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Relevant bibliographies by topics / Stress data

Academic literature on the topic 'Stress data'

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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Journal articles on the topic "Stress data"

1

Šleger, V., and P. Neuberger. "Using meteorological data to determine the risk of heat stress." Research in Agricultural Engineering 52, No. 2 (February 7, 2012): 39–47. http://dx.doi.org/10.17221/4878-rae.

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This paper first proposes a technique of computing air temperature and humidity in stables based on outdoor air parameters and biological production of animals. The computation technique is outlined. The calculated values are then used to assess the potential of evaporation cooling in mild climatic conditions. Graphs illustrate the assumed effect of evaporation cooling equipment inside a stable housing of egg laying hens. Used in the computation were hourly meteorological readings obtained during the period May to August in years 2000 to 2002, in the locality with a potential installation of a cooling system. Other Graphs illustrate the time the animals spent in an environment with a particular air temperature. For instance in June 2002, the time animals in the stable were exposed to temperatures 27°C or higher was reduced by using an air cooling system from 39 h to 22 h, and in July 2002 from 33 h to 4 h. The envisaged model can be modified for other kinds of gallinaceous poultry and pigs.

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Krueger, Alan B. "Stress Testing Economic Data." Business Economics 45, no. 2 (April 2010): 110–15. http://dx.doi.org/10.1057/be.2010.4.

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Bradley, W. V., and H. Leverne Williams. "Prediction of stress–relaxation data of some nylons from stress–strain data." Journal of Applied Polymer Science 32, no. 1 (July 1986): 2889–95. http://dx.doi.org/10.1002/app.1986.070320105.

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Haderka, P., and A. N. Galybin. "Plastic stress field reconstruction based on stress orientations data." Russian Journal of Earth Sciences 12, no. 4 (June 5, 2012): 1–15. http://dx.doi.org/10.2205/2012es000516.

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Zhang, Bo, Yann Morère, Loïc Sieler, Cécile Langlet, Benoît Bolmont, and Guy Bourhis. "Stress Recognition from Heterogeneous Data." Journal of Image and Graphics 4, no. 2 (2016): 116–21. http://dx.doi.org/10.18178/joig.4.2.116-121.

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Hsu, T. H., and H. Saunders. "Stress and Strain—Data Handbook." Journal of Pressure Vessel Technology 114, no. 2 (May 1, 1992): 254. http://dx.doi.org/10.1115/1.2929038.

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Zouani, A., T. Bui-Quoc, and M. Bernard. "Cyclic stress-strain data analysis under biaxial tensile stress state." Experimental Mechanics 39, no. 2 (June 1999): 92–102. http://dx.doi.org/10.1007/bf02331111.

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Rummel, F., G. M�hring-Erdmann, and J. Baumg�rtner. "Stress constraints and hydrofracturing stress data for the continental crust." Pure and Applied Geophysics PAGEOPH 124, no. 4-5 (1986): 875–95. http://dx.doi.org/10.1007/bf00879616.

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IWATA, Takaki, Keisuke YOSHIDA, and Yukitoshi FUKAHATA. "Stress Tensor Inversion Using Seismological Data." Journal of Geography (Chigaku Zasshi) 128, no. 5 (October 25, 2019): 797–811. http://dx.doi.org/10.5026/jgeography.128.797.

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Breuer, Thomas, and Martin Summer. "Systematic stress tests on public data." Journal of Banking & Finance 118 (September 2020): 105886. http://dx.doi.org/10.1016/j.jbankfin.2020.105886.

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Dissertations / Theses on the topic "Stress data"

1

Jelinek, Lena. "Memory fragmentation in posttraumatic stress disorder content specific or generalised." Berlin wvb, Wiss. Verl, 2006. http://www.wvberlin.de/data/inhalt/jelinek.html.

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Jelinek, Lena. "Memory fragmentation in posttraumatic stress disorder : content specific or generalised /." Berlin : Wvb, Wiss. Verl, 2007. http://www.wvberlin.de/data/inhalt/jelinek.html.

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Lindqvist, Anton. "Stress Response Analysis Using Centralised Expression Data." Thesis, Umeå universitet, Institutionen för fysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-177326.

