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This study investigated the point-wise superconvergence of block finite elements for the variable coefficient elliptic equation in a regular family of rectangular partitions of the domain in three-dimensional space. Initially, the estimates for the three-dimensional discrete Greens function and discrete derivative Greens function were presented. Subsequently, employing an interpolation operator of projection type, two essential weak estimates were derived, which were crucial for superconvergence analysis. Ultimately, by combining the aforementioned estimates, we achieved superconvergence estimates for the derivatives and function values of the finite element approximation in the point-wise sense of the $ L^\infty $-norm. A numerical example illustrated the theoretical results.

PurposeTo explore the role of deep learning (DL) and radiomics-based integrated approach based on contrast enhanced magnetic resonance imaging (CEMRI) for predicting early recurrence (ER) in hepatocellular carcinoma (HCC) patients after curative resection.MethodsTotal 165 HCC patients (ER, n = 96 vs. non-early recurrence (NER), n = 69) were retrospectively collected and divided into a training cohort (n = 132) and a validation cohort (n = 33). From pretreatment CEMR images, a total of 3111 radiomics features were extracted, and radiomics models were constructed using five machine learning classifiers (logistic regression, support vector machine, k-nearest neighbor, extreme gradient Boosting, and multilayer perceptron). DL models were established via three variations of ResNet architecture. The clinical-radiological (CR), radiomics combined with clinical-radiological (RCR), and deep learning combined with RCR (DLRCR) models were constructed. Model discrimination, calibration, and clinical utilities were evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis, respectively. The best-performing model was compared with the widely used staging systems and preoperative prognostic indexes.ResultsThe RCR model (area under the curve (AUC): 0.841 and 0.811) and the optimal radiomics model (AUC: 0.839 and 0.804) achieved better performance than the CR model (AUC: 0.662 and 0.752) in the training and validation cohorts, respectively. The optimal DL model (AUC: 0.870 and 0.826) outperformed the radiomics model in the both cohorts. The DL, radiomics, and CR predictors (aspartate aminotransferase (AST) and tumor diameter) were combined to construct the DLRCR model. The DLRCR model presented the best performance over any model, yielding an AUC, an accuracy, a sensitivity, a specificity of 0.917, 0.886, 0.889, and 0.882 in the training cohort and of 0.844, 0.818, 0.800, and 0.846 in the validation cohort, respectively. The DLRCR model achieved better clinical utility compared to the clinical staging systems and prognostic indexes.ConclusionBoth radiomics and DL models derived from CEMRI can predict HCC recurrence, and DL and radiomics-based integrated approach can provide a more effective tool for the precise prediction of ER for HCC patients undergoing resection.

This study focused on the superconvergence of the finite element method for the five-dimensional Poisson equation in the $ W^{1, \infty} $-seminorm. Specifically, we investigated the block finite element method, which is a tensor-product finite element approach applied to regular rectangular partitions of the domain. First, we introduced the finite element scheme for the equation and discussed various functions related to it, along with their properties. Next, we proposed a weight function and established its important properties, which play a crucial role in the theoretical analysis. By utilizing the properties of the weight function and employing weighted-norm analysis techniques, we derived an optimal order estimate in the $ W^{2, 1} $-seminorm for the discrete derivative Green's function (DDGF). Furthermore, we provided an interpolation fundamental estimate of the second type, also known as the weak estimate of the second type, for the block finite element. This weak estimate is based on a five-dimensional interpolation operator of the projection type. Finally, by combining the derived $ W^{2, 1} $-seminorm estimate for the DDGF and the weak estimate for the block finite element, we obtained a superconvergence estimate for the block finite element approximation in the pointwise sense of the $ W^{1, \infty} $-seminorm. The correctness of the theoretical results was demonstrated through a numerical example.

Abstract Background Noninvasive and precise methods to estimate treatment response and identify hepatocellular carcinoma (HCC) patients who could benefit from transarterial chemoembolization (TACE) are urgently required. The present study aimed to investigate the ability of intratumoral and peritumoral radiomics based on contrast-enhanced magnetic resonance imaging (CE-MRI) to preoperatively predict tumor response to TACE in HCC patients. Methods A total of 138 patients with HCC who received TACE were retrospectively included and randomly divided into training and validation cohorts at a ratio of 7:3. Total 1206 radiomics features were extracted from arterial, venous, and delayed phases images. The inter- and intraclass correlation coefficients, the spearman’s rank correlation test, and the gradient boosting decision tree algorithm were used for radiomics feature selection. Radiomics models on intratumoral region (TR) and peritumoral region (PTR) (3 mm, 5 mm, and 10 mm) were established using logistic regression. Three integrated radiomics models, including intratumoral and peritumoral region (T-PTR) (3 mm), T-PTR (5 mm), and T-PTR (10 mm) models, were constructed using TR and PTR radiomics scores. A clinical-radiological model and a combined model incorporating the optimal radiomics score and selected clinical-radiological predictors were constructed, and the combined model was presented as a nomogram. The discrimination, calibration, and clinical utilities were evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis, respectively. Results The T-PTR radiomics models performed better than the TR and PTR models, and the T-PTR (3 mm) radiomics model demonstrated preferable performance with the AUCs of 0.884 (95%CI, 0.821–0.936) and 0.911 (95%CI, 0.825–0.975) in both training and validation cohorts. The T-PTR (3 mm) radiomics score, alkaline phosphatase, tumor size, and satellite nodule were fused to construct a combined nomogram. The combined nomogram [AUC: 0.910 (95%CI, 0.854–0.958) and 0.918 (95%CI, 0.831–0.986)] outperformed the clinical-radiological model [AUC: 0.789 (95%CI, 0.709–0.863) and 0.782 (95%CI, 0.660–0.902)] in the both cohorts and achieved good calibration capability and clinical utility. Conclusions CE-MRI-based intratumoral and peritumoral radiomics approach can provide an effective tool for the precise and individualized estimation of treatment response for HCC patients treated with TACE.

