An infrared target intrusion detection method based on feature fusion and enhancement

Infrared target intrusion detection has significant applications in the fields of military defence and intelligent warning.In view of the characteristics of intrusion targets as well as inspection difficulties,an infrared target intrusion detection algorithm based on feature fusion and enhancement was proposed.This algorithm combines static target mode analysis and dynamic multi-frame correlation detection to extract infrared target features at different levels.Among them,LBP texture analysis can be used to effectively identify the posterior feature patterns which have been contained in the target library,while motion frame difference method can detect the moving regions of the image,improve the integrity of target regions such as camouflage,sheltering and deformation.In order to integrate the advantages of the two methods,the enhanced convolutional neural network was designed and the feature images obtained by the two methods were fused and enhanced.The enhancement module of the network strengthened and screened the targets,and realized the background suppression of infrared images.Based on the experiments,the effect of the proposed method and the comparison method on the background suppression and detection performance was evaluated,and the results showed that the SCRG and BSF values of the method in this paper had a better performance in multiple data sets,and it’s detection performance was far better than the comparison algorithm.The experiment results indicated that,compared with traditional infrared target detection methods,the proposed method could detect the infrared invasion target more accurately,and suppress the background noise more effectively.

Heterostructured ZnFe_2O_4/Fe_2TiO_5/TiO_2 Composite Nanotube Arrays with an Improved Photocatalysis Degradation Efficiency Under Simulated Sunlight Irradiation

To improve the visible light absorption and photocatalytic activity of titanium dioxide nanotube arrays(TONTAs), ZnFe_2O_4(ZFO) nanocrystals were perfused into pristine TONTA pipelines using a novel bias voltageassisted perfusion method. ZFO nanocrystals were well anchored on the inner walls of the pristine TONTAs when the ZFO suspensions(0.025 mg m L^(-1)) were kept under a60 V bias voltage for 1 h. After annealing at 750 °C for2 h, the heterostructured ZFO/Fe_2 TiO_5(FTO)/TiO_2 composite nanotube arrays were successfully obtained. Furthermore, Fe^(3+)was reduced to Fe^(2+)when solid solution reactions occurred at the interface of ZFO and the pristine TONTAs. Introducing ZFO significantly enhanced thevisible light absorption of the ZFO/FTO/TONTAs relative to that of the annealed TONTAs. The coexistence of type I and staggered type II band alignment in the ZFO/FTO/TONTAs facilitated the separation of photogenerated electrons and holes, thereby improving the efficiency of the ZFO/FTO/TONTAs for photocatalytic degradation of methylene blue when irradiated with simulated sunlight.

Biomimetic multi-channel nerve conduits with micro/nanostructures for rapid nerve repair

Peripheral nervous system(PNS)injuries often lead to significant sensory and motor impairments.Traditional artificial nerve conduits,lacking anisotropic structures,have been associated with prolonged repair time and failures in nerve regeneration.This study aimed to address these challenges by developing a novel approach for rapid repair of peripheral nerve injuries(PNI).A 3D oriented fibers scaffold featuring distinct radial(RFs)and longitudinal(LFs)fibers orientations was engineered using coaxial electrospinning and gas directional foaming techniques.This scaffold was then integrated with a shape memory conduit to form a directional multi-channel nerve conduit with micro/nanostructures.The results revealed that the grooved surface of the fibers significantly improved cellular directional guidance,effectively facilitating the migration of SCs from the periphery towards the center and from the base to the apex of the scaffold.In a rat model with a 10 mm nerve defect,the ND-PLATMC/LF ND-PCL scaffold significantly enhanced nerve regeneration and motor function recovery within 4 weeks.These results suggest the potential of this innovative scaffold for efficient repair of the nerve injuries.

Protonated-chitosan sponge with procoagulation activity for hemostasis in coagulopathy

