Stress Analysis Using Machine Learning

Stress is one of the major health issues of the world and one of the major reasons for committing suicide. Also, it leads to other mental health issues such as depression, anxiety etc., and damage to organs related to respiratory, cardiovascular and nervous systems. In recent years, stress has impacted many individuals due to the pandemic situation. Since the governments across the globe had started to impose lockdowns, the levels of stress significantly raised because of the disturbances led by covid infections, losing loved ones, continuous engagement with laptops and mobiles etc. It is also found that stress has not only disturbed the health condition but also disturbed the relationships and became a self-destruction component. This project is aimed to help those people to understand their stress and consult a psychologist at right time to overcome the situation. Though stress is an active area of research and achieved high performance of models, those were based on signal and speech which were computationally costlier and text-based research work using a state-of-the-art model called the BERT has achieved an f1-score i.e. 80.65%. This project focuses on text-domain and uses open-sourced Stress Analysis on Social Media dataset available on Kaggle which contains 3.6 K samples. In this project, both Machine Learning and Deep Learning Models were trained with 80% of the data and validated with 20% of the data. After, optimization and evaluation of several models, the best model has achieved a benchmark result of 83.74% f1-score on test data using a new network architecture i.e. combination of stacked Transformer Encoder layers with stacked Bi-directional-LSTM. In addition to this, an explainable AI has been implemented for an embedding layer to inspect input attributions in predicting the results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Editorial

The Italian Association for Stress Analysis (AIAS) was founded in 1971 by researchers from academia, research centers, and industry. AIAS was intended as a community where to discuss, share, and develop scientific knowledge related to all technical aspects of stress analysis. In the years, from an initial focus on experimental techniques, AIAS contributed considerably to the development of modern numerical methods and computational techniques for mechanical engineering design. In 2015, AIAS turned in the Italian Scientific Society of Mechanical Engineering Design.Today, AIAS is an institutional partner that supports the instances from academia in the subject area of mechanical engineering design. Every year, AIAS organizes a technical conference offering the possibility to present research updates, share new ideas, and foster collaborations. The AIAS conference has become a fundamental event for all those interested in current developments in mechanical engineering design and stress analysis, where to meet researchers, testing equipment, and software developers.The 51st AIAS Conference edition was held in Padova, Italy again in presence after two years of online-only events due to the COVID-19 pandemic.The response of researchers and students has been outstanding: over 200 oral contributions have been presented during the three days of the conference, with four parallel sessions. In addition to the thematic sessions on AIAS traditional subjects, special sessions on additive manufacturing, energy methods for structural analysis, circular design in mechanical engineering, and mechanical behavior under extreme conditions, have been successfully organized with the contribution of the AIAS technical committees.Among all contributions presented at the conference, 48 have been selected to be published after peer review, in this volume. This was made possible thanks to the active participation of all AIAS members, to the work of the AIAS Scientific Committee and Conference Papers Review panel (Profs Giovanni Meneghetti, AIAS Scientific Coordinator, Luciano Afferrante, Francesco Bucchi, Filippo Cianetti, Enrico Armentani, Marco Sasso). Their outstanding contribution is gratefully acknowledged.DisclaimerAIAS2022 Conference was held in presence on Sept. 7-10, 2022. Presentations were arranged in four parallel sessions with a time slot of 15 min assigned to each presenter. Approximately 250 participants attended the conference during the three days event. All sessions have been continuously monitored by the organizers to provide technical support. The conference ran smoothly, and the participants' feedback was very positive.

Mental Stress Analysis of Bangladeshi Job Holders during Covid-19 pandemic: A Machine Learning Approach

Due to the Covid-19 epidemic, the work market has changed in the twenty-first century. The management of the organization should check to see if jobholders are mentally competent and suitable before they begin and enter the work environment. The job holders do their business in various ways, such as someone working from the office and someone working from home, to maintain physical distance and protect themselves from the epidemic. We are in office zones for twothirds of the day. We were inspired and informed about the psychological strain caused by the coronavirus. The study paper's goal is to assist and enlighten employment business administrators so that employees who work from home and offices may maintain their regular mental wellness. We employed the machine learning methods Logistic Regression, KNearest Neighbors (KNN), Naive Bayes, and Decision Tree. In the Decision Tree, this research paper's accuracy was approximately 73%. © 2022 IEEE.

