Front Door Pillar 2 Revisi

## Overview

Front Door Pillar 2 Revisi is a dataset for object detection tasks - it contains Objects YAZB annotations for 290 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Details of completed (processed) COVID-19 antigen tests booked through the NHS-Digital portals.

This is a dataset created for the Medicaid Scorecard website (https://www.medicaid.gov/state-overviews/scorecard/index.html), and is not intended for use outside that application.

Pillar 2 data is processed by NHS Digital and extracts for NI residents are sent to the NI Public Health Agency.

https://www.gov.uk/government/publications/coronavirus-covid-19-testing-data-methodology/covid-19-testing-data-methodology-note

COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2) data is required by NHS Digital to support COVID-19 requests for linkage, analysis and dissemination to other organisations. These requests are often urgent and in support of direct care and service monitoring, planning and research. These are all functions that NHS Digital have been asked to deliver as a national resource in response to COVID-19, through the recent direction from the SoS.

Antigen test results relate to subjects who have had swab testing in the community at drive through test centres, walk in centres, home kits returned by posts, care homes, prisons etc.

The dataset is composed of:

• Patient identity and contact details

• Testing centre and laboratory details

• Test results • Test kit types (manufacturer)

The data cover the UK and is collected under SoS Covid Direction under s254 of the HSCA 2012 and s255 requests from devolved administrations for Scotland, Northern Ireland and Wales. This is an expansion of the original scope which only included data for welsh patients tested in other parts of the UK.

Data is currently available for dissemination through the NHS Digital DARS service for England. If your extract is to include data from the devolved administrations their approval will also be required.

Timescales for dissemination can be found under 'Our Service Levels' at the following link: https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process

Data forming the Covid-19 Second Generation Surveillance Systems data set relate to demographic and diagnostic information from Pillar 1 swab testing in PHE labs and NHS hospitals for those with a clinical need, and health and care workers and Pillar 2 Swab testing in the community at drive through test centres, walk in centres, home kits returned by posts, care homes, prisons etc).

Timescales for dissemination can be found under 'Our Service Levels' at the following link: https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process

Front Door Pillar

## Overview

Front Door Pillar is a dataset for object detection tasks - it contains Objects annotations for 290 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset tracks the updates made on the dataset "CoreSEt pillar v2.10.6 (coreset-etl-test)" as a repository for previous versions of the data and metadata.

ECOSS is a database that holds surveillance data on various microorganisms (e.g. influenza virus, coronavirus) and infections reported from NHS diagnostic and reference laboratories and Pillar 2 facilities/Lighthouse laboratories [high-throughput facilities dedicated to COVID-19 viral Reverse Transcription-Polymerase Chain Reaction (RT-PCR) testing for the National Testing Programme]. Data on laboratory results for all SARS-CoV-2 RT-PCR tests carried out in Scotland are being collated by ECOSS and can be linked to other data sources

This dataset tracks the updates made on the dataset "CoreSEt pillar v2.10.6 (coreset-etl-test)" as a repository for previous versions of the data and metadata.

This publication was archived on 12 October 2023. Please see the Viral Respiratory Diseases (Including Influenza and COVID-19) in Scotland publication for the latest data. This dataset provides information on number of new daily confirmed cases, negative cases, deaths, testing by NHS Labs (Pillar 1) and UK Government (Pillar 2), new hospital admissions, new ICU admissions, hospital and ICU bed occupancy from novel coronavirus (COVID-19) in Scotland, including cumulative totals and population rates at Scotland, NHS Board and Council Area levels (where possible). Seven day positive cases and population rates are also presented by Neighbourhood Area (Intermediate Zone 2011). Information on how PHS publish small are COVID figures is available on the PHS website. Information on demographic characteristics (age, sex, deprivation) of confirmed novel coronavirus (COVID-19) cases, as well as trend data regarding the wider impact of the virus on the healthcare system is provided in this publication. Data includes information on primary care out of hours consultations, respiratory calls made to NHS24, contact with COVID-19 Hubs and Assessment Centres, incidents received by Scottish Ambulance Services (SAS), as well as COVID-19 related hospital admissions and admissions to ICU (Intensive Care Unit). Further data on the wider impact of the COVID-19 response, focusing on hospital admissions, unscheduled care and volume of calls to NHS24, is available on the COVID-19 Wider Impact Dashboard. Novel coronavirus (COVID-19) is a new strain of coronavirus first identified in Wuhan, China. Clinical presentation may range from mild-to-moderate illness to pneumonia or severe acute respiratory infection. COVID-19 was declared a pandemic by the World Health Organisation on 12 March 2020. We now have spread of COVID-19 within communities in the UK. Public Health Scotland no longer reports the number of COVID-19 deaths within 28 days of a first positive test from 2nd June 2022. Please refer to NRS death certificate data as the single source for COVID-19 deaths data in Scotland. In the process of updating the hospital admissions reporting to include reinfections, we have had to review existing methodology. In order to provide the best possible linkage of COVID-19 cases to hospital admissions, each admission record is required to have a discharge date, to allow us to better match the most appropriate COVID positive episode details to an admission. This means that in cases where the discharge date is missing (either due to the patient still being treated, delays in discharge information being submitted or data quality issues), it has to be estimated. Estimating a discharge date for historic records means that the average stay for those with missing dates is reduced, and fewer stays overlap with records of positive tests. The result of these changes has meant that approximately 1,200 historic COVID admissions have been removed due to improvements in methodology to handle missing discharge dates, while approximately 820 have been added to the cumulative total with the inclusion of reinfections. COVID-19 hospital admissions are now identified as the following: A patient's first positive PCR or LFD test of the episode of infection (including reinfections at 90 days or more) for COVID-19 up to 14 days prior to admission to hospital, on the day of their admission or during their stay in hospital. If a patient's first positive PCR or LFD test of the episode of infection is after their date of discharge from hospital, they are not included in the analysis. Information on COVID-19, including stay at home advice for people who are self-isolating and their households, can be found on NHS Inform. Data visualisation of Scottish COVID-19 cases is available on the Public Health Scotland - Covid 19 Scotland dashboard. Further information on coronavirus in Scotland is available on the Scottish Government - Coronavirus in Scotland page, where further breakdown of past coronavirus data has also been published.

