Analysis of Bridge Inspection Technology for Highway Reconstruction and Expansion

This article focuses on bridge testing technologies in highway construction and expansion projects.It provides an overview of the inspection process for bridges in highway reconstruction and expansion projects,the main inspections,and strategies for improving the quality of inspections.Relevant units should conduct bridge inspections using appearance inspections,special inspections,and bearing capacity assessments in highway reconstruction and expansion projects.To effectively improve testing quality,relevant units should also focus on establishing and improving testing standards,strengthening material testing,and improving the overall quality of the testing personnel.This research aims to enhance the quality of highway reconstruction and expansion projects by supporting the sensible application of bridge inspection technology.

Integrating GPT into the Journalism and Communication Industries:The Technological Logic,Ecosystem Reconstruction,and Ethical Chal enges

Generative AI,represented by GPT(Generative Pre-trained Transformer),is now leading the technological revolution and is reconstructing the journalism and communication industries'ecosystems because of its powerful generative capacity and diverse range of outputs.While GPT is busy revolutionizing and innovating the production of news content,working patterns,and operation modes,it has also given rise to ethical concerns in regard to news authenticity,data security,humanistic values,and other related aspects.Therefore,it is imperative to initiate strategies and approaches,such as establishing a mechanism for verifying information authenticity,enhancing data security and privacy regulations,and instituting an ethical supervision and governance framework for AI,in order to facilitate the systematic advancement of AI-based news production while reinstating public trust.

Application of multi-planar reconstruction technique in endovascular repair of aortic dissection

BACKGROUND Endovascular repair of aortic dissection is an effective method commonly used in the treatment of Stanford type B aortic dissection.Stent placement during the operation was one-time and could not be repeatedly adjusted during the operation.Therefore,it is of great significance for cardiovascular physicians to fully understand the branch status,position,angle,and other information regarding aortic arch dissection before surgery.AIM To provide more references for clinical cardiovascular physicians to develop treatment plans.METHODS Data from 153 patients who underwent endovascular repair of aortic dissection at our hospital between January 2021 and December 2022 were retrospectively collected.All patients underwent multi-slice spiral computed tomography angiography.Based on distinct post-image processing techniques,the patients were categorized into three groups:Multiplanar reconstruction(MPR)(n=55),volume reconstruction(VR)(n=46),and maximum intensity projection(MIP)(n=52).The detection rate of aortic rupture,accuracy of the DeBakey classification,rotation,and tilt angles of the C-arm during the procedure,dispersion after stent release,and the incidence of late complications were recorded and compared.RESULTS The detection rates of interlayer rupture in the MPR and VR groups were significantly higher than that in the MIP group(P<0.05).The detection rates of De-Bakey subtypesⅠ,Ⅱ,andⅢin the MPR group were higher than those in the MIP group,and the detection rate of typeⅢin the MPR group was significantly higher than that in the VR group(P<0.05).There was no statistically significant difference in the detection rates of typesⅠandⅡcompared to the VR group(P>0.05).The scatter rate of markers and the incidence of complications in the MPR group were significantly lower than those in the VR and MIP groups(P<0.05).CONCLUSION The application of MPR in the endovascular repair of aortic dissection has improved the detection rate of dissection rupture,the accuracy of anatomical classification,and safety.

Research on CT characteristics reconstruction technology

The traditional computed tomography(CT)reconstruction methods are noisy,low resolution,poor contrast,and generally not suitable to detect the smaller flaws.Besides,the filter design is also difficult.The CT characteristics reconstruction technology was brought forward to improve in these aspects,which is defined to directly reconstruct the characteristics of the projection for the best requirements not the overall image quality.The two-dimension(2D)and three-dimension(3D)CT characteristics reconstruction algorithm were firstly introduced,then by detailed analysis,experimental results and comparsion of parameters calculated,its advantages in keeping better high-frequency feature,better noise immunity,short time-consuming and easier design are verified.

Single frame super-resolution reconstruction based on sparse representation

In order to effectively improve the quality of recovered images, a single frame super-resolution reconstruction method based on sparse representation is proposed. The combination method of local orientation estimation-based image patch clustering and principal component analysis is used to obtain a series of geometric dictionaries of different orientations in the dictionary learning process. Subsequently, the dictionary of the nearest orientation is adaptively assigned to each of the input patches that need to be represented in the sparse coding process. Moreover, the consistency of gradients is further incorporated into the basic framework to make more substantial progress in preserving more fine edges and producing sharper results. Two groups of experiments on different types of natural images indicate that the proposed method outperforms some state-of- the-art counterparts in terms of both numerical indicators and visual quality.

