Search Results (105)

The offshore reservoir development involves large injection and production rates and high injection pressures. High-permeability flow channels usually occur in offshore unconsolidated heavy-oil reservoirs during long-term water flux, substantially impacting the production performance. As one important method for identifying channeling, the numerical simulation method with a full-fidelity model is hampered by the low computational efficiency of the history matching process. The GPSNet model is extended for polymer flooding simulations, incorporating complex mechanisms including adsorption and shear-thinning effects, with solutions obtained through a fully implicit numerical scheme. Four flow channel characteristic parameters are proposed, and an evaluation factor M for flow channel identification is established with the comprehensive evaluation method. Finally, the field application of the GPSNet model is made and validated by the tracer interpretation result. The history matching speed based on the GPSNet model is 58 times faster than the full-fidelity ECLIPSE model. In addition, the application demonstrates a high degree of consistency with tracer monitoring results, confirming the accuracy and field feasibility. The new method enables rapid and accurate identification and prediction of large and dominant channels, offering effective guidance for targeted treatment of channels and sustainable development of polymer flooding. Full article

Early and accurate identification of maize diseases is crucial for ensuring sustainable agricultural development. However, existing maize disease identification models face challenges including high inter-class similarity, intra-class variability, and limited capability in identifying early-stage symptoms. To address these limitations, we proposed DSTANet (decomposed spatial token aggregation network), a lightweight and high-performance model for maize leaf disease identification. In this study, we constructed a comprehensive maize leaf image dataset comprising six common disease types and healthy samples, with early and late stages of northern leaf blight and eyespot specifically differentiated. DSTANet employed MobileViT as the backbone architecture, combining the advantages of CNNs for local feature extraction with transformers for global feature modeling. To enhance lesion localization and mitigate interference from complex field backgrounds, DSFM (decomposed spatial fusion module) was introduced. Additionally, the MSTA (multi-scale token aggregator) was designed to leverage hidden-layer feature channels more effectively, improving information flow and preventing gradient vanishing. Experimental results showed that DSTANet achieved an accuracy of 96.11%, precision of 96.17%, recall of 96.11%, and F1-score of 96.14%. With only 1.9M parameters, 0.6 GFLOPs (floating point operations), and an inference speed of 170 images per second, the model meets real-time deployment requirements on edge devices. This study provided a novel and practical approach for fine-grained and early-stage maize disease identification, offering technical support for smart agriculture and precision crop management. Full article

In response to the need for precise blossom identification and optimization of key operational parameters in intelligent cherry spraying pollination, the SMD-YOLO (You Only Look Once with spatial and channel reconstruction convolution, multi-scale channel attention, and dual convolution modules) cherry blossom detection model is proposed, along with a pollination experiment platform for parameter optimization. The SMD-YOLO model, built upon YOLOv11, enhances feature extraction through the ScConvC3k2 (spatial and channel reconstruction convolution C3k2) module, incorporates the MSCA (multi-scale channel attention) attention mechanism, and employs the DualConv module for a lightweight design, ensuring both detection accuracy and operational efficiency. Tested on a self-constructed cherry blossom dataset, the model delivered a precision of 87.6%, a recall rate of 86.1%, and an mAP (mean average precision) reaching 93.1% with a compact size of 4765 KB, 2.28 × 10⁶ parameters, a computational cost of 5.8 G, and a detection speed of 75.76 FPS, demonstrating strong practicality and potential for embedded real-time detection in edge devices, such as cherry pollination robots. To further enhance pollination effectiveness, a dedicated pollination experiment bench was designed, and a second-order orthogonal rotational combination experiment method was employed to systematically optimize three key parameters: spraying distance, spraying time, and liquid flow rate. Experimental results indicate that the optimal pollination effect occurs when the spraying distance is 3.4 cm, spraying time is 1.9 s, and liquid flow rate is 339 mL/min, with a deposition amount of 0.18 g and a coverage rate of 97.25%. This study provides a high-precision image detection algorithm and operational parameter optimization basis for intelligent and precise cherry blossom pollination. Full article

The development of inter-well channeling pathways has become a major challenge restricting the effectiveness of the thermal recovery process for heavy oil reservoirs, which leads to non-uniform sweep and reduced oil recovery. This is especially true for the characteristics of the higher injection–production intensity in offshore operations, making the issue more prominent. In this study, a quick and widely applicable approach is proposed for channeling identification, utilizing the static reservoir parameters and injection–production performance. The results show that the cumulative injection–production pressure differential (CIPPD) over the cumulative water equivalent (CWE) exhibits a linear relationship when connectivity exists between the injection and production wells. Thereafter, the seepage resistance could be analyzed quantitatively by the slope of the linear relationship during the steam injection process. Simultaneously, a channeling identification chart could be obtained based on the data of injection–production performance, dividing the steam flooding process into three different stages, including the energy recharge zone, interference zone, and channeling zone. Then, the established channeling identification chart is applied to injection–production data from two typical wells in the Bohai oilfield. From the obtained channeling identification chart, it is shown that Well X1 exhibits no channeling, while Well X2 exhibited channeling in the late stage of the steam flooding process. These findings are validated against the field performance (i.e., the liquid rate, water cut, flowing temperature, and flowing pressure) to confirm the accuracy. The channeling identification approach in this paper provides a guide for operational adjustments to improve the effect of the thermal recovery process in the field. Full article

