Online Fatigue Research Articles

Designing and delivering bioinformatics project-based learning in East Africa

BackgroundThe Eastern Africa Network for Bioinformatics Training (EANBiT) has matured through continuous evaluation, feedback, and codesign. We highlight how the program has evolved to meet challenges and achieve its goals and how experiential learning through mini projects enhances the acquisition of skills and collaboration. We continued to learn and grow through honest feedback and evaluation of the program, trainers, and modules, enabling us to provide robust training even during the Coronavirus disease 2019 (COVID-19) pandemic, when we had to redesign the program due to restricted travel and in person group meetings.ResultsIn response to the pandemic, we developed a program to maintain “residential” training experiences and benefits remotely. We had to answer the following questions: What must change to still achieve the RT goals? What optimal platforms should be used? How would we manage connectivity and data challenges? How could we avoid online fatigue? Going virtual presented an opportunity to reflect on the essence and uniqueness of the program and its ability to meet the objective of strengthening bioinformatics skills among the cohorts of students using different delivery approaches. It allowed an increase in the number of participants. Evaluating each program component is critical for improvement, primarily when feedback feeds into the program's continuous amendment. Initially, the participants noted that there were too many modules, insufficient time, and a lack of hands-on training as a result of too much focus on theory. In the subsequent iterations, we reduced the number of modules from 27 to five, created a harmonized repository for the materials on GitHub, and introduced project-based learning through the mini projects.ConclusionWe demonstrate that implementing a program design through detailed monitoring and evaluation leads to success, especially when participants who are the best fit for the program are selected on an appropriate level of skills, motivation, and commitment.

Clinical experiences of staff and students in transitioning from in-person to blended teaching.

This paper describes some of the lessons learned during the COVID-19 pandemic from a study conducted with a group of clinical teachers and undergraduate dental students at the Faculty of Dentistry, Oral & Craniofacial Sciences (FoDOCS) at King's College London about the use of a combination of remote, online and in-person teaching methods that resumed from June 2020. In the narrative research, participants shared their experiences delivering online clinical workshops and their previous experiences delivering face-to-face sessions online, both during and before the pandemic. We conducted remote interviews with the participants via video conferencing, which were recorded, transcribed, and analysed using thematic analysis. Narrative accounts revealed commonalities organised into seven themes, highlighting some of the challenges encountered during the pandemic and providing insights into addressing different curricular constraints and concerns when utilising various delivery modes during emergency situations, such as pandemics. In our study, we concluded that students and teachers benefit from dissociating clinical learning from clinical treatment sessions to focus on the educational intent and content before applying them chairside with patients. Throughout the course, students and teachers were challenged by a lack of engagement. In addition, it is important to examine the online fatigue highlighted by both students and teachers and identify ways to improve time, literacy, and facilitation to create a more conducive learning environment.

Study on the multi‐scale online fatigue crack deformation and growth behaviors under overload in Q&P steel

AbstractThe multi‐scale online fatigue crack deformation and growth behaviors under single overload (SOL) and periodic overload (POL) case in Quenching and Partitioning (Q&P) steel were studied using two micro cameras and sub‐image stitching and matching digital image correlation (DIC) method. Firstly, the microscopic crack tip speckle images and crack images on both sides of the compact tension (CT) specimen during the fatigue crack growth (FCG) process were collected under different overload parameters. Then, the FCG overload retardation, crack tip closing and blunting behaviors, and the crack tip strain distribution and evolution, overload retardation mechanism were analyzed. In addition, the corresponding multi‐scale FCG morphological characteristics and material microstructure under overload case were observed. The results indicate that the overload ratio and interval affect the overload retardation effect of Q&P steel seriously; the overload crack tip blunting and plastic zone are the major factors inducing the overload retardation effect in Q&P steel.

