Degenerative disc disease (DDD), regardless of its phenotype and clinical grade, is widely associated with low back pain (LBP), which remains the single leading cause of disability worldwide. This work provides a quantitative methodology for comparatively investigating artificial IVD degeneration via two popular approaches: enzymatic denaturation and fatigue loading. An in-vitro animal study was used to study the time-dependent responses of forty fresh juvenile porcine thoracic IVDs in conjunction with inverse and forward finite element (FE) simulations. The IVDs were dissected from 6-month-old-juvenile pigs and equally assigned to 5 groups (intact, denatured, low-level, medium-level, high-level fatigue loading). Upon preloading, a sinusoid cyclic load (Peak-to-peak/0.1-to-0.8 MPa) was applied (0.01-10 Hz), and dynamic-mechanical-analyses (DMA) was performed. The DMA outcomes were integrated with a robust meta-model analysis to quantify the poroelastic IVD characteristics, while specimen-specific FE models were developed to study the detailed responses. The results demonstrated that enzymatic denaturation had a more significantly pronounced effect on the resistive strength and shock attenuation capabilities of the intervertebral discs. This can be attributed to the simultaneous disruption of the collagen fibers and water-proteoglycan bonds induced by trypsin digestion. Fatigue loading, on the other hand, primarily influenced the disc's resistance to deformation in a frequency-dependent pattern, where alterations were most noticeable at low loading frequencies. This study confirms the intricate interplay between the biochemical changes induced by enzymatic processes and the mechanical behavior stemming from fatigue loading, suggesting the need for a comprehensive approach to closely mimic the interrelated multifaceted processes of human disc degeneration.
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http://dx.doi.org/10.1016/j.jbiomech.2024.112159 | DOI Listing |
J Appl Biomech
January 2025
Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, USA.
Gait abnormalities affect an individual's ability to navigate the world independently and occur in 10% of older adults. Examining age-related gait symmetry in nonlaboratory environments is necessary for understanding mobility limitations in older adults. This study examined gait symmetry differences between older and younger adults using in-shoe force sensors.
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January 2025
Department of Electrical Engineering, Technical University Eindhoven, 5612 AZ Eindhoven, The Netherlands.
The effects of mechanical vibrations on control system stability could be significant in control systems designed on the assumption of rigid-body dynamics, such as launch vehicles. Vibrational loads can also cause damage to launch vehicles due to fatigue or excitation of structural resonances. This paper investigates a method to control structural vibrations in real time using a finite number of strain measurements from a fiber Bragg grating (FBG) sensor array.
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December 2024
College of Healthcare Sciences, Nova Southeastern University, Fort Lauderdale, FL 33328, USA.
The purpose was to create a systematic approach for analyzing data to improve predictive models for fatigue and neuromuscular performance in volleyball, with potential applications in other sports. The study aimed to assess whether average, peak, or peak-to-average ratios of countermovement jump (CMJ) force plate metrics exhibit stronger correlations and determine which metric most effectively predicts performance. Data were obtained from nine division I female volleyball athletes over a season, recording daily jump loads (total jumps, jump counts >38.
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December 2024
Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai 200240, China.
Taking the titanium alloy wing-body connection joint at the rear beam of a certain type of aircraft as the research object, this study analyzed the failure mechanism and verified the structural safety of the wing-body connection joint under actual flight loads. Firstly, this study verified the validity of the loading system and the measuring system in the test system through the pre-test, and the repeatability of the test was analyzed for error to ensure the accuracy of the experimental data. Then, the test piece was subjected to 400,000 random load tests of flight takeoffs and landings, 100,000 Class A load tests, and ground-air-ground load tests, and the test piece fractured under the ground-air-ground load tests.
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December 2024
School of Science, Harbin Institute of Technology, Shenzhen 518055, China.
Fatigue failure poses a serious challenge for ensuring the operational safety of critical components subjected to cyclic/random loading. In this context, various machine learning (ML) models have been increasingly explored, due to their effectiveness in analyzing the relationship between fatigue life and multiple influencing factors. Nevertheless, existing ML models hinge heavily on numeric features as inputs, which encapsulate limited information on the fatigue failure process of interest.
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