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Background: The last two decades have seen several new methods for analysing gene expression data. One such method is pathway analysis in which it’s possible to decipher response patterns between pathways (groups of interacting genes) and external stress factors. A critical issue in pathway analysis is often limited sample sizes resulting in undesirable batch affects. A method for reducing such effects, called Centralization Within Sub-Experiments (CSE), has recently been developed. This method makes it possible to predict pathways using data from experiments with a diversity of external conditions. Aims: We propose a method for identifying stress-responsive pathways predicted using CSE pre-processed expression data. Method: 27 CSE predicted pathways were analysed and tested for stress-responsiveness associated with specific external stress factors. Firstly, we screened the complete gene list for DE genes. Secondly, we analysed the occurrences of DE genes within each pathway and finally, an over-representation analysis was performed with the aim of identifying pathways with significantly larger portions of differently expressed genes categorised as stress-responsive. Results: The analysis resulted in a list of pathways with significantly larger proportions of DE genes. This was only when screened for salt-responsive pathways. We also found that these pathways contained several mitochondrial genes confirmed to be associated with salt stress. Conclusion: The results show great promise in using the method for extracting information regarding the stress responsiveness amongst pathways predicted using CSE-pre-processing expression data.

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Weber, Annegret. "Einfluss von Stress und sozialen Faktoren auf die Immunreaktivität sechsjähriger Kinder /." Leipzig : UFZ, 2008. http://www.ufz.de/data/ufzdiss_07_20089040.pdf.

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Ollander, Simon. "Wearable Sensor Data Fusion for Human Stress Estimation." Thesis, Linköpings universitet, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-122348.

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With the purpose of classifying and modelling stress, different sensors, signal features, machine learning methods, and stress experiments have been compared. Two databases have been studied: the MIT driver stress database and a new experimental database, where three stress tasks have been performed for 9 subjects: the Trier Social Stress Test, the Socially Evaluated Cold Pressor Test and the d2 test, of which the latter is not classically used for generating stress. Support vector machine, naive Bayes, k-nearest neighbor and probabilistic neural network classification techniques were compared, with support vector machines achieving the highest performance in general (99.5 ±0.6 %$on the driver database and 91.4 ± 2.4 % on the experimental database). For both databases, relevant features include the mean of the heart rate and the mean of the galvanic skin response, together with the mean of the absolute derivative of the galvanic skin response signal. A new feature is also introduced with great performance in stress classification for the driver database. Continuous models for estimating stress levels have also been developed, based upon the perceived stress levels given by the subjects during the experiments, where support vector regression is more accurate than linear and variational Bayesian regression.
I syfte att klassificera och modellera stress har olika sensorer, signalegenskaper, maskininlärningsmetoder och stressexperiment jämförts. Två databaser har studerats: MIT:s förarstressdatabas och en ny databas baserad på egna experiment, där stressuppgifter har genomförts av nio försökspersoner: Trier Social Stress Test, Socially Evaluated Cold Pressor Test och d2-testet, av vilka det sistnämnda inte normalt används för att generera stress. Support vector machine-, naive Bayes-, k-nearest neighbour- och probabilistic neural network-algoritmer har jämförts, av vilka support vector machine har uppnått den högsta prestandan i allmänhet (99.5 ± 0.6 % på förardatabasen, 91.4 ± 2.4 % på experimenten). För båda databaserna har signalegenskaper såsom medelvärdet av hjärtrytmen och hudens ledningsförmåga, tillsammans med medelvärdet av beloppet av hudens ledningsförmågas derivata identifierats som relevanta. En ny signalegenskap har också introducerats, med hög prestanda i stressklassificering på förarstressdatabasen. En kontinuerlig modell har också utvecklats, baserad på den upplevda stressnivån angiven av försökspersonerna under experimenten, där support vector regression har uppnått bättre resultat än linjär regression och variational Bayesian regression.

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Nytorpe, Piledahl Staffan, and Daniel Dahlberg. "Detektering av stress från biometrisk data i realtid." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-31248.

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At the time of writing, stress and stress related disease have become the most common reasons for absence in the workplace in Sweden. The purpose of the work presented here is to identify and notify people managing unhealthy levels of stress. Since symptoms of mental stress manifest through functions of the Sympathetic Nervous System (SNS), they are best measured through monitoring of SNS changes and phenomena. In this study, changes in the sympathetic control of heart rate were recorded and analyzed using heart rate variability analysis and a simple runner’s heart rate sensor connected to a smartphone. Mental stress data was collected through stressful video gaming. This was compared to data from non-stressful activities, physical activity and extremely stressful activities such as public speaking events. By using the period between heartbeats and selecting features from the frequency domain, a simple machine learning algorithm could differentiate between the types of data and thus could effectively recognize mental stress. The study resulted in a collection of 100 data points, an algorithm to extract features and an application to continuously collect and classify sequences of heart periods. It also revealed an interesting relationship in the data between different subjects. The fact that continuous stress monitoring can be achieved using minimally intrusive sensors is the greatest benefit of these results, especially when connsidering its potential value in the identification and prevention of stress related disease.