Volunteer Online Counseling during COVID-19: Insights, Practices, and Future Directions

This study investigated the transition to online counseling by 34 counselors during the COVID-19 pandemic, addressing the gap in access to mental health services due to restrictions of in-person interactions. The research employed online questionnaires and a virtual focus group discussion to gather qualitative data, which were analyzed using thematic analysis. Counselors reported addressing various client issues, including anxiety, depression, academic challenges, suicidal ideation, and family conflicts. The primary technical challenges identified were connectivity issues, clients' lack of technological resources, and limited digital literacy. Effective online counseling was associated with active listening, adaptability, trustworthiness, and technological competence. Although valued for accessibility, convenience, and confidentiality, online counseling poses challenges, such as impersonal communication and online fatigue. The findings underscore the need for improving tele-counseling practices, particularly in overcoming technological and communication barriers, to ensure the continued growth and development of the field.

Looking into the “Dark Mirror”: Autoethnographic Reflections on the Impact of COVID-19 and Change Fatigue on the Wellbeing of Enabling Practitioners

The COVID-19 pandemic brought global disruptions to the way universities operate. Online learning abruptly took priority, as the physical campuses in Australian universities became deserted. Staff had to instantly adapt to major changes in work practices, whilst continuing to support students’ engagement and maintain quality teaching and learning. This article discusses how change fatigue during the pandemic impacted the wellbeing of staff working in the enabling education sector. As staff and student wellbeing is interdependent, gaining a better understanding of the influences on staff wellbeing in the post-pandemic era is worth exploring in the context of discussions around student wellbeing and success. Autoethnographical reflections of eight practitioners at six Australian universities working in teaching, leadership and professional practice in enabling education, were thematically analysed. Emergent data reveals the superordinate theme of change fatigue and sub-themes of time, online fatigue, and emotional labour. This article highlights the impact of workload intensification and change fatigue in educators. Our findings demonstrated that practitioners prioritise their workload and students, to the detriment of their own wellbeing. These findings hold relevance for institutions as they look to address student wellbeing and success, and highlights the value of embedding cultures of care and compassion across all levels of the university.

Fatigue crack size evaluation using acoustic emission signals for wire and arc additive manufactured material

As one of the promising additive remanufacturing techniques, the wire and arc additive manufacturing has shown great potential in automated repair and restoration. However, the layer-by-layer fabrication process may lead to an uneven heating and cooling rates, which has a significant effect on the microstructure and mechanical behavior, as compared to conventionally processed materials. In this study, the fatigue performance of the low-carbon steel components made by wire and arc additive manufacturing is studied systematically. The components made by conventional hot-rolled method is used for comparison purposes. By leveraging the advanced causal network and graph neural networks techniques, a novel method for online fatigue crack size evaluations is proposed using acoustic emission signals. Low dimensional latent variables are extracted from the acoustic emission signal through a one-dimensional convolutional autoencoder (1D-CAE), and the data-driven causal discovery algorithm is adopted to construct the causal network of these latent variables, which characterizes their inherent cause-effect relations. The graph attention network (GAT) is adopted to aggregate a node information with its parent nodes as the node embedding in a causal network. Fully connected layers are constructed between the node embeddings and fatigue crack size label. For online scenarios where limited data is available, the above GAT model previously trained is transferred with different strategies for crack size evaluation. Experimental data with three different scenarios: specimens with same direction, specimens with different direction and hot-rolled specimens are employed to validate the proposed online fatigue crack size evaluation method.

Dynamic muscle fatigue assessment using s-EMG technology towards human-centric human-robot collaboration

Human-centric human-robot collaboration (HHRC) allows seamless collaboration between humans and robots to fulfill flexible manufacturing operations in a shared workspace while maximizing operator autonomy and well-being toward optimal team performance. Therefore, assessing and monitoring an operator’s physical health, specifically fatigue, is paramount to maintaining a comfortable working environment. However, current fatigue assessment models are not suitable for characterizing the fatigue profile of target muscle groups against repetitive dynamic manufacturing operations non-invasively. To solve this problem, we created a theory for quantifying localized muscular fatigue by just understanding the relative task load and the number of repetitive operations the operator conducted. This was achieved by an experimentally proved multivariable linear relationship between localized muscular fatigue, task load, base muscle strength, and number of repetitive operations for an operation type via s-EMG technologies. We also showed the procedures for developing a personalized muscle fatigue profile for a variety of assembly operations via specialized s-EMG experiment design and measurement. This simple fatigue measurement mechanism allows us to constantly monitor operator fatigue via just monitoring repetitive operations conducted using non-invasive sensors, e.g., cameras. We also provide a framework for integrating our work for online fatigue monitoring in a human-robot collaboration system.

