Porcine reproductive and respiratory syndrome(PRRS)is one of the most significant diseases affecting the pig industry worldwide.The PRRSV mutation rate is the highest among the RNA viruses.To date,NADC30-like PRRSV an...Porcine reproductive and respiratory syndrome(PRRS)is one of the most significant diseases affecting the pig industry worldwide.The PRRSV mutation rate is the highest among the RNA viruses.To date,NADC30-like PRRSV and highly pathogenic PRRSV(HP-PRRSV)are the dominant epidemic strains in China;however,commercial vaccines do not always provide sufficient cross-protection,and the reasons for insufficient protection are unclear.This study isolated a wild-type NADC30-like PRRSV,SX-YL1806,from Shaanxi Province.Vaccination challenge experiments in piglets showed that commercial modified live virus(MLV)vaccines provided good protection against HP-PRRSV.However,it could not provide sufficient protection against the novel strain SXYL1806.To explore the reasons for this phenomenon,we compared the genomic homology between the MLV strain and HP-PRRSV or NADC30-like PRRSV and found that the MLV strain had a lower genome similarity with NADC30-like PRRSV.Serum neutralization assay showed that MLV-immune serum slightly promoted the homologous HP-PRRSV replication and significantly promoted the heterologous NADC30-like PRRSV strain replication in vitro,suggesting that antibody-dependent enhancement(ADE)might also play a role in decreasing MLV protective efficacy.These findings expand our understanding of the potential factors affecting the protective effect of PRRSV MLV vaccines against the NADC30-like strains.展开更多
Strong coupling between resonantly matched surface plasmons of metals and excitons of quantum emitters results in the formation of new plasmon-exciton hybridized energy states.In plasmon-exciton strong coupling,plasmo...Strong coupling between resonantly matched surface plasmons of metals and excitons of quantum emitters results in the formation of new plasmon-exciton hybridized energy states.In plasmon-exciton strong coupling,plasmonic nanocavities play a significant role due to their ability to confine light in an ultrasmall volume.Additionally,two-dimensional transition metal dichalcogenides(TMDCs) have a significant exciton binding energy and remain stable at ambient conditions,making them an excellent alternative for investigating light-matter interactions.As a result,strong plasmon-exciton coupling has been reported by introducing a single metallic cavity.However,single nanoparticles have lower spatial confinement of electromagnetic fields and limited tunability to match the excitonic resonance.Here,we introduce the concept of catenary-shaped optical fields induced by plasmonic metamaterial cavities to scale the strength of plasmon-exciton coupling.The demonstrated plasmon modes of metallic metamaterial cavities offer high confinement and tunability and can match with the excitons of TMDCs to exhibit a strong coupling regime by tuning either the size of the cavity gap or thickness.The calculated Rabi splitting of Au-MoSe_2 and Au-WSe_2 heterostructures strongly depends on the catenary-like field enhancement induced by the Au cavity,resulting in room-temperature Rabi splitting ranging between 77.86 and 320 me V.These plasmonic metamaterial cavities can pave the way for manipulating excitons in TMDCs and operating active nanophotonic devices at ambient temperature.展开更多
BACKGROUND This study aimed to evaluate the safety of enhanced recovery after surgery(ERAS)in elderly patients with gastric cancer(GC).AIM To evaluate the safety of ERAS in elderly patients with GC.METHODS The PubMed,...BACKGROUND This study aimed to evaluate the safety of enhanced recovery after surgery(ERAS)in elderly patients with gastric cancer(GC).AIM To evaluate the safety of ERAS in elderly patients with GC.METHODS The PubMed,EMBASE,and Cochrane Library databases were used to search for eligible studies from inception to April 1,2023.The mean difference(MD),odds ratio(OR)and 95%confidence interval(95%CI)were pooled for analysis.The quality of the included studies was evaluated using the Newcastle-Ottawa Scale scores.We used Stata(V.16.0)software for data analysis.RESULTS This study consists of six studies involving 878 elderly patients.By analyzing the clinical outcomes,we found that the ERAS group had shorter postoperative hospital stays(MD=-0.51,I2=0.00%,95%CI=-0.72 to-0.30,P=0.00);earlier times to first flatus(defecation;MD=-0.30,I²=0.00%,95%CI=-0.55 to-0.06,P=0.02);less intestinal obstruction(OR=3.24,I2=0.00%,95%CI=1.07 to 9.78,P=0.04);less nausea and vomiting(OR=4.07,I2=0.00%,95%CI=1.29 to 12.84,P=0.02);and less gastric retention(OR=5.69,I2=2.46%,95%CI=2.00 to 16.20,P=0.00).Our results showed that the conventional group had a greater mortality rate than the ERAS group(OR=0.24,I2=0.00%,95%CI=0.07 to 0.84,P=0.03).However,there was no statistically significant difference in major complications between the ERAS group and the conventional group(OR=0.67,I2=0.00%,95%CI=0.38 to 1.18,P=0.16).CONCLUSION Compared to those with conventional recovery,elderly GC patients who received the ERAS protocol after surgery had a lower risk of mortality.展开更多
Multimodal lung tumor medical images can provide anatomical and functional information for the same lesion.Such as Positron Emission Computed Tomography(PET),Computed Tomography(CT),and PET-CT.How to utilize the lesio...Multimodal lung tumor medical images can provide anatomical and functional information for the same lesion.Such as Positron Emission Computed Tomography(PET),Computed Tomography(CT),and PET-CT.How to utilize the lesion anatomical and functional information effectively and improve the network segmentation performance are key questions.To solve the problem,the Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network(Guide-YNet)is proposed in this paper.Firstly,a double-encoder single-decoder U-Net is used as the backbone in this model,a single-coder single-decoder U-Net is used to generate the saliency guided feature using PET image and transmit it into the skip connection of the backbone,and the high sensitivity of PET images to tumors is used to guide the network to accurately locate lesions.Secondly,a Cross Scale Feature Enhancement Module(CSFEM)is designed to extract multi-scale fusion features after downsampling.Thirdly,a Cross-Layer Interactive Feature Enhancement Module(CIFEM)is designed in the encoder to enhance the spatial position information and semantic information.Finally,a Cross-Dimension Cross-Layer Feature Enhancement Module(CCFEM)is proposed in the decoder,which effectively extractsmultimodal image features through global attention and multi-dimension local attention.The proposed method is verified on the lung multimodal medical image datasets,and the results showthat theMean Intersection overUnion(MIoU),Accuracy(Acc),Dice Similarity Coefficient(Dice),Volumetric overlap error(Voe),Relative volume difference(Rvd)of the proposed method on lung lesion segmentation are 87.27%,93.08%,97.77%,95.92%,89.28%,and 88.68%,respectively.It is of great significance for computer-aided diagnosis.展开更多
