Relevant bibliographies by topics / Stress detection and management

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Academic literature on the topic 'Stress detection and management'

Author: Grafiati
Published: 25 January 2025

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Journal articles on the topic "Stress detection and management"

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Shrawankar, Urmila, and Chaitali Chandankhede. "Sarcasm Detection for Workplace Stress Management." International Journal of Synthetic Emotions 10, no. 2 (July 2019): 1–17. http://dx.doi.org/10.4018/ijse.2019070101.

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Working stress is becoming very common. Handling working stress at the workplace is really going to be challenging. As a result, most of the time most of the time people start behaving in sarcastic ways through verbal communication, through different gestures, using emoticons, or writing reviews or comments that leads to increasing their anxiety sometimes promotes depression. It is difficult to identify sarcasm in written notes or communication. Feedback analysis is not a direct method since feedback or employer reviews are written in more formal language. This motivates the authors to work on the employee feedback system. The currently developed system helps to detect the sarcastic emotions by applying different methodologies on several types of statements. This will help corporations and other big organizations to identify reasons behind sarcastic behavior or increased anxiety. As a result, they can promote counseling programs, psychological treatment, or yoga-meditation camps.
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G, Divya Shree. "Stress-Level Detection Using RepVGG Neural Network." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 31, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem35132.

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Stress used to impact our mental and physical well-being,productivity,and overall quality of life. Detecting stress accurately is vital for timely intervention and effective management. In this study, we introduce a new method for detecting stress levels using the RepVGG deep learning architecture. RepVGG stands out for its efficient performance and straightforward structure, making it ideal for analyzing physiological signals and other stress indicators. We using standard metrics to calculate the things like accuracy, precision, recall and etc. Ourfindings reveal that the RepVGG-based method excels in detecting stress levels, surpassing many traditional methods and other deep learning models. Moreover, the model shows strong generalization capabilities across various datasets and conditions. This research underscores the potential of advanced deep learning models like RepVGG in stress detection, opening doors for real-time, scalable, and precise stress monitoring systems. Looking ahead, weaim to integrate this model into wearable devices andmobile apps, enabling continuous stress monitoring and offering personalized stress management advice. Some approach utilizes a rich dataset consists of signals such (HRV) and (GSR), along with other relevant biomarkers. To ensure the model's robustness, we preprocess and augment this data. We then train the RepVGG architecture on this dataset, harnessing its Neural layers for feature extraction and its unique re-parameterizable design for efficient use.
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Zhou, Quan, Jinjia Kuang, Linfeng Yu, Xudong Zhang, Lili Ren, and Youqing Luo. "Discriminating between Biotic and Abiotic Stress in Poplar Forests Using Hyperspectral and LiDAR Data." Remote Sensing 16, no. 19 (October 9, 2024): 3751. http://dx.doi.org/10.3390/rs16193751.

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Sustainable forest management faces challenges from various biotic and abiotic stresses. The Asian longhorned beetle (ALB) and drought stress both induce water shortages in poplar trees, but require different management strategies. In northwestern China, ALB and drought stress caused massive mortality in poplar shelterbelts, which seriously affected the ecological functions of poplars. Developing a large-scale detection method for discriminating them is crucial for applying targeted management. This study integrated UAV-hyperspectral and LiDAR data to distinguish between ALB and drought stress in poplars of China’s Three-North Shelterbelt. These data were analyzed using a Partial Least Squares-Support Vector Machine (PLS-SVM). The results showed that the LiDAR metric (elev_sqrt_mean_sq) was key in detecting drought, while the hyperspectral band (R970) was key in ALB detection, underscoring the necessity of integrating both sensors. Detection of ALB in poplars improved when the poplars were well watered. The classification accuracy was 94.85% for distinguishing well-watered from water-deficient trees, and 80.81% for detecting ALB damage. Overall classification accuracy was 78.79% when classifying four stress types: healthy, only ALB affected, only drought affected, and combined stress of ALB and drought. The results demonstrate the effectiveness of UAV-hyperspectral and LiDAR data in distinguishing ALB and drought stress in poplar forests, which contribute to apply targeted treatments based on the specific stress in poplars in northwest China.
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Hanchate, Rohini, Harshal Narute, and Siddharam Shavage. "Stress Detection Using Machine Learning." International Journal of Science and Healthcare Research 8, no. 2 (May 25, 2023): 307–11. http://dx.doi.org/10.52403/ijshr.20230239.

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Stress management systems are essential to identify and address stress levels that can disrupt our socioeconomic functioning. According to the World Health Organization (WHO), one in four people experience stress, which can result in mental and socioeconomic issues, poor work relationships, and depression, and in severe cases, suicide. Counselling is crucial to help people cope with stress, and while stress cannot be avoided, preventive measures can be taken to mitigate its effects. Currently, only medical and physiological experts can determine whether someone is experiencing stress. Traditional stress detection methods rely on self-reported answers, which can be unreliable. Automated stress detection can minimize health risks and improve societal welfare. Therefore, there is a need for a scientific tool that can automate stress detection using physiological signals. Stress detection is an important social contribution that potential to improve quality of life. As IT industries bring new technologies and products to the market, stress levels in employees are also increasing. While some organizations offer mental health services to their employees, more needs to be done to address this issue. Keywords: Machine Learning, Stress Detection, Haar cascade algorithm, Convolutional Neural Network
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Ferreira, Simão, Matilde A. Rodrigues, and Nuno Rocha. "P-434 NEXT-GEN STRESS MANAGEMENT: AI’S REVOLUTIONARY ROLE IN WORKPLACE OCCUPATIONAL HEALTH." Occupational Medicine 74, Supplement_1 (July 1, 2024): 0. http://dx.doi.org/10.1093/occmed/kqae023.1148.

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Abstract Introduction Occupational stress accounts for nearly half of all lost workdays, underscoring the urgent need for real-time, practical solutions. To address this, we introduced a non-intrusive, multimodal approach for detecting work-related stress, leveraging videoplethysmography and self-reported metrics to capture a comprehensive picture of stress dynamics in real-life settings. Methods In our study involving 28 participants over three months, we collected physiological data during their regular 8-hour workdays, supplemented by self-reported measures collected through a specially designed app. This integrative method facilitated the labeling of physiological data with perceived stress levels, subsequently aiding in the development of an accurate stress detection model. Results The refined model exhibits an accuracy that exceeds 90% and an F1 score above 92%, highlighting its potential in fostering a healthier work environment. Discussion Moving forward, we aim to couple this detection model with a personalized digital coaching module, focusing on user-centric recommendations. This innovative strategy, grounded in user preferences, seeks not only to alert individuals to immediate stress peaks but also offers tailored suggestions for quick stress relief, thereby nurturing sustained engagement and adherence. Conclusion Our initiative showcases the feasibility of ethical, unobtrusive stress monitoring, promising a pathway to a healthier workforce by averting potential stress episodes and reducing chronic stress conditions. Future efforts will concentrate on assessing the positive impacts and efficacy of implementing such a recommender system.
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KONNI, SRIKANTH REDDY, and KHAJA ZIAUDDIN. "UTILIZING MACHINE LEARNING AND IMAGE PROCESSING TO DETECT SIGNS OF STRESS IN INDIVIDUALS." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 3 (December 15, 2020): 2838–48. http://dx.doi.org/10.61841/turcomat.v11i3.14588.

