The fracture behavior at high temperatures of the Ti−22Al−26Nb alloy,which features duplex lamellar,bimodal,and Widmanstätten structures,was studied.Samples of the alloy were prepared through compression deformat...The fracture behavior at high temperatures of the Ti−22Al−26Nb alloy,which features duplex lamellar,bimodal,and Widmanstätten structures,was studied.Samples of the alloy were prepared through compression deformation in the trans-phase region followed by subsequent heat treatment.The results indicate that at 650℃,the fracture toughness of the Ti−22Al−26Nb alloy is increased by 41.7%compared to that with original microstructures.The content of the B2 phase significantly influences the inherent fracture toughness of the material,while the morphology and distribution of the precipitated phases primarily affect the tortuosity of the crack propagation path.Among the microstructural features,the morphology and geometric orientation of the lamellae most significantly impact the crack path;consequently,the Widmanstätten structure exhibits the most tortuous fracture path.Additionally,a predictive model for fracture toughness is developed,which effectively predicts the fracture toughness of Ti−22Al−26Nb alloys with various microstructures at 650℃.展开更多
The suddenness, uncertainty, and randomness of rockbursts directly affect the safety of tunnel construction. The prediction of rockbursts is a fundamental aspect of mitigating or even eliminating rockburst hazards. To...The suddenness, uncertainty, and randomness of rockbursts directly affect the safety of tunnel construction. The prediction of rockbursts is a fundamental aspect of mitigating or even eliminating rockburst hazards. To address the shortcomings of the current rockburst prediction models, which have a limited number of samples and rely on manual test results as the majority of their input features, this paper proposes rockburst prediction models based on multi-featured drilling parameters of rock drilling jumbo. Firstly, four original drilling parameters, namely hammer pressure (Ph), feed pressure (Pf), rotation pressure (Pr), and feed speed (VP), together with the rockburst grades, were collected from 1093 rockburst cases. Then, a feature expansion investigation was performed based on the four original drilling parameters to establish a drilling parameter feature system and a rockburst prediction database containing 42 features. Furthermore, rockburst prediction models based on multi-featured drilling parameters were developed using the extreme tree (ET) algorithm and Bayesian optimization. The models take drilling parameters as input parameters and rockburst grades as output parameters. The effects of Bayesian optimization and the number of drilling parameter features on the model performance were analyzed using the accuracy, precision, recall and F1 value of the prediction set as the model performance evaluation indices. The results show that the Bayesian optimized model with 42 drilling parameter features as inputs performs best, with an accuracy of 91.89%. Finally, the reliability of the models was validated through field tests.展开更多
With the aid of the latest fiber optic sensing technology parameters in the cure process of ther- mosetting resin-matrix composite, such as temperature, viscosity,void and residual stress, can be monitored entirely an...With the aid of the latest fiber optic sensing technology parameters in the cure process of ther- mosetting resin-matrix composite, such as temperature, viscosity,void and residual stress, can be monitored entirely and efficiently.In this paper, experiment results of viscosity measurement in composite cure process in autoclave using fiber optic sensors are presented. Based on the sensed information, a computer program is utilized to control the cure process. With this technology, the cure process becomes more apparent and controllable, which will greatly improve the cured products and reduce the cost.展开更多
This work reports the structural feature and internal motion of one novel hyperbranching cluster system in dilution solution.The cluster system is composed of HB-PS_(300)-g-Pt BA_(45) hypergraft copolymer chains with ...This work reports the structural feature and internal motion of one novel hyperbranching cluster system in dilution solution.The cluster system is composed of HB-PS_(300)-g-Pt BA_(45) hypergraft copolymer chains with uniform subchain,high molar mass and low polydispersity(M_(w)=1.73×106 g/mol and<M_(w)/M_(n)>≈1.07),where HB-PS and Pt BA represent hyperbranched polystyrene core and poly(tert-butyl polyacrylate)graft,respectively.In the selective solvent of PS blocks(cyclohexane,T_(θ)=34.5℃),the aggregation kinetics and structural feature are found to be precisely tunable for assembled clusters by the aggregation temperature(11℃<T<17℃)and time(0 h<t<24 h).An interesting structural evolution kinetics is observed,namely,the fractal dimension(d_(f))of clusters is found to first increases and then decreases with t,eventually,it reaches a plateau value of d_(f)≈3.0,corresponds to a uniform spherical structure.By using dynamic light scattering(DLS)to monitor the number and strength of relaxation modes inΓ(q)withΓbeing the decay rate and q being the scattering vector,it is quantitatively revealed that the relaxation,intensity contribution and mode origin of internal motions of clusters are neither similar with previously reported cluster systems with high polydispersity,nor with the classical linear chain systems.In particular,in the broad range of 2.0<qR_(h)<6.0,we have observed that the reduced first cumulant[Γ^(*)=Γ(q)/(q^(3)k_(B)T/η_(0))]does not display an asymptotic behavior.Whereas,a better asymptotic behavior is observed by plottingΓ(q)/q^(4) versus qRh.For the first time,our observation provides direct evidence supporting that,for hyperbranching cluster system with low polydispersity and high local chain segment density,the hydrodynamic interaction is greatly weakened due to the enhanced hydrodynamic shielding effect.展开更多
