This study investigates the impact of annealing time, temperature, and layer height on the tensile strength and dimensional change of three 3D printing materials (PLA, PETG, and carbon fiber-reinforced PETG). Samples with varying layer heights (0.1 mm, 0.2 mm, and 0.3 mm) were annealed at temperatures ranging from 60–100 °C for 30, 60, and 90 min. Tensile tests were conducted, and regression models were developed to analyze the effects of these parameters on tensile strength. The models exhibited high accuracy, with a maximum deviation of only 5% from measured validation values. The models showed that layer height has a significantly bigger influence on tensile strength than annealing time and temperature. Optimal combinations of parameters were identified for each material, with PLA performing best at 0.1 mm/60 min/90 °C and PETG and PETGCF achieving optimal tensile strength at 0.1 mm/90 min/60 °C. PETGCF demonstrated smallest dimensional change after annealing and had the best modulus of elasticity of all the materials. The study employed experimental testing and regression models to assess the results across multiple materials under consistent conditions, contributing valuable insights to the ongoing discussion on the influence of annealing in 3D-printed parts.

Proper posture, characterized by the appropriate alignment of the cervical, thoracic, and lumbar segments of the spine, enables these regions to maintain their normal curvature. Body composition is recognized as one of the factors that can influence overall postural alignment of the spine. The objective of this study was to determine the influence of the parameters of body composition on the prevalence of postural disorders in the sagittal plane. The cross-sectional study was conducted on 152 children of both genders (78 boys), at a mean age of 11 years ± 6 months. In order to evaluate postural disorders, the Formetric 4D System, a tool manufactured by Diers, Schlangenbad, Germany was used. Based on its output data, the following variables were obtained: hyperkyphosis, hyperlordosis, kypholordosis, flatback, and normal alignment of the body in the sagittal plane. The evaluation of body composition parameters was conducted using the InBody 770 device. To determine how body composition influences the postural status of the spinal column, a discriminant analysis was employed. The results showed that approximately 65.8% of children exhibit various types of postural disorders when assessing the alignment of the spine in the sagittal plane. The most prevalent disorder observed was hyperkyphosis, affecting 34.2% of the subjects, followed by kypholordosis at 16.4%. Moreover, the results demonstrated that body composition significantly influences body posture (p = 0.004). An increase in fat mass corresponds to a deviation from normal body posture, whereas an increase in the percentage of skeletal muscle mass and fat-free mass is associated with a reduction in postural abnormalities in the sagittal plane. Considering the results, it is clear that body composition parameters serve as more reliable predictors of the influence on body posture compared to simply calculating the body mass index. Furthermore, it can be concluded that there are consistent patterns of influence by specific body composition parameters, including fat mass, percentage of skeletal muscle, and fat-free mass, on body posture among children from various climates. These results underscore the significance of implementing strength exercises in children, particularly during periods of rapid growth and development, as a means of preventing and correcting postural disorders.

Rail transport plays a crucial role in promoting sustainability and reducing the environmental impact of transport. Ongoing efforts to improve the sustainability of rail transport through technological advancements and operational improvements are further enhancing its reputation as a sustainable mode of transport. Autonomous obstacle detection in railways is a critical aspect of railway safety and operation. While the widespread deployment of autonomous obstacle detection systems is still under consideration, the ongoing advancements in technology and infrastructure are paving the way for their full implementation. The SMART2 project developed a holistic obstacle detection (OD) system consisting of three sub-systems: long-range on-board, trackside (TS), and Unmanned Aerial Vehicle (UAV)-based OD sub-systems. All three sub-systems are integrated into a holistic OD system via interfaces to a central Decision Support System (DSS) that analyzes the inputs of all three sub-systems and makes decision about locations of possible hazardous obstacles with respect to trains. A holistic approach to autonomous obstacle detection for railways increases the detection area, including areas behind a curve, a slope, tunnels, and other elements blocking the train’s view on the rail tracks, in addition to providing long-range straight rail track OD. This paper presents a demonstration of the SMART2 holistic OD performed during the operational cargo haul with in-service trains. This paper defines the demonstration setup and scenario and shows the performance of the developed holistic OD system in a real environment.

