scholarly journals Technology and Skill Upgrading: Results from Linked Worker-Plant Data for Finnish Manufacturing

1999 ◽  
pp. 115-145 ◽  
Author(s):  
Jari Vainiomaki
Keyword(s):  
Skill Upgrading ◽  
Plant Data

Pilot Plant Data for Synthetic Iron Oxides

1952 ◽  
Vol 44 (3) ◽  
pp. 449-449
Author(s):  
C DeWitt ◽  
M Livingood ◽  
K Miller
Keyword(s):  
Iron Oxides ◽  
Pilot Plant ◽  
Plant Data

EVALUASI ANR PADA TRANSMISI DATA NETWORK TERHADAP WP-REST API DALAM APLIKASI ANDROID

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Agung Riyadi
Keyword(s):  
Large Data ◽  
Data Retrieval ◽  
Test Results ◽  
Android Application ◽  
Retrieval Process ◽  
Request Queue ◽  
Plant Data ◽  
Android Development ◽  
Rest Api ◽  
The One

The One of many way to connect to the database through the android application is using volleyball and RESTAPI. By using RestAPI, the android application does not directly connect to the database but there is an intermediary in the form of an API. In android development, Android-volley has the disadvantage of making requests from large and large data, so an evaluation is needed to test the capabilities of the Android volley. This research was conducted to test android-volley to retrieve data through RESTAPI presented in the form of an application to retrieve medicinal plant data. From the test results can be used by volley an error occurs when the back button is pressed, in this case another process is carried out if the previous volley has not been loaded. This error occurred on several android versions such as lollipops and marshmallows also on some brands of devices. So that in using android-volley developer need to check the request queue process that is carried out by the user, if the data retrieval process by volley has not been completed, it is necessary to stop the process to download data using volley so that there is no Android Not Responding (ANR) error.Keywords: Android, Volley, WP REST API, ANR Error

scholarly journals MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Daniel E. Runcie ◽  
Jiayi Qu ◽  
Hao Cheng ◽  
Lorin Crawford
Keyword(s):  
Genomic Prediction ◽  
Large Scale ◽  
Mixed Model ◽  
Human Genetics ◽  
Linear Mixed Effect Model ◽  
Mixed Effect ◽  
Statistical Framework ◽  
Effect Model ◽  
Plant Data ◽  
Genetic Value

AbstractLarge-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present , a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.

scholarly journals Revision of the femoralis group of Blepharoneura Loew (Diptera: Tephritidae)

Zootaxa ◽  
2010 ◽  
Vol 2374 (1) ◽  
pp. 1 ◽  
Author(s):  
ALLEN L. NORRBOM ◽  
MARTY CONDON
Keyword(s):  
Phylogenetic Analysis ◽  
New Species ◽  
Costa Rica ◽  
Species Group ◽  
Southern Brazil ◽  
Key To Species ◽  
Northwestern Argentina ◽  
New Synonym ◽  
Northern Brazil ◽  
Plant Data

