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Journal articles on the topic 'Stress data'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Chandra, N., and Mashroor Ahmad Khan. "Maximum Likelihood Estimation for Step-Stress Partially Accelerated Life Tests based on Censored Data." MATHEMATICAL JOURNAL OF INTERDISCIPLINARY SCIENCES 3, no. 1 (September 1, 2014): 37–54. http://dx.doi.org/10.15415/mjis.2014.31004.

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12

Chengjie Xiong and G. A. Milliken. "Step-stress life-testing with random stress-change times for exponential data." IEEE Transactions on Reliability 48, no. 2 (June 1999): 141–48. http://dx.doi.org/10.1109/24.784272.

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13

Hamada, M. S., G. M. Hemphill, and and R. E. Hackenberg. "Combined Analysis of Accelerated Fixed Stress Lab and Varying Stress Field Data." Quality Engineering 27, no. 2 (April 3, 2015): 139–43. http://dx.doi.org/10.1080/08982112.2014.920508.

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14

Kasula, Lavankumar Reddy, J. Murali Krishnan, K. R. Rajagopal, and D. N. Little. "Normal stress and stress relaxation data for sand asphalt undergoing torsional flow." Mechanics Research Communications 32, no. 1 (January 2005): 43–52. http://dx.doi.org/10.1016/j.mechrescom.2004.05.004.

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15

Gu, Jingjing, Zhiteng Dong, Cai Zhang, Xiaojiang Du, and Mohsen Guizani. "Dynamic Stress Measurement with Sensor Data Compensation." Electronics 8, no. 8 (August 2, 2019): 859. http://dx.doi.org/10.3390/electronics8080859.

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Applying parachutes-deployed Wireless Sensor Network (WSN) in monitoring the high-altitude space is a promising solution for its effectiveness and cost. However, both the high deviation of data and the rapid change of various environment factors (air pressure, temperature, wind speed, etc.) pose a great challenge. To this end, we solve this challenge with data compensation in dynamic stress measurements of parachutes during the working stage. Specifically, we construct a data compensation model to correct the deviation based on neural network by taking into account a variety of environmental parameters, and name it as Data Compensation based on Back Propagation Neural Network (DC-BPNN). Then, for improving the speed and accuracy of training the DC-BPNN, we propose a novel Adaptive Artificial Bee Colony (AABC) algorithm. We also address its stability of solution by deriving a stability bound. Finally, to verify the real performance, we conduct a set of real implemented experiments of airdropped WSN.

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16

Mukherjee, S. P., and Sudhansu S. Maiti. "Reliability from Damaged Stress and Strength Data." Calcutta Statistical Association Bulletin 46, no. 1-2 (March 1996): 135–42. http://dx.doi.org/10.1177/0008068319960112.

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In case of stress-strength reliability R = P( X > Y), inference is made under various assumptions regarding the variables X and Y. In reality instead of observing X and Y one observes U and V which imply stress and strength subject to some sort of errors. In this article, procedures have been Indicated to estimate true reliability using U and V values under the assumption of exponentiality. Over and/or under-reporting has been treated generally as damage.

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17

Meira, J. B. C., R. R. Braga, R. Y. Ballester, C. B. Tanaka, and A. Versluis. "Understanding Contradictory Data in Contraction Stress Tests." Journal of Dental Research 90, no. 3 (November 22, 2010): 365–70. http://dx.doi.org/10.1177/0022034510388039.

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18

Becker, Ami B., Traci L. Galinsky, Naomi G. Swanson, and Steven L. Sauter. "Stress Control Interventions in Data Entry Work." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 40, no. 24 (October 1996): 1279. http://dx.doi.org/10.1177/154193129604002456.

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19

Indikawati, Fitri Indra, and Sri Winiarti. "Stress Detection from Multimodal Wearable Sensor Data." IOP Conference Series: Materials Science and Engineering 771 (March 19, 2020): 012028. http://dx.doi.org/10.1088/1757-899x/771/1/012028.

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20

Oost, W. A. "The KNMI HEXMAX stress data – a reanalysis." Boundary-Layer Meteorology 86, no. 3 (March 1998): 447–68. http://dx.doi.org/10.1023/a:1000824918910.

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21

Zhdankin, N. A., and S. B. Kolokolov. "Interpretation of experimental data during stress measurement." Soviet Mining Science 26, no. 1 (January 1990): 43–46. http://dx.doi.org/10.1007/bf02499764.