Perceived Stress Analysis of Undergraduate Students during COVID-19: A Machine Learning Approach

Stress is a state of mind when an individual experiences emotional or physical tensions originating from any event that results in frustration, anger, or nervousness. Unfortunately, since the inception of the COVID-19 pandemic, it has been massively witnessed among university students due to persistent usage of e-learning gears for the last two years. Due to the severity of the observed stress, accurate and early prediction and detection should play a pivotal role in treating a student. In this work, a questionnaire-based dataset on Jordanian students has been analyzed using the 5-point Likert Scale. One of the most widely used psychological instrument Perceived Stress Scale (PSS) is used to identify the stressrelated symptoms of the students. Based on the dataset, several machine learning (ML) algorithms were applied for regression and classification analysis by which mental stress has been predicted and classified. After simulation in Python, the ML regressors were evaluated through the performance metrics such as R2 Score, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Percentage Absolute Error (MAPE), and ML classifiers were assessed by accuracy, precision, recall, and F1-Score. It is observed that Linear Regression (LR) performed the best among all the regression models whereas the Logistic Regression Classifier (LRC) portrayed the highest accuracy of 97.8% among all the classifiers. Therefore, ML-based stress analysis can significantly contribute to analyzing students' mental stress during COVID-19 in an automated manner.

Diseño de prótesis para amputación transmetatarsal y de Chopart

La Diabetes Mellitus es una enfermedad crónica no transmisible de origen multifactorial, cuya prevalencia en Chile es de 12,3% en la población mayor a 15 años. Dentro de las consecuencias de esta enfermedad se encuentra el pie diabético, cuyo tratamiento principal es la amputación parcial del pie. En este contexto, el objetivo de la investigación es diseñar una prótesis parcial de pie para los niveles de amputación transmetatarsal y de Chopart. La metodología empleada incluyó el establecimiento del perfil del paciente, la definición de las especificaciones de diseño y la posterior propuesta del diseño conceptual. Luego se abordó el diseño de detalle, realizando los cálculos de las fuerzas a las que está sometida la prótesis, la selección del material y el análisis de esfuerzos empleando el método de elementos finitos, para establecer el material y dimensiones definitivas. La prótesis diseñada está compuesta por una plantilla de material poliuretano-termoplástico o de polipropileno, y una cubierta estética de silicona;incluye en la misma plantilla los dos niveles de amputación parcial de pie, no restringe los grados de libertad del tobillo y es personalizada para el paciente, lo cual es relevante cuando se trabaja con pacientes con diabetes.Alternate :Diabetes Mellitus is a non-communicable chronic disease of multifactorial origin, whose prevalence in Chile is 12.3% in the population over 15 years of age. Among the consequences of this disease there is the diabetic foot, whose main treatment is the partial amputation of the foot. In this context, the objective of the research is to design a partial foot prosthesis for the transmetatarsal and Chopart levels of amputation. The used methodology included the establishment of the patient's profile, the definition of the design specifications and the subsequent proposal of the conceptual design. Then the detailed design was approached, carrying out the calculations of the forces to which the prosthesis is subjected, the material selection and the stress analysis using the finite element method to establish the final material and dimensions of the prosthesis. The designed prosthesis is composed of a polyurethane-thermoplastic or polypropylene material insole, and an aesthetic silicone cover;It includes both levels of partial foot amputation in the same template, does not restrict the degrees of freedom of the ankle, and is personalized for the patient, which is relevant when working with patients with diabetes.

A Novel Deep Learning Based Healthcare Model for COVID-19 Pandemic Stress Analysis

Coronavirus (COVID-19) has impacted nearly every person across the globe either in terms of losses of life or as of lockdown. The current coronavirus (COVID-19) pandemic is a rare/special situation where people can express their feelings on Internet-based social networks. Social media is emerging as the biggest platform in recent years where people spend most of their time expressing themselves and their emotions. This research is based on gathering data from Twitter and analyzing the behavior of the people during the COVID-19 lockdown. The research is based on the logic expressed by people in this perspective and emotions for the suffering of COVID-19 and lockdown. In this research, we have used a Long Short-Term Memory (LSTM) network model with Convolutional Neural Network using Keras python deep-learning library to determine whether social media platform users are depressed in terms of positive, negative, or neutral emotional out bust based on their Twitter posts. The results showed that the model has 88.14% accuracy (representation of the correct prediction over the test dataset) after 10 epochs which most tweets showed had neutral polarity. The evaluation shows interesting results in positive (1), negative (-1), and neutral (0) emotions through different visualization. © 2022 Tech Science Press. All rights reserved.

