Fatigue Model Research Articles

Development of a prediction model for clinically-relevant fatigue: a multi-cancer approach.

Fatigue is the most prevalent symptom across cancer types. To support clinicians in providing fatigue-related supportive care, this study aims to develop and compare models predicting clinically relevant fatigue (CRF) occurring between two and three years after diagnosis, and to assess the validity of the best-performing model across diverse cancer populations. Patients with non-metastatic bladder, colorectal, endometrial, ovarian, or prostate cancer who completed a questionnaire within three months after diagnosis and a subsequent questionnaire between two and three years thereafter, were included. Predictor variables included clinical, socio-demographic, and patient-reported variables. The outcome was CRF (EORTC QLQC30 fatigue ≥ 39). Logistic regression using LASSO selection was compared to more advanced Machine Learning (ML) based models, including Extreme gradient boosting (XGBoost), support vector machines (SVM), and artificial neural networks (ANN). Internal-external cross-validation was conducted on the best-performing model. 3160 patients were included. The logistic regression model had the highest C-statistic (0.77) and balanced accuracy (0.65), both indicating good discrimination between patients with and without CRF. However, sensitivity was low across all models (0.22-0.37). Following internal-external validation, performance across cancer types was consistent (C-statistics 0.73-0.82). Although the models' discrimination was good, the low balanced accuracy and poor calibration in the presence of CRF indicates a relatively high likelihood of underdiagnosis of future CRF. Yet, the clinical applicability of the model remains uncertain. The logistic regression performed better than the ML-based models and was robust across cohorts, suggesting an advantage of simpler models to predict CRF.

Ren-Shen-Bu-Qi decoction alleviates exercise fatigue through activating PI3K/AKT/Nrf2 pathway in mice.

Fatigue is a prevalent issue that can lead individuals to a sub-health condition, impacting their work efficiency and quality of life. There are limited effective treatment options available for fatigue. Ren-Shen-Bu-Qi decoction (RSBQD) is a proprietary herbal remedy that is designed to address fatigue. However, the specific pharmacological mechanisms and basis of RSBQD are not yet fully understood. This study aimed to investigate the pharmacological effects and mechanisms of RSBQD in a mouse model of exercise fatigue. UPLC-Q-Orbitrap HRMS was used to analyze the chemical composition of RSBQD. The pharmacological basis and molecular mechanism of RSBQD on exercise fatigue were predicted using network pharmacology analysis. Subsequently, an exercise fatigue mouse model was established and used to analysis the effects of RSBQD. The potential mechanisms were verified by hematoxylin-eosin (HE) staining, real-time fluorescence quantitative PCR (RT-qPCR), Western blot (WB) and molecular docking. The results showed that 88 main components of RSBQD were identified, which have mainly belonged to flavonoids and carboxylic acid compounds. The network pharmacology analysis indicated that RSBQD ameliorate fatigue through PI3K/AKT signaling pathway. Notably, RSBQD prolonged the swimming time and diminished body weight loss of exercise fatigue mice (P < 0.05). Meanwhile, RSBQD significantly alleviated the injury of liver and kidney induced by exhaustive exercise, and decreasing the serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), urea and BUN levels (P < 0.05). In addition, RSBQD was found could relieve exercise fatigue by decreasing the content of creatine kinase (CK), lactate dehydrogenase (LDH), and lactic acid (LA), but increasing the blood glucose (GLU) and liver glycogen (HG) levels (P < 0.05). RSBQD also significantly increased the hepatic superoxide dismutase (SOD), glutathione peroxidase (GSH-Px) but decreased hepatic malondialdehyde (MDA) levels. Moreover, RSBQD was able to upregulate protein level of activated Nrf2 and PI3K/AKT signaling pathways. RSBQD mitigates exercise fatigue by reversing metabolic changes and reducing oxidative damage through the PI3K/AKT/Nrf2 signaling pathway. This study offers pharmacological support for the utilization of RSBQD in exercise fatigue treatment.

Trajectory Prediction Analysis of Basketball Players’ Movements Based on DSM Optimization Algorithm

In this paper, the action trajectory of basketball players is predicted and analyzed by the DSM optimization algorithm. Data on the movements of basketball players, including position, speed and acceleration are collected. The motion trajectory is predicted by the DSM optimization algorithm. At present, computer technology is more and more applied in sports training. Based on this, this paper studies the simulation of basketball players’ motion trajectory prediction. After describing the research background and significance, this paper constructs a human joint model and then proposes the motion process DSM algorithm to simulate the jump shot action of basketball players. In this paper, the algorithm and model are tested, and the experiment adopts the run-jump fatigue model to produce fatigue. The infrared high-speed motion capture system and three-dimensional force measuring table are used to collect the kinematics and dynamics data of the sudden stop jump shot before and after fatigue. The experimental results show that the trajectory prediction simulation proposed in this paper is feasible.