To solve the problem that using a single feature cannot play the role of multiple features of Android application in malicious code detection, an Android malicious code detection mechanism is proposed based on integra...To solve the problem that using a single feature cannot play the role of multiple features of Android application in malicious code detection, an Android malicious code detection mechanism is proposed based on integrated learning on the basis of dynamic and static detection. Considering three types of Android behavior characteristics, a three-layer hybrid algorithm was proposed. And it combined the malicious code detection based on digital signature to improve the detection efficiency. The digital signature of the known malicious code was extracted to form a malicious sample library. The authority that can reflect Android malicious behavior, API call and the running system call features were also extracted. An expandable hybrid discriminant algorithm was designed for the above three types of features. The algorithm was tested with machine learning method by constructing the optimal classifier suitable for the above features. Finally, the Android malicious code detection system was designed and implemented based on the multi-layer hybrid algorithm. The experimental results show that the system performs Android malicious code detection based on the combination of signature and dynamic and static features. Compared with other related work, the system has better performance in execution efficiency and detection rate.展开更多
BeiDou Global Navigation Satellite System(BDS-3)not only performs the normal positioning,navigation and timing(PNT)functions,but also provides featured services,which are divided into geostationary orbit(GEO)and mediu...BeiDou Global Navigation Satellite System(BDS-3)not only performs the normal positioning,navigation and timing(PNT)functions,but also provides featured services,which are divided into geostationary orbit(GEO)and medium earth orbit(MEO)satellite-based featured services in this paper.The former refers to regional services consisting of the regional short message communication service(RSMCS),the radio determination satellite service(RDSS),the BDS satellite-based augmented service(BDSBAS)and the satellite-based precise point positioning service via B2b signal(B2b-PPP).The latter refers to global services consisting of the global short message communication service(GSMCS)and the MEO satellite-based search and rescue(MEOSAR)service.The focus of this paper is to describe these featured services and evaluate their performances.The results show that the inter-satellite link(ISL)contributes a lot to the accuracy improvement of orbit determination and time synchronization for the whole constellation.Compared with some other final products,the root mean squares(RMS)of the BDS-3 precise orbits and broadcast clock are 25.1 cm and 2.01 ns,respectively.The positioning accuracy of single frequency is better than 6 m,and that of the generalized RDSS is usually better than 12 m.For featured services,the success rates of RSMCS and GSMCS are better than 99.9% and 95.6%,respectively;the positioning accuracies of single and dual frequency BDSBAS are better than 3 and 2 m,respectively;the positioning accuracy of B2b-PPP is better than 0.6 m,and the convergence time is usually smaller than 30 min;the single station test shows that the success rate of MEOSAR is better than 99%.Due to the ISL realization in the BDS-3 constellation,the performance and capacities of the global featured services are improved significantly.展开更多
AIM:To investigate the clinical features and prognosis of patients with orbital inflammatory myofibroblastic tumor(IMT).METHODS:This retrospective study collected clinical data from 22 patients diagnosed with orbital ...AIM:To investigate the clinical features and prognosis of patients with orbital inflammatory myofibroblastic tumor(IMT).METHODS:This retrospective study collected clinical data from 22 patients diagnosed with orbital IMT based on histopathological examination.The patients were followed up to assess their prognosis.Clinical data from patients,including age,gender,course of disease,past medical history,primary symptoms,ophthalmologic examination findings,general condition,as well as imaging,laboratory,histopathological,and immunohistochemical results from digital records were collected.Orbital magnetic resonance imaging(MRI)and(or)computed tomography(CT)scans were performed to assess bone destruction of the mass,invasion of surrounding tissues,and any inflammatory changes in periorbital areas.RESULTS:The mean age of patients with orbital IMT was 28.24±3.30y,with a male-to-female ratio of 1.2:1.Main clinical manifestations were proptosis,blurred vision,palpable mass,and pain.Bone destruction and surrounding tissue invasion occurred in 72.73%and 54.55%of cases,respectively.Inflammatory changes in the periorbital site were observed in 77.27%of the patients.Hematoxylin and eosin staining showed proliferation of fibroblasts and myofibroblasts,accompanied by infiltration of lymphocytes and plasma cells.Immunohistochemical staining revealed that smooth muscle actin(SMA)and vimentin were positive in 100%of cases,while anaplastic lymphoma kinase(ALK)showed positivity in 47.37%.The recurrence rate of orbital IMT was 27.27%,and sarcomatous degeneration could occur.There were no significant correlations between recurrence and factors such as age,gender,laterality,duration of the disease,periorbital tissue invasion,bone destruction,periorbital inflammation,tumor size,fever,leukocytosis,or treatment(P>0.05).However,lymphadenopathy and a Ki-67 index of 10%or higher may be risk factors for recurrence(P=0.046;P=0.023).CONCLUSION:Orbital IMT is a locally invasive disease that may recur or lead to sarcomatoid degeneration,primarily affecting young and middle-aged patients.The presence of lymphadenopathy and a Ki-67 index of 10%or higher may signify a poor prognosis.展开更多
Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate ...Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate this challenge,we present an enhanced semi-supervised learning approach based on the Mean Teacher framework,incorporating a novel feature loss module to maximize classification performance with limited labeled samples.The model studies show that the proposed model surpasses both the baseline Mean Teacher model and fully supervised method in accuracy.Specifically,for datasets with 20%,30%,and 40%label ratios,using a single training iteration,the model yields accuracies of 78.61%,82.21%,and 85.2%,respectively,while multiple-cycle training iterations achieves 82.09%,81.97%,and 81.59%,respectively.Furthermore,scenario-specific training schemes are introduced to support diverse deployment need.These findings highlight the potential of the proposed technique in minimizing labeling requirements and advancing intelligent blast furnace diagnostics.展开更多
