Search Results (164,374)

Mangrove ecosystems are important blue carbon systems and play a critical role in understanding carbon cycling and responses to climate change. However, accurate regional estimation of Net Ecosystem Exchange (NEE) remains challenging due to the environmental complexity and spatial heterogeneity. This study combined eddy covariance observations from four mangrove sites along China’s southeastern coast (natural and restored mangrove forests) with multi-source remote sensing and environmental reanalysis data to construct three variable schemes (site observations only, with added vegetation indices, and comprehensive multi-source variables). We compared three machine learning models for daily NEE prediction, including eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM). The results showed that: (1) Restored and natural mangroves exhibited similar temporal NEE dynamics and consistently functioned as carbon sinks, restored mangrove sites showed greater cross-site variability. Among the study sites, CN-LZR exhibited the strongest cumulative carbon uptake. (2) Scheme 3 combined with the XGBoost algorithm achieved the highest predictive accuracy, reaching an R² of 0.73 across sites. Differences among machine learning models were primarily associated with their ability to capture nonlinear interactions between atmospheric and hydrological variables, with tree-based models outperforming SVM. (3) SHAP analysis indicated that radiation-related variables were the dominant drivers of NEE, while hydrological influences were site-dependent; and (4) Regional upscaling indicated that all sites consistently functioned as long-term carbon sinks, with CN-LZR exhibiting slightly higher daily mean carbon uptake than the other sites. This study presented the first machine learning framework for estimating daily-scale NEE in mangroves, providing methodological and data support for regional carbon flux assessment and blue carbon management. Full article

Thermo-hydro-mechanical (THM) densification is an effective method for improving the mechanical performance of low-density, fast-growing hardwoods such as poplar. This study examined the bending performance of THM-densified poplar wood at different compression ratios (CR = 0%, 50%, 60%, and 65%), with emphasis on the effects of ultrasonic pretreatment (US) and thermal modification posttreatment (TM), applied individually and in combination. A paired sampling design was used to reduce material variability, and modulus of rupture (MOR) and modulus of elasticity (MOE) were evaluated using linear mixed-effects models (LMM). Bending tests were performed in accordance with EN 310:1993. Increasing the compression ratio led to substantial increases in MOR and MOE; compared with non-densified specimens, MOR increased by approximately 240% and MOE by about 140% at CR = 65%, confirming densification as the dominant factor controlling bending performance. US did not affect non-densified wood but significantly enhanced MOR and MOE after densification, particularly at CR = 50%. In contrast, TM consistently reduced MOR and, to a lesser extent, MOE across all compression ratios. The results demonstrate that the bending performance of densified poplar wood is governed by both compression ratio and compression-dependent treatment effects. Full article

Strip intercropping of maize and soybean is a key practice for improving land productivity and ensuring food and oil security in the hilly regions of the Loess Plateau. However, complex interspecific interactions generate highly heterogeneous canopy structures, making it difficult for traditional linear models to capture yield variability within mixed pixels. Based on a single-season (2025) field experiment, this study developed a UAV multispectral imagery-based yield estimation framework integrating multiple machine-learning algorithms. Shapley additive explanations (SHAP) and partial dependence plots (PDP) were used to interpret the spectral–yield relationships under different spatial configurations. The predictive performance of linear regression and eight nonlinear algorithms was compared using 20 spectral features. Ensemble learning outperformed linear approaches in all intercropping scenarios. In the maize–soybean 3:2 pattern, the GBDT model delivered the highest accuracy (R² = 0.849; NRMSE = 9.28%), whereas in the 4:2 pattern with stronger shading stress on soybean, the random forest model showed the greatest robustness (R² = 0.724). Interpretation results indicated that yield in monoculture systems was mainly driven by physiological traits characterized by visible-band indices, while yield in intercropping systems was dominated by structural and stress-response traits represented by near-infrared and soil-adjusted vegetation indices. The generated centimeter-scale yield maps revealed clear strip-like spatial variability driven by interspecific competition. Overall, explainable machine learning combined with UAV multispectral data shows promise for within-season yield estimation in intercropping systems and can support spatially differentiated precision management under the sampled conditions. Full article

