Fatigue Conditions Research Articles

The damage mechanism of tension-tension fatigue interaction with creep damage of the compacted graphite cast iron alloy at high temperatures

The tension-tension fatigue test of the compacted graphite cast iron (CGI) alloy was carried out by RDL100 universal testing at 500 °C and 550 °C, respectively. A tension-tension trapezoidal load is applied to the CGI specimen. Because of the time-dependent deformation at elevated temperatures, the stress–strain curve presents hysteresis loops, and the area of the hysteresis loop increases gradually with continuous cyclic loading and sustained loading times. Intergranular and transgranular cracks in the microstructure accelerate the CGI alloy fracture failure. The fatigue life is sensitive to the short loading time and decreases with the sustained loading time exponentially under the tension-tension fatigue condition. The short holding time has a great influence on the fatigue life of CGI. The fatigue behavior of CGI alloys and the influence of holding time on the fatigue life can be characterized by y = aexp(bx) (a and b are constants, can be fitted through the test data). In addition, the fatigue life of CGI alloy can be predicted by the ductility depletion method. But the equivalent stress amplitude needs to be modified to eliminate the effects of oxidation damage.

Deep Learning for Pothole Detection on Indonesian Roadways

Accidents are common on Indonesian roadways. Accidents are caused by vehicles, motorcycles, and public transportation. Road fatalities are caused by speeding, alcohol, distraction, fatigue, and poor road conditions. There are numerous car accidents on Indonesian roadways. 30% of Indonesian traffic incidents are explained by road infrastructure and environmental conditions, 61% by driver skill and personality, and 9% by vehicle variables such as vehicle standardization. Cars are damaged, immobilized, and crashed as a result of road conditions. Every hour, three people pass away in traffic in Indonesia, according to authorities. According to the BPS's 2021 Land Transportation Statistics report, 31.91 percent of Indonesia's roads were damaged, totaling 174,298 kilometers. Accidents among Indonesian motorists are becoming more common as roads deteriorate. Using a single camera, a deep learning algorithm can recognize and detect road degradation such as potholes and road cracks. Train and process the model using transfer learning and fine-tuning on the Nano YOLOv5 model architecture. After being validated in three major scenarios, the model performs well with the appropriate confidence level. The precision metric for the model is 0.8, while recall and mAP:0.5 are both 0.5.

Lifting velocity predicts the maximum number of repetitions to failure with comparable accuracy during the Smith machine and free-weight prone bench pull exercises

This study compared the accuracy of the fastest mean velocity from set (MVfastest) to predict the maximum number of repetitions to failure (RTF) between 2 variants of prone bench pull (PBP) exercise (Smith machine and free-weight) and 3 methods (generalized, individualized multiple-point, and individualized 2-point). Twenty-three resistance-trained males randomly performed 2 sessions during Smith machine PBP and 2 sessions during free-weight PBP in different weeks. The first weekly session determined the RTF-MVfastest relationships and subjects completed single sets of repetitions to failure against 60-70-80-90%1RM. The second weekly session explored the accuracy of RTFs prediction under fatigue conditions and subjects completed 2 sets of 65%1RM and 2 sets of 85%1RM with 2 min of rest. The MVfastest associated with RTFs from 1 to 15 were greater for Smith machine compared to free-weight PBP (F ≥ 42.9; P < 0.001) and for multiple-point compared to 2-point method (F ≥ 4.6; P ≤ 0.043). The errors when predicting RTFs did not differ between methods and PBP variants, whereas all RTF-MVfastest relationships overestimated the RTF under fatigue conditions. These results suggest that RTF–MVfastest relationships present similar accuracy during Smith machine and free-weight PBP exercises and it should be constructed under similar training conditions.

Association of Short-Chain Fatty Acids with Gut Microbiota and Lipid Metabolism in Mice with Diarrhea Induced by High-Fat Diet in a Fatigued State.