Fatigue life prediction of workpiece with 3D rough surface topography based on surface reconstruction technology

The fatigue performance of a workpiece depends on its surface quality.In traditional fatigue life prediction,the effect of surface quality is commonly accounted for by using empirical correction factors,which is imprecise when safety is of great concern.For surface quality,the surface topography is an important parameter,which introduces stress concentration that reduces the fatigue life.It is not feasible to test the stress concentration of different surface topographies.On the one hand,it is time-consuming and high-cost,and on the other hand,it cannot reflect the general statistical characteristics.With the help of surface reconstruction technology and interpolation method,a more efficient and economic approach is proposed,where FE simulation of workpiece with the reconstructed surface topography is used as a foundation for fatigue life prediction.The relationship between surface roughness(Sa)and fatigue life of the workpiece is studied with the proposed approach.

Super-resolution reconstruction of synthetic-aperture radar image using adaptive-threshold singular value decomposition technique

A super-resolution reconstruction approach of （SVD） technique was presented, and its performance was radar image using an adaptive-threshold singular value decomposition analyzed, compared and assessed detailedly. First, radar imaging model and super-resolution reconstruction mechanism were outlined. Then, the adaptive-threshold SVD super-resolution algorithm, and its two key aspects, namely the determination method of point spread function （PSF） matrix T and the selection scheme of singular value threshold, were presented. Finally, the super-resolution algorithm was demonstrated successfully using the measured synthetic-aperture radar （SAR） images, and a Monte Carlo assessment was carried out to evaluate the performance of the algorithm by using the input/output signal-to-noise ratio （SNR）. Five versions of SVD algorithms, namely 1 ） using all singular values, 2） using the top 80% singular values, 3） using the top 50% singular values, 4） using the top 20% singular values and 5） using singular values s such that S2≥/max（s2）/rinsNR were tested. The experimental results indicate that when the singular value threshold is set as Smax/（rinSNR）1/2, the super-resolution algorithm provides a good compromise between too much noise and too much bias and has good reconstruction results.

Image super-resolution reconstruction based on sparse representation and residual compensation

A super-resolution reconstruction algorithm is proposed. The algorithm is based on the idea of the sparse representation of signals, by using the fact that the sparsest representation of a sig- nal is unique as the constraint of the patched-based reconstruction, and compensating residual errors of the reconstruction results both locally and globally to solve the distortion problem in patch-based reconstruction algorithms. Three reconstruction algorithms are compared. The results show that the images reconstructed with the new algorithm have the best quality.

Research on Application of Low Technology in Landscape Reconstruction of Watercourses and Embankments in Wanjiang Urban Fringe in China

Urban fringe is a dynamic development area and its development in a certain period and region makes greater damage on the environment;hence,it is important to select proper technologies for landscape reconstruction.Based on this theme,the paper has analyzed the development and functional orientation of watercourses and embankments landscapes in Wanjiang urban fringe.On this basis,from the perspective of the selection and application of materials for watercourse and embankment landscape reconstruction,the respect of specific environmental culture of Wanjiang urban fringe,and the adaptation to the integration of watercourse landscape reconstruction technologies of Wanjiang urban fringe,the paper has illustrated the application of low technologies in landscape reconstruction of watercourses and embankments in Wanjiang urban fringe,so as to improve the surrounding environment and even the built-up environment based on the eco-environment of watercourses and embankments.

Super-resolution image reconstruction based on three-step-training neural networks

A new method of super-resolution image reconstruction is proposed, which uses a three-step-training error backpropagation neural network （BPNN） to realize the super-resolution reconstruction （SRR） of satellite image. The method is based on BPNN. First, three groups learning samples with different resolutions are obtained according to image observation model, and then vector mappings are respectively used to those three group learning samples to speed up the convergence of BPNN, at last, three times consecutive training are carried on the BPNN. Training samples used in each step are of higher resolution than those used in the previous steps, so the increasing weights store a great amount of information for SRR, and network performance and generalization ability are improved greatly. Simulation and generalization tests are carried on the well-trained three-step-training NN respectively, and the reconstruction results with higher resolution images verify the effectiveness and validity of this method.

