Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from...Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.展开更多
Cloud computing describes highly scalable computing resources provided as an external service via the internet. Economically, the main feature of cloud computing is that customers only use what they need, and only pay...Cloud computing describes highly scalable computing resources provided as an external service via the internet. Economically, the main feature of cloud computing is that customers only use what they need, and only pay for what they actually use. Resources are available to be accessed from the cloud at any time, and from any location via the internet. There’s no need to worry about how things are being maintained behind the scenes—you simply purchase the IT service you require. This new, web-based generation of computing utilizes remote servers for data storage and management. One of the challenging issues tackled in the cloud computing is the security of data stored in the service providers’ site. In this paper, we propose a new architecture for secure data storage in such a way that users’ data are encrypted and split into various cipher blocks and distributed among different service providers site rather than solely depend on single provider for data storage. This architecture ensures better reliability, availability, scalability and security.展开更多
Sodium-ion batteries have emerged as promising alternatives to lithium-ion batteries due to their abundant raw material reserves,low cost,enhanced safety,and environmental sustainability.Na_(2)Fe_(2)OS_(2),featuring a...Sodium-ion batteries have emerged as promising alternatives to lithium-ion batteries due to their abundant raw material reserves,low cost,enhanced safety,and environmental sustainability.Na_(2)Fe_(2)OS_(2),featuring a layered anti-perovskite structure,has attracted significant interest for its high capacity and facile synthesis.In this study,density functional theory calculations were performed to systematically investigate the phase stability,ionic conductivity,and voltage characteristics of Na_(2)Fe_(2)OS_(2)as a model system for anti-perovskite layered cathode materials.The compound exhibits excellent phase stability,and its equilibrium potential was calculated for the series Na_(x)Fe_(2)OCh_(2)(0<±<2)(where Ch represents chalcogenides).Naion transport analysis using the climbing image nudged elastic band method reveals a relatively low migration barrier(~0.47eV)along a dingonal pathway,indicating efficient Na^(+)mobility.To expand the materials design space,we systematically explored the effects of substituting Fe with various transition metals and replacing S with Se in NaaTM_(2)OCh_(2)structures.Among the variants studied,Na_(2)Mn_(2)OS_(2) demonstrates the most favorable combination of high voltage(~2.51V),robust phase stability,and superior energy density(~427 W-h/kg).This comprehensive comparison of transition metal substitutions provides vnluable insights for the rational design and experimental development of next-generation anti-perovskite layered cathode materials for sodium-ion batteries.展开更多
Breast cancer diagnosis relies heavily on many kinds of information from diverse sources—like mammogram images,ultrasound scans,patient records,and genetic tests—but most AI tools look at only one of these at a time...Breast cancer diagnosis relies heavily on many kinds of information from diverse sources—like mammogram images,ultrasound scans,patient records,and genetic tests—but most AI tools look at only one of these at a time,which limits their ability to produce accurate and comprehensive decisions.In recent years,multimodal learning has emerged,enabling the integration of heterogeneous data to improve performance and diagnostic accuracy.However,doctors cannot always see how or why these AI tools make their choices,which is a significant bottleneck in their reliability,along with adoption in clinical settings.Hence,people are adding explainable AI techniques that show the steps the model takes.This review investigates previous work that has employed multimodal learning and XAI for the diagnosis of breast cancer.It discusses the types of data,fusion techniques,and XAI models employed.It was done following the PRISMA guidelines and included studies from 2021 to April 2025.The literature search was performed systematically and resulted in 61 studies.The review highlights a gradual increase in current studies focusing on multimodal fusion and XAI,particularly in the years 2023–2024.It found that studies using multi-modal data fusion achieved the highest accuracy by 5%–10%on average compared to other studies that used single-modality data,an intermediate fusion strategy,and modern fusion techniques,such as cross attention,achieved the highest accuracy and best performance.The review also showed that SHAP,Grad-CAM,and LIME techniques are the most used in explaining breast cancer diagnostic models.There is a clear research shift toward integrating multimodal learning and XAI techniques into the breast cancer diagnostics field.However,several gaps were identified,including the scarcity of public multimodal datasets.Lack of a unified explainable framework in multimodal fusion systems,and lack of standardization in evaluating explanations.These limitations call for future research focused on building more shared datasets and integrating multimodal data and explainable AI techniques to improve decision-making and enhance transparency.展开更多
