Search Results (5,032)

Free-floating bike-sharing (FFBS) systems play a valuable role in alleviating traffic congestion and reducing carbon emissions, making them vital to sustainable urban transportation. Although extensive research has investigated the relationship between the built environment and cycling behavior, the adverse effects of air pollution and its interaction with the built environment remain insufficiently understood. In this study, multisource data from Shenzhen are used, and an XGBoost–SHAP model is employed to comprehensively investigate the nonlinear associations among the FFBS trip volume, built environment, and air pollution while considering the spatial heterogeneity in interaction effects. The results indicate that population density, road density, building density, and PM2.5 are the most influential factors. In addition, significant temporal heterogeneity is observed between weekdays and weekends. The effects of the built environment variables and their interactions are more pronounced on weekdays than on weekends. More importantly, an interaction analysis reveals that the positive influence of compact urban development on cycling is conditional: in high-density areas with elevated pollution exposure, the health risks associated with air pollution can offset or even outweigh the mobility benefits of compactness. Overall, this study identifies the complex, spatially heterogeneous mechanisms through which the built environment and air quality jointly shape FFBS usage. These findings provide important evidence for integrating environmental health considerations into compact city planning and offer practical insights for promoting cycling and sustainable urban mobility in high-density cities. Full article

The deterioration of steel reinforcement through corrosion triggers cracking and loss of concrete cover, ultimately weakening the structure’s strength and ductility. In practical design and assessment, it is vital to precisely quantify the shear capacity of corroded reinforced concrete beams (CRCBs). In this paper, machine learning (ML) models are developed to predict the shear capacity of CRCBs, including kernel ridge regression (KRR), K-nearest neighbors (KNN), decision trees (DT), random forest (RF), gradient-boosted regression trees (GBRT), and extreme gradient boosting (XGBoost). A total of 408 data entries on the shear strength of CRCBs under different corrosion conditions were collected to establish an extensive database. The reliability of the proposed ML models is examined by contrasting their outputs with the experimental data. The XGBoost model demonstrated superior predictive capability, achieving an R² value of 0.994 and outperforming all other tested models, including RF, GBRT, and DT. The Shapley Additive Explanations (SHAP) algorithm is adopted to reveal the contribution of each input feature to the predicted shear capacity of CRCBs. The interpretive SHAP results show that the ultimate shear capacity of CRCBs is most influenced by beam depth (h), with the shear span-to-depth ratio (λ) and concrete compressive strength ($f_{cl,150}$) being the subsequent key contributors. A comparative assessment between the XGBoost model and traditional analytical models was carried out to estimate the shear strength of CRCBs. Results demonstrate that the XGBoost model delivers enhanced predictive accuracy and improved performance. A parametric investigation examined its robustness under variations in geometry and material properties, while a user-friendly interface was created to support its practical use. Full article

Accurately estimating leaf area index (LAI) is vital for evaluating crop growth and predicting yields. Conventional approaches, however, often struggle due to the limited representativeness of available data and the complex structure of plant canopies, which reduce their reliability across diverse canopy architectures and observation conditions. To overcome these challenges, this work introduces an LAI retrieval framework that combines a three-dimensional radiative transfer model (3D RTM) with deep learning techniques. Representative 3D maize canopy scenarios were generated using the LESS model, producing synthetic LiDAR point clouds constrained by realistic structural parameters. A deep learning model based on PointNet++ was trained, and transfer learning (TL) was employed to facilitate knowledge transfer from simulated to actual measured data. The TL-enhanced model demonstrated significant improvement, with R² rising from 0.537 to 0.842 and RMSE dropping from 0.541 to 0.288 m²·m⁻². Moreover, retrieval performance was notably affected by scanning mode, angle, and stem diameter, achieving optimal results under TLS acquisition, moderate scanning angles, and intermediate stem widths. These findings suggest that integrating 3D RTM-generated synthetic point clouds with transfer learning is an effective strategy for enhancing the robustness and generalization of LiDAR-based LAI retrieval. Full article

