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The Effects of GlyNAC on the Regeneration Rate and Movement of Dugesia tigrina
No full textThis study aims to investigate the effects of GlyNAC on planaria tissue regeneration and behavioral changes. There has been a focus on anti-aging in the older population due to the adverse effects that come with it such as muscle fatigue, a decrease in tissue regeneration, etc. As a result, there was an increase in research on finding an effective supplement to combat health issues and aging. GlyNAC, a combination of Glycine and N-acetylcysteine, has been shown to reverse and improve health by increasing glutathione levels. Glutathione is an antioxidant with multiple health benefits such as protecting against oxidative stress, a main factor in causing cell death. This experiment was conducted using planarians because they have a similar central nervous system, tissue regeneration, and glutathione production to humans. There were 1 control group and 3 experimental groups. The control group was fed regular ground beef while the experimental group was fed ground beef with GlyNAC. For tissue regeneration, each group would be measured and observed before and after the ground beef or GlyNAC-mixed ground beef. The planarians’ motility was recorded for 20 minutes with grid paper underneath their petri dishes. The results indicate that GlyNAC treated groups had increased regeneration and movement. With this support, our hypothesis of GlyNAC increases the regeneration and movement in planarians. Future Research can be done by having bigger experimental groups for more accurate data. Introduction GlyNAC There has been an interest in anti-aging in the modern day due to one’s fear of aging and the many symptoms that come with it, such as muscle fatigue, inflammation, a decrease in tissu
Effect of Environmental Degradation on the Fatigue Resistance of Mode II Adhesive Joints in Carbon/Epoxy Composite Materials
No full textThis study analyzes delamination in CFRP adhesive joints under mode II loading in both static and dynamic regimes. The ENF test was used to evaluate the effect of exposure to salt spray and a climatic chamber over various periods—one, two, four, and twelve weeks—as well as for unaged specimens. Based on the experimental data, fatigue initiation curves (ΔG-N) and fatigue crack growth curves (G-da/dN) were constructed to analyze both degradation processes. In the fatigue initiation phase, the data were analyzed using a probabilistic model based on a Weibull distribution. The most relevant findings of this study are as follows: regarding the fatigue limits obtained for the adhesive joint under mode II fracture, a decrease in load-bearing capacity was observed due to degradation processes—around 20% under static loading conditions for salt spray exposure, and 25% for hygrothermal degradation. As for the fatigue crack growth phase, the crack propagation rates were found to depend on the specific environmental degradation process to which the tested specimens were subjected
Matrix cracking effect in thermoset and thermoplastic CFRP: development of an analysis tool for the design of hydrogen tanks
No full textLiquid hydrogen (LH₂) is a promising alternative to reduce carbon dioxide (CO₂) emissions in the aeronautical industry. However, current tanks do not meet their rigorous design and safety requirements. Carbon Fibre Reinforced Polymers (CFRP) offer advantages over classic metallic materials, such as higher stiffness and toughness at low temperatures, as well as lower density. Nevertheless, matrix cracking in composites can cause leaks through the tank walls, compromising their tightness, even with a low crack density. Therefore, a deeper understanding of this phenomenon could prevent permeability loss and ensure the operational safety of the tank. Currently, thermoplastic composites are gaining distinction over thermosets in the aeronautical industry due to their better range of properties and the possibility of out-of-autoclave manufacturing. In this context, CF/PEEK composite has shown greater resistance to damage propagation compared to epoxy-based materials, positioning it as a promising candidate for LH₂ storage tanks. This research evaluates the initiation and propagation of transverse cracks in the matrix in specimens with 0º and 90º layer orientations of thermoplastic (CF/PEEK) and thermoset (M21E/IMA-12K) matrices under static loads and at room temperature. Two different stacking sequences, [0/90/0₂/90₂]s and [90/0/90₂/0₂]s, have been analysed. X-ray tomography images were captured at different deformation states. From these images, crack density was calculated using a digital processing method developed by the authors, which quantifies the number of cracks in each layer across the entire width of the specimen. This method allows for a three-dimensional (3D) visualization of the inspected area, facilitating the tracking of possible paths that could compromise the tightness of the tanks. These results contribute to the design of safer and more efficient tanks for liquid hydrogen storage in aeronautical applications
Adaptive Federated Fault Diagnosis Framework for Wind Turbine Reliability
No full textWind turbine reliability is critical for sustainable energy production, yet fault diagnosis faces challenges due to data privacy concerns, heterogeneous operational conditions, and resource constraints in distributed wind farms. Traditional centralized Machine Learning (ML) approaches struggle with these issues, necessitating decentralized solutions. This study introduces the Adaptive Federated Fault Diagnosis (AF2D) framework, a novel Federated Learning (FL) approach for wind turbine fault diagnosis that ensures data privacy while addressing non-i.i.d. data distributions. Using a dataset of 35 uniaxial vibration recordings from six turbines at the University of Mustansiriyah, AF2D leverages two key modules: Adaptive Model Aggregation (AMA) and Lightweight Model Optimization (LMO). AMA employs Jensen-Shannon divergence and cosine similarity to adaptively aggregate local model updates, mitigating data heterogeneity, while LMO applies structured pruning (60% filter reduction) and 8bit quantization to enable deployment on resource-constrained SCADA systems. Results show AF2D achieves 91.3% accuracy (±1.2%, 95% confidence interval), a 3.5% improvement over FedAvg (87.8%± 1.4%), with