Search Results (981)

The changes in charge distribution caused by mutations in the spike protein may play a crucial role in balancing infectivity and immune evasion during the evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To explore how charge increments in spike protein variants influence viral evolution, a statistical analysis was conducted on 57 SARS-CoV-2 variants, examining relationships between charge distribution, lineage divergence, angiotensin-converting enzyme 2 (ACE2) affinity, immune evasion, and receptor-binding domain (RBD) expression. A phylogenetic tree was also reconstructed using only the charge properties of mutation sites. Results indicated that with increasing lineage divergence, overall positive charge initially rose sharply and then more gradually. Partitioning the spike protein into three domains—the RBD, the N-terminal flanking region (B-RBD), and the C-terminal flanking region (A-RBD)—revealed distinct patterns: positive charge increased in the RBD and A-RBD, whereas the B-RBD accumulated negative charge. Charge increments were negatively associated with ACE2 affinity and RBD expression but positively correlated with immune evasion. The k-mer-based tree derived from charge-reduced sequences showed a topology consistent with the whole-genome tree. These findings suggest that charge distribution in spike proteins is closely linked to viral evolution, with the opposing trends in the RBD and B-RBD potentially reflecting a balance between infectivity and immune escape. Full article

The recommendation of electricity retail packages is challenged by diversified user demands and the complexity of evaluation information in liberalized electricity markets. Existing approaches are often limited by the subjectivity of user clustering and the difficulty of accurately capturing cognitive fuzziness and dynamic weight variations in the decision-making process. To address these challenges, this paper proposes a novel recommendation framework that integrates Improved Density Peaks Clustering (IDPC) with group decision-making based on trapezoidal fuzzy numbers. First, an IDPC-based model is constructed to objectively identify and partition users into homogeneous groups based on similar electricity consumption characteristics. Subsequently, a dynamic multi-attribute group decision-making model, which synergizes trapezoidal fuzzy numbers and the Multi-Criteria Compromise Ranking Method (MCRM), is designed to aggregate evaluation information from these user groups and to score the retail packages. Furthermore, a full-ranking recommendation strategy is established based on group satisfaction levels. Finally, a case study using a real-world dataset from a region in Eastern China is conducted. The empirical results demonstrate the framework’s superior performance: the IDPC algorithm achieves a stable Davies–Bouldin index of approximately 1.4, and the final recommendation ranking yields a Spearman correlation coefficient of 0.9 against simulated actual choices, significantly outperforming benchmark methods. This study shows that the proposed method can effectively enhance the precision and relevance of package recommendations, providing crucial decision support for electricity retailers in implementing refined marketing strategies. Full article

Unsupervised anomaly detection has been widely applied to industrial scenarios. Recently, transformer-based methods have also been developed and have produced good performance. Although the global dependencies in anomaly images are considered, the typical patch partition strategy in the vanilla self-attention mechanism ignores the content consistencies in anomaly defects or normal regions. To sufficiently exploit the content consistency in images, we propose the multi-granularity content-aware network with semantic integration (MGCA-Net), in which superpixel segmentation is introduced into feature space to divide images according to their spatial structures. Specifically, we adopt a pre-trained ResNet as the encoder to extract features. Then, we design content-aware attention blocks (CAABs) to capture the global information in features at different granularities. In this block, we impose superpixel segmentation on the features from the encoder and employ the superpixels as tokens for the learning of global relationships. Because the superpixels are divided according to their content consistencies, the spatial structures of objects in anomaly or normal regions are preserved. Meanwhile, the multi-granularity semantic integration block is devised to further integrate the global information of all granularities. Next, we use semantic-guided fusion blocks (SGFBs) to progressively upsample the features with the help of CAABs. Finally, the differences between the outputs of CAABs and SGFBs are calculated and merged to predict the anomaly defects. Thanks to the preservation of content consistency of objects, experimental results on two benchmark datasets demonstrate that our proposed MGCA-Net achieves superior anomaly detection performance over state-of-the-art methods. Full article