Total minor ginsenosides exert anti-fatigue effects via antioxidant, anti-inflammatory, regulating gut microbiota and serum metabolism

Minor ginsenosides have demonstrated notable anti-fatigue capabilities. The aim of this study was to investigate the anti-fatigue mechanisms of total minor ginsenosides (TMGs) derived from a process involving probiotic fermentation and high-pressure steam treatment. The fatigue model was established in BALB/c male mice using weight-bearing swimming and TMGs were administered by orally at a dosage of 200 mg/kg for four weeks. The anti-fatigue mechanisms of TMGs were explored by assessing liver oxidative stress, skeletal muscle inflammation markers, as well as their impact on gut microbiota and serum metabolism. The results indicated that TMGs could significantly increase the levels of SOD, CAT, ATP and Na+-K+-ATPase and enhance the antioxidant capacity by modulating the PGC-1α/KEAP1/NRF2/HO-1 pathway. Meanwhile, TMGs reducing the levels of inflammatory factors TNF-α, IL-1β and IL-6 and inhibited inflammation by modulating the AMPK/TORC2/CREB/PGC-1α pathway. TMGs also regulated the gut microbiota, increasing the abundance of probiotic bacteria and the content of short-chain fatty acids (SCFAs) in the cecum. Serum metabolomics analyses have shown that TMGs can significantly affect the serum metabolic profile of fatigue model mice, regulating metabolic markers through affecting anti-fatigue-related metabolic pathways. In conclusion, TMGs exerted significant anti-fatigue effects through antioxidant and anti-inflammatory effects, and alleviate fatigue by regulating gut microbiota and serum metabolism.

Mode I fatigue delamination growth behavior and model for multidirectional laminates with the same overall stiffness

In order to isolate the influence of ply orientation on the mode I fatigue delamination propagation behavior of carbon fiber reinforced composite laminates, multidirectional laminates with the same overall stiffness are designed while three different interfaces (0//0, 45//45, and 90//90), which can avoid the coupling effect of remote plies. Test results show that the fatigue delamination behavior is obviously affected by the interface angle. In addition, a novel and simple method for determining the fatigue delamination resistance is proposed. The measured fatigue delamination resistance is lower than the quasi-static one for all interfaces. The specimen with 45//45 interface exhibits more significant delamination resistance, whereas the delamination resistances of specimens with 0/0 and 90//90 interfaces are similar, which is consistent with the phenomenon of more bridging fibers accompanying in the delamination process of 45//45 interface. In order to consider the effect of fiber bridging and reduce the dispersion of the data, experimental data are further processed using a normalized model considering the effect of fiber bridging. The normalized results can be characterized by a single curve, suggesting that the normalized model is effective in describing the fatigue delamination behavior in the presence of fiber bridging.

Menu fatigue: Exploring an obscure concept with problem-centred expert interviews

The study aims to explore menu fatigue by defining its dimensions and the actors involved. It also aims to establish a model that reflects the paradigmatic nature of the concept. Given the paucity of literature to support the objectives, it was considered appropriate to conduct the study using a qualitative research approach. Accordingly, a grounded theory approach with problem-centred expert interviews was used to collect data, which is recommended for investigating issues that are not clearly defined and not well established, with the direct contribution of those who have sufficient knowledge of the concept being researched. First, the "what" and "who" questions were put to the only actor identified in the literature, the customer. In this way, the definition of menu fatigue was broadened and other actors to be studied were identified. Similar questions were asked of other actors such as administrative and owner/manager chefs, kitchen staff and service staff, who were also found to be affected. This cumulative approach made it possible to select 24 experts to contribute to the study. Participants were identified through purposive and theoretical sampling. The results of the two-stage coding showed that menu fatigue is not only about monotony, which is the only dimension associated with the concept in the literature, but also about excessiveness, appearance and extra effort. While previous literature accepted menu fatigue in industrial settings as an issue of customer boredom and perceived monotony, the current paper extended the concept to include newly explored dimensions and actors. Secondly, the highlighted dimensions and actors were linked to their occurrence and subsequent outcomes to be presented in a model of menu fatigue that provides a more comprehensive understanding of the issue. The paper concludes with implications for both academic and industrial practitioners.

