With the complexity of the composition process and the rapid growth of candidate services,realizing optimal or near-optimal service composition is an urgent problem.Currently,the static service composition chain is ri...With the complexity of the composition process and the rapid growth of candidate services,realizing optimal or near-optimal service composition is an urgent problem.Currently,the static service composition chain is rigid and cannot be easily adapted to the dynamic Web environment.To address these challenges,the geographic information service composition(GISC) problem as a sequential decision-making task is modeled.In addition,the Markov decision process(MDP),as a universal model for the planning problem of agents,is used to describe the GISC problem.Then,to achieve self-adaptivity and optimization in a dynamic environment,a novel approach that integrates Monte Carlo tree search(MCTS) and a temporal-difference(TD) learning algorithm is proposed.The concrete services of abstract services are determined with optimal policies and adaptive capability at runtime,based on the environment and the status of component services.The simulation experiment is performed to demonstrate the effectiveness and efficiency through learning quality and performance.展开更多
Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)meth...Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)method that reformulates the task as a Markov Decision Process(MDP)through the integration of Monte Carlo Tree Search(MCTS)and deep reinforcement learning(DRL).The framework employs an action space of 25 enhancement operators,strategically grouped for basic attribute adjustment,color component balance,correction,and deblurring.Exploration within MCTS is guided by a dual-branch convolutional network,enabling intelligent sequential operator selection.Our core contributions include:(1)a multimodal state representation combining CIELab color histograms with deep perceptual features,(2)a dual-objective reward mechanism optimizing chromatic fidelity and perceptual consistency,and(3)an alternating training strategy co-optimizing enhancement sequences and network parameters.We further propose two inference schemes:an MCTS-based approach prioritizing accuracy at higher computational cost,and an efficient network policy enabling real-time processing with minimal quality loss.Comprehensive evaluations on the UIEB Dataset and Color correction and haze removal comparisons on the U45 Dataset demonstrate AquaTree’s superiority,significantly outperforming nine state-of-the-art methods across five established underwater image quality metrics.展开更多
Background:Chronic endometritis(CE)is an important pathological factor contributing to female infertility and recurrent pregnancy loss.Although antibiotics are the primary clinical treatment for CE,they do not effecti...Background:Chronic endometritis(CE)is an important pathological factor contributing to female infertility and recurrent pregnancy loss.Although antibiotics are the primary clinical treatment for CE,they do not effectively improve pregnancy outcomes.Wen Yang Hua Zhuo(WYHZ)is a clinically employed classical formula known for its effects in warming yang,tonifying the spleen and kidneys,and resolving dampness.However,its underlying mechanisms remain unclear.This study aimed to elucidate how WYHZ modulates the immunometabolic microenvironment at the maternal-fetal interface in CE by targeting the MCT/HIF-1α/LDHA pathway to promote embryo implantation.Methods:In vivo,the model of CE was established by intrauterine injection of lipopolysaccharide(LPS)(1 mg/mL)into female C57/BL mice,followed by WYHZ treatment for 3 weeks to evaluate its effects on embryo implantation.Mechanistic studies were further conducted using the MCT-1 inhibitor AZD3965 and adeno-associated virus-mediated HIF-1αknockdown.In vitro,an in vitro CE model consisting of M1 macrophages and Ishikawa,as well as an in vitro embryo implantation model mediated by JAR cells,were constructed using Transwell,and the therapeutic mechanisms of WYHZ was validated using AZD3965 and lentiviral sh HIF-1αintervention.Metabolic enzyme activity assays,protein antibody microarrays,immunofluorescence,Western blotting,Seahorse analysis,and ELISA were employed.Results:WYHZ improved the immune-inflammatory microenvironment at the maternal-fetal interface by reducing pro-inflammatory cytokines and increasing anti-inflammatory factors.In parallel,WYHZ reprogrammed endometrial metabolism by enhancing glycolysis and suppressing mitochondrial oxidative phosphorylation,thereby improving endometrial receptivity and embryo implantation.Mechanistically,WYHZ activated the MCT/HIF-1α/LDHA pathway in endometrial epithelial cells,alleviating inflammatory stress and restoring receptivity.Both AZD3965 intervention and HIF-1αknockdown impaired endometrial receptivity and implantation,effects that were reversed by WYHZ.Conclusion:WYHZ modulates the immunometabolic microenvironment of the endometrium in the context of CE by targeting the activation of the MCT/HIF-1α/LDHA pathway,which improves endometrial receptivity and promotes embryo implantation.展开更多
