Search Results (29,491)

The increasing number of vehicles in cities reduces the efficiency of parking infrastructure and increases traffic congestion, making it challenging to achieve sustainable transportation goals. This situation necessitates a re-evaluation of urban mobility systems in conjunction with spatial organization and digital technologies. This article examines smart parking systems as “data-oriented spaces”, analyzing their impact on urban mobility, energy efficiency and spatial organization from a multidimensional perspective. The research adopts a qualitative, multi-level approach, structured through a comprehensive literature review, a comparative analysis of five international case studies and a conceptual synthesis of the findings. The data obtained were evaluated using criteria such as technological infrastructure, spatial structure, sustainability performance and user interaction. The findings reveal that smart parking systems not only serve as vehicle storage but can also function as digital–spatial interfaces that direct urban data flows. This study presents a conceptual framework that treats smart parking systems as data-oriented architectural spaces, offering a holistic approach to the design of sustainable urban mobility infrastructures. This perspective allows for redesigning parking structures as adaptable, data-oriented architectural systems that optimize circulation patterns, reduce search-related emissions, increase spatial efficiency and support sustainable urban mobility networks. Full article

The successful application of 3D and 4D food printing is fundamentally governed by the rheology and microstructure of edible inks. These factors control every step, from extrusion and nozzle deposition to the final product functionality. This review systematically examines how formulation variables, including starch/protein composition, water content, and hydrocolloids, determine the network architecture and critical rheological properties, such as yield stress and viscoelasticity. These properties determine printing outcomes such as filament formation, stacking accuracy, and the stability of sensitive components. This review explores 4D printing as a “3D + 1D function,” where printed structures provide additional features over time, such as a controlled color change or bioactive release, while post-printing treatment often activates these features. Through case studies of novel inks, we show how interfacial chemistry and process parameters influence texture and stability. Finally, we discuss the application of rheological metrics for predicting printability and outline the critical need for developing multi-parameter, process-relevant printability indices to advance the field of digital food manufacturing. Full article

With the increasing adoption of electric vehicles (EV), congestion at charging stations during peak hours has become a prominent issue, imposing significant pressure on station scheduling. Furthermore, the large-scale integration of photovoltaics (PV) introduces dual uncertainties in both generation and load, negatively impacting grid voltage. To tackle the above problems, a strategy for demand response balancing and congestion alleviation of charging stations under grid–road network partition mapping is proposed in this paper. Firstly, a user demand response capability assessment method based on the Fogg Behavior Model is proposed to evaluate the demand response potential of individual users in each zone. The results are aggregated to obtain the demand response participation capability of each zone, thereby realizing capability-based allocation and achieving demand response balancing. Secondly, the road network is divided into several zones and mapped to the power grid, and a two-layer cross-zone collaborative autonomy model is established. The upper layer aims to alleviate inter-zone congestion and balance inter-station power, taking into account the grid voltage level. A tripartite benefit model involving the power grid, charging stations and users is constructed, and an inter-zone mutual-aid model for the upper layer is established and solved optimally. The lower layer establishes an intra-zone self-consistency model, which subdivides different functional zone types within the road network zone, allocates and accommodates the cross-zone power from the upper-layer output inside the zone, and synchronously performs intra-zone cross-zone judgment to avoid congestion at charging stations. Simulation verification is carried out on the IEEE 33-bus system. The results show that the proposed method can effectively alleviate the congestion of charging stations, the balance degree among all zones is increased by 43.58%, and the power grid voltage quality is improved by about 38%. This study offers feasible guidance for exploring large-scale planned participation of electric vehicles in power system demand response. Full article

In this work, an adaptive event-triggered-based consensus control strategy is proposed for the quadrotor unmanned aerial vehicle (QUAV) formation system in the presence of external disturbances and state constraints. Firstly, the disturbed QUAV formation system dynamic model is established. Then, to address the initial peaking explosion problem in the traditional active disturbance rejection control method, a time-varying gain extended state observer (TGESO) is designed to suppress external disturbances. Meanwhile, a novel barrier Lyapunov function (BLF) is constructed to cope with the adverse effects caused by state constraints. Furthermore, aiming to alleviate network congestion and reduce communication burden, the adaptive event-triggered mechanism (AETM) is adopted to design the formation flight controller. Finally, the stability of the developed consensus controller and the boundedness of all error signals are proved via Lyapunov theory. Comparative simulation results demonstrate the practicality of the presented control algorithm. Full article