Analysis of fatigue degradation process and modelling of an elliptical crack in the locking system of a PET bottle blower

In the dynamic world of Polyethylene Terephthalate (PET) bottle production, ensuring the reliability of blow molding machines is paramount, particularly the locking mechanisms that endure significant cyclic loads during operation. This study investigates the fatigue degradation and crack propagation within the locking system of a two-cavity, 2-liter PET bottle blow molder. By modeling an elliptical crack in the most stressed component, we explore the complex stress distribution and the impact of repeated opening and closing cycles on the system’s integrity. Utilizing advanced finite element analysis (FEA), our research provides a comprehensive understanding of the stress intensity factors and safety margins under varying loading conditions. The results not only enhance predictive maintenance strategies but also offer valuable insights into extending the operational lifespan of these machines, thereby improving safety and reliability in industrial applications. This study opens new avenues in the field of component lifespan prediction. By combining artificial intelligence, machine learning, and innovative materials, it aims to improve the reliability and lifespan of PET blow molders.

The pivotal role of positive emotions in maintaining nurses’ trust and preventing pandemic fatigue

Abstract Background Nursing is a challenging profession that demands quick adaptation to changing conditions. The COVID-19 pandemic has placed unprecedented pressure on nursing teams, leading to high-stress levels and uncertainty. This study examines how nurses’ emotional response at the start of the pandemic predicts their trust in the healthcare system and pandemic fatigue after three morbidity waves. Methods This study included collecting self-reported data among Israeli nurses in three time periods: 346 nurses during March-May 2020 (T1), 260 nurses during June-July 2020 (T2), and 91 nurses during February 2022 (T3). Change in the study variables was assessed using repeated-measure ANOVA. Two multiple linear regression models were calculated for trust and pandemic fatigue at T3, with the emotions at T1 and professional seniority as predictors. Results A significant increase in negative emotions was observed between T1 and T2 (M = 2.65, SD = 1.12 and M = 3.20, SD = 1.29 respectively). Furthermore, there was a significant decrease in trust levels between T1 and T3 (M = 4.68, SD = 1.14 and M = 4.07, SD = 1.08 respectively). The regression model for trust was significant, with 20% of the variance explained in it, revealing that higher positive emotions at T1 predicted higher trust at T3. Similarly, the regression model for pandemic fatigue was significant (R2=22%), showing that lower professional seniority and lower positive emotions at T1 were predictors of higher pandemic fatigue at T3. Conclusions During a crisis, nurses may experience intense negative emotions like fear and anxiety. However, positive emotions, such as pride and a sense of mission, are crucial in maintaining their trust and preventing the onset of pandemic fatigue. Healthcare organizations should promote emotional support and provide emotional resources so that nurses can continue to deliver high-quality care even during challenging times. Key messages • During times of crisis, it’s crucial to foster positivity and encourage a sense of pride and accomplishment in nursing staff. • Healthcare organizations must provide emotional support to nursing teams in routine and crisis situations to prevent burnout and maintain trust and long-term functioning.

Fault diagnosis of floating offshore wind turbine based on bidirectional long short-term memory networks with sliding window

Owing to its ability to harvest more power in deeper waters than other types of offshore wind turbines, the floating offshore wind turbine (FOWT) has attracted significant interest from both academics and industry. However, deterioration in system performance and unscheduled shutdowns will significantly increase operation and maintenance costs. To avoid system damage and ensure operation safety, there is a great demand for developing FOWT fault diagnosis to detect and identify faults as early as feasible. Due to the enormous complexity of FOWT including dynamics and nonlinearity, effective FOWT fault diagnosis poses a significant challenge. This paper develops a novel FOWT fault diagnosis method using bidirectional long short-term memory (BiLSTM) with sliding window. Firstly, the dynamics are embedded by stacking time-dependent measurements using the sliding window technique. Then, the augmented data is fed into a BiLSTM network to exploit the nonlinearity of FOWT. Consequently, a softmax classifier is used for fault diagnosis. A high-fidelity 10 MW spar FOWT benchmark based on the NREL Fatigue, Aerodynamics, Structures, and Turbulence (FAST) model is used to validate the capability and effectiveness of the proposed method. Experimental results show that the proposed method outperforms other related methods in diagnosing critical faults in FOWTs under various operating conditions.

