Interfacial Modification of NiO_(x)by Self-assembled Monolayer for Efficient and Stable Inverted Perovskite Solar Cells

NiO_(x)as a hole transport material for inverted perovskite solar cells has received great attention owing to its high transparency,low fabrication temperature,and superior stability.However,the mismatched energy levels and possible redox reactions at the NiO_(x)/perovskite interface severely limit the performance of NiO_(x) based inverted perovskite solar cells.Herein,we introduce a p-type self-assembled monolayer between NiO_(x)and perovskite layers to modify the interface and block the undesirable redox reaction between perovskite and NiO_(x)The selfassembled monolayer molecules all contain phosphoric acid function groups,which can be anchored onto the NiOr surface and passivate the surface defect.Moreover,the introduction of self-assembled monolayers can regulate the energy level structure of NiO_(x),reduce the interfacial band energy offset,and hence promote the hole transport from perovskite to NiO_(x)layer.Consequently,the device performance is significantly enhanced in terms of both power conversion efficiency and stability.

Single-atom catalysts based on polarization switching of ferroelectric In_(2)Se_(3) for N_(2) reduction

The polarization switching plays a crucial role in controlling the final products in the catalytic pro-cess.The effect of polarization orientation on nitrogen reduction was investigated by anchoring transition metal atoms to form active centers on ferroelectric material In_(2)Se_(3).During the polariza-tion switching process,the difference in surface electrostatic potential leads to a redistribution of electronic states.This affects the interaction strength between the adsorbed small molecules and the catalyst substrate,thereby altering the reaction barrier.In addition,the surface states must be considered to prevent the adsorption of other small molecules(such as *O,*OH,and *H).Further-more,the V@↓-In_(2)Se_(3) possesses excellent catalytic properties,high electrochemical and thermody-namic stability,which facilitates the catalytic process.Machine learning also helps us further ex-plore the underlying mechanisms.The systematic investigation provides novel insights into the design and application of two-dimensional switchable ferroelectric catalysts for various chemical processes.

Design and implementation of low-cost geomagnetic field monitoring equipment for high-density deployment

The observation of geomagnetic field variations is an important approach to studying earthquake precursors.Since 1987,the China Earthquake Administration has explored this seismomagnetic relationship.In particular,they studied local magnetic field anomalies over the Chinese mainland for earthquake prediction.Owing to the years of research on the seismomagnetic relationship,earthquake prediction experts have concluded that the compressive magnetic effect,tectonic magnetic effect,electric magnetic fluid effect,and other factors contribute to preearthquake magnetic anomalies.However,this involves a small magnitude of magnetic field changes.It is difficult to relate them to the abnormal changes of the extremely large magnetic field in regions with extreme earthquakes owing to the high cost of professional geomagnetic equipment,thereby limiting large-scale deployment.Moreover,it is difficult to obtain strong magnetic field changes before an earthquake.The Tianjin Earthquake Agency has developed low-cost geomagnetic field observation equipment through the Beijing–Tianjin–Hebei geomagnetic equipment test project.The new system was used to test the availability of equipment and determine the findings based on big data..

Deactivation mechanism of acetone to isobutene conversion over Y/Beta catalyst

The conversion of acetone derived from biomass to isobutene has attracted extensive attentions.In comparison with Brønsted acidic catalyst,Lewis acidic catalyst could exhibit a better catalytic performance with a higher isobutene selectivity.However,the catalyst stability remains a key problem for the long-running acetone conversion and the reasons for catalyst deactivation are poorly understood up to now.Herein,the deactivation mechanism of Lewis acidic Y/Beta catalyst during the acetone to isobutene conversion was investigated by various characterization techniques,including acetone-temperature-programmed surface reaction,gas chromatography-mass spectrometry,in situ ultraviolet-visible,and ^(13)C cross polarization magic angle spinning nuclear magnetic resonance spectroscopy.A successive aldol condensation and cyclization were observed as the main side-reactions during the acetone conversion at Lewis acidic Y sites.In comparison with the low reaction temperature,a rapid formation and accumulation of the larger cyclic unsaturated aldehydes/ketones and aromatics could be observed,and which could strongly adsorb on the Lewis acidic sites,and thus cause the catalyst deactivation eventually.After a simple calcination,the coke deposits could be easily removed and the catalytic activity could be well restored.

