Search Results (28,028)

Urban Water Systems (UWS) are complex infrastructures that interact with energy, food, ecosystems and socio-political systems, and are under growing pressure from climate change and resource depletion. Planning circular interventions in this context requires system-level analysis to avoid fragmented, siloed decisions. This paper develops the Hydrosocial Resource Urban Nexus (HRUN) framework that integrates hydrosocial thinking with the Water–Energy–Food–Ecosystems (WEFE) nexus to guide UWS design. We conduct a structured literature review and analyse different configurations of circular interventions, mapping their synergies and trade-offs across socioeconomic and environmental functions of hydrosocial systems. The framework is operationalised through a typology of circular interventions based on their circularity purpose (water reuse, resource recovery and reuse, or water-cycle restoration) and management scale (from on-site to centralised), while greening degree (from grey to green infrastructure) and digitalisation (integration of sensors and control systems) are treated as transversal strategies that shape their operational profile. Building on this typology, we construct cause–effect matrices for each intervention type, linking recurring operational patterns to hydrosocial functionalities and revealing associated synergies and trade-offs. Overall, the study advances understanding of how circular interventions with different configurations can strengthen or weaken system resilience and sustainability outcomes. The framework provides a basis for integrated planning and for quantitative and participatory tools that can assess trade-offs and governance effects of different circular design choices, thereby supporting the transition to more resilient and just water systems. Full article

The Erasmus+ YOU4BLUE project represents an interdisciplinary educational initiative aimed at fostering a youth culture of sustainability through hands-on learning, scientific literacy, and critical thinking focused on the marine environment. The project aimed to encourage lasting behavioural change and empower young people to act. It engaged secondary school students aged 14 to 18 on three Mediterranean islands (Sardinia, Crete, and Mallorca) through a blended Place-Based Education (PBE) model that integrates online learning with local, experiential activities. Forty-nine students completed a pre-assessment questionnaire measuring baseline marine ecosystem knowledge, sustainability-related behaviours, and attitudes toward the sea. Following three international exchanges involving the learning activities, roughly the same cohort of students completed post-activity surveys assessing self-perceived knowledge gains and intercultural interaction. Qualitative data from emotional mapping, field observations, and group reflections complemented the quantitative analysis. The results indicate substantial self-perceived increases in students’ understanding of marine ecosystems (+1.0 to +1.7 points on a 5-point scale), enhanced collaboration with international peers, and strengthened environmental awareness. Across all three sites, students applied their learning by co-designing proposals addressing local coastal challenges, demonstrating emerging civic responsibility and the ability to integrate scientific observations into real-world problem solving. These findings suggest that combining place-based education, citizen science, and participatory methods can effectively support the development of sustainability competences among youth in coastal contexts. This study contributes empirical evidence to the growing literature on education for sustainable development and highlights the value of blended, experiential, and intercultural approaches in promoting environmentally responsible behaviour. Full article

Natural recruitment of Manila clam (Ruditapes philippinarum) often persists in degraded estuaries, yet the environmental thresholds enabling this resilience remain quantitatively undefined. We employed binomial generalized additive model (GAM) coupled with field surveys (n = 168) in the Xiaoqing River estuary (Laizhou Bay, China) to identify critical limits for adult occurrence, which served as a field-based proxy for recruitment potential. Sediment median grain size (D₅₀), salinity (Sal) and dissolved inorganic nitrogen (DIN) were identified as the key factors, collectively explaining 79.30% of the deviance (AUC = 0.98). The probability of occurrence decreased sharply beyond two distinct thresholds: D₅₀ > 95 μm and salinity < 17.50‰. While DIN had a positive effect, it did not offset the strong negative associations with coarse sediment or low salinity. These field-validated thresholds provide quantifiable criteria to guide habitat suitability mapping, activation of early-warning systems against salinity-driven mortality, and site prioritization for ecological restoration in the Xiaoqing River estuary. Our findings offer a framework for developing management strategies to support clam resilience under environmental stress. Full article