Current aqueous battery electrolytes,including conve ntional hydrogel electrolytes,exhibit unsatisfactory water retention capabilities.The sustained water loss will lead to subsequent polarization and increased intern...Current aqueous battery electrolytes,including conve ntional hydrogel electrolytes,exhibit unsatisfactory water retention capabilities.The sustained water loss will lead to subsequent polarization and increased internal resistance,ultimately resulting in battery failure.Herein,a double network(DN) orga no hydrogel electrolyte based on dimethyl sulfoxide(DMSO)/H_(2)O binary solvent was proposed.Through directionally reconstructing hydrogen bonds and reducing active H_(2)O molecules,the water retention ability and cathode/anode interfaces were synergistic enhanced.As a result,the synthesized DN organohydrogel demonstrates exceptional water retention capabilities,retaining approximately 75% of its original weight even after the exposure to air for 20 days.The Zn MnO_(2) battery delivers an outstanding specific capacity of275 mA h g^(-1) at 1 C,impressive rate performance with 85 mA h g^(-1) at 30 C,and excellent cyclic stability(95% retention after 6000 cycles at 5 C).Zn‖Zn symmetric battery can cycle more than 5000 h at 1 mA cm^(-2) and 1 mA h cm^(-2) without short circuiting.This study will encourage the further development of functional organohydrogel electrolytes for advanced energy storage devices.展开更多
Due to the selective absorption of light and the existence of a large number of floating media in sea water, underwater images often suffer from color casts and detail blurs. It is therefore necessary to perform color...Due to the selective absorption of light and the existence of a large number of floating media in sea water, underwater images often suffer from color casts and detail blurs. It is therefore necessary to perform color correction and detail restoration. However,the existing enhancement algorithms cannot achieve the desired results. In order to solve the above problems, this paper proposes a multi-stream feature fusion network. First, an underwater image is preprocessed to obtain potential information from the illumination stream, color stream and structure stream by histogram equalization with contrast limitation, gamma correction and white balance, respectively. Next, these three streams and the original raw stream are sent to the residual blocks to extract the features. The features will be subsequently fused. It can enhance feature representation in underwater images. In the meantime, a composite loss function including three terms is used to ensure the quality of the enhanced image from the three aspects of color balance, structure preservation and image smoothness. Therefore, the enhanced image is more in line with human visual perception.Finally, the effectiveness of the proposed method is verified by comparison experiments with many stateof-the-art underwater image enhancement algorithms. Experimental results show that the proposed method provides superior results over them in terms of MSE,PSNR, SSIM, UIQM and UCIQE, and the enhanced images are more similar to their ground truth images.展开更多
Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images.Current methods feed the whole image directly into the model for enhancement.However,they ign...Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images.Current methods feed the whole image directly into the model for enhancement.However,they ignored that the R,G and B channels of underwater degraded images present varied degrees of degradation,due to the selective absorption for the light.To address this issue,we propose an unsupervised multi-expert learning model by considering the enhancement of each color channel.Specifically,an unsupervised architecture based on generative adversarial network is employed to alleviate the need for paired underwater images.Based on this,we design a generator,including a multi-expert encoder,a feature fusion module and a feature fusion-guided decoder,to generate the clear underwater image.Accordingly,a multi-expert discriminator is proposed to verify the authenticity of the R,G and B channels,respectively.In addition,content perceptual loss and edge loss are introduced into the loss function to further improve the content and details of the enhanced images.Extensive experiments on public datasets demonstrate that our method achieves more pleasing results in vision quality.Various metrics(PSNR,SSIM,UIQM and UCIQE) evaluated on our enhanced images have been improved obviously.展开更多
With the widespread use of machine learning(ML)technology,the operational efficiency and responsiveness of power grids have been significantly enhanced,allowing smart grids to achieve high levels of automation and int...With the widespread use of machine learning(ML)technology,the operational efficiency and responsiveness of power grids have been significantly enhanced,allowing smart grids to achieve high levels of automation and intelligence.However,tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks,making it urgent to enhance their robustness.To address this,we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree ensembles.Our algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws,ensuring training data accurately reflects possible attack scenarios while adhering to physical rules.In our experiments,the proposed method increased robustness against adversarial attacks by 100%when applied to real grid data under physical constraints.These results highlight the advantages of our method in maintaining efficient and secure operation of smart grids under adversarial conditions.展开更多