Two-Line Element (TLE) datasets are the only orbital data source of Earth-orbiting space objects for many civil users for their research and applications. The datasets have uneven qualities that may affect the reliability of the propagated positions of space objects using a single TLE. The least squares approach to use multiple TLEs also suffers from the poor quality of some TLEs, and reliable error information cannot be available. This paper proposes a simplex algorithm to estimate an optimal TLE from multiple TLEs and obtain the uncertainty of each element. It is a derivative-free technique that can deal with various orbit types. Experiments have demonstrated that using the TLE estimated from the simplex method is more reliable, stable, and effective than those from the batch least squares method. As an application example, the optimal TLE and its uncertainty are used for predicting the fallen area, keeping the actual fallen site in the prediction areas.

Cancer-associated fibroblasts (CAFs) are significantly involved in determining the patient's prognosis and response to bladder cancer (BLCA) therapy. CAFs can induce epithelial-mesenchymal transformation (EMT) as well as complex interaction with immune cells. Hence, it is imperative to identify potential markers for enhancing our understanding of CAFs in BLCA progression and immune regulation. A variety of algorithms and analyses were employed in the study, leading to the development of a novel prognostic feature for CAFs-Stromal-EMT (CSE)-prognostic feature. This feature was constructed based on the genes MFAP5, PCOLCE2, and JUN. Furthermore, we revealed that patients with higher CSE risk scores responded to immunotherapy better compared to those with lower. Finally, we verified two CSE-related genes using in vitro experiments. Our results suggested that the CSE-prognostic feature could predict the prognosis and evaluate the response of patients to immune and chemotherapies. This would aid clinicians in designing treatment strategies for patients with BLCA.

Background: Adenoidal hypertrophy (AH) is one of the most common causes of upper airway obstruction in children. Drug and surgical treatment are the typical treatment of AH. The study on the inflammatory mechanism of AH in children provides a new idea for preoperative intervention and non-surgical treatment with anti-inflammatory drugs such as montelukast sodium (a cysteine leukotriene receptor antagonist). The aim of this study is to evaluate the effect of montelukast sodium on adenoidal lymphoid tissue pathology in children with AH under light microscope.Objective: To study whether there is any change in pathology of the adenoidal lymphoid tissue under the light microscope compared with the control group in children with moderate to severe simple AH treated with montelukast sodium for 1 month before operation.Materials and methods: Twenty patients (8 males, 12 females, 3–8 years old) with moderate to severe AH who were prepared for surgical treatment were selected. All the patients were examined by Nasopharyngeal CT and hemocyte analysis before operation. 20 subjects were randomly divided into two groups: One group was given montelukast chewable tablets 5 mg/d, qn, for 4 weeks; The control group was given placebo 5 mg/d, qn, for 4 weeks. After 4 weeks, the adenoids were removed and examined histopathology.Results: Compared with the control group, the number of lymphocytes in the blood cell analysis of the study group was significantly reduced, with a statistically significant difference (p < 0.05). And the number of germinal centers in adenoid tissue of the study group was relatively reduced, no small cyst was found in the epithelium, and the degree of inflammatory cell infiltration was reduced, with a statistically significant difference (p < 0.05).Conclusion: Montelukast can reduce the number of reactive cells, the number of lymphocytes in blood cells and blood vessels in adenoid lymphoid tissue, which can provide a new idea for preoperative intervention and non-surgical treatment of adenoid hypertrophy in children. However, this is only a pilot study and a longer treatment period is needed to assess the long-term effects of montelukast sodium on adenoid lymphoid tissue.Clinical Trial Registration: www.Chictr.org.cn, identifier ChiCTR2300075040.