Reliable probabilistic wind speed forecasts are essential for integrating renewable energy into power grids and managing operational uncertainty. This study compares Quantile Regression Forests (QRF), Bayesian Additive Regression Trees (BART), and Gaussian Process Regression (GPR) under at-site and regional pooled frameworks using 21 years (2000–2020) of daily wind data from eleven stations in New South Wales and Queensland, Australia. Models are evaluated via strict year-based holdout validation across seven metrics: RMSE, MAE, R², bias, correlation, coverage, and Continuous Ranked Probability Score (CRPS). Regional QRF achieves exceptional point forecast stability with minimal RMSE increase but suffers persistent under-coverage, rendering probabilistic bounds unreliable. BART attains near-nominal coverage at individual sites but experiences catastrophic calibration collapse under regional pooling, driven by fixed noise priors inadequate for spatially heterogeneous data. In contrast, GPR maintains robust probabilistic skill regionally despite larger point forecast RMSE penalties, achieving the lowest overall CRPS and near-nominal coverage through kernel-based variance inflation. Variable importance analysis identifies surface pressure and minimum temperature as dominant predictors (60–80%), with spatial covariates critical for regional differentiation. Operationally, regional QRF is prioritised for point accuracy, regional GPR for calibrated probabilistic forecasts in risk-sensitive applications, and at-site BART when local data suffice. These findings show that Bayesian machine learning methods can effectively navigate the trade-off between local specificity and regional pooling, a challenge common to wind forecasting in diverse terrain globally. The methodology and insights are transferable to other heterogeneous regions, providing guidance for probabilistic wind forecasting and renewable energy grid integration. Full article

Urban street greening structure plays a crucial role in promoting environmental justice and enhancing residents’ daily well-being, yet existing studies have primarily focused on vegetation quantity while neglecting how perception and governance interact to shape fairness. This study develops an integrated analytical framework that combines deep learning, machine learning, and spatial analysis to examine the impact of perceptual experience and socio-economic indicators on the equity of greening structure distribution in urban streets, and to reveal the underlying mechanisms driving this equity. Using DeepLabV3+ semantic segmentation, perception indices derived from street-view imagery, and population-weighted Gini coefficients, the study quantifies both the structural and perceptual dimensions of greening equity. XGBoost regression, SHAP interpretation, and Partial Dependence Plot analysis were applied to reveal the influence mechanism of the “Matthew effect” of perception and the Site governance responsiveness on the fairness of the green structure. The results identify two key findings: (1) perception has a positive driving effect and a negative vicious cycle effect on the formation of fairness, where positive perceptions such as beauty and safety gradually enhance fairness, while negative perceptions such as depression and boredom rapidly intensify inequality; (2) Site management with environmental sensitivity and dynamic mutual feedback to a certain extent determines whether the fairness of urban green structure can persist under pressure, as diverse Tree–Bush–Grass configurations reflect coordinated management and lead to more balanced outcomes. Policy strategies should therefore emphasize perceptual monitoring, flexible maintenance systems, and transparent public participation to achieve resilient and equitable urban street greening structures. Full article

CYP3A4 is the main xenobiotic metabolizing enzyme in the human body, as it is in volved in metabolism of ~30% of drugs on the market as well as of many other compounds, from small molecules such as ethanol to large drugs such as erythromycin and cyclosporine [...] Full article