Guest editorial

[...]the following 13 conference papers have been selected for publication in this peer-reviewed special issue: Topczewski S., Żugaj M.;Bibik P., “Impact of Actuators Backlash on the Helicopter Control During Landing on the Moving Vessel Deck” Kopecki G., “Aircraft Control With the Use of Model Reference Adaptive Control” Rodzewicz M., “Comparative fatigue properties estimation of composite structural nodes” Kowalski M., Goraj Z.;Goliszek B., “The use of FEA and semi-empirical equations for weight estimation of a passenger aircraft” Olejnik A., Dziubiński, A.: Kiszkowiak. Ł., “CFD simulation of engine nacelle cooling on pusher configuration aircraft” Goraj Z., Kowalski M., Kiszkowiak Ł.;Olejnik A., “Simulation of a passenger aircraft flight with the wing tip cut” Bridel G., Goraj Z.;Kiszkowiak Ł., Brevot Jean-G. and Devaux J., Szczepański C., Vrchota P., “Air combat training - high energy at lowest cost” Kwiek A., Galiński C., Bogdański K., Hajduk J.;Tarnowski A., “Results of Simulation and Scaled Flight Tests Performed on a Rocket-Plane at High Angles of Attack” Rogalski T., Rzucidło P., Noga St.;Prusik J., “Unmanned Aircraft Automatic Flight Control Algorithm in an Immelmann Manoeuvre” Kretov A., “Sensitivity Factors of Aircraft Mass for the Conceptual Design” Noga St., Maciejowska K.;Rogalski T., “Vibration Analysis of an Aviation Engine Turbine Shaft Shield” Samolej S., Dec G., Rzońca D., Majka A.;Rogalski T., “Regular Graph-based Free Route Flight Planning Approach” Pieniążek J.;Cieciński P., “Safety analysis of the optionally-piloted airplane landing” The articles deal with different solutions for future aerospace challenges in the areas of novel configuration, aerodynamics, design methods, multidisciplinary optimization, UAV and stress analysis. [...]I would like to thank the Editor-in-Chief of the AEAT, Prof. Philip Webb, for his agreement, and support of all editorial team of AEAT in making this issue possible.

Technologically Advanced Reusable 3D Face Shield for Health Workers Confronting COVID19

The probability of medical staff to get affected from COVID19 is much higher due to their working environment which is more exposed to infectious diseases. So, as a preventive measure the body temperature monitoring of medical staff at regular intervals is highly recommended. Infrared temperature sensing guns have proved its effectiveness and therefore such devices are used to monitor the body temperature. These devices are either used on hands or forehead. As a result, there are many issues in monitoring the temperature of frontline healthcare professionals. Firstly, these healthcare professionals keep wearing PPE (Personal Protective Equipment) kits during working hours and as a result it would be very difficult to monitor their body temperature. Secondly, these healthcare professionals also wear face shields and in such cases monitoring temperature by exposing forehead needs removal of face shield. Doing so after regular intervals is surely uncomfortable for healthcare professionals. To avoid such issues, this paper is disclosing a technologically advanced face shield equipped with sensors capable of monitoring body temperature instantly without the hassle of removing the face shield. This face shield is integrated with a built-in infrared temperature sensor. A total of 10 such face shields were printed and assembled within the university lab and then handed over to a group of ten members including faculty and students of nursing and health science department. This sequence was repeated four times and as a result 40 healthcare workers participated in the study. Thereafter, feedback analysis was conducted on questionnaire data and found a significant overall mean score of 4.59 out of 5 which indicates that the product is effective and worthy in every facet. Stress analysis is also performed in the simulated environment and found that the device can easily withstand the typically applied forces. The limitations of this product are difficulty in cleaning the product and comparatively high cost due to the deployment of electronic equipment.

