The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method f...The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.展开更多
Aiming at the problems of inaccuracy in detecting theαphase contour of TB6 titanium alloy.By combining computer vision technology with human vision mechanisms,the spatial characteristics of theαphase can be simulate...Aiming at the problems of inaccuracy in detecting theαphase contour of TB6 titanium alloy.By combining computer vision technology with human vision mechanisms,the spatial characteristics of theαphase can be simulated to obtain the contour accurately.Therefore,an algorithm forαphase contour detection of TB6 titanium alloy fused with multi-scale fretting features is proposed.Firstly,through the response of the classical receptive field model based on fretting and the suppression of new non-classical receptive field model based on fretting,the information maps of theαphase contour of the TB6 titanium alloy at different scales are obtained;then the information map of the smallest scale contour is used as a benchmark,the neighborhood is constructed to judge the deviation of other scale contour information,and the corresponding weight value is calculated;finally,Gaussian function is used to weight and fuse the deviation information,and the contour detection result of TB6 titanium alloyαphase is obtained.In the Visual Studio 2013 environment,484 metallographic images with different temperatures,strain rates,and magnifications were tested.The results show that the performance evaluation F value of the proposed algorithm is 0.915,which can effectively improve the accuracy ofαphase contour detection of TB6 titanium alloy.展开更多
With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods ...With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.展开更多
Improving the volumetric energy density of supercapacitors is essential for practical applications,which highly relies on the dense storage of ions in carbon-based electrodes.The functional units of carbon-based elect...Improving the volumetric energy density of supercapacitors is essential for practical applications,which highly relies on the dense storage of ions in carbon-based electrodes.The functional units of carbon-based electrode exhibit multi-scale structural characteristics including macroscopic electrode morphologies,mesoscopic microcrystals and pores,and microscopic defects and dopants in the carbon basal plane.Therefore,the ordered combination of multi-scale structures of carbon electrode is crucial for achieving dense energy storage and high volumetric performance by leveraging the functions of various scale structu re.Considering that previous reviews have focused more on the discussion of specific scale structu re of carbon electrodes,this review takes a multi-scale perspective in which recent progresses regarding the structureperformance relationship,underlying mechanism and directional design of carbon-based multi-scale structures including carbon morphology,pore structure,carbon basal plane micro-environment and electrode technology on dense energy storage and volumetric property of supercapacitors are systematically discussed.We analyzed in detail the effects of the morphology,pore,and micro-environment of carbon electrode materials on ion dense storage,summarized the specific effects of different scale structures on volumetric property and recent research progress,and proposed the mutual influence and trade-off relationship between various scale structures.In addition,the challenges and outlooks for improving the dense storage and volumetric performance of carbon-based supercapacitors are analyzed,which can provide feasible technical reference and guidance for the design and manufacture of dense carbon-based electrode materials.展开更多
Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportatio...Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks.展开更多
Globally,diabetic retinopathy(DR)is the primary cause of blindness,affecting millions of people worldwide.This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure p...Globally,diabetic retinopathy(DR)is the primary cause of blindness,affecting millions of people worldwide.This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure prompt diagnosis and effective treatment.Deep learning-based automated diagnosis for diabetic retinopathy can facilitate early detection and treatment.However,traditional deep learning models that focus on local views often learn feature representations that are less discriminative at the semantic level.On the other hand,models that focus on global semantic-level information might overlook critical,subtle local pathological features.To address this issue,we propose an adaptive multi-scale feature fusion network called(AMSFuse),which can adaptively combine multi-scale global and local features without compromising their individual representation.Specifically,our model incorporates global features for extracting high-level contextual information from retinal images.Concurrently,local features capture fine-grained details,such as microaneurysms,hemorrhages,and exudates,which are critical for DR diagnosis.These global and local features are adaptively fused using a fusion block,followed by an Integrated Attention Mechanism(IAM)that refines the fused features by emphasizing relevant regions,thereby enhancing classification accuracy for DR classification.Our model achieves 86.3%accuracy on the APTOS dataset and 96.6%RFMiD,both of which are comparable to state-of-the-art methods.展开更多
Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells an...Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size.Pathologists often refer to surrounding cells to identify abnormalities.To emulate this slide examination behavior,this study proposes a Multi-Scale Feature Fusion Network(MSFF-Net)for detecting cervical abnormal cells.MSFF-Net employs a Cross-Scale Pooling Model(CSPM)to effectively capture diverse features and contextual information,ranging from local details to the overall structure.Additionally,a Multi-Scale Fusion Attention(MSFA)module is introduced to mitigate the impact of cell size variations by adaptively fusing local and global information at different scales.To handle the complex environment of cervical cell images,such as cell adhesion and overlapping,the Inner-CIoU loss function is utilized to more precisely measure the overlap between bounding boxes,thereby improving detection accuracy in such scenarios.Experimental results on the Comparison detector dataset demonstrate that MSFF-Net achieves a mean average precision(mAP)of 63.2%,outperforming state-of-the-art methods while maintaining a relatively small number of parameters(26.8 M).This study highlights the effectiveness of multi-scale feature fusion in enhancing the detection of cervical abnormal cells,contributing to more accurate and efficient cervical cancer screening.展开更多
With the rapid growth of socialmedia,the spread of fake news has become a growing problem,misleading the public and causing significant harm.As social media content is often composed of both images and text,the use of...With the rapid growth of socialmedia,the spread of fake news has become a growing problem,misleading the public and causing significant harm.As social media content is often composed of both images and text,the use of multimodal approaches for fake news detection has gained significant attention.To solve the problems existing in previous multi-modal fake news detection algorithms,such as insufficient feature extraction and insufficient use of semantic relations between modes,this paper proposes the MFFFND-Co(Multimodal Feature Fusion Fake News Detection with Co-Attention Block)model.First,the model deeply explores the textual content,image content,and frequency domain features.Then,it employs a Co-Attention mechanism for cross-modal fusion.Additionally,a semantic consistency detectionmodule is designed to quantify semantic deviations,thereby enhancing the performance of fake news detection.Experimentally verified on two commonly used datasets,Twitter and Weibo,the model achieved F1 scores of 90.0% and 94.0%,respectively,significantly outperforming the pre-modified MFFFND(Multimodal Feature Fusion Fake News Detection with Attention Block)model and surpassing other baseline models.This improves the accuracy of detecting fake information in artificial intelligence detection and engineering software detection.展开更多
To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network mo...To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network model is proposed by fusing multi-scale feature information.Firstly,a multi-scale feature extraction module is designed to obtain multi-scale information on feature images by using different scales of convolution kernels.Meanwhile,the channel attention mechanism is used to increase the global information acquisition of the network.Secondly,the feature images processed by the multi-scale feature extraction module are fused with the deep feature images through short links to guide the full learning of the network,thus reducing the loss of texture details of the deep network feature images,and improving network generalization ability and recognition accuracy.Finally,the validity of the MSFResNet model is verified using public datasets and applied to wild mushroom identification.Experimental results show that compared with ResNeXt50 network model,the accuracy of the MSFResNet model is improved by 6.01%on the FGVC-Aircraft common dataset.It achieves 99.13%classification accuracy on the wild mushroom dataset,which is 0.47%higher than ResNeXt50.Furthermore,the experimental results of the thermal map show that the MSFResNet model significantly reduces the interference of background information,making the network focus on the location of the main body of wild mushroom,which can effectively improve the accuracy of wild mushroom identification.展开更多
Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE...Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively.展开更多
Advanced chemical engineering for simultaneous modulation of nanomaterial morphology, defects, interfaces, and structure to enhance electromagnetic and microwave absorption (MA) performance. However, accurately distin...Advanced chemical engineering for simultaneous modulation of nanomaterial morphology, defects, interfaces, and structure to enhance electromagnetic and microwave absorption (MA) performance. However, accurately distinguishing the MA contributions of different scale factors and tuning the optimal combined effects remains a formidable challenge. This study employs a synergistic approach combining template protection etching and vacuum annealing to construct a controlled system of micrometer-sized cavities and amorphous carbon matrices in metal-organic framework (MOF) derivatives. The results demonstrate that the spatial effects introduced by the hollow structure enhance dielectric loss but significantly weaken impedance matching. By increasing the proportion of amorphous carbon, the balance between electromagnetic loss and impedance matching can be effectively maintained. Importantly, in a suitable graphitization environment, the presence of oxygen vacancies in amorphous carbon can induce significant polarization to compensate for the reduced conductivity loss due to the absence of sp2 carbon. Through the synergistic effects of morphology and composition, the samples exhibit a broader absorption bandwidth (6.28 GHz) and stronger reflection loss (−61.64 dB) compared to the original MOF. In conclusion, this study aims to elucidate the multiscale impacts of macroscopic micro-nano structure and microscopic defect engineering, providing valuable insights for future research in this field.展开更多