With the advancement of remote sensing technology, image acquisition has become more convenient and the amount of information captured has significantly increased, playing a vital role in numerous fields. However, cloud cover often results in missing image data, severely affecting data usability. In recent years, although deep learning methods have made progress in cloud removal tasks, the complexity of modeling multispectral band relationships and the scarcity of paired data remain major challenges. To address this, this paper proposes a two-stage codebook fusion vector-quantized generative adversarial network (TCF-VQ GAN) and a training framework. The first stage employs synthetic aperture radar (SAR), MODIS, and cloud-free data for unsupervised training; the second stage performs fusion fine-tuning using SAR and MODIS on paired cloudy/cloud-free data. The model incorporates a space-channel jointed gated convolution (SCGC) module to model irregular cloud cover and combines channel attention for band selection, while a dynamically weighted wavelet alignment loss function (DW²A) is designed to enhance multiscale feature representation. Experiments on the SEN12MS-CR and SMILE-CR datasets demonstrate that the proposed method outperforms existing methods across all metrics: on SEN12MS-CR, PSNR is 31.0397 and SAM is 4.7243; they are 33.5191 and 2.1663, respectively, on SMILE-CR. Furthermore, under fixed paired data conditions, simply adding auxiliary and cloud-free data further improves performance, validating the method’s effectiveness in data-scarce scenarios. Full article

Blood disorders encompass a wide range of diseases including anemia, hemophilia, thrombotic disorders, platelet dysfunction, and hematological cancers, making blood disorders a major global health concern. These conditions can impair processes vital to human physiology including oxygenation, coagulation, and immune defense. Hematologic malignancies, both chronic and acute, require timely diagnosis and ongoing disease monitoring for effective clinical management. Microfluidic technologies have emerged as promising alternatives to benchtop techniques for diagnosing and monitoring hematological disorders. For example, microfluidic assays can be used for the isolation and characterization of liquid biopsy markers such as rare cells, extracellular vesicles, and cell-free molecules to support disease management in a minimally invasive manner while the process automation afforded by microfluidics decentralizes healthcare, making it more accessible. Advances in lab-on-a-chip technologies, including large-scale fabrication methods and novel design strategies, will provide tools for the clinical validation of biomarkers and the translation of these technologies from the laboratory bench to the patient bedside. In this review, we will show that microfluidic devices enable disease monitoring via high-throughput analysis of liquid biopsy samples for the detection of rare disease-specific biomarkers found in blood, plasma, urine, etc., providing an alternative to standard benchtop testing using specimens secured via invasive bone marrow procedures, typically used for managing blood-based diseases. A key advantage of microfluidics is their ability to manipulate blood components at scales that closely mimic the body’s microvascular environment. Not surprisingly, microfluidic vascular models have been developed to replicate physiological rheology enabling quantitative assessment of blood cell deformability, aggregation, or clot formation. We provide a critical perspective on the use of the microfluidic “organ-on-chip” designed for blood disorders’ modeling and employed to recapitulate the blood cancer microenvironment. A summary of advances in microfluidic strategies for detection, diagnosis, drug screening, and mechanistic investigations of blood disorders, and future directions for precision testing, will be presented. Full article

Introduction: Multiple Sclerosis (MS) is frequently associated with a range of neurological, cognitive and psychological issues, presenting significant challenges to patients’ Quality of Life (QoL). Among non-invasive neuromodulation techniques, transcutaneous spinal Direct Current Stimulation (tsDCS) is emerging as a potential approach for symptom management in neurological conditions. However, the effects of tsDCS on MS remain poorly explored. Thus, this preliminary study aimed to evaluate the effects of tsDCS on MS symptomatology, focusing on cognitive and psychological variables. Methods: Six patients with MS were recruited for a randomized, sham-controlled, double-blind crossover study, and received anodal tsDCS or sham stimulation in two separate sessions at least one month apart. Assessment outcomes included cognitive and attentional-executive functions, depressive symptoms, and several QoL components. The tests were administered at baseline (T0), immediately after treatment (T1), one week (T2) and one month (T3) post-treatment. Results: Although protocol-by-time interactions did not reach statistical significance across all measures, protocol-independent improvements over time were observed in various QoL subscales, including Physical Functioning, Role Limitations due to Physical Health, Vitality, Health Distress, and Overall QoL. Conclusions: Our findings indicate that tsDCS is a feasible and well-tolerated intervention in patients with MS, with possible implications for QoL. Given the small sample size and the exploratory nature of this study, further research is needed to clarify whether tsDCS may represent a potentially beneficial non-invasive neuromodulation approach for improving well-being in patients with MS across both physical and mental dimensions. Full article

