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Готові списки джерел за темами / Stress classification

Добірка наукової літератури з теми "Stress classification"

Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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Статті в журналах з теми "Stress classification"

1

Kaeding, Christopher C., and Robert G. Najarian. "Stress Fractures: Classification and Management." Physician and Sportsmedicine 38, no. 3 (October 2010): 45–54. http://dx.doi.org/10.3810/psm.2010.10.1807.

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2

Roche, Roland L. "Practical procedure for stress classification." International Journal of Pressure Vessels and Piping 37, no. 1-4 (January 1989): 27–44. http://dx.doi.org/10.1016/0308-0161(89)90138-5.

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3

Kalejaiye, Odunayo, Monika Vij, and Marcus John Drake. "Classification of stress urinary incontinence." World Journal of Urology 33, no. 9 (June 25, 2015): 1215–20. http://dx.doi.org/10.1007/s00345-015-1617-1.

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4

Miller, Timothy L., and Christopher Kaeding. "Stress Fractures: A New Classification System." Medicine & Science in Sports & Exercise 41 (May 2009): 84. http://dx.doi.org/10.1249/01.mss.0000353531.90405.97.

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5

Blaivas, Jerry G., and Carl A. Olsson. "Stress Incontinence: Classification and Surgical Approach." Journal of Urology 139, no. 4 (April 1988): 727–31. http://dx.doi.org/10.1016/s0022-5347(17)42611-5.

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6

Dhalla, A. K. "Stress Classification for Elevated Temperature Service." Journal of Pressure Vessel Technology 113, no. 4 (November 1, 1991): 488–96. http://dx.doi.org/10.1115/1.2928785.

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The paper presents two procedures which can be used to classify the stresses originating at the structural discontinuities of pressure vessel and piping components designed for elevated temperature service. The stress classification is based upon the definitions of primary and secondary stress intensities which are provided in the ASME Boiler and Pressure Vessel Code, Sections III and VIII. The intent is not to reclassify primary stresses, but to provide a designer with practical guidance in evaluating the primary characteristics of thermal and structural discontinuity pressure stresses. Two objectives for an appropriate classification of stresses are: (a) to reduce undue conservatism in elevated temperature Code Case N-47 design rules when primary stress is only a small fraction of secondary stress, and (b) to provide a sufficient margin of safety for extreme (Level D) loadings when the primary local and general membrane stress levels are close to the yield point of the material at structural discontinuities.

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7

Cipresso, Pietro, Andrea Gaggioli, Silvia Serino, and Giuseppe Riva. "Stress Diffusion through Complex Networks." International Journal of Adaptive, Resilient and Autonomic Systems 3, no. 1 (January 2012): 46–64. http://dx.doi.org/10.4018/jaras.2012010103.

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Research has proven that stress reduces quality of life and causes many diseases. It is not clear how stress spreads among the population and how its diffusion in a society can be estimated. From a complex system perspective, this paper defines the rules of stress transmission, including input and output factors. Stress transmission flow is defined to describe an entropy-derived measure of stress between two interconnected individuals, and the analysis is extended to networked individuals to analyze stress diffusion in a theoretical setting that includes the modeling of complex networks and the use of agent-based models in a simulated framework. These approaches endow artificial, interacting agents with behavioral rules, allowing the authors to determine the important components that must be considered as the nature of the equilibrium that exists between two distinctly different classifications of individuals. The first classification is “isolated individuals” who experience self-induced stress. The second classification consists of “too connected individuals” who have a high perception of social pressure, have a higher probability of being stressed, and who are surrounded by a higher number of stressed people.

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8

Liu, Xinyu, Yuhao Shan, Min Peng, Huanyu Chen, and Tong Chen. "Human Stress and StO2: Database, Features, and Classification of Emotional and Physical Stress." Entropy 22, no. 9 (August 31, 2020): 962. http://dx.doi.org/10.3390/e22090962.

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Emotional and physical stress can cause various health problems. In this paper, we used tissue blood oxygen saturation (StO2), a newly proposed physiological signal, to classify the human stress. We firstly constructed a public StO2 database including 42 volunteers subjected to two types of stress. During the physical stress experiment, we observed that the facial StO2 right after the stress can be either increased or decreased comparing to the baseline. We investigated the StO2 feature combinations for the classification and found that the average StO2 values from left cheek, chin, and the middle of the eyebrow can provide the highest classification rate of 95.56%. Comparison with other stress classification method shows that StO2 based method can provide best classification performance with lowest feature dimension. These results suggest that facial StO2 can be used as a promising features to identify stress states, including emotional and physical stress.

