Search Results (3,369)

Aerosol chemical components are critical parameters that influence the atmospheric environment, climate effects, and human health. Retrieving global columnar atmospheric aerosol components from satellite observations provides foundational data and practical value. This study develops a method for retrieving aerosol component composition from polarized satellite data by synergizing a chemical transport model with ground-based remote sensing data. The method enables the rapid acquisition of columnar mass concentrations for seven aerosol components on a global scale, including black carbon (BC), brown carbon (BrC), organic carbon (OC), ammonium sulfate (AS), aerosol water (AW), dust (DU), and sea salt (SS). We first establish a remote sensing model based on the multiple solution mixing mechanism (MSM²) to obtain aerosol chemical components using AERONET ground-based measurements. We then employ a cross-layer adaptive fusion (CAF)-Transformer model to learn the spatial distribution characteristics of aerosol components from the MERRA-2 model. Furthermore, we optimize the retrieval model by transfer learning from the ground-based composition data to achieve satellite remote sensing of aerosol components. Residual analysis indicates that the retrieval model exhibits robust generalization capabilities for components such as BC, OC, AS, and DU, achieving a coefficient of determination of 0.7. Moreover, transfer learning effectively enhances the consistency between satellite retrievals and ground-based remote sensing results, with an average improvement of 0.23 in the correlation coefficient. We present annual and seasonal means of global distributions of the retrieved aerosol component concentrations, with a major focus on the spatial and temporal variations of BC and DU. Additionally, we analyze three typical atmospheric environmental cases, wildfire, dust storm, and particulate pollution, by comparing our retrievals with model data and other datasets. This demonstrates the ability of satellite remote sensing to identify the location, intensity, and impact range of environmental pollution events. Satellite-retrieved aerosol component data offers high spatial resolution and efficiency, particularly providing significant advantages for near-real-time monitoring of regional atmospheric environmental events. Full article

As an emerging technology, digital twin (DT) is increasingly valued in bridge management for its potential to optimize asset operation and maintenance (O&M). However, traditional bridge management systems (BMS) and existing DT applications typically rely on standalone building information modeling (BIM) or geographic information system (GIS) platforms, with limited integration between BIM and GIS or consideration for their underlying graph structures. This study addresses these limitations by developing an integrated DT system that combines WebGIS, WebBIM, and graph algorithms within a three-layer architecture. The system design includes a common data environment (CDE) to address cross-platform compatibility, enabling real-time monitoring, drone-enabled inspection, maintenance planning, traffic diversion, and logistics optimization. Additionally, it features an adaptive data structure incorporating JSON-based bridge defect information modeling and triple-based roadmap graphs to streamline data management and decision-making. This comprehensive approach demonstrates the potential of DTs to enhance bridge O&M efficiency, safety, and decision-making. Future research will focus on further improving cross-platform interoperability to expand DT applications in infrastructure management. Full article

Subjectivity has been an inherent issue in the conventional Fugl-Meyer assessment, which has been the focus of impairment-level recognition in several studies. This study continues our previous work on the use of EMG to recognize finger movement impairment levels. In contrast to our previous work, this study provided a better and more reliable recognition result with improved experimental settings, such as an increased sampling frequency, EMG channels, and extensive patient data. This study employed two data processing mechanisms, inter-subject cross-validation (ISCV) and data-scaled inter-subject cross-validation (DS-ISCV), resulting in two evaluation methods. The machine learning algorithms employed in this study were SVM, random forest (RF), and multi-layer perceptron (MLP). MLP_ISCV achieved the highest average recall score of 0.73 across impairment levels in the spherical grasp task. Subsequently, the highest average recall score of 0.72 among non-majority classes was achieved by SVM_DS-ISCV in the mass extension task. The cross-validation result shows that the proposed method effectively handled the imbalanced dataset without being biased toward the majority class. The proposed method demonstrated the potential to assist doctors in clarifying the subjective assessment of finger movement impairment levels. Full article

