Search Results (170)

Nucleic acid strand displacement reactions (SDRs) are fundamental building blocks of dynamic DNA nanotechnology. A detailed understanding of their kinetics is crucial for designing efficient sequences and regulating reaction networks with applications in biosensing, synthetic biology, biocomputing, and medical diagnostics. Since the development of toehold-mediated strand displacement, researchers have devised many strategies to adjust reaction kinetics. These efforts have expanded the available tools in DNA nanotechnology. This review summarizes the basic principles and recent advances in activation strategies, emphasizing the role of strand proximity as a central driving force. Proximity-based approaches include toehold docking, associative toeholds, remote toeholds, and allosteric designs, as well as strategies that operate without explicit toehold motifs. These methods enable flexible and scalable construction of DNA reaction networks. We further discuss how combining different activation and kinetic control approaches gives rise to dynamic networks with complex and dissipative behaviors, providing new directions for DNA-based nanotechnology. Full article

After long-term water injection development in Block M of the Gudong Oilfield, the fluid seepage field in the formation has become increasingly complex, and the heterogeneity of the reservoir has intensified. The uneven injection-production and the differences in lithological permeability have further led to a series of problems such as uneven reserve utilization, a low injection-production correspondence rate, and a poor overall development effect. During the development process, severe water flooding at the bottom and rich remaining oil at the top within the layer also emerged. These issues not only affected the economic benefits of the oilfield but also put forward higher requirements for the subsequent development strategies. In order to solve the above problems, based on a detailed analysis of geological characteristics, reservoir engineering and monitoring data, this paper uses the numerical simulation method to systematically simulate the dynamic changes in the residual oil saturation and pressure field in the formation under different development stages. The simulation results show that the pressure and saturation in each layer are both on a downward trend, especially in the layers with large pressure changes, where the oil saturation changes are more significant. Therefore, combined with the results of numerical simulation, a series of profile control and water shutoff schemes were systematically designed. These schemes covered different types of profile control agents and technological parameters. Representative well groups were selected to predict and evaluate the profile control effect. In the technical and economic evaluation of the profile control effect, the input–output ratio method was adopted. Finally, an optimal scheme was selected and applied in the field. The results show that after the implementation of this scheme, the daily oil increment of the well group was remarkable. During the test period, the cumulative oil increment reached 100 t, and the total expected oil increment could reach 190 t. The input–output ratio reached 1:2.1. The profile control measures significantly improved the injection-production correspondence, slowed down the in-layer water flooding, and further enhanced the recovery rate of remaining oil. In conclusion, the methods and achievements of this study can provide important technical references and support for the efficient and long-term development of similar high-water-cut and heterogeneous complex oil reservoirs, and have guiding significance for the subsequent adjustment, potential tapping, stable production and efficiency improvement of oilfields. Full article

To address the challenges of multi-scale distribution, low contrast and background interference in rock crack identification, this paper proposes an improved Mask R-CNN model (CBAM-BiFPN-Mask R-CNN) that integrates the convolutional block attention mechanism (CBAM) module and the bidirectional feature pyramid network (BiFPN) module. A dataset of 1028 rock surface crack images was constructed. The robustness of the model was improved by dynamically combining Gaussian blurring, noise overlay, and color adjustment to enhance data augmentation strategies. The model embeds the CBAM module after the residual block of the ResNet50 backbone network, strengthens the crack-related feature response through channel attention, and uses spatial attention to focus on the spatial distribution of cracks; at the same time, it replaces the traditional FPN with BiFPN, realizes the adaptive fusion of cross-scale features through learnable weights, and optimizes multi-scale crack feature extraction. Experimental results show that the improved model significantly improves the crack recognition effect in complex rock mass scenarios. The mAP index, precision and recall rate are improved by 8.36%, 9.1% and 12.7%, respectively, compared with the baseline model. This research provides an effective solution for rock crack detection in complex geological environments, especially the missed detection of small cracks and complex backgrounds. Full article