BACKGROUND SMARCB1/INI1-deficient pancreatic undifferentiated rhabdoid carcinoma is a highly aggressive tumor,and spontaneous splenic rupture(SSR)as its presenting manifestation is rarely reported among pancreatic mal...BACKGROUND SMARCB1/INI1-deficient pancreatic undifferentiated rhabdoid carcinoma is a highly aggressive tumor,and spontaneous splenic rupture(SSR)as its presenting manifestation is rarely reported among pancreatic malignancies.CASE SUMMARY We herein report a rare case of a 59-year-old female who presented with acute left upper quadrant abdominal pain without any history of trauma.Abdominal imaging demonstrated a heterogeneous splenic lesion with hemoperitoneum,raising clinical suspicion of SSR.Emergency laparotomy revealed a pancreatic tumor invading the spleen and left kidney,with associated splenic rupture and dense adhesions,necessitating en bloc resection of the distal pancreas,spleen,and left kidney.Histopathology revealed a biphasic malignancy composed of moderately differentiated pancreatic ductal adenocarcinoma and an undifferentiated carcinoma with rhabdoid morphology and loss of SMARCB1 expression.Immunohistochemical analysis confirmed complete loss of SMARCB1/INI1 in the undifferentiated component,along with a high Ki-67 index(approximately 80%)and CD10 positivity.The ductal adenocarcinoma component retained SMARCB1/INI1 expression and was positive for CK7 and CK-pan.Transitional zones between the two tumor components suggested progressive dedifferentiation and underlying genomic instability.The patient received adjuvant chemotherapy with gemcitabine and nab-paclitaxel and maintained a satisfactory quality of life at the 6-month follow-up.CONCLUSION This study reports a rare case of SMARCB1/INI1-deficient undifferentiated rhabdoid carcinoma of the pancreas combined with ductal adenocarcinoma,presenting as SSR-an exceptionally uncommon initial manifestation of pancreatic malignancy.展开更多
Tariq RahmanTariq Rahman earned his Ph.D in English from theUniversity of Sheffield in England.He did his post-doctoral research as a Fulbright scholar(1995-96)in the USA.He is presently National DistinguishedProfesso...Tariq RahmanTariq Rahman earned his Ph.D in English from theUniversity of Sheffield in England.He did his post-doctoral research as a Fulbright scholar(1995-96)in the USA.He is presently National DistinguishedProfessor at the Quaid-i-Azam University in Islamabad,Pakistan.Dr.Rahman is a highly published scholar havingmore than eighty research papers in academic journals and six booksto his credit.He is considered an authority on the politics and historyof languages and language policy in Pakistan.His book Language andPolitics in Pakistan(Oxford University Press,1996)is regarded as aclassic in the field and has been reprinted four times.His latest bookDenizens of Alien Worhls(Oxford University Press,2004)is on展开更多
The remodeling of axonal connections following injury is an important feature driving functional recovery.The reticulospinal tract is an interesting descending motor tract that contains both excitatory and inhibitory ...The remodeling of axonal connections following injury is an important feature driving functional recovery.The reticulospinal tract is an interesting descending motor tract that contains both excitatory and inhibitory fibers.While the reticulospinal tract has been shown to be particularly prone to axonal growth and plasticity following injuries of the spinal cord,the differential capacities of excitatory and inhibitory fibers for plasticity remain unclear.As adaptive axonal plasticity involves a sophisticated interplay between excitatory and inhibitory input,we investigated in this study the plastic potential of glutamatergic(vGlut2)and GABAergic(vGat)fibers originating from the gigantocellular nucleus and the lateral paragigantocellular nucleus,two nuclei important for locomotor function.Using a combination of viral tracing,chemogenetic silencing,and AI-based kinematic analysis,we investigated plasticity and its impact on functional recovery within the first 3 weeks following injury,a period prone to neuronal remodeling.We demonstrate that,in this time frame,while vGlut2-positive fibers within the gigantocellular and lateral paragigantocellular nuclei rewire significantly following cervical spinal cord injury,vGat-positive fibers are rather unresponsive to injury.We also show that the acute silencing of excitatory axonal fibers which rewire in response to lesions of the spinal cord triggers a worsening of the functional recovery.Using kinematic analysis,we also pinpoint the locomotion features associated with the gigantocellular nucleus or lateral paragigantocellular nucleus during functional recovery.Overall,our study increases the understanding of the role of the gigantocellular and lateral paragigantocellular nuclei during functional recovery following spinal cord injury.展开更多
To promote the level of rural construction,the provincial party committee and the provincial government of Jiangsu officially issued the Action Plan for Featured Countryside Construction of Jiangsu Province on Jun.20,...To promote the level of rural construction,the provincial party committee and the provincial government of Jiangsu officially issued the Action Plan for Featured Countryside Construction of Jiangsu Province on Jun.20,2017.The Plan proposed that,by sticking to the principles of innovation,coordination,green,open,and sharing,and in line展开更多
The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more e...The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.展开更多
