Search Results (174)

Camellia oleifera, a woody oil crop unique to China, plays a crucial role in alleviating the global pressure of edible oil supply and maintaining ecological security. However, it remains challenging to accurately forecast Camellia oleifera yield in complex landscapes using only remote sensing data. The aim of this study is to develop an interpretable deep learning model, namely Shapley Additive Explanations–guided Attention–long short-term memory (SALSTM), for estimating Camellia oleifera yield by integrating an improved spectral bloom index and environmental parameters. The study area is located in Hengyang City in Hunan Province. Sentinel-2 imagery, meteorological observation from 2019 to 2023, and topographic data were collected. First, an improved spectral bloom index (ISBI) was constructed as a proxy for flowering density, while average temperature, precipitation, accumulated temperature, and wind speed were selected to represent environmental regulation variables. Second, a SALSTM model was designed to capture temporal dynamics from multi-source inputs, in which the LSTM module extracts time-dependent information and an attention mechanism assigns time-step-wise weights. Feature-level importance derived from SHAP analysis was incorporated as a guiding prior to inform attention distribution across variable dimensions, thereby enhancing model transparency. Third, model performance was evaluated using root mean square error (RMSE) and coefficient of determination (R²). The result show that the constructed SALSTM model achieved strong predictive performance in predicting Camellia oleifera yield in Hengyang City (RMSE = 0.5738 t/ha, R² = 0.7943). Feature importance analysis results reveal that ISBI weight > 0.26, followed by average temperature and precipitation from flowering to fruit stages, these features are closely associated with C. oleifera yield. Spatially, high-yield zones were mainly concentrated in the central–southern hilly regions throughout 2019–2023, In contrast, low-yield zones were predominantly distributed in the northern and western mountainous areas. Temporally, yield hotspots exhibited a gradual increasing while low-yield zones showed mild fluctuations. This framework provides an effective and transferable approach for remote sensing-based yield estimation of flowering and fruit-bearing crops in complex landscapes. Full article

As a type of cell with self-renewal ability and multi-directional differentiation potential, stem cells are closely related to their functions, such as reprogramming transcription factors, histone modifications, and energy metabolism. m⁶A (N⁶-methyladenosine modification) is one of the most abundant modifications in RNA, and dynamic reversible m⁶A modification plays an important role in regulating stem cell function. This review moves beyond listing isolated functions and instead adopts an integrated perspective, viewing m⁶A as a temporal regulator of cellular state transitions. We discuss how m⁶A dynamically regulates stem cell pluripotency, coordinates epigenetic and metabolic reprogramming, and serves as a central hub integrating key signaling pathways (Wnt, PI3K-AKT, JAK-STAT, and Hippo). Finally, using somatic reprogramming as an example, we elucidate the stage-specific role of m⁶A in complex fate transitions. This comprehensive exposition not only clarifies the context-dependent logic of m⁶A regulation but also provides a precise framework for targeting the m⁶A axis in regenerative medicine and cancer therapy. Full article

The advance and delay of the rainy season is among the most frequently cited effects of climate change in the central Ecuadorian Andes. However, its assessment is not feasible using the indicators recommended by the standardized indices of the Expert Team on Climate Change Detection and Indices (ETCCDI), designed to detect changes in intensity, frequency, or duration of intense events. This study aims to analyze such advances and delays through harmonic analysis in Tungurahua, a predominantly agricultural province in the Tropical Central Andes, where in situ data are scarce. Daily in situ data from five meteorological stations were used, including precipitation, maximum, and minimum temperature records spanning 39 to 68 years. The study involved an analysis of the region’s climatology, climate change indices, and harmonic analysis using Cross-Wavelet Transform (XWT) and Wavelet Coherence Transform (WCT) to identify seasonal patterns and their variability (advance or delay) by comparing historical and recent time series, and Krigging for regionalization. The year 2000 was used as a study point for comparing past and present trends. Results show a generalized increase in both minimum and maximum temperatures. In the case of extreme rainfall events, no significant changes were detected. Harmonic analysis was found to be fruitful despite of the missing data. Furthermore, the observed advances and delays in seasonality were not statistically significant and appeared to be more closely related to the geographic location of the stations than to temporal shifts. Full article