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This study's main objective is to identify stress in the human body using vivid machine learning and image processing techniques. Our system is an improved version of earlier stress detection systems that lacked personal counseling or live detection. Instead, it detects employees' levels of both physical and mental stress and offers appropriate stress management strategies through a survey form. Additionally, our system includes periodic analysis of employees and live detection. To make the most of employees during working hours, our approach is mainly concerned with stress management and fostering a positive, flexible work environment.
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Radhika R, Devika A, Sreeranjani S, and Dharshini KR. "A CHATBOT-BASED APPROACH FOR STRESS LEVEL DETECTION AND MANAGEMENT." International Journal of Trendy Research in Engineering and Technology 07, no. 05 (2023): 14–17. http://dx.doi.org/10.54473/ijtret.2023.7504.

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Jerath, Ravinder, Mohammad Syam, and Shajia Ahmed. "The Future of Stress Management: Integration of Smartwatches and HRV Technology." Sensors 23, no. 17 (August 22, 2023): 7314. http://dx.doi.org/10.3390/s23177314.

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In the modern world, stress has become a pervasive concern that affects individuals’ physical and mental well-being. To address this issue, many wearable devices have emerged as potential tools for stress detection and management by measuring heart rate, heart rate variability (HRV), and various metrics related to it. This literature review aims to provide a comprehensive analysis of existing research on HRV tracking and biofeedback using smartwatches pairing with reliable 3rd party mobile apps like Elite HRV, Welltory, and HRV4Training specifically designed for stress detection and management. We apply various algorithms and methodologies employed for HRV analysis and stress detection including time-domain, frequency-domain, and non-linear analysis techniques. Prominent smartwatches, such as Apple Watch, Garmin, Fitbit, Polar, and Samsung Galaxy Watch, are evaluated based on their HRV measurement accuracy, data quality, sensor technology, and integration with stress management features. We describe the efficacy of smartwatches in providing real-time stress feedback, personalized stress management interventions, and promoting overall well-being. To assist researchers, doctors, and developers with using smartwatch technology to address stress and promote holistic well-being, we discuss the data’s advantages and limitations, future developments, and the significance of user-centered design and personalized interventions.
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Solanke, Prof S. A., Shreyash S. Tidke, Tejas G. Malokar, Sarvesh S. Udapurkar, Faiz Mohammad Sheikh, and Prajakta G. Gaikwad. "Stress Detection System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (February 29, 2024): 1676–80. http://dx.doi.org/10.22214/ijraset.2024.58627.

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Abstract: Stress is a pervasive aspect of modern life, posing significant health risks if left unmanaged. Early detection of stress is crucial for preventing adverse health outcomes and promoting well-being. This paper presents a novel approach to stress monitoring and management using machine learning (ML) techniques and wearable physiological sensors. By analyzing multimodal datasets, including electrocardiogram (ECG) signals and other physiological parameters, our proposed model aims to accurately detect stress levels in individuals. Leveraging low-cost wearable sensors and IoT technology, our system provides real-time feedback and alerts individuals to their stress levels, enabling proactive intervention to mitigate health risks. Through a comprehensive review of existing stress detection approaches and integration of ML algorithms, our study contributes to the development of more efficient and effective stress monitoring systems. This research holds promise for improving health outcomes and enhancing quality of life in individuals facing stress-related challenges.
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Jambhale, Kiran, Smridhi Mahajan, Benjamin Rieland, Nilanjan Banerjee, Abhijit Dutt, Sai Praveen Kadiyala, and Ramana Vinjamuri. "Identifying Biomarkers for Accurate Detection of Stress." Sensors 22, no. 22 (November 11, 2022): 8703. http://dx.doi.org/10.3390/s22228703.

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Substance use disorder (SUD) is a dangerous epidemic that develops out of recurrent use of alcohol and/or drugs and has the capability to severely damage one’s brain and behaviour. Stress is an established risk factor in SUD’s development of addiction and in reinstating drug seeking. Despite this expanding epidemic and the potential for its grave consequences, there are limited options available for management and treatment, as well as pharmacotherapies and psychosocial treatments. To this end, there is a need for new and improved devices dedicated to the detection, management, and treatment of SUD. In this paper, the negative effects of SUD-related stress were discussed, and based on that, a few significant biomarkers were selected from a set of eight features collected by a chest-worn device, RespiBAN Professional, on fifteen individuals. We used three machine learning classifiers on these optimal biomarkers to detect stress. Based on the accuracies, the best biomarkers to detect stress and those considered as features for classification were determined to be electrodermal activity (EDA), body temperature, and a chest-worn accelerometer. Additionally, the differences between mental stress and physical stress, as well as different administrations of meditation during the study, were identified and analysed. Challenges, implications, and applications were also discussed. In the near future, we aim to replicate the proposed methods in individuals with SUD.
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Dissertations / Theses on the topic "Stress detection and management"

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Keryakos, Youssef. "Detection and management of stressful situations to help the blind persons in their daily lives." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCD043.