Inverse design has long been an efficient and powerful design tool in the aircraft industry.In this paper,a novel inverse design method for supercritical airfoils is proposed based on generative models in deep learnin...Inverse design has long been an efficient and powerful design tool in the aircraft industry.In this paper,a novel inverse design method for supercritical airfoils is proposed based on generative models in deep learning.A Conditional Variational Auto Encoder(CVAE)and an integrated generative network CVAE-GAN that combines the CVAE with the Wasserstein Generative Adversarial Networks(WGAN),are conducted as generative models.They are used to generate target wall Mach distributions for the inverse design that matches specified features,such as locations of suction peak,shock and aft loading.Qualitative and quantitative results show that both adopted generative models can generate diverse and realistic wall Mach number distributions satisfying the given features.The CVAE-GAN model outperforms the CVAE model and achieves better reconstruction accuracies for all the samples in the dataset.Furthermore,a deep neural network for nonlinear mapping is adopted to obtain the airfoil shape corresponding to the target wall Mach number distribution.The performances of the designed deep neural network are fully demonstrated and a smoothness measurement is proposed to quantify small oscillations in the airfoil surface,proving the authenticity and accuracy of the generated airfoil shapes.展开更多
In this paper,a class of new immersed interface finite element methods (IIFEM) is developed to solve elasticity interface problems with homogeneous and non-homogeneous jump conditions in two dimensions.Simple non-body...In this paper,a class of new immersed interface finite element methods (IIFEM) is developed to solve elasticity interface problems with homogeneous and non-homogeneous jump conditions in two dimensions.Simple non-body-fitted meshes are used.For homogeneous jump conditions,both non-conforming and conforming basis functions are constructed in such a way that they satisfy the natural jump conditions. For non-homogeneous jump conditions,a pair of functions that satisfy the same non-homogeneous jump conditions are constructed using a level-set representation of the interface.With such a pair of functions,the discontinuities across the interface in the solution and flux are removed;and an equivalent elasticity interface problem with homogeneous jump conditions is formulated.Numerical examples are presented to demonstrate that such methods have second order convergence.展开更多
Large-scale multi-objective optimization problems(MOPs)that involve a large number of decision variables,have emerged from many real-world applications.While evolutionary algorithms(EAs)have been widely acknowledged a...Large-scale multi-objective optimization problems(MOPs)that involve a large number of decision variables,have emerged from many real-world applications.While evolutionary algorithms(EAs)have been widely acknowledged as a mainstream method for MOPs,most research progress and successful applications of EAs have been restricted to MOPs with small-scale decision variables.More recently,it has been reported that traditional multi-objective EAs(MOEAs)suffer severe deterioration with the increase of decision variables.As a result,and motivated by the emergence of real-world large-scale MOPs,investigation of MOEAs in this aspect has attracted much more attention in the past decade.This paper reviews the progress of evolutionary computation for large-scale multi-objective optimization from two angles.From the key difficulties of the large-scale MOPs,the scalability analysis is discussed by focusing on the performance of existing MOEAs and the challenges induced by the increase of the number of decision variables.From the perspective of methodology,the large-scale MOEAs are categorized into three classes and introduced respectively:divide and conquer based,dimensionality reduction based and enhanced search-based approaches.Several future research directions are also discussed.展开更多
Enzymatic hydrolysis of proteins is a breakdown process of peptide bond in proteins,releasing some peptides with potential biological functions.Previous studies on enzymatic hydrolysis of whey proteins have not identi...Enzymatic hydrolysis of proteins is a breakdown process of peptide bond in proteins,releasing some peptides with potential biological functions.Previous studies on enzymatic hydrolysis of whey proteins have not identified the complete peptide profiles after hydrolysis.In this study,we reconstructed a profile of peptides from whey hydrolysates with two enzymes and different processing conditions.We also developed an ensemble machine learning predictor to classify peptides obtained from whey hydrolysis.A total of 2572 peptides were identified over three process conditions with two enzymes in duplicate.499 peptides were classified and chosen as potential antioxidant peptides from whey proteins.The peptides classified as antioxidants in the hydrolysates had a proportion of 13.1%-24.5%regarding all peptides identified.These results facilitate the selection of promising peptides involved in the antioxidant properties during the enzymatic hydrolysis of whey proteins,aiding the discovery of novel antioxidant peptides.展开更多
Non-equilibrium turbulence phenomena have raised great interests in recent years. Significant efforts have been devoted to non-equilibrium turbulence properties in canonical flows, e.g., grid turbulence, turbulent wak...Non-equilibrium turbulence phenomena have raised great interests in recent years. Significant efforts have been devoted to non-equilibrium turbulence properties in canonical flows, e.g., grid turbulence, turbulent wakes, and homogeneous isotropic turbulence(HIT). The non-equilibrium turbulence in non-canonical flows, however, has rarely been studied due to the complexity of the flows. In the present contribution, a directnumerical simulation(DNS) database of a turbulent flow is analyzed over a backwardfacing ramp, the flow near the boundary is demonstrated, and the non-equilibrium turbulent properties of the flow in the wake of the ramp are presented by using the characteristic parameters such as the dissipation coefficient C and the skewness of longitudinal velocity gradient Sk, but with opposite underlying turbulent energy transfer properties. The equation of Lagrangian velocity gradient correlation is examined, and the results show that non-equilibrium turbulence is the result of phase de-coherence phenomena, which is not taken into account in the modeling of non-equilibrium turbulence. These findings are expected to inspire deeper investigation of different non-equilibrium turbulence phenomena in different flow conditions and the improvement of turbulence modeling.展开更多
