Search Results (811)

This study investigated spring water in the core area and buffer zone of the Shibing Dolomite Karst World Heritage Site using one-year monthly monitoring, hydrochemistry, nitrate dual isotopes, and the MixSIAR model. The buffer zone spring exhibits shallow fissure-conduit flow with rapid hydrological response, anthropogenic nitrate dominance (>62%), nitrification as the main process, and limited denitrification. Its nitrate concentration shows seasonal peaks. In contrast, the core area spring is recharged by deep fissure water, with natural nitrate sources (>80%), stable nitrate levels (5–7.4 mg/L), and potential local denitrification. Nitrogen export in the buffer zone increases 4.5 times in the rainy season (NO₃⁻ accounting for 93% of TN). The core area shows higher TN export flux per unit area (3.34 vs. 0.4 g/m²/a) and greater DON proportion. Nitrogen export far exceeds that from rocky desertified areas, suggesting that dissolved nitrogen leaching drives karst rocky desertification evolution. Full article

Dam constructions alter the river flow, leading to a cascade of physical, chemical, and biological changes in the ecosystem’s structure and function. This study presents a systematic framework for assessing the impact of these built structures on adjacent surface water bodies. The approach integrates mandatory long-term monitoring data with a multivariate statistical approach (Partial Least Squares Discriminant Analysis, PLS-DA) to provide a robust assessment of fourteen of Bulgaria’s major and significant reservoirs’ influence on nearby rivers and streams. Datasets for studied reservoirs include basic physicochemical parameters, and for 8 out of 14 dams—potentially toxic elements (PTEs). To assess the influence of each reservoir on the river, two sampling locations were selected per dam: upstream (U) and downstream (D). Results for the water quality parameters, identified as significant discriminators in each PLS-DA model, are presented. A clear upstream dominance was observed for Pchelina, Saedinenie, and Ticha, a strong downstream pattern was observed for Dospat and Yovkovtsi, and a mixed spatial pattern for the remaining dams. The hierarchical clustering revealed three groups of parameters studied. The first cluster (EC, NO₂⁻, NO₃⁻, TN) likely reflects diffuse inputs. The second cluster (TP, PO₄³⁻) describes the relationship between total and dissolved phosphorus fractions. The third cluster (pH, NH₄⁺, DO, BOD) highlights organic matter decomposition and oxygen dynamics. The results highlight that reservoir impacts are governed by the interplay of hydrological conditions, catchment characteristics, and in-reservoir biogeochemical processes, leading to distinct functional behaviours such as retention, transformation, or release of substances. Full article

Large trees are keystone ecological structures that sustain biodiversity and ecosystem services, particularly in human-altered landscapes. However, their persistence is increasingly threatened by land-use change, urban expansion, and inadequate monitoring. This study develops and validates a scalable, automated framework for monitoring the loss of large individual trees using satellite image time series and breakpoint detection. We compared four spectral indices (SIs): Enhanced Vegetation Index 2–EVI2; Normalized Burn Ratio–NBR; Normalized Difference Red Edge–NDRE, and the Normalized Difference Vegetation Index–NDVI derived from Sentinel-2 imagery (2015–2025) for 691 georeferenced trees in Lousada, northern Portugal. Data were accessed and processed in Google Earth Engine and analyzed using a custom R-based workflow, including cloud masking, gap-filling, temporal interpolation, upper-envelope smoothing, deseasonalization, and break detection. Five breakpoint detection algorithms were compared: BFAST, energy-divisive, linear regression of structural changes, wild-binary segmentation, and change point models. Detected breakpoints were subsequently post-validated to determine whether they were associated with declines in SIs, using three pre-/post-breakpoint methods: comparisons of short- and long-term medians and a randomized trend analysis. As a baseline, these algorithms/post-validation logic were compared against the Continuous Change Detection and Classification (CCDC) approach. The results indicate moderate but consistent break detection performance, with a maximum balanced accuracy of 73% (for EVI2 or NDVI and using the energy-divisive algorithm coupled with the long-term median post-validator) under conservative validation criteria and high specificity for surviving trees. CCDC ranked comparatively lower at 62%. Algorithm performance varied substantially, with the energy-divisive providing the most conservative detection and the wild-binary segmentation yielding higher sensitivity. Performance was further influenced by tree structural attributes and species identity, with larger, taller and isolated trees, as well as particular genera, showing higher detection accuracy, with genus Eucalyptus, Tilia and Celtis yielding top performance results (79–65%) and Quercus, Castanea and Platanus the lowest (62–60%). By integrating satellite observations with large-tree inventory data from the Green Giants citizen science project, this study demonstrates the potential of decentralized, Earth observation-based monitoring to support tree-level loss assessments in fragmented landscapes. The proposed framework provides a transferable foundation for wide-scale monitoring of large trees in peri-urban and mixed-use environments. Full article