Analysis and Study of Fatigue Driving Detection System based on Multimodal Fusion

Fatigue driving always endangers road traffic safety and is one of the main causes of traffic accidents. How to effectively monitor and determine the fatigue driving state, so as to realize the fatigue early warning, has become a hot spot of scientific research. The traditional detection methods are mostly based on a certain human physiological parameters, through the monitoring of the physiological parameters changes, to complete the determination of the fatigue state. Compared with the traditional detection methods, the fatigue detection method based on computer vision has the advantages of accurate and reliable detection results, detection not relying on wearable devices, and good human-computer interaction experience. However, in the specific algorithm application scenarios, there are often interference from environmental factors such as partial occlusion of the inspected image, low image resolution, complex illumination of the detection environment, and unstable acquisition equipment. In the face of many challenges, how to effectively improve the detection accuracy of fatigue state, and the algorithm has the practical application space, still needs to be explored. The traditional fatigue detection method based on facial information often detects and analyzes a certain subordination. Due to the detection of individual differences and the fuzzy definition of fatigue state, the algorithm has the problems of high recognition error rate and poor robustness. Moreover, the single attribute model separates the potential integration and exclusion relationship between facial attributes. At the same time, the huge model size and complex model structure also limit the actual implementation of the algorithm. In view of these urgent problems, this paper first proposes the fusion model of face detection and head posture analysis based on the multi-task convolutional neural network (MTCNN). The model uses the architecture strategy of multi-task cascade to complete the complete facial posture analysis task while completing the facial detection task. Secondly, based on the lightweight convolutional neural network (SqueezeNet) of the network cascade, the facial key point detection network is designed to obtain the 72-point key point coordinates of the face. For the obtained facial attribute information, extract fatigue features and propose a fatigue determination method of multimodal fusion. Finally, combined with the research results, the construction of the online fatigue detection and early warning system platform was completed.

An Explainable Student Fatigue Monitoring Module with Joint Facial Representation

Online fatigue estimation is, inevitably, in demand as fatigue can impair the health of college students and lower the quality of higher education. Therefore, it is essential to monitor college students' fatigue to diminish its adverse effects on the health and academic performance of college students. However, former studies on student fatigue monitoring are mainly survey-based with offline analysis, instead of using constant fatigue monitoring. Hence, we proposed an explainable student fatigue estimation model based on joint facial representation. This model includes two modules: a spacial-temporal symptom classification module and a data-experience joint status inferring module. The first module tracks a student's face and generates spatial-temporal features using a deep convolutional neural network (CNN) for the relevant drivers of abnormal symptom classification; the second module infers a student's status with symptom classification results with maximum a posteriori (MAP) under the data-experience joint constraints. The model was trained on the benchmark NTHU Driver Drowsiness Detection (NTHU-DDD) dataset and tested on an Online Student Fatigue Monitoring (OSFM) dataset. Our method outperformed the other methods with an accuracy rate of 94.47% under the same training-testing setting. The results were significant for real-time monitoring of students' fatigue states during online classes and could also provide practical strategies for in-person education.

Digital dependence: Online fatigue and coping strategies during the COVID-19 lockdown.

As the COVID-19 pandemic lockdowns forced populations across the world to become completely dependent on digital devices for working, studying, and socializing, there has been no shortage of published studies about the possible negative effects of the increased use of digital devices during this exceptional period. In seeking to empirically address how the concern with digital dependency has been experienced during the pandemic, we present findings from a study of daily self-reported logbooks by 59 university students in Copenhagen, Denmark, over 4 weeks in April and May 2020, investigating their everyday use of digital devices. We highlight two main findings. First, students report high levels of online fatigue, expressed as frustration with their constant reliance on digital devices. On the other hand, students found creative ways of using digital devices for maintaining social relations, helping them to cope with isolation. Such online interactions were nevertheless seen as a poor substitute for physical interactions in the long run. Our findings show how the dependence on digital devices was marked by ambivalence, where digital communication was seen as both the cure against, and cause of, feeling isolated and estranged from a sense of normality.

