Search Results (45,914)

Background: Extracorporeal membrane oxygenation (ECMO) can affect the disposition of drugs, notably by sequestering them in a circuit. This review aimed to provide a comprehensive summary of existing ex vivo studies investigating the impact of contemporary ECMO circuits on drug sequestration, and to examine the associations between the physicochemical properties of drugs, the features and settings of ECMO devices, and the extent of drug sequestration. Method: A comprehensive search was conducted to identify ex vivo studies that determined drug concentrations in ECMO circuits. Studies that did not allow for the proper assessment of drug loss by degradation were excluded. Drug characteristics and experimental conditions were recorded. Drug sequestration in the circuit was calculated as the difference between the drug loss measured in the ECMO circuit and the drug loss due to spontaneous degradation measured under control conditions. To identify predictors of drug sequestration, a stepwise multiple linear meta-regression was applied by testing the physicochemical properties of drugs and ECMO device features/settings. Results: A total of 40 studies were identified, of which 21 were included in the analysis, covering 41 drugs. The Maquet membrane oxygenator was the most used brand (73%). About half of the circuits were adult and half were pediatric. Our final regression model retained lipophilicity, and to a lesser extent ionization at a physiological pH, as significant predictors of drug sequestration (R² 0.44, relative standard error 23%). Protein binding had no additional effect. Anti-infectives were the most studied class of drugs (n = 28). Antibiotics were overall not significantly sequestered, while lipophilic drugs such as posaconazole, voriconazole, paracetamol, fentanyl, sufentanil, propofol, thiopental, dexmedetomidine and amiodarone were highly sequestered (≥50%). However, this sequestration occurred mainly within the first few hours of the experiments, possibly reflecting a saturation effect. Conclusions: Lipophilic drugs are significantly sequestered in ex vivo ECMO circuits, although this effect may be limited by early saturation. Full article

Accurate traffic modeling is essential for effective urban mobility planning within Smart Cities. Conventional capacity assessment methods assume rule-based driver behavior and therefore neglect psychological priority, an informal interaction in which drivers negotiate right-of-way contrary to traffic regulations. This study investigates how the absence of this behavioral factor affects the accuracy of delay and capacity evaluation at unsignalized intersections. A 12 h field observation was conducted at an intersection in Prešov, Slovakia, and 28 driver interactions were analyzed using linear regression modeling. The derived model (R² = 0.83, p < 0.05) demonstrates that incorporating psychological priority significantly improves the agreement between calculated and observed waiting times. Unrealistic results occurring under oversaturated conditions in standard methodologies were eliminated. The findings confirm that behavioral variability has a measurable impact on traffic performance and should be reflected in analytical and simulation models. Integrating these behavioral parameters into Smart City traffic modeling contributes to more realistic and human-centered decision-making in intersection design and capacity management, supporting the development of safer and more efficient urban mobility systems. Full article

A scientifically systematic credit evaluation system serves as a crucial safeguard mechanism for maintaining a healthy business environment in the construction market, effectively regulating industry entities’ behaviors and promoting ecosystem optimization. Current credit risk assessment relies excessively on financial data, neglecting the importance of corporate operational conditions. This study focuses on constructing a credit risk assessment model for construction general contractors. Innovatively incorporating both short-term financial status and long-term operational development factors, the research integrates grey relational analysis with a two-dimensional cloud model to establish a comprehensive credit risk assessment system featuring visualization of evaluation results. The methodology involves three key steps: (1) establishing a dual-dimensional credit risk indicator system covering financial and operational aspects; (2) determining risk factor weights through grey relational analysis and generating three-dimensional cloud diagrams using reverse cloud generators; (3) visualizing corporate credit risk levels through cloud mapping. Empirical analysis of representative Contractor A, utilizing Wind Financial Database data and field research, demonstrates the model’s significant advantages in critical risk factor identification and comprehensive credit risk assessment. Full article