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9

Dhole, N. P., and S. N. Kale. "Multilayer Perceptron Classification in Stress Speech Identification." International Journal of Computer Sciences and Engineering 6, no. 4 (April 30, 2018): 471–75. http://dx.doi.org/10.26438/ijcse/v6i4.471475.

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10

Kang, Jun-Su, Giljin Jang, and Minho Lee. "Stress status classification based on EEG signals." Journal of the Institute of Internet Broadcasting and Communication 16, no. 3 (June 30, 2016): 103–8. http://dx.doi.org/10.7236/jiibc.2016.16.3.103.

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Дисертації з теми "Stress classification"

1

Manser, Sarina. "Wall motion classification of stress echocardiography." Thesis, University of Oxford, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.526081.

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2

GOYAL, DEVENDRA. "EVALUATING WORK-FACTOR CLASSIFICATION SYSTEM." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin990816550.

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3

Aigrain, Jonathan. "Multimodal detection of stress : evaluation of the impact of several assessment strategies." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066681/document.

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Il est maintenant largement accepté que le stress joue un rôle important dans les sociétés modernes. Le stress impacte en effet le corps et l'esprit à différents niveaux. De plus, le lien entre stress et maladie a été observé dans plusieurs études. Cependant, il n'y a pas encore de définition consensuelle du stress, et par conséquent il n'y a pas de manière consensuelle de le mesurer. Ainsi, bien que la qualité de la mesure joue un rôle majeur dans la réalisation de solutions robustes de détection du stress, les chercheurs doivent choisir une stratégie de mesure parmi un grand nombre de possibilités. Cette hétérogénéité impacte la validité des comparaisons faites entre les différentes solutions. Dans cette thèse, nous évaluons l'impact de plusieurs stratégies de mesure pour la détection du stress. Dans un premier temps, nous résumons comment différents domaines de recherche définissent et mesurent le stress. Nous décrivons ensuite comment nous avons collecté des données de sujets en situation stressante ainsi que plusieurs mesures du stress. Nous étudions également les liens entre ces différentes mesures. Par la suite, nous présentons les descripteurs comportementaux et physiologiques que nous avons extraits pour nos expériences. Enfin, nous présentons les résultats obtenus concernant l'impact des stratégies de mesure sur 1) la normalisation de données, 2) la performance des descripteurs pour la classification et 3) sur la conception d'algorithmes d'apprentissage automatique. De manière générale, nous défendons l'idée qu'il faut adopter une approche globale pour concevoir une solution de détection du stress
It is now widely accepted that stress plays an important role in modern societies. It impacts the body and the mind at several levels and the association between stress and disease has been observed in several studies. However, there is no consensual definition of stress yet, and therefore there is no consensual way of assessing it either. Thus, although the quality of assessment is a key factor to build robust stress detection solutions, researchers have to choose among a wide variety of assessment strategies. This heterogeneity impacts the validity of comparing solutions among them. In this thesis, we evaluate the impact of several assessment strategies for stress detection. We first review how different fields of research define and assess stress. Then, we describe how we collected stress data along with multiple assessments. We also study the association between these assessments. We present the behavioural and physiological features that we extracted for our experiments. Finally, we present the results we obtained regarding the impact of assessment strategies on 1) data normalization, 2) feature classification performance and 3) on the design of machine learning algorithms. Overall, we argue that one has to take a global and comprehensive approach to design stress detection solutions

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4

Narber, Cody G. "Classification of emotion using sub audible frequencies in vocal data." Thesis, Manhattan, Kan. : Kansas State University, 2008. http://hdl.handle.net/2097/776.

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5

Nazarov, Andrei V., and Alexandr A. Mikheev. "Theory of diffusion under stress." Diffusion fundamentals 2 (2005) 32, S. 1-2, 2005. https://ul.qucosa.de/id/qucosa%3A13312.

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6

Shin, Sauyeun. "Classification du cancer du pancréas via une analyse du traductome." Thesis, Toulouse 3, 2020. http://www.theses.fr/2020TOU30121.

