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199 results for “Adaptive Systems”
Evolution of left-right asymmetry in the sensory system and foraging behavior during adaptation to food-sparse cave environments
<p>Laterality in relation to behavior and sensory systems is found commonly in a variety of animal taxa. Despite the advantages conferred by laterality (e.g., the startle response and complex motor activities), little is known about the evolution of laterality and its plasticity in response to ecological demands. In the present study, a comparative study model, the Mexican tetra (<em>Astyanax mexicanus</em>), composed of two morphotypes, i.e., riverine surface fish and cave-dwelling cavefish, was used to address the relationship between environment and laterality. The use of a machine learning-based fish posture detection system and sensory ablation revealed that the left cranial lateral line significantly supports one type of foraging behavior, i.e., vibration attraction behavior, in one cave population. Additionally, left-right asymmetric approaches toward a vibrating rod became symmetrical after fasting in one cave population but not in the other populations. Based on these findings, we propose a model explaining how the observed sensory laterality and behavioral shift could help adaptation in terms of the tradeoff in energy gain and loss during foraging according to differences in food availability among caves.</p> <p>This repository contains all of raw videos used in this study.</p> <p>Please let us know if you have any question on these videos</p>
Data for Analysis for "A Framework for Adapting Conversational Intelligent Tutoring Systems to enable Collaborative Learning"
<p>This dataset includes, the data files for validating the statistical analysis from "A Framework for Adapting Conversational Intelligent Tutoring Systems to enable Collaborative Learning"</p> <p> </p> <p>The dataset is composed of 1500 files named following the pattern `Test-R-N-User-C-P.csv` where</p> <ul> <li>R is the n-th repetition. From 0 to 50</li> <li>N is the number of concurrent users. From 100 to 1000</li> <li>C is the treatment. chat for the framework version. chat-session for the legacy version.</li> <li>P is the problem number. 16 or 352.</li> </ul> <p>The data files corresponding to chat and problem 16 are those that in the paper are identified as Framework. The files por problem 352 are the collaborative version with students grouped.</p> <p>Each csv, is composed following the standard formate by Apache JMeter, and contains XX columns:</p> <ul> <li>timeStamp - UNIX timestamp of the request</li> <li>elapsed - Time taken to finish the request</li> <li>label - which step</li> <li>responseCode - HTTP response code</li> <li>responseMessage</li> <li>threadName</li> <li>dataType</li> <li>success - true|false</li> <li>failureMessage</li> <li>sentBytes</li> <li>grpThreads</li> <li>allThreads - Threads running</li> <li>URL - Endpoint URL</li> <li>Latency</li> <li>SampleCount</li> <li>ErrorCount - Cumulative amount of errors</li> <li>IdleTime </li> <li>Connect - Connection time</li> </ul> <p> </p>
ADAPTING SYSTEMS ENGINEERING TO EVALUATE TECH STARTUPS: AN INNOVATIVE FRAMEWORK BASED ON OMG ESSENCE
<p>The adaptation of system engineering to evaluate technical startups through an innovative framework based on the OMG Essence standard is a modern approach to simplify the process of analyzing and managing startups at various stages of their development. In this study, a universal framework was proposed that allows evaluating technical startups from the point of view of system engineering. This approach takes into account key aspects of startup development, such as requirements, stakeholders, technology, and team, which makes it an important tool for evaluating innovative projects.</p> <p>The main purpose of the proposed framework is to apply the principles of systems engineering to the evaluation of startups, focusing on technical aspects such as system architecture, integration capabilities, and the ability of the team to solve complex tasks. Traditional methods of evaluating startups, often focusing on business aspects, may not always take into account all the technical difficulties and risks that arise when developing software products. The OMG Essence-based framework fills this gap by providing a more comprehensive tool for analyzing and managing startup development.</p> <p>OMG Essence, as a standardized method, acts as a basis for formalizing practices and facilitating interaction between various project participants. The framework based on it offers the possibility of modular adaptation, which allows you to evaluate startups regardless of their complexity and current stage of development. An important feature of OMG Essence is the ability to integrate with other methodologies, which makes it universal for various fields. The application of this standard in the evaluation of startups allows you to obtain more objective results and improve the decision-making process.</p>
