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Abstract Fibre‐reinforced polymer (FRP)/rigid polyurethane foam (RPUF) interface is susceptible to moisture intrusion in addition to natural ageing during operation. This study investigated the influence of moisture intrusion on the FRP/RPUF interface under natural ageing using experiments and molecular dynamics (MD) simulations. Water absorption, shear strength, and leakage current tests were used to clarify the changes in the interface performance. The degradation mechanism of moisture intrusion at the interface under natural ageing was revealed by MD simulation and micro‐characterisation. The results show that the natural ageing of interfacial materials will slightly reduce the quality of the interface but will reduce the water resistance of the material and promote the process of water intrusion into the interface. In addition, water mainly invades the interface through RPUF. After water penetration, the interfacial bonding strength and insulation performance significantly decreased. Plasticisation and hydrolysis of interfacial materials are the main reasons for decreased interfacial adhesion. Hydrolysis can cause irreversible damage to the interface, increasing interface defects and water absorption. The vicious cycle of material hydrolysis is the ultimate cause of interfacial debonding. The polar molecules produced by this process and the water absorbed by the interface caused the degradation of the interface insulation performance.

The most common method for interpreting strata with seismic data is to relate the peaks and troughs of adjacent traces based on the seismic waveform characteristics. This can be captured by machine learning and deep learning methods to stratigraphic segmentation as many people investigated in industry. However, the spatial variability and instability of the peaks and troughs of seismic signals increases the difficulty of applying this technology. In addition, the nonlinear relationship and complicated subsurface geological setting make it more difficult. Thus, we propose a new seismic attribute extraction method based on the Gabor wavelet transform and linear dimensionality reduction. This method does not use the peaks or troughs of the seismic signal and instead focuses on the energy change in the seismic signal at the strata interface. It uses the characteristics of the energy change to identify strata. A sliding window Fourier transforms (STFTs) pretreatment is applied to convert the seismic signal to a spectrum energy form. On this basis, the local texture information of the spectrum can be processed by the Gabor wavelet transform to obtain the Gabor attribute of the seismic signal. The seismic Gabor attributes extracted using the above method contain time, frequency, and energy features, solving the problem of single seismic amplitude data features. Finally, the validity of the extracted seismic attributes is verified by a field data. In this process, the seismic amplitude, spectrum data and Gabor attributes are used as sample data for the support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost) models and deep residual shrinkage network (DRSN) for comparison. The results show that when the seismic Gabor attributes are used, the accuracy and root mean square error (RMSE) of the stratigraphic identification with the SVM, RF and XGBoost models are significantly better than those of the seismic amplitude and spectrum data only.

BackgroundPrevious observational studies have shown a correlation between leisure sedentary behaviors (LSB) and physical activity (PA) with the incidence of obstructive sleep apnea (OSA). However, the causal associations remain unknown. Therefore, our study used bidirectional two-sample Mendelian randomization (MR) to identify potential causal relationships between LSB/PA and OSA.MethodsWe sourced genetic variation data for LSB and PA from the UK Biobank, while data on OSA were collected from the FinnGen study. The primary analysis method employed was the inverse variance weighted (IVW) approach, complemented by the weighted median and MR-Egger methods. For sensitivity analyses, we conducted Cochran’s Q test, the MR-Egger intercept test, the MR-PRESSO global test, and the leave-one-out analysis.ResultsIVW analyses showed that genetically predicted leisure television watching (odds ratio [OR] = 1.38, 95% confidence interval [CI] = 1.09–1.75, p = 0.007) and computer use (OR = 1.48, 95% CI = 1.15–1.92, p = 0.002) significantly increased the risk of OSA. Conversely, self-reported vigorous physical activity (VPA) (OR = 0.33, 95% CI = 0.11–0.98, p = 0.046) may reduce the risk of OSA. No causal effects on OSA risk were observed for driving or self-reported moderate-to-vigorous physical activity. Furthermore, the reverse MR analysis indicated no significant causal relationship between OSA and any LSB/PA phenotype. Sensitivity tests showed no significant heterogeneity or horizontal pleiotropy.ConclusionThis study suggests that leisurely television watching and computer use are risk factors for OSA, while VPA may be a protective factor. Additionally, OSA does not affect PA or LSB levels. We recommend reducing sedentary activities, particularly television watching and computer use, and prioritizing VPA to reduce the risk of OSA. Further research in diverse populations and settings is needed to validate these findings.

