Sakarya University of Applied Sciences AXSIS
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A study protocol to develop virtual reality software in the care management of patients in intensive care
No full textBackground: The use of virtual reality is increasing in nursing to ensure patient safety and to improve the quality of care in the education of nurses. Aim: To develop a virtual reality software for intensive care patient care management and to investigate the effect of this software on novice intensive care nurses. Study Design: This study protocol contains a randomized controlled experimental design research. The nurses will be divided into control (n = 34) and study groups (n = 34) by randomization. The research will be conducted in four steps: (1) according to Kolcaba's Comfort Theory, the protocol for care management of an intensive care patient will be prepared and transferred to the virtual reality software, (2) the nurses in both groups will be given theoretical training, (3) a routine orientation training programme will be applied to the nurses in the control group, and those in the study group will be given virtual reality goggles. (4) Tools such as a ‘Clinical Practice Skills Observation Form and Knowledge Level Questionnaire’ and ‘Problem-Solving Inventory’, ‘Clinical Decision-Making Scale in Nursing’, ‘State Anxiety Inventory’ and ‘Satisfaction Level Questionnaire’ will be applied to both groups before commencing the theoretical training, 1 week after the application and in the first month of the application. Results: This protocol describes an experimental study aiming to test the impact of virtual reality software on novice intensive care nurses in the care management of an intensive care patient. Conclusions: The results and recommendations will be shared after the study is completed. Relevance to Clinical Practice: Within the scope of the research, the virtual reality software to be developed for the care management of an intensive care patient will provide important contributions to the development of nurses' problem-solving and clinical decision-making skills and reduce state anxiety levels in orientation to the intensive care unit (ICU). © 2025 British Association of Critical Care Nurses
Determination of job stress levels of physical education and sport teachers; [Determinación de los niveles de estrés laboral de los profesores de educación física y deporte]
No full textThis study investigates how the job stress levels of physical education and sport teachers vary according to gender, age, educational status and job position. Within the scope of the research, the data obtained from 520 teachers were used and the job stress questionnaire was evaluated under four main subheadings. The results of Bartlett's test and Kaiser-Meyer-Olkin (KMO) coefficient for the suitability of the data for factor analysis showed that the data were quite suitable for factor analysis; while the workload sub-heading explained the highest variance with 26.7%, skill use explained 14.2%, decision freedom explained 12.03% and social support explained 11.6% of the total variance. In the analyses based on gender differences, it was determined that male teachers had higher levels of workload stress, while female teachers had higher levels of skill use, decision freedom and social support stress. In the analyses conducted between age groups, no significant differences were found in the sub-dimensions. Similar results were obtained in the analyses conducted according to education levels. In the analyses conducted according to job positions, it was determined that administrators did not differ from non-administrators in all dimensions. These results show that it is important to customise job stress management strategies according to gender, age and job positions. It is emphasised that general stress management strategies should be developed to cover all demographic groups. © 2025, Campus EDUCA SPORTIS S.L.. All rights reserved
Current status, opportunities, and challenges of exosomes in diagnosis and treatment of osteoarthritis
No full textOsteoarthritis (OA) is a progressive joint disease that is a frequent reason for pain and physical dysfunction in adults, with enormous social and economic burden. Although ongoing scientific efforts in recent years have made considerable progress towards understanding of the disease's molecular mechanism, the pathogenesis of OA is still not fully known, and its clinical challenge remains. Thus, elucidating molecular events underlying the initiation and progression of OA is crucial for developing novel diagnostic and therapeutic approaches that could facilitate effective clinical management of the illness. Exosomes, extracellular vesicles containing various cellular components with approximately a diameter of 100 nm, act as essential mediators in physiological and pathological processes by modulating cell-to-cell communications. Exosomes have crucial roles in biological events such as intercellular communication, regulation of gene expression, apoptosis, inflammation, immunity, maturation and differentiation due to their inner composition, which includes nucleic acids, proteins, and lipids. We focus on the roles of exosomes in OA pathogenesis and discuss how they might be used in clinical practice for OA diagnosis and treatment. Our paper not only provides a comprehensive review of exosomes in OA but also contributes to the development efforts of diagnostic and therapeutic tools for OA. © 2025 Elsevier Inc