Fushun oil shale(FOS) was subjected to thermal dissolution(TD) under different conditions. The results show that the optimal solvent, temperature, time, and ratio of solvent to FOS are ethanol, 300 °C, 2 h, and 5...Fushun oil shale(FOS) was subjected to thermal dissolution(TD) under different conditions. The results show that the optimal solvent, temperature, time, and ratio of solvent to FOS are ethanol, 300 °C, 2 h, and 5 ml·g^(-1),respectively and the corresponding yield of the soluble portion(SP) is 32.2%(daf), which is much higher than the oil content of FOS(ca. 6%), suggesting that TD in ethanol is an excellent way to extract organics from FOS.According to 3 direct analyses, aliphatic moieties in FOS are the most abundant followed by C\\O-containing moieties and each cluster in FOS has 3 conjugated aromatic rings on average with fewer substituents. According to the analysis with a gas chromatograph/mass spectrometer, alkanes are predominant in all the SPs. A number of alkenes were identified in the SPs from the TD, while none of the alkenes were detected in acetone-SP obtained at room temperature, implying that the TD can destroy the π-π and intertwining interactions between alkenes and macromolecular structures in FOS. Moreover, a small amount of alkyl-substituted phenols and alkoxysubstituted phenols were detected in ethanol-SP from the TD, which could be the products from ethanolyzing the macromolecular moiety of FOS.展开更多
Fortunella margarita（Lour.） Swingle, commonly known as kumquat, is the smallest citrus fruit. It thrives in southeastern China and is widely cultivated and consumed in the world due to its multiple health benefits. ...Fortunella margarita（Lour.） Swingle, commonly known as kumquat, is the smallest citrus fruit. It thrives in southeastern China and is widely cultivated and consumed in the world due to its multiple health benefits. It has been used as an important herbal medicine in traditional Chinese medicine and also as one of the most popular fruits. There are various kinds of bioactive compounds in F. margarita, such as polysaccharides, limonoids, essential oils, flavonoids, phenolic acids, vitamins, dietary fiber, etc. In addition, many studies have reported that these bioactive compounds can be used as antioxidant, antimicrobial, hypolipidemic, drosophila lure components in functional foods, pharmaceuticals and daily chemical products due to their biological activities. This review focuses on the structural features and biological activities of polysaccharides, limonoids, essential oils and flavonoids and other bioactive substances from F. margarita and their potential applications in food, daily chemical and pharmaceutical industries.展开更多
Working conditions of rolling bearings of wind turbine generators are complicated, and their vibration signals often show non-linear and non-stationary characteristics. In order to improve the efficiency of feature ex...Working conditions of rolling bearings of wind turbine generators are complicated, and their vibration signals often show non-linear and non-stationary characteristics. In order to improve the efficiency of feature extraction of wind turbine rolling bearings and to strengthen the feature information, a new structural element and an adaptive algorithm based on the peak energy are proposed,which are combined with spectral correlation analysis to form a fault diagnosis algorithm for wind turbine rolling bearings. The proposed method firstly addresses the problem of impulsive signal omissions that are prone to occur in the process of fault feature extraction of traditional structural elements and proposes a "W" structural element to capture more characteristic information. Then, the proposed method selects the scale of multi-scale mathematical morphology, aiming at the problem of multi-scale mathematical morphology scale selection and structural element expansion law. An adaptive algorithm based on peak energy is proposed to carry out morphological scale selection and structural element expansion by improving the computing efficiency and enhancing the feature extraction effect.Finally, the proposed method performs spectral correlation analysis in the frequency domain for an unknown signal of the extracted feature and identifies the fault based on the correlation coefficient. The method is verified by numerical examples using experimental rig bearing data and actual wind field acquisition data and compared with traditional triangular and flat structural elements. The experimental results show that the new structural elements can more effectively extract the pulses in the signal and reduce noise interference,and the fault-diagnosis algorithm can accurately identify the fault category and improve the reliability of the results.展开更多
In this paper, a series of Sb-doped and Bi-doped Cu_(2)Sn_(1-x)M_(x)Se_(3) samples(M = Sb, Bi) are prepared by vacuum melting combined with the spark plasma sintering process. The effects of different atomic doping am...In this paper, a series of Sb-doped and Bi-doped Cu_(2)Sn_(1-x)M_(x)Se_(3) samples(M = Sb, Bi) are prepared by vacuum melting combined with the spark plasma sintering process. The effects of different atomic doping amounts on their properties are discussed. Structural studies indicate that all obtained samples comprise a single Cu_(2)SnSe_(3) phase. Sb and Bi atoms are experimentally demonstrated to be efficient cation dopants for increasing the transport performance. Compared with that doping on the cation site,Bi doping is much more efficient in increasing the electron concentration of the Cu_(2)SnSe_(3) system. Ultimately, a high figure of merit of 0.36 is achieved in the Cu_(2)Sn_(0.94)Sb_(0.06) Se_(3) sample at 773 K due to the enhanced power factor and lowered lattice thermal conductivity,which are 1.73 times higher than those of the pure sample.Our results provide an efficient approach to enhance thermoelectric performance via other doping atoms, which could also be applied to copper-based chalcogenide materials.展开更多