The overall knowledge structure, developmental context, and research frontiers in the field of bronze cultural relic corrosion and protection are lacking. This study employs bibliometric methods to comprehensively analyze 2614 relevant publications from 1906 to 2025 in the Web of Science Core Collection, utilizing the software Citespace 6.2.R3 to construct a knowledge map. The research results based on the number of publications and keyword statistics indicate that the research in this field has undergone a temporal evolution of research trends. Since 2010, the annual number of publications has grown rapidly, peaking in 2024, which reflects the continuously increasing academic attention given to the subject. Globally, China, Italy, and the United States are the leading contributors, forming a closely knit international cooperation network. Among these, China leads in total publications, though there remains room for improvement in its centrality within the collaborative network. Major research institutions are primarily large scientific organizations, such as the National Research Council of Italy and the Chinese Academy of Sciences. Keyword analysis demonstrates that research hotspots have long centered on “corrosion mechanisms and control” and “innovative protection materials and technologies”. Temporal evolution analysis further indicates that the research paradigm is shifting: from the early investigations of mechanisms, through a middle phase focused on material development, to the current emphasis on the development of preventative and intelligent protection systems via multidisciplinary integration. This study systematically reviews the field’s evolutionary trajectory, collaboration networks, and thematic dynamics, providing a comprehensive reference for research planning and future development. Full article

This paper presents a finite difference method for solving the Caputo generalized time fractional diffusion equation. The method extends the $L1$ scheme to discretize the time fractional derivative and employs the central difference for the spatial diffusion term. Theoretical analysis demonstrates that the proposed numerical scheme achieves a convergence rate of order $2-\alpha$ in time and second order in space. These theoretical findings are further validated through numerical experiments. Compared to existing methods that only achieve a temporal convergence of order $1-\alpha$, the proposed approach offers improved accuracy and efficiency, particularly when the fractional order $\alpha$ is close to zero. This makes the method highly suitable for simulating transport processes with memory effects, such as oil pollution dispersion and biological population dynamics. Full article

In the context of global warming, hydroclimatic conditions in the monsoon marginal zone are governed by two primary drivers: the East Asian monsoon and the westerly winds. As a sensitive indicator of climatic change, this region experiences disproportionately amplified adverse effects of climate change are markedly amplified, positioning it as a focal area for climatological research. However, the limited temporal coverage of instrumental records poses significant challenges for understanding historical hydroclimatic variability and its underlying mechanisms. To address this limitation, tree-ring width indices derived from 73 cores of Styphnolobium japonicum ((L.) Schott (1830)) are hereby employed to reconstruct summer maximum temperatures over a 433-year period in the central monsoon fringe zone—specifically, the northwestern Yan Mountains. Results confirm a strong correlation between the tree-ring width index of Styphnolobium japonicum and local summer maximum temperatures (r = 0.770, p < 0.01). Compared to the 19th century, the frequency of temperature fluctuations has increased substantially, with four abrupt regime shifts identified in the reconstructed series (1707, 1817, 1878, and 1994). Spectral analysis reveals cyclical patterns at interannual (2–7 years), decadal (10–30 years), and multidecadal (50 years) timescales. These oscillations align closely with known climate modes, including the EI Niño–Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), and the Atlantic Multidecadal Oscillation (AMO). Among them, the AMO presents particularly strong coherence with the reconstructed temperature variability. These outcomes improve insights into long-term temperature dynamics in the region and highlight the value of dendroclimatic proxies in reconstructing past climate conditions. Full article

