Search Results (109)

The passenger car market has experienced a radical shift: the rise of SUV, crossover vehicles, but also Battery Electric Vehicle (BEV) and Plug-In Hybrid Vehicle (PHEV), has blurred the borders between traditional vehicle segments as well as body types, resulting in reduced applicability of conventional taxonomies of vehicle types. This study aims to provide an overview of the vehicle market by proposing a new, machine-learning-based segmentation of the entire German vehicle fleet covering the past years. We merge over 40 million registered vehicles with a technical specifications database and apply data-mining techniques to derive an improved market segmentation. We demonstrate that unsupervised learning techniques, specifically Ward and k-means clustering, yield clusters with enhanced separation, clarity, and practical usability. Clustering was applied to both raw technical features and engineered features designed to capture aspects of economy, ecology, usability, and performance. The silhouette scores can reach 0.19, a significant increase over the +0.05/−0.05 scores of the existing vehicle segments or chassis types. Full article

Bedside echocardiography stands as a cornerstone diagnostic tool in internal medicine, offering rapid, real-time evaluation of cardiac structure and function across a wide spectrum of acute and chronic conditions. Its application, particularly when combined with lung and inferior vena cava (IVC) ultrasound, significantly enhances diagnostic accuracy for fluid balance assessment, dyspnea, and hypotensive states, guiding timely therapeutic decisions. Focused cardiac ultrasound (FoCUS) enables internists to assess left ventricular function, right atrial pressure, valvular abnormalities, and pericardial effusion, facilitating differentiation between cardiac and non-cardiac causes of symptoms such as dyspnea, chest pain, and hemodynamic instability. While operator-dependent, echocardiography can be effectively integrated into internal medicine practice through structured training programs that combine theoretical knowledge with supervised hands-on experience. This integration enhances clinical decision-making, optimizes patient management, and reduces the need for immediate specialist consultation. Widespread adoption of focused ultrasound techniques in internal medicine wards promises not only improved patient outcomes but also more efficient utilization of healthcare resources. Continued education and institutional support are fundamental to embedding echocardiography into routine care, ensuring internists are equipped to leverage this powerful bedside modality. This narrative review aims to underscore the transformative impact of bedside echocardiography in internal medicine, demonstrating its capacity, when combined with lung and IVC ultrasound, to optimize diagnostic pathways and treatment decisions across diverse acute and chronic settings. Full article

C. canephora exhibits high genetic variability, and to estimate this variability, morphological descriptors associated with coffee quality are used. Bean size is a physical trait of great importance for coffee classification. Manual classification is known to be inaccurate and time-consuming, which is why researchers have adopted digital imaging techniques to improve classification efficiency. The objective of this study was to quantify the genetic diversity in 43 C. canephora clones using the Ward-MLM strategy and to estimate genetic parameters and correlations from digital phenotyping of beans and cherries. The experiment was conducted on a crop consisting of 43 C. canephora genotypes, where the cherries were manually pulped and dried until they reached 12% moisture content. Using GroundEye^® equipment, four replicates of 50 beans and cherries were evaluated for each treatment, and the software generated spreadsheets with the results of the geometric traits. To determine the existence of genetic variability among the genotypes, the data obtained were subjected to analysis of variance, estimation of genetic parameters, Ward-MLM analysis, and Pearson correlation. The genotypic variance was higher than the environmental variance for all variables analyzed, both for beans and cherries, indicating that the genotypes evaluated have high genetic variability. The greatest genetic distance was observed between groups I and IV, suggesting favorable conditions for crosses between the genotypes of these groups. Phenotypic correlation analysis revealed significant positive and negative correlations between the variables. Digital seed analysis successfully detected genetic divergence among the 43 C. canephora clones. The variables ‘area’, ‘maximum diameter’, and ‘minimum diameter’ are the most suitable for selecting genotypes with larger beans. Full article