Finesse is a critical parameter for describing the characteristics of an optical enhancement cavity(OEC). This paper first presents a review of finesse measurement techniques, including a comparative analysis of the a...Finesse is a critical parameter for describing the characteristics of an optical enhancement cavity(OEC). This paper first presents a review of finesse measurement techniques, including a comparative analysis of the advantages, disadvantages, and potential limitations of several main methods from both theoretical and practical perspectives. A variant of the existing method called the free spectral range(FSR) modulation method is proposed and compared with three other finesse measurement methods, i.e., the fast-switching cavity ring-down(CRD) method, the rapidly swept-frequency(SF) CRD method, and the ringing effect method. A high-power OEC platform with a high finesse of approximately 16000 is built and measured with the four methods. The performance of these methods is compared, and the results show that the FSR modulation method and the fast-switching CRD method are more suitable and accurate than the other two methods for high-finesse OEC measurements. The CRD method and the ringing effect method can be implemented in open loop using simple equipment and are easy to perform. Additionally, recommendations for selecting finesse measurement methods under different conditions are proposed, which benefit the development of OEC and its applications.展开更多
In recent times,an image enhancement approach,which learns the global transformation function using deep neural networks,has gained attention.However,many existing methods based on this approach have a limitation:thei...In recent times,an image enhancement approach,which learns the global transformation function using deep neural networks,has gained attention.However,many existing methods based on this approach have a limitation:their transformation functions are too simple to imitate complex colour transformations between low-quality images and manually retouched high-quality images.In order to address this limitation,a simple yet effective approach for image enhancement is proposed.The proposed algorithm based on the channel-wise intensity transformation is designed.However,this transformation is applied to the learnt embedding space instead of specific colour spaces and then return enhanced features to colours.To this end,the authors define the continuous intensity transformation(CIT)to describe the mapping between input and output intensities on the embedding space.Then,the enhancement network is developed,which produces multi-scale feature maps from input images,derives the set of transformation functions,and performs the CIT to obtain enhanced images.Extensive experiments on the MIT-Adobe 5K dataset demonstrate that the authors’approach improves the performance of conventional intensity transforms on colour space metrics.Specifically,the authors achieved a 3.8%improvement in peak signal-to-noise ratio,a 1.8%improvement in structual similarity index measure,and a 27.5%improvement in learned perceptual image patch similarity.Also,the authors’algorithm outperforms state-of-the-art alternatives on three image enhancement datasets:MIT-Adobe 5K,Low-Light,and Google HDRþ.展开更多
Low-light images suffer from low quality due to poor lighting conditions,noise pollution,and improper settings of cameras.To enhance low-light images,most existing methods rely on normal-light images for guidance but ...Low-light images suffer from low quality due to poor lighting conditions,noise pollution,and improper settings of cameras.To enhance low-light images,most existing methods rely on normal-light images for guidance but the collection of suitable normal-light images is difficult.In contrast,a self-supervised method breaks free from the reliance on normal-light data,resulting in more convenience and better generalization.Existing self-supervised methods primarily focus on illumination adjustment and design pixel-based adjustment methods,resulting in remnants of other degradations,uneven brightness and artifacts.In response,this paper proposes a self-supervised enhancement method,termed as SLIE.It can handle multiple degradations including illumination attenuation,noise pollution,and color shift,all in a self-supervised manner.Illumination attenuation is estimated based on physical principles and local neighborhood information.The removal and correction of noise and color shift removal are solely realized with noisy images and images with color shifts.Finally,the comprehensive and fully self-supervised approach can achieve better adaptability and generalization.It is applicable to various low light conditions,and can reproduce the original color of scenes in natural light.Extensive experiments conducted on four public datasets demonstrate the superiority of SLIE to thirteen state-of-the-art methods.Our code is available at https://github.com/hanna-xu/SLIE.展开更多
Foam concrete is a prospective material in defense engineering to protect structures due to its high energy absorption capability resulted from the long plateau stage.However,stress enhancement rather than stress miti...Foam concrete is a prospective material in defense engineering to protect structures due to its high energy absorption capability resulted from the long plateau stage.However,stress enhancement rather than stress mitigation may happen when foam concrete is used as sacrificial claddings placed in the path of an incoming blast load.To investigate this interesting phenomenon,a one-dimensional difference model for blast wave propagation in foam concrete is firstly proposed and numerically solved by improving the second-order Godunov method.The difference model and numerical algorithm are validated against experimental results including both the stress mitigation and the stress enhancement.The difference model is then used to numerically analyze the blast wave propagation and deformation of material in which the effects of blast loads,stress-strain relation and length of foam concrete are considered.In particular,the concept of minimum thickness of foam concrete to avoid stress enhancement is proposed.Finally,non-dimensional analysis on the minimum thickness is conducted and an empirical formula is proposed by curve-fitting the numerical data,which can provide a reference for the application of foam concrete in defense engineering.展开更多
The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study intro...The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.展开更多