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22

Greasley, A., and T. Sheppard. "Flow-stress mapping from extrapolated laboratory data." Journal of Mechanical Working Technology 11, no. 2 (May 1985): 201–14. http://dx.doi.org/10.1016/0378-3804(85)90025-7.

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23

Hakala, M., J. A. Hudson, and R. Christiansson. "Quality control of overcoring stress measurement data." International Journal of Rock Mechanics and Mining Sciences 40, no. 7-8 (October 2003): 1141–59. http://dx.doi.org/10.1016/j.ijrmms.2003.07.005.

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24

Gao, Gelin, Bud Mishra, and Daniele Ramazzotti. "Causal data science for financial stress testing." Journal of Computational Science 26 (May 2018): 294–304. http://dx.doi.org/10.1016/j.jocs.2018.04.003.

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25

赵, 伟. "Calculating Stress Method with Well-Logging Data." Journal of Oil and Gas Technology 42, no. 01 (2020): 74–80. http://dx.doi.org/10.12677/jogt.2020.421008.

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26

Zimmermann, Juliane, Ryan L. Hayes, Markus Basan, José N. Onuchic, Wouter-Jan Rappel, and Herbert Levine. "Intercellular Stress Reconstitution from Traction Force Data." Biophysical Journal 107, no. 3 (August 2014): 548–54. http://dx.doi.org/10.1016/j.bpj.2014.06.036.

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27

Gawade, Shriya, Riya Sawant, Aakash Rathod, and Prof Chhaya Dhavale. "Psychological Analysis Using Social Media Data." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1936–41. http://dx.doi.org/10.22214/ijraset.2022.41510.

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Abstract: Mental Stress is an important aspect of our life that is given the least importance. We tend to ignore the fact that we need to be emotionally stable along with physical stability. To keep your mental state sound, we proposed this system where the psychological state of a person is being predicted. One such place where a person comes up and shares his/her thoughts, through texts is on social media with their friends. To detect such a state, we made use of NLP techniques accompanied by a reliable scale, the Perceived Stress Scale (PSS) developed by Cohen, Kamarck and Mermelstein. The huge texts were cleaned using text processing methods. In Machine Learning, there are many ways for sentimental analysis such: decision-based systems, Bayesian classifiers, support vector machines, neural networks and sample-based methods. We have performed sentimental analysis and in order to give the severity of the condition we made use of the Perceived Stress Scale (PSS). The model will be predicting whether the given text indicates stress or not and further classifies it as low, medium or high-level stress. Keywords: TF-IDF, Natural Language Processing (NLP), Stress, WordCloud, Perceived Stress Scale

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28

Gawade, Shriya, Riya Sawant, Aakash Rathod, and Prof Chhaya Dhavale. "Psychological Analysis Using Social Media Data." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1936–41. http://dx.doi.org/10.22214/ijraset.2022.41510.

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Abstract: Mental Stress is an important aspect of our life that is given the least importance. We tend to ignore the fact that we need to be emotionally stable along with physical stability. To keep your mental state sound, we proposed this system where the psychological state of a person is being predicted. One such place where a person comes up and shares his/her thoughts, through texts is on social media with their friends. To detect such a state, we made use of NLP techniques accompanied by a reliable scale, the Perceived Stress Scale (PSS) developed by Cohen, Kamarck and Mermelstein. The huge texts were cleaned using text processing methods. In Machine Learning, there are many ways for sentimental analysis such: decision-based systems, Bayesian classifiers, support vector machines, neural networks and sample-based methods. We have performed sentimental analysis and in order to give the severity of the condition we made use of the Perceived Stress Scale (PSS). The model will be predicting whether the given text indicates stress or not and further classifies it as low, medium or high-level stress. Keywords: TF-IDF, Natural Language Processing (NLP), Stress, WordCloud, Perceived Stress Scale

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29

Hwu, Chyanbin, and Y. C. Liang. "Evaluation of stress concentration factors and stress intensity factors from remote boundary data." International Journal of Solids and Structures 37, no. 41 (October 2000): 5957–72. http://dx.doi.org/10.1016/s0020-7683(99)00245-0.

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30

Irša, J., and A. N. Galybin. "Stress trajectories element method for stress determination from discrete data on principal directions." Engineering Analysis with Boundary Elements 34, no. 5 (May 2010): 423–32. http://dx.doi.org/10.1016/j.enganabound.2009.12.004.

