Occupational burnout is common among intensive-care-unit (ICU) staff and adversely affects staff well-being and patient care. We hypothesized that a multicomponent intervention based on organizational support and workplace climate improvement would reduce burnout. The 1:1 cluster-randomized Hello trial involved 370 ICUs from sixty countries allocated to either the intervention or usual care. The four-week intervention designed to promote a positive workplace culture and within-team support used posters, email nudges, greetings during morning meetings, role modeling, and positive messages in boxes and on noticeboards. The primary endpoint was burnout prevalence, measured using the Maslach Burnout Inventory. Secondary outcomes included MBI subscale scores, well-being, job satisfaction, ethical climate, intention to leave, work safety, and professional conflicts. Before the intervention, burnout prevalence was 59.4% (95% CI, 58.6–60.5), with no difference between arms. After the intervention, 4966 intervention-arm and 4602 control-arm healthcare professionals completed the MBI. Burnout prevalence was significantly lower in the intervention arm relative to controls (52.2% vs. 63.3%; adjusted odds ratio, 0.56; 95%CI 0.46–0.68; P < 0.001). Among MBI sub-scales scores, emotional exhaustion and depersonalization were lower, and personal accomplishment was higher in the intervention arm. Staff in the intervention arm reported better job satisfaction, workplace safety, ethical climate, and patient- and family-centered care; they were less often considering a job change. The Hello intervention reduced burnout and improved workplace culture among ICU staff. Given the pragmatic design, the intervention tested may have broad applicability. The trial was registered on ClinicalTrials.gov on June 18, 2024 (NCT06453616).

Background and purpose Fatigue is a physiological phenomenon that reduces the ability of muscles to maintain maximal force during repeated activities. Anaerobic capacity and the Fatigue Index (FI) are key indicators of the body’s ability to sustain explosive power, and the Running Anaerobic Sprint Test (RAST) is a recognized tool for their assessment. Purpose: this study was to assess the anaerobic abilities of active sportsmen, using the RAST, with a focus on Peak Power (PP) and the Fatigue Index (FI). Material and methods The study included two male active sportsmen. (U.G., 70 kg and A.V., 85 kg) who performed six 35-meter sprints with 10 seconds of rest between sprints. Sprint time and lower limb power (W) were measured, while FI was calculated based on the difference between maximal and minimal power. Results Both participants showed a linear decline in speed and power during repeated sprints. A.V. achieved a higher level of power and a lower FI (11.6%) compared to U.G. (18 %). Power decreased from (both) sample 732W to 515W, while total average sprint time increased by 0.57s. Conclusions The Rast aerobic sprint test is an effective and practical tool for assessing anaerobic capacity and monitoring individual progress. The results confirm lactate accumulation and muscle fatigue. Targeted high-intensity anaerobic training can reduce FI and improve the anaerobic capacity of physical education and sport students.

The ever-evolving nature of cyber-attacks within the Internet of Things (IoT) highlights the urgent need for developing advanced detection methods suitable for the limited resources of edge environments. Traditional detection methods face challenges in adapting to the rapidly changing and expanding landscape of IoT applications. This situation underscores the necessity for advanced detection methods that can achieve high detection accuracy while minimizing resource consumption. To address this issue, this paper proposes a lightweight framework for intrusion detection that combines the strengths of lightweight vision transformers with transfer learning. By incorporating a pre-trained MobileViT model into the domain of IoT intrusion detection, the proposed framework utilizes transfer learning to efficiently extract features and classify data, thereby enhancing detection performance. The approach involves converting traffic data into images by segmenting the data into blocks of successive samples and converting these blocks into grayscale images. Experimental results demonstrate the proposed framework's superior performance, achieving a high accuracy of 99.97% and an F1 score of 99.92% in a multi-class classification scenario on the Edge-IIoTset dataset, outperforming existing methods.