Micro-expressions are extremely subtle and short-lived facial muscle movements that often reveal an individual’s genuine emotions. However, micro-expression recognition (MER) remains highly challenging due to its short duration, low motion intensity, and the imbalanced distribution of training samples. To address these issues, this paper proposes a Global–Local Feature Fusion Network (GLFNet) to effectively extract discriminative features for MER. Specifically, GLFNet consists of three core modules: the Global Attention (LA) module, which captures subtle variations across the entire facial region; the Local Block (GB) module, which partitions the feature map into four non-overlapping regions to emphasize salient local movements while suppressing irrelevant information; and the Adaptive Feature Fusion (AFF) module, which employs an attention mechanism to dynamically adjust channel-wise weights for efficient global–local feature integration. In addition, a class-balanced loss function is introduced to replace the conventional cross-entropy loss, mitigating the common issue of class imbalance in micro-expression datasets. Extensive experiments are conducted on three benchmark databases, SMIC, CASME II, and SAMM, under two evaluation protocols. The experimental results demonstrate that under the Composite Database Evaluation protocol, GLFNet consistently outperforms existing state-of-the-art methods in overall performance. Specifically, the unweighted F1-scores on the Combined, SAMM, CASME II, and SMIC datasets are improved by 2.49%, 2.02%, 0.49%, and 4.67%, respectively, compared to the current best methods. These results strongly validate the effectiveness and superiority of the proposed global–local feature fusion strategy in micro-expression recognition tasks. Full article

Protecting sensitive electronic interfaces is critical in industrial applications, where exposure to harsh conditions and fault events is common. This paper reviews and compares circuit techniques for the design of bidirectional protection switches, highlighting key features such as analog switching, high-voltage capability, thermal shutdown, galvanic input isolation, and adjustable current limiting. Based on this review, we propose a universal architecture that combines the most suitable building blocks identified in the literature, with a focus on options that would enable monolithic integration in high-voltage silicon-on-insulator (SOI) technology and capable of delivering up to 2 A at a maximum voltage of 200 V. The proposed architecture is intended as a design guide for realizing a universal switch, rather than a fabricated implementation. To demonstrate system-level interactions, behavioral MATLAB/Simulink (R2024b) simulations are presented using generic components, which show expected functional responses but are not tied to process-specific device models. Full article

Cotton is one of the world’s most important economic crops, and its yield and quality have a significant impact on the agricultural economy. However, Verticillium wilt of cotton, as a widely spread disease, severely affects the growth and yield of cotton. Due to the typically small and densely distributed characteristics of this disease, its identification poses considerable challenges. In this study, we introduce YOLO-MSPM, a lightweight and accurate detection framework, designed on the YOLOv11 architecture to efficiently identify cotton Verticillium wilt. In order to achieve a lightweight model, MobileNetV4 is introduced into the backbone network. Moreover, a single-head self-attention (SHSA) mechanism is integrated into the C2PSA block, allowing the network to emphasize critical areas of the feature maps and thus enhance its ability to represent features effectively. Furthermore, the PC3k2 module combines pinwheel-shaped convolution (PConv) with C3k2, and the mobile inverted bottleneck convolution (MBConv) module is incorporated into the detection head of YOLOv11. Such adjustments improve multi-scale information integration, enhance small-target recognition, and effectively reduce computation costs. According to the evaluation, YOLO-MSPM achieves precision (0.933), recall (0.920), mAP50 (0.970), and mAP50-95 (0.797), each exceeding the corresponding performance of YOLOv11n. In terms of model lightweighting, the YOLO-MSPM model has 1.773 M parameters, which is a 31.332% reduction compared to YOLOv11n. Its GFLOPs and model size are 5.4 and 4.0 MB, respectively, representing reductions of 14.286% and 27.273%. The study delivers a lightweight yet accurate solution to support the identification and monitoring of cotton Verticillium wilt in environments with limited resources. Full article

Hydraulic fracturing technology significantly enhances reservoir conductivity by creating artificial fractures, serving as a crucial means for the economically viable development of low-permeability reservoirs. Accurate prediction of post-fracturing productivity is essential for optimizing fracturing parameter design and establishing scientific production strategies. However, current limitations in understanding post-fracturing production dynamics and the lack of efficient prediction methods severely constrain the evaluation of fracturing effectiveness and the adjustment of development plans. This study proposes a machine learning-based method for predicting post-fracturing productivity in multi-layer commingled production wells and validates its effectiveness using a key block from the PetroChina North China Huabei Oilfield Company. During the data preprocessing stage, the three-sigma rule, median absolute deviation, and density-based spatial clustering of applications with noise were employed to detect outliers, while missing values were imputed using the K-nearest neighbors method. Feature selection was performed using Pearson correlation coefficient and variance inflation factor, resulting in the identification of twelve key parameters as input features. The coefficient of determination served as the evaluation metric, and model hyperparameters were optimized using grid search combined with cross-validation. To address the multi-layer commingled production challenge, seven distinct datasets incorporating production parameters were constructed based on four geological parameter partitioning methods: thickness ratio, porosity–thickness product ratio, permeability–thickness product ratio, and porosity–permeability–thickness product ratio. Twelve machine learning models were then applied for training. Through comparative analysis, the most suitable productivity prediction model for the block was selected, and the block’s productivity patterns were revealed. The results show that after training with block-partitioned data, the accuracy of all models has improved; further stratigraphic subdivision based on block partitioning has led the models to reach peak performance. However, data volume is a critical limiting factor—for blocks with insufficient data, stratigraphic subdivision instead results in a decline in prediction performance. Full article

