Understanding the statistics of fluctuation driven flows in the boundary layer of magnetically confined plasmas is desired to accurately model the lifetime of the vacuum vessel components. Mirror Langmuir probes (MLPs) are a novel diagnostic that uniquely allow us to sample the plasma parameters on a time scale shorter than the characteristic time scale of their fluctuations. Sudden large-amplitude fluctuations in the plasma degrade the precision and accuracy of the plasma parameters reported by MLPs for cases in which the probe bias range is of insufficient amplitude. While some data samples can readily be classified as valid and invalid, we find that such a classification may be ambiguous for up to 40% of data sampled for the plasma parameters and bias voltages considered in this study. In this contribution, we employ an autoencoder (AE) to learn a low-dimensional representation of valid data samples. By definition, the coordinates in this space are the features that mostly characterize valid data. Ambiguous data samples are classified in this space using standard classifiers for vectorial data. In this way, we avoid defining complicated threshold rules to identify outliers, which require strong assumptions and introduce biases in the analysis. By removing the outliers that are identified in the latent low-dimensional space of the AE, we find that the average conductive and convective radial heat fluxes are between approximately 5% and 15% lower as when removing outliers identified by threshold values. For contributions to the radial heat flux due to triple correlations, the difference is up to 40%.

Jurisdictional Unit, 2022-05-21. For use with WFDSS, IFTDSS, IRWIN, and InFORM.This is a feature service which provides Identify and Copy Feature capabilities. If fast-drawing at coarse zoom levels is a requirement, consider using the tile (map) service layer located at https://nifc.maps.arcgis.com/home/item.html?id=3b2c5daad00742cd9f9b676c09d03d13.OverviewThe Jurisdictional Agencies dataset is developed as a national land management geospatial layer, focused on representing wildland fire jurisdictional responsibility, for interagency wildland fire applications, including WFDSS (Wildland Fire Decision Support System), IFTDSS (Interagency Fuels Treatment Decision Support System), IRWIN (Interagency Reporting of Wildland Fire Information), and InFORM (Interagency Fire Occurrence Reporting Modules). It is intended to provide federal wildland fire jurisdictional boundaries on a national scale. The agency and unit names are an indication of the primary manager name and unit name, respectively, recognizing that:There may be multiple owner names.Jurisdiction may be held jointly by agencies at different levels of government (ie State and Local), especially on private lands, Some owner names may be blocked for security reasons.Some jurisdictions may not allow the distribution of owner names. Private ownerships are shown in this layer with JurisdictionalUnitIdentifier=null,JurisdictionalUnitAgency=null, JurisdictionalUnitKind=null, and LandownerKind="Private", LandownerCategory="Private". All land inside the US country boundary is covered by a polygon.Jurisdiction for privately owned land varies widely depending on state, county, or local laws and ordinances, fire workload, and other factors, and is not available in a national dataset in most cases.For publicly held lands the agency name is the surface managing agency, such as Bureau of Land Management, United States Forest Service, etc. The unit name refers to the descriptive name of the polygon (i.e. Northern California District, Boise National Forest, etc.).These data are used to automatically populate fields on the WFDSS Incident Information page.This data layer implements the NWCG Jurisdictional Unit Polygon Geospatial Data Layer Standard.Relevant NWCG Definitions and StandardsUnit2. A generic term that represents an organizational entity that only has meaning when it is contextualized by a descriptor, e.g. jurisdictional.Definition Extension: When referring to an organizational entity, a unit refers to the smallest area or lowest level. Higher levels of an organization (region, agency, department, etc) can be derived from a unit based on organization hierarchy.Unit, JurisdictionalThe governmental entity having overall land and resource management responsibility for a specific geographical area as provided by law.Definition Extension: 1) Ultimately responsible for the fire report to account for statistical fire occurrence; 2) Responsible for setting fire management objectives; 3) Jurisdiction cannot be re-assigned by agreement; 4) The nature and extent of the incident determines jurisdiction (for example, Wildfire vs. All Hazard); 5) Responsible for signing a Delegation of Authority to the Incident Commander.See also: Unit, Protecting; LandownerUnit IdentifierThis data standard specifies the standard format and rules for Unit Identifier, a code used within the wildland fire community to uniquely identify a particular government organizational unit.Landowner Kind & CategoryThis data standard provides a two-tier classification (kind and category) of landownership. Attribute Fields JurisdictionalAgencyKind Describes the type of unit Jurisdiction using the NWCG Landowner Kind data standard. There are two valid values: Federal, and Other. A value may not be populated for all polygons.JurisdictionalAgencyCategoryDescribes the type of unit Jurisdiction using the NWCG Landowner Category data standard. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State. A value may not be populated for all polygons.JurisdictionalUnitNameThe name of the Jurisdictional Unit. Where an NWCG Unit ID exists for a polygon, this is the name used in the Name field from the NWCG Unit ID database. Where no NWCG Unit ID exists, this is the “Unit Name” or other specific, descriptive unit name field from the source dataset. A value is populated for all polygons.JurisdictionalUnitIDWhere it could be determined, this is the NWCG Standard Unit Identifier (Unit ID). Where it is unknown, the value is ‘Null’. Null Unit IDs can occur because a unit may not have a Unit ID, or because one could not be reliably determined from the source data. Not every land ownership has an NWCG Unit ID. Unit ID assignment rules are available from the Unit ID standard, linked above.LandownerKindThe landowner category value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. There are three valid values: Federal, Private, or Other.LandownerCategoryThe landowner kind value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State, Private.DataSourceThe database from which the polygon originated. Be as specific as possible, identify the geodatabase name and feature class in which the polygon originated.SecondaryDataSourceIf the Data Source is an aggregation from other sources, use this field to specify the source that supplied data to the aggregation. For example, if Data Source is "PAD-US 2.1", then for a USDA Forest Service polygon, the Secondary Data Source would be "USDA FS Automated Lands Program (ALP)". For a BLM polygon in the same dataset, Secondary Source would be "Surface Management Agency (SMA)."SourceUniqueIDIdentifier (GUID or ObjectID) in the data source. Used to trace the polygon back to its authoritative source.MapMethod:Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality. MapMethod will be Mixed Method by default for this layer as the data are from mixed sources. Valid Values include: GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; DigitizedTopo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Phone/Tablet; OtherDateCurrentThe last edit, update, of this GIS record. Date should follow the assigned NWCG Date Time data standard, using 24 hour clock, YYYY-MM-DDhh.mm.ssZ, ISO8601 Standard.CommentsAdditional information describing the feature. GeometryIDPrimary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature.JurisdictionalUnitID_sansUSNWCG Unit ID with the "US" characters removed from the beginning. Provided for backwards compatibility.JoinMethodAdditional information on how the polygon was matched information in the NWCG Unit ID database.LocalNameLocalName for the polygon provided from PADUS or other source.LegendJurisdictionalAgencyJurisdictional Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.LegendLandownerAgencyLandowner Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.DataSourceYearYear that the source data for the polygon were acquired.Data InputThis dataset is based on an aggregation of 4 spatial data sources: Protected Areas Database US (PAD-US 2.1), data from Bureau of Indian Affairs regional offices, the BLM Alaska Fire Service/State of Alaska, and Census Block-Group Geometry. NWCG Unit ID and Agency Kind/Category data are tabular and sourced from UnitIDActive.txt, in the WFMI Unit ID application (https://wfmi.nifc.gov/unit_id/Publish.html). Areas of with unknown Landowner Kind/Category and Jurisdictional Agency Kind/Category are assigned LandownerKind and LandownerCategory values of "Private" by use of the non-water polygons from the Census Block-Group geometry.PAD-US 2.1:This dataset is based in large part on the USGS Protected Areas Database of the United States - PAD-US 2.`. PAD-US is a compilation of authoritative protected areas data between agencies and organizations that ultimately results in a comprehensive and accurate inventory of protected areas for the United States to meet a variety of needs (e.g. conservation, recreation, public health, transportation, energy siting, ecological, or watershed assessments and planning). Extensive documentation on PAD-US processes and data sources is available.How these data were aggregated:Boundaries, and their descriptors, available in spatial databases (i.e. shapefiles or geodatabase feature classes) from land management agencies are the desired and primary data sources in PAD-US. If these authoritative sources are unavailable, or the agency recommends another source, data may be incorporated by other aggregators such as non-governmental organizations. Data sources are tracked for each record in the PAD-US geodatabase (see below).BIA and Tribal Data:BIA and Tribal land management data are not available in PAD-US. As such, data were aggregated from BIA regional offices. These data date from 2012 and were substantially updated in 2022. Indian Trust Land affiliated with Tribes, Reservations, or BIA Agencies: These data are not considered the system of record and are not intended to be used as such. The Bureau of Indian Affairs (BIA), Branch of Wildland Fire Management (BWFM) is not the originator of these data. The

