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visualization and error compensation of demolition robot attachment changing
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Abstract Planning for power systems with high penetrations of variable renewable energy requires higher spatial and temporal granularity. However, most publicly available test systems are of insufficient fidelity for developing methods and tools for high- resolution planning. This paper presents methods to construct open-access test systems of high spatial granularity to more accurately represent current infrastructure and high temporal granularity to represent variability of demand and renewable resources. To demonstrate, a high-resolution test system representing the United States is created using only publicly available data. This test system is validated by running it in a production cost model, with results validated against historical generation to ensure that they are representative. The resulting open source test system can support power system transition planning and aid in development of tools to answer questions around how best to reach decarbonization goals, using the most effective combinations of transmission expansion, renewable generation, and energy storage. Documentation of dataset development A paper describing the process of developing the dataset is available at https://arxiv.org/abs/2002.06155. Please cite as: Y. Xu, Nathan Myhrvold, Dhileep Sivam, Kaspar Mueller, Daniel J. Olsen, Bainan Xia, Daniel Livengood, Victoria Hunt, Benjamin Rouillé d'Orfeuil, Daniel Muldrew, Merrielle Ondreicka, Megan Bettilyon, "U.S. Test System with High Spatial and Temporal Resolution for Renewable Integration Studies," 2020 IEEE PES General Meeting, Montreal, Canada, 2020. Dataset version history 0.1, January 31, 2020: initial data upload. 0.2, March 10, 2020: addition of Tabular Data Package metadata, modifications to cost curves and transmission capacities aimed at more closely matching optimization results to historical data. 0.2.1, March 25, 2020: corrected a bug in the wind profile generation process which was pulling the wrong locations for wind farms outside the Western Interconnection.
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Abstract Planning for power systems with high penetrations of variable renewable energy requires higher spatial and temporal granularity. However, most publicly available test systems are of insufficient fidelity for developing methods and tools for high- resolution planning. This paper presents methods to construct open-access test systems of high spatial granularity to more accurately represent current infrastructure and high temporal granularity to represent variability of demand and renewable resources. To demonstrate, a high-resolution test system representing the United States is created using only publicly available data. This test system is validated by running it in a production cost model, with results validated against historical generation to ensure that they are representative. The resulting open source test system can support power system transition planning and aid in development of tools to answer questions around how best to reach decarbonization goals, using the most effective combinations of transmission expansion, renewable generation, and energy storage. Documentation of dataset development A paper describing the process of developing the dataset is available at https://arxiv.org/abs/2002.06155. Please cite as: Y. Xu, Nathan Myhrvold, Dhileep Sivam, Kaspar Mueller, Daniel J. Olsen, Bainan Xia, Daniel Livengood, Victoria Hunt, Benjamin Rouillé d'Orfeuil, Daniel Muldrew, Merrielle Ondreicka, Megan Bettilyon, "U.S. Test System with High Spatial and Temporal Resolution for Renewable Integration Studies," 2020 IEEE PES General Meeting, Montreal, Canada, 2020. Dataset version history 0.1, January 31, 2020: initial data upload. 0.2, March 10, 2020: addition of Tabular Data Package metadata, modifications to cost curves and transmission capacities aimed at more closely matching optimization results to historical data. 0.2.1, March 25, 2020: [erroneous upload] 0.2.2, March 26, 2020: [erroneous upload]
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Planning for power systems with high penetrations of variable renewable energy requires higher spatial and tempo- ral granularity. However, most publicly available test systems are of insufficient fidelity for developing methods and tools for high- resolution planning. This paper presents methods to construct open-access test systems of high spatial granularity to more accurately represent current infrastructure and high temporal granularity to represent variability of demand and renewable resources. To demonstrate, a high-resolution test system representing the United States is created using only publicly available data. This test system is validated by running it in a production cost model, with results validated against historical generation to ensure that they are representative. The resulting open source test system can support power system transition planning and aid in development of tools to answer questions around how best to reach decarbonization goals, using the most effective combinations of transmission expansion, renewable generation, and energy storage. A paper describing the process of developing the dataset is available at https://arxiv.org/abs/2002.06155. Version history 0.1, January 31, 2020: initial data upload. 0.2, March 10, 2020: addition of Tabular Data Package metadata, modifications to cost curves and transmission capacities aimed at more closely matching optimization results to historical data.
