Search Results (8,791)

Direct core-scale evidence remains insufficient for evaluating hydraulic fracture–natural fracture coupling and stimulation effectiveness in tight sandstone oil reservoirs. In this study, post-fracturing full-diameter cores from the Chang 8₁ tight oil reservoir in the Xi 119 well block, Xifeng Oilfield, Ordos Basin, were investigated using core observation, computed tomography (CT) scanning, fracture-source evidence and three-dimensional fracture-network reconstruction. A total of 87.56 m of core from 11 core runs was scanned at a voxel size of 50.62 μm. Natural fractures, hydraulic fractures and engineering-induced fractures were identified and distinguished based on fracture-surface features, CT expression, spatial continuity, proppant/tracer evidence and their relationship with bedding and lithological boundaries. The results show that lithological structure exerts a first-order control on hydraulic-fracture surface morphology. Massive sandstone tends to generate straight and continuous high-conductivity main fractures, argillaceous laminated sandstone promotes bedding-controlled discontinuous fractures with limited connectivity, and cross-bedded sandstone favors fracture diversion, branching and natural-fracture activation. Based on fracture assemblage, spatial connectivity and seepage behavior, three hydraulic fracture–natural fracture coupling types were classified: single hydraulic-fracture type, single main fracture–diverted fracture–natural fracture type, and dual main fractures–diverted fractures–natural fractures type. Their equivalent permeability increases stepwise from 155 mD to 345 mD and 586 mD, respectively, indicating a positive relationship between fracture-network complexity and seepage capacity. A CT-derived stimulation-effect evaluation framework was further established by integrating pore–fracture structural modification, fracture volume increase, aperture improvement and seepage-capacity enhancement. The dual main fractures–diverted fractures–natural fractures type shows the strongest stimulation response, with the largest reduction in small-aperture pore/fracture proportion, the greatest lamina-fracture aperture enlargement and the most significant permeability improvement. These results provide direct core-scale evidence for understanding fracture-network formation in continental tight sandstone reservoirs and support more targeted hydraulic-fracturing design and stimulation-effect evaluation. Full article

Accurate crop classification is essential for sustainable agriculture activities and food security studies. Recent advancements in remote sensing data acquisition and analysis techniques enable various solutions for cropland detection; however, reliable crop maps are still lacking in many heterogeneous semi-arid regions (e.g., Central Asia). Machine learning approaches address such challenges and distinguish different crop types using multiple datasets. The main aim of this study is to optimize crop classification outcomes by integrating multi-sensor datasets leveraging numerous vegetation indices through different machine learning models. Four datasets: Landsat-8 (DS-1), Sentinel-2 (DS-2), optical Sentinel-2 integrated with SAR Sentinel-1 (DS-3), and Sentinel-1 (DS-4) were used for the developed experiments. Five vegetation indices, NDVI, GNDVI, EVI, SAVI, and MSAVI, were derived using Sentinel-2 and Landsat-8 bands; in addition, NDRE was only obtained for Sentinel-2 exploiting the red edge band. Three input scenarios were considered for model training and image classification, featuring solely NDVI and its related bands; a set of vegetation indices and their associated bands for optical imagery; and VV, VH, and VV/VH ratio bands for SAR data. Five classifiers, Gradient Boosting Tree (GBT), Random Forest (RF), K-Nearest Neighbor (KNN), Classification and Regression Tree (CART), and Minimum Distance (MD), were employed to assess the machine learning quality for scene classification. Findings demonstrated that Sentinel-2 outperforms Landsat-8 images due to the higher spatial resolution and red edge bands. DS-3 consistently outperforms both DS-2 (optical-only) and DS-4 (SAR-only) across all classifiers, enhancing the overall accuracy up to 2.38% over the optical dataset, and up to 13.28% over the SAR data, demonstrating the added details on canopy spectral reflectance, structure and moisture content. Using multiple vegetation indices consistently improves performance over NDVI alone across DS-1, DS-2, and DS-3, with gains reaching up to 96.22% due to the complementary information captured by multi-index spectral sensitivity. The GBT and RF classifiers consistently achieved the highest classification performance, effectively combining multiple decision trees to capture complex nonlinear relationships and decision boundaries; meanwhile, the MD classifier exhibited the lowest accuracy due to its reliance solely on distances to class mean vectors. All in all, the presented approach offers a robust framework for crop classification supplemented with multiple data sources using different VI feature scenarios and variable machine learning tools for precise farming applications in semi-arid regions. Full article