The femoralis species group of the genus Blepharoneura is revised. The following 42 species, including 32 new species, are recognized: amplihyalina, n. sp. (northwestern Argentina), apaapa, n. sp. (Bolivia), aspiculosa, n. sp. (Mexico), bidigitata, n. sp. (southern Brazil), bipunctata, n. sp. (Ecuador), biseriata Wulp (Mexico), bivittata, n. sp. (Nicaragua, Costa Rica), brevivittata, n. sp. (Costa Rica to Peru), chaconi, n. sp. (Costa Rica), cornelli, n. sp. (Costa Rica), cyclantherae, n. sp. (Mexico), femoralis Wulp (Mexico to Brazil), fernandezi, n. sp. (Venezuela, northern Brazil), furcifer Hendel (Venezuela to Bolivia and Brazil), hirsuta Bates (Venezuela, Guyana, Brazil), hyalinella, n. sp. (Bolivia), io Giglio-Tos (Mexico), isolata, n. sp. (Guatemala), lutea, n. sp. (Costa Rica), macwilliamsae, n. sp. (Costa Rica), marshalli, n. sp. (northwestern Argentina), mexicana, n. sp. (Guatemala, Mexico), mikenoltei, n. sp. (Costa Rica), multipunctata, n. sp. (Ecuador), nigriapex, n. sp. (Bolivia), nigrifemur, n. sp. (Bolivia), osmundsonae, n. sp. (Mexico), punctistigma, n. sp. (Mexico to Costa Rica), quadristriata Wulp (Mexico to Costa Rica; possibly Colombia), quetzali, n. sp. (Guatemala), regina Giglio-Tos (Mexico), rupta (Wulp) (Mexico to Costa Rica), ruptafascia, n. sp. (Ecuador), septemdigitata, n. sp. (Peru, Bolivia), sinepuncta, n. sp. (Costa Rica), splendida Giglio-Tos (Mexico to Ecuador), tau, n. sp. (Costa Rica), thetis Hendel (southern Brazil), unifasciata, n. sp. (Ecuador), variabilis, n. sp. (Mexico), wasbaueri, n. sp. (Ecuador), and zumbadoi, n. sp. (Costa Rica). Blepharoneura amazonensis Lima & Leite, 1952 is considered a new synonym of B. hirsuta Bates, 1933, and a lectotype is designated for Blepharoneura furcifer Hendel, 1914. A key to species and phylogenetic analysis are provided, as well as descriptions, illustrations, distributions, and host plant data (as available) for each species.

New species of leaf-mining Nepticulidae (Lepidoptera) from the Neotropical and Ando-Patagonian regions, with new data on host plants

Zootaxa ◽  
2017 ◽  
Vol 4272 (1) ◽  
pp. 1 ◽  
Author(s):  
JONAS R. STONIS ◽  
ANDRIUS REMEIKIS ◽  
ARŪNAS DIŠKUS ◽  
NICK MEGORAN
Keyword(s):  
New Species ◽  
Host Plants ◽  
Virgin Islands ◽  
Distribution Map ◽  
Leaf Miners ◽  
New Host ◽  
British Virgin Islands ◽  
Leaf Mining ◽  
Plant Data ◽  
First Time

The paper treats fifteen species of leaf-mining pygmy moths (Insecta, Lepidoptera, Nepticulidae) discovered in the Neotropics (British Virgin Islands, Belize, Costa Rica, Venezuela, and Ecuador), and Ando-Patagonian region (Argentina and Chile). Except for two species, all belong to Stigmella Schrank. Twelve species are new, and are named and described in the current paper: Stigmella apicibrunella Diškus & Stonis, sp. nov.; S. decora Diškus & Stonis, sp. nov.; S. unicaudata Remeikis & Stonis, sp. nov.; S. sanmartini Remeikis & Stonis, sp. nov.; S. patula Remeikis & Stonis, sp. nov.; S. torosa Remeikis & Stonis, sp. nov.; S. monstrata Remeikis & Stonis, sp. nov.; S. huahumi Remeikis & Stonis, sp. nov.; S. venezuelica Remeikis & Stonis, sp. nov.; S. virginica Remeikis & Stonis, sp. nov.; Fomoria miranda Diškus & Stonis, sp. nov.; and Hesperolyra robinsoni Stonis, sp. n. Newly discovered variation of male genitalia of the Andean Stigmella rudis Puplesis & Robinson, 2000 is briefly discussed, and the formerly poorly understood Stigmella hylomaga (Meyrick, 1931) is redescribed and documented with photographs for the first time. We also present more photographs and add some addtional information on Stigmella gallicola van Nieukerken & Nishida, a recently described gall-maker from Costa Rica.The paper also provides new host-plant data: some of the described (or redescribed) species are reported for the first time as leaf-miners on plants belonging to Euphorbiaceae (Acalypha padifolia Kunth), Salicaceae (Azara microphylla Hook. f.), Fabaceae (Inga spectabilis (Vahl) Willd. or I. edulis Mart.), Rhamnaceae (Colletia spinosissima J. F. Gmel.), Geraniaceae or Vivianiaceae (Rhynchotheca spinosa Ruiz & Pav.), and Asteraceae (Mutisia decurrens Cav.). All species treated in the paper are illustrated with photographs of the adults and genitalia, a distribution map, and also photographs of the leaf-mines and host plants when available.