Recently,there have been significant advancements in the study of semantic communication in single-modal scenarios.However,the ability to process information in multi-modal environments remains limited.Inspired by the...Recently,there have been significant advancements in the study of semantic communication in single-modal scenarios.However,the ability to process information in multi-modal environments remains limited.Inspired by the research and applications of natural language processing across different modalities,our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos.Specifically,we propose a deep learning-basedMulti-ModalMutual Enhancement Video Semantic Communication system,called M3E-VSC.Built upon a VectorQuantized Generative AdversarialNetwork(VQGAN),our systemaims to leverage mutual enhancement among different modalities by using text as the main carrier of transmission.With it,the semantic information can be extracted fromkey-frame images and audio of the video and performdifferential value to ensure that the extracted text conveys accurate semantic information with fewer bits,thus improving the capacity of the system.Furthermore,a multi-frame semantic detection module is designed to facilitate semantic transitions during video generation.Simulation results demonstrate that our proposed model maintains high robustness in complex noise environments,particularly in low signal-to-noise ratio conditions,significantly improving the accuracy and speed of semantic transmission in video communication by approximately 50 percent.展开更多
Handheld ultrasound devices are known for their portability and affordability,making them widely utilized in underdeveloped areas and community healthcare for rapid diagnosis and early screening.However,the image qual...Handheld ultrasound devices are known for their portability and affordability,making them widely utilized in underdeveloped areas and community healthcare for rapid diagnosis and early screening.However,the image quality of handheld ultrasound devices is not always satisfactory due to the limited equipment size,which hinders accurate diagnoses by doctors.At the same time,paired ultrasound images are difficult to obtain from the clinic because imaging process is complicated.Therefore,we propose a modified cycle generative adversarial network(cycleGAN) for ultrasound image enhancement from multiple organs via unpaired pre-training.We introduce an ultrasound image pre-training method that does not require paired images,alleviating the requirement for large-scale paired datasets.We also propose an enhanced block with different structures in the pre-training and fine-tuning phases,which can help achieve the goals of different training phases.To improve the robustness of the model,we add Gaussian noise to the training images as data augmentation.Our approach is effective in obtaining the best quantitative evaluation results using a small number of parameters and less training costs to improve the quality of handheld ultrasound devices.展开更多
The plasma density enhancement outside hollow electrodes in capacitively coupled radio-frequency(RF) discharges is investigated by a two-dimensional(2D) particle-in-cell/Monte-Carlo collision(PIC/MCC) model. Results s...The plasma density enhancement outside hollow electrodes in capacitively coupled radio-frequency(RF) discharges is investigated by a two-dimensional(2D) particle-in-cell/Monte-Carlo collision(PIC/MCC) model. Results show that plasma exists inside the cavity when the sheath inside the hollow electrode hole is fully collapsed, which is an essential condition for the plasma density enhancement outside hollow electrodes. In addition, the existence of the electron density peak at the orifice is generated via the hollow cathode effect(HCE), which plays an important role in the density enhancement. It is also found that the radial width of bulk plasma at the orifice affects the magnitude of the density enhancement, and narrow radial plasma bulk width at the orifice is not beneficial to obtain high-density plasma outside hollow electrodes.Higher electron density at the orifice, combined with larger radial plasma bulk width at the orifice,causes higher electron density outside hollow electrodes. The results also imply that the HCE strength inside the cavity cannot be determined by the magnitude of the electron density outside hollow electrodes.展开更多
In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of ea...In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.展开更多
Determining whether sevoflurane sedation in children leads to“pseudo”prominent leptomeningeal contrast enhancement(pLMCE)on 3 Tesla magnetic resonance imaging will help reduce overdiagnosis by radiologists and clari...Determining whether sevoflurane sedation in children leads to“pseudo”prominent leptomeningeal contrast enhancement(pLMCE)on 3 Tesla magnetic resonance imaging will help reduce overdiagnosis by radiologists and clarify the pathophysiological changes of pLMCE.展开更多
This research aimed to analyze the enhancement of competitiveness among fruit and vegetable export entrepreneurs,with the case study of the Su-ngai Kolok checkpoint in Narathiwat Province.This research focused on thre...This research aimed to analyze the enhancement of competitiveness among fruit and vegetable export entrepreneurs,with the case study of the Su-ngai Kolok checkpoint in Narathiwat Province.This research focused on three objectives:(1)examining how competitiveness is enhanced through government support and the development of border trade zones,(2)studying the supply chain dynamics,including strategic factors and opportunities in agricultural product management,and(3)developing a model for border trade to strengthen exporters’capabilities.The research employed qualitative methods,with data collected from 25 informants through interviews and employed descriptive methods.The research results revealed that:(1)government agencies play a crucial role in improving competitiveness by developing border economic zones and trade policies;additionally,(2)the research highlighted the importance of strategic partnerships,network building,and efficient logistics along the value chain;(3)the proposed Border Trade Model includes elements for instance government policy,partnerships,entrepreneurial potential,and transportation channels,aiming to enhance the overall efficiency and effectiveness of vegetable and fruit exporters in the region.展开更多
基金This research was supported by the National Natural Science Foundation of China(32172846)the Earmarked Fund for CARS-35,the Science and Technology Major Project of Gansu Province(22ZD6NA001)+4 种基金the Science Foundation for Distinguished Young Scholars of Shaanxi Province(2021JC-18)the Natural Science Foundation of Gansu Province(23JRRA1153)the Science and Technology Plan Project of Gansu Province(23JRRA561)the Chinese Academy of Agricultural Science and Technology Innovation Project(CAAS-ASTIP-JBGS-20210602)the Strategic Priority Research Program of the National Center of Technology Innovation for Pigs(NCTIP-XD/C03).