Background/Objectives: Online adaptive radiotherapy (oART) generates plans at each fraction by exploiting AI-assisted optimization engines without explicit user control over modulation. This process challenges quality assurance since measurement-based Patient Specific Quality Assurance (PSQA) cannot be performed daily. This study aimed: (i) to characterize plan complexity in IOE-generated plans for prostate cancer using a reproducible set of PCMs, including the decomposition of inter-patient and intra-patient variability sources; (ii) to evaluate the association between PCMs and delivery accuracy within a cohort-informed SPC framework validated through leave-one-patient-out cross-validation; (iii) to investigate whether inter-fraction anatomical variations explain the observed plan complexity patterns, or whether complexity is predominantly an intrinsic signature of the AI-assisted optimizer. Methods: Twenty-one prostate cancer patients treated on a CBCT-based oART platform were retrospectively analyzed across three anatomical targets: prostatic bed (PrB), prostate (Pr), and prostate with seminal vesicles (PrSV). Six PCMs, namely MU/cGy, Modulation Complexity Score (MCS), Aperture Area Variability (AAV), Leaf Sequence Variability (LSV), Average Leaf Gap (ALG) and Plan Irregularity, were extracted. Additionally, five anatomical metrics (AMs) were computed from daily contours. Linear mixed-effects models (LMEMs) compared reference/online plans, decomposed variance via intraclass correlation coefficients (ICCs), and assessed PCM–gamma passing rate (GPR) associations. Leave-one-patient-out cross-validation (LOPO-CV) evaluated SPC threshold stability. The relationships between PCMs and AMs were investigated using LMEMs. Results: The AI-assisted optimization engine generated plans characterized by elevated monitor unit demand (average MU/cGy ≥ 6.8 ± 0.9) and narrow MLC apertures (ALG ≤ 17.7 mm ± 1.9 mm). No complexity differences emerged between offline and online-adapted plans, nor between anatomical targets. All PCMs showed significant associations with global GPR (p ≤ 0.027), though marginal R² remained low (≤ 0.122). Notably, GPR dispersion increased systematically at higher complexity values, indicating that highly modulated plans exhibit reduced delivery predictability. LOPO-CV demonstrated stable tolerance/action limits. Anatomical variations explained less than 35% of the total variance in PCMs. Conclusions: Plan complexity in oART reflects the optimization paradigm and patient-specific anatomy rather than daily adaptation. PCMs can serve as surveillance indicators flagging high-risk fractions to support SPC-based monitoring. Full article

In a media ecosystem marked by misinformation and disinformation, democratic resilience requires new strategies for digital and media literacy and participation. In the proposed model, the University, through transnational Cooperation Partnerships, activates applied research laboratories that generate high-social-impact communication prototypes. The European case studies Respectnet and DigiFunCollab demonstrate that the conscious use of digital media, transforming students from passive users into conscious creators, reduces vulnerability to cognitive biases, filter bubbles, and echo chambers, thereby limiting manipulation in democratic processes and stimulating civic participation. The imitative diffusion of such practices generates virtuous circles of collective learning. The theoretical framework combines institutional isomorphism, reinterpreted as a virtuous isomorphism of best practices, with democratic resilience and the UNESCO MIL and DigComp 2.2 frameworks. The methodology adopts a mixed-methods design with a quantitative prevalence. The qualitative phase includes focus groups with national stakeholders and a national report (regulatory analysis, training needs, SWOT on social entrepreneurship) preliminary to course design. The quantitative phase involves monitoring training pathways (online course and project work) and a final questionnaire. Indicators include the number of participants, certifications, projects developed, and engagement levels. By systematically implementing this approach, the Academy fuels multi-stakeholder institutional dialogue. Knowledge transfer creates communicative culture and strengthens the democratic capacity of communities. This approach confirms the role of Visual Education as a tool to integrate the University’s three missions, thus structurally reinforcing democratic resilience. Full article