statistical significance (p < 0.05), and outperforms state-of-the-art methods like Clustered FL (88.5%) and Privacy-Preserving FL (87.2%). LMO reduces inference time by 64.44% and memory usage by 53.71%, enhancing edge deployment feasibility. However, the small dataset raises overfitting risks, and scalability tests reveal a threefold communication cost increase (54.5 to 150.6 MB) for 18 clients, mitigated by proposed compression (30%–50% reduction) and asynchronous updates (20%–40% overhead reduction). Privacy is maintained with a differential privacy guarantee of= 1.0, though advanced techniques like secure multiparty computation could achieve <1. Despite limitations in severe fault detection and dataset diversity, AF2D demonstrates robust performance. Future work includes integrating multi-modal data (SCADA, vibration, environmental), testing real-time deployment, and expanding federated datasets to enhance generalizability and scalability.OPEN ACCESS Received: 11/09/2025 Accepted: 16/10/2025 Published: 23/01/202
Study on the Annular Fluid Level IdentificationAlgorithmfor Ultra-Deep Wells Based on AcousticVelocityCorrection
No full textThe geological conditions and pressure system of ultra-deep oil and gas wells are complex, and formation leakage conditions are prone to occur. Accurately obtaining the annular liquid level depth during leakage is crucial for treatment decisions. In the high-temperature and high-pressure wellbore environment of ultra-deep wells, the sound velocity shows a nonuniform variation trend, which seriously affects the accuracy of annular liquid level identification. Therefore, it is of significance to carry out a study on the annular fluid level identification algorithm based on acoustic velocity correction. Based on establishing a calculation model for the annular temperature and pressure field, a sound velocity calculation model in the annular air section was constructed. By combining the actual sound velocity of the clear signal segment identified through the echo signal, the basic parameters in the sound velocity calculation model can be calibrated to obtain the calibrated wellbore sound velocity distribution. Finally, tests were conducted on simulated well sites and noisy production wells to verify the accuracy of the annular liquid level identification algorithm constructed in this study. The results showed that the identification algorithm had an error of less than 2%. All in all, this study can effectively meet the demand for dynamic annular liquid level data during the leakage conditions, which is of great significance for the treatment and decisionmaking in deep and ultra-deep wells.OPEN ACCESS Received: 30/07/2025 Accepted: 25/09/2025 Published: 23/01/202
Functional H∞ Filtering for Descriptor Systems with Monotone Nonlinearities
No full textThis paper proposes a new framework for the design of functional H∞ filters tailored for nonlinear descriptor systems affected by disturbances. Earliermethods have some significant drawbacks: they rely on the restrictive assumption of system regularity, employ implicit descriptor-form filters that complicate implementation, and emphasize full-order filtering, which is often unnecessary and computationally expensive. To overcome these drawbacks, the proposed filter is developed in an explicit state-space form that allows simple implementation with arbitrary initial conditions. Moreover, its order is minimized by matching it to the dimension of the functional vector, which reduces computational complexity compared to conventional filters. A new set of sufficient conditions is presented for the existence of a functionalH∞ filter, expressed through a rank condition and a linear matrix inequality (LMI) formulation. These conditions guarantee the stability of the estimation error dynamics while ensuring that the L2 gain from disturbances to errors remains below a specified bound. A numerical example based on a simple constrained mechanical system is presented to illustrate the effectiveness of the proposed method
Evolution Families and Time-Varying Processes in Modular Function Spaces
No full textThis paper develops a modular framework for the study of time--dependent linear evolution processes via evolution families. We consider non--autonomous abstract Cauchy problems generated by families of operators depending on time and introduce a notion of --strong continuity compatible with the modular topology. Under suitable uniform --boundedness assumptions, we establish the existence of evolution families and derive modular growth estimates formulated in terms of the associated modular growth function. To address regularity issues, a Steklov--type averaging technique is employed, allowing differentiability and domain inclusion to be treated in the modular sense. Several examples, including time--varying multiplication processes in integral and Orlicz--type modular spaces, are presented to illustrate the scope and effectiveness of the proposed approach
Large Language Model-Driven Demand Forecasting and Inventory Optimization for University Physical Education Resource Supply Chain
No full textThis study proposes an intelligent management system for university physical education resource supply chains based on Large Language Models (LLMs), aiming to address the problems of inaccurate demand forecasting and inefficient inventory management in traditional physical education resource allocation. By constructing a deep learning framework incorporating LLMs and combining multi-dimensional information including historical data, seasonal factors, course schedules, and student preferences, precise demand forecasting for sports equipment, facilities, and teaching resources is achieved. The research employs a pre-trained language model based on the Transformer architecture, combined with time series analysis and reinforcement learning algorithms, to develop dynamic inventory optimization strategies. Experimental results demonstrate that compared to traditional methods, this system improves demand forecasting accuracy by 23.7%, increases inventory turnover rate by 31.2%, and achieves a resource utilization rate of 89.6%. This research provides a novel solution for intelligent management of university physical education resources, offering significant theoretical value and practical implications.OPEN ACCESS Received: 24/08/2025 Accepted: 28/10/2025 Published: 03/02/202