Improving comprehensive properties of Cu-11.9Al-2.5Mn shape memory alloy by adding multi-layer graphene carried by Cu_(51)Zr_(14)inoculant particles

In order to improve the comprehensive properties of the Cu-11.9Al-2.5Mn shape memory alloy(SMA),multilayer graphene(MLG)carried by Cu_(51)Zr_(14)inoculant particles was incorporated and dispersed into this alloy through preparing the preform of the cold-pressed MLG-Cu_(51)Zr_(14)composite powders.In the resultant novel MLG/Cu-Al-Mn composites,MLG in fragmented or flocculent form has a good bonding with the Cu-Al-Mn matrix.MLG can prevent the coarsening of grains of the Cu-Al-Mn SMA and cause thermal mismatch dislocations near the MLG/Cu-Al-Mn interfaces.The damping and mechanical properties of the MLG/Cu-Al-Mn composites are significantly improved.When the content of MLG reaches 0.2 wt.%,the highest room temperature damping of 0.0558,tensile strength of 801.5 MPa,elongation of 10.8%,and hardness of HV 308 can be obtained.On the basis of in-depth observation of microstructures,combined with the theory of internal friction and strengthening and toughening theories of metals,the relevant mechanisms are discussed.

Conjugated microporous polymers-scaffolded enzyme cascade systems with enhanced catalytic activity

Enhancing catalytic activity of multi-enzyme in vitro through substrate channeling effect is promis-ing yet challenging.Herein,conjugated microporous polymers(CMPs)-scaffolded integrated en-zyme cascade systems(I-ECSs)are constructed through co-entrapping glucose oxidase(GOx)and horseradish peroxidase(HRP),in which hydrogen peroxide(H_(2)O_(2)) is the intermediate product.The interplay of low-resistance mass transfer pathway and appropriate pore wall-H_(2)O_(2) interactions facilitates the directed transfer of H_(2)O_(2),resulting in 2.4-fold and 5.0-fold elevation in catalytic activ-ity compared to free ECSs and separated ECSs,respectively.The substrate channeling effect could be regulated by altering the mass ratio of GOx to HRP.Besides,I-ECSs demonstrate excellent stabili-ties in harsh environments and multiple recycling.

Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm

A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.

Recent Advances on Ruthenium-based Electrocatalysts for Lithium-oxygen Batteries

Rechargeable lithium-oxygen(Li-O_(2))batteries have attracted wide attention due to their high energy density.However,the sluggish cathode kinetics results in high overvoltage and poor cycling performance.Ruthenium(Ru)-based electrocatalysts have been demonstrated to be promising cathode catalysts to promote oxygen evolution reaction(OER).It facilitates decomposition of lithium peroxide(Li_(2)O_(2))by adjusting Li_(2)O_(2) morphologies,which is due to the strong interaction between Ru-based catalyst and superoxide anion(O_(2))intermediate.In this review,the design strategies of Ru-based electrocatalysts are introduced to enhance their OER catalytic kinetics in Li-O_(2) batteries.Different configurations of Ru-based catalysts,including metal particles(Ru metal and alloys),single-atom catalysts,and Ru-loaded compounds with various substrates(carbon materials,metal oxides/sulfides),have been summarized to regulate the electronic structure and the matrix architecture of the Ru-based electrocatalysts.The structure-property relationship of Ru-based catalysts is discussed for a better understanding of the Li_(2)O_(2) decomposition mechanism at the cathode interface.Finally,the challenges of Ru-based electrocatalysts are proposed for the future development of Li-O_(2) batteries.

