Robustness of complex networks has been studied for decades,with a particular focus on network attack.Research on network repair,on the other hand,has been conducted only very lately,given the even higher complexity a...Robustness of complex networks has been studied for decades,with a particular focus on network attack.Research on network repair,on the other hand,has been conducted only very lately,given the even higher complexity and absence of an effective evaluation metric.A recently proposed network repair strategy is self-healing,which aims to repair networks for larger components at a low cost only with local information.In this paper,we discuss the effectiveness and efficiency of self-healing,which limits network repair to be a multi-objective optimization problem and makes it difficult to measure its optimality.This leads us to a new network repair evaluation metric.Since the time complexity of the computation is very high,we devise a greedy ranking strategy.Evaluations on both real-world and random networks show the effectiveness of our new metric and repair strategy.Our study contributes to optimal network repair algorithms and provides a gold standard for future studies on network repair.展开更多
The journal of Global Geology(English Edition) sponsored by the International Center of Geoscience Research and Education in Northeast Asia,Jilin University of China(ISSN 1673-9736,CN22-1371/P).The former name of the ...The journal of Global Geology(English Edition) sponsored by the International Center of Geoscience Research and Education in Northeast Asia,Jilin University of China(ISSN 1673-9736,CN22-1371/P).The former name of the journal was the Journal of International Geoscientific Research in Northeast Asia,which was started in 1998 and served the scientists and teachers in geosciences in the world.The Editorial Board is composed of 42 scientists from various countries in the world,including China,German,Japan,Korea,Russia,the UK and USA.展开更多
The journal of Global Geology (English Edition) sponsored by the International Center of Geoscience Research and Education in Northeast Asia, Jilin University of China(ISSN 1673-9736 CN22-1371/P). The former name of t...The journal of Global Geology (English Edition) sponsored by the International Center of Geoscience Research and Education in Northeast Asia, Jilin University of China(ISSN 1673-9736 CN22-1371/P). The former name of the journal was the Journal of International Geoscientific Research in Northeast Asia, which was started in 1998 and served the scientists and teachers in geosciences in the world. The Editorial Board is composed of 42 scientists from various countries in the world including China, German, Japan, Korea, Russia, the UK and USA. The journal was biannually in publication from 1998 to 2007. It is changed quarterly in publication since 2008. We hope that the readers worldwide will be more interested in and continue to support our journal by papers contributed to the journal. Form of manuscriptManuscripts should be concise and consistent in style, spelling, and the use of abbreviations. Authors should submit their manuscripts in English. The title page should include: the title, the names of the authors and their affiliations. If possible, please use the "Times New Roman" font. References In the text, reference to the literature cited should list author's name, year of publication, title, source, volume (issue) and page.展开更多
Accessible communication based on sign language recognition(SLR)is the key to emergency medical assistance for the hearing-impaired community.Balancing the capture of both local and global information in SLR for emerg...Accessible communication based on sign language recognition(SLR)is the key to emergency medical assistance for the hearing-impaired community.Balancing the capture of both local and global information in SLR for emergency medicine poses a significant challenge.To address this,we propose a novel approach based on the inter-learning of visual features between global and local information.Specifically,our method enhances the perception capabilities of the visual feature extractor by strategically leveraging the strengths of convolutional neural network(CNN),which are adept at capturing local features,and visual transformers which perform well at perceiving global features.Furthermore,to mitigate the issue of overfitting caused by the limited availability of sign language data for emergency medical applications,we introduce an enhanced short temporal module for data augmentation through additional subsequences.Experimental results on three publicly available sign language datasets demonstrate the efficacy of the proposed approach.展开更多