Prevalence of Online Fatigue during COVID-19: A Cross-Sectional Study on Academicians of Rawalpindi

Objective: To determine prevalence of online fatigue among academicians and to analyse factors associated with online fatigue.
 Study Design: Analytical, cross-sectional study.
 Place and During of Study: The study was conducted online from Nov 2021 to Apr 2022 on academicians belonging to educational institutions and teaching hospitals of Rawalpindi Pakistan.
 Methodology: One hundred fifty academicians, aged 20-75 years, belonging to Rawalpindi were enrolled through snowball sampling. Data was collected online by using google forms. A validated questionnaire having an online fatigue scale was used. Partial responses were excluded. Qualitative variables were expressed as frequency and percentage while quantitative variables were expressed as mean and S.D. Chi-square test was applied.
 Results: Out of 150 academics, 72(48%) were males and 78(52%) were females. Mean age of participants was 32±9.4 years. Amongst the study participants 27(18%), 67(44.7%), 50(33.3%) and 6(4%) suffered from mild, moderate, severe and extreme online fatigue respectively. The degree of online fatigue was found to be significantly associated with female gender (p=0.000), older age (p=0.005) and higher level of education (p=0.002). Association of online fatigue with factors such as workplace(p=0.054), residence (p=0.129) and amount of technology usage (p=0.995) was found to be statistically insignificant.
 Conclusions: Our study showed that online fatigue was prevalent among the academic community of Rawalpindi, especially females. Online fatigue can be reduced by training the academicians about work-life balance and updating their knowledge on practical technology.

Load and fatigue monitoring in musicians using an online app: A pilot study.

High occupational injury rates are reported in musicians, with a career prevalence of up to 89%. Fatigue and playing (over)load are identified as key risk factors for musicians' injuries. Self-report fatigue management strategies in sport have demonstrated preventive effects. A self-report fatigue management tool for musicians was developed based on a Delphi survey of international experts and hosted in an online app. The aims of this study are to evaluate the content validity and uptake of this new tool, and explore associations between collected performance quality, physical/psychological stress, pain, injury and fatigue data. University and professional musicians were asked to provide entries into the online app twice per week for 1-6 months. Entries into the app were designed to take 2-3 min to complete and consisted of the following: 6 questions regarding playing load over the previous 72 h; 5 questions regarding current levels in key physical/psychological stress domains (sleep, recovery, overplaying, pain, fitness); one question self-rating of performance quality over the previous day; one question regarding current musculoskeletal symptoms; a reaction time task to evaluate psychomotor fatigue. N = 96 participants provided an average of 2 app entries (range 0-43). Increased playing time, rating of perceived exertion (RPE), and feelings of having to "play too much" were consistently associated with increased self-rated performance quality (p ≤ 0.004; 6.7 <| t |< 2148.5). Increased ratings of feeling fit and recovering well were consistently associated with reduced pain severity (p < 0.001; 3.8 <| t |< 20.4). Pain severity was increased (6.5/10 vs. 2.5/10; p < 0.001) in participants reporting playing-related musculoskeletal disorders (PRMDs; symptoms affecting playing). The prospective value of regular individual self-report playing load, stress, and performance data collection in musicians is clear. However, limited uptake of the online fatigue management app piloted in this study indicates that new approaches to the collection of these data are needed to realize their potential impact.