As vital carbon pools within terrestrial ecosystems, wetlands store sediment organic carbon (SOC), a process influenced by plant communities, seasonal variations, and wetland types. Microbial communities, fundamental to wetland ecosystems, are hypothesized to regulate carbon storage. We investigated sediment microbial communities and carbon storage in different seasonal and plant conditions in two types of wetlands. Sediment organic carbon, the associated environmental factors, and microbial community characteristics were detected to explore the impacts of seasons and plants on SOC. Plants and seasons significantly influenced the content of SOC in constructed wetland, while only altered the content of dissolved organic carbon (DOC) in river wetland. In river wetland, plants increased the microbial function of Amino Acid Metabolism through the input of exogenous dissolved organic carbon (DOC) and the effect on moisture content. The functional traits of Carbohydrate Metabolism in sediment were higher in river wetland than that in constructed wetland. Our results indicated that plants and seasons influenced SOC in wetlands through their effects on sediment microbial community and function. Compared with the river wetland, the constructed wetland had more stable microbial communities and might be easier to fix organic carbon from plants. This study highlights the importance of the carbon sequestration potential of constructed wetlands due to the stable microbial communities. Full article

Acoustic emission (AE) is a vital non-destructive technique for monitoring damage in materials, yet its simulation via the Discrete Element Method (DEM) has historically been limited to material-scale analysis. This research presents a novel application of block-based DEM to simulate AE signals in masonry structures at the structural scale under quasi-static in-plane loading. Using a simplified micro-modeling approach, the study first validates the method by monitoring crack initiation and AE energy in single mortar bed joints under tensile and shear conditions. The methodology is then scaled to a large-scale masonry wall panel (1.835 × 1.170 × 0.15 m³) subjected to monotonic shear loading. A critical finding is the influence of local damping; a reduced damping ratio of 0.3 is recommended to preserve the kinetic energy necessary for capturing clear velocity signals. Numerical results show strong agreement with experimental force-displacement and cumulative AE energy curves, confirming the model’s robustness. Furthermore, frequency analysis of the simulated signals successfully distinguishes between tensile and shear failure modes. This study fills a significant gap in the literature by demonstrating that DEM is an effective predictive tool for structural-scale failure analysis and AE monitoring in heterogeneous masonry. Full article

Traffic assignment is crucial for urban traffic regulation and management. Based on this background, this study proposes a heterogeneous graph neural network that integrates Transformer-based multi-head self-attention for traffic assignment in urban road networks. The model builds a heterogeneous graph with both physical road links and virtual origin–destination links. It features a dual-encoder structure: the V-Encoder and the R-Encoder. The V-Encoder employs Transformer multi-head self-attention to capture long-range spatial relationships between origin and destination nodes. In contrast, the R-Encoder aggregates local topological features to characterize the transmission of flow across road segments. A combined loss function that includes flow conservation constraints is designed to ensure predictions are both accurate and physically realistic. Experiments on the Sioux Falls and EMA networks demonstrate that the method outperforms baseline models under various congestion conditions, exhibiting high accuracy and efficiency. Ablation tests show that Transformer multi-head self-attention is vital for performance enhancement. The approach also remains robust under abnormal conditions, such as in the case of incomplete OD demands, making it a practical solution for efficient, low-carbon, and sustainable traffic management. Full article