Usually, deep oil and gas accumulation is often controlled by strike–slip faults. However, in the Tarim Basin, deep Ordovician oil and gas accumulations are also found in areas far from the fault zone. The process of oil and gas accumulation in deep reservoirs far from strike–slip fault zones is still unclear at present. The source and evolution of Ordovician fluids were analyzed using inclusion geochemical methods and the U–Pb dating technique. The analysis of rare earth elements and carbon–oxygen–strontium isotopes in the reservoirs showed that the reservoirs were weakly modified by diagenetic fluid. The fluid was derived from the fluid formation during the same period as the seawater, and no oxidizing fluid invaded the reservoir. The late oil and gas reservoirs had good sealing properties. The U–Pb dating results combined with homogenization temperature data revealed that the first-stage oil was charged during the Late Caledonian Period, and the second-stage natural gas was charged during the Middle Yanshanian Period. The evolution of the paleo-pressure showed that the charging of natural gas in the Middle Yanshanian was the main reason for the formation of reservoir overpressure. The strike–slip fault zone was basically inactive in the Middle Yanshanian. During this period, the charged natural gas mainly migrated to the reservoir along the unconformity surface and the open strike–slip fault zone in the upper part of the Ordovician reservoir. The source of the fluid shows that the reservoir in the late stage had good sealing properties, and there was no intrusion of exogenous fluid. The overpressure in the reservoir is well preserved at present.

The uplift–depression framework controls the source–reservoir assemblage. However, the exploration breakthrough is restricted by an insufficient understanding of the uplift–depression differentiation framework in the Early Cambrian Keping–Wensu area. In this paper, based on field outcrops evaluations, thin section analysis, logging data, drilling data, and 3D seismic data, Wensu low paleo-uplift was discovered in the northern Tarim Basin, and the planar distribution was demonstrated in detail, generally shown as a SW–NE trending nose structure, extending roughly 114 km in length to the southwest, about 35 km in width to the northeast, and with the overall characteristic of being high in the west and low in the east. During the Early Cambrian, the Tabei paleo-uplift evolved into the Wensu low paleo-uplift and largely died out by the Middle Cambrian, with the development of ramps and rimmed carbonate platforms. The tectonic-sedimentary evolution of the uplift–depression framework controlled the development of a set of main source rocks and two sets of large-scale effective reservoir rocks in the Lower Cambrian, constituting two sets of effective hydrocarbon accumulation in the upper and lower stratigraphic parts of the basin. Among them, the upper assemblage holds more potential for hydrocarbon exploration, and is expected to be a next strategic target area for hydrocarbon exploration of Cambrian subsalt in the Keping–Wensu area.

Most single-object trackers currently employ either a convolutional neural network (CNN) or a vision transformer as the backbone for object tracking. In CNNs, convolutional operations excel at extracting local features but struggle to capture global representations. On the other hand, vision transformers utilize cascaded self-attention modules to capture long-range feature dependencies but may overlook local feature details. To address these limitations, we propose a target-tracking algorithm called CVTrack, which leverages a parallel dual-branch backbone network combining CNN and Transformer for feature extraction and fusion. Firstly, CVTrack utilizes a parallel dual-branch feature extraction network with CNN and transformer branches to extract local and global features from the input image. Through bidirectional information interaction channels, the local features from the CNN branch and the global features from the transformer branch are able to interact and fuse information effectively. Secondly, deep cross-correlation operations and transformer-based methods are employed to fuse the template and search region features, enabling comprehensive interaction between them. Subsequently, the fused features are fed into the prediction module to accomplish the object-tracking task. Our tracker achieves state-of-the-art performance on five benchmark datasets while maintaining real-time execution speed. Finally, we conduct ablation studies to demonstrate the efficacy of each module in the parallel dual-branch feature extraction backbone network.