The quantum telebroadcasting of a cat-like state in combination with the quantum teleportation and the local copying of entanglement is presented. This gives a general way of distributing entanglement among distant pa...The quantum telebroadcasting of a cat-like state in combination with the quantum teleportation and the local copying of entanglement is presented. This gives a general way of distributing entanglement among distant parties. All of the operations of our scenario are local and within the reach of current technology.展开更多
Many recent laboratory experiments and numerical simulations support a non-equilibrium dissipation scaling in decaying turbulence before it reaches an equilibrium state.By analyzing a direct numerical simulation(DNS)d...Many recent laboratory experiments and numerical simulations support a non-equilibrium dissipation scaling in decaying turbulence before it reaches an equilibrium state.By analyzing a direct numerical simulation(DNS)database of a transitional boundary-layer flow,we show that the transition region and the non-equilibrium turbulence region,which are located in different streamwise zones,present different non-equilibrium scalings.Moreover,in the wall-normal direction,the viscous sublayer,log layer,and outer layer show different non-equilibrium phenomena which differ from those in grid-generated turbulence and transitional channel flows.These findings are expected to shed light on the modelling of various types of non-equilibrium turbulent flows.展开更多
In this study, we aimed to （1） identify white matter （WM） deficits underlying the consciousness level in patients with disorders of consciousness （DOCs） using diffusion tensor imaging （DTI）, and （2） evaluate...In this study, we aimed to （1） identify white matter （WM） deficits underlying the consciousness level in patients with disorders of consciousness （DOCs） using diffusion tensor imaging （DTI）, and （2） evaluate the relationship between DTI metrics and clinical measures of the consciousness level in DOC patients. With a cohort of 8 comatose, 8 unresponsive wakefulness syndrome/ vegetative state, and 14 minimally conscious state patients and 25 patient controls, we performed group comparisons of the DTI metrics in 48 core WM regions of interest （ROIs）, and examined the clinical relevance using correlation analysis. We identified multiple abnormal WM ROIs in DOC patients compared with normal controls, and the DTI metrics in these ROIs were significantly correlated with clinical measures of the consciousness level. Therefore, our findings suggested that multiple WM tracts are involved in the impaired consciousness levels in DOC patients and demonstrated the clinical relevance of DTI for DOC patients.展开更多
Using an unmanned aerial vehicle (UAV) paired with image semantic segmentation to classify land cover within natural vegetation can promote the development of forest and grassland field. Semantic segmentation normally...Using an unmanned aerial vehicle (UAV) paired with image semantic segmentation to classify land cover within natural vegetation can promote the development of forest and grassland field. Semantic segmentation normally excels in medical and building classification, but its usefulness in mixed forest-grassland ecosystems in semi-arid to semi-humid climates is unknown. This study proposes a new semantic segmentation network of LResU-net in which residual convolution unit (RCU) and loop convolution unit (LCU) are added to the U-net framework to classify images of different land covers generated by UAV high resolution. The selected model enhanced classification accuracy by increasing gradient mapping via RCU and modifying the size of convolution layers via LCU as well as reducing convolution kernels. To achieve this objective, a group of orthophotos were taken at an altitude of 260 m for testing in a natural forest-grassland ecosystem of Keyouqianqi, Inner Mongolia, China, and compared the results with those of three other network models (U-net, ResU-net and LU-net). The results show that both the highest kappa coefficient (0.86) and the highest overall accuracy (93.7%) resulted from LResU-net, and the value of most land covers provided by the producer’s and user’s accuracy generated in LResU-net exceeded 0.85. The pixel-area ratio approach was used to calculate the real areas of 10 different land covers where grasslands were 67.3%. The analysis of the effect of RCU and LCU on the model training performance indicates that the time of each epoch was shortened from U-net (358 s) to LResU-net (282 s). In addition, in order to classify areas that are not distinguishable, unclassified areas were defined and their impact on classification. LResU-net generated significantly more accurate results than the other three models and was regarded as the most appropriate approach to classify land cover in mixed forest-grassland ecosystems.展开更多
Eye health has become a global health concern and attracted broad attention.Over the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing oc...Eye health has become a global health concern and attracted broad attention.Over the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing ocular diseases efficiently and precisely.However,most existing methods were dedicated to constructing sophisticated CNNs,inevitably ignoring the trade-off between performance and model complexity.To alleviate this paradox,this paper proposes a lightweight yet efficient network architecture,mixeddecomposed convolutional network(MDNet),to recognise ocular diseases.In MDNet,we introduce a novel mixed-decomposed depthwise convolution method,which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low-resolution and high-resolution patterns by using fewer computations and fewer parameters.We conduct extensive experiments on the clinical anterior segment optical coherence tomography(AS-OCT),LAG,University of California San Diego,and CIFAR-100 datasets.The results show our MDNet achieves a better trade-off between the performance and model complexity than efficient CNNs including MobileNets and MixNets.Specifically,our MDNet outperforms MobileNets by 2.5%of accuracy by using 22%fewer parameters and 30%fewer computations on the AS-OCT dataset.展开更多