文摘Porcine reproductive and respiratory syndrome(PRRS)is one of the most significant diseases affecting the pig industry worldwide.The PRRSV mutation rate is the highest among the RNA viruses.To date,NADC30-like PRRSV and highly pathogenic PRRSV(HP-PRRSV)are the dominant epidemic strains in China;however,commercial vaccines do not always provide sufficient cross-protection,and the reasons for insufficient protection are unclear.This study isolated a wild-type NADC30-like PRRSV,SX-YL1806,from Shaanxi Province.Vaccination challenge experiments in piglets showed that commercial modified live virus(MLV)vaccines provided good protection against HP-PRRSV.However,it could not provide sufficient protection against the novel strain SXYL1806.To explore the reasons for this phenomenon,we compared the genomic homology between the MLV strain and HP-PRRSV or NADC30-like PRRSV and found that the MLV strain had a lower genome similarity with NADC30-like PRRSV.Serum neutralization assay showed that MLV-immune serum slightly promoted the homologous HP-PRRSV replication and significantly promoted the heterologous NADC30-like PRRSV strain replication in vitro,suggesting that antibody-dependent enhancement(ADE)might also play a role in decreasing MLV protective efficacy.These findings expand our understanding of the potential factors affecting the protective effect of PRRSV MLV vaccines against the NADC30-like strains.
基金supported by the Australian Research Council (DP200101353)。
文摘Strong coupling between resonantly matched surface plasmons of metals and excitons of quantum emitters results in the formation of new plasmon-exciton hybridized energy states.In plasmon-exciton strong coupling,plasmonic nanocavities play a significant role due to their ability to confine light in an ultrasmall volume.Additionally,two-dimensional transition metal dichalcogenides(TMDCs) have a significant exciton binding energy and remain stable at ambient conditions,making them an excellent alternative for investigating light-matter interactions.As a result,strong plasmon-exciton coupling has been reported by introducing a single metallic cavity.However,single nanoparticles have lower spatial confinement of electromagnetic fields and limited tunability to match the excitonic resonance.Here,we introduce the concept of catenary-shaped optical fields induced by plasmonic metamaterial cavities to scale the strength of plasmon-exciton coupling.The demonstrated plasmon modes of metallic metamaterial cavities offer high confinement and tunability and can match with the excitons of TMDCs to exhibit a strong coupling regime by tuning either the size of the cavity gap or thickness.The calculated Rabi splitting of Au-MoSe_2 and Au-WSe_2 heterostructures strongly depends on the catenary-like field enhancement induced by the Au cavity,resulting in room-temperature Rabi splitting ranging between 77.86 and 320 me V.These plasmonic metamaterial cavities can pave the way for manipulating excitons in TMDCs and operating active nanophotonic devices at ambient temperature.
基金Supported by Chongqing Medical University Program for Youth Innovation in Future Medicine,No.W0190.
文摘BACKGROUND This study aimed to evaluate the safety of enhanced recovery after surgery(ERAS)in elderly patients with gastric cancer(GC).AIM To evaluate the safety of ERAS in elderly patients with GC.METHODS The PubMed,EMBASE,and Cochrane Library databases were used to search for eligible studies from inception to April 1,2023.The mean difference(MD),odds ratio(OR)and 95%confidence interval(95%CI)were pooled for analysis.The quality of the included studies was evaluated using the Newcastle-Ottawa Scale scores.We used Stata(V.16.0)software for data analysis.RESULTS This study consists of six studies involving 878 elderly patients.By analyzing the clinical outcomes,we found that the ERAS group had shorter postoperative hospital stays(MD=-0.51,I2=0.00%,95%CI=-0.72 to-0.30,P=0.00);earlier times to first flatus(defecation;MD=-0.30,I²=0.00%,95%CI=-0.55 to-0.06,P=0.02);less intestinal obstruction(OR=3.24,I2=0.00%,95%CI=1.07 to 9.78,P=0.04);less nausea and vomiting(OR=4.07,I2=0.00%,95%CI=1.29 to 12.84,P=0.02);and less gastric retention(OR=5.69,I2=2.46%,95%CI=2.00 to 16.20,P=0.00).Our results showed that the conventional group had a greater mortality rate than the ERAS group(OR=0.24,I2=0.00%,95%CI=0.07 to 0.84,P=0.03).However,there was no statistically significant difference in major complications between the ERAS group and the conventional group(OR=0.67,I2=0.00%,95%CI=0.38 to 1.18,P=0.16).CONCLUSION Compared to those with conventional recovery,elderly GC patients who received the ERAS protocol after surgery had a lower risk of mortality.
基金supported in part by the National Natural Science Foundation of China(Grant No.62062003)Natural Science Foundation of Ningxia(Grant No.2023AAC03293).
