Search Results (56,197)

The present study investigates the twofold effect of home–work spillover on life satisfaction through intrinsic work motivation and meaning derived from work, with family flexibility as a moderator. Based on Self-Determination Theory and the Work–Home Resources model, we test a moderated parallel mediation model whereby both positive and negative spillover from home affect life satisfaction through motivational and meaning pathways, depending on the level of family flexibility. 735 working adults completed validated measures of work-related flow, work meaning, home–work interaction (negative and positive), family flexibility, and life satisfaction. PROCESS macro (Model 59) via 5000 bootstrapped samples indicated that home negatively influencing work was associated with lower life satisfaction, mainly via reduced work meaning, particularly for individuals with low family flexibility. Conversely, positive work–home interaction was associated with higher work meaning and, indirectly, greater life satisfaction, with this effect being stronger when family flexibility was lower. Intrinsic motivation was associated with life satisfaction through mediation only when family flexibility was higher. These results indicate work meaning and family context compensatory and buffering effects on well-being. The research adds to integrative work–life interface models by delineating conditional psychological processes that enable employee flourishing. Full article

Background and Objectives: Cardiac injury caused by cancer therapy can be detected early using high-sensitivity cardiac troponins (hs-cTns), and this is crucial for preventing irreversible consequences. Clinically relevant issues regarding hs-cTns in oncologic settings—such as reliable cut-off values, the optimal assessment timeframe, factors influencing their levels, and their prognostic ability in relation to functional echocardiographic parameters—require further investigation. In this study, we aimed to examine the determinants of hs-cTnT variations during cancer therapy and the relationship between the biomarker and functional conventional echocardiographic parameters. Materials and Methods: We prospectively evaluated adult patients scheduled for chemotherapy for either breast or gastrointestinal cancers, excluding those with pulmonary and cardiac disorders. We enrolled 40 patients who underwent a minimum of one cycle of potentially cardiotoxic regimens containing at least one of the following agents: anthracyclines, cyclophosphamide, taxanes, 5-fluorouracil, platinum compounds, trastuzumab, or bevacizumab. We observed two-dimensional and tissue Doppler echocardiographic parameters and hs-cTnT levels for a median of 360 days (IQR 162, 478) following the start of chemotherapy. Results: The generalised estimating equation (GEE) analysis revealed significant elevations in hs-cTnT levels at three months (β = 1.2; p = 0.005) and six months (β = 2.3; p = 0.02) from baseline, influenced by anthracycline treatment (p = 0.009), renal function (p = 0.003), and increased cardiotoxicity risk (high: p = 0.013; medium: p < 0.001). Elevated hs-cTnT levels independently predicted the deterioration of the LV longitudinal myocardial function, measured by the systolic tissue velocities, according to the GEE analysis. The receiver operating characteristic curve-derived hs-cTnT thresholds—of 8.23 ng/L and 8.08 ng/L—had a high negative predictive value for identifying Average and Lateral LVS′ decreases, respectively. Conclusions: Our research supports the use of baseline and continuing hs-cTnT testing in cancer patients, showing the dependence of the biomarker on renal function, cardiovascular toxicity risk level, and anthracycline treatment. The hs-cTnT cut-off value of approximately 8 ng/L may suggest a low probability of longitudinal myocardial function impairment and this observation needs further validation in larger cohorts. Full article

Additively manufactured (AM) aluminum (Al) alloys are very useful in sectors like automotive, manufacturing, and aerospace because they have unique mechanical properties, such as their light weight, etc. AlSi10Mg made by laser powder bed fusion (LPBF) is one of the most promising materials because it has a high strength-to-weight ratio, good thermal resistance, and good corrosion resistance. But machining AlSi10Mg parts is still hard because they have unique microstructural properties from the way they were produced. This research investigates the machining efficacy of the AM-AlSi10Mg alloy in distinct cutting conditions (dry, flood, chilled air, and minimal quantity lubrication with castor oil). The study assesses how different cooling conditions affect important performance metrics such as cutting temperature, surface roughness, and tool wear. Due to castor oil’s superior lubricating and film-forming properties, MQL (Minimal Quantity Lubrication) reduces heat generation between 80 °C and 98 °C for the distinct speed–feed combinations. The Multi-Objective Optimization by Ratio Analysis (MOORA) approach is used to determine the ideal cooling and machining conditions (MQL, Vc of 90 m/min, and fr of 0.05 mm/rev). The relative closeness values derived from the MOORA approach were used to predict machining results using machine learning (ML) models (MLP, GPR, and RF). The MLP showed the strongest relationship between the measured and predicted values, with R values of 0.9995 in training and 0.9993 in testing. Full article