Quantitative security metrics are desirable for measuring the performance of information security controls. Security metrics help to make functional and business decisions for improving the performance and cost of the...Quantitative security metrics are desirable for measuring the performance of information security controls. Security metrics help to make functional and business decisions for improving the performance and cost of the security controls. However, defining enterprise-level security metrics has already been listed as one of the hard problems in the Info Sec Research Council's hard problems list. Almost all the efforts in defining absolute security metrics for the enterprise security have not been proved fruitful. At the same time, with the maturity of the security industry, there has been a continuous emphasis from the regulatory bodies on establishing measurable security metrics. This paper addresses this need and proposes a relative security metric model that derives three quantitative security metrics named Attack Resiliency Measure(ARM), Performance Improvement Factor(PIF), and Cost/Benefit Measure(CBM) for measuring the performance of the security controls. For the effectiveness evaluation of the proposed security metrics, we took the secure virtual machine(VM) migration protocol as the target of assessment. The virtual-ization technologies are rapidly changing the landscape of the computing world. Devising security metrics for virtualized environment is even more challenging. As secure virtual machine migration is an evolving area and no standard protocol is available specifically for secure VM migration. This paper took the secure virtual machine migration protocol as the target of assessment and applied the proposed relative security metric model for measuring the Attack Resiliency Measure, Performance Improvement Factor, and Cost/Benefit Measure of the secure VM migration protocol.展开更多
Trust is one of the core components of any ad hoc network security system.Trust management(TM)has always been a challenging issue in a vehicular network.One such developing network is the Internet of vehicles(IoV),whi...Trust is one of the core components of any ad hoc network security system.Trust management(TM)has always been a challenging issue in a vehicular network.One such developing network is the Internet of vehicles(IoV),which is expected to be an essential part of smart cities.IoV originated from the merger of Vehicular ad hoc networks(VANET)and the Internet of things(IoT).Security is one of the main barriers in the on-road IoV implementation.Existing security standards are insufficient to meet the extremely dynamic and rapidly changing IoV requirements.Trust plays a vital role in ensuring security,especially during vehicle to vehicle communication.Vehicular networks,having a unique nature among other wireless ad hoc networks,require dedicated efforts to develop trust protocols.Current TM schemes are inflexible and static.Predefined scenarios and limited parameters are the basis for existing TM models that are not suitable for vehicle networks.The vehicular network requires agile and adaptive solutions to ensure security,especially when it comes to critical messages.The vehicle network’s wireless nature increases its attack surface and exposes the network to numerous security threats.Moreover,internet involvement makes it more vulnerable to cyberattacks.The proposed TM framework is based on context-based cognition and machine learning to be best suited to IoV dynamics.Machine learning is the best solution to utilize the big data produced by vehicle sensors.To handle the uncertainty Bayesian machine learning statistical model is used.The proposed framework can adapt scenarios dynamically and infer using the maximum possible parameter available.The results indicated better performance than existing TM methods.Furthermore,for future work,a high-level machine learning model is proposed.展开更多
基金supported by the Major Science and Technology Programs in Henan Province(No.241100210100)Henan Provincial Science and Technology Research Project(No.252102211085,No.252102211105)+3 种基金Endogenous Security Cloud Network Convergence R&D Center(No.602431011PQ1)The Special Project for Research and Development in Key Areas of Guangdong Province(No.2021ZDZX1098)The Stabilization Support Program of Science,Technology and Innovation Commission of Shenzhen Municipality(No.20231128083944001)The Key scientific research projects of Henan higher education institutions(No.24A520042).
文摘Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.
文摘Cloud computing describes highly scalable computing resources provided as an external service via the internet. Economically, the main feature of cloud computing is that customers only use what they need, and only pay for what they actually use. Resources are available to be accessed from the cloud at any time, and from any location via the internet. There’s no need to worry about how things are being maintained behind the scenes—you simply purchase the IT service you require. This new, web-based generation of computing utilizes remote servers for data storage and management. One of the challenging issues tackled in the cloud computing is the security of data stored in the service providers’ site. In this paper, we propose a new architecture for secure data storage in such a way that users’ data are encrypted and split into various cipher blocks and distributed among different service providers site rather than solely depend on single provider for data storage. This architecture ensures better reliability, availability, scalability and security.
