Drinking water safety is a safety issue that the whole society attaches great importance to currently. For sudden water pollution accidents, it is necessary to trace the water pollution source in ...real time to determine the pollution source’s characteristic information and provide technical support to emergency management departments for decision making. The problems of water pollution’s real-time traceability are as follows: non-uniqueness and dynamic real time of pollution sources. Aiming at these two difficulties, an intelligent traceability algorithm based on dynamic multi-mode optimization was designed and proposed in the work. As a multi-mode optimization problem, pollution traceability could have multiple similar optimal solutions. Firstly, the new algorithm divided the population reasonably through the optimal subpopulation division strategy, which made the nodes’ distribution in a single subpopulation more similar and conducive to local optimization. Then, a similar peak penalty strategy was used to eliminate similar solutions and reduce the non-unique solutions’ number, since real-time traceability required higher algorithm convergence than traditional offline traceability and dynamic problems with parameter changes, historical information preservation, and adaptive initialization strategies could make reasonable use of the algorithm’s historical knowledge to improve the population space and increase the population convergence rate when the problem changed. The experimental results showed the proposed new algorithm’s effectiveness in solving problems—accurately tracing the source of pollution, and obtain corresponding characteristic information in a short time.
This research examined the impacts of market conditions on the choice of contract and timing for information system outsourcing. Real options approach is applied to develop several analytical models ...to investigate the decision making for outsourcing information system. The results show that the information asymmetry, requirement uncertainty, cost structure and vendor’s competition have important impacts on the client’s cost reduction, value of outsourcing option and probability of outsourcing. With symmetric information, the cost reduction, value of outsourcing option and probability of outsourcing under cost-plus contract and fixed-price contract are indifferent. With asymmetric information, however, the two contracts will generate different cost reductions and values of outsourcing option. We identify market conditions under which a contract is superior to others and also characterize the market conditions under which the client can switch to outsourcing or postpone outsourcing.
The difference between the convolutional neural network and the ordinary neural network is that the convolutional neural network contains a feature extractor composed of a convolutional layer and a ...subsampling layer. With the development of society and economy, the pace of urbanization is accelerating, and the number and types of urban buildings are also growing rapidly. Digital management has put forward higher requirements for 3D reconstruction of urban buildings. Aiming at explanation the question that sharpness form are proetrate to be blea or bewildered in CNN-supported structure birth from lofty-separation airy conception, an optimise construction birth algorithmic rule is converse to increase the construction brink of proud-resoluteness atmospheric semblance and the twist projection. Remote sensing image target recognition, as the main research content in the current remote sensing image application field, has important theoretical significance and extensive application value. In recent years, deep learning has become an emerging research direction in the field of machine learning, and convolutional neural network is a deep learning model that has been widely studied and applied. More specifically, the construction brink is better by realm vary recursive filter out, and the better appearance is fed into the U-Net nerval netting for making. Afterward, in custom to plentifully take advantage of the sumptuous detail shape of buildings on supercilious sake picture, we tempt to en plot impair from the manage copy and pigeonhole supported on the origin U-Net edifice to increase the school data. These beauty spot can remarkably fortify the procurement of edifice hie viterbilt characteristic in eager and invert intense lore. Finally, construction essence is instrument by mechanical advantage the quotation intense characteristic. The trial effect of edifice extract from the Panjin City have demonstrated that for the hoagie-optimum pattern data with division of shade areas, the everywhere assortment propriety of buildings recognized by U-Net is above 80%, and the zenith everywhere assortment truth of the amended regularity extension 83%. In this paper, through the research on the application of convolutional neural network in the field of image segmentation, the problems of low segmentation accuracy, long time and high cost in the task of aerial image building image segmentation are solved to a certain extent. The detection and segmentation method of buildings in aerial images based on CNN can automatically detect and segment buildings, and can segment a large number of buildings in aerial images in batches. In scenarios with high segmentation efficiency requirements.
With the iterative update of computer technology, the penetration of computer and other Internet technologies in human–computer interaction systems has become more and more extensive, and the ...human–computer interaction methods have quietly undergone huge changes. Gesture recognition has gradually become a hot spot in the field of human–computer interaction now, which has a wide range of application prospects and research value. The color segmentation experiment shows that the skin color of the gesture in the YCrCg space has better clustering properties than in the YCrCb space. In the preprocessing of gesture images, an improved Otsu method is proposed to improve the real-time performance to realize the threshold segmentation of the human hand; then the morphological processing is carried out, and the median filter method is used to achieve image denoising to improve image quality. A gesture recognition algorithm is designed: First, use Graph-SAGE to recognize the graph-structured data of the gesture, and then use the Adaboost algorithm to combine the two strong classifiers of the random forest and the support vector machine into a cascade classifier through the cascade structure. The output information of Graph-SAGE is classified and the meaning of the gesture is analyzed. On the test set, the average detection accuracy of the algorithm is 91.70%, the recall rate is 94.23%, and the average detection time per frame is 330 ms.