The catalyst layers(CLs) electrode is the key component of the membrane electrode assembly(MEA) in proton exchange membrane fuel cells(PEMFCs). Conventional electrodes for PEMFCs are composed of carbon-supported, iono...The catalyst layers(CLs) electrode is the key component of the membrane electrode assembly(MEA) in proton exchange membrane fuel cells(PEMFCs). Conventional electrodes for PEMFCs are composed of carbon-supported, ionomer, and Pt nanoparticles, all immersed together and sprayed with a micron-level thickness of CLs. They have a performance trade-off where increasing the Pt loading leads to higher performance of abundant triple-phase boundary areas but increases the electrode cost. Major challenges must be overcome before realizing its wide commercialization. Literature research revealed that it is impossible to achieve performance and durability targets with only high-performance catalysts, so the controllable design of CLs architecture in MEAs for PEMFCs must now be the top priority to meet industry goals. From this perspective, a 3D ordered electrode circumvents this issue with a support-free architecture and ultrathin thickness while reducing noble metal Pt loadings. Herein, we discuss the motivation in-depth and summarize the necessary CLs structural features for designing ultralow Pt loading electrodes. Critical issues that remain in progress for 3D ordered CLs must be studied and characterized. Furthermore, approaches for 3D ordered CLs architecture electrode development, involving material design, structure optimization, preparation technology, and characterization techniques, are summarized and are expected to be next-generation CLs for PEMFCs. Finally, the review concludes with perspectives on possible research directions of CL architecture to address the significant challenges in the future.展开更多
In this study, we analyzed the gravity and, magnetic characteristics, and the occurrence of a fault zone and discussed the relationships between the two locations. The results reveal that the subsurface structures str...In this study, we analyzed the gravity and, magnetic characteristics, and the occurrence of a fault zone and discussed the relationships between the two locations. The results reveal that the subsurface structures strikes are different compared with those in the research region. In other words, the geophysical advantageous directions from the gravity and magnetic anomalies are not the same as those caused by the surface structures. The local horizontal gradient results from the gravity and magnetic anomalies show that the majority of earthquakes occur along an intense fault zone, which is a zone of abrupt gravity and negative magnetic change, where the shapes match very well. From the distribution of earthquakes in this area, we find that it has experienced more than 11 earthquake events with magnitude larger than Ms7.0. In addition, water development sites such as Jinshajiang, Lancangjiang, and the Red River and Pearl River watersheds have been hit ten times by earthquakes of this magnitude. It is observed that strong earthquakes occur frequently in the Holocene active fault zone.展开更多
In order to explore the structural features of neural networks and the ap-proaches to local interconnection,the geometrical structural information is introduced tothe Hopfield neural network model which is applied to ...In order to explore the structural features of neural networks and the ap-proaches to local interconnection,the geometrical structural information is introduced tothe Hopfield neural network model which is applied to associative memory.The dynamicsof the recalling is studied theoretically and cxpcrimcntally.The rcsults show that the geo-metrical structural information is helpless to the associative memory of monolayeredneural networks,furthermore,it makes the error probability increased.If the geometricalstructural information of the stored patterns is necessary to be introduced,somc new ap-proaches have to be explored.展开更多
Spin-polarized first-principle was performed to study the structural stability and the electronic states of Cr doped ZnS with the Cr component of 50% in zincblende （ZB）, wurtzite （W） and rocksalt （RS） structures...Spin-polarized first-principle was performed to study the structural stability and the electronic states of Cr doped ZnS with the Cr component of 50% in zincblende （ZB）, wurtzite （W） and rocksalt （RS） structures under pressure. The results show that the zincblende and wurtzite structures become unstable under low pressures of about 4.68 and 9.61 GPa, respectively, but the rocksalt structure can be maintained up to an extremely high pressure of about 32.92 GPa. Both zincblende and wurtzite Zno.sCro.5S display half metallic features under pressure, while rocksalt Zno.sCro.sS exhibits metallic feature. The half metallic features can be ascribed to the stronger interactions between S-3p and Cr-3d states and the metallic feature is due to the higher crystal symmetry of rocksalt Zn0.5Cr0.5S. These results can provide helpful guidance for Cr doped ZnS to be used in spintronic devices.展开更多
The 1°×1° distribution map of crustmantle structural ratio R for the lithosphere along the Longitudinal Seismic Belt of China has been compiled using computer based on the results of geophysical prospec...The 1°×1° distribution map of crustmantle structural ratio R for the lithosphere along the Longitudinal Seismic Belt of China has been compiled using computer based on the results of geophysical prospecting by previous researchers, and the latest results by the present authors. Based on this map, an insight into the structural features of the crustmantle assemblage along the Longitudinal Seismic Belt has been gained, while their relation to seismic activity and the distributions of geothermal flux and intracrustal high conductivitylow velocity layers, as well as their tectonic effect to seismicity have been discussed.展开更多
基金Supported by the Henan Province Key Research and Development Project(231111211300)the Central Government of Henan Province Guides Local Science and Technology Development Funds(Z20231811005)+2 种基金Henan Province Key Research and Development Project(231111110100)Henan Provincial Outstanding Foreign Scientist Studio(GZS2024006)Henan Provincial Joint Fund for Scientific and Technological Research and Development Plan(Application and Overcoming Technical Barriers)(242103810028)。
文摘The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.
基金Supported by Hebei Provincial Key Laboratory for Software Engineering(Grant No.22567637H)the"Rail Vehicle Application Engineering"National International Science and Technology Cooperation Base Open Project Fund(Grant No.BMRV21KF09).