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Franaszek, Krzysztof. "Translation-mediated stress responses : mining of ribosome profiling data." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/269473.

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Advances in next-generation sequencing platforms during the past decade have resulted in exponential increases in biological data generation. Besides applications in determining the sequences of genomes and other DNA elements, these platforms have allowed the characterization of cell-wide mRNA pools under different conditions and in different tissues. In 2009, Ingolia and colleagues developed an extension of high-throughput sequencing that provides a snapshot of all cellular mRNA fragments protected by translating ribosomes, dubbed ribosome profiling. This approach allows detection of differential translation activity, annotation of novel protein coding sequences and variants, identification of ribosome pause sites and estimates of de novo protein synthesis. As with other sequencing based methodologies, a major challenge of ribosome profiling has been sorting, filtering and interpreting the gigabytes of data produced during the course of a typical experiment. In this thesis, I developed and applied computational pipelines to interrogate ribosome profiling data in relation to gene expression in several viruses and eukaryotic species, as well as to identify sites of ribosomal pausing and sites of non-canonical translation activity. Specifically, I applied various control analyses for characterizing the quality of profiling data and developed scripts for visualizing genome-based (exon-by-exon) rather than transcript-based ribosome footprint alignments. I also examined the challenge of mapping footprints to repetitive sequences in the genome and propose ways to mitigate the associated problems. I performed differential expression analyses on data from coronavirus-infected murine cells, retrovirus-infected human cells and temperature-stressed Arabidopsis thaliana plants. Dissection of translational responses in Arabidopsis thaliana during heat shock or cold shock revealed several groups of genes that were highly upregulated within 10 minutes of temperature challenge. Analysis of the branches of the unfolded protein and integrated stress responses during coronavirus infection allowed for deconvolution of transcriptional and translational contributions. During the course of these analyses, I identified errors in a recently publicized algorithm for detection of differential translation, and wrote corrections that have now been pulled into the repository for this package. Comparison of the translational kinetics of the dengue virus infection in mosquito and human cell lines revealed host-specific sites of ribosome pausing and RNA accumulation. Analysis of HIV profiling data revealed footprint peaks which were in agreement with previously proposed models of peptide or RNA mediated ribosome stalling. I also developed a simulation to identify transcripts that are prone to generating RPFs with multiple alignments during the read mapping process. Together, the scripts and pipelines developed during the course of this work will serve to expedite future analyses of ribosome profiling data, and the results will inform future studies of several important pathogens and temperature stress in plants.

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Zhang, Su Juan. "Automated reflection photoelasticity : digital data acquisition and use." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340144.

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Agnér, Christian, and Anneli Blomqvist. "Evaluating Stress through Machine Learning based on Brain Activity Data." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214709.

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More people are experiencing stressrelatedsymptoms, which is not only causing worsenhealth, but also causing economical drawbacks for thesociety, businesses and individuals. The aim of thisproject is to create a tool that evaluates stress frombrain activity data and can help to avoid develop thesymptoms.An EMOTIV Epoch EEG headset is used to recorddata. The stress level is evaluated from the brainactivity data by the parameters, feeling of pleasure(valence) and the mental workload. k-NN machinelearning is utilized to create a valence classificationalgorithm and the theta power density spectrum is usedto determine the workload. Eye movement disturbancesin the recordings are also addressed.Tests with Stroop word color games as stress stimuliare conducted and the project concludes that it ispossible to determine the stress level correctly, onaverage, 17.56% and when allowing one level difference,48.71% .

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Vickers, Stephen R. "Examining the Duplication of Flight Test Data Centers." International Foundation for Telemetering, 2011. http://hdl.handle.net/10150/595653.

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ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada
Aircraft flight test data processing began with on site data analysis from the very first aircraft design. This method of analyzing flight data continued from the early 1900's to the present day. Today each new aircraft program builds a separate data center for post flight processing (PFP) to include operations, system administration, and management. Flight Test Engineers (FTE) are relocated from geographical areas to ramp up the manpower needed to analyze the PFP data center products and when the first phase of aircraft design and development is completed the FTE headcount is reduced with the FTE either relocated to another program or the FTE finds other employment. This paper is a condensed form of the research conducted by the author on how the methodology of continuing to build PFP data centers cost the aircraft company millions of dollars in development and millions of dollars on relocation plus relocation stress effects on FTE which can hinder productivity. This method of PFP data center development can be avoided by the consolidation of PFP data centers using present technology.