Preface

The Italian Association for Stress Analysis (AIAS) was founded in 1971 by researchers from academia, research centers and industry. AIAS was intended as a community where to discuss, to share and to develop scientific knowledge related to all technical aspects of stress analysis. In the years, from an initial focus on experimental techniques, AIAS contributed considerably to the development of modern numerical methods and computational techniques for the mechanical engineering design. In 2015, AIAS turned in the Italian Scientific Society of Mechanical Engineering Design.Today, AIAS is an institutional partner that supports the instances from academia in subject area of the mechanical engineering design. Every year, AIAS organizes a technical conference offering the possibility to present research updates, share new ideas and foster collaborations. The AIAS conference has become a fundamental event for all those interested in current developments in mechanical engineering design and stress analysis, where to meet researchers, testing equipment and software developers.The 50th AIAS Conference edition was initially re-scheduled to be held in Genoa, Italy after it was switched as virtual conference in 2020 due to COVID-19 emergency. Unfortunately, due to the continuing pandemic emergency, it was decided to go virtual again to keep continuity with the tradition, confident that, despite the difficulties of the moment, united we stand.As for the previous year, the response of researchers and students has been outstanding: over 180 oral contribution have been presented in synchronous, during the three days of the conference, with three simultaneous parallel sessions. In addition to the thematic sessions on AIAS traditional subjects, special sessions on additive manufacturing, energetic methods for structural analysis and smart materials and MEMS have been successfully organized with the contribution of the AIAS technical committees.Among all contributions presented at the conference, 54 have been selected to be published after peer review, in this volume. This was made possible thanks to the active participation of all AIAS members, to the work of the AIAS Scientific Committee and Conference Papers Review panel (Profs Giovanni Meneghetti, AIAS Scientific Coordinator, Luciano Afferrante, Francesco Bucchi, Filippo Cianetti, Enrico Armentani, Marco Sasso). Their outstanding contribution is gratefully acknowledged.List of Disclaimer, Editors, Scientific Committee are available in this pdf.

Stress Analysis and Care Prediction System for Online Workers

Working from home (WFH) online during the covid-19 pandemic has caused increased stress level. Online workers/students have been affecting by the crisis according to new researches. Natural response of body, to external and internal stimuli is stress. Even though stress is a natural occurrence, prolonged exposure while working Online to stressors can lead to serious health problems if any action will not be applied to control it. Our research has been conducted deeply to identify the best parameters, which have connection with stress level of online workers. As a result of our research, a desktop application has been created to identify the users stress level in real time. According to the results, our overall system was able to provide outputs with more than 70% accuracy. It will give best predictions to avoid the health problems. Our main goal is to provide best solution for the online workers to have healthy lifestyles. Updates for the users will be provided according to the feedback we will have in the future from the users. Our System will be a most valuable application in the future among online workers. © 2021 IEEE.

MOBILE SOLAR POWERED INSTRUCTIONAL TECHNOLOGY EQUIPMENT FOR ONLINE TEACHING AND LEARNING DURING COVID-19 PANDEMIC FOR REMOTE COMMUNITY

As the COVID-19 pandemic continues globally, the disruption of the traditional face-to-face classes in educational institutions is evident. In the Philippines, these educational institutions have shifted to Flexible Learning System (FLS). However, in the implementation of FLS, the teachers and the learners in the remote communities experience internet connectivity problems. This problem is minimized using mobile solar-powered instructional technology equipment for online teaching and learning activities. This equipment is designed to carry all the necessary accessories in the online delivery of FLS like television, solar panel, WiFi routers, inverters, and other power accessories. It is made of Polypropylene Random Copolymer (PPR) pipe material and is mounted on a four-wheel bicycle. Several tests were conducted to evaluate the performance of the equipment. These were the following: stress analysis, solar energy utilization test, portability assessment, connectivity speed test, and quality assessment. Results suggest that the developed equipment is ready for actual deployment in a remote community in the Philippines. With such deployment, internet connectivity problems can be minimized. As a result, the less fortunate learners can have free and reliable internet access. Moreover, a possible related future work is designing bigger mounting equipment that can carry more accessories due to its portability and mobility aspects. Copyright © 2021 by ASME

Understanding job stress and organizational effectiveness of airline employees due to changes in the employment environment under COVID-19—focusing on the difference in the GAD-7 of employees

