A new species of the genus Sinacosa is described from Nangunhe Nature Reserve,Yunnan,China:S.nan-gunheensis sp.n.(♂♀).Morphological descriptions,photos and illustrations of copulatory organs are provided,and a key t...A new species of the genus Sinacosa is described from Nangunhe Nature Reserve,Yunnan,China:S.nan-gunheensis sp.n.(♂♀).Morphological descriptions,photos and illustrations of copulatory organs are provided,and a key to the Sinacosa species is also provided.展开更多
Particulate photocatalytic systems using nanoscale photocatalysts have been developed as an attractive promising route for solar energy utilization to achieve resource sustainability and environmental harmony.Dynamic ...Particulate photocatalytic systems using nanoscale photocatalysts have been developed as an attractive promising route for solar energy utilization to achieve resource sustainability and environmental harmony.Dynamic obstacles are considered as the dominant inhibition for attaining satisfactory energy-conversion efficiency.The complexity in light absorption and carrier transfer behaviors has remained to be further clearly illuminated.It is challenging to trace the fast evolution of charge carriers involved in transfer migration and interfacial reactions within a micro–nano-single-particle photocatalyst,which requires spatiotemporal high resolution.In this review,comprehensive dynamic descriptions including irradiation field,carrier separation and transfer,and interfacial reaction processes have been elucidated and discussed.The corresponding mechanisms for revealing dynamic behaviors have been explained.In addition,numerical simulation and modeling methods have been illustrated for the description of the irradiation field.Experimental measurements and spatiotemporal characterizations have been clarified for the reflection of carrier behavior and probing detection of interfacial reactions.The representative applications have been introduced according to the reported advanced research works,and the relationships between mechanistic conclusions from variable spatiotemporal measurements and photocatalytic performance results in the specific photocatalytic reactions have been concluded.This review provides a collective perspective for the full understanding and thorough evaluation of the primary dynamic processes,which would be inspired for the improvement in designing solar-driven energy-conversion systems based on nanoscale particulate photocatalysts.展开更多
Towards optimal k-prototype discovery,k-means-like algorithms give us inspirations of central samples collection,yet the unstable seed samples selection,the hypothesis of a circle-like pattern,and the unknown K are st...Towards optimal k-prototype discovery,k-means-like algorithms give us inspirations of central samples collection,yet the unstable seed samples selection,the hypothesis of a circle-like pattern,and the unknown K are still challenges,particularly for non-predetermined data patterns.We propose an adaptive k-prototype clustering method(kProtoClust)which launches cluster exploration with a sketchy division of K clusters and finds evidence for splitting and merging.On behalf of a group of data samples,support vectors and outliers from the perspective of support vector data description are not the appropriate candidates for prototypes,while inner samples become the first candidates for instability reduction of seeds.Different from the representation of samples in traditional,we extend sample selection by encouraging fictitious samples to emphasize the representativeness of patterns.To get out of the circle-like pattern limitation,we introduce a convex decomposition-based strategy of one-cluster-multiple-prototypes in which convex hulls of varying sizes are prototypes,and accurate connection analysis makes the support of arbitrary cluster shapes possible.Inspired by geometry,the three presented strategies make kProtoClust bypassing the K dependence well with the global and local position relationship analysis for data samples.Experimental results on twelve datasets of irregular cluster shape or high dimension suggest that kProtoClust handles arbitrary cluster shapes with prominent accuracy even without the prior knowledge K.展开更多
The Knowledge Economic City (KEC) of Al Madinah Al Munawwarah is one of the major projects and represents the cornerstone for the new development activities for Al Madinah. The study area contains different geological...The Knowledge Economic City (KEC) of Al Madinah Al Munawwarah is one of the major projects and represents the cornerstone for the new development activities for Al Madinah. The study area contains different geological units dominated by basalt and overlain by surface deposits. The surface soils vary in thickness and can be classified into well-graded SAND with silt and gravel (SW-SM), silty SAND with gravel (SM), silty GRAVEL with sand (GM), and sandy SILTY clay (CL-ML). The subsurface soil obtained from the drilled boreholes can be classified into poorly graded GRAVEL (GP), well-graded GRAVEL with sand (GW), poorly graded GRAVEL with silt (GP-GM), silty CLAYEY gravel with sand (GC-GM), silty SAND with gravel (SM), silt with SAND (ML), and silty CLAY with sand (CL-ML), sandy lean CLAY (CL), and lean CLAY (CL). The relative density of the deposit and the different gravel sizes intercalated with the soil influenced the Standard Penetration Test (SPT) values. The SPT N values are high and approach refusal even at shallow depths. The shallow refusal depth (0.10 to 0.90 m) of the Dynamic Cone Penetration Test (DCPT) was observed. Generally, the soil can be described as inactive with low plasticity and dense to very dense consistency. The basalt of the KEC site is characterized by slightly (W2) to highly (W4) weathering, their strength ranges from moderate (S4) to very strong (S2), and the Rock Quality Designation (RQD) ranges from very poor (R5) to excellent (R1). The engineering geological map of the KEC characterized the geoengineering properties of the soil and rock materials and classified them into many zones. The high sulphate (SO42−) and chloride (Cl−) contents in groundwater call for protective measures for foundation concrete. The current study revealed that geohazard(s) mitigation measures concerning floods, volcanic eruptions, and earthquakes should be considered.展开更多
Ti-Hf-Zr-Nb-Ta refractory high-entropy alloys(RHEAs)exhibiting a dual-phase structure resulting from martensitic transformation offer significant ductility enhancement,but their design requires precise control of the ...Ti-Hf-Zr-Nb-Ta refractory high-entropy alloys(RHEAs)exhibiting a dual-phase structure resulting from martensitic transformation offer significant ductility enhancement,but their design requires precise control of the phase stability between body-centred cubic(BCC)and hexagonal close-packed(HCP)phases.This study establishes a comprehensive thermodynamic database for the Ti-Hf-Zr-Nb-Ta system using the 3rd-generation Calculation of Phase Diagrams(CALPHAD)model.The reliability of the database is validated by the strong agreement between the calculated thermodynamic properties and phase equilibria and the experimental data for pure element,as well as for binary and ternary systems.Utilizing this database,the phase stability of various RHEAs within this system was predicted,showing that all RHEAs exhibit a BCC single phase over a wide temperature range.The HCP phase is stable and coexists with BCC phase in both quaternary and quinary RHEAs at lower temepratures.Calculations of the Gibbs energy difference between the BCC and HCP phases(ΔG^(HCP−BCC))in TiHfZrTa_(x) and TiHfZrNb_(x) alloys reveal that both Nb and Ta stabilize the BCC phase,with Nb exerting a stronger influence.Significantly,a metastable BCC+HCP region in the TiHfZrTa_(x) and TiHfZrNb_(x) alloys with ΔG^(HCP−BCC) ranging from 1786 to 2230 J/mol.Utilizing this finding,the critical Nb composition range(0.0367–0.0712)to achieve the metastable BCC+HCP phase is precisely predicted in TiHfZrTa_(0.2)Nb_(x) alloys,enabling targeted design for martensitic transformation.The predictions show excellent agreement with existing experimental measurements.展开更多