In this study, to meet the development and application requirements for high-strength and hightoughness energetic structural materials, a representative volume element of a TA15 matrix embedded with a TaZrNb sphere wa...In this study, to meet the development and application requirements for high-strength and hightoughness energetic structural materials, a representative volume element of a TA15 matrix embedded with a TaZrNb sphere was designed and fabricated via diffusion bonding. The mechanisms of the microstructural evolution of the TaZrNb/TA15 interface were investigated via SEM, EBSD, EDS, and XRD.Interface mechanical property tests and in-situ tensile tests were conducted on the sphere-containing structure, and an equivalent tensile-strength model was established for the structure. The results revealed that the TA15 titanium alloy and joint had high density and no pores or cracks. The thickness of the planar joint was approximately 50-60 μm. The average tensile and shear strengths were 767 MPa and 608 MPa, respectively. The thickness of the spherical joint was approximately 60 μm. The Zr and Nb elements in the joint diffused uniformly and formed strong bonds with Ti without forming intermetallic compounds. The interface exhibited submicron grain refinement and a concave-convex interlocking structure. The tensile fracture surface primarily exhibited intergranular fracture combined with some transgranular fracture, which constituted a quasi-brittle fracture mode. The shear fracture surface exhibited brittle fracture with regular arrangements of furrows. Internal fracture occurred along the spherical interface, as revealed by advanced in-situ X-ray microcomputed tomography. The experimental results agreed well with the theoretical predictions, indicating that the high-strength interface contributes to the overall strength and toughness of the sphere-containing structure.展开更多
Medical image segmentation is a crucial task in clinical applications.However,obtaining labeled data for medical images is often challenging.This has led to the appeal of semi-supervised learning(SSL),a technique adep...Medical image segmentation is a crucial task in clinical applications.However,obtaining labeled data for medical images is often challenging.This has led to the appeal of semi-supervised learning(SSL),a technique adept at leveraging a modest amount of labeled data.Nonetheless,most prevailing SSL segmentation methods for medical images either rely on the single consistency training method or directly fine-tune SSL methods designed for natural images.In this paper,we propose an innovative semi-supervised method called multi-consistency training(MCT)for medical image segmentation.Our approach transcends the constraints of prior methodologies by considering consistency from a dual perspective:output consistency across different up-sampling methods and output consistency of the same data within the same network under various perturbations to the intermediate features.We design distinct semi-supervised loss regression methods for these two types of consistencies.To enhance the application of our MCT model,we also develop a dedicated decoder as the core of our neural network.Thorough experiments were conducted on the polyp dataset and the dental dataset,rigorously compared against other SSL methods.Experimental results demonstrate the superiority of our approach,achieving higher segmentation accuracy.Moreover,comprehensive ablation studies and insightful discussion substantiate the efficacy of our approach in navigating the intricacies of medical image segmentation.展开更多
基金Supported by the National Natural Science Foundation of China(No.41971356,41671400,41701446)National Key Research and Development Program of China(No.2017YFB0503600,2018YFB0505500)Hubei Province Natural Science Foundation of China(No.2017CFB277)。
文摘With the complexity of the composition process and the rapid growth of candidate services,realizing optimal or near-optimal service composition is an urgent problem.Currently,the static service composition chain is rigid and cannot be easily adapted to the dynamic Web environment.To address these challenges,the geographic information service composition(GISC) problem as a sequential decision-making task is modeled.In addition,the Markov decision process(MDP),as a universal model for the planning problem of agents,is used to describe the GISC problem.Then,to achieve self-adaptivity and optimization in a dynamic environment,a novel approach that integrates Monte Carlo tree search(MCTS) and a temporal-difference(TD) learning algorithm is proposed.The concrete services of abstract services are determined with optimal policies and adaptive capability at runtime,based on the environment and the status of component services.The simulation experiment is performed to demonstrate the effectiveness and efficiency through learning quality and performance.
基金supported by theHubei Provincial Technology Innovation Special Project and the Natural Science Foundation of Hubei Province under Grants 2023BEB024,2024AFC066,respectively.
文摘Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)method that reformulates the task as a Markov Decision Process(MDP)through the integration of Monte Carlo Tree Search(MCTS)and deep reinforcement learning(DRL).The framework employs an action space of 25 enhancement operators,strategically grouped for basic attribute adjustment,color component balance,correction,and deblurring.Exploration within MCTS is guided by a dual-branch convolutional network,enabling intelligent sequential operator selection.Our core contributions include:(1)a multimodal state representation combining CIELab color histograms with deep perceptual features,(2)a dual-objective reward mechanism optimizing chromatic fidelity and perceptual consistency,and(3)an alternating training strategy co-optimizing enhancement sequences and network parameters.We further propose two inference schemes:an MCTS-based approach prioritizing accuracy at higher computational cost,and an efficient network policy enabling real-time processing with minimal quality loss.Comprehensive evaluations on the UIEB Dataset and Color correction and haze removal comparisons on the U45 Dataset demonstrate AquaTree’s superiority,significantly outperforming nine state-of-the-art methods across five established underwater image quality metrics.
基金supported by the National Natural Science Foundation of China(grant number:82205172,82274570).
