Search Results (103,749)

Globally, Central Asian wild fruit forests are critical repositories of wild fruit germplasm resources and provide essential ecosystem services. However, their habitats are facing escalating degradation risks driven by climate warming, shifting precipitation regimes, and intensifying anthropogenic disturbances. Accurately quantifying climate-driven spatiotemporal variations in habitat suitability for keystone wild fruit tree species is therefore an essential prerequisite for formulating targeted conservation and management strategies in arid and semi-arid landscapes. In this study, we applied the maximum entropy (MaxEnt) model to simulate the current (2000–2020 baseline) and future (2030s, 2050s, 2070s) potential suitable habitats of two dominant wild fruit tree species, Malus sieversii (Ledeb.) M.Roem. and Prunus cerasifera Ehrh., in the Ili Valley, a core distribution area of Central Asian wild fruit forests in northwestern China. We integrated rigorously screened species occurrence records with key environmental predictors and characterized future climate conditions using three Shared Socioeconomic Pathways (SSPs; SSP126, SSP245, and SSP585) spanning low to high radiative forcing levels. The model exhibited excellent predictive performance (AUC > 0.85), confirming the robustness and reliability of our habitat suitability simulations. Elevation and annual precipitation were identified as the dominant environmental variables governing habitat suitability for both species, highlighting the critical role of terrain–hydroclimate interactions in maintaining viable dryland refugia for wild fruit forests. Under the baseline climate scenario, the total area of suitable habitats reached 24.014 × 10³ km² for Malus sieversii and 18.990 × 10³ km² for Prunus cerasifera. Future climate projections revealed a consistent and significant contraction trend in suitable habitats for both species, with the magnitude of habitat loss escalating with increasing radiative forcing and longer projection time horizons. Specifically, under the high-emission SSP585 scenario by the 2070s, the suitable habitat area is projected to decline by 7.579 × 10³ km² for Malus sieversii and 9.883 × 10³ km² for Prunus cerasifera relative to the baseline. Our findings delineate climate-vulnerable hotspots of wild fruit forests and provide a robust spatial scientific basis for prioritizing in situ conservation, targeted habitat restoration, and anthropogenic disturbance regulation to support the long-term persistence of these irreplaceable wild fruit germplasm resources under accelerating global climate change. Full article

Pozzolanic concrete demonstrates intricate, highly nonlinear material interactions that pose significant challenges for the accurate prediction of compressive strength (CS). This study introduces a novel, interpretable ensemble machine learning (ML) framework for predicting CS based on 759 mixture records encompassing cement, aggregates, supplementary cementitious materials (pozzolans), water/binder (W/B), superplasticizer, water, and curing age. Descriptive analysis and ANOVA were used to identify key predictors, followed by an 80/20 train–test split with 10-fold cross-validation to ensure robust and generalizable modeling. To further enhance model reliability, 5% of outliers were removed using an isolation forest algorithm, after which data were normalized and ensemble hyperparameters optimized. Among the evaluated models, the extra trees algorithm with standard scaling demonstrated the most stable generalization, achieving a coefficient of determination (R²) of 0.978 and a root mean square error (RMSE) of 4.197 MPa on the test set, and R² = 0.966 (RMSE = 5.053 MPa) under 10-fold cross-validation. Feature importance, SHAP, and partial dependence analyses consistently demonstrated that W/B, curing age, and cement are the principal determinants of CS. Finally, multi-objective optimization generated high-strength, low-impact mixtures, confirming the framework’s effectiveness as a transparent decision-support tool for performance- and sustainability-oriented pozzolanic concrete design. This study is novel in combining interpretable ensemble ML with multi-objective optimization to simultaneously achieve precise CS prediction and the formulation of sustainable, performance-optimized pozzolanic concrete mixtures. Full article