Background/Objectives: Diffuse sinonasal exophytic papillomas (DSNEPs) are rare entities, with similarities to recurrent respiratory papillomatosis (RRPs). DSNEP treatment is usually based on surgical excision, but the recurrence rate is high. Bevacizumab injections have been increasingly used as an adjuvant option for RRP, but their role in DSNEP treatment remains unknown. The current study describes the preliminary experience, safety profile, and exploratory outcomes of sublesional bevacizumab injections following surgical excision. Methods: We undertook a retrospective, single-centre study of a cohort of patients diagnosed with DSNEP between 2011 and 2018. All patients were treated with surgical excision and sublesional bevacizumab injections. The effect of bevacizumab was evaluated using a severity score developed to quantify lesion size and the extent of affected areas in each patient. Results: Seven patients diagnosed with DSNEP were treated. All patients were male, with a median age at diagnosis of 42 years [38–44.5]. Human papillomavirus (HPV) DNA was detected in all patients: HPV-11 in six cases (85.7%) and HPV-6 in one case (14.3%). Bevacizumab was injected into the submucosa of their surgical sites. The median follow-up was 55.5 months [40.85–82.73]. Most patients (85.72%) presented recurrence, with a median of 3 years [1.5–4]. A statistically significant reduction in the severity score was observed (p = 0.017), although this finding cannot be attributed solely to bevacizumab due to study design limitations. No relevant complications were reported. Conclusions: Nasal sublesional bevacizumab injections were well tolerated and feasible as an adjuvant approach to DSNEP. Larger prospective studies are needed to confirm its safety and assess its potential benefit. Full article

Background/Objectives: Chemical and metabolic kinetics have historically been derived from mass balance differential equations expressed in terms of amounts, and this framework was later extended to pharmacokinetics by converting amount-based equations to concentration-based clearance relationships. That conversion is valid for fixed-volume in vitro experiments, but may be unreliable in vivo, where input, distribution, and elimination can occur in different volumes of distribution. The objective of this study is to present an alternate, mechanistically agnostic framework for deriving pharmacokinetic relationships by adapting Kirchhoff’s Laws to treat pharmacokinetic systems as networks of parallel and in-series rate-defining processes, and to identify where differential equation approaches fail in vivo. Methods: Clearance and rate constant equations were derived using the adapted Kirchhoff’s Laws by summing parallel rate-defining processes and summing inverses for in-series processes, explicitly incorporating organ blood flow, net transporter, and delivery site effects. The resulting expressions were compared with differential equation hepatic disposition elimination models (well-stirred, parallel tube, dispersion) and the Extended Clearance Concept (ECC). Mean residence time concepts were used to extend the framework to oral input, and the full approach was applied to a case study of a hypothetical drug (KL25A). Results: The adapted Kirchhoff-based approach reproduced standard pharmacokinetic analyses without mechanistic organ assumptions and yielded model-independent hepatic and renal clearance equations that include blood flow, net transport, and delivery kinetics. Inconsistencies with the traditional differential-based derivations were highlighted, including the interpretation of pharmacokinetics associated with slow absorption site clearance, as illustrated by KL25A. Conclusions: For linear drug metabolism and pharmacokinetics, clearance and rate constant relationships can be derived by summing parallel and in-series rate-defining processes, without differential equations. Differential equation methods may misestimate in vivo clearance and bioavailability when drug input is slow or when volumes of distribution differ across processes. The adapted Kirchhoff framework offers a simpler, model-independent basis for interpreting clinical data. Full article

Machine Learning (ML) and Deep Learning (DL) have shown significant promise in healthcare, particularly in medical image segmentation, which is crucial for accurate disease diagnosis and treatment planning. Despite their potential, challenges such as data privacy concerns, limited annotated data, and inadequate training data persist. Decentralized learning approaches such as federated learning (FL), split learning (SL), and split federated learning (SplitFed/SFL) address these issues effectively. This paper introduces “MedSegNet10,” a publicly accessible repository designed for medical image segmentation using split-federated learning. MedSegNet10 provides a collection of pre-trained neural network architectures optimized for various medical image types, including microscopic images of human blastocysts, dermatoscopic images of skin lesions, and endoscopic images of lesions, polyps, and ulcers. MedSegNet10 implements SplitFed versions of ten established segmentation architectures, enabling collaborative training without centralizing raw data and labels, reducing the computational load required at client sites. This repository supports researchers, practitioners, trainees, and data scientists, aiming to advance medical image segmentation while maintaining patient data privacy. Full article

