This study examines the impact of AI-based tutoring systems on developing mathematical reasoning among secondary school students. It investigates the overall effect on reasoning skills, the influence on reasoning components (conjecture, justification, representation, and metacognition), the relationship between students’ interaction behaviors and reasoning performance, and the perceptions of students and teachers. Pedagogical and contextual factors affecting AI effectiveness are also explored.

A quasi-experimental design was employed with 120 students from Ibn Majid and Ibn Khaldun Schools in Amman, with each school contributing an experimental group (receiving AI-based tutoring) and a control group (following traditional teaching methods), each consisting of 30 students. Data were collected through pre- and post-tests, engagement logs, surveys, and interviews. Quantitative data were analyzed using t-tests, effect sizes, and correlation analyses, while qualitative data were analyzed thematically.

Results showed that AI tutoring significantly improved overall reasoning, with all components positively influenced—particularly conjecture and representation. Higher engagement and frequent interaction were strongly associated with better outcomes. Students and teachers reported positive perceptions, and effective AI implementation depended on teacher guidance, scaffolding, and a supportive learning environment.

Background: The thyroid gland is a cornerstone of the endocrine system, regulating metabolism, growth, and development through its synthesis of thyroid hormones. The micronutrient iodine is an indispensable component of these hormones, and its availability is a critical determinant of thyroid health. Both deficiency and excess of iodine can lead to a spectrum of thyroid pathologies, representing a significant global health challenge.

Objective: This review aims to provide a comprehensive analysis of the intricate relationship between iodine and thyroid function. It synthesizes evidence on iodine metabolism, dietary requirements, the pathophysiology of iodine-related disorders, and the public health strategies implemented to mitigate them.

Methods: A systematic literature review was conducted, analyzing 56 seminal and contemporary research articles. The selected literature covers the foundational biochemistry of thyroid hormone synthesis, the epidemiology of iodine deficiency disorders (IDD), the neurodevelopmental consequences of iodine insufficiency, the impact of environmental and dietary goitrogens, and the global outcomes of salt iodization programs.

Results: The synthesis of the literature confirms that iodine uptake is a tightly regulated process involving the sodium-iodide symporter (NIS) and pendrin. Iodine deficiency is causally linked to hypothyroidism, goiter, and cretinism, with profound, irreversible effects on fetal and childhood neurodevelopment. Factors such as perchlorates, thiocyanates, and deficiencies in selenium and iron can exacerbate iodine-related disorders. While universal salt iodization has been a landmark public health success, challenges remain, including monitoring population iodine status, addressing deficiencies in vulnerable groups (e.g., pregnant women, vegans), and managing the risks of iodine-induced hyperthyroidism in previously deficient populations.

Conclusion: Maintaining optimal iodine nutrition is paramount for the prevention of thyroid pathologies. Effective and continuous monitoring of salt iodization programs and public education are essential to eradicate iodine deficiency disorders and ensure global thyroid health.

Effective cybersecurity in critical infrastructure systems has become an urgent global necessity. As industries increasingly deploy continuous integration/continuous deployment (CI/CD) pipelines, surge in interconnected systems, and pervasive use of artificial intelligence (AI), vulnerabilities proliferate in both software and operational layers. This paper presents a novel, integrated framework that synthesizes established cybersecurity standards with cutting‑edge AI-based vulnerability detection and demand forecasting models, aiming to secure CI/CD-powered critical infrastructure systems. Leveraging the principles of the National Institute of Standards and Technology (NIST) cybersecurity framework (NIST, 2018) as a foundational scaffold, the proposed methodology incorporates AI-driven code analysis, anomaly detection, resource demand forecasting, and continuous vulnerability management. We discuss theoretical underpinnings, detail a comprehensive methodology, and enumerate expected results. Our discussion explores potential limitations, ethical considerations, and a roadmap for future research. This work contributes to bridging the gap between standardized cybersecurity guidelines and dynamic, AI-enhanced defense in modern CI/CD and critical infrastructure environments.