Background/Objectives: Understanding the mechanisms of drug response plays an essential role in predicting effects prior to drug administration and advancing personalized medicine by optimizing treatment strategies. This study aimed to identify gene combinations that can predict the antitumor activity of lenvatinib, which is a multi-targeted tyrosine kinase inhibitor. Methods: Cancer- and drug-response-related gene sets were identified by mapping gene expression profiles of previously reported syngeneic mouse tumor models onto a protein–protein interaction network and extracting subnetworks comprising nodes where high expression levels were clustered. The scores for these network modules were calculated using the expression data of mouse tumor models prior to drug administration. These scores were used to train a machine learning (ML) model of drug response to lenvatinib by narrowing down the parameter space using hepatocellular carcinoma patient-derived xenograft (HCC PDX) models acquired in this study. Results: Using this integrative framework, we identified several network modules including those involved in the nerve growth factor signaling pathway, Wnt signaling pathway, and interleukin signaling pathways, that were consistently prioritized as informative features across PDX models and human patient data from The Cancer Genome Atlas. Conclusions: These network modules exhibit biological functions that are linked to the known targets of lenvatinib in the cancer cells or the tumor microenvironment, highlighting their potential relevance as determinants of drug response. Full article

Numerous studies have demonstrated the potential of Synthetic Aperture Radar (SAR) in monitoring glacier velocity. However, owing to the complex dynamics of glaciers and the variability of their surface features, velocity fields derived from even short-interval SAR image pairs often exhibit missing parts. This study proposes a missing glacier velocity recovery method based on a spatiotemporal hybrid neural network to solve the above problem. Considering the spatiotemporal characteristics of glacier velocity fields, a hybrid network combining an Artificial Neural Network (ANN) and a Denoising Autoencoder (DAE) is developed. The ANN is first employed to capture spatial correlations associated with missing values, after which it is integrated with the DAE to model temporal variations using a time-aware loss function. An iterative weighting strategy adaptively balances spatial and temporal features during training. The method is applied to SAR–derived velocity fields of Petermann Glacier. Experimental results show that the method significantly improves the performance of glacier velocity recovery compared to traditional methods. Additionally, the study compares and analyzes the velocity of Petermann Glacier in different seasons, and the findings indicate that the glacier exhibits more pronounced seasonal differences in the accumulation zone. Full article

Fluid mechanics in disordered structures gives rise to rich multiscale dynamics through the interplay of topology, symmetry breaking, and fluid–structure interactions. Heterogeneous networks encode mechanical responses, regulate flow organization, and shape energy dissipation, enabling memory effects and emergent collective behaviors across both natural and engineered systems. These principles operate across vast scales: from seamounts with characteristic scales of $L\approx {10}^3\mathrm{m}$ and Froude numbers $Fr\approx {10}^{-2}--{10}^{-1}$ generating deep-ocean turbulent mixing, to marine tidal turbines operating at Reynolds numbers $Re\approx {10}^7--{10}^8$ and Euler numbers $Eu\approx {10}^{-1}--{10}^0$, where inertial forces dominate flow dynamics. Although the dominant physical forces may vary across scales—for example, planetary rotation and stratification in large-scale oceanic flows versus viscous or interfacial effects in microscale systems—the comparison of dimensionless parameters provides a useful framework for discussing similarities in flow organization and scaling behavior. Empirical observations, network-based descriptions, and multiscale simulations collectively demonstrate how topological features constrain symmetry, organize transport pathways, and support predictive reconstruction and inverse design. These principles underpin applications ranging from engineered systems that exploit broken symmetries to rectify chaotic transport, to biological architectures where flows mediate information transfer, locomotion, and structural self-organization. In this Review, we synthesize recent advances to propose a unifying physical paradigm: fluid flows actively interact with disorder, reorganize dissipation, and convert structural asymmetry into functional mechanical performance across scales. Full article

