LMA) leads to the expression and protein accumulation of high pI α-amylases during late grain development. This α-amylase is maintained through harvest and leads to an unacceptable low falling number (FN), the wheat industry’s standard measure for predicting end-use quality. Unfortunately, low FN leads to significant financial losses for growers. As a result, wheat researchers are working to understand and eliminate LMA from wheat breeding programs, with research aims that include unraveling the genetic, biochemical, and physiological mechanisms that lead to LMA expression. In addition, cereal chemists and quality scientists are working to determine if and how LMA-affected grain impacts end-use quality. This review is a comprehensive overview of studies focused on LMA and includes open questions and future directions.

Breeding for grain yield, biotic and abiotic stress resistance, and end-use quality are important goals of wheat breeding programs. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Genomic selection provides an alternative to predict performance using genome-wide markers under forward and across location predictions, where a previous year’s dataset can be used to build the models. Due to large datasets in breeding programs, we explored the potential of the machine and deep learning models to predict fourteen end-use quality traits in a winter wheat breeding program. The population used consisted of 666 wheat genotypes screened for five years (2015–19) at two locations (Pullman and Lind, WA, USA). Nine different models, including two machine learning (random forest and support vector machine) and two deep learning models (convolutional neural network and multilayer perceptron) were explored for cross-validation, forward, and across locations predictions. The prediction accuracies for different traits varied from 0.45–0.81, 0.29–0.55, and 0.27–0.50 under cross-validation, forward, and across location predictions. In general, forward prediction accuracies kept increasing over time due to increments in training data size and was more evident for machine and deep learning models. Deep learning models were superior over the traditional ridge regression best linear unbiased prediction (RRBLUP) and Bayesian models under all prediction scenarios. The high accuracy observed for end-use quality traits in this study support predicting them in early generations, leading to the advancement of superior genotypes to more extensive grain yield trails. Furthermore, the superior performance of machine and deep learning models strengthens the idea to include them in large scale breeding programs for predicting complex traits.

Sustainable urban water supply management requires, ideally, accurate evidence based estimations on per capita consumption and a good understanding of the factors influencing the consumption. The information can then be used to achieve improved water demand forecasts. Water consumption patterns in the developed countries have been extensively investigated. However, very little is known for the developing world. This paper investigates per capita water consumption resulting from water use activities in different types of households typically found in urban areas of the developing world. A data collection programme was executed for 407 households to extract information on household characteristics, water user behaviour and intensity and the nature of indoor and outdoor water use activities. The rigorous statistical analysis of the data shows that per capita water consumption increases with income: 241, 272 and 290 l/capita/day for low, medium and high income households, respectively. Additionally, the results suggest that per capita consumption increases with the number of adult female members in the household and almost one-third of consumption is via taps. The collected data has been used to develop statistical models using two different regression techniques: multiple linear (STEPWISE) and evolutionary polynomial regression (EPR). The inclusion of demographic parameters in the developed models considerably improved the prediction accuracy. Two of the best performing models are used to forecast the water demand for the city, using four future scenarios: market forces, fortress world, policy reform and great transition. The results suggest that the domestic water demand would be highest in the fortress world scenario due to the increase in population and size of built-up area.