Image quality assessment has become increasingly important in image quality monitoring and reliability assuring of image processing systems.Most of the existing no-reference image quality assessment methods mainly exp...Image quality assessment has become increasingly important in image quality monitoring and reliability assuring of image processing systems.Most of the existing no-reference image quality assessment methods mainly exploit the global information of image while ignoring vital local information.Actually,the introduced distortion depends on a slight difference in details between the distorted image and the non-distorted reference image.In light of this,we propose a no-reference image quality assessment method based on a multi-scale convolutional neural network,which integrates both global information and local information of an image.We first adopt the image pyramid method to generate four scale images required for network input and then provide two network models by respectively using two fusion strategies to evaluate image quality.In order to better adapt to the quality assessment of the entire image,we use two different loss functions in the training and validation phases.The superiority of the proposed method is verified by several different experiments on the LIVE datasets and TID2008 datasets.展开更多
Monocular depth estimation is the basic task in computer vision.Its accuracy has tremendous improvement in the decade with the development of deep learning.However,the blurry boundary in the depth map is a serious pro...Monocular depth estimation is the basic task in computer vision.Its accuracy has tremendous improvement in the decade with the development of deep learning.However,the blurry boundary in the depth map is a serious problem.Researchers find that the blurry boundary is mainly caused by two factors.First,the low-level features,containing boundary and structure information,may be lost in deep networks during the convolution process.Second,themodel ignores the errors introduced by the boundary area due to the few portions of the boundary area in the whole area,during the backpropagation.Focusing on the factors mentioned above.Two countermeasures are proposed to mitigate the boundary blur problem.Firstly,we design a scene understanding module and scale transformmodule to build a lightweight fuse feature pyramid,which can deal with low-level feature loss effectively.Secondly,we propose a boundary-aware depth loss function to pay attention to the effects of the boundary’s depth value.Extensive experiments show that our method can predict the depth maps with clearer boundaries,and the performance of the depth accuracy based on NYU-Depth V2,SUN RGB-D,and iBims-1 are competitive.展开更多
The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have ...The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have the following two shortcomings:On the one hand,they mostly use global average pooling to generate context descriptors,without highlighting the guiding role of salient information on descriptor generation,resulting in insufficient ability of the final generated attention mask representation;On the other hand,the design of most attention modules is complicated,which greatly increases the computational cost of the model.To solve these problems,this paper proposes an attention module called self-supervised recalibration(SR)block,which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask.In particular,a special"Squeeze-Excitation"(SE)unit is designed in the SR block to further process the generated intermediate masks,both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels.Furthermore,we combine the most commonly used Res Net-50 to construct the instantiation model of the SR block,and verify its effectiveness on multiple Re-ID datasets,especially the mean Average Precision(m AP)on the Occluded-Duke dataset exceeds the state-of-the-art(SOTA)algorithm by 4.49%.展开更多
Due to the popularity of group activities in social media,group recommendation becomes increasingly significant.It aims to pursue a list of preferred items for a target group.Most deep learning-based methods on group ...Due to the popularity of group activities in social media,group recommendation becomes increasingly significant.It aims to pursue a list of preferred items for a target group.Most deep learning-based methods on group recommendation have focused on learning group representations from single interaction between groups and users.However,these methods may suffer from data sparsity problem.Except for the interaction between groups and users,there also exist other interactions that may enrich group representation,such as the interaction between groups and items.Such interactions,which take place in the range of a group,form a local view of a certain group.In addition to local information,groups with common interests may also show similar tastes on items.Therefore,group representation can be conducted according to the similarity among groups,which forms a global view of a certain group.In this paper,we propose a novel global and local information fusion neural network(GLIF)model for group recommendation.In GLIF,an attentive neural network(ANN)activates rich interactions among groups,users and items with respect to forming a group′s local representation.Moreover,our model also leverages ANN to obtain a group′s global representation based on the similarity among different groups.Then,it fuses global and local representations based on attention mechanism to form a group′s comprehensive representation.Finally,group recommendation is conducted under neural collaborative filtering(NCF)framework.Extensive experiments on three public datasets demonstrate its superiority over the state-of-the-art methods for group recommendation.展开更多
