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.

OBJECTIVE This study aimed to assess the prevalence and analyze characteristics and symptoms of non-infectious sore-throat in teachers. STUDY DESIGN AND METHODS Adult persons employed as teachers were screened for non-infectious sore throat. A cross-sectional study was performed in patients with non-infectious sore throat confirmed based on clinical examination. RESULTS Out of 1008 teachers who participated in the screening, non-infectious sore throat was found in 70 respondents (6.9%). Most of the participants with non-infectious sore throat were women with a mean age of 45.5 years and a mean length of service 18.1 years. A small number of participants (n = 7, 10%) often visited a medical doctor because of throat problems. Over 80% of participants commonly used throat therapeutic agents. The most common symptoms were dry throat, tickling, and scratching in the throat. The oropharyngeal mucosa in most subjects was slightly red to red, the tonsils were normal, and there was no oropharyngeal enanthema. The median subjective assessments using the visual analog scale from zero to ten were four (interquartile range (IQR) 0-5) for pain, four (IQR 1-5) for difficulty in swallowing, and two (IQR 0-4) for swelling of the pharynx back wall. Of the analyzed risk factors, the highest percentages of participants were using air conditioning and consuming chilled and carbonated drinks, 21.4% were smokers and 10% of participants had a confirmed respiratory allergy. CONCLUSIONS The prevalence of non-infectious sore throat was 6.9% with the most common symptoms being dry throat, tickling, and scratching in the throat. While a small percentage of participants often visited a medical doctor because of throat problems, the majority of them used throat therapeutic agents. Additional multicentric prospective studies are needed to increase our knowledge about symptoms and therapeutic strategies for non-infectious sore throat and voice disorders in general.

Electrochemical biosensors transduce chemical reactions into measurable electrical signals by incorporating recognition components. Although they are capable of detecting a broad range of target molecules, their application in complex matrices, such as food, at minimum or no sample preparation, is challenging and requires the introduction of innovative and effective strategies. This review explores the recent advances in electrochemical biosensors for on-site food safety and quality analysis. We first discuss the presence of chemical contaminants and biohazards in food and the need for robust, rapid, low-cost, and point-of-care (POC) analytical techniques. We then address the critical aspects of sensitivity and selectivity of electrochemical biosensors in detecting chemical and biological contaminants in real food samples. We finally investigate the major drawbacks of these biosensors and provide future perspectives on the field.

Background/Objectives: This cross-sectional study aimed to produce an adapted Croatian version of the Negative Behaviors in Health Care Questionnaire and to validate it. Methods: The process comprised the translation, cultural adaptation, and psychometric evaluation of the questionnaire. Clinical specialists and qualified bilingual speakers participated in both forward and backward translation. Face validity was tested. The survey’s original developer approved the final version. The reliability of the questionnaire was assessed using the test–retest method and Cronbach’s alpha coefficient. Exploratory and confirmatory factor analyses and assessments of divergent and convergent validity were conducted. The collected data were analyzed using SPSS 21.0 and R, program version 3.5.2., for Windows. Results: A five-factor structure was obtained and confirmed via CFA, although not all fit coefficients were satisfactory. The internal consistency reliability was 0.86 for the contributing factors and the seriousness of aggression, 0.79 for the use of aggression, 0.95 for the fear of retaliation, and 0.83 for the frequency of aggression; in total, α = 0.88. Test–retest reliability was moderate. All correlations were statistically significant, and the correlation was the highest for seriousness (0.754) and frequency of aggression (0.725) and the lowest for contributing factors (0.528). Test–retest reliability was satisfactory. Statistically significant differences were found when comparing respondents by gender, age, work experience, education, and hierarchical position. Conclusions: The adapted, translated, and validated survey provides a valuable tool for assessing lateral and vertical aggression between and towards nurses in terms of contributing factors, frequency, severity, uses of aggression, and fear of retaliation.

OBJECTIVE To systematically evaluate timely reporting of clinical trial results at medical universities and university hospitals in the Nordic countries. STUDY DESIGN AND SETTING In this cross-sectional study, we included trials (regardless of intervention) registered in the EU Clinical Trials Registry and/or ClinicalTrials.gov, completed 2016-2019, and led by a university with medical faculty or university hospital in Denmark, Finland, Iceland, Norway, or Sweden. We identified summary results posted at the trial registries, and conducted systematic manual searches for results publications (e.g., journal articles, preprints). We present proportions with 95% confidence intervals (CI), and medians with interquartile range (IQR). PROTOCOL https://osf.io/wua3r RESULTS: Among 2,112 included clinical trials, 1,650 (78.1%, 95%CI 76.3-79.8%) reported any results during our follow-up; 1,097 (51.9%, 95%CI 49.8-54.1%) reported any results within 2 years of the global completion date; and 48 (2.3%, 95%CI 1.7-3.0%) posted summary results in the registry within 1 year. Median time from global completion date to results reporting was 690 days (IQR 1,103). 856/1,681 (50.9%) of ClinicalTrials.gov-registrations were prospective. Denmark contributed approximately half of all trials. Reporting performance varied widely between institutions. CONCLUSION Missing and delayed results reporting of academically led clinical trials is a pervasive problem in the Nordic countries. We relied on trial registry information, which can be incomplete. Institutions, funders, and policy makers need to support trial teams, ensure regulation adherence, and secure trial reporting before results are permanently lost.

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.