METHODS OF TRAINING AND ADAPTATION OF AI AGENTS IN COMPLEX PROCESS CONTROL SYSTEMS
<p>The article presents a study of modern methods of training and adaptation of artificial agents used in managing complex processes, which are characterized by a high level of uncertainty and the need for prompt response to changes. Key methodological approaches such as machine learning and neuroevolution are discussed. These approaches allow AI agents to accumulate knowledge about the behavior of systems continuously, analyze external changes, and adjust the management strategy depending on environmental conditions, which significantly increases their ability to predict and prevent possible failures in management.</p> <p>In the course of the study, models were considered that allow automating the execution of complex, multitasking processes, minimizing human intervention, and reducing the likelihood of errors. In addition, the presented methods provide high flexibility and scalability of systems, which is especially important in industrial and technological industries, where stability and reliability are critical. The results showed that AI agents with adaptive learning capabilities can increase operational efficiency while reducing costs and optimizing resource use. The conclusion highlights the prospects of using artificial intelligence to build highly autonomous control systems capable of responding to dynamic challenges, which opens up new horizons for automation and intellectual support in industrial production, logistics, and other key areas.</p> <p>Thus, the article makes a significant contribution to understanding the role of AI in management modernization, offering practical recommendations on the implementation of intelligent agents in real-world scenarios to increase productivity and sustainability.</p>
(c) simulation on Repast: after queen adaptive development-MODELING SELF-ORGANIZING SYSTEMS WITH SOCIAL INSECTS ALGORITHMS
<p>On figures (b) and (c), simulations on RePast [11, 16, 18] are<br> provided at successive times. The last figure shows the adaptive mechanism<br> of the queen which grows with time according to the material density around<br> it, like in natural observations.</p>
Accurate modeling and efficient QoS analysis of scalable adaptive systems under bursty workload
<p>The datasets include the traces used for the research and experiments on modelling and analyzing systems that execute under bursty workload:</p> <ul> <li>numReq10secondsfrom360000to660000-Paris contains a summary of the requests traces published in <a href="http://ita.ee.lbl.gov/html/contrib/WorldCup.html">http://ita.ee.lbl.gov/html/contrib/WorldCup.html</a> , by grouping into a single count the number of requests that servers in Paris region received every 10 seconds .</li> <li>mawi10seconds contains a summary of the traces published in <a href="http://mawi.wide.ad.jp/mawi/ditl/ditl2009/">http://mawi.wide.ad.jp/mawi/ditl/ditl2009</a> , by grouping the number of packets every 10 seconds into a single count. </li> </ul>
Dataset for "Adaptive connectivity control in networked multi-agent systems: A distributed approach"
<div> <div>Dataset accompanying the paper "<em>Adaptive connectivity control in networked multi-agent systems: A distributed approach</em>" by M. Krizmancic and S. Bogdan submitted to PLOS ONE journal on April 30, 2024.</div> <div> </div> <div> <div> <div>Contains:</div> <ul> <li>Vector images of the figures presented in the paper.</li> <li>Data files containing the values used to build the figures.</li> </ul> <p>Detailed information and instructions are available in the README file within the dataset.</p> </div> </div> </div>
Figure 1. DiagAgentExpertDM architecture (adapted from Ioniță, 2014) -Intelligent System for Diagnosis of a Three-Phase Separator
<p>For the current hybrid system</p> <p>(DiagAgentExpertDM)</p> <p>, data from different sources and in</p> <p>various forms are preprocessed, in order to represe</p> <p>nt them in a unified way to be able to upload</p> <p>them in a learning module. Simultaneously, inconsis</p> <p>tency tests are made to eliminate the</p> <p>measurement errors caused by improper calibration o</p> <p>f transducers etc.</p> <p>In condition of using a diagnosis method based on p</p> <p>rocess history, the next step in fault</p> <p>identification referring to the gas-oil separation</p> <p>process is to scan the historical data. Retrieved d</p> <p>ata</p> <p>will possess a label with a certain priority which</p> <p>will be used in the next retrieval process. The</p> <p>learning module is supplied with multiple preproces</p> <p>sed data samples, in order to extract knowledge</p> <p>from them. For each data samples, a data mining alg</p> <p>orithm will be applied (figure 1).</p> <p> </p>