This study examines the determinants of apartment prices in 17 post-WWII multi-family housing estates in Ljubljana, Slovenia, constructed between 1947 and 1986. Using 1973 verified transactions from 2020 to 2025, the analysis evaluates spatial, structural, environmental, and accessibility-related variables through a combination of statistical and machine learning techniques. A hedonic price model based on ordinary least squares (OLS) demonstrates modest explanatory power (R² = 0.171), identifying local market reference prices, floor level, noise exposure, and window renovation as significant predictors. In contrast, seven machine learning models—Random Forest, XGBoost, and Gradient Boosting Machines (GBMs), including optimized versions—achieve notably higher predictive accuracy. The best-performing model, GBM with Randomized Search CV, explains 59.6% of price variability (R² = 0.5957), with minimal prediction error (MAE = 0.03). Feature importance analysis confirms the dominant role of localized price references and structural indicators, while environmental and accessibility variables contribute variably. In addition, three clustering methods (Ward, k-means, and HDBSCAN) are employed to identify typological groups of neighborhoods. While Ward’s and k-means methods consistently identify four robust clusters, HDBSCAN captures greater internal heterogeneity, suggesting five distinct groups and detecting outlier neighborhoods. The integrated approach enhances understanding of spatial housing price dynamics and supports data-driven valuation, urban policy, and regeneration strategies for post-WWII housing estates in Central and Eastern European contexts. Full article

Introduction: Pain represents a significant threat to the physical and psychological well-being of children, negatively affecting their quality of life during hospitalization. Pain is considered the fifth vital sign and must be regularly assessed and managed, as also emphasized by the nursing code of ethics. The interdisciplinary approach to pediatric pain management includes both pharmacological treatments and non-pharmacological techniques (NPTs), taking into account the child’s age and specific needs. NPTs comprise a broad set of methods, ranging from simple to complex, that can be applied to children to help them manage pain. The main objective of this study was to explore and analyze which non-pharmacological methods are adopted by nurses in their clinical practice to relieve pain in school-aged children (6–12 years) undergoing surgery. Materials and Methods: This observational study involved nursing staff from pediatric wards in the Italian provinces of Ravenna, Forlì-Cesena, and Rimini, and used a validated online questionnaire. The study focused on school-aged children (6–12 years) who had undergone surgical procedures. The questionnaire included items on which NPTs nurses used to relieve pain in pediatric patients. Participants responded using a Likert scale from 1 (never) to 5 (always), and anonymity and voluntary participation were guaranteed. Data were collected between February and October 2024, involving the pediatric units of three hospitals in the provinces of Ravenna, Forlì-Cesena, and Rimini. Statistical analyses included t-tests, ANOVA, and Kruskal–Wallis tests to identify significant differences. Results: A total of 46 nurses completed the questionnaire. No significant differences were found between nurses’ backgrounds and the use of NPTs. Overall, nurses did report using NPTs, although there was limited use of such techniques in the preoperative phase. The study also highlighted a discrepancy in the information provided to children versus parents, with nurses tending to give more information to parents during the preoperative period. Notably, nurses who reported effective multidisciplinary collaboration were also those who better prepared children using NPTs. Conclusions: This study emphasizes the importance of NPTs in pediatric pain management and highlights the need to improve direct communication with children. Adopting an effective multidisciplinary approach is essential to ensuring a less traumatic surgical experience for young patients. Full article

Background: Overcrowded emergency departments (EDs) create significant challenges for patient management and hospital efficiency. In response, Amiens Picardy University Hospital (APUH) developed the “Prediction of the Patient Pathway in the Emergency Department” (3P-U) model to enhance patient flow management. Objectives: To develop and clinically validate an explainable artificial intelligence (XAI) model for hospital admission predictions, using structured triage data, and demonstrate its real-world applicability in the ED setting. Methods: Our retrospective, single-center study involved 351,019 patients consulting in APUH’s EDs between 2015 and 2018. Various models (including a cross-validation artificial neural network (ANN), a k-nearest neighbors (KNN) model, a logistic regression (LR) model, and a random forest (RF) model) were trained and assessed for performance with regard to the area under the receiver operating characteristic curve (AUROC). The best model was validated internally with a test set, and the F1 score was used to determine the best threshold for recall, precision, and accuracy. XAI techniques, such as Shapley additive explanations (SHAP) and partial dependence plots (PDP) were employed, and the clinical explanations were evaluated by emergency physicians. Results: The ANN gave the best performance during the training stage, with an AUROC of 83.1% (SD: 0.2%) for the test set; it surpassed the RF (AUROC: 71.6%, SD: 0.1%), KNN (AUROC: 67.2%, SD: 0.2%), and LR (AUROC: 71.5%, SD: 0.2%) models. In an internal validation, the ANN’s AUROC was 83.2%. The best F1 score (0.67) determined that 0.35 was the optimal threshold; the corresponding recall, precision, and accuracy were 75.7%, 59.7%, and 75.3%, respectively. The SHAP and PDP XAI techniques (as assessed by emergency physicians) highlighted patient age, heart rate, and presentation with multiple injuries as the features that most specifically influenced the admission from the ED to a hospital ward. These insights are being used in bed allocation and patient prioritization, directly improving ED operations. Conclusions: The 3P-U model demonstrates practical utility by reducing ED crowding and enhancing decision-making processes at APUH. Its transparency and physician validation foster trust, facilitating its adoption in clinical practice and offering a replicable framework for other hospitals to optimize patient flow. Full article

