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3,225 results for “Case of study”
Safety of Perception System for Automated Driving: A Case Study on Apollo
<p>This replication package consists of the detailed results of the safety assessment process of Apollo 7.0’s perception system for its use on a 3.4-kilometer segment of the Dutch highway, A270.<br> <br> <strong>‘Operational design domain description’</strong> consists of a detailed description of the operational area (operational design domain).</p> <p><strong>‘Hazard Analysis and Risk Assessment’ </strong>is a Microsoft Excel workbook of 6 sheets comprising of all intermediate results from the first two steps of safety requirement elicitation (hazard analysis, risk assessment), along with the final result (safety goals and their risk levels).</p> <p><strong>‘Safety Analysis’</strong> is a pdf document showing the translation of system-wide safety goals to the safety goals specific to components using fault tree analysis.</p> <p><strong>‘Safety Requirements’ </strong>is a Microsoft Excel workbook of 2 sheets comprising the final result of the safety requirement elicitation process, i.e., safety requirements (1) for traditional software (2) specific to ML-based systems.</p> <p><strong>‘Design assessment’ </strong>is a Microsoft Excel workbook of 2 sheets comprising the design assessment results. Specifically, the sheets consist of (1) the safety requirements and applicable design choices for each requirement; (2) where did we assess each requirement; (3) the final verdict for assessment of each requirement; (4) the reason for the verdict and the design decisions found in Apollo’s perception system related artifacts.<br> <br> </p> <p><strong>How to navigate this replication package with a complete running example.<br> —-------------------------------------------------------------------------------------------------------</strong><br> The identification of safety requirements starts from the Microsoft Excel workbook: ‘Hazard Analysis and Risk Assessment’ of 6 sheets presenting all intermediate results from the first two steps of safety requirement elicitation (hazard analysis, risk assessment). In the rest of this document, we demarcate running examples with bullets. <br> <br> In the sheet “Homepage” of this Excel workbook are descriptions and pointers to the other sheets in the same workbook if readers want to skip directly to the other pages. The second sheet “Operational scenarios” describes the operational scenarios and associated operational modes. The combination of operational scenarios and associated operational modes form operational conditions, each with a unique identifier. These operational conditions are used further. The sheet also describes which variables are considered from the Operational design domain description (see the separate document with the same name for a detailed description of the operational area) to form these operational conditions.</p> <ul> <li> <p>An example operational condition is “driving in lane” + “slower-moving vehicle in front” with its unique identifier OC1 (see row 11 in the second sheet).</p> </li> </ul> <p>The third sheet “Operational modes & functions” describes the functions (specific to functional safety or functional insufficiency) of the automated driving vehicle and which operational modes (from the second sheet) these functions are activated. Each function is associated with a unique identifier.</p> <ul> <li> <p>An example function is “avoid collision with an object in driving lane” and its unique identifier is F1 (see rows 2-5 of the third sheet).</p> </li> </ul> <p>The fourth sheet “Hazards identification” shows hazards identified by crossing each function from the third sheet with guide words. Each hazard has a unique identifier.</p> <ul> <li> <p>An example hazard created from the above example function F1 is “does not avoid collision with an object in driving lane” with its unique identifier H1 (see second row of the fourth sheet).