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31

Murty, G. S., and S. Banerjee. "Evaluation of threshold stress from the stress — Strain rate data of superplastic materials." Scripta Metallurgica et Materialia 31, no. 6 (September 1994): 707–12. http://dx.doi.org/10.1016/0956-716x(94)90214-3.

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32

Guo, Jin Quan, Long Tian, Hui Chao Shi, and Wu Zhou Meng. "Using Stress Relaxation Data to Predict Creep Behavior." Advanced Materials Research 842 (November 2013): 382–85. http://dx.doi.org/10.4028/www.scientific.net/amr.842.382.

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An estimation method to predict creep performances of high temperature structural materials has been proposed. A Stress relaxation equation is obtained by fitting stress relaxation testing curves and modifying Tanaka-Ohba reloading stress relaxation constitutive equation. Based on the relationship between stress relaxation and creep, a unified prediction equation of creep is deduced. The method is to use the unified equation to derive creep strain rates or creep strain vs. time curves from stress relaxation measurements through some specified time increments. In order to validate the approach, the predicted results are compared to the experimental results of uni-axial isothermal creep tests conducted on 1Cr10NiMoW2VNbN steel. Good agreement between results of creep tests and the predicted results indicates that the developed method can be recommended in the creep behavior evaluation of high temperature materials.

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33

Moi, Marta, Irenilza de A. Nääs, Fabiana R. Caldara, Ibiara C. de L. Almeida Paz, Rodrigo G. Garcia, and Alexandra F. S. Cordeiro. "Vocalization data mining for estimating swine stress conditions." Engenharia Agrícola 34, no. 3 (June 2014): 445–50. http://dx.doi.org/10.1590/s0100-69162014000300008.

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This study aimed to identify differences in swine vocalization pattern according to animal gender and different stress conditions. A total of 150 barrow males and 150 females (Dalland® genetic strain), aged 100 days, were used in the experiment. Pigs were exposed to different stressful situations: thirst (no access to water), hunger (no access to food), and thermal stress (THI exceeding 74). For the control treatment, animals were kept under a comfort situation (animals with full access to food and water, with environmental THI lower than 70). Acoustic signals were recorded every 30 minutes, totaling six samples for each stress situation. Afterwards, the audios were analyzed by Praat® 5.1.19 software, generating a sound spectrum. For determination of stress conditions, data were processed by WEKA® 3.5 software, using the decision tree algorithm C4.5, known as J48 in the software environment, considering cross-validation with samples of 10% (10-fold cross-validation). According to the Decision Tree, the acoustic most important attribute for the classification of stress conditions was sound Intensity (root node). It was not possible to identify, using the tested attributes, the animal gender by vocal register. A decision tree was generated for recognition of situations of swine hunger, thirst, and heat stress from records of sound intensity, Pitch frequency, and Formant 1.

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34

Craft, Jeremy L., and A. John Bailer. "Comparison of step‐stress data among multiple groups." Environmental Toxicology and Chemistry 24, no. 4 (April 2005): 1004–6. http://dx.doi.org/10.1897/04-242r.1.

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35

Yan, Jun. "Data Point to Enzyme’s Role in Stress, Depression." Psychiatric News 48, no. 12 (June 12, 2013): 1. http://dx.doi.org/10.1176/appi.pn.2013.6b5.

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36

Herwanger, Jorg, and Steve Horne. "Predicting time-lapse stress effects in seismic data." Leading Edge 24, no. 12 (December 2005): 1234–42. http://dx.doi.org/10.1190/1.2149632.

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37

Grzywinski, G. G., and D. A. Woodford. "Design data for polycarbonate from stress-relaxation tests." Materials & Design 14, no. 5 (January 1993): 279–84. http://dx.doi.org/10.1016/0261-3069(93)90127-h.

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38

Sodnomsambuu, Demberel, and Anatoly V. Klyuchevskii. "Lithospheric stress in Mongolia, from earthquake source data." Geoscience Frontiers 8, no. 6 (November 2017): 1323–37. http://dx.doi.org/10.1016/j.gsf.2017.01.003.

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39

Segall, Richard S., Gauri S. Guha, and Sarath A. Nonis. "Data mining of environmental stress tolerances on plants." Kybernetes 37, no. 1 (February 15, 2008): 127–48. http://dx.doi.org/10.1108/03684920810851041.