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Le cancer du pancréas, ou encore l'adénocarcinome pancréatique (ACP) est la 4ème cause de décès liée au cancer dans les pays industrialisés. Non seulement l'absence de signes cliniques spécifiques à la maladie conduit à un diagnostic tardif, souvent à un stade métastasique, la classification actuelle des patients atteints de ce cancer ne permet pas d'orienter le choix de prise en charge des patients. Cette absence de traitement efficace se traduit par un très faible taux de réponse aux chimiothérapies et une médiane de survie à 5 ans, inférieure à 5%. L'importante hétérogénéité génomique inter-tumorale ainsi que l'abondance du stroma perturbent l'analyse du processus de carcinogénèse pancréatique, et rendent particulièrement complexe le ciblage d'une voie spécifique. Par conséquent, les différentes classifications établies par des analyses génomiques et transcriptomiques ont permis de séparer les patients principalement en deux sous-groupes "basal" et "classique" avec des différences marquées de survie, mais pas de sensibilité particulière à un agent thérapeutique. Cependant, plusieurs groupes de recherche, dont le nôtre, ont montré que l'ACP comportait d'importantes dérégulations au niveau de la traduction des ARN messagers, démontrant ainsi que l'abondance des ARNm et le niveau de protéines ne sont pas toujours corrélés. Mes travaux ont permis d'identifier un sous-groupe de tumeurs portant une signature traductionnelle spécifique, indécelable au niveau transcriptionnel, grâce à l'analyse du traductome de 28 PDX pancréatiques par une approche bio-informatique non-supervisée. Dans ces tumeurs, la voie de "Integrated Stress response" (ISR) est activée de manière constitutive, associée à une baisse de la synthèse protéique globale. Cette particularité semble contribuer à un mécanisme de résistance aux stress, notamment aux chimiothérapies activatrices de l'ISR. Ce phénotype pourrait être expliqué par une résistance accrue à l'apoptose dans ces cellules cancéreuses. Néanmoins, la caractérisation de ce sous-type démontre une dépendance très particulière liée au métabolisme d'un acide aminé. En conséquence, le ciblage de cette voie pourrait constituer une nouvelle approche thérapeutique, spécifique à ces tumeurs pancréatiques
Pancreatic cancer, also known as pancreatic ductal adenocarcinoma (PDA) is the 4th cause of cancer related death in industrialized countries. The absence of specific clinical symptoms of this disease leads to a late diagnosis, mostly at a metastatic stage. Moreover, the current classification of PDA patients does not allow personalized patient management, which may explain the lack of efficient treatment, resulting in a poor treatment response rate and a 5-year survival rate not exceeding 7%. The important genomic heterogeneity between tumors and the high abundance of the stroma hinder the analysis of the pancreatic carcinogenesis process, and make particularly complex the targeting of a specific pathway. Different classifications of PDA tumors based on mutational landscape and genome wide transcriptomic analysis emerged principally two subtypes "basal-like" and "classical", correlating with patient overall survival but not to a particular sensitivity to a molecule class. Nonetheless, several research groups including ours, demonstrated the presence of a high deregulation in mRNA translation mechanisms in PDA, showing that the abundance of mRNA and the level of the associated protein do not always correlate. My work allowed the identification of a tumor subgroup with a particular translational signature, undetectable at the transcriptional level, through the translatome analysis of 28 pancreatic patient-derived xenografts (PDX) by unsupervised bioinformatics approaches. The Integrated Stress Response (ISR) pathway is constituvely activated in these tumors, in association with a global protein synthesis attenuation. This particularity seems to contribute in increasing resistance to stress, including ISR-activating chemotherapies, and could be explained by an increased resistance to apoptosis in these cancer cells. Nonetheless, the characterization of this subtype revealed an important dependency on a specific amino acid metabolism. As a consequence, targeting this metabolic pathway might be an interesting therapeutic approach for these pancreatic tumors

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7

Murphy, Nova Annette. "An analysis of the relationship of gender and school geographic classification and teacher stressors in selected Oklahoma schools /." Full-text version available from OU Domain via ProQuest Digital Dissertations, 1986.

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8

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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9

Borén, Mirjam. "Classification of discrete stress levels in users using eye tracker and K- Nearest Neighbour algorithm." Thesis, Umeå universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-176258.