</p> </li> </ul> <p>The fifth sheet “ASIL parameters” describes the parameters for identifying risk levels. The parameters are given to each of the operational modes and operational situations from the prior sheets. The sixth sheet “Hazardous events & safety goals” describes the derivation of safety goals from hazards identified from the fourth sheet and the identification of the risk level for each safety goal based on the risk parameters from the fifth sheet (they are automatically applied using the formula function in Excel). This sheet gives at least four new pieces of information (and how this information is derived): (1) hazardous event; (2) safety goal derived from each of the hazardous events; (3) aggregate safety goal; and (4) risk level (ASIL) for the aggregated safety goal. Each of these has its own unique identifiers. An example of each of these four (following from the example hazard given above) is given below,</p> <ul> <li> <p>An example hazardous event (in the second row of the sixth sheet) is “does not avoid collision with an object in driving lane” (third column) + “driving in lane” (fifth column) + “slower-moving vehicle in front” (sixth column). This can be combined as “the automated driving vehicle does not avoid collision with a slower-moving vehicle in its driving lane”. Its unique identifier is HE1.</p> </li> <li> <p>The safety goal derived from the above hazardous event (HE1) is “avoid collision in scenario: slower-moving vehicle in front in operational mode: driving in lane” (see second row, 15th column, of the sixth sheet) with its unique identifier SG1.</p> </li> <li> <p>The above safety goal (SG1) along with other similar safety goals, forms the aggregated safety goal: “avoid collision with an object (obstacle or vehicle) in driving lane in all operational modes” (see rows two to ten, column 17, of the sixth sheet) with its unique id Aggreg_SG1.</p> </li> <li> <p>The risk level associated with the aggregated safety goal, Aggreg_SG1, is ASIL D (see rows two to ten, column 18, of the sixth sheet). This aggregated risk level is the highest risk level of the associated safety goals (see rows two to ten, column 13 of the sixth sheet). The risk level in each row of column 13 is derived from the risk parameters in columns 10-12 (sixth sheet). The risk parameters in columns 10-12 (sixth sheet) come from the fifth sheet and are associated with the operational mode in column 5 (sixth sheet) and the operational scenario in column 6 (sixth sheet). </p> </li> </ul> <p>The next part of requirements elicitation is presented in the pdf titled ‘Safety Analysis’. The pdf document shows the translation of system-wide safety goals to the safety goals specific to components using fault tree analysis. Pipeline-level safety-critical events derived from the fault tree analysis are marked with unique identifiers.</p> <ul> <li> <p>The fault tree for aggregated safety goal with the unique identifier Aggreg_SG1 is shown from page 2 of the document. One of the safety-critical event resulting from the aggregated safety goal is “Lidar obstacle detection, classification and tracking pipeline estimates incorrect state of vehicle / obstacle” with unique identifier SCE1 (see page 13).</p> </li> </ul> <p>The final part of the safety requirement elicitation is shown in the Microsoft Excel workbook ‘Safety Requirements’. The workbook consists of 2 sheets comprising the final result of the safety requirement elicitation process, i.e., safety requirements (1) for traditional software (2) specific to ML-based systems. </p> <ul> <li> <p>An example traditional software safety requirement resulting from the safety-critical event with the unique identifier SCE1 is "if any component in LiDAR obstacle detection, classification, and tracking pipeline becomes non-operational then this failure shall not lead to an incorrect estimation of the state of vehicles or other obstacles.” and its unique identifier is 26262_3 (see the first sheet, rows 28-40, fourth column)</p> </li> <li> <p>An example ML pipeline safety requirement resulting from the safety-critical event with the unique identifier SCE1 is “If the performance of Lidar obstacle detection, classification, and tracking pipeline is deteriorated due to strong sunlight, then this deterioration in performance shall not lead to an incorrect estimation of the state of vehicles or other obstacles.” with unique identifier SOTIF_8 (see the second sheet, rows 73-82, sixth column)</p> </li> </ul> <p>Finally, the design assessment of the safety requirements is shown in the Microsoft Excel workbook ‘Design assessment’. The workbook consists of 2 sheets comprising the design assessment results. Specifically, the sheets provide the following information: (1) the safety requirements and applicable design choices for each requirement; (2) where did we assess each requirement; (3) the final verdict for assessment of each requirement; (4) the reason for the verdict and the design decisions found in Apollo’s perception system related artifacts.</p> <ul> <li> <p>The assessment of the traditional safety requirement with the identifier 26262_3 (from above) is shown in the first sheet in rows 28-40.</p> </li> <li> <p>The assessment of the ML pipeline related safety requirement with the identifier SOTIF_8 is shown in the second sheet in rows 73-82.</p> </li> </ul>