LaFeO₃ nanoperovskites were synthesized through a sugarcane-molasses-assisted combustion route using mechanically stirred (MLP) and ultrasonic-assisted (ULP) activation strategies to investigate the influence of synthesis conditions on structural and interfacial electrochemical properties. X-ray diffraction and Rietveld refinement confirmed the formation of orthorhombic LaFeO₃, while ultrasonic-assisted synthesis promoted improved phase homogeneity and reduced crystallite size compared with mechanically stirred combustion. Transmission electron microscopy revealed lower agglomeration and improved particle dispersion for ULP materials, whereas thermal and vibrational analyses confirmed the formation of thermally stable LaFeO₃ nanoperovskites containing residual biomass-derived species associated with the combustion process. Electrochemical characterization at screen-printed carbon electrodes demonstrated that ultrasonically synthesized LaFeO₃ significantly enhanced interfacial charge-transfer behavior, yielding lower charge-transfer resistance (435 Ω), increased electroactive surface area (0.149 cm²), and improved heterogeneous electron-transfer kinetics relative to MLP and bare electrodes. The LaFeO₃-modified interfaces additionally exhibited distinct electrochemical oxidation behavior toward 2-aminothiazole (2AT) and 2-aminooxazole (2AO) under acidic conditions. Scan-rate analyses revealed predominantly diffusion-controlled irreversible oxidation processes, while pH-dependent studies indicated proton-coupled electron-transfer behavior during electrooxidation. The combined structural and electrochemical results establish clear process–structure–property relationships linking ultrasonic-assisted green synthesis, nanostructural organization, and interfacial electrochemical performance in LaFeO₃ nanoperovskites. Full article

Middle ear neuroendocrine tumours (MeNETs) are rare epithelial neoplasms with neuroendocrine differentiation that pose significant diagnostic and therapeutic challenges. The clinical presentation of MeNETs is often nonspecific and can mimic other middle ear pathologies, such as chronic otitis media, cholesteatoma, or paraganglioma Common symptoms include conductive hearing loss, otalgia, intermittent or persistent tinnitus, ear fullness, and dizziness. We present five patients who underwent surgery in our University Otolaryngology Centre between 2019 and 2025, in whom histopathological examination confirmed the diagnosis of MeNET. Although MeNET is typically considered an indolent tumour, rare cases of locally aggressive behaviour and distant metastases have been reported in the literature. Metastatic potential appears to correlate with histopathological features such as increased mitotic activity, Ki-67 proliferation index > 5%. The treatment of choice for MeNET is surgical resection of the tumour, with the choice of surgical technique depending on the stage of the tumour, its relationship to surrounding anatomical structures, and the possibility of hearing preservation. In our study, we highlighted the importance of radical tumour excision to minimize the risk of recurrence. Given the risk of recurrence and the risk of potential metastases, long-term follow-up is necessary, particularly in patients with advanced-stage tumours. Full article

As China transitions to a higher development stage, the environmental dividends of smart-digital upgrades are growing more prominent. Boosting green total factor productivity (GTFP) in manufacturing is critical for fostering emerging quality-driven productive forces. Employing provincial data from 30 regions (2013–2023), this research utilizes the super-efficiency SBM, fixed-effects, mediation, and moderation models to investigate both the impact and operative pathways of digital-intelligent transformation on manufacturing GTFP. The empirical evidence suggests that: (1) The positive effect of digital-intelligent transformation on manufacturing greening proves robust across endogeneity corrections and various sensitivity tests. (2) Heterogeneity tests indicate that the promotional effect is more pronounced in eastern provinces and areas with weaker pollution loads. (3) Mechanism analysis identifies a dual-edged pathway: while technological innovation serves as a positive conduit, labor structure optimization unexpectedly acts as a suppression channel, weakening the overall positive impact. (4) Moderating effect analysis demonstrates that factor market development strengthens the positive relationship between digital-intelligent transformation and manufacturing GTFP. (5) Threshold analysis based on environmental regulation intensity indicates that the marginal effect of digital-intelligent transformation gradually declines as environmental regulation becomes more stringent. Overall, the findings suggest that the green effects of digital-intelligent transformation are not automatic but depend on regional industrial structures, factor market conditions, and environmental regulatory intensity. Crucially, this study reveals a potential “green paradox” in China’s manufacturing digitalization process: although digital-intelligent technologies stimulate innovation and labor upgrading, their contribution to emission reduction is partially offset by a misalignment in labor allocation and technological orientation, resulting in a net weakening of the green transformation effect. Therefore, policymakers should promote the coordinated development of digital infrastructure, factor market reform, and environmental regulation, while actively guiding skilled labor toward green innovation activities to accelerate manufacturing green transformation and foster new quality productive forces. Full article