Accurate detection and counting of rice ears serve as a critical indicator for yield estimation, but the complex conditions of paddy fields limit the efficiency and precision of traditional sampling methods. We propose REU-YOLO, a model specifically designed for UAV low-altitude remote sensing to collect images of rice ears, to address issues such as high-density and complex spatial distribution with occlusion in field scenes. Initially, we combine the Additive Block containing Convolutional Additive Self-attention (CAS) and Convolutional Gated Linear Unit (CGLU) to propose a novel module called Additive-CGLU-C2F (AC-C2f) as a replacement for the original C2f in YOLOv8. It can capture the contextual information between different regions of images and improve the feature extraction ability of the model, introduce the Dropblock strategy to reduce model overfitting, and replace the original SPPF module with the SPPFCSPC-G module to enhance feature representation and improve the capacity of the model to extract features across varying scales. We further propose a feature fusion network called Multi-branch Bidirectional Feature Pyramid Network (MBiFPN), which introduces a small object detection head and adjusts the head to focus more on small and medium-sized rice ear targets. By using adaptive average pooling and bidirectional weighted feature fusion, shallow and deep features are dynamically fused to enhance the robustness of the model. Finally, the Inner-PloU loss function is introduced to improve the adaptability of the model to rice ear morphology. In the self-developed dataset UAVR, REU-YOLO achieves a precision (P) of 90.76%, a recall (R) of 86.94%, an mAP_0.5 of 93.51%, and an mAP_0.5:0.95 of 78.45%, which are 4.22%, 3.76%, 4.85%, and 8.27% higher than the corresponding values obtained with YOLOv8 s, respectively. Furthermore, three public datasets, DRPD, MrMT, and GWHD, were used to perform a comprehensive evaluation of REU-YOLO. The results show that REU-YOLO indicates great generalization capabilities and more stable detection performance. Full article

To tackle the challenges of complex construction environment interference (e.g., lighting variations, occlusion, and marker contamination) and the demand for high-precision alignment during the hoisting process of bridge-erecting machines, this paper presents a two-stage marker detection–localization network tailored to hoisting alignment. The proposed network adopts a “coarse detection–fine estimation” phased framework; the first stage employs a lightweight detection module, which integrates a dynamic hybrid backbone (DHB) and dynamic switching mechanism to efficiently filter background noise and generate coarse localization boxes of marker regions. Specifically, the DHB dynamically switches between convolutional and Transformer branches to handle features of varying complexity (using depthwise separable convolutions from MobileNetV3 for low-level geometric features and lightweight Transformer blocks for high-level semantic features). The second stage constructs a Transformer-based homography estimation module, which leverages multi-head self-attention to capture long-range dependencies between marker keypoints and the scene context. By integrating enhanced multi-scale feature interaction and position encoding (combining the absolute position and marker geometric priors), this module achieves the end-to-end learning of precise homography matrices between markers and hoisting equipment from the coarse localization boxes. To address data scarcity in construction scenes, a multi-dimensional data augmentation strategy is developed, including random homography transformation (simulating viewpoint changes), photometric augmentation (adjusting brightness, saturation, and contrast), and background blending with bounding box extraction. Experiments on a real bridge-erecting machine dataset demonstrate that the network achieves detection accuracy (mAP) of 97.8%, a homography estimation reprojection error of less than 1.2 mm, and a processing frame rate of 32 FPS. Compared with traditional single-stage CNN-based methods, it significantly improves the alignment precision and robustness in complex environments, offering reliable technical support for the precise control of automated hoisting in bridge-erecting machines. Full article