Quality Attributes Assessment in Self-Adaptive Systems: An Empirical Evaluation
<p>Self-adaptive Systems (SAS) can monitor themselves and their context. They can detect changes and react to unexpected conditions with minimal human supervision during their execution. One of the challenges behind developing SAS is dealing with the decision-making process while analyzing the tradeoff points among the multiple quality attributes (QA). In Software Engineering, a widely accepted method of evaluating QA goals in software projects is the Architecture Tradeoff Analysis Method (ATAM). However, despite its importance and wide acceptance, there are few reports of empirical studies on analyzing QA tradeoffs in SAS. In this sense, the present investigation proposes an adapted version of ATAM called ATAM-4SAS to deal with the particularities of SAS. To achieve the research goal, we employed the UPPAAL SMC (statistical verification model) to analyze a set of QA. To evaluate the feasibility of the proposed method, we performed an empirical study on the execution of the ATAM-4SAS in a SAS developed according to the MAPE-K model. This model encompasses the Monitoring, Analysis, Planning, and Execution phases. Such steps share a knowledge base (K), which is fundamental in supporting decision-making. We complemented the empirical evaluation by conducting a focus group, which sought to assess the perceived ease of use and the perceived usefulness of the ATAM-4SAS to support the strategic choice of QA in a SAS. As a result, we observed that most participants agreed that ATAM-4SAS provides adequate support for the strategic choice of QA in SAS.</p>
Going underwater! Multiple origins and functional morphology of piercing-sucking feeding and tracheal system adaptations in water scavenger beetle larvae (Coleoptera: Hydrophiloidea)
<p>Supplementary videos:</p> <p>Video S1. Typical feeding behavior of chewing larvae.<em> Tropisternus latus</em> Brullé, 1837 first-instar larva. Note that feeding occurs above water surface.</p> <p>Video S2. Alternative chewing feeding strategy of moluscivorous larvae. <em>Hydrophilus (Dibolocelus) palpalis </em>Brullé, 1837 second-instar larva feeding.</p> <p>Video S3. Piercing-sucking feeding behavior of <em>Hemiosus dejeanii </em>(Solier, 1849) third-instar larva. Note that feeding occurs under water surface.</p> <p>Video S4. Piercing-sucking feeding behavior of <em>Oocyclus magnifica </em>Hebauer & Wang, 1998. Note that feeding occurs inside water film.</p>
Data supplement for "Adaptive stochastic continuation with a modified lifting procedure applied to complex systems"
<p>This dataset contains the data and source files for the diagrams of the following preprint:</p> <p><em>Clemens Willers, Uwe Thiele, Andrew J. Archer, David J. B. Lloyd, and Oliver Kamps<br> Adaptive stochastic continuation with a modified lifting procedure applied to complex systems<br> arXiv preprint arXiv:2002.01705, 2020 </em></p> <p>Please follow the instructions given in 'Readme.txt'.</p>
Sequential maturation of stimulus-specific adaptation in the mouse lemniscal auditory system
<p>Stimulus-specific adaptation (SSA), the reduction of neural activity to a common stimulus that does not generalize to other, rare stimuli, is an essential property of our brain. Although well characterized in adults, it is still unknown how it develops during adolescence and what neuronal circuits are involved. Using in vivo electrophysiology and optogenetics in the lemniscal pathway of the mouse auditory system, we observed SSA to be stable from postnatal day 20 (P20) in the inferior colliculus, to develop until P30 in the auditory thalamus (MGV) and even later in the primary auditory cortex (A1). We found this maturation process to be experience-dependent in A1 but not in MGV, and to be related to alterations in deep but not input layers of A1. We also identified corticothalamic projections to be implicated in MGV SSA development. Together, our results reveal different circuits underlying the sequential SSA maturation and provide a unique perspective to understand predictive coding and surprise across sensory systems.</p>
Featured Scents: Assessing Architectural Smells for Self-Adaptive Systems at Runtime