Preliminary research finds that a high-fat diet (HFD) in a fatigued state triggers diarrhea, but the exact mechanism has not been clarified. To address concerns about the pathogenesis of diarrhea, the study evaluates the composition and metabolomics of the gut microbiota. The study uses the multiple platform apparatus device to induce fatigue in mice, combined with intragastric administration of lard-caused diarrhea. Subsequently, the characteristics and interaction relationship of gut microbiota, short-chain fatty acids (SCFAs), inflammatory biomarkers, brain-gut peptides, and lipid metabolism are analyzed at the end of the experiment. HFD in a fatigued state results in a significant increase in interleukin-17, interleukin-6, cholecystokinin, somatostatin, and malondialdehyde content in mice (p < 0.05), along with a substantial decrease in high-density lipoprotein (p < 0.05). Additionally, an HFD in a fatigued state causes changes in the structure and composition of the gut microbiota, with Lactobacillus murinus as its characteristic bacteria, and reduces the production of SCFAs. An HFD in a fatigued state triggers diarrhea, possibly associated with gut content microbiota dysbiosis, SCFAs deprivation, increased inflammation, and dysregulated lipid metabolism.

Human muscle fatigue monitoring method and its application for exoskeleton interactive control

Aiming at the human-computer interaction problem during the movement of the rehabilitation exoskeleton robot, this paper proposes an adaptive human-computer interaction control method based on real-time monitoring of human muscle state. Considering the efficiency of patient health monitoring and rehabilitation training, a new fatigue assessment algorithm was proposed. The method fully combined the human neuromuscular model, and used the relationship between the model parameter changes and the muscle state to achieve the classification of muscle fatigue state on the premise of ensuring the accuracy of the fatigue trend. In order to ensure the safety of human-computer interaction, a variable impedance control algorithm with this algorithm as the supervision link was proposed. On the basis of not adding redundant sensors, the evaluation algorithm was used as the perceptual decision-making link of the control system to monitor the muscle state in real time and carry out the robot control of fault-tolerant mechanism decision-making, so as to achieve the purpose of improving wearing comfort and improving the efficiency of rehabilitation training. Experiments show that the proposed human-computer interaction control method is effective and universal, and has broad application prospects.

Prediction of the effect of shot peening residual stress on fretting fatigue behaviour

Fretting occurs at two contact bodies, which sustain oscillation loading. The fretting phenomenon decreases the fatigue lifetime dramatically. Shot peening is a commonly used surface treatment to enhance the resistance to damage because the residual stress is introduced to the surface of the material. Some studies showed that residual stress was helpful to improve fatigue lifetime. Under fretting fatigue condition, there are two main stages: crack initiation stage and the crack propagation stage. In this paper, the authors apply the Critical Plane (CP) method combined with the Theory of Critical Distance (TCD) to study the fretting fatigue crack initiation behaviour under the influence of shot peening residual stress, including the crack initiation position, orientation and the crack initiation lifetime. Meanwhile, the Linear Elastic Fracture Mechanics (LEFM) theory and the Extension Maximum Tangential Stress (EMTS) criterion are adopted to study crack propagation behaviour considering shot peening residual stress effect. The results show that the methods applied in this paper can make good predictions of fretting fatigue behaviour, including both initiation and propagation stages. Meanwhile, the influence of residual stress in these two stages are studied as well.

EEG-based neural networks approaches for fatigue and drowsiness detection: A survey

Drowsiness is a state of fatigue or sleepiness characterized by a strong urge to sleep. It is correlated with a progressive decline in response time, compromised processing of available information, more errors in short-term memory, and reduced vigilance behaviors. The electroencephalogram (EEG), a recording of the brain’s electrical activities, has demonstrated the most robust association with drowsiness. As a result, EEG is widely recognized as a dependable indicator for evaluating drowsiness, fatigue, and performance levels. In this survey paper, we thoroughly investigate the application of shallow and deep neural network approaches utilizing EEG signals for the detection of fatigue and drowsiness. As far as our knowledge extends, this is the pioneering survey paper dedicated to exploring this specific research domain. The paper presents a comprehensive overview of the diverse EEG features utilized in the detection of fatigue and drowsiness, the different types of neural networks, and the reported performance of these methods in the literature. Additionally, the paper thoroughly examines the challenges and limitations associated with EEG-based fatigue and drowsiness detection and highlights directions for future research. The survey aims to offer a comprehensive overview of the existing methods in EEG-based fatigue and drowsiness detection, serving as a valuable resource for researchers and practitioners working in the respective field.