Groundwater nitrate contamination, coupled with long-term overexploitation and intensive anthropogenic perturbations, has become a critical environmental challenge in the northwestern North China Plain, underscoring the urgent need to elucidate groundwater hydrochemical characteristics and their genetic mechanisms. Taking the upper section of the Yongding River alluvial–proluvial fan as the study area, this research aims to quantitatively decipher the hydrochemical characteristic and genetic mechanism of high-nitrate groundwater, identify the sources of nitrate contamination, and assess the associated human health risks. By leveraging over a decade of continuous hydrochemical monitoring data, an integrated analytical approach is adopted, including hydrochemical ionic ratio analysis, Positive Matrix Factorization, and Human Health Risk Assessment. The results indicate that the groundwater is characterized by HCO₃-Ca. The pH values range from 7.2 to 8.2 while the total dissolved solids concentrations vary between 695 mg/L and 949 mg/L. Ionic ratio analysis demonstrates that water–rock interaction is the dominant controlling process, involving silicate hydrolysis, dissolution of carbonates, gypsum dissolution, and cation exchange. The Positive Matrix Factorization model quantitatively identifies four key factors controlling the hydrochemical characteristics of groundwater. Factor 1 is dominated by NO₃⁻ (76.67%) and associated with exogenous nitrate inputs from nitrogen fertilizer application. Factor 2 is dominated by Na⁺ (72.26%) and Mg²⁺ (81.67%), deriving from silicate weathering and dolomite dissolution. Factor 3 is governed by pH (59.62%) and K⁺ (71.65%), with its driving mechanism being the weathering and dissolution of potassium-bearing silicate minerals. Factor 4 is dominated by SO₄²⁻ (50.12%) and constitutes a mixed source associated with sulfur-containing fertilizer application and livestock breeding. Groundwater NO₃⁻ concentrations range from 4.2 mg/L to 23.3 mg/L, with 69% of dry-season and 77% of wet-season samples exceeding the 10 mg/L threshold, primarily originating from manure and domestic wastewater. HHRA results show that nitrate poses significant non-carcinogenic health risks, with the highest risk observed in children (100% of samples at high risk), followed by adult females (92% at high risk) and adult males (77~92% at high risk). This study provides quantitative insights into the genetic mechanisms of groundwater nitrate contamination and offers a scientific basis for groundwater quality management and health risk mitigation in the NCP and other similar agricultural regions worldwide. Full article

Within the context of the UN 2030 Agenda for Sustainable Development and Education for Sustainable Development (ESD), this study draws on a Web of Science dataset (n = 815, 1991–2025) and employs a mixed approach combining scientometric mapping with framework analysis and tool comparison. It systematically reviews the knowledge structure, methodological evolution, and tool genealogy of Campus Sustainability Assessment (CSA). The results reveal a paradigmatic shift from an operations-oriented focus to a whole-of-institution and impact-oriented perspective. Representative tools can be grouped into five categories by purpose—improvement-oriented, ranking and benchmarking, education and curriculum, standards and certification, and policy advocacy and recognition—and can be mapped onto the four domains of governance, academics, operations, and engagement in alignment with the Sustainable Development Goals (SDGs). Synthesizing quantitative and qualitative evidence, three systemic shortcomings are identified: excessive reliance on self-reporting with limited verification, insufficient evidence of learning outcomes and key competencies, and weak interoperability of indicators across educational stages and frameworks. Looking ahead, four actionable research pathways are proposed: (1) assessment of key competencies centered on learning outcomes with stronger curriculum–practice alignment; (2) policy–indicator interoperability and vertical integration grounded in SDGs and national or sectoral standards; (3) stakeholder co-design enabling an assessment–improvement loop; and (4) remote-sensing-based multi-scale monitoring and data governance. The contribution of this study lies in advancing a unified four-domain framework under a process–outcome–impact evidence chain, while suggesting cross-stage and cross-tool alignment and complementarity. This provides methodological support and an implementation roadmap for shifting CSA from measuring performance to empowering improvement. Full article

