Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image dis...Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image distortion,inaccurate localization of the tampered regions,and difficulty in recovering content.Given these shortcomings,a fragile image watermarking algorithm for tampering blind-detection and content self-recovery is proposed.The multi-feature watermarking authentication code(AC)is constructed using texture feature of local binary patterns(LBP),direct coefficient of discrete cosine transform(DCT)and contrast feature of gray level co-occurrence matrix(GLCM)for detecting the tampered region,and the recovery code(RC)is designed according to the average grayscale value of pixels in image blocks for recovering the tampered content.Optimal pixel adjustment process(OPAP)and least significant bit(LSB)algorithms are used to embed the recovery code and authentication code into the image in a staggered manner.When detecting the integrity of the image,the authentication code comparison method and threshold judgment method are used to perform two rounds of tampering detection on the image and blindly recover the tampered content.Experimental results show that this algorithm has good transparency,strong and blind detection,and self-recovery performance against four types of malicious attacks and some conventional signal processing operations.When resisting copy-paste,text addition,cropping and vector quantization under the tampering rate(TR)10%,the average tampering detection rate is up to 94.09%,and the peak signal-to-noise ratio(PSNR)of the watermarked image and the recovered image are both greater than 41.47 and 40.31 dB,which demonstrates its excellent advantages compared with other related algorithms in recent years.展开更多
In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The e...In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.展开更多
BACKGROUND Data on clinical characteristics,treatment outcomes,and prognosis of pancreatic primitive neuroectodermal tumors(PNETs)are limited.AIM To analyze the clinical data of 30 patients with pancreatic PNETs to id...BACKGROUND Data on clinical characteristics,treatment outcomes,and prognosis of pancreatic primitive neuroectodermal tumors(PNETs)are limited.AIM To analyze the clinical data of 30 patients with pancreatic PNETs to identify their clinical characteristics and factors associated with prognosis.METHODS We used the keywords"primary neuroectodermal tumor,""digestive tract,""pancreas,""pancreatic,"and"gastrointestinal,"individually or in combination,to collect data from a global database for all patients with pancreatic PNET to date.Univariate and Cox regression analyses were performed to identify prognostic factors for patient survival.RESULTS A total of 30 cases of pancreatic PNET were included in this study:15 males and 15 females with a mean age of 24 years.The main symptom was abdominal pain(73.3%),and the median tumor size was 7.85 cm.Twenty-four patients(80.0%)underwent surgery and nineteen patients received adjuvant therapy.Local metastasis was observed in 13 patients(43.3%),lymph node metastasis in 10 patients(33.3%),and distant metastasis in 6 patients(20.0%).Local recurrence was observed in 13 patients(43.3%).The median survival time of all patients was 29.4 months,and the overall estimated 1-year and 3-year survival rates were approximately 66.0%and 36.4%,respectively.Univariate analysis showed that chemotherapy(P=0.036),local metastasis(P=0.041),lymph node metastasis(P=0.003),distant metastasis(P=0.049),and surgical margins(P=0.048)were the prognostic factors affecting survival.Multivariate analysis revealed only lymph node metastasis(P=0.012)as a prognostic factor.CONCLUSION Pancreatic PNET is extremely rare,occurs in young adults,has no apparent sex predisposition,has a high rate of metastasis and early recurrence,and has a very poor prognosis.The diagnosis of pancreatic PNET requires a combination of clinical symptoms,pathologic features,immunohistochemistry,and cytogenetic analysis.Univariate analysis suggested that chemotherapy,metastasis,and surgical margins were prognostic factors affecting survival,and multivariate analysis suggested that lymph node metastasis is an important prognostic factor.Therefore,early diagnosis,early and extensive resection,and adjuvant chemoradiotherapy may help improve prognosis.展开更多
This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data a...This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data and historical academic performance)with dynamic behavioral patterns(e.g.,real-time interactions and evolving interests over time).The research employs Term Frequency-Inverse Document Frequency(TF-IDF)for semantic feature extraction,integrates the Analytic Hierarchy Process(AHP)for feature weighting,and introduces a time decay function inspired by Newton’s law of cooling to dynamically model changes in learners’interests.Empirical results demonstrate that this framework effectively captures the dynamic evolution of learners’behaviors and provides context-aware learning resource recommendations.The study introduces a novel paradigm for learner modeling in educational technology,combining methodological innovation with a scalable technical architecture,thereby laying a foundation for the development of adaptive learning systems.展开更多
The traditional EnFCM(Enhanced fuzzy C-means)algorithm only considers the grey-scale features in image segmentation,resulting in less than satisfactory results when the algorithm is used for remote sensing woodland im...The traditional EnFCM(Enhanced fuzzy C-means)algorithm only considers the grey-scale features in image segmentation,resulting in less than satisfactory results when the algorithm is used for remote sensing woodland image segmentation and extraction.An EnFCM remote sensing forest land extraction method based on PCA multi-feature fusion was proposed.Firstly,histogram equalization was applied to improve the image contrast.Secondly,the texture and edge features of the image were extracted,and a multi-feature fused pixel image was generated using the PCA technique.Moreover,the fused feature was used as a feature constraint to measure the difference of pixels instead of a single grey-scale feature.Finally,an improved feature distance metric calculated the similarity between the pixel points and the cluster center to complete the cluster segmentation.The experimental results showed that the error was between 1.5%and 4.0%compared with the forested area counted by experts’hand-drawing,which could obtain a high accuracy segmentation and extraction result.展开更多