The b-value is a critical parameter for gauging seismic activity and is essential for seismic hazard assessment, monitoring stress evolution in focal zones, and forecasting major earthquakes. The minimum magnitude of completeness ($M_c$), a key indicator of the completeness of an earthquake catalog, reflects the monitoring capability of a seismic network and serves as a crucial foundation for the accurate calculation of the b-value. We began by integrating multi-source earthquake catalogs for mainland China using the nearest-neighbor method. Building on this, we employed a combination of partitioned time-series analysis and a grid-based spatial scanning technique to systematically investigate the spatiotemporal evolution of the $M_c$ and the b-value across mainland China and its adjacent regions. Our findings indicate the following: (1) Since the 1980s, the overall trend of $M_c$ has shifted from high and unstable values to low and stable ones. However, significant earthquake events can cause a notable short-term increase in the $M_c$. (2) The b-value exhibits strong fluctuations, primarily influenced by the dual effects of the tectonic stress field and catalog completeness. These fluctuations are particularly pronounced in highly active seismic regions such as the Sichuan–Yunnan area and Taiwan, whereas the western Tibetan Plateau has consistently maintained a low b-value. (3) The spatial distributions of both the $M_c$ and the b-value are markedly heterogeneous. By developing a unified and complete earthquake catalog for mainland China, our research highlights the qualitative leap in monitoring capabilities brought about by the continuous densification and technological upgrading of seismic networks. This dataset provides a solid foundation for future seismological research, disaster prevention practices, and especially for the development of AI-based earthquake prediction models. Full article

This study systematically benchmarks the performance of four single soft magnetic powders—water-atomized Fe–Si–Cr (FeSiCr), silica-coated reduced iron powder (RIP), silica-coated carbonyl iron powder (CIP), and phosphate-coated CIP (CIP-P)—to establish quantitative relationships between powder attributes, deformation substructure, and high-frequency loss for molded power inductors (100 kHz–1 MHz). We prepared toroidal compacts at 200 MPa and characterized them by initial permeability (μ_i), core-loss (P_cv(f)), partitioning (P_cv(f) = K_hf + K_ef², K_h, $K_{\mathrm{e}}$: hysteresis and eddy-current loss coefficients), and EBSD (electron backscatter diffraction)-derived microstrain metrics (Kernel Average Misorientation, KAM; low-/high-angle grain-boundary fractions). Corrosion robustness was assessed using a 5 wt% NaCl, 35 °C, 24 h salt-spray protocol. Our findings reveal that FeSiCr achieves the highest μ_i across the frequency band, despite its lowest compaction density. This is attributed to its coarse particle size (D₅₀ ≈ 18 µm) and the resulting lower intragranular pinning. The loss spectra are dominated by hysteresis over this frequency range, with FeSiCr exhibiting the largest K_h, while the fine, silica-insulated Fe powders (RIP/CIP) most effectively suppress K_e. EBSD analysis shows that the high coercivity and hysteresis loss in CIP (and, to a lesser extent, RIP) are correlated with dense, deformation-induced subgrain networks, as evidenced by higher mean KAM and a lower low-angle grain boundary fraction. In contrast, FeSiCr exhibits the lowest KAM, with strain confined primarily to particle contact regions. Corrosion testing ranked durability as FeSiCr ≳ CIP ≈ RIP ≫ CIP-P, which is consistent with the Cr-rich passivation of FeSiCr and the superior barrier properties of the SiO₂ shells compared to low-dose phosphate. At 15 A, inductance retention ranks CIP (67.9%) > RIP (55.7%) > CIP-P (48.8%) > FeSiCr (33.2%), tracking a rise in effective anisotropy and—for FeSiCr—lower M_s that precipitate earlier roll-off. Collectively, these results provide a microstructure-informed selection map for single-powder formulations. We demonstrate that particle size and shell chemistry are the primary factors governing eddy currents (K_e), while the KAM-indexed substructure dictates hysteresis loss (K_h) and DC-bias superposition characteristics. This framework enables rational trade-offs between magnetic permeability, core loss, and environmental durability. Full article