Graph neural networks(GNNs)have shown great power in learning on graphs.However,it is still a challenge for GNNs to model information faraway from the source node.The ability to preserve global information can enhance...Graph neural networks(GNNs)have shown great power in learning on graphs.However,it is still a challenge for GNNs to model information faraway from the source node.The ability to preserve global information can enhance graph representation and hence improve classification precision.In the paper,we propose a new learning framework named G-GNN(Global information for GNN)to address the challenge.First,the global structure and global attribute features of each node are obtained via unsupervised pre-training,and those global features preserve the global information associated with the node.Then,using the pre-trained global features and the raw attributes of the graph,a set of parallel kernel GNNs is used to learn different aspects from these heterogeneous features.Any general GNN can be used as a kernal and easily obtain the ability of preserving global information,without having to alter their own algorithms.Extensive experiments have shown that state-of-the-art models,e.g.,GCN,GAT,Graphsage and APPNP,can achieve improvement with G-GNN on three standard evaluation datasets.Specially,we establish new benchmark precision records on Cora(84.31%)and Pubmed(80.95%)when learning on attributed graphs.展开更多
In this paper,we propose an Unsupervised Nonlinear Adaptive Manifold Learning method(UNAML)that considers both global and local information.In this approach,we apply unlabeled training samples to study nonlinear manif...In this paper,we propose an Unsupervised Nonlinear Adaptive Manifold Learning method(UNAML)that considers both global and local information.In this approach,we apply unlabeled training samples to study nonlinear manifold features,while considering global pairwise distances and maintaining local topology structure.Our method aims at minimizing global pairwise data distance errors as well as local structural errors.In order to enable our UNAML to be more efficient and to extract manifold features from the external source of new data,we add a feature approximate error that can be used to learn a linear extractor.Also,we add a feature approximate error that can be used to learn a linear extractor.In addition,we use a method of adaptive neighbor selection to calculate local structural errors.This paper uses the kernel matrix method to optimize the original algorithm.Our algorithm proves to be more effective when compared with the experimental results of other feature extraction methods on real face-data sets and object data sets.展开更多
The paper introduces the business-based interorganizational information platform（IOP） and analyzes the feasibility and mechanism of business-based IOP governing global supply chains vulnerability,and then aims to de...The paper introduces the business-based interorganizational information platform（IOP） and analyzes the feasibility and mechanism of business-based IOP governing global supply chains vulnerability,and then aims to develop a risk evaluation software under reliable algorithm to appraise the capability of an interorganizational information platform resisting to global supply chains risks that supports platform users and providers to make decisions.The paper respectively starts with a basic conceptual model of global supply chains vulnerability and a conceptual model of global supply chains vulnerability in business-based IOP,and then gives the simulation model of governance of global supply chain vulnerability in business-based IOP;then has a discussion with the beneficial model of governing global supply chains vulnerability by using business-based IOP or not.The results of research：（1） If given the ratio of expense per income on global supply chains using business-based IOP, we can estimate the costs to take precautions against risks that decides to the maximum value of the average income of per transaction on global supply chains using business-based IOP.（2） If given total income of transaction on global supply chains using business-based IOP,we can estimate the maximum value of the ratio of expense per income on global supply chains using business-based IOP, which would help to make pricing policy for IOP service provider.展开更多