Small object detection in UAV-based remote sensing imagery is crucial for applications such as traffic monitoring, emergency response, and urban management. However, aerial images often suffer from low object resolution, complex backgrounds, and varying lighting conditions, leading to missed or false detections. To address these challenges, we propose DCS-YOLOv8, an enhanced object detection framework tailored for small target detection in UAV scenarios. The proposed model integrates a Dynamic Convolution Attention Mixture (DCAM) module to improve global feature representation and combines it with the C2f module to form the C2f-DCAM block. The C2f-DCAM block, together with a lightweight SCDown module for efficient downsampling, constitutes the backbone DCS-Net. In addition, a dedicated P2 detection layer is introduced to better capture high-resolution spatial features of small objects. To further enhance detection accuracy and robustness, we replace the conventional CIoU loss with a novel Scale-based Dynamic Balanced IoU (SDBIoU) loss, which dynamically adjusts loss weights based on object scale. Extensive experiments on the VisDrone2019 dataset demonstrate that the proposed DCS-YOLOv8 significantly improves small object detection performance while maintaining efficiency. Compared to the baseline YOLOv8s, our model increases precision from 51.8% to 54.2%, recall from 39.4% to 42.1%, $mA{P}_{0.5}$ from 40.6% to 44.5%, and $mA{P}_{0.5:0.95}$ from 24.3% to 26.9%, while reducing parameters from 11.1 M to 9.9 M. Moreover, real-time inference on RK3588 embedded hardware validates the model’s suitability for onboard UAV deployment in remote sensing applications. Full article

The G Block of Daqing Oilfield is a crucial area for sustainable development and stable production. In addressing the technical bottlenecks of high-resolution logging data interpretation for reservoir evaluation in the Block, this study proposes a resistivity curve prediction method based on machine learning algorithms. Traditional interpretation models relying on DLS logging data face two major challenges when applied to 0.2 m high-resolution logging: first, the interpreted effective thickness of the reservoir tends to be overestimated, and second, the accuracy of fluid property identification declines. Additionally, the lack of corresponding well-test data for new logging datasets further constrains the development of interpretation models. To tackle these challenges, this study employs the XGBoost algorithm to construct a high-precision resistivity prediction model. Through systematic analysis of various logging parameter combinations, the optimal feature set comprising HAC, MSFL, and GR curves was identified. Training and testing results demonstrate that the model achieves a mean absolute error (MAE) of 0.94 Ω·m and a root mean square error (RMSE) of 1.79 Ω·m in predicting resistivity. After optimization, the model’s performance improved significantly, with MAE and RMSE reduced to 0.75 Ω·m and 1.31 Ω·m, respectively. To evaluate the model’s reliability, an external validation test was conducted on Well GFX2, yielding MAE and RMSE values of 0.91 Ω·m and 1.43 Ω·m, confirming the model’s strong generalization capability. Furthermore, the RLLD-AC and RLLD-DEN crossplots constructed from the predicted results exhibit excellent fluid identification performance in practical applications, achieving an accuracy rate exceeding 89%, which aligns well with production test data. The findings of this study provide new technical support for fine reservoir characterization in the study area and offer significant practical guidance for development plan adjustments. Full article

Well pattern infill adjustment combined with chemical flooding is an important technical approach for significantly improving oil recovery in high-water-cut reservoirs after polymer flooding. Current research predominantly focuses on the evaluation of oil displacement potential through either well pattern infilling or chemical flooding alone, while systematic experimental investigations and mechanism studies on the synergistic effect of well pattern infilling and chemical flooding remain insufficient. To overcome the limitations of single adjustment measures, this study proposes a synergistic improved oil recovery (IOR) strategy integrating branched preformed particle gel (B-PPG) heterogeneous phase composite flooding (HPCF) with well pattern infill adjustment. Two-dimensional visual physical simulation experiments are conducted to evaluate the synergistic oil displacement effects of different displacement systems and well pattern adjustment strategies after polymer flooding and to elucidate the synergistic IOR mechanisms under the coupling of dense well patterns and chemical flooding. The experimental results demonstrate that, under well pattern infill conditions, the HPCF system exhibits significant water control and oil enhancement effects during the chemical flooding stage, achieving a 29.95% increase in stage recovery compared to the water flooding stage. The system effectively blocks high-permeability channels while enhancing displacement in low-permeability zones through a coupling effect, thereby significantly expanding the displacement sweep volume, improving displacement uniformity, and efficiently mobilizing the remaining oil in low-permeability and residual oil-rich areas. Meanwhile, well pattern infill adjustment optimizes the injection–production well pattern layout, shortens the inter-well spacing, and effectively increases the displacement pressure differential between injection and production wells. This induces disturbances and reconfiguration of the streamline field, disrupts the original high-permeability channel-dominated flow regime, further expands the sweep range of the remaining oil, and substantially improves overall oil recovery. The findings of this study enrich and advance the theoretical framework of water control and potential tapping, as well as synergistic IOR mechanisms, in high-water-cut and strongly heterogeneous reservoirs, providing a reliable theoretical and technical basis for the efficient development and remaining oil recovery in such reservoirs during the late production stage. Full article