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Le stress, une expérience commune à diverses activités humaines, affecte de manière significative la vie quotidienne des individus, en particulier ceux ayant des déficiences visuelles. Défini comme un état de tension physique ou mentale, le stress provient de situations qui induisent frustration, nervosité ou colère. Le Syndrome d'Adaptation Générale (SAG) décrit la réponse du corps au stress, englobant les phases d'Alarme, de Résistance et d''Epuisement, et implique la libération d'hormones de stress comme le cortisol et l'adrénaline. Identifier et gérer le stress chez les personnes aveugles est particulièrement difficile en raison de leur dépendance aux indices sensoriels non visuels pour la navigation.Les personnes aveugles éprouvent un stress accru lors de la navigation en raison de l'incapacité à voir les obstacles et les changements dans leur environnement. Ce stress est exacerbé dans les environnements extérieurs et intérieurs inconnus ou complexes, où les aides traditionnelles à la navigation, telles que les GPS et les images satellites, sont inefficaces. Les résultats de notre travail ont établi que la présence d'obstacles et la complexité des itinéraires de navigation sont directement corrélées à des niveaux de stress accrus. Malgré le développement de divers outils d'assistance, tels que les compas en braille et les cannes blanches, ces solutions sont souvent insuffisantes pour fournir un soutien complet.En réponse à ces défis, nous proposons un nouveau système de navigation hybride conçu pour aider les personnes aveugles en intégrant la détection du stress en temps réel et une aide à l'orientation lors de la navigation. Notre système utilise une canne blanche intelligente équipée d'un réseau de capteurs biologiques pour surveiller les indicateurs physiologiques du stress, notamment la fréquence cardiaque, la conductance de la peau et l'activité musculaire. En analysant ces biosignaux, le système vise à identifier les situations induisant du stress et à recommander des itinéraires de navigation moins stressants.Le système proposé répond aux défis de la navigation en extérieur et en intérieur. Pour les environnements extérieurs, le système génère des indices de difficulté et de stress des itinéraires, permettant aux utilisateurs de choisir et de suivre le chemin le moins stressant. La détection en temps réel des obstacles est réalisée grâce à des techniques avancées de traitement d'images, et les utilisateurs reçoivent des indications vocales pour naviguer efficacement. Pour les environnements intérieurs, où les données GPS et satellites sont indisponibles, le système utilise la réalité augmentée (RA) et un système de gestion de contenu (SGC) pour créer des cartes virtuelles basées sur des images de plans d'étage. Des codes QR dans l'espace intérieur permettent aux utilisateurs de charger et d'interagir avec ces cartes virtuelles via une application mobile.L'intégration de la détection du stress en temps réel et du soutien à la navigation représente un avancement significatif dans la technologie d'assistance. En réduisant le stress et en améliorant l'efficacité de la navigation, le système vise à améliorer la qualité de vie des personnes malvoyantes. Les recherches futures se concentreront sur l'évaluation des performances du système, l'analyse des retours des utilisateurs et le raffinement de la technologie pour garantir une fonctionnalité optimale et la satisfaction des utilisateurs. Cette approche améliore non seulement l'indépendance, mais fournit également une expérience de navigation plus gérable et moins stressante pour les utilisateurs aveugles
Stress is a universal human experience that has a substantial impact on people's day-to-day functioning, particularly for those who are visually impaired. Stress is characterized as a condition of tension, either bodily or mental, that results from events that cause anxiety, agitation, or frustration. The alarm, resistance, and exhaustion phases of the body's reaction to stress are outlined by the General Adaptation Syndrome (GAS), which also includes the release of stress hormones like cortisol and adrenaline. Blindness makes it especially difficult to recognize and manage stress, as blind people rely on non-visual sensory signals to navigate.Being unable to notice barriers and changes in their surroundings causes blind persons to experience more stress when navigating. In unfamiliar or complicated outdoor and indoor environments, when conventional navigational aids like GPS and satellite images are useless, this stress is increased. Our research's findings have demonstrated a clear relationship between higher stress levels and the difficulty of navigation routes and the existence of barriers. Even with the invention of numerous assistive devices, such as white canes and braille compasses, these solutions frequently fail to offer complete assistance.We address these issues by putting forth a novel hybrid navigation system that combines real-time stress detection with navigation assistance to help blind people. Our method makes use of a smart white cane that is outfitted with a network of biosensors to keep an eye on physiological stress markers including heart rate, muscular activity, and skin conductance. The system's goal is to identify stressful conditions and suggest less stressful navigation routes by evaluating these biosignals.The suggested solution tackles navigational issues in both indoor and outdoor environments. The system creates stress and route difficulty indices for outdoor surroundings, enabling users to select and follow the least stressful path. Advanced image processing techniques are used to detect obstacles in real-time, and verbal commentary is provided to users to help them navigate safely. The technology uses augmented reality (AR) and a content management system (CMS) to build virtual maps based on floor plan photos for indoor environments where GPS and satellite data are not available. Thanks to a mobile application, users can load and interact with these virtual maps by scanning QR codes located within the indoor space.Real-time stress detection and navigation support are integrated into the system, which is a major leap in assistive technology. The system's goal is to make navigation more efficient and less stressful for people who are blind or visually impaired. Subsequent studies will concentrate on assessing the system's effectiveness, examining user input, and improving the technology to guarantee peak performance and user contentment. For blind individuals, this method not only increases independence but also makes navigation easier and less stressful
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Ostovar, Alireza. "Business process drift: Detection and characterization." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/127157/1/Alireza_Ostovar_Thesis.pdf.

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This research contributes a set of techniques for the early detection and characterization of process drifts, i.e. statistically significant changes in the behavior of business operations, as recorded in transactional data. Early detection and subsequent characterization of process drifts allows organizations to take prompt remedial actions and avoid potential repercussions resulting from unplanned changes in the behavior of their operations.
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Gaur, Sudhanshu. "Interference management in MIMO networks." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24769.

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Thesis (Ph.D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2008.
Committee Chair: Mary Ann Ingram; Committee Member: Geoffrey Li; Committee Member: Gregory Durgin; Committee Member: Prasad Tetali; Committee Member: Raghupathy Sivakumar.
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Xu, Cheng. "Scalable Validation of Data Streams." Doctoral thesis, Uppsala universitet, Avdelningen för datalogi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-291530.

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In manufacturing industries, sensors are often installed on industrial equipment generating high volumes of data in real-time. For shortening the machine downtime and reducing maintenance costs, it is critical to analyze efficiently this kind of streams in order to detect abnormal behavior of equipment. For validating data streams to detect anomalies, a data stream management system called SVALI is developed. Based on requirements by the application domain, different stream window semantics are explored and an extensible set of window forming functions are implemented, where dynamic registration of window aggregations allow incremental evaluation of aggregate functions over windows. To facilitate stream validation on a high level, the system provides two second order system validation functions, model-and-validate and learn-and-validate. Model-and-validate allows the user to define mathematical models based on physical properties of the monitored equipment, while learn-and-validate builds statistical models by sampling the stream in real-time as it flows. To validate geographically distributed equipment with short response time, SVALI is a distributed system where many SVALI instances can be started and run in parallel on-board the equipment. Central analyses are made at a monitoring center where streams of detected anomalies are combined and analyzed on a cluster computer. SVALI is an extensible system where functions can be implemented using external libraries written in C, Java, and Python without any modifications of the original code. The system and the developed functionality have been applied on several applications, both industrial and for sports analytics.
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Protsenko, I. M. "Stress management." Thesis, Видавництво СумДУ, 2012. http://essuir.sumdu.edu.ua/handle/123456789/26022.

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Stress management refers to a wide spectrum of techniques and psychotherapies aimed at controlling a person's levels of stress, especially chronic stress, usually for the purpose of improving everyday functioning. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/26022
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Lau, Shing-hon. "Stress Detection for Keystroke Dynamics." Research Showcase @ CMU, 2018. http://repository.cmu.edu/dissertations/1208.