Thunderstorm wind gusts are small in scale,typically occurring within a range of a few kilometers.It is extremely challenging to monitor and forecast thunderstorm wind gusts using only automatic weather stations.There...Thunderstorm wind gusts are small in scale,typically occurring within a range of a few kilometers.It is extremely challenging to monitor and forecast thunderstorm wind gusts using only automatic weather stations.Therefore,it is necessary to establish thunderstorm wind gust identification techniques based on multisource high-resolution observations.This paper introduces a new algorithm,called thunderstorm wind gust identification network(TGNet).It leverages multimodal feature fusion to fuse the temporal and spatial features of thunderstorm wind gust events.The shapelet transform is first used to extract the temporal features of wind speeds from automatic weather stations,which is aimed at distinguishing thunderstorm wind gusts from those caused by synoptic-scale systems or typhoons.Then,the encoder,structured upon the U-shaped network(U-Net)and incorporating recurrent residual convolutional blocks(R2U-Net),is employed to extract the corresponding spatial convective characteristics of satellite,radar,and lightning observations.Finally,by using the multimodal deep fusion module based on multi-head cross-attention,the temporal features of wind speed at each automatic weather station are incorporated into the spatial features to obtain 10-minutely classification of thunderstorm wind gusts.TGNet products have high accuracy,with a critical success index reaching 0.77.Compared with those of U-Net and R2U-Net,the false alarm rate of TGNet products decreases by 31.28%and 24.15%,respectively.The new algorithm provides grid products of thunderstorm wind gusts with a spatial resolution of 0.01°,updated every 10minutes.The results are finer and more accurate,thereby helping to improve the accuracy of operational warnings for thunderstorm wind gusts.展开更多
Excellent detonation performances and low sensitivity are prerequisites for the deployment of energetic materials.Exploring the underlying factors that affect impact sensitivity and detonation performances as well as ...Excellent detonation performances and low sensitivity are prerequisites for the deployment of energetic materials.Exploring the underlying factors that affect impact sensitivity and detonation performances as well as exploring how to obtain materials with desired properties remains a long-term challenge.Machine learning with its ability to solve complex tasks and perform robust data processing can reveal the relationship between performance and descriptive indicators,potentially accelerating the development process of energetic materials.In this background,impact sensitivity,detonation performances,and 28 physicochemical parameters for 222 energetic materials from density functional theory calculations and published literature were sorted out.Four machine learning algorithms were employed to predict various properties of energetic materials,including impact sensitivity,detonation velocity,detonation pressure,and Gurney energy.Analysis of Pearson coefficients and feature importance showed that the heat of explosion,oxygen balance,decomposition products,and HOMO energy levels have a strong correlation with the impact sensitivity of energetic materials.Oxygen balance,decomposition products,and density have a strong correlation with detonation performances.Utilizing impact sensitivity of 2,3,4-trinitrotoluene and the detonation performances of 2,4,6-trinitrobenzene-1,3,5-triamine as the benchmark,the analysis of feature importance rankings and statistical data revealed the optimal range of key features balancing impact sensitivity and detonation performances:oxygen balance values should be between-40%and-30%,density should range from 1.66 to 1.72 g/cm^(3),HOMO energy levels should be between-6.34 and-6.31 eV,and lipophilicity should be between-1.0 and 0.1,4.49 and 5.59.These findings not only offer important insights into the impact sensitivity and detonation performances of energetic materials,but also provide a theoretical guidance paradigm for the design and development of new energetic materials with optimal detonation performances and reduced sensitivity.展开更多
Anomaly Detection (AD) has been extensively adopted in industrial settings to facilitate quality control of products. It is critical to industrial production, especially to areas such as aircraft manufacturing, which ...Anomaly Detection (AD) has been extensively adopted in industrial settings to facilitate quality control of products. It is critical to industrial production, especially to areas such as aircraft manufacturing, which require strict part qualification rates. Although being more efficient and practical, few-shot AD has not been well explored. The existing AD methods only extract features in a single frequency while defects exist in multiple frequency domains. Moreover, current methods have not fully leveraged the few-shot support samples to extract input-related normal patterns. To address these issues, we propose an industrial few-shot AD method, Feature Extender for Anomaly Detection (FEAD), which extracts normal patterns in multiple frequency domains from few-shot samples under the guidance of the input sample. Firstly, to achieve better coverage of normal patterns in the input sample, we introduce a Sample-Conditioned Transformation Module (SCTM), which transforms support features under the guidance of the input sample to obtain extra normal patterns. Secondly, to effectively distinguish and localize anomaly patterns in multiple frequency domains, we devise an Adaptive Descriptor Construction Module (ADCM) to build and select pattern descriptors in a series of frequencies adaptively. Finally, an auxiliary task for SCTM is designed to ensure the diversity of transformations and include more normal patterns into support features. Extensive experiments on two widely used industrial AD datasets (MVTec-AD and VisA) demonstrate the effectiveness of the proposed FEAD.展开更多
An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyram...An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.展开更多
基金financially supported by the National Natural Science Foundation of China(Nos.51975175,51875158)。
文摘The fracture behavior at high temperatures of the Ti−22Al−26Nb alloy,which features duplex lamellar,bimodal,and Widmanstätten structures,was studied.Samples of the alloy were prepared through compression deformation in the trans-phase region followed by subsequent heat treatment.The results indicate that at 650℃,the fracture toughness of the Ti−22Al−26Nb alloy is increased by 41.7%compared to that with original microstructures.The content of the B2 phase significantly influences the inherent fracture toughness of the material,while the morphology and distribution of the precipitated phases primarily affect the tortuosity of the crack propagation path.Among the microstructural features,the morphology and geometric orientation of the lamellae most significantly impact the crack path;consequently,the Widmanstätten structure exhibits the most tortuous fracture path.Additionally,a predictive model for fracture toughness is developed,which effectively predicts the fracture toughness of Ti−22Al−26Nb alloys with various microstructures at 650℃.