Walking is the basic locomotion pattern for bipedal robots.The walking pattern is widely generated using the linear inverted pendulum model.The linear inverted pendulum motion of each support period can be designed as...Walking is the basic locomotion pattern for bipedal robots.The walking pattern is widely generated using the linear inverted pendulum model.The linear inverted pendulum motion of each support period can be designed as a walk primitive to be connected to form a walking trajectory.A novel method of integrating double support phase into the walk primitive was proposed in this article.The method describes the generation of walking patterns using walk primitives with double support,specifically for lateral plane including walking in place,walking for lateral,and walking initiation,and for sagittal plane including fixed step length walking,variable step length walking,and walking initiation.Compared to walk primitives without double support phase,those with double support phase reduce the maximum speed required by the robot and eliminate the need to adjust foothold for achieving continuous speed.The performance of the proposed method is validated by simulations and experiments on Neubot,a position-controlled biped robot.展开更多
Path planning is crucial for autonomous flight of fixed-wing Unmanned Aerial Vehicles(UAVs).However,due to the high-speed flight and complex control of fixed-wing UAVs,ensuring the feasibility and safety of planned pa...Path planning is crucial for autonomous flight of fixed-wing Unmanned Aerial Vehicles(UAVs).However,due to the high-speed flight and complex control of fixed-wing UAVs,ensuring the feasibility and safety of planned paths in complex environments is challenging.This paper proposes a feasible path planning algorithm named Closed-loop Radial Ray A^(*)(CL-RaA^(*)).The core components of the CL-RaA^(*)include an adaptive variable-step-size path search and a just-in-time expansion primitive.The former enables fast path search in complex environments,while the latter ensures the feasibility of the generated paths.By integrating these two components and conducting safety checks on the trajectories to be expanded,the CL-RaA^(*)can rapidly generate safe and feasible paths that satisfy the differential constraints that comprehensively consider the dynamics and control characteristics of six-degree-of-freedom fixed-wing UAVs.The final performance tests and simulation validations demonstrate that the CL-RaA^(*)can generate safe and feasible paths in various environments.Compared to feasible path planning algorithms that use the rapidlyexploring random trees,the CL-RaA^(*)not only ensures deterministic planning results in the same scenarios but also generates smoother feasible paths for fixed-wing UAVs more efficiently.In environments with dense grid obstacles,the feasible paths generated by the CL-RaA^(*)are more conducive to UAV tracking compared to those planned using Dubins curves.展开更多
This paper investigates the ergodicity and weak convergence of transition probabilities for two-dimensional stochastic primitive equations driven by multiplicative noise.The existence of invariant measures is establis...This paper investigates the ergodicity and weak convergence of transition probabilities for two-dimensional stochastic primitive equations driven by multiplicative noise.The existence of invariant measures is established using the classical Krylov-Bogoliubov theory.The uniqueness of invariant measures and the weak convergence of transition probabilities are demonstrated through the application of the asymptotic coupling method and Foias-Prodi estimate.展开更多
The accelerating urbanization process leads to aggravated environmental problems, thus garden design which is a creative activity connecting human and nature has attracted much attention, and also garden designers hav...The accelerating urbanization process leads to aggravated environmental problems, thus garden design which is a creative activity connecting human and nature has attracted much attention, and also garden designers have had to rethink about environmental ethics and morals. This study, from the perspective of environmental ethics, duly proposed the brand-new concept of 'appropriate garden of primitive ecology', proceeded from the orientation, form, value, theoretical basis, realistic significance, culture and consumption psychology of 'garden of primitive ecology', fully developed outstanding ecological wisdoms and morals in traditional Chinese garden culture, boosted garden designs to the environmental ethics level of eco-justice, to seek for the approach to 'garden of primitive ecology' with indigenous Chinese environmental ethic characteristics.展开更多
This paper is devoted to considering the three-dimensional viscous primitive equations of the large-scale atmosphere. First, we prove the global well-posedness for the primitive equations with weaker initial data than...This paper is devoted to considering the three-dimensional viscous primitive equations of the large-scale atmosphere. First, we prove the global well-posedness for the primitive equations with weaker initial data than that in [11]. Second, we obtain the existence of smooth solutions to the equations. Moreover, we obtain the compact global attractor in V for the dynamical system generated by the primitive equations of large-scale atmosphere, which improves the result of [11].展开更多
Peripheral primitive neuroectodermal tumor(PNET) of the kidney is a rare, aggressive tumor known for its recurrence and metastatic potential. Despite the frequency of venous extension to the renal veins and inferior v...Peripheral primitive neuroectodermal tumor(PNET) of the kidney is a rare, aggressive tumor known for its recurrence and metastatic potential. Despite the frequency of venous extension to the renal veins and inferior vena cava, pulmonary tumor embolism at the initial presentation is not common. We report a case of 22-year-old female with PNET of the kidney who presented with tumor embolism in the inferior vena cava(IVC) and bilateral pulmonary artery. The patient underwent surgical resection and histopathological analysis confirmed the presence of tumor within the IVC and pulmonary arteries. The patient received adjuvant chemotherapy and is currently doing well on follow-up.展开更多