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Background. Stress can profoundly affect human behavior. Critical-infrastructure operators (e.g., at nuclear power plants) may make more errors when overstressed; malicious insiders may experience stress while engaging in rogue behavior; and chronic stress has deleterious effects on mental and physical health. If stress could be detected unobtrusively, without requiring special equipment, remedies to these situations could be undertaken. In this study a common computer keyboard and everyday typing are the primary instruments for detecting stress. Aim. The goal of this dissertation is to detect stress via keystroke dynamics – the analysis of a user’s typing rhythms – and to detect the changes to those rhythms concomitant with stress. Additionally, we pinpoint markers for stress (e.g., a 10% increase in typing speed), analogous to the antigens used as markers for blood type. We seek markers that are universal across all typists, as well as markers that apply only to groups or clusters of typists, or even only to individual typists. Data. Five types of data were collected from 116 subjects: (1) demographic data, which can reveal factors (e.g., gender) that influence subjects’ reactions to stress; (2) psychological data, which capture a subject’s general susceptibility to stress and anxiety, as well as his/her current stress state; (3) physiological data (e.g., heart-rate variability and blood pressure) that permit an objective and independent assessment of a subject’s stress level; (4) self-report data, consisting of subjective self-reports regarding the subject’s stress, anxiety, and workload levels; and (5) typing data from subjects, in both neutral and stressed states, measured in terms of keystroke timings – hold and latency times – and typographical errors. Differences in typing rhythms between neutral and stressed states were examined to seek specific markers for stress. Method. An ABA, single-subject design was used, in which subjects act as their own controls. Each subject provided 80 typing samples in each of three conditions: (A) baseline/neutral, (B) induced stress, and (A) post-stress return/recovery-to-baseline. Physiological measures were analyzed to ascertain the subject’s stress level when providing each sample. Typing data were analyzed, using a variety of statistical and machine learning techniques, to elucidate markers of stress. Clustering techniques (e.g., K-means) were also employed to detect groups of users whose responses to stress are similar. Results. Our stressor paradigm was effective for all 116 subjects, as confirmed through analysis of physiological and self-report data. We were able to identify markers for stress within each subject; i.e., we can discriminate between neutral and stressed typing when examining any subject individually. However, despite our best attempts, and the use of state-of-the-art machine learning techniques, we were not able to identify universal markers for stress, across subjects, nor were we able to identify clusters of subjects whose stress responses were similar. Subjects’ stress responses, in typing data, appear to be highly individualized. Consequently, effective deployment in a realworld environment may require an approach similar to that taken in personalized medicine.
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Madjar, Nicole, and Filip Lindblom. "Machine Learning implementation for Stress-Detection." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280897.

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This project is about trying to apply machine learning theories on a selection of data points in order to see if an improvement of current methodology within stress detection and measure selecting could be applicable for the company Linkura AB. Linkura AB is a medical technology company based in Linköping and handles among other things stress measuring for different companies employees, as well as health coaching for selecting measures. In this report we experiment with different methods and algorithms under the collective name of Unsupervised Learning, to identify visible patterns and behaviour of data points and further on we analyze it with the quantity of data received. The methods that have been practiced on during the project are “K-means algorithm” and a dynamic hierarchical clustering algorithm. The correlation between the different data points parameters is analyzed to optimize the resource consumption, also experiments with different number of parameters are tested and discussed with an expert in stress coaching. The results stated that both algorithms can create clusters for the risk groups, however, the dynamic clustering method clearly demonstrate the optimal number of clusters that should be used. Having consulted with mentors and health coaches regarding the analysis of the produced clusters, a conclusion that the dynamic hierarchical cluster algorithm gives more accurate clusters to represent risk groups were done. The conclusion of this project is that the machine learning algorithms that have been used, can categorize data points with stress behavioral correlations, which is usable in measure testimonials. Further research should be done with a greater set of data for a more optimal result, where this project can form the basis for the implementations.
Detta projekt handlar om att försöka applicera maskininlärningsmodeller på ett urval av datapunkter för att ta reda på huruvida en förbättring av nuvarande praxis inom stressdetektering och  åtgärdshantering kan vara applicerbart för företaget Linkura AB. Linkura AB är ett medicintekniskt företag baserat i Linköping och hanterar bland annat stressmätning hos andra företags anställda, samt hälso-coachning för att ta fram åtgärdspunkter för förbättring. I denna rapport experimenterar vi med olika metoder under samlingsnamnet oövervakad maskininlärning för att identifiera synbara mönster och beteenden inom datapunkter, och vidare analyseras detta i förhållande till den mängden data vi fått tillgodosett. De modeller som har använts under projektets gång har varit “K-Means algoritm” samt en dynamisk hierarkisk klustermodell. Korrelationen mellan olika datapunktsparametrar analyseras för att optimera resurshantering, samt experimentering med olika antal parametrar inkluderade i datan testas och diskuteras med expertis inom hälso-coachning. Resultaten påvisade att båda algoritmerna kan generera kluster för riskgrupper, men där den dynamiska modellen tydligt påvisar antalet kluster som ska användas för optimalt resultat. Efter konsultering med mentorer samt expertis inom hälso-coachning så drogs en slutsats om att den dynamiska modellen levererar tydligare riskkluster för att representera riskgrupper för stress. Slutsatsen för projektet blev att maskininlärningsmodeller kan kategorisera datapunkter med stressrelaterade korrelationer, vilket är användbart för åtgärdsbestämmelser. Framtida arbeten bör göras med ett större mängd data för mer optimerade resultat, där detta projekt kan ses som en grund för dessa implementeringar.
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Uggla, Gabriel. "Pause — Stress Management." Thesis, Umeå universitet, Designhögskolan vid Umeå universitet, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-171773.

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Stress affects us all more or less, but over the last decade people who suffer from severe stress and its symptomshas increased considerably. Women are especially affected, with absence from Swedish workplaces due tostress growing 370% between 2011—2019. As of 2020, stress is treated when we experience symptoms suchas anxiety, panic attacks or insomnia, but it is more sustainable to prevent stress than treating it in terms ofmoney, time, health and effort.Through collaboration with people affected by stress, psychiatrists & researchers specialized on stress, anddesigners from different disciplines, the topic of stress was explored and researched to create a conceptualsolution that could be able to help users manage their stress better.“Pause” is the result of this work. It is a conceptual product ecosystem consisiting of an application andwatch that facilitates monitoring and management of stress in day-to-day scenarios, in order to prevent andreduce stress.
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Kangas, Persson Emil. "Stress Management for Pilots." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-35606.