基金supported by the National Natural Science Foundation of China(Grant Nos.12404264 and 22209067)Shenzhen Basic Research Program(Natural Science Foundation)Key Project of Basic Research(Grant No.JCYJ20241202123916023)Shenzhen Science and Technology Program(Grant No.KQTD20200820113047086)。
文摘Sodium-ion batteries have emerged as promising alternatives to lithium-ion batteries due to their abundant raw material reserves,low cost,enhanced safety,and environmental sustainability.Na_(2)Fe_(2)OS_(2),featuring a layered anti-perovskite structure,has attracted significant interest for its high capacity and facile synthesis.In this study,density functional theory calculations were performed to systematically investigate the phase stability,ionic conductivity,and voltage characteristics of Na_(2)Fe_(2)OS_(2)as a model system for anti-perovskite layered cathode materials.The compound exhibits excellent phase stability,and its equilibrium potential was calculated for the series Na_(x)Fe_(2)OCh_(2)(0<±<2)(where Ch represents chalcogenides).Naion transport analysis using the climbing image nudged elastic band method reveals a relatively low migration barrier(~0.47eV)along a dingonal pathway,indicating efficient Na^(+)mobility.To expand the materials design space,we systematically explored the effects of substituting Fe with various transition metals and replacing S with Se in NaaTM_(2)OCh_(2)structures.Among the variants studied,Na_(2)Mn_(2)OS_(2) demonstrates the most favorable combination of high voltage(~2.51V),robust phase stability,and superior energy density(~427 W-h/kg).This comprehensive comparison of transition metal substitutions provides vnluable insights for the rational design and experimental development of next-generation anti-perovskite layered cathode materials for sodium-ion batteries.
基金supported by the Deanship of Scientific Research,King Saud University through the Vice Deanship of Scientific Research Chairs,Chair of Pervasive and Mobile Computing.
文摘Breast cancer diagnosis relies heavily on many kinds of information from diverse sources—like mammogram images,ultrasound scans,patient records,and genetic tests—but most AI tools look at only one of these at a time,which limits their ability to produce accurate and comprehensive decisions.In recent years,multimodal learning has emerged,enabling the integration of heterogeneous data to improve performance and diagnostic accuracy.However,doctors cannot always see how or why these AI tools make their choices,which is a significant bottleneck in their reliability,along with adoption in clinical settings.Hence,people are adding explainable AI techniques that show the steps the model takes.This review investigates previous work that has employed multimodal learning and XAI for the diagnosis of breast cancer.It discusses the types of data,fusion techniques,and XAI models employed.It was done following the PRISMA guidelines and included studies from 2021 to April 2025.The literature search was performed systematically and resulted in 61 studies.The review highlights a gradual increase in current studies focusing on multimodal fusion and XAI,particularly in the years 2023–2024.It found that studies using multi-modal data fusion achieved the highest accuracy by 5%–10%on average compared to other studies that used single-modality data,an intermediate fusion strategy,and modern fusion techniques,such as cross attention,achieved the highest accuracy and best performance.The review also showed that SHAP,Grad-CAM,and LIME techniques are the most used in explaining breast cancer diagnostic models.There is a clear research shift toward integrating multimodal learning and XAI techniques into the breast cancer diagnostics field.However,several gaps were identified,including the scarcity of public multimodal datasets.Lack of a unified explainable framework in multimodal fusion systems,and lack of standardization in evaluating explanations.These limitations call for future research focused on building more shared datasets and integrating multimodal data and explainable AI techniques to improve decision-making and enhance transparency.
基金co-supported by the National Key Project of China(No.GJXM92579)the National Natural Science Foundation of China(Nos.92052203,61903178 and61906081)。
文摘Inverse design has long been an efficient and powerful design tool in the aircraft industry.In this paper,a novel inverse design method for supercritical airfoils is proposed based on generative models in deep learning.A Conditional Variational Auto Encoder(CVAE)and an integrated generative network CVAE-GAN that combines the CVAE with the Wasserstein Generative Adversarial Networks(WGAN),are conducted as generative models.They are used to generate target wall Mach distributions for the inverse design that matches specified features,such as locations of suction peak,shock and aft loading.Qualitative and quantitative results show that both adopted generative models can generate diverse and realistic wall Mach number distributions satisfying the given features.The CVAE-GAN model outperforms the CVAE model and achieves better reconstruction accuracies for all the samples in the dataset.Furthermore,a deep neural network for nonlinear mapping is adopted to obtain the airfoil shape corresponding to the target wall Mach number distribution.The performances of the designed deep neural network are fully demonstrated and a smoothness measurement is proposed to quantify small oscillations in the airfoil surface,proving the authenticity and accuracy of the generated airfoil shapes.