This paper presents a methodology enabling the use of a Pitot probe for static pressure measurement in supersonic flow under severely confined spatial conditions where standard design guidelines cannot be satisfied. In particular, the recommended placement of a static pressure tapping at a distance of 10–20 tube diameters is not feasible; the proposed approach allows for the tapping to be located immediately downstream of the static tube cone. The methodology combines theoretical analysis, experimental measurements, and Computational Fluid Dynamics (CFD) simulations. Experiments were performed using appropriately selected pressure sensors, while detailed simulations in Ansys Fluent (Ansys 2024 R2) included both a high-fidelity probe model and free-stream flow analysis. By comparing experimental and numerical results, a correction coefficient was established based on the free-stream dynamic pressure obtained from CFD. This enables the accurate estimation of static pressure even in non-ideal probe configurations. The measurement error did not exceed 20%, while in most cases, very good agreement between experimental and CFD results was achieved. The main contribution of this paper is the validated methodology, which extends the applicability of Pitot probes to geometrically constrained environments where conventional static pressure measurement techniques cannot be implemented. Full article

Aerosol optical depth (AOD) is a key indicator for quantifying aerosol radiative effects and evaluating air quality. However, atmospheric chemical transport models often exhibit systematic AOD biases, and model capability for column-integrated optical properties is not always consistent with that for near-surface particulate matter concentrations. Here, we evaluate AOD simulated by the Community Multiscale Air Quality (CMAQ) model over five major urban clusters in China, including the Beijing-Tianjin-Hebei (BTH) region, Fenwei Plain (FWP), Sichuan Basin (SCB), Yangtze River Delta (YRD), and Pearl River Delta (PRD), using satellite retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS), ground-based retrievals from the Aerosol Robotic Network (AERONET), and vertical extinction profiles from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). CMAQ reproduces the major spatial patterns and exhibits relatively small biases in near-surface PM_2.5. However, it persistently underestimates AOD relative to MODIS, with the largest negative bias occurring in April (i.e., a typical spring month). This contrast indicates a pronounced inconsistency between column-integrated aerosol amount and surface mass density. Relative to AERONET, CMAQ shows a negative bias (NMB = −38%), whereas MODIS shows a positive bias (NMB = 56%), suggesting that both model and retrieval uncertainties contribute to the CMAQ–MODIS disagreements. CALIPSO-constrained vertical analysis further suggests that insufficient extinction above the planetary boundary layer (PBL) is an important contributor to the negative AOD bias, although the relative roles of boundary-layer and upper-layer contributions vary across regions, underscoring the importance of accurately representing aerosol vertical transport and optical processes. These results indicate that evaluations based solely on surface observations may fail to fully capture the overall structure of AOD errors, particularly given the clear differences between near-surface mass concentrations and column optical properties, which vary across regions. This also highlights the importance of improving the representation of aerosol vertical transport and optical processes in chemical transport models. Full article

Deep learning models have achieved high accuracy in forecasting problems, but at the cost of large computational energy demand. Brain-inspired spiking neural networks (SNNs) offer a promising, low-power alternative, yet their adoption for time-series forecasting has been limited by information loss from binary spikes and degraded performance in deeper networks. This paper proposes a fully spiking framework that bridges this gap by improving both the encoding and propagation of information in SNNs. The framework introduces a hybrid Delta-Rate encoding mechanism that captures both abrupt changes and gradual trends in time-series data, and a Mem-Spike mechanism that transmits analog membrane potential values to preserve fine-grained information between spiking layers. We further employ residual membrane connections to maintain signal flow in deep spiking networks. Using two public energy load datasets, our enhanced SNNs consistently outperform conventional spiking models, improving prediction accuracy by up to 61.6% and mitigating degradation in multi-layer networks. Notably, it narrows the gap to the selected deep learning baseline (LSTM), achieving comparable accuracy in some settings while requiring only about 10% of the estimated inference energy of that baseline under a common operation-level model. These results show that, within the empirical scope considered here, enhanced conventional SNNs can improve time-series forecasting accuracy while retaining favorable estimated efficiency. Full article