基金supported by the China Railway Corporation Science and Technology Research and Development Program(Grant Nos.K2020G035 and K2021G024)the National Natural Science Foundation of China(Grant No.52378411).
文摘The suddenness, uncertainty, and randomness of rockbursts directly affect the safety of tunnel construction. The prediction of rockbursts is a fundamental aspect of mitigating or even eliminating rockburst hazards. To address the shortcomings of the current rockburst prediction models, which have a limited number of samples and rely on manual test results as the majority of their input features, this paper proposes rockburst prediction models based on multi-featured drilling parameters of rock drilling jumbo. Firstly, four original drilling parameters, namely hammer pressure (Ph), feed pressure (Pf), rotation pressure (Pr), and feed speed (VP), together with the rockburst grades, were collected from 1093 rockburst cases. Then, a feature expansion investigation was performed based on the four original drilling parameters to establish a drilling parameter feature system and a rockburst prediction database containing 42 features. Furthermore, rockburst prediction models based on multi-featured drilling parameters were developed using the extreme tree (ET) algorithm and Bayesian optimization. The models take drilling parameters as input parameters and rockburst grades as output parameters. The effects of Bayesian optimization and the number of drilling parameter features on the model performance were analyzed using the accuracy, precision, recall and F1 value of the prediction set as the model performance evaluation indices. The results show that the Bayesian optimized model with 42 drilling parameter features as inputs performs best, with an accuracy of 91.89%. Finally, the reliability of the models was validated through field tests.
文摘With the aid of the latest fiber optic sensing technology parameters in the cure process of ther- mosetting resin-matrix composite, such as temperature, viscosity,void and residual stress, can be monitored entirely and efficiently.In this paper, experiment results of viscosity measurement in composite cure process in autoclave using fiber optic sensors are presented. Based on the sensed information, a computer program is utilized to control the cure process. With this technology, the cure process becomes more apparent and controllable, which will greatly improve the cured products and reduce the cost.
基金financially supported by the National Natural Science Foundation of China(No.21973088)Shenzhen Science and Technology Program(Nos.RCYX20210706092101012 and ZDSYS20210623100800001)。
文摘This work reports the structural feature and internal motion of one novel hyperbranching cluster system in dilution solution.The cluster system is composed of HB-PS_(300)-g-Pt BA_(45) hypergraft copolymer chains with uniform subchain,high molar mass and low polydispersity(M_(w)=1.73×106 g/mol and<M_(w)/M_(n)>≈1.07),where HB-PS and Pt BA represent hyperbranched polystyrene core and poly(tert-butyl polyacrylate)graft,respectively.In the selective solvent of PS blocks(cyclohexane,T_(θ)=34.5℃),the aggregation kinetics and structural feature are found to be precisely tunable for assembled clusters by the aggregation temperature(11℃<T<17℃)and time(0 h<t<24 h).An interesting structural evolution kinetics is observed,namely,the fractal dimension(d_(f))of clusters is found to first increases and then decreases with t,eventually,it reaches a plateau value of d_(f)≈3.0,corresponds to a uniform spherical structure.By using dynamic light scattering(DLS)to monitor the number and strength of relaxation modes inΓ(q)withΓbeing the decay rate and q being the scattering vector,it is quantitatively revealed that the relaxation,intensity contribution and mode origin of internal motions of clusters are neither similar with previously reported cluster systems with high polydispersity,nor with the classical linear chain systems.In particular,in the broad range of 2.0<qR_(h)<6.0,we have observed that the reduced first cumulant[Γ^(*)=Γ(q)/(q^(3)k_(B)T/η_(0))]does not display an asymptotic behavior.Whereas,a better asymptotic behavior is observed by plottingΓ(q)/q^(4) versus qRh.For the first time,our observation provides direct evidence supporting that,for hyperbranching cluster system with low polydispersity and high local chain segment density,the hydrodynamic interaction is greatly weakened due to the enhanced hydrodynamic shielding effect.