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A human brain is capable to achieve great things, to endure heavy stress and to calculate complex problems. What happens when it fails to do so? Is there anything that could be done to prevent this from happening? Is it possible to help a pilot in command manage his or her stress during flight by measuring finger temperature, skin conductance, ECG and heart rate variations? This thesis study processes that can help pilots manage high stress with simple tools such as closing their eyes and taking a few deep breaths. To determine when the calming measures are needed and to evaluate potential effects, all the signal data stated above are used combined with scoring of every participant performance. The statistical methods used involves an ANOVA-test and mean value calculations. The results were also analyzed using CBR to get a better understanding of the results and to not only rely on statistical methods. The thesis is limited to a minor study of 10 student pilots participating in two sessions with a heavy workload departure in a simulator. The test did result in a small difference between the two sessions which pointed at the calming measures giving a slight improvement for the pilots. However, when ANOVA was applied it showed that the difference between the two sessions was not a significant one. This could be due to several reasons; the pilots were not used to the calming measures and because of this felt more stressed when trying to use the calming measures as well. There might have been a difference if calming measures were included into pilot training already from the beginning. Another reason might be because this is only a minor study and the difference was not apparent on so few participants. In future research, however, there might be a breakthrough on how to handle stress in a cockpit environment involving similar sensors.
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Bonner, Tanya. "Stress, coping, and corporate stress management : a review." Thesis, Stellenbosch : Stellenbosch University, 2004. http://hdl.handle.net/10019.1/50108.

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Thesis (MA)--University of Stellenbosch, 2004.
Hierdie artikel bied 'n teoretiese oorsig van stres en streshantering in die korporatiewe omgewing. Die artikel begin met 'n kort oorsig van die historiese ontwikkeling van die streskonsep, gevolg deur 'n kort bespreking van die konsep streshantering. Vervolgens word verskeie faktore wat tot stres in die werkplek bydra, in oënskou geneem. Dit word opgevolg deur 'n bespreking van stres wat kenmerkend is van bestuursposisies en 'n kort oorsig van onlangse navorsing oor streshanteringsprogramme. Sommige van die probleme wat met navorsing oor streshanteringsprogramme ondervind word, word kortliks oorweeg. Die artikel word afgesluit met 'n poging om, op grond van gepubliseerde bevindings, te illustreer watter stresverminderende intervensies doeltreffend is en watter nie.
AFRIKAANSE OPSOMMING: Hierdie artikel bied 'n teoretiese oorsig van stres en streshantering in die korporatiewe omgewing. Die artikel begin met 'n kort oorsig van die historiese ontwikkeling van die streskonsep, gevolg deur 'n kort bespreking van die konsep streshantering. Vervolgens word verskeie faktore wat tot stres in die werkplek bydra, in oënskou geneem. Dit word opgevolg deur 'n bespreking van stres wat kenmerkend is van bestuursposisies en 'n kort oorsig van onlangse navorsing oor streshanteringsprogramme. Sommige van die probleme wat met navorsing oor streshanteringsprogramme ondervind word, word kortliks oorweeg. Die artikel word afgesluit met 'n poging om, op grond van gepubliseerde bevindings, te illustreer watter stresverminderende intervensies doeltreffend is en watter nie.
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Books on the topic "Stress detection and management"

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Selye, Hans. Stress without distress. London: Corgi, 1987.

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Gordon, James S. Stress management. New York: Chelsea House Publishers, 1990.

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College, National Extension, ed. Stress management. Cambridge: National Extension College, 1989.

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Cottrell, Randall R. Stress management. Guilford, CT: Dushkin Pub. Group, 1992.

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Botswana. Workplace Wellness Program for Health Workers., ed. Stress management. [Gaborone]: Workplace Wellness Program for Health Workers, 2008.

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Council, National Safety, ed. Stress management. Boston, MA: Jones and Bartlett, 1995.

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Ruthstiver, Wendy. Stress management. Torrance, CA: Homestead Pub. Co., 1995.

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Inc, Great Performance, ed. Stress management. [Chicago, IL: Great Performance, Inc., 1987.

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Clarke, Deborah. Stress management. Cambridge: National Extension College, 1994.

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United States. Air Force. Tactical Air Command. Medical Services. Stress management. Langley AFB, Va.]: Tactical Air Command, Medical Services, 1989.

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Book chapters on the topic "Stress detection and management"

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Suni Lopez, Franci, Nelly Condori-Fernandez, and Alejandro Catala. "Towards Real-Time Automatic Stress Detection for Office Workplaces." In Information Management and Big Data, 273–88. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11680-4_27.

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Jain, Khushboo, Ayushi Malik, Avinash Marwal, R. K. Gaur, and S. M. Paul Khurana. "Detection Techniques for Potato Viruses and Viroids." In Approaches for Potato Crop Improvement and Stress Management, 325–48. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1223-6_11.

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Bakir, Nagihan. "Anger Management and Stress Management Within the Family." In To Be a Happy Family, 55–73. Istanbul: Nobel Tip Kitabevleri, 2024. http://dx.doi.org/10.69860/nobel.9786053359500.4.

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This article talks about stress and anger management within the family. It aims to provide a socially and emotionally happier family relationship by identifying the factors underlying anger and stress and then taking steps accordingly. Individuals in our society try to continue by ignoring the factors underlying anger and stress, but this turns into unsatisfactory relationships. The importance of detecting this correctly and moving forward is emphasized.
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Malviya, Lokesh, Sandip Mal, Radhikesh Kumar, Bishwajit Roy, Umesh Gupta, Deepika Pantola, and Madhuri Gupta. "Mental Stress Level Detection Using LSTM for WESAD Dataset." In Proceedings of Data Analytics and Management, 243–50. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7615-5_22.

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Gupta, Rupali, Gautam Anand, Satyendra Pratap Singh, Dinesh Yadav, and Rakesh Pandey. "Harnessing Beneficial Rhizospheric Microorganisms for Biotic Stress Management in Medicinal and Aromatic Plants." In Detection, Diagnosis and Management of Soil-borne Phytopathogens, 283–308. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8307-8_11.

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Jain, Khushboo, Shalini Tailor, Ayushi Malik, Mayank Suthar, Chitra Nehra, Rajarshi Gaur, Mukesh Meena, and Avinash Marwal. "Identification, Detection, and Management of Geminiviruses as Biotic Stress of Vegetable Crops." In Molecular Dynamics of Plant Stress and its Management, 387–401. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1699-9_17.

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Yadav, Sarika, Neelam Yadav, S. M. Paul Khurana, and Dinesh Kumar Yadav. "Barcoding of Life for Detection and Diagnosis of Diseases and Pests in Potato." In Approaches for Potato Crop Improvement and Stress Management, 445–77. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1223-6_16.

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Peng, Shanglian, Zhanhuai Li, Qiang Li, Qun Chen, Wei Pan, Hailong Liu, and Yanming Nie. "Event Detection over Live and Archived Streams." In Web-Age Information Management, 566–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23535-1_48.

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Calcina-Ccori, Pablo, Eduardo S. Rodriguez-Canales, and Edgar Sarmiento-Calisaya. "A Persuasive System for Stress Detection and Management in an Educational Environment." In Internet of Things, 239–49. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20936-9_19.