Accurate and efficient bacterial detection is essential for public health and medical diagnostics. However, traditional detection methods are constrained by limited dataset size, complex bacterial morphology, and dive...Accurate and efficient bacterial detection is essential for public health and medical diagnostics. However, traditional detection methods are constrained by limited dataset size, complex bacterial morphology, and diverse detection environments, hindering their effectiveness. In this study, we present EagleEyeNet, a novel multi-scale information fusion model designed to address these challenges. EagleEyeNet leverages large models as teacher networks in a knowledge distillation framework, significantly improving detection performance. Additionally, a newly designed feature fusion architecture, integrating Transformer modules, is proposed to enable the efficient fusion of global and multi-scale features, overcoming the bottlenecks posed by Feature Pyramid Networks (FPN) structures, which in turn reduces information transmission loss between feature layers. To improve the model’s adaptability for different scenarios, we create our own QingDao Bacteria Detection (QDBD) dataset as a comprehensive evaluation benchmark for bacterial detection. Experimental results demonstrate that EagleEyeNet achieves remarkable performance improvements, with mAP50 increases of 3.1% on the QDBD dataset and 4.9% on the AGRA dataset, outperforming the State-Of-The-Art (SOTA) methods in detection accuracy. These findings underscore the transformative potential of integrating large models and deep learning for advancing bacterial detection technologies.展开更多
Enterprise management information system (EMIS) in Manufacturing CIMS Integrating Platform (MACIP), refers to a computer system that manages the information for running an enterprise. A typical EMIS consists of a grou...Enterprise management information system (EMIS) in Manufacturing CIMS Integrating Platform (MACIP), refers to a computer system that manages the information for running an enterprise. A typical EMIS consists of a group of closely connected functions such as production planning, material management, accounting, quality management, etc. The EMIS exchanges information with the CAD/CAPP system in the design department, and the shop floor controller (SFC) in the manufacturing department, while the global information system (GIS) of MACIP supplies the mechanism for information sharing within the enterprise. This paper introduces the EMIS model for a typical manufacturing enterprise, then analyses the interface of the EMIS with the CAD/CAPP system and the SFC. A technical scheme for integrating the EMIS with the GIS is given. This scheme considers the integration of some MRPII systems in the market, and adopts advanced industrial standards to ensure its flexibility and reusability.展开更多
The role of taxation in promoting economic recovery has attracted great-er attention in recent years,with economic dislocation following the Global Financial Crisis and the COVID-19 pandemic.While taxation is only one...The role of taxation in promoting economic recovery has attracted great-er attention in recent years,with economic dislocation following the Global Financial Crisis and the COVID-19 pandemic.While taxation is only one of the factors impacting economic recovery,both economic literature and practical experience show that tax policy can contribute to enhanced growth and therefore greater economic activity.Tax instruments used as a means for promoting economic recovery include tax holidays,preferential tax rates,investment allowances,tax credits and special economic zones.However,there are a range of constraints over tax incentive design imposed by bodies such as the OECD/G20 Inclusive Framework on Base Erosion and Profit Shifting,the Forum on Harmful Tax Practices of the OECD and the Code of Conduct on Business Taxation of the European Union.Given the above,this paper sets out practical issues to inform governments seeking to promote economic activity through taxation.展开更多
基金supported by the Research Fund from the National Natural Science Foundation of China(Nos.61521091,61650110516,and 61601013)
文摘Robustness of complex networks has been studied for decades,with a particular focus on network attack.Research on network repair,on the other hand,has been conducted only very lately,given the even higher complexity and absence of an effective evaluation metric.A recently proposed network repair strategy is self-healing,which aims to repair networks for larger components at a low cost only with local information.In this paper,we discuss the effectiveness and efficiency of self-healing,which limits network repair to be a multi-objective optimization problem and makes it difficult to measure its optimality.This leads us to a new network repair evaluation metric.Since the time complexity of the computation is very high,we devise a greedy ranking strategy.Evaluations on both real-world and random networks show the effectiveness of our new metric and repair strategy.Our study contributes to optimal network repair algorithms and provides a gold standard for future studies on network repair.
文摘The journal of Global Geology(English Edition) sponsored by the International Center of Geoscience Research and Education in Northeast Asia,Jilin University of China(ISSN 1673-9736,CN22-1371/P).The former name of the journal was the Journal of International Geoscientific Research in Northeast Asia,which was started in 1998 and served the scientists and teachers in geosciences in the world.The Editorial Board is composed of 42 scientists from various countries in the world,including China,German,Japan,Korea,Russia,the UK and USA.