Benefiting from the powerful feature extraction capability of deep learning, the Siamese tracker stands out due to its advanced tracking performance. However, constrained by the complex backgrounds of aerial tracking, such as low resolution, occlusion, similar objects, small objects, scale variation, aspect ratio change, deformation and limited computational resources, efficient and accurate aerial tracking is still difficult to realize. In this work, we design a lightweight and efficient adaptive temporal contextual aggregation Siamese network for aerial tracking, which is designed with a parallel atrous module (PAM) and adaptive temporal context aggregation model (ATCAM) to mitigate the above problems. Firstly, by using a series of atrous convolutions with different dilation rates in parallel, the PAM can simultaneously extract and aggregate multi-scale features with spatial contextual information at the same feature map, which effectively improves the ability to cope with changes in target appearance caused by challenges such as aspect ratio change, occlusion, scale variation, etc. Secondly, the ATCAM adaptively introduces temporal contextual information to the target frame through the encoder-decoder structure, which helps the tracker resist interference and recognize the target when it is difficult to extract high-resolution features such as low-resolution, similar objects. Finally, experiments on the UAV20L, UAV123@10fps and DTB70 benchmarks demonstrate the impressive performance of the proposed network running at a high speed of over 75.5 fps on the NVIDIA 3060Ti.

In this paper, we address aerial tracking tasks by designing multi-phase aware networks to obtain rich long-range dependencies. For aerial tracking tasks, the existing methods are prone to tracking drift in scenarios with high demand for multi-layer long-range feature dependencies such as viewpoint change caused by the characteristics of the UAV shooting perspective, low resolution, etc. In contrast to the previous works that only used multi-scale feature fusion to obtain contextual information, we designed a new architecture to adapt the characteristics of different levels of features in challenging scenarios to adaptively integrate regional features and the corresponding global dependencies information. Specifically, for the proposed tracker (SiamMAN), we first propose a two-stage aware neck (TAN), where first a cascaded splitting encoder (CSE) is used to obtain the distributed long-range relevance among the sub-branches by the splitting of feature channels, and then a multi-level contextual decoder (MCD) is used to achieve further global dependency fusion. Finally, we design the response map context encoder (RCE) utilizing long-range contextual information in backpropagation to accomplish pixel-level updating for the deeper features and better balance the semantic and spatial information. Several experiments on well-known tracking benchmarks illustrate that the proposed method outperforms SOTA trackers, which results from the effective utilization of the proposed multi-phase aware network for different levels of features.

Summary: Advances in the understanding of the tumor microenvironment have led to development of immunotherapeutic strategies, such as chimeric antigen receptor T cells (CAR-Ts). However, despite success in blood malignancies, CAR-T therapies in solid tumors have been hampered by their restricted infiltration. Here, we used our understanding of early cytotoxic lymphocyte infiltration of human lymphocytes in solid tumors in vivo to investigate the receptors in normal, adjacent, and tumor tissues of primary non-small-cell lung cancer specimens. We found that CX3CL1-CX3CR1 reduction restricts cytotoxic cells from the solid-tumor bed, contributing to tumor escape. Based on this, we designed a CAR-T construct using the well-established natural killer group 2, member D (NKG2D) CAR-T expression together with overexpression of CX3CR1 to promote their infiltration. These CAR-Ts infiltrate tumors at higher rates than control-activated T cells or IL-15-overexpressing NKG2D CAR-Ts. This construct also had similar functionality in a liver-cancer model, demonstrating potential efficacy in other solid malignancies.

Abstract Growing empirical evidence reveals the dramatic expansion in the risk of in-work poverty on a global scale over the last half-century. The current article reviews research on in-work poverty, illustrates how in-work poverty developed from a regional phenomenon into a global issue, and considers recent studies that have reexamined the concept of “in-work poverty” from the original “male family head” to further call on respecting the individual perspective and gender dimension. On the one hand, few studies have provided evidence on the gendered trends in in-work poverty; women’s situation in in-work poverty has not been particularly researched, and the gender dimension is often invisible. On the other hand, the existing literature does not consider this poverty issue much in developing countries, even though this does not mean that in-work poverty in developed countries is only a “side effect.” Hence, an international comparative setting with the gender dimension is needed, and more research is required to explore this construct within the context of the developing world.

Ship detection technology has achieved significant progress recently. However, for practical applications, lightweight ship detection still remains a very challenging problem since small ships have small relative scales in wide images and are easily missed in the background. To promote the research and application of small-ship detection, we propose a new remote sensing image dataset (VRS-SD v2) and provide a fog simulation method that reflects the actual background in remote sensing ship detection. The experiment results show that the proposed fog simulation is beneficial in improving the robustness of the model for extreme weather. Further, we propose a lightweight detector (LMSD-Net) for ship detection. Ablation experiments indicate the improved ELA-C3 module can efficiently extract features and improve the detection accuracy, and the proposed WGC-PANet can reduce the model parameters and computation complexity to ensure a lightweight nature. In addition, we add a Contextual Transformer (CoT) block to improve the localization accuracy and propose an improved localization loss specialized for tiny-ship prediction. Finally, the overall performance experiments demonstrate that LMSD-Net is competitive in lightweight ship detection among the SOTA models. The overall performance achieves 81.3% in AP@50 and could meet the lightweight and real-time detection requirements.