Vehicle re-identification(ReID)aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera scenario.It has gradually become a core technology of int...Vehicle re-identification(ReID)aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera scenario.It has gradually become a core technology of intelligent transportation system.Most existing vehicle re-identification models adopt the joint learning of global and local features.However,they directly use the extracted global features,resulting in insufficient feature expression.Moreover,local features are primarily obtained through advanced annotation and complex attention mechanisms,which require additional costs.To solve this issue,a multi-feature learning model with enhanced local attention for vehicle re-identification(MFELA)is proposed in this paper.The model consists of global and local branches.The global branch utilizes both middle and highlevel semantic features of ResNet50 to enhance the global representation capability.In addition,multi-scale pooling operations are used to obtain multiscale information.While the local branch utilizes the proposed Region Batch Dropblock(RBD),which encourages the model to learn discriminative features for different local regions and simultaneously drops corresponding same areas randomly in a batch during training to enhance the attention to local regions.Then features from both branches are combined to provide a more comprehensive and distinctive feature representation.Extensive experiments on VeRi-776 and VehicleID datasets prove that our method has excellent performance.展开更多
Urban land provides a suitable location for various economic activities which affect the development of surrounding areas. With rapid industrialization and urbanization, the contradictions in land-use become more noti...Urban land provides a suitable location for various economic activities which affect the development of surrounding areas. With rapid industrialization and urbanization, the contradictions in land-use become more noticeable. Urban administrators and decision-makers seek modern methods and technology to provide information support for urban growth. Recently, with the fast development of high-resolution sensor technology, more relevant data can be obtained, which is an advantage in studying the sustainable development of urban land-use. However, these data are only information sources and are a mixture of ＂information＂ and ＂noise＂. Processing, analysis and information extraction from remote sensing data is necessary to provide useful information. This paper extracts urban land-use information from a high-resolution image by using the multi-feature information of the image objects, and adopts an object-oriented image analysis approach and multi-scale image segmentation technology. A classification and extraction model is set up based on the multi-features of the image objects, in order to contribute to information for reasonable planning and effective management. This new image analysis approach offers a satisfactory solution for extracting information quickly and efficiently.展开更多
Extraskeletal Ewing's sarcoma/peripheral primitive neuroectodermal tumor(E-EWS/pP NET) is a rare aggressive malignant small round cell tumor. In this report, we present the case of a 15-year-old boy who suffered f...Extraskeletal Ewing's sarcoma/peripheral primitive neuroectodermal tumor(E-EWS/pP NET) is a rare aggressive malignant small round cell tumor. In this report, we present the case of a 15-year-old boy who suffered from acute abdominal pain accompanied by hematemesis and melena, and was eventually diagnosed with E-EWS/p PNET. To date, there have been only five reported cases of E-EWS/pP NET of the small bowel including the patient in this report. To the best of our knowledge, this is the first documentation of a pP NET of the small bowel mesentery at nonage. All these have made this report rare and significant.展开更多
The design of a total energy conserving semi-implicit scheme for the multiple-level baroclinic primitive equation has remained an unsolved problem for a long time. In this work, however, we follow an energy perfect co...The design of a total energy conserving semi-implicit scheme for the multiple-level baroclinic primitive equation has remained an unsolved problem for a long time. In this work, however, we follow an energy perfect conserving semi-implicit scheme of a European Centre for Medium-Range Weather Forecasts (ECMWF) type sigma-coordinate primitive equation which has recently successfully formulated. Some real-data contrast tests between the model of the new conserving scheme and that of the ECMWF-type of global spectral semi-implicit scheme show that the RMS error of the averaged forecast Height at 850 hPa can be clearly improved after the first integral week. The reduction also reaches 50 percent by the 30th day. Further contrast tests demonstrate that the RMS error of the monthly mean height in the middle and lower troposphere also be largely reduced, and some well-known systematical defects can be greatly improved. More detailed analysis reveals that part of the positive contributions comes from improvements of the extra-long wave components. This indicates that a remarkable improvement of the model climate drift level can be achieved by the actual realizing of a conserving time-difference scheme, which thereby eliminates a corresponding computational systematic error source/sink found in the currently-used traditional type of weather and climate system models in relation to the baroclinic primitive equations.展开更多