The long-term stability of rock–concrete composites largely depends on the mechanical properties and durability of the rock–concrete interface. This study investigated the coupling effect of interfacial roughness and acid sulfate corrosion on sandstone–concrete composites by using uniaxial compression tests combined with acoustic emission (AE) monitoring. The results showed that corrosion continuously reduces the mechanical properties of the specimens with peak strength and elastic modulus, exhibiting a two-stage evolution: rapid degradation in the early stage followed by a slow decline in the later stage. After 60 days of corrosion, the peak strength for composites with JRC = 5, JRC = 10, and JRC = 15 interfaces decreased by 46.59%, 44.34%, and 50.43%, respectively. The elastic modulus exhibited the same pattern of variation, and the decreasing rate was 68.90%, 66.96%, and 76.46% for the JRC = 5, JRC = 10, and JRC = 15 groups. Acoustic emission activities appeared earlier and were more significant after corrosion. With the effect of corrosion, the fracture mode evolved from tensile-dominated cracks to mixed tensile–shear cracks with a stronger shear component. Fractal analysis of AE energy revealed that the Hurst exponent decreased from 0.842–0.864 in the natural state to 0.503–0.567 after 60 days of immersion, whereas the fractal dimension increased from 1.136–1.182 to 1.433–1.497, indicating a decrease in the persistence and increase in complexity of the acoustic emission energy release process. Overall, the moderately rough interface (JRC = 10) achieved a better balance between initial strengthening and long-term corrosion resistance. These findings provide experimental support for evaluating the durability of sandstone–concrete composites in acidic sulfate environments. Full article

Water pollution control for wetland lakes has undergone a fluctuating development process. Effective pollution management requires not only scientific water quality monitoring data but also clear identification of pollution sources within the study area. Accordingly, this study investigated Mudong Lake, the core area of the Huixian Wetland, and conducted water quality monitoring in January 2023 (dry season) and June 2023 (wet season). Based on the Water Quality Index (WQI) assessment results, water quality was better in the wet season than in the dry season. To identify pollution sources, the Absolute Principal Component Score-Multiple Linear Regression (APCS-MLR) model was applied. The results showed that pollution in the dry season was mainly derived from aquaculture and agricultural non-point source pollution, anthropogenic point source pollution, and internal release from sediments, while pollution in the wet season exhibited mixed characteristics, driven by agricultural non-point sources, domestic sewage discharge, and natural factors. Source apportionment analysis indicated that composite pollution sources (domestic sewage and aquaculture wastewater), agricultural non-point source pollution, and other unidentified sources contributed 43.71%, 34.11%, and 22.18% of the total pollution load, respectively. The findings of this study can provide a scientific basis for pollution control, emission reduction, and the targeted management of Mudong Lake. Full article

Multi-UAV cooperative search is important for applications such as target reconnaissance, environmental monitoring, and emergency response. In practice, communication is often spatially heterogeneous due to terrain occlusion and environmental interference, which may delay information sharing and weaken coordination efficiency when UAVs traverse communication-blocked areas. To address this issue, we propose an Age of Information (AoI)- and Dempster–Shafer (DS)-enhanced cooperative search framework for multi-UAV systems under spatially structured communication constraints. Specifically, a DS belief map is introduced to fuse uncertain observations, while AoI is used to characterize the freshness of delayed information. An AoI-aware update mechanism further integrates buffered observations into the global belief map after communication recovery. The search process is then formulated as a communication-aware multi-agent sequential decision-making problem and solved using reinforcement learning. To demonstrate the generality of the proposed framework, we instantiate it with Proximal Policy Optimization (PPO), Multi-Agent Proximal Policy Optimization (MAPPO), and Q-value Mixing Network (QMIX). Experimental results show that the proposed framework consistently outperforms the baseline methods under heterogeneous environments and different communication conditions. Among all variants, AoI-DS-MAPPO achieves the best overall performance, improving average reward, success rate, and the number of detected targets by 26.13%, 24.32%, and 3.65%, respectively, while reducing episode length by 31.96% relative to the strongest baseline. Full article

