Objective: The objective of this study was to determine the effectiveness of the conservative Schroth method in patients with adolescent idiopathic scoliosis. Methods: A total of 34 respondents, 24 male and 10 female, aged between 11 and 16 years, participated in the research. The study was a single-blind randomized trial, in which subjects were divided into control and experimental groups by stratified randomization according to the stratum of the Cobb angle. The control group performed the Schroth method at home without the supervision of Schroth therapists, while the experimental group performed the Schroth method under the supervision of Schroth therapists three times a week for 90 min over eight weeks. Initial and final outcome measurements were performed: Cobb angle, angle of trunk rotation, vital capacity, forced vital capacity, forced expiratory volume in the first second, the percentage of forced expiratory volume in the first second in forced vital capacity, and chest expansion. Results: There was a statistically significant improvement in all measured outcomes in the experimental group, while no statistically significant changes were recorded in the control group. The Cobb angle decreased by 2.12°, while ATR decreased by 2.88°; VC increased by 0.15 L, FVC by 0.13 L, FEV1 by 0.1 L, and CE increased by 0.78 cm. Conclusions: The application of an eight-week therapy program using Schroth method by subjects with adolescent idiopathic scoliosis had statistically significant changes in all measured outcomes in the group that was supervised by Schroth’s therapists, while there was no statistically significant improvement in the group that applied therapy at home without supervision.

Background: Chronic low back pain is a leading cause of disability worldwide, necessitating effective interventions to alleviate pain and improve function. This meta-analysis aimed to evaluate the effectiveness of stabilization exercises for pain relief and disability reduction in patients with chronic low back pain. Methods: A meta-analysis was conducted following PRISMA and Cochrane guidelines. Randomized controlled trials evaluating stabilization exercises for chronic low back pain were included. Subgroup analyses were performed based on treatment duration, type of pain (specific vs. non-specific), study quality, and exercise type. Effect sizes were calculated using standardized mean differences (SMD), and evidence quality was assessed using the GRADE tool. Results: A total of 23 studies involving 1132 participants were included. The meta-analysis revealed that longer treatment durations (8–12 weeks) showed the strongest effects on pain reduction (SMD = −0.88) and disability improvement (SMD = −0.85). For pain type, non-specific low back pain responded better (SMD = −0.81 for pain, −0.73 for disability) compared to specific LBP (SMD = −0.61 and −0.42, respectively). The 6-week duration also demonstrated moderate effects (SMD = −0.72 for pain). Core stability exercises had superior pain reduction (SMD = −0.90, large effect) compared to spinal stability exercises (SMD = −0.57), while spinal stability exercises showed higher-quality evidence for disability improvement (SMD = −0.56, high-quality) versus core stability (SMD = −0.62, low-quality). Conclusion: Stabilization exercises are a highly effective intervention for chronic low back pain, offering significant pain relief and functional improvement. They outperform other common interventions and should be prioritized in clinical practice, particularly in longer-duration, supervised programs. These findings provide strong evidence to guide treatment protocols and improve outcomes for patients with chronic low back pain.

This paper applies a hybrid multi-criteria decision-making (MCDM) model that integrates the Analytic Hierarchy Process (AHP) for structured weighting of evaluation criteria with the Net Worth Analysis (NWA) method for value-based aggregation of scores. The proposed framework was employed to evaluate Learning Management Systems (LMS) in higher education, involving two independent expert panels representing management and IT perspectives. Results of the AHP analysis show that cost (28%), security (22%), and usability (17%) are the most influential criteria in the decision-making process, reflecting institutional priorities for financial efficiency, safety and ease of use. Based on the combined AHP-NWA model, Moodle 4.3 emerged as the most sustainable choice (0.586), followed by Atutor 2.2.1 (0.541) and Blackboard (SaaS edition) (0.490). The inclusion of sensitivity and scenario analyses confirmed the robustness of the model, demonstrating that the ranking of alternatives remains stable under variations in weighting factors and different strategic priorities. By framing LMS evaluation within the context of sustainable digital transformation, the study emphasizes how transparent and systematic decision-making supports long-term institutional resilience and aligns with the principles of Education for Sustainable Development (ESD). In addition, the framework contributes to the achievement of Sustainable Development Goal 4 (Quality Education), by guiding higher education institutions toward inclusive, resilient and cost-effective digital solutions.

In the age of digitization and the ever-present use of artificial intelligence (AI), it is essential to develop methodologies that enable the systematic evaluation and ranking of different AI algorithms. This paper investigated the application of the PIPRECIA-S model as a methodological framework for the multi-criteria ranking of AI algorithms. Analyzing relevant criteria such as efficiency, flexibility, ease of implementation, stability and scalability, the paper provided a comprehensive overview of existing algorithms and identified their strengths and weaknesses. The research results showed that the PIPRECIA-S model enabled a structured and objective assessment, which facilitated decision-making in selecting the most suitable algorithms for specific applications. This approach not only advances the understanding of AI algorithms but also contributes to the development of strategies for their implementation in various industries.

This paper presents a comprehensive model for cyber security risk assessment using the PIPRECIA-S method within decision theory, which enables organizations to systematically identify, assess and prioritize key cyber threats. The study focuses on the evaluation of malware, ransomware, phishing and DDoS attacks, using criteria such as severity of impact, financial losses, ease of detection and prevention, impact on reputation and system recovery. This approach facilitates decision making, as it enables the flexible adaptation of the risk assessment to the specific needs of an organization. The PIPRECIA-S model has proven to be useful for identifying the most critical threats, with a special emphasis on ransomware and DDoS attacks, which represent the most significant risks to businesses. This model provides a framework for making informed and strategic decisions to reduce risk and strengthen cyber security, which are critical in a digital environment where threats become more and more sophisticated.

This paper provides a bibliometric analysis of current research trends in the field of artificial intelligence (AI), focusing on key topics such as deep learning, machine learning, and security in AI. Through the lens of bibliometric analysis, we explore publications published from 2020 to 2024, using primary data from the Clarivate Analytics Web of Science Core Collection. The analysis includes the distribution of studies by year, the number of studies and citation rankings in journals, and the identification of leading countries, institutions, and authors in the field of AI research. Additionally, we investigate the distribution of studies by Web of Science categories, authors, affiliations, publication years, countries/regions, publishers, research areas, and citations per year. Key findings indicate a continued growth of interest in topics such as deep learning, machine learning, and security in AI over the past few years. We also identify leading countries and institutions active in researching this area. Awareness of data security is essential for the responsible application of AI technologies. Robust security frameworks are important to mitigate risks associated with AI integration into critical infrastructure such as healthcare and finance. Ensuring the integrity and confidentiality of data managed by AI systems is not only a technical challenge but also a societal necessity, demanding interdisciplinary collaboration and policy development. This analysis provides a deeper understanding of the current state of research in the field of AI and identifies key areas for further research and innovation. Furthermore, these findings may be valuable to practitioners and decision-makers seeking to understand current trends and innovations in AI to enhance their business processes and practices.