Effect of interrupted aging on mechanical properties and corrosion resistance of 7A75 aluminum alloy

The effects of interrupted aging on mechanical properties and corrosion resistance of 7A75 aluminum alloy extruded bar were investigated through various analyses,including electrical conductivity,mechanical properties,local corrosion properties,and slow strain rate tensile stress corrosion tests.Microstructure characterization techniques such as metallographic microscopy,scanning electron microscopy(SEM),and transmission electron microscopy(TEM)were also employed.The results indicate that the tensile strength of the alloy produced by T6I6 aging is similar to that produced by T6I4 aging,and it even exceeds 700 MPa.Furthermore,the yield strength increases by 52.7 MPa,reaching 654.8 MPa after T6I6 aging treatment.The maximum depths of intergranular corrosion(IGC)and exfoliation corrosion(EXCO)decrease from 116.3 and 468.5μm to 89.5 and 324.3μm,respectively.The stress corrosion factor also decreases from 2.1%to 1.6%.These findings suggest that the alloy treated with T6I6 aging exhibits both high strength and excellent stress corrosion cracking resistance.Similarly,when the alloy is treated with T6I4,T6I6 and T6I7 aging,the sizes of grain boundary precipitates(GBPs)are found to be 5.2,18.4,and 32.8 nm,respectively.The sizes of matrix precipitates are 4.8,5.7 and 15.7 nm,respectively.The atomic fractions of Zn in GBPs are 9.92 at.%,8.23 at.%and 6.87 at.%,respectively,while the atomic fractions of Mg are 12.66 at.%,8.43 at.%and 7.00 at.%,respectively.Additionally,the atomic fractions of Cu are 1.83 at.%,2.47 at.%and 3.41 at.%,respectively.

Advances in the studies of the supported ruthenium catalysts for CO_(2) methanation

CO_(2) methanation has a potential in the large-scale utilization of carbon dioxide.It has also been considered to be useful for the renewable energy storage.The commercial pipeline for natural gas transportation can be directly applied for the methane product of CO_(2) methanation.The supported ruthenium(Ru)catalyst has been confirmed to be active and stable for CO_(2) methanation with its high ability in the dissociation of hydrogen and the strong binding of carbon monoxide.CO_(2) methanation over the supported Ru catalyst is structure sensitive.The size of the Ru catalyst and the support have significant effects on the activity and the mechanism.A significant challenge re-mained is the structural controllable preparation of the supported Ru catalyst toward a sufficiently high low-temperature activity.In this review,the recent progresses in the investigations of the supported Ru catalysts for CO_(2) methanation are summarized.The challenges and the future devel-opments are also discussed.

Improved YOLOv5-Based Inland River Floating Garbage Detection Model

Detection of floating garbage in inland rivers is crucial for water environmental protection,as it effectively reduces ecological damage and ensures the safety of water resources.To address the inefficiency of traditional cleanup methods and the challenges in detecting small targets,an improved YOLOv5 object detection model was proposed in this study.In order to enhance the model’s sensitivity to small targets and mitigate the impact of redundant information on detection performance,a bi-level routing attention mechanism was introduced and embedded into the backbone network.Additionally,a multi-scale detection head was incorporated into the model,allowing for more comprehensive coverage of floating garbage of various sizes through multi-scale feature extraction and detection.The Focal-EIoU loss function was also employed to optimize the model parameters,improving localization accuracy.Experimental results on the publicly available FloW_Img dataset demonstrated that the improved YOLOv5 model outperforms the original YOLOv5 model in terms of precision and recall,achieving a mAP(mean average precision)of 86.12%,with significant improvements and faster convergence.