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Books on the topic "Stress data"

1

Hsu, Teng H. Stress and strain data handbook. Houston: Gulf Pub. Co., Book Division, 1986.

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Gross, Thomas F. Bottom boundary layer stress measurements with BASS tripods: Data report STRESS 1988-89. [Woods Hole, Mass.]: Woods Hole Oceanographic Institution, 1993.

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Caputo, Michele. Altimetry data and the elastic stress tensor of subduction zones. [Washington, DC: National Aeronautics and Space Administration, 1985.

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Caputo, Michele. Altimetry data and the elastic stress tensor of subduction zones. Greenbelt, Maryland: National Aeronautics and Space Administration, Goddard Space Flight Center, 1987.

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Caputo, Michele. Altimetry data and the elastic stress tensor of subduction zones. [Washington, DC: National Aeronautics and Space Administration, 1985.

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Gross, Thomas F. Data report: Stress measurements in the bottom boundary layer with BASS tripods STRESS II 1990-91. [Woods Hole, Mass.]: Woods Hole Oceanographic Institution, 1993.

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Hwang, C. Robin. Computer aided analysis of the stress/strain response of high polymers. Edited by Lin Chiah C, Matis Gary, and Hopfe H. H. Lancaster, PA: Technomic, 1989.

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Verderaime, V. Test load verification through strain data analysis. Washington, DC: National Aeronautics and Space Administration, 1995.

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Naor, Ellen M. Indicators of potential family stress: Data from the pregnancy risk assessment monitoring system. Augusta, Me: Maine Dept. of Human Services, Office of Data, Research, and Vital Statistics, 1990.

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Tiani, Alaina. Remote Physiological Data Collection During a Virtual Stress Protocol: Sleep and Cardiovascular Reactivity. 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2022. http://dx.doi.org/10.4135/9781529601435.

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Book chapters on the topic "Stress data"

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Pakala, Aneesh Venkat. "Stress Test." In Data Interpretation in Anesthesia, 383–87. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55862-2_69.

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Zang, Arno, and Ove Stephansson. "Generic Stress Data." In Stress Field of the Earth’s Crust, 225–52. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-1-4020-8444-7_10.

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Zang, Arno, and Ove Stephansson. "Local Stress Data." In Stress Field of the Earth’s Crust, 195–223. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-1-4020-8444-7_9.

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Brown, Roger. "Stress and Strain Data." In Physical Test Methods for Elastomers, 109–18. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66727-0_8.

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Patterson, Eann A., and Robert E. Rowlands. "Stress Separation Using Thermoelastic Data." In Experimental Analysis of Nano and Engineering Materials and Structures, 845–46. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-6239-1_420.

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Chen, Wei, Shixin Zheng, and Xiao Sun. "Introducing MDPSD, a Multimodal Dataset for Psychological Stress Detection." In Big Data, 59–82. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0705-9_5.

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Baqer, Khaled, Danny Yuxing Huang, Damon McCoy, and Nicholas Weaver. "Stressing Out: Bitcoin “Stress Testing”." In Financial Cryptography and Data Security, 3–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-53357-4_1.

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Allison, I. M., and P. Nurse. "The Benefits and Pitfalls of Automatic Processing For Photoelastic Test Data." In Applied Stress Analysis, 536–44. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0779-9_51.

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Kostopoulos, Panagiotis, Athanasios I. Kyritsis, Michel Deriaz, and Dimitri Konstantas. "Stress Detection Using Smart Phone Data." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 340–51. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49655-9_41.

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Shukla, A., and R. Chona. "Determination of Dynamic Mode I and Mode II Fracture Mechanics Parameters From Photoelastic Data." In Experimental Stress Analysis, 245–54. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4416-9_28.

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Conference papers on the topic "Stress data"

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Iida, Shizuku, and Kazuo Hara. "Finding Experiential Stress in Tweets by Utilizing both Explicit and Implicit Stress Data." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671341.

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Marques, Hugo, Hugo Carvalho, Jose Morgado, Nuno M. Garcia, Ivan Miguel Pires, and Eftim Zdravevski. "Control and Prevention of Personal Stress." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378311.