文摘Background:Chronic endometritis(CE)is an important pathological factor contributing to female infertility and recurrent pregnancy loss.Although antibiotics are the primary clinical treatment for CE,they do not effectively improve pregnancy outcomes.Wen Yang Hua Zhuo(WYHZ)is a clinically employed classical formula known for its effects in warming yang,tonifying the spleen and kidneys,and resolving dampness.However,its underlying mechanisms remain unclear.This study aimed to elucidate how WYHZ modulates the immunometabolic microenvironment at the maternal-fetal interface in CE by targeting the MCT/HIF-1α/LDHA pathway to promote embryo implantation.Methods:In vivo,the model of CE was established by intrauterine injection of lipopolysaccharide(LPS)(1 mg/mL)into female C57/BL mice,followed by WYHZ treatment for 3 weeks to evaluate its effects on embryo implantation.Mechanistic studies were further conducted using the MCT-1 inhibitor AZD3965 and adeno-associated virus-mediated HIF-1αknockdown.In vitro,an in vitro CE model consisting of M1 macrophages and Ishikawa,as well as an in vitro embryo implantation model mediated by JAR cells,were constructed using Transwell,and the therapeutic mechanisms of WYHZ was validated using AZD3965 and lentiviral sh HIF-1αintervention.Metabolic enzyme activity assays,protein antibody microarrays,immunofluorescence,Western blotting,Seahorse analysis,and ELISA were employed.Results:WYHZ improved the immune-inflammatory microenvironment at the maternal-fetal interface by reducing pro-inflammatory cytokines and increasing anti-inflammatory factors.In parallel,WYHZ reprogrammed endometrial metabolism by enhancing glycolysis and suppressing mitochondrial oxidative phosphorylation,thereby improving endometrial receptivity and embryo implantation.Mechanistically,WYHZ activated the MCT/HIF-1α/LDHA pathway in endometrial epithelial cells,alleviating inflammatory stress and restoring receptivity.Both AZD3965 intervention and HIF-1αknockdown impaired endometrial receptivity and implantation,effects that were reversed by WYHZ.Conclusion:WYHZ modulates the immunometabolic microenvironment of the endometrium in the context of CE by targeting the activation of the MCT/HIF-1α/LDHA pathway,which improves endometrial receptivity and promotes embryo implantation.
基金supported by the National Natural Science Foundation of China(Grant No.12372351).
文摘In this study, to meet the development and application requirements for high-strength and hightoughness energetic structural materials, a representative volume element of a TA15 matrix embedded with a TaZrNb sphere was designed and fabricated via diffusion bonding. The mechanisms of the microstructural evolution of the TaZrNb/TA15 interface were investigated via SEM, EBSD, EDS, and XRD.Interface mechanical property tests and in-situ tensile tests were conducted on the sphere-containing structure, and an equivalent tensile-strength model was established for the structure. The results revealed that the TA15 titanium alloy and joint had high density and no pores or cracks. The thickness of the planar joint was approximately 50-60 μm. The average tensile and shear strengths were 767 MPa and 608 MPa, respectively. The thickness of the spherical joint was approximately 60 μm. The Zr and Nb elements in the joint diffused uniformly and formed strong bonds with Ti without forming intermetallic compounds. The interface exhibited submicron grain refinement and a concave-convex interlocking structure. The tensile fracture surface primarily exhibited intergranular fracture combined with some transgranular fracture, which constituted a quasi-brittle fracture mode. The shear fracture surface exhibited brittle fracture with regular arrangements of furrows. Internal fracture occurred along the spherical interface, as revealed by advanced in-situ X-ray microcomputed tomography. The experimental results agreed well with the theoretical predictions, indicating that the high-strength interface contributes to the overall strength and toughness of the sphere-containing structure.
基金the Innovation Program of Shanghai Industrial Synergy(No.XTCX-KJ-2023-2-12)。
文摘Medical image segmentation is a crucial task in clinical applications.However,obtaining labeled data for medical images is often challenging.This has led to the appeal of semi-supervised learning(SSL),a technique adept at leveraging a modest amount of labeled data.Nonetheless,most prevailing SSL segmentation methods for medical images either rely on the single consistency training method or directly fine-tune SSL methods designed for natural images.In this paper,we propose an innovative semi-supervised method called multi-consistency training(MCT)for medical image segmentation.Our approach transcends the constraints of prior methodologies by considering consistency from a dual perspective:output consistency across different up-sampling methods and output consistency of the same data within the same network under various perturbations to the intermediate features.We design distinct semi-supervised loss regression methods for these two types of consistencies.To enhance the application of our MCT model,we also develop a dedicated decoder as the core of our neural network.Thorough experiments were conducted on the polyp dataset and the dental dataset,rigorously compared against other SSL methods.Experimental results demonstrate the superiority of our approach,achieving higher segmentation accuracy.Moreover,comprehensive ablation studies and insightful discussion substantiate the efficacy of our approach in navigating the intricacies of medical image segmentation.