The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems. Based on image multi-feature fusion and support vector machine, a new method to ide...The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems. Based on image multi-feature fusion and support vector machine, a new method to identify flow regime in two-phase flow was presented. Firstly, gas-liquid two-phase flow images including bub- bly flow, plug flow, slug flow, stratified flow, wavy flow, annular flow and mist flow were captured by digital high speed video systems in the horizontal tube. The image moment invariants and gray level co-occurrence matrix texture features were extracted using image processing techniques. To improve the performance of a multiple classifier system, the rough sets theory was used for reducing the inessential factors. Furthermore, the support vector machine was trained by using these eigenvectors to reduce the dimension as flow regime samples, and the flow regime intelligent identification was realized. The test results showed that image features which were reduced with the rough sets theory could excellently reflect the difference between seven typical flow regimes, and successful training the support vector machine could quickly and accurately identify seven typical flow regimes of gas-liquid two-phase flow in the horizontal tube. Image multi-feature fusion method provided a new way to identify the gas-liquid two-phase flow, and achieved higher identification ability than that of single characteristic. The overall identification accuracy was 100%, and an estimate of the image processing time was 8 ms for online flow regime identification.展开更多
Mafic rocks comprising tholeiitic pillow basalt, dolerite and minor gabbro form the basal stratigraphic unit in the ca. 2.8 to 2.6 Ga Geita Greenstone Belt situated in the NW Tanzania Craton. They outcrop mainly along...Mafic rocks comprising tholeiitic pillow basalt, dolerite and minor gabbro form the basal stratigraphic unit in the ca. 2.8 to 2.6 Ga Geita Greenstone Belt situated in the NW Tanzania Craton. They outcrop mainly along the southern margin of the belt, and are at least 50 million years older than the supracrustal assemblages against which they have been juxtaposed. Geochemical analyses indicate that parts of the assemblage approach high Mg-tholeiite （more than 8 wt.% MgO）. This suite of samples has a restricted compositional range suggesting derivation from a chemically homogenous reservoir. Trace element modeling suggests that the mafic rocks were derived by partial melting within the spinel peridotite field from a source rock with a primitive mantle composition. That is, trace elements maintain primitive mantle ratios （Zr/Hf = 32-35, Ti/Zr - 107-147）, producing flat REE and HFSE profles [（La/Yb）pm = 0.9 -1.3], with abundances of 3-10 times primitive mantle and with minor negative anomalies of Nb [（Nb/ La）pm - 0.6-0.8] and Th [（Th/La）pm = 0.6-0.9]. Initial isotope compositions （εNd） range from 1.6 to 2.9 at 2.8 Ga and plot below the depleted mantle line suggesting derivation from a more enriched source compared to present day MORB mantle. The trace element composition and Nd isotopic ratios are similar to the mafic rocks outcropping -50 km south. The mafic rocks outcropping in the Geita area were erupted through oceanic crust over a short time period, between -2830 and-2820 Ma; are compositionally homogenous, contain little to no associated terrigenous sediments, and their trace element composition and short emplacement time resemble oceanic plateau basalts. They have been interpreted to be derived from a plume head with a primitive mantle composition.展开更多
Massive open online courses(MOOC)have recently gained worldwide attention in the field of education.The manner of MOOC provides a new option for learning various kinds of knowledge.A mass of data miming algorithms hav...Massive open online courses(MOOC)have recently gained worldwide attention in the field of education.The manner of MOOC provides a new option for learning various kinds of knowledge.A mass of data miming algorithms have been proposed to analyze the learner’s characteristics and classify the learners into different groups.However,most current algorithms mainly focus on the final grade of the learners,which may result in an improper classification.To overcome the shortages of the existing algorithms,a novel multi-feature weighting based K-means(MFWK-means)algorithm is proposed in this paper.Correlations between the widely used feature grade and other features are first investigated,and then the learners are classified based on their grades and weighted features with the proposed MFWK-means algorithm.Experimental results with the Canvas Network Person-Course(CNPC)dataset demonstrate the effectiveness of our method.Moreover,a comparison between the new MFWK-means and the traditional K-means clustering algorithm is implemented to show the superiority of the proposed method.展开更多
BACKGROUND Adrenal primitive neuroectodermal tumor(PNET) is an extremely rare malignant tumor with poor prognosis and of neural crest origin. Herein, we report a case of adrenal PNET and summarized its clinical and pa...BACKGROUND Adrenal primitive neuroectodermal tumor(PNET) is an extremely rare malignant tumor with poor prognosis and of neural crest origin. Herein, we report a case of adrenal PNET and summarized its clinical and pathological characteristics on the basis of 16 patients reported recently.CASE SUMMARY A female patient aged 25 years presented with right lumbago for 12 mo, and preoperative computed tomography showed a huge right adrenal mass. She received tumorectomy, and post-operative pathological examination showed adrenal PNET. After surgery, she underwent adjuvant chemotherapy and was followed up 31 mo after surgery. She received brachytherapy for right paracolic and hepatic metastases. She was alive and followed up for 60 mo. In available studies, only 57.14%(4/7) and 44.44%(4/9) were positive for the expression of neuron-specific enolase and synaptophysin, respectively, although CD99 expression was found in all the patients(100%; 10/10).CONCLUSION It is concluded that adrenal PNET is very rare and highly malignant, and histology is a golden standard in its diagnosis. Surgery and adjuvant therapy is the main treatment.展开更多
In this paper, we study the bases and base sets of primitive symmetric loop-free （generalized） signed digraphs on n vertices. We obtain sharp upper bounds of the bases, and show that the base sets of the classes of ...In this paper, we study the bases and base sets of primitive symmetric loop-free （generalized） signed digraphs on n vertices. We obtain sharp upper bounds of the bases, and show that the base sets of the classes of such digraphs are （2, 3,..., 2n - 1}. We also give a new proof of an important result obtained by Cheng and Liu.展开更多
基金supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.SJCX24_1332)Jiangsu Province Education Science Planning Project in 2024(Grant No.B-b/2024/01/122)High-Level Talent Scientific Research Foundation of Jinling Institute of Technology,China(Grant No.jit-b-201918).