文摘To solve the problem that using a single feature cannot play the role of multiple features of Android application in malicious code detection, an Android malicious code detection mechanism is proposed based on integrated learning on the basis of dynamic and static detection. Considering three types of Android behavior characteristics, a three-layer hybrid algorithm was proposed. And it combined the malicious code detection based on digital signature to improve the detection efficiency. The digital signature of the known malicious code was extracted to form a malicious sample library. The authority that can reflect Android malicious behavior, API call and the running system call features were also extracted. An expandable hybrid discriminant algorithm was designed for the above three types of features. The algorithm was tested with machine learning method by constructing the optimal classifier suitable for the above features. Finally, the Android malicious code detection system was designed and implemented based on the multi-layer hybrid algorithm. The experimental results show that the system performs Android malicious code detection based on the combination of signature and dynamic and static features. Compared with other related work, the system has better performance in execution efficiency and detection rate.
基金supported by the National Natural Science Foundation of China(41931076,L1924033,and 41904042)National Key Research and Development Program of China(2020YFB0505800)。
文摘BeiDou Global Navigation Satellite System(BDS-3)not only performs the normal positioning,navigation and timing(PNT)functions,but also provides featured services,which are divided into geostationary orbit(GEO)and medium earth orbit(MEO)satellite-based featured services in this paper.The former refers to regional services consisting of the regional short message communication service(RSMCS),the radio determination satellite service(RDSS),the BDS satellite-based augmented service(BDSBAS)and the satellite-based precise point positioning service via B2b signal(B2b-PPP).The latter refers to global services consisting of the global short message communication service(GSMCS)and the MEO satellite-based search and rescue(MEOSAR)service.The focus of this paper is to describe these featured services and evaluate their performances.The results show that the inter-satellite link(ISL)contributes a lot to the accuracy improvement of orbit determination and time synchronization for the whole constellation.Compared with some other final products,the root mean squares(RMS)of the BDS-3 precise orbits and broadcast clock are 25.1 cm and 2.01 ns,respectively.The positioning accuracy of single frequency is better than 6 m,and that of the generalized RDSS is usually better than 12 m.For featured services,the success rates of RSMCS and GSMCS are better than 99.9% and 95.6%,respectively;the positioning accuracies of single and dual frequency BDSBAS are better than 3 and 2 m,respectively;the positioning accuracy of B2b-PPP is better than 0.6 m,and the convergence time is usually smaller than 30 min;the single station test shows that the success rate of MEOSAR is better than 99%.Due to the ISL realization in the BDS-3 constellation,the performance and capacities of the global featured services are improved significantly.
基金Supported by the National Key R&D Program of China(No.2023YFC2410203)Beijing Hospitals Authority Clinical Medicine Development of Special Funding Support(No.ZLRK202503).
文摘AIM:To investigate the clinical features and prognosis of patients with orbital inflammatory myofibroblastic tumor(IMT).METHODS:This retrospective study collected clinical data from 22 patients diagnosed with orbital IMT based on histopathological examination.The patients were followed up to assess their prognosis.Clinical data from patients,including age,gender,course of disease,past medical history,primary symptoms,ophthalmologic examination findings,general condition,as well as imaging,laboratory,histopathological,and immunohistochemical results from digital records were collected.Orbital magnetic resonance imaging(MRI)and(or)computed tomography(CT)scans were performed to assess bone destruction of the mass,invasion of surrounding tissues,and any inflammatory changes in periorbital areas.RESULTS:The mean age of patients with orbital IMT was 28.24±3.30y,with a male-to-female ratio of 1.2:1.Main clinical manifestations were proptosis,blurred vision,palpable mass,and pain.Bone destruction and surrounding tissue invasion occurred in 72.73%and 54.55%of cases,respectively.Inflammatory changes in the periorbital site were observed in 77.27%of the patients.Hematoxylin and eosin staining showed proliferation of fibroblasts and myofibroblasts,accompanied by infiltration of lymphocytes and plasma cells.Immunohistochemical staining revealed that smooth muscle actin(SMA)and vimentin were positive in 100%of cases,while anaplastic lymphoma kinase(ALK)showed positivity in 47.37%.The recurrence rate of orbital IMT was 27.27%,and sarcomatous degeneration could occur.There were no significant correlations between recurrence and factors such as age,gender,laterality,duration of the disease,periorbital tissue invasion,bone destruction,periorbital inflammation,tumor size,fever,leukocytosis,or treatment(P>0.05).However,lymphadenopathy and a Ki-67 index of 10%or higher may be risk factors for recurrence(P=0.046;P=0.023).CONCLUSION:Orbital IMT is a locally invasive disease that may recur or lead to sarcomatoid degeneration,primarily affecting young and middle-aged patients.The presence of lymphadenopathy and a Ki-67 index of 10%or higher may signify a poor prognosis.
