Search Results (6,810)

Vehicle loads constitute the dominant source of dynamic excitation for small- and medium-span bridges, exerting a critical influence on bridge safety and service performance. However, vehicle load characteristics exhibit pronounced temporal variability and strong regional heterogeneity, which poses challenges for accurately characterizing the in-service loading conditions of bridges in specific regions using conventional dynamic load models. Therefore, this study focuses on the actual operational characteristics of vehicles on the Lieshihe bridge and the effects of vehicle loads and proposes a stochastic vehicle load simulation method based on the Monte Carlo sampling technique and weigh-in-motion (WIM) measured data. Initially, the recorded vehicle data are classified into representative vehicle models, and statistical analyses are conducted to characterize lane-dependent traffic flow variations and the occurrence patterns of vehicle overloading. Subsequently, axle number and axle spacing are selected as the core indicators for vehicle classification, based on which vehicles are categorized into five representative vehicle types. The changing patterns of axle load, vehicle weight, vehicle speed, etc., for each vehicle type are studied, and corresponding probability density distribution models are established to describe the stochastic nature of vehicle characteristics. Finally, using the Monte Carlo method combined with important attributes of vehicle flows, a stochastic vehicle load model is established based on the spatial–temporal characteristics. The results demonstrate that the vehicle weight on the bridge exhibits a Gaussian mixture distribution with multi-peaks, characterized by similar peak magnitudes but markedly different occurrence frequencies; axle load shows a single-peak distribution of Gaussian distribution with small differences in peak values and frequencies. Full article

The vitality dynamics of historic urban areas under high tourism pressure and their underlying mechanisms remain not fully understood, posing a challenge to sustaining their uniqueness against homogenized redevelopment. To explore this issue, this study utilises human mobility data and an XGBoost-SHAP model to examine the spatio-temporal dynamics of block-level vitality and to uncover the nonlinear effects of built environment factors in Shenyang, China. The results indicate that: (1) Diverging from the commuting patterns of general urban areas, the vitality of historic urban areas presents unique spatio-temporal shifts, transitioning from commercial centers on weekdays to a commercial-cultural mix during holidays. (2) The determinants of vitality vary temporally, shifting from accessibility-oriented (subway) on weekdays to heritage-oriented (state historic sites) during holidays. (3) By applying the ‘Three-Factor Theory’ from satisfaction research to decode nonlinear effects, the study classifies factors into Performance (functional density), Basic (proximity to water bodies), and Excitement (distance to subway and state historic sites). The findings guide urban renewal to prioritize systemic and sustainable vitality across the historic urban areas rather than maximizing vitality in specific locations. Full article

Background/Objectives: This study aimed to describe temporal trends in deep brain stimulation (DBS) use for Parkinson’s disease (PD), essential tremor (ET), and dystonia; characterize patient age and sex distribution and comorbidity; assess postoperative complications and in-hospital mortality (IHM) after implantation and explantation; and explore sex-specific differences in utilization and outcomes. Methods: We conducted a retrospective nationwide population-based study using the Spanish National Hospital Discharge Database (RAE-CMBD) from 2002 to 2019. All hospital admissions with DBS implantation or explantation/revision and a diagnosis of PD, ET, or dystonia were identified. Sociodemographic variables, the Charlson Comorbidity Index (CCI), length of hospital stay (LOHS), postoperative complications, and IHM were analyzed across three calendar periods and stratified by diagnosis and sex. Results: A total of 4883 admissions for DBS electrode implantations and 497 admissions for DBS explantation/revision were recorded. PD accounted for 82.6% of implantations, followed by ET (11.2%) and dystonia (6.3%). DBS activity increased significantly over time, while median LOHS declined from 12 to 6 days for implantations and from 13 to 5 days for explantations. Overall IHM after implantation was 0.27%, decreasing to 0.05% in 2014–2019; IHM after explantation was 0.6%. Most hospitalizations had low comorbidity (CCI = 0 in 87.8%), although comorbidity increased over time. Men represented approximately 60% of procedures in PD and ET. Women with PD underwent DBS at older ages, despite similar LOHS and IHM. Postoperative complications were recorded in 14.6% of implantations, mainly hardware-related issues (5–6%) and infections (1–2%), whereas infections (33%) and mechanical problems (27%) predominated among explantations. Conclusions: DBS use in Spain has expanded substantially, with shorter hospital stays and very low in-hospital mortality. Sex-related differences in utilization are increasing, and hardware complications and infections remain the most frequent conditions associated with explantation. As complications were identified only during the same hospitalization as the DBS procedure, late post-discharge events are not captured and could be underestimated; patient-level risks cannot be derived. Full article