To address lubricant film oscillation and rotor whirl instability caused by unreasonable bearing configurations in X-ray tubes, this study systematically investigated the dynamic performance of asymmetrically distributed herringbone-groove journal bearings lubricated with gallium-based liquid metal. On the basis of hydrodynamic lubrication theory and turbulence effects, an unsteady dynamic Reynolds equation and a perturbation pressure differential equation are established. The physical definitions and coordinate transformation relationships of the lubricant film stiffness and damping coefficients are clarified. Comparative analyses of symmetric and asymmetric bearing structures are conducted on the COMSOL Multiphysics platform under varying eccentricities, rotational speeds, bearing clearances, and groove depths. Compared with the symmetric design, the asymmetric structure generates a significantly higher damping peak in the medium-to-high eccentricity range, achieving an optimal combination of high stiffness and moderate damping. A stable, directional, high-pressure zone can form at zero eccentricity, which actively guides the lubricant to establish a steady hydrodynamic film under misaligned operating conditions. This study provides theoretical support for the optimal design of high-speed bearing systems. Full article

Urban parks contribute to emotional well-being, yet the relationship among park environments, users’ perceptions, recreational activities, and emotions across seasons remains insufficiently understood. Using Jiefang Park in Wuhan, China, as a case study, this study analyzed 2233 valid questionnaires from four-season field surveys across six surveyed spaces, together with measured spatial environmental data. Partial least squares structural equation modeling (PLS-SEM) was used to examine the associations and interaction effects of landscape perception, thermal perception, recreational activities, and positive and negative emotions. Hierarchical linear modeling (HLM) was used to account for respondents nested within 24 space-season units and examine cross-level statistical indirect associations. Emotional responses varied across seasons and among the six surveyed locations. The surveyed lawn space showed the highest positive-emotion and lowest negative-emotion scores, whereas the surveyed fitness activity space had the highest negative-emotion score. The surveyed fitness activity and waterfront spaces displayed smaller seasonal fluctuations. Thermal and visual perceptions were consistently associated with emotional responses in the season-specific models. Measurement invariance was not established, so path coefficients were not formally compared. Spatial environmental characteristics showed significant indirect associations with emotions mainly through perceptual variables, whereas indirect associations through recreational activities were not significant. Vegetation color richness showed favorable indirect associations, while green view, pavement visibility, and fitness facility quantity showed adverse indirect associations at the surveyed locations. These findings suggest that seasonally responsive park design should emphasize perceptual quality, thermal adaptability, and context-sensitive spatial configuration. Full article