A flexible model to reconstruct education-specific fertility rates: Sub-saharan Africa case study
<p><strong>A flexible model to reconstruct education-specific fertility rates: Sub-saharan Africa case study</strong></p> <p>The fertility rates are consistent with the United Nation World Population Prospects (UN WPP) 2022 fertility rates.</p> <p>The Bayesian model developed to reconstruct the fertility rates using Demographic and Health Surveys and the UN WPP is published in a <a href="https://epub.oeaw.ac.at/0xc1aa5576_0x003e65e0.pdf">working paper</a>. </p> <p><strong>Abstract</strong></p> <p>The future world population growth and size will be largely determined by the pace of fertility decline in sub-Saharan Africa. Correct estimates of education-specific fertility rates are crucial for projecting the future population. Yet, consistent cross-country comparable estimates of education-specific fertility for sub-Saharan African countries are still lacking. We propose a flexible Bayesian hierarchical model to reconstruct education-specific fertility rates by using the patchy Demographic and Health Surveys (DHS) data and the United Nations’ (UN) reliable estimates of total fertility rates (TFR). Our model produces estimates that match the UN TFR to different extents (in other words, estimates of varying levels of consistency with the UN). We present three model specifications: consistent but not identical with the UN, fully-consistent (nearly identical) with the UN, and consistent with the DHS. Further, we provide a full time series of education-specific TFR estimates covering five-year periods between 1980 and 2014 for 36 sub-Saharan African countries. The results show that the DHS-consistent estimates are usually higher than the UN-fully-consistent ones. The differences between the three model estimates vary substantially in size across countries, yielding 1980-2014 fertility trends that differ from each other mostly in level only but in some cases also in direction.</p> <p><strong>Funding</strong></p> <p>The data set are part of the <a href="https://www.oeaw.ac.at/vid/research/research-projects/bayesedu">BayesEdu Project </a>at Wittgenstein Centre for Demography and Global Human Capital (IIASA, OeAW, University of Vienna) funded from the “Innovation Fund Research, Science and Society” by the Austrian Academy of Sciences (ÖAW).</p> <p>We provide education-specific total fertility rates (ESTFR) from three model specifications: (1) estimated TFR consistent but not identical with the TFR estimated by the UN (“Main model (UN-consistent)”; (2) estimated TFR fully consistent (nearly identical) with the TFR estimated by the UN ( “UN-fully -consistent”, and (3) estimated TFR consistent only with the TFR estimated by the DHS ( “DHS-consistent”).</p> <p>For education- and age-specific fertility rates that are UN-fully consistent, please see https://doi.org/10.5281/zenodo.8182960</p> <p><strong>Variables</strong></p> <p>Country: Country names</p> <p>Education: Four education levels, No Education, Primary Education, Secondary Education and Higher Education.</p> <p>Year: Five-year periods between 1980 and 2015.</p> <p>ESTFR: Median education-specific total fertility rate estimate </p> <p>sd: Standard deviation</p> <p>Upp50: 50% Upper Credible Interval</p> <p>Lwr50: 50% Lower Credible Interval</p> <p>Upp80: 80% Upper Credible Interval</p> <p>Lwr80: 80% Lower Credible Interval</p> <p>Model: Three model specifications as explained above and in the working paper. DHS-consistent, Main model (UN-consistent) and UN-fully consistent.</p> <p>List of countries:</p> <p>Angola, Benin, Burkina Faso, Burundi, Cote D'Ivoire, Cameroon, Central African Republic, Chad, Comoros, Congo, Democratic Republic of Congo, Eswatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, South Africa, Tanzania, Togo, Uganda, Zambia, Zimbabwe</p> <p> </p>
Miniaturization does not change conserved spider anatomy, a case study on spider Rayforstia (Araneae: Anapidae).
