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.

Comorbidity networks have become a valuable tool to support data-driven biomedical research. Yet, studies often are severely hindered by the availability of the necessary comprehensive data, often due to the sensitivity of health care information. This study presents a population-wide comorbidity network dataset derived from 45 million hospital stays of 8.9 million patients over 17 years in Austria. We present co-occurrence networks of hospital diagnoses, stratified by age, sex, and observation period in a total of 96 different subgroups. For each of these groups we report a range of association measures (e.g., count data, and odds ratios) for all pairs of diagnoses. The dataset provides the possibility to researchers to create their own, tailor-made comorbidity networks from real patient data that can be used as a starting point in quantitative and machine learning methods. This data platform is intended to lead to deeper insights into a wide range of epidemiological, public health, and biomedical research questions.

This paper introduces a novel statistical simulator designed to model propagation in two-way diffuse power (TWDP) fading channels. The simulator employs two zero-mean stochastic sinusoids to simulate specular components, while a sum of sinusoids is used to model the diffuse one. Using the developed simulator, the autocorrelation and cross-correlation functions of the quadrature components, as well as the autocorrelation of the complex and squared envelope, are derived for the first time in literature for channels experiencing TWDP fading. The statistical properties of the proposed simulator are thoroughly validated through extensive simulations, which closely align with the theoretical results.

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.

Objectives Early diagnostic separation between glioblastoma (GBM) and solitary metastases (MET) is important for patient management but remains challenging when based on imaging only. The objective of this study was to assess whether amide proton transfer weighted (APTw) MRI alone or combined with dynamic susceptibility contrast (DSC) MRI parameters, including cerebral blood volume (CBV), cerebral blood flow (CBF), and leakage parameter (K2) measurements, can differentiate GBM from MET. Methods APTw MRI and DSC-MRI were performed on 18 patients diagnosed with GBM (N = 10) or MET (N = 8). Quantitative parameter maps were calculated, and regions-of-interest (ROIs) were placed in whole tumor, contrast-enhanced tumor (ET), edema, necrosis and normal-appearing white matter (NAWM). The mean and max of the APTw signal, CBF, leakage-corrected CBV and K2 were obtained from each ROI. Except for K2, all were normalized to NAWM (nAPTwmean/max, nCBFmean/max, ncCBVmean/max,). Receiver Operating Characteristic (ROC) curves and area-under-the-curve (AUC) were assessed for different parameter combinations. Statistical analyses were performed using Mann–Whitney U test. Results When comparing GBM to MET, nAPTmax, nCBFmax, ncCBVmax and ncCBVmean were significantly increased (p < 0.05) in ET with AUC being 0.81, 0.83, 0.85, and 0.83, respectively. Combinations of nAPTwmax + ncCBVmax, nAPTwmean + ncCBVmean, nAPTwmax + nCBFmax, nAPTwmax + K2max and nAPTwmax + ncCBVmax + K2max in ET showed significant prediction in differentiating GBM and MET (AUC = 0.92, 0.82, 0.92, 0.85, and 0.92 respectively). Conclusion When assessed in Gd-enhanced tumor areas, nAPTw MRI signal intensity alone or combined with DSC-MRI parameters, was an excellent predictor for differentiating GBM and MET. However, the small cohort warrants future studies.

Background: Midwives are globally recognized as health professionals who specialize in the care of women in labor with a vital role in maternal and newborn health care. Midwives specialize in the care of women in labor and play a key role globally in managing normal vaginal birth, caring for pregnant women including supporting women and their families, providing consultations, managing normal birth for low-risk pregnant women and helping them maintain a healthy pregnancy. Despite the fact that the midwifery profession is an autonomous profession, in some countries there are many struggles to achieve recognition within its formal scope of work. The role of the midwife/midwife remains unclear in many countries due to poorly articulated policies and a lack of regulatory frameworks, which results in a lack of public clarity regarding the role of the midwife. Objective: The purpose of this expert report is to present the role of the midwife in protecting the health of mothers before, during and after childbirth, to clearly define their role and importance, and the need to improve midwifery as a profession in order to reduce the number of caesarean sections. Methods: This systematic review includes a comprehensive literature search of published scientific articles, in English, from 2020 to 2024, using electronic databases considered most relevant to the topics; CINAHL, EMBASE and PubMed. In this systematic review and meta-analysis, we included studies on the role of midwives in different countries, including Thailand, the United States, Australia, Canada, the UK, the Netherlands, Bosnia and Herzegovina, Slovenia, Croatia and Serbia, to arrive at results on what the role of midwives is in these countries. Citations without abstracts and/or full text, anonymous reports, editorials, case reports, case series and qualitative studies were excluded. Results: In the Law on Health Care of the FBiH, and the Law on Nursing and Midwifery of the FBiH, the role of the midwife is insufficiently defined and she is not given sufficient authority to work. For childbirth in BiH, in addition to midwives, a doctor must always be present. In European and foreign countries, the role of the midwife is put in the foreground during childbirth, so there are also hospitals where women give birth and are cared for by midwives. Midwife-led care, an approach that is already widely practiced in developed countries; however, it is a relatively new approach in lower-income countries. In midwife-led care, a midwife who is well known to the mother provides care for the low-risk pregnant woman during antenatal care, delivery and the postnatal period, rather than being cared for by different medical staff led by an obstetrician. The primary focus of care led by midwives is to support a healthy physiological pregnancy and birth and to empower women to give birth naturally with little or no regular intervention. Conclusion: It is very worrying for midwifery as a profession that there is currently a lack of visibility of midwives in practice within their scope of practice in Bosnia and Herzegovina. More research is needed on demonstrating the value of midwives as a primary role in the context of midwifery practice in Bosnia and Herzegovina.

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.

Eutrophication of coastal areas is a global problem. A full-scale coastal remediation project was initiated in Björnöfjärden bay in the Stockholm archipelago in 2011. Measures to reduce external nutrient inputs from the surrounding catchment (15 km2) targeted agriculture, on-site wastewater treatment facilities, and horse keeping. The effects were evaluated at 22 water quality monitoring stations over 11 years (2012–2022) to determine temporal trends in nutrient concentrations, spatial correlations within and between monitored sub-catchments, and effects of individual mitigation measures at local and catchment scale. The effect of individual measures varied from no significant effect to significant nutrient decreases (21% reduction in dissolved P concentrations in one lime filter) or increases (11% higher concentrations in total P in one constructed wetland). However, few significant trends were detected at sub-catchment outlet stations. Tailored placement, design, dimensioning, and maintenance of implemented mitigation measures are needed to improve their nutrient retention effect.

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.