In wireless communication,the problem of authenticating the transmitter’s identity is challeng-ing,especially for those terminal devices in which the security schemes based on cryptography are approxi-mately unfeasib...In wireless communication,the problem of authenticating the transmitter’s identity is challeng-ing,especially for those terminal devices in which the security schemes based on cryptography are approxi-mately unfeasible owing to limited resources.In this paper,a physical layer authentication scheme is pro-posed to detect whether there is anomalous access by the attackers disguised as legitimate users.Explicitly,channel state information(CSI)is used as a form of fingerprint to exploit spatial discrimination among de-vices in the wireless network and machine learning(ML)technology is employed to promote the improve-ment of authentication accuracy.Considering that the falsified messages are not accessible for authenticator during the training phase,deep support vector data de-scription(Deep SVDD)is selected to solve the one-class classification(OCC)problem.Simulation results show that Deep SVDD based scheme can tackle the challenges of physical layer authentication in wireless communication environments.展开更多
In modern society,the globalization of literary works is evident,with exceptional literary pieces from various countries spreading worldwide.Among these,children’s literature,due to the specificity of its target audi...In modern society,the globalization of literary works is evident,with exceptional literary pieces from various countries spreading worldwide.Among these,children’s literature,due to the specificity of its target audience,imposes distinct requirements on children’s books,compelling translators to approach the text from a child’s perspective.“The Little Prince”has renowned both within and outside of China,and a careful reading of this work can provide us with much inspiration.To this end,the present study adopts the perspective of Gideon Toury’s Descriptive Translation Studies to conduct an in-depth analysis of the different English and Chinese translations in conjunction with the original French novel.This approach aims to better guide literary research and explores translation methods for children’s literature through the analysis of translation norms and rules.展开更多
The simultaneous description for nuclear matter and finite nuclei has been a long-standing challenge in nuclear ab initio theory.With the success for nuclear matter,the relativistic Brueckner-Hartree-Fock(RBHF)theory ...The simultaneous description for nuclear matter and finite nuclei has been a long-standing challenge in nuclear ab initio theory.With the success for nuclear matter,the relativistic Brueckner-Hartree-Fock(RBHF)theory with covariant chiral interactions is a promising ab initio approach to describe both nuclear matter and finite nuclei.In the description of finite nuclei with the current RBHF theory,the covariant chiral interactions have to be localized to make calculations feasible.In order to examine the reliability and validity,in this letter,the RBHF theory with local and nonlocal covariant chiral interactions at leading order is applied to nuclear matter.The low-energy constants in the covariant chiral interactions determined with the local regularization are close to those with the nonlocal regularization.Moreover,the RBHF theory using covariant chiral interactions with local and nonlocal regulators provides an equally good description of the saturation properties of nuclear matter.The present work paves the way for the implementation of covariant chiral interactions in RBHF theory for finite nuclei.展开更多
Hot deformation is a commonly employed processing technique to enhance the ductility and workability of Mg alloy.However,the hot deformation of Mg alloy is highly sensitive to factors such as temperature,strain rate,a...Hot deformation is a commonly employed processing technique to enhance the ductility and workability of Mg alloy.However,the hot deformation of Mg alloy is highly sensitive to factors such as temperature,strain rate,and strain,leading to complex flow behavior and an exceptionally narrow processing window for Mg alloy.To overcome the shortcomings of the conventional Arrhenius-type(AT)model,this study developed machine learning-based Arrhenius-type(ML-AT)models by combining the genetic algorithm(GA),particle swarm optimization(PSO),and artificial neural network(ANN).Results indicated that when describing the flow behavior of the AQ80 alloy,the PSO-ANN-AT model demonstrates the most prominent prediction accuracy and generalization ability among all ML-AT and AT models.Moreover,an activation energy-processing(AEP)map was established using the reconstructed flow stress and activation energy fields based on the PSO-ANN-AT model.Experimental validations revealed that this AEP map exhibits superior predictive capability for microstructure evolution compared to the one established by the traditional interpolation methods,ultimately contributing to the precise determination of the optimum processing window.These findings provide fresh insights into the accurate constitutive description and workability characterization of Mg alloy during hot deformation.展开更多
The research aims to improve the performance of image recognition methods based on a description in the form of a set of keypoint descriptors.The main focus is on increasing the speed of establishing the relevance of ...The research aims to improve the performance of image recognition methods based on a description in the form of a set of keypoint descriptors.The main focus is on increasing the speed of establishing the relevance of object and etalon descriptions while maintaining the required level of classification efficiency.The class to be recognized is represented by an infinite set of images obtained from the etalon by applying arbitrary geometric transformations.It is proposed to reduce the descriptions for the etalon database by selecting the most significant descriptor components according to the information content criterion.The informativeness of an etalon descriptor is estimated by the difference of the closest distances to its own and other descriptions.The developed method determines the relevance of the full description of the recognized object with the reduced description of the etalons.Several practical models of the classifier with different options for establishing the correspondence between object descriptors and etalons are considered.The results of the experimental modeling of the proposed methods for a database including images of museum jewelry are presented.The test sample is formed as a set of images from the etalon database and out of the database with the application of geometric transformations of scale and rotation in the field of view.The practical problems of determining the threshold for the number of votes,based on which a classification decision is made,have been researched.Modeling has revealed the practical possibility of tenfold reducing descriptions with full preservation of classification accuracy.Reducing the descriptions by twenty times in the experiment leads to slightly decreased accuracy.The speed of the analysis increases in proportion to the degree of reduction.The use of reduction by the informativeness criterion confirmed the possibility of obtaining the most significant subset of features for classification,which guarantees a decent level of accuracy.展开更多