Siamese-based trackers have been widely used in object tracking. However, aerial remote tracking suffers from various challenges such as scale variation, viewpoint change, background clutter and occlusion, while most existing Siamese trackers are limited to single-scale and local features, making it difficult to achieve accurate aerial tracking. We propose the global multi-scale optimization and prediction head attentional Siamese network to solve this problem and improve aerial tracking performance. Firstly, a transformer-based multi-scale and global feature encoder (TMGFE) is proposed to obtain global multi-scale optimization of features. Then, the prediction head attentional module (PHAM) is proposed to add context information to the prediction head by adaptively adjusting the spatial position and channel contribution of the response map. Benefiting from these two components, the proposed tracker solves these challenges of aerial remote sensing tracking to some extent and improves tracking performance. Additionally, we conduct ablation experiments on aerial tracking benchmarks, including UAV123, UAV20L, UAV123@10fps and DTB70, to verify the effectiveness of the proposed network. The comparisons of our tracker with several state-of-the-art (SOTA) trackers are also conducted on four benchmarks to verify its superior performance. It runs at 40.8 fps on the GPU RTX3060ti.

Hemerocallis citrina Baroni [Asphodelaceae], which is traditional herbal medicine, has been widely used for treating depressive disorders in Eastern-Asia countries. However, the active compounds and corresponding mechanism of anti-depression are not yet completely clarified. In this study, the anti-depressive activities of six H. citrina extracts were primarily evaluated. The results showed that the water extract of H. citrina flowers (HCW) displays significant anti-depressive activity. A total of 32 metabolites were identified from HCW by high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (HPLC-Q-TOF-MS) and nuclear magnetic resonance (NMR). And then, the anti-depressive activity of the high-level compound (rutin) in HCW was also estimated. The results indicated that rutin displayed significant anti-depressive activity and was one of the main active ingredients. Finally, the anti-depressive mechanisms of HCW and rutin were investigated based on the intestinal microorganisms. The results showed that HCW and rutin increase the diversity and richness of the intestinal flora and regulate the specific intestinal microorganisms such as Bacteroides and Desulfovibrio genera in depressed mice. This work marks the first comprehensive study of the active components, anti-depressive activities and corresponding mechanisms of different H. citrina extracts, which provide a potential possibility for developing new antidepressants.

Moving object detection in remote sensing image sequences has been widely used in military and civilian fields. However, the complex background of remote sensing images and the small sizes of moving objects bring great difficulties for effective detection. To solve this problem, we propose a real-time moving object detection method for remote sensing image sequences. This method works by fusing the semantic information from a single image extracted by the object detection branch with the motion information of multiple frames extracted by the motion detection branch. Specifically, in the motion detection branch, we design a motion feature enhancement module (MFE) to improve the interframe motion information. Then, we design a Motion Information Extraction network (MIE) to extract motion information. Finally, the moving object information is directly output by fusing the motion and semantic information extracted by the object detection branch. Based on the experimental results of the two datasets, the proposed method achieves an accuracy rate of 93.21%, a recall rate of 92.72%, an average frame rate of 25.25 frames (fps), and a performance of 96.71% in terms of AP@0.5. The performance of the proposed method is better than that of other methods, and the overall detection effect is better; therefore, it meets the needs of the detection task.

For the Siamese network-based trackers utilizing modern deep feature extraction networks without taking full advantage of the different levels of features, tracking drift is prone to occur in aerial scenarios, such as target occlusion, scale variation, and low-resolution target tracking. Additionally, the accuracy is low in challenging scenarios of visual tracking, which is due to the imperfect utilization of features. To improve the performance of the existing Siamese tracker in the above-mentioned challenging scenes, we propose a Siamese tracker based on Transformer multi-level feature enhancement with a hierarchical attention strategy. The saliency of the extracted features is enhanced by the process of Transformer Multi-level Enhancement; the application of the hierarchical attention strategy makes the tracker adaptively notice the target region information and improve the tracking performance in challenging aerial scenarios. Meanwhile, we conducted extensive experiments and qualitative or quantitative discussions on UVA123, UAV20L, and OTB100 datasets. Finally, the experimental results show that our SiamHAS performs favorably against several state-of-the-art trackers in these challenging scenarios.

Non-uniformity commonly exists in the infrared focal plane, which behaves as the fixed-pattern noise (FPN) and seriously affects the image quality of long-wave infrared (LWIR) detection systems. The two-point correction (TPC) method is commonly used to reduce image FPN in engineering. However, when a wide-spectrum LWIR detection system calibrated with a black body is used to detect weak and small targets in the sky, FPN still appears in the image, affecting its uniformity. The effects of atmospheric transmittance characteristics of long-range paths on the non-uniformity of wide-spectrum long-wave infrared systems have not been studied. This paper proposes a modified TPC model based on spectral subdivision that introduces atmospheric transmittance. Additionally, the effects of atmospheric transmittance characteristics on the long-wave infrared non-uniform correction coefficient are analyzed. The experimental results for a black body scene and sky scene using a weak and small target detection system with a long-wave Sofradir FPA demonstrate that the wide-spectrum LWIR detection system fully considers atmospheric transmittance when performing calibration based on the TPC method, which can reduce the non-uniformity of the image.