Increasingly, scholars have been researching how ESG ratings appear to impact the returns of a merger as well as the expected synergies of the merger. This paper adds to the literature by using a non-linear method to test the impact that ESG ratings, differences in ESG ratings between the acquirer and the target, and ESG components have on the deal premium. We find overwhelming evidence, using multiple deal premium measurements, of an inverted U-shaped relationship between the target’s ESG scores at the time of the announcement and the deal premium. Moreover, we find some evidence that differences between the ESG scores of the acquirer and the target also impact the deal premium but in a U-shaped relationship. Finally, our results show that the social scores of both the acquirer and the target impact the deal premium, again in an inverted U-shaped manner, as does the governance rating of target, but only in relatively smaller deals. Full article

Establishing a sustainable human presence on the Moon and Mars will require the use of locally available resources for construction. A binder material similar to concrete is a promising candidate, provided that its production and performance under reduced gravity can be reliably understood. Previous microgravity investigations demonstrated the feasibility of mixing cementitious materials in space but produced irregular or low-quality specimens that limited standardized mechanical testing. To address these limitations, the MASON (Material Science on Solidification of Concrete) team developed the first-generation MASON Concrete Mixer (MCM), which enabled the safe production of cylindrical specimens aboard the International Space Station (ISS). However, its fully manual operation introduced variability and required significant astronaut time. Building on this foundation, the development of an automated MCM prototype is presented in this study. It integrates motorized mixing and programmable process control into the established containment architecture. This system enables reproducible specimen production by eliminating operator-dependent variations while reducing crew workload. In comparison to manually mixed samples, the automated MCM demonstrated reduced variability in the tested concrete properties. The automated MCM represents a first step toward autonomous space instrumentation for high-quality materials research and provides a scalable path to uncrewed missions and future extraterrestrial construction technologies. Full article

Bud break is a critical phenological stage in muscadine grapevines, marking the start of the growing season and the increasing need for irrigation management. Real-time bud detection enables irrigation to match muscadine grape phenology, conserving water and enhancing performance. This study presents BudCAM , a low-cost, solar-powered, edge computing camera system based on Raspberry Pi 5 and integrated with a LoRa radio board , developed for real-time bud detection. Nine BudCAMs were deployed at Florida A&M University Center for Viticulture and Small Fruit Research from mid-February to mid-March, 2024, monitoring three wine cultivars (A27, noble, and Floriana)with three replicates each. Muscadine grape canopy images were captured every 20 min between 7:00 and 19:00, generating 2656 high-resolution (4656 × 3456 pixels) bud break images as a database for bud detection algorithm development. The dataset was divided into 70% training, 15% validation, and 15% test. YOLOv11 models were trained using two primary strategies: a direct single-stage detector on tiled raw images and a refined two-stage pipeline that first identifies the grapevine cordon. Extensive evaluation of multiple model configurations identified the top performers for both the single-stage (mAP@0.5 = 86.0%) and two-stage (mAP@0.5 = 85.0%) approaches. Further analysis revealed that preserving image scale via tiling was superior to alternative inference strategies like resizing or slicing. Field evaluations conducted during the 2025 growing season demonstrated the system’s effectiveness, with the two-stage model exhibiting superior robustness against environmental interference, particularly lens fogging. A time-series filter smooths the raw daily counts to reveal clear phenological trends for visualization. In its final deployment, the autonomous BudCAM system captures an image, performs on-device inference, and transmits the bud count in under three minutes, demonstrating a complete, field-ready solution for precision vineyard management. Full article