To commercialize supersonic and hypersonic passenger aircraft and reusable spaceplanes, we are developing a small-scale supersonic flight experiment vehicle as a flying testbed for technical demonstrations in high-speed flight environments. This experiment vehicle is equipped with a fuel tank and an oxidizer tank, and the propellants inside the tanks slosh due to changes in acceleration during flight. In this situation, there is a risk of gas entrainment during liquid discharge, which could potentially cause an engine malfunction. To avoid such a situation, we considered installing a propellant management device (PMD) inside the tank to suppress the gas entrainment. In this study, a capillary type PMD with a screen channel structure, commonly used in satellites featuring no moving parts, was adopted due to its applicability to a wide acceleration range. The PMD was designed with a structure featuring cylindrical mesh screen nozzles installed at the top and bottom of a cylindrical tank. A one-dimensional flow analysis model was developed taking into account factors such as the pressure loss across the mesh screens and the flow loss within the mesh screen nozzles, which enabled the identification of conditions under which gas entrainment occurred. In this analytical model, separate formulations were developed using Hartwig’s and Ingmanson’s formulas for evaluating the flow losses through the mesh screens. Furthermore, by applying the flow analysis model, the specifications of the mesh screens as key parameters of the PMD, together with the nozzle diameter and nozzle length, were selected. Moreover, we fabricated prototype PMDs with each nozzle and conducted visualization tests using a transparent tank. The tests were conducted under static conditions, where a gravitational acceleration acted downward, and the effects of the cylindrical mesh screen length and discharge flow rate on the free surface height at which gas entrainment occurred were investigated. This experiment demonstrated the effectiveness of the propellant acquisition mechanism of the present PMD. The height of the free surface was also compared with the experimental and analytical results, and it was shown that the results obtained by using Ingmanson’s formula for pressure loss through the screen mesh were closer to the experimental results. These findings demonstrated the validity of the one-dimensional flow analysis model. Full article

As air transportation demand continues to rise, congestion in air route networks has seriously compromised the safe and efficient operation of air traffic. Few studies have examined the spatiotemporal characteristics of congestion propagation under different time lag conditions. To address this gap, this study proposes a cross-segment congestion-propagation causal time-lag analysis framework. First, to account for the interdependency across segments in air route networks, we construct a point–line congestion state assessment model and introduce the FCM-WBO algorithm for precise congestion state identification. Next, the Multi-Channel Attention DSNG-BiLSTM model is designed to estimate the causal weights of congestion propagation between segments. Finally, based on these causal weights, two indicators—CPP and CPF—are derived to analyze the spatiotemporal characteristics of congestion propagation under various time lag levels. The results indicate that our method achieves over 90% accuracy in estimating causal weights. Moreover, the propagation features differ significantly in their spatiotemporal distributions under different time lags. Spatially, congestion sources tend to spread as time lag increases. We also identify segments that are likely to become overloaded, which serve as the primary receivers of congestion. Temporally, analysis of time-lag features reveals that because of higher traffic flow during peak periods, congestion propagates 36.92% more slowly than during the early-morning hours. By analyzing congestion propagation at multiple time lags, controllers can identify potential congestion sources in advance. They can then implement targeted interventions during critical periods, thereby alleviating congestion in real time and improving route-network efficiency and safety. Full article

Climate changes have increased heavy rainfall, intensifying flood damage, especially along small streams with steep slopes, fast flows, and narrow widths. In Korea, nearly half of flood-related casualties occur in these regions, underscoring the need for effective flood early warning systems. However, predicting flood depths is challenging due to the complex channels and rapid flood wave propagation in small streams. This study developed a flood early warning framework (FEWF) tailored for small streams in Korea, optimizing rainfall–discharge nomographs using hydro-informatic data from four streams. The FEWF integrates a four-parameter logistic model with real-time updates with a nomograph using a robust constrained nonlinear optimization algorithm. A simplified two-level early warning system (attention and severe) is based on field-verified thresholds. Discharge predictions estimate the water depth in unmeasured cross-sections using the Manning formula, with real-time data updates allowing for the dynamic identification of the flood depth. The framework was validated during the 2022 flood event, where no inundation or bank failures were observed. By improving flood prediction and adaptive management, this framework can significantly enhance disaster response and reduce casualties in vulnerable small stream areas. Full article

