Search Results (1,113)

Accurate monitoring of rice-planting areas underpins food security and evidence-based farm management. Recent work has advanced along three complementary lines—multi-source data fusion (to mitigate cloud and spectral confusion), temporal feature extraction (to exploit phenology), and deep-network architecture optimization. However, even the best fusion- and time-series-based approaches still struggle to preserve fine spatial details in sub-meter scenes. Targeting this gap, we propose an HRNet-CA-enhanced DeepLabV3+ that retains the original model’s strengths while resolving its two key weaknesses: (i) detail loss caused by repeated down-sampling and feature-pyramid compression and (ii) boundary blurring due to insufficient multi-scale information fusion. The Xception backbone is replaced with a High-Resolution Network (HRNet) to maintain full-resolution feature streams through multi-resolution parallel convolutions and cross-scale interactions. A coordinate attention (CA) block is embedded in the decoder to strengthen spatially explicit context and sharpen class boundaries. The rice dataset consisted of 23,295 images (11,295 rice + 12,000 non-rice) via preprocessing and manual labeling and benchmarked the proposed model against classical segmentation networks. Our approach boosts boundary segmentation accuracy to 92.28% MIOU and raises texture-level discrimination to 95.93% F1, without extra inference latency. Although this study focuses on architecture optimization, the HRNet-CA backbone is readily compatible with future multi-source fusion and time-series modules, offering a unified path toward operational paddy mapping in fragmented sub-meter landscapes. Full article

Tourist satisfaction models typically assume that service performance dimensions carry the same weight for all travelers. Drawing on Bourdieu, we reconceptualize age, gender, and region of origin as demographic capital, durable resources that mediate how visitors decode service cues. Using a SERVPERF-based survey of 407 international travelers departing Quito (Ecuador), we test measurement invariance across six sociodemographic strata with multi-group confirmatory factor analysis. The four-factor SERVPERF core (Access, Lodging, Extra-hotel Services, Attractions) holds, yet partial metric invariance emerges: specific loadings flex with demographic capital. Gen-Z travelers penalize transport reliability and safety; female visitors reward cleanliness and empathy; and Latin American guests are the most critical of basic organization. These patterns expose a boundary condition for universalistic satisfaction models and elevate demographic capital from a descriptive tag to a structuring construct. Managerially, we translate the findings into segment-sensitive levers, visible security for youth and regional markets, gender-responsive facility upgrades, and dual eco-luxury versus digital-detox bundles for long-haul segments. By demonstrating when and how SERVPERF fractures across sociodemographic lines, this study intervenes in three theoretical conversations: (1) capital-based readings of consumption, (2) the search for boundary conditions in service-quality measurement, and (3) the shift from segmentation to capital-sensitive interpretation in emerging markets. The results position Ecuador as a critical case and provide a template for destinations facing similar performance–perception mismatches in the Global South. Full article

The point-kernel (PK) technique has a long history in applied radiation shielding, originating from the early days of digital computers. The PK technique applied to gamma-ray attenuation is one of many successful applications, based on the linear superposition principle applied to distributed radiation sources. Mathematically speaking, the distributed source will produce a detector response equivalent to the numerical integration of the radiation received from an equivalent number of point sources. In this treatment, there is no interference between individual point sources, while inherent limitations of the PK method are its inability to simulate gamma scattering in shields and the usage of simple boundary conditions. The PK method generally works for gamma-ray shielding with corrective B-factor for scattering and only specifically for fast neutron attenuation in a hydrogenous medium with the definition of cross section removal. This paper presents theoretical and programming aspects of the PK program developed for a distributed source of photons (line, disc, plane, sphere, slab volume, etc.) and slab shields. The derived flux solutions go beyond classical textbooks as they include the analytical integration of Taylor B-factor, obtaining a closed form readily suitable for programming. The specific computational modules are unified with a graphical user interface (GUI), assisting users with input/output data and visualization, developed for the fast radiological characterization of simple shielding problems. Numerical results of the selected PK test cases are presented and verified with the CADIS hybrid shielding methodology of the MAVRIC/SCALE6.1.3 code package from the ORNL. Full article