文摘The journal of Global Geology (English Edition) sponsored by the International Center of Geoscience Research and Education in Northeast Asia, Jilin University of China(ISSN 1673-9736 CN22-1371/P). The former name of the journal was the Journal of International Geoscientific Research in Northeast Asia, which was started in 1998 and served the scientists and teachers in geosciences in the world. The Editorial Board is composed of 42 scientists from various countries in the world including China, German, Japan, Korea, Russia, the UK and USA. The journal was biannually in publication from 1998 to 2007. It is changed quarterly in publication since 2008. We hope that the readers worldwide will be more interested in and continue to support our journal by papers contributed to the journal. Form of manuscriptManuscripts should be concise and consistent in style, spelling, and the use of abbreviations. Authors should submit their manuscripts in English. The title page should include: the title, the names of the authors and their affiliations. If possible, please use the "Times New Roman" font. References In the text, reference to the literature cited should list author's name, year of publication, title, source, volume (issue) and page.
基金supported by the National Natural Science Foundation of China(No.62376197)the Tianjin Science and Technology Program(No.23JCYBJC00360)the Tianjin Health Research Project(No.TJWJ2025MS045).
文摘Accessible communication based on sign language recognition(SLR)is the key to emergency medical assistance for the hearing-impaired community.Balancing the capture of both local and global information in SLR for emergency medicine poses a significant challenge.To address this,we propose a novel approach based on the inter-learning of visual features between global and local information.Specifically,our method enhances the perception capabilities of the visual feature extractor by strategically leveraging the strengths of convolutional neural network(CNN),which are adept at capturing local features,and visual transformers which perform well at perceiving global features.Furthermore,to mitigate the issue of overfitting caused by the limited availability of sign language data for emergency medical applications,we introduce an enhanced short temporal module for data augmentation through additional subsequences.Experimental results on three publicly available sign language datasets demonstrate the efficacy of the proposed approach.
基金supported by the National Natural Science Foundation of China(Grant No.61772171)the Major Science and Technology Platform Project of the Normal Universities in Liaoning(Grant No.JP2017005).
文摘Image quality assessment has become increasingly important in image quality monitoring and reliability assuring of image processing systems.Most of the existing no-reference image quality assessment methods mainly exploit the global information of image while ignoring vital local information.Actually,the introduced distortion depends on a slight difference in details between the distorted image and the non-distorted reference image.In light of this,we propose a no-reference image quality assessment method based on a multi-scale convolutional neural network,which integrates both global information and local information of an image.We first adopt the image pyramid method to generate four scale images required for network input and then provide two network models by respectively using two fusion strategies to evaluate image quality.In order to better adapt to the quality assessment of the entire image,we use two different loss functions in the training and validation phases.The superiority of the proposed method is verified by several different experiments on the LIVE datasets and TID2008 datasets.
基金supported in part by School Research Projects of Wuyi University (No.5041700175).
文摘Monocular depth estimation is the basic task in computer vision.Its accuracy has tremendous improvement in the decade with the development of deep learning.However,the blurry boundary in the depth map is a serious problem.Researchers find that the blurry boundary is mainly caused by two factors.First,the low-level features,containing boundary and structure information,may be lost in deep networks during the convolution process.Second,themodel ignores the errors introduced by the boundary area due to the few portions of the boundary area in the whole area,during the backpropagation.Focusing on the factors mentioned above.Two countermeasures are proposed to mitigate the boundary blur problem.Firstly,we design a scene understanding module and scale transformmodule to build a lightweight fuse feature pyramid,which can deal with low-level feature loss effectively.Secondly,we propose a boundary-aware depth loss function to pay attention to the effects of the boundary’s depth value.Extensive experiments show that our method can predict the depth maps with clearer boundaries,and the performance of the depth accuracy based on NYU-Depth V2,SUN RGB-D,and iBims-1 are competitive.
基金supported in part by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(Grant No.2022D01B186 and No.2022D01B05)。
文摘The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have the following two shortcomings:On the one hand,they mostly use global average pooling to generate context descriptors,without highlighting the guiding role of salient information on descriptor generation,resulting in insufficient ability of the final generated attention mask representation;On the other hand,the design of most attention modules is complicated,which greatly increases the computational cost of the model.To solve these problems,this paper proposes an attention module called self-supervised recalibration(SR)block,which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask.In particular,a special"Squeeze-Excitation"(SE)unit is designed in the SR block to further process the generated intermediate masks,both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels.Furthermore,we combine the most commonly used Res Net-50 to construct the instantiation model of the SR block,and verify its effectiveness on multiple Re-ID datasets,especially the mean Average Precision(m AP)on the Occluded-Duke dataset exceeds the state-of-the-art(SOTA)algorithm by 4.49%.