Rip currents are fast-moving, narrow channels of water that flow seaward from the shoreline, typically forming within the surf zone and extending beyond the wave-breaking region. These currents pose significant hazards to swimmers, contributing to numerous drowning incidents, especially with the increasing popularity of ocean recreation. Despite their prevalence, rip currents remain difficult to detect visually, and no universally reliable method exists for their identification by beachgoers. To address this challenge, this study presents a novel approach for detecting rip currents in optical images using wavelet-based edge detection and image convolution techniques. Five identification criteria were established based on previous literature and expert observations. The proposed program incorporates image augmentation, averaging, and frame aggregation to enhance generalization and accuracy. Experimental analysis involving four iterations and four wavelet bases demonstrated that using two iterations with the Daubechies wavelet yielded the highest interpretation accuracy (88.3%). Performance evaluation using a confusion matrix further confirmed an accuracy rate of 83.0%. The results indicate that the proposed method identifies rip currents in images, offering a valuable tool for researchers studying rip current patterns. This approach lays the groundwork for future advancements in rip current detection and related research. Full article

Encrypted network traffic classification remains a critical component in network security monitoring. However, existing approaches face two fundamental limitations: (1) conventional methods rely on manual feature engineering and are inadequate in handling high-dimensional features; and (2) they lack the capability to capture dynamic temporal patterns. This paper introduces TransECA-Net, a novel hybrid deep learning architecture that addresses these limitations through two key innovations. First, we integrate ECA-Net modules with CNN architecture to enable automated feature extraction and efficient dimension reduction via channel selection. Second, we incorporate a Transformer encoder to model global temporal dependencies through multi-head self-attention, supplemented by residual connections for optimal gradient flow. Extensive experiments on the ISCX VPN-nonVPN dataset demonstrate the superiority of our approach. TransECA-Net achieved an average accuracy of 98.25% in classifying 12 types of encrypted traffic, outperforming classical baseline models such as 1D-CNN, CNN + LSTM, and TFE-GNN by 6.2–14.8%. Additionally, it demonstrated a 37.44–48.84% improvement in convergence speed during the training process. Our proposed framework presents a new paradigm for encrypted traffic feature disentanglement and representation learning. This paradigm enables cybersecurity systems to achieve fine-grained service identification of encrypted traffic (e.g., 98.9% accuracy in VPN traffic detection) and real-time responsiveness (48.8% faster than conventional methods), providing technical support for combating emerging cybercrimes such as monitoring illegal transactions on darknet networks and contributing significantly to adaptive network security monitoring systems. Full article

Calorimetry at the High Luminosity LHC (HL-LHC) faces many challenges, particularly in the forward direction, such as radiation tolerance and large in-time event pileup. To meet these challenges, the CMS Collaboration is preparing to replace its current endcap calorimeters from the HL-LHC era with a high-granularity calorimeter (HGCAL), featuring an unprecedented transverse and longitudinal segmentation, for both the electromagnetic and hadronic compartments, with 5D information (space–time–energy) read out. The proposed design uses silicon sensors for the electromagnetic section (with fluences above ${10}^{16} {\mathrm{n}}_{eq}{\mathrm{/cm}}^2$) and high-irradiation regions (with fluences above ${10}^{14} {\mathrm{n}}_{eq}{\mathrm{/cm}}^2$) of the hadronic section, while in the low-irradiation regions of the hadronic section, plastic scintillator tiles equipped with on-tile silicon photomultipliers (SiPMs) are used. Full HGCAL will have approximately 6 million silicon sensor channels and about 280 thousand channels of scintillator tiles. This will allow for particle-flow-type calorimetry, where the fine structure of showers can be measured and used to enhance particle identification, energy resolution and pileup rejection. In this overview we present the ideas behind HGCAL, the current status of the project, results of the beam tests and the challenges that lie ahead. Full article

Human pose estimation is an important research direction in the field of computer vision, which aims to accurately identify the position and posture of keypoints of the human body through images or videos. However, multi-person pose estimation yields false detection or missed detection in dense crowds, and it is still difficult to detect small targets. In this paper, we propose a Mamba-based human pose estimation. First, we design a GMamba structure to be used as a backbone network to extract human keypoints. A gating mechanism is introduced into the linear layer of Mamba, which allows the model to dynamically adjust the weights according to the different input images to locate the human keypoints more precisely. Secondly, GMamba as the backbone network can effectively solve the long-sequence problem. The direct use of convolutional downsampling reduces selectivity for different stages of information flow. We used slice downsampling (SD) to reduce the resolution of the feature map to half the original size, and then fused local features from four different locations. The fusion of multi-channel information helped the model obtain rich pose information. Finally, we introduced an adaptive threshold focus loss (ATFL) to dynamically adjust the weights of different keypoints. We assigned higher weights to error-prone keypoints to strengthen the model’s attention to these points. Thus, we effectively improved the accuracy of keypoint identification in cases of occlusion, complex background, etc., and significantly improved the overall performance of attitude estimation and anti-interference ability. Experimental results showed that the AP and AP50 of the proposed algorithm on the COCO 2017 validation set were 72.2 and 92.6. Compared with the typical algorithm, it was improved by 1.1% on AP50. The proposed method can effectively detect the keypoints of the human body, and provides stronger robustness and accuracy for the estimation of human posture in complex scenes. Full article