Abstract Background Aortic dissection is a rare and emergent condition. Aortic dissection during pregnancy is not much known but it is quite lethal to both mother and infant. Earlier reports published show that clinicians conducted hysterectomies during cesarean section to avoid anticoagulant-induced uterine bleeding during the following aortic surgery. Case presentation A woman (38, gravida 1, para 0) in the 37th gestational week suffered an acute, severe, sharp pain in the chest and back. She was diagnosed with Standford type A aortic dissection and suspected with Marfan syndrome. An emergency cesarean section was performed immediately to deliver the baby. Since the patient was on anticoagulants during aortic replacement, so Cook balloon was inserted into the uterus to prevent postpartum hemorrhage. This helped to maintain the uterus intact. Family genetic testing showed that the patient was a carrier of FBN1 mutation which was inherited from the patient’s mother, and the newborn also carried the mutation. Hence the patient was concluded to be positive for Marfan syndrome. Conclusion It is important that clinicians should pay attention to the possibility of aortic dissection in a pregnant woman with chest, abdominal or back pain. In this case study, we employed Cook balloon during cesarean section to avoid anticoagulant-induced uterine bleeding during the following aortic surgery.

Abstract In this article, for an elliptic equation with varying coefficients, we first derive an interpolation fundamental estimate for the P 2 ( x , y ) ⊗ P 2 ( z ) $\mathcal{P}_{2}(x,y)\otimes \mathcal{P}_{2}(z)$ pentahedral finite element over uniform partitions of the domain. Then combined with the estimate for the W 2 , 1 $W^{2,1}$ -seminorm of the discrete Green function, superconvergence of the function value between the finite element approximation and the corresponding interpolant to the true solution is given.

Wind erosion is one of the most widespread and severe natural hazards in arid, semiarid, and semihumid regions worldwide. The Three-North region (TNR) (Northeast China, North China, and Northwest China) of China includes 90% of the wind erosion area in China. In response to the harsh environmental conditions in the TNR, China initiated a series of ecological programs, including the Three-North Afforestation Program and Grain for Green. However, little is known about the effect of these ecological programs on wind erosion. Therefore, within our study, we estimated the spatiotemporal variations in wind erosion in the TNR between 1981–2020 with a revised wind erosion model and analyzed its driving mechanism. Then, the ecological programs’ effects on wind erosion changes was identified. The results showed the following. (1) From 1981 to 2020, wind erosion showed a clear downward trend of 99.02 t km−2 a−1, with a slope. On average, the areas of mild, moderate, severe, more severe, and very severe wind erosion accounted for 28.76%, 7.17%, 3.92%, 3.72%, and 13.29% of the total in the TNR, respectively. (2) Wind erosion variation was inconsistent in different parts of the TNR. The wind erosion expressed a long-term decreasing trend in Northeast China and the Loess Plateau, a nonsignificant change in North Central China, and an increasing trend in Northwest China. (3) On average, ecological programs were very limited in reducing erosion at the regional scale, with a contribution of approximately 5.93% in the TNR because of the relatively small scope of ecological programs’ implementation. Climate change played a key role in adjusting wind erosion; wind speed, temperature, and precipitation affected 57.58% of the TNR. Human interference (proportion of cropland and grassland areas in a 1 km ×1 km grid) affected 8.78% of the TNR. Thus, the persistent complement of ecological programs, reasonable human activities, and timely observation is a method to alleviate wind erosion in the TNR.

PurposeTo investigate the role of contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics for pretherapeutic prediction of the response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC).MethodsOne hundred and twenty-two HCC patients (objective response, n = 63; non-response, n = 59) who received CE-MRI examination before initial TACE were retrospectively recruited and randomly divided into a training cohort (n = 85) and a validation cohort (n = 37). All HCCs were manually segmented on arterial, venous and delayed phases of CE-MRI, and total 2367 radiomics features were extracted. Radiomics models were constructed based on each phase and their combination using logistic regression algorithm. A clinical-radiological model was built based on independent risk factors identified by univariate and multivariate logistic regression analyses. A combined model incorporating the radiomics score and selected clinical-radiological predictors was constructed, and the combined model was presented as a nomogram. Prediction models were evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis.ResultsAmong all radiomics models, the three-phase radiomics model exhibited better performance in the training cohort with an area under the curve (AUC) of 0.838 (95% confidence interval (CI), 0.753 - 0.922), which was verified in the validation cohort (AUC, 0.833; 95% CI, 0.691 - 0.975). The combined model that integrated the three-phase radiomics score and clinical-radiological risk factors (total bilirubin, tumor shape, and tumor encapsulation) showed excellent calibration and predictive capability in the training and validation cohorts with AUCs of 0.878 (95% CI, 0.806 - 0.950) and 0.833 (95% CI, 0.687 - 0.979), respectively, and showed better predictive ability (P = 0.003) compared with the clinical-radiological model (AUC, 0.744; 95% CI, 0.642 - 0.846) in the training cohort. A nomogram based on the combined model achieved good clinical utility in predicting the treatment efficacy of TACE.ConclusionCE-MRI radiomics analysis may serve as a promising and noninvasive tool to predict therapeutic response to TACE in HCC, which will facilitate the individualized follow-up and further therapeutic strategies guidance in HCC patients.