Background/Objectives: Fetoscopic repair of open spinal dysraphism (OSD) is a rare intrauterine procedure performed in specialized fetal surgery centers. Conducted under restrictive fluid management and continuous tocolysis, it poses substantial challenges to maternal hemodynamic stability. Blood pressure optimization with vasopressor boluses is often required, yet intraoperative hemodynamic data remain limited. Methods: This prospective observational study was conducted between December 2023 and January 2025 during fetoscopic repair of OSD at Marburg University Hospital, Germany. Maternal hemodynamics were continuously monitored using pulse contour analysis with the Acumen IQ sensor and HemoSphere platform (Edwards Lifesciences, Irvine, CA, USA). To stabilize arterial pressure, cafedrine/theodrenaline (Akrinor, Ratiopharm, Ulm, Germany) was administered as intravenous boluses. Hemodynamic parameters were analyzed immediately before and after each bolus. Fetal heart rate was assessed as a secondary parameter at predefined intraoperative time points when available. Results: A total of 13 patients and 110 vasopressor boluses were analyzed. Reported values reflect median percent changes; parentheses indicate the total range. Following maternal blood pressure optimization, mean arterial pressure increased by 13.7% (5.9–21.6), systemic vascular resistance index by 23.1% (8.3–36.7), and dP/dtmax by 21.7% (6.3–29.9): p < 0.001 for all. Cardiac index and stroke volume index decreased by −6.7% (−11.8 to −0.6), p < 0.001, and −4.3% (−9.8 to 1.8), p = 0.048, respectively. Fetal heart rate remained stable (+0.4% (−0.8 to 1.5); p = 0.470). A total of 38 HPI alerts were followed by hypotension, with a median latency of 120 s (80–235); 73 alerts were not followed by hypotension during the observation period. Conclusions: Intermittent cafedrine/theodrenaline boluses significantly increased arterial pressure, dP/dtmax, and systemic vascular resistance under conditions of fluid restriction and tocolysis-induced vasodilation. Maternal heart rate remained stable, and cardiac output showed only minor reductions. Fetal heart rate was unchanged following maternal blood pressure treatment, indicating no adverse fetal response to C/T within the observed intraoperative period. Full article

Public perception of traditional villages’ streetscape is a crucial link for unlocking their benefits in promoting physical and mental health and realizing environmental value transformation. Current studies on the influence mechanisms of rural streetscape characteristics on perception largely rely on subjective ratings and mostly depend on linear models. To address this, this study takes a traditional village in eastern China, which is rich in natural and cultural conditions, as an example and constructs an evaluation framework comprising 29 streetscape feature indicators. Based on multimodal data including electroencephalography (EEG), image segmentation, color, and spatial depth computation, XGBoost-SHAP was employed to reveal the nonlinear influence mechanisms of streetscape features on neurophysiological indicators (alpha-band power spectral density, α PSD) in the traditional rural context, which differs from the blue–green spaces and residential, campus, and urban environments in previous studies. The results indicate that (1) the dominant factors affecting α PSD in traditional villages are tree, color consistency, architectural aesthetics, spatial enclosure index, P_EBG, and road, in descending order. (2) Threshold effects and interaction effects that differ from previous studies on campuses, window views, and other contexts were identified. The positive effect of tree view index on α activity peaks at the threshold of 0.09, beyond which diminishing returns occur. Color complexity, including high color difference from the primary village scheme (i.e., low color consistency, color diversity, and visual entropy), inhibits α activity. The effect of spatial enclosure index (SEI) on α activity exhibits an inverted U-shape, peaking at 0.35. Tree–VE_nats, road–SEI, and building–SEI show antagonistic effects. Road–sky and SEI–P_FG display conditional interaction effects. (3) Based on k-means clustering analysis, the “key factor identification—threshold effect management—multi-factor synergy optimization” design can directionally regulate α PSD, promoting relaxed and calm streetscape schemes. This approach can be applied to urban and rural environment assessment and design, providing theoretical and technical support for scientific decision-making. Full article

Designing reliable fault detection and diagnosis (FDD) systems remains difficult when only limited fault-free data are available. Kolmogorov–Arnold Networks (KANs) have recently been proposed as parameter-efficient alternatives to multilayer perceptrons, yet their effectiveness for unsupervised FDD has not been systematically established. This study presents a statistically grounded comparison of Kolmogorov–Arnold Autoencoders (KAN-AEs) against an orthogonal autoencoder and a PCA baseline using the Tennessee Eastman Process benchmark. Four KAN-AE variants (EfficientKAN-AE, FastKAN-AE, FourierKAN-AE, and WavKAN-AE) were trained on fault-free data subsets ranging from 625 to 250,000 samples and evaluated over 30 independent runs. Detection performance was assessed using Bayesian signed-rank tests to estimate posterior probabilities of model superiority across fault scenarios. The results show that WavKAN-AE and EfficientKAN-AE achieve approximately 90–92% fault detection rate with only 2500 samples. In contrast, the orthogonal autoencoder requires over 30,000 samples to reach comparable performance, while PCA remains markedly below this level regardless of data size. Under data-rich conditions, Bayesian tests show that the orthogonal autoencoder matches or slightly outperforms the KAN-AEs on the more challenging fault scenarios, while remaining computationally more efficient. These findings position KAN-AEs as compact, data-efficient tools for industrial fault detection when historical fault-free data are scarce. Full article