<p>Self-adaptive systems (SAS) change their behavior and structure at runtime to answer the changes in their environment. Such systems combine different architectural fragments or solutions via feature binding/unbinding at runtime. Moreover, this combination may negatively impact the system's architectural qualities, exhibiting architectural bad smells (ABS). These issues are challenging to detect in the code due to the combinatorial explosion of interactions amongst features. Since SAS do not document these features in their source code, design time smell detection ignores them and risks reporting spurious smells. This paper assesses this risk to understand how ABS occur at runtime for different feature combinations. We look for cyclic dependency and hub-like ABS in various runtime adaptations of two SAS, Adasim and mRubis. Our results indicate that architectural smells are feature-dependent and that their number is highly variable from one adaptation to the other. Some ABS appear in all runtime adaptations, some in only a few. We discuss the reasons behind these architectural smells for each system and draw some lessons for targeted analyses of ABS in SAS.</p>
Featured Scents: Towards Assessing ArchitecturalSmells for Self-Adaptive Systems at Runtime
<p>Self-adaptive systems (SAS) change their behavior and structure at runtime to answer the changes in their environment. Such systems combine different architectural fragments or solutions via feature binding/unbinding at runtime. Moreover, this combination may negatively impact the system's architectural qualities, exhibiting architectural bad smells (ABS). These issues are challenging to detect in the code due to the combinatorial explosion of interactions amongst features. Since SAS does not document these features in their source code, design time smell detection ignores them and risks reporting smells that are different than those observed at runtime. This paper assesses this risk to understand how ABS occurs at runtime for different feature combinations. We look for cyclic dependency and hub-like ABS in various runtime adaptations of two SAS, Adasim and mRubis. Our results indicate that architectural smells are feature-dependent and that their number is highly variable from one adaptation to the other. Some ABS appear in all runtime adaptations, some in only a few. We discuss the reasons behind these architectural smells for each system and motivate the need for targeted ABS analyses in SAS.</p>
Population genomic consequences of life history and mating system adaptation to a geothermal soil mosaic in yellow monkeyflowers (common garden phenotype data)
<p>Local selection can promote phenotypic divergence despite gene flow across habitat mosaics, but adaptation itself may generate substantial barriers to genetic exchange. In plants, life-history, phenology, and mating system divergence have been proposed to promote genetic differentiation in sympatry. In this study, we investigate phenotypic and genetic variation in <em>Mimulus guttatus</em> (yellow monkeyflowers) across a geothermal soil mosaic in Yellowstone National Park (YNP). Plants from thermal annual and nonthermal perennial habitats were heritably differentiated for life history and mating system traits, consistent with local adaptation to the ephemeral thermal-soil growing season. However, genome-wide genetic variation primarily clustered plants by geographic region, with little variation sorting by habitat. The one exception was an extreme thermal population also isolated by a 200m geographical gap of no intermediate habitat. Individual inbreeding coefficients (F<sub>IS</sub>) were higher (and predicted by trait variation) in annual plants and annual pairs showed greater isolation by distance at local (<1km) scales. Finally, YNP adaptation does not re-use a widespread inversion that underlies <em>M. guttatus</em> life-history ecotypes range-wide, suggesting a novel genetic mechanism. Overall, this work suggests that life history and mating system adaptation strong enough to shape individual mating patterns does not necessarily generate incipient speciation without geographical barriers.</p>
Dataset: Two-dimensional wavefront characterization of adaptable corrective optics and Kirkpatrick–Baez mirror system using ptychography
<p>The ptychography datasets and processed wavefront data in support of the publication "Two-dimensional wavefront characterization of adaptable corrective optics and Kirkpatrick–Baez mirror system using ptychography". File are in the HDF format and contain a number of datasets detailed below. If you require more information, please contact the corresponding author of the publication or thomas.moxham@eng.ox.ac.uk</p>
MultiCardioNER Corpus: Multilingual Adaptation of Clinical NER Systems to the Cardiology Domain
