There are several reasons for the development of nanopolymer compounds, such as improving physical, mechanical, and chemical properties, increasing lifespan, reducing costs, and decreasing negative environmental impact. The compatibility of two rubbers and mineral nanofillers in nanocomposites is a challenge that needs to be studied, and the effect of nanofillers on morphological, physical, and mechanical properties should be investigated accordingly. In this study, calcium carbonate nanoparticles were added to a polymer compound that included natural rubber (NR), styrene-butadiene rubber (SBR), vulcanization accelerators, and other additives. For mixing nanoparticles in the polymer matrix, various methods were used, including the solvent method in toluene and W410 solvents and the surface modification of calcium carbonate nanoparticles with stearic acid. The effect of dispersion nanoparticles in nanocomposite specimens on morphology, curing characteristics, and mechanical properties was studied. The morphologies of specimens were determined by X-ray diffraction (XRD) analysis and field emission scanning electron microscopy (FESEM). The particle size of the nanocomposite was approximately 34 nm, and the interlayer spacing between crystal plates increased from 2.81 nm to 3.03 nm. These results indicate a uniform dispersion of nanoparticles, specifically with an optimum content of 3.52%, in the compounds prepared through all mixing methods, with no agglomeration observed in the nanocomposites. The results of the nanocomposites’ curing characterization demonstrate that with the addition of nanoparticles, a strong bond is created in the polymer chains, and curing properties are improved. Among the dispersion methods, the highest percentage improvement in curing properties is observed with the solvent method W410. To evaluate the effect of the addition of calcium carbonate nanoparticles and the dispersion method on improving mechanical properties, tensile, tear, hardness, and rebound resilience tests were performed. In tensile tests, the surface modification method showed the highest enhancement in ultimate stress (80%), followed by the W410 method (64%) and toluene method (63.7%). Tear strength improvements were highest in the W410-solvent sample (80%), followed by the surface modification method (57%) and the solvent-toluene method (50%). The W410 method resulted in the hardest samples, while the surface-modified samples had the lowest hardness. The addition of CaCO3 nanofillers reduced rebound resilience, with the W410 method experiencing the largest reduction (10.64%), followed by the toluene method (6.38%), and with the surface-modified samples showing the lowest reduction (4.25%). The results show that in the W410 solvent method, the nanocomposite is more elastic than for other methods. Additionally, for most of the mechanical properties, the W410 method results in the most growth in improvement.

This chapter explores the diverse testing methodologies employed to characterize the elastic and viscoelastic properties of materials. It covers both static and dynamic testing techniques, including tensile testing, dynamic mechanical analysis (DMA), creep testing, stress relaxation, and rheometry. The chapter delves into the principles behind these methods, their practical applications, and the interpretation of results. Special attention is given to how these tests reveal crucial information about a material’s behavior under various conditions, such as different loading rates, temperatures, and frequencies. With the rise of smart and additive manufacturing, understanding material behavior is essential for optimizing processes, ensuring reliability, and maintaining quality control in automated production systems. By understanding these testing methodologies, researchers and engineers can better predict material performance, optimize material selection, and develop new materials with tailored elastic and viscoelastic properties for specific applications. The chapter emphasizes the importance of time-temperature superposition (TTS) in extending the range of measurable viscoelastic properties. It also discusses the unique capabilities of DMA in determining glass transition temperatures, analyzing phase transitions, and predicting long-term material behavior. The relationship between stress and strain, including the significance of stress-strain curves in understanding material properties, is thoroughly examined. Additionally, the chapter covers the distinctions between engineering and true stress-strain curves, and the application of Hooke’s law in the linear elastic region.

Rotator cuff injuries are among the most common musculoskeletal conditions that affect shoulder function and can ultimately impact quality of life. While physical therapy is essential in the care of rotator cuff injuries, the ideal dose of therapeutic exercises continues to be a significant clinical dilemma because of the generalized nature of rehabilitation protocols. This pilot study proposes a machine learning approach to personalize rehabilitation using surface electromyography (sEMG) data collected from eight healthy individuals by testing four key shoulder movements: scaption, internal rotation, external rotation, and external rotation at 90° abduction. In this research, the XGBoost algorithm was used to model muscle activation patterns by achieving a high predictive accuracy (R² = 0.9875; MSE = 15.0983 μV2). Because sEMG reliably measures superficial muscle activity, a linear programming model was used to divide a 60-minute therapy session in a way that increases activation of superficial muscles (such as deltoid and trapezius) while reducing strain on deep muscles (such as supraspinatus and infraspinatus). Three optimization scenarios were tested by reflecting a different clinical goal: prioritizing superficial muscles, minimizing deep muscle strain, or balancing both. Optimized time allocations assigned more time to external rotation at 90° abduction and scaption. This research demonstrates the potential for data-driven methods to transform rotator cuff rehabilitation through personalized and evidence- based treatment plans. The results are a step toward improving clinical practices by providing physical therapists with tools for more effective and adaptive rehabilitation planning. These findings suggest that a machine learning approach could play a valuable role in supporting clinical decision making specifically in applications such as the analysis of complicated muscle activation patterns. Its strong predictive performance and computational efficiency identify machine learning as a promising candidate and can be deployed in clinical workflows where reliability and speed plays an important role.