Hemostatic materials are essential for managing acute bleeding in medical settings.Chitosan(CS)shows promise in hemostasis but its underlying mechanism remains incompletely understood.We unexpectedly discovered that certain protonated-chitosan(PCS)rapidly assembled plasma proteins to form protein membrane(PM)upon contact with platelet-poor plasma(PPP).We hypothesized that the novel observation was intricately related to the procoagulant effect of chitosan.Herein,the study aimed to elucidate the conditions necessary and mechanism for PM formation,identify the proteins within the PM and PCS's procoagulant action at the molecule levels.We confirmed that the amount of–NH3+groups(>4.9 mmol/g)on PCS molecules played a crucial role in promoting coagulation.The–NH3+group interacted with blood's multiple active components to exert hemostatic effects:assembling plasma proteins including coagulation factors such as FII,FV,FX,activating blood cells and promoting the secretion of coagulation-related substances(FV,ADP,etc)by platelets.Notably,the hemostatic mechanism can be extended to protonated-chitosan derivatives like quaternized,alkylated,and catechol-chitosan.In the blood clotting index(BCI)experiment,compared to other groups,PCS95 achieved the lowest BCI value(∼6%)within 30 s.Protonated-chitosan exhibited excellent biocompatibility and antibacterial properties,with PCS95 demonstrating inhibition effectiveness of over 95%against Escherichia coli(E.coil)and Staphylococcus aureus(S.aureus).Moreover,PCS performed enhanced hemostatic effectiveness over chitosan-based commercially agents(Celox™and ChitoGauze®XR)in diverse bleeding models.In particular,PCS95 reduced bleeding time by 70%in rabbit models of coagulopathy.Overall,this study investigated the coagulation mechanism of materials at the molecular level,paving the way for innovative approaches in designing new hemostatic materials.

Mineral Potential Mapping for Tungsten Polymetallic Deposits in the Nanling Metallogenic Belt, South China

The Nanling belt in South China has considerable resources of tungsten polymetallic commodities and is one of the most important metallogenic belts in the world. Data-driven weights-of-evidence （WofE） and fuzzy logic models are used to evaluate the tungsten polymetallic potential of the Nanling belt. Initially, seven ore-controlling factors derived from multi-source geospatial datasets （e.g., geological, geochemical, and geophysical） are used for data integration in the two models. Two mineral potential maps are generated that efficiently predicate the locations of the deposits. The WofE map predicate 81% of the deposits within 13.6% of the study area, whereas the fuzzy logic map predicate 81.5% of the deposits within 13% of the area. The predictive maps are syntheses of spatial association rules, which provide better understanding of those factors that control the distribution of mineralization and trigger eventual exploration work in new areas. Subsequently, in order to evaluate the success rate accuracy, the receiver operating characteristic curves and area under the curves （AUCs） for the two potential maps are constructed. The results show that the AUCs for the WofE and fuzzy logic models are 0.775 7 and 0.840 6, respectively. The higher AUC value for the fuzzy logic model implies that it delineate a greater number of favorable areas compared with the WorE model. Overall, the capabilities of both models for correctly classifying areas with existing mineral deposits are satisfactory.

The use of evidential belief functions for mineral potential mapping in the Nanling belt, South China

In this study, the evidential belief functions （EBFs） were applied for mapping tungsten polymetallic potential in the Nanling belt, South China. Seven evidential layers （e.g., geological, geochemical, and geophysical） related to tungsten polymetallic deposits were extracted from a multi-source geospatial database. The relationships between evidential layers and the target deposits were quantified using EBFs model. Four EBF maps （belief map, disbelief map, uncertainty map, and plausibility map） are generated by integrating seven evidential layers which provide meaningful interpretations for tungsten polymetallic potential. On the final predictive map, the study area was divided into three target zones of high potential, moderate potential, and low potential areas, among which high potential and moderate potential areas accounted for 17.8% of the total area, containing 81% of the total deposits. To evaluate the success rate accuracy, the receiver operating characteristic （ROC） curves and the area under the curves （AUC） for the belief map were calculated. The area under the curve is 0.81 which indicates that the capability for correctly classifying the areas with existing mineral deposits is satisfactory. The results of this study indicate that the EBFs were effectively used for mapping mineral potential and for managing uncertainties asso- ciated with evidential layers.

Realization and application of geological cloud platform

In recent years,with the progress of computer technology,some traditional industries such as geology are facing changes in industrial structure and application mode.So we try to apply big data and virtualization technology in the field of geoscience.This study aims at addressing the existing problems in geological applications,such as data sharing,data processing and computing performance.A Geological Cloud Platform has been designed and realized preliminarily with big data and virtualization technology.The application of the Geological Cloud Platform can be divided into two parts:1)to nest the geological computing model in cloud platform and visualize the results and 2)to use relevant software to conduct data analysis and processing in virtual machines of Windows or Linux system.Finally,we prospect Carlin-type deposits in Nevada by using the spatial data model ArcSDM in the virtual machine.