文摘Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image distortion,inaccurate localization of the tampered regions,and difficulty in recovering content.Given these shortcomings,a fragile image watermarking algorithm for tampering blind-detection and content self-recovery is proposed.The multi-feature watermarking authentication code(AC)is constructed using texture feature of local binary patterns(LBP),direct coefficient of discrete cosine transform(DCT)and contrast feature of gray level co-occurrence matrix(GLCM)for detecting the tampered region,and the recovery code(RC)is designed according to the average grayscale value of pixels in image blocks for recovering the tampered content.Optimal pixel adjustment process(OPAP)and least significant bit(LSB)algorithms are used to embed the recovery code and authentication code into the image in a staggered manner.When detecting the integrity of the image,the authentication code comparison method and threshold judgment method are used to perform two rounds of tampering detection on the image and blindly recover the tampered content.Experimental results show that this algorithm has good transparency,strong and blind detection,and self-recovery performance against four types of malicious attacks and some conventional signal processing operations.When resisting copy-paste,text addition,cropping and vector quantization under the tampering rate(TR)10%,the average tampering detection rate is up to 94.09%,and the peak signal-to-noise ratio(PSNR)of the watermarked image and the recovered image are both greater than 41.47 and 40.31 dB,which demonstrates its excellent advantages compared with other related algorithms in recent years.
基金supported by the National Natural Science Foundation of China(Nos.12072027,62103052,61603346 and 62103379)the Henan Key Laboratory of General Aviation Technology,China(No.ZHKF-230201)+3 种基金the Funding for the Open Research Project of the Rotor Aerodynamics Key Laboratory,China(No.RAL20200101)the Key Research and Development Program of Henan Province,China(Nos.241111222000 and 241111222900)the Key Science and Technology Program of Henan Province,China(No.232102220067)the Scholarship Funding from the China Scholarship Council(No.202206030079).
文摘In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.
文摘BACKGROUND Data on clinical characteristics,treatment outcomes,and prognosis of pancreatic primitive neuroectodermal tumors(PNETs)are limited.AIM To analyze the clinical data of 30 patients with pancreatic PNETs to identify their clinical characteristics and factors associated with prognosis.METHODS We used the keywords"primary neuroectodermal tumor,""digestive tract,""pancreas,""pancreatic,"and"gastrointestinal,"individually or in combination,to collect data from a global database for all patients with pancreatic PNET to date.Univariate and Cox regression analyses were performed to identify prognostic factors for patient survival.RESULTS A total of 30 cases of pancreatic PNET were included in this study:15 males and 15 females with a mean age of 24 years.The main symptom was abdominal pain(73.3%),and the median tumor size was 7.85 cm.Twenty-four patients(80.0%)underwent surgery and nineteen patients received adjuvant therapy.Local metastasis was observed in 13 patients(43.3%),lymph node metastasis in 10 patients(33.3%),and distant metastasis in 6 patients(20.0%).Local recurrence was observed in 13 patients(43.3%).The median survival time of all patients was 29.4 months,and the overall estimated 1-year and 3-year survival rates were approximately 66.0%and 36.4%,respectively.Univariate analysis showed that chemotherapy(P=0.036),local metastasis(P=0.041),lymph node metastasis(P=0.003),distant metastasis(P=0.049),and surgical margins(P=0.048)were the prognostic factors affecting survival.Multivariate analysis revealed only lymph node metastasis(P=0.012)as a prognostic factor.CONCLUSION Pancreatic PNET is extremely rare,occurs in young adults,has no apparent sex predisposition,has a high rate of metastasis and early recurrence,and has a very poor prognosis.The diagnosis of pancreatic PNET requires a combination of clinical symptoms,pathologic features,immunohistochemistry,and cytogenetic analysis.Univariate analysis suggested that chemotherapy,metastasis,and surgical margins were prognostic factors affecting survival,and multivariate analysis suggested that lymph node metastasis is an important prognostic factor.Therefore,early diagnosis,early and extensive resection,and adjuvant chemoradiotherapy may help improve prognosis.
基金This work is supported by the Ministry of Education of Humanities and Social Science projects in China(No.20YJCZH124)Guangdong Province Education and Teaching Reform Project No.640:Research on the Teaching Practice and Application of Online Peer Assessment Methods in the Context of Artificial Intelligence.
文摘This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data and historical academic performance)with dynamic behavioral patterns(e.g.,real-time interactions and evolving interests over time).The research employs Term Frequency-Inverse Document Frequency(TF-IDF)for semantic feature extraction,integrates the Analytic Hierarchy Process(AHP)for feature weighting,and introduces a time decay function inspired by Newton’s law of cooling to dynamically model changes in learners’interests.Empirical results demonstrate that this framework effectively captures the dynamic evolution of learners’behaviors and provides context-aware learning resource recommendations.The study introduces a novel paradigm for learner modeling in educational technology,combining methodological innovation with a scalable technical architecture,thereby laying a foundation for the development of adaptive learning systems.