This paper introduces a novel and efficient approach for Gaussian Splatting in dynamic scenes that leverages symmetry principles for enhanced computational efficiency and visual fidelity. First, we diverge from conventional methods that process static and dynamic regions uniformly by implementing an adaptive separation mechanism. This approach exploits the inherent symmetry-breaking properties between static and dynamic Gaussian points, utilizing motion differentials to identify and isolate dynamic elements. This symmetry-aware partitioning allows for the application of specialized processing techniques to each region type, with static regions benefiting from their temporal symmetry while dynamic regions receive targeted deformation modeling. Second, through this fine-grained partitioning of static and dynamic components guided by symmetry analysis, we achieve more judicious allocation of computational resources. The symmetric treatment of spatially coherent static regions and the focused processing of symmetry-breaking dynamic elements substantially reduce memory requirements and training time while preserving reconstruction quality. This optimization effectively conserves valuable computational resources without compromising visual fidelity. Third, we introduce a sophisticated deformation modeling framework that learns the transformational characteristics of grids composed of multiple Gaussian points. By incorporating radial basis function principles, which inherently preserve local rotational and translational symmetries, our method efficiently encodes complex motion information of dynamic Gaussian points. This symmetry-preserving deformation approach not only enables high-fidelity reconstruction of dynamic regions but also significantly improves the rendering of continuously evolving shadow interactions by maintaining physical consistency. The result is a marked reduction in visual distortion and rendering outputs that demonstrate exceptional correspondence to ground truth imagery across diverse dynamic scenes. Full article

Here, we report on the complexity of the taxonomy and species evolution within the monophyletic genus Rhamphus, which includes some of the smallest members of the Curculionidae family and whose species are morphologically almost indistinguishable from each other. Despite their similar appearance, we found high divergence and varying evolutionary rates among observed species groups living both in sympatry and allopatry in the western Palearctic. On the basis of subtle morphological differences and molecular evidence, we defined eight morphotypic groups and 14 species, of which 6 are newly described in this paper: R. diottii sp. nov. and R. ibericus sp. nov. (monzinii-group), R. cypricus sp. nov. and R. macedonicus sp. nov. (cypricus-group), R. betulae sp. nov. and R. crypticus sp. nov. (pulicarius group). Rhamphus morphotypic groups showed intense species radiation and cryptic speciation, with an estimated genetic divergence of 4.2–18.8% (uncorrected) in the barcoding region of the mitochondrial COI gene. The estimated divergence of the two nuclear markers, nEF-1α and nCAD, ranged from 1 to 11.9% and 0.5 to 15%, respectively. Phylogenetic analyses using both single and partitioned multigene adequately resolved the relationships between Rhamphus species and identified all groups and the species with high nodal support. According to our study, Rhamphus species cluster into monophyletic groups that are partly defined by their host plant associations and by subtle differences in penis shape. No substantial differences in female genitalia were found. Most of the species exhibit relatively rapid species radiation, which is cryptic by nature. Full article