The degree of anisotropy and heterogeneity in coarse-grained materials significantly affects ultrasonic propagation behavior and scattering. This paper proposes a vector coherent factor threshold total focusing imaging method (VCF-T-TFM) for austenitic stainless steel, based on material properties, through a combination of simulation and experimentation. Three types of austenitic stainless steel weld test blocks with varying degrees of heterogeneity were selected containing multiple side-drilled hole defects, each with a diameter of 2 mm. Full-matrix data were collected using a 32-element phased array probe with a center frequency of 5 MHz. The grain size and orientation of the material were quantitatively observed via electron backscatter diffraction (EBSD). By combining the instantaneous phase distribution of the TFM image, the coarse-grained material coherence compensation value (C_A) and probability threshold (P_T) were optimized for different heterogeneous regions, and the vector coherence imaging threshold (γ) was adjusted. The defect imaging results of homogeneous material (carbon steel) and three austenitic stainless steels with different levels of heterogeneity were compared, and the influence of coarse-grained, anisotropic heterogeneous structures on the imaging signal-to-noise ratio was analyzed. The results show that the VCF-T-TFM, which considers the influence of material properties on phase coherence, can suppress structural noise. Compared to compensation results that did not account for material properties, the signal-to-noise ratio was improved by 97.3%. Full article

The problem of protecting 7075 Al alloy trekking poles from corrosion in complex outdoor environments was addressed using a composite conversion coating system. This system comprised Na₂MoO₄, NaF, CoSO₄·7H₂O, ethylenediaminetetraacetic acid-2Na, and H₂(TiF₆). The influences of this system on the properties of the coating layer were systematically studied by adjusting the pH of the coating solution. The conversion temperature and pH were the pivotal parameters influencing the formation of the conversion coating. The pH substantially influenced the compactness of the coating layer, acting as a regulatory agent of the coating kinetics. When the conversion temperature and pH were set to 40 °C and 3.8, respectively, the prepared coating layer displayed optimal performance in terms of compactness and protective properties. Therefore, this parameter combination favours the synthesis of high-performance conversion coatings. Microscopy confirmed the formation of a continuous, dense composite oxide film structure under these conditions, effectively blocking erosion in corrosive media. Furthermore, the optimised process led to substantial enhancements in the environmental adaptabilities and service lives of the components of trekking poles, thus establishing a theoretical foundation and technical reference for use in the surface protection of outdoor equipment. Full article

This article presents a probabilistic methodology for assessing corrosion initiation in reinforced concrete structures exposed to chloride ingress. The approach addresses key limitations of conventional analytical models by accounting for non-diffusive barriers and incorporating a rigorous statistical treatment of censored data to mitigate biases introduced by limited simulation durations. A combination of analytical solutions for diffusion from opposite sides with time-dependent boundary conditions is also proposed and validated. The probabilistic study includes the depassivation assessment of a hollow pier section. The blocking effect caused by rebars is statistically characterised through correction factors derived from finite element simulations. These factors are used to adjust analytical solutions, which are computationally inexpensive. Results show that neglecting the rebar blocking effect can overestimate the mean corrosion initiation time by up to 42%, while the use of censored data reduces bias in lifetime estimates. The observed frequency of censored events reached up to 20% when simulations were truncated at 100 years. The corrected analytical models closely match the finite element results, statistically validating their application. The case study indicates premature corrosion initiation (less than 10 years to achieve target reliability), underscoring the need to better reconcile the desired levels of reliability with realistic input parameters for depassivation. Full article