DUO-GAIT: A gait dataset for walking under dual-task and fatigue conditions with inertial measurement units

In recent years, there has been a growing interest in developing and evaluating gait analysis algorithms based on inertial measurement unit (IMU) data, which has important implications, including sports, assessment of diseases, and rehabilitation. Multi-tasking and physical fatigue are two relevant aspects of daily life gait monitoring, but there is a lack of publicly available datasets to support the development and testing of methods using a mobile IMU setup. We present a dataset consisting of 6-minute walks under single- (only walking) and dual-task (walking while performing a cognitive task) conditions in unfatigued and fatigued states from sixteen healthy adults. Especially, nine IMUs were placed on the head, chest, lower back, wrists, legs, and feet to record under each of the above-mentioned conditions. The dataset also includes a rich set of spatio-temporal gait parameters that capture the aspects of pace, symmetry, and variability, as well as additional study-related information to support further analysis. This dataset can serve as a foundation for future research on gait monitoring in free-living environments.

Effect of sustained load on fatigue crack growth behavior of FGH96 at elevated temperature

Accurate prediction of fatigue crack growth behavior is crucial to damage tolerance assessment of turbine disk. Powder nickel-based superalloy is a kind of preferred material of turbine disk. For the purpose of better understanding the characteristics of its crack growth behavior under dwell fatigue conditions at elevated temperature, dwell fatigue crack growth tests of powder nickel-based superalloy FGH96 were conducted with compliance method at three temperatures (600℃, 650℃, 700℃) and multiple dwell time conditions (from 0 s to 60 s). The results show that sustained load can accelerate fatigue crack growth rate of FGH96 significantly at high temperature, and it is more pronounced with higher temperature and prolonged dwell time. At higher ΔK level, the acceleration effect brought by sustained load seems to diminish, this is ascribed to rapid crack growth rate which leads to insufficient time for time-dependent damage occurring at crack tip zone. The effect of temperature on dwell fatigue crack growth rate is more remarkable from 650℃ to 700℃, compared with that from 600℃ to 650℃. The fatigue crack growth resistance of FGH96 is sensitive to sustained load at 700℃, just 1 s dwell loading can produce remarkable acceleration effect on fatigue crack growth rate. The time-dependent crack extension in a loading cycle increases with dwell time in a decelerated manner. Finally, A phenomenological-based crack growth model considering time-dependent damage was proposed, the comparation between the proposed model and the experiment data confirms that it works well for a wide range of temperature and dwell time.

Fatigue Life Evaluation of Wind Turbine Tower Based on Measured Data

Fatigue life is a crucial design factor which dictates the safe operation of the wind turbines, but it is influenced by uncertain factors such as environmental loads, analytical models, material properties, and manufacturing methods. In this study, a 1.5 MW wind turbine was monitored in operation to understand the fatigue mechanism and enhance wind turbine design. The influence of different operating conditions on fatigue damage was analyzed by correlating strain monitoring data with supervisory control and data acquisition (SCADA) data. Furthermore, a fatigue evaluation method based on measured strain data was proposed. Fatigue damage increases with the increase of wind and rotation speed. More than 50% of the damage occurred at the rated rotation speed state, the corresponding wind speed was greater than the rated wind speed and the pitch control system was active. The findings of this study provide insights for investigating the real fatigue state of similar wind turbine towers and improving the return on investment by closely estimating their service life.

ELearning improves allied health professionals' knowledge and confidence to manage medically unexplained chronic fatigue states: A randomized controlled trial