The article is devoted to a theoretical and applied examination of the synergetics of interaction between offensive (Red Team) and defensive (Blue Team) units in cybersecurity. The relevance of the topic is determined by the increasing complexity and frequency of cyberattacks, which necessitates abandoning fragmented, isolated defense practices in favor of integrated, proactive strategies. The scientific novelty lies in formulating a holistic model of the cyber resilience lifecycle based on continuous cooperative interaction between teams (Purple Teaming). The study revisits the classical roles and identifies the limitations of autonomous activities of the Red Team and Blue Team, examines existing options for their integration, and provides an analytical assessment of the advantages of a synergistic approach. Special emphasis is placed on designing a continuous feedback loop between offense and defense that ensures iterative improvement of protective mechanisms. The purpose of the research is to confirm that formalized coordination of offensive and defensive team actions is a decisive factor in enhancing an organization’s cyber resilience. To achieve this goal, methods of analysis and synthesis of the scientific literature, comparative analysis, and conceptual modeling are applied. Sources describing both theoretical foundations and practical frameworks for implementing Purple Teaming are examined. The conclusion formulates findings on the superiority of the integrated model and offers practical recommendations for its implementation. The material is intended for information security professionals, managers, and researchers in the field of cybersecurity.

In this study, cordierite ceramics were synthesized by the solid-state reaction method from a mixture of high-purity oxide powders, including magnesium oxide (MgO), aluminum oxide (Al₂O₃), and silicic acid (SiO₂·nH₂O). The cordierite phase was formed through sintering at 1400 °C and 1430 °C for 1 hour (h). The phase formation and structural transformations during sintering were analyzed using X-ray diffraction (XRD). The XRD analysis results showed that cordierite was a completely formed phase.

The article describes the specific features of an integrated sustainable-development model for solo-operator nail salons in which environmental and social responsibility cease to be an “optional extra” and instead constitute the nucleus of competitive strategy. Drawing on a comprehensive review of sectoral statistics and accumulated practitioner experience, the study demonstrates that the dominant conveyor-style paradigm—designed to maximise customer throughput and rely on inexpensive materials—entails elevated risks of physical injury, allergic reactions and ecological contamination and therefore fails to secure long-term profitability. In response, a three-component framework is proposed. First, chemical and physical service safety is ensured through the elimination of toxic ingredients and the introduction of a proprietary atraumatic cuticle-treatment technique. Second, environmental responsibility is realised via resource conservation, circular waste management and the deliberate selection of suppliers holding recognised eco-certifications. Third, economic viability is achieved by means of premium pricing, relationship-oriented client retention and zero marketing expenditure enabled by word-of-mouth referrals. The resulting findings confirm the hypothesis that sustainability drives not only ethical benefits but also financial advantages for enterprises, and that the systemic approach outlined here can be scaled to other niches within personalised beauty services on a global scale.

Amorphous silica was synthesized from rice husk and doped with Ag, Cu, Zn, or Bi to develop iodine sorbents. The resulting metal-loaded silica materials, produced via thermal treatment after wet impregnation, were tested for iodine vapor uptake at 25 °C for 1 hour. Bismuth- and copper-modified silica achieved the highest capacities (0.5–0.6 g I2/g), outperforming silver (0.2 g/g) and zinc (0.15 g/g). The enhanced performance stems from chemisorption via metal iodide formation (AgI, CuI, ZnI2, BiI3). These results align with literature values and demonstrate a sustainable route to efficient iodine capture using low-cost rice husk silica combined with targeted metal additives.

This study investigates the adsorption of Ag⁺ ions by polymer sorbents impregnated with organic ligands using IR spectroscopy. Comparative spectra recorded before and after adsorption show systematic shifts of characteristic vibrational bands, confirming coordination-driven complex formation between Ag⁺ and donor functional groups on the sorbent surface. Ligands such as MAB, DEDTK, and DADTFK bind within the polymer matrix and yield stable metal–ligand complexes; diagnostic changes are observed in bands assigned to N–H, C=N, and, for phosphorus-containing ligands, P=S and P–S modes. The polymer matrix exhibits the expected polystyrene signatures, against which new or shifted bands are benchmarked to infer interaction strength and site specificity. The results underscore the decisive role of ligand functionality in designing selective sorbents for silver ion separation and purification.

The article presents a broad analysis of methods for applying artificial intelligence to predict injuries and fatalities in the Thoroughbred industry. The study is based on an interdisciplinary approach that incorporates elements of veterinary medicine, sensor technologies, radiomics, image analysis, and the processing of clinical records. Particular attention is given to a comparative-analytical review of modern models, including computer vision techniques, radiomic analysis (μCT), machine learning algorithms, and deep neural network architectures (CNN, ANN), as well as the use of inertial measurement units (IMU) for quantitative risk stratification. The limitations of individual approaches are identified: small sample sizes and low classification stability for eye regions in computer vision systems, high cost and limited accessibility of radiomics, and the dependence of sensor technologies on track surface and conditions. The integration of domain transfer methods, facial expression analysis, and NLP services for unstructured medical data processing is emphasized. The impact of multimodal integration of images, sensor metrics, and clinical records on improving the accuracy and robustness of predictions is examined. The need to shift from universal monitoring strategies to targeted control of high-risk groups is substantiated, enabling proactive equine health management and reducing fatality rates in the racing industry. The article proposes an original classification of promising development directions, including complex architectures, the use of Retrieval Augmented Generation to link sensor and clinical data, and the implementation of integrated platforms in the practice of insurance, breeding, and injury prevention. The material will be of interest to researchers in veterinary medicine, sports analytics, artificial intelligence, and digital technologies, as well as to specialists engaged in the development and implementation of risk prediction systems in the equine industry.