文摘Aiming at the problems of inaccuracy in detecting theαphase contour of TB6 titanium alloy.By combining computer vision technology with human vision mechanisms,the spatial characteristics of theαphase can be simulated to obtain the contour accurately.Therefore,an algorithm forαphase contour detection of TB6 titanium alloy fused with multi-scale fretting features is proposed.Firstly,through the response of the classical receptive field model based on fretting and the suppression of new non-classical receptive field model based on fretting,the information maps of theαphase contour of the TB6 titanium alloy at different scales are obtained;then the information map of the smallest scale contour is used as a benchmark,the neighborhood is constructed to judge the deviation of other scale contour information,and the corresponding weight value is calculated;finally,Gaussian function is used to weight and fuse the deviation information,and the contour detection result of TB6 titanium alloyαphase is obtained.In the Visual Studio 2013 environment,484 metallographic images with different temperatures,strain rates,and magnifications were tested.The results show that the performance evaluation F value of the proposed algorithm is 0.915,which can effectively improve the accuracy ofαphase contour detection of TB6 titanium alloy.
文摘With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.
基金funded by the Joint Fund for Regional Innovation and Development of National Natural Science Foundation of China(U21A20143)the National Science Fund for Excellent Young Scholars(52322607)the Excellent Youth Foundation of Heilongjiang Scientific Committee(YQ2022E028)。
文摘Improving the volumetric energy density of supercapacitors is essential for practical applications,which highly relies on the dense storage of ions in carbon-based electrodes.The functional units of carbon-based electrode exhibit multi-scale structural characteristics including macroscopic electrode morphologies,mesoscopic microcrystals and pores,and microscopic defects and dopants in the carbon basal plane.Therefore,the ordered combination of multi-scale structures of carbon electrode is crucial for achieving dense energy storage and high volumetric performance by leveraging the functions of various scale structu re.Considering that previous reviews have focused more on the discussion of specific scale structu re of carbon electrodes,this review takes a multi-scale perspective in which recent progresses regarding the structureperformance relationship,underlying mechanism and directional design of carbon-based multi-scale structures including carbon morphology,pore structure,carbon basal plane micro-environment and electrode technology on dense energy storage and volumetric property of supercapacitors are systematically discussed.We analyzed in detail the effects of the morphology,pore,and micro-environment of carbon electrode materials on ion dense storage,summarized the specific effects of different scale structures on volumetric property and recent research progress,and proposed the mutual influence and trade-off relationship between various scale structures.In addition,the challenges and outlooks for improving the dense storage and volumetric performance of carbon-based supercapacitors are analyzed,which can provide feasible technical reference and guidance for the design and manufacture of dense carbon-based electrode materials.
基金funded by the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia through research group No.(RG-NBU-2022-1234).
文摘Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks.
基金supported by the National Natural Science Foundation of China(No.62376287)the International Science and Technology Innovation Joint Base of Machine Vision and Medical Image Processing in Hunan Province(2021CB1013)the Natural Science Foundation of Hunan Province(Nos.2022JJ30762,2023JJ70016).
文摘Globally,diabetic retinopathy(DR)is the primary cause of blindness,affecting millions of people worldwide.This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure prompt diagnosis and effective treatment.Deep learning-based automated diagnosis for diabetic retinopathy can facilitate early detection and treatment.However,traditional deep learning models that focus on local views often learn feature representations that are less discriminative at the semantic level.On the other hand,models that focus on global semantic-level information might overlook critical,subtle local pathological features.To address this issue,we propose an adaptive multi-scale feature fusion network called(AMSFuse),which can adaptively combine multi-scale global and local features without compromising their individual representation.Specifically,our model incorporates global features for extracting high-level contextual information from retinal images.Concurrently,local features capture fine-grained details,such as microaneurysms,hemorrhages,and exudates,which are critical for DR diagnosis.These global and local features are adaptively fused using a fusion block,followed by an Integrated Attention Mechanism(IAM)that refines the fused features by emphasizing relevant regions,thereby enhancing classification accuracy for DR classification.Our model achieves 86.3%accuracy on the APTOS dataset and 96.6%RFMiD,both of which are comparable to state-of-the-art methods.
基金funded by the China Chongqing Municipal Science and Technology Bureau,grant numbers 2024TIAD-CYKJCXX0121,2024NSCQ-LZX0135Chongqing Municipal Commission of Housing and Urban-Rural Development,grant number CKZ2024-87+3 种基金the Chongqing University of Technology graduate education high-quality development project,grant number gzlsz202401the Chongqing University of Technology-Chongqing LINGLUE Technology Co.,Ltd.,Electronic Information(Artificial Intelligence)graduate joint training basethe Postgraduate Education and Teaching Reform Research Project in Chongqing,grant number yjg213116the Chongqing University of Technology-CISDI Chongqing Information Technology Co.,Ltd.,Computer Technology graduate joint training base.