The health and productivity of rabbits are highly sensitive to the environmental conditions within the rabbit house, particularly to fluctuations and deviations in temperature, relative humidity, and carbon dioxide (CO₂) concentration. However, owing to the thermal inertia and residual evaporation effects inherent in ventilation and cooling systems, environmental changes often exhibit delayed responses, rendering real-time control inadequate. Accurate prediction of key environmental parameters is indispensable for formulating effective environmental control strategies, as it enables consideration of their future dynamics and thereby enhances the rationality of regulation in rabbit farming. Existing prediction models often exhibit unsatisfactory accuracy and weak generalization, which restricts the incorporation of prediction into effective environmental control strategies. To address these limitations, summer indoor and outdoor environmental data were collected from rabbit houses in Nanping, Fujian; Jiyuan, Henan; and Qingyang, Gansu, China—three climatically distinct regions—forming three datasets. Based on these datasets, a multi-parameter time-series prediction model, Patch and Cross-Attention Enhanced Transformer for Rabbit House Prediction (PatchCrossFormer-RHP), is introduced, integrating patching and attention mechanisms. The model partitions the sequences of rabbit house temperature, relative humidity, and CO₂ concentration into patches and incorporates auxiliary parameters, such as indoor air velocity and outdoor temperature and humidity, to enhance feature representation. Furthermore, it applies cross-attention with differentiated encoding to disentangle multi-parameter relationships and improve predictive performance. This study used the Fujian dataset as the primary benchmark. On this dataset, PatchCrossFormer-RHP achieved root mean square error (RMSE) values of 0.290 °C, 1.554%, and 38.837 ppm for rabbit house temperature, humidity, and CO₂ concentration, respectively, with corresponding R² values of 0.963, 0.956, and 0.838, consistently outperforming RNN, GRU, and LSTM. Transfer experiments with single- and multi-source pretraining followed by fine-tuning on Fujian demonstrated that strong cross-regional generalization can be achieved with only limited target-domain data. Full article

Normal faults play a key role in accommodating extensional deformation within the South Tibet Rift. The M_S 6.8 Tingri earthquake of 7 January 2025 therefore provides a rare opportunity to investigate how these normal faults accommodate east–west extension driven by India–Eurasia convergence. Using Sentinel-1 synthetic aperture radar (SAR) imagery, we measured coseismic surface deformation and inverted the slip distribution, revealing a maximum line-of-sight (LOS) displacement of 1.85 m. Combining Bayesian inference with joint fault-slip inversion, we constrain the seismogenic fault as a west-dipping normal fault (strike 183°, dip 42.5°, rake ~–115°), exhibiting a maximum slip of 5.36 m at shallow depth. The derived moment magnitude (M_W 7.12, seismic moment 3.32 × 10¹⁹ N·m) agrees well with the USGS estimate (M_W 7.1). Coulomb stress modeling suggests stress decreases along fault flanks and significant stress loading (>0.01 MPa) at rupture terminations and adjacent north–south trending faults, implying elevated aftershock potential and possible fault triggering. GNSS velocity fields and strain rate inversion indicate a regional stress regime with a principal compressive axis (σ₁) oriented ~341° (NNW) and extensional axis (σ₃) at ~73° (ESE), consistent with east–west extension and north–south shortening. The fault exhibits oblique-normal slip, attributed to the non-orthogonal orientation of the fault plane relative to the stress field, resulting in right-lateral shear. Within the framework of the paired general-shear (PGS) deformation, this oblique slip reflects localized extensional deformation within a distributed dextral shear zone. These findings support a model of strain partitioning under regional shear and provide insights into fault segmentation and kinematics in rift systems. Full article

Severe leaf galling by the jujube gall midge Dasineura jujubifolia (Diptera: Cecidomyiidae) compromises photosynthesis and yield in arid-zone jujube orchards, yet Xinjiang-specific evidence to guide biological control has been scarce. Here we provide the first systematic characterization in Xinjiang (Hami, China) of D. jujubifolia and its parasitoid complex, integrating region-specific field surveys with gall dissection and laboratory assays. We documented five parasitoid wasps, including two species newly recorded in China—Pseudotorymus samsatensis (Hymenoptera: Torymidae) and Baryscapus adalia (Hymenoptera: Eulophidae). In Hami, the host completed 4–5 generations per year with a 19–24-day generation time. Functional roles were partitioned: P. samsatensis (dominant), Systasis parvula (Hymenoptera: Pteromalidae), and B. adalia were larval ectoparasitoids, whereas Aprostocetus sp. (Hymenoptera: Eulophidae) and Synopeas sp. (Hymenoptera: Platygastridae) were endoparasitoids. Time-series data revealed tight temporal synchrony between P. samsatensis and host peaks. Controlled experiments quantified daily emergence rhythms, diet-dependent adult longevity, and sex ratios, providing parameters to inform release timing and conservation in biological control programs. Collectively, these findings establish management-ready baselines for D. jujubifolia and its parasitoids in arid jujube systems and support conservation-oriented, reduced-pesticide integrated pest management (IPM). Full article