For decades, studies have been conducted on the efficiency of gas purification processes with wet scrubbers, including the Venturi scrubbers, and this is the most commonly addressed issue in the field literature. The Venturi scrubber consists of a Venturi nozzle and a cyclone. The article addresses the empirical and analytical studies on the annular–mist flow regime that exists in the throat of the Venturi nozzle with a square cross-section. The uniform distribution of droplets over the cross-section area of the Venturi’s throat strongly correlates with the efficiency of the gas cleaning process using Venturi scrubbers. Due to the above, studies on the physics of the phenomena that affect the quantity of small droplets present in the core of the flow are highly justified. The influence of body forces and diffusive mechanisms impacting the number of droplets in the core flow were investigated to tackle the problem in question. Consequently, the fractions of droplets susceptible to turbulent or inertial–turbulent diffusion mechanisms can now be predicted using the outcomes of the research carried out. The droplets were divided into three fractions that differed by their sizes as follows: airborne droplets I confirm thar italic can be removed in all cases. $(d_d\le$ 10 µm), medium-sized droplets $(d_d\le$ 20 µm), and largest droplets $(d_d$ = (50 − 150) µm). The estimation of diffusion coefficients $\left({\epsilon }_{d,M},{\epsilon }_{d,ref}\right)$ and stopping distances $\left(s_M,s_{ref}\right)$ of all fractions of droplets was carried out with the inclusion $\left({\epsilon }_{d,M},s_M\right)$ and exclusion $\left({\epsilon }_{d,ref},s_{ref}\right)$ of the Magnus lift force $\left(M\right)$ in equations of both the droplet’s stopping distance and its diffusion coefficient. The outcomes revealed that the inclusion of the M force translates significantly to the growth in values of ${\epsilon }_{d,M},s_M$ compared to ${\epsilon }_{d,ref},s_{ref}$. Hence, it was concluded that the M force impacts the increase in the speed of the diffusion of the droplets with $d_d\ge$ 16.45 µm, which is favorable. Hence, the inertial–turbulent diffusion of larger droplets and the turbulent diffusion of medium ones seem to be supported by the M force. The local velocity gradient, which varied within the region of the flow’s hydraulic stabilization also impacted the mass content of droplets with diameter $d_d\le$ 10 µm in the core of the flow. As the flow development progressed, the number of droplets measured at n = 5 Hz varied nonlinearly up to the point where the boundary layer thickness reached the channel radius. The quantity of small droplets in the main flow was significantly influenced by turbulence intensity $\left(Tu\right)$. The desired high number of small droplets in the core of the flow (mist flow) was estimated empirically, and it was achieved when gas flows at high speed and has a mean value of Tu. The former benefits the efficiency of gas purification. Investigations on the effects of body forces of inertia of the continuous phase on the separation of droplets with diameters of a few microns and sub-microns from the flow were performed by employing two channel elbows, namely $e_4$ and $e_1$. The curved channels were subsequently mounted at the end of the straight channel ($SC{h}_2$). The curvature angle ($\alpha$) of the $e_4$ and $e_1$ equaled $90{}^{\circ }$C and $30{}^{\circ }$C, respectively. The number of droplets existing in the mist flow was higher in value, as desired, when the $e_4$ was used, unlike $e_1$. Two-dimensional flow fields of the mist have been obtained using the Particle Imaging Velocimetry (PIV) technique and analyzed further . Topas LAP 332 Aerosol Spectrometer was used for the determination of droplet ($d_d\le$ 40 µm) size distribution (DSD) and particle concentrations, while the Droplet Size Analyzer D Kamika Instruments (DSA) was exploited to ascertain DSD of droplets with diameter $d_d>40$ µm . Full article

Sambucus sibirica, a deciduous shrub from the Adoxaceae family, is a traditional Kazakh medicine used in Xinjiang, China. Its branches, leaves, and fruits are used to treat fractures, rheumatoid arthritis, and nephritis. To advance research on S. sibirica, we conducted studies on its microscopic identification, chemical composition, and biological activity. The cross-sectional features of the branches, leaves, and fruits were observed under a microscope, revealing different types of ducts, cork cells, non-glandular hairs, oil droplets, stone cells, scale hairs, and star-shaped hairs in the S. sibirica powders. Fourier transform infrared spectroscopy (FTIR) was used to characterize the presence of specific chemical groups, revealing similarities and differences between different parts. Thin-layer chromatography (TLC) confirmed that chlorogenic acid was present in the branches, leaves, and fruits, whereas rutin was more prominent in the leaves. The total flavonoid contents were determined by a photocolorimetric approach and resulted in values of 7419.80, 5193.10, and 3629.10 μg·g⁻¹ (dry weight) for the leaves, branches, and fruits, respectively. Further qualitative and quantitative analyses via ultra-performance liquid chromatography coupled with triple quadrupole tandem mass spectrometry (UPLC-QqQ-MS/MS) identified rutin, chlorogenic acid, quercetin, isoquercetin, and astragalin, with contents ranging from 1.00 to 4535.60 μg·g⁻¹ (dry weight). Antioxidant tests revealed that the branches, leaves, and fruits of S. sibirica presented antioxidant properties, with the leaves demonstrating the highest activity, followed by the branches and fruits. These results align with the results of the quantitative analysis. This study provides valuable insights into the microscopic features, chemical composition, and antioxidant activity of S. sibirica, laying the foundation for its pharmacognosy research and quality standards and offering a reference for its future development and utilization. Full article