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The advancement of the Head Mounted Display (HMD) used for Virtual Reality (VR) has come a long way and now the option of eye tracking is available in some HMD. The eyes show physiological responses when healthy individuals are stressed, justifying eye tracking as a tool to estimate at minimum, the very presence of stress. Stress can present itself in many shapes and may be caused by different factors such as work, social situations, cognitive load and many others. The stress test Group Stroop Color Word Test (GSCWT) can induce four different levels of stress in users; no stress, low stress, medium stress and high stress. In this thesis GSCWT was implemented in a virtual reality and users had their pupil dilation and blinking rate recorded. The data was then used to train and test a K-Nearest Neighbour algorithm (KNN). The KNN- algorithm could not accurately predict between the four different stress classes but it could predict the presence or absence of stress. VR has been used successfully as a tool for practicing different social skills and other everyday life skills for individuals with Autism Spectrum Disorder (ASD). By correctly identifying the stress level in the user in VR, tools for practicing social skills for ASD individuals could be more personalized and improved.

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10

Stange, Roland, Kenechukwu David Nnetu, and Josef A. Käs. "Cell stiffening and softening evoked by optical stress application." Diffusion fundamentals 20 (2013) 30, S. 1, 2013. https://ul.qucosa.de/id/qucosa%3A13596.

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Книги з теми "Stress classification"

1

Davidson, Jonathan R. T., 1943- and Foa Edna B, eds. Posttraumatic stress disorder: DSM-IV and beyond. Washington, DC: American Psychiatric Press, 1993.

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2

Eberhardshtayner, Yozef, Sergey Leonovich, and Valentin Dorkin. Design models of structural building materials under multiaxial stress. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1082947.

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The monograph presents the results of experimental and theoretical studies of the behavior of wood and concrete of various structures under biaxial and triaxial compression. It contains a systematic classification of existing models for concrete that link three-axis nonlinear elastic stresses and deformations, as well as research and subsequent evaluation of some basic models from the point of view of their possible use in the framework of spatial load analysis using FEM. It is intended for scientific and engineering workers of research and design organizations.

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3

N, Petroff John, Garic G, Jones David P. 1943-, Becht C, American Society of Mechanical Engineers. Pressure Vessels and Piping Division., and Pressure Vessels and Piping Conference (1994 : Minneapolis, Minn.), eds. Recertification and stress classification issues: Presented at the 1994 Pressure Vessels and Piping Conference, Minneapolis, Minnesota, June 19-23, 1994. New York, N.Y: American Society of Mechanical Engineers, 1994.

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4

Borchelt, Gretchen. Break them down: Systematic use of psychological torture by U.S. Forces. Cambridge, MA: Physicians for Human Rights, 2005.

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5

Ellmin, Roger. Läraryrkets tillfredsställelse och påfrestning: En empirisk metodstudie för klassificering av kritiska situationer i lärares yrkesutövning = The satisfactions and strains of the teaching profession : an empirical method study for classification of critical situations in the practice of the teaching profession. Stockholm: Stockholms universitet, 1988.

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6

Rosegen, D. L. A stream classification system. S.l: s.n, 1988.

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7

Armstrong, David S. Characteristics and classification of least altered streamflows in Massachusetts. Reston, Va: U.S. Dept. of the Interior, U.S. Geological Survey, 2008.

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8

Pflieger, William L. Aquatic community classification system for Missouri. Jefferson City, Mo: Missouri Dept. of Conservation, 1989.

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9

McCain, Michael E. Stream habitat classification and inventory procedures for northern California. Eureka, CA?]: U.S. Dept. of Agriculture, Forest Service, Pacific Southwest Region, 1990.

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10

Graffiti art styles: A classification system and theoretical analysis. Jefferson, NC: McFarland & Co., 2008.

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Частини книг з теми "Stress classification"

1

Kaeding, Christopher C., and Timothy L. Miller. "Classification of Stress Fractures." In Stress Fractures in Athletes, 65–75. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46919-1_5.

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2

Kaeding, Christopher C., and Timothy L. Miller. "Classification of Stress Fractures." In Stress Fractures in Athletes, 51–61. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09238-6_4.