Transforming growth factor-β (TGF-β) signaling plays a central role in lung tissue homeostasis, coordinating epithelial repair, immune resolution, and stromal remodeling following injury. However, persistent or dysregulated TGF-β activation is a hallmark of both idiopathic pulmonary fibrosis (IPF) and lung cancer, two devastating pulmonary diseases that are traditionally studied as distinct entities. Emerging evidence suggests that this dichotomous view may obscure shared pathogenic mechanisms driven by aberrant TGF-β signaling dynamics. In this review, we synthesize experimental, translational, and clinical findings to propose a unifying framework in which IPF and lung cancer represent endpoints along a shared TGF-β–driven pathological continuum. We highlight how the duration and intensity of TGF-β signaling determine divergent cellular outcomes across epithelial cells, fibroblasts, and immune compartments—ranging from physiological wound repair to irreversible fibrotic remodeling and the establishment of a pro-tumorigenic niche. Particular emphasis is placed on the temporal transition from acute injury responses to chronic signaling states that promote epithelial plasticity, fibroblast fixation, immune suppression, and genomic instability. By integrating fibrosis and tumorigenesis into a single pathophysiological model, this review reframes TGF-β signaling as a time-dependent disease modifier rather than a disease-specific factor. This perspective provides a conceptual basis for therapeutic strategies targeting TGF-β signaling windows to intercept disease progression before irreversible fibrosis or malignant transformation occurs. Full article

A cross-immunity model for influenza A(H3N2) based on convolutional neural networks (CNNs) was developed and validated under temporally structured conditions that mimic real-world forecasting. Antigenic distance was derived from hemagglutination inhibition (HI) titers. The model was trained on WHO data (2011–2023) and tested in a time-split fashion on independent recent data (2022–2024). Hemagglutinin sequences (HA/HA1) were encoded into 3D tensors using five physicochemical indices from AAindex. Two- and three-layer CNN architectures were tested. Performance was evaluated using Accuracy, Sensitivity, Specificity, and Matthews Correlation Coefficient (MCC) with 95% confidence intervals. Validation on the classic Smith’s dataset showed high accuracy (Accuracy = 0.9996, MCC = 0.9964), serving as a necessary sanity check. Testing on current data yielded lower but robust results (Accuracy: 0.73–0.81, MCC: 0.48–0.60), reflecting real-world forecasting complexity. ROC analysis confirmed the strong discriminative ability (AUC ≥ 0.805) and good calibration (Brier scores ≤ 0.192). The three-layer CNN demonstrated greater robustness on challenging data. This CNN model is an effective tool for assessing influenza A(H3N2) antigenic distances and holds promise for integration into epidemiological models to aid vaccine strain selection. Further accuracy improvements may arise from modeling the structural impact of amino acid substitutions and polyclonal immune responses. Full article

Unmanned aerial vehicles (UAVs) have emerged as a promising platform for supporting integrated sensing, computing, and communication (ISCC) functionality in Internet of Things (IoT) applications. This paper investigates a UAV-enabled ISCC network, where the UAV performs radar sensing and onboard edge computing with the computational assistance of ground access points (APs). Given the limited onboard energy, ensuring energy-efficient operation of UAVs is crucial to support the long-term sustainability of network performance. In this paper, we define computing efficiency as the ratio between the total number of successfully processed computational bits and the overall UAV energy consumption, under the constraint of a required sensing threshold. To maximize this performance metric, this paper jointly optimizes the beamforming vector, the CPU frequency, and the trajectory of the UAV. This optimization problem is modeled as a Markov decision process (MDP) and solved using a deep reinforcement learning (DRL) approach based on a memory mechanism. Specifically, a long short-term memory (LSTM) and twin delayed deep deterministic policy gradient (TD3)-based trajectory design and resource allocation (LTTDRA) algorithm is proposed. LSTM units are integrated into the actor and critic to effectively capture the temporal correlations in dynamic environments, thereby enhancing policy stability and accelerating algorithm convergence. The reward function is meticulously designed to alleviate sparse-penalty effects and learn high-performance strategies in complex environments with multiple constraints. Extensive simulations are conducted under various settings and network scenarios, and the results consistently indicate that the proposed approach substantially outperforms the baseline schemes. Full article