Predicting the energetic performance of an automobile heat pump utilising a fixed capacity compressor and R1234yf using ANN modelling
No full textThis study used experimental data to illustrate the accuracy of artificial neural network modelling for vehicle heat pump systems. The system had a four-way valve, thermostatic expansion valves, and a fixed-capacity compressor. The system used R1234yf refrigerant instead of R134a in automotive air conditioning systems. The system was evaluated using varying compressor speeds, indoor unit intake air flow rates, interior and outdoor unit inlet air flow temperatures, and relative humidity. The experimental system was tested 72 times using different control and data-collecting technologies to determine steady-state performance and how artificial intelligence may enhance it. The projected performance parameter of the automotive heat pump system employing R1234yf refrigerant was assessed using an artificial neural network model. Six scenarios were examined: compressor discharge temperature, indoor unit output airflow temperature, refrigerant mass flow rate, compressor power, heating capacity, and performance coefficient. Data was divided into training (269 patterns, 68.27 %) and testing sets (125 patterns, 31.73 %) to ensure accurate model development and performance assessment across different experimental configurations. This approach guarantees robust data handling and reliable artificial neural network predictions. The training and testing of the artificial neural network model of the automobile heat pump system with R1234yf was evaluated. In the best case, training R² was 0.99817, MSE 0.0012, and MEP 0.005. High prediction accuracy and robust linear associations were observed with R² = 0.99969, MSE = 0.0008, and MEP = 0.003. Future vehicle heat pump research using alternative refrigerants will benefit from this study's shortened experimental techniques and system performance estimates. © 2024 Elsevier Ltd and II
Prediction of mass attenuation coefficients in mixed alkali and borosilicate glasses using machine learning approaches
No full textThe increasing demand for effective radiation shielding materials in different sectors, including medical, nuclear, and industrial applications, has promoted exploring new approaches for optimizing material properties. Mixed alkali glasses, such as those containing B2O3, SiO2, CaO and ZnO, offer a combination of protective and transparent properties, making them promising candidates for radiation shielding. Recent advances in machine learning (ML) have accelerated the development and evaluation of these materials. This study utilizes ML techniques to predict the mass attenuation coefficients (MAC) of mixed alkali glasses across a photon energy range of 0.015 MeV-15 MeV. A comprehensive dataset generated using XCOM served as the basis for training and validating several ML models, including Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression, Random Forest Regression, K-Nearest Neighbors, Multi-Layer Perceptron, and a customized Neural Network (NN). Among these models, Random Forest Regression ended as the most accurate, achieving an R-squared value of 0.99958 and demonstrating minimal error (MAE: 0.01481, MSE: 0.00561, RMSE: 0.07493), indicating its superior ability to capture the complex relationships between glass composition, energy levels, and radiation attenuation properties. While other models like MLP and NN performed decently, they lagged behind the Random Forest model. The results highlight machine learning's potential to advance radiation shielding by providing reliable models for material design and parameter calculations like MAC, offering faster, more efficient alternatives to conventional tools like Monte Carlo simulations
The solution of the equation AX = B using commutative elliptic octonion matrix analysis and its applications in colour image restoration
No full textIn this study, the algebraic properties of commutative elliptic octonions and their relations with the base (Formula presented.) are examined. Then, the matrix structure is analyzed based on these properties and an algorithm for solving the equation AX = B is presented. In image processing, the Octonion Elliptic Linear Shift Invariant (OELSI) method for enhancing images is defined and applied represented by commutative elliptic octonion matrices. Finally, the obtained results are supported by examples and compared with other image restoration techniques in the literature. © 2025 Informa UK Limited, trading as Taylor & Francis Group
Advanced applications of artificial neural networks in scientific research
No full textThis chapter is focused on discussing various aspects of the application of Artificial Neural Networks (ANNs) in scientific research with reference to physics, biology, chemistry, engineering, and environmental sciences. ANNs are discussed with the base on the modeling of complicated systems, the handling of massive amounts of data, as well as forecasting in different fields. Among them are CNNs, RNNs, the increasingly popular Transformers depending on the type of data and the type of tasks they are intended for. It also contains the training, the optimization and the performance measures which are the bare minimum indices defining the stability and the efficiency of ANN structures. Additionally, the proposed system is expanded to incorporate other technologies, including IoT and blockchain, while providing future prospects and directions for Explainable AI (XAI) for improved model interpretability. © 2025, IGI Global Scientific Publishing. All rights reserved