Debris-covered glaciers, with their intricate thermal dynamics and significant spatial heterogeneity, play a pivotal role in elucidating glacier ablation processes and their responses to climate change. However, existing research on their energy balance predominantly focuses on short-term or localized processes, while the long-term evolution of energy fluxes and the combined effects of debris cover and ice cliffs remain underexplored. This study, focused on the Lirung glacier in the Central Himalaya, leverages multi-source remote sensing data (Landsat 8, MODIS, Planet) in conjunction with meteorological observations and an energy balance model to investigate the spatiotemporal variations in the glacier’s surface energy balance from October 2017 to August 2019. Key findings are as follows: (1) Net radiation flux emerges as the predominant energy driver for ablation, reaching its peak during May–June and substantially outpacing both sensible and latent heat fluxes in magnitude; (2) The energy balance exhibits pronounced spatial heterogeneity, with lower-altitude regions receiving enhanced energy inputs and displaying reduced albedo, thereby magnifying the local ablation flux; (3) The average debris thickness is quantified at 0.55 ± 0.02 m, with thicker debris layers mitigating ablation, while thinner layers exacerbate it; (4) Ice cliffs are characterized by significantly elevated ablation fluxes, with certain areas recording values as high as 1.73 times the glacier-wide mean; (5) The proglacial lake has expanded by 21.1 ± 11.4%, with its temporal variations closely tracking the fluctuations in net radiation flux. These findings provide crucial insights into the energy balance and climate responses of debris-covered glaciers in the Central Himalaya. Full article

The European squid Loligo vulgaris inhabits the continental shelf of the North and Central Atlantic and the Mediterranean Sea, with significant socio-economic value for the associated fisheries. Globally, the stock appears to be maintained at levels close to the optimal sustainable yield, but capture statistics indicate high fluctuations in fisheries production, and some regions might be affected by overexploitation. In this study, we used the mitochondrial marker mtCOI to investigate temporal and spatial genetic structure and variability in the European squid in the eastern part of the Adriatic Sea and put it into context with its Mediterranean and Atlantic conspecifics using data from public databases. High haplotype and low nuclear diversity of mtCOI were detected, with no significant genetic differentiation, suggesting one panmictic homogeneous population in the North and Central Adriatic Sea. The Adriatic cluster appears to diverge from its Mediterranean–Atlantic conspecifics; however, this pattern should be considered preliminary due to the limited and uneven geographic sampling available in public databases. The current dataset lacks comprehensive coverage of several Mediterranean sub-basins, which restricts the resolution of connectivity patterns and may mask subtle population structure. Despite these limitations, our results provide an important baseline for understanding the L. vulgaris Adriatic stock and for developing joint management policies among all countries that exploit this shared resource. Full article

Flight safety is central to the certification process and relies on assessment methods that provide evidence acceptable to regulators. For drones operating as Advanced Air Mobility (AAM) platforms, this requires an accurate representation of the complex wind fields in urban areas. Large-eddy simulations (LES) of such environments generate datasets from hundreds of gigabytes to several terabytes, imposing heavy storage demands and limiting real-time use in simulation frameworks. To address this challenge, we apply a Convolutional Autoencoder (CAE) to compress a 40 m-deep section of an LES wind field. The dataset size was reduced from 7.5 GB to 651 MB, corresponding to a 91% compression ratio, while maintaining maximum magnitude errors within a few tenths of the spatio-temporal wind velocity. Predicted vehicle responses showed only marginal differences, with close agreement between the full LES and CAE reconstructions. These findings demonstrate that CAEs can significantly reduce the computational cost of urban wind field integration without compromising fidelity, thereby enabling the use of larger domains in real-time and supporting efficient sharing of disturbance models in collaborative studies. Full article