Potentially toxic element (PTE) contamination of urban soils poses long-term ecological and public health risks. Ornamental vegetation is increasingly discussed within green-infrastructure-based risk management. We screened and synthesised 167 field studies (>120 ornamental and horticultural plant species) to characterise the scope, reporting structure and design features of the available phytoremediation-related evidence. Studies assessed a mean of 3.21 elements (SD = 1.37); Pb, Cd and Zn were most frequently investigated (67%), whereas Ni, Cr and B occurred in <10%. Reported element richness differed by setting, averaging 3.8 ± 1.5 in wastewater-affected sites versus 2.6 ± 1.1 in urban parks. Using a study-by-element presence/absence matrix, co-reporting patterns separated three recurrent co-reporting profiles. The first three PCs explained 64.5% of variance (PC1: Pb–Zn–B; PC2: Cu–Ni; PC3: Cd–Cr). Accumulation was reported most often (56.8%), while stabilisation (17.9%) and translocation (25.3%) were less commonly addressed. For public space applications, accumulation-focused plantings require a defined maintenance pathway (pruning/harvest, biomass removal, and safe handling or disposal) to avoid recirculation of metal-bearing material within the urban environment. Sampling focused on aboveground tissues (73.4%) more than roots (28.9%). In multiple regression, environmental type was associated with element richness (Adj. R² = 0.08, p = 0.001). Here, richness is treated as an index of reporting breadth. Overall, the dominant quantitative signals reflect context-dependent reporting and study design patterns. They do not represent harmonised, concentration-based remediation outcomes. These patterns provide an evidence map to support context-aware interpretation and future study standardisation. Full article

Rapid urbanization and climate change are intensifying heat exposure in cities, making effective adaptation strategies essential. This study presents a streamlined digital twin modeling framework for simulating the impact of nature-based solutions (NBSs) on outdoor thermal comfort, developed within the Intelligent Communities Lifecycle (ICL) software suite. The approach automates the import of urban geometry from OpenStreetMap and integrates geolocated weather data, enabling users to efficiently test scenarios involving NBSs and surface material modifications. Outdoor thermal comfort is quantified using the Universal Thermal Climate Index (UTCI), with results visualized through an interactive cloud-based 3D platform to support participatory urban planning. The methodology is demonstrated in Meunierstraat, Leuven (Belgium), where three planning alternatives are compared across seasonal extremes. Simulations show that targeted NBS interventions, particularly temporary participatory measures, can improve thermal comfort under extreme heat. However, the benefits are seasonally dependent and spatially heterogeneous, emphasizing the value of high-resolution, scenario-based analysis. This integrated workflow enhances both technical evidence and stakeholder engagement. While the tool is capable of linking outdoor comfort improvements with building energy performance and carbon emissions, the present paper focuses solely on the outdoor thermal comfort results, leaving indoor–outdoor coupling analysis as a direction for future work. Full article

Accurate multi-modal traffic demand forecasting is key to optimizing intelligent transportation systems (ITSs). To overcome the shortcomings of existing methods in capturing dynamic high-order correlations between heterogeneous spatial units and decoupling intra- and inter-mode dependencies at multiple time scales, this paper proposes a Dynamic Multi-Relation Learning with Multi-Scale Hypergraph Transformer method (MST-Hype Trans). The model integrates three novel modules. Firstly, the Multi-Scale Temporal Hypergraph Convolutional Network (MSTHCN) achieves collaborative decoupling and captures periodic and cross-modal temporal interactions of transportation demand at multiple granularities, such as time, day, and week, by constructing a multi-scale temporal hypergraph. Secondly, the Dynamic Multi-Relationship Spatial Hypergraph Network (DMRSHN) innovatively integrates geographic proximity, passenger flow similarity, and transportation connectivity to construct structural hyperedges and combines KNN and K-means algorithms to generate dynamic hyperedges, thereby accurately modeling the high-order spatial correlations of dynamic evolution between heterogeneous nodes. Finally, the Conditional Meta Attention Gated Fusion Network (CMAGFN), as a lightweight meta network, introduces a gate control mechanism based on multi-head cross-attention. It can dynamically generate node features based on real-time traffic context and adaptively calibrate the fusion weights of multi-source information, achieving optimal prediction decisions for scene perception. Experiments on three real-world datasets (NYC-Taxi, -Bike, and -Subway) demonstrate that MST-Hyper Trans achieves an average reduction of 7.6% in RMSE and 9.2% in MAE across all modes compared to the strongest baseline, while maintaining interpretability of spatiotemporal interactions. This study not only provides good model interpretability but also offers a reliable solution for multi-modal traffic collaborative management. Full article