The tribological performance of PFPE oil and the Ti:WS₂/PFPE composite lubricating system with different oil amounts was investigated under a proton radiation (PR) irradiation environment. After PR irradiation, PFPE molecules occurred during cross-linking and a polymerization reaction and formed a volatile small molecular compound, which deteriorates the tribological performance of the Ti:WS₂/PFPE system. The tribological properties of the Ti:WS₂/PFPE system rely strongly on oil amount. For an unirradiated Ti:WS₂/PFPE system, the amorphous layer of transfer film near the sliding contact area was converted into a well-defined crystalline WS₂ layer with a (002) plane induced by the friction process. After PR irradiation, the transfer film became thicker and showed a wholly amorphous structure due to the difficulty in preventing the entrance of O and showed no reorientation with induced friction. Full article

Electroencephalography (EEG) is a non-invasive technique widely used in neuroscience to diagnose neural disorders and analyse brain activity. However, ocular and myogenic artifacts from eye movements and facial muscle activity often contaminate EEG signals, compromising signal analysis accuracy. While deep learning models are a popular choice for denoising EEG signals, most focus on removing either ocular or myogenic artifacts independently. This paper introduces a novel EEG denoising model capable of handling the simultaneous occurrence of both artifacts. The model uses convolutional layers to extract spatial features and a fully connected layer to reconstruct clean signals from learned features. The model integrates the Adam optimiser, average pooling, and ReLU activation to effectively capture and restore clean EEG signals. It demonstrates superior performance, achieving low training and validation losses with a significantly reduced $RRMSE$ value of 0.35 in both the temporal and spectral domains. A high cross-correlation coefficient of 0.94 with ground-truth EEG signals confirms the model’s fidelity. Compared to the existing architectures and models (FPN, UNet, MCGUNet, LinkNet, MultiResUNet3+, Simple CNN, Complex CNN) across a range of signal-to-noise ratio values, the model shows superior performance for artifact removal. It also mitigates overfitting, underscoring its robustness in artifact suppression. Full article

Fire represents a serious challenge to the safety and integrity of buildings, especially timber structures exposed to high temperatures and intense heat radiation. The combustibility of timber is one of the main reasons why regulations strictly limit timber as a building material, especially in multi-storey structures. This investigation seeks to assess the fire behaviour of cross-laminated timber (CLT) edifices and examine the ramifications for structural integrity and fire protection. Utilising computational fluid dynamics (CFD) simulations, critical variables including charring rate, heat emission, and smoke generation were analysed across two scenarios: one featuring exposed CLT and another incorporating protected CLT. The outcomes indicated that protective layers markedly diminish charring rates and heat emission, thereby augmenting fire resistance and constraining smoke dissemination. These revelations imply that CFD-based methodologies can proficiently inform fire protection design paradigms for CLT structures, presenting potential cost efficiencies by optimising material utilisation and minimising structural impairment. Full article

In order to improve the accuracy and efficiency of flow pattern recognition and to solve the problem of the real-time monitoring of flow patterns, which is difficult to achieve with traditional visual recognition methods, this study introduced a flow pattern recognition method based on a convolutional neural network (CNN), which can recognize the flow pattern under different pressure and flow conditions. Firstly, the complex gas–liquid distribution and its velocity field in the annulus were investigated using a computational fluid dynamics (CFDs) simulation, and the gas–liquid distribution and velocity vectors in the annulus were obtained to clarify the complexity of the flow patterns in the annulus. Subsequently, a sequence model containing three convolutional layers and two fully connected layers was developed, which employed a CNN architecture, and the model was compiled using the Adam optimizer and the sparse classification cross entropy as a loss function. A total of 450 images of different flow patterns were utilized for training, and the trained model recognized slug and annular flows with probabilities of 0.93 and 0.99, respectively, confirming the high accuracy of the model in recognizing annulus flow patterns, and providing an effective method for flow pattern recognition. Full article