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Chowhan, Papan, and Arka Pratim Chakraborty. "Remote Sensing Technology—A New Dimension in Detection, Quantification and Tracking of Abiotic and Biotic Stresses." In Plant Stress: Challenges and Management in the New Decade, 445–57. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95365-2_27.

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Conference papers on the topic "Stress detection and management"

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Singh, Amisha, Akshita Agarwal, Ajeet Kumar, Umang Rastogi, and Sushil Kumar. "A Comprehensive Approach to Stress Detection and Management Using Machine Learning, Deep Learning, and Chatbot Integration." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725990.

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Brunel, G., L. Pichon, J. Taylor, and B. Tisseyre. "Easy water stress detection system for vineyard irrigation management." In 12th European Conference on Precision Agriculture. The Netherlands: Wageningen Academic Publishers, 2019. http://dx.doi.org/10.3920/978-90-8686-888-9_115.

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Bubulac, Lucia, Loretta Ichim, Dan Popescu, Lorin Victor Purcarea, Stefan Cirneanu, Gheorghe Lazar, and Ovidiu Popa-Velea. "DETECTION AND MANAGEMENT OF STUDENT STRESS IN THE LEARNING PROCESS." In 10th annual International Conference of Education, Research and Innovation. IATED, 2017. http://dx.doi.org/10.21125/iceri.2017.1690.

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Jyothsna, R., V. Rohini, and Joy Paulose. "Novel deep neural network based stress detection system." In ETLTC-ICETM2023 INTERNATIONAL CONFERENCE PROCEEDINGS: ICT Integration in Technical Education & Entertainment Technologies and Management. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0181900.

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Praveena, G., and J. M. Mathana. "Review on Stress Detection and Management Techniques using Nano EEG Sensors." In 2022 IEEE International Conference on Nanoelectronics, Nanophotonics, Nanomaterials, Nanobioscience & Nanotechnology (5NANO). IEEE, 2022. http://dx.doi.org/10.1109/5nano53044.2022.9828889.

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Agarwal, Aditi, Astha Goel, Shristy Sharma, Soniya Jain, Mani Dwivedi, and Deep Kumar. "A Novel Model for Stress Detection and Management using Machine Learning." In 2023 International Conference on Disruptive Technologies (ICDT). IEEE, 2023. http://dx.doi.org/10.1109/icdt57929.2023.10150768.

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Rajkumar, Shivaani Dushya, Ihill Ushan Dewpura, Piyoshila Fernandez, Chanuth Maduka, Harinda Fernando, and Samantha Rajapakshe. "MindRelax: Smart System for Emotion and Mental Stress Monitoring, Detection and Management." In 2023 5th International Conference on Advancements in Computing (ICAC). IEEE, 2023. http://dx.doi.org/10.1109/icac60630.2023.10417592.

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Thompson, Ron, Ray Gardner, Katrina Dwyer, Richard Gonzales, Andrew Corbett, and Guillermo Solano. "Use of ILI Technology for the Detection and Measurement of Elevated Stress Associated with CSCC." In 34th Pipeline Pigging and Integrity Management. Houston, TX USA: Clarion Technical Conferences, 2022. http://dx.doi.org/10.52202/069251-0030.

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Tehsin, Nauman, Mohammed A. Al-Rabeeah, Abdullah S. Alghamdi, Murtadah A. Al-Hashem, and Mohammed F. Aljama. "Stress Corrosion Cracking (SCC) Management Program for Onshore Pipelines." In 2024 15th International Pipeline Conference. American Society of Mechanical Engineers, 2024. https://doi.org/10.1115/ipc2024-134127.

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Abstract Stress Corrosion Cracking (SCC) on buried pipelines results from simultaneous presence of tensile stress, corrosive environment, and material that is susceptible to SCC. In-Line-Inspection (ILI) using smart tools have been effective in the industry for locating and sizing SCC on pipelines. However, there are some inherent limitations in ILI tools, therefore pipeline operators must develop a comprehensive SCC Integrity Management plan. Saudi Aramco operates a combined pipeline network of approximately 26,000 kilometers in length. A significant number of Saudi Aramco pipelines were built prior to 1980 with tape coated pipelines, thereby increasing the pipeline network susceptibility to SCC. Saudi Aramco initiated the inspection of these vintage pipelines using Ultrasonic Crack Detection In-Line-Inspection (UTCD ILI) tools and Electromagnetic Acoustic Transducer ILI (EMAT ILI) tools respectively to detect the presence of Stress Corrosion Cracking (SCC). In-Line-Inspection (ILI) results from a total of 21 pipelines showed presence of SCC. These cracks were later verified through direct assessment results. ASME B31.8S provides a criterion for gauging the severity of SCC by comparing the ratio between failure pressure and maximum allowable operating pressure (MAOP). This paper will cover details of the SCC integrity management planning including but not limited to the pre-validation SCC control and mitigation measures and post-inspection SCC mitigation measure.
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Pascual, Rodney Karlo C., John Paul D. Serrano, Jamie Mitchelle A. Soltez, John Christopher D. Castillo, Jumelyn L. Torres, and Febus Reidj G. Cruz. "Artificial Neural Network Based Stress Level Detection System using Physiological Signals." In 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM). IEEE, 2018. http://dx.doi.org/10.1109/hnicem.2018.8666339.

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Reports on the topic "Stress detection and management"

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Bubenik, Tom, Cara Macrory, and Tara McMahan. PR727-223805-Z01 Guidelines for When to Perform Crack Detection In-Line Inspection. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), September 2024. http://dx.doi.org/10.55274/r0000085.

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The goal of this project was to develop flow charted decision processes for operators to determine when ILI is appropriate and recommended. Two initial tasks developed the background material needed for developing the decision processes. Task 1 is a review of publicly available and industry research reports pertaining to the linkage between susceptibility to crack-related failure and performing ILI. Primary references are the results from industry work that informed the development of TTO5 and the development of other industry guidance documents. Task 2 is a survey of operators to understand the extent to which TTO5 does or does not address their integrity management needs regarding reassessments for cracks, and to learn the details of their solutions to perceived shortcomings with TTO5. Task 3 is the development of an updated decision process for determining susceptibility to cracking and the necessary intervals for assessment of the pipeline condition with respect to fatigue cracking and stress-corrosion cracking. Separate decision processes were developed for cracks that grow by fatigue and corrosion (either stress corrosion or corrosion fatigue). The decision process for fatigue crack growth susceptibility and the associated assessment schedule has three frameworks. The process for stress corrosion crack and corrosion fatigue has one. This Bundle includes the Final Report (PR727-223805-R01 Guidelines for When to Perform Crack Detection In-Line Inspection) and 2 White Papers (PR727-223805-R02 Decision Process for Fatigue Crack Growth Susceptibility and Assess White Paper and PR727-223805-R03 Stress Corrosion Cracking Susceptibility White Paper)
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Cohen, Yafit, Carl Rosen, Victor Alchanatis, David Mulla, Bruria Heuer, and Zion Dar. Fusion of Hyper-Spectral and Thermal Images for Evaluating Nitrogen and Water Status in Potato Fields for Variable Rate Application. United States Department of Agriculture, November 2013. http://dx.doi.org/10.32747/2013.7594385.bard.