Endothelial dysfunction, chronic inflammation, immune dysregulation, oxidative stress, mitochondrial dysfunction, and metabolic disturbances collectively contribute to cardiovascular diseases (CVDs) associated with blood stasis patterns. Xuefu Zhuyu Decoction (XFZYD) is widely used clinically for the management of CVDs. Based on serum-exposed prototype profiling in rats, two pharmacology-driven core component sets of XFZYD were defined as the core set for the promotion of blood circulation and the elimination of blood stasis (CPBEB; HSYA, GRo, FA, β-ECD, AMY, ALB, PF) and the core set for the regulation of qi and the relief of pain (CRQRP; LIQ, NR, NAR, ROF, HSD, NHP, LTG, NRG, ISL, FNT, NOB, PD, SSa). CPBEB primarily targets vascular pathology by regulating endothelial dysfunction with dyslipidemia-driven arterial lipid deposition. Mechanistically, CPBEB is associated with improved endothelial function, reduced plaque instability, attenuated chronic inflammation and oxidative stress, normalized lipid and bile acid metabolism, and decreased thrombosis. CRQRP primarily modulates vascular tone and systemic energy metabolism. These effects are linked to enhanced AMPK/SIRT1-driven antioxidant defenses and mitochondrial homeostasis, increased NO/cGMP signaling, coordinated crosstalk among the TLR4/NF-κB, JAK/STAT, NLRP3, and PPAR pathways, and remodeling of the gut microbiota–immune network. In summary, this review integrates modern analytical approaches with network pharmacology and the literature evidence to clarify the material basis underlying XFZYD’s therapeutic effects in CVDs, thereby supporting the modernization and internationalization of traditional Chinese medicine. Full article

Accurate forecasting of mining-induced surface deformation is critical for coal-mine safety assessment and hazard mitigation. InSAR deformation time series are often short, temporally sparse, and strongly nonlinear. These characteristics can make purely data-driven predictors unreliable in small-sample settings. To address this issue, we propose a generation–prediction–interpretation framework that combines generative augmentation with physics-informed forecasting. We first develop a TCN-TimeGAN model to synthesize high-fidelity deformation sequences and expand the training set. Recurrent modules in the generator and discriminator are replaced with causal TCN residual blocks, and a temporal self-attention layer is further stacked on top of the TCN backbone to adaptively reweight informative time steps. We then construct a physics-informed Kolmogorov–Arnold Network, termed PI-KAN. Subsidence-consistency and smoothness priors are embedded in the learning objective to promote physically plausible predictions while retaining spline-based interpretability. Experiments on SBAS-InSAR deformation series from the Guqiao coal mine show that the framework achieves an RMSE of 0.825 mm and an R² of 0.968. It outperforms TGAN-KAN, CNN-BiGRU, and BiGRU under the same evaluation protocol. Visualizations of the learned spline-based edge functions further reveal stronger nonlinear responses for lagged inputs closer to the forecast horizon, providing interpretable evidence of short-term temporal sensitivity under sparse observations. Full article

Background: MicroRNAs (miRNAs) are stable, small non-coding RNAs involved in asthma-related pathways and are promising diagnostic biomarkers and therapeutic targets in childhood asthma. Objective: To identify miRNAs differentially expressed in preschool wheezing and childhood asthma, evaluate their association with asthma diagnosis and severity-related phenotypes, and explore their potential translational relevance through exploratory bioinformatic analyses. Methods: A systematic search of Medline, Embase, SCOPUS, PubMed, CINAHL, and Web of Science was conducted for English-language articles published up to March 19, 2025. Eligible human studies reported that miRNAs were differentially expressed in children with wheeze or asthma versus healthy controls (p < 0.05, fold change ≥ 1.5). Bioinformatic analysis identified hub genes, constructed protein–protein interaction networks, and predicted drug–gene interactions. Results: Forty-seven studies met the inclusion criteria, yielding 58 differentially expressed miRNAs (31 up, 27 down). Recurrently reported miRNAs included miR-497, let-7e, miR-98, miR-21, miR-126a, miR-196a2, miR-1, miR-146a-5p, miR-210-3p, miR-145-5p, and miR-200c-3p across blood, nasal swabs, BALF, and exhaled breath condensate. miR-26a showed strong diagnostic performance (sensitivity 83%, specificity 93%; p < 0.002, 95% CI 0.831–0.987). Functional enrichment implicated 56 differentially expressed genes in metabolic and immune processes. Ten hub genes (including TNF, IL5, IL13, TLR4) were linked to 339 potential therapeutic agents; the exploratory network analysis highlighted overlap between predicted miRNA-regulated hub genes and existing asthma-relevant drug targets, including approved biologics. Conclusions: Our review findings suggest that several miRNAs are promising candidate biomarkers for childhood asthma phenotyping and severity assessment; however, their diagnostic utility remains exploratory and requires rigorous external validation and standardisation before clinical application. Full article