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Abstract Motivation Antibodies are widely used experimental reagents to test expression of proteins. However, they might not always provide the intended tests because they do not specifically bind to the target proteins that their providers designed them for, leading to unreliable and irreproducible research results. While many proposals have been developed to deal with the problem of antibody specificity, they may not scale well to deal with the millions of antibodies that have ever been designed and used in research. In this study, we investigate the feasibility of automatically extracting statements about antibody specificity reported in the literature by text mining, and generate reports to alert scientist users of problematic antibodies. Results We developed a deep neural network system called Antibody Watch and tested its performance on a corpus of more than two thousand articles that report uses of antibodies. We leveraged the Research Resource Identifiers (RRID) to precisely identify antibodies mentioned in an input article and the BERT language model to classify if the antibodies are reported as nonspecific, and thus problematic, as well as inferred the coreference to link statements of specificity to the antibodies that the statements referred to. Our evaluation shows that Antibody Watch can accurately perform both classification and linking with F-scores over 0.8, given only thousands of annotated training examples. The result suggests that with more training, Antibody Watch will provide useful reports about antibody specificity to scientists.
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Early detection of infectious diseases is the most cost-effective strategy in disease surveillance for reducing the risk of outbreaks. Latest deep learning and computer vision improvements are powerful tools that open up a new field of research in epidemiology and disease control. In this work, these techniques were employed to develop an algorithm aimed to track and compute individual animal motion in real time. This algorithm was used in experimental trials in order to assess African swine fever (ASF) infection course in Eurasian wild boar. Overall, the outcomes showed a strong correlation between motion reduction and fever caused by ASF infection. In addition, infected animals computed significant low movements compared to uninfected animals. The obtained results suggest that a motion monitoring system based on artificial intelligence may be used to trigger suspicions of fever. It would help farmers and animal health services to early detect clinical signs compatible with infectious diseases. This technology shows a promising start up for implementing non-intrusive, economic and real time solutions in the livestock industry with especial interest in ASF, considering the current concern in the world pig industry.
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Compressed fastqs for raw sequences of clinical isolates of Escherichia coli infection from Toronto, Canada in 2018 (Dataset 2). Sequencing details outlined in associated publication. Performed using Illumina NextSeq platform.
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Pequenos Cientistas em Casa Acompanha as atividades que o Centro Ciência Viva do Algarve publica para poderes fazer em casa. Diverte-te em família, fazendo as nossas atividade e partilha connosco os resultado através de fotografias ou vídeo. #PequenosCientistasEmCasa #PequenosCientistasCCVAlg HOJE: Sistema de manivela O que será uma manivela? Puxa pelo teu lado criativo! Queremos ver as tuas ideias! ATENÇÃO!! Não saias de casa para adquirir nenhum destes materiais. Se não os tiveres todos, aguarda pelo nosso próximo desafio! #PequenosCientistasEmCasa #PequenosCientistasCCVAlg #ccvalg #cienciaviva #ficaemcasa #manivela #movimento #engenhocarias
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  • Video
This dataset contains the monitoring results of veterinary medicinal product residues and other substances measured in live animals and animal products analysed by the national competent authority of Estonia. The presence of unauthorised substances, residues of veterinary medicinal products or chemical contaminants in food may pose a risk factor for public health. For this reason and in order to ensure a high level of consumer protection, a comprehensive legislative framework has been established in the European Union (EU) which defines maximum limits permitted in food and monitoring programmes for the control of the presence of these substances in the food chain. Regulation (EU) No 37/2010 establishes maximum limits for residues of veterinary medicinal products in food-producing animals and animal products. Maximum residue levels for pesticides in or on food and feed of plant and animal origin are laid down in Regulation (EC) No 396/2005. Commission Regulation (EC) 1881/2006 lays down the maximum limits for the presence of