The Xiangshan Uranium Deposit located in Jiangxi Province represents the largest volcanic-hosted uranium deposit in Asia and serves as a critical uranium production base in China. Clay alteration serves as a key prospecting indicator for this deposit, and its quantitative characterization through high-resolution spectroscopy is crucial for elucidating the relationship between hydrothermal activity and uranium mineralization. Addressing the current shortcoming in hyperspectral alteration mapping—which primarily focuses on qualitative mineral identification while lacking systematic quantitative characterization of diagnostic absorption trough parameters—this study utilized ZY1-02E (Chinese Resource Satellite-1-02E) satellite hyperspectral imagery as the data source. Following preprocessing, key parameters of the diagnostic absorption trough at 2205 nm for clay-altered minerals—including depth, area, and symmetry—were quantitatively extracted to construct a spectral index reflecting the intensity of clay alteration. Subsequently, the spatial distribution characteristics of these parameters were systematically analyzed. It was found that, with the line connecting Yankeng, Youjiashan, Xiangshan, and Yunji serving as a boundary, clay alteration generally exhibits a distribution trend of being stronger in the northwest and weaker in the southeast, with morphological features characterized by a combination of strip-like, ring-shaped, and nodular patterns. Based on these findings, nine prospective exploration areas were delineated, including five Class-I and four Class-II prospective exploration areas. High-value anomalies in the absorption trough parameters show good spatial correlation with known uranium deposits and fault structures, effectively indicating the centers of hydrothermal alteration and fluid migration pathways. This study advances the remote sensing identification of clay alteration from qualitative mapping to the quantitative analysis stage, providing a scientific basis for further exploration in the Xiangshan mineralized area. The established method is highly applicable to similar volcanic-type uranium mineralized areas. Full article

Event cameras provide high temporal resolution and sparse asynchronous output for small-target monitoring. However, distant weak targets often generate sparse and fragmented events that are easily obscured by responses from background structures and sensor background activity (BA) noise. This paper proposes a Spatio-Temporal Polarity Interaction Filter (SPIF), which assigns a reliability score to each incoming event. SPIF constructs weighted neighborhood support from temporal proximity, spatial distance, and polarity relationships, and uses the recent firing history of the center pixel to discount unreliable evidence caused by persistent activation. On the complete test split of the event-based unmanned aerial vehicle (EV-UAV) benchmark, SPIF achieves a macro-averaged F1 score (macro-F1) of 0.7732 and a target retention rate of 0.8896, exceeding the corresponding values of 0.7583 and 0.6740 obtained by the supervised EV-SpSegNet. Driving and the ED24 real-world denoising dataset further evaluate separation between valid events and BA noise. SPIF obtains the highest area under the receiver operating characteristic curve (AUC) on Driving at 3–10 Hz/pixel and under all evaluated ED24 settings. The C++ implementation processes 6.72 million events per second on the tested CPU. Full article

Water and nitrogen application rates directly affect crop yield formation and protein accumulation. Nitrogen metabolism, as a key physiological process linking water and nitrogen supply with crop growth, plays a critical role in regulating nitrogen uptake, transformation, and accumulation. However, the functional relationships among different nitrogen metabolism indicators in mediating the formation of high-quality and high-yield alfalfa under water–nitrogen regulation remain unclear. In this study, alfalfa (Medicago sativa L.) was subjected to four nitrogen application levels [N0 (0 kg·hm⁻²), N1 (80 kg·hm⁻²), N2 (160 kg·hm⁻²), N3 (240 kg·hm⁻²)] and four irrigation gradients [severe deficit (W0, 45–60% θ_f), moderate deficit (W1, 55–70% θ_f), mild deficit (W2, 65–80% θ_f), and full irrigation (W3, 75–90% θ_f), where θ_f represents field capacity]. The relationships between water–nitrogen regulation and alfalfa nitrogen metabolism and productive performance were analyzed. The results showed that (1) both irrigation amount and nitrogen application rate significantly affected the activities of leaf nitrate reductase (NR), glutamine synthetase (GS), glutamate synthase (GOGAT), and soluble protein (SP) content (p < 0.05). Aboveground nitrogen content (TN) initially increased and then decreased with increasing irrigation and nitrogen application, reaching its maximum under W2N2, with leaves being the primary site of aboveground nitrogen accumulation. (2) Under W2N2, alfalfa yield and crude protein accumulation (CP-a) both reached their maximum values, averaging 10.22 t·ha⁻¹ and 1187.10 kg·ha⁻¹, respectively. (3) Structural equation modeling (SEM) indicated that water–nitrogen regulation primarily influenced yield and CP-a formation through its effects on GS activity and TN. Based on these findings, a comprehensive evaluation model (GS–TN–Yield–CP-a) was constructed, and the preliminary suitable water–nitrogen regulation ranges for high-quality and high-yield alfalfa were identified as an irrigation amount of 69.4–85.9% θ_f and a nitrogen application rate of 102.6–222.3 kg·hm⁻². These results can provide a theoretical basis for high-quality and high-yield alfalfa management in arid and semi-arid regions, but further verification through field experiments is still required. Full article