Background/Objective: Timely and successful treatments for Alzheimer’s disease (AD) depend on early detection. The Multimer Detection System (MDS-OAβ) for quantifying plasma oligomeric amyloid-β (OAβ) has shown promise as a biomarker of amyloid disease. The theta-to-alpha ratio (TAR) and theta-to-beta ratio (TBR) are two examples of spectral power metrics that can be used in resting-state quantitative EEG (qEEG) to evaluate brain function non-invasively. This study used resting-state EEG (rEEG) recordings obtained while the subjects were both eyes-open (EO) and eyes-closed (EC) to investigate the relationship between regional qEEG power ratios and plasma MDS-OAβ levels in older adults. Methods: The analysis comprised 174 patients between the ages of 60 and 85, with 2 in the low-MDS-OAβ group and 82 in the high-MDS-OAβ group. The clinical plasma cutoff was 0.78 ng/mL. All participants underwent rEEG recordings and plasma OAβ quantification. EEG pre-processing included bandpass filtering (0.5–100 Hz), average re-referencing, artifact rejection using independent component analysis (ICA), and spectral power estimation using Welch’s method. The TAR and TBR were calculated across five lobar regions (frontal, central, parietal, occipital, and temporal) during both EO and EC conditions. To normalize data distributions, EEG ratio variables were log-transformed prior to statistical analysis. Group comparisons and linear regression analyses were conducted to evaluate the associations between EEG power ratios and MDS-OAβ levels. Adjusted regression models included age, years of education, and neuropsychological test scores as covariates. Statistical significance was set at p < 0.05. Results: No significant associations were found between TAR and plasma MDS-OAβ levels across any lobar regions under either EO or EC conditions. In contrast, TBR exhibited consistent and significant negative associations with MDS-OAβ levels, particularly under EC conditions. Adjusted regression models revealed that higher MDS-OAβ levels were associated with lower TBR values in the central (β = −0.059, p = 0.015), parietal (β = −0.072, p = 0.006), occipital (β = −0.067, p = 0.040), and temporal (β = −0.053, p = 0.018) lobes, with the strongest inverse relationship observed in the parietal lobe. A similar, though slightly weaker, pattern was observed during EO conditions, with significant inverse associations in the frontal, central, and temporal lobes. Conclusions: Our findings indicate that, after adjusting for covariates, increased plasma MDS-OAβ levels are significantly associated with a reduced TBR, particularly in the parietal and central lobes, under both EO and EC resting-state conditions. In contrast, no significant associations were observed with TAR. These results suggest that a lower TBR may reflect an increased peripheral amyloid burden and highlight its potential as a sensitive qEEG biomarker for early amyloid-related brain changes in older adults. Full article

Spontaneous liquid–liquid capillary imbibition in axially varying capillaries is central to petroleum engineering applications such as water-driven enhanced oil recovery, where the dynamic contact angle (DCA) governs interfacial motion. We extend the classical Lucas–Washburn (LW) formulation to account for axial variations in the hydraulic radius, early-time inertia, and viscous dissipation in the displaced non-wetting phase. In parallel, we develop a cascaded multicomponent Shan–Chen lattice Boltzmann model (LBM) that resolves the in situ evolution of the DCA and simulate imbibition in three area-matched geometries: convergent conical, divergent conical, and parabolic. The axial profile is shown to control both the imbibition rate and the DCA. For a viscosity-matched binary fluid, the temporal variation in the DCA is set by the local contraction rate: the DCA decreases as the capillary widens and increases as it narrows. Stronger intrinsic wettability enlarges the discrepancy between the DCA and the static contact angle (SCA). Moreover, at fixed non-wetting-phase viscosity, decreasing the wetting/non-wetting viscosity ratio reduces the imbibition rate and drives the DCA toward the SCA. Predictions from the extended LW equation that neglect DCA exhibit systematic deviations from LBM results, whereas supplying the time-resolved DCA yields close quantitative agreement across all geometries. These findings identify the DCA as a critical state variable for reduced-order prediction of imbibition in axially varying capillaries and inform the design of enhanced-oil-recovery and microfluidic systems. Full article

The rapid growth of Distributed Energy Resources (DERs) exerts significant pressure on distribution network margins, requiring predictive and safe coordination. This paper presents a closed-loop framework combining a topology-aware Spatio-Temporal Transformer (STT) for multi-horizon forecasting, a cooperative multi-agent reinforcement learning (MARL) controller under Centralized Training and Decentralized Execution (CTDE), and a real-time safety layer that enforces feeder limits via sensitivity-based quadratic programming. Evaluations on three SimBench feeders, with OLTC/capacitor hybrid control and a stress protocol amplifying peak demand and mid-day PV generation, show that the method reduces tail violations by 31% and 56% at the 99th percentile voltage deviation, and lowers branch overload rates by 71% and 90% compared to baselines. It mitigates tail violations and discrete switching while ensuring real-time feasibility and cost efficiency, outperforming rule-based, optimization, MPC, and learning baselines. Stress maps reveal robustness envelopes and identify MV–LV bottlenecks; ablation studies show that diffusion-based priors and coordination contribute to performance gains. The paper also provides convergence analysis and a suboptimality decomposition, offering a practical pathway to scalable, safe, and interpretable DER coordination. Full article