Study on stress-based fatigue monitoring methodology for Class 1 nuclear pipe based on the RCC-M code

On-line fatigue monitoring technology plays a key role in the LTO of nuclear power plants, for which the main challenges are: how to gain the thermal shock stress quickly and reliably, how to order the random stress history, and how to do the code-coforming fatigue assessment (eg. the RCC-M Code and ASME Code). Compared to the calculation process of FEM, the numerical technique based on the Green’s function is efficient and reliable. The stress due to a linear radial temperature distribution which was previously classified as a secondary stress is re-classified as a peak stress in the ASME Code while this requirement is not supported in the mainstream FEM software (such as ANSYS and ABAQUS). Traditionally, detection of peaks and valleys in the stress history is simply based on the change on sign of the slope of the major stress component (1-D rain flow) which is not strict compliance with the requirement in the codes. Therefore, a refined strategy for detecting peaks and valleys needs to be developed. In this paper, the Green’s function is used to calculate the thermal shock stress for the TECMAN on-line fatigue monitoring software which is developed by the author's team, and the method to separate the stress due to the linear radial temperature distribution and the 3-D rain flow technology for the random stress history with a multiaxial stress state are discussed. In the study case of a prototypical nozzle under rapid thermal shock, the results show that the simple integration method of the Green's function gives identical results as the conventional FEM, and the maximum axial stress caused by the radial linear temperature distribution under the reference transient accounts for 7.03% of the design specification limit 3Sm. Compared to the design requirements in the ASME Code, the criteria in the RCC-M Code is unnecessarily conservative. When the extreme values of the stress components are obtained almost at the same time, the nonconservative for the 1-D rain flow is limited, while the nonconservative cannot be negligible under a multiaxial stress state.

Train Driver Fatigue Detection Using Eye Feature Vector and Support Vector Machine

Fatigue driving is one of the main causes of traffic accidents. The eye features are the important cues of fatigue detection. In order to improve the accuracy and robustness of detection based on a single eye feature, we propose a fatigue detection algorithm based on the eye feature (EFV) vector. Firstly, the coordinates of the eye region were localized with facial landmarks detector and the landmarks geometric relation (LGR) was calculated as a feature value. Secondly, a deep transfer learning network was designed to classify the driver eye state on a small dataset. The probability value of the eyes being open state was calculated. Then an eye feature vector was constructed to overcome the limitations of a single fixed threshold and a support vector machine (SVM) model was trained for eye state classification recognition. Finally, the performance of the proposed detection model was evaluated by the percentage of eyelid closure over time (PERCLOS) criterion. The results show that the accuracy of this model can reach 91.67% on the test database, which is higher than the single-feature-based method. This work lays a foundation for the online fatigue detection of train drivers and the deployment of the train driving monitoring system.

Effect of physical exercise on cognitive function after chemotherapy in patients with breast cancer: a randomized controlled trial (PAM study)

BackgroundUp to 60% of breast cancer patients treated with chemotherapy is confronted with cognitive problems, which can have a significant impact on daily activities and quality of life (QoL). We investigated whether exercise training improves cognition in chemotherapy-exposed breast cancer patients 2–4 years after diagnosis.MethodsChemotherapy-exposed breast cancer patients, with both self-reported cognitive problems and lower than expected performance on neuropsychological tests, were randomized to an exercise or control group. The 6-month exercise intervention consisted of supervised aerobic and strength training (2 h/week), and Nordic/power walking (2 h/week). Our primary outcome was memory functioning (Hopkins Verbal Learning Test-Revised; HVLT-R). Secondary outcomes included online neuropsychological tests (Amsterdam Cognition Scan; ACS), self-reported cognition (MD Anderson Symptom Inventory for multiple myeloma; MDASI-MM), physical fitness (relative maximum oxygen uptake; VO2peak), fatigue (Multidimensional Fatigue Inventory), QoL (European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire; EORTC QLQ C-30), depression (Patient Health Questionnaire-9, Hospital Anxiety and Depression Scale; HADS), and anxiety (HADS). HVLT-R total recall was analyzed with a Fisher exact test for clinically relevant improvement (≥ 5 words). Other outcomes were analyzed using multiple regression analyses adjusted for baseline and stratification factors.ResultsWe randomized 181 patients to the exercise (n = 91) or control group (n = 90). Two-third of the patients attended ≥ 80% of the exercise sessions, and physical fitness significantly improved compared to control patients (B VO2peak 1.4 ml/min/kg, 95%CI:0.6;2.2). No difference in favor of the intervention group was seen on the primary outcome. Significant beneficial intervention effects were found for self-reported cognitive functioning [MDASI-MM severity (B-0.7, 95% CI − 1.2; − 0.1)], fatigue, QoL, and depression. A hypothesis-driven analysis in highly fatigued patients showed positive exercise effects on tested cognitive functioning [ACS Reaction Time (B-26.8, 95% CI − 52.9; − 0.6) and ACS Wordlist Learning (B4.4, 95% CI 0.5; 8.3)].ConclusionsA 6-month exercise intervention improved self-reported cognitive functioning, physical fitness, fatigue, QoL, and depression in chemotherapy-exposed breast cancer patients with cognitive problems. Tested cognitive functioning was not affected. However, subgroup analysis indicated a positive effect of exercise on tested cognitive functioning in highly fatigued patients.Trial Registration Netherlands Trial Registry: Trial NL5924 (NTR6104). Registered 24 October 2016, https://www.trialregister.nl/trial/5924.