The cement industry is under constant pressure to reduce its environmental footprint while ensuring economic competitiveness and technological reliability. One of the most effective strategies to achieve this goal is the substitution of traditional raw materials with alternative ones derived from industrial (by)products, waste, or secondary resources. This paper presents a structured methodology for the selection and evaluation of potential raw materials for clinker production. The proposed approach integrates four key criteria: physical compatibility, which determines whether the raw material can be handled by existing processing equipment; chemical compatibility, which ensures compliance with clinker quality requirements; environmental compliance, which assesses adherence to local and international environmental regulations; and economic viability, including the costs of material acquisition, processing, equipment adaptation, and CO2 emissions associated with the raw mix. The research procedure involves initial communication with suppliers, visual inspection of the material, laboratory analysis (chemical and environmental), raw mix modelling, and full economic evaluation. If at any stage the material fails to meet the required criteria, feedback is provided to the supplier, avoiding unnecessary costs and efforts. Results indicate that this integrated methodology offers a systematic and transparent making of decision framework that can accelerate the acceptance of alternative raw materials, improve resource efficiency, and contribute to sustainable cement production.

ABSTRACT Objective To examine the associations between parent perceived environmental factors, nighttime sleep duration and 24-h sleep duration among an international sample of preschool-aged children. Methods Secondary analyses of cross-sectional data from preschoolers across 23 countries (19 LMICs), collected during the third pilot phase (January 2021–August 2024) of the SUNRISE Study. Parents completed a questionnaire which asked about their child’s sleep patterns and environmental factors that impacted their child’s sleep in the previous 3 days. Results Data from 2,219 children were analyzed. A significant difference was observed between nighttime sleep (F = 14.27, p = <0.0001) and nap duration (F = 9.10, p = 0.0004) across country income level. Environmental factors such as heat (−12.87, 95% CI: −11.54, −0.61) and cold (−17.70, 95% CI: −34.53, −0.85) were negatively associated with nighttime sleep duration. Conclusions Public health researchers and professionals should prioritize context-specific strategies to minimize the impact of weather conditions on sleep to promote healthy levels of sleep among preschoolers from diverse settings.

Background: The study investigates newly developed composite materials with advanced filler technology and modified resin matrices, designed to enhance esthetic quality, clinical efficiency, and mechanical properties. This study evaluated the effect of two light-curing protocols—a conventional low-voltage (LV) protocol (10 s at 1200 mW/cm2) and a high-voltage (HV) protocol (3 s at 3000 mW/cm2)—on the microhardness (MH), bottom/top MH ratio, and the correlation between filler content (wt%, vol%) and MH of bulk-fill resin-based composites (RBCs). Four RBCs were tested: Tetric PlusFill (TPF), Tetric Plus Flow(TPFW), Tetric PowerFill (PFL), and Tetric PowerFlow (PFW). Materials and Methods: Samples were fabricated in the laboratory using specially designed cylindrical molds (diameter = 8 mm, height = 4 mm). Initial MH was measured on the top and bottom surfaces of composite specimens 24 h after light curing using a digital microhardness tester (QNESS 60 M EVO, ATM Qness GmbH, Mammelzen, Germany). The correlation between the filler content (wt%, vol%) and the MH of the RBCs was tested. For the calculation of depth-dependent curing effectiveness, the bottom/top ratio for initial MH was used. Conclusions: The MH of bulk-fill RBCs was found to be influenced by both material composition and the applied light-curing protocol. An increase in filler content resulted in higher MH values under both protocols, with the filler volume fraction exhibiting a stronger correlation than the weight fraction. While both flowable and sculptable Tetric Plus composites exhibited higher MH values under the HV protocol, Tetric Power composites demonstrated greater initial hardness under LV protocol. The flowable composite PFW showed the most pronounced reduction in MH under HV curing. The bottom/top MH ratio exceeded 80% in all tested materials, confirming adequate polymerization throughout the composite layers.