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Potato yield and quality are highly dependent on an adequate supply of nitrogen and water. Opportunities exist to use airborne hyperspectral (HS) remote sensing for the detection of spatial variation in N status of the crop to allow more targeted N applications. Thermal remote sensing has the potential to identify spatial variations in crop water status to allow better irrigation management and eventually precision irrigation. The overall objective of this study was to examine the ability of HS imagery in the visible and near infrared spectrum (VIS-NIR) and thermal imagery to distinguish between water and N status in potato fields. To lay the basis for achieving the research objectives, experiments in the US and in Israel were conducted in potato with different irrigation and N-application amounts. Thermal indices based merely on thermal images were found sensitive to water status in both Israel and the US in three potato varieties. Spectral indices based on HS images were found suitable to detect N stress accurately and reliably while partial least squares (PLS) analysis of spectral data was more sensitive to N levels. Initial fusion of HS and thermal images showed the potential of detecting both N stress and water stress and even to differentiate between them. This study is one of the first attempts at fusing HS and thermal imagery to detect N and water stress and to estimate N and water levels. Future research is needed to refine these techniques for use in precision agriculture applications.
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Alchanatis, Victor, Stephen W. Searcy, Moshe Meron, W. Lee, G. Y. Li, and A. Ben Porath. Prediction of Nitrogen Stress Using Reflectance Techniques. United States Department of Agriculture, November 2001. http://dx.doi.org/10.32747/2001.7580664.bard.

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Commercial agriculture has come under increasing pressure to reduce nitrogen fertilizer inputs in order to minimize potential nonpoint source pollution of ground and surface waters. This has resulted in increased interest in site specific fertilizer management. One way to solve pollution problems would be to determine crop nutrient needs in real time, using remote detection, and regulating fertilizer dispensed by an applicator. By detecting actual plant needs, only the additional nitrogen necessary to optimize production would be supplied. This research aimed to develop techniques for real time assessment of nitrogen status of corn using a mobile sensor with the potential to regulate nitrogen application based on data from that sensor. Specifically, the research first attempted to determine the system parameters necessary to optimize reflectance spectra of corn plants as a function of growth stage, chlorophyll and nitrogen status. In addition to that, an adaptable, multispectral sensor and the signal processing algorithm to provide real time, in-field assessment of corn nitrogen status was developed. Spectral characteristics of corn leaves reflectance were investigated in order to estimate the nitrogen status of the plants, using a commercial laboratory spectrometer. Statistical models relating leaf N and reflectance spectra were developed for both greenhouse and field plots. A basis was established for assessing nitrogen status using spectral reflectance from plant canopies. The combined effect of variety and N treatment was studied by measuring the reflectance of three varieties of different leaf characteristic color and five different N treatments. The variety effect on the reflectance at 552 nm was not significant (a = 0.01), while canonical discriminant analysis showed promising results for distinguishing different variety and N treatment, using spectral reflectance. Ambient illumination was found inappropriate for reliable, one-beam spectral reflectance measurement of the plants canopy due to the strong spectral lines of sunlight. Therefore, artificial light was consequently used. For in-field N status measurement, a dark chamber was constructed, to include the sensor, along with artificial illumination. Two different approaches were tested (i) use of spatially scattered artificial light, and (ii) use of collimated artificial light beam. It was found that the collimated beam along with a proper design of the sensor-beam geometry yielded the best results in terms of reducing the noise due to variable background, and maintaining the same distance from the sensor to the sample point of the canopy. A multispectral sensor assembly, based on a linear variable filter was designed, constructed and tested. The sensor assembly combined two sensors to cover the range of 400 to 1100 nm, a mounting frame, and a field data acquisition system. Using the mobile dark chamber and the developed sensor, as well as an off-the-shelf sensor, in- field nitrogen status of the plants canopy was measured. Statistical analysis of the acquired in-field data showed that the nitrogen status of the com leaves can be predicted with a SEP (Standard Error of Prediction) of 0.27%. The stage of maturity of the crop affected the relationship between the reflectance spectrum and the nitrogen status of the leaves. Specifically, the best prediction results were obtained when a separate model was used for each maturity stage. In-field assessment of the nitrogen status of corn leaves was successfully carried out by non contact measurement of the reflectance spectrum. This technology is now mature to be incorporated in field implements for on-line control of fertilizer application.
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Skow. PR-244-093703-R01 Uncertainties of In-line Inspection Crack Detection Tools Phases 1-2. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), October 2014. http://dx.doi.org/10.55274/r0010828.

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This project was carried� to develop better approaches to deal with in line crack inspection tool uncertainty. The primary objectives of the project were as follows: To develop practical and defendable methodologies to quantify the de facto tool accuracy for a specific pipeline, taking into account the limitations associated with field excavation programs.� To develop methodologies that can be used to incorporate the above mentioned tool characterizations into the crack management process. The objectives were addressed in two parts: first by estimating the number of defects in the pipeline, and second by estimating the reliability of the pipeline given the defect count and size distributions. Sections 2 and 3 of this report detail the models used to estimate the number and size of defects in the pipeline. Section 4 details the reliability models used as a basis for crack management. API Standard 1163 titled �In line Inspection System Qualification Standards� provides performance based requirements for in line inspection (ILI) systems, including procedures, personnel and equipment. The standard provides a starting point and terminology for discussions on in line tool performance and is referred to throughout this report. In line tool accuracy is defined in API 1163 in terms of probability of detection (POD), probability of identification (POI), probability of false call (POFC) and sizing accuracy. Models for each of these performance measures is developed and exercised using four operator ILI and excavation data sets for stress corrosion cracking (SCC) defects. The model results are then applied to a crack management process that utilizes pipeline reliability as a basis for decision making. An overview of the models developed for POD, POI, POFC and sizing, as well as key results from the crack management process, is presented.
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Seginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, February 2001. http://dx.doi.org/10.32747/2001.7575271.bard.