The evaluation of biodiversity is an essential tool for conservation, management of natural resources, and assessment of ecosystem functioning. Choosing an appropriate and understandable diversity metric is critical to ultimately make better decisions and apply more sustainable resource management. However, biodiversity metrics are numerous, and care must be taken when using them. So, should one consider all these metrics to obtain the right information? If not, how should one choose? This paper aims to demonstrate the importance of understanding and selecting the appropriate diversity metrics to reach accurate conclusions. We simulated theoretical plant communities for which calculations of different biodiversity metrics were carried out to understand why and how to use them. We explored Richness, Evenness and Disparity components of biodiversity using both scales of diversity partitioning (i.e., alpha and beta diversity). In doing so, a decision tree is proposed to select diversity metrics according to user objectives. We also suggest an add-in term if alpha metrics are calculated with subsamples to better reflect biodiversity. Finally, we recommend that when dealing with ecosystem functioning or conservation concerns, species-dependent metrics should be used, as they reflect Disparity. However, there is a critical need to increase knowledge and data availability on species traits or phylogeny to be able to better analyze Disparity. Full article

To address face occlusion, low detection rates of small-scale faces, and sample imbalance in dense visual scenarios, we propose a YOLOv7-based detector with four key improvements: (1) an optimized MPConv module to enhance feature extraction; (2) a novel CFPM to boost sensitivity to occluded samples; (3) an integration of the DyHead block in IDetect to mitigate feature loss from sample imbalance; (4) an SW-SCE loss function with a dual-input network to better detect small faces. Experiments on the WiderFace dataset show that our method improves detection performance by 1.2%, 1.8%, and 3% on the easy, medium, and hard subsets over the baseline. These gains strengthen face detection in dense, challenging environments with heavy occlusion and small-scale targets. Full article

The global rise in the incidence and severity of invasive fungal infections, particularly among immunocompromised and immunodeficient patients, has created an urgent need for rapid and accurate diagnostic techniques. Therefore, fungal-specific positron emission tomography imaging agents are increasingly in demand, as they offer the potential for early-stage detection of fungal infections. Recently, 2-deoxy-2-[¹⁸F]fluorocellobiose ([¹⁸F]FCB), a fluorine-18-labeled analog of cellobiose that is selectively metabolized by fungal pathogens possessing cellulose-degrading mechanisms (cellulolytic), was developed for the targeted imaging of Aspergillus infections. However, the final [¹⁸F]FCB contained less than 2% unreacted 2-deoxy-2-[¹⁸F]fluoroglucose ([¹⁸F]FDG), which can potentially interfere with image interpretation. Accordingly, this study aims to eliminate residual [¹⁸F]FDG from the final product by enzymatically converting it to [¹⁸F]FDG-6-phosphate through hexokinase-mediated phosphorylation. A Trasis AllInOne (Trasis AIO) module was used to automate the radiolabeling procedure. The reagent vials contain [¹⁸F]FDG, glucose-1-phosphate, cellobiose phosphorylase, adenosine triphosphate (ATP), and hexokinase. A Sep-Pak cartridge was used to purify the tracer. The overall radiochemical yield was 45–50% (n = 3, decay-corrected) in a 40 min synthesis time, with a radiochemical purity of >99% (no detectable [¹⁸F]FDG). This is a highly reliable protocol to produce current good manufacturing practice (cGMP)-compliant [¹⁸F]FCB for clinical PET imaging. Full article

Against the backdrop of smart cities and digital cultural tourism, the accurate prediction of urban passenger flow is of great significance for public security management and resource allocation. However, existing studies mostly rely on single data sources or only perform a simple concatenation of multi-source features, lacking systematic indicator system design. Meanwhile, weekly or monthly data are commonly used with coarse temporal granularity, making it difficult to capture short-term fluctuations and lag effects. To overcome these limitations, this paper collects the daily passenger flow data of Hangzhou from 15 March 2024 to 15 March 2025; integrates multi-dimensional factors such as keyword search trends across platforms, holidays and major events, and online public opinion; and constructs three daily characteristic indicators: online search index, humanistic–meteorological index, and textual sentiment index. The data denoising, dimensionality reduction, and sentiment quantification are realized through methods including SSA, PCA, and SnowNLP. On this basis, a hybrid CNN-GRU model integrated with the attention mechanism is proposed. An improved artificial bee colony (ABC) algorithm is adopted for global hyperparameter optimization, and a weighted hybrid loss function (JQHL) is introduced to enhance the model’s adaptability to extreme values. The results show that the ABC-CNN-GRU-Attention model, incorporating multi-dimensional indicators, outperforms traditional methods on evaluation metrics, including MAE, RMSE, MAPE, R², and RPD, demonstrating a higher prediction accuracy and robustness. Full article