ObjectivesTo evaluate the impact of eLearning by allied health professionals on improving the knowledge and confidence to manage people with medically unexplained chronic fatigue states (FS). MethodsUsing a parallel randomized controlled trial design, participants were randomized 1:1 to a 4-week eLearning or wait-list control group. Knowledge and self-reported confidence in clinical skills to implement a therapeutic intervention for patients with FS were assessed at baseline, post-intervention and follow-up. Secondary outcomes (adherence and satisfaction with online education, knowledge retention) were also assessed. Data was analyzed using intention-to-treat. ResultsThere were 239 participants were randomized (eLearning n = 119, control n = 120), of whom 101 (85%) eLearning and 107 (89%) control participants completed baseline assessments and were included in the analysis. Knowledge (out of 100) improved significantly more in the eLearning group compared to the control group [mean difference (95% CI) 8.6 (5.9 to 11.4), p < 0.001]. Knowledge was reduced in the eLearning group at follow-up but was still significantly higher than baseline [6.0 (3.7 to 8.3), p < 0.001]. Median change (out of 5) in confidence in clinical skills to implement the FS intervention was also significantly greater in the eLearning group compared to the control group [knowledge: eLearning (1.2), control (0); clinical skills: eLearning (1), control (0.1); both p < 0.001)]. Average time spent on the eLearning program was 8.8 h. Most participants (80%) rated the lesson difficulty as at the “right level”, and 91% would recommend it to others. ConclusionseLearning increased knowledge and confidence to manage FS amongst allied health professionals and was well-accepted.Registration: ACTRN12616000296437 https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370222&isReview=true.

Evaluation of Junk Food Consumption and the Risk Related to Consumer Health among the Romanian Population.

Premature aging and degradative processes are mainly generated by unhealthy habits and an unbalanced diet. Quality of food and lifestyle are important factors in sano-genesis. Many imbalances and ailments have their origin in the adoption of an unbalanced diet and a disordered lifestyle. With the help of a transversal study carried out on the basis of a questionnaire, the consumption of junk food products among the population of Romania was evaluated; at the same time, an evaluation of the characteristics of the associated diet, as well as a series of lifestyle components (quality of rest, physical activity, evaluation of the state of health) was carried out. The data collected and processed indicate an increased tendency to consume junk food products in the 18-23 age group, and especially among obese respondents. Female respondents show a lower tendency toward an increased consumption of junk food products (OR = 0.703, 95% CI)-0.19-0.95, p = 0.011) compared to male respondents. The most consumed junk food products are fried potatoes (46.2%) and pastries (41.4%). Junk food products that show an increased tendency toward consumption addiction are fried potatoes (13.8%), sweets (12.4%), pastry products (11.1%), and sweetened drinks (11.2%). The poor quality of food from a nutritional point of view, and reduced physical activity, are reflected in the varied range of problems faced by the respondents: states of fatigue (62.4%), nervousness (37.5%), depression, anxiety, emotional eating, etc.

Effects of video display terminal fatigue on situational awareness ability of operators and modeling study

In order to study the changing pattern of the posture perception ability of video display terminal operators in the main control room of energy power plants such as nuclear power plants in a state of fatigue, subjective and objective data of 19 energy system operators were collected through experiments. The effect of video display terminal fatigue on the operator's level of situational awareness was analyzed, and the subjective and objective data were selected to construct an XGboost model to predict the identified operator's situational awareness capability. The study results showed that the subjective and objective data showed significant differences, and it could be inferred that the subjects had developed video display terminal fatigue. The combination of data showed that fatigue affected the manipulator's ability to perceive and understand, decreasing the manipulator's situational awareness. The XGboost model, based on subjective and objective data, achieved a validation rate of 88.90%, indicating that the model can identify and predict the situational awareness of video display terminal manipulators. The study's results can be used to assess the situational awareness of video display terminal operators in nuclear power plants and provide a scientific basis for safety training in nuclear power plants and other energy power plants.

Lower Limb Muscle Fatigue Alters Spatiotemporal Gait Parameters and Turning Difficulty Characteristics in Parkinson's Disease.

To determine the effects of lower limb muscle fatigue on spatiotemporal gait parameters and turning difficulty characteristics during the extended Timed Up and Go (extended TUG) test in individuals with different severity stages of Parkinson's disease (PD). Forty individuals with PD, classified as Hoehn and Yahr (H&Y) stages 2 and 3 participated in this pre- and post-experimental study design. The participants performed a continuous sit-to-stand task from a chair based on 30 cycles/min set-up to induce lower limb muscle fatigue. They performed extended TUG test immediately before and after completing the fatigue protocol. Spatiotemporal gait parameters and turning difficulty characteristics were recorded using two GoPro® Hero 4 Silver cameras. Data were subjected to a repeated-measure ANOVA. Individuals with PD experience significant changes in spatiotemporal gait parameters, specifically stride velocity and length, under conditions of lower limb muscle fatigue (p=0.001). These changes were more pronounced in individuals with PD in the H&Y stage 3 group. Additionally, both PD groups exhibited difficulty with turning, requiring more than five steps to complete a 180° turn and taking more than 3 seconds to accomplish it. These findings highlight the impact of muscle fatigue on gait performance in PD and suggest that individuals in later stages of the disease may be particularly affected. Further research is needed to explore interventions that can mitigate these gait impairments and improve mobility in individuals with PD.