Recording and tracking the long-term dynamic changes in microbial populations is as essential as monitoring other soil properties for evaluating soil quality and health; however, this area has significantly lagged due to technical constraints and challenges in storing fresh soil samples. Historically archived soil samples offer a unique opportunity to characterize the temporal dynamics of microorganisms over several decades. To determine whether archived air-dried soils can be utilized for this purpose, we compared the structure and composition of bacterial communities across fresh soils, air-dried soil archives stored for varying durations, and their corresponding rewetted counterparts, all sourced from a long-term fertilization experiment on calcareous loess soil. Soil microbial features were characterized using the MiSeq sequencing platform. The results indicated that the similarity of DNA-based bacterial community composition between fresh soil and both archived and rewetted soils followed a downward quadratic curve as archiving time increased. Specifically, the DNA-based community structure of soils air-dried and preserved for one year, as well as those rewetted after eight years of archiving, remained highly similar to that of fresh soil. Regarding taxonomic shifts, the relative abundance of Actinobacteria in both air-dried and rewetted soils increased with storage time. Conversely, the relative abundances of Acidobacteria and Gemmatimonadetes significantly increased in air-dried soils but decreased upon rewetting over time. The relative abundances of Chloroflexi and Firmicutes remained stable in air-dried soils; however, after rewetting, the former decreased while the latter increased dramatically. Furthermore, Proteobacteria, Rokubacteria, Planctomycetes, Bacteroidetes, and Latescibacteria exhibited a decreasing trend in both air-dried and rewetted soils. These findings suggest that air-dried soils preserve DNA-based community profiles more effectively than rewetted soils, particularly for samples stored for less than eight years. This study provides a valuable reference for utilizing archived historical soil samples from long-term experiments to investigate microbial community evolution. Full article

Motor imagery (MI) classification using EEG signals is central to brain–computer interfaces but remains challenging due to low signal-to-noise ratio, non-stationarity, and high inter-subject variability. We introduce EEG-TriNet++, a multi-branch deep learning architecture that enhances both classification accuracy and cross-subject generalization. The model integrates three complementary components: convolutional spatial–spectral encoders for channel-wise and frequency-specific patterns, bidirectional LSTMs to model temporal dynamics, and a Transformer head for global relational reasoning. A patchwise tokenization strategy and neural architecture search optimize the trade-off between efficiency and representational capacity. To address individual differences, a model-agnostic meta-learning (MAML) module enables rapid adaptation to new users with limited data. Evaluated on two public MI datasets under within-subject and leave-one-subject-out (LOSO) protocols, EEG-TriNet++ achieves 79.1% and 78.6% accuracy in within-subject tasks, and 72.4% and 71.3% in LOSO settings. Ablation studies validate the contribution of each module, and comparisons with state-of-the-art methods demonstrate consistent performance gains under identical conditions. Full article

This article investigates how intuition operates when its referent is structurally absent or non-objectifiable. While phenomenology has traditionally linked intuition to fulfilment and object-givenness, a growing range of contemporary experiences, such as climate change, future generations, and technologically mediated processes, resist such modes of presentation in principle. Their absence is not contingent but structural. The article argues that phenomenology can nonetheless account for these experiences by articulating a mode of intuition that does not depend on presentable objects, but arises through mediation, temporal articulation, and responsiveness. Drawing on Husserl’s analyses of intuition and temporality, the first part identifies the limits of object-centred accounts of evidence in contexts characterized by mediation and diachronic dispersion. The second part turns to Levinas, whose account of diachrony and responsibility discloses a relation to the future that is ethically binding without being anticipable or reciprocable. The third part elaborates this insight through Waldenfels’s phenomenology of the alien and of responsiveness, showing how experience is structured by pathos, delay, and asymmetry. Here, intuition without objects appears not as a lack of evidence, but as a specific mode of experiential articulation grounded in interruption and answerability. The article concludes by showing how this phenomenological reconstruction clarifies central problems in contemporary climate ethics, particularly those concerning intergenerational responsibility. It suggests that what is often described as a motivational or institutional deficit can also be understood as a failure to recognize a distinctive intuitive relation to the future, i.e., one that binds without presenting and calls for response despite structural absence. In doing so, the notion of intuition without objects contributes to broader reflections on temporality, responsibility, and ethical agency under conditions of deep temporal asymmetry. Full article