Current approaches in neuro-technologies aim to design artificial devices capable of collecting information on in vitro and in vivo brain activities. In this view, a major challenge for new processing technologies is to integrate the peculiar properties of biomaterials and electrical circuits into engineered devices. Herein, the optimization of electroconductive polyvinyl alcohol (PVA) fibers loaded with polyanilines (PANIs) and produced via electrospinning is proposed. Two different polyaniline forms were selected, i.e., doped emeraldine base (dPANI-EB) and doped PANI nanofibers (dPANI-NFs) synthesized by a rapid mixing process. SEM morphological investigation indicated that conductive phases do not remarkably affect fiber morphology, slightly increasing the average diameter. Conversely, PANI fibers remarkably affect the PVA surface’s hydrophilicity, as confirmed by the increase in contact angle. The presence of conductive phases enhances the intrinsic ionic conductivity of PVA fibers, through protonic currents, which also increases the electronic conductivity from 10⁻¹⁰ to 10⁻⁷ S/cm. Preliminary in vitro studies performed on a human neuroblastoma cell line (SH-SY5Y) confirmed the biocompatibility of PVA/PANI nanofibers. These data demonstrate the potential of such nanofibers to be used as biotextiles, and specifically as electroactive interfaces capable of monitoring changes in the levels of biochemical signals (i.e., neurotransmitters) related to the brain’s microenvironment. Full article

Plant extracts are widely used as natural pesticides, cosmetic ingredients, and in pharmaceutical applications. However, their poor water solubility and stability limit their usability. Lipid nanoparticles (LNPs) offer an effective encapsulation strategy to overcome these challenges. This study demonstrates the encapsulation of three representative substances from these industries: quercetin as a pesticide, irones as a cosmetic ingredient, and nucleic acids for pharmaceutical use. Ultrasonic treatment was used for the encapsulation of quercetin and irones, and a concept for continuous encapsulation in a plug flow reactor was proposed for process intensification. Inline multi-angle light scattering and dynamic light scattering measurements proved effective for real-time monitoring and enabled the replacement of traditional batch measurements. In the pharmaceutical area, mRNA-based therapies require LNP encapsulation to prevent nucleic acid degradation. Plant-based β-sitosterol was used as an alternative helper lipid to cholesterol, resulting in an average particle diameter of 72 nm and an encapsulation efficiency of 91%, comparable to commercial formulations such as the Comirnaty vaccine. Furthermore, a novel process model based on population balances was developed to simulate the entire manufacturing process, from rapid mixing in a T-mixer to particle stabilization via buffer exchange during diafiltration. By applying a quantitative and distinctive model validation workflow, the model was shown to be as accurate and precise as the experimental data, enabling its use as a digital twin for autonomous continuous operation. In summary, this study contributes to reducing the facility footprint and cost of goods through the implementation of continuous processing and model-based control. This approach improves productivity by 20% and reduces process time by a factor of two. Full article

