scholarly journals Multinational Production: Data and Stylized Facts

2015 ◽  
Vol 105 (5) ◽  
pp. 530-536 ◽  
Author(s):  
Natalia Ramondo ◽  
Andrés Rodríguez-Clare ◽  
Felix Tintelnot
Keyword(s):  
Multinational Firms ◽  
Missing Values ◽  
Basic Data ◽  
Production Data ◽  
Stylized Facts ◽  
Data Set ◽  
Extrapolation Procedure ◽  
Comprehensive Data ◽  
Multinational Production

We present a comprehensive data set on the bilateral activity of multinational firms, with focus on two variables: affiliate revenues and the number of affiliates across country pairs. Our basic data are from UNCTAD and include 59 countries, an average over 1996-2001. We implement an extrapolation procedure that fills in missing values using, alternately, FDI stocks and the bilateral number of M&A transactions. Our dataset allows for the analysis of new patterns of multinational production activities across countries, by taking into account firm rather than balance of payment variables, and both the intensive and extensive margins of multinational activities.

scholarly journals Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)

2021 ◽  
Vol 18 (3) ◽  
pp. 1333
Author(s):  
Ahmad R. Alsaber ◽  
Jiazhu Pan ◽  
Adeeba Al-Hurban 
Keyword(s):  
Air Quality ◽  
Missing Data ◽  
Random Forest ◽  
Missing Values ◽  
Imputation Method ◽  
Environmental Data ◽  
Environmental Research ◽  
Quality Data ◽  
Data Set ◽  
Air Quality Data

In environmental research, missing data are often a challenge for statistical modeling. This paper addressed some advanced techniques to deal with missing values in a data set measuring air quality using a multiple imputation (MI) approach. MCAR, MAR, and NMAR missing data techniques are applied to the data set. Five missing data levels are considered: 5%, 10%, 20%, 30%, and 40%. The imputation method used in this paper is an iterative imputation method, missForest, which is related to the random forest approach. Air quality data sets were gathered from five monitoring stations in Kuwait, aggregated to a daily basis. Logarithm transformation was carried out for all pollutant data, in order to normalize their distributions and to minimize skewness. We found high levels of missing values for NO2 (18.4%), CO (18.5%), PM10 (57.4%), SO2 (19.0%), and O3 (18.2%) data. Climatological data (i.e., air temperature, relative humidity, wind direction, and wind speed) were used as control variables for better estimation. The results show that the MAR technique had the lowest RMSE and MAE. We conclude that MI using the missForest approach has a high level of accuracy in estimating missing values. MissForest had the lowest imputation error (RMSE and MAE) among the other imputation methods and, thus, can be considered to be appropriate for analyzing air quality data.

scholarly journals The Core of the Global Corporate Network

2021 ◽  
Author(s):  
Ricardo Giglio ◽  
Thomas Lux
Keyword(s):  
Network Topology ◽  
World Wide ◽  
Data Set ◽  
The Core ◽  
Comprehensive Data ◽  
The World ◽  
Corporate Network ◽  
Board Membership ◽  
National Networks ◽  
Wide Population

AbstractWe investigate the network topology of a comprehensive data set of the world-wide population of corporate entities. In particular, we have extracted information on the boards of all companies listed in Bloomberg’s archive of company profiles in October, 2015, a total of almost 100,000 firms. We provide information on board membership overlaps at various levels, and, in particular, show that there exists a core of directors who accumulate a large number of seats and are highly connected among themselves both at the level of national networks and at the worldwide aggregated level.

Symmetry Breaking and Training from Incomplete Data with Radial Basis Boltzmann Machines

1997 ◽  
Vol 08 (03) ◽  
pp. 301-315 ◽  
Author(s):  
Marcel J. Nijman ◽  
Hilbert J. Kappen
Keyword(s):  
Symmetry Breaking ◽  
Incomplete Data ◽  
Missing Values ◽  
Nearest Neighbor ◽  
Boltzmann Machine ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Input Space ◽  
Learning Rules ◽  
Radial Basis

A Radial Basis Boltzmann Machine (RBBM) is a specialized Boltzmann Machine architecture that combines feed-forward mapping with probability estimation in the input space, and for which very efficient learning rules exist. The hidden representation of the network displays symmetry breaking as a function of the noise in the dynamics. Thus, generalization can be studied as a function of the noise in the neuron dynamics instead of as a function of the number of hidden units. We show that the RBBM can be seen as an elegant alternative of k-nearest neighbor, leading to comparable performance without the need to store all data. We show that the RBBM has good classification performance compared to the MLP. The main advantage of the RBBM is that simultaneously with the input-output mapping, a model of the input space is obtained which can be used for learning with missing values. We derive learning rules for the case of incomplete data, and show that they perform better on incomplete data than the traditional learning rules on a 'repaired' data set.