Effects of Motivational Interviewing and Mindfulness-based Cognitive Therapies on Academic Burnout among Extra-mural Students in Ekiti State

Academic burnout is a mental and emotional fatigue state caused by continuous stress among extra-mural students. This syndrome is usually caused by repeated failure and overload of work in the school, which eventually leads to high dropout rate, truancy and failure. Academic burnout hinders academic success of students, as it truncates their advancement in moving to the next academic stage. Previous studies focused largely on predictors of academic burnout of in-school adolescents, while little attention was paid to extra-mural students. This study, therefore, was designed to determine the effects of Motivational Interviewing Therapy (MIT) and Mindfulness-Based Cognitive Therapy (M-BCT) on academic burnout among extra-mural students in Ekiti State, Nigeria. Moderating effects of academic self-efficacy and goal setting were also examined. Pretest-posttest control group quasi experimental design with 3x3x3 factorial matrix was adopted. Three extra-mural centres were randomly selected in Ekiti State, while the simple random sampling technique was used to select participants from each of the remedial study centres. The remedial centres were randomly assigned to MIT (29), M-BCT (51) and control (25) groups, making a total of 105 participants. The instruments used were Academic Burnout Indicator Inventory (for screening – ?=0.92), Maslash Academic Burnout (?=0.70), Academic self-Efficacy (?=0.86) and Goal Setting (?=0.80) scales and instructional guides. Data were subjected to descriptive statistics, Analysis of covariance and Scheffe post-hoc test at 0.05 level of significance. Participants’ age was 22.14±3.15years. The majority of the participants were female (61.5%). There was a significant main effect of treatment on academic burnout of the participants (F(2;84)=140.13. partial ?2=0.77). The participants in the control group (111.04) had the highest mean scores on academic burnout, followed by the M-BCT (88.20) and the MIT (61.59) groups. There was no significant main effect of academic self-efficacy and goal setting on academic burnout. There was a significant interaction effect of treatment and goal setting on academic burnout (F(2;84) =3.93, partial ?2=.16), in favour of the participants with high goal setting from the MIT group. Motivational interviewing and mindfulness-based cognitive therapies reduced academic burnout among extra-mural students in Ekiti State, Nigeria. Counselling psychologists and other help professionals should adopt these therapies.

Management of Fatigue in Post-Stroke Patients

Post-stroke patients often have sequelae that are difficult to avoid. One of the impacts that can occur after a stroke is the onset of cognitive impairment, physical disability, and even death due to the extent of brain damage that occurs. Another impact of stroke is Fatigue Post Stroke (FPS), where most stroke patients experience fatigue, and this condition can persist even though other symptoms caused by stroke have improved. This literature review aims to determine the appropriate management of fatigue after a stroke. The method in this study is a literature review by collecting published literature from databases such as ResearchGate, Google Scholar, and PLOS using Boolean stroke management and post-stroke fatigue. The literature search focused on articles related to fatigue in stroke patients. Both used RCT (Randomized Control Trial) design, Quasi Experiment, Qualitative study, and Survey study from the year (2015-2022). Based on the results of data extraction from the author, two themes can be taken by the author, post-stroke patients who experience fatigue and fatigue management in stroke. The update in this literature review is the manifestation of fatigue conditions that, on average, will be experienced by stroke patients. If not handled properly, it will have a worse impact physically and psychologically, which can certainly interfere with the recovery process. So, it is necessary to appropriately manage both physical exercise and psychological interventions following the fatigue conditions of post-stroke patients. We recommend that in health services, nurses pay more attention to patient responses related to fatigue to provide appropriate nursing interventions and increase patients' desire to undergo rehabilitation.