The core body temperature serves as a pivotal physiological metric indicative of sow health, with rectal thermometry prevailing as a prevalent method for estimating core body temperature within sow farms. Nonetheless, employing contact thermometers for rectal temperature measurement proves to be time-intensive, labor-demanding, and hygienically suboptimal. Addressing the issues of minimal automation and temperature measurement accuracy in sow temperature monitoring, this study introduces an automatic temperature monitoring method for sows, utilizing a segmentation network amalgamating YOLOv5s and DeepLabv3+, complemented by an adaptive genetic algorithm-random forest (AGA-RF) regression algorithm. In developing the sow vulva segmenter, YOLOv5s was synergized with DeepLabv3+, and the CBAM attention mechanism and MobileNetv2 network were incorporated to ensure precise localization and expedited segmentation of the vulva region. Within the temperature prediction module, an optimized regression algorithm derived from the random forest algorithm facilitated the construction of a temperature inversion model, predicated upon environmental parameters and vulva temperature, for the rectal temperature prediction in sows. Testing revealed that vulvar segmentation IoU was 91.50%, while the predicted MSE, MAE, and R2 for rectal temperature were 0.114 °C, 0.191 °C, and 0.845, respectively. The automatic sow temperature monitoring method proposed herein demonstrates substantial reliability and practicality, facilitating an autonomous sow temperature monitoring.

The measurement of pig weight holds significant importance for producers as it plays a crucial role in managing pig growth, health, and marketing, thereby facilitating informed decisions regarding scientific feeding practices. On one hand, the conventional manual weighing approach is characterized by inefficiency and time consumption. On the other hand, it has the potential to induce heightened stress levels in pigs. This research introduces a hybrid 3D point cloud denoising approach for precise pig weight estimation. By integrating statistical filtering and DBSCAN clustering techniques, we mitigate weight estimation bias and overcome limitations in feature extraction. The convex hull technique refines the dataset to the pig’s back, while voxel down-sampling enhances real-time efficiency. Our model integrates pig back parameters with a convolutional neural network (CNN) for accurate weight estimation. Experimental analysis indicates that the mean absolute error (MAE), mean absolute percent error (MAPE), and root mean square error (RMSE) of the weight estimation model proposed in this research are 12.45 kg, 5.36%, and 12.91 kg, respectively. In contrast to the currently available weight estimation methods based on 2D and 3D techniques, the suggested approach offers the advantages of simplified equipment configuration and reduced data processing complexity. These benefits are achieved without compromising the accuracy of weight estimation. Consequently, the proposed method presents an effective monitoring solution for precise pig feeding management, leading to reduced human resource losses and improved welfare in pig breeding.

Sow body condition scoring has been confirmed as a vital procedure in sow management. A timely and accurate assessment of the body condition of a sow is conducive to determining nutritional supply, and it takes on critical significance in enhancing sow reproductive performance. Manual sow body condition scoring methods have been extensively employed in large-scale sow farms, which are time-consuming and labor-intensive. To address the above-mentioned problem, a dual neural network-based automatic scoring method was developed in this study for sow body condition. The developed method aims to enhance the ability to capture local features and global information in sow images by combining CNN and transformer networks. Moreover, it introduces a CBAM module to help the network pay more attention to crucial feature channels while suppressing attention to irrelevant channels. To tackle the problem of imbalanced categories and mislabeling of body condition data, the original loss function was substituted with the optimized focal loss function. As indicated by the model test, the sow body condition classification achieved an average precision of 91.06%, the average recall rate was 91.58%, and the average F1 score reached 91.31%. The comprehensive comparative experimental results suggested that the proposed method yielded optimal performance on this dataset. The method developed in this study is capable of achieving automatic scoring of sow body condition, and it shows broad and promising applications.

In order to improve the image quality of the aerial optoelectronic sensor over a wide range of temperature changes, high thermal adaptability of the primary mirror as the critical components is considered. Integrated optomechanical analysis and optimization for mounting primary mirrors are carried out. The mirror surface shape error caused by uniform temperature decrease was treated as the objective function, and the fundamental frequency of the mirror assembly and the surface shape error caused by gravity parallel or vertical to the optical axis are taken as the constraints. A detailed size optimization is conducted to optimize its dimension parameters. Sensitivities of the optical system performance with respect to the size parameters are further evaluated. The configuration of the primary mirror and the flexure are obtained. The simulated optimization results show that the size parameters differently affect the optical performance and which factors are the key. The mirror surface shape error under 30 °C uniform temperature decrease effectively decreased from 26.5 nm to 11.6 nm, despite the weight of the primary mirror assembly increases by 0.3 kg. Compared to the initial design, the value of the system’s modulation transfer function (0° field angle) is improved from 0.15 to 0.21. Namely, the optical performance of the camera under thermal load has been enhanced and thermal adaptability of the primary mirror has been obviously reinforced after optimization. Based on the optimized results, a prototype of the primary mirror assembly is manufactured and assembled. A ground thermal test was conducted to verify difference in imaging quality at room and low temperature, respectively. The image quality of the camera meets the requirements of the index despite degrading.