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Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, control, structure and crop). 2. Using these models, develop algorithms for an early detection of deviations from the normal. 3. Develop identifying procedures for the most important faults. 4. Develop accommodation procedures while awaiting a repair. The Technion team focused on the shoot environment and the Cornell University team focused on the root environment. Achievements Models: Accurate models were developed for both shoot and root environment in the greenhouse, utilizing neural networks, sometimes combined with robust physical models (hybrid models). Suitable adaptation methods were also successfully developed. The accuracy was sufficient to allow detection of frequently occurring sensor and equipment faults from common measurements. A large data base, covering a wide range of weather conditions, is required for best results. This data base can be created from in-situ routine measurements. Detection and isolation: A robust detection and isolation (formerly referred to as 'identification') method has been developed, which is capable of separating the effect of faults from model inaccuracies and disturbance effects. Sensor and equipment faults: Good detection capabilities have been demonstrated for sensor and equipment failures in both the shoot and root environment. Water stress detection: An excitation method of the shoot environment has been developed, which successfully detected water stress, as soon as the transpiration rate dropped from its normal level. Due to unavailability of suitable monitoring equipment for the root environment, crop faults could not be detected from measurements in the root zone. Dust: The effect of screen clogging by dust has been quantified. Implications Sensor and equipment fault detection and isolation is at a stage where it could be introduced into well equipped and maintained commercial greenhouses on a trial basis. Detection of crop problems requires further work. Dr. Peleg was primarily responsible for developing and implementing the innovative data analysis tools. The cooperation was particularly enhanced by Dr. Peleg's three summer sabbaticals at the ARS, Northem Plains Agricultural Research Laboratory, in Sidney, Montana. Switching from multi-band to hyperspectral remote sensing technology during the last 2 years of the project was advantageous by expanding the scope of detected plant growth attributes e.g. Yield, Leaf Nitrate, Biomass and Sugar Content of sugar beets. However, it disrupted the continuity of the project which was originally planned on a 2 year crop rotation cycle of sugar beets and multiple crops (com and wheat), as commonly planted in eastern Montana. Consequently, at the end of the second year we submitted a continuation BARD proposal which was turned down for funding. This severely hampered our ability to validate our findings as originally planned in a 4-year crop rotation cycle. Thankfully, BARD consented to our request for a one year extension of the project without additional funding. This enabled us to develop most of the methodology for implementing and running the hyperspectral remote sensing system and develop the new analytical tools for solving the non-repeatability problem and analyzing the huge hyperspectral image cube datasets. However, without validation of these tools over a ful14-year crop rotation cycle this project shall remain essentially unfinished. Should the findings of this report prompt the BARD management to encourage us to resubmit our continuation research proposal, we shall be happy to do so.
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Wolfenson, David, William W. Thatcher, Rina Meidan, Charles R. Staples, and Israel Flamenbaum. Hormonal and Nutritional Stretegies to Optimize Reproductive Function and Improve Fertility of Dairy Cattle during Heat Stress in Summer. United States Department of Agriculture, August 1994. http://dx.doi.org/10.32747/1994.7568773.bard.

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The BARD program includes two main parts. In the first, experiments were conducted to complete our understanding of the mechanisms responsible for the impairment of reproductive functions under heat stress. Experiments focused on follicular development and function, since results obtained in our previous BARD project indicate that the preovulatory follicle is susceptible to heat stress. The theca cells, sensitive to thermal stress, produced less androgen during the summer, as well as during the autumn. Similarly, luteinized theca cells obtained from cows in summer produced much less progesterone than in winter. Granulosa cells and luteinized granulosa cells were less susceptible to heat stress. A delayed effect of heat stress on follicular development, on suppression of dominance and on steroid production by theca and granulosa cells was noted. This may be related to the low fertility of cows during the cool months of autumn. In the second part, experiments were conducted aiming to improve fertility in summer. The timed AI program was developed using two injections of GnRH coupled with PGF2a. It was found effective in improving reproductive performance in lactating cows. Limitations induced by heat stress on estrus detection were eliminated with the timed AI management program. Replacing the second injection of GnRH with hCG instead of GnRH agonist increased plasma progesterone levels post ovulation but did not improve fertility. Use of the timed AI program in summer, shortened days open and increased the net revenue per cow, however, it did not protect the embryo fiom temperature-induced embryonic mortality. Incorporation of a GnRH-agonist implant into the timed AJ program was examined. The implant increased plasma progesterone and LH concentrations and altered follicular dynamics. The use of a GnRH-implant enhanced pregnancy rate in cows with low body conditions. In a timed embryo transfer experiment, the use of fresh or frozen in vitro produced embryos was compared in the summer to improve fertility. The use of flesh embryos (but not frozen ones) improved pregnancy rate, however, substantial embryonic death occurred between 21 and 45 days. The timed AI program, which is now being used commercially, shortened days open, and increased pregnancy rate during summer. Other approaches which were found to improve fertility in small-scale studies, need to be tested again in large-scale field trials.
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Haines and Rosenfeld. L52125 Physical Performance and Inspection Objectives to Inspect Currently Non Piggable Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), January 2004. http://dx.doi.org/10.55274/r0011137.

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A significant portion of the pipeline infrastructure cannot be assessed for integrity using in-line inspection. There may also be reasons why some segments cannot be readily assessed by hydrostatic testing or by direct assessment. New standards and regulations for integrity management provide for using other technology that can be demonstrated to be as effective in assessing for the presence of integrity threats as the standard techniques. The first part of the report discusses a survey of PRCI member pipeline operating companies investigating the reasons why their systems are not piggable. The results are intended to be an aid to determining the direction of future PRCI research and development efforts. The results revealed that 62% of the pipe surveyed is presently piggable, and those members answering the survey plan to make another 21% of the system piggable. The survey further indicated that members plan to use direct assessment as the primary choice for inspecting nonpiggable segments. Consequently, the survey suggests that the primary goal of the Committee in addressing nonpiggable lines should be to validate direct assessment, followed by developing new above ground direct assessment and metal loss detection tools. The second part of the report discusses an analysis to establish suitable detection criteria for alternative assessment methods. Criteria for metal loss due to corrosion were established for 2 NPS to 24 NPS pipe of Grades B, X42, and X52 in all standard class locations so as to assure the same margin of safety afforded by the minimum hydrostatic test required per class. Criteria were also proposed for stress-corrosion cracking, selective corrosion in ERW seams, and mechanical damage.
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Bryan, Charles R., Kent B. Pfeifer, Stephen Buerger, and Eric John Schindelholz. Innovative Technologies for Optical Detection of Stress Corrosion Cracks. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1488645.

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Allendorf, Mark D., Peter J. Hesketh, Kenneth A. Gall, A. Choudhury, J. Pikarsky, Leanne Andruszkiewicz, Ronald J. T. Houk, and Albert Alec Talin. Stress-induced chemical detection using flexible metal-organic frameworks. Office of Scientific and Technical Information (OSTI), September 2009. http://dx.doi.org/10.2172/993628.

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Schramm, Raymond E., Alfred V. Clark, Dragan V. Mitrakovic, Stephen R. Schaps, and Todd J. McGuire. Report no. 23- residual stress detection in railroad wheels :. Gaithersburg, MD: National Institute of Standards and Technology, 1991. http://dx.doi.org/10.6028/nist.ir.3968.

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