Monilinia laxa and Colletotrichum fioriniae are major fungal pathogens causing brown rot and anthracnose in stone fruits and shell fruits, leading to significant economic losses. Chemical fungicides are widely applied but can result in resistance development, environmental contamination, and food safety concerns. Biological control using entomopathogenic bacteria (EPB) of the genus Photorhabdus has emerged as an eco-friendly alternative. This study evaluated the in vitro antifungal activity of selected Photorhabdus species (P. kayaii 1723B, P. temperata 3017, P. cinerea 3086, P. laumondii 3196, and P. thracensis 3210) against M. laxa (M3) and C. fioriniae (VV081) using drop-to-drop confrontation and poisoned agar assays. Effects of fermentation time, preparation mode (original vs. centrifuged and filtered), and concentration (5, 10, 20%) were examined. Species-specific inhibition was observed, with Median Inhibition Index values indicated relatively higher antifungal activity for P. thracensis 3210 against M. laxa (0.718) and C. fioriniae (0.552), followed by P. cinerea 3086 (0.643 and 0.552) and P. kayaii 1723B (0.629 and 0.541). Fermentation time and preparation mode influenced antifungal activity in a strain-dependent manner, with longer fermentation periods and original culture preparations generally showing stronger inhibitory trends. Higher concentrations, especially 20%, were often associated with increased inhibition, although the magnitude of these effects varied among strain–pathogen combinations. Overall, these findings demonstrate that the strain- and pathogen-specific nature of antifungal responses in Photorhabdus, supporting their potential as components of targeted biological control strategies rather than uniform broad-spectrum agents. Full article

Banker plants are non-crop plants that sustain populations of biological control agents prior to pest outbreaks, offering a preventive strategy within integrated pest management (IPM). Their benefits have primarily been attributed to top-down regulation via natural enemy-mediated pest suppression; however, their potential bottom-up effects remain largely unexplored. Here, we show that airborne cues emitted from banker plants infested with the zoophytophagous mirid bug Nesidiocoris tenuis altered the performance of the two-spotted spider mite Tetranychus urticae on neighboring tomato plants Solanum lycopersicum. Exposure to airborne cues from infested sesame Sesamum indicum significantly reduced mite fecundity, whereas those from tomato and spider flower Cleome hassleriana had no detectable effect, indicating that the induction of crop resistance is dependent on banker plant species. Moreover, T. urticae infestation of banker plants consistently suppressed mite oviposition on neighboring tomato plants across all banker plant species tested. These findings suggest that banker plants can exert previously unrecognized bottom-up effects by modulating crop resistance-related responses through airborne cues. Therefore, selecting banker plant species that emit effective airborne cues may strengthen crop protection and stabilize biological control performance in sustainable IPM strategies. Full article

Background: Early detection of dental caries is essential for effective oral health management. Current diagnostic workflows rely heavily on radiographic imaging, which involves infrastructure requirements, workflow coordination, and resource considerations that may limit frequent use in high-throughput or resource-constrained settings. These contextual factors motivate exploration of adjunct screening concepts that could support front-end triage decisions within existing care pathways. This study evaluates, in simulation, whether modeled tooth-percussion response signals contain sufficient discriminative information to justify further translational and managerial investigation. Implementation costs, workflow optimization, and economic outcomes are not evaluated directly; rather, the objective is to assess whether the technical preconditions for a potentially scalable screening concept are satisfied under controlled in silico conditions. Methods: An in silico model of tooth percussion was developed in which enamel, dentin, and pulp/root structures were represented as a simplified layered mechanical system. Impulse responses generated from simulated tapping were used to compute the modeled surface-vibration response (enamel-layer displacement), which served as a proxy for a measurable percussion-related signal (e.g., contact vibration), rather than a recorded acoustic waveform. Carious conditions were simulated through depth-dependent reductions in stiffness and effective mass and increases in damping to represent enamel and dentin demineralization. A synthetic dataset of labeled simulated signals was generated under varying structural parameters and measurement-noise assumptions. Machine-learning models using Mel-frequency cepstral coefficient (MFCC) features were trained to classify healthy teeth, enamel caries, and dentin caries at a screening (triage) level. Results: Under baseline simulation conditions, the classifier achieved an overall accuracy of 0.97 with balanced macro-averaged F1-score (0.97). Misclassifications occurred primarily between healthy and enamel-caries categories, whereas dentin-caries cases were most consistently identified. When measurement noise and structural variability were increased, performance declined gradually, reaching approximately 0.90 accuracy under the most challenging simulated scenario. These results indicate that discriminative information is present within the modeled signals at a screening (triage) level, meaning that higher-risk categories can be distinguished probabilistically rather than with definitive diagnostic certainty. Sensitivity and specificity trade-offs were not optimized in this study, as the objective was to assess separability rather than to define clinical decision thresholds. Conclusions: Within the constraints of the in silico model, simulated tooth-percussion response signals demonstrated discriminative patterns between healthy, enamel caries, and dentin caries categories at a screening (triage) level. These findings establish technical plausibility under controlled simulation conditions and support further investigation of percussion-based screening as a potential adjunct to clinical assessment. From a healthcare management perspective, the present results address a prerequisite question—whether such signals contain sufficient information to justify translational research, rather than demonstrating workflow optimization, cost reduction, or system-level impact. Clinical validation, threshold optimization, and implementation studies are required before managerial or operational benefits can be evaluated. Full article