<p>Stacks of histological sections and data from CLSM of spider <em>Rayforstia</em> (Araneae: Anapidae).</p>
Ground-based observation data from the article " Elucidating the impacts of various atmospheric ventilation conditions on local and transboundary ozone pollution patterns: A case study of Beijing, China"
<p>Hourly ground-level O<sub>3</sub>, CO and NO<sub>2</sub> observational data were retrieved from the China Environmental Monitoring Station, ultimately comprising 714 stations in North China and 35 stations in Beijing after data quality control. The hourly data from meteorological stations in China were obtained from the China Meteorological Data Service Centre, including observational values of such weather elements as temperature, pressure, relative humidity, wind, total cloud cover, and precipitation.</p>
Supplementary material 1 from: Seehausen ML, Branco M, Afonso C, Kenis M (2023) Testing a modified version of the EPPO decision-support scheme for release of classical biological control agents of plant pests using Ganaspis cf. brasiliensis and Cleruchoides noackae as case studies. NeoBiota 87: 121-141. https://doi.org/10.3897/neobiota.87.103187
Decision-support scheme for release of classical biological control agents of plant pests – Environmental impact assessment (EIA)
Estimation of the Uncertainties Introduced in Thermal Map Mosaic: A Case of Study with PIX4D Mapper Software
<p>Available data sets, used to analyse problems related to thermal mapping obtained from thermal data acquired from unmanned aerial systems (UAS) equipped with thermal cameras. We focused on an accurate analysis of uncertainties introduced by the PIX4D Mapper software.</p>
How do host-plant use and seasonal life cycle relate to insect body size: A case study on European geometrid moths (Lepidoptera: Geometridae)
<p><span>We used European geometrid moths (> 630 species) as a model group to investigate how life history traits linked to larval host plant use (i.e., diet breadth and host-plant growth form) and seasonal life cycle (i.e., voltinism, overwintering stage, and caterpillar phenology) are related to adult body size in holometabolous insect herbivores. To do so, we applied phylogenetic comparative methods to account for shared evolutionary history among herbivore species. We further categorised larval diet breadth based on the phylogenetic structure of utilised host plant genera. Our results indicate that species associated with woody plants are, on average, larger than herb feeders and increase in size with increasing diet breadth. Obligatorily univoltine species are larger than multivoltine species, and attain larger sizes when their larvae are restricted to the early season. Furthermore, adult body size is significantly smaller in species that overwinter in the pupal stage compared to those that overwinter as egg or caterpillar. In summary, our results indicate that the ecological niche of an holometabolous insect herbivore is strongly interrelated with its size at maturity.</span></p>
Dataset for "Built world parameters and the nesting habits of the European white stork: a case study of Segovia, Spain."
<p>Dataset for "Built world parameters and the nesting habits of the European white stork: a case study of Segovia, Spain" covering nesting information of European white stork population in Segovia, Spain for 2021-2023.</p>
Data of : Assessment of CLEC4M and GSH in Iraqi Newly Diagnosed Cervical Cancer Women: A Case Control Study
<p>Data of : Assessment of CLEC4M and GSH in Iraqi Newly Diagnosed Cervical Cancer Women: A Case Control Study</p>
Data for "Impact of time-dependent data assimilation on ice flow model initialization and projections: a case study of Kjer Glacier, Greenland"
<p>Data for the main figures for "Impact of time-dependent data assimilation on ice flow model initialization: A case study of Kjer Glacier, Greenland"</p>
Olfactory and Trigeminal Functions in Patients With Multiple Sclerosis: Case-control Study
ClinicalTrials.gov study NCT06020937. IPD Sharing: UNDECIDED. Countries: 1. Publications: 14.
Postoperative Opioid Use and Ileus Occurrence---A Case Control Study
ClinicalTrials.gov study NCT05262569. IPD Sharing: UNDECIDED. Countries: 1. Publications: 20.
Insulin Resistance in Nonalcoholic Fatty Liver Disease: A Case Control Study
ClinicalTrials.gov study NCT00637520. IPD Sharing: Not stated. Countries: 1. Publications: 18.
Case-Control Study of the Glycotest™ HCC Panel Vs AFP for the Detection of Early-stage Hepatocellular Carcinoma
ClinicalTrials.gov study NCT03878550. IPD Sharing: NO. Countries: 2. Publications: 1.
The Smartphone and Wearable Detected Atrial Arrhythmia in Older Adults Case Finding Study (Smart in OAC - AFNET 9)
ClinicalTrials.gov study NCT04579159. IPD Sharing: Not stated. Countries: 3. Publications: 2.
Assessing LncRNA DQ786243 and IL-17 Expression in Oral Lichen Planus: A Case Control Study
ClinicalTrials.gov study NCT04503824. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Unstimulated Gluteus Maximus Sphincteroplasty for Treatment of Bowel Incontinnence Case Series Study
ClinicalTrials.gov study NCT07239076. IPD Sharing: YES. Countries: 1. Publications: 0.
Effects of Training Using Volume and Flow-oriented Incentive Spirometry in Healthy Subjects - Case-control Study
ClinicalTrials.gov study NCT02094131. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Binge Drinking And Addiction : Case-Control Study in Hospital
ClinicalTrials.gov study NCT03204214. IPD Sharing: NO. Countries: 1. Publications: 1.
Nutritional Risk Factors for Hip Fracture: a Case Control Study
ClinicalTrials.gov study NCT01738776. IPD Sharing: Not stated. Countries: 1. Publications: 2.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.