This paper presents the diversification of fluvial sediments caused by the occurrence of coarse woody debris (CWD), boulder steps (BSs), and mixed structures (MSs), understood as a combination of CWD and BSs in a stream channel in a small forested catchment in the Polish Carpathians. This research is crucial for understanding the role of this kind of threshold present in a stream channel in shaping fluvial sediment characteristics in small forested mountain catchments. Our hypothesis is that the threshold type in a stream channel determines fluvial sediment diversification. This was verified in field research, including identification of the channel’s morphodynamic structure and the morphometric characteristics of CWD, BSs, and MSs as well as the collection of fluvial sediments upstream and downstream of them. In order to preserve research objectivity, tests were performed during comparable flow conditions in the summer (EX1) and autumn (EX2) periods. The statistical analysis showed that the type of threshold significantly affects the processing, size, and shape diversification of mineral material. This diversity is particularly noticeable in fluvial sediments within CWD and MSs, which retain material of more diverse sizes and shapes. Full article

In terms of facial expressions, micro-expressions are more realistic than macro-expressions and provide more valuable information, which can be widely used in psychological counseling and clinical diagnosis. In the past few years, deep learning methods based on optical flow and Transformer have achieved excellent results in this field, but most of the current algorithms are mainly concentrated on establishing a serialized token through the self-attention model, and they do not take into account the spatial relationship between facial landmarks. For the locality and changes in the micro-facial conditions themselves, we propose the deep learning model MCCA-VNET on the basis of Transformer. We effectively extract the changing features as the input of the model, fusing channel attention and spatial attention into Vision Transformer to capture correlations between features in different dimensions, which enhances the accuracy of the identification of micro-expressions. In order to verify the effectiveness of the algorithm mentioned, we conduct experimental testing in the SAMM, CAS (ME) II, and SMIC datasets and compared the results with other former best algorithms. Our algorithms can improve the UF1 score and UAR score to, respectively, 0.8676 and 0.8622 for the composite dataset, and they are better than other algorithms on multiple indicators, achieving the best comprehensive performance. Full article

A novel methodology is proposed for the development of empirical flow pattern maps for pulsating heat pipes (PHPs), which relies on the concept of virtual superficial velocity of the liquid and vapour phases. The virtual superficial velocity of each phase is defined using solely the design and operational parameters of the pulsating heat pipe, allowing the resulting flow pattern map to serve as a predictive instrument. This contrasts with existing flow pattern maps that necessitate direct measurements of temperatures and/or velocities within one or more channels of the pulsating heat pipe. Specifically, the virtual superficial velocities are derived from the relative significance of the driving forces and the resistances encountered by each phase during flow. The proposed methodology is validated using flow visualisation datasets obtained from two separate experimental campaigns conducted on flat-plate polypropylene pulsating heat pipe prototypes featuring transparent walls and meandering channels with three turns, five turns, seven turns, and eleven turns, respectively. The PHP prototypes were tested for gravity levels ranging between 0 g and 1 g and heat inputs ranging from 5 W to 35 W. The proposed approach enables the identification of empirical boundaries for flow pattern transitions as well as the establishment of an empirical criterion for start-up. Full article

Aquaporins (AQPs) are transmembrane proteins permeable to water and a series of small solutes. AQPs play a key role in pathways of hepatobiliary secretion at the level of the liver, bile ducts, and gallbladder. AQP8 and -9 are pivotal in facilitating the osmotic water movement of hepatic bile, which is composed of 95% water. In the biliary tract, AQP1 and -4 are involved in the rearrangement of bile composition by mechanisms of reabsorption/secretion of water. In the gallbladder, AQP1 and -8 are also involved in trans-epithelial bidirectional water flow with the ultimate goal of bile concentration. Pathophysiologically, AQPs have been indicated as players in several hepatobiliary disorders, including cholestatic diseases and cholesterol cholelithiasis. Research on AQP function and the modulation of AQP expression is in progress, with the identification of potent and homolog-specific compounds modulating the expression or inhibiting these membrane channels with promising pharmacological developments. This review summarizes the contribution of AQPs in physiological and pathophysiological stages related to hepatobiliary function. Full article