This research examines the pursuit of behavioral change for climate-resilient tourism along the Catalan coast by engaging territorial stakeholders in a co-creation process. This study is guided by the following research question: how can the co-creation of integrated climate services, water and energy management, and beach-dune conservation foster behavioral change among stakeholders towards climate-resilient tourism along the Catalan coast? Focusing on two destinations in Catalonia (Costa Daurada and Terres de l’Ebre), it examines three interconnected dimensions of tourism activity: (1) weather, climate, and climate change; (2) energy and water; and (3) beach-dune systems. Through our analysis, we pursue three secondary objectives: (1) to assess the influence of meteo-climatic conditions on tourist activity, (2) to identify necessary adaptation measures related to water and energy management, and (3) to explore how historical photographs can shape stakeholders’ perceptions regarding the relevance and conservation of the beach-dune system. By bringing together expertise in climate services, resource management, and ecosystem conservation, this study explores how collaborative engagement with public and private stakeholders can foster adaptive strategies that enhance the sustainability and resilience of coastal tourism. The findings directly respond to the research question by showing that co-creation processes integrating climate, resource, and ecosystem management can effectively foster behavioral change among stakeholders. Specifically, the main results highlight (1) a clear relationship between meteo-climatic conditions and tourism activities, underscoring the importance of climate awareness; (2) stakeholder recognition of practical adaptation measures focused on water and energy management to increase sector resilience; and (3) the use of the historical photographs as an effective tool to enhance participants’ understanding of beach-dune systems, improving their knowledge of these ecosystems’ dynamics, formation, and evolution. Full article

Clear aligners have gained popularity in orthodontics due to their aesthetics, comfort, and removability; however, their prolonged intraoral wear and frequent removal–reinsertion cycles create favorable conditions for microbial colonization. This in vitro study evaluated the efficacy of seven commercially available mouthwash formulations in inhibiting biofilms of Streptococcus mutans, Streptococcus oralis, and Candida albicans formed on four different clear aligner materials. Standardized aligner fragments were incubated for 24 h with microbial suspensions to allow biofilm formation, treated for 1 min with one of the mouthwashes, and then assessed for residual viability through spectrophotometric optical density measurements after a further 24 h incubation. Biofilm inhibition varied according to both mouthwash composition and aligner material. The chlorhexidine-based rinse (MW-D) consistently showed the highest inhibition across microorganisms, while the fluoride–cetylpyridinium chloride rinse (MW-B) performed strongly for S. oralis and C. albicans. An essential oil-based formulation with xylitol (MW-G) showed notable antifungal activity against C. albicans. Monolayer polyurethane aligners generally achieved higher inhibition rates than multilayer or copolyester-based materials. These findings indicate that antimicrobial efficacy on aligners depends on both mouthwash type and material, supporting a tailored approach to biofilm management in clear aligner therapy to reduce the risk of caries, periodontal disease, and candidiasis. Full article

This paper presents an Explainable Artificial Intelligence (XAI) framework for the automated generation of lesson plans aligned with Bloom’s Taxonomy. The proposed system addresses the dual challenges of accurate cognitive classification and pedagogical transparency by integrating a multi-task transformer-based classifier with a taxonomy-conditioned content generation module. Drawing from a locally curated dataset of 3000 annotated lesson objectives, the model predicts both cognitive process levels and knowledge dimensions using attention-enhanced representations, while offering token-level explanations via SHAP to support interpretability. A GPT-based generator leverages these predictions to produce instructional activities and assessments tailored to the taxonomy level, enabling educators to scaffold learning effectively. Empirical evaluations demonstrate strong classification performance (F1-score of 91.8%), high pedagogical alignment in generated content (mean expert rating: 4.43/5), and robust user trust in the system’s explanatory outputs. The framework is designed with a feedback loop for continuous fine-tuning and incorporates an educator-facing interface conceptually developed for practical deployment. This study advances the integration of trustworthy AI into curriculum design by promoting instructional quality and human-in-the-loop explainability within a theoretically grounded implementation. Full article