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3

Bodo, Giovanni, and Enrico Ammirati. "Incontinence: Definition and Classification." In Male Stress Urinary Incontinence, 35–44. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19252-9_3.

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4

Guido, Maria, Samantha Sarcognato, Matteo Fassan, and Marco Pizzi. "Liver Cancer Classification." In Oxidative Stress in Applied Basic Research and Clinical Practice, 417–48. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15539-5_19.

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5

Phutela, Nishtha, Devanjali Relan, Goldie Gabrani, and Ponnurangam Kumaraguru. "EEG Based Stress Classification in Response to Stress Stimulus." In Artificial Intelligence and Speech Technology, 354–62. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95711-7_30.

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6

Kesimli, Iffet, Serol Karalar, and Özgür Tasdemir. "Auditor Stress: Literature Review and Classification." In Accounting, Finance, Sustainability, Governance & Fraud: Theory and Application, 317–46. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-3212-7_18.

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7

Riecher-Rössler, Anita, and Anke Rohde. "Diagnostic Classification of Perinatal Mood Disorders." In Perinatal Stress, Mood and Anxiety Disorders, 6–27. Basel: KARGER, 2005. http://dx.doi.org/10.1159/000087442.

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8

Latour, Joris B., Rudo Reiling, and Jaap Wiertz. "A flexible multiple stress model: who needs a priori classification?" In Ecosystem Classification for Environmental Management, 183–98. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-017-1384-9_9.

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Šorf, Milan, Vladimír Eck, Ladislava Janku, and Lenka Lhotska. "Application of Neural Network for Stress Classification." In The State of the Art in Computational Intelligence, 373–75. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1844-4_59.

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10

Sharma, Nandita, and Tom Gedeon. "Stress Classification for Gender Bias in Reading." In Neural Information Processing, 348–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24965-5_39.

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Тези доповідей конференцій з теми "Stress classification"

1

"Stress and Heart Rate Variability." In The First International Workshop on Biosignal Processing and Classification. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0001194000680077.

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2

"Continuous Blood Pressure Measurements in Stress Situations." In The Second International Workshop on Biosignal Processing and Classification. SciTePress - Science and and Technology Publications, 2006. http://dx.doi.org/10.5220/0001225401260134.

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3

Yao, Xiao, Xiaofeng Liu, Aiming Jiang, and Ning Xu. "Stress classification in speech based on stress levels." In 2015 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW). IEEE, 2015. http://dx.doi.org/10.1109/icce-tw.2015.7216824.

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4

Thanungkul, Peeranat, Nantawachara Jirakittayakorn, and Yodchanan Wongsawat. "Stress Classification During Dental Procedure." In 2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2022. http://dx.doi.org/10.1109/ecti-con54298.2022.9795647.

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5

Sysoev, Mikhail, Andrej Kos, Urban Sedlar, and Matev Pogacnik. "Sensors Classification for Stress Analysis: Toward Automatic Stress Recognition." In 2014 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI). IEEE, 2014. http://dx.doi.org/10.1109/iiki.2014.31.

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6

Sueaseenak, Direk, Pruek Apichontivong, Pitchayes Sripitak, and Saran Sukplang. "Stress Classification System for Intelligent Wheelchair." In 2019 IEEE 1st Global Conference on Life Sciences and Technologies (LifeTech). IEEE, 2019. http://dx.doi.org/10.1109/lifetech.2019.8883963.

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7

Kalnins, Arturs. "Stress Classification Lines Straight Through Singularities." In ASME 2008 Pressure Vessels and Piping Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/pvp2008-61746.

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The paper considers geometries of pressure vessels and components for which the theoretical models contain sharp corners, representing singularities. The idea is proposed that a stress classification line passed straight through the singularity can yield linearized stresses that are applicable in pressure vessel design. Using elastic finite element analysis, details of the procedures by which this result can be achieved are given for two examples. One is a sharp corner at the toe of a fillet weld. Membrane and bending stresses are calculated directly in the toe plane, showing little or no dependence on mesh size. The other is an axisymmetric shell with a flat head and a sharp corner at the joint. The objective is to determine the primary-plus-secondary stress intensity on a Stress Classification Line (SCL) through the joint. Two methods are used. One is by determining the zone of valid SCLs and extrapolating the linearized stresses to the joint. The other is by calculating the linearized stresses directly on the SCL through the joint. Conditions for the use of the SCL through the joint for the shell/flat head model are established.