EMG-driven fatigue-based self-adapting admittance control of a hand rehabilitation robot

Upper-limb rehabilitation therapy sessions for post-stroke people generally contain rhythmic hand movements in a tiresome manner to rebuild the injured neural circuits. Fatigue formation causes breaks in the training and limits the therapy duration. Therefore, it is essential to establish a correlation between the patient’s muscle condition and the rehabilitation exercises to improve the physiotherapy sessions. A self-adapting control method based on online fatigue detection in rhythmic arm movements is presented. Experimental tests were performed on twenty healthy subjects to validate the method’s feasibility. Electromyography (EMG) and force signals considered the interfaces between users and the robot. In the first stage of the experiment, utilizing the frequency features from EMG signals, a neural network for fatigue detection trained; however, in the end, it substituted with a simple function as a refinement in the time-consuming aspect for the online employment. The initiation of the fatigue process is followed by reducing the admittance controller damping term based on the EMG signal processing. Trajectory tracking with the robot employs the self-adapting admittance controller (SAAC) method and the non-adapting admittance controller (NAAC). Movement accuracy and smoothness were measured and showed a better performance of the SAAC method related to the NAAC. Simulations with two different stiffness levels were performed on an upper-limb OpenSim model to study a stroke-injured arm and evaluate the proposed method’s proficiency. The metabolic cost indicated the movement’s superiority in a fatigue situation for reduced environment stiffness.

A Bayesian approach for fatigue damage diagnosis and prognosis of wind turbine blades

This paper proposes a Bayesian framework based on particle filters for online fatigue damage diagnosis and prognosis for wind turbine blades (WTBs). The framework integrates theoretical and practical aspects with the purpose of developing a robust monitoring tool. Besides, a damage indicator based on identified modal frequencies of the WTB is defined to quantify the degree of damage to the monitored blade. Furthermore, feature extraction techniques on vibration signals are considered to obtain the online observations and inputs needed by the Bayesian framework. Experimental data collected from a fatigue test performed on a WTB was used to validate the proposed methodology. The results obtained by the damage diagnosis algorithm show the great potential of the Bayesian processor for damage estimation of the monitored WTB and uncertainty quantification related to the estimated variable. According to the damage prognosis results, the proposed algorithm could generate suitable long-term predictions of the damage indicator so as to estimate the time-of-failure (ToF) probability mass function (PMF) for the monitored WTB. Consequently, the computed ToF-PMF was able to include the experimental ToF within its 95% confidence interval, demonstrating the accuracy of the Bayesian approach.