Protein–RNA interactions are essential to many cellular functions, and missense mutations in RNA-binding proteins can disrupt these interactions, often leading to disease. To address this, we developed PRITrans, a specialized computational method aimed at predicting the effects of missense mutations on protein–RNA interactions, which is vital for understanding disease mechanisms and advancing molecular biology research. PRITrans is a novel deep learning model designed to predict the effects of missense mutations on protein–RNA interactions, which employs a Transformer architecture enhanced with multiscale convolution modules for comprehensive feature extraction. Its primary innovation lies in integrating protein language model embeddings with a deep feature fusion strategy, effectively handling high-dimensional feature representations. By utilizing multi-layer self-attention mechanisms, PRITrans captures nuanced, high-level sequence information, while multiscale convolutions extract features across various depths, thereby enhancing predictive accuracy. Consequently, this architecture enables significant improvements in ΔΔG prediction compared to traditional approaches. We validated PRITrans using three different cross-validation strategies on two newly reconstructed mutation datasets, S315 and S630 (containing 315 forward and 315 reverse mutations). The results consistently demonstrated PRITrans’s strong performance on both datasets. PRITrans demonstrated strong predictive capability, achieving a Pearson correlation coefficient of 0.741 and a root mean square error (RMSE) of 1.168 kcal/mol on the S630 dataset. Moreover, its robust performance extended to independent test sets, achieving a Pearson correlation of 0.699 and an RMSE of 1.592 kcal/mol. These results underscore PRITrans’s potential as a powerful tool for protein-RNA interaction studies. Moreover, when tested against existing prediction methods on an independent dataset, PRITrans showed improved predictive accuracy and robustness. Full article

This work’s objective is to investigate the laminar steady flow characteristics of non-Newtonian nano-fluids in a developed chaotic microdevice known as a two-layer crossing channels micromixer (TLCCM). The continuity equation, the 3D momentum equations, and the species transport equations have been solved numerically at low Reynolds numbers with the commercial CFD software Fluent. A procedure has been verified for non-Newtonian flow in studied geometry that is continuously heated. Secondary flows and thermal mixing performance with two distinct intake temperatures of nano-shear thinning fluids is involved. For an extensive range of Reynolds numbers (0.1 to 25), the impact of fluid characteristics and various concentrations of Al₂O₃ nanoparticles on thermal mixing capabilities and pressure drop were investigated. The simulation for performance enhancement was run using a power-law index (n) at intervals of different nanoparticle concentrations (0.5 to 5%). At high nano-fluid concentrations, our research findings indicate that hydrodynamic and thermal performances are considerably improved for all Reynolds numbers because of the strong chaotic flow. The mass fraction visualization shows that the suggested design has a fast thermal mixing rate that approaches 0.99%. As a consequence of the thermal and hydrodynamic processes, under the effect of chaotic advection, the creation of entropy governs the second law of thermodynamics. Thus, with the least amount of friction and thermal irreversibilities compared to other studied geometries, the TLCCM arrangement confirmed a significant enhancement in the mixing performance. Full article

A passive flow control technique in the form of microfiber coatings with a diverging pillar cross-section area was applied to the wing suction surface of a small tailless unmanned aerial vehicle (UAV). The coatings are inspired from ‘gecko feet’ surfaces, and their impact on steady and unsteady aerodynamics is assessed through wind tunnel testing. Angles of attack from −2° to 17° were used for static experiments, and for some cases, the elevon control surface was deflected to study its effectiveness. In forced oscillation, various combinations of mean angle of attack, frequency and amplitude were explored. The aerodynamic coefficients were calculated from load cell measurements for experimental variables such as microfiber size, the region of the wing coated with microfibers, Reynolds number and angle of attack. Microfibers with a 140 µm pillar height reduce drag by a maximum of 24.7% in a high-lift condition and cruise regime, while 70 µm microfibers work best in the stall flow regime, reducing the drag by 24.2% for the same high-lift condition. Elevon deflection experiments showed that pitch moment authority is significantly improved near stall when microfibers cover the control surface and upstream, with an increase in CM magnitude of up to 22.4%. Dynamic experiments showed that microfibers marginally increase dynamic damping in pitch, improving load factor production in response to control surface actuation at low angles of attack, but reducing it at higher angles. In general, the microfiber pillars are within the laminar boundary layer, and they create a periodic slip condition on the top surface of the pillars, which increases the near-wall momentum over the wing surface. This mechanism is particularly effective in mitigating flow separation at high angles of attack, reducing pressure drag and restoring pitching moment authority provided by control surfaces. Full article