Osteoporosis constitutes a significant public health concern necessitating proactive prevention, treatment, and monitoring efforts. Timely identification holds paramount importance in averting fractures and alleviating the overall disease burden. The realm of osteoporosis diagnosis has witnessed a surge in interest in machine learning applications. This burgeoning technology excels at recognizing patterns and forecasting the onset of osteoporosis, paving the way for more efficacious preventive and therapeutic interventions. Smart walkers emerge as valuable tools in this context, serving as data acquisition platforms for datasets tailored to machine learning techniques. These datasets, trained to discern patterns indicative of osteoporosis, play a pivotal role in enhancing diagnostic accuracy. In this study, encompassing 40 participants—20 exhibiting robust health and 20 diagnosed with osteoporosis—data from force sensors embedded in the handlebars of conventional walkers were gathered. A windowing action was used to increase the size of the dataset. The data were normalized, and k-fold cross-validation was applied to assess how well our model performs on untrained data. We used multiple machine learning algorithms to create an accurate model for automatic monitoring of users’ gait, with the Random Forest classifier performing the best with 95.40% accuracy. To achieve the best classification accuracy on the validation dataset, the hyperparameters of the Random Forest classifier were further adjusted on the training data. The results suggest that machine learning-based automatic monitoring of gait parameters could lead to accurate, non-laborious, cost-effective, and efficient diagnostic tools for osteoporosis and other musculoskeletal disorders. Further research is needed to validate these findings.

The aim of this study was to implement a familiarization protocol and to determine the constructive validity of four different non-reactive agility tests on a sample of professional female basketball players (n = 12; age: 18.17 ± 1.90 years). Following the protocol, the participants performed standardized tests three times (the Agility t-test and the Sprint 9-3-6-3-9 m with 180° Turns), while the lateral agility, universal agility, frontal agility, and semicircular agility tests were repeated five times each, with a three-minute rest period between each repetition. A single-factor analysis of variance for repeated measures (ANOVA) determined significant differences between a series of test repetitions for familiarization. Hierarchical cluster analysis grouped the samples based on the results of the standardized tests into two groups. A t-test for independent samples determined a significant difference between these groups (p < 0.05) on all four tests (lateral, universal, frontal, and semicircular agility). Pearson’s linear correlation determined a correlation between the results of the standardized agility tests and the lateral, universal, frontal, and semicircular agility tests, with correlation coefficients ranging from r = 0.936 to r = 0.987. A high value was observed for the discriminant analysis (0.821), while the maximum value was observed for the Kendall coefficient of concordance (W = 1). The study confirmed the validity of the applied tests and determined that the results are influenced by the number of series performed during the preparation for testing. In practice, it is necessary to perform two series of universal and frontal agility tests, i.e., four series of repetitions of lateral and semicircular agility tests in professional women’s basketball.

Proper fixation techniques are crucial in orthopedic surgery for the treatment of various medical conditions. Fractures of the distal humerus can occur due to either high-energy trauma with skin rupture or low-energy trauma in osteoporotic bone. The recommended surgical approach for treating these extra-articular distal humerus fractures involves performing an open reduction and internal fixation procedure using plate implants. This surgical intervention plays a crucial role in enhancing patient recovery and minimizing soft tissue complications. Dynamic Compression Plates (DCPs) and Locking Compression Plates (LCPs) are commonly used for bone fixation, with LCP extra-articular distal humerus plates being the preferred choice for extra-articular fractures. These fixation systems have anatomically shaped designs that provide angular stability to the bone. However, depending on the shape and position of the bone fracture, additional plate bending may be required during surgery. This can pose challenges such as increased surgery time and the risk of incorrect plate shaping. To enhance the accuracy of plate placement, the study introduces the Method of Anatomical Features (MAF) in conjunction with the Characteristic Product Features methodology (CPF). The utilization of the MAF enables the development of a parametric model for the contact surface between the plate and the humerus. This model is created using specialized Referential Geometrical Entities (RGEs), Constitutive Geometrical Entities (CGEs), and Regions of Interest (ROI) that are specific to the human humerus bone. By utilizing this anatomically tailored contact surface model, the standard plate model can be customized (bent) to precisely conform to the distinct shape of the patient’s humerus bone during the pre-operative planning phase. Alternatively, the newly designed model can be fabricated using a specific manufacturing technology. This approach aims to improve geometrical accuracy of plate fixation, thus optimizing surgical outcomes and patient recovery.