Integrated Production Data Analysis and Water Injection Optimization in A Giant Carbonate Field

2021 ◽  
Author(s):  
Adel Mehrabadi ◽  
Gabriele Urbani ◽  
Simona Renna ◽  
Lucia Rossi ◽  
Italo Luciani ◽  
...  
Keyword(s):  
Data Analysis ◽  
Water Injection ◽  
Water Flooding ◽  
Integrated Analysis ◽  
Production Data ◽  
Data Set ◽  
Proper Actions ◽  
Injection Scheme ◽  
Vertical Heterogeneity ◽  
Current Water

Abstract In case of giant brown fields, a proper water injection management can result in a very complex process, due to the quality and quantity of data to be analysed. Main issue is the understanding of the injected water preferential paths, especially in carbonate environment characterized by strong vertical and areal heterogeneities (karst). A structured workflow is presented to analyze and integrate a massive data set, in order to understand and optimize the water injection scheme. An extensive Production Data Analysis (PDA) has been performed, based on the integration of available geological data (including NMR and Cased Hole Logs), production (allocated rates, Well Tests, PLT), pressure (SBHP, RFT, MDT, ESP) and salinity data. The applied workflow led to build a Fluid Path Conceptual Model (FPCM), an easy but powerful tool to visualize the complex dynamic connections between injectors-producers and aquifer influence areas. Several diagnostic plots were performed to support and validate the main outcomes. On this basis, proper actions were implemented to optimize the current water injection scheme. The workflow was applied on a carbonate giant brown field characterized by three different reservoir members, hydraulically communicating at original conditions, characterized by high vertical heterogeneity and permeability contrast. Moreover, dissolution phenomena, localized in the uppermost reservoir section, led to important permeability enhancement through a wide network of connected vugs, acting as water preferential communication pathways. The geological analysis played a key role to investigate the reservoir water flooding mechanism in dynamic conditions. The water rising mechanism was identified to be driven by the high permeability contrast, hence characterized by lateral independent movements in the different reservoir members. The integrated analysis identified room for optimization of the current water injection strategy. In particular, key factor was the analysis and optimization at block scale, intended as areal and vertical sub-units, as identified by the PDA and visualized through the FPCM. Actions were suggested, including injection rates optimization and the definition of new injections points. A detailed surveillance plan was finally implemented to monitor the effects of the proposed actions on the field performances, proving the robustness of the methodology. Eni workflow for water injection analysis and optimization was previously successfully tested only in sandstone reservoirs. This paper shows the robustness of the methodology also in carbonate environment, where water encroachment is strongly driven by karst network. The result is a clear understanding of the main dynamics in the reservoir, which allows to better tune any action aimed to optimize water injection and increase the value of mature assets.

scholarly journals Reliability of the radiographic variables in the International Spinal Cord Injury Spinal Column Injury Basic Data Set compared with the AO classification

Spinal Cord ◽  
2016 ◽  
Vol 54 (10) ◽  
pp. 884-888 ◽  
Author(s):  
C Lucantoni ◽  
R G Krishnan ◽  
M Gehrchen ◽  
D W Hallager ◽  
F Biering-Sørensen ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Spinal Column ◽  
Basic Data ◽  
Data Set ◽  
Ao Classification ◽  
Cord Injury

scholarly journals The Development of a Comprehensive Data Set for Systematic Studies of Machine Translation

2021 ◽  
pp. 248-262
Author(s):  
Jörg Tiedemann
Keyword(s):  
Machine Translation ◽  
Training Data ◽  
Sufficient Information ◽  
Data Set ◽  
Neural Machine Translation ◽  
High Resource ◽  
Language Groups ◽  
Comprehensive Data ◽  
Parallel Data ◽  
Initial Results

This paper presents our on-going efforts to develop a comprehensive data set and benchmark for machine translation beyond high-resource languages. The current release includes 500GB of compressed parallel data for almost 3,000 language pairs covering over 500 languages and language variants. We present the structure of the data set and demonstrate its use for systematic studies based on baseline experiments with multilingual neural machine translation between Finno-Ugric languages and other language groups. Our initial results show the capabilities of training effective multilingual translation models with skewed training data but also stress the shortcomings with low-resource settings and the difficulties to obtain sufficient information through straightforward transfer from related languages.