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Moon, Junhyung, Juneil Lee, Dongmi Cheon, Munhee Lee, and Kyoungwoo Lee. "Stress Recognition with State Classification Considering Temporal Variation of Stress Responses." In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8983372.

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9

Womack, Brian D., and John H. L. Hansen. "Stress independent robust HMM speech recognition using neural network stress classification." In 4th European Conference on Speech Communication and Technology (Eurospeech 1995). ISCA: ISCA, 1995. http://dx.doi.org/10.21437/eurospeech.1995-487.

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"TOWARDS UNOBTRUSIVE AUTOMATED SLEEP STAGE CLASSIFICATION - Polysomnography using Electrodes on the Face." In Special Session on Biofeedback Systems for Stress Reduction. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003890104870492.

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Звіти організацій з теми "Stress classification"

1

Messner, M. C., Robert I. Jetter, Yanli Wang, and T. L. Sham. Initial Development of an Improved Creep-Fatigue Design Method that Avoids the Separate Evaluation of Creep and Fatigue Damage and Eliminates the Requirement for Stress Classification. Office of Scientific and Technical Information (OSTI), August 2019. http://dx.doi.org/10.2172/1601808.

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2

Mersel, Matthew K., Robert W. Lichvar, and Lindsey E. Lefebvre. A Review of Land and Stream Classifications in Support of Developing a National Ordinary High Water Mark (OHWM) Classification. Fort Belvoir, VA: Defense Technical Information Center, August 2014. http://dx.doi.org/10.21236/ada608563.

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3

Barnett, J. M. Data quality objectives for the B-Cell waste stream classification sampling. Office of Scientific and Technical Information (OSTI), May 1998. http://dx.doi.org/10.2172/10148902.

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4

Bevelhimer, Mark, Chris DeRolph, and Adam Witt. Site Classification for Standard Modular Hydropower Development: Characterizing Stream Reaches by Module Need. Office of Scientific and Technical Information (OSTI), August 2018. http://dx.doi.org/10.2172/1463991.

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5

Singledecker, Steven John. Classification of the Z-Pinch Waste Stream as Low-Level Waste for Disposal. Office of Scientific and Technical Information (OSTI), March 2017. http://dx.doi.org/10.2172/1346836.

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6

McManamay, Ryan A., Matthew J. Troia, Christopher R. DeRolph, and Nicole M. Samu. Stream Classification Tool User Manual: For Use in Applications in Hydropower-Related Evironmental Mitigation. Office of Scientific and Technical Information (OSTI), January 2016. http://dx.doi.org/10.2172/1240565.

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7

Evans, Julie, Kendra Sikes, and Jamie Ratchford. Vegetation classification at Lake Mead National Recreation Area, Mojave National Preserve, Castle Mountains National Monument, and Death Valley National Park: Final report (Revised with Cost Estimate). National Park Service, October 2020. http://dx.doi.org/10.36967/nrr-2279201.