Flash boiling atomization(FBA)is a promising approach for enhancing spray atomization,which can generate a fine and more evenly distributed spray by increasing the fuel injection temperature or reducing the ambient pr...Flash boiling atomization(FBA)is a promising approach for enhancing spray atomization,which can generate a fine and more evenly distributed spray by increasing the fuel injection temperature or reducing the ambient pressure.However,when the outlet speed of the nozzle exceeds 400 m/s,investigating high-speed flash boiling atomization(HFBA)becomes quite challenging.This difficulty arises fromthe involvement ofmany complex physical processes and the requirement for a very fine mesh in numerical simulations.In this study,an HFBA model for gasoline direct injection(GDI)is established.This model incorporates primary and secondary atomization,as well as vaporization and boilingmodels,to describe the development process of the flash boiling spray.Compared to lowspeed FBA,these physical processes significantly impact HFBA.In this model,the Eulerian description is utilized for modeling the gas,and the Lagrangian description is applied to model the droplets,which effectively captures the movement of the droplets and avoids excessive mesh in the Eulerian coordinates.Under various conditions,numerical solutions of the Sauter mean diameter(SMD)for GDI show good agreement with experimental data,validating the proposed model’s performance.Simulations based on this HFBA model investigate the influences of fuel injection temperature and ambient pressure on the atomization process.Numerical analyses of the velocity field,temperature field,vapor mass fraction distribution,particle size distribution,and spray penetration length under different superheat degrees reveal that high injection temperature or low ambient pressure significantly affects the formation of small and dispersed droplet distribution.This effect is conducive to the refinement of spray particles and enhances atomization.展开更多
DD4hep serves as a generic detector description toolkit recommended for offline software development in next-generation high-energy physics(HEP)experiments.Conversely,Filmbox(FBX)stands out as a widely used 3D modelin...DD4hep serves as a generic detector description toolkit recommended for offline software development in next-generation high-energy physics(HEP)experiments.Conversely,Filmbox(FBX)stands out as a widely used 3D modeling file format within the 3D software industry.In this paper,we introduce a novel method that can automatically convert complex HEP detector geometries from DD4hep description into 3D models in the FBX format.The feasibility of this method was dem-onstrated by its application to the DD4hep description of the Compact Linear Collider detector and several sub-detectors of the super Tau-Charm facility and circular electron-positron collider experiments.The automatic DD4hep–FBX detector conversion interface provides convenience for further development of applications,such as detector design,simulation,visualization,data monitoring,and outreach,in HEP experiments.展开更多
Predictive maintenance has emerged as an effective tool for curbing maintenance costs,yet prevailing research predominantly concentrates on the abnormal phases.Within the ostensibly stable healthy phase,the reliance o...Predictive maintenance has emerged as an effective tool for curbing maintenance costs,yet prevailing research predominantly concentrates on the abnormal phases.Within the ostensibly stable healthy phase,the reliance on anomaly detection to preempt equipment malfunctions faces the challenge of sudden anomaly discernment.To address this challenge,this paper proposes a dual-task learning approach for bearing anomaly detection and state evaluation of safe regions.The proposed method transforms the execution of the two tasks into an optimization issue of the hypersphere center.By leveraging the monotonicity and distinguishability pertinent to the tasks as the foundation for optimization,it reconstructs the SVDD model to ensure equilibrium in the model’s performance across the two tasks.Subsequent experiments verify the proposed method’s effectiveness,which is interpreted from the perspectives of parameter adjustment and enveloping trade-offs.In the meantime,experimental results also show two deficiencies in anomaly detection accuracy and state evaluation metrics.Their theoretical analysis inspires us to focus on feature extraction and data collection to achieve improvements.The proposed method lays the foundation for realizing predictive maintenance in a healthy stage by improving condition awareness in safe regions.展开更多
Video description generates natural language sentences that describe the subject,verb,and objects of the targeted Video.The video description has been used to help visually impaired people to understand the content.It...Video description generates natural language sentences that describe the subject,verb,and objects of the targeted Video.The video description has been used to help visually impaired people to understand the content.It is also playing an essential role in devolving human-robot interaction.The dense video description is more difficult when compared with simple Video captioning because of the object’s interactions and event overlapping.Deep learning is changing the shape of computer vision(CV)technologies and natural language processing(NLP).There are hundreds of deep learning models,datasets,and evaluations that can improve the gaps in current research.This article filled this gap by evaluating some state-of-the-art approaches,especially focusing on deep learning and machine learning for video caption in a dense environment.In this article,some classic techniques concerning the existing machine learning were reviewed.And provides deep learning models,a detail of benchmark datasets with their respective domains.This paper reviews various evaluation metrics,including Bilingual EvaluationUnderstudy(BLEU),Metric for Evaluation of Translation with Explicit Ordering(METEOR),WordMover’s Distance(WMD),and Recall-Oriented Understudy for Gisting Evaluation(ROUGE)with their pros and cons.Finally,this article listed some future directions and proposed work for context enhancement using key scene extraction with object detection in a particular frame.Especially,how to improve the context of video description by analyzing key frames detection through morphological image analysis.Additionally,the paper discusses a novel approach involving sentence reconstruction and context improvement through key frame object detection,which incorporates the fusion of large languagemodels for refining results.The ultimate results arise fromenhancing the generated text of the proposedmodel by improving the predicted text and isolating objects using various keyframes.These keyframes identify dense events occurring in the video sequence.展开更多