Wireless sensor networks increasingly support time-critical monitoring applications, where coverage optimization must often be performed under limited computational resources. This work addresses a previously underexplored WSN coverage problem involving range-free, angular-limited sensors with transmitter-induced sensing degradation and discrete sector orientation. We formulate a mixed combinatorial problem that jointly optimizes $K$-out-of-$N$ sensor activation and sector assignment under strict feasibility constraints. A constraint-aware genetic algorithm with repair-based feasibility enforcement is proposed and validated against the global optimum obtained via exhaustive enumeration, enabling direct quantification of optimality. The repair mechanism corrects infeasible offspring after each genetic operation to guarantee that exactly K sensors remain active, eliminating the need for penalty-based constraint handling. A brute-force search is used to establish the global optimum of our small-scale scenario, serving as a ground-truth optimality benchmark for evaluating the proposed method. The purpose of this comparison is not to assess competitiveness against other metaheuristic algorithms, but to quantify how closely the proposed approach approximates the true optimal solution under strict problem constraints. The constraint-aware genetic algorithm is developed using an integer chromosome encoding, two initialization strategies, two crossover pairing schemes, elitism, and per-gene mutation, combined with alternative constraint-handling strategies. Two experimental series evaluate the impact of population size, crossover method, mutation probability, and constraint handling using problem-specific metrics, alongside convergence and fitness statistics. The proposed algorithm reliably reaches near-optimal solutions with significantly reduced computational cost when compared to exhaustive search. By integrating problem-specific constraints directly into the process, the proposed evolutionary optimization method effectively balances solution quality and execution time, making it well suited for scenarios requiring rapid sensor reconfiguration. Full article

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is essential for prognostics and health management. However, standard Gaussian processes (GPs) face challenges in scalability and capturing complex global degradation trends, while deep learning models often lack principled uncertainty quantification. To bridge this gap, this study proposes a novel deep mixed-effect Gaussian process (DME-GP) model, which decomposes the predictive function into a global multi-layer perceptron (MLP)-based feature mapping component and a sample-specific local GP component under the mixed-effect paradigm. This hybrid architecture synergistically captures intricate global patterns and provides probabilistic uncertainty estimates. The model’s performance was rigorously validated on a real-world battery RUL dataset. Quantitative results demonstrate its superior accuracy, achieving a reduction in root mean square error (RMSE) by up to 63.41% and in mean absolute error (MAE) by up to 62.63% compared to a standard GP baseline. The proposed DME-GP framework provides a robust and reliable data-driven solution for advancing battery health monitoring systems. Full article

Background/Objectives: Hypertensive disorders of pregnancy (HDPs) affect approximately one in seven hospital deliveries in the United States and increase the risk of pregnancy-associated mortality. Home blood pressure monitoring (HBPM) for patients with HDPs has emerged as a model of care poised to improve ascertainment of blood pressure and triage of care during pregnancy and postpartum periods. However, the strength of evidence supporting HBPM approaches has been variable. This rapid review aimed to understand how HBPM approaches for pregnant and postpartum populations with HDPs have been evaluated in order to strengthen future research. Methods: Search criteria included peer-reviewed literature in English and French published during 2018–2024 that assessed HBPM approaches for pregnant and postpartum populations in high-income countries. A total of 370 records were screened and reviewed to identify 52 eligible articles. Key study characteristics, methodologies, and outcome measures were extracted. Identified outcome measures were mapped by outcome type (implementation, health service, and client) to assess gaps in evaluation of HBPM approaches. Results: A range of study designs were employed to evaluate HBPM approaches: experimental (17%), observational (52%), qualitative (10%), mixed method (10%), and economic (11%) designs. Over a third employed a comparison group, most of which compared HBPM approaches to usual antepartum or postpartum care. Only 11 studies reported on impact outcomes (long-term blood pressure control, adverse maternal and perinatal outcomes). Significant gaps were identified among the implementation outcomes examined. While patient engagement measures were common, assessment of provider adherence and engagement was limited. Hospital admissions and emergency department visits were often employed as proxies to measure HBPM effectiveness, efficiency, and safety. However, no studies adequately reported effectiveness measures for remote patient triage. Conclusions: Our results call for improved HBPM metrics to ensure patients are receiving high-quality care responsive to their clinical condition. Future studies on HBPM approaches should prioritize more transparent reporting on health actor engagement. A composite measure including both patient and provider adherence to monitoring and triage processes will provide stronger evidence on the effectiveness of HBPM for pregnant and postpartum patients and share impactful learning for health systems interested in adopting HBPM approaches. Full article