From in-person to virtual engagement: Adaptations of a participative process for designing a marine litter public policy in Brazi

Marine litter is a transversal issue that affects the envi- ronment and society in a multitude of ways. As such, solutions to this problem are complex and demand the engagement of multiple sectors of society. The São Paulo Strategic Plan for Monitoring and Assessment of Marine Litter (PEMALM) is the first public policy of its kind, seeking to establish indicators and build knowledge to move towards a plan to combat marine litter in the most populous state in Brazil. From its inception, PEMALM has sought to establish a participative construction frame- work, involving key stakeholders at each step. When the Covid-19 pandemic struck, the participative construction process had to be adapted. Here we present and discuss&nbsp;the strategies applied in the participatory process of PEMALM to guarantee the remote engagement of stakeholders. Three participatory milestones were part of the final policy-making process: a first in-person workshop which gathered stakeholders in a single location, a series of in-person meetings in which the project team travelled to where the stake- holders are located, and, due to the Covid-19 pandemic, an entirely virtual workshop. Sector participation was found to be alike for online and on-site events, with higher participation of the public sector, followed by academia, NGOs and the private sector in both. The adjustments and the adaptive effort placed on the participatory process due to the Covid-19 pandemic, such as being dedicated and attentive to the needs of attendees, expanding the modes of interaction and promoting a flexible and light schedule to reduce online fatigue, guaranteed the quality of stakeholder engagement and participation. The positive accomplishments of the hybrid strategy used in building PEMALM as a public policy exemplifies ways to facilitate and broaden participation in the co-construction under mobility restrictions.

Fatigue Crack Evaluation with the Guided Wave-Convolutional Neural Network Ensemble and Differential Wavelet Spectrogram.

On-line fatigue crack evaluation is crucial for ensuring the structural safety and reducing the maintenance costs of safety-critical systems. Among structural health monitoring (SHM), guided wave (GW)-based SHM has been deemed as one of the most promising techniques. However, the traditional damage index-based method and machine learning methods require manual processing and selection of GW features, which depend highly on expert knowledge and are easily affected by complicated uncertainties. Therefore, this paper proposes a fatigue crack evaluation framework with the GW–convolutional neural network (CNN) ensemble and differential wavelet spectrogram. The differential time–frequency spectrogram between the baseline signal and the monitoring signal is processed as the CNN input with the complex Gaussian wavelet transform. Then, an ensemble of CNNs is trained to jointly determine the crack length. Real fatigue tests on complex lap joint structures were carried out to validate the proposed method, in which several structures were tested preliminarily for collecting the training dataset and a new structure was adopted for testing. The root mean square error of the training dataset is 1.4 mm. Besides, the root mean square error of the evaluated crack length in the testing lap joint structure was 1.7 mm, showing the effectiveness of the proposed method.

Prevalence and health correlates of Onine Fatigue: A cross-sectional study on the Italian academic community during the COVID-19 pandemic.

BackgroundDuring the COVID-19 pandemic, many people had to shift their social and work life online. A few researchers and journalists described a new form of fatigue associated with a massive use of technology, including videoconferencing platforms. In this study, this type of fatigue was referred to as Online Fatigue. A new tool (the Online Fatigue Scale) was developed, and its psychometric properties were evaluated. This tool was used to assess Online Fatigue among Italian academics and to examine its associations with psychological and physical health.MethodsAn online survey was conducted in December 2020 on a sample of Italian academics. Besides the Online Fatigue Scale (11 items) used to assess Online Fatigue, the survey was composed of questionnaires (including validated measures) focused on sociodemographic and job-related information, technostress creators, health status, psychological well-being, and COVID-related perceived distress. The psychometric properties of the Online Fatigue Scale were evaluated, and statistical analyses were conducted to examine the associations between Online Fatigue and all the other variables.ResultsParticipants were 307 academics aged 24–70 years old (mean age = 40.7; SD = 10.1). The Online Fatigue Scale showed good psychometric properties. Two subscales were identified: Off-Balance Fatigue and Virtual Relations Fatigue. High levels of Off-Balance Fatigue were associated with a greater use of technology, female gender, and presence of minor children. Participants with high scores on both subscales reported a greater frequency of psychosomatic symptoms, unhealthy habits, poorer psychological well-being, and greater Covid-related perceived distress.ConclusionsThe Online Fatigue Scale can be considered a reliable tool to assess Online Fatigue, which was significantly detected in our sample of Italian academics, along with its negative effects on physical and psychological health. Being a woman and having young children represent important risk factors. Universities should promote the separation between work and private life by encouraging self-care activities.