A Hybrid Framework for Characterizing and Benchmarking Fatigue S‐N Curves in Aluminum Alloys by Integrating Empirical and Data‐Driven Approaches

ABSTRACTThe complicated and stochastic nature, coupled with uncertainties and data scatter, poses challenges in developing a general fatigue model. This study introduces a hybrid framework that integrates an empirical model with data‐driven approaches, aiming to address data scarcity and streamline the fatigue characterization of aluminum alloys. It was found that support vector regression (SVR) and neural network (NN) exhibit superior performance, with SVR achieving a mean absolute error (MAE) of 0.13 (cycles to failure in log scale) for training and 0.14 for testing, and NN reaching an MAE of 0.15 for training and 0.16 for testing data. The employment of leave‐one‐group‐out‐cross‐validation (LOGOCV) ensured the generalizability of the models, with the effectiveness confirmed by the actual‐predicted life plot. The results demonstrated that almost 98% of predicted data fell within the life factor of ±1. This methodology reduces the requirement for experimentation and provides the prospect of automating fatigue design and characterization.

An adaptive acceleration scheme for phase-field fatigue computations

Abstract Phase-field models of fatigue are capable of reproducing the main phenomenology of fatigue behavior. However, phase-field computations in the high-cycle fatigue regime are prohibitively expensive due to the need to resolve spatially the small length scale inherent to phase-field models and temporally the loading history for several millions of cycles. As a remedy, we propose a fully adaptive acceleration scheme based on the cycle jump technique, where the cycle-by-cycle resolution of an appropriately determined number of cycles is skipped while predicting the local system evolution during the jump. The novelty of our approach is a cycle-jump criterion to determine the appropriate cycle-jump size based on a target increment of a global variable which monitors the advancement of fatigue. We propose the definition and meaning of this variable for three general stages of the fatigue life. In comparison to existing acceleration techniques, our approach needs no parameters and bounds for the cycle-jump size, and it works independently of the material, specimen or loading conditions. Since one of the monitoring variables is the fatigue crack length, we introduce an accurate, flexible and efficient method for its computation, which overcomes the issues of conventional crack tip tracking algorithms and enables the consideration of several cracks evolving at the same time. The performance of the proposed acceleration scheme is demonstrated with representative numerical examples, which show a speedup reaching up to four orders of magnitude in the high-cycle fatigue regime with consistently high accuracy. Graphical abstract

Neural-driven viscosity modeling and fatigue optimization for cycloidal gear systems

This study presents a comprehensive approach to enhancing RV reducer performance. A novel RAM viscosity-pressure model is developed, combining traditional stability with neural network precision. Surface micro-morphological analysis is integrated with contact fatigue modeling, revealing critical insights into surface characteristics and their impact on fatigue. A neural proxy-based optimization strategy is employed to identify optimal operational parameters, significantly reducing fatigue risks. Additionally, a new method for real-time transmission ratio monitoring is introduced, validating the model's effectiveness and offering a robust framework for improving the reliability and lifespan of RV reducers in industrial applications.

Comparison of the Young’s modulus of the lead free solder alloy Sn-Ag3.8-Cu0.7 determined by hot tensile tests, ultrasonic measurements and [formula omitted]-Sn single crystal calculations

Sn-based solders are known for their tendency to form coarse grained microstructures. In combination with the high elastic anisotropy of β-Sn, the overall elastic properties and their interpretation require a careful discussion of the structure–property-relationship as elastic constants are often important input parameters for creep or fatigue models. This study therefore investigates the influence of microstructure and testing method on the Young’s modulus E for the widespread lead free solder alloy Sn-Ag3.8-Cu0.7 (SAC387) using bulk specimens. Due to its already high homologous temperature at room temperature (Thom≈ 0.6 = T/Tm for Tm being the melting temperature), hot tensile tests generally bear the risk of superimposed creep deformation. Mechanical testing becomes even more challenging, since yield strengths are usually low for these alloys. Consequently, in addition to the hot tensile tests executed for the engineering strain rate ϵ̇e=1⋅10−3 s−1, two supplemental methods are used to determine the Young’s modulus comprising of the dynamic resonance frequency measurement and the calculation of the Young’s modulus based on single crystal compliance data of the majority phase β-Sn.Young’s moduli yielded from dynamic resonance frequency measurement and calculations based on single crystal compliance data showed comparable results of (E35 °C≈ 55 GPa, E80 °C≈ 51 GPa and E125 °C≈ 48 GPa). Hot tensile tests showed similar data with the largest deviation at 80 °C, where E80 °C≈ 53 GPa was determined. These absolute values and their temperature dependence can be attributed to the microstructure of the cast specimens which show a general preferred orientation of β-Sn grains close to <110> after analysis via electron backscattered diffraction (EBSD). A comparison with literature data revealed significant differences in the Young’s moduli which are likely to be attributed to differences in the preferred orientation of β-Sn grains.