This study evaluates hierarchical clustering methodologies to identify patterns associated with timeout requests for EuroLeague basketball games. Using play-by-play data from 3743 games spanning the 2008–2023 seasons (over 1.9 million instances), we applied Principal Component Analysis to reduce dimensionality and tested multiple agglomerative and divisive clustering techniques (e.g., Ward and DIANA) with different distance metrics (Euclidean, Manhattan, and Minkowski). Clustering quality was assessed using internal validation indices such as Silhouette, Dunn, Calinski–Harabasz, Davies–Bouldin, and Gap statistics. The results show that Ward.D and Ward.D2 methods using Euclidean distance generate well-balanced and clearly defined clusters. Two clusters offer the best overall quality, while four clusters allow for meaningful segmentation of game situations. The analysis revealed that teams that did not request timeouts often exhibited better scoring efficiency, particularly in the advanced game phases. These findings offer data-driven insights into timeout dynamics and contribute to strategic decision-making in professional basketball. Full article

Precisely predicting photovoltaic (PV) output is crucial for reliable grid integration; so far, most models rely on site-specific sensor data or treat large meteorological datasets as black boxes. This study proposes an explainable machine-learning framework that simultaneously ranks the most informative weather parameters and reveals their physical relevance to PV generation. Starting from 27 local and plant-level variables recorded at 15 min resolution for a 1 MW array in Çanakkale region, Türkiye (1 August 2022–3 August 2024), we apply a three-stage feature-selection pipeline: (i) variance filtering, (ii) hierarchical correlation clustering with Ward linkage, and (iii) a meta-heuristic optimizer that maximizes a neural-network R² while penalizing poor or redundant inputs. The resulting subset, dominated by apparent temperature and diffuse, direct, global-tilted, and terrestrial irradiance, reduces dimensionality without significantly degrading accuracy. Feature importance is then quantified through two complementary aspects: (a) tree-based permutation scores extracted from a set of ensemble models and (b) information gain computed over random feature combinations. Both views converge on shortwave, direct, and global-tilted irradiance as the primary drivers of active power. Using only the selected features, the best model attains an average R² ≅ 0.91 on unseen data. By utilizing transparent feature-reduction techniques and explainable importance metrics, the proposed approach delivers compact, more generalized, and reliable PV forecasts that generalize to sites lacking embedded sensor networks, and it provides actionable insights for plant siting, sensor prioritization, and grid-operation strategies. Full article

Non-invasive ventilation (NIV) is a cornerstone in the management of acute and chronic respiratory failure, offering critical support without the risks of intubation. However, successful weaning from NIV remains a complex, high-stakes process. Poorly timed or improperly executed weaning significantly increases morbidity and mortality, yet current clinical practice often relies on subjective judgment rather than evidence-based protocols. This manuscript reviews the current landscape of NIV weaning, emphasizing structured approaches, objective monitoring, and predictors of weaning success or failure. It examines guideline-based indications, monitoring strategies, and various weaning techniques—gradual and abrupt—with evidence of their efficacy across different patient populations. Predictive tools such as the Rapid Shallow Breathing Index, Lung Ultrasound Score, Diaphragm Thickening Fraction, ROX index, and HACOR score are analyzed for their diagnostic value. Additionally, this review underscores the importance of care setting—ICU, step-down unit, or general ward—and how it influences outcomes. Finally, it highlights critical gaps in research, especially around weaning in non-ICU environments. By consolidating current evidence and identifying predictors and pitfalls, this article aims to support clinicians in making safe, timely, and patient-specific NIV weaning decisions. In the current literature, there are gaps regarding patient selection and lack of universal protocolization for initiation and de-escalation of NIV as the data has been scattered. This review aims to consolidate the relevant information to be utilized by clinicians throughout multiple levels of care in all hospital systems. Full article