With the rapid acceleration of industrialization and urbanization in China, PM2.5 pollution has emerged as a major challenge to public health and sustainable development of the society and economy. At the interprovincial level, PM2.5 exhibits a complex spatial correlation network structure. Using data from 31 provinces in China from 2000 to 2023, this study constructed a spatial correlation network of PM2.5 and analyzed its structural characteristics and formation mechanisms. The results reveal that China’s PM2.5 spatial correlation network is both complex and stable, underscoring the severity of the pollution problem. The network demonstrates a distinct ‘core–periphery’ distribution, with provinces such as Jiangsu, Shandong, and Henan occupying central positions and functioning as critical bridges. Block model analysis showed a clear role of differentiation among provinces in the diffusion of pollution. Temporal exponential random graph model suggests that geographical proximity, industrial structure, vehicle ownership, and government intervention are key factors shaping the network. Geographically adjacent provinces are more likely to form close connections, whereas environmental regulation and vehicle ownership tend to constrain the spread of pollution. This study provides a novel theoretical framework for understanding the spatial diffusion pathways of PM2.5 pollution and offers important policy implications for optimizing and implementing cross-regional air quality governance strategies in China. Full article

Accurate land use/land cover (LULC) maps generated through cloud computing can support large-scale land management. Leveraging the rich resources of Google Earth Engine (GEE) is essential for developing historical maps that facilitate the analysis of regional LULC dynamics. We implemented the best-performing scheme on GEE to produce 30 m LULC maps for the Shandong Peninsula urban agglomeration (SPUA) and to detect LULC changes, while closely observing the spatio-temporal trends of landscape patterns during 2004–2024 using the Shannon Diversity Index, Patch Density, and other metrics. The results indicate that (a) Gradient Tree Boost (GTB) marginally outperformed Random Forest (RF) under identical feature combinations, with overall accuracies consistently exceeding 90.30%; (b) integrating topographic features, remote sensing indices, spectral bands, land surface temperature, and nighttime light data into the GTB classifier yielded the highest accuracy (OA = 93.68%, Kappa = 0.92); (c) over the 20-year period, cultivated land experienced the most substantial reduction (11,128.09 km²), accompanied by impressive growth in built-up land (9677.21 km²); and (d) landscape patterns in central and eastern SPUA changed most noticeably, with diversity, fragmentation, and complexity increasing, and connectivity decreasing. These results underscore the strong potential of GEE for LULC mapping at the urban agglomeration scale, providing a robust basis for long-term dynamic process analysis. Full article

This study investigates urban heat vulnerabilities in Seville, Spain, using a multidimensional framework that integrates remote sensing, Space Syntax, and social vulnerability metrics. This research identifies Heat Boundaries (HBs), which are critical urban entities with elevated Land Surface Temperatures (LSTs) that act as barriers to adjacent vulnerable neighbourhoods, disrupting both physical and social continuity and environmental equity, and examines their relationship with the urban syntax and social vulnerability. The analysis spans two temporal scenarios: a Category 3 heatwave on 26 June 2023 and a normal summer day on 14 July 2024, incorporating both daytime and nighttime satellite-derived LST data (Landsat 9 and ECOSTRESS). The results reveal pronounced spatial disparities in thermal exposure. During the heatwave, peripheral zones recorded extreme LSTs exceeding 53 °C, while river-adjacent neighbourhoods recorded up to 7.28 °C less LST averages. In the non-heatwave scenario, LSTs for advantaged neighbourhoods close to the Guadalquivir River were 2.55 °C lower than vulnerable high-density zones and 3.77 °C lower than the peripheries. Nocturnal patterns showed a reversal, with central high-density districts retaining more heat than the peripheries. Correlation analyses indicate strong associations between LST and built-up intensity (NDBI) and a significant inverse correlation with vegetation cover (NDVI). Syntactic indicators revealed that higher Mean Depth values—indicative of spatial segregation—correspond with elevated thermal stress, particularly during nighttime and heatwave scenarios. HBs occupy 17% of the city, predominantly composed of barren land (42%), industrial zones (30%), and transportation infrastructure (28%), and often border areas with high social vulnerability. This study underscores the critical role of spatial configuration in shaping heat exposure and advocates for targeted climate adaptation measures, such as HB rehabilitation, greening interventions, and Connectivity-based design. It also presents preliminary insights for future deep learning applications to automate HB detection and support predictive urban heat resilience planning. Full article