文摘Multimodal lung tumor medical images can provide anatomical and functional information for the same lesion.Such as Positron Emission Computed Tomography(PET),Computed Tomography(CT),and PET-CT.How to utilize the lesion anatomical and functional information effectively and improve the network segmentation performance are key questions.To solve the problem,the Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network(Guide-YNet)is proposed in this paper.Firstly,a double-encoder single-decoder U-Net is used as the backbone in this model,a single-coder single-decoder U-Net is used to generate the saliency guided feature using PET image and transmit it into the skip connection of the backbone,and the high sensitivity of PET images to tumors is used to guide the network to accurately locate lesions.Secondly,a Cross Scale Feature Enhancement Module(CSFEM)is designed to extract multi-scale fusion features after downsampling.Thirdly,a Cross-Layer Interactive Feature Enhancement Module(CIFEM)is designed in the encoder to enhance the spatial position information and semantic information.Finally,a Cross-Dimension Cross-Layer Feature Enhancement Module(CCFEM)is proposed in the decoder,which effectively extractsmultimodal image features through global attention and multi-dimension local attention.The proposed method is verified on the lung multimodal medical image datasets,and the results showthat theMean Intersection overUnion(MIoU),Accuracy(Acc),Dice Similarity Coefficient(Dice),Volumetric overlap error(Voe),Relative volume difference(Rvd)of the proposed method on lung lesion segmentation are 87.27%,93.08%,97.77%,95.92%,89.28%,and 88.68%,respectively.It is of great significance for computer-aided diagnosis.
基金Joint Funds of the National Natural Science Foundation of China (U22A20140)University of Jinan Disciplinary Cross-Convergence Construction Project 2023 (XKJC-202309, XKJC-202307)+4 种基金Jinan City-School Integration Development Strategy Project (JNSX2023015)Independent Cultivation Program of Innovation Team of Ji’nan City (202333042)Youth Innovation Group Plan of Shandong Province (2022KJ095)Shenzhen Stable Support Plan Program for Higher Education Institutions Research Program (20220816131408001)Shenzhen Science and Technology Program (JCYJ20230807091802006)。
文摘Current aqueous battery electrolytes,including conve ntional hydrogel electrolytes,exhibit unsatisfactory water retention capabilities.The sustained water loss will lead to subsequent polarization and increased internal resistance,ultimately resulting in battery failure.Herein,a double network(DN) orga no hydrogel electrolyte based on dimethyl sulfoxide(DMSO)/H_(2)O binary solvent was proposed.Through directionally reconstructing hydrogen bonds and reducing active H_(2)O molecules,the water retention ability and cathode/anode interfaces were synergistic enhanced.As a result,the synthesized DN organohydrogel demonstrates exceptional water retention capabilities,retaining approximately 75% of its original weight even after the exposure to air for 20 days.The Zn MnO_(2) battery delivers an outstanding specific capacity of275 mA h g^(-1) at 1 C,impressive rate performance with 85 mA h g^(-1) at 30 C,and excellent cyclic stability(95% retention after 6000 cycles at 5 C).Zn‖Zn symmetric battery can cycle more than 5000 h at 1 mA cm^(-2) and 1 mA h cm^(-2) without short circuiting.This study will encourage the further development of functional organohydrogel electrolytes for advanced energy storage devices.
基金supported by the national key research and development program (No.2020YFB1806608)Jiangsu natural science foundation for distinguished young scholars (No.BK20220054)。
文摘Due to the selective absorption of light and the existence of a large number of floating media in sea water, underwater images often suffer from color casts and detail blurs. It is therefore necessary to perform color correction and detail restoration. However,the existing enhancement algorithms cannot achieve the desired results. In order to solve the above problems, this paper proposes a multi-stream feature fusion network. First, an underwater image is preprocessed to obtain potential information from the illumination stream, color stream and structure stream by histogram equalization with contrast limitation, gamma correction and white balance, respectively. Next, these three streams and the original raw stream are sent to the residual blocks to extract the features. The features will be subsequently fused. It can enhance feature representation in underwater images. In the meantime, a composite loss function including three terms is used to ensure the quality of the enhanced image from the three aspects of color balance, structure preservation and image smoothness. Therefore, the enhanced image is more in line with human visual perception.Finally, the effectiveness of the proposed method is verified by comparison experiments with many stateof-the-art underwater image enhancement algorithms. Experimental results show that the proposed method provides superior results over them in terms of MSE,PSNR, SSIM, UIQM and UCIQE, and the enhanced images are more similar to their ground truth images.
基金supported in part by the National Key Research and Development Program of China(2020YFB1313002)the National Natural Science Foundation of China(62276023,U22B2055,62222302,U2013202)+1 种基金the Fundamental Research Funds for the Central Universities(FRF-TP-22-003C1)the Postgraduate Education Reform Project of Henan Province(2021SJGLX260Y)。
文摘Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images.Current methods feed the whole image directly into the model for enhancement.However,they ignored that the R,G and B channels of underwater degraded images present varied degrees of degradation,due to the selective absorption for the light.To address this issue,we propose an unsupervised multi-expert learning model by considering the enhancement of each color channel.Specifically,an unsupervised architecture based on generative adversarial network is employed to alleviate the need for paired underwater images.Based on this,we design a generator,including a multi-expert encoder,a feature fusion module and a feature fusion-guided decoder,to generate the clear underwater image.Accordingly,a multi-expert discriminator is proposed to verify the authenticity of the R,G and B channels,respectively.In addition,content perceptual loss and edge loss are introduced into the loss function to further improve the content and details of the enhanced images.Extensive experiments on public datasets demonstrate that our method achieves more pleasing results in vision quality.Various metrics(PSNR,SSIM,UIQM and UCIQE) evaluated on our enhanced images have been improved obviously.