Policy and practice in L2 classroom assessment: policy implementation at a state high school in Türkiye
No full textThis study investigates how the explicit policies set for assessing English achievement in the instructional policy documents come to life at a particular program of a state high school. Junior-year students and their English-as-a-foreign-language teachers were the participants. Data were gathered through field notes, observations, interviews, and documents. Findings suggested a discrepancy between policy and practice in assessing English achievement. Instructional policy documents created at different layers of the policy conveyed a mixture of traditional and performance-based assessment types as the leading features of the intended assessment. However, the field data demonstrated that though principles of intended assessment were achieved to a degree, features of traditional assessment dominated classroom assessment practices. Several contextual factors ranging from teacher beliefs to top-down policy implementation were found influential in the realisation of the policy. The study presents implications for instructional policymaking, language classroom assessment, and in-service training for language teachers. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
Development of free-standing h-BN/rGO/S composite cathodes for Li-S batteries: h-BN content and temperature effect
No full textThis study aims to improve the properties of Li-S batteries and overcome their disadvantages by utilizing hexagonal boron nitride (h-BN) nanocomposites with unique features that provide advantages in their applications. For this purpose, composite films were produced using h-BN with superior mechanical and chemical properties along with reduced graphene oxide (rGO) possessing high electrical conductivity. Free-standing and flexible h-BN/rGO/S composite paper electrodes containing different weight ratios of functionalized h-BN were prepared. The obtained binder-free composite papers were employed as cathodes in Li-S batteries and applied at different temperatures. In this study, the structural, morphological, and thermal analyses of the composite cathodes were conducted using X-ray diffraction (XRD), field emission gun scanning electron microscopy (FEG-SEM), energy dispersive X-ray spectroscopy (EDS), transmission electron microscopy (TEM) and thermogravimetric analysis (TGA). The optical measurements were carried out by Fourier transform infrared spectroscopy (FT-IR), Raman spectroscopy and ultraviolet–visible spectroscopy (UV–Vis). After assembling CR2032 button cells, electrochemical performance tests were applied to assess the charge–discharge capacities. A high discharge capacity of 427 mAh g−1 was achieved after 1000 cycles. As a result, h-BN/rGO-based composites have been developed as environmentally friendly and metal-free materials, further enhancing the electrochemical performance and electron transport of lithium batteries. © 2025 Elsevier B.V
Optimizing callogenesis in five potential medicinal herbs for the bioactive constituents: a sustainable approach to pharmaceutical production
No full textThe search for natural antioxidants to safeguard against several diseases is expanding rapidly. Interestingly, the levels of antioxidants have been discovered to be greater in the in vitro-raised calli than the plant extracts in vivo. The aim of this research was to standardize the protocols for culturing calli of five potential medicinal herbs and determine their antioxidant and polyphenolic compounds. The calli of carnation, goji berry, harmal, bitter cucumber, and datura were developed from young leaves using Murashige and Skoog media with varied forms and concentrations of cytokinin and auxin in combination after their optimization. Goji berry, carnation, and datura initiated callus in 13 days, faster than bitter cucumber (20 days). Datura had a 28.7% higher callus induction rate than bitter cucumber. The callus weight of goji berry was three times higher than harmal, with a 25.4% greater diameter than bitter cucumber. The callus of goji berry had 4.3 times more phenolic and ascorbic content than datura and 1.9× more than harmal. The callus of datura had twice the total antioxidant capacity of harmal. The callus of goji berry exhibited 5.7% increased radical-scavenging activities than datura. The enzyme activities of catalase and superoxide dismutase were 2.6% and 2.4% greater in the callus of goji berry than datura. The callus of goji berry also had 2.1% and 2.4% increased peroxidase and ascorbate peroxidase activities than datura and bitter cucumber, respectively. From the findings, it can be concluded that the callus of goji berry is a highly promising source of natural antioxidants, exhibiting significantly higher levels of antioxidant and polyphenolic compounds compared to other medicinal herbs. © The Author(s), under exclusive licence to Springer Nature B.V. 2024