This paper analyzes the transient voltage stability of the dual-source DC power system. The system’s equivalent model is first established. Subsequently, the effect mechanisms of line parameters and voltage-source rectifiers’ current control inner loops on the system’s transient voltage instability are investigated. It indicates that these factors reduce the power supply capacity of the source, increasing the risk of transient instability in the system. Then, considering the influence of fault depths, the influence of different large disturbances on the transient voltage stability is investigated. Furthermore, the critical cutting voltage and critical cutting time for DC power systems are determined and then validated on the MATLAB R2023b/Simulink platform. Finally, based on the mixed potential function theory, the impact of system parameter variations on stability boundaries is analyzed quantitatively. Simulation verification is conducted on the MATLAB R2023b/Simulink platform, and experimental verification is conducted on the RT-LAB Hardware-in-the-Loop platform. The results of the quantitative analysis and experiments corroborate the conclusions drawn from the mechanistic analysis, underscoring the critical role of line parameters and converter control parameters in the system’s transient voltage stability. Full article

The galaxy NGC 7469 is a bright infrared source with an active galactic nucleus (AGN) and an intense star-forming region with a radius of approximately 500 parsecs, where the star formation rate is estimated to be 20–50 $M_{\odot }{\mathrm{yr}}^{-1}$. This study presents the results of spectral and photometric observations carried out during the period from 2020 to 2024 at the Fesenkov Astrophysical Institute (Almaty, Kazakhstan) and the Nasreddin Tusi Shamakhy Astrophysical Observatory (Shamakhy, Azerbaijan). Photometric data were obtained using B, V, and Rc filters, while spectroscopic observations covered the wavelength range of $\lambda$ 4000–7000 Å. Data reduction was performed using the IRAF and MaxIm DL Pro6 software packages. An analysis of the light curves revealed that after the 2019–2020 outburst, the luminosity level of NGC 7469 remained relatively stable until the end of 2024. In November–December 2024, an increase in brightness (∼0.3–0.5 magnitudes) was recorded. Spectral data show variations in the Ha fluxes and an enhancement of them at the end of 2024. On BPT diagrams, the emission line flux ratios [OIII]/H $\beta$ and [NII]/H $\alpha$ place NGC 7469 on the boundary between regions dominated by different ionization sources: AGN and star-forming regions. The electron density of the gas, estimated from the intensity ratios of the [SII] 6717, 6731 Ålines, is about $900–1000{\mathrm{cm}}^{-3}$. Continued observations will help to determine whether the trend of increasing brightness and emission line fluxes recorded at the end of 2024 will persist. Full article

Drought impacts agricultural production and regional sustainable development. Accordingly, timely and accurate drought monitoring is essential for ensuring food security in rain-fed agricultural regions. Alternating drought and flood events frequently occur in the Heilongjiang River Basin, the largest grain-producing area in Far East Asia. However, spatiotemporal variability in drought is not well understood, in part owing to the limitations of the traditional Temperature Vegetation Dryness Index (TVDI). In this study, an Improved Temperature Vegetation Dryness Index (ITVDI) was developed by incorporating Digital Elevation Model data to correct land surface temperatures and introducing a constraint line method to replace the traditional linear regression for fitting dry–wet boundaries. Based on MODIS (Moderate-resolution Imaging Spectroradiometer) normalized vegetation index and land surface temperature products, the Heilongjiang River Basin, a cross-border basin between China, Mongolia, and Russia, exhibited pronounced spatiotemporal variability in drought conditions of the growing season from 2001 to 2023. Drought severity demonstrated clear geographical zonation, with a higher intensity in the western region and lower intensity in the eastern region. The Mongolian Plateau and grasslands were identified as drought hotspots. The Far East Asia forest belt was relatively humid, with an overall lower drought risk. The central region exhibited variation in drought characteristics. From the perspective of cross-national differences, the drought severity distribution in Northeast China and Inner Mongolia exhibits marked spatial heterogeneity. In Mongolia, regional drought levels exhibited a notable trend toward homogenization, with a higher proportion of extreme drought than in other areas. The overall drought risk in the Russian part of the basin was relatively low. A trend analysis indicated a general pattern of drought alleviation in western regions and intensification in eastern areas. Most regions showed relatively stable patterns, with few areas exhibiting significant changes, mainly surrounding cities such as Qiqihar, Daqing, Harbin, Changchun, and Amur Oblast. Regions with aggravation accounted for 52.29% of the total study area, while regions showing slight alleviation account for 35.58%. This study provides a scientific basis and data infrastructure for drought monitoring in transboundary watersheds and for ensuring agricultural production security. Full article