基金supported by National Natural Science Foundation of China (Nos.61872363 and 61672507)Natural Foundation of Beijing Municipal Commission of Education,China (No.21JD0044)+1 种基金National Key Research and Development Program of China (No.2016YFB0401202)the Research and Development Fund of Institute of Automation,Chinese Academy of Sciences,China(No.Y9J2FZ0801)
文摘Due to the popularity of group activities in social media,group recommendation becomes increasingly significant.It aims to pursue a list of preferred items for a target group.Most deep learning-based methods on group recommendation have focused on learning group representations from single interaction between groups and users.However,these methods may suffer from data sparsity problem.Except for the interaction between groups and users,there also exist other interactions that may enrich group representation,such as the interaction between groups and items.Such interactions,which take place in the range of a group,form a local view of a certain group.In addition to local information,groups with common interests may also show similar tastes on items.Therefore,group representation can be conducted according to the similarity among groups,which forms a global view of a certain group.In this paper,we propose a novel global and local information fusion neural network(GLIF)model for group recommendation.In GLIF,an attentive neural network(ANN)activates rich interactions among groups,users and items with respect to forming a group′s local representation.Moreover,our model also leverages ANN to obtain a group′s global representation based on the similarity among different groups.Then,it fuses global and local representations based on attention mechanism to form a group′s comprehensive representation.Finally,group recommendation is conducted under neural collaborative filtering(NCF)framework.Extensive experiments on three public datasets demonstrate its superiority over the state-of-the-art methods for group recommendation.
基金partially supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No.18kJB510010the Social Science Foundation of Jiangsu Province of China under Grant No.19TQD002the National Nature Science Foundation of China under Grant No.61976114.
文摘Graph neural networks(GNNs)have shown great power in learning on graphs.However,it is still a challenge for GNNs to model information faraway from the source node.The ability to preserve global information can enhance graph representation and hence improve classification precision.In the paper,we propose a new learning framework named G-GNN(Global information for GNN)to address the challenge.First,the global structure and global attribute features of each node are obtained via unsupervised pre-training,and those global features preserve the global information associated with the node.Then,using the pre-trained global features and the raw attributes of the graph,a set of parallel kernel GNNs is used to learn different aspects from these heterogeneous features.Any general GNN can be used as a kernal and easily obtain the ability of preserving global information,without having to alter their own algorithms.Extensive experiments have shown that state-of-the-art models,e.g.,GCN,GAT,Graphsage and APPNP,can achieve improvement with G-GNN on three standard evaluation datasets.Specially,we establish new benchmark precision records on Cora(84.31%)and Pubmed(80.95%)when learning on attributed graphs.
基金supported in part by the National Natural Science Foundation of China(Nos.61373093,61402310,61672364,and 61672365)the National Key Research and Development Program of China(No.2018YFA0701701)。
文摘In this paper,we propose an Unsupervised Nonlinear Adaptive Manifold Learning method(UNAML)that considers both global and local information.In this approach,we apply unlabeled training samples to study nonlinear manifold features,while considering global pairwise distances and maintaining local topology structure.Our method aims at minimizing global pairwise data distance errors as well as local structural errors.In order to enable our UNAML to be more efficient and to extract manifold features from the external source of new data,we add a feature approximate error that can be used to learn a linear extractor.Also,we add a feature approximate error that can be used to learn a linear extractor.In addition,we use a method of adaptive neighbor selection to calculate local structural errors.This paper uses the kernel matrix method to optimize the original algorithm.Our algorithm proves to be more effective when compared with the experimental results of other feature extraction methods on real face-data sets and object data sets.