Segmentation of brain tissue is an essential task in medical image analysis, particularly in neuroimaging and disease diagnosis. This study evaluates and compares three major segmentation approaches in the ISBR18 dataset: atlas-based methods, machine learning techniques, and deep learning architectures. The atlas-based Majority Voting method achieved the highest performance within its category with a dice similarity coefficient of 0.8477, utilizing anatomical templates for segmentation. Among machine learning techniques, K-means clustering demonstrated robust performance with 96% classification accuracy, offering computational efficiency despite limitations in spatial resolution. The deep learning U-Net model trained for binary segmentation achieved 93% accuracy, benefiting from its encoder-decoder architecture for precise boundary detection. While traditional atlas-based approaches provide robust anatomical consistency and machine learning methods offer computational advantages, deep learning models show promise in handling complex segmentation tasks. Future research could integrate these approaches to enhance segmentation performance in the ISBR18 dataset and lead to more accurate and reliable brain tissue segmentation for clinical applications.

Contemporary approaches to teaching children’s literature and music education are increasingly replacing traditional pedagogical methods. The emergence of artificial intelligence (AI) has made these processes more dynamic and complex, presenting both opportunities and challenges for educators and pupils. This development raises important questions about teachers’ readiness to adopt innovative methodologies and pupils’ receptiveness to deeper learning and improved outcomes through AI-enhanced instruction. Children’s literature and music possess the capacity to educate, nurture, and heal. Their interdisciplinary nature provides primary school teachers with a rich foundation for creative and integrative teaching strategies, particularly relevant in the age of AI. This paper advocates for the deliberate integration of AI and healing education into the teaching of children’s literature and music at the primary level. It proposes that such integration can be achieved through creative and interdisciplinary applications of AI tools, alongside bibliotherapeutic and musicotherapeutic methods. The central methodological framework employed is mood mapping. The study examines the use of the Donna AI Song Generator within healing education, aiming to identify optimal strategies for both teachers and pupils. These findings may inform the development of diverse teaching methodologies and offer insights into the creative use of AI in interdisciplinary primary education. Additionally, the paper introduces an innovative conceptual framework – the Bibliotherapy and Musicotherapy Questionnaire (BMQ) – proposed as a theoretical model for future implementation in primary education settings. Although conceptual and theoretical in nature, the study is grounded in extensive practical teaching experience and the integration of AI tools, particularly within healing education. The BMQ model demonstrates adaptability to diverse instructional contexts and age groups, offering potential for future empirical validation and practical classroom application. Ultimately, this research highlights the transformative potential of AI in fostering holistic, creative, and therapeutic learning environments in primary education.

Background Fermented foods are an integral part of diets worldwide, and emerging epidemiological data suggest their significant beneficial health effects. However, assessing their intake is challenging since many of these foods are sporadically and/or locally consumed, hence current traditional nutritional assessment tools lack the specificity to capture this variability. To address this gap, the Fermented Food Frequency Questionnaire (3FQ) was developed and this study aimed to evaluate its relative validity and repeatability across European regions. Methods In the validation study of the 3FQ, 12,646 adult participants were recruited across four European regions to assess consumption of sixteen major fermented food groups. Repeatability was assessed by administering the 3FQ twice, ~6 weeks apart, to a subset of participants (n = 2,315). Validity was evaluated using 24-h dietary recalls (24 h). Statistical analyses included Spearman's rank correlation coefficients and Intra-Class Correlation coefficients (ICC) for repeatability, and Bland-Altman plots for validity. Results Results showed high repeatability, overall and by region, for estimated quantities and frequencies of consumption for most of the fermented food groups (from 0.4 to 1.0), with a few exceptions for infrequently consumed items (e.g., fermented fish). Validity assessment via Bland-Altman plots revealed excellent agreement between the 3FQ and 24 h for most of the food groups, with over 90% of values falling within the agreement interval. Notably, fermented dairy products, coffee, and bread categories showed the strongest agreement (>95%). Conclusion The 3FQ is a robust and reliable tool for estimating the consumption of diverse fermented food groups across four European regions and provides valid estimates of the frequency and quantity of intake for specific fermented foods. The 3FQ could be a valuable instrument for epidemiological research aiming to elucidate associations between certain fermented foods and health parameters in European diets.