Integrated Project Delivery (IPD) is a collaborative approach proposed to address fragmentation and performance issues in the AEC industry, yet its adoption remains limited. This study examines Brazilian professionals’ perceptions of IPD and identifies the barriers, challenges, and enablers associated with it. Drawing on a survey and a systematic review, the findings indicate that although benefits such as improved collaboration are recognized, concerns about contractual feasibility, shared risks, and organizational readiness persist. Technological aspects are seen as more familiar than contractual or managerial changes, diverging from international empirical evidence, which typically identifies contractual and governance-related challenges as the primary barriers to IPD adoption. The study reveals both shared global challenges and unique Brazilian issues, particularly regarding implementation complexity. Adoption depends more on organizational and contractual preparedness than on technology, informing strategies for introducing collaborative models in emerging markets. Full article

Many species within the family Moronidae possess biological traits that facilitate their success as invasive species in freshwater ecosystems. In Iowa, USA, non-native Yellow Bass (Morone mississippiensis) have expanded their range into at least 19 glacial natural lakes, yet their trophic interactions in these complex systems remain poorly understood. From 2018 to 2020, we evaluated the open-water diet composition of 1300 Yellow Bass across six Iowa natural lakes to quantify diet composition, feeding intensity, and ontogenetic dietary shifts. While zooplankton numerically dominated diets across most systems (>80% by number) biomass was driven primarily by benthic invertebrates and fish. Feeding intensity was not uniform, characterized by a distinct suppression of foraging during late spring followed by intense feeding in early summer. Overall, we found that Yellow Bass foraging is highly plastic but heavily constrained by spatial (lake identity, season, and year) and biological (ontogeny, age, and sex) filters. Spatial heterogeneity was the primary driver of diet composition ${(R}^2=0.407)$, with individual lakes explaining the largest portion of variance ${(R}^2=0.126)$. The interaction between lake size and population history ${(R}^2=0.054)$ was also significant, highlighting that the ecological impact of Yellow Bass is context-dependent, differing among established populations in small lakes versus recent invasions in large lakes. We identified distinct ontogenetic breakpoints at 114 mm and 252 mm; fish < 114 mm were obligate zooplanktivores, while significant piscivory was restricted to large adults (>252 mm). These results suggest that the successful colonization of Yellow Bass is supported by high dietary plasticity, which may lead to intensive resource competition with native juveniles. Our findings provide a critical baseline for fisheries managers to assess the ecological risks associated with Yellow Bass expansion and emphasize the importance of monitoring trophic shifts to preserve the integrity of native fish communities in the Midwest. Full article

Autophagy plays a key role in the development and homeostasis of the cochlear organ. Alterations in the autophagic pathways have been associated with damage to auditory cell types and hearing impairment caused by an array of factors like age, ototoxicity, exposure to high levels of noise, or genetic mutations. Cochlear damage frequently entails mitochondrial dysfunction, impaired mitophagy and the accumulation of high concentrations of free radicals. This review summarizes the observations made to date on the autophagic function in response to cochlear damage and the results of either activating or inhibiting these processes. The data demonstrate that autophagic activity is cell context-dependent and varies according to the cochlear cell type, the toxic agent, its levels and the length and timing of its administration; other factors that influence the autophagic response may be external to the auditory system or related to epigenetic changes or the expression of genetic variants. Modulation of the autophagic status has an effect on auditory cell loss and the progression to hearing impairment and this approach has thus become a promising avenue towards the protection of the hearing function. Nonetheless, this is no easy task and it will require the identification of reliable biomarkers to evaluate the dynamics of autophagic activity as well as the development of specific autophagy modulators that do not exert toxicity. Full article