Background/Objectives: Dihydroartemisinin (DHA), a bioactive metabolite of Artemisia annua, displays potent antitumor activity in multiple cancers. However, its dose-dependent transcriptional regulatory networks in colorectal cancer (CRC) remain insufficiently understood. This study aimed to clarify the molecular mechanisms of low- and high-dose DHA in human CRC cells and reveal the dose-dependent crosstalk among related biological processes. Methods: We integrated RNA-seq transcriptomic profiling and functional validation in HCT116 cells treated with 20 μM (low-dose) or 50 μM (high-dose) DHA. Differentially expressed genes (DEGs) were screened at FDR ≤ 0.05 and |log₂(fold change)| ≥ 1, followed by GO and KEGG enrichment analyses. Results: DHA inhibited cell viability dose-dependently, with an IC₅₀ of 50 μM. We identified 280 and 678 DEGs in low-and high-dose groups, respectively. Low-dose DHA induced apoptosis via GADD45α/β and ATF4/DDIT3-mediated endoplasmic reticulum stress and triggered senescence through G2/M phase arrest. High-dose DHA mainly modulated gene expression signatures associated with ferroptosis by regulating iron homeostasis and lipid peroxidation at the transcriptional level. Both doses suppressed glycolysis, lipid, and folate metabolism; high-dose DHA also inhibited MGAT5B-mediated glycosylation. DHA regulated five core signaling pathways dose-dependently, with high-dose DHA further repressing Wnt3a/16 and BMP4/6. Conclusions: This study first identifies ferroptosis-related gene networks as key transcriptional targets. It reveals dose-dependent crosstalk among cell death, senescence, metabolic reprogramming, and signaling, providing a transcriptomic framework and gene targets for optimizing DHA-based colorectal cancer therapy. Full article

This work reports the isolation and structural characterization of 1-(2-aminophenyl)-3-(4-pyridyl)-3-hydroxy-1-propanone (1), a β-hydroxyketone intermediate that crystallized unexpectedly during the base-catalyzed aldol condensation of 2-aminoacetophenone with pyridine-4-carbaldehyde, a reaction intended to afford the corresponding pyridyl chalcone (2). The formation of (1) highlights the sensitivity of Claisen–Schmidt reactions to the electronic and steric features of the substrates and to the applied reaction conditions. Single-crystal X-ray diffraction unambiguously confirmed the molecular structure of (1), revealing a hydrogen-bonding network involving the amino, carbonyl, and β-hydroxyl functionalities. These interactions contribute to the solid-state stabilization of the β-hydroxyketone and hinder its dehydration to chalcone (2). The present results provide experimental insight into the mechanistic landscape of aldol condensations and emphasize the relevance of isolable intermediates as structurally defined precursors for further synthetic transformations. Full article