Autonomic nervous system (ANS) dysfunction has emerged as a central feature of post-infectious syndromes, including post-COVID syndrome (PCS), chronic fatigue syndrome (CFS), and late-stage Lyme disease. This cross-sectional study included 1036 patients evaluated in the Neurocardiological Laboratory of the Institute for Cardiovascular Diseases “Dedinje,” divided into four groups: PCS, CFS after COVID-19, CFS of insidious onset, and Lyme disease. All patients underwent head-up tilt testing (HUTT), and serological testing was performed in accredited institutions. The Lyme disease group demonstrated the highest prevalence of positive HUTT responses and a significantly greater frequency of orthostatic hypotension and syncope. Approximately 50–65% of patients in the PCS and Lyme groups were positive for IgM antibodies against at least one microorganism, with more than 10% showing positivity for three or more pathogens. Logistic regression analysis revealed that, beyond classical hemodynamic parameters, antibody status served as a significant predictor of HUTT outcomes, with specific associations identified for HSV-1, HHV-6, Coxiella burnetii, Toxoplasma gondii, and Borrelia spp. Multinomial regression further indicated that negative IgG antibodies, particularly to HSV-1 and VZV, predicted Lyme disease group membership. These findings support the hypothesis that ANS dysfunction in post-infectious syndromes may be driven by persistent or prior infections, highlighting the need for integrative diagnostic approaches.

Background In the dot perspective taking task – a task commonly used to assess implicit mentalizing - participants are typically slower in judging how many dots they see when there is a difference in the number of dots seen by themselves and a centrally placed avatar than when both perspectives align. This finding has been termed the ‘altercentric interference’ effect and taken as evidence that participants spontaneously and automatically compute the avatar’s perspective. In this study, we focus on one line of critiques regarding the interpretation that the altercentric interference effect is automatic, namely by assessing whether the effect is purely stimulus-driven. Specifically, we tested the proposal that for the altercentric interference effect to emerge, participants must first focus their attention to the avatar, which then directs their attention to the dots and that this is achieved by a social word prompt (typically ‘YOU’) inducing a social mindset that then draws the participants’ attention to the avatar once the avatar and the dots appear. Methods We tested two groups of participants: one with a ‘YOU’ prompt and one with a non-social ‘NOW’ prompt. The semantics of both prompts were irrelevant because information about the colour of the dots participants needed to judge was presented through the ink colour of the prompt, not its text. Results Our results revealed no statistically significant difference in the altercentric interference effect between groups and our exploratory analyses showed that this was due to the altercentric interference effect being present in both groups. Conclusions Our findings do not provide empirical support for the hypothesis that the word prompt used in typical dot perspective tasking tasks promotes a social mindset that leads to the altercentric interference effect, however they may be aligned with the hypothesis that the effect requires participants’ attention to be drawn to the avatar.