<h1><strong>MultiCardioNER</strong></h1> <p><strong>MultiCardioNER</strong> is a shared task about the adaptation of clinical NER systems to the cardiology domain. It uses a combination of two existing datasets (DisTEMIST for diseases and the newly-released DrugTEMIST for medications), as well as a new, smaller dataset of cardiology clinical cases annotated using the same guidelines.</p> <p>Participants are provided DisTEMIST and DrugTEMIST as training data to use as they see fit (1,000 documents, with the original partitions splitting them into 750 for training and 250 for testing). The cardiology clinical cases (cardioccc) are meant to be used as a development or validation set (258 documents), although participants are encourage to experiment with the documents and annotations as they see fit. The evaluation is done using a different collection of cardiology clinical cases (250).</p> <p>MultiCardioNER proposes two tracks:</p> <p>- Track 1: Spanish adaptation of disease recognition systems to the cardiology domain.<br>- Track 2: Multilingual (Spanish, English and Italian) adaptation of medication recognition systems to the cardiology domain.</p> <p>Please read the README file attached for more information on folder structure and file format.</p> <p><strong>MultiCardioNER</strong> was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of BioASQ 2024. For more information on the corpus, annotation scheme and task in general, please visit: <a href="https://temu.bsc.es/multicardioner" target="_blank" rel="noopener">https://temu.bsc.es/multicardioner</a>. This task is promoted by Spanish and European projects such as DataTools4Heart, AI4HF, BARITONE and AI4ProfHealth.</p> <p><strong>UPDATE MAY 28th 2024: </strong>The test set annotations are now out! We've also included the original background set files, as well as a file with the mappings from the masked filenames used during the evaluation phase to the original filenames. Please check the README for more information.</p> <h2><strong>Resources</strong></h2> <ul> <li><a href="https://temu.bsc.es/multicardioner" target="_blank" rel="noopener">MultiCardioNER website</a></li> <li><a href="http://bioasq.org/" target="_blank" rel="noopener">BioASQ website</a></li> <li><a href="../doi/10.5281/zenodo.6458078" target="_blank" rel="noopener">DisTEMIST Guidelines</a></li> <li><a href="../doi/10.5281/zenodo.11065432" target="_blank" rel="noopener">DrugTEMIST Guidelines</a></li> </ul> <h2><strong>License</strong></h2> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <h2><strong>Contact</strong></h2> <p>If you have any questions or suggestions, please contact us at:</p> <p>- Salvador Lima-López (<salvador [dot] limalopez [at] gmail [dot] com>)<br>- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p> <h2><strong>Additional resources and corpora</strong></h2> <p>If you are interested in MultiCardioNER, you might want to check out these corpora and resources:</p> <ul> <li><a href="../records/7614764">DisTEMIST</a> (Corpus of disease mentions and normalization to SNOMED CT)</li> <li><a href="../records/8224056">MedProcNER </a>(Corpus of clinical procedure mentions and normalization to SNOMED CT)</li> <li><a href="../records/10635215">SympTEMIST</a> (Corpus of clinical findings and normalization to SNOMED CT)</li> <li><a href="../records/4270158">PharmaCoNER</a> (Corpus of medications, drugs, chemical substances, genes, proteins and vaccine mentions and normalization)</li> <li><a href="../records/7116201">MEDDOPROF</a> (Corpus of mentions of professions, occupations and working status and normalization)</li> <li><a href="../records/8403498">MEDDOPLACE</a> (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization)</li> <li><a href="../records/4279323">MEDDOCAN</a> (Corpus of mentions of Personal Health Identifiers (PHI))</li> <li><a href="../records/3978041">CANTEMIST</a> (Corpus of cancer tumor morphology mentions and normalization)</li> <li><a href="../records/3837305">CodiESP</a> (Corpus of clinical case reportes with assigned clinical codes from ICD10, Spanish version)</li> <li><a href="../records/7684093">LivingNER</a> (Corpus of mentions of species, including human/family members, pathogens, food, etc.. and normalization to NCBI Taxonomy)</li> <li><a href="../records/2560344">SPACCC-POS</a> (Corpus of clinical case reports in Spanish annotated with POS-tags)</li> <li><a href="../records/2560338">SPACCC-TOKEN</a> (Corpus of clinical case reports in Spanish annotated with token-tags (word mention boundaries))</li> <li><a href="../records/2560338">SPACCC-SPLIT</a> (Corpus of clinical case reports in Spanish annotated with sentence boundary-tags)</li> <li><a href="../records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts)</li> </ul>
Data and Code for "Climate impacts and adaptation in US dairy systems 1981-2018"