Cross-lingual image description,the task of generating image captions in a target language from images and descriptions in a source language,is addressed in this study through a novel approach that combines neural net...Cross-lingual image description,the task of generating image captions in a target language from images and descriptions in a source language,is addressed in this study through a novel approach that combines neural network models and semantic matching techniques.Experiments conducted on the Flickr8k and AraImg2k benchmark datasets,featuring images and descriptions in English and Arabic,showcase remarkable performance improvements over state-of-the-art methods.Our model,equipped with the Image&Cross-Language Semantic Matching module and the Target Language Domain Evaluation module,significantly enhances the semantic relevance of generated image descriptions.For English-to-Arabic and Arabic-to-English cross-language image descriptions,our approach achieves a CIDEr score for English and Arabic of 87.9%and 81.7%,respectively,emphasizing the substantial contributions of our methodology.Comparative analyses with previous works further affirm the superior performance of our approach,and visual results underscore that our model generates image captions that are both semantically accurate and stylistically consistent with the target language.In summary,this study advances the field of cross-lingual image description,offering an effective solution for generating image captions across languages,with the potential to impact multilingual communication and accessibility.Future research directions include expanding to more languages and incorporating diverse visual and textual data sources.展开更多
Rotating Space Slender Flexible Structures(RSSFS)are extensively utilized in space operations because of their light weight,mobility,and low energy consumption.To realize the accurate space operation of the RSSFS,it i...Rotating Space Slender Flexible Structures(RSSFS)are extensively utilized in space operations because of their light weight,mobility,and low energy consumption.To realize the accurate space operation of the RSSFS,it is necessary to establish a precise mechanical model and develop a control algorithm with high precision.However,with the application of traditional control strategies,the RSSFS often suffers from the chattering phenomenon,which will aggravate structure vibration.In this paper,novel deformation description is put forward to balance modeling accuracy and computational efficiency of the RSSFS,which is better appropriate for real-time control.Besides,the Neural Network Sliding Mode Control(NNSMC)strategy modified by the hyperbolic tangent(tanh)function is put forward to compensate for modeling errors and reduce the chattering phenomenon,thereby improving the trajectory tracking accuracy of the RSSFS.Firstly,a mathematical model for the RSSFS is developed according to the novel deformation description and the vibration theory of flexible structure.Comparison of the deformation accuracy between different models proves that the novel modeling method proposed has high modeling accuracy.Next,the universal approximation property of the Radial Basis Function(RBF)neural network is put forward to determine and compensate for modeling errors,which consist of higher-order modes and the uncertainties of external disturbances.In addition,the tanh function is proposed as the reaching law in the conventional NNSMC strategy to suppress driving torque oscillation.The control law of modified NNSMC strategy and the adaptive law of weight coefficients are developed according to the Lyapunov theorem to guarantee the RSSFS stability.Finally,the simulation and physical experimental tests of the RSSFS with different control strategies are conducted.Experimental results show that the control law according to the novel deformation description and the modified NNSMC strategy can obtain accurate tracking of the rotation and reduce the vibration of the RSSFS simultaneously.展开更多
Image description task is the intersection of computer vision and natural language processing,and it has important prospects,including helping computers understand images and obtaining information for the visually imp...Image description task is the intersection of computer vision and natural language processing,and it has important prospects,including helping computers understand images and obtaining information for the visually impaired.This study presents an innovative approach employing deep reinforcement learning to enhance the accuracy of natural language descriptions of images.Our method focuses on refining the reward function in deep reinforcement learning,facilitating the generation of precise descriptions by aligning visual and textual features more closely.Our approach comprises three key architectures.Firstly,it utilizes Residual Network 101(ResNet-101)and Faster Region-based Convolutional Neural Network(Faster R-CNN)to extract average and local image features,respectively,followed by the implementation of a dual attention mechanism for intricate feature fusion.Secondly,the Transformer model is engaged to derive contextual semantic features from textual data.Finally,the generation of descriptive text is executed through a two-layer long short-term memory network(LSTM),directed by the value and reward functions.Compared with the image description method that relies on deep learning,the score of Bilingual Evaluation Understudy(BLEU-1)is 0.762,which is 1.6%higher,and the score of BLEU-4 is 0.299.Consensus-based Image Description Evaluation(CIDEr)scored 0.998,Recall-Oriented Understudy for Gisting Evaluation(ROUGE)scored 0.552,the latter improved by 0.36%.These results not only attest to the viability of our approach but also highlight its superiority in the realm of image description.Future research can explore the integration of our method with other artificial intelligence(AI)domains,such as emotional AI,to create more nuanced and context-aware systems.展开更多
This paper presents five Theridion species from the Yuelu Mt.,Changsha,including one new species and four known species:Theridion albioculum(♀♂);T.longipalpum(♂);T.obscuratum(♂);T.subundatum sp.nov.(♂♀);T.undat...This paper presents five Theridion species from the Yuelu Mt.,Changsha,including one new species and four known species:Theridion albioculum(♀♂);T.longipalpum(♂);T.obscuratum(♂);T.subundatum sp.nov.(♂♀);T.undatum(♀♂).We provided the morphological description,photos for the new species and photos for the known species in current paper.展开更多
Combining the strengths of Lagrangian and Eulerian descriptions,the coupled Lagrangian–Eulerian methods play an increasingly important role in various subjects.This work reviews their development and application in o...Combining the strengths of Lagrangian and Eulerian descriptions,the coupled Lagrangian–Eulerian methods play an increasingly important role in various subjects.This work reviews their development and application in ocean engineering.Initially,we briefly outline the advantages and disadvantages of the Lagrangian and Eulerian descriptions and the main characteristics of the coupled Lagrangian–Eulerian approach.Then,following the developmental trajectory of these methods,the fundamental formulations and the frameworks of various approaches,including the arbitrary Lagrangian–Eulerian finite element method,the particle-in-cell method,the material point method,and the recently developed Lagrangian–Eulerian stabilized collocation method,are detailedly reviewed.In addition,the article reviews the research progress of these methods with applications in ocean hydrodynamics,focusing on free surface flows,numerical wave generation,wave overturning and breaking,interactions between waves and coastal structures,fluid–rigid body interactions,fluid–elastic body interactions,multiphase flow problems and visualization of ocean flows,etc.Furthermore,the latest research advancements in the numerical stability,accuracy,efficiency,and consistency of the coupled Lagrangian–Eulerian particle methods are reviewed;these advancements enable efficient and highly accurate simulation of complicated multiphysics problems in ocean and coastal engineering.By building on these works,the current challenges and future directions of the hybrid Lagrangian–Eulerian particle methods are summarized.展开更多
基金supported by the Science&Technology Fundamental Resources Investigation Program(grant no.2022FY202100)the Second Tibetan Plateau Scientific Expedition and Research Program(STEP,#2024QZKK0200).