3D (three-dimensional) property volume is an important data carrier for 3D land administration by using 3D cadastral technology, which can be used to express the legal space (property rights) scope matching with physical entities such as buildings and land. A 3D property volume is represented by a dense set of 3D coordinate points arranged in a predefined order and is displayed alongside the parcel map for reference and utilization by readers. To store a 3D property volume in the database, it is essential to record the connectivity relationships among the original 3D coordinate points, the associations between points and lines for representing boundary lines, and the relationships between lines for defining surfaces. Only by preserving the data structure that represents the relationships among points, lines, and surfaces can the 3D property volume in a parcel map be fully reconstructed. This approach inevitably results in the database storage volume significantly exceeding the original size of the point set, thereby causing storage redundancy. Consequently, this paper introduces a reversible 3D property volume compression coding method (called 3DPV-CC) to address this issue. By analyzing the distribution characteristics of the coordinate points of the 3D property volume, a specific rule for sorting the coordinate points is designed, enabling the database to have the ability of data storage and recovery by merely storing a reordered point set. The experimental results show that the 3DPV-CC method has excellent support capabilities for 3D property volumes of the vertical and slopped types, and can compress and restore the coordinate point set of the 3D property volume for drawing 3D parcel maps. The compression capacity of our method in the test is between 23.66% and 38.42%, higher than the general data compression methods (ZIP/7Z/RAR: 8.37–10.32%). By means of this method, land or real estate administrators from government departments can store 3D property volume data at a lower cost. This is conducive to enhancing the informatization level of land management. Full article

In double-shield TBM tunnel construction, grouting plays a vital role in consolidating the gravel backfill and maintaining the integrity of the segmental lining. To investigate the permeation behavior of grout within the pea gravel layer, a fluid dynamics model was developed in this study. The model directly simulates the flow of grout through the porous medium by solving the Navier–Stokes equations and employs the level set method to track the evolving interface between the grout and air phases. Unlike conventional continuum approaches, this model incorporates particle-scale heterogeneity, allowing for a more realistic analysis of grout infiltration through the non-uniform pore structures formed by gravel packing. Three different grouting port positions and two boundary conditions are considered in the simulation. The results indicate that under pressure boundary conditions, the grout flow rate increases rapidly in the initial stage, and then decreases and stabilizes, with the flow rate peak increasing as the grout port moves upward. Under velocity boundary conditions, the injection pressure grows slowly in the early stage but accelerates with time. Additionally, the rate of pressure change is faster when the grout port is located lower in the backfilling layer. Through theoretical analysis, the existing analytical formula was extended by introducing a gravitational correction term. When the grouting port is near the upper part of the tunnel, the analytical solution aligns well with the numerical simulation results, but as the grout port moves downward, the discrepancy between the two increases. Full article

Glacier classification offers a structured framework for assessing glacier characteristics and understanding their responses to climate change. In this study, we apply the Shi–Xie glacier classification system, proposed by Chinese glaciologists Shi and Xie, to evaluate the transformation of extremely continental, subcontinental, and maritime glaciers across China over the past four decades. Our results show a widespread rise in equilibrium line altitudes (ELAs), alongside complex changes in climatic and glaciological parameters. Notably, despite ongoing warming trends, nearly half of the glaciers experienced cooling at the ELA, and over two-thirds showed a decline in summer mean temperatures. This apparent contradiction is explained by elevation-induced cooling; as ELAs rise to higher altitudes, the corresponding summer air temperatures decline due to the lapse rate effect. Near-surface ice temperatures (20 m depth) were strongly consistent with changes in annual air temperature. Precipitation trends were spatially heterogeneous, yet around 70% of glaciers experienced stable or slightly increasing annual precipitation. In contrast, maritime glaciers, particularly those in the southeastern glacierized regions, exhibited marked decreases. Glacier surface velocities generally declined, with 90% of glaciers flowing at speeds below 50 m a⁻¹. Threshold-based analysis reveals that glaciers in transitional zones frequently exhibit multi-indicator deviations. Extremely continental glaciers near classification boundaries showed a shift toward warmer, wetter subcontinental conditions, while maritime glaciers tended toward drier, colder subcontinental characteristics. These findings offer new insights into the differentiated responses and ongoing transformation of glacier types in China under climate change. Full article