基金supported by the US ARO grants 49308-MA and 56349-MAthe US AFSOR grant FA9550-06-1-024+1 种基金he US NSF grant DMS-0911434the State Key Laboratory of Scientific and Engineering Computing of Chinese Academy of Sciences during a visit by Z.Li between July-August,2008.
文摘In this paper,a class of new immersed interface finite element methods (IIFEM) is developed to solve elasticity interface problems with homogeneous and non-homogeneous jump conditions in two dimensions.Simple non-body-fitted meshes are used.For homogeneous jump conditions,both non-conforming and conforming basis functions are constructed in such a way that they satisfy the natural jump conditions. For non-homogeneous jump conditions,a pair of functions that satisfy the same non-homogeneous jump conditions are constructed using a level-set representation of the interface.With such a pair of functions,the discontinuities across the interface in the solution and flux are removed;and an equivalent elasticity interface problem with homogeneous jump conditions is formulated.Numerical examples are presented to demonstrate that such methods have second order convergence.
基金This work was supported by the Natural Science Foundation of China(Nos.61672478 and 61806090)the National Key Research and Development Program of China(No.2017YFB1003102)+4 种基金the Guangdong Provincial Key Laboratory(No.2020B121201001)the Shenzhen Peacock Plan(No.KQTD2016112514355531)the Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-inspired Intelligence Fund(No.2019028)the Fellowship of China Postdoctoral Science Foundation(No.2020M671900)the National Leading Youth Talent Support Program of China.
文摘Large-scale multi-objective optimization problems(MOPs)that involve a large number of decision variables,have emerged from many real-world applications.While evolutionary algorithms(EAs)have been widely acknowledged as a mainstream method for MOPs,most research progress and successful applications of EAs have been restricted to MOPs with small-scale decision variables.More recently,it has been reported that traditional multi-objective EAs(MOEAs)suffer severe deterioration with the increase of decision variables.As a result,and motivated by the emergence of real-world large-scale MOPs,investigation of MOEAs in this aspect has attracted much more attention in the past decade.This paper reviews the progress of evolutionary computation for large-scale multi-objective optimization from two angles.From the key difficulties of the large-scale MOPs,the scalability analysis is discussed by focusing on the performance of existing MOEAs and the challenges induced by the increase of the number of decision variables.From the perspective of methodology,the large-scale MOEAs are categorized into three classes and introduced respectively:divide and conquer based,dimensionality reduction based and enhanced search-based approaches.Several future research directions are also discussed.
基金supported and funded by the Gobernación del Cesar-Ministry of Science,Technology,and Innovation through resources for the higher education(grant 736/2015)the Natural Sciences and Engineering Research Council of Canada(NSERC)。
文摘Enzymatic hydrolysis of proteins is a breakdown process of peptide bond in proteins,releasing some peptides with potential biological functions.Previous studies on enzymatic hydrolysis of whey proteins have not identified the complete peptide profiles after hydrolysis.In this study,we reconstructed a profile of peptides from whey hydrolysates with two enzymes and different processing conditions.We also developed an ensemble machine learning predictor to classify peptides obtained from whey hydrolysis.A total of 2572 peptides were identified over three process conditions with two enzymes in duplicate.499 peptides were classified and chosen as potential antioxidant peptides from whey proteins.The peptides classified as antioxidants in the hydrolysates had a proportion of 13.1%-24.5%regarding all peptides identified.These results facilitate the selection of promising peptides involved in the antioxidant properties during the enzymatic hydrolysis of whey proteins,aiding the discovery of novel antioxidant peptides.
基金Project supported by the National Natural Science Foundation of China(Nos.11572025,11772032,and 51420105008)the National Basic Research Program of China(No.2014CB046405)the U.K.Engineering and Physical Sciences Research Council(EPSRC)(Nos.EP/K024574/1 and EP/L000261/1)
文摘Non-equilibrium turbulence phenomena have raised great interests in recent years. Significant efforts have been devoted to non-equilibrium turbulence properties in canonical flows, e.g., grid turbulence, turbulent wakes, and homogeneous isotropic turbulence(HIT). The non-equilibrium turbulence in non-canonical flows, however, has rarely been studied due to the complexity of the flows. In the present contribution, a directnumerical simulation(DNS) database of a turbulent flow is analyzed over a backwardfacing ramp, the flow near the boundary is demonstrated, and the non-equilibrium turbulent properties of the flow in the wake of the ramp are presented by using the characteristic parameters such as the dissipation coefficient C and the skewness of longitudinal velocity gradient Sk, but with opposite underlying turbulent energy transfer properties. The equation of Lagrangian velocity gradient correlation is examined, and the results show that non-equilibrium turbulence is the result of phase de-coherence phenomena, which is not taken into account in the modeling of non-equilibrium turbulence. These findings are expected to inspire deeper investigation of different non-equilibrium turbulence phenomena in different flow conditions and the improvement of turbulence modeling.