文摘A new species of the genus Sinacosa is described from Nangunhe Nature Reserve,Yunnan,China:S.nan-gunheensis sp.n.(♂♀).Morphological descriptions,photos and illustrations of copulatory organs are provided,and a key to the Sinacosa species is also provided.
基金supported by the Project of National Natural Science Foundation of China(22102095,21773153)the National Key Basic Research and Development Program(2018YFB1502001)financial support from the program of China Scholarships Council(No.202306230242).
文摘Particulate photocatalytic systems using nanoscale photocatalysts have been developed as an attractive promising route for solar energy utilization to achieve resource sustainability and environmental harmony.Dynamic obstacles are considered as the dominant inhibition for attaining satisfactory energy-conversion efficiency.The complexity in light absorption and carrier transfer behaviors has remained to be further clearly illuminated.It is challenging to trace the fast evolution of charge carriers involved in transfer migration and interfacial reactions within a micro–nano-single-particle photocatalyst,which requires spatiotemporal high resolution.In this review,comprehensive dynamic descriptions including irradiation field,carrier separation and transfer,and interfacial reaction processes have been elucidated and discussed.The corresponding mechanisms for revealing dynamic behaviors have been explained.In addition,numerical simulation and modeling methods have been illustrated for the description of the irradiation field.Experimental measurements and spatiotemporal characterizations have been clarified for the reflection of carrier behavior and probing detection of interfacial reactions.The representative applications have been introduced according to the reported advanced research works,and the relationships between mechanistic conclusions from variable spatiotemporal measurements and photocatalytic performance results in the specific photocatalytic reactions have been concluded.This review provides a collective perspective for the full understanding and thorough evaluation of the primary dynamic processes,which would be inspired for the improvement in designing solar-driven energy-conversion systems based on nanoscale particulate photocatalysts.
基金supported by the National Natural Science Foundation of China under Grant No.62162009the Key Technologies R&D Program of He’nan Province under Grant No.242102211065+1 种基金the Scientific Research Innovation Team of Xuchang University under GrantNo.2022CXTD003Postgraduate Education Reform and Quality Improvement Project of Henan Province under Grant No.YJS2024JD38.
文摘Towards optimal k-prototype discovery,k-means-like algorithms give us inspirations of central samples collection,yet the unstable seed samples selection,the hypothesis of a circle-like pattern,and the unknown K are still challenges,particularly for non-predetermined data patterns.We propose an adaptive k-prototype clustering method(kProtoClust)which launches cluster exploration with a sketchy division of K clusters and finds evidence for splitting and merging.On behalf of a group of data samples,support vectors and outliers from the perspective of support vector data description are not the appropriate candidates for prototypes,while inner samples become the first candidates for instability reduction of seeds.Different from the representation of samples in traditional,we extend sample selection by encouraging fictitious samples to emphasize the representativeness of patterns.To get out of the circle-like pattern limitation,we introduce a convex decomposition-based strategy of one-cluster-multiple-prototypes in which convex hulls of varying sizes are prototypes,and accurate connection analysis makes the support of arbitrary cluster shapes possible.Inspired by geometry,the three presented strategies make kProtoClust bypassing the K dependence well with the global and local position relationship analysis for data samples.Experimental results on twelve datasets of irregular cluster shape or high dimension suggest that kProtoClust handles arbitrary cluster shapes with prominent accuracy even without the prior knowledge K.
文摘The Knowledge Economic City (KEC) of Al Madinah Al Munawwarah is one of the major projects and represents the cornerstone for the new development activities for Al Madinah. The study area contains different geological units dominated by basalt and overlain by surface deposits. The surface soils vary in thickness and can be classified into well-graded SAND with silt and gravel (SW-SM), silty SAND with gravel (SM), silty GRAVEL with sand (GM), and sandy SILTY clay (CL-ML). The subsurface soil obtained from the drilled boreholes can be classified into poorly graded GRAVEL (GP), well-graded GRAVEL with sand (GW), poorly graded GRAVEL with silt (GP-GM), silty CLAYEY gravel with sand (GC-GM), silty SAND with gravel (SM), silt with SAND (ML), and silty CLAY with sand (CL-ML), sandy lean CLAY (CL), and lean CLAY (CL). The relative density of the deposit and the different gravel sizes intercalated with the soil influenced the Standard Penetration Test (SPT) values. The SPT N values are high and approach refusal even at shallow depths. The shallow refusal depth (0.10 to 0.90 m) of the Dynamic Cone Penetration Test (DCPT) was observed. Generally, the soil can be described as inactive with low plasticity and dense to very dense consistency. The basalt of the KEC site is characterized by slightly (W2) to highly (W4) weathering, their strength ranges from moderate (S4) to very strong (S2), and the Rock Quality Designation (RQD) ranges from very poor (R5) to excellent (R1). The engineering geological map of the KEC characterized the geoengineering properties of the soil and rock materials and classified them into many zones. The high sulphate (SO42−) and chloride (Cl−) contents in groundwater call for protective measures for foundation concrete. The current study revealed that geohazard(s) mitigation measures concerning floods, volcanic eruptions, and earthquakes should be considered.
基金financially supported by the Natural Science Foundation of Hebei Province,China(No.E202302154).
文摘Ti-Hf-Zr-Nb-Ta refractory high-entropy alloys(RHEAs)exhibiting a dual-phase structure resulting from martensitic transformation offer significant ductility enhancement,but their design requires precise control of the phase stability between body-centred cubic(BCC)and hexagonal close-packed(HCP)phases.This study establishes a comprehensive thermodynamic database for the Ti-Hf-Zr-Nb-Ta system using the 3rd-generation Calculation of Phase Diagrams(CALPHAD)model.The reliability of the database is validated by the strong agreement between the calculated thermodynamic properties and phase equilibria and the experimental data for pure element,as well as for binary and ternary systems.Utilizing this database,the phase stability of various RHEAs within this system was predicted,showing that all RHEAs exhibit a BCC single phase over a wide temperature range.The HCP phase is stable and coexists with BCC phase in both quaternary and quinary RHEAs at lower temepratures.Calculations of the Gibbs energy difference between the BCC and HCP phases(ΔG^(HCP−BCC))in TiHfZrTa_(x) and TiHfZrNb_(x) alloys reveal that both Nb and Ta stabilize the BCC phase,with Nb exerting a stronger influence.Significantly,a metastable BCC+HCP region in the TiHfZrTa_(x) and TiHfZrNb_(x) alloys with ΔG^(HCP−BCC) ranging from 1786 to 2230 J/mol.Utilizing this finding,the critical Nb composition range(0.0367–0.0712)to achieve the metastable BCC+HCP phase is precisely predicted in TiHfZrTa_(0.2)Nb_(x) alloys,enabling targeted design for martensitic transformation.The predictions show excellent agreement with existing experimental measurements.