基金financial support provided by the Natural Science Foundation of Hebei Province,China(No.E2024105036)the Tangshan Talent Funding Project,China(Nos.B202302007 and A2021110015)+1 种基金the National Natural Science Foundation of China(No.52264042)the Australian Research Council(No.IH230100010)。
文摘Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate this challenge,we present an enhanced semi-supervised learning approach based on the Mean Teacher framework,incorporating a novel feature loss module to maximize classification performance with limited labeled samples.The model studies show that the proposed model surpasses both the baseline Mean Teacher model and fully supervised method in accuracy.Specifically,for datasets with 20%,30%,and 40%label ratios,using a single training iteration,the model yields accuracies of 78.61%,82.21%,and 85.2%,respectively,while multiple-cycle training iterations achieves 82.09%,81.97%,and 81.59%,respectively.Furthermore,scenario-specific training schemes are introduced to support diverse deployment need.These findings highlight the potential of the proposed technique in minimizing labeling requirements and advancing intelligent blast furnace diagnostics.
文摘BACKGROUND SMARCB1/INI1-deficient pancreatic undifferentiated rhabdoid carcinoma is a highly aggressive tumor,and spontaneous splenic rupture(SSR)as its presenting manifestation is rarely reported among pancreatic malignancies.CASE SUMMARY We herein report a rare case of a 59-year-old female who presented with acute left upper quadrant abdominal pain without any history of trauma.Abdominal imaging demonstrated a heterogeneous splenic lesion with hemoperitoneum,raising clinical suspicion of SSR.Emergency laparotomy revealed a pancreatic tumor invading the spleen and left kidney,with associated splenic rupture and dense adhesions,necessitating en bloc resection of the distal pancreas,spleen,and left kidney.Histopathology revealed a biphasic malignancy composed of moderately differentiated pancreatic ductal adenocarcinoma and an undifferentiated carcinoma with rhabdoid morphology and loss of SMARCB1 expression.Immunohistochemical analysis confirmed complete loss of SMARCB1/INI1 in the undifferentiated component,along with a high Ki-67 index(approximately 80%)and CD10 positivity.The ductal adenocarcinoma component retained SMARCB1/INI1 expression and was positive for CK7 and CK-pan.Transitional zones between the two tumor components suggested progressive dedifferentiation and underlying genomic instability.The patient received adjuvant chemotherapy with gemcitabine and nab-paclitaxel and maintained a satisfactory quality of life at the 6-month follow-up.CONCLUSION This study reports a rare case of SMARCB1/INI1-deficient undifferentiated rhabdoid carcinoma of the pancreas combined with ductal adenocarcinoma,presenting as SSR-an exceptionally uncommon initial manifestation of pancreatic malignancy.
文摘Tariq RahmanTariq Rahman earned his Ph.D in English from theUniversity of Sheffield in England.He did his post-doctoral research as a Fulbright scholar(1995-96)in the USA.He is presently National DistinguishedProfessor at the Quaid-i-Azam University in Islamabad,Pakistan.Dr.Rahman is a highly published scholar havingmore than eighty research papers in academic journals and six booksto his credit.He is considered an authority on the politics and historyof languages and language policy in Pakistan.His book Language andPolitics in Pakistan(Oxford University Press,1996)is regarded as aclassic in the field and has been reprinted four times.His latest bookDenizens of Alien Worhls(Oxford University Press,2004)is on
基金supported by the Deutsche Forschungsgemeinschaft(DFG),TRR274(Project ID 408885537,Sy Nergy,EXC 2145/ID 390857198,to FMB)。
文摘The remodeling of axonal connections following injury is an important feature driving functional recovery.The reticulospinal tract is an interesting descending motor tract that contains both excitatory and inhibitory fibers.While the reticulospinal tract has been shown to be particularly prone to axonal growth and plasticity following injuries of the spinal cord,the differential capacities of excitatory and inhibitory fibers for plasticity remain unclear.As adaptive axonal plasticity involves a sophisticated interplay between excitatory and inhibitory input,we investigated in this study the plastic potential of glutamatergic(vGlut2)and GABAergic(vGat)fibers originating from the gigantocellular nucleus and the lateral paragigantocellular nucleus,two nuclei important for locomotor function.Using a combination of viral tracing,chemogenetic silencing,and AI-based kinematic analysis,we investigated plasticity and its impact on functional recovery within the first 3 weeks following injury,a period prone to neuronal remodeling.We demonstrate that,in this time frame,while vGlut2-positive fibers within the gigantocellular and lateral paragigantocellular nuclei rewire significantly following cervical spinal cord injury,vGat-positive fibers are rather unresponsive to injury.We also show that the acute silencing of excitatory axonal fibers which rewire in response to lesions of the spinal cord triggers a worsening of the functional recovery.Using kinematic analysis,we also pinpoint the locomotion features associated with the gigantocellular nucleus or lateral paragigantocellular nucleus during functional recovery.Overall,our study increases the understanding of the role of the gigantocellular and lateral paragigantocellular nuclei during functional recovery following spinal cord injury.