This dataset tracks the updates made on the dataset "CoreSEt pillar v2.10.11 (coreset-etl-test)" as a repository for previous versions of the data and metadata.

This dataset tracks the updates made on the dataset "CoreSet pillar v3.2.4 (etl-test)" as a repository for previous versions of the data and metadata.

Overview

• Introduction
• Object Classes
  • Hammer
  • Hanger Bar
  • Hook
  • Pedestal
  • Pillar
  • Pillar With Bracket
  • Safety Joint

Introduction

This dataset is designed to solve the task of object detection for various construction-related items. The objective is to accurately annotate objects within images using bounding boxes. The classes included are:

• Hammer: Typically used for driving nails into surfaces.
• Hanger Bar: A long bar or rod for hanging items.
• Hook: A curved or angular device used for catching or holding objects.
• Pedestal: A base or support for larger structures.
• Pillar: A vertical, elongated support structure.
• Pillar With Bracket: A pillar with an attached bracket for support.
• Safety Joint: A component designed to ensure secure connections in assemblies.

Object Classes

Hammer

Description

Tools with a handle and a heavy end, often wrapped or covered.

Instructions

• Identify the metal head and the handle.
• The hammer appears wrapped in packaging; include this in the bounding box.

Hanger Bar

Description

Long, thin, rectangular metallic or plastic rods.

Instructions

• Annotate groups of these bars with one box
• Extend the bounding box along the entire visible length of the bar.
• If bars overlap, annotate individually where distinct sections are visible.
• Exclude bars if more than 80% obscured.

Hook

Description

Curved or angled, small metallic pieces, often grouped.

Instructions

• Enclose the entire hook, ensuring all curves are within the boundary.
• Do not separate hooks if grouped closely; annotate collectively.
• Ignore isolated parts with unidentified purposes.

Pedestal

Description

Rectangular or cylindrical bases, typically longer in form.

Instructions

• Align the bounding box tightly along the edges.
• Ensure differentiation from similar long objects by checking the base support features.
• Exclude any significantly blurred sections.

Pillar

Description

Long, hollow structures with cut-out rectangular patterns.

Instructions

• Cover the full visible length, including pattern cut-outs.
• Annotate distinctly from similar structures without patterns.
• Avoid grouping; maximum one box per pillar.

Pillar With Bracket

Description

Pillars similar to standard ones but with bracket attachments.

Instructions

• Highlight both the pillar and the attached bracket distinctly.
• Ensure the bounding box encloses both components without external objects.
• Clearly separate from regular pillars by identifying bracket presence.

Safety Joint

Description

Angular, zig-zag structures, often paired or grouped tightly.

Instructions

• Contain all visible zig-zag components within one box if adjacent.
• Clearly separate overlapping joints into distinct annotations.
• Avoid annotating unrelated angular objects nearby.