Generative Artificial Intelligence (GenAI)-assisted Virtual Reality (VR) heritage serious game design constitutes a complex adaptive socio-technical system in which natural language prompts act as control levers shaping designers’ cognition and action. However, the systemic effects of prompt type on agency construction, decision boundaries, and process strategy remain unclear. Treating the design setting as adaptive, we captured real-time interactions by collecting think-aloud data from 48 novice designers. Nine prompt categories were extracted and their cognitive effects were systematically analyzed through the Repertory Grid Technique (RGT), principal component analysis (PCA), and Ward clustering. These analyses revealed three perception profiles: tool-based, collaborative, and mentor-like. Strategy coding of 321 prompt-aligned utterances showed cluster-specific differences in path length, first moves, looping, and branching. Tool-based prompts reinforced boundary control through short linear refinements; collaborative prompts sustained moderate iterative enquiry cycles; mentor-like prompts triggered divergent exploration via self-loops and frequent jumps. We therefore propose a stage-adaptive framework that deploys mentor-like prompts for ideation, collaborative prompts for mid-phase iteration, and tool-based prompts for final verification. This approach balances creativity with procedural efficiency and offers a reusable blueprint for integrating prompt-driven agency modelling into GenAI design workflows. Full article

The article aims to identify consumer types based on their attitudes and behaviors toward phygital tools and solutions. The analysis was based on the authors’ empirical research. The research was conducted on a sample of 2160 Polish consumers. The study employed an online survey technique. To determine the types of consumers, a 20-item scale was used, allowing the respondents to express their attitudes toward solutions and tools that improve shopping in the phygital space. The extraction of types was carried out in two steps. The first was cluster analysis, conducted using the hierarchical Ward method with the square of the Euclidean distance, and the second was non-hierarchical cluster analysis using the k-means method. As a result of the analyses, three relatively homogeneous types of consumers were distinguished: phygital integrators, digital frequenters, and physical reality anchors. The behaviours of consumers from each type were examined in the context of their impact on sustainable consumption and the sustainable development of the planet. The proposed typology contributes to developing consumer behavior theory in sustainable consumption environments. It provides practical implications for designing customer experiences that are more inclusive, resource-efficient, and aligned with responsible consumption patterns. Understanding how different consumer groups engage with phygital tools allows businesses and policymakers to tailor strategies that support equitable access to digital services and foster more sustainable, adaptive consumption journeys in an increasingly digitized marketplace. Full article

In hospital environments, pathogenic bacteria spread easily and acquire virulence and antibiotic resistance genes. The aim of the study was an evaluation of the genetic diversity of 109 K. pneumoniae isolates recovered from patients of a district hospital in central Poland. The frequencies of genes coding for β-lactamases, efflux pumps, and virulence factors were determined. Genotyping of the isolates was performed with ERIC (Enterobacterial Repetitive Intergenic Consensus) and REP (Repetitive Element Sequence Based) PCR techniques, with 21 and 19 genotypes being identified, respectively. The bla_SHV-1 (92.7%), bla_CTX-M group 1 (83.5%), bla_TEM-1 (28.4%), bla_NDM-1 (16.5%), bla_VEB-1 (11.0%), bla_CTX-M group 9 (3.7%), bla_KPC (1.8%), bla_IMP, bla_OXA-48, bla_CTX-M group 2, bla_CTX-M groups 8, and 25/26 (0% each) and efflux pumps: AcrAB (100%), tolC (93.6%), and mdtk (60.5%), and virulence genes coding: urease subunit ureA (94.5%) endotoxins wabG (92.7%) and uge (64.2%), and siderophore iucB (3.7%) were detected. The bla_SHV-1, bla_CTX-M group 1, mdtk, tolC, AcrAB (16.5%); bla_SHV-1, bla_CTX-M group 1, tolC, AcrAB (15.6%), and bla_SHV-1, bla_CTX-M group 1, bla_NDM-1, mdtk, tolC, AcrAB (11.9%) were the most common resistance patterns. The distribution of resistance and virulence genes varied more between hospital wards than between different clinical materials. Hospital’s antibiotic-resistant and virulent K. pneumoniae, able to spread among humans, animals, and in the environment, pose a significant threat to public health. Full article