Introduction Black entrepreneurship in Canada makes important contributions to the Canadian economy, from fostering innovation to creating employment and building generational wealth. At least 1.3% of Black adults in Canada are business owners, relative to the 2.3% that are business owners from the entire Canadian population (Business Development Bank of Canada (BDC), 2025). Similarly, Black people account for 2.4% of all business owners in the country even though they only represent 4.3% of the entire population. Women account for 33% of these Black businesses, as compared to their 20% share in the country’s total business ownership (Diversity Institute, 2024), highlighting the potential of Black entrepreneurship for economic empowerment. However, Black businesses are faced with some systemic barriers that impede their ability to thrive, including but not limited to underrepresentation among entrepreneurs in Canada and limited access to finance, restricted networking opportunities, and insufficient specialized support programs (Gueye et al., 2022; Gueye, 2023; Diversity Institute, 2024). These challenges stem in part from a lack of comprehensive and reliable data on Black businesses and the absence of standardized definitions for key concepts such as Black entrepreneurs, Black enterprises, and Black entrepreneurship. Without a clear understanding of who constitutes a Black entrepreneur and the scale of their contributions, policymakers and stakeholders struggle to provide the necessary support and resources to advance this community. In fact, the development of policies and initiatives for Black businesses faces difficulties because current data about Black entrepreneurship remains fragmented and inconsistent, with different sources reporting different numbers (Grekou et al., 2021; Gueye, 2023). These discrepancies highlight the urgent need for a unified approach to data collection and analysis, as accurate and comprehensive data are critical to understanding the size, scope, and needs of Black entrepreneurs, enabling targeted policy interventions and resource allocation. Current data fragmentation problems combined with non-standardized definitions create a situation where Black business owners are frequently ignored or inaccurately classified or omitted (Coletto et al., 2021). The significance of this research, therefore, lies in its ability to resolve systemic barriers through an improved representation of Black entrepreneurs. This research aims to harmonise missing data points and set specific criteria to establish sound tools for policymakers, researchers, and community groups who want to better assist Black entrepreneurs. With this, Black-owned business support will be strengthened through targeted policies and programs that develop sustainable growth for these businesses in Canada. The main objectives of this study are threefold. The research seeks to reconcile disparate Black entrepreneurship statistics from Afrobiz.ca alongside Canadian Black Chamber of Commerce records and Statistics Canada databases. Also, the research seeks to develop unified criteria to define Black business owners together with their enterprises to improve both data collection precision and reporting consistency. Lastly, the research will establish procedures to build a standardized database of Black entrepreneurs by integrating present data sources and making sure both formal and informal businesses receive proper representation. These research efforts will establish fundamental principles for developing an inclusive and equal entrepreneurial system throughout Canada. Introduction L'entrepreneuriat noir au Canada apporte d'importantes contributions à l'économie canadienne, qu'il s'agisse de favoriser l'innovation, de créer des emplois ou de constituer un patrimoine générationnel. Au moins 1,3 % des adultes noirs au Canada sont propriétaires d'une entreprise, contre 2,3 % pour l'ensemble de la population canadienne (Banque de développement du Canada (BDC), 2025). De même, les Noirs représentent 2,4 % de tous les propriétaires d'entreprise du pays, alors qu'ils ne représentent que 4,3 % de la population totale. Les femmes représentent 33 % de ces entreprises noires, alors qu'elles représentent 20 % de l'ensemble des entreprises du pays (Diversity Institute, 2024), ce qui souligne le potentiel de l'entrepreneuriat noir en matière d'émancipation économique. Toutefois, les entreprises noires sont confrontées à certains obstacles systémiques qui entravent leur capacité à prospérer, notamment la sousreprésentation des entrepreneurs au Canada et l'accès limité au financement, les possibilités de réseautage restreintes et l'insuffisance des programmes de soutien spécialisés (Gueye et al., 2022 ; Gueye, 2023 ; Diversity Institute, 2024). Ces défis découlent en partie d'un manque de données complètes et fiables sur les entreprises noires et de l'absence de définitions normalisées pour des concepts clés tels que les entrepreneurs...

