Search Results (6,769)

Assessing the sustainability of urban development, including road changes, is increasing from year to year and requires clear indicators for robust decision-making tools to gain knowledge across regions. This study conducts the selection of transportation routes over a longer period as an example to evaluate the sustainability of historical official routes in achieving economically cost-efficient operation and maintenance. Official ways in the Qiantang River Basin connected the Jiangnan region, the economic center of China, with surrounding provinces were assessed. During the past six hundred years, the official road network in this area gradually simplified, evolving from valley roads to river banks, which covered longer distances. However, this transformation lacks a systematic explanation. By applying the analytic hierarchy process (AHP) with geographic information system (GIS), quantitative analysis was gained and the results are as follows: (1) Among the influencing factors, the weights of transportation cost and population related to economic needs are 39.54% and 29.52% respectively, with a combined total of 69.06%. (2) The official road network is often designed for governing the people, but in places such as the Qiantang River Basin, economic logic superseded political imperatives, becoming the dominant factor in reshaping the official ways. (3) In the pre-industrial era characterized by limited technological capacity, the physical environment had a greater impact on economic costs, ultimately reshaping the spatial configuration of official route networks. Overall, the evolution of official routes reflects the decline in their military-political function, driven by sustained peace and long-term decline in strategic position. The evolution of the official ways in the Qiantang River Basin reveals the importance of economic benefits in road selection. Full article

Highway corridors are increasingly being discussed not only as zones of ecological disturbance but also as components of regional green infrastructure with potential carbon sequestration functions, yet their long-term evolutionary characteristics and multi-scale associated factors remain insufficiently understood. Using multi-source time-series data from 2000 to 2023, we developed an analytical framework integrating the CASA model, Random Forest, and geographically weighted regression (GWR). To ensure methodological rigor, we implemented a Spatial K-fold Cross-Validation strategy and incorporated Partial Dependence Analysis (PDA) to identify non-linear thresholds. The results indicate that: (1) Vegetation carbon sequestration within Shandong’s highway corridors increased significantly, with total sequestration rising from 5.54 × 10⁶ t in 2000 to 1.55 × 10⁷ t in 2023, representing an average annual growth rate of approximately 5.0%. This growth transitioned from a relatively stable phase to a more rapid growth phase. (2) A clear distance-related ecological pattern was observed. Statistical tests (Kruskal–Wallis H test) confirmed that vegetation carbon sequestration exhibited a significant non-monotonic gradient ($p<0.05$), with a stable peak zone observed 50–100 m from the roadbed. This peak zone is associated with a spatial “trade-off” pattern between the attenuation of traffic-related stressors and roadside ecological management. (3) The observed spatial pattern was associated with a nonlinear coupling of natural background conditions and human disturbance. Precipitation and temperature were the dominant associated factors, while PDA further identified a critical precipitation threshold (~750 mm) and localized tipping points for human interference, with a distinct road-disturbance-sensitive zone evident within 200–500 m. The results suggest that high-standard ecological design and active restoration measures are associated with lower ecological disturbance and higher vegetation carbon sequestration performance in some highway corridors. However, these relationships should be interpreted cautiously, as they may also be influenced by differences in climate background, topography, land-use context, and road construction history. These findings provide empirical evidence to inform differentiated ecological restoration and low-carbon management of traffic corridors. Full article

Assembling expert teams under strict skill-coverage and communication-distance constraints is a fundamental challenge in collaborative knowledge work. Existing learning-based approaches excel at probabilistic link prediction but cannot reliably enforce hard logical constraints or provide interpretable justifications. This study presents a constructive heuristic framework for team formation on Heterogeneous Information Networks (HINs), integrating authors, papers, departments, and organizations into a unified graph database. Seven algorithms exploit distinct structural features—topological proximity, co-authorship history, organizational affiliation, citation impact, and temporal recency—and guarantee constraint satisfaction by construction. Experiments on a subset of the AMiner DBLP dataset (≈625,000 nodes, 973,000 edges) covering 12,045 formation requests across 147 configurations show that algorithm choice is the dominant determinant of runtime, while skill frequency governs feasibility: success rates decline from 81.2% under abundant keywords to 14.9% in the long tail. Algorithms further form statistically distinct clusters in communication cost, and specialized heuristics operate in nearly disjoint solution spaces—supporting a toolbox approach over single-algorithm deployment. These results provide actionable selection guidance: proximity-based algorithms for communication-efficient teams; citation- or recency-aware algorithms when impact matters; cohesion-based algorithms when internal collaboration is the priority. Full article