Background: Untargeted metabolomics enables comprehensive profiling of biological systems, but accurate metabolite annotation remains a critical bottleneck due to incomplete spectral libraries and structural isomerism. The use of in silico annotation tools can increase the coverage of annotated compounds, but it remains unclear whether these tools, in the absence of reference standards, can reliably annotate real-world experimental LC-HRMS data and whether they are sufficient for this task. Methods: This study assesses the performance and limitations of four widely used in silico structure prediction tools (MassFrontier, MetFrag, MS-FINDER, and SIRIUS/CSI:FingerID) when applied to an experimentally acquired feature set previously used to differentiate patients with depressive disorders from healthy controls. To ensure uniform evaluation across tools under realistic but optimized conditions, the quality of MS/MS data was improved using a parallel reaction monitoring method, allowing acquisition of interpretable fragmentation spectra for 26 of the 28 detected features. Results: For most features, all tools were able to suggest structure candidates. However, none of the tools proved sufficient as a standalone solution for reliable metabolite annotation. Due to their different algorithms, each tool had strengths and weaknesses in fragmentation interpretation, candidate generation, and ranking, resulting in incomplete or inconsistent annotations. While the combined application of all four tools provided a substantial improvement in putative annotation over conventional spectral library matching, the in silico structure prediction tools often prioritized chemically implausible, biologically irrelevant, or artifactual candidates. Consequently, manual expert evaluation was required to assess the chemical plausibility and biological relevance of the proposed structures. This ultimately reduced the number of biologically plausible metabolites putatively associated with disease to ten. Conclusions: Overall, these results demonstrate that existing in silico annotation tools can substantially support the annotation of experimental metabolomics data, but are insufficient on their own. Reliable identification of metabolites in complex biological matrices still depends on high-quality MS/MS data acquisition, the combined use of complementary tools, and mandatory post-annotation expert curation. Full article

The accurate and timely identification of urban building footprints is critical for sustainable urban planning and disaster management. Traditional remote sensing methods for this task often face limitations in scalability, accuracy, and adaptability to complex urban morphologies. This paper addresses these challenges by developing and evaluating a novel data-centric framework that synergistically integrates Graph Neural Networks (GNNs) with zero-shot superpixel segmentation derived from the Segment Anything Model (SAM) applied to Sentinel-2 imagery. A cornerstone of our methodology is a rigorous assessment of OpenStreetMap (OSM) data, refined through temporal NDVI stability analysis to generate high-quality ground truth. We propose an optimized UrbanGraphSAGE model, enhanced with spectral data augmentation and trained using a robust loss function with label smoothing to mitigate label noise. In the complex urban landscape of Algiers, Algeria, our approach achieves a Test F1-Score of 0.7131, demonstrating highly competitive performance with standard pixel-based baselines like U-Net while offering significant topological and computational advantages. Specifically, our model operates with merely 19,585 parameters—orders of magnitude fewer than pixel-based CNNs. A rigorous Gold Standard evaluation against manually labeled imagery confirms the model’s high recall (0.8484) and reliability for automated urban monitoring. Full article