SPARQL query example 1. This text file contains the SPARQL query we apply on our PGx linked data to obtain the data graph represented in Fig. 3. This query includes the definition of prefixes mentioned in Figs. 2 and 3. This query takes about 30 s on our https://pgxlod.loria.fr server. (TXT 2 kb)

It is understood that ensuring equation balance is a necessary condition for a valid model of times series data. Yet, the definition of balance provided so far has been incomplete and there has not been a consistent understanding of exactly why balance is important or how it can be applied. The discussion to date has focused on the estimates produced by the GECM. In this paper, we go beyond the GECM and be- yond model estimates. We treat equation balance as a theoretical matter, not merely an empirical one, and describe how to use the concept of balance to test theoretical propositions before longitudinal data have been gathered. We explain how equation balance can be used to check if your theoretical or empirical model is either wrong or incomplete in a way that will prevent a meaningful interpretation of the model. We also raise the issue of “I(0) balance” and its importance. The replication dataset includes the Stata .do file and .dta file to replicate the analysis in section 4.1 of the Supplementary Information.

Based on the work described in D8.1 and D8.2, we developed initial prototypes for modeling Data Life Cycles and directives. In this document, we are describing both the processes and the results of this initial implementation. We will discuss the issues we faced in developing these prototypes and the technical choices we made. This deliverable provides an overview of the current status of the work. This work has been used as a basis for concrete implementations of community use-cases, described in D8.6.

🇺🇸 United States

The cosmopolitan ascidian Ciona intestinalis is the most common model species of Tunicata, the sister-group of Vertebrata, and widely used in developmental biology, genomics and evolutionary studies. Recently, molecular studies suggested the presence of cryptic species hidden within the C. intestinalis species, namely C. intestinalis type A and type B. So far, no substantial morphological differences have been identified between individuals belonging to the two types. Here we present morphometric, immunohistochemical, and histological analyses, as well as 3-D reconstructions, of late larvae obtained by cross-fertilization experiments of molecularly determined type A and type B adults, sampled in different seasons and in four different localities. Our data point to quantitative and qualitative differences in the trunk shape of larvae belonging to the two types. In particular, type B larvae exhibit a longer pre-oral lobe, longer and relatively narrower total body length, and a shorter ocellus-tail distance than type A larvae. All these differences were found to be statistically significant in a Discriminant Analysis. Depending on the number of analyzed parameters, the obtained discriminant function was able to correctly classify > 93% of the larvae, with the remaining misclassified larvae attributable to the existence of intra-type seasonal variability. No larval differences were observed at the level of histology and immunohistochemical localization of peripheral sensory neurons. We conclude that type A and type B are two distinct species that can be distinguished on the basis of larval morphology and molecular data. Since the identified larval differences appear to be valid diagnostic characters, we suggest to raise both types to the rank of species and to assign them distinct names.

This data set contains a series of land surface parameters simulated from the Noah land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing and range from Jan 1979 to the present. The temporal resolution is monthly. The file format is WMO GRIB-1. The NLDAS-2 monthly Noah model data were generated from the NLDAS-2 hourly Noah model data, as monthly accumulation for rainfall, snowfall, subsurface runoff, surface runoff, total evapotranspiration, snow melt, and monthly averages for other variables. The monthly period of each month is from 00Z at start of the month to 23:59Z at end of the month, with the exception of the very first month in the data set (Jan 1979) which starts at 00Z 02 Jan 1979. Also for the first month (Jan 1979), because the variables listed as instantaneous in the README file do not have valid data exactly on 00Z 02 Jan 1979, and this one hour is not included in the average for this month only.

A brief description about the NLDAS-2 monthly Noah model can be found from the dataset landing page for NLDAS_NOAH0125_H_002 and the NLDAS-2 README document.

Details about the NLDAS-2 configuration of the Noah LSM can be found in Xia et al. (2012).

The NLDAS-2 Noah monthly data contain fifty-two fields. The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units.

For information about the vertical layers of the Soil Moisture Content (PDS 086), Soil Temperature (PDS 085), and Liquid Soil Moisture Content (PDS 151) please see the README Document or the GrADS ctl file.