Despite growing attention to sustainability in the global tourism industry, empirical evidence explaining how internal organizational capabilities translate into superior environmental performance remains scarce—particularly in emerging markets. This study investigates the performance effects of green dynamic capabilities (GDC) in driving environmental performance in the hotel industry, with a particular focus on the mediating effect of eco-innovation (ECI) and the moderating effect of environmental management accounting (EMA). Although environmental sustainability in tourism has become a global imperative, limited empirical evidence exists on how internal capabilities and accounting practices jointly enhance hotels’ green performance—particularly within emerging economies such as Türkiye. Drawing on dynamic capabilities theory and resource orchestration perspectives, this study addresses this research gap by analyzing survey data collected from 108 managers of Green Key-certified hotels in Türkiye. The developed research framework was tested through Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 4. The results reveal that GDCs positively influence environmental performance, and this relationship is significantly mediated by ECI. Moreover, EMA strengthens the positive effect of GDCs on ECI, highlighting its role as an enabling internal infrastructure. These findings suggest that the realization of sustainability outcomes depends not only on the presence of capabilities but also on how these are embedded within innovation processes and internal organizing systems. The study contributes to sustainability and management literature by offering a context-specific understanding of the capability–infrastructure–performance nexus and providing actionable insights for hotel managers in emerging tourism markets. Full article

Introduction: Cigarette smoking is unquestionably associated with an increase in morbidity and mortality worldwide, exerting significant adverse effects on respiratory health. The impact of tobacco persists in the epigenome long after smoking cessation. Furthermore, the offspring of smokers may also be affected by the detrimental effects of smoking. Material and methods: The modifications made to the body, such as DNA methylation, histone modification, and regulation by non-coding RNAs, do not change the DNA sequence but can influence gene expression. In respiratory disease, multigenerational effects have been reported in humans, with an increased risk of asthma or COPD and decreased lung function in offspring, despite them not being exposed to smoke. Prenatal nicotine exposure leads to pulmonary pathology that persists across three consecutive generations, supported by animal studies conducted by Rehan et al. Significant advances in high-throughput genomic and epigenomic technologies have enabled the discovery of molecular phenotypes. These either reflect or are influenced by them. Due to the hidden environmental effects and the rise of artificial intelligence (AI) in biomedical research, new predictive models are emerging that not only explain complex data but also enable earlier detection and prevention of smoking-related diseases. In this narrative review, we synthesise the latest research on how smoking affects gene regulation and chromatin structure, emphasising how tobacco can increase vulnerability to multiple diseases. Discussion: For many years, it was widely believed that diseases are solely inherited through genetics. However, recent research in epigenetics has led to a significant realisation: environmental factors play a crucial role in an individual’s life. External influences leave a mark on DNA that can influence future health and offer insights into potential illnesses. In this context, it is possible that in the future, doctors might treat people not as a whole but as individual beings, with personalised medication, tests, and other approaches. Conclusions: The accumulated evidence suggests that exposure to various environmental factors is associated with multigenerational changes in gene expression patterns, which may contribute to increased disease risk. The application of artificial intelligence in this domain is currently a crucial tool for researching potential future health issues in individuals, and it holds a powerful prospect that could transform current medical and scientific practice. Full article

Background: Artificial intelligence (AI), the overarching field that includes machine learning (ML) and its subfield deep learning (DL), is rapidly transforming clinical research by enabling the analysis of high-dimensional data and automating the output of diagnostic and prognostic tests. As clinical trials become increasingly complex and costly, ML-based approaches (especially DL for image and signal data) offer promising solutions, although they require new approaches in clinical education. Objective: Explore current and emerging AI applications in oncology and cardiology, highlight real-world use cases, and discuss the challenges and future directions for responsible AI adoption. Methods: This narrative review summarizes various aspects of AI technology in clinical research, exploring its promise, use cases, and its limitations. The review was based on a literature search in PubMed covering publications from 2019 to 2025. Search terms included “artificial intelligence”, “machine learning”, “deep learning”, “oncology”, “cardiology”, “digital twin”. and “AI-ECG”. Preference was given to studies presenting validated or clinically applicable AI tools, while non-English articles, conference abstracts, and gray literature were excluded. Results: AI demonstrates significant potential in improving diagnostic accuracy, facilitating biomarker discovery, and detecting disease at an early stage. In clinical trials, AI improves patient stratification, site selection, and virtual simulations via digital twins. However, there are still challenges in harmonizing data, validating models, cross-disciplinary training, ensuring fairness, explainability, as well as the robustness of gold standards to which AI models are built. Conclusions: The integration of AI in clinical research can enhance efficiency, reduce costs, and facilitate clinical research as well as lead the way towards personalized medicine. Realizing this potential requires robust validation frameworks, transparent model interpretability, and collaborative efforts among clinicians, data scientists, and regulators. Interoperable data systems and cross-disciplinary education will be critical to enabling the integration of scalable, ethical, and trustworthy AI into healthcare. Full article