Constitutive modeling of functional fatigue with tension–compression asymmetry for superelastic NiTi shape memory alloy

Under cyclic loads, superelastic shape memory alloys (SMAs) exhibit stress–strain responses featured by functional fatigue, i.e., degradation of superelasticity and accumulation of irrecoverable deformation as cycling number increases, together with an asymmetry between tensile and compressive responses. Comprehensive understanding and modeling of these material complexities are crucial for the design and analysis of various superelastic SMA structures in practical applications. This work has developed a novel constitutive model based on irreversible thermodynamics to account for functional fatigue with tension–compression asymmetry. A potential function, defined as a weighted sum of two potentials that are calibrated against the tensile and compressive responses respectively, is employed to generate the asymmetric responses, and functional fatigue is represented by degradation of superelastic properties and growth of plastic strain as martensitic transformation accumulates. The model is adopted in numerical simulations for superelastic SMA tubes under cyclic lateral compression, which is experimentally investigated as a model problem. The agreement between simulations and experiments shows the validity and effectiveness of this constitutive modeling. Through additional finite element simulations incorporating this model, the effects of tension–compression asymmetry under cycling and diameter-to-thickness ratio of the tubular geometry upon mechanical responses of laterally compressed SMA tubes are also unveiled.

Mechanistic Model of Fatigue in Ultrasonic Assisted Machining.

Anti-fatigue design in the machining process of aviation material requires advanced processes to enhance the surface integrity and a holistic model which can optimize the process aiming at maximum fatigue life. In the present study, the axial ultrasonic assisted milling process was utilized to machine the Inconel 718 while the process executes the thermomechanical cutting and peening action simultaneously. To optimize the process factors, a hybrid model using a combination of regression analysis and an analytical model was developed to correlate the machining factors, i.e., vibration amplitude, cutting velocity and feed rate to fatigue life. Herein, the former was used to map the process inputs to surface integrity aspects (SIAs), viz. roughness, hardness and residual stress; then, the SIA was mapped to fatigue life through a stress-based approach. The obtained results revealed that there is close agreement between the measured and predicted values of fatigue life where the prediction error is less than two times the dispersion. On the other hand, applying ultrasonic vibration at the highest amplitude together with the maximum feed rate and cutting velocity yield significant improvement in fatigue life, i.e., three times the same condition without ultrasonic vibration in light of the enhancement of compressive residual stress and work hardening of the surface layers.

Predicting fatigue life of multi-defect materials using the fracture mechanics-based physics-informed neural network framework

To address the limitations of traditional methods (such as the S-N curves and Paris’s law) in evaluating the fatigue life of multi-defect materials, this study developed a fracture mechanics-based physics-informed neural network (PINN) to predict the lifetime of multi-defect materials using cyclic loading (Δσ) and the equivalent damage area (AD). Influences of multiple defects were unified characterized through the equivalent damage area, which was calculated based on the M−integral fatigue model. This model reflects the energy evolution of multi-defect fatigue damage. By embedding the prior knowledge of fracture mechanics derived from the M−integral fatigue model into the loss function of PINN, crucial physical information was captured during the training progress, enhancing the interpretability of the neural network. By integrating the advantage of the M−integral fatigue model in characterizing the fatigue performance of multi-defect materials and the nonlinear fitting ability of neural networks, the proposed approach effectively improves the generalization ability and predictive accuracy of limited fatigue data. The presented PINN models accurately forecast the fatigue life of multi-defect materials, with a squared correlation coefficient (R2) exceeding 0.9. The presented methodological framework addresses the existing gap in methods for evaluating the fatigue performance of multi-defect materials and reliance on fatigue testing.