The management of large and heterogeneous building stocks requires decision-support tools capable of prioritising interventions under limited technical and financial resources. In this framework, the role of structural deterioration is rarely integrated within a unified prioritisation framework. This study proposes a rapid deterioration-based assessment for prioritising maintenance within heterogenous portfolios. The assessment is articulated into two levels. A Project Level (PL) is based on visual inspections and component-level condition ratings, while a Network Level (NL) introduces contextual and functional modifiers related to the relevance of each structural unit within the building stock. A seismic assessment procedure is integrated in proposed decision-making system for optimising intervention planning. The two assessments are integrated through a decision-tree logic providing an overall classification of buildings within portfolios. The proposed framework is applied to an industrial-oriented building stock located in Italy, comprising 79 structural units characterised by significant typological heterogeneity, including masonry, reinforced concrete, precast reinforced concrete, and steel buildings. The application illustrates the internal consistency of the proposed framework and its ability to support a transparent and articulated prioritisation process for maintenance and risk mitigation within heterogeneous building portfolios. Further applications to different building stocks are required to explore the general applicability of the methodology. Full article

Hyperspectral image (HSI) classification has achieved substantial progress with deep learning. However, existing methods still underexploit frequency-domain information, particularly the complementary roles of high- and low-frequency components. The recently proposed Kolmogorov-Arnold Network (KAN) shows strong nonlinear feature extraction ability for HSI classification, but its lack of frequency-domain learning and reliance on B-spline activation functions often causes unstable training and convergence issues. To address these limitations, this study introduces a Frequency-aware Filtering Activation-based KAN (FFAKAN) for HSI classification. In this framework, the conventional B-spline activation functions in KAN are replaced with learnable high-pass and low-pass spatial filters, enabling explicit frequency decomposition while preserving spectral sequence modeling capacity. Specifically, the proposed framework includes three modules: spectral-spatial feature embedding (S2FE), adaptive filtering KAN (AFKAN), and sequence feature extraction (SeqFE) modules. First, the S2FE module generates highly discriminative feature representations, providing a strong foundation for subsequent processing. Second, the AFKAN module, serving as the core component, employs learnable cutoff frequencies together with cosine-based smooth transition functions to achieve physically interpretable high-low frequency separation, adaptively capturing fine-grained details and structural characteristics in HSI data. Finally, the SeqFE module leverages multi-layer stacked 3D convolutions to perform deep spectral-spatial correlation modeling, extracting high-level discriminative joint features for the classification task. Experiments on four public HSI datasets demonstrate that FFAKAN consistently outperforms state-of-the-art methods. Overall, the proposed method achieves significant performance gains, with maximum improvements of 6.82%, 1.83%, 4.35%, and 5.93% compared with conventional approaches. Moreover, compared with strong baseline models, FFAKAN further improves the overall accuracy by 1.70%, 0.10%, 0.02%, and 0.37%, respectively. These results clearly demonstrate the effectiveness, robustness, and superior generalization capability of the proposed method across different datasets. This study introduces a new paradigm that incorporates physically interpretable frequency-domain priors, showing strong adaptability and promising potential in complex land-cover scenarios. Full article

Against the backdrop of dietary structure evolution and the “big food concept” strategy, there has been a shift from the traditional grain-centric perspective toward a diversified supply system. Taking Hubei Province—a major grain-producing region in China—as a case study, this research establishes a multi-criteria evaluation system and conducts analysis using statistical yearbooks and land survey data. By integrating natural conditions, economic benefits, and production capacity, the suitability of cultivated land for growing grain crops, cash crops, and forage crops is assessed. Concurrently, landscape pattern indices were applied to quantify the degree of farmland fragmentation. Employing a self-organizing mapping (SOM) neural network model, we synthesized suitability and fragmentation data to delineate differentiated farmland conservation zones. The results revealed significant spatial heterogeneity in crop suitability and fragmentation levels. High-suitability zones for grain crops were concentrated in the Jianghan Plain, while forage crops exhibited higher suitability in northeastern and southeastern Hubei. Farmland fragmentation showed a spatial pattern of lower levels in central Jianghan Plain, gradually increasing toward surrounding hilly and mountainous areas. SOM clustering effectively partitioned farmland into six functional zones: multifunctional agricultural zones, mixed farming zones, grain crop zones, cash crop zones, forage crop zones, and production improvement zones. This multi-source geographic and statistical data-driven zoning framework provides scientific basis for targeted policy interventions. It enables the quantitative management, quality enhancement, and spatial optimization of farmland resources, thereby operationalizing the big food concept to strengthen regional food security. Full article