These results cover which environmental activities are being carried out and the reasons for doing so. They also cover the area of various habitats found on farms. Source agency: Environment, Food and Rural Affairs Designation: National Statistics Language: English Alternative title: Environment and Countryside Management: Results from the Farm Business Survey Countryside Maintenance and Management in England 2010/11 The latest statistics produced by Defra on Countryside Maintenance and Management Activities (CMMA) were released on 28 June 2012 according to the arrangements approved by the UK Statistics Authority. The release shows final estimates for countryside maintenance and management activities on farms in England. These are sourced from the 2010/11 Farm Business Survey (FBS) which covers the 2010 harvest and can be accessed via the link below. This workbook provides tables of data used in the release or to create charts used in the release. In addition there is further data from the survey that was not used in the release. Link to main release http://www.defra.gov.uk/statistics/foodfarm/farmmanage/fbs/publications/envcountryman/ Background to the Survey Countryside and agri-environment practices have become increasingly important in English agriculture. Whilst many farmers have always been aware of the habitats on and around their farms, the reforms in government payments to farmers have ensured that nearly all farmers now need to consider these issues. In particular, the concept of ‘cross-compliance’ introduced in 2005 alongside the Single Payment Scheme means that most farmers have to follow basic environmental standards. There are a number of potential sources of data on the management practices adopted by farmers to protect and enhance the environment. Questions on these issues were included in the Farm Business Survey (FBS) for the first time in the 2005/06 survey. The FBS is an interview survey specifically aimed at collecting accounting information, and allows the inclusion of more complex questions. By using the FBS, relationships between countryside maintenance and management activities and farm type, size, profitability and location can be explored. The countryside maintenance and management module was repeated in 2006/07 and in 2008/09 was expanded to give a more detailed picture of activities being carried out. For the 2010/11 survey the module was further expanded to include questions on the Campaign for the Farmed Environment (CFE), results from which were published on 16th February 2012 here: http://www.defra.gov.uk/statistics/foodfarm/farmmanage/fbs/publications/envcountryman/ Information on countryside maintenance and management activities is important in helping to understand what famers are doing to protect and enhance the environment and their reasons for doing so; which in turn can help shape policy decisions. It is important, for example, in the context of structural change and CAP reform, including issues surrounding the balance between Pillar 1 and Pillar 2 payments. The results from this module will also inform the planning of the next Rural Development Programme, in particular the agri-environment measures. The information will also feed into wider research examining competitiveness of the farming industry, e.g. any links between agricultural performance and countryside maintenance and management activities. Survey Methodology The results provided in this release are from the questions relating to Countryside Maintenance and Management Activities (CMMA) which were included in the 2010/11 FBS campaign. The questions were asked during the period January to September 2011 The FBS is an annual survey providing information on the financial position and physical and economic performance of farm businesses in England. The sample of around 1,900 farm businesses covers all regions of England and all types of farming with the data being collected by face to face interview with the farmer. Results are weighted to represent the whole population of farm businesses that have at least 25,000 Euros of standard output as recorded in the annual June Survey of Agriculture and Horticulture. In 2010 there were just over 56,000 farm businesses meeting this criteria. For the 2010/11 FBS, an additional countryside maintenance and management ‘module’ was included to collect areas of land under various environmental activities and the associated costs of managing this land. Only those farms in the FBS which were managing the land in a positive manner were eligible to complete the module (henceforth referred to as eligible farms). Positive management was defined as any land management measures or activities that deliver a positive environmental outcome. Details of the questions asked can be found here: http://www.defra.gov.uk/statistics/foodfarm/farmmanage/fbs/aboutfbs/datacollection/forms/ For further information about the Farm Business Survey please see: http://www.defra.gov.uk/statistics/foodfarm/farmmanage/fbs/ Data analysis As stated above, the results from the FBS relate to farms which have a standard output of at least 25,000 Euros. Initial weights are applied to the FBS records based on the inverse sampling fraction. These weights are then adjusted (calibration weighting) so that they can produce unbiased estimators of a number of different target variables. As detailed in the Survey Methodology section above, the countryside maintenance and management module was a voluntary addition to the main FBS commitment and achieved a response rate of 77% for eligible farms. In order to take account of non-response, the results have been reweighted using a method that preserves marginal totals for populations according to farm type, farm size groups and agri-environment scheme membership. The results have been further restricted to relate only to the population of eligible farms i.e. those managing some of their land in a positive manner. Comparisons between 2008/09 and 2010/11 Results from the 2008/09 and 2010/11 countryside maintenance and management modules are not directly comparable due to changes in the coverage of the survey and changes in the classification of farms for the 2010/11 campaign. In 2010/11 the survey was restricted to include farms which have a least 25,000 Euros of standard output; prior to this the survey was restricted to farms with ½ Standard Labour Requirement or more. The classification of farms into farm types was also revised for the 2010/11 Farm Business Survey, to bring the classification in line with European guidelines. Equivalent results from 2008/09 have been presented alongside 2010/11 results in many of the charts and tables; however comparisons should be treated with extreme caution due to the reasons given above. To enable more robust comparisons between the 2008/09 and 2010/11 countryside maintenance and management modules to be reported, we have examined the subset of farms that participated and have some form of activity in both years (approximately 900 farms). For all analyses we have used the farm type, farm size and tenure groups as defined on the 2010/11 dataset. For this subset of farms we have carried out significance testing using the Wilcoxon signed rank test to determine whether the differences observed between the two time periods are statistically significant. Where a statistically significant difference has been observed this has been indicated on the tables and charts for the full module results with a *. Commentary alongside the charts and tables will refer to this analysis rather than make comparisons with the 2008/09 data displayed. Accuracy and reliability of the results Where possible, relative standard error (RSE) and 95% confidence intervals have been shown in the tables. RSE is derived from the standard error, which is a measure of the variation in the data. Typically, large estimates also have large standard errors. The standard error divided by the estimated total gives the RSE. This is expressed as a percentage and is easier to interpret than the standard error. Low RSEs indicate greater reliability in the figures, whereas estimates with high RSEs should be treated with caution. 95% confidence intervals show the range of values that may apply to the figures. They mean that we are 95% confident that the true value lies within this range either side of the estimate. They are calculated as the standard errors (se) multiplied by 1.96 to give the 95% confidence interval (95% CI). The standard errors only give an indication of the sampling error. They do not reflect any other sources of survey errors, such as non-response bias. The confidence limits shown are appropriate for comparing groups within the same year; they should not be used for comparing 2010/11 results with those from 2008/09 since they do not allow for the fact that many of the same farms contributed to both surveys. Estimates based on less than 5 observations have been suppressed to prevent disclosure of the identity of the contributing farms. Estimates based on less than 15 observations have been highlighted in italics in the tables and should be treated with caution as they are likely to be less precise. Availability of results Defra statistical notices can be viewed on the Food and Farming Statistics pages on the Defra website at http://www.defra.gov.uk/statistics/foodfarm/. This site also shows details of future publications, with pre-announced dates. ? Other publications Results from the 2010/11 FBS: http://www.defra.gov.uk/statistics/foodfarm/farmmanage/fbs/publications/farmaccounts/ Provisional estimates of farm business income for 2011/12: http://www.defra.gov.uk/statistics/foodfarm/farmmanage/fbs/publications/fbsincomes/ Campaign for the Farmed Environment, Results from the Farm Business Survey 2010/11: http://www.defra.gov.uk/statistics/foodfarm/farmmanage/fbs/publications/envcountryman/ Definitions Countryside maintenance and management activities The