Multi-channel fast super-resolution image reconstruction based on matrix observation model

A multi-channel fast super-resolution image reconstruction algorithm based on matrix observation model is proposed in the paper,which consists of three steps to avoid the computational complexity: a single image SR reconstruction step,a registration step and a wavelet-based image fusion. This algorithm decomposes two large matrixes to the tensor product of two little matrixes and uses the natural isomorphism between matrix space and vector space to transform cost function based on matrix-vector products model to matrix form. Furthermore,we prove that the regularization part can be transformed to the matrix formed. The conjugate-gradient method is used to solve this new model. Finally,the wavelet fusion is used to integrate all the registered highresolution images obtained from the single image SR reconstruction step. The proposed algorithm reduces the storage requirement and the calculating complexity,and can be applied to large-dimension low-resolution images.

A NOVEL METHOD TO REALIZE COMPRESSED VIDEO SUPER-RESOLUTION RECONSTRUCTION

This letter proposes a novel method of compressed video super-resolution reconstruction based on MAP-POCS (Maximum Posterior Probability-Projection Onto Convex Set). At first assuming the high-resolution model subject to Poisson-Markov distribution, then constructing the projecting convex based on MAP. According to the characteristics of compressed video, two different convexes are constructed based on integrating the inter-frame and intra-frame information in the wavelet-domain. The results of the experiment demonstrate that the new method not only outperforms the traditional algorithms on the aspects of PSNR (Peak Signal-to-Noise Ratio), MSE (Mean Square Error) and reconstruction vision effect, but also has the advantages of rapid convergence and easy extension.

Super-resolution reconstruction based on CNN:A case study of Jilin-1 multispectral data

MS or MS+PAN is usually applied separately in convolutional neural network(CNN)resolution reconstruction to obtain high-resolution MS images,but the difference between the two datasets is rarely studied.This paper introduced a dual-channel network and took MS and MS+PAN of Jilin-1 spectrum satellites as two datasets to evaluate the performance of CNN resolution reconstruction,and analyzed the difference with bicubic and GS methods.The result of CNN reconstruction shows that MS+PAN dataset performed better than MS,with about 6%improvement in spatial and spectral components,and the overall quality of MS+PAN dataset was slightly higher than that of MS dataset,with QNR from 0.9559 to 0.9584.The bicubic performed best in spectral components with the quality value of 0.017,and GS performed best in spatial components with the quality values of 0.0443.CNN showed similar performance in spectral and spatial components with the two traditional methods and achieved the best overall quality with QNR value of 0.9584.

Application of digital technology in nasal reconstruction

Nasal defects are facial defects caused by trauma,tumors,or congenital diseases that seriously damage a patient’s physical and mental health.Nasal defects,from skin defects to total nasal defects,require surgical repair and reconstruction to restore the appearance and function of the nose,which have always been challenges for rhinoplasty.The development of digital technology has increased the possibility of nasal reconstruction.Digital technology is involved in the preoperative,intraoperative,and postoperative stages of nasal construction and is of great significance in improving the effect of this surgery.This article reviews the application of major digital technologies,including three-dimensional(3D)imaging technology,computer-assisted surgical navigation,and 3D printing,in nasal reconstruction and discusses the shortcomings of the current application of digital technology.

Digital measurement of bone tumor volume by CT three-dimensional reconstruction technology

Objective To discuss the measurement of bone tumor volume on the basis of three dimensional images segmentation technology. Methods Twenty patients with lacunar bone tumor from Tianjin Hospital and Tongji Hospital were included in the

Accurate resection of hilar cholangiocarcinoma using eOrganmap 3D reconstruction and full quantization technique

BACKGROUND For treatment of hilar cholangiocarcinoma(HCCA),the rate of radical resection is low and prognosis is poor,and preoperative evaluation is not sufficiently accurate.3D visualization has the advantage of giving a stereoscopic view,which makes accurate resection of HCCA possible.AIM To establish precise resection of HCCA based on eOrganmap 3D reconstruction and full quantification technology.METHODS We retrospectively analyzed the clinical data of 73 patients who underwent HCCA surgery.All patients were assigned to two groups.The traditional group received traditional 2D imaging planning before surgery(n=35).The eOrganmap group underwent 3D reconstruction and full quantitative technical planning before surgery(n=38).The preoperative evaluation,anatomical classification of hilar hepatic vessels,indicators associated with surgery,postoperative complications,liver function,and stress response indexes were compared between the groups.RESULTS Compared with the traditional group,the amount of intraoperative blood loss in the eOrganmap group was lower,the operating time and postoperative intestinal ventilation time were shorter,and R0 resection rate and lymph node dissection number were higher(P<0.05).The total complication rate in the eOrganmap group was 21.05%compared with 25.71%in the traditional group(P>0.05).The levels of total bilirubin,Albumin(ALB),aspartate transaminase,and alanine transaminase in the eOrganmap group were significantly different from those in the traditional group(intergroup effect:F=450.400,79.120,95.730,and 13.240,respectively;all P<0.001).Total bilirubin,aspartate transaminase,and alanine transaminase in both groups showed a decreasing trend with time(time effect:F=30.270,17.340,and 13.380,respectively;all P<0.001).There was an interaction between patient group and time(interaction effect:F=3.072,2.965,and 2.703,respectively;P=0.0282,0.032,and 0.046,respectively);ALB levels in both groups tended to increase with time(time effect:F=22.490,P<0.001),and there was an interaction effect between groups and time(interaction effect:F=4.607,P=0.004).In the eOrganmap group,there was a high correlation between the actual volume of intraoperative liver specimen resection and the volume of preoperative virtual liver resection(t=0.916,P<0.001).CONCLUSION The establishment of accurate laparoscopic resection of hilar cholangiocarcinoma based on preoperative eOrganmap 3D reconstruction and full quantization technology can make laparoscopic resection of hilar cholangiocarcinoma more accurate and safe.