Automated water body (WB) extraction is one of the hot research topics in the field of remote sensing image processing. To address the challenges of over-extraction and incomplete extraction in complex water scenes, we propose an encoder–decoder architecture semantic segmentation network for high-precision extraction of WBs called EDWNet. We integrate the Cross-layer Feature Fusion (CFF) module to solve difficulties in segmentation of WB edges, utilizing the Global Attention Mechanism (GAM) module to reduce information diffusion, and combining with the Deep Attention Module (DAM) module to enhance the model’s global perception ability and refine WB features. Additionally, an auxiliary head is incorporated to optimize the model’s learning process. In addition, we analyze the feature importance of bands 2 to 7 in Landsat 8 OLI images, constructing a band combination (RGB 763) suitable for algorithm’s WB extraction. When we compare EDWNet with various other semantic segmentation networks, the results on the test dataset show that EDWNet has the highest accuracy. EDWNet is applied to accurately extract WBs in the Weihe River basin from 2013 to 2021, and we quantitatively analyzed the area changes of the WBs during this period and their causes. The results show that EDWNet is suitable for WB extraction in complex scenes and demonstrates great potential in long time-series and large-scale WB extraction. Full article

Routine visual inspection is fundamental to the preventive maintenance of power equipment. Convolutional neural networks (CNNs) substantially reduce the number of parameters and efficiently extract image features for classification tasks. In the actual production and operation process of substations, due to the limitation of safety distance, camera monitoring, inspection robots, etc., cannot be very close to the target. The operational environment of power equipment leads to scale variations in the main target and thus compromises the performance of conventional models. To address the challenges posed by scale fluctuations in power equipment image datasets, while adhering to the requirements for model efficiency and enhanced inter-channel communication, this paper proposed the ResNet Cross-Layer Parameter Sharing (ResNetCPS) framework. The core idea is that the network output should remain consistent for the same object at different scales. The proposed framework facilitates weight sharing across different layers within the convolutional network, establishing connections between pertinent channels across layers and leveraging the scale invariance inherent in image datasets. Additionally, for substation image processing mainly based on edge devices, smaller models must be used to reduce the expenditure of computing power. The Cross-Layer Parameter Sharing framework not only reduces the overall number of model parameters but also decreases training time. To further enhance the representation of critical features while suppressing less important or redundant ones, an Inserting and Adjacency Attention (IAA) module is designed. This mechanism improves the model’s overall performance by dynamically adjusting the importance of different channels. Experimental results demonstrate that the proposed method significantly enhances network efficiency, reduces the total parameter storage space, and improves training efficiency without sacrificing accuracy. Specifically, models incorporating the Cross-Layer Parameter Sharing module achieved a reduction in the number of parameters and model size by 10% to 30% compared to the baseline models. Full article

For feedback to be productive, it relies on the interactions of participants, design elements, and resources. Yet, complexities in clinical education pose challenges for feedback practices in students and teachers, and efforts to improve feedback often ignore the influence of culture and context. A recent sociocultural approach to feedback practices recognized three layers to understand the complexity of productive feedback: the encounter layer, the design layer, and the knowledge layer. This study explores the sociocultural factors that influence productive feedback practices in clinical settings from the clinical teacher–student dyad perspective. A cross-sectional qualitative study in a physiotherapy clerkship involved semi-structured interviews with ten students and eight clinical educators. Convenience sampling was used, and participation was voluntary. Employing thematic analysis from a sociocultural perspective, this study examined feedback practices across the three layers of feedback practices. The analysis yielded different elements along the three layers that enable productive feedback practices in the clinical workplace: (1) the feedback encounter layer: dyadic relationships, mutual trust, continuity of supervision, and dialogue; (2) the feedback design layer: enabled learning opportunities and feedback scaffolding; (3) the knowledge domain layer in the clinical culture: Growing clinical experience and accountability. In the context of undergraduate clinical education, productive feedback practices are shaped by social–cultural factors. Designing feedback practices should consciously integrate these components, such as cultivating relationships, fostering guidance, enhancing feedback agency, and enabling supervised autonomy to promote productive feedback. Full article