基金partially supported by the National Key Research and Development Project under Grant2020YFB1806805Social Development Projects of Jiangsu Science and Technology Department under Grant No.BE2018704
文摘In wireless communication,the problem of authenticating the transmitter’s identity is challeng-ing,especially for those terminal devices in which the security schemes based on cryptography are approxi-mately unfeasible owing to limited resources.In this paper,a physical layer authentication scheme is pro-posed to detect whether there is anomalous access by the attackers disguised as legitimate users.Explicitly,channel state information(CSI)is used as a form of fingerprint to exploit spatial discrimination among de-vices in the wireless network and machine learning(ML)technology is employed to promote the improve-ment of authentication accuracy.Considering that the falsified messages are not accessible for authenticator during the training phase,deep support vector data de-scription(Deep SVDD)is selected to solve the one-class classification(OCC)problem.Simulation results show that Deep SVDD based scheme can tackle the challenges of physical layer authentication in wireless communication environments.
文摘In modern society,the globalization of literary works is evident,with exceptional literary pieces from various countries spreading worldwide.Among these,children’s literature,due to the specificity of its target audience,imposes distinct requirements on children’s books,compelling translators to approach the text from a child’s perspective.“The Little Prince”has renowned both within and outside of China,and a careful reading of this work can provide us with much inspiration.To this end,the present study adopts the perspective of Gideon Toury’s Descriptive Translation Studies to conduct an in-depth analysis of the different English and Chinese translations in conjunction with the original French novel.This approach aims to better guide literary research and explores translation methods for children’s literature through the analysis of translation norms and rules.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.12435006,12435007,12475117,12141501,and 123B2080)the National Key R&D Program of China(Grant No.2024YFE0109803)the National Key Laboratory of Neutron Science and Technology(Grant No.NST202401016)。
文摘The simultaneous description for nuclear matter and finite nuclei has been a long-standing challenge in nuclear ab initio theory.With the success for nuclear matter,the relativistic Brueckner-Hartree-Fock(RBHF)theory with covariant chiral interactions is a promising ab initio approach to describe both nuclear matter and finite nuclei.In the description of finite nuclei with the current RBHF theory,the covariant chiral interactions have to be localized to make calculations feasible.In order to examine the reliability and validity,in this letter,the RBHF theory with local and nonlocal covariant chiral interactions at leading order is applied to nuclear matter.The low-energy constants in the covariant chiral interactions determined with the local regularization are close to those with the nonlocal regularization.Moreover,the RBHF theory using covariant chiral interactions with local and nonlocal regulators provides an equally good description of the saturation properties of nuclear matter.The present work paves the way for the implementation of covariant chiral interactions in RBHF theory for finite nuclei.
基金supported by the National Natural Science Foundation of China(Grant Nos.52305361,51775194,52090043)China Postdoctoral Science Foundation(2023M741245)the National Key Research and Development Program of China(2022YFB3706903).
文摘Hot deformation is a commonly employed processing technique to enhance the ductility and workability of Mg alloy.However,the hot deformation of Mg alloy is highly sensitive to factors such as temperature,strain rate,and strain,leading to complex flow behavior and an exceptionally narrow processing window for Mg alloy.To overcome the shortcomings of the conventional Arrhenius-type(AT)model,this study developed machine learning-based Arrhenius-type(ML-AT)models by combining the genetic algorithm(GA),particle swarm optimization(PSO),and artificial neural network(ANN).Results indicated that when describing the flow behavior of the AQ80 alloy,the PSO-ANN-AT model demonstrates the most prominent prediction accuracy and generalization ability among all ML-AT and AT models.Moreover,an activation energy-processing(AEP)map was established using the reconstructed flow stress and activation energy fields based on the PSO-ANN-AT model.Experimental validations revealed that this AEP map exhibits superior predictive capability for microstructure evolution compared to the one established by the traditional interpolation methods,ultimately contributing to the precise determination of the optimum processing window.These findings provide fresh insights into the accurate constitutive description and workability characterization of Mg alloy during hot deformation.
基金This research was funded by Prince Sattam bin Abdulaziz University(Project Number PSAU/2023/01/25387).
文摘The research aims to improve the performance of image recognition methods based on a description in the form of a set of keypoint descriptors.The main focus is on increasing the speed of establishing the relevance of object and etalon descriptions while maintaining the required level of classification efficiency.The class to be recognized is represented by an infinite set of images obtained from the etalon by applying arbitrary geometric transformations.It is proposed to reduce the descriptions for the etalon database by selecting the most significant descriptor components according to the information content criterion.The informativeness of an etalon descriptor is estimated by the difference of the closest distances to its own and other descriptions.The developed method determines the relevance of the full description of the recognized object with the reduced description of the etalons.Several practical models of the classifier with different options for establishing the correspondence between object descriptors and etalons are considered.The results of the experimental modeling of the proposed methods for a database including images of museum jewelry are presented.The test sample is formed as a set of images from the etalon database and out of the database with the application of geometric transformations of scale and rotation in the field of view.The practical problems of determining the threshold for the number of votes,based on which a classification decision is made,have been researched.Modeling has revealed the practical possibility of tenfold reducing descriptions with full preservation of classification accuracy.Reducing the descriptions by twenty times in the experiment leads to slightly decreased accuracy.The speed of the analysis increases in proportion to the degree of reduction.The use of reduction by the informativeness criterion confirmed the possibility of obtaining the most significant subset of features for classification,which guarantees a decent level of accuracy.