Transformer and GAN-Based Super-Resolution Reconstruction Network for Medical Images

Super-resolution reconstruction in medical imaging has become more demanding due to the necessity of obtaining high-quality images with minimal radiation dose,such as in low-field magnetic resonance imaging(MRI).However,image super-resolution reconstruction remains a difficult task because of the complexity and high textual requirements for diagnosis purpose.In this paper,we offer a deep learning based strategy for reconstructing medical images from low resolutions utilizing Transformer and generative adversarial networks(T-GANs).The integrated system can extract more precise texture information and focus more on important locations through global image matching after successfully inserting Transformer into the generative adversarial network for picture reconstruction.Furthermore,we weighted the combination of content loss,adversarial loss,and adversarial feature loss as the final multi-task loss function during the training of our proposed model T-GAN.In comparison to established measures like peak signal-to-noise ratio(PSNR)and structural similarity index measure(SSIM),our suggested T-GAN achieves optimal performance and recovers more texture features in super-resolution reconstruction of MRI scanned images of the knees and belly.

Deep-learning-based methods for super-resolution fluorescence microscopy

The algorithm used for reconstruction or resolution enhancement is one of the factors affectingthe quality of super-resolution images obtained by fluorescence microscopy.Deep-learning-basedalgorithms have achieved stateof-the-art performance in super-resolution fluorescence micros-copy and are becoming increasingly attractive.We firstly introduce commonly-used deep learningmodels,and then review the latest applications in terms of the net work architectures,the trainingdata and the loss functions.Additionally,we discuss the challenges and limits when using deeplearning to analyze the fluorescence microscopic data,and suggest ways to improve the reliability and robustness of deep learning applications.

RESEARCH ACTIVITIES ON LAND USE/COVER CHANGE IN THE PAST TEN YEARS IN CHINA USING SPACE TECHNOLOGY

Land use/cover change, which in China is characterized by urbanization resulting in a decrease in arable land in the east along with a large area of grassland being cultivated in the west, has been accelerated by rapid economic development in the last years. All of the above changes will affect sustainable development in the next century. The Chinese Academy of Sciences is conducting a study of land use/cover change over the last ten years based on the integration of remote sensing and GIS technology to establish a multitemporal database covering all of China. Fundamental data for land use/cover for the year 1996 has already been developed by the Chinese Academy of Sciences. In order to reconstruct fundamental land use/cover data for the year 1986, a central data processing and analyzing system and a regional data acquisition, processing and analyzing system have been established and are joined together as a network. After the 1986 database is established, the comparative research on the reduction in arable land, urbanization, desertification, changes in forest and grassland, and lake and wetland land use/cover change will be carried out. In addition, a transect for a key regional comparative study was selected along the Changjiang (Yangtze) River. The driving forces of these changes also will be extracted. The result of this study will be not only make a contribution to global land use/cover change research, but will also support decision making for sustainable national development.

Research on the Application of Super Resolution Reconstruction Algorithm for Underwater Image

Underwater imaging is widely used in ocean,river and lake exploration,but it is affected by properties of water and the optics.In order to solve the lower-resolution underwater image formed by the influence of water and light,the image super-resolution reconstruction technique is applied to the underwater image processing.This paper addresses the problem of generating super-resolution underwater images by convolutional neural network framework technology.We research the degradation model of underwater images,and analyze the lower-resolution factors of underwater images in different situations,and compare different traditional super-resolution image reconstruction algorithms.We further show that the algorithm of super-resolution using deep convolution networks(SRCNN)which applied to super-resolution underwater images achieves good results.