Geographic routing is an appealing method for wireless sensor networks, as it routes data packets solely based on nodes’ location information rather than global network topology. A fundamental requirement for geographic routing is that source nodes must know the locations of sink nodes to deliver their data. To efficiently provide sink location information, quorum-based sink location service schemes have been introduced, using crossing points between sink location announcement (SLA) and sink location query (SLQ) quorums. However, existing quorum-based schemes typically construct quorums along fixed paths, causing rapid energy depletion of particular sensor nodes and resulting in shorter network lifetimes, especially in irregular sensor fields. To overcome this limitation, we propose an energy-efficient quorum-based sink location service scheme that extends network lifetime by reducing and balancing sensor nodes’ energy consumption. Specifically, our scheme constructs a quadrilateral-shaped SLA quorum using four randomly selected points, and a line-shaped SLQ quorum defined by two randomly chosen points located inside and outside the SLA quorum, respectively. We also address key issues of the proposed scheme, including network holes, irregular boundaries, multiple sources and sinks, and Base Zone sizing, and present methods to handle them. Simulation results demonstrate that the proposed scheme outperforms existing approaches, achieving approximately 29% lower total energy consumption and 27% higher energy balancing across sensor nodes on average. Full article

This study investigates the taxonomic status, phylogenetic relationships, and genetic diversity of Japanese chestnut (Castanea crenata Siebold & Zucc.) in Liaodong, China, and across East Asia. Additionally, it evaluates core germplasm resources through cluster and population structure analyses using simple sequence repeat (SSR) marker data from 13 Castanea henryi, 18 Castanea seguinii, and 27 Castanea mollissima, and 142 Japanese chestnut resources. The results show that the East Asian Castanea genus forms a monophyletic group with distinct interspecific boundaries. Japanese chestnut and two varieties/lines of C. seguinii (187 and 170) form a sister clade, indicating a close phylogenetic relationship. All Japanese chestnut resources are divided into two branches, with considerable admixture. The genetic diversity analysis revealed that the 142 Japanese chestnut varieties/lines collectively possessed 141 allelic loci, with genetic distances (GDs) ranging from 0.429 to 0.880 with an average of 0.740. Based on unique characteristics, seven resources with distinctive features were selected as mandatory. A total of 41 core germplasm resources were finally determined using the simulated annealing method. The comparative analysis revealed that, aside from a notable difference in polymorphic information loci, the core germplasm and original germplasm showed no significant differences in other genetic diversity parameters. This indicates that the 41 core germplasm resources effectively preserve the genetic diversity of the original germplasm and have been influenced by artificial selection. This study provides a scientific basis for conserving and using C. crenata germplasm resources. Full article

With the development of intelligent transportation, vehicle motion state recognition has become a crucial method for enhancing the reliability of vehicle navigation and ensuring driving safety. Currently, machine learning is the main approach for recognizing vehicle motion states. The symmetry characteristics of sensor data have also been studied to better recognize motion states. However, the existing approaches face challenges during motion state changes due to indeterminate state boundaries, resulting in reduced recognition accuracy. To address this problem, this paper proposes a vehicle motion state recognition method based on the Hidden Markov Model (HMM) and Support Vector Machine (SVM). Firstly, Kalman filtering is applied to denoise the data of inertial sensors. Then, HMM is employed to capture the subtle state transition, enabling the recognition of complex dynamic state changes. Finally, SVM is utilized to classify motion states. The sensor data were collected in various vehicle motion states, including stationary, straight-line driving, lane changing, turning, and then the proposed method is compared with SVM, KNN (K-Nearest Neighbor), DT (Decision Tree), RF (Random Forest), and NB (Naive Bayes). The results of the experiment show that the proposed method improves the recognition accuracy of motion state transitions in the case of boundary ambiguity and is superior to the existing methods. Full article