基金supported by the National Natural Science Foundation of China(Project Nos.12272270,11972261).
文摘Flash boiling atomization(FBA)is a promising approach for enhancing spray atomization,which can generate a fine and more evenly distributed spray by increasing the fuel injection temperature or reducing the ambient pressure.However,when the outlet speed of the nozzle exceeds 400 m/s,investigating high-speed flash boiling atomization(HFBA)becomes quite challenging.This difficulty arises fromthe involvement ofmany complex physical processes and the requirement for a very fine mesh in numerical simulations.In this study,an HFBA model for gasoline direct injection(GDI)is established.This model incorporates primary and secondary atomization,as well as vaporization and boilingmodels,to describe the development process of the flash boiling spray.Compared to lowspeed FBA,these physical processes significantly impact HFBA.In this model,the Eulerian description is utilized for modeling the gas,and the Lagrangian description is applied to model the droplets,which effectively captures the movement of the droplets and avoids excessive mesh in the Eulerian coordinates.Under various conditions,numerical solutions of the Sauter mean diameter(SMD)for GDI show good agreement with experimental data,validating the proposed model’s performance.Simulations based on this HFBA model investigate the influences of fuel injection temperature and ambient pressure on the atomization process.Numerical analyses of the velocity field,temperature field,vapor mass fraction distribution,particle size distribution,and spray penetration length under different superheat degrees reveal that high injection temperature or low ambient pressure significantly affects the formation of small and dispersed droplet distribution.This effect is conducive to the refinement of spray particles and enhances atomization.
基金supported by the National Natural Science Foundation of China(Nos.12175321,11975021,11675275,and U1932101)National Key Research and Development Program of China(Nos.2023YFA1606000 and 2020YFA0406400)+2 种基金State Key Laboratory of Nuclear Physics and Technology,Peking University(Nos.NPT2020KFY04 and NPT2020KFY05)Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA10010900)National College Students Science and Technology Innovation Project,and Undergraduate Base Scientific Research Project of Sun Yat-sen University。
文摘DD4hep serves as a generic detector description toolkit recommended for offline software development in next-generation high-energy physics(HEP)experiments.Conversely,Filmbox(FBX)stands out as a widely used 3D modeling file format within the 3D software industry.In this paper,we introduce a novel method that can automatically convert complex HEP detector geometries from DD4hep description into 3D models in the FBX format.The feasibility of this method was dem-onstrated by its application to the DD4hep description of the Compact Linear Collider detector and several sub-detectors of the super Tau-Charm facility and circular electron-positron collider experiments.The automatic DD4hep–FBX detector conversion interface provides convenience for further development of applications,such as detector design,simulation,visualization,data monitoring,and outreach,in HEP experiments.
基金Supported by Sichuan Provincial Key Research and Development Program of China(Grant No.2023YFG0351)National Natural Science Foundation of China(Grant No.61833002).
文摘Predictive maintenance has emerged as an effective tool for curbing maintenance costs,yet prevailing research predominantly concentrates on the abnormal phases.Within the ostensibly stable healthy phase,the reliance on anomaly detection to preempt equipment malfunctions faces the challenge of sudden anomaly discernment.To address this challenge,this paper proposes a dual-task learning approach for bearing anomaly detection and state evaluation of safe regions.The proposed method transforms the execution of the two tasks into an optimization issue of the hypersphere center.By leveraging the monotonicity and distinguishability pertinent to the tasks as the foundation for optimization,it reconstructs the SVDD model to ensure equilibrium in the model’s performance across the two tasks.Subsequent experiments verify the proposed method’s effectiveness,which is interpreted from the perspectives of parameter adjustment and enveloping trade-offs.In the meantime,experimental results also show two deficiencies in anomaly detection accuracy and state evaluation metrics.Their theoretical analysis inspires us to focus on feature extraction and data collection to achieve improvements.The proposed method lays the foundation for realizing predictive maintenance in a healthy stage by improving condition awareness in safe regions.
文摘Video description generates natural language sentences that describe the subject,verb,and objects of the targeted Video.The video description has been used to help visually impaired people to understand the content.It is also playing an essential role in devolving human-robot interaction.The dense video description is more difficult when compared with simple Video captioning because of the object’s interactions and event overlapping.Deep learning is changing the shape of computer vision(CV)technologies and natural language processing(NLP).There are hundreds of deep learning models,datasets,and evaluations that can improve the gaps in current research.This article filled this gap by evaluating some state-of-the-art approaches,especially focusing on deep learning and machine learning for video caption in a dense environment.In this article,some classic techniques concerning the existing machine learning were reviewed.And provides deep learning models,a detail of benchmark datasets with their respective domains.This paper reviews various evaluation metrics,including Bilingual EvaluationUnderstudy(BLEU),Metric for Evaluation of Translation with Explicit Ordering(METEOR),WordMover’s Distance(WMD),and Recall-Oriented Understudy for Gisting Evaluation(ROUGE)with their pros and cons.Finally,this article listed some future directions and proposed work for context enhancement using key scene extraction with object detection in a particular frame.Especially,how to improve the context of video description by analyzing key frames detection through morphological image analysis.Additionally,the paper discusses a novel approach involving sentence reconstruction and context improvement through key frame object detection,which incorporates the fusion of large languagemodels for refining results.The ultimate results arise fromenhancing the generated text of the proposedmodel by improving the predicted text and isolating objects using various keyframes.These keyframes identify dense events occurring in the video sequence.
文摘Cross-lingual image description,the task of generating image captions in a target language from images and descriptions in a source language,is addressed in this study through a novel approach that combines neural network models and semantic matching techniques.Experiments conducted on the Flickr8k and AraImg2k benchmark datasets,featuring images and descriptions in English and Arabic,showcase remarkable performance improvements over state-of-the-art methods.Our model,equipped with the Image&Cross-Language Semantic Matching module and the Target Language Domain Evaluation module,significantly enhances the semantic relevance of generated image descriptions.For English-to-Arabic and Arabic-to-English cross-language image descriptions,our approach achieves a CIDEr score for English and Arabic of 87.9%and 81.7%,respectively,emphasizing the substantial contributions of our methodology.Comparative analyses with previous works further affirm the superior performance of our approach,and visual results underscore that our model generates image captions that are both semantically accurate and stylistically consistent with the target language.In summary,this study advances the field of cross-lingual image description,offering an effective solution for generating image captions across languages,with the potential to impact multilingual communication and accessibility.Future research directions include expanding to more languages and incorporating diverse visual and textual data sources.