The primary objective from this project was to acquire historical shoreline information for all of the Northern Ireland coastline. Having this detailed understanding of the coast’s shoreline position and geometry over annual to decadal time periods is essential in any management of the coast.The historical shoreline analysis was based on all available Ordnance Survey maps and aerial imagery information. Analysis looked at position and geometry over annual to decadal time periods, providing a dynamic picture of how the coastline has changed since the start of the early 1800s.Once all datasets were collated, data was interrogated using the ArcGIS package – Digital Shoreline Analysis System (DSAS). DSAS is a software package which enables a user to calculate rate-of-change statistics from multiple historical shoreline positions. Rate-of-change was collected at 25m intervals and displayed both statistically and spatially allowing for areas of retreat/accretion to be identified at any given stretch of coastline.The DSAS software will produce the following rate-of-change statistics:Net Shoreline Movement (NSM) – the distance between the oldest and the youngest shorelines.Shoreline Change Envelope (SCE) – a measure of the total change in shoreline movement considering all available shoreline positions and reporting their distances, without reference to their specific dates.End Point Rate (EPR) – derived by dividing the distance of shoreline movement by the time elapsed between the oldest and the youngest shoreline positions.Linear Regression Rate (LRR) – determines a rate of change statistic by fitting a least square regression to all shorelines at specific transects.Weighted Linear Regression Rate (WLR) - calculates a weighted linear regression of shoreline change on each transect. It considers the shoreline uncertainty giving more emphasis on shorelines with a smaller error.The end product provided by Ulster University is an invaluable tool and digital asset that has helped to visualise shoreline change and assess approximate rates of historical change at any given coastal stretch on the Northern Ireland coast.

This workshop is a continuation of the DDI power point presentation given at the previous year's DLI Training in Kingston. It is intended as a primer for those interested in understanding the basic concepts of the Data Documentation Initiative (DDI) and the Data Type Definition (DTD) statements. This time participants will have the opportunity to take a closer look, examine the tags, determine criteria for selection and create an XML template.

Presentation Number of weddings and pacs per month since 1980 in the city of Martigues. The data are updated daily. # Description of the fields in the table (in columns): ## Year * Definition: corresponds to the legal calendar year. * Possible values: 1980-2021 ## MONTHS * Definition: corresponds to the number of one of the twelve months of a legal year * Possible values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 and 12. ## STR_MOIS * Definition: corresponds to the names of the twelve months of a legal year * Possible values: January, February, March, April, May, June, July, August, September, October, November and December ## ACTE_DE * Definition: corresponds to the nature of the act * Possible values: Marriage — PACS ## ETAT * Definition: corresponds to the legal status of the act * Possible values: Valid — ANNUAL ## TOTAL * Definition: corresponds to the total number of first names in the year in question. * Possible values: 1-93

Understanding the statistics of fluctuation driven flows in the boundary layer of magnetically confined plasmas is desired to accurately model the lifetime of the vacuum vessel components. Mirror Langmuir probes (MLPs) are a novel diagnostic that uniquely allow us to sample the plasma parameters on a time scale shorter than the characteristic time scale of their fluctuations. Sudden large-amplitude fluctuations in the plasma degrade the precision and accuracy of the plasma parameters reported by MLPs for cases in which the probe bias range is of insufficient amplitude. While some data samples can readily be classified as valid and invalid, we find that such a classification may be ambiguous for up to 40% of data sampled for the plasma parameters and bias voltages considered in this study. In this contribution, we employ an autoencoder (AE) to learn a low-dimensional representation of valid data samples. By definition, the coordinates in this space are the features that mostly characterize valid data. Ambiguous data samples are classified in this space using standard classifiers for vectorial data. In this way, we avoid defining complicated threshold rules to identify outliers, which require strong assumptions and introduce biases in the analysis. By removing the outliers that aremore » identified in the latent low-dimensional space of the AE, we find that the average conductive and convective radial heat fluxes are between approximately 5% and 15% lower as when removing outliers identified by threshold values. For contributions to the radial heat flux due to triple correlations, the difference is up to 40%.« less

The Joint Army Navy NASA Air Force Modeling and Simulation Subcommittee's Integrated Health Management panel was started about 6 years ago to help foster communication and collaboration in health management related issues for liquid and solid rocket propulsion systems. The panel is co-chaired by Mr. Scott Hyde (ATK) and Ashok N. Srivastava, Ph.D. (NASA). In order to have a common langauge for health management, we need to have a common set of definitions. We have attached a MS Excel spreadsheet that covers the many terms that are of interest to us in the field. Please take a look at the definitions and provide comments and additional terms (with or without definitions) using the feedback box below. We will compile all the definitions into a master list for submittal to the Modeling and Simulation Subcommittee.