Weather radar reflectivity plays a critical role in precipitation estimation and convective storm identification. However, due to terrain limitations and the uneven spatial distribution of radar stations, oceanic regions have long suffered from a lack of radar observations, resulting in extensive monitoring gaps. Geostationary meteorological satellites have wide-area coverage and near-real-time observation capability, offering a viable solution for synthesizing radar reflectivity in these regions. Most previous synthesis studies have adopted fixed time-window data partitioning, which introduces significant noise into visible-light observations under large-scale, low-illumination conditions, thereby degrading synthesis quality. To address this issue, we propose an integrated deep-learning method that combines illumination-based classification and reflectivity synthesis to enhance the accuracy of radar reflectivity synthesis from geostationary meteorological satellites. This approach integrates a classification network with a synthesis network. First, visible-light observations from the Himawari-8 satellite are classified based on illumination conditions to separate valid signals from noise; then, noise-free infrared observations and multimodal fused data are fed into dedicated synthesis networks to generate composite reflectivity products. In experiments, the proposed method outperformed the baseline approach in regions with strong convection (≥35 dBZ), with a 9.5% improvement in the critical success index, a 7.5% increase in the probability of detection, and a 6.1% reduction in the false alarm rate. Additional experiments confirmed the applicability and robustness of the method across various complex scenarios. Full article

Long-term intensive cultivation has caused soil fertility decline and structural degradation in the Songnen Plain, thereby constraining maize root development and yield formation. As a fundamental conservation tillage practice, straw return enhances soil function by incorporating exogenous organic matter and regulating root-shoot physiological processes. However, the mechanism underlying yield improvement through root–photosynthesis–nitrogen synergy remains insufficiently understood. A field experiment was conducted to assess the effects of conventional tillage (CT), straw incorporation (SI), straw mulching (SM), and deep straw incorporation (DF) on maize physiological traits and yield. Compared with CT, DF markedly enhanced root morphology and physiology, increasing the root length, surface area, volume, and root-shoot ratio by 16.46%, 23.87%, 26.64%, and 51.34%, respectively. The root bleeding intensity increased by 23.63%, whereas amino acid and nitrate contents in the bleeding sap increased by 29.20% and 65.93%, respectively, indicating improved root nutrient transport capacity. The enhanced root system positively influenced shoot photosynthesis by increasing the chlorophyll SPAD value by 16.05%, net photosynthetic rate (P_n) by 11.28%, and the activities of RuBP, PEP, nitrate reductase (NR), and glutamine synthetase (GS) by 10.59%, 24.36%, 29.94%, and 12.47%, respectively. These synergistic improvements significantly promoted post-anthesis biomass accumulation and yield formation. DF increased nitrogen and dry matter accumulation at the R3 stage by 26.61% and 15.67%, respectively, and resulted in an average yield increase of 8.34%, which was primarily due to an 11.96% increase in 100-grain weight. Although SI and SM also improved certain physiological indices, their effects were weaker than those of DF. RF analysis identified sap nitrate content (RNO), bleeding intensity (RBI), root length (RL), and root volume (RV) as key yield determinants. PLS-SEM further revealed that straw return enhanced root morphology and bleeding traits (path coefficients: 0.96 and 0.82), which subsequently improved leaf photosynthetic traits (path coefficients: 0.52 and 0.39) and biomass accumulation (path coefficient: 0.71). Collectively, these improvements promoted post-anthesis nitrogen accumulation and dry matter partitioning into grains. These findings elucidated the physiological mechanism by which deep straw incorporation increased maize yield through root system optimization, providing a theoretical basis for conservation tillage optimization in the thin-layer Mollisol region of the Songnen Plain. Full article