基金supported by National Natural Science Foundation of China(No.61761027)Gansu Young Doctor’s Fund for Higher Education Institutions(No.2021QB-053)。
文摘The traditional EnFCM(Enhanced fuzzy C-means)algorithm only considers the grey-scale features in image segmentation,resulting in less than satisfactory results when the algorithm is used for remote sensing woodland image segmentation and extraction.An EnFCM remote sensing forest land extraction method based on PCA multi-feature fusion was proposed.Firstly,histogram equalization was applied to improve the image contrast.Secondly,the texture and edge features of the image were extracted,and a multi-feature fused pixel image was generated using the PCA technique.Moreover,the fused feature was used as a feature constraint to measure the difference of pixels instead of a single grey-scale feature.Finally,an improved feature distance metric calculated the similarity between the pixel points and the cluster center to complete the cluster segmentation.The experimental results showed that the error was between 1.5%and 4.0%compared with the forested area counted by experts’hand-drawing,which could obtain a high accuracy segmentation and extraction result.
基金supported in part by the National Key R&D Program under Grant 2018YFB1304504.
文摘Walking is the basic locomotion pattern for bipedal robots.The walking pattern is widely generated using the linear inverted pendulum model.The linear inverted pendulum motion of each support period can be designed as a walk primitive to be connected to form a walking trajectory.A novel method of integrating double support phase into the walk primitive was proposed in this article.The method describes the generation of walking patterns using walk primitives with double support,specifically for lateral plane including walking in place,walking for lateral,and walking initiation,and for sagittal plane including fixed step length walking,variable step length walking,and walking initiation.Compared to walk primitives without double support phase,those with double support phase reduce the maximum speed required by the robot and eliminate the need to adjust foothold for achieving continuous speed.The performance of the proposed method is validated by simulations and experiments on Neubot,a position-controlled biped robot.
基金supported by the National Natural Science Foundation of China(No.52272382)the Fundamental Research Funds for the Central Universities,China。
文摘Path planning is crucial for autonomous flight of fixed-wing Unmanned Aerial Vehicles(UAVs).However,due to the high-speed flight and complex control of fixed-wing UAVs,ensuring the feasibility and safety of planned paths in complex environments is challenging.This paper proposes a feasible path planning algorithm named Closed-loop Radial Ray A^(*)(CL-RaA^(*)).The core components of the CL-RaA^(*)include an adaptive variable-step-size path search and a just-in-time expansion primitive.The former enables fast path search in complex environments,while the latter ensures the feasibility of the generated paths.By integrating these two components and conducting safety checks on the trajectories to be expanded,the CL-RaA^(*)can rapidly generate safe and feasible paths that satisfy the differential constraints that comprehensively consider the dynamics and control characteristics of six-degree-of-freedom fixed-wing UAVs.The final performance tests and simulation validations demonstrate that the CL-RaA^(*)can generate safe and feasible paths in various environments.Compared to feasible path planning algorithms that use the rapidlyexploring random trees,the CL-RaA^(*)not only ensures deterministic planning results in the same scenarios but also generates smoother feasible paths for fixed-wing UAVs more efficiently.In environments with dense grid obstacles,the feasible paths generated by the CL-RaA^(*)are more conducive to UAV tracking compared to those planned using Dubins curves.
基金supported in part by the NSFC(12171084,12326367)the Jiangsu Provincial Scientific Research Center of Applied Mathematics(BK20233002)the fundamental Research Funds for the Central Universities(RF1028623037)。
文摘This paper investigates the ergodicity and weak convergence of transition probabilities for two-dimensional stochastic primitive equations driven by multiplicative noise.The existence of invariant measures is established using the classical Krylov-Bogoliubov theory.The uniqueness of invariant measures and the weak convergence of transition probabilities are demonstrated through the application of the asymptotic coupling method and Foias-Prodi estimate.
文摘The accelerating urbanization process leads to aggravated environmental problems, thus garden design which is a creative activity connecting human and nature has attracted much attention, and also garden designers have had to rethink about environmental ethics and morals. This study, from the perspective of environmental ethics, duly proposed the brand-new concept of 'appropriate garden of primitive ecology', proceeded from the orientation, form, value, theoretical basis, realistic significance, culture and consumption psychology of 'garden of primitive ecology', fully developed outstanding ecological wisdoms and morals in traditional Chinese garden culture, boosted garden designs to the environmental ethics level of eco-justice, to seek for the approach to 'garden of primitive ecology' with indigenous Chinese environmental ethic characteristics.
基金supported in part by the NSF of China (90511009, 10801017)National Basic Research Program of China (973 Program, 2007CB814800)
文摘This paper is devoted to considering the three-dimensional viscous primitive equations of the large-scale atmosphere. First, we prove the global well-posedness for the primitive equations with weaker initial data than that in [11]. Second, we obtain the existence of smooth solutions to the equations. Moreover, we obtain the compact global attractor in V for the dynamical system generated by the primitive equations of large-scale atmosphere, which improves the result of [11].
