In this paper, the conditional phase distribution of the two-wave with diffuse power (TWDP) process is derived as a closed-form and as an infinite-series expression. For the obtained infinite series expression, a truncation analysis is performed and the truncated expression is used to examine the influence of different channel conditions on the behavior of the TWDP phase. All the results are verified through Monte Carlo simulations.

This study evaluated the stability of acetylsalicylic acid (ASA) in commercial Aspirin Protect 100 mg tablets under eight different storage conditions, including varying exposure to moisture, light, and temperature, with a focus on tablets stored in dosette boxes. Acid-base titration methods were used to assess ASA degradation and stability. Elevated moisture had the greatest impact on ASA stability, significantly reducing recovery factors to 85.38% and 81.10% under high humidity, while temperature influenced ASA stability, with notable deviations from control values at temperatures above 25°C (13.26% and 7.16% for two methods). Although storage at 18–25°C yielded acceptable results, reduced temperatures (<8°C) provided better stability. Direct sunlight exposure caused further degradation, reducing recovery values to as low as 82.5% and increasing deviations from control (-10.82% to -16.77%). Hydrolysis, exacerbated by environmental factors, was identified as the primary degradation pathway, leading to the formation of salicylic acid and acetic acid. Samples stored in under recommended conditions had the best stability, with recovery factors meeting pharmacopoeia standards (101.08% and 99.16% of labelled content). These findings underscore the importance of proper storage practices for ASA tablets to maintain their quality, safety, and therapeutic efficacy. While repackaging tablets into dosette boxes may improve compliance, it can compromise stability, highlighting the need for stricter storage guidelines to ensure optimal patient outcomes.

The paper presents the most comprehensive and large-scale global study to date on how higher education students perceived the use of ChatGPT in early 2024. With a sample of 23,218 students from 109 countries and territories, the study reveals that students primarily used ChatGPT for brainstorming, summarizing texts, and finding research articles, with a few using it for professional and creative writing. They found it useful for simplifying complex information and summarizing content, but less reliable for providing information and supporting classroom learning, though some considered its information clearer than that from peers and teachers. Moreover, students agreed on the need for AI regulations at all levels due to concerns about ChatGPT promoting cheating, plagiarism, and social isolation. However, they believed ChatGPT could potentially enhance their access to knowledge and improve their learning experience, study efficiency, and chances of achieving good grades. While ChatGPT was perceived as effective in potentially improving AI literacy, digital communication, and content creation skills, it was less useful for interpersonal communication, decision-making, numeracy, native language proficiency, and the development of critical thinking skills. Students also felt that ChatGPT would boost demand for AI-related skills and facilitate remote work without significantly impacting unemployment. Emotionally, students mostly felt positive using ChatGPT, with curiosity and calmness being the most common emotions. Further examinations reveal variations in students’ perceptions across different socio-demographic and geographic factors, with key factors influencing students’ use of ChatGPT also being identified. Higher education institutions’ managers and teachers may benefit from these findings while formulating the curricula and instructions/regulations for ChatGPT use, as well as when designing the teaching methods and assessment tools. Moreover, policymakers may also consider the findings when formulating strategies for secondary and higher education system development, especially in light of changing labor market needs and related digital skills development.

Terrestrial laser scanners (TLS) are widely employed in structural health monitoring (SHM) of large objects due to their superior capabilities compared to traditional geodetic methods. TLS provides rapid and detailed data on the geometric properties of objects, enabling various types of analyses. In this study, TLS was utilized to examine the minaret of the Bjelave Mosque, located in Sarajevo, Bosnia and Herzegovina. The inclination of the minaret was assessed using principal component analysis (PCA) and linear regression (LR) applied to sampled data from four edges of the minaret’s body. The geodetically determined inclination values were used as input data for subsequent static and pushover analyses conducted in DIANA FEA, where the minaret was modeled. The analyses indicate that the inclination increases stress and strain, leading to larger cracks and reduced structural capacity, as demonstrated by the pushover analysis curves. This study highlights the combined impact of structural inclination, water infiltration, and settlement on the minaret’s integrity and proposes these findings as a basis for future maintenance and strengthening measures.