Real-Time Prediction of Choke Health Using Production Data Integrated with AI

2021 ◽  
Author(s):  
Ahmed Alghamdi ◽  
Olakunle Ayoola ◽  
Khalid Mulhem ◽  
Mutlaq Otaibi ◽  
Abdulazeez Abdulraheem
Keyword(s):  
High Pressure ◽  
Real Time ◽  
Large Scale ◽  
Production Systems ◽  
Rate Of Change ◽  
Production Data ◽  
Ann Model ◽  
Sand Production ◽  
Data Set ◽  
Pressure Drops

Abstract Chokes are an integral part of production systems and are crucial surface equipment that faces rough conditions such as high-pressure drops and erosion due to solids. Predicting choke health is usually achieved by analyzing the relationship of choke size, pressure, and flow rate. In large-scale fields, this process requires extensive-time and effort using the conventional techniques. This paper presents a real-time proactive approach to detect choke wear utilizing production data integrated with AI analytics. Flowing parameters data were collected for more than 30 gas wells. These wells are producing gas with slight solids production from a high-pressure high-temperature field. In addition, these wells are equipped with a multi-stage choke system. The approach of determining choke wear relies on training the AI model on a dataset constructed by comparison of the choke valve rate of change with respect to a smoother slope of the production rate. If the rate of change is not within a tolerated range of divergence, an abnormal choke behavior is detected. The data set was divided into 70% for training and 30% for testing. Artificial Neural Network (ANN) was trained on data that has the following inputs: gas specific gravity, upstream & downstream pressure and temperature, and choke size. This ANN model achieved a correlation coefficient above 0.9 with an excellent prediction on the data points exhibiting normal or abnormal choke behaviors. Piloting this application on large fields, where manual analysis is often impractical, saves a substantial man-hour and generates significant cost-avoidance. Areas for improvement in such an application depends on equipping the ANN network with long-term production profile prediction abilities, such as water production, and this analysis relies on having an accurate reading from the venturi meters, which is often the case in single-phase flow. The application of this AI-driven analytics provides tremendous improvement for remote offshore production operations surveillance. The novel approach presented in this paper capitalizes on the AI analytics for estimating proactively detecting choke health conditions. The advantages of such a model are that it harnesses AI analytics to help operators improve asset integrity and production monitoring compliance. In addition, this approach can be expanded to estimate sand production as choke wear is a strong function of sand production.

A Novel Imputation Approach for Sharing Protected Public Health Data

2021 ◽  
pp. e1-e9
Author(s):  
Elizabeth A. Erdman ◽  
Leonard D. Young ◽  
Dana L. Bernson ◽  
Cici Bauer ◽  
Kenneth Chui ◽  
...  
Keyword(s):  
Public Health ◽  
Missing Values ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Surveillance Data ◽  
Prescription Data ◽  
Data Set ◽  
Opioid Prescription ◽  
Mean Imputation ◽  
Imputation Approach

Objectives. To develop an imputation method to produce estimates for suppressed values within a shared government administrative data set to facilitate accurate data sharing and statistical and spatial analyses. Methods. We developed an imputation approach that incorporated known features of suppressed Massachusetts surveillance data from 2011 to 2017 to predict missing values more precisely. Our methods for 35 de-identified opioid prescription data sets combined modified previous or next substitution followed by mean imputation and a count adjustment to estimate suppressed values before sharing. We modeled 4 methods and compared the results to baseline mean imputation. Results. We assessed performance by comparing root mean squared error (RMSE), mean absolute error (MAE), and proportional variance between imputed and suppressed values. Our method outperformed mean imputation; we retained 46% of the suppressed value’s proportional variance with better precision (22% lower RMSE and 26% lower MAE) than simple mean imputation. Conclusions. Our easy-to-implement imputation technique largely overcomes the adverse effects of low count value suppression with superior results to simple mean imputation. This novel method is generalizable to researchers sharing protected public health surveillance data. (Am J Public Health. Published online ahead of print September 16, 2021: e1–e9. https://doi.org/10.2105/AJPH.2021.306432 )

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