Background: The application of large language model (LLM) in surgical decision-making is rapidly expanding, yet its potential in hand and peripheral nerve surgery remains largely unexplored. This study assessed the diagnostic and therapeutic performance of a large language model (ChatGPT-4o) in scenarios characterized by multiple valid management strategies and absent expert consensus. Methods: Three representative cases—thumb carpometacarpal (CMC I) arthritis, scaphoid nonunion, and carpal tunnel syndrome (CTS)—were developed to reflect frequent conditions in hand surgery with competing but accepted treatment options. Each case was submitted to ChatGPT-4o using a standardized prompt. LLM-generated responses were evaluated by 52 participants (34 board-certified hand surgeons and 18 residents) across diagnostic accuracy, clinical relevance, and completeness. Readability indices, including Flesch–Kincaid Grade Level, were analyzed to assess appropriateness for a medical audience. Results: ChatGPT-4o demonstrated coherent but limited diagnostic accuracy (mean 2.9 ± 1.2 SD), moderate clinical relevance (3.5 ± 1.0 SD), and slightly higher completeness (3.4 ± 1.1 SD). Performance was strongest in the standardized scenario (carpal tunnel syndrome, CTS) and weakest in individualized reasoning (CMC I arthritis). No significant differences were observed between experts and residents (p > 0.05). In higher-level reasoning, ChatGPT-4o performed best in CTS and weakest in CMC I arthritis. Readability confirmed professional-level language (mean Flesch–Kincaid Grade Level: 16.4). Conclusions: ChatGPT-4o shows promise as a supportive tool for diagnostic reasoning and surgical education, particularly where standardized frameworks exist. Its limitations in ambiguous scenarios highlight the ongoing need for expert oversight. Future large language model development should emphasize specialty-specific training and context-aware reasoning to enhance their role in surgical decision support. Full article

The consumption of raw milk has been demonstrated to carry a potential risk of transmission of Campylobacter spp., with Campylobacter jejuni (C. jejuni) and Campylobacter coli (C. coli) being the major causes for foodborne gastroenteritis cases. The present study assessed the prevalence and species distribution of Campylobacter spp. in 633 raw milk samples collected over a one-year climatic cycle from small, medium, and large producers in Pichincha and Manabí, Ecuador. Samples were augmented and analyzed by qPCR for Campylobacter spp., while species identification was performed by duplex PCR and confirmed by 16S rDNA sequencing. The average prevalence of Campylobacter spp. was 49.9% (316/633), with a higher detection rate in Manabí (57.6%, 182/316) compared to Pichincha (42.4%, 134/316). C. coli was the most prevalent species, accounting for 46.2% (146/316) of the cases, followed by C. jejuni at 23.1% (73/316), co-contaminations at 13.3% (42/316), and non-identified Campylobacter at 44.0% (139/316). Phylogenetic analysis was employed to confirm species identity, thereby confirming the presence of Campylobacter fetus and Campylobacter lari. The increased diversity and frequency of isolates in Manabí, particularly during periods of elevated temperature, imply that coastal environmental conditions and production practices promote the persistence of bacteria. The findings of this study indicate a high prevalence of Campylobacter in Ecuadorian raw milk, posing a significant health risk to the population and underscoring the need for enhanced hygiene practices and continuous monitoring to mitigate public health risks. Full article

Conventional approaches to building energy retrofit decision-making struggle to generalize across diverse building characteristics, climate conditions, and occupant behaviors, and often lack interpretability. Generative AI, particularly Large Language Models (LLMs), offers a promising solution because they learn from extensive, heterogeneous data and can articulate inferences in transparent natural language. However, their capabilities in retrofit decision-making remain underexplored. This study evaluates six widely used LLMs on two objectives: determining the retrofit measure that maximizes CO₂ reduction (a technical task) and minimizes the payback period (a sociotechnical task). We assessed performance across accuracy, consistency, sensitivity, and reasoning. The evaluation used 400 residential buildings from a nationwide, simulation-based database. The results reveal that LLMs vary across cases, with consistently strong technical-task performance but notably weaker performance on the sociotechnical one, highlighting limitations in handling complex economic and contextual trade-offs. The models consistently identify a near-optimal solution for the technical task (Top-5 accuracy reaching 92.8%), although their ability to pinpoint the single best option is limited (Top-1 accuracy reaching 54.5%). While models approximate engineering logic by prioritizing location and geometry, their reasoning processes are oversimplified. These findings suggest LLMs are promising for technical advisory tools but not yet reliable for standalone retrofit decision-making. Full article