Implementing mating disruption (MD) programs to manage codling moth (CM), Cydia pomonella (L.), should be based on knowledge of how effectively each program disrupts female mating. A recent survey of 142 pome fruit orchards under MD in Washington State and Oregon found that, on average, about half of the CM females caught in traps baited with a kairomone-based lure were mated. However, significantly lower proportions of mated females were sampled when the intensity of the MD program was increased. A standardized protocol that could reduce the large inter-orchard variability was developed, involving weekly releases of sterilized CM adults. Eleven trials were conducted in 2023 and 2024 across 82 orchards treated with 20 MD programs. The intensive MD programs were significantly more effective in reducing mating of both wild and sterile CM females. Three advantages of using sterile moths to assess CM MD were identified: (i) it minimized the impact of wild immigrant females or individuals previously exposed to sublethal spray residues; (ii) it allowed greater numbers of females to be dissected, thus increasing the precision of the mean value; and (iii) it and allowed the collection of sufficient sampling data (>5 CM females per site) from 30% more orchards than relying on wild moth catch. Full article

This study investigates water-based gel and gel-like cleaning treatments on Superficie 553, an oil painting on canvas by Giuseppe Capogrossi, using portable NMR to assess their impact. The objective was to evaluate the effects of four cleaning systems composed of a buffer solution released in free form and combined with xanthan gum, a cross-linked silicone polymer gel, and an agar gel matrix. Two distinct NMR experiments were conducted. The first involved the acquisition of ¹H depth profiles to detect the distribution of the cleaning solution within the painted layer and the thickness variations resulting from cleaning procedures. The second employed the acquisition of relaxation times, facilitating the investigation of molecular mobility within the organic components of the paint layer. NMR results indicated that the agar gel system caused negligible structural changes, whereas the silicone gel induced rigidification, and the other systems permanently increased molecular mobility. These measurements provided insights into alterations in the dynamic behavior of the polymerized oil. A key strength of this investigation lies in the direct application of diagnostic methods on Superficie 553, made possible by the non-invasive nature and portability of the NMR-MOUSE system. Additionally, portable FTIR was used to detect residues and obtain chemical information, confirming that the silicone gel left detectable residues and identifying the agar gel as the most conservative cleaning method. This enabled in situ analysis of the original artwork without sampling or relocation—a crucial advantage given the difficulty of replicating the complex physicochemical conditions of historical paint surfaces under laboratory constraints. Such real-time, on-site monitoring ensured an authentic evaluation of the treatment effects, preserving the integrity of the artwork throughout the conservation process. Full article

This study evaluates the accuracy of various Geographic Information System interpolation methods in predicting the stratified spatial distribution of organic pollutants (Benzene, Total Petroleum Hydrocarbons [TPH], and Methyl Tert-butyl Ether [MTBE]) in groundwater at a petrochemical-contaminated site. Given the limitations of traditional monitoring methods in predicting spatial distribution, this study focuses on the spatial computational prediction of volatile organic compound concentrations at a former petrochemical industrial site. Three interpolation methods—Inverse Distance Weighting (IDW), Radial Basis Function (RBF), and Ordinary Kriging (OK)—were applied and evaluated. Prediction accuracy was assessed using leave-one-out cross-validation, with performance quantified through key metrics: Root Mean Square Error, Coefficient of Determination, and Spearman’s Rank Correlation Coefficient. Results demonstrate significant variations in optimal prediction methods depending on pollutant type and depth stratum. For pollutants predominantly enriched in shallow and middle layers (Benzene, TPH), OK yielded the highest accuracy and stability. Conversely, for predictions of pollutants primarily concentrated in deeper layers, RBF achieved superior performance. IDW consistently underperformed across all strata and pollutants. All interpolation methods generally exhibited systematic overestimation of pollutant concentrations (mean cross-validation error > 0). Through a hierarchical evaluation of the accuracy and interpolation effectiveness of these methods, this study develops a more accurate modeling framework to describe the composite groundwater contamination patterns at petrochemical sites. This study systematically evaluates the spatial prediction accuracy of various non-aqueous phase liquid species under differing groundwater-table depths, identifies the most robust interpolation method, and thereby provides a benchmark for enhancing predictive fidelity in subsurface contaminant mapping. Full article