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40

Cetin, K. Onder, and Nihat S. Isik. "Probabilistic Assessment of Stress Normalization for CPT Data." Journal of Geotechnical and Geoenvironmental Engineering 133, no. 7 (July 2007): 887–97. http://dx.doi.org/10.1061/(asce)1090-0241(2007)133:7(887).

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41

Kaloupek, Danny G., Kathleen M. Chard, Michael C. Freed, Alan L. Peterson, David S. Riggs, Murray B. Stein, and Farris Tuma. "Common Data Elements for Posttraumatic Stress Disorder Research." Archives of Physical Medicine and Rehabilitation 91, no. 11 (November 2010): 1684–91. http://dx.doi.org/10.1016/j.apmr.2010.06.032.

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42

Grzywinski, G. G., and D. A. Woodford. "Creep analysis of thermiplastics using stress relaxation data." Polymer Engineering and Science 35, no. 24 (December 1995): 1931–37. http://dx.doi.org/10.1002/pen.760352404.

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43

Lee, Yonghee, and Sangmun Shin. "Job stress evaluation using response surface data mining." International Journal of Industrial Ergonomics 40, no. 4 (July 2010): 379–85. http://dx.doi.org/10.1016/j.ergon.2010.03.003.

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44

Dogan, Daghan, Seta Bogosyan, and Tankut Acarman. "Evaluation of driver stress level with survey, galvanic skin response sensor data, and force-sensing resistor data." Advances in Mechanical Engineering 11, no. 12 (December 2019): 168781401989155. http://dx.doi.org/10.1177/1687814019891555.

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Thousands of lives are lost in traffic accidents every year, and most traffic accidents are caused by driver errors. Causes and impairments such as fatigue, inattentiveness, alcohol usage, stress, and drugs are the main factors of these accidents. When a driver is subject to changing and complicated driving tasks in traffic, he or she should be able to assure driving authority to prevent potential hazards and accidents. In this context, the purpose of this study is to determine the stress level of the driver when driving in urban traffic in such situations requiring delegation of driving authority. Thus, the work combines stress questionnaire and galvanic skin response sensor to validate results and fuses with a force-sensing resistor. In this study, a prototype electric vehicle is equipped with sensors providing various drivers’ data including the responses of a force-sensing resistor sensor while galvanic skin is being collected on a specified route. At the end of the trip, the stress level of the drivers is determined by the collected data. Results indicate that the galvanic skin sensor stress results are consistent with the results of the survey with an average accuracy of 87.5%. The force-sensing resistor sensor is only used to determine gender stress. And the force-sensing resistor sensor gender-stress results are consistent with results of the survey with an accuracy of 100%. These results are used to validate the results of post-driving stress survey evaluated by SPSS 23.0 windows statistics software. Data analysis is particularly focused on demographic properties of participators, factor analysis, reliability tests, correlation, T-test, and one-way analysis of variance.

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45

Šidlauskaitė, Ieva. "PSICHOLOGINIO-SOCIALINIO STRESO IR GYVENIMO STILIAUS SĄSAJOS." Psichologija 24 (January 1, 2001): 21–37. http://dx.doi.org/10.15388/psichol.2001..4413.

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Darbe siekta nustatyti, ar psichologinis-socialinis stresas siejasi su gyvenimo stiliaus ypatumais: priklausomybes sukeliančių medžiagų (alkoholio, tabako, narkotikų) vartojimo kiekiu bei polinkiu tai keisti; su mityba ir fiziniu aktyvumu bei noru keisti gyvenimo stiliaus ypatumus sveikatingumo linkme.Tyrimas atliktas naudojantis sveikatos apklausos anketa. Tyrimo rezultatai pateikiami naudojantis 18-22 metų amžiaus tirtų vaikinų 616 anketų.Nustatyta, kad: 1. Jaunuoliai, kuriems būdingas aukštas psichologinio-socialinio streso lygis, vartoja daugiau priklausomybę sukeliančių medžiagų (tabako, alkoholio, narkotikų), nei tie jaunuoliai, kuriems būdingas žemas psichologinio-socialinio streso lygis. 2. Aukšto psichologinio-socialinio streso lygio jaunuoliai labiau linkę nekeisti priklausomybę sukeliančių medžiagų vartojimo nei žemo psichologinio-socialinio streso lygio jaunuoliai. 3. Aukštas psichologinis-socialinis stresas yra susijęs su nesveikesniu gyvenimo stiliumi (mažesniu fiziniu aktyvumu, didesniu druskos vartojimu). THE LINK BETWEEN PSYCHOLOGICAL-SOCIAL STRESS AND LIFE STYLE Ieva Šidlauskaitė SummaryThe purposes of this work was to state the connection between psychological-social stress (PS stress) and life style's features, such as: nicotine use; alcohol use; drugs use; nutrition and physical activity. To state the connection between psychological-social stress level and quantity of nicotine, alcohol, drugs use; tendency to change habits in the direction of healthier life style. Research has been carried according to health questionnaire (translated by A. Gostautas, 2000). For data analysis we used results from common research with 616 youngsters (form 18 till 22 years).Conclusions are following:• Youngsters with higher PS stress level use more cigarettes, alcohol and drugs comparing with youngsters with lover PS stress level.• Youngsters with higher PS stress level are liable not to change using of nicotine, alcohol and drugs, comparing with youngsters with lover PS stress level.• Higher PS stress level is related with unhealthier life style.