基金Supported by the National Natural Science Foundation of China under Grant No.19874056.
文摘The quantum telebroadcasting of a cat-like state in combination with the quantum teleportation and the local copying of entanglement is presented. This gives a general way of distributing entanglement among distant parties. All of the operations of our scenario are local and within the reach of current technology.
基金Project supported by the National Natural Science Foundation of China(Nos.12002318,11572025,11772032,and 51420105008)the Science Foundation of North University of China(No.XJJ201929)。
文摘Many recent laboratory experiments and numerical simulations support a non-equilibrium dissipation scaling in decaying turbulence before it reaches an equilibrium state.By analyzing a direct numerical simulation(DNS)database of a transitional boundary-layer flow,we show that the transition region and the non-equilibrium turbulence region,which are located in different streamwise zones,present different non-equilibrium scalings.Moreover,in the wall-normal direction,the viscous sublayer,log layer,and outer layer show different non-equilibrium phenomena which differ from those in grid-generated turbulence and transitional channel flows.These findings are expected to shed light on the modelling of various types of non-equilibrium turbulent flows.
基金supported by the Natural Science Foundation of China (81571025)International Cooperation Project from Shanghai Science Foundation (18410711300)+13 种基金the National Science Foundation for Distinguished Young Scholars of China (81025013)National Basic Research Development Program (973 Program) of China (2012CB720700, 2010CB945500, 2012CB966300, and 2009CB941100)the National Natural Science Foundation of China (81322021)the Beijing Nova Program (Z121110002512032)the Project for National 985 Engineering of China (985III-YFX0102)the ‘‘Dawn Tracking’’ Program of Shanghai Education Commission (10GG01)the Shanghai Natural Science Foundation (08411952000 and 10ZR1405400)the National Natural Science Young Foundation in China (81201033)the grants of Shanghai Health Bureau (20114358)the National High-Technology Development Project (863 Project) of China (2015AA020501)the Program for New Century Excellent Talents in University of China (NCET-10-0356)the National Program for the Support of TopNotch Young Professionalssupported by the Michael Smith Foundation, the CRC, and the CIHRsupported by the China Scholarship Council
文摘In this study, we aimed to （1） identify white matter （WM） deficits underlying the consciousness level in patients with disorders of consciousness （DOCs） using diffusion tensor imaging （DTI）, and （2） evaluate the relationship between DTI metrics and clinical measures of the consciousness level in DOC patients. With a cohort of 8 comatose, 8 unresponsive wakefulness syndrome/ vegetative state, and 14 minimally conscious state patients and 25 patient controls, we performed group comparisons of the DTI metrics in 48 core WM regions of interest （ROIs）, and examined the clinical relevance using correlation analysis. We identified multiple abnormal WM ROIs in DOC patients compared with normal controls, and the DTI metrics in these ROIs were significantly correlated with clinical measures of the consciousness level. Therefore, our findings suggested that multiple WM tracts are involved in the impaired consciousness levels in DOC patients and demonstrated the clinical relevance of DTI for DOC patients.
基金The work was supported by the Fundamental Research Funds for the Central Universities(NO.2021ZY92)major program of State Administration of Forestry and Grassland“Study on the assessment technologies for ecologically restoring the degraded grasslands”(20,200,507).
文摘Using an unmanned aerial vehicle (UAV) paired with image semantic segmentation to classify land cover within natural vegetation can promote the development of forest and grassland field. Semantic segmentation normally excels in medical and building classification, but its usefulness in mixed forest-grassland ecosystems in semi-arid to semi-humid climates is unknown. This study proposes a new semantic segmentation network of LResU-net in which residual convolution unit (RCU) and loop convolution unit (LCU) are added to the U-net framework to classify images of different land covers generated by UAV high resolution. The selected model enhanced classification accuracy by increasing gradient mapping via RCU and modifying the size of convolution layers via LCU as well as reducing convolution kernels. To achieve this objective, a group of orthophotos were taken at an altitude of 260 m for testing in a natural forest-grassland ecosystem of Keyouqianqi, Inner Mongolia, China, and compared the results with those of three other network models (U-net, ResU-net and LU-net). The results show that both the highest kappa coefficient (0.86) and the highest overall accuracy (93.7%) resulted from LResU-net, and the value of most land covers provided by the producer’s and user’s accuracy generated in LResU-net exceeded 0.85. The pixel-area ratio approach was used to calculate the real areas of 10 different land covers where grasslands were 67.3%. The analysis of the effect of RCU and LCU on the model training performance indicates that the time of each epoch was shortened from U-net (358 s) to LResU-net (282 s). In addition, in order to classify areas that are not distinguishable, unclassified areas were defined and their impact on classification. LResU-net generated significantly more accurate results than the other three models and was regarded as the most appropriate approach to classify land cover in mixed forest-grassland ecosystems.