基金Supported by the Applied Basic Research Program of Liaoning Province,China(No.2023JH2/101300159)the National Natural Science Foundation of China(No.52275090).
文摘Rotating Space Slender Flexible Structures(RSSFS)are extensively utilized in space operations because of their light weight,mobility,and low energy consumption.To realize the accurate space operation of the RSSFS,it is necessary to establish a precise mechanical model and develop a control algorithm with high precision.However,with the application of traditional control strategies,the RSSFS often suffers from the chattering phenomenon,which will aggravate structure vibration.In this paper,novel deformation description is put forward to balance modeling accuracy and computational efficiency of the RSSFS,which is better appropriate for real-time control.Besides,the Neural Network Sliding Mode Control(NNSMC)strategy modified by the hyperbolic tangent(tanh)function is put forward to compensate for modeling errors and reduce the chattering phenomenon,thereby improving the trajectory tracking accuracy of the RSSFS.Firstly,a mathematical model for the RSSFS is developed according to the novel deformation description and the vibration theory of flexible structure.Comparison of the deformation accuracy between different models proves that the novel modeling method proposed has high modeling accuracy.Next,the universal approximation property of the Radial Basis Function(RBF)neural network is put forward to determine and compensate for modeling errors,which consist of higher-order modes and the uncertainties of external disturbances.In addition,the tanh function is proposed as the reaching law in the conventional NNSMC strategy to suppress driving torque oscillation.The control law of modified NNSMC strategy and the adaptive law of weight coefficients are developed according to the Lyapunov theorem to guarantee the RSSFS stability.Finally,the simulation and physical experimental tests of the RSSFS with different control strategies are conducted.Experimental results show that the control law according to the novel deformation description and the modified NNSMC strategy can obtain accurate tracking of the rotation and reduce the vibration of the RSSFS simultaneously.
基金This research was funded by the Natural Science Foundation of Gansu Province with Approval Numbers 20JR10RA334 and 21JR7RA570Funding is provided for the 2021 Longyuan Youth Innovation and Entrepreneurship Talent Project with Approval Number 2021LQGR20+1 种基金the University Level Innovation Project with Approval NumbersGZF2020XZD18jbzxyb2018-01 of Gansu University of Political Science and Law.
文摘Image description task is the intersection of computer vision and natural language processing,and it has important prospects,including helping computers understand images and obtaining information for the visually impaired.This study presents an innovative approach employing deep reinforcement learning to enhance the accuracy of natural language descriptions of images.Our method focuses on refining the reward function in deep reinforcement learning,facilitating the generation of precise descriptions by aligning visual and textual features more closely.Our approach comprises three key architectures.Firstly,it utilizes Residual Network 101(ResNet-101)and Faster Region-based Convolutional Neural Network(Faster R-CNN)to extract average and local image features,respectively,followed by the implementation of a dual attention mechanism for intricate feature fusion.Secondly,the Transformer model is engaged to derive contextual semantic features from textual data.Finally,the generation of descriptive text is executed through a two-layer long short-term memory network(LSTM),directed by the value and reward functions.Compared with the image description method that relies on deep learning,the score of Bilingual Evaluation Understudy(BLEU-1)is 0.762,which is 1.6%higher,and the score of BLEU-4 is 0.299.Consensus-based Image Description Evaluation(CIDEr)scored 0.998,Recall-Oriented Understudy for Gisting Evaluation(ROUGE)scored 0.552,the latter improved by 0.36%.These results not only attest to the viability of our approach but also highlight its superiority in the realm of image description.Future research can explore the integration of our method with other artificial intelligence(AI)domains,such as emotional AI,to create more nuanced and context-aware systems.
基金supported by the Scientific Research Foundation of Education Department of Jiangxi Province(Grant No.GJJ201434)the Key Natural Science Foundation of Chongqing(cstc2019jcyj-zdxmX0006)+1 种基金Chongqing Provincial Funding for Postdoc to Muhammad Irfan(cstc2021jcyj-bsh0196)Foreign Youth Talent Program Funding(QN2022168002L).
文摘This paper presents five Theridion species from the Yuelu Mt.,Changsha,including one new species and four known species:Theridion albioculum(♀♂);T.longipalpum(♂);T.obscuratum(♂);T.subundatum sp.nov.(♂♀);T.undatum(♀♂).We provided the morphological description,photos for the new species and photos for the known species in current paper.
基金the support received from the Laoshan Laboratory(No.LSKJ202202000)the National Natural Science Foundation of China(Grant Nos.12032002,U22A20256,and 12302253)the Natural Science Foundation of Beijing(No.L212023)for partially funding this work.
文摘Combining the strengths of Lagrangian and Eulerian descriptions,the coupled Lagrangian–Eulerian methods play an increasingly important role in various subjects.This work reviews their development and application in ocean engineering.Initially,we briefly outline the advantages and disadvantages of the Lagrangian and Eulerian descriptions and the main characteristics of the coupled Lagrangian–Eulerian approach.Then,following the developmental trajectory of these methods,the fundamental formulations and the frameworks of various approaches,including the arbitrary Lagrangian–Eulerian finite element method,the particle-in-cell method,the material point method,and the recently developed Lagrangian–Eulerian stabilized collocation method,are detailedly reviewed.In addition,the article reviews the research progress of these methods with applications in ocean hydrodynamics,focusing on free surface flows,numerical wave generation,wave overturning and breaking,interactions between waves and coastal structures,fluid–rigid body interactions,fluid–elastic body interactions,multiphase flow problems and visualization of ocean flows,etc.Furthermore,the latest research advancements in the numerical stability,accuracy,efficiency,and consistency of the coupled Lagrangian–Eulerian particle methods are reviewed;these advancements enable efficient and highly accurate simulation of complicated multiphysics problems in ocean and coastal engineering.By building on these works,the current challenges and future directions of the hybrid Lagrangian–Eulerian particle methods are summarized.