Background: Developing an evidence base for physiotherapy programs for patients with Cerebral Palsy (CP) requires an understanding of the microscopic and metabolic processes in striated muscle. The gracilis muscle represents a logical object of study due to the significant morphological changes in individuals with cerebral palsy. This research aims to study morphological and biochemical alterations in the gracilis muscle depending on the severity of motor impairments in CP patients. Methods: The cross-sectional study included 24 patients stratified by the severity of motor impairment. Intraoperative gracilis muscle samples were obtained during tenomyotomies. Nutritional status of patients, morphometric, and biochemical parameters were evaluated. Results: Initial body mass and Quetelet index (p = 0.02) were lower in GMFCS V patients (p = 0.01) compared to GMFCS IV and GMFCS II-III. Muscle tissue predominated in histological samples of GMFCS II-III and GMFCS V patients (p = 0.79), while connective tissue content was higher in the GMFCS IV group (p = 0.03). Strong, fast-twitch, anaerobic fibers (p = 0.761) with reduced creatine phosphokinase activity (p = 0.012) were more frequently observed in the intraoperative samples of GMFCS V patients. Low creatine phosphokinase activity was revealed in children in the GMFCS V group (p = 0.012). Conclusions: The structural and metabolic abnormalities observed in gracilis muscle of patients with spastic cerebral palsy indicates profound functional muscular dysfunction, representing one of the factors limiting children’s motor ability. The morphological and biochemical alterations in the striated muscle of CP children correlate with severity of motor dysfunction conditioned by the primary upper motor neuron disorders. Less significant changes in muscles in ambulatory children reflect favorable basis for physical therapy. Full article

Coverage expansions and affordability reforms often presume that improved access to care yields better population health. We examine this premise in a capacity-constrained healthcare system, where congestion and throughput determine whether potential access translates into realized care. Using U.S. state-year panel data from 2006 to 2023, we study (i) how healthcare workforce density relates to multiple access margins and (ii) whether the mortality effects of access improvements depend on local delivery capacity. Reduced-form estimates show that higher workforce density is associated with higher insurance coverage and fewer cost-related barriers to care, while associations with having a usual source of care are weaker. With full controls these relationships attenuate, and Medicaid expansion and poverty explain much of the remaining variation. Instrumental variable models suggest that policy-driven improvements in effective access are associated with lower mortality, although the first-stage strength varies across specifications. Interaction-IV estimates indicate capacity dependence: for all-cause and external-cause mortality, implied benefits are larger in lower-capacity settings and diminish as workforce density increases; for endocrine mortality, benefits are concentrated in higher-capacity settings, while respiratory effects are not detectable. Overall, the results support a systems perspective in which the health returns to access expansions depend on local delivery capacity, underscoring the importance of aligning access reforms with constraints in healthcare production and flow. Full article

The continuous scaling of microelectronic technology nodes has imposed fundamental physical constraints on conventional floating-gate (FG) non-volatile memory, driving the adoption of charge-trapping memory such as Silicon–Oxide–Nitride–Oxide–Silicon (SONOS) technology. SONOS devices offer advantages in scalability, endurance, and compatibility with advanced CMOS processes, yet their high-temperature reliability remains challenging due to charge loss mechanisms influenced by device structure and material properties. In this work, we systematically evaluate the reliability of two-transistor SONOS memory fabricated using a 28 nm high-K metal gate (HKMG) process. A refined temperature-dependent charge loss model (T-model) is introduced, which, by incorporating a characteristic temperature parameter (T₀) that captures the dynamic shift in activation energy, fundamentally departs from the constant-activation energy assumption of the conventional Arrhenius model. This approach more accurately describes charge retention behavior across a wide temperature range. Experimental results demonstrate excellent device performance, including endurance exceeding 10⁴ program/erase cycles at 85 °C and data retention over 10 years at 85 °C. The T-model shows strong agreement with measured data, providing a physically grounded framework for predicting long-term reliability. This study not only validated a novel charge loss model, providing insights for predicting the failure time of SONOS memory, but also demonstrated that HKMG-integrated SONOS memory exhibits high reliability. Full article