<p>This data and code archive provides all the files that are necessary to replicate the empirical analyses that are presented in the paper "Climate impacts and adaptation in US dairy systems 1981-2018" authored by Maria Gisbert-Queral, Arne Henningsen, Bo Markussen, Meredith T. Niles, Ermias Kebreab, Angela J. Rigden, and Nathaniel D. Mueller and published in 'Nature Food' (2021, DOI: <a href="https://doi.org/10.1038/s43016-021-00372-z">10.1038/s43016-021-00372-z</a>). The empirical analyses are entirely conducted with the "R" statistical software using the add-on packages "car", "data.table", "dplyr", "ggplot2", "grid", "gridExtra", "lmtest", "lubridate", "magrittr", "nlme", "OneR", "plyr", "pracma", "quadprog", "readxl", "sandwich", "tidyr", "usfertilizer", and "usmap". The R code was written by Maria Gisbert-Queral and Arne Henningsen with assistance from Bo Markussen. Some parts of the data preparation and the analyses require substantial amounts of memory (RAM) and computational power (CPU). Running the entire analysis (all R scripts consecutively) on a laptop computer with 32 GB physical memory (RAM), 16 GB swap memory, an 8-core Intel Xeon CPU E3-1505M @ 3.00 GHz, and a GNU/Linux/Ubuntu operating system takes around 11 hours. Running some parts in parallel can speed up the computations but bears the risk that the computations terminate when two or more memory-demanding computations are executed at the same time.</p> <p>This data and code archive contains the following files and folders:</p> <p>* README<br> Description: text file with this description</p> <p>* flowchart.pdf<br> Description: a PDF file with a flow chart that illustrates how R scripts transform the raw data files to files that contain generated data sets and intermediate results and, finally, to the tables and figures that are presented in the paper.</p> <p>* runAll.sh<br> Description: a (bash) shell script that runs all R scripts in this data and code archive sequentially and in a suitable order (on computers with a "bash" shell such as most computers with MacOS, GNU/Linux, or Unix operating systems)</p> <p>* Folder "DataRaw"<br> Description: folder for raw data files<br> This folder contains the following files:</p> <p>- DataRaw/COWS.xlsx<br> Description: MS-Excel file with the number of cows per county<br> Source: USDA NASS Quickstats<br> Observations: All available counties and years from 2002 to 2012</p> <p>- DataRaw/milk_state.xlsx<br> Description: MS-Excel file with average monthly milk yields per cow<br> Source: USDA NASS Quickstats<br> Observations: All available states from 1981 to 2018</p> <p>- DataRaw/TMAX.csv<br> Description: CSV file with daily maximum temperatures<br> Source: PRISM Climate Group (spatially averaged)<br> Observations: All counties from 1981 to 2018</p> <p>- DataRaw/VPD.csv<br> Description: CSV file with daily maximum vapor pressure deficits<br> Source: PRISM Climate Group (spatially averaged)<br> Observations: All counties from 1981 to 2018</p> <p>- DataRaw/countynamesandID.csv<br> Description: CSV file with county names, state FIPS codes, and county FIPS codes<br> Source: US Census Bureau<br> Observations: All counties</p> <p>- DataRaw/statecentroids.csv<br> Descriptions: CSV file with latitudes and longitudes of state centroids<br> Source: Generated by Nathan Mueller from Matlab state shapefiles using the Matlab "centroid" function<br> Observations: All states</p> <p>* Folder "DataGenerated"<br> Description: folder for data sets that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these generated data files so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis).</p> <p>* Folder "Results"<br> Description: folder for intermediate results that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these intermediate results so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis).</p> <p>* Folder "Figures"<br> Description: folder for the figures that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these figures so that people who replicate our analysis can more easily compare the figures that they get with the figures that are presented in our paper. Additionally, this folder contains CSV files with the data that are required to reproduce the figures.</p> <p>* Folder "Tables"<br> Description: folder for the tables that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these tables so that people who replicate our analysis can more easily compare the tables that they get with the tables that are presented in our paper.</p> <p>* Folder "logFiles"<br> Description: the shell script runAll.sh writes the output of each R script that it runs into this folder. We provide these log files so that people who replicate our analysis can more easily compare the R output that they get with the R output that we got.