Landslides are among the most common and destructive geological hazards and pose a significant threat to the long-term stability of infrastructure systems. In particular, long-distance power transmission corridors often traverse mountainous and forested regions, where landslides can endanger tower foundations and transmission line safety. Such landslides predominantly occur in sloped forested areas, where dense vegetation causes severe occlusion that blurs landslide boundaries and creates strong visual similarity with surrounding land covers. Consequently, accurate and efficient landslide identification from remote sensing imagery remains a significant challenge. To address these challenges, we propose a structural constrained contrastive learning network (SC-Net) for reliable landslide extraction from remote sensing images. First, a multi-structural feature extraction module is designed to capture landslide-specific geometric characteristics. These features are further enhanced by fusing multi-scale semantic representations extracted from a pretrained backbone network through an attention-based adaptive feature fusion module. Additionally, a mask-constrained object-level contrastive learning strategy is introduced to enforce global structural consistency at the landslide object-level, thereby improving the discriminability between landslide and non-landslide regions. Extensive experiments conducted on the publicly available CAS landslide dataset demonstrate the effectiveness of the proposed method. The proposed SC-Net achieves IoU scores of 89.89% and 79.76% on the CAS-UAV and CAS-SAT datasets, respectively, outperforming the best-performing baseline by 2.09% and 0.46%. The proposed method provides an effective solution for large-scale landslide monitoring and demonstrates potential for applications in power transmission corridor inspection and infrastructure safety assessment. Full article

With the continuous advancement of ocean exploration and development, energy supply for underwater electronic equipment has become a key bottleneck restricting long-term operation. Traditional wired power supply and battery-powered operation suffer from corrosion, high maintenance costs, limited endurance, and replacement difficulties. Underwater ultrasonic wireless power transfer (UUWPT) achieves contactless electric–acoustic–electric conversion via piezoelectric transducers. It offers unique advantages, including insensitivity to electromagnetic interference, metal-penetration capability, excellent directivity, and medium-to-long-distance transmission. This paper systematically reviews the technical principles and development history of UUWPT. We trace its evolution from early feasibility verification, through theoretical improvements, to current system engineering and industrialization. Key frontier research directions are highlighted, such as MIMO/MISO arrays, simultaneous wireless power and data transfer (SWPDT), adaptive tuning, and novel transducer structures. Application prospects in marine monitoring, AUV endurance replenishment, marine energy development, and the Internet of Underwater Things are also analyzed. Finally, we discuss remaining challenges, including the trade-off between transmission efficiency and distance, insufficient adaptability to complex marine environments, and the lack of standardized system frameworks. Future research should prioritize high-efficiency long-distance power transfer, system reliability, and engineering applications. Full article

Class-incremental learning allows a model to continuously acquire new classes from sequentially arriving data while preserving its ability to recognize previously learned ones, which is essential for improving adaptability and supporting long-term evolution. However, the class arrival order is inherently random, and highly similar classes may appear consecutively, which intensifies catastrophic forgetting. Although replay-based methods can effectively alleviate this problem, they usually require storing or accessing historical raw samples, which introduces additional data-retention and storage burdens. To address these challenges, this paper proposes ORACIL, an Order-Robust Analytic Class-Incremental Learning framework. First, ORACIL constructs a conflict graph based on class centroids and dynamically partitions newly arriving classes into multiple low-similarity groups, thereby reducing inter-class interference and mitigating forgetting. Second, for each class group, it trains an analytic incremental classification head and performs recursive closed-form updates for the analytic heads using current-stage data and accumulated second-order statistics, without replaying raw historical samples. For group recognition, ORACIL uses feature-derived distance representations rather than raw historical images, making the incremental process raw-sample-free with respect to original image replay. Third, during inference, the group probabilities generated by the group-recognition router are softly fused with the class scores produced by each analytic head, and the class with the highest fused probability is selected as the final prediction. Extensive experiments on CIFAR-100, CUB200, and OmniBenchmark demonstrate the effectiveness of ORACIL. Without replaying historical images, ORACIL achieves final-phase average forgetting rates of 0.16%, 0.77%, and 1.04%, and final-phase accuracies of 95.77%, 93.86%, and 88.12%, respectively. In addition, the MOPD and AOPD results show that ORACIL maintains strong robustness under different class arrival orders. Full article

