This article investigates the influence of different solvents on the mechanical properties of biocompatible and biodegradable polycaprolactone (PCL) scaffolds. During the research, using electrospinning technology, 27 samples of polycaprolactone nanofibers exposed to different solvents were produced. A tensile test was performed on the produced nanofiber samples, and the nanofiber mechanical properties, yield strength, elastic modulus, and elastic elongation were calculated, and load–displacement and stress–strain dependence diagrams were compared from the obtained results. The strongest nanofiber was singled out, and its mechanical properties were compared with those of biological tissues and its application in tissue engineering. The structure was determined using a scanning electron microscope, and the structures of nanofibers exposed to different solvents were compared. After calculating the influence of different solvents on the mechanical properties of the nanofibers, the strongest structure was identified, PCL and chloroform, which has an elastic modulus of 9.86 MPa and a yield strength of 1.11 ± 0.32 MPa. The type of solvent used in the production of the solution affects the homogeneity of the fibre and the shape of the filaments. In solvents with lower viscosity, the fibre filaments are more homogeneous and more evenly distributed.

Background and Objectives: Surface electromyography (EMG) is widely used to assess muscle activation. However, direct interpretation of its functional biomechanical consequences remains challenging. This study aimed to develop and evaluate an EMG-driven musculoskeletal simulation framework for investigating how controlled modifications of muscle activation patterns influence joint-level biomechanics in the upper limb. The objective was not to reproduce specific clinical pathologies but to enable systematic virtual scenario analysis of activation-dependent movement alterations. Materials and Methods: Surface EMG signals were recorded from five healthy adults (3 males, 2 females; age 22 ± 1 years) during cyclic elbow flexion/extension tasks using a wireless system (sampling frequency: 2000 Hz). Processed and normalized EMG envelopes were directly applied as prescribed neural inputs in forward dynamic simulations implemented in OpenSim, without optimization-based muscle recruitment. Controlled virtual scenarios were generated through parametric modification of activation signals to represent reduced activation capacity, increased antagonist co-activation, spasticity-like activation modulation, and tremor-like oscillatory modulation. Joint kinematics, joint moments, and movement stability were evaluated. A Movement Quality Index (MQI) was introduced as a comparative research metric integrating biomechanical performance indicators. Simulations were deterministic and analyzed descriptively. Results: Distinct activation modifications produced characteristic kinematic and kinetic responses. Reduced activation capacity decreased simulated joint moment output, increased co-activation altered joint moment timing and mechanical stability, and tremor-like oscillatory modulation generated periodic fluctuations in joint kinematics and kinetics. The MQI enabled quantitative differentiation between simulated scenarios and severity levels within the controlled modelling framework. Conclusions: The proposed EMG-driven forward dynamic simulation framework provides a methodological platform for controlled virtual scenario analysis of activation-dependent biomechanical changes. The findings highlight the sensitivity of joint-level mechanics to altered muscle activation patterns, within the deterministic modelling environment. The framework is intended for research-oriented biomechanical investigation and hypothesis testing rather than direct clinical diagnosis of neuromuscular disorders.