We herein report the anticancer activity and molecular docking studies of a series of amide derivatives of two nonsteroidal anti-inflammatory drugs (mefenamic acid and ibuprofen). The hypothesis of drug repurposing has been successfully employed to explore the promising anticancer activity of analogs of known anti-inflammatory agents. The compounds have been tested for their inhibitory potential against cervical cancer cell lines by MTT assay using 5-fluorouracil as the reference standard. Among the compounds screened, 3aa [2-(2,3-dimethylamino)phenyl)(1H-indol-1-yl)methanone] and 3ad [2-(2,3-dimethylphenylamino)phenyl)(9H-carbazol-9-yl)methanone] displayed good potency of less than 25 µg/mL half-maximal inhibitory concentration (IC₅₀). The docking analysis has confirmed that molecule 3aa effectively binds to the active site of the target protein CDK2, with a docking score of −9.21 Kcal/mol and a binding energy of −46.44 Kcal/mol, involving a hydrogen bond with Ile 10. The molecule 3ad also exhibited a good glide score of −6.78 Kcal/mol with the binding energy of −45.90 Kcal/mol. As many of the tested compounds displayed promising potency against cervical cancer cell lines, our investigation revealed the importance of drug repurposing in the development of lead molecules in medicinal chemistry. Full article

Coastal peri-urban parks must simultaneously support ecological restoration, public recreation, cultural continuity, and long-term operation, yet reproducible methods for matching local cultural resources with renewal needs remain limited. Taking Yandao Mountain Park in Qingdao, China, as a case study, this research develops a culture–need matching and scenario-based evaluation framework integrating the Local Cultural Resource Identification Index (LCRI), Renewal Need Index (RNI), Culture–Renewal Need Matching Matrix (CRNM), Public Behaviour Activation Index (PBAI), and Sustainable Renewal Effect Index (SREI). Data were obtained from four field surveys, 318 valid questionnaires, 12 semi-structured interviews, GIS-supported spatial interpretation and Average Nearest Neighbor analysis, AHP weighting, two-round Delphi consultation, and sensitivity analysis. Beer culture, residents’ living memory, mountain–sea landscape imagery, and Liyuan courtyard culture showed strong identification bases, while functional integration, operational improvement, all-age sharing, and ecological restoration were the main renewal needs. CRNM revealed differentiated culture–need relationships that informed spatial translation and functional embedding. Under the proposed renewal scenario, the field-observed PBAI baseline of 0.46 was compared with a Delphi-calibrated scenario estimate of 0.82, while the estimated overall SREI was 0.87. These values indicate scenario-based potential for behavioural activation and sustainable renewal rather than verified post-implementation performance. Full article

A large number of potential knowledge associations in scientific and technological innovation activities have not yet become explicit. How to identify potential valuable innovation opportunity clues from complex knowledge structures has therefore become an important issue in intelligence analysis research. This study takes the field of rice drought-tolerant breeding as an empirical case. Based on PMC full-text literature data, the LightRAG model was employed to extract innovation-related entities and their semantic relationships, including varieties, genes, proteins, phenotypes, and technological methods. A technology-data heterogeneous network for rice drought-tolerant breeding was then constructed, and the HetGNN link prediction method was introduced to predict potential relationships within the network. To evaluate the effectiveness of the proposed model, literature published from 2006 to 2020 was used to construct the training network, while newly emerging relationships extracted from literature published between 2021 and 2023 were used as a future validation set. Adamic-Adar and Node2Vec were further selected as baseline models for comparison. The experimental results show that the proposed method achieved an AUC of 0.8901, an AP of 0.9190, and an F1@0.5 of 0.8322 on the internal testing set, outperforming the baseline models in overall performance. In the temporal holdout validation, the model was able to identify some newly emerging knowledge associations that subsequently appeared in the 2021–2023 literature. The prediction results based on the full dataset indicate that the potential relationships are mainly concentrated in influence relationships between data elements and drought-tolerant phenotypes, as well as support relationships between technological methods and drought-tolerant phenotype research. This study constructs an analytical framework consisting of “innovation element extraction, heterogeneous network modeling, temporal holdout validation, and potential relationship interpretation,” thereby providing a methodological reference for identifying potential innovation opportunity clues from complex scientific knowledge structures. Full article