In carbon sequestration in saline aquifers, many factors affect multiphase fluid migration and reservoir pressure change. This study developed a high-resolution three-dimensional numerical model to investigate large-scale CO₂ geological storage in the Ordovician carbonate aquifer of the Tarim Basin, China. This study focuses on the quantitative prediction of CO₂ plume migration, multiphase flow interactions between supercritical CO₂ and brine, and formation pressure evolution under coupled injection operations. Injection strategies were compared by constant rate (CR) and variable rate (VR) injection, and two caprock morphology-type selection by placing wells into monocline traps (wells 1/3/5) and anticline traps (wells 2/4) with varying limb dip angles and closure depths. The results demonstrate that both injection speed and caprock morphology strongly control CO₂ trapping evolution and storage security. At the end of the 500-year simulation, the dissolved-CO₂ migration distance followed the order CR > VR, indicating that, under the studied conditions, VR injection most effectively limited the lateral spread of dissolved CO₂ and thereby enhanced dissolved-CO₂ immobilization. In addition, CR and VR injection schedules have a subtle impact on long-term pressure change; Across all cases, formation pressure remained below the caprock breakthrough pressure. CR injection promotes the fastest CO₂ dissolution and pressure dissipation but yields the weakest long-term immobilization, whereas VR injection trades early dissolution rate for more effective plume containment. This result indicates that injection-strategy selection should be matched to dominant site controlled near-term pressure management versus long-term containment and to the trapping behavior imposed by caprock morphology. This study provides a mechanistically grounded optimization framework linking injection-speed control and caprock morphology to the coupled evolution of pressure-buildup safety and long-term CO₂ immobilization, supporting CCUS decision-making in the Tarim Basin. Full article

Non-uniform sound velocity profiles (SVPs) cause sound-ray refraction and propagation-path bending. The straight-line mapping among propagation time, propagation distance, and target position is, therefore, disrupted, leading to systematic errors in constant-sound-speed localization. To improve the consistency between propagation correction and geometric localization, an iterative ray-path correction method based on propagation-time consistency is proposed. The method contains three coupled steps. First, a path-dependent local layered SVP model is constructed for each target-to-base-station path, rather than using a global or fixed sound-speed model. Second, the ray parameter is inverted under the constraint of measured time-of-arrival (TOA), so that the corrected ray path remains consistent with the observed propagation time. Third, the corrected slant range obtained by layered ray tracing is fed back into a known-depth weighted least squares (WLS) localization model, forming a closed-loop position update. The method is evaluated through long-baseline (LBL) simulations with multiple SVPs and propagation geometries and is validated using measured TOA data and an observation-derived SVP. The simulation results show that sub-meter accuracy can be achieved under the tested TOA-noise conditions. In measured-data validation, the planar localization error is reduced from 4.6866 m to 0.1923 m. No divergence is observed in the tested small SVP-perturbation cases. Full article

