Selection of Optimal Time-Controlled Release Granule Formula of Metominostrobin

Four starch-encapsulated formulations of EPTC(S-ethyl dipropylthiocarbamate) and of butylate(S-ethyl diisobutylthiocarbamate) were prepared and evaluated by comparison with their respective emulsifiable concentrate formulations for their slow-release capabilities and efficacies. Chemical and biological evaluation indicated that difference in controlled-release could be achieved by the selection of the starch xanthate and oxidant used in the formualtion process. EPTC and butylate released slower when formulated as starch-encapsulated granules than when formulated as emulsifiable concentrates under soil conditions that favored rapid release. The initial release was adequate for weed control and slow enough for desired residual activity. Repeated seeding and harvesting the treated soils and bioassays of treated soils generally produced release rate anticipated from short term dry and wet chemical tests.

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Impulse Artificial Neural Networks in Internal Transport

Management and Production Engineering Review ◽

10.2478/mper-2014-0015 ◽

2014 ◽

Vol 5 (2) ◽

pp. 33-44

Author(s):

Joanna Ochelska-Mierzejewska

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Route Planning ◽

Human Operator ◽

Optimal Time ◽

Automated Warehouses ◽

Time Gap ◽

Artificial Neural ◽

Internal Transport ◽

Selection Of

Abstract The second most important function of a warehouse, apart from the storing of goods, is internal transport with a focus on time-effectiveness. When there is a time gap between the production and export of products, the goods need to be stored until they are dispatched to the consumers. An important problem that concerns both large and small warehouses is the selection of priorities, that is handling the tasks in order of importance. Another problem is to identify the most efficient routes for forklift trucks to transport goods from a start-point to a desired destination and prevent the routes from overlapping. In automated warehouses, the transport of objects (the so called pallets of goods) is performed by machines controlled by a computer instead of a human operator. Thus, it is the computer, not the man, that makes the difficult decisions regarding parallel route planning, so that the materials are transported within the warehouse in near-optimal time. This paper presents a method for enhancing this process.

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Selection of optimal time-to-go in generalized vector explicit guidance

2015 IEEE Conference on Control Applications (CCA) ◽

10.1109/cca.2015.7320708 ◽

2015 ◽

Cited By ~ 2

Author(s):

Sabyasachi Mondal ◽

Radhakant Padhi

Keyword(s):

Optimal Time ◽

Selection Of

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Selection of time points for costly experiments: a comparison between human intuition and computer-aided experimental design

10.1101/301796 ◽

2018 ◽

Cited By ~ 1

Author(s):

Daphne Ezer ◽

Joseph C. Keir

Keyword(s):

Experimental Design ◽

Functional Data ◽

Optimal Time ◽

Design Decisions ◽

Time Points ◽

Research Outcomes ◽

Human Decision ◽

Computational Strategy ◽

Selection Of ◽

Critical Aspects

AbstractMotivationThe design of an experiment influences both what a researcher can measure, as well as how much confidence can be placed in the results. As such, it is vitally important that experimental design decisions do not systematically bias research outcomes. At the same time, making optimal design decisions can produce results leading to statistically stronger conclusions. Deciding where and when to sample are among the most critical aspects of many experimental designs; for example, we might have to choose the time points at which to measure some quantity in a time series experiment. Choosing times which are too far apart could result in missing short bursts of activity. On the other hand, there may be time points which provide very little information regarding the overall behaviour of the quantity in question.ResultsIn this study, we design a survey to analyse how biologists use previous research outcomes to inform their decisions about which time points to sample in subsequent experiments. We then determine how the choice of time points affects the type of perturbations in gene expression that can be observed. Finally, we present our main result: NITPicker, a computational strategy for selecting optimal time points (or spatial points along a single axis), that eliminates some of the biases caused by human decision-making while maximising information about the shape of the underlying curves, utilising ideas from the field of functional data analysis.AvailabilityNITPicker is available on GIThub (https://github.com/ezer/NITPicker).

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