Requirements for Unmanned Aerial Vehicle (UAV) applications in low-altitude operations are escalating, which demands resilient Position, Navigation and Timing (PNT) solutions incorporating global navigation satellite system (GNSS) services. However, UAVs often operate in stringent environments with degraded GNSS performance. Practical challenges often arise from dense, dynamic, complex, and uncertain obstacles. When flying in complex environments, it is important to consider signal degradation caused by reflections (multipath) and obscuration (Non-Line of Sight (NLOS)), which can lead to positioning errors that must be minimized to ensure mission reliability. Recent works integrate GNSS reliability maps derived from pseudorange error estimations into path planning to reduce loss-of-GNSS risks with PNT degradations. To accommodate multiple constraint conditions attempting to improve flight resilience against GNSS-degraded environments, this paper proposes a reinforcement learning (RL) approach to feature GNSS signal quality awareness during path planning. The non-linear relations between GNSS signal quality in the form of dilution of precision (DoP), geographic locations, and the policy of searching sub-minima points are learned by the clipped Proximal Policy Optimization (PPO) method. Other constraints considered include static obstacle occurrence, altitude boundary, forbidden flying regions, and operational volumes. The reward and punishment functions and the training method are designed to maximize the success criteria of approaching destinations. The proposed RL approach is demonstrated using a real 3D map of Indianapolis, USA, in the Godot engine, incorporating forecasted DoP data generated by a Geospatial Augmentation system named GNSS Foresight from Spirent. Results indicate a 36% enhancement in mission success rates when GNSS performance is included in the path planning training. Additionally, the varying tensor size, representing the UAV’s DoP perception range, exhibits a positive proportion relation to a higher mission rate, despite an increment in computational complexity. Full article

Leaves are the most ubiquitous plant organs, whose macrostructures exhibit close correlations with environmental factors while simultaneously reflecting inherent genetic and evolutionary patterns. These characteristics render them highly significant for plant taxonomy, ecology, and related disciplines. Therefore, this study presents the first comprehensive evaluation of Malus leaf macrostructures for infraspecific classification. By establishing a trait-screening system, we conducted a numerical taxonomic analysis of leaf phenotypic variation across 73 Malus germplasm (34 species and 39 cultivars). Through ancestor-inclined distribution characteristic analysis, we investigated phylogenetic relationships at both the genus level and infraspecific ranks within Malus. A total of 21 leaf phenotypic traits were selected from 50 candidate traits based on the following criteria: high diversity, abundance, and evenness (D ≥ 0.50, H ≥ 0.80, and E ≥ 0.60); significant intraspecific uniformity and interspecific distinctness ($\overline{CV}$ ≤ 10% and $CV$ ≥ 15%). Notably, the selected traits with low intraspecific variability ($\overline{CV}$ ≤ 10%) exhibit environmental robustness, likely reflecting low phenotypic plasticity of these specific traits under varying conditions. This stability enhances their taxonomic utility. It was found that the highest ancestor-inclined distribution probability reached 90% for 10 traceable cultivars, demonstrating reliable breeding lines. Based on morphological evidence, there was a highly significant correlation between the evolutionary orders of (Sect. Docyniopsis → Sect. Sorbomalus → Sect. Malus) and group/sub-groups (B₁ → B₂ → A). This study demonstrates that phenotypic variation in leaf macrostructures can effectively explore the affinities among Malus germplasm, exhibiting taxonomic significance at the infraspecific level, thereby providing references for variety selection. However, hybrid offspring may exhibit mixed parental characteristics, leading to blurred species boundaries. And convergent evolution may create false homologies, potentially misleading morphology-based taxonomic inferences. The inferred taxonomic relationships present certain limitations that warrant further investigation. Full article

This paper proposes an improved analytical model for a line-start explosion-proof magnet synchronous motor that considers the effect of magnetic bridge saturation. Under the condition of maintaining the air-gap magnetic field unchanged, and taking into account the topological structures of embedded magnets, squirrel cages, and rotor slot openings, a subdomain model partitioning method is systematically investigated. Considering the saturation effect of the magnetic bridge of the rotor, the equivalent magnetic circuit method was utilized to calculate the permeance of the saturated region. It not only facilitates the establishment of subdomain equations and corresponding subdomain boundary conditions, but also ensures the maximum accuracy of the equivalence by maintaining the topology of the rotor. The motor was partitioned into subdomains, and in conjunction with the boundary conditions, the Poisson equation and Laplace equation are solved to obtain the electromagnetic performance of the motor. The accuracy of the analytical model is verified through finite element analysis. The accuracy of the analytical model is verified through finite element analysis (FEA). Compared to the FEA, the improved model maintains high precision while reducing computational time and exhibiting better generality, making it suitable for the initial design and optimization of industrial motors. Full article