The gaze information channel paradigm models fixation sequences as a first-order Markov chain and quantifies gaze behaviour through Shannon entropy and mutual information (MI), where $I(X;Y)$ measures the reduction in uncertainty about the next fixation state given the current one. This paper extends the framework by introducing two new channels: the saccade amplitude channel, which discretises saccade angular distance into three categories (short, medium, long) with a four-category variant also analysed, and the pupil diameter channel, which discretises fixation-period pupil size into three categories. Both are applied to 10 observers viewing 12 Van Gogh paintings. The amplitude channel shows that observer-driven variation exceeds stimulus-driven variation. The pupil channel yields the highest $I(X;Y)$ among the two new channels ($0.489\pm 0.209$ bits per participant), consistent with the slow dynamics of pupil responses. Goodness-of-fit tests confirm significantly non-random sequential structure in both channels ($p<0.01$) for all pooled matrices. A simultaneous cross-channel association analysis across all five channels finds that 19 of 20 pairwise Spearman correlations are non-significant; the single nominally significant result (pupil–duration, $\rho =+0.697$, $p=0.025$) does not survive Bonferroni correction and is not robust to outlier removal. Two theoretical observations are presented: an upper bound on conditional entropy in terms of transition persistence (Proposition 1), and a refinement monotonicity result showing that finer discretisation cannot decrease channel MI (Remark 2). An exploratory comparison with five computational aesthetics measures finds a nominally significant negative correlation between pupil $I(X;Y)$ and Bense’s palette redundancy ($\rho =-0.692$, $p=0.013$, uncorrected), suggesting that diverse colour palettes are associated with stronger sequential pupil dynamics; permutation entropy and statistical complexity show no association with any channel. Full article

Outdoor industrial sites, including logistics terminals, construction yards, and civil infrastructure worksites, increasingly require supervisory systems for monitoring autonomous mobile units under variable wireless and operational conditions. This study presents an edge-cloud telemetry platform that connects V2X on-board and roadside units to a normalized data pipeline and an operator dashboard. The architecture assigns frame reception and data validation to the edge layer, while cloud services perform stream ingestion, storage, querying, and visualization using a Kafka-Elasticsearch-Grafana stack. A fixed supervisory schema was defined for position, heading, speed, mission state, battery level, and error flags so that virtual fields used in early validation can later be replaced by measured signals without changing downstream interfaces. Physical field validation was conducted using a single test vehicle in a construction-site emulation environment to evaluate communication continuity and dashboard refresh behavior. Multi-unit applicability was examined at the architecture and schema levels, and a preliminary payload-level capacity estimate was derived using the telemetry frequency and payload-length assumptions. Under the tested site conditions, the system maintained continuous reception and visualization over an approximately 700 m distance from the RSU-side reference location. The measured end-to-end display delay averaged 0.78 s, with a standard deviation of 0.059 s and a maximum of 0.96 s. Under a 10 Hz status-message condition, the estimated pure-payload traffic was approximately 23 kbps per mobile unit. These results indicate that V2X-based edge-cloud telemetry can provide a practical baseline for supervisory monitoring in outdoor industrial sites, while simultaneous multi-vehicle validation, detailed network-load evaluation, and long-term field testing remain necessary future work. Full article

Extended-range three-dimensional (3D) single-molecule localization microscopy (SMLM) and single-particle tracking (SPT) require precise emitter localization across cellular-scale axial distances. However, long-rangeengineered point-spread functions (PSFs) spread photons over wider camera footprints, lowering the signal-to-noise ratio (SNR) and localization precision. We numerically evaluated a physics-guided deep learning workflow for 3D localization over a 10.0 µm axial range using simulated electron-multiplying charge-coupled device (EMCCD) images. The workflow combines an analytical secondary-astigmatism phase mask, frequency-domain cross-filtering, a cross-filtering generative adversarial network (CFGAN), and coarse-to-fine fitting. The optical model and engineered PSF provide physical signal priors, cross-filtering preserves directional Fourier-domain energy, and CFGAN suppresses residual structured noise before model-based localization. In low-SNR simulations, lateral, axial, and radial root-mean-squared localization errors (RMSEs) decreased from 54.11, 96.12, and 112.79 nm without denoising to 31.14, 39.06, and 50.12 nm after CFGAN denoising—close to Cramér–Rao lower-bound (CRLB) references of 34.39, 38.94, and 51.95 nm. High-SNR RMSE values were 8.78, 12.00, and 14.96 nm, comparable to CRLB references of 10.36, 11.71, and 15.64 nm. These simulations suggest that physics-guided restoration can improve extended-range 3D SMLM precision, while experimental validation remains necessary. Full article

