Release Date: 2014-12-09...Table Name. Geographic Area Series: Shipment Characteristics by Origin Geography by Commodity: 2012 and 2007 ....ReleaseSchedule. The data in this file are scheduled for release in December 2014.....Key TableInformation.None.....Universe. The 2012 Commodity Flow Survey (CFS) covers business establishments with paid employees that are located in the United States and are classified using the 2007 North American Industry Classification System (NAICS) in mining, manufacturing, wholesale trade, and selected retail trade and services industries, namely, electronic shopping and mail-order houses, fuel dealers, and publishers. Establishments classified in transportation, construction, and all other retail and services industries are excluded from the survey. Farms, fisheries, foreign establishments, and most government-owned establishments are also excluded.The survey also covers auxiliary establishments (i.e., warehouses and managing offices) of multi-establishments companies..For the 2012 CFS, an advance survey (pre-canvass) of approximately 100,000 establishments was conducted to identify establishments with shipping activity. and to try and obtain an accurate measure of their shipping activity. Surveyed establishments that indicated undertaking shipping activities and the .non-respondents to the pre-canvass were included in the CFS sample universe....GeographyCoverage. The data are shown at the U.S., region, division, state, and CFS metropolitan area levels.....IndustryCoverage.None.....Data ItemsandOtherIdentifyingRecords. This file contains data on:..Value ($ Millions).Tons (Thousands).Ton-miles (Millions).Average miles per shipment (Number).Percentage change from 2007 and coefficient of variation or standard error for all above data items. . The data are shown by commodity code (COMM)......Sort Order.Data are presented in descending year by ascending geography (GEO_ID) by COMM sequence.....FTP Download. Download the entire table at Table 6 FTP. ....ContactInformation.U.S. Census Bureau.Commodity Flow Survey.Tel: (301)763-2108.Email: erd.cfs@census.gov...The estimates presented are based on data from the 2012 and 2007 Commodity Flow Surveys (CFS) and supersede data previously released in the 2012 CFS Preliminary Report. These estimates only cover businesses with paid employees. All dollar values are expressed in current dollars relative to each sample year (2012 and 2007), i.e., they are based on price levels in effect at the time of each sample. Estimates may not be additive due to rounding. ...For information on Commodity Flow Survey geographies, including changes for 2012, see Census Geographies. .Symbols:.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page..Z - Rounds to Zero..X - Not Applicable..For a complete list of all economic programs symbols, see the Symbols Glossary..Commodity Code changes for 2012 CFS.. (CFS10) 07-R - Prior to the 2012 CFS, oils and fats treated for use as biodiesel were included in Commodity Code 07. In the 2012 CFS, oils and fats treated for use as biodiesel moved to Commodity Code 18. . (CFS20) 08-R - Prior to the 2012 CFS, alcohols intended for use as fuel such as ethanol, although not specifically identified, were included in Commodity Code 08. In the 2012 CFS, ethanol moved to Commodity Code 17. . (CFS30) 17-R - Prior to the 2012 CFS, fuel alcohols such as ethanol were included in Commodity Code 08, although not specifically identified. Also, kerosene was included in Commodity Code 19. In the 2012 CFS, ethanol, fuel alcohols and kerosene moved to Commodity Code 17. . (CFS40) 18-R - Prior to the 2012 CFS, biodiesel, although not specifically identified, was included in Commodity Code 07. In the 2012 CFS, biodiesel moved to Commodity Code 18. . (CFS11) 074-R - Prior to the 2012 CFS, oils and fats treated for use as biodiesel were included in Commodity Code 074. In the 2012 CFS, oils and fats treated for use as biodiesel moved to Commodity Code 182. . (CFS21) 083-R - Prior to the 2012 CFS, denatured alcohol of more than 80% by volume was included in Commodity Code 083. In the 2012 CFS, denatured alcohol of more than 80% by volume moved to Commodity Code 084. . (CFS31) 171-R - Prior to the 2012 CFS, Commodity Code 171 only included gasoline. In the 2012 CFS, mixtures of 10% ethanol and gasoline moved to Commodity Code 171. . (CFS32) 172-R - Prior to the 2012 CFS, kerosene was included in Commodity Code 192. In the 2012 CFS, kerosene moved to Commodity Code 172. . (CFS12) 0743-R - Prior to the 2012 CFS, oils and fats treated for use in biodiesel were included in Commodity C...