文摘Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size.Pathologists often refer to surrounding cells to identify abnormalities.To emulate this slide examination behavior,this study proposes a Multi-Scale Feature Fusion Network(MSFF-Net)for detecting cervical abnormal cells.MSFF-Net employs a Cross-Scale Pooling Model(CSPM)to effectively capture diverse features and contextual information,ranging from local details to the overall structure.Additionally,a Multi-Scale Fusion Attention(MSFA)module is introduced to mitigate the impact of cell size variations by adaptively fusing local and global information at different scales.To handle the complex environment of cervical cell images,such as cell adhesion and overlapping,the Inner-CIoU loss function is utilized to more precisely measure the overlap between bounding boxes,thereby improving detection accuracy in such scenarios.Experimental results on the Comparison detector dataset demonstrate that MSFF-Net achieves a mean average precision(mAP)of 63.2%,outperforming state-of-the-art methods while maintaining a relatively small number of parameters(26.8 M).This study highlights the effectiveness of multi-scale feature fusion in enhancing the detection of cervical abnormal cells,contributing to more accurate and efficient cervical cancer screening.
基金supported by Communication University of China(HG23035)partly supported by the Fundamental Research Funds for the Central Universities(CUC230A013).
文摘With the rapid growth of socialmedia,the spread of fake news has become a growing problem,misleading the public and causing significant harm.As social media content is often composed of both images and text,the use of multimodal approaches for fake news detection has gained significant attention.To solve the problems existing in previous multi-modal fake news detection algorithms,such as insufficient feature extraction and insufficient use of semantic relations between modes,this paper proposes the MFFFND-Co(Multimodal Feature Fusion Fake News Detection with Co-Attention Block)model.First,the model deeply explores the textual content,image content,and frequency domain features.Then,it employs a Co-Attention mechanism for cross-modal fusion.Additionally,a semantic consistency detectionmodule is designed to quantify semantic deviations,thereby enhancing the performance of fake news detection.Experimentally verified on two commonly used datasets,Twitter and Weibo,the model achieved F1 scores of 90.0% and 94.0%,respectively,significantly outperforming the pre-modified MFFFND(Multimodal Feature Fusion Fake News Detection with Attention Block)model and surpassing other baseline models.This improves the accuracy of detecting fake information in artificial intelligence detection and engineering software detection.
基金supported by National Natural Science Foundation of China(No.61862037)Lanzhou Jiaotong University Tianyou Innovation Team Project(No.TY202002)。
文摘To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network model is proposed by fusing multi-scale feature information.Firstly,a multi-scale feature extraction module is designed to obtain multi-scale information on feature images by using different scales of convolution kernels.Meanwhile,the channel attention mechanism is used to increase the global information acquisition of the network.Secondly,the feature images processed by the multi-scale feature extraction module are fused with the deep feature images through short links to guide the full learning of the network,thus reducing the loss of texture details of the deep network feature images,and improving network generalization ability and recognition accuracy.Finally,the validity of the MSFResNet model is verified using public datasets and applied to wild mushroom identification.Experimental results show that compared with ResNeXt50 network model,the accuracy of the MSFResNet model is improved by 6.01%on the FGVC-Aircraft common dataset.It achieves 99.13%classification accuracy on the wild mushroom dataset,which is 0.47%higher than ResNeXt50.Furthermore,the experimental results of the thermal map show that the MSFResNet model significantly reduces the interference of background information,making the network focus on the location of the main body of wild mushroom,which can effectively improve the accuracy of wild mushroom identification.
基金supported by the National Key Research and Development Program of China(2023YFB3307800)National Natural Science Foundation of China(62394343,62373155)+2 种基金Major Science and Technology Project of Xinjiang(No.2022A01006-4)State Key Laboratory of Industrial Control Technology,China(Grant No.ICT2024A26)Fundamental Research Funds for the Central Universities.
文摘Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively.
基金supported by the National Natural Science Foundation of China(52172091,52172295)Defense Industrial Technology Development Program(JCKY2023605C002)+4 种基金Frontier Leading Technology Basic Research Major Project of Jiangsu Province(SBK2023050110)the National Key Laboratory on Electromagnetic Environmental Effects and Electro-optical Engineering(NO.61422062301)the Opening Project of Science and Technology on Reliability Physics and Application Technology of Electronic Component Laboratory(ZHD202305)the Opening Project of Jiangsu Key Laboratory of Advanced Structural Materials and Application Technology(ASMA202303)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX23_0371).
文摘Advanced chemical engineering for simultaneous modulation of nanomaterial morphology, defects, interfaces, and structure to enhance electromagnetic and microwave absorption (MA) performance. However, accurately distinguishing the MA contributions of different scale factors and tuning the optimal combined effects remains a formidable challenge. This study employs a synergistic approach combining template protection etching and vacuum annealing to construct a controlled system of micrometer-sized cavities and amorphous carbon matrices in metal-organic framework (MOF) derivatives. The results demonstrate that the spatial effects introduced by the hollow structure enhance dielectric loss but significantly weaken impedance matching. By increasing the proportion of amorphous carbon, the balance between electromagnetic loss and impedance matching can be effectively maintained. Importantly, in a suitable graphitization environment, the presence of oxygen vacancies in amorphous carbon can induce significant polarization to compensate for the reduced conductivity loss due to the absence of sp2 carbon. Through the synergistic effects of morphology and composition, the samples exhibit a broader absorption bandwidth (6.28 GHz) and stronger reflection loss (−61.64 dB) compared to the original MOF. In conclusion, this study aims to elucidate the multiscale impacts of macroscopic micro-nano structure and microscopic defect engineering, providing valuable insights for future research in this field.