Linearity studies.

The vegetable sector is a vital pillar of society and an indispensable part of the national economic structure. As a significant segment of the agricultural market, accurately forecasting vegetable prices holds significant importance. Vegetable market pricing is subject to a myriad of complex influences, resulting in nonlinear patterns that conventional time series methodologies often struggle to decode. In this paper, we exploit the average daily price data of six distinct types of vegetables sourced from seven key wholesale markets in Beijing, spanning from 2009 to 2023. Upon training an LSTM model, we discovered that it exhibited exceptional performance on the test dataset. Demonstrating robust predictive performance across various vegetable categories, the LSTM model shows commendable generalization abilities. Moreover, LSTM model has a higher accuracy compared to several machine learning methods, including CNN-based time series forecasting approaches. With R2 score of 0.958 and MAE of 0.143, our LSTM model registers an enhancement of over 5% in forecast accuracy relative to conventional machine learning counterparts. Therefore, by predicting vegetable prices for the upcoming week, we envision this LSTM model application in real-world settings to aid growers, consumers, and policymakers in facilitating informed decision-making. The insights derived from this forecasting research could augment market transparency and optimize supply chain management. Furthermore, it contributes to the market stability and the balance of supply and demand, offering a valuable reference for the sustainable development of the vegetable industry.

As two pillars of the green transition, clean energy and tourism have gained growing strategic prominence in the landscape of sustainable finance, warranting a deeper investigation into their financial interdependencies. However, empirical research exploring their interaction in financial markets, particularly from a regional perspective, remains relatively limited. This study contributes to that objective by examining the predictive relationships between the WILDERHILL Clean Energy Index and tourism indices from the United States, Europe, China, and Australia. Using monthly data from 2010 to 2023, the analysis applies quantile Granger causality and wavelet coherence methods to capture asymmetric and time-varying dynamics. Additionally, a structural VAR model is used to assess region-specific responses to clean energy shocks. While conventional Granger tests do not indicate significant linkages, quantile-based approaches uncover heterogeneous connections that emerge under extreme market conditions. The findings reveal increasing co-movement between clean energy and tourism sectors and emphasize the importance of distribution-sensitive and regionally contextualized approaches in guiding investment and policy-making strategies.