The purpose of this study was to examine the relationship between the job stress of employees due to changes in the employment environment of airlines under COVID-19 and job burnout and turnover intention. GAD-7 was applied to verify the influence relationship linking job stress, job burnout and turnover intention, using a structural equation model according to anxiety, namely, COVID blues. The results of this study can provide a theoretical basis for the relationship linking job stress, job burnout and turnover intention of employees due to changes in the employment environment resulting from the business crisis of airlines under the COVID-19 pandemic, which has been ongoing since 2020. In addition, it was possible to understand the degree of impact on stress, job burnout and turnover intention by measuring the anxiety disorder of employees, i.e., COVID-19 blues, due to the prolonged pandemic. This study provides basic data on human management measures in the aviation field. Furthermore, according to the results of this study, changes in the employment environment led to job stress in employees, which in turn increased employee job burnout and turnover intention. These results can provide practical implications and help human resource managers to better manage employees, as it was revealed that both changes in the organization of employees and changes in the employment environment of temporary employees increase job stress. Lastly, in the situation where COVID-19 and its associated blues continue, the GAD-7 scale, typically used for patients in hospitals, was applied to airline employees to determine their anxiety levels and psychological state, representing an effective method of lowering intention to leave, job stress and job burnout. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

70th Annual Denver X-ray Conference Report: A Virtual Event

Materials Identification Quantitative Phase Analysis Advanced PDF Modelling EDS Detectors Sample Preparation of XRF Basic XRF Trace Analysis Layered Structures Intermediate to Advanced XRD Imaging Micro XRF Non-ambient XRD Quantitative Analysis of XRF Handheld XRF III. New Developments in XRD and XRF Instrumentation Quantitative Phase Analysis Advanced Total Scattering Methods for Complex Material Studies Quantitative Analysis of XRF Trace Analysis including TXRF Machine Learning Techniques in X-ray Analysis General XRD Imaging Stress Analysis Functional Materials General XRF Non-ambient Analysis X-ray Absorption Spectroscopy Industrial Applications of XRD and XRF Cultural Heritage V. POSTER SESSIONS Both XRD and XRF Posters were available on the first day of the conference. Residual Stress and Microstructural Analysis of Laser Powder Bed Fusion Processed Ti-5Al-5V-5Mo-3Cr Alloy Using Synchrotron X-ray Diffraction C. XRF best poster award M. Sunder*, M.A. Zaitz, IBM, USA, for their work: Andrés Servando Aguirre Sánchez, Instituto Tecnológico y de Estudios Superiores de Monterrey, Mexico Adam Corrao, Stony Brook University, USA Hannah Cross, Keele University, United Kingdom Samantha Davies, Cranfield University, United Kingdom Benedikt Eger, Bonn-Rhein-Sieg University of Applied Sciences, Germany Gesa Goetzke, TU Berlin, Germany Sarah Gosling, Cranfield University, United Kingdom Sven Hampel, Clausthal University of Technology, Germany Benjamin Hulbert, University of Illinois at Urbana-Champaign, USA Rahul Lalge, University of Minnesota, USA Marcelo Augusto Malagutti, Universidade Federal de Santa Catarina, Brazil Otavio Jovino Marques, Illinois Institute of Technology, USA Ana Cecilia Murrieta Muñoz, Tecnológico de Monterrey, Mexico Steffen Staeck, TU Berlin, Germany VIII.

Analyzing a community worker’s stress during the COVID-19 pandemic in China

PurposeCommunity governance plays an important role in the prevention and control of the Coronavirus disease 2019 (COVID-19) pandemic in China. Community workers, the main executors in community governance, experience a huge amount of stress, which affects their physical and mental health. Thus, it is crucial to pay more attention to the stressors and stress responses of community workers and propose strategies to alleviate such responses. This paper aims to analyze the work stress of community workers during the COVID-19 pandemic in China.Design/methodology/approachBased on a questionnaire survey of 602 community workers during COVID-19 in China, the four main stressors and 14 stress factors of community workers were identified and six factors at three levels of stress responses were defined. A stress analysis model is proposed that tests the mediating role of psychological capital and the moderating role of organizational climate.FindingsThe results show that stressors influence stress responses through the moderating role of psychological capital, organizational climate plays a negative mediator role between stressors and psychological capital and the main stressors for community workers are work, safety and performance stress.Originality/valueThis paper contributes to existing research because it offers suggestions for reducing the impact of stress on the community workers during the COVID-19 pandemic. Further, it can promote the control and prevention of the COVID-19.