基金Supported by the Fundamental Research Funds for the Central Universities(2017BSCXB27)the Research and the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX17_1507)
文摘Fushun oil shale(FOS) was subjected to thermal dissolution(TD) under different conditions. The results show that the optimal solvent, temperature, time, and ratio of solvent to FOS are ethanol, 300 °C, 2 h, and 5 ml·g^(-1),respectively and the corresponding yield of the soluble portion(SP) is 32.2%(daf), which is much higher than the oil content of FOS(ca. 6%), suggesting that TD in ethanol is an excellent way to extract organics from FOS.According to 3 direct analyses, aliphatic moieties in FOS are the most abundant followed by C\\O-containing moieties and each cluster in FOS has 3 conjugated aromatic rings on average with fewer substituents. According to the analysis with a gas chromatograph/mass spectrometer, alkanes are predominant in all the SPs. A number of alkenes were identified in the SPs from the TD, while none of the alkenes were detected in acetone-SP obtained at room temperature, implying that the TD can destroy the π-π and intertwining interactions between alkenes and macromolecular structures in FOS. Moreover, a small amount of alkyl-substituted phenols and alkoxysubstituted phenols were detected in ethanol-SP from the TD, which could be the products from ethanolyzing the macromolecular moiety of FOS.
基金Supported by the Natural Science Foundation of Fujian Province(2016J05068)High Level University Construction Projects of Fujian Agriculture and Forestry University(612014042)+2 种基金Science and Technology Development Foundation Project of Fujian Agriculture and Forestry University(KF2015101)Leading Talents Support Program of Science and Technology Innovation in Fujian Province(KRC16002A)Excellent Talents Support Program of Colleges and Universities in Fujian Province(JA14094)
文摘Fortunella margarita（Lour.） Swingle, commonly known as kumquat, is the smallest citrus fruit. It thrives in southeastern China and is widely cultivated and consumed in the world due to its multiple health benefits. It has been used as an important herbal medicine in traditional Chinese medicine and also as one of the most popular fruits. There are various kinds of bioactive compounds in F. margarita, such as polysaccharides, limonoids, essential oils, flavonoids, phenolic acids, vitamins, dietary fiber, etc. In addition, many studies have reported that these bioactive compounds can be used as antioxidant, antimicrobial, hypolipidemic, drosophila lure components in functional foods, pharmaceuticals and daily chemical products due to their biological activities. This review focuses on the structural features and biological activities of polysaccharides, limonoids, essential oils and flavonoids and other bioactive substances from F. margarita and their potential applications in food, daily chemical and pharmaceutical industries.
基金supported by National Natural Science Foundation of China (No. 61763037)Inner Mongolia Autonomous Region Natural Science Foundation of China(No. 2019LH06007)Science and Technology Plan Project of Inner Mongolia (No. 2019,2020GG028)。
文摘Working conditions of rolling bearings of wind turbine generators are complicated, and their vibration signals often show non-linear and non-stationary characteristics. In order to improve the efficiency of feature extraction of wind turbine rolling bearings and to strengthen the feature information, a new structural element and an adaptive algorithm based on the peak energy are proposed,which are combined with spectral correlation analysis to form a fault diagnosis algorithm for wind turbine rolling bearings. The proposed method firstly addresses the problem of impulsive signal omissions that are prone to occur in the process of fault feature extraction of traditional structural elements and proposes a "W" structural element to capture more characteristic information. Then, the proposed method selects the scale of multi-scale mathematical morphology, aiming at the problem of multi-scale mathematical morphology scale selection and structural element expansion law. An adaptive algorithm based on peak energy is proposed to carry out morphological scale selection and structural element expansion by improving the computing efficiency and enhancing the feature extraction effect.Finally, the proposed method performs spectral correlation analysis in the frequency domain for an unknown signal of the extracted feature and identifies the fault based on the correlation coefficient. The method is verified by numerical examples using experimental rig bearing data and actual wind field acquisition data and compared with traditional triangular and flat structural elements. The experimental results show that the new structural elements can more effectively extract the pulses in the signal and reduce noise interference,and the fault-diagnosis algorithm can accurately identify the fault category and improve the reliability of the results.
基金financially supported by the National Key Research and Development Program of China(No.2019YFA0210003)the National Natural Science Foundation of China(Nos.11775163 and 11875208)。
文摘In this paper, a series of Sb-doped and Bi-doped Cu_(2)Sn_(1-x)M_(x)Se_(3) samples(M = Sb, Bi) are prepared by vacuum melting combined with the spark plasma sintering process. The effects of different atomic doping amounts on their properties are discussed. Structural studies indicate that all obtained samples comprise a single Cu_(2)SnSe_(3) phase. Sb and Bi atoms are experimentally demonstrated to be efficient cation dopants for increasing the transport performance. Compared with that doping on the cation site,Bi doping is much more efficient in increasing the electron concentration of the Cu_(2)SnSe_(3) system. Ultimately, a high figure of merit of 0.36 is achieved in the Cu_(2)Sn_(0.94)Sb_(0.06) Se_(3) sample at 773 K due to the enhanced power factor and lowered lattice thermal conductivity,which are 1.73 times higher than those of the pure sample.Our results provide an efficient approach to enhance thermoelectric performance via other doping atoms, which could also be applied to copper-based chalcogenide materials.