Soil thermal conductivity (STC) is vital for environmental and engineering modeling, yet traditional unfrozen STC estimates often perform poorly under field conditions. This study develops an enhanced Johansen–Tarnawski model incorporating vegetation parameters (JT-V) and applies geospatial data for regional simulation. Residuals from mechanistic predictions were analyzed using Geodetector and Random Forest, revealing strong vegetation-type effects. Validation with 88 samples from 18 sites across five vegetation types showed the JT-V model significantly improved accuracy: R² rose from 0.426 to 0.716, and RMSE decreased by 53%. The best performance occurred at the surface layer (RMSE = 0.074 W·m⁻¹·K⁻¹), with errors increasing with depth. Over 83% of sites achieved R² > 0.7, and most linear regression slopes fell between 0.8 and 1.1. Applying JT-V to simulate thawing-season STC in Northeast China, it was found that lower values predominated in the Khingan Mountains and the Inner Mongolia Plateau, while higher values occurred across the Northeast Plain. Temporal dynamics exhibited three stages: stability (May–mid-July), rapid rise (mid-July–mid-August), and gradual decline (mid-August–September). The improved model advances regional land surface simulations and supports agricultural and engineering applications. Full article

Papain-like cysteine proteases (PLCPs) are vital enzymes involved in plant development, acting as key regulators of processes such as seed germination, nutrient mobilization, senescence, and programmed cell death. In the present study, we analyzed active PLCPs in various barley organs, including roots, leaves, stems, and seeds at different stages of plant development. Protein extracts obtained from barley samples (4-day-old seedlings; plants at 2, 4, 7, and 11 weeks after sowing; developing seeds from 11-week-old plants; and mature dry seeds) were subjected to anion-exchange chromatography. Fractions containing active PLCPs were pooled, biotinylated using the DCG-04 probe, affinity-purified using streptavidin-agarose, and subsequently analyzed via SDS-PAGE. Bands corresponding to biotinylated PLCPs (detected using streptavidin-peroxidase and a chemiluminescent substrate) were excised from the gel and analyzed by tandem mass spectrometry, enabling the identification of up to 23 distinct PLCPs belonging to nine known PLCP subfamilies. Among the identified PLCPs, HvPap-6 from the L-like D subfamily proved to be the most abundant across all barley samples. In seedlings, B-like and L-like D proteases constituted the largest proportion of all PLCP classes, and their levels continued to increase as the plants developed. Although the relative abundance of L-like B and L-like C proteases was high in seedlings, their levels declined in the roots and leaves of developing plants, as three PLCPs from the L-like B subfamily were identified only during the seedling stage. These results suggest that L-like B and L-like C proteases play an important role in seed germination and seedling development. Organ-specific expression was also observed for certain PLCPs: HvPap-26 from the L-Like C subfamily was identified only in the shoots and roots of seedlings; four PLCPs of the L-like E subfamily were detected solely in the roots, whereas two other proteases from this subfamily were identified exclusively in the leaves and shoots under our experimental conditions. Thus, our results suggest that certain active PLCPs are organ-specific, and that the relative importance of identified PLCPs varies within these organs during plant development. Full article

The pantograph–catenary system is a crucial component of rail transit vehicles, performing the vital function of electric energy transmission. During train operation, the current-carrying components continuously emit particulate matter into the surrounding environment due to friction, and these particulate emissions have a significant impact on human health. However, research on the correlation between the current-carrying friction of carbon contact strips and particulate matter emission characteristics is rarely reported. Based on a semi-enclosed pin-on-disc current-carrying friction and wear test rig, this paper investigates the effects of varying current intensity under different contact load conditions on the friction and wear performance of carbon/copper pairs, as well as the associated particulate matter emission behavior. It reveals the damage characteristics of carbon contact strips, the particulate matter emission characteristics, and the relationship between them under different service conditions. The results indicate that the wear mechanism and particulate matter emission behavior of carbon contact strips are jointly influenced by current magnitude and contact load. In the absence of current, increasing the load exacerbates the mechanical wear on the carbon friction pair surface, while elevating the emission concentration of particles of various sizes and stabilizing the particle size distribution. Under current-carrying conditions, a higher contact load effectively reduces the frequency of arc discharges between the friction pair. Meanwhile, the degree of arc erosion on the contact surface worsens with increasing current intensity. Arc discharges instantaneously lead to a sharp increase in particulate emissions, and the higher the discharge intensity or the greater the number of discharges, the higher the particulate concentration around the contact pair. Full article