基金This work was supported by Natural Science Foundation of China(Nos.62303126,62362008,62066006,authors Zhenyong Zhang and Bin Hu,https://www.nsfc.gov.cn/,accessed on 25 July 2024)Guizhou Provincial Science and Technology Projects(No.ZK[2022]149,author Zhenyong Zhang,https://kjt.guizhou.gov.cn/,accessed on 25 July 2024)+1 种基金Guizhou Provincial Research Project(Youth)forUniversities(No.[2022]104,author Zhenyong Zhang,https://jyt.guizhou.gov.cn/,accessed on 25 July 2024)GZU Cultivation Project of NSFC(No.[2020]80,author Zhenyong Zhang,https://www.gzu.edu.cn/,accessed on 25 July 2024).
文摘With the widespread use of machine learning(ML)technology,the operational efficiency and responsiveness of power grids have been significantly enhanced,allowing smart grids to achieve high levels of automation and intelligence.However,tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks,making it urgent to enhance their robustness.To address this,we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree ensembles.Our algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws,ensuring training data accurately reflects possible attack scenarios while adhering to physical rules.In our experiments,the proposed method increased robustness against adversarial attacks by 100%when applied to real grid data under physical constraints.These results highlight the advantages of our method in maintaining efficient and secure operation of smart grids under adversarial conditions.
基金Project supported by National Key Research and Development Program of China (Grant No.2022YFA1603403)。
文摘Finesse is a critical parameter for describing the characteristics of an optical enhancement cavity(OEC). This paper first presents a review of finesse measurement techniques, including a comparative analysis of the advantages, disadvantages, and potential limitations of several main methods from both theoretical and practical perspectives. A variant of the existing method called the free spectral range(FSR) modulation method is proposed and compared with three other finesse measurement methods, i.e., the fast-switching cavity ring-down(CRD) method, the rapidly swept-frequency(SF) CRD method, and the ringing effect method. A high-power OEC platform with a high finesse of approximately 16000 is built and measured with the four methods. The performance of these methods is compared, and the results show that the FSR modulation method and the fast-switching CRD method are more suitable and accurate than the other two methods for high-finesse OEC measurements. The CRD method and the ringing effect method can be implemented in open loop using simple equipment and are easy to perform. Additionally, recommendations for selecting finesse measurement methods under different conditions are proposed, which benefit the development of OEC and its applications.
基金National Research Foundation of Korea,Grant/Award Numbers:2022R1I1A3069113,RS-2023-00221365Electronics and Telecommunications Research Institute,Grant/Award Number:2014-3-00123。
文摘In recent times,an image enhancement approach,which learns the global transformation function using deep neural networks,has gained attention.However,many existing methods based on this approach have a limitation:their transformation functions are too simple to imitate complex colour transformations between low-quality images and manually retouched high-quality images.In order to address this limitation,a simple yet effective approach for image enhancement is proposed.The proposed algorithm based on the channel-wise intensity transformation is designed.However,this transformation is applied to the learnt embedding space instead of specific colour spaces and then return enhanced features to colours.To this end,the authors define the continuous intensity transformation(CIT)to describe the mapping between input and output intensities on the embedding space.Then,the enhancement network is developed,which produces multi-scale feature maps from input images,derives the set of transformation functions,and performs the CIT to obtain enhanced images.Extensive experiments on the MIT-Adobe 5K dataset demonstrate that the authors’approach improves the performance of conventional intensity transforms on colour space metrics.Specifically,the authors achieved a 3.8%improvement in peak signal-to-noise ratio,a 1.8%improvement in structual similarity index measure,and a 27.5%improvement in learned perceptual image patch similarity.Also,the authors’algorithm outperforms state-of-the-art alternatives on three image enhancement datasets:MIT-Adobe 5K,Low-Light,and Google HDRþ.
基金supported by the National Natural Science Foundation of China(62276192)。
文摘Low-light images suffer from low quality due to poor lighting conditions,noise pollution,and improper settings of cameras.To enhance low-light images,most existing methods rely on normal-light images for guidance but the collection of suitable normal-light images is difficult.In contrast,a self-supervised method breaks free from the reliance on normal-light data,resulting in more convenience and better generalization.Existing self-supervised methods primarily focus on illumination adjustment and design pixel-based adjustment methods,resulting in remnants of other degradations,uneven brightness and artifacts.In response,this paper proposes a self-supervised enhancement method,termed as SLIE.It can handle multiple degradations including illumination attenuation,noise pollution,and color shift,all in a self-supervised manner.Illumination attenuation is estimated based on physical principles and local neighborhood information.The removal and correction of noise and color shift removal are solely realized with noisy images and images with color shifts.Finally,the comprehensive and fully self-supervised approach can achieve better adaptability and generalization.It is applicable to various low light conditions,and can reproduce the original color of scenes in natural light.Extensive experiments conducted on four public datasets demonstrate the superiority of SLIE to thirteen state-of-the-art methods.Our code is available at https://github.com/hanna-xu/SLIE.