基金funded by the Natural Science Foundation of Shandong Province, China (ZR2023MB049)the China Postdoctoral Science Foundation (2020M670483)the Science Foundation of Weifang University (2023BS11)。
文摘The catalyst layers(CLs) electrode is the key component of the membrane electrode assembly(MEA) in proton exchange membrane fuel cells(PEMFCs). Conventional electrodes for PEMFCs are composed of carbon-supported, ionomer, and Pt nanoparticles, all immersed together and sprayed with a micron-level thickness of CLs. They have a performance trade-off where increasing the Pt loading leads to higher performance of abundant triple-phase boundary areas but increases the electrode cost. Major challenges must be overcome before realizing its wide commercialization. Literature research revealed that it is impossible to achieve performance and durability targets with only high-performance catalysts, so the controllable design of CLs architecture in MEAs for PEMFCs must now be the top priority to meet industry goals. From this perspective, a 3D ordered electrode circumvents this issue with a support-free architecture and ultrathin thickness while reducing noble metal Pt loadings. Herein, we discuss the motivation in-depth and summarize the necessary CLs structural features for designing ultralow Pt loading electrodes. Critical issues that remain in progress for 3D ordered CLs must be studied and characterized. Furthermore, approaches for 3D ordered CLs architecture electrode development, involving material design, structure optimization, preparation technology, and characterization techniques, are summarized and are expected to be next-generation CLs for PEMFCs. Finally, the review concludes with perspectives on possible research directions of CL architecture to address the significant challenges in the future.
基金supported by the Chinese Earthquake Administration,Institute of Seismology Foundation(IS201326126)Chinese earthquake scientific array exploration northern section of North South Seismic Belt gravity profile Foundation(201308011)
文摘In this study, we analyzed the gravity and, magnetic characteristics, and the occurrence of a fault zone and discussed the relationships between the two locations. The results reveal that the subsurface structures strikes are different compared with those in the research region. In other words, the geophysical advantageous directions from the gravity and magnetic anomalies are not the same as those caused by the surface structures. The local horizontal gradient results from the gravity and magnetic anomalies show that the majority of earthquakes occur along an intense fault zone, which is a zone of abrupt gravity and negative magnetic change, where the shapes match very well. From the distribution of earthquakes in this area, we find that it has experienced more than 11 earthquake events with magnitude larger than Ms7.0. In addition, water development sites such as Jinshajiang, Lancangjiang, and the Red River and Pearl River watersheds have been hit ten times by earthquakes of this magnitude. It is observed that strong earthquakes occur frequently in the Holocene active fault zone.
文摘In order to explore the structural features of neural networks and the ap-proaches to local interconnection,the geometrical structural information is introduced tothe Hopfield neural network model which is applied to associative memory.The dynamicsof the recalling is studied theoretically and cxpcrimcntally.The rcsults show that the geo-metrical structural information is helpless to the associative memory of monolayeredneural networks,furthermore,it makes the error probability increased.If the geometricalstructural information of the stored patterns is necessary to be introduced,somc new ap-proaches have to be explored.
基金the financial support of the project from the National Natural Science Foundation of China (No. 11104175)the National Basic Research Program of China (No. 2011CB610406)+1 种基金the "111 Project" of China (No. B08040)the Research Fund of the State Key Laboratory of Solidification Processing, China
文摘Spin-polarized first-principle was performed to study the structural stability and the electronic states of Cr doped ZnS with the Cr component of 50% in zincblende （ZB）, wurtzite （W） and rocksalt （RS） structures under pressure. The results show that the zincblende and wurtzite structures become unstable under low pressures of about 4.68 and 9.61 GPa, respectively, but the rocksalt structure can be maintained up to an extremely high pressure of about 32.92 GPa. Both zincblende and wurtzite Zno.sCro.5S display half metallic features under pressure, while rocksalt Zno.sCro.sS exhibits metallic feature. The half metallic features can be ascribed to the stronger interactions between S-3p and Cr-3d states and the metallic feature is due to the higher crystal symmetry of rocksalt Zn0.5Cr0.5S. These results can provide helpful guidance for Cr doped ZnS to be used in spintronic devices.
文摘The 1°×1° distribution map of crustmantle structural ratio R for the lithosphere along the Longitudinal Seismic Belt of China has been compiled using computer based on the results of geophysical prospecting by previous researchers, and the latest results by the present authors. Based on this map, an insight into the structural features of the crustmantle assemblage along the Longitudinal Seismic Belt has been gained, while their relation to seismic activity and the distributions of geothermal flux and intracrustal high conductivitylow velocity layers, as well as their tectonic effect to seismicity have been discussed.