BACKGROUND After 25 years of implementing the Medical Devices Directive (MDD), in 2017, the new Medical Devices Regulation (MDR) came into force, establishing stricter requirements for post-market surveillance of the safety and performance of medical devices (MD). For electrocardiogram (ECG) devices, which are crucial for monitoring cardiac activities, these requirements are essential to ensure the reliability and accuracy of diagnosing cardiac conditions and timely treatment. OBJECTIVE This study aims to enhance post-market surveillance of ECG devices by leveraging Machine Learning (ML) algorithms to predict the operational status of these devices. Specifically, the research focuses on classifying the success or failure of ECG device operations based on performance and safety parameters. The ultimate goal is to improve the management strategies of ECG devices in healthcare institutions, ensuring optimal functionality and increasing the reliability of diagnostic procedures. METHOD During the inspection process of ECG devices conducted by an accredited laboratory in accordance with ISO 17020 standard in numerous healthcare institutions in Bosnia and Herzegovina, a total of 5577 samples were collected. Various machine learning algorithms, including Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Gaussian Naive Bayes (NB), and Support Vector Machine (SVM), were employed for result comparison and selection of the most accurate algorithm. RESULTS All algorithms demonstrated good performance, but the Random Forest (RF) algorithm stood out, achieving 100% accuracy in predicting the success/unsuccess status of the device. While the results of this research are specific to the collected data from EKG devices, the developed algorithms can be applied to other similar datasets, offering opportunities for broader use in the medical environment. CONCLUSION Implementing machine learning algorithms for automated systems in healthcare institutions can significantly enhance the quality of patient diagnosis and treatment. Additionally, these systems can optimize costs associated with managing medical devices. Improved post-market surveillance using ML can address challenges related to ensuring device reliability and safety.

This study investigates the use of deep learning algorithms to predict the discharge coefficient (Cd) of contaminated multi-hole orifice flow meters with circular opening. Datasets (MHO1 and MHO2) were obtained from computational fluid dynamic simulations for two circular multi-hole orifice flow meters of different geometries. To evaluate the performance and generalization capabilities of different models, three distinct scenarios, each involving different dataset configurations and normalization techniques were designed. For each scenario, three deep learning models (feedforward neural networks, convolutional neural network, and recurrent neural network) were implemented and evaluated based on their performance metrics, including mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). For all three scenarios eight models for each neural network model were developed (FFNN – four models, CNN – two models, RNN – two models). The same structure of models was used across all scenarios to ensure consistency in the evaluation process. Key input parameters include geometrical and flow variables such as β – parameter, contamination thickness, radial distance, Reynolds number, and orifice diameters. Results demonstrate the effectiveness of deep learning in accurately predicting discharge coefficient for different contamination conditions and different geometries. This study showed that deep learning models can be used for prediction of discharge coefficients for multi-hole orifice flow meters of similar geometry, based on data obtained from one orifice flow meter for different contamination parameters.

Background and Objectives: Depression is a common mental problem in the older population and has a significant impact on recovery and general well-being. A comprehensive understanding of the prevalence of depressive symptoms and their effects on functional outcomes is essential for improving care strategies. The primary aim of this study was to determine the prevalence of depressive symptoms in older patients undergoing geriatric rehabilitation and to assess their specific impact on their functional abilities. Materials and Methods: A retrospective study was conducted at the Lucerne Cantonal Hospital in Wolhusen, Switzerland, spanning from 2015 to 2020 and including 1159 individuals aged 65 years and older. The presence of depressive symptoms was assessed using the Geriatric Depression Scale (GDS) Short Form, while functional abilities were evaluated using the Functional Independence Measure (FIM) and the Tinetti test. Data analysis was performed using TIBCO Statistica 13.3, with statistical significance set at p < 0.05. Results: Of the participants, 22.9% (N = 266) exhibited depressive symptoms, with no notable differences between genders. Although all patients showed functional improvements, the duration of rehabilitation was prolonged by two days (p = 0.012, d = 0.34) in those with depressive symptoms. Alarmingly, 76% of participants were classified as at risk of falling based on the Tinetti score. However, no significant correlation was found between the GDS and Tinetti scores at admission (p = 0.835, r = 0.211) or discharge (p = 0.336, r = 0.184). The results from the non-parametric Wilcoxon matched-pairs test provide compelling evidence of significant changes in FIM scores when comparing admission scores to those at discharge across all FIM categories. Conclusions: Depressive symptoms are particularly common in geriatric rehabilitation patients, leading to prolonged recovery time and increased healthcare costs. While depressive symptoms showed no correlation with mobility impairments, improvements in functional status were directly associated with reduced GDS scores. Considering mental health during admission and planning is critical in optimizing rehabilitation outcomes.