Resilience, Positivity and Social Support as Perceived Stress Predictors among University Students.

Since the COVID-19 pandemic, researchers have been trying to identify which personal resources can contribute to minimizing the mental health costs in students incurred due to the restrictions that disrupted safety and predictability in their academic lives. The aim of the study was to verify if and how individual factors (resilience and positivity) and socio-environmental factors (social support and nationality) allow prediction of the level of perceived stress. University students (n = 559) from Poland, Serbia, and Italy were surveyed using the Perceived Stress Scale (PSS-10), the Brief Resilience Scale (BRS), the Positivity Scale (PS), and the Interpersonal Support Evaluation List (ISEL-12). Personal resources-positivity, resilience, and support-were found to be positively interrelated and significantly associated with stress levels. Additionally, gender and nationality differentiated stress levels. A general linear model (GLM) showed that levels of perceived stress are best explained by resilience, positivity, tangible support, and gender. The results obtained can strengthen students' awareness of personal resources and their protective role in maintaining mental health, as well as contribute to the creation of prevention-oriented educational activities. Nationality was not a significant predictor of the level of perceived stress, which highlights the universality of examined predictors among university students from different countries and suggests that interventions aimed at enhancing these resources could benefit students across different cultural contexts.

AI-Based Support System for Monitoring the Quality of a Product within Industry 4.0 Paradigm.

Three-dimensional (3D) printing, also known as additive manufacturing (AM), has already shown its potential in the fourth technological revolution (Industry 4.0), demonstrating remarkable applications in manufacturing, including of medical devices. The aim of this publication is to present the novel concept of support by artificial intelligence (AI) for quality control of AM of medical devices made of polymeric materials, based on the example of our own elbow exoskeleton. The methodology of the above-mentioned inspection process differs depending on the intended application of 3D printing as well as 3D scanning or reverse engineering. The use of artificial intelligence increases the versatility of this process, allowing it to be adapted to specific needs. This brings not only innovative scientific and technological solutions, but also a significant economic and social impact through faster operation, greater efficiency, and cost savings. The article also indicates the limitations and directions for the further development of the proposed solution.

Traditional Artificial Neural Networks Versus Deep Learning in Optimization of Material Aspects of 3D Printing.

3D printing of assistive devices requires optimization of material selection, raw materials formulas, and complex printing processes that have to balance a high number of variable but highly correlated variables. The performance of patient-specific 3D printed solutions is still limited by both the increasing number of available materials with different properties (including multi-material printing) and the large number of process features that need to be optimized. The main purpose of this study is to compare the optimization of 3D printing properties toward the maximum tensile force of an exoskeleton sample based on two different approaches: traditional artificial neural networks (ANNs) and a deep learning (DL) approach based on convolutional neural networks (CNNs). Compared with the results from the traditional ANN approach, optimization based on DL decreased the speed of the calculations by up to 1.5 times with the same print quality, improved the quality, decreased the MSE, and a set of printing parameters not previously determined by trial and error was also identified. The above-mentioned results show that DL is an effective tool with significant potential for wide application in the planning and optimization of material properties in the 3D printing process. Further research is needed to apply low-cost but more computationally efficient solutions to multi-tasking and multi-material additive manufacturing.

Reducing Waste in 3D Printing Using a Neural Network Based on an Own Elbow Exoskeleton.

Traditional rehabilitation systems are evolving into advanced systems that enhance and improve rehabilitation techniques and physical exercise. The reliable assessment and robotic support of the upper limb joints provided by the presented elbow exoskeleton are important clinical goals in early rehabilitation after stroke and other neurological disorders. This allows for not only the support of activities of daily living, but also prevention of the progression neuromuscular pathology through proactive physiotherapy toward functional recovery. The prices of plastics are rising very quickly, as is their consumption, so it makes sense to optimize three dimensional (3D) printing procedures through, for example, improved artificial intelligence-based (AI-based) design or injection simulation, which reduces the use of filament, saves material, reduces waste, and reduces environmental impact. The time and cost savings will not reduce the high quality of the products and can provide a competitive advantage, especially in the case of thinly designed mass products. AI-based optimization allows for one free print after every 6.67 prints (i.e., from materials that were previously wasted).