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46

Henning, Steven W., Dinesh Jaishankar, Levi W. Barse, Emilia R. Dellacecca, Nicola Lancki, Kirsten Webb, Linda Janusek, Herbert L. Mathews, Ronald N. Price, and I. Caroline Le Poole. "The relationship between stress and vitiligo: Evaluating perceived stress and electronic medical record data." PLOS ONE 15, no. 1 (January 27, 2020): e0227909. http://dx.doi.org/10.1371/journal.pone.0227909.

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47

Dhillon, S. S., J. C. Thompson, and K. J. Negus. "A simplified method for predicting the stress concentration in notches from experimental stress data." Strain 24, no. 3 (August 1988): 95–98. http://dx.doi.org/10.1111/j.1475-1305.1988.tb00659.x.

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48

Barba, Salvatore, Michele M. C. Carafa, Maria Teresa Mariucci, Paola Montone, and Simona Pierdominici. "Present-day stress-field modelling of southern Italy constrained by stress and GPS data." Tectonophysics 482, no. 1-4 (February 2010): 193–204. http://dx.doi.org/10.1016/j.tecto.2009.10.017.

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49

Aarbogh, H. M., M. M'Hamdi, A. Mo, and H. G. Fjær. "Simplified method for establishing constitutive equations and flow stress data for welding stress modelling." Science and Technology of Welding and Joining 13, no. 8 (November 2008): 705–13. http://dx.doi.org/10.1179/174329308x349539.

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50

Lei, Ting, Bikash K. Sinha, and Michael Sanders. "Estimation of horizontal stress magnitudes and stress coefficients of velocities using borehole sonic data." GEOPHYSICS 77, no. 3 (May 1, 2012): WA181—WA196. http://dx.doi.org/10.1190/geo2011-0277.1.

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We described a nondestructive method to estimate the maximum and minimum horizontal stresses and formation nonlinear elastic constants using sonic data from a vertical wellbore. This method for the estimation of horizontal stress magnitudes consists of using radial profiles of the three shear moduli obtained from the Stoneley and cross-dipole sonic data in a vertical wellbore. These shear moduli change as a function of formation stresses, which in turn change as a function of the radial position away from the wellbore. Two difference equations were constructed from the three far-field shear moduli and the other two were constructed from differences in the shear moduli at radial positions with different stresses in the presence of near-wellbore stress concentrations. Outputs from this inversion algorithm included the maximum and minimum horizontal stress magnitudes, and two rock nonlinear constants referred to a local hydrostatically loaded reference state. The underlying acoustoelastic theory behind this inversion algorithm assumes that differences in the three shear moduli are caused by differences in the formation principal stresses. Additionally, the orientation of the maximum horizontal stress direction was identified from the fast-shear azimuth in the presence of a dipole dispersion crossover. Hence, the principal horizontal stress state was fully determined. Good agreement was obtained between the predicted minimum horizontal stress magnitude and that measured from an extended leak-off test in a vertical offshore wellbore in Malaysia. One of the nonlinear constants was obtained from differences between compressional velocity at two depths caused by differences in the overburden stress and the maximum and minimum horizontal stresses. Estimates were obtained for the stress coefficients of the compressional, fast-shear, and slow-shear velocities referred to a local reference state. These stress coefficients of velocities helped in the interpretation of observed time-lapse changes in seismic traveltimes caused by fluid saturation and reservoir stress changes.

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