This paper presents an examination of the relationship between international operations and corporate R&D investment. Using a large sample of Chinese listed firms for the 2009–2022 period and the ordinary least squares method, we find that international operations have a positive effect on corporate R&D investment. The finding remains valid after a battery of robustness tests. Mechanism tests show that international operations increase corporate R&D investment by diversifying product demand instead of increasing firms’ international knowledge acquisition. This paper provides new evidence on the role of international operations in innovation activities.

Data Converters Market Outlook

The global data converters market size is projected to experience significant growth from 2023 to 2032, expanding from $4.5 billion in 2023 to an estimated $8.1 billion by 2032, with a compound annual growth rate (CAGR) of 6.7%. This robust growth is primarily driven by the increasing demand for high-performance electronics and communication devices that require efficient data conversion. The ongoing advancements in technology, especially in consumer electronics and telecommunication industries, are making data converters indispensable components for various applications. The integration of data converters in smart devices and the proliferation of the Internet of Things (IoT) are further propelling the market forward, as these technologies rely heavily on precise and rapid data conversion processes.

The growth of the data converters market is significantly influenced by the rising demand for consumer electronics, which are becoming increasingly sophisticated and multifunctional. Modern gadgets like smartphones, tablets, and wearable devices incorporate advanced data conversion technologies to handle complex tasks such as high-definition video streaming and real-time data processing. Additionally, the growing trend towards smart homes and connected devices is expanding the role of data converters in managing and processing large volumes of data with precision. This demand is further bolstered by the integration of artificial intelligence and machine learning in consumer devices, necessitating efficient data conversion to improve performance and user experience.

Another significant growth factor is the automotive industry's rapid evolution towards autonomous and electric vehicles. These vehicles require advanced data converters to process information from a myriad of sensors and communication systems. As vehicles become more connected and autonomous, the need for high-speed, high-precision data conversion becomes crucial. Moreover, the increasing focus on vehicle safety and driver assistance systems is pushing the demand for reliable data converters that can seamlessly handle real-time data processing. The automotive sector's push towards electrification and smart technologies will continue to drive the demand for innovative data conversion solutions.

The industrial sector is also a major contributor to the growth of the data converters market. Industries are increasingly adopting automation and smart manufacturing practices, which rely heavily on data conversion for operational efficiency and productivity improvements. The need for real-time data monitoring, analysis, and decision-making in industrial environments necessitates the use of advanced data converters. Furthermore, the trend towards Industry 4.0, characterized by the use of IoT, robotics, and artificial intelligence in manufacturing, is expected to further fuel the demand for data converters, enhancing their role in optimizing industrial processes and improving operational efficiencies.

Regionally, North America currently holds a significant share of the data converters market, primarily due to the presence of major technology companies and a high adoption rate of advanced technologies. The Asia Pacific region, however, is expected to exhibit the fastest growth during the forecast period, driven by rapid industrialization and the increasing demand for consumer electronics and automotive solutions. The region's burgeoning economies, such as China and India, are investing heavily in technology-driven infrastructure, supporting the growth of the data converters market. Europe also remains a key market, with a strong focus on automotive and industrial applications, contributing to steady growth in the region.

In the context of the expanding data converters market, Logic Level Converters play a pivotal role in ensuring compatibility between different voltage levels in electronic circuits. These converters are essential in bridging the gap between components that operate at different voltage levels, a common scenario in modern electronic devices. As the demand for more complex and multifunctional consumer electronics grows, the use of Logic Level Converters becomes increasingly important. They facilitate seamless communication between various parts of a device, ensuring that data is accurately transmitted and processed without voltage mismatches causing errors. This capability is crucial in maintaining the performance and reliability of devices, especially as they become more integrated and sophisticated.

Abstract

The survey on financial literacy among the citizens of Bosnia and Herzegovina was conducted within a larger project that aims at creating the Action Plan for Consumer Protection in Financial Services.

The conclusion about the need for an Action Plan was reached by the representatives of the World Bank, the Federal Ministry of Finance, the Central Bank of Bosnia and Herzegovina, supervisory authorities for entity financial institutions and non-governmental organizations for the protection of consumer rights, based on the Diagnostic Review on Consumer Protection and Financial Literacy in Bosnia and Herzegovina conducted by the World Bank in 2009-2010. This diagnostic review was conducted at the request of the Federal Ministry of Finance, as part of a larger World Bank pilot program to assess consumer protection and financial literacy in developing countries and middle-income countries. The diagnostic review in Bosnia and Herzegovina was the eighth within this project.