Hailstones exhibit variations in diameter and impact objects at different velocities influenced by airflow. The extent of damage inflicted by hailstorms is contingent upon both the size and speed of the hailstones. Accurately simulating hailstones is essential for conducting impact tests. In this research, artificial hailstones were created using a polyvinyl acetate (PVA) additive. Previous investigations indicate that a mixture comprising 12% PVA and 88% demineralized water is most effective in replicating the behavior of natural hailstones. The primary objective of this study is to establish an experimental setup for assessing the impact of hailstones on aircraft canopies. To support this goal, specific requirements for impact testing were outlined. Dynamic impact tests were conducted using two different aircraft dimensions. Artificial hailstones of 20 mm and 50 mm in diameter were successfully manufactured for the experiments. The designated velocities for these artificial hailstones were 20 m/s, 30 m/s, 60 m/s, and 120 m/s, for which the necessary air pressures were calculated. Experimental results confirmed that artificial hailstones of varying diameters could be effectively produced and that they impacted predetermined areas on the aircraft canopies. However, the study also found that artificial hailstones traveling at velocities exceeding 120 m/s failed to produce visible deformation on the aircraft canopies. Full article

Dengue is a life-threatening disease that is transmitted by mosquitoes. Dengue fever has no proper treatment. Early, proper diagnosis is essential to minimize complications and enhance outcomes in patients. This research uses a clinical and hematological dataset of dengue to assess the effectiveness of the Gradient Boosting (GB) classification model with and without feature selection. It initially employs a standalone GB model, achieving impeccable results for classification, at 100% accuracy, F1-score, precision, and recall. In addition, the Bird Swarm Algorithm (BSA)-based metaheuristic technique is implemented on the GB classifier to execute wrapper-based feature selection so that features are reduced and achieve better results. The BSA-GB model yielded an accuracy of 99.49%, F1-score of 99.62%, recall of 99.24%, and precision of 100%, but it only selected five features in total. An additional test with a five-fold cross-validation was employed for better performance and model evaluation. Folds 1 and 2 showed especially good results. Although fold 2 selected only four features, it still showed high results, compared to fold 1, which selected five features. In this context, fold 2 achieved an accuracy of 99.49%, F1-score of 99.65%, recall of 99.30%, and precision of 100%. Means of hyperparameters were also calculated across folds to make a generalized GB model, which maintained 99.49% of accuracy with just three features, namely, Hemoglobin, WBC Count, and Platelet Count. To enhance transparency, counterfactual explanations were performed to analyze the misclassified cases, which indicated that minimum changes in input features modify the predictions. Also, an evaluation of the SHAP value result designated WBC Count and Platelet Count as the most important features. Full article

This study proposes a comprehensive and computationally efficient system for the recognition of heart valve diseases (HVDs) in phonocardiogram (PCG) signals, emphasizing an end-to-end workflow suitable for real-world deployment. The core of the system is a lightweight weighted convolutional neural network (WCNN) featuring a key weighting calculation (KWC) layer, which enhances noise robustness by adaptively weighting feature map channels based on global average pooling. The proposed system incorporates optimized feature extraction using Mel-frequency cepstral coefficients (MFCCs) guided by GradCAM, and a band energy ratio (BER) metric to assess signal quality, showing that lower BER values are associated with higher misclassification rates due to noise. Experimental results demonstrated classification accuracies of 99.6% and 90.74% on the GitHub PCG and PhysioNet/CinC Challenge 2016 databases, respectively, where the models were trained and tested independently. The proposed model achieved superior accuracy using significantly fewer parameters (312,357) and lower computational cost (4.5 M FLOPs) compared with previously published research. Compared with the model proposed by Karhade et al., the proposed model use 74.9% fewer parameters and 99.3% fewer FLOPs. Furthermore, the proposed model was implemented on a Raspberry Pi, achieving real-time HVDs detection with a detection time of only 1.87 ms for a 1.4 s signal. Full article

