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Optical and synthetic aperture radar (SAR) images exhibit non-negligible intensity differences due to their unique imaging mechanisms, which makes it difficult for classical SIFT-based algorithms to obtain sufficiently correct correspondences when processing the registration of these two types of images. To tackle this problem, an accurate optical and SAR image registration algorithm based on the SIFT algorithm (OS-PSO) is proposed. First, a modified ratio of exponentially weighted averages (MROEWA) operator is introduced to resolve the sudden dark patches in SAR images, thus generating more consistent gradients between optical and SAR images. Next, we innovatively construct the Harris scale space to replace the traditional difference in the Gaussian (DoG) scale space, identify repeatable key-points by searching for local maxima, and perform localization refinement on the identified key-points to improve their accuracy. Immediately after that, the gradient location orientation histogram (GLOH) method is adopted to construct the feature descriptors. Finally, we propose an enhanced matching method. The transformed relation is obtained in the initial matching stage using the nearest neighbor distance ratio (NNDR) and fast sample consensus (FSC) methods. And the re-matching takes into account the location, scale, and main direction of key-points to increase the number of correctly corresponding points. The proposed OS-PSO algorithm has been implemented on the Gaofen and Sentinel series with excellent results. The superior performance of the designed registration system can also be applied in complex scenarios, including urban, suburban, river, farmland, and lake areas, with more efficiency and accuracy than the state-of-the-art methods based on the WHU-OPT-SAR dataset and the BISTU-OPT-SAR dataset.

In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry, with numerous applications which guide engineers in better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in multiple modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all of these applications lead to considerable computational time and computer resource-associated costs, rendering reservoir simulators as not fast and robust enough, and thus introducing the need for more time-efficient and intelligent tools, such as ML models which are able to adapt and provide fast and competent results that mimic the simulator’s performance within an acceptable error margin. In a recent paper, the developed ML applications in a subsurface reservoir simulation were reviewed, focusing on improving the speed and accuracy of individual reservoir simulation runs and history matching. This paper consists of the second part of that study, offering a detailed review of ML-based Production Forecast Optimization (PFO). This review can assist engineers as a complete source for applied ML techniques in reservoir simulation since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications.

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.

Objective This study aims to evaluate the real-world effectiveness of applying different levels of Enhanced Recovery After Surgery (ERAS) guidelines to video-assisted thoracic day surgery (VATS). The goal is to determine the optimal degree of ERAS protocols and management requirements to improve postoperative recovery outcomes. Methods It was designed as a single-centre, prospective pragmatic randomized controlled trial (PRCT), including patients who underwent VATS at the Day Surgery Center of West China Hospital, between January 2021 and November 2022. Patients were divided into Group A and Group B through convenience sampling to implement different levels of ERAS management protocols. Data collection included the baseline characteristics (gender, age, marital status, education level, BMI, PONV risk score, ASA classification), surgery-related indicators (type of surgery, pathological results, hospitalization costs, duration of surgery, intraoperative blood loss, intraoperative rehydration volume), postoperative recovery indicators (postoperative chest tube duration time, time to first postoperative ambulation and urination, postoperative complications, follow-up condition), pain-related indicators (pain threshold score, pain score at 6 h postoperatively, bedtime, and predischarge), psychological state indicators (anxiety level), Athens Insomnia Scale (AIS) scores, and social support scores. Propensity score matching (PSM) was utilized and statistical analyses were conducted using R version 4.4.1. Comparisons of categorical variables were performed using the χ² test, while comparisons of continuous variables were conducted using ANOVA or the Kruskal-Wallis rank-sum test. A significance level of α = 0.05 was set for statistical tests. Result A total of 340 patients were included, with 187 in Group A and 153 in Group B. After propensity score matching (PSM), there were 142 patients in Group A and 105 in Group B, with no significant baseline differences. Group A had a significantly higher proportion of chest tube removals within 24 h postoperatively ( P  < 0.001) and earlier mobilization ( P  < 0.001). Despite a higher pain threshold in Group A ( P  = 0.016), their postoperative pain scores were not higher than those in Group B. Additionally, Group A had a lower incidence of postoperative complications. Conclusion The more comprehensive ERAS protocol significantly improved postoperative recovery, confirming its value in day-case VATS and supporting its clinical adoption. However, the study has limitations; future research should focus on standardizing ERAS protocols and expanding their application to a broader patient population to validate these findings further. Trail Registration This study underwent review by the Ethics Committee of West China Hospital of Sichuan University under No. 2020 (1001). It has been officially registered with the China Clinical Trial Registry, TRN: ChiCTR2100051372 and registration date is Sept. 22, 2021.