Ship detection in visible remote sensing (VRS) images has been widely used in the military and civil fields. However, the various backgrounds and the variable scale and orientation bring great difficulties to effective detection. In this paper, we propose a novel ship target detection scheme based on small training samples. The scheme contains two main stages: candidate region extraction and ship identification. In the first stage, we propose a visual saliency detection model based on the difference in covariance statistical characteristics to quickly locate potential ships. Moreover, the multi-scale fusion for the saliency model is designed to overcome the problem of scale variation. In the second stage, we propose a three-channel aggregate feature, which combines a rotation-invariant histogram of oriented gradient and the circular frequency feature. The feature can identify the ship target well by avoiding the impact of its rotation and shift. Finally, we propose the VRS ship dataset that contains more realistic scenes. The results on the VRS ship dataset demonstrate that the saliency model achieves the best AUC value with 0.9476, and the overall detection achieves a better performance of 65.37% in terms of AP@0.5:0.95, which basically meets the need of the detection tasks.

Siamese network-based trackers satisfy the balance between performance and efficiency for visual tracking. However, they do not have enough robustness to handle the challenges of target occlusion and similar objects. In order to improve the robustness of the tracking algorithm, this paper proposes visual tracking with FPN based on Transformer and response map enhancement. In this paper, a feature pyramid structure based on Transformer is designed to encode robust target-specific appearance features, as well as the response map enhanced module to improve the tracker’s ability to distinguish object and background. Extensive experiments and ablation experiments are conducted on many challenging benchmarks such as UAV123, GOT-10K, LaSOT and OTB100. These results show that the tracking algorithm we proposed in this paper can effectively improve the tracking robustness against the challenges of target occlusion and similar object, and thus improve the precision rate and success rate of the tracking algorithm.

In order to solve the technical problem of damage localization in the concrete damage process, the Computerized Tomography (CT) scanning damage localization test was carried out under uniaxial compression. In addition to the three-dimensional meso model, the porosity variation law of concrete pore or cracks at different loading stages are obtained. A damage variable is created based on CT images. The results show that the change of porosity, pore volume and damage variable is consistent with the loading process. The rapid increase of concrete pore volume and damage variable can be the precursor of concrete damage. The whole process of the development and evolution of concrete cracks was observed and analyzed comprehensively by using the three-dimensional reconstruction images. A method for prognosist the evolution and localization of cracks in concrete by comprehensively analyze the CT reconstruction images, rapid increase of concrete pore volume and rapid growth of damage variable is presented.

In order to improve the geo-location accuracy of the airborne optoelectronic platform and eliminate the influence of assembly systematic error on the accuracy, a systematic geo-location error correction method is proposed. First, based on the kinematic characteristics of the airborne optoelectronic platform, the geo-location model was established. Then, the error items that affect the geo-location accuracy were analyzed. The installation error between the platform and the POS was considered, and the installation error of platform’s pitch and azimuth was introduced. After ignoring higher-order infinitesimals, the least square form of systematic error is obtained. Therefore, the systematic error can be obtained through a series of measurements. Both Monte Carlo simulation analysis and in-flight experiment results show that this method can effectively obtain the systematic error. Through correction, the root-mean-square value of the geo-location error have reduced from 45.65 m to 12.62 m, and the mean error from 16.60 m to 1.24 m. This method can be widely used in systematic error correction of relevant photoelectric equipment.

Correlation filter (CF) based trackers have gained significant attention in the field of visual single-object tracking, owing to their favorable performance and high efficiency; however, existing trackers still suffer from model drift caused by boundary effects and filter degradation. In visual tracking, long-term occlusion and large appearance variations easily cause model degradation. To remedy these drawbacks, we propose a sparse adaptive spatial-temporal context-aware method that effectively avoids model drift. Specifically, a global context is explicitly incorporated into the correlation filter to mitigate boundary effects. Subsequently, an adaptive temporal regularization constraint is adopted in the filter training stage to avoid model degradation. Meanwhile, a sparse response constraint is introduced to reduce the risk of further model drift. Furthermore, we apply the alternating direction multiplier method (ADMM) to derive a closed-solution of the object function with a low computational cost. In addition, an updating scheme based on the APCE-pool and Peak-pool is proposed to reveal the tracking condition and ensure updates of the target’s appearance model with high-confidence. The Kalam filter is adopted to track the target when the appearance model is persistently unreliable and abnormality occurs. Finally, extensive experimental results on OTB-2013, OTB-2015 and VOT2018 datasets show that our proposed tracker performs favorably against several state-of-the-art trackers.