An experimentally validated multiscale machine learning fatigue damage model for fiber reinforced composites

In this paper, a fatigue model is formulated and implemented to predict fatigue life of fiber reinforced composite laminates. The developed model is based on experimental data collected at different spatial and temporal scales, and reported in [1] and [2]. The model is first validated against quasistatic tensile test data and is shown to accurately capture damage modes and the sequences in which the modes occur. Next the fatigue damage model is formulated and for computational efficiency the cycle jumping approach is used to implement the model for predicting fatigue life. The computational efficiency is further enhanced through the use of a neural network that allows for very fast computation. Details of the model are presented and shown to be an efficient way to predict fatigue life of composite laminates.

In-plane biaxial fatigue life prediction model for high-cycle fatigue under synchronous sinusoidal loading

The unique advantage of in-plane biaxial loading is no rotation of the maximum principal stress during cyclic loading, which has a distinct effect on fatigue life. This study aims to propose a life prediction model for in-plane biaxial fatigue encompassing both in-phase and out-of-phase conditions. For synchronous sinusoidal loadings, an equivalent stress expression is derived using the integral method, accounting for the influences of shear and normal stresses across all planes. The equivalent stress is then compared to a reversed uniaxial constant amplitude loading to predict fatigue life. To validate and compare the model, in-phase and out-of-phase in-plane biaxial fatigue experiments were conducted using 24 nickel-based superalloy cruciform specimens at temperatures of 420℃, 550℃, and 600℃. The results show that the proposed model is superior to conventional stress-invariant based models, with most results staying within the ± 2 error bands. Using the proposed model, the effects of mean tensile stress, biaxiality and phase shift are discussed. Additionally, a novel non-proportional factor is introduced to enhance multiaxial fatigue life prediction by distinctly separating the effects of principal stress and its directional variations.

Mechanism-based shift factors to predict the fatigue performance of cemented pavement materials

Cemented pavement materials (CPMs) are essential components in pavement structures, yet accurately predicting their service life due to fatigue damage remains challenging. Laboratory fatigue test results are commonly employed to predict the service life of CPMs by applying a lab-to-field shift factor (SF). However, traditional approaches rely heavily on experimental data, posing challenges in ensuring the certainty of lab-to-field results. Additionally, inconsistencies in lab-to-field fatigue failure criteria further complicate SF development. To address these challenges, this study proposes a mechanism-based methodology for developing SF. This methodology comprises a rigorous two-scale fatigue model developed by the authors to characterise the fatigue performance of CPMs at the lab scale and predict their performance at the field scale, thereby facilitating the development of SFs. These SFs are established based on a consistent lab-to-field fatigue failure criterion (i.e. the modulus reduction of CPMs). By accounting for strain differences between laboratory and field scales, SFs are derived in the strain-fatigue life space. Application of this approach to typical Australian CPMs, namely siltstone and hornfels, yields mechanism-based SFs of 1.19 and 1.21, respectively.

Dual-factor model of sleep and diet: a new approach to understanding central fatigue.

Numerous studies have recently examined the impact of dietary factors such as high-fat diets on fatigue. Our study aims to investigate whether high-fat diet (HFD) alone or combined with alternate-day fasting (ADF) can lead to the central fatigue symptoms and to investigate the potential integration of dietary and sleep variables in the development of central fatigue models. Seventy-five male Wistar rats were divided into five groups: control, HFD, HFD + ADF, modified multiple platform method (MMPM), and MMPM+HFD + ADF. Each group underwent a 21-day modeling period according to their respective protocol. Their behavioral characteristics, fatigue biochemical markers, hippocampal pathological changes, mitochondrial ultrastructure, and oxidative stress damage were analyzed. Our findings demonstrate that using only HFD did not cause central fatigue, but combining it with ADF did. This combination led to reduced exercise endurance, decreased locomotor activity, impaired learning and memory abilities, along with alterations in serum levels of alanine aminotransferase (ALT), creatine kinase (CK), and lactate (LAC), as well as hippocampal pathological damage and other central fatigue symptoms. Moreover, the MMPM+HFD + ADF method led to the most obvious central fatigue symptoms in rats, including a variety of behavioral changes, alterations in fatigue-related biochemical metabolic markers, prominent pathological changes in hippocampal tissue, severe damage to the ultrastructure of mitochondria in hippocampal regions, changes in neurotransmitters, and evident oxidative stress damage. Additionally, it was observed that rats subjected to HFD + ADF, MMPM, and MMPM+HFD + ADF modeling method exhibited significant brain oxidative stress damage. We have demonstrated the promotive role of dietary factors in the development of central fatigue and have successfully established a more stable and clinically relevant animal model of central fatigue by integrating dietary and sleep factors.