This paper presents a review of history matching and oil field development optimization techniques with a focus on optimization algorithms. History matching algorithms are reviewed as a precursor to production optimization algorithms. Techniques for history matching and production optimization are reviewed including global and local methods. Well placement, well control, and combined well placement-control optimization using both secondary and tertiary oil production techniques are considered. Secondary and tertiary recovery techniques are commonly referred to as waterflooding and enhanced oil recovery (EOR), respectively. Benchmark models for comparison of methods are summarized while other applications of methods are discussed throughout. No single optimization method is found to be universally superior. Key areas of future work are combining optimization methods and integrating multiple optimization processes. Current challenges and future research opportunities for improved model validation and large scale optimization algorithms are also discussed.

Vehicle Logo Recognition (VLR) is an important part of vehicle behavior analysis and can provide supplementary information for vehicle identification, which is an essential research topic in robotic systems. However, the inaccurate extraction of vehicle logo candidate regions will affect the accuracy of logo recognition. Additionally, the existing methods have low recognition rate for most small vehicle logos and poor performance under complicated environments. A VLR method based on enhanced matching, constrained region extraction and SSFPD network is proposed in this paper to solve the aforementioned problems. A constrained region extraction method based on segmentation of the car head and car tail is proposed to accurately extract the candidate region of logo. An enhanced matching method is proposed to improve the detection performance of small objects, which augment each of training images by copy-pasting small objects many times in the unconstrained region. A single deep neural network based on a reduced ResNeXt model and Feature Pyramid Networks is proposed in this paper, which is named as Single Shot Feature Pyramid Detector (SSFPD). The SSFPD uses the reduced ResNeXt to improve classification performance of the network and retain more detailed information for small-sized vehicle logo detection. Additionally, it uses the Feature Pyramid Networks module to bring in more semantic context information to build several high-level semantic feature maps, which effectively improves recognition performance. Extensive evaluations have been made on self-collected and public vehicle logo datasets. The proposed method achieved 93.79% accuracy on the Common Vehicle Logos Dataset and 99.52% accuracy on another public dataset, respectively, outperforming the existing methods.

Background The application of Enhanced Recovery After Surgery (ERAS) protocol in gastrointestinal surgery has been widely accepted. The aim of this study was to compare the effect of ERAS in total robotic distal gastrectomy (TRDG) versus 3D total laparoscopic distal gastrectomy (3D-TLDG) for gastric cancer. Methods We retrospectively evaluated 73 patients underwent TRDG and 163 patients who received 3D-TLDG. The propensity score was used for matching analysis according to a 1:1 ratio, so that there was no significant difference in the baseline data between the two groups. The short-term effect and safety of the two groups were compared. Results The TRDG group had a less intraoperative bleeding (30.21 ± 13.78 vs. 41.44 ± 17.41 ml, P  < 0.001), longer intraoperative preparation time (31.05 ± 4.93 vs. 15.48 ± 2.43 min, P  < 0.001), shorter digestive tract reconstruction time (32.67 ± 4.41 vs. 39.78 ± 4.95 min, P  < 0.001), shorter postoperative ambulation time (14.07 ± 8.97 vs. 17.49 ± 5.98 h, P  = 0.007), shorter postoperative anal exhaust time (1.78 ± 0.79 vs. 2.18 ± 0.79 days, P  = 0.003), shorter postoperative hospital stay (7.74 ± 3.15 vs. 9.97 ± 3.23 days, P  < 0.001), lower postoperative pain score ( P  = 0.006) and higher hospitalization cost (89,907.15 ± 17,147.19 vs. 125,615.82 ± 11,900.80 RMB, P  < 0.001) than the 3D-TLDG group. Conclusion TRDG and 3D-TLDG under ERAS protocol are safe and feasible. Compared with 3D-TLDG, the TRDG has better intraoperative bleeding control effect and greater advantages in digestive tract reconstruction. After the combination of ERAS protocol, TRDG also has certain advantages in the recovery process of patients after surgery.