While multilane roundabouts follow geometric design standards, they often overlook motorcycle-dominated traffic behavior. This study evaluates lane-reduction strategies to create safer and more inclusive urban corridors in mixed-traffic conditions, focusing on a case study in Southern Thailand. High-resolution unmanned aerial vehicle (UAV) trajectory data were analyzed using the Macroscopic Fundamental Diagram (MFD), Cell Transmission Model (CTM), and Time-To-Collision (TTC) frameworks under three configurations: full lane availability, partial inner-lane closure, and full inner-lane closure. Results indicate progressive deterioration in performance under restricted-lane conditions. Under full closure, total flow decreased by 31%, and average travel time increased by 43%. The MFD curve shifted toward higher critical densities, indicating earlier congestion onset, while CTM results revealed longer discharge times, queue spillback, and increased merging friction. Conversely, safety outcomes (TTC) improved significantly: extreme rear-end conflicts were reduced by 48%, and severe lane-change conflicts were nearly eliminated (99%). Behavioral evidence suggests that full closure constrains motorcycles to a single circulating path, reducing erratic filtering and promoting more stable interactions. Overall, this study identifies a systemic trade-off between safety and efficiency, highlighting how geometric interventions catalyze behavioral adaptation. The findings highlight how geometric constraints shape collective behavior in motorcycle-dominated roundabouts and demonstrate the value of an integrated UAV-based framework as a vital tool for inclusive urban management, providing the granular data needed to balance safety and mobility in complex traffic landscapes. Full article

Hydrotreatment is vital for producing high-quality liquid fuels in petroleum refining and its air coolers are critical components prone to severe corrosion under high-temperature and high-pressure conditions. Ammonium salts from NH₃-HCl and NH₃-H₂S reactions, particularly ammonium chloride precipitated during cooling, readily deposit on tube surfaces. Strong temperature gradients and complex flow conditions may severely affect air cooler inlets and front sections. To enhance the refining process reliability, an experimental setup was established to investigate the water injection removal of ammonium chloride particle deposits in air cooler tube bundles. Results show that water injection effectively removes ammonium chloride particles. Particle size has a minor influence, whereas inlet velocity, temperature, and water injection rate significantly affect removal efficiency. Increasing inlet velocity from 2 to 5 m/s, temperature from 80 to 110 °C, and water injection rate all enhance removal efficiency. Furthermore, differences between two-row tubes were also observed: the second-row tube exhibits a higher removal ratio due to liquid film formation, which increases Reynolds number and shear force, thereby enhancing dissolution. These findings provide experimental support for optimizing water injection strategies to mitigate corrosion, improving hydrotreatment unit reliability and safety, ensuring the continuous operation of the petroleum and fuel processing industry. Full article

Underwater Wireless Sensor Networks (UWSNs) play a vital role in ocean monitoring and exploration. However, harsh underwater conditions and frequent topology changes caused by node and sink mobility pose significant challenges for reliable routing. Conventional routing protocols that depend on global route reconstruction and static paths generate excessive control overhead and degrade performance in large-scale underwater environments. In this paper, we propose an energy-efficient virtual cell-based mobile-sink adaptive routing (VC-MAR) protocol for UWSNs. The sensing field is logically partitioned into a three-dimensional grid of virtual cells, where a cell-gateway is elected in each cell to construct a low-overhead routing backbone. To support sink mobility, VC-MAR introduces a localized route-adjustment mechanism that updates only the affected backbone segments rather than reconstructing the entire routing structure. By confining routing updates to neighboring cells influenced by sink movement, the proposed protocol significantly reduces control packet exchanges while ensuring stable and reliable data delivery. Simulation results show that the proposed VC-MAR improves the packet delivery ratio by up to 20% and reduces routing control overhead by about 34% compared with traditional grid-based routing methods. These results confirm the suitability of VC-MAR for dynamic and realistic underwater sensing scenarios. Full article