Hydrological models with the conceptual tipping bucket and the process-based evapotranspiration models are the most common tools in hydrology. However, these models consistently fail to replicate long-term and slow dynamics of a hydrological system, indicating the need for model augmentation and shift in approach. This study employed an entirely different approach - system dynamics - towards more realistic replication of long-term and slow hydrological behaviors by removing limits of exogenous climate on evapotranspiration and involving endogenous soil water-vegetation feedback loop. Using the headwaters of Baiyang Lake in China as a case study, the mechanisms of slow hydrological dynamics were gradually unraveled from 1982 to 2015 through wavelet analysis, Granger's causality test, and system dynamics. The wavelet analysis and Granger's causality test identified a negative-correlated, bidirectional causal relationship between evapotranspiration and the water budget across distinct climatic periodicities, suggesting a robust endogenous soil water-vegetation feedback structure operating on a long-term scale. The system dynamics approach successfully captured the slow behavior of the hydrological system under both natural and human-intervention scenarios, demonstrating a self-sustained oscillation arising within the system's boundary. Conventional hydrological models, which rely on process-based evapotranspiration models, operate on an instantaneous scale and are thus susceptible to short-time climatic and vegetation physiological variations. This results in inaccurate depletion rate of soil water stock and in turn, can lead to incorrect calculations of other hydrological variables. However, long-term and slow hydrological dynamics typically involves in endogenous state-dependent modulation and feedback related to changes in vegetation structure, thus are insensitive to exogenous disturbances and can be well replicated using system dynamics approach. This insight that the failure of hydrological models to replicate slow dynamics can be attributed to a time-scale mismatch may offer potential solutions for improving conventional hydrological models. [ABSTRACT FROM AUTHOR]

This study focused on the superconvergence of the finite element method for the fivedimensional Poisson equation in the W1,∞-seminorm. Specifically, we investigated the block finite element method, which is a tensor-product finite element approach applied to regular rectangular partitions of the domain. First, we introduced the finite element scheme for the equation and discussed various functions related to it, along with their properties. Next, we proposed a weight function and established its important properties, which play a crucial role in the theoretical analysis. By utilizing the properties of the weight function and employing weighted-norm analysis techniques, we derived an optimal order estimate in the W2,1-seminorm for the discrete derivative Green's function (DDGF). Furthermore, we provided an interpolation fundamental estimate of the second type, also known as the weak estimate of the second type, for the block finite element. This weak estimate is based on a five-dimensional interpolation operator of the projection type. Finally, by combining the derived W2,1-seminorm estimate for the DDGF and the weak estimate for the block finite element, we obtained a superconvergence estimate for the block finite element approximation in the pointwise sense of the W1,∞-seminorm. The correctness of the theoretical results was demonstrated through a numerical example. [ABSTRACT FROM AUTHOR]

To clarify how the fire performance of ultra-low density fiberboard (ULDF) can be improved by complex fire-retardants, the limiting oxygen index (LOI) and microstructure of ULDFs with different additive amounts of complex fire-retardants was analyzed. The char yield, chemical bonding, and thermostability of ULDFs treated by different temperatures were also tested. Results showed that the LOI values and compactness of ULDFs were increased with increased amounts of fire-retardants. Three steps of char yield curves in control fiberboard (CF) and mixed fiberboard (MF) were apparent. The preliminary degradation in lignin and cellulose of CF occurred at 300 °C. The cellulose had completely decomposed at 400 °C, but in the case of MF, the lignin and cellulose were not completely decomposed at 400 °C. It was shown that there are different ways to improve the fire resistance of ULDF using boron, nitrogen-phosphorus, silica, and halogen-based fire-retardants. The fiberboard with silicium compounds had the lowest mass loss in three stages and total mass loss. Compared with CF, MF had a lower mass loss. Furthermore, the exothermic peak for MF at around 400.0 °C was decreased, indicating that the fire resistance of ULDF was improved by the complex fire-retardants.

To improve the mechanical properties of ultra-low density plant fiber composite (ULD_PFC), a suitable beating process to improve the fibrillation of cellulose fibers and maintain their length was investigated. The physical properties of cellulose fibers and papers, surface chemical bonds, and internal bond strength (IB) of ULD_PFCs were analyzed. The results showed that the beating degrees, degree of fibrillation, and fiber fines increased with the decreasing of beating gap, except for the fiber weight-average length, width, kink index, and curl index. The tensile index and burst index of paper showed an increasing trend with an increase in beating degree, while the tear index showed a decreasing trend. FTIR results showed that intermolecular and intramolecular hydrogen bonds in ULDF were broken. A suitable beating gap of 30 μm with a beating degree of 35 °SR was obtained. The corresponding IB was 50.9 kPa, which represented an increase of 73.1% over fibers with a beating degree of 13 °SR.