Various image tampering detection approaches are used to find the variations or inconsistencies in statistical image features. But still these techniques lack behind to identify copy-move and splicing based manipulations. The manipulation in digital data encourages the crimes, particularly in the domain of image processing and computer vision-based applications. Therefore, to find image forgeries, new method needs to be designed so that originality of data is authenticated in the court of law or jurisdiction. To achieve, a pixel based forgery detection framework for copy-move and splicing based forgeries is suggested in this paper. Initially, pre-processing over image data is performed to enhance the textural information. The proposed system estimates various features using enhanced SURF and template matching for the identification of fake image regions. The relevant key parameters are estimated and compared with the calculated threshold value. The evaluation is carried out using CASIA forged image dataset. The results are evaluated and compared with other existing methods through a comprehensive set of experiments. The enhanced SURF method produces a forgery detection accuracy of 97%, while template matching gives 100% forgery detection. As a whole system, the accuracy is 97.5%. Thus, the demonstrated result shows that the proposed framework attains considerably more detection accuracy compared to other state-of-art techniques.

Kalman filter (KF)-based methods for 3D multi-object tracking (MOT) in autonomous driving often face challenges when detections are missed due to occlusions, sensor noise, or objects moving out of view. This leads to data association failures and cumulative errors in the update stage, as traditional Kalman filters rely on linear state estimates that can drift significantly without measurement updates. To address this issue, we propose an enhanced Kalman filter with dummy nodes and prediction confidence (KDPBTracker) to improve tracking continuity and robustness in these challenging scenarios. First, we designed dummy nodes to act as pseudo-observations generated from past and nearby frame detections in cases of missed detection, allowing for stable associations within the data association matrix when real detections were temporarily unavailable. To address the uncertainty in these dummy nodes, we then proposed a prediction confidence score to reflect their reliability in data association. Additionally, we modified a constant acceleration motion model combined with position-based heading estimation to better control high-dimensional numerical fluctuations in the covariance matrix, enhancing the robustness of the filtering process, especially in highly dynamic scenarios. We further designed bipartite graph data association to refine Kalman filter updates by integrating geometric and motion information weighted by the prediction confidence of the dummy nodes. Finally, we designed a confidence-based retention track management module to dynamically manage track continuity and deletion based on temporal and reliability thresholds, improving tracking accuracy in complex environments. Our method achieves state-of-the-art performance on the nuScenes validation set, improving AMOTA by 1.8% over the baseline CenterPoint. Evaluation on the nuScenes dataset demonstrates that KDPBTracker significantly improves tracking accuracy, reduces ID switches, and enhances overall tracking continuity under challenging conditions.

Suitable elastic modulus and particle size of preformed particle gel are the keys to both diverting water flow and avoiding permanent impairment to reservoirs. Therefore, the paper aims at finding the best matched preformed particle gel for given reservoirs using sand-pack displacement experiments. The results show that the injection pressure of preformed particle gel with excessively small size and elastic modulus is relatively low, indicating poor capacity to increase flow resistance and reduce water channeling. On the other hand, if the particle size and elastic modulus of preformed particle gel are excessively large, the reservoir may be plugged and irreversibly damaged, affecting oil development performance. In fact, the best matched particle size and elastic modulus of preformed particle gel increase with the increase in reservoir permeability. Furthermore, the paper establishes a quantitative logarithmic model between the particle size of preformed particle gel and reservoir permeability. Finally, the established matching relationship is validated via microscopic visualization oil displacement experiments using a glass etching model. The validation experiments indicate that the preformed particle gel (60–80 mesh; 2–4 Pa) selected according to the matching relationship can effectively reduce water channeling and increase sweeping efficiency by as much as 55% compared with water flooding in the glass etching model with an average permeability of 2624 × 10−3 μm2. Therefore, the established matching relationship can provide an effective guide when selecting the best suitable preformed particle gel for a given reservoir in more future applications.