</p> <p>* PrepareCowsData.R<br> Description: R script that imports the raw data set COWS.xlsx and prepares it for the further analyses</p> <p>* PrepareWeatherData.R<br> Description: R script that imports the raw data sets TMAX.csv, VPD.csv, and countynamesandID.csv, merges these three data sets, and prepares the data for the further analyses</p> <p>* PrepareMilkData.R<br> Description: R script that imports the raw data set milk_state.xlsx and prepares it for the further analyses</p> <p>* CalcFrequenciesTHI_Temp.R<br> Description: R script that calculates the frequencies of days with the different THI bins and the different temperature bins in each month for each state</p> <p>* CalcAvgTHI.R<br> Description: R script that calculates the average THI in each state</p> <p>* PreparePanelTHI.R<br> Description: R script that creates a state-month panel/longitudinal data set with exposure to the different THI bins</p> <p>* PreparePanelTemp.R<br> Description: R script that creates a state-month panel/longitudinal data set with exposure to the different temperature bins</p> <p>* PreparePanelFinal.R<br> Description: R script that creates the state-month panel/longitudinal data set with all variables (e.g., THI bins, temperature bins, milk yield) that are used in our statistical analyses</p> <p>* EstimateTrendsTHI.R<br> Description: R script that estimates the trends of the frequencies of the different THI bins within our sampling period for each state in our data set</p> <p>* EstimateModels.R<br> Description: R script that estimates all model specifications that are used for generating results that are presented in the paper or for comparing or testing different model specifications</p> <p>* CalcCoefStateYear.R<br> Description: R script that calculates the effects of each THI bin on the milk yield for all combinations of states and years based on our 'final' model specification</p> <p>* SearchWeightMonths.R<br> Description: R script that estimates our 'final' model specification with different values of the weight of the temporal component relative to the weight of the spatial component in the temporally and spatially correlated error term</p> <p>* TestModelSpec.R<br> Description: R script that applies Wald tests and Likelihood-Ratio tests to compare different model specifications and creates Table S10</p> <p>* CreateFigure1a.R<br> Description: R script that creates subfigure a of Figure 1</p> <p>* CreateFigure1b.R<br> Description: R script that creates subfigure b of Figure 1</p> <p>* CreateFigure2a.R<br> Description: R script that creates subfigure a of Figure 2</p> <p>* CreateFigure2b.R<br> Description: R script that creates subfigure b of Figure 2</p> <p>* CreateFigure2c.R<br> Description: R script that creates subfigure c of Figure 2</p> <p>* CreateFigure3.R<br> Description: R script that creates the subfigures of Figure 3</p> <p>* CreateFigure4.R<br> Description: R script that creates the subfigures of Figure 4</p> <p>* CreateFigure5_TableS6.R<br> Description: R script that creates the subfigures of Figure 5 and Table S6</p> <p>* CreateFigureS1.R<br> Description: R script that creates Figure S1</p> <p>* CreateFigureS2.R<br> Description: R script that creates Figure S2</p> <p>* CreateTableS2_S3_S7.R<br> Description: R script that creates Tables S2, S3, and S7</p> <p>* CreateTableS4_S5.R<br> Description: R script that creates Tables S4 and S5</p> <p>* CreateTableS8.R<br> Description: R script that creates Table S8</p> <p>* CreateTableS9.R<br> Description: R script that creates Table S9<br> </p>
An in-silico analysis of information sharing systems for adaptable resources management: a case study of oyster farmers
<p>Model and data outcomes --> We developed an agent-based models involving oyster farmers sharing information to adapt to an ill-understood virus. Various scenarios of heterogeneity and information sharing (through social networks and centralized information system) are simulated.</p>
Testing Dynamically Adaptive Systems: A Preliminary Literature Review
<p><strong>Context.</strong> Dynamically Adaptive Systems (DAS) encompasses a large class of systems that adapt their behaviours in response to changes in their context and/or predefined optimisation goals. Advanced software architectures support these adaptations robustly and efficiently at runtime. Yet, testing such architectures remains challenging as tests also need to cope with contextual and behavioural changes (addition, modification or removal) to be useful and prevent undesired behaviours to be deployed.<br> <strong>Objective.</strong> The objective of this study is to identify what strategies are used to test DAS and raise evidence on techniques and tools that achieve high defect detection even in unpredictable contexts.<br> <strong>Method.</strong> We performed a literature review of 389 studies published from the year 2001 until 2019. These studies cover the testing of Dynamic Software Product Lines, Context-Aware Systems and Dynamically Adaptive Systems.<br> <strong>Results.</strong> Our analysis comprised three fundamental aspects of testing strategies for DAS: executing the test cases, the test methods, and the test tools. Our preliminary findings indicate that the literature offers a large number of strategies to cope with such aspects. However, the studies analyzed do not show how to design or execute the test cases in practice.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.