Improved Small Target Detection Method for SAR Image Based on YOLOv7

In order to solve the problems that the current synthetic aperture radar(SAR)image target detection method cannot adapt to targets of different sizes,and the complex image background leads to low detection accuracy,an improved SAR image small target detection method based on YOLOv7 was proposed in this study.The proposed method improved the feature extraction network by using Switchable Around Convolution(SAConv)in the backbone network to help the model capture target information at different scales,thus improving the feature extraction ability for small targets.Based on the attention mechanism,the DyHead module was embedded in the target detection head to reduce the impact of complex background,and better focus on the small targets.In addition,the NWD loss function was introduced and combined with CIoU loss.Compared to the CIoU loss function typically used in YOLOv7,the NWD loss function pays more attention to the processing of small targets,so as to further improve the detection ability of small targets.The experimental results on the HRSID dataset indicate that the proposed method achieved mAP@0.5 and mAP@0.95 scores of 93.5%and 71.5%,respectively.Compared to the baseline model,this represents an increase of 7.2%and 7.6%,respectively.The proposed method can effectively complete the task of SAR image small target detection.

Improved YOLOv8-Based Target Detection Algorithm for UAV Aerial Image

In response to the challenge of low detection accuracy and susceptibility to missed and false detections of small targets in unmanned aerial vehicles(UAVs)aerial images,an improved UAV image target detection algorithm based on YOLOv8 was proposed in this study.To begin with,the CoordAtt attention mechanism was employed to enhance the feature extraction capability of the backbone network,thereby reducing interference from backgrounds.Additionally,the BiFPN feature fusion network with an added small object detection layer was used to enhance the model's ability to perceive for small objects.Furthermore,a multi-level fusion module was designed and proposed to effectively integrate shallow and deep information.The use of an enhanced MPDIoU loss function further improved detection performance.The experimental results based on the publicly available VisDrone2019 dataset showed that the improved model outperformed the YOLOv8 baseline model,mAP@0.5 improved by 20%,and the improved method improved the detection accuracy of the model for small targets.

Improving autoencoder-based unsupervised damage detection in uncontrolled structural health monitoring under noisy conditions

Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.

Underwater Image Enhancement Based on Multi-scale Adversarial Network

In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.

Network Intrusion Detection Model Based on Ensemble of Denoising Adversarial Autoencoder

Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research significance for network security.Due to the strong generalization of invalid features during training process,it is more difficult for single autoencoder intrusion detection model to obtain effective results.A network intrusion detection model based on the Ensemble of Denoising Adversarial Autoencoder(EDAAE)was proposed,which had higher accuracy and reliability compared to the traditional anomaly detection model.Using the adversarial learning idea of Adversarial Autoencoder(AAE),the discriminator module was added to the original model,and the encoder part was used as the generator.The distribution of the hidden space of the data generated by the encoder matched with the distribution of the original data.The generalization of the model to the invalid features was also reduced to improve the detection accuracy.At the same time,the denoising autoencoder and integrated operation was introduced to prevent overfitting in the adversarial learning process.Experiments on the CICIDS2018 traffic dataset showed that the proposed intrusion detection model achieves an Accuracy of 95.23%,which out performs traditional self-encoders and other existing intrusion detection models methods in terms of overall performance.

Oriented Bounding Box Object Detection Model Based on Improved YOLOv8

In the study of oriented bounding boxes(OBB)object detection in high-resolution remote sensing images,the problem of missed and wrong detection of small targets occurs because the targets are too small and have different orientations.Existing OBB object detection for remote sensing images,although making good progress,mainly focuses on directional modeling,while less consideration is given to the size of the object as well as the problem of missed detection.In this study,a method based on improved YOLOv8 was proposed for detecting oriented objects in remote sensing images,which can improve the detection precision of oriented objects in remote sensing images.Firstly,the ResCBAMG module was innovatively designed,which could better extract channel and spatial correlation information.Secondly,the innovative top-down feature fusion layer network structure was proposed in conjunction with the Efficient Channel Attention(ECA)attention module,which helped to capture inter-local cross-channel interaction information appropriately.Finally,we introduced an innovative ResCBAMG module between the different C2f modules and detection heads of the bottom-up feature fusion layer.This innovative structure helped the model to better focus on the target area.The precision and robustness of oriented target detection were also improved.Experimental results on the DOTA-v1.5 dataset showed that the detection Precision,mAP@0.5,and mAP@0.5:0.95 metrics of the improved model are better compared to the original model.This improvement is effective in detecting small targets and complex scenes.