Innovative Approach to Build a Scalable Hybrid Model for Prescriptive Maintenance

2021 ◽  
Author(s):  
Richard Büssow ◽  
Bruno Hain ◽  
Ismael Al Nuaimi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Hybrid Model ◽  
Domain Knowledge ◽  
Spare Parts ◽  
Good Prediction ◽  
First Principle ◽  
Plant Data ◽  
Rotating Equipment

Abstract Objective and Scope Analysis of operational plant data needs experts in order to interpret detected anomalies which are defined as unusual operation points. The next step on the digital transformation journey is to provide actionable insights into the data. Prescriptive Maintenance defines in advance which kind of detailed maintenance and spare parts will be required. This paper details requirements to improve these predictions for rotating equipment and show potential to integrate the outcome into an operational workflow. Methods, Procedures, Process First principle or physics-based modelling provides additional insights into the data, since the results are directly interpretable. However, such approaches are typically assumed to be expensive to build and not scalable. Identification of and focus on the relevant equipment to be modeled in a hybrid model using a combination of first principle physics and machine learning is a successful strategy. The model is trained using a machine learning approach with historic or current real plant data, to predict conditions which have not occurred before. The better the Artificial Intelligence is trained, the better the prediction will be. Results, Observations, Conclusions The general aim when operating a plant is the actual usage of operational data for process and maintenance optimization by advanced analytics. Typically a data-driven central oversight function supports operations and maintenance staff. A major lesson-learned is that the results of a rather simple statistical approach to detect anomalies fall behind the expectations and are too labor intensive. It is a widely spread misinterpretation that being able to deal with big data is sufficient to come up with good prediction quality for Prescriptive Maintenance. What big data companies are normally missing is domain knowledge, especially on plant critical rotating equipment. Without having domain knowledge the relevant input into the model will have shortcomings and hence the same will apply to its predictions. This paper gives an example of a refinery where the described hybrid model has been used. Novel and Additive Information First principle models are typically expensive to build and not scalable. This hybrid model approach, combining first principle physics based models with artificial intelligence and integration into an operational workflow shows a new way forward.

scholarly journals Boron nutrition and yield of alfalfa cultivar crioula in relation to boron supply

2004 ◽  
Vol 61 (5) ◽  
pp. 496-500 ◽  
Author(s):  
Anacleto Ranulfo dos Santos ◽  
Waldssimiler Teixeira de Mattos ◽  
Ana Aparecida da Silva Almeida ◽  
Francisco Antonio Monteiro ◽  
Beatriz Dias Corrêa ◽  
...  
Keyword(s):  
Dry Matter ◽  
Boron Concentration ◽  
Critical Level ◽  
External Solution ◽  
Dry Mass ◽  
Dry Matter Accumulation ◽  
Critical Levels ◽  
Production And Distribution ◽  
Plant Data ◽  
South Brazil

Alfalfa cultivar Crioula (Medicago sativa cv. Crioula) is grown in South Brazil and only a few studies on the plants' boron requirement are available. A greenhouse experiment was carried out with alfalfa to measure boron acquisition, production and distribution in the plant; data on critical level and production potentials were recorded. Plants were grown in ground quartz added with 1 L of solution, with the following boron rates: 0, 0.0625, 0.125, 0.25, 0.50, 1.00, and 2.00 mg L-1. Plants were harvested at 46 days of growth. Forage dry mass was increased by boron supply and dry matter accumulation was considerably low in control. Boron concentration in the leaves was higher than in the stems or roots. Boron utilization from the external solution reached 90% at 0.0625 mg L-1 and sharply decreased with further increasing boron rates. Boron concentration and content in the leaves and in plant tops were at maximum when applied boron was between 1.5 and 1.6 mg L-1. Critical levels of boron in plant were 61 mg kg-1 in the leaves and 39 mg kg-1 in plant tops for this cultivar of alfalfa.

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