基金supported by National Nature Science Foundation of China(G021004,71171054)Major Special Project of Fujian Province(2004HZ02)
文摘The paper introduces the business-based interorganizational information platform（IOP） and analyzes the feasibility and mechanism of business-based IOP governing global supply chains vulnerability,and then aims to develop a risk evaluation software under reliable algorithm to appraise the capability of an interorganizational information platform resisting to global supply chains risks that supports platform users and providers to make decisions.The paper respectively starts with a basic conceptual model of global supply chains vulnerability and a conceptual model of global supply chains vulnerability in business-based IOP,and then gives the simulation model of governance of global supply chain vulnerability in business-based IOP;then has a discussion with the beneficial model of governing global supply chains vulnerability by using business-based IOP or not.The results of research：（1） If given the ratio of expense per income on global supply chains using business-based IOP, we can estimate the costs to take precautions against risks that decides to the maximum value of the average income of per transaction on global supply chains using business-based IOP.（2） If given total income of transaction on global supply chains using business-based IOP,we can estimate the maximum value of the ratio of expense per income on global supply chains using business-based IOP, which would help to make pricing policy for IOP service provider.
基金supported by the Shandong Provincial Department of Education.
文摘Accurate and efficient bacterial detection is essential for public health and medical diagnostics. However, traditional detection methods are constrained by limited dataset size, complex bacterial morphology, and diverse detection environments, hindering their effectiveness. In this study, we present EagleEyeNet, a novel multi-scale information fusion model designed to address these challenges. EagleEyeNet leverages large models as teacher networks in a knowledge distillation framework, significantly improving detection performance. Additionally, a newly designed feature fusion architecture, integrating Transformer modules, is proposed to enable the efficient fusion of global and multi-scale features, overcoming the bottlenecks posed by Feature Pyramid Networks (FPN) structures, which in turn reduces information transmission loss between feature layers. To improve the model’s adaptability for different scenarios, we create our own QingDao Bacteria Detection (QDBD) dataset as a comprehensive evaluation benchmark for bacterial detection. Experimental results demonstrate that EagleEyeNet achieves remarkable performance improvements, with mAP50 increases of 3.1% on the QDBD dataset and 4.9% on the AGRA dataset, outperforming the State-Of-The-Art (SOTA) methods in detection accuracy. These findings underscore the transformative potential of integrating large models and deep learning for advancing bacterial detection technologies.
文摘Enterprise management information system (EMIS) in Manufacturing CIMS Integrating Platform (MACIP), refers to a computer system that manages the information for running an enterprise. A typical EMIS consists of a group of closely connected functions such as production planning, material management, accounting, quality management, etc. The EMIS exchanges information with the CAD/CAPP system in the design department, and the shop floor controller (SFC) in the manufacturing department, while the global information system (GIS) of MACIP supplies the mechanism for information sharing within the enterprise. This paper introduces the EMIS model for a typical manufacturing enterprise, then analyses the interface of the EMIS with the CAD/CAPP system and the SFC. A technical scheme for integrating the EMIS with the GIS is given. This scheme considers the integration of some MRPII systems in the market, and adopts advanced industrial standards to ensure its flexibility and reusability.
文摘The role of taxation in promoting economic recovery has attracted great-er attention in recent years,with economic dislocation following the Global Financial Crisis and the COVID-19 pandemic.While taxation is only one of the factors impacting economic recovery,both economic literature and practical experience show that tax policy can contribute to enhanced growth and therefore greater economic activity.Tax instruments used as a means for promoting economic recovery include tax holidays,preferential tax rates,investment allowances,tax credits and special economic zones.However,there are a range of constraints over tax incentive design imposed by bodies such as the OECD/G20 Inclusive Framework on Base Erosion and Profit Shifting,the Forum on Harmful Tax Practices of the OECD and the Code of Conduct on Business Taxation of the European Union.Given the above,this paper sets out practical issues to inform governments seeking to promote economic activity through taxation.