Concealed shallow karst along long-distance pipeline corridors can trigger subsidence, uneven settlement, and leakage, creating environmental and infrastructure hazards. In the Huyuanxi area (Fuyang District, Hangzhou, Zhejiang, China), strong electromagnetic interference and limited site access motivated an integrated electrical resistivity tomography (ERT) plus ambient-noise microtremor (SPAC) workflow for shallow-karst screening. Three ERT lines (900 m each) were deployed along the pipeline axis and at ±15 m offsets with 10 m spacing using a WDJD-4 system (100 V constant-voltage; Wenner array, 30 layers), followed by resistivity inversion; Res2Dinv v3.65 was adopted as the inversion software. The L2 norm was selected for the objective function, and the error model was set to the default error floor plus 5%. The regularization parameter was set as λ = 0.01, and adaptive gridding was used for the mesh with a minimum cell size of 0.5 m × 0.5 m. The number of iterations was set to 15, with a final root mean square (RMS) misfit of 3.2%. The depth of investigation (DOI) was calculated via the built-in algorithm of the software, yielding a maximum value of 30 m. Low-resistivity anomalies were used to focus eight perpendicular microtremor profiles (3 m spacing) acquired with SmartSolo IGU-16HR 1C and 10 geophones (5 Hz; 1 ms sampling interval) in a nested SPAC array (0.5/1/2 m radii); processing removed segments with SNR < 3 and inverted 2-D Vs structure by damped least-squares. Resistivity sections show 50–8600 Ω·m near surface, including a <100 Ω·m fracture-zone anomaly (28–30 m wide; 8–18 m depth) and a cavity-zone anomaly (55–70 m wide; 10–20 m depth). Joint interpretation places karst development mainly at 10–25 m depth near the bedrock–cover interface (~16 m). At HYXK3, microtremor versus shear-wave logging yielded a void-layer bottom depth of 21.28 m versus 20.12 m (5.76% error) and Vs of 457 versus 446 m/s (2.40% error). Example profiles show microtremor-derived depths (18.3/14.5/18.7 m) consistent with ERT (16.8/15.1/19.5 m; 4.1–8.1% errors). Drilling verification accuracy was approximately 81.7%, precision approximately 90%, recall approximately 74.0%, and the F1-score 81.1% supporting practical corridor risk screening under complex field constraints. Full article

To address the issues of low detection accuracy and high miss rates in long-range small traffic sign detection, which are caused by insufficient feature information and susceptibility to background interference, this paper proposes an improved real-time traffic sign detection algorithm based on YOLO11n. First, a cross-guided feature extraction module, C3k2_CGPEMA, is designed within the neck network. By embedding the Efficient Multi-Scale Attention (EMA) mechanism into the feature extraction branch of Partial Convolution (PConv), this module utilizes the spatial attention mask generated by the convolutional branch to provide cross-branch guidance and filter out complex background noise from the identity branch. This achieves precise fine-grained feature focusing while preserving high-frequency spatial details. Furthermore, a joint bounding box regression loss function combining Complete Intersection over Union (CIoU) and Gaussian Combined Distance (GCD) is adopted. This preserves the stable convergence properties of CIoU while leveraging the scale invariance of GCD to enhance the regression accuracy for small targets. Finally, the detection layers are reconstructed by removing the P5 layer and introducing a high-resolution P2 layer (160 × 160), significantly strengthening the localization capability for distant, tiny targets. Experimental results demonstrate that the proposed algorithm achieves improvements of 5.4, 7.4, and 6.6 points in precision, recall, and mAP@0.5, respectively, on the TT100K dataset compared to the baseline YOLO11n. While boosting detection accuracy, the model maintains an inference speed of 114.5 frames per second (FPS), fully satisfying the requirements for real-time detection in in-vehicle environments. Generalization experiments conducted on the CCTSDB dataset further validate the robustness of the proposed algorithm in complex environments. Full article