基金Stable Support Plan Program,Grant/Award Number:20200925174052004Shenzhen Natural Science Fund,Grant/Award Number:JCYJ20200109140820699+2 种基金National Natural Science Foundation of China,Grant/Award Number:82272086Guangdong Provincial Department of Education,Grant/Award Numbers:2020ZDZX3043,SJZLGC202202Guangdong Provincial Key Laboratory,Grant/Award Number:2020B121201001。
文摘Eye health has become a global health concern and attracted broad attention.Over the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing ocular diseases efficiently and precisely.However,most existing methods were dedicated to constructing sophisticated CNNs,inevitably ignoring the trade-off between performance and model complexity.To alleviate this paradox,this paper proposes a lightweight yet efficient network architecture,mixeddecomposed convolutional network(MDNet),to recognise ocular diseases.In MDNet,we introduce a novel mixed-decomposed depthwise convolution method,which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low-resolution and high-resolution patterns by using fewer computations and fewer parameters.We conduct extensive experiments on the clinical anterior segment optical coherence tomography(AS-OCT),LAG,University of California San Diego,and CIFAR-100 datasets.The results show our MDNet achieves a better trade-off between the performance and model complexity than efficient CNNs including MobileNets and MixNets.Specifically,our MDNet outperforms MobileNets by 2.5%of accuracy by using 22%fewer parameters and 30%fewer computations on the AS-OCT dataset.
文摘Quantitative security metrics are desirable for measuring the performance of information security controls. Security metrics help to make functional and business decisions for improving the performance and cost of the security controls. However, defining enterprise-level security metrics has already been listed as one of the hard problems in the Info Sec Research Council's hard problems list. Almost all the efforts in defining absolute security metrics for the enterprise security have not been proved fruitful. At the same time, with the maturity of the security industry, there has been a continuous emphasis from the regulatory bodies on establishing measurable security metrics. This paper addresses this need and proposes a relative security metric model that derives three quantitative security metrics named Attack Resiliency Measure(ARM), Performance Improvement Factor(PIF), and Cost/Benefit Measure(CBM) for measuring the performance of the security controls. For the effectiveness evaluation of the proposed security metrics, we took the secure virtual machine(VM) migration protocol as the target of assessment. The virtual-ization technologies are rapidly changing the landscape of the computing world. Devising security metrics for virtualized environment is even more challenging. As secure virtual machine migration is an evolving area and no standard protocol is available specifically for secure VM migration. This paper took the secure virtual machine migration protocol as the target of assessment and applied the proposed relative security metric model for measuring the Attack Resiliency Measure, Performance Improvement Factor, and Cost/Benefit Measure of the secure VM migration protocol.
基金The work is partially funded by CGS Universiti Teknologi PETRONAS,Malaysia.
文摘Trust is one of the core components of any ad hoc network security system.Trust management(TM)has always been a challenging issue in a vehicular network.One such developing network is the Internet of vehicles(IoV),which is expected to be an essential part of smart cities.IoV originated from the merger of Vehicular ad hoc networks(VANET)and the Internet of things(IoT).Security is one of the main barriers in the on-road IoV implementation.Existing security standards are insufficient to meet the extremely dynamic and rapidly changing IoV requirements.Trust plays a vital role in ensuring security,especially during vehicle to vehicle communication.Vehicular networks,having a unique nature among other wireless ad hoc networks,require dedicated efforts to develop trust protocols.Current TM schemes are inflexible and static.Predefined scenarios and limited parameters are the basis for existing TM models that are not suitable for vehicle networks.The vehicular network requires agile and adaptive solutions to ensure security,especially when it comes to critical messages.The vehicle network’s wireless nature increases its attack surface and exposes the network to numerous security threats.Moreover,internet involvement makes it more vulnerable to cyberattacks.The proposed TM framework is based on context-based cognition and machine learning to be best suited to IoV dynamics.Machine learning is the best solution to utilize the big data produced by vehicle sensors.To handle the uncertainty Bayesian machine learning statistical model is used.The proposed framework can adapt scenarios dynamically and infer using the maximum possible parameter available.The results indicated better performance than existing TM methods.Furthermore,for future work,a high-level machine learning model is proposed.
