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Applications of Stress01:04

Applications of Stress

400
Consider a structure made of a boom and a rod designed to support a load. These two components are connected by a pin and stabilized by brackets and pins. The boom and the rod are detached from their supports to assess the different stresses imposed on this structure, and a free-body diagram is drawn. Then, all the forces applied, including the load acting on the structure, are identified. The reaction forces exerted on both the boom and the rod are computed using the equilibrium equations.
The...
400
Stress Prevention and Stress Management Techniques V01:28

Stress Prevention and Stress Management Techniques V

63
A social support system is a structured network of personal relationships that provides assistance to individuals facing various challenges, offering a buffer against psychological and physical stressors. This network may consist of family members, friends, neighbors, colleagues, or other community members who provide resources and companionship. Social support can take many forms, including advice, emotional comfort, practical help, and companionship. Research indicates that these networks can...
63
Physiological Foundation of Stress01:24

Physiological Foundation of Stress

157
Stress triggers a coordinated physiological response involving the sympathetic nervous system (SNS) and the hypothalamic-pituitary-adrenal (HPA) axis. This dual activation ensures that the body is prepared for both immediate and prolonged stress management. The process begins with the perception of a stressor. This initial phase activates the SNS, leading to the rapid release of adrenaline (epinephrine) from the adrenal glands.
Role of the Sympathetic Nervous System
Adrenaline triggers the...
157
Stress Prevention and Stress Management Techniques IV01:26

Stress Prevention and Stress Management Techniques IV

62
Stress often leads to unhealthy habits like smoking, excessive drinking, and overeating, which offer short-term relief but ultimately increase long-term health risks. These behaviors create a cycle that temporarily lowers stress levels but can result in severe long-term health consequences. Breaking these habits is essential to reduce the risk of chronic diseases and improve overall well-being. Three primary changes that support better health include quitting smoking, reducing alcohol intake,...
62
Stress Prevention and Stress Management Techniques I01:26

Stress Prevention and Stress Management Techniques I

92
Stress prevention and management are crucial for maintaining well-being and building resilience. Techniques to manage stress include cultivating qualities like conscientiousness, a sense of personal control, and self-efficacy. Each of these traits significantly reduces stress and promotes healthier lifestyle choices and outcomes.
Conscientiousness
Conscientious individuals tend to be organized, responsible, and disciplined. They prioritize completing tasks and following structured routines,...
92
Stress Prevention and Stress Management Techniques II01:23

Stress Prevention and Stress Management Techniques II

71
Personality types, particularly Type A and Type B, significantly influence how individuals respond to stress. These personality distinctions are marked by varying levels of ambition, competitiveness, and coping styles, all of which shape an individual's resilience to stressors.
Type A Personality: Driven and Easily Stressed
Individuals with Type A personalities are often highly competitive and ambitious and operate with a strong sense of urgency. Commonly labeled as...
71

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Related Experiment Video

Updated: Sep 10, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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A novel and efficient personalized stress detection technique using a deep learning model.

Ulligaddala Srinivasarao1, Gopisetty Rathnamma2, M Satish Kumar3

  • 1Department of CSE, GITAM (Deemed to be) University, Rudraram Village, Hyderabad, India. ulligaddalasrinu@gmail.com.

Scientific Reports
|August 21, 2025
PubMed
Summary

This study introduces an efficient method for detecting stress from social media text. The novel approach integrates advanced text representations with a deep learning model, achieving high accuracy in stress detection.

Keywords:
DeepMojiFastTextFox optimizationResidual networkStemmingStress detectionTokenization

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Area of Science:

  • Computational linguistics
  • Artificial intelligence
  • Psychology

Background:

  • Stress significantly impacts adult and elder health, leading to chronic conditions.
  • Detecting stress from social media text presents challenges due to complexity and computational demands.
  • Existing machine learning and deep learning models face limitations like long training times and feature constraints.

Purpose of the Study:

  • To develop an efficient and accurate technique for stress detection from social media text.
  • To overcome the limitations of existing methods in terms of training time and feature utilization.
  • To improve the accuracy of stress detection using novel model integration and optimization.

Main Methods:

  • Integration of advanced text representation techniques: FastText, Global Vectors for Word Representation (Glove), DeepMoji, and XLNet.
  • Utilizing a Depth-wise Separable Convolution with Residual Network (DSC-ResNet) for accurate stress detection.
  • Employing the Chaotic Fennec Fox Optimization Algorithm (CFFO) for hyperparameter tuning.

Main Results:

  • The proposed technique achieved a high accuracy of 98.42%.
  • Precision, recall, specificity, and F1-score were reported at 97.58%, 98.12%, 98.28%, and 98.38%, respectively.
  • The model demonstrated superior performance compared to existing techniques.

Conclusions:

  • The novel integration of text representations and DSC-ResNet offers an efficient solution for stress detection.
  • The proposed method effectively addresses limitations of previous approaches, providing high accuracy and performance.
  • This technique shows promise for real-world applications in mental health monitoring via social media analysis.