Urban coastal areas are increasingly vulnerable to compound flooding due to the convergence of extreme rainfall, storm surges, and infrastructure aging, especially in high-density settings. This study proposes and empirically validates a multi-scale strategy for enhancing urban flood resilience in the Macau Peninsula, a densely built coastal city with complex flood exposure patterns. Building on a previously developed network-based resilience assessment framework, the study integrates hydrodynamic simulation and complex network analysis to evaluate the effectiveness of targeted interventions, including segmented storm surge defense barriers, drainage infrastructure upgrades, and spatially optimized low-impact development (LID) measures. The Macau Peninsula was partitioned into multiple shoreline defense zones, each guided by context-specific design principles and functional zoning. Based on our previously developed flood simulation framework covering extreme rainfall, storm surge, and compound events in high-density coastal zones, this study validates resilience strategies that achieve significant reductions in inundation extent, water depth, and recession time. Additionally, the network-based resilience index showed marked improvement in system connectivity and recovery efficiency, particularly under compound hazard conditions. The findings highlight the value of integrating spatial planning, ecological infrastructure, and systemic modeling to inform adaptive flood resilience strategies in compact coastal cities. The framework developed offers transferable insights for other urban regions confronting escalating hydrometeorological risks under climate change. Full article

Discontinuity trace provides critical geological data for engineering design and construction optimization. However, current extraction methods relying on discontinuity intersection fitting are highly sensitive to the segmentation accuracy of individual discontinuity, while trace segment connectivity remains suboptimal. To address these challenges, we propose an ARCG (Adaptive Region Contour Growing) method using 3D point clouds. By dynamically adjusting parameter thresholds, our approach simultaneously extracts both discontinuities and their boundaries. We then evaluate the fitting performance of different discontinuity models using area ratios, identifying the parallelogram as the most suitable representation. The method then detects intersection lines between paired discontinuities through spatial intersection analysis, with dynamic partitioning preserving original geometric properties. Finally, a bidirectional weighted graph-based growth algorithm connects intersection lines belonging to the same discontinuity, generating the final trace results. The proposed method was validated using slope data from two case studies. Results demonstrate that, compared to existing methods and point cloud processing software, our approach achieves robust extraction of complex traces while maintaining high connectivity. Moreover, it improves computational efficiency by 48.8% without compromising trace accuracy. Thus, this method offers a novel solution for the digital characterization of rock mass discontinuity parameters. Full article

As a distinct plant functional group, climbers critically sustain ecosystem structure and function globally. However, little is known about those in China. Here, we examine the diversity and distribution of Chinese climbers at a regional scale. First, climbing species data were collected. Then, Pearson correlations were conducted to assess relationships between environmental variables and climber species richness. Also, variation partitioning was used to reveal the pure and shared effects of four explanatory variable groups on species richness. A total of 3485 climber species (551 genera, 105 families) were recorded in China. Woody lianas dominated the climbing flora (64.73% of species) relative to herbaceous vines; twining represented the predominant mechanism (1829 species, 52.48%) relative to the others. Chinese climbers largely presented a pattern of species richness that decreased from south to north in China. Moreover, endemic and threatened climbers exhibited strong distributional congruence with all climbers. Additionally, four predictor groups (temperature, precipitation, geography, human impact) were found to jointly account for over 70% of species density variance across different climber types through variation partitioning, with precipitation’s pure effect dominating. Thus, Chinese climbers exhibit high diversity and an uneven distribution, primarily driven by precipitation. This study also provides a valuable reference on climbers at the regional scale for future studies. Full article