Research on the LA-UMamba Model for Asymmetric Modules with Added Auxiliary Information

Deep learning techniques are revolutionizing the developmentof medical image segmentation.With the advancement of Transformer models,especially ViT and Swin-Transformer,which enhances the remote-dependent modeling capability of the model through the self-attention mechanism,better segmentation performance can be achieve.Moreover,the high computational cost of Transformer has motivated researchers to explore more efficient models,such as the Mamba model based on state-space modeling(SSM),and for the field of medical segmentation,reducing the number of model parameters is also necessary.In this study,a novel asymmetric model called LA-UMamba was proposed,which integrates visual Mamba module to efficiently capture complex visual features and remote dependencies.The classical design of U-Net was adopted in the upsampling phase to help reduce the number of references and recover more details.To mitigate the information loss problem,an auxiliary U-Net downsampling layer was designed to focus on sizing without extracting features,thus enhancing the protection of input information while maintaining the efficiency of the model.The experiments were conducted on the ACDC MRI cardiac segmentation dataset,and the results showed that the proposed LA-UMamba achieves proved performance compared to the baseline model in several evaluation metrics,such as IoU,Accuracy,Precision,HD and ASD,which improved that the model is successful in optimizing the detail processing and reducing the complexity of the model,providing a new perspective for further optimization of medical image segmentation techniques.

Traffic Sign Detection Model Based on Improved RT-DETR

The correct identification of traffic signs plays an important role in automatic driving technology and road safety driving.Therefore,to address the problems of misdetection and omission in traffic sign detection due to the variety of sign types,significant size differences and complex background information,an improved traffic sign detection model for RT-DETR was proposed in this study.Firstly,the HiLo attention mechanism was added to the Attention-based Intra-scale Feature Interaction,which further enhanced the feature extraction capability of the network and improved the detection efficiency on high-resolution images.Secondly,the CAFMFusion feature fusion mechanism was designed,which enabled the network to pay attention to the features in different regions in each channel.Based on this,the model could better capture the remote dependencies and neighborhood feature correlation,improving the feature fusion capability of the model.Finally,the MPDIoU was used as the loss function of the improved model to achieve faster convergence and more accurate regression results.The experimental results on the TT100k-2021 traffic sign dataset showed that the improved model achieves the performance with a precision value of 90.2%,recall value of 88.1%and mAP@0.5 value of 91.6%,which are 4.6%,5.8%,and 4.4%better than the original RT-DETR model respectively.The model effectively improves the problem of poor traffic sign detection and has greater practical value.

Research on BIM Model Reshaping Method Based on 3D Point Cloud Recognition

In view of the limitations of traditional measurement methods in the field of building information,such as complex operation,low timeliness and poor accuracy,a new way of combining three-dimensional scanning technology and BIM(Building Information Modeling)model was discussed.Focused on the efficient acquisition of building geometric information using the fast-developing 3D point cloud technology,an improved deep learning-based 3D point cloud recognition method was proposed.The method optimised the network structure based on RandLA-Net to adapt to the large-scale point cloud processing requirements,while the semantic and instance features of the point cloud were integrated to significantly improve the recognition accuracy and provide a precise basis for BIM model remodeling.In addition,a visual BIM model generation system was developed,which systematically transformed the point cloud recognition results into BIM component parameters,automatically constructed BIM models,and promoted the open sharing and secondary development of models.The research results not only effectively promote the automation process of converting 3D point cloud data to refined BIM models,but also provide important technical support for promoting building informatisation and accelerating the construction of smart cities,showing a wide range of application potential and practical value.