文摘Peripheral primitive neuroectodermal tumor(PNET) of the kidney is a rare, aggressive tumor known for its recurrence and metastatic potential. Despite the frequency of venous extension to the renal veins and inferior vena cava, pulmonary tumor embolism at the initial presentation is not common. We report a case of 22-year-old female with PNET of the kidney who presented with tumor embolism in the inferior vena cava(IVC) and bilateral pulmonary artery. The patient underwent surgical resection and histopathological analysis confirmed the presence of tumor within the IVC and pulmonary arteries. The patient received adjuvant chemotherapy and is currently doing well on follow-up.
基金This work was supported,in part,by the National Nature Science Foundation of China under Grant Numbers 61502240,61502096,61304205,61773219in part,by the Natural Science Foundation of Jiangsu Province under grant numbers BK20201136,BK20191401+1 种基金in part,by the Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant Numbers SJCX21_0363in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund.
文摘Vehicle re-identification(ReID)aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera scenario.It has gradually become a core technology of intelligent transportation system.Most existing vehicle re-identification models adopt the joint learning of global and local features.However,they directly use the extracted global features,resulting in insufficient feature expression.Moreover,local features are primarily obtained through advanced annotation and complex attention mechanisms,which require additional costs.To solve this issue,a multi-feature learning model with enhanced local attention for vehicle re-identification(MFELA)is proposed in this paper.The model consists of global and local branches.The global branch utilizes both middle and highlevel semantic features of ResNet50 to enhance the global representation capability.In addition,multi-scale pooling operations are used to obtain multiscale information.While the local branch utilizes the proposed Region Batch Dropblock(RBD),which encourages the model to learn discriminative features for different local regions and simultaneously drops corresponding same areas randomly in a batch during training to enhance the attention to local regions.Then features from both branches are combined to provide a more comprehensive and distinctive feature representation.Extensive experiments on VeRi-776 and VehicleID datasets prove that our method has excellent performance.
基金The paper is supported by the Research Foundation for OutstandingYoung Teachers , China University of Geosciences ( Wuhan) ( No .CUGQNL0616) Research Foundationfor State Key Laboratory of Geo-logical Processes and Mineral Resources ( No . MGMR2002-02)Hubei Provincial Depart ment of Education (B) .
文摘Urban land provides a suitable location for various economic activities which affect the development of surrounding areas. With rapid industrialization and urbanization, the contradictions in land-use become more noticeable. Urban administrators and decision-makers seek modern methods and technology to provide information support for urban growth. Recently, with the fast development of high-resolution sensor technology, more relevant data can be obtained, which is an advantage in studying the sustainable development of urban land-use. However, these data are only information sources and are a mixture of ＂information＂ and ＂noise＂. Processing, analysis and information extraction from remote sensing data is necessary to provide useful information. This paper extracts urban land-use information from a high-resolution image by using the multi-feature information of the image objects, and adopts an object-oriented image analysis approach and multi-scale image segmentation technology. A classification and extraction model is set up based on the multi-features of the image objects, in order to contribute to information for reasonable planning and effective management. This new image analysis approach offers a satisfactory solution for extracting information quickly and efficiently.
文摘Extraskeletal Ewing's sarcoma/peripheral primitive neuroectodermal tumor(E-EWS/pP NET) is a rare aggressive malignant small round cell tumor. In this report, we present the case of a 15-year-old boy who suffered from acute abdominal pain accompanied by hematemesis and melena, and was eventually diagnosed with E-EWS/p PNET. To date, there have been only five reported cases of E-EWS/pP NET of the small bowel including the patient in this report. To the best of our knowledge, this is the first documentation of a pP NET of the small bowel mesentery at nonage. All these have made this report rare and significant.
基金This research was jointly supported by the National Key Programme for Developing Basic Sciences (G1998040911) and the National Natural Science Foundation of China under Grant Nos. 49675267, 49205058, and 49975020.
文摘The design of a total energy conserving semi-implicit scheme for the multiple-level baroclinic primitive equation has remained an unsolved problem for a long time. In this work, however, we follow an energy perfect conserving semi-implicit scheme of a European Centre for Medium-Range Weather Forecasts (ECMWF) type sigma-coordinate primitive equation which has recently successfully formulated. Some real-data contrast tests between the model of the new conserving scheme and that of the ECMWF-type of global spectral semi-implicit scheme show that the RMS error of the averaged forecast Height at 850 hPa can be clearly improved after the first integral week. The reduction also reaches 50 percent by the 30th day. Further contrast tests demonstrate that the RMS error of the monthly mean height in the middle and lower troposphere also be largely reduced, and some well-known systematical defects can be greatly improved. More detailed analysis reveals that part of the positive contributions comes from improvements of the extra-long wave components. This indicates that a remarkable improvement of the model climate drift level can be achieved by the actual realizing of a conserving time-difference scheme, which thereby eliminates a corresponding computational systematic error source/sink found in the currently-used traditional type of weather and climate system models in relation to the baroclinic primitive equations.
基金Supported by the National Natural Science Foundation of China （50706006） and the Science and Technology Development Program of Jilin Province （20040513）.