Background and Objectives: COVID-19 has been associated with prolonged inactivity and reduced physical performance, even in mild and moderate cases. This study aimed to evaluate changes in functional mobility and gait speed, assessed with the Timed Up and Go (TUG) and 10-Meter Walk Test (10MWT), in patients with mild to moderate post-COVID-19 conditions undergoing a structured rehabilitation program. Materials and Methods: A controlled observational study was conducted on 193 patients (115 women, 78 men) who had recovered from mild to moderate COVID-19. Participants were divided into a rehabilitation group (n = 160) and a control group (n = 33) who did not undergo structured physical therapy. Functional performance was assessed with TUG and 10MWT at admission and at one-year follow-up. Results: Both tests showed significant improvements following rehabilitation. In the rehabilitation group, the proportion of patients classified as functionally independent increased significantly for both the TUG (Cramér’s V = 0.468, p < 0.001) and 10MWT (Cramér’s V = 0.500, p < 0.001). The McNemar test confirmed a moderate within-group improvement for 10MWT (p = 0.001). Older adults (≥60 years) exhibited functional gains comparable to younger participants. A strong association between final TUG and 10MWT categories (Cramér’s V = 0.40, p < 0.001) confirmed the consistency of outcomes. Conclusions: Structured rehabilitation significantly improves balance, gait speed, and functional independence in mild-to-moderate post-COVID-19 patients. These findings highlight that rehabilitation should be integrated into the continuum of post-COVID care, as meaningful recovery is achievable even outside severe cases. Full article

The management of the olive fly using sustainable methods includes strategies based on attract-and-kill techniques. Although some studies have shown that lure-and-kill and mass-trapping methods can be effective in certain contexts, their performance under conditions of highly variable olive production remains unclear. In this study, we evaluated the effectiveness of two sustainable control techniques in olive groves located at the northernmost boundary of olive cultivation in Europe. The efficacy of a lure-and-kill product (Spintor^TM Fly) and a mass-trapping product (Flypack^® Dacus Trap) was assessed over a three-year period by monitoring olive fly population density and infestation levels. We found that the efficacy of attract-and-kill techniques varied over the years. In years of abundant production, the high availability of fruits may reduce the detectability of damage. In contrast, in low production years, the limited number of fruits can lead to higher infestation rates, potentially reducing the effectiveness of the control techniques. Both techniques tested, particularly lure-and-kill, can help maintain low Bactrocera oleae population densities. However, they are insufficient to maintain fruit infestation at acceptable levels during years of low olive production, when the adoption of control measures is not economically justified. Full article

Salinity is a long-standing global environmental stressor of terrestrial agroecosystems, with important implications for viticulture sustainability, especially in arid and semi-arid environments. Salt-induced physiological and biochemical disruptions to grapevines undermine yield and long-term vineyard sustainability. This review aims to integrate physiological, molecular, and omics-based insights to elucidate how grapevine rootstocks confer salinity tolerance and to identify future breeding directions for sustainable viticulture. This review critically assesses the ecological and molecular processes underlying salt stress adaptation in grapevine (Vitis spp.) rootstocks, with an emphasis on their contribution to modulating scion performance under saline conditions. Core adaptive mechanisms include morphological plasticity, ion compartmentalization, hormonal regulation, antioxidant defense, and activation of responsive genes to stress. Particular emphasis is given to recent integrative biotechnological developments—including transcriptomics, proteomics, metabolomics, and genomics—that reveal the intricate signaling and regulatory networks enabling rootstock-mediated tolerance. By integrating advances across eco-physiological, agronomic, and molecular realms, this review identifies rootstock selection as a promising strategy for bolstering resilience in grapevine production systems confronted by salinization, a phenomenon increasingly exacerbated by anthropogenic land use and climate change. The research highlights the value of stress ecology and adaptive root system strategies for alleviating the environmental consequences of soil salinity for perennial crop systems. Full article