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Vegetation inventory and mapping is a process to document the composition, distribution and abundance of vegetation types across the landscape. The National Park Service’s (NPS) Inventory and Monitoring (I&M) program has determined vegetation inventory and mapping to be an important resource for parks; it is one of 12 baseline inventories of natural resources to be completed for all 270 national parks within the NPS I&M program. The Mojave Desert Network Inventory & Monitoring (MOJN I&M) began its process of vegetation inventory in 2009 for four park units as follows: Lake Mead National Recreation Area (LAKE), Mojave National Preserve (MOJA), Castle Mountains National Monument (CAMO), and Death Valley National Park (DEVA). Mapping is a multi-step and multi-year process involving skills and interactions of several parties, including NPS, with a field ecology team, a classification team, and a mapping team. This process allows for compiling existing vegetation data, collecting new data to fill in gaps, and analyzing the data to develop a classification that then informs the mapping. The final products of this process include a vegetation classification, ecological descriptions and field keys of the vegetation types, and geospatial vegetation maps based on the classification. In this report, we present the narrative and results of the sampling and classification effort. In three other associated reports (Evens et al. 2020a, 2020b, 2020c) are the ecological descriptions and field keys. The resulting products of the vegetation mapping efforts are, or will be, presented in separate reports: mapping at LAKE was completed in 2016, mapping at MOJA and CAMO will be completed in 2020, and mapping at DEVA will occur in 2021. The California Native Plant Society (CNPS) and NatureServe, the classification team, have completed the vegetation classification for these four park units, with field keys and descriptions of the vegetation types developed at the alliance level per the U.S. National Vegetation Classification (USNVC). We have compiled approximately 9,000 existing and new vegetation data records into digital databases in Microsoft Access. The resulting classification and descriptions include approximately 105 alliances and landform types, and over 240 associations. CNPS also has assisted the mapping teams during map reconnaissance visits, follow-up on interpreting vegetation patterns, and general support for the geospatial vegetation maps being produced. A variety of alliances and associations occur in the four park units. Per park, the classification represents approximately 50 alliances at LAKE, 65 at MOJA and CAMO, and 85 at DEVA. Several riparian alliances or associations that are somewhat rare (ranked globally as G3) include shrublands of Pluchea sericea, meadow associations with Distichlis spicata and Juncus cooperi, and woodland associations of Salix laevigata and Prosopis pubescens along playas, streams, and springs. Other rare to somewhat rare types (G2 to G3) include shrubland stands with Eriogonum heermannii, Buddleja utahensis, Mortonia utahensis, and Salvia funerea on rocky calcareous slopes that occur sporadically in LAKE to MOJA and DEVA. Types that are globally rare (G1) include the associations of Swallenia alexandrae on sand dunes and Hecastocleis shockleyi on rocky calcareous slopes in DEVA. Two USNVC vegetation groups hold the highest number of alliances: 1) Warm Semi-Desert Shrub & Herb Dry Wash & Colluvial Slope Group (G541) has nine alliances, and 2) Mojave Mid-Elevation Mixed Desert Scrub Group (G296) has thirteen alliances. These two groups contribute significantly to the diversity of vegetation along alluvial washes and mid-elevation transition zones.

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Eibling, R. SALTSTONE VAULT CLASSIFICATION SAMPLES MODULAR CAUSTIC SIDE SOLVENT EXTRACTION UNIT/ACTINIDE REMOVAL PROCESS WASTE STREAM APRIL 2011. Office of Scientific and Technical Information (OSTI), September 2011. http://dx.doi.org/10.2172/1025803.

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9

Bryant, M. D., B. E. Wright, and B. J. Davies. Application of a hierarchical habitat unit classification system: stream habitat and salmonid distribution in Ward Creek, southeast Alaska. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 1992. http://dx.doi.org/10.2737/pnw-rn-508.

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10

Kumar, Indraneel, Lionel Beaulieu, Annie Cruz-Porter, Chun Song, Benjamin St. Germain, and Andrey Zhalnin. An Assessment of the Workforce and Occupations in the Highway, Street, and Bridge Construction Industries in Indiana. Purdue University, 2020. http://dx.doi.org/10.5703/1288284315018.

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This project explores workforce and occupations within the highway, street, and bridge construction industries (NAICS 237310) in Indiana. There are five specific deliverable comprised of three data reports, one policy document, and a website. The first data report includes an assessment of the workforce based on the eight-part framework, which are industry, occupations, job postings, hard-to-fill jobs, Classification of Instructional Programs (CIP), GAP Analysis, compatibility, and automation. The report defines a cluster followed by a detailed analysis of the occupations, skills, job postings, etc., in the NAICS 237310 industry in Indiana. The report makes use of specialized labor market databases, such as the Economic Modeling Specialists International (EMSI), CHMURA JobsEQ, etc. The analysis is based only on the jobs covered under the unemployment insurance or the Quarterly Census of Employment and Wages (QCEW) data. The second data report analyzes jobs to jobs flows to and from the construction industry in Indiana, with a particular emphasis on the Great Recession, by utilizing the Bureau of Labor Statistics (BLS) data. The third data report looks into the equal employment opportunity or Section 1391 and 1392 data for Indiana and analyzes specific characteristics of that data. The policy report includes a set of recommendations for workforce development for INDOT and a summary of the three data reports. The key data on occupations within the NAICS 237310 are provided in an interactive website. The website provides a data dashboard for individual INDOT Districts. The policy document recommends steps for development of the highways, streets and bridges construction workforce in INDOT Districts.

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