Electro-hydrostatic actuators (EHAs) are increasingly deployed in modern aircraft due to their compact size, fast response, and high power-to-weight ratio. However, existing airborne QAR and EICAS data are typically recorded as independent parameters without explicit correspondence to system health states, making degradation assessment and remaining useful life (RUL) prediction challenging. To address this issue, this paper proposes a spatiotemporal degradation modeling framework, termed PreDyn-ST, based on multivariate time series (MTS) data. The method integrates SimCLR-based contrastive pretraining and a dynamic feature fusion mechanism to capture evolving temporal dependencies and spatial sensor correlations. Specifically, graph convolutional networks (GCNs) incorporating physical connectivity priors are employed for spatial modeling, while a Transformer extracts long-range temporal patterns. A learnable dynamic weighting mechanism adaptively balances spatial and temporal features during training. The adaptive behavior is further analyzed using correlation statistical index (CSI) curves for interpretability. Experimental validation on a self-developed EHA degradation test bench and the C-MAPSS benchmark dataset demonstrates that PreDyn-ST achieves competitive and stable prediction performance. In particular, the method shows robust performance under complex operating conditions such as FD004. These results indicate the effectiveness of the proposed framework for accurate and interpretable degradation modeling in aerospace applications. Full article

Pediatric brain tumor survivors remain at high risk of recurrence, yet current surveillance strategies relying on neuroimaging and cerebrospinal fluid (CSF) cytology have limited sensitivity for early or minimal disease. Tumor-specific peptides (TSPs) derived from individual tumors represent a promising class of highly specific biomarkers for longitudinal disease monitoring through CSF-based proteomic analysis. In this study, tumor tissue and serial CSF samples from six pediatric brain tumor patients (five medulloblastomas and one atypical teratoid/rhabdoid tumor (ATRT)) were analyzed using an integrated proteogenomic workflow combining discovery and targeted mass spectrometry. TSPs were identified from resected tumor tissue and matched against shotgun CSF proteomic datasets to nominate candidate biomarkers. High-confidence peptides were synthesized as isotopically labeled standards and quantified longitudinally using targeted multiple reaction monitoring. Two TSP biomarkers derived from individualized pediatric brain tumors (one medulloblastoma and one ATRT) demonstrated robust detection in serial CSF samples and exhibited temporal concordance with radiographic disease course, declining with treatment response and increasing during disease progression. These findings establish the feasibility of detecting and longitudinally quantifying TSPs in CSF and support further investigation of individualized proteomic biomarkers for treatment response monitoring and disease surveillance in pediatric brain tumors. Full article

The inherent randomness, high volatility, and non-stationarity of photovoltaic (PV) power generation pose substantial threats to the stability of modern power grids. Developing high-precision forecasting models is essential for grid operation, yet conventional architectures often encounter a performance bottleneck: they struggle to simultaneously achieve high computational efficiency for long-range dependency modeling and robust perception for local, abrupt fluctuations. To address these limitations, this paper proposes HDCF-Mamba, a novel forecasting framework that resolves the feature distribution gap between long-range trends and short-term volatility. The core innovation lies in the Heterogeneous Dual-branch Cross-Fusion (HDCF) mechanism, which enables the synergetic integration of a Mamba-based global branch and a Multi-Kernel Filter Unit-based multi-scale local branch. Specifically, we integrate the Mamba Selective State Space Mechanism into the global branch to efficiently capture long-term dependencies with $O\left(L\right)$ linear complexity, fundamentally overcoming the quadratic computational bottleneck of Transformers. Meanwhile, the Multi-Scale Feature Extraction Module (MSFEM) acts as a local compensator to capture high-frequency power fluctuations caused by transient weather changes. Unlike simple hybrid models that rely on linear addition, our HDCF design utilizes a temporal concatenation mechanism to ensure non-linear alignment of these heterogeneous features. Extensive experiments on four real-world PV operational datasets (including publicly available benchmark datasets and actual photovoltaic power station monitoring data: ECD-PV, LSP-PV, APS-PV, and PSB-PV) demonstrate that HDCF-Mamba consistently outperforms state-of-the-art models, achieving a reduction in Mean Absolute Error (MAE) of up to 11.4% compared to iTransformer and 8% compared to SCINet, while maintaining superior computational efficiency. Full article