文摘To promote the level of rural construction,the provincial party committee and the provincial government of Jiangsu officially issued the Action Plan for Featured Countryside Construction of Jiangsu Province on Jun.20,2017.The Plan proposed that,by sticking to the principles of innovation,coordination,green,open,and sharing,and in line
文摘The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFC3004104)the National Natural Science Foundation of China(Grant No.U2342204)+4 种基金the Innovation and Development Program of the China Meteorological Administration(Grant No.CXFZ2024J001)the Open Research Project of the Key Open Laboratory of Hydrology and Meteorology of the China Meteorological Administration(Grant No.23SWQXZ010)the Science and Technology Plan Project of Zhejiang Province(Grant No.2022C03150)the Open Research Fund Project of Anyang National Climate Observatory(Grant No.AYNCOF202401)the Open Bidding for Selecting the Best Candidates Program(Grant No.CMAJBGS202318)。
文摘Thunderstorm wind gusts are small in scale,typically occurring within a range of a few kilometers.It is extremely challenging to monitor and forecast thunderstorm wind gusts using only automatic weather stations.Therefore,it is necessary to establish thunderstorm wind gust identification techniques based on multisource high-resolution observations.This paper introduces a new algorithm,called thunderstorm wind gust identification network(TGNet).It leverages multimodal feature fusion to fuse the temporal and spatial features of thunderstorm wind gust events.The shapelet transform is first used to extract the temporal features of wind speeds from automatic weather stations,which is aimed at distinguishing thunderstorm wind gusts from those caused by synoptic-scale systems or typhoons.Then,the encoder,structured upon the U-shaped network(U-Net)and incorporating recurrent residual convolutional blocks(R2U-Net),is employed to extract the corresponding spatial convective characteristics of satellite,radar,and lightning observations.Finally,by using the multimodal deep fusion module based on multi-head cross-attention,the temporal features of wind speed at each automatic weather station are incorporated into the spatial features to obtain 10-minutely classification of thunderstorm wind gusts.TGNet products have high accuracy,with a critical success index reaching 0.77.Compared with those of U-Net and R2U-Net,the false alarm rate of TGNet products decreases by 31.28%and 24.15%,respectively.The new algorithm provides grid products of thunderstorm wind gusts with a spatial resolution of 0.01°,updated every 10minutes.The results are finer and more accurate,thereby helping to improve the accuracy of operational warnings for thunderstorm wind gusts.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.2682024GF019)。
文摘Excellent detonation performances and low sensitivity are prerequisites for the deployment of energetic materials.Exploring the underlying factors that affect impact sensitivity and detonation performances as well as exploring how to obtain materials with desired properties remains a long-term challenge.Machine learning with its ability to solve complex tasks and perform robust data processing can reveal the relationship between performance and descriptive indicators,potentially accelerating the development process of energetic materials.In this background,impact sensitivity,detonation performances,and 28 physicochemical parameters for 222 energetic materials from density functional theory calculations and published literature were sorted out.Four machine learning algorithms were employed to predict various properties of energetic materials,including impact sensitivity,detonation velocity,detonation pressure,and Gurney energy.Analysis of Pearson coefficients and feature importance showed that the heat of explosion,oxygen balance,decomposition products,and HOMO energy levels have a strong correlation with the impact sensitivity of energetic materials.Oxygen balance,decomposition products,and density have a strong correlation with detonation performances.Utilizing impact sensitivity of 2,3,4-trinitrotoluene and the detonation performances of 2,4,6-trinitrobenzene-1,3,5-triamine as the benchmark,the analysis of feature importance rankings and statistical data revealed the optimal range of key features balancing impact sensitivity and detonation performances:oxygen balance values should be between-40%and-30%,density should range from 1.66 to 1.72 g/cm^(3),HOMO energy levels should be between-6.34 and-6.31 eV,and lipophilicity should be between-1.0 and 0.1,4.49 and 5.59.These findings not only offer important insights into the impact sensitivity and detonation performances of energetic materials,but also provide a theoretical guidance paradigm for the design and development of new energetic materials with optimal detonation performances and reduced sensitivity.
基金supported by the National Natural Science Foundation of China(No.52188102).
文摘Anomaly Detection (AD) has been extensively adopted in industrial settings to facilitate quality control of products. It is critical to industrial production, especially to areas such as aircraft manufacturing, which require strict part qualification rates. Although being more efficient and practical, few-shot AD has not been well explored. The existing AD methods only extract features in a single frequency while defects exist in multiple frequency domains. Moreover, current methods have not fully leveraged the few-shot support samples to extract input-related normal patterns. To address these issues, we propose an industrial few-shot AD method, Feature Extender for Anomaly Detection (FEAD), which extracts normal patterns in multiple frequency domains from few-shot samples under the guidance of the input sample. Firstly, to achieve better coverage of normal patterns in the input sample, we introduce a Sample-Conditioned Transformation Module (SCTM), which transforms support features under the guidance of the input sample to obtain extra normal patterns. Secondly, to effectively distinguish and localize anomaly patterns in multiple frequency domains, we devise an Adaptive Descriptor Construction Module (ADCM) to build and select pattern descriptors in a series of frequencies adaptively. Finally, an auxiliary task for SCTM is designed to ensure the diversity of transformations and include more normal patterns into support features. Extensive experiments on two widely used industrial AD datasets (MVTec-AD and VisA) demonstrate the effectiveness of the proposed FEAD.
基金supported by the National Natural Science Foundation of China(No.62241109)the Tianjin Science and Technology Commissioner Project(No.20YDTPJC01110)。
文摘An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.