The financial literacy survey, whose results are presented in this report, aims at establishing the basic situation with respect to financial literacy, serving on the one hand as a preparation for the educational activities plan, and on the other as a basis for measuring the efficiency of activities undertaken.

Geographic coverage

Data collection was based on a random, nation-wide sample of citizens of Bosnia and Herzegovina aged 18 or older (N = 1036).

Analysis unit

Household, individual

Universe

Population aged 18 or older

Kind of data

Sample survey data [ssd]

Sampling procedure

SUMMARY

In Bosnia and Herzegovina, as is well known, there is no completely reliable sample frame or information about universe. The main reasons for such a situation are migrations caused by war and lack of recent census data. The last census dates back to 1991, but since then the size and distribution of population has significantly changed. In such a situation, researchers have to combine all available sources of population data to estimate the present size and structure of the population: estimates by official statistical offices and international organizations, voters? lists, list of polling stations, registries of passport and ID holders, data from large random surveys etc.

The sample was three-stage stratified: in the first stage by entity, in the second by county/region and in the third by type of settlement (urban/rural). This means that, in the first stage, the total sample size was divided in two parts proportionally to number of inhabitants by entity, while in the second stage the subsample size for each entity was further divided by regions/counties. In the third stage, the subsample for each region/county was divided in two categories according to settlement type (rural/urban).

Taking into the account the lack of a reliable and complete list of citizens to be used as a sample frame, a multistage sampling method was applied. The list of polling stations was used as a frame for the selection of primary sampling units (PSU). Polling station territories are a good choice for such a procedure since they have been recently updated, for the general elections held in October 2010. The list of polling station territories contains a list of addresses of housing units that are certainly occupied.

In the second stage, households were used as a secondary sampling unit. Households were selected randomly by a random route technique. In total, 104 PSU were selected with an average of 10 respondents per PSU. The respondent from the selected household was selected randomly using the Trohdal-Bryant scheme.

In total, 1036 citizens were interviewed with a satisfactory response rate of around 60% (table 1). A higher refusal rate is recorded among middle-age groups (table 2). The theoretical margin of error for a random sample of this size is +/-3.0%.

Due to refusals, the sample structure deviated from the estimated population structure by gender, age and education level. Deviations were corrected by RIM weighting procedure.

MORE DETAILED INFORMATION

IPSOS designed a representative sample of approximately 1.000 residents age 18 and over, proportional to the adult populations of each region, based on age, sex, region and town (settlement) type.

For this research we designed three-stage stratified representative sample. First we stratify sample at entity level, regional level and then at settlement type level for each region.

Sample universe:

Population of B&H -18+; 1991 Census figures and estimated population dynamics, census figures of refugees and IDPs, 1996. Central Election Commision - 2008; CIPS - 2008;

Sampling frame:

Polling stations territory (approximate size of census units) within strata defined by regions and type of settlements (urban and rural) Polling stations territories are chosen to be used as primary units because it enables the most reliable sample selection, due to the fact that for these units the most complete data are available (dwelling register - addresses)

Type of sample:

Three stage random representative stratified sample

Definition and number of PSU, SSU, TSU, and sampling points

• PSU - Polling station territory Definition: Polling stations territories are defined by street(s) name(s) and dwelling numbers; each polling station territory comprises approximately 300 households, with exception of the settlements with less than 300 HH which are defined as one unite. Number of PSUs in sample universe: 4710
• SSU - Household Definition: One household comprises people living in the same apartment and sharing the expenditure for food
• TSU - Respondent Definition: Member of the HH , 18+ Number of TSUs in sample universe: = 2.966.766
• Sampling points Approximately 10 respondents per one PSU, total 104

Stratification, purpose and method

• First level strata: Federation of B&H Republika Srpska Brc ko District
• Second level strata: 10 cantons 2 regions -
• Third level strata: urban and rural settlements
• Purpose: Optimisation of the sample plan, and reducing the sampling error

• Method: The strata are defined by criteria of optimal geographical and cultural uniformity

• Selection procedure of PSU, SSU, and respondent Stratification, purpose and method