The article presents a theoretical and analytical review of the transformation of management practices in the context of implementing artificial intelligence and advanced analytics. The study is based on an interdisciplinary approach that combines methods of strategic management, innovation analysis, and digital technologies. Particular attention is paid to the formation of the hybrid role of the "product-program manager," which integrates the functions of product and program management. Scenarios of AI applications are examined, including the automation of routine management processes and predictive resource allocation. A comparative analysis of traditional and hybrid managerial roles is conducted, with an emphasis on the changing role of data and the integration of analytical platforms into business processes. It is established that the implementation of AI ensures a shift from reactive to predictive management, contributes to cost reduction and faster time-to-market, and strengthens the manager’s role as an architect of the innovation cycle. Identified limitations are associated with differences in technological maturity and the need to adapt organizational structures. The article proposes a conceptual model of the evolution of managerial functions based on AI, integrating strategy, tactics, and operational execution into a single analytical framework. This work will be useful to researchers in innovation management, practitioners of digital transformation, analytics specialists, and project leaders interested in building sustainable business models in the digital economy.

Background: The rapidly expanding concept, the Internet of Things (IoT), connects a large number of devices to the internet for effective real-time data sharing and communication. But as IoT technology advances, new security risks also arise, and for this reason, machine learning (ML)-based intrusion detection systems (IDS), particularly anomaly-based ones, have emerged as a crucial defence against these new dangers to IoT networks.

Objective: The primary goal of this study is to develop and assess anomaly-based IDS based on ML techniques for use in Internet of Things environments. The goal is to improve the models' performance in terms of accuracy and usefulness.

Methods: The first phase of the study is a thorough review of the recent studies in the field to support the model design. The steps of the experimental stage include data preprocessing, encoding, and normalization. Data balancing was achieved using the SMOTEENN technique. The experiments and validation studies were performed on the UNSW-NB15 dataset. The employed ML algorithms for the study include DT, DNN, RF, XGBoost, and KNN, and the performance was in terms of precision, recall, accuracy, and F1-score.

Results: XGBoost recorded the best detection accuracy (96.37%), followed by RF and DNN. The models were evaluated on both normal and attack traffic, and the suggested model outperformed many existing approaches in recent literature.

Conclusion: The experimental findings confirm the suitability of hybrid and ensemble ML models in improving intrusion detection performance in IoT systems. Future research should consider integrating real-time datasets and combining deep learning with ensemble methods to develop more robust and adaptive models.

The article analyzes the influence of rheological properties on viscosity, workability, fluidity and degree of compaction of concrete mix prepared with the addition of superplasticizer, microsilica, fly ash and basalt fiber in order to create an optimal composition of fine-grained fiber-reinforced concrete (FRC). According to the conclusions made on the basis of the results of the study, it is stated that the use of the specified superplasticizers and additives in optimal quantities improves the rheological properties of fiber-reinforced concrete mix, has a positive effect on the uniformity of distribution of micro-reinforcing elements in the composition of the FRC mix throughout the volume and increases its formability.

The influence of the medium’s acidity and temperature on the sorption of silver ions by impregnated polymer sorbents was investigated. The sorption process was studied in the pH range of 1–12 using PAD400, PAD600, and FK-based sorbents modified with chelating ligands such as MAB, Dtz, and DEDTK. The results demonstrated that the sorption efficiency strongly depends on the acidity of the solution, with maximum sorption observed at pH = 3. The kinetics of silver ion sorption were analyzed using pseudo-first-order, pseudo-second-order, and intra-particle diffusion models. Experimental data showed that the sorption process reaches equilibrium within 60 minutes. Comparison of the kinetic parameters revealed that the pseudo-second-order model best describes the sorption process for all sorbents, with correlation coefficients (R² > 0.99) and calculated equilibrium capacities (Qₑ,calc) consistent with experimental data (Qₑ,obs). These findings indicate that the rate-limiting step of the sorption process is the surface chemical reaction between the functional groups of the sorbent and silver ions.