Background: Testicular torsion scoring systems, based on a combination of clinical and imaging factors, have been developed to improve the diagnostic accuracy of testicular torsion in patients presenting with acute scrotum. This study aimed to validate and compare two current testicular torsion scores the Boettcher Alert Score (BAL) and the Testicular Workup for Ischemia and Suspected Torsion (TWIST)-in a retrospective cohort of pediatric patients with acute scrotum. Methods: We conducted a retrospective study of all pediatric patients admitted to our institution for acute scrotum between January 2010 and December 2022. Patients were categorized into the testicular torsion (TT) group and the non-testicular torsion (NTT) group. Collected data were used to calculate the scoring systems and perform statistical analyses. Results: A total of 241 patients were included, of whom 80 (33.2%) had testicular torsion. The mean age in the TT group was 13 years. The optimal individual cut-off value for the BAL score was >1 (sensitivity 90%, specificity 80.75%), and for the TWIST score >4 (sensitivity 82.5%, specificity 80.75%). A high-risk TWIST score >5 had a specificity of 80.75% and a negative predictive value (NPV) of 90.28%, while a BAL score of 4 showed a specificity of 98.48% and NPV of 94.2%. The area under the ROC curve was slightly higher for the BAL score (0.917; 95% CI, 0.875–0.949) than for the TWIST score (0.897; 95% CI, 0.851–0.932). The difference between the two scores was not statistically significant. Conclusion: The TWIST and BAL clinical scores have significant diagnostic value and may assist in the evaluation of testicular torsion in children. Both scores could be incorporated into a standardized approach for assessing pediatric acute scrotum, potentially reducing time to definitive diagnosis, and minimizing ischemia duration.

The energy transition intensifies spatial conflicts between energy infrastructure (EI) and migratory birds, fueling public resistance, litigation and permitting delays. These conflicts are often addressed only at the project level, after EI sites are chosen, leaving little room for proactive impact avoidance. We present a globally scalable method using existing weather radar infrastructure and open data to map nocturnal bird migration at high spatial resolution across large areas, demonstrating its application in the Netherlands. We find that around 50% of migration occurs at altitudes of EI. Contrary to assumed homogeneous distributions or discrete routes, our maps reveal that migration intensity varies over distances relevant for spatial planning. We demonstrate how such maps can inform selection of wind energy sites with considerable variation in migration and wind potential. Our method facilitates early-stage impact avoidance through strategic EI siting, targeted mitigation and faster permitting, offering a low-cost, scalable path toward a more nature-inclusive energy transition.

Future wireless communications will rely on multiple-input multiple-output (MIMO) beamforming operating at millimeter wave (mmWave) frequency bands to deliver high data rates. To support flexible spatial processing and meet the demands of latency-critical applications, it is essential to use fully digital mmWave MIMO beamforming, which relies on accurate channel estimation. However, ensuring power efficiency in fully digital mmWave MIMO systems requires the use of low-resolution digital-to-analog converters (DACs) and analog-to-digital converters (ADCs). The reduced resolution of these quantizers introduces distortion in both transmitted and received signals, ultimately degrading system performance. In this paper, we investigate the channel estimation performance of mmWave MIMO systems employing fully digital beamforming with low-resolution quantization, under practical system constraints. We evaluate the system performance in terms of spectral efficiency (SE) and energy efficiency (EE). Simulation results demonstrate that a moderate quantization resolutions of 4-bit per DAC/ADC offers a favorable trade-off between energy consumption and achievable data rate.

Within the digital pulse of modern industry, digital strategic orientation acts as an architect of the future, laying the foundation for technological adaptability and data mastery while constructing a pathway to sustainability that transcends conventional business norms. This study aimed to uncover the extent to which digital strategic orientation shapes sustainability performance. Anchored in the resource-based view theory, this study employs a thoroughly structured methodology, utilizing multiple regression analysis to derive insights from data gathered across 115 enterprises operating within the business landscape of Bosnia and Herzegovina. The research findings underscore a strong, positive impact of digital strategic orientation on sustainable performance, manifesting across its three critical dimensions—financial outcomes, social responsibility, and environmental performance. This study provides a novel contribution to the literature and offers tangible managerial insights by empirically showcasing how digital strategic orientation serves as a catalyst for enhancing sustainable performance. More specifically, the practical implications indicate that firms actively integrating digital strategic orientation into their operations can enhance efficiency, minimize resource waste, and improve financial stability, while simultaneously strengthening stakeholder trust and ensuring regulatory compliance, ultimately positioning themselves for long-term sustainable growth in an increasingly digital and environmentally conscious market.