• PSU Type of sampling of the PSU: Polling station territory chosen with probability proportional to size (PPS) Method of selection: Cumulative (Lachirie method)

• SSU Type of sampling of the SSU: Sample random sampling without replacement Method of selection: Random walk - Random choice of the starting point

• TSU - Respondent Type of sampling of respondent: Sample random sampling without replacement Method of selection: TCB (Trohdal-Bryant scheme)

• Sample size N=1036 respondents

• Sampling error Marginal error +/-3.0%

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey was modelled after the identical survey conducted in Romania. The questionnaire used in the Financial Literacy Survey in Romania was localized for Bosnia and Herzegovina, including adaptations to match the Bosnian context and methodological improvements in wording of questions.

Cleaning operations

Before data entry, 100% logic and consistency controls are performed first by local supervisors and once later by staff in central office.

Verification of correct data entry is assured by using BLAISE system for data entry (commercial product of Netherlands statistics), where criteria for logical and consistency control are defined in advance.

Response rate

• Nobody at home: 2,8%
• Eligible person is not home: 2,8%
• Refusal : 32,79%
• Given up after a minimum of two visits: 0,82%
• Other (excluded after control): 0,29%
• Finished: 60,5%

According to the Open Knowledge Foundation, open data is data that can be used freely and reused and redistributed by anyone. The definition of open data can be summarized as follows. The Satu Data Palapa Portal is a data-sharing medium accessible through the utilization of information and communication technology, providing complete, up-to-date, valid, and accountable data and information within the scope of Mojokerto Regency. Satu Data Palapa fundamentally aims to regulate the management of data produced by Data Producers to support development planning, implementation, control, and evaluation. Through Satu Data Palapa, let's generate Integrated, Sustainable, and Reliable Data. Based on the established vision, the missions of the Satu Data Palapa implementation are as follows: Provide a reference for implementation and guidelines for Regional Agencies in the context of implementing Satu Data Palapa to support development planning, implementation, control, and evaluation. Realize the availability of integrated, sustainable, reliable, accurate, up-to-date, accountable, and easily accessible and shareable data between Regional Agencies as a basis for development planning, implementation, evaluation, and control. Encourage data openness and transparency, creating data-driven development planning and policy formulation. Support the national statistical system of laws and regulations. In accordance with applicable regulations, if you wish to request data/information, please visit the PPID (Public Information and Documentation Management Officer) website of Mojokerto Regency. Translated from Indonesian Original Text: Menurut Open Knowledge Foundation, open data adalah data yang dapat digunakan secara bebas serta digunakan dan didistribusikan ulang oleh siapa saja. Definisi open data dapat diringkas sebagai berikut. Portel Satu Data Palapa merupakan media bagi-pakai Data yang dapat diakses melalui pemanfaatan teknologi informasi dan komunikasi yang menyediakan data dan informasi yang lengkap, aktual, valid, dan akuntabel dalam lingkup Kabupaten Mojokerto. Satu Data Palapa pada dasarnya bertujuan untuk mengatur penyelenggaraan pengelolaan Data yang dihasilkan oleh Produsen Data untuk mendukung perencanaan, pelaksanaan, pengendalian dan evaluasi pembangunan. Melalui Satu Data Palapa, Mari Kita Hasilkan Data yang Terpadu, Berkelanjutan, dan Pasti. Berdasarkan visi yang telah ditetapkan, misi dari implementasi Satu Data Palapa adalah sebagai berikut: Memberikan acuan pelaksanaan dan pedoman bagi Instansi Daerah dalam rangka Penyelenggaraan Satu Data Palapa untuk mendukung perencanaan, pelaksanaan, pengendalian dan evaluasi pembangunan. Mewujudkan ketersediaan Data yang terpadu, berkelanjutan, pasti, akurat, mutakhir, dapat dipertanggungjawabkan, serta mudah diakses dan dibagipakaikan antar Instansi Daerah sebagai dasar perencanaan, pelaksanaan, evaluasi, dan pengendalian pembangunan. Mendorong keterbukaan dan transparansi Data sehingga tercipta perencanaan dan perumusan kebijakan pembangunan yang berbasis pada Data. Mendukung sistem statistik nasional peraturan perundang-undangan. Sesuai dengan aturan yang berlaku, apabila ingin melakukan permohonan data/informasi, silahkan untuk mengunjungi website PPID Kabupaten Mojokerto