certain contaminants in animal products. Council Directive 96/23/EC lays down measures to monitor certain substances and residues thereof, mainly veterinary medicinal products, in live animals and animal products. Additionally, Commission Decision 97/747/EC lays down levels and frequencies of sampling for certain animal products. The dataset contains the results of laboratory tests from samples taken from bovines, pigs, sheep, goats, horses, poultry, rabbits, farmed game, wild game aquaculture, milk, eggs and honey. Targeted samples are taken with the aim of detecting illegal treatment or controlling compliance with the maximum levels laid down in the relevant legislation. This means that, in their national plans Member States target the groups of animals (species, gender, age) where the probability of finding residues is the highest. Suspect samples are taken as a consequence of i) non-compliant results on samples taken in accordance with the monitoring plan, ii) possession or presence of prohibited substances at any point during manufacture, storage, distribution or sale through the food and feed production chain, or iii) suspicion or evidence of illegal treatment or non-compliance with the withdrawal period for an authorised medicinal veterinary product. Residues of pharmacologically active substances mean active substances, excipients or degradation products and their metabolites, which remain in food. Unauthorised substances or products mean substances or products prohibited under European Union legislation. Non-compliant sample is a sample that has been analysed for the presence of one or more substances and failed to comply with the legal provisions for at least one substance. Thus, a sample can be non-compliant for one or more substances. REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: VMPR_2018 – Veterinary and Food Board VMPR_2017 – Veterinary and Food Board
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This dataset contains the monitoring results of veterinary medicinal product residues and other substances measured in live animals and animal products analysed by the national competent authority of Cyprus. The presence of unauthorised substances, residues of veterinary medicinal products or chemical contaminants in food may pose a risk factor for public health. For this reason and in order to ensure a high level of consumer protection, a comprehensive legislative framework has been established in the European Union (EU) which defines maximum limits permitted in food and monitoring programmes for the control of the presence of these substances in the food chain. Regulation (EU) No 37/2010 establishes maximum limits for residues of veterinary medicinal products in food-producing animals and animal products. Maximum residue levels for pesticides in or on food and feed of plant and animal origin are laid down in Regulation (EC) No 396/2005. Commission Regulation (EC) 1881/2006 lays down the maximum limits for the presence of certain contaminants in animal products. Council Directive 96/23/EC lays down measures to monitor certain substances and residues thereof, mainly veterinary medicinal products, in live animals and animal products. Additionally, Commission Decision 97/747/EC lays down levels and frequencies of sampling for certain animal products. The dataset contains the results of laboratory tests from samples taken from bovines, pigs, sheep, goats, horses, poultry, rabbits, farmed game, wild game aquaculture, milk, eggs and honey. Targeted samples are taken with the aim of detecting illegal treatment or controlling compliance with the maximum levels laid down in the relevant legislation. This means that, in their national plans Member States target the groups of animals (species, gender, age) where the probability of finding residues is the highest. Suspect samples are taken as a consequence of i) non-compliant results on samples taken in accordance with the monitoring plan, ii) possession or presence of prohibited substances at any point during manufacture, storage, distribution or sale through the food and feed production chain, or iii) suspicion or evidence of illegal treatment or non-compliance with the withdrawal period for an authorised medicinal veterinary product. Residues of pharmacologically active substances mean active substances, excipients or degradation products and their metabolites, which remain in food. Unauthorised substances or products mean substances or products prohibited under European Union legislation. Non-compliant sample is a sample that has been analysed for the presence of one or more substances and failed to comply with the legal provisions for at least one substance. Thus, a sample can be non-compliant for one or more substances. REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: VMPR_2018 – State General Laboratory - Ministry of Health VMPR_2017 – State General Laboratory - Ministry of Health
Data Types:
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