Weed management has become a critical agricultural practice, as weeds compete with crops for nutrients, host pests and diseases, and cause major economic losses. The invasive weed Ambrosia artemisiifolia (common ragweed) is particularly problematic in Hungary, endangering crop productivity and public health through its fast proliferation and allergenic pollen. This review examines the current challenges and impacts of A. artemisiifolia while exploring sustainable approaches to its management through precision agriculture. Recent advancements in unmanned aerial vehicles (UAVs) equipped with advanced imaging systems, remote sensing, and artificial intelligence, particularly deep learning models such as convolutional neural networks (CNNs) and Support Vector Machines (SVMs), enable accurate detection, mapping, and classification of weed infestations. These technologies facilitate site-specific weed management (SSWM) by optimizing herbicide application, reducing chemical inputs, and minimizing environmental impacts. The results of recent studies demonstrate the high potential of UAV-based monitoring for real-time, data-driven weed management. The review concludes that integrating UAV and AI technologies into weed management offers a sustainable, cost-effective, mitigate the socioeconomic impacts and environmentally responsible solution, emphasizing the need for collaboration between agricultural researchers and technology developers to enhance precision agriculture practices in Hungary. Full article

As sustainable tourism gains global momentum, extended reality (XR) technologies have emerged as important tools for enhancing visitor experiences at overburdened World Heritage Sites while mitigating physical deterioration through non-consumptive engagement. However, existing research on immersive technologies in heritage tourism has largely relied on single-cultural samples and has paid limited attention to theoretically grounded boundary conditions in post-adoption behaviour. To address these gaps, this study extends the Expectation–Confirmation Model (ECM) by incorporating cultural distance (CD) and prior visitation experience (PVE) as moderating variables, and empirically tests the proposed framework using a mixed domestic–international sample exposed to an on-site XR application at the Badaling Great Wall World Heritage Site. Data were collected immediately after the XR experience and analysed using structural equation modelling. The results validate the core relationships of ECM while identifying significant moderating effects. Cultural distance attenuates the positive effects of confirmation on perceived usefulness as well as the effect of perceived usefulness on continuance intention, while prior visitation experience weakens the influences of enjoyment and visual appeal on satisfaction. These findings establish important boundary conditions for ECM in immersive heritage contexts. From a practical perspective, the study demonstrates that high-quality, culturally responsive XR can complement physical visitation and support sustainable conservation strategies at large-scale linear heritage sites. Full article

Objectives: This study aims to evaluate the efficacy of hydroxyapatite-tricalcium phosphate (HAP-TCP) as a bone substitute in subantral augmentation for dental implants. Specifically, it investigates the effects of HAP-TCP on bone quality, density, and integration with implants over time. Methods: A prospective controlled longitudinal study was conducted on 22 patients (39–75 years of age) undergoing subantral augmentation and dental implantation. A total of 52 sites of augmented bone and 67 sites of native bone were analyzed using computed tomography (CT) to assess bone density in Hounsfield Units (HU), insertion torque measurements, and the Misch classification for bone quality. Augmented and native bone measurements were compared within each patient. Results: The augmented bone exhibited an average density of 1132.6 ± 334.9 HU, which is significantly higher (45.9%) than the average density of native bone at 519.3 ± 395.0 HU. Insertion torque values in the HAP-TCP augmented sites averaged 35 N·cm, showing a 71.4% increase compared to adjacent native bone sites (25 N·cm). The study found notable improvements in bone homogeneity and vascularization within the augmented zones. Conclusion: HAP-TCP demonstrates significant potential as a reliable and effective synthetic bone substitute for subantral augmentation in dental implants. It yields higher radiodensity and insertion torque than adjacent native bone, while mitigating complications associated with autogenous grafts. These observational findings support the potential clinical use of HAP-TCP for sinus augmentation. Full article