Soil salinization represents one of the most severe abiotic constraints on global forest productivity. Populus, the most widely cultivated fast-growing timber tree and a premier model woody plant, exhibits striking intrageneric variation in salt tolerance—from the extremely halophytic Populus euphratica to highly salt-sensitive cultivated clones. Understanding the molecular basis of this variation has profound implications for saline–alkali land reclamation and salt-tolerant variety breeding. This review systematically synthesizes current knowledge on Populus salt-stress responses, covering three primary injury mechanisms (osmotic stress, ionic toxicity, and oxidative damage) and the corresponding physiological countermeasures. We further survey functional genes across four major categories: ion transporters, osmotic-adjustment enzymes, antioxidant-defense components, and transcription factors. Crucially, we extend beyond the herbaceous-plant paradigm by examining salt-tolerance strategies that are specific to the woody architecture of Populus: long-distance radial and axial Na⁺ transport through tall stems, salt sequestration in senescent bark and wood parenchyma, and deep-root ion exclusion strategies. Comparative insights from other woody genera are incorporated to highlight convergent and divergent mechanisms. On this basis, we propose an integrated multi-level regulatory model in which Na⁺ compartmentalization/efflux serves as the core, ROS homeostasis as the key regulatory axis, and osmotic adjustment as the auxiliary strategy. Outstanding challenges—including unresolved primary salt-signal perception, insufficient pathway integration, and limited in planta gene-function verification—are critically assessed, and future research priorities encompassing multi-omics integration, CRISPR-based gene editing, and natural-population genomics are outlined. Full article

Background/Objectives: The acute complications of COVID-19 have been well characterized and are frequently associated with increased mortality. Although substantial knowledge regarding long COVID has accumulated since the beginning of the pandemic, important uncertainties remain regarding the long-term clinical, functional, radiological, and metabolic consequences of SARS-CoV-2 infection. Identification of post-COVID-19 complications is therefore essential for appropriate recognition and management. This study aimed to evaluate the long-term complications of COVID-19 at 3 and 9 months after infection. Methods: This prospective study was conducted at Inonu University Turgut Ozal Medical Center. Patients who presented with active post-COVID-19 complaints or for routine follow-up were enrolled. Participants were evaluated at the pulmonology outpatient clinic at 3 and 9 months. At each visit, persistent or new-onset symptoms were assessed, and pulmonary function tests (PFT), the six-minute walk test (6MWT), echocardiography (ECHO), and thoracic computed tomography (CT) were performed as clinically indicated. Patients were stratified into three groups according to the severity of acute illness: outpatient, ward-hospitalized, and ICU-hospitalized. Results: A total of 205 patients (120 male, 85 female) were included. Male patients had significantly higher rates of ward and ICU hospitalization than female patients (p = 0.006). At 9 months, 85.3% of patients had at least one persistent symptom; dyspnea (69.6%), cough (35.6%), and chest pain (32.5%) were the most common. FVC showed a statistically significant increase between months 3 and 9 (p = 0.014), and the 6MWT distance improved significantly (423.56 m vs. 464.10 m; p = 0.008). Ground-glass opacity, present in 90.2% of patients at admission, persisted in 44.3% at 9 months (p < 0.001). Reticular opacities, pleuroparenchymal bands, and mosaic perfusion patterns increased over time. ICU patients had significantly lower ejection fraction values compared with ward and outpatient groups at 9 months (p = 0.046). During follow-up, 13 patients developed pulmonary embolism and 7 developed new-onset diabetes mellitus. Conclusions: Despite the well-characterized acute phase, the long-term sequelae of COVID-19 remain a significant clinical challenge. Identification of late complications is critical for reducing morbidity and understanding the long-term societal and healthcare burden of the pandemic. Multidisciplinary long-term follow-up is warranted, particularly for patients who experienced severe acute illness. Full article