Abstract

The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.

Geographic coverage

National

Analysis unit

Schools, teachers, students, public officials

Kind of data

Sample survey data [ssd]

Sampling procedure

The aim of the Global Education Policy Dashboard school survey is to produce nationally representative estimates, which will be able to detect changes in the indicators over time at a minimum power of 80% and with a 0.05 significance level. We also wish to detect differences by urban/rural location. For our school survey, we will employ a two-stage random sample design, where in the first stage a sample of typically around 200 schools, based on local conditions, is drawn, chosen in advance by the Bank staff. In the second stage, a sample of teachers and students will be drawn to answer questions from our survey modules, chosen in the field. A total of 10 teachers will be sampled for absenteeism. Five teachers will be interviewed and given a content knowledge exam. Three 1st grade students will be assessed at random, and a classroom of 4th grade students will be assessed at random. Stratification will be based on the school’s urban/rural classification and based on region. When stratifying by region, we will work with our partners within the country to make sure we include all relevant geographical divisions. For our Survey of Public Officials, we will sample a total of 200 public officials. Roughly 60 officials are typically surveyed at the federal level, while 140 officials will be surveyed at the regional/district level. For selection of officials at the regional and district level, we will employ a cluster sampling strategy, where roughly 10 regional offices (or whatever the secondary administrative unit is called) are chosen at random from among the regions in which schools were sampled. Then among these 10 regions, we also typically select around 10 districts (tertiary administrative level units) from among the districts in which schools were sampled. The result of this sampling approach is that for 10 clusters we will have links from the school to the district office to the regional office to the central office. Within the regions/districts, five or six officials will be sampled, including the head of organization, HR director, two division directors from finance and planning, and one or two randomly selected professional employees among the finance, planning, and one other service related department chosen at random. At the federal level, we will interview the HR director, finance director, planning director, and three randomly selected service focused departments. In addition to the directors of each of these departments, a sample of 9 professional employees will be chosen in each department at random on the day of the interview.

Sampling deviation

In order to visit two schools per day, we clustered at the sector level choosing two schools per cluster. With a sample of 200 schools, this means that we had to allocate 100 PSUs. We combined this clustering with stratification by district and by the urban rural status of the schools. The number of PSUs allocated to each stratum is proportionate to the number of schools in each stratum (i.e. the district X urban/rural status combination).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.

More information pertaining to each of the three instruments can be found below: - School Survey: The School Survey collects data primarily on practices (the quality of service delivery in schools), but also on some de facto policy indicators. It consists of streamlined versions of existing instruments—including Service Delivery Surveys on teachers and inputs/infrastructure, Teach on pedagogical practice, Global Early Child Development Database (GECDD) on school readiness of young children, and the Development World Management Survey (DWMS) on management quality—together with new questions to fill gaps in those instruments. Though the number of modules is similar to the full version of the Service Delivery Indicators (SDI) Survey, the number of items and the complexity of the questions within each module is significantly lower. The School Survey includes 8 short modules: School Information, Teacher Presence, Teacher Survey, Classroom Observation, Teacher Assessment, Early Learner Direct Assessment, School Management Survey, and 4th-grade Student Assessment. For a team of two enumerators, it takes on average about 4 hours to collect all information in a given school. For more information, refer to the Frequently Asked Questions.

• Policy Survey: The Policy Survey collects information to feed into the policy de jure indicators. This survey is filled out by key informants in each country, drawing on their knowledge to identify key elements of the policy framework (as in the SABER approach to policy-data collection that the Bank has used over the past 7 years). The survey includes questions on policies related to teachers, school management, inputs and infrastructure, and learners. In total, there are 52 questions in the survey as of June 2020. The key informant is expected to spend 2-3 days gathering and analyzing the relavant information to answer the survey questions.