Multi-Modality Tensor Fusion Based Human Fatigue Detection

Multimodal learning is an expanding research area and aims to pursue a better understanding of given data by regarding different modals. Multimodal approaches for qualitative data are used for the quantitative proofing of ground-truth datasets and discovering unexpected phenomena. In this paper, we investigate the effect of multimodal learning schemes of quantitative data to assess its qualitative state. We try to interpret human fatigue levels through analyzing video, thermal image and voice data together. The experiment showed that the multimodal approach using three types of data was more effective than the method of using each dataset individually. As a result, we identified the possibility of predicting human fatigue states.

DESKRIPSI PENGETAHUAN DAN INDEKS DMF-T DALAM RANGKA PENCEGAHAN MASALAH KESEHATAN GIGI DAN MULUT PADA SISWA BINA TALENTA GRAHA HARAPAN INDAH BEKASI

The health of school students is part of the health development and prevention program by the Ministry of Health Republic of Indonesia. Improvement of general health will affect oral health. Prevention of oral health problems for Bina Talenta Graha students ranged from DMF-TT intraoral examination, dental health education, pre-test and post-test 33questionnaires, and mass toothbrushing activities. 44 kindergarten and 227 elementary school students participated in the program. DMF-T index was categorized according to WHO, in which the index for kindergarten is medium (3.92), while for elementary school classes is very high (8.7). Caries in both classes were more prevalent in girls than boys. The average score for kindergarten was 97.89 (pre-test) and 95.95 (post-test), while that of elementary school was 69 (pre-test) and 66.50 (post-test). Statistical tests for pre- and post-test yielded p = 0.623. for kindergarten and p= 0.151 for primary school. As p&gt;0.05, there was no significant difference between pre- and-post tests for both groups. The decline in scores can be caused by short duration of dental health education, pre-, and post-tests. Environmental factors such as noise, visual disturbances, fatigue, and uncomfortable room conditions also played a role. By considering those factors, next activites can be planned and done more optimally

Dual task scheduling strategy for personalized multi-objective optimization of cycle time and fatigue in human-robot collaboration

The increased adoption of collaborative robots into human dominated work cells improves production efficiency of hard-to-automate manufacturing tasks. System-centric task scheduling objectives aim to fully utilize all available agents, often physically burdening workers with schedules designed maximized efficiency. Therefore, the modeling and optimization of human fatigue, as one of the main contributors to efficiency decline and adverse health conditions, is significant to task scheduling in Human-Robot Collaboration (HRC). Furthermore, HRC teams often involve drastically different workers with differing capabilities and muscle strengths – with direct implication toward their personalized fatigue responses. As such, we present a dual scheduling strategy for personalized multi-objective optimization of cycle time and fatigue considering recovery in HRC. The scheduling strategy involves two fatigue minimization objectives which either minimize the team’s fatigue state or an individual worker’s fatigue state. This balances fatigue accumulation between workers, providing targeted rest and recovery to specific workers while maintaining production efficiency. We designed a custom NSGA-III genetic algorithm for simultaneously minimizing cycle time and fatigue. Our model and algorithm are applied to an HRC assembly case and show promising results in redistributing tasks between agents to minimize personalized fatigue.

A Novel Approach to Detecting Muscle Fatigue Based on sEMG by Using Neural Architecture Search Framework.

Muscle fatigue detection is of great significance to human physiological activities, but many complex factors increase the difficulty of this task. In this article, we integrate several effective techniques to distinguish muscle states under fatigue and nonfatigue conditions via surface electromyography (sEMG) signals. First, we perform an isometric contraction experiment of biceps brachii to collect sEMG signals. Second, we propose a neural architecture search (NAS) framework based on reinforcement learning to autogenerate neural networks. Finally, we present an effective two-step training strategy to improve the performance by combining CNN with three types of commonly used statistical algorithms. Meanwhile, we propose a data enhancement algorithm based on empirical mode decomposition (EMD) to generate time-series data for expanding the dataset. The results show that this search algorithm can hunt for high-performing networks, and the accuracy of the best-selected model combined with support vector machine (SVM) for the group is 96.5%. With the same architecture, the average accuracy in individual models is 97.8%. The proposed data enhancement technique can effectively improve the fatigue detection performance, which allows further implementations in the human-exoskeleton interaction systems.