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Hamman, Emrich, Kobus du Plooy, and Joe Seery. "Data management and geotechnical models." In Eighth International Conference on Deep and High Stress Mining. Australian Centre for Geomechanics, Perth, 2017. http://dx.doi.org/10.36487/acg_rep/1704_33.2_hamman.

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Hadjigeorgiou, John. "Where do the data come from?" In Sixth International Seminar on Deep and High Stress Mining. Australian Centre for Geomechanics, Perth, 2012. http://dx.doi.org/10.36487/acg_rep/1201_19_hadjigeorgiou.

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Bakker, Jorn, Mykola Pechenizkiy, and Natalia Sidorova. "What's Your Current Stress Level? Detection of Stress Patterns from GSR Sensor Data." In 2011 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2011. http://dx.doi.org/10.1109/icdmw.2011.178.

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Smith, Reuel, and Mohammad Modarres. "Tools for analysis of accelerated life and degradation test data." In 2016 IEEE Accelerated Stress Testing & Reliability Conference (ASTR). IEEE, 2016. http://dx.doi.org/10.1109/astr.2016.7762296.

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Etienne, Nichole, and Emmanuel Agu. "Investigating Transfer Learning of Smartphone-Sensed Stress in University Populations." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378078.

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Budnick, Larry. "Emergency Data Awareness: Sharing Under Stress." In 2008 IEEE Conference on Technologies for Homeland Security. IEEE, 2008. http://dx.doi.org/10.1109/ths.2008.4534469.

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Malviya, Lokesh, Sandip Mal, and Praveen Lalwani. "EEG Data Analysis for Stress Detection." In 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2021. http://dx.doi.org/10.1109/csnt51715.2021.9509713.

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Palleske, Cortney, Katherine Kalenchuk, Colin Hume, and William Bawden. "Strategic use of geotechnical data for maximised value added." In Eighth International Conference on Deep and High Stress Mining. Australian Centre for Geomechanics, Perth, 2017. http://dx.doi.org/10.36487/acg_rep/1704_33_palleske.

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Reports on the topic "Stress data"

1

Adams, J. Canadian crustal stress data - a compilation to 1985. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1986. http://dx.doi.org/10.4095/293511.

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Adams, J. Canadian crustal stress data-a compilation to 1987. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1987. http://dx.doi.org/10.4095/130317.

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Harris, W. D., and R. W. Kohrumel. Calculating Creep and Stress Relaxation from Long Term Data,. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada307537.

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Patterson, Timothy, and Sathya Motupally. Improved Accelerated Stress Tests Based on Fuel Cell Vehicle Data. Office of Scientific and Technical Information (OSTI), June 2012. http://dx.doi.org/10.2172/1123145.

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Byun, TS, Jin Weon Kim, Ivan Dunbar, and John D. Hunn. Fracture Stress Data for SiC Layers in the TRISO-Coated Fuel Particles. Office of Scientific and Technical Information (OSTI), September 2008. http://dx.doi.org/10.2172/1616803.

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Gina, Owens, and Keller Emily. Data from Traditional rural values and posttraumatic stress among rural and urban undergraduates. University of Tennessee, Knoxville Libraries, 2017. http://dx.doi.org/10.7290/e8gazry7hf.

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Kelley, Mark, Bob Hardage, Valerie Smith, Allen Modroo, and Richard Dok. TASK 2 REPORT EXTRACTING STRESS DATA FROM SEISMIC DATA: A Non-Invasive Approach for Elucidating the Spatial Distribution of In Situ Stress in Deep Subsurface Geologic Formations Considered for CO2 Storage. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1890650.

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Wackerbarth, D. E., M. U. Anderson, and R. A. Graham. PVDFSTRESS: A PC-based computer program to reduce Bauer PVDF stress-rate gauge data. Office of Scientific and Technical Information (OSTI), February 1992. http://dx.doi.org/10.2172/5413767.

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Harikrishnan, R., G. Hareland, and N. R. Warpinski. Comparison and verification of two models which predict minimum principal in situ stress from triaxial data. Office of Scientific and Technical Information (OSTI), November 1993. http://dx.doi.org/10.2172/10104161.

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Davies, J. B. Extension of in-situ stress test analysis to fractured media with reference to Yucca Mountain data. Office of Scientific and Technical Information (OSTI), May 1993. http://dx.doi.org/10.2172/227031.

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