基金supported by the National Natural Science Foundation of China (Grant No.52178515)。
文摘Foam concrete is a prospective material in defense engineering to protect structures due to its high energy absorption capability resulted from the long plateau stage.However,stress enhancement rather than stress mitigation may happen when foam concrete is used as sacrificial claddings placed in the path of an incoming blast load.To investigate this interesting phenomenon,a one-dimensional difference model for blast wave propagation in foam concrete is firstly proposed and numerically solved by improving the second-order Godunov method.The difference model and numerical algorithm are validated against experimental results including both the stress mitigation and the stress enhancement.The difference model is then used to numerically analyze the blast wave propagation and deformation of material in which the effects of blast loads,stress-strain relation and length of foam concrete are considered.In particular,the concept of minimum thickness of foam concrete to avoid stress enhancement is proposed.Finally,non-dimensional analysis on the minimum thickness is conducted and an empirical formula is proposed by curve-fitting the numerical data,which can provide a reference for the application of foam concrete in defense engineering.
文摘The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.
基金supported by the National Key Research and Development Project under Grant 2020YFB1807602Key Program of Marine Economy Development Special Foundation of Department of Natural Resources of Guangdong Province(GDNRC[2023]24)the National Natural Science Foundation of China under Grant 62271267.
文摘Recently,there have been significant advancements in the study of semantic communication in single-modal scenarios.However,the ability to process information in multi-modal environments remains limited.Inspired by the research and applications of natural language processing across different modalities,our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos.Specifically,we propose a deep learning-basedMulti-ModalMutual Enhancement Video Semantic Communication system,called M3E-VSC.Built upon a VectorQuantized Generative AdversarialNetwork(VQGAN),our systemaims to leverage mutual enhancement among different modalities by using text as the main carrier of transmission.With it,the semantic information can be extracted fromkey-frame images and audio of the video and performdifferential value to ensure that the extracted text conveys accurate semantic information with fewer bits,thus improving the capacity of the system.Furthermore,a multi-frame semantic detection module is designed to facilitate semantic transitions during video generation.Simulation results demonstrate that our proposed model maintains high robustness in complex noise environments,particularly in low signal-to-noise ratio conditions,significantly improving the accuracy and speed of semantic transmission in video communication by approximately 50 percent.
文摘Handheld ultrasound devices are known for their portability and affordability,making them widely utilized in underdeveloped areas and community healthcare for rapid diagnosis and early screening.However,the image quality of handheld ultrasound devices is not always satisfactory due to the limited equipment size,which hinders accurate diagnoses by doctors.At the same time,paired ultrasound images are difficult to obtain from the clinic because imaging process is complicated.Therefore,we propose a modified cycle generative adversarial network(cycleGAN) for ultrasound image enhancement from multiple organs via unpaired pre-training.We introduce an ultrasound image pre-training method that does not require paired images,alleviating the requirement for large-scale paired datasets.We also propose an enhanced block with different structures in the pre-training and fine-tuning phases,which can help achieve the goals of different training phases.To improve the robustness of the model,we add Gaussian noise to the training images as data augmentation.Our approach is effective in obtaining the best quantitative evaluation results using a small number of parameters and less training costs to improve the quality of handheld ultrasound devices.
文摘The plasma density enhancement outside hollow electrodes in capacitively coupled radio-frequency(RF) discharges is investigated by a two-dimensional(2D) particle-in-cell/Monte-Carlo collision(PIC/MCC) model. Results show that plasma exists inside the cavity when the sheath inside the hollow electrode hole is fully collapsed, which is an essential condition for the plasma density enhancement outside hollow electrodes. In addition, the existence of the electron density peak at the orifice is generated via the hollow cathode effect(HCE), which plays an important role in the density enhancement. It is also found that the radial width of bulk plasma at the orifice affects the magnitude of the density enhancement, and narrow radial plasma bulk width at the orifice is not beneficial to obtain high-density plasma outside hollow electrodes.Higher electron density at the orifice, combined with larger radial plasma bulk width at the orifice,causes higher electron density outside hollow electrodes. The results also imply that the HCE strength inside the cavity cannot be determined by the magnitude of the electron density outside hollow electrodes.
文摘In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.
基金Supported by the Chongging Medical Scientific Research Project(Joint Project of Chongqing Health Commission and Science and Technology Bureau),No.2022QNXM013 and No.2023MSXM016.
文摘Determining whether sevoflurane sedation in children leads to“pseudo”prominent leptomeningeal contrast enhancement(pLMCE)on 3 Tesla magnetic resonance imaging will help reduce overdiagnosis by radiologists and clarify the pathophysiological changes of pLMCE.
文摘This research aimed to analyze the enhancement of competitiveness among fruit and vegetable export entrepreneurs,with the case study of the Su-ngai Kolok checkpoint in Narathiwat Province.This research focused on three objectives:(1)examining how competitiveness is enhanced through government support and the development of border trade zones,(2)studying the supply chain dynamics,including strategic factors and opportunities in agricultural product management,and(3)developing a model for border trade to strengthen exporters’capabilities.The research employed qualitative methods,with data collected from 25 informants through interviews and employed descriptive methods.The research results revealed that:(1)government agencies play a crucial role in improving competitiveness by developing border economic zones and trade policies;additionally,(2)the research highlighted the importance of strategic partnerships,network building,and efficient logistics along the value chain;(3)the proposed Border Trade Model includes elements for instance government policy,partnerships,entrepreneurial potential,and transportation channels,aiming to enhance the overall efficiency and effectiveness of vegetable and fruit exporters in the region.