• Survey of Public Officials: The Survey of Public Officials collects information about the capacity and orientation of the bureaucracy, as well as political factors affecting education outcomes. This survey is a streamlined and education-focused version of the civil-servant surveys that the Bureaucracy Lab (a joint initiative of the Governance Global Practice and the Development Impact Evaluation unit of the World Bank) has implemented in several countries. The survey includes questions about technical and leadership skills, work environment, stakeholder engagement, impartial decision-making, and attitudes and behaviors. The survey takes 30-45 minutes per public official and is used to interview Ministry of Education officials working at the central, regional, and district levels in each country.

Cleaning operations

Data quality control was performed in R and Stata Code to calculate all indicators can be found on github here: https://github.com/worldbank/GEPD/blob/master/Countries/Rwanda/2019/School/01_data/03_school_data_cleaner.R

Sampling error estimates

The aim of the Global Education Policy Dashboard school survey is to produce nationally representative estimates, which will be able to detect changes in the indicators over time at a minimum power of 80% and with a 0.05 significance level.

Citation Request : 1. OZKAN IA., KOKLU M. and SARACOGLU R. (2021). Classification of Pistachio Species Using Improved K-NN Classifier. Progress in Nutrition, Vol. 23, N. 2, pp. DOI:10.23751/pn.v23i2.9686. (Open Access) 2. SINGH D, TASPINAR YS, KURSUN R, CINAR I, KOKLU M, OZKAN IA, LEE H-N., (2022). Classification and Analysis of Pistachio Species with Pre-Trained Deep Learning Models, Electronics, 11 (7), 981. https://doi.org/10.3390/electronics11070981. (Open Access) DATASET: https://www.muratkoklu.com/datasets/

https://www.mattioli1885journals.com/index.php/progressinnutrition/article/view/9686/9178 ABSTRACT: A computer vision system has been developed to distinguish two different species of pistachios with different characteristics that address different market types. 2148 sample image for these two kinds of pistachios were taken with a high-resolution camera. The image processing techniques, segmentation and feature extraction were applied on the obtained images of the pistachio samples. A pistachio dataset that has sixteen attributes was created. An advanced classifier based on k-NN method, which is a simple and successful classifier, and principal component analysis was designed on the obtained dataset. In this study; a multi-level system including feature extraction, dimension reduction and dimension weighting stages has been proposed. Experimental results showed that the proposed approach achieved a classification success of 94.18%. The presented high-performance classification model provides an important need for the separation of pistachio species and increases the economic value of species. In addition, the developed model is important in terms of its application to similar studies. Keywords: Classification, Image processing, k nearest neighbor classifier, Pistachio species

https://doi.org/10.3390/electronics11070981 Within the scope of the study, images of Kirmizi and Siirt pistachio types were obtained through the computer vision system. The pre-trained dataset includes a total of 2148 images, 1232 of Kirmizi type and 916 of Siirt type. Three different convolutional neural network models were used to classify these images. Models were trained by using the transfer learning method, with AlexNet and the pre-trained models VGG16 and VGG19. The dataset is divided as 80% training and 20% test. As a result of the performed classifications, the success rates obtained from the AlexNet, VGG16, and VGG19 models are 94.42%, 98.84%, and 98.14%, respectively. Models’ performances were evaluated through sensitivity, specificity, precision, and F-1 score metrics. In addition, ROC curves and AUC values were used in the performance evaluation. The highest classification success was achieved with the VGG16 model. The obtained results reveal that these methods can be used successfully in the determination of pistachio types. View Full-Text Keywords: pistachio; genetic varieties; machine learning; deep learning; food recognition

When we designed the Valkyrie trial several different considerations were made on how to calculate the number of patients to be recruited for the trial. In the end, the decision fell on the way as described in the project [methodology]meto: - #### 5.7.Sample size: - The sample size does not depend on the initial question, which is not comparative. However, in order to ascertain secondary outcomes, it was necessary to calculate the expected number of patients to be recruited. Using data available from patients already treated in our hospital (as discussed above) as a pilot group, assuming a hazard ratio of 0.05 and choosing a statistical power of 80% and 0.5% alpha the minimum number of patients required in the experimental group (in a 1:2 design) is 16 patients. We expect to require 2 years to recruit this sample. The comparison group to be used will be the historic control group of patients treated at our institution between 2000 and 2013, with schemes other than HIT (number of patients 31, suitable for this experimental design). The powerSurvEpi package of R language was used to calculate the sample size (R Development Core Team, 2012).(excerpt)