Fried Tengjiao oil is commonly used for seasoning spicy dishes, and both fresh and dried Tengjiao are used in its preparation. However, the flavor differences between fried Tengjiao oils prepared from these two types of raw materials have not yet been studied. The aim of this study was to compare and analyze the flavor differences between fresh fried Tengjiao oil (FFTO) and dried fried Tengjiao oil (DFTO). In this study, molecular sensory science was employed to reveal the flavor differences between the two at the molecular level. FFTO had a stronger pepper and spice aroma, while DFTO exhibited a more marked oily aroma. A total of 82 volatile compounds were identified via SAFE-GC-MS (solvent-assisted flavor evaporation–gas chromatography–mass spectrometry). Based on AEDA (aroma extract concentration analysis), 36 aroma-active compounds with FD ≥ 27 were accurately quantified. Following the AEDA, OAV analysis, and recombination experiments and omission tests, linalool and β-caryophyllene were identified as key flavor compounds in FTOs. α-thujone, 3-buten-1-yl isothiocyanate, citronellal, linalyl acetate, and 3-phenylpropionitrile were key flavor compounds in FFTO, and β-pinene, α-terpinene, β-phellandrene, and 3-ethyl-2,5-dimethylpyrazine were key flavor compounds in DFTO. Finally, chiral analysis suggests that the ratio of linalool enantiomers may be the potential cause of the flavor differences between FFTO and DFTO. This study provides theoretical guidance for the industrial production of FTO. Full article

Quantitatively characterizing the dynamic evolution of fracture swarms under offset well low-frequency distributed acoustic sensing (LF-DAS) monitoring remains a significant challenge. This study proposes a physics-data dual-driven closed-loop inversion framework to address this problem. The framework consists of three core modules: (1) a fluid–solid coupled semi-analytical forward model applicable to variable-rate injection and shut-in conditions; (2) an automatic key feature identification method based on multi-scale scanning and physical polarity constraints; and (3) a dynamic inversion model for fracture swarms based on adaptive particle swarm optimization (APSO). Validation against the classical PKN model confirms that the proposed forward model accurately reproduces the fundamental fracture propagation behavior, with good agreement in fracture half-length and net pressure evolution. In synthetic inversion cases, the method successfully recovers the number of fractures, the dynamic flow rate allocation history, fracture length evolution, and the spatiotemporal strain rate response. A field application further demonstrates that three dominant fractures were generated during stimulation, reaching the vicinity of the monitoring well at 18, 27, and 46 min with corresponding spacings of approximately 21 m and 16 m. The proposed framework provides a new route for advancing LF-DAS monitoring from qualitative interpretation to quantitative dynamic inversion. Full article

The determination of the arc rating of arc-resistant materials for the manufacture of personal protective clothing is conducted by measuring the incident and transmitted energies through calorimetry using thermocouples coupled to copper discs during the electric arc events. In this study, custom calorimeters were constructed by incorporating both a thermocouple wire and an embedded optical-fiber temperature sensor, and the arc ratings of different fabrics were determined in terms of their arc-thermal-performance value (ATPV). The results revealed differences between the measurements obtained with the two sensor types. Notably, the absence of electromagnetic interferences generated by the arc current and the enhanced time response achieved with the optical-fiber temperature sensor signal led to an ATPV arc rating approximately 27% lower than that measured with the thermocouple. These findings underscore the importance of investigating the current methodology used for determining arc ratings to ensure accurate measurement of incident and transmitted energy. Full article

Ocean Thermal Energy Conversion (OTEC) is characterized by its abundant reserves and pollution-free nature, enabling stable power generation around the clock. Since the power output of an OTEC system is significantly influenced by the energy available from cold and warm seawater, the accurate evaluation of the outlet temperature of the cold-water pipe (CWP) is crucial. To analyze the heat transfer performance of the CWP, this paper investigates the temperature field of the OTEC CWP and employs numerical simulation methods to conduct finite element analysis of the temperature field under different discharge conditions. The results indicate that during the pumping of deep-sea cold water through the CWP, heat is absorbed from the warmer upper seawater layers. When the pumping discharge rate is higher, the shorter fluid residence time due to higher flow velocity results in a lower outlet temperature. Compared to steel CWPs, high-density polyethylene (HDPE) is more suitable for OTEC systems due to its lower thermal conductivity and density. Full article