To address the global plastic crisis, recycled plastics from food packaging were used as road materials by the dry method for practical application research. First, the main components of the recycled plastics were identified based on FTIR, and their thermal stability was evaluated through DSC, TG, and microscopic analysis. Then, the workability of the plastic–asphalt mixture was evaluated using the gyratory compaction indicator, void content, and compaction energy index (CEI). Finally, the effect of reused plastics on the cracking resistance of bituminous mixtures was examined with the Superpave IDT test. The results indicate that recycled plastics from food packaging are polyolefin composite materials, primarily consisting of Low-Density Polyethylene (LDPE), Linear Low-Density Polyethylene (LLDPE), High-Density Polyethylene (HDPE), and Polypropylene (PP), and that their thermal stability meets production requirements. Good compaction performance was observed with plastic content below 2% of the aggregate weight, while higher contents reduced void content due to the space occupied by plastics. When the plastic content increased from 0.5% to 2.0%, creep compliance decreased from 68.4% to 77.87%, while the m-value, tensile strength, and elastic energy maximum decreased by 30.77%, 5.6%, and 7%, respectively. In contrast, the failure strain, fracture energy, and maximum DSCE increased by 25.86%, 87.43%, and 133.05%, respectively. The recycled plastic enhanced the toughness of the asphalt mixture, increasing the dissipated energy during crack propagation and improving its resistance to permanent deformation. Moreover, the plastics hindered crack propagation through a bridging effect, leading to fewer cracks within plastic zones compared with surrounding areas. This study provides actionable guidance for the application of composite plastics in asphalt pavements and supports their sustainable development. Full article

Background: Conflicts are an inevitable part of interpersonal relationships, and personality traits influence how they are resolved. In the nursing work environment, conflicts often arise from poor communication and stress, negatively impacting nurses’ well-being and quality of care. The “Big Five” personality model highlights how traits such as extraversion, agreeableness, and emotional stability shape conflict approaches. Understanding these traits aids in developing effective conflict management strategies. This study investigates intragroup conflicts among nurses by identifying their types and examining how sociodemographic factors and personality traits predict their occurrence. The aim is to provide insights that support targeted interventions and improve team dynamics in nursing practice. Methods: The study was conducted as a cross-sectional analysis within the University Hospital Centre Osijek from March to August 2024, involving nurses and technicians. Data was collected using structured questionnaires with clearly defined inclusion and exclusion criteria. The questionnaire included the Process Conflict Scale, the Big Five Inventory, and a Demographic questionnaire. Appropriate statistical analyses were conducted, including descriptive statistics, normality testing with the Kolmogorov–Smirnov test, non-parametric Spearman and Point-Biserial correlations, and linear regression to examine predictors of intragroup conflicts. All assumptions for regression were met, with significance set at p < 0.05, and analyses were performed using JASP software version 0.17.2.1. Results: The research reveals significant differences among various types of team conflicts, where personality traits such as neuroticism increase, while conscientiousness decreases conflicts. The professional competence of respondents also positively correlates with logistical conflicts, and personality explains the variance in conflicts among nurses. Conclusions: Intragroup conflicts among nurses, particularly task-related, stem from communication issues and high care standards. Neuroticism negatively affects team dynamics, while conscientiousness can reduce conflicts but may also lead to disagreements if expectations are unmet. Education on conflict management and clearly defined roles can improve teamwork and quality of care. Full article

Body image dissatisfaction, particularly related to breast appearance, plays an important role in cosmetic breast surgery (CBS) decisions and psychological wellbeing. However, existing measures are often lengthy, overlook healthy women considering CBS, and fail to adequately address the nipple–areola complex (NAC), a critical component of breast satisfaction. This study introduces the 12-item Breast Appearance Concerns Scale (BACS), a brief screening tool developed to address existing gaps and to document breast-specific body image concerns among women considering CBS. Data were collected from a diverse sample of 589 young adult women who completed the BACS along with measures of related constructs such as self-esteem and anxiety. Exploratory and confirmatory factor analyses supported a two-subscale structure: NAC Satisfaction and General Breast Satisfaction. The BACS total score demonstrated strong internal consistency (α = 0.785) and test–retest reliability (r = 0.741). Predictive validity analyses revealed that the General Breast Satisfaction subscale effectively distinguished women who had considered CBS from those who had not (classification accuracy = 72.1%). Receiver Operating Characteristic (ROC) analysis was conducted with the General Breast Satisfaction subscale to establish a preliminary cutoff score. This cutoff provides initial support for use of this subscale as a screening tool to help classify individuals based on their consideration of CBS. Although clinically important, the NAC subscale is still in an early stage of development and requires additional research before cutoff scores can be established to inform surgical decision-making and evaluate patient-reported satisfaction outcomes. Both subscales require further investigation in older populations and clinical settings to support their use as screening tools. These findings position the BACS as a promising screening tool for assessing breast-specific body image concerns, particularly general breast satisfaction, with potential applications in clinical, pre-surgical settings. Full article