High-fidelity 4D reconstruction of dynamic scenes is pivotal for immersive simulation yet remains challenging due to the photometric inconsistencies inherent in multi-view sensor arrays. Standard 3D Gaussian Splatting (3DGS) strictly adheres to the brightness constancy assumption, failing to distinguish between intrinsic scene radiance and transient brightness shifts caused by independent auto-exposure (AE), auto-white-balance (AWB), and non-linear ISP processing. This misalignment often forces the optimization process to compensate for spectral discrepancies through incorrect geometric deformation, resulting in severe temporal flickering and spatial floating artifacts. To address these limitations, we present Lumina-4DGS, a robust framework that harmonizes spatiotemporal geometry modeling with a hierarchical exposure compensation strategy. Our approach explicitly decouples photometric variations into two levels: a Global Exposure Affine Module that neutralizes sensor-specific AE/AWB fluctuations and a Multi-Scale Bilateral Grid that residually corrects spatially varying non-linearities, such as vignetting, using luminance-based guidance. Crucially, to prevent these powerful appearance modules from masking geometric flaws, we introduce a novel SSIM-Gated Optimization mechanism. This strategy dynamically gates the gradient flow to the exposure modules based on structural similarity. By ensuring that photometric enhancement is only activated when the underlying geometry is structurally reliable, we effectively prioritize geometric accuracy over photometric overfitting. Extensive experiments validate the quantitative superiority of Lumina-4DGS. On the Waymo Open Dataset, our method achieves a state-of-the-art Full Image PSNR of 31.12 dB while minimizing geometric errors to a Depth RMSE of 1.89 m and Chamfer Distance of 0.215 m. Furthermore, on our highly challenging self-collected surround-view dataset featuring severe unconstrained illumination shifts, Lumina-4DGS yields a significant 2.13 dB PSNR improvement over recent driving-scene baselines. These results confirm that our framework achieves photorealistic, exposure-invariant novel view synthesis while maintaining superior geometric consistency across heterogeneous camera inputs. Full article

The peri-implantation window is a tightly regulated temporal phase during which the human endometrium undergoes coordinated molecular remodeling to establish receptivity. MicroRNAs (miRNAs) contribute to implantation-related processes; however, whether dynamic endometrial regulatory signals are functionally reflected in circulation within a defined temporal framework remains unclear. We hypothesized that although individual miRNA identities differ between endometrial tissue and plasma, temporally regulated miRNAs in both compartments may exhibit overlap at the level of enriched biological pathways during the peri-implantation window. To test this hypothesis, we performed time-resolved small RNA sequencing on paired endometrial and plasma samples collected from 62 participants across progesterone exposure days P+3 to P+7 in hormonally controlled cycles. Temporal modeling identified 27 dynamic miRNAs in endometrial tissue and 17 in plasma (FDR < 0.05). Despite limited overlap at the individual miRNA level, functional enrichment analysis revealed recurrent overlap in apoptosis-, cell cycle-, aging-, inflammatory-, and metabolic-related pathways across compartments. Four miRNAs exhibited concordant directional temporal trends between tissue and plasma with moderate correlation coefficients. These findings suggest that dynamic miRNA-associated enrichment patterns during the peri-implantation window may exhibit pathway-level overlap despite divergence in specific molecular identities. This temporally aligned integrative framework provides a pathway-centric perspective for interpreting cross-compartment miRNA-associated temporal patterns and supports a hypothesis-generating systems-level view of human implantation biology. Full article

Bamboo shoots are valued as traditional vegetables, but their palatability is often compromised by bitter and astringent compounds. The spatial and temporal distribution of these compounds across species, tissues, and developmental stages remains poorly characterized. This study systematically investigated key taste-active compounds (tannins, oxalic acid, flavonoids, cyanide compounds, and free amino acids) in three sympodial bamboo species (Bambusa chungii, Dendrocalamus farinosus, and Bambusa oldhamii). We integrated quantitative chemical analysis of shoots at different emergence stages and tissue parts with descriptive sensory evaluation. The results revealed pronounced, species-specific accumulation patterns. For instance, tannin content increased with shoot emergence in all species, whereas oxalic acid and cyanide showed divergent temporal trends among them. Tissue-specific gradients were also evident for most compounds. Correlation analysis with sensory data indicated distinct associations for each species. Bitterness in D. farinosus was most strongly correlated with oxalic acid, while in B. oldhamii, it was closely linked to tannins and cyanide. In B. chungii, specific amino acids (aspartic acid, histidine) and tannins showed significant correlations with bitterness perception. The perception of astringency involved multiple contributing factors. These findings elucidate the distinct biochemical bases of flavor variation in sympodial bamboos. They provide a scientific rationale for optimizing harvest timing and tissue selection, offering targeted strategies for post-harvest processing to improve edible quality and market value. Full article