Background/Objectives: Accurate and timely triage is essential for optimizing clinical outcomes and resource allocation in emergency departments (EDs). The Peripheral Perfusion Index (PPI), a non-invasive and objective parameter derived from pulse oximetry, may offer added value in early risk stratification. This study aimed to analyze the correlation between the PPI measured at triage and at Emergency Severity Index (ESI) levels, as well as to determine if the PPI may function as a predictive tool to facilitate early risk identification before patient disposition. Methods: In this prospective cross-sectional study, adult ambulatory patients presenting to a tertiary care ED were enrolled. At triage, PPI and standard vital signs were recorded, and patients were classified using the five-level ESI system. The diagnostic performance of PPI and ESI in predicting ED discharge was assessed using receiver operating characteristic (ROC) curve analysis, with comparative evaluation performed via DeLong’s test. Results: Lower PPI values were consistently associated with higher ESI acuity levels and more intensive care requirements. Patients who were discharged had significantly higher median PPI values (4.0) compared to those admitted to wards (2.1) or intensive care units (1.9). PPI also distinguished survivors from non-survivors (median PPI: 3.60 vs. 1.15). ROC analysis showed that the PPI demonstrated a good discriminative capacity for forecasting ED discharge, equal to the efficacy of ESI (AUC: 0.926 vs. 0.903; p < 0.001). Conclusions: The PPI could improve post-triage risk classification and enhance current triage techniques like ESI, especially in cases of unclear or borderline presentations, but further validation in prospective trials is required. Full article

A mathematical model of the anomalous diffusion of surfactant and the process of adsorption–desorption on an interface is analyzed using a fractional calculus approach. The model is based on time-fractional partial differential equations in the bulk phases and the corresponding time-fractional description of the flux bulk–interface. The general case, when the surfactant is soluble in both phases, is considered under the assumption that the adsorption–desorption process is diffusion-controlled. Some of the most popular kinetic models of Henry, Langmuir, and Volmer are considered. Applying the Laplace transform, the partial differential model is transformed into a single multi-term time-fractional nonlinear ordinary differential equation for the surfactant concentration on the interface. Based on existing analytical solutions of linear time-fractional differential equations, the exact solution in the case of the Henry model is derived in terms of multinomial Mittag–Leffler functions, and its asymptotic behavior is studied. Further, the fractional differential model in the general nonlinear case is rewritten as an integral equation, which is a generalization of the well-known Ward–Tordai equation. For computer simulations, based on the obtained integral equation, a predictor–corrector numerical technique is developed. Numerical results are presented and analyzed. Full article

Background: Bile leakage remains a significant challenge following major liver resection, with potential for improvement depending on the transection technique used. In this study, we aimed to evaluate the effectiveness of our hybrid resection technique—utilizing both LigaSure and stapler devices—in reducing bile leakage after major liver resection compared to our conventional stapler-only technique. As a secondary aim, we compared overall morbidity, costs, and reimbursements. Method: Patients who underwent major hepatectomy without biliary reconstruction using either the hybrid or stapler technique between August 2014 and December 2021 were included in the study. Propensity score matching was performed using a one-to-two algorithm. Perioperative data, bile leakage rates, and cost and reimbursement information based on the diagnosis-related group (DRG) system were analyzed. Results: In total, data from 492 patients were evaluated (hybrid = 152; stapler = 340). After one-to-two propensity score matching, the operation time was significantly longer in the hybrid group (p = 0.005). A cost analysis showed no significant difference in total operative costs between the two techniques (p = 0.092). However, the hybrid group had a significantly lower rate of bile leakage (p = 0.002), as well as shorter intensive care unit (ICU) and overall hospital stays (p = 0.034 and p = 0.007, respectively). Consequently, ICU and ward costs were significantly lower in the hybrid group (p = 0.024 and p = 0.014, respectively) compared to the stapler group. The financial difference calculated as DRG reimbursement minus costs was two-fold higher in the hybrid group (p = 0.02). Conclusions: Although the hybrid technique resulted in a longer operating time, it proved superior to the stapler technique in reducing postoperative bile leakage and shortening ICU and hospital stays. Furthermore, the use of the hybrid technique was more cost efficient and resulted in a greater positive financial margin. Full article