Localized densification of nanoporous silica under combined mechanical compression and elevated temperature involves coupled pore collapse, skeletal rearrangement, and thermally activated sintering. Clarifying how local pre-compression regulates these processes is important for understanding the surface and near-surface densification of nanoporous silica and related porous materials. In this study, the microscopic sintering behavior of a silica-inspired aerogel-like nanoporous model under the coupling of non-uniform local stress and high-temperature fields (indentation depths of 50–150 Å and temperatures of 298–1800 K) was systematically investigated using molecular dynamics simulations combined with a three-dimensional (3D) topological recognition algorithm (probe sphere method and DBSCAN clustering). The results indicate that the sintering densification of the silica-inspired aerogel model exhibits significant pore-size dependence and a “depth-temperature inverse relationship”: the local pre-compression induced by the 150 Å indentation facilitates thermally activated atomic rearrangement and shifts the onset of densification to a lower temperature, leading to an early bimodal splitting of the pore size distribution at 1300 K, accompanied by a significant jump in the elastic modulus from 3.0 to 10.07 GPa. In contrast, the 50 Å shallow region requires heating to 1800 K to achieve an equivalent densification effect. Furthermore, topological analysis quantitatively reveals the phase transition process of the pore network from connected to isolated: taking 1300 K as an example, the number of connected pore clusters decreases from the initial 86 to 70 (at 1000 ps), marking the fracture of the connected network; subsequently, the number of isolated pores surges to 4861, and the residual connected framework is severely fragmented into 136 micro-clusters. Based on the above microstructural and topological evolution data, a four-stage thermo-mechanical synergistic evolution process of the silica-inspired aerogel model is summarized. These findings provide quantitative fundamental data that conceptually supports the design of functional gradient structures with alternating “dense-thermally-conductive” and “porous-thermally-insulating” layers within a single continuous aerogel matrix; such structures may be realized in the future through strategies such as arrayed nanoindentation combined with high-temperature sintering. Full article

Background/Objectives: Human islet amyloid polypeptide (IAPP) aggregation plays a critical role in the pathogenesis of type 2 diabetes mellitus (T2DM). Although metabolic alterations are a hallmark of T2DM, the functional roles of differential metabolites in regulating disease-associated molecular processes remain largely unexplored. This study aimed to establish a metabolomics-guided strategy for identifying endogenous metabolites with anti-amyloid activity and to investigate their underlying chemical interactions with IAPP. Methods: Untargeted metabolomic profiling of clinical samples from T2DM patients, obesity patients and healthy controls was performed to identify differential metabolites. Candidate metabolites were subsequently screened for their ability to modulate IAPP aggregation. Transmission electron microscopy (TEM), thioflavin T (ThT) fluorescence assays, and cell viability measurements were employed to evaluate their effects on fibril formation and cytotoxicity. Mass spectrometry was further used to characterize metabolite–IAPP interactions. Results: Metabolomic analysis identified 3-hydroxypyruvic acid (also known as β-hydroxypyruvic acid, hereafter referred to as HPA) as a significantly altered endogenous metabolite associated with T2DM and a candidate regulator of IAPP aggregation. Functional assays demonstrated that HPA effectively inhibited amyloid fibril formation, as evidenced by the absence of typical fibrillar structures and a prolonged lag phase during aggregation. HPA also significantly alleviated IAPP-induced cytotoxicity. Mass spectrometric analysis revealed the formation of HPA–IAPP oligomer complexes (n < 4), suggesting that HPA directly interacts with early oligomeric intermediates and interferes with their progression toward mature fibrils. Conclusions: This work demonstrates that untargeted metabolomics of clinical samples can serve as an effective strategy for discovering bioactive endogenous metabolites involved in disease-related molecular processes. The identification of HPA as a potential endogenous inhibitor of IAPP aggregation provides new chemical insight into the relationship between metabolic dysregulation and amyloidogenesis and highlights endogenous metabolites as a valuable source of potential therapeutic lead compounds. Full article