To obtain a suitable refining process for Pinus massoniana cellulose fibers (PMCF) and China fir cellulose fibers (CFCF), the effects of the beating gap and the pulp consistency on the physical properties and the morphology of the two cellulose fibers were investigated. The results showed that the physical properties of the PMCF and the CFCF were well affected by the beating gap and the pulp consistency. The CFCF showed a smaller weight-average length and width than that of the PMCF. The CFCF exhibited smaller weight-average length, width, and kink index than the PMCF. It is easy to get the high beating degree, indicating it is more easily to be refined. Additionally, the tensile index and burst index of PMCFP and CFCFP increased with increasing beating degree, while the tear index decreased. Compared to the CFCF, the paper made from PMCF had superior strength properties. Consequently, the PMCF was suitable for refining with a high pulp consistency and a medium beating gap, whereas the CFCF had a medium pulp consistency and a big beating gap.

Automatic registration of optical and synthetic aperture radar (SAR) images is a challenging task due to the influence of SAR speckle noise and nonlinear radiometric differences. This study proposes a robust algorithm based on phase congruency to register optical and SAR images (ROS-PC). It consists of a uniform Harris feature detection method based on multi-moment of the phase congruency map (UMPC-Harris) and a local feature descriptor based on the histogram of phase congruency orientation on multi-scale max amplitude index maps (HOSMI). The UMPC-Harris detects corners and edge points based on a voting strategy, the multi-moment of phase congruency maps, and an overlapping block strategy, which is used to detect stable and uniformly distributed keypoints. Subsequently, HOSMI is derived for a keypoint by utilizing the histogram of phase congruency orientation on multi-scale max amplitude index maps, which effectively increases the discriminability and robustness of the final descriptor. Finally, experimental results obtained using simulated images show that the UMPC-Harris detector has a superior repeatability rate. The image registration results obtained on test images show that the ROS-PC is robust against SAR speckle noise and nonlinear radiometric differences. The ROS-PC can tolerate some rotational and scale changes.

The dynamics model used for inertially or strapdown inertially stabilized platforms is based on the rotor and motor load, and it either does not consider the stator or it implicitly assumes a fixed stator. It has been determined that vibrations occur in the system when a controller is used in strapdown inertially stabilized platforms with a light base support. As the system is also affected by multi-source disturbances, which are the main factors that affect the control accuracy. For the above two problems, this paper originally establishes a multi-body dynamics model including the controller. The composite controller not only suppresses the vibration successfully, but also greatly improves the disturbance compensation and tracking performance of the strapdown inertially stabilized platforms. Specifically, a modified feedback controller is used to suppress the vibrations analyzed according to the dynamics model. The friction feedforward and residual disturbance observer facilitates the design of compound disturbance compensation on the basis of composite hierarchical anti-disturbance control. To emphasize the advantages of strapdown inertially stabilized platforms, the feedforward controller employs feedforward angular velocity and acceleration. The results of the numerical analysis and experiments indicate that vibrations are successfully suppressed and tracking accuracy and disturbance isolation ability are improved.

In order to solve the technical problem of damage localization in the concrete damage process, the Computerized Tomography (CT) scanning test and Acoustic Emission (AE) damage localization test was carried out under uniaxial compression. In addition to the three-dimensional meso model, the porosity variation law of concrete pore or cracks at different loading stages are obtained. A damage variable is created based on CT images and AE damage points. A damage variable based on CT image and AE damage point is established. The results show that the change of porosity, pore volume and damage variable is consistent with the loading process. The rapid increase of concrete pore volume and damage variable can be the precursor of concrete damage. The whole process of the development and evolution of concrete cracks was observed and analyzed comprehensively by using the three-dimensional reconstruction images and acoustic emission damage location map. A new method for analyzing the evolution and localization of cracks in concrete is presented.

The preparation conditions of complex fire-retardant (FR) agents containing boron compounds (BF, X1), nitrogen-phosphorus compounds (NPF, X2), silicon compounds (SF, X3), and halogen compounds (HF, X4) for ultra-low density fiberboard (ULDF) were optimized using a response surface methodology. The effects and interactions of X1, X2, X3, and X4 on the fire properties of ULDF were investigated. An optimum char yield of 61.4% was obtained when the complex fire-retardant agents contained 33.9% boron, 27.2% nitrogen-phosphorus, 15.0% silicon, and 28.6% halogen. Compared with control fiberboard (CF), the heat release rate (HRR) profiles of all fiberboards with FRs were reduced. The peak HRR reduction in BF and NPF was more pronounced than for SF and HF at this stage. And the mixed fiberboard (MF) had the lowest pkHRR of 75.02 kW m−2. In total heat release (THR) profiles, all fiberboards with FRs were lower than the CF. Unlike the HRR profiles, HF had the lowest THR profile of 15.33 MJ m−2. Additionally, Si compounds showed greater effectiveness in preventing ULDF mass loss than BF, NPF, and HF. MF showed the highest residual mass (40.94%). Furthermore, the synergistic effect between four FR agents showed more significant results in ULDFs.