Silk receptivity and kernel set vary with time of pollination and environment. Our objectives were to evaluate differences in kernel set, silk elongation patterns, and duration of silk receptivity for four hybrids. These experiments used pollen‐saturating hand pollinations of four field‐grown maize (Zea mays L.) hybrids at varying days after first silk (DAFS) appearance in 1992, 1993, and 1994. Kernel set on primary ears was determined for B73 × MO17 (low silk turgor), WF9 × A632 (high silk turgor), Pioneer 3343, and Pioneer 3379 (Pioneer Hi‐Bred Int., Inc., Johnston, IA). WF9 × A632 had the fastest initial silk growth rate, resulting in earlier successful pollination of midbase floret positions. However, these floret positions declined in kernel set at later DAFS due to loss in silk receptivity and poor silk health. Thus, it had a lower final kernel number and a faster decline in kernel set when pollinated 10 DAFS in 1992 and 8 DAFS in 1993 and 1994. Hybrid WF9 × A632 had the lowest receptive trichome surface area, trichome density, and trichome number on its exposed silk surface 3 DAFS compared with the other three hybrids. Hybrid silk trichome length varied and was longer near the silk tip. Although silk elongation patterns were different, silk growth rates for all four hybrids gradually declined to near zero by 8 to 10 DAFS in 1992. We conclude that kernel set and yield stability are impacted by variation among hybrids for silk elongation and senescence.
Monolithic approaches to functional language arrays, such aa Haskell array comprehensions, define elements all at once, at the time the array is created, instead of incrementally. Although monolithic arrays are elegant, a naive implementation can be very inefficient.For example, if a compiler does not know whether an element has zero or many definitions, it must compile runtime tests. If a compiler does not know inter-element data dependences, it must resort to pessimistic strategies such as compiling elements as thunks, or making unnecessary copies when updating an array. Subscriptanalysis, originally developed for imperative language vectorizing and parallelising compilers, can be adapted to provide a functional language compiler with the information needed for efficient compilation of monolithic arrays. Our contribution is to develop the number-theoretic basis of subscript analysis with assumptions appropriate to functional arrays, detail the kinds of dependence information subscript analysis can uncover, and apply that dependence information to sequential efficient compilation of functional arrays.
A new framework is presented, based on the notion of a partially ordered multiset (or pomset), which is able to provide not only a precise operational semantics of parallel functional program evaluation, but also a handle through which to control such behavior. As an operational semantics, pomsets are able to distinguish between call-by-value, call-by-name, call-by-need, and call-by-speculation evaluation strategies (even though all but the first of these have the same standard semantics); and as a "handle" from which to control operational behavior, pomsets can express most of the behaviors achieved by previously proposed annotations that control not only evaluation order but also the spatial mapping of program to machine.
Technological development prior to industrial revolution 4.0 incentivized manufacturing industries to invest into digital industry with the aim of increasing the capability and efficiency in manufacturing activity. Major manufacturing industry has begun implementing cyber-physical system in industrial monitoring and control. The system itself will generate large volumes of data. The ability to process those big data requires algorithm called machine learning because of its ability to read patterns of big data for producing useful information. This study conducted on premises of Indonesia’s current network infrastructure and workforce capability on supporting the implementation of machine learning especially in large-scale manufacture. That will be compared with countries that have a positive stance in implementing machine learning in manufacturing. The conclusions that can be drawn from this research are Indonesia current infrastructure and workforce is still unable to fully support the implementation of machine learning technology in manufacturing industry and improvements are needed.
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