文摘The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems. Based on image multi-feature fusion and support vector machine, a new method to identify flow regime in two-phase flow was presented. Firstly, gas-liquid two-phase flow images including bub- bly flow, plug flow, slug flow, stratified flow, wavy flow, annular flow and mist flow were captured by digital high speed video systems in the horizontal tube. The image moment invariants and gray level co-occurrence matrix texture features were extracted using image processing techniques. To improve the performance of a multiple classifier system, the rough sets theory was used for reducing the inessential factors. Furthermore, the support vector machine was trained by using these eigenvectors to reduce the dimension as flow regime samples, and the flow regime intelligent identification was realized. The test results showed that image features which were reduced with the rough sets theory could excellently reflect the difference between seven typical flow regimes, and successful training the support vector machine could quickly and accurately identify seven typical flow regimes of gas-liquid two-phase flow in the horizontal tube. Image multi-feature fusion method provided a new way to identify the gas-liquid two-phase flow, and achieved higher identification ability than that of single characteristic. The overall identification accuracy was 100%, and an estimate of the image processing time was 8 ms for online flow regime identification.
文摘Mafic rocks comprising tholeiitic pillow basalt, dolerite and minor gabbro form the basal stratigraphic unit in the ca. 2.8 to 2.6 Ga Geita Greenstone Belt situated in the NW Tanzania Craton. They outcrop mainly along the southern margin of the belt, and are at least 50 million years older than the supracrustal assemblages against which they have been juxtaposed. Geochemical analyses indicate that parts of the assemblage approach high Mg-tholeiite （more than 8 wt.% MgO）. This suite of samples has a restricted compositional range suggesting derivation from a chemically homogenous reservoir. Trace element modeling suggests that the mafic rocks were derived by partial melting within the spinel peridotite field from a source rock with a primitive mantle composition. That is, trace elements maintain primitive mantle ratios （Zr/Hf = 32-35, Ti/Zr - 107-147）, producing flat REE and HFSE profles [（La/Yb）pm = 0.9 -1.3], with abundances of 3-10 times primitive mantle and with minor negative anomalies of Nb [（Nb/ La）pm - 0.6-0.8] and Th [（Th/La）pm = 0.6-0.9]. Initial isotope compositions （εNd） range from 1.6 to 2.9 at 2.8 Ga and plot below the depleted mantle line suggesting derivation from a more enriched source compared to present day MORB mantle. The trace element composition and Nd isotopic ratios are similar to the mafic rocks outcropping -50 km south. The mafic rocks outcropping in the Geita area were erupted through oceanic crust over a short time period, between -2830 and-2820 Ma; are compositionally homogenous, contain little to no associated terrigenous sediments, and their trace element composition and short emplacement time resemble oceanic plateau basalts. They have been interpreted to be derived from a plume head with a primitive mantle composition.
文摘Massive open online courses(MOOC)have recently gained worldwide attention in the field of education.The manner of MOOC provides a new option for learning various kinds of knowledge.A mass of data miming algorithms have been proposed to analyze the learner’s characteristics and classify the learners into different groups.However,most current algorithms mainly focus on the final grade of the learners,which may result in an improper classification.To overcome the shortages of the existing algorithms,a novel multi-feature weighting based K-means(MFWK-means)algorithm is proposed in this paper.Correlations between the widely used feature grade and other features are first investigated,and then the learners are classified based on their grades and weighted features with the proposed MFWK-means algorithm.Experimental results with the Canvas Network Person-Course(CNPC)dataset demonstrate the effectiveness of our method.Moreover,a comparison between the new MFWK-means and the traditional K-means clustering algorithm is implemented to show the superiority of the proposed method.
基金Supported by National Natural Science Foundation of China,No.81572621Medical and Technology Intercrossing Research Foundation of Shanghai Jiaotong University,No.YG2016QN65
文摘BACKGROUND Adrenal primitive neuroectodermal tumor(PNET) is an extremely rare malignant tumor with poor prognosis and of neural crest origin. Herein, we report a case of adrenal PNET and summarized its clinical and pathological characteristics on the basis of 16 patients reported recently.CASE SUMMARY A female patient aged 25 years presented with right lumbago for 12 mo, and preoperative computed tomography showed a huge right adrenal mass. She received tumorectomy, and post-operative pathological examination showed adrenal PNET. After surgery, she underwent adjuvant chemotherapy and was followed up 31 mo after surgery. She received brachytherapy for right paracolic and hepatic metastases. She was alive and followed up for 60 mo. In available studies, only 57.14%(4/7) and 44.44%(4/9) were positive for the expression of neuron-specific enolase and synaptophysin, respectively, although CD99 expression was found in all the patients(100%; 10/10).CONCLUSION It is concluded that adrenal PNET is very rare and highly malignant, and histology is a golden standard in its diagnosis. Surgery and adjuvant therapy is the main treatment.
基金Supported by the National Natural Science Foundation of China(Grant Nos.1090106111071088)the Zhujiang Technology New Star Foundation of Guangzhou(Grant No.2011J2200090)
文摘In this paper, we study the bases and base sets of primitive symmetric loop-free （generalized） signed digraphs on n vertices. We obtain sharp upper bounds of the bases, and show that the base sets of the classes of such digraphs are （2, 3,..., 2n - 1}. We also give a new proof of an important result obtained by Cheng and Liu.