Public health machinery learning platform based on cloud-native is a system platform that combines machine learning frameworks and cloud-native technology for public health services. The problem of ...how its flexible value is realized has been widely concerned by all public health network intelligent researchers. Thus, this article examines the relationship between cloud-native architecture flexibility and cloud provider value and the processes and the boundary condition by which cloud-native architecture flexibility affects cloud provider value based on innovation theory and dynamic capability theory. The results of a survey of 509 platform-related respondents in China show that cloud-native architecture flexibility is positively related to cloud provider value, and both absorptive capacity and supply chain agility mediate the above-mentioned effect. Moreover, R&D subsidies strengthen both the positive relationship between absorptive capacity and cloud provider value and the relationship between supply chain agility and cloud provider value. In this study, cloud-native architecture flexibility, unit absorptive capacity, supply chain agility and R&D subsidies are considered into a flexible value generation mechanism model that extend the relevant research on the value generation mechanism of information system under the background of network intelligence, and to provide relevant enterprises with suggestions on upgrade strategies.
Gallbladder cancer is a relatively rare but highly malignant tumor. This study mainly explores the CT findings of gallbladder cancer based on neural networks. This study designed a gallbladder cancer ...LDCT image denoising network. Ability to process different doses of gallbladder cancer LDCT images with significant differences in noise and artifact distribution, this study designed the noise level estimation sub-network as a codec structure; the decoding part is used to generate the noise level of the gallbladder cancer LDCT image Artifact image. Artificial neural network is a kind of artificial neural network that simulates the behavior characteristics of animal neural network and achieves the purpose of processing information by adjusting the interconnection between a large number of internal nodes. In order to meet the requirements of medical diagnosis for gallbladder cancer LDCT image quality, this study designed the backbone noise reduction network as a GAN framework that can be internally optimized. The discriminator network structure of this study is a multi-scale inception structure. As a sub-network of GAN, the discriminator network is used to distinguish true and false images and constrain the generator to make the generated images close to real images. In addition, it can be used as a noise evaluation sub-network to evaluate the noise gallbladder cancer LDCT. The treatment methods of gallbladder cancer include surgery, chemotherapy, radiation therapy, arterial interventional perfusion therapy, targeted therapy, etc. Surgery is currently the first choice for the treatment of gallbladder cancer, and the choice of surgery depends on the stage and growth site of gallbladder cancer. The image denoising network was used to evaluate the quality of the noise-reduced image. The average precision of GAN network for gallbladder cancer area is 91.0%, and the highest value is 95.2%. This study will provide a reliable reference value for the auxiliary diagnosis of gallbladder cancer.
In order to track the loan lost-linking customers, we analyzed their historical daily consumption transaction network records (DCTNR), which include bank card transaction records, third-party payment ...transaction records, and network trading system order details records. We extracted the transaction date, time and address information from their daily consumption transaction path, analyzed the key factors affecting the tracking work, and constructed loan lost-linking customer path correlated index model which is applied to quantify the correlation between the initial search address and other addresses. In addition, we also establish loan customer daily consumption transaction network based on big data environment, propose the network sorting rules and searching rules, and construct the network sorting search algorithm to track loan lost-linking customers in different address types. In the case study, we analyzed the historical DCTNR data of a Chinese bank’s loan lost-linking customer, and applied loan lost-linking customer path correlated index model and network sorting search algorithm to track him in big data environment. The results represent that the method can achieve the purpose of tracking, and the tracking time and cost can be reduced by using network sorting rules and searching rules. It is of great practical significance and scientific guiding significance for banks, financial institutions and major financial platforms to apply big data, artificial intelligence and other information technologies to track loan lost-linking customers and recover economic losses.
Since the new century, there have been more and more survey reports on the use of acupuncture in the field of tremor and paralysis, reflect the health and accuracy of acupuncture in the field of ...tremor and paralysis, this article discusses the comparison of the therapeutic effects of acupuncture treatment for tremor paralysis with non-exercise disease, and proposes clinical guiding significance for the treatment of tremor paralysis by acupuncture and moxibustion, and the future-related clinical research can be improved. This paper aims to make acupuncture give full play to its own advantages in the treatment of non-motor symptoms of Parkinson's disease by studying the Meta-analysis of the efficacy of acupuncture and moxibustion in the treatment of non-motor symptoms of Parkinson's disease. This paper proposes a classification model of fifty-layer cyclotron neural network based on deep residual framework. After more than 80,000 clinical ECG assessments, the accurate classification of positive abnormal tremor paralysis has been achieved. In this study, a simple random method was used to randomly divide 120 patients into the treatment group (combined governor tremor group), the control group one (Tongdu acupuncture group), and the control group two (tremor three-needle group). After maintaining modern treatment, three types of acupuncture treatments with different main points are added. The combined group of governor and tremor takes Baihui, Shenting, Yintang, Suliao, Houxi, Shenmai, Sishenzhen, Fengchi, Hegu, and Taichong, Xingping replenishing and reducing technique; Tongdu acupuncture group takes Baihui, Shenting, Yintang, Suliao, Houxi, Shenmai, Xingping replenishing and reducing technique; trembling three-needle group takes Sishen needle, Fengchi, Hegu, Tai Chong, the method of replenishing, replenishing and reducing. Meta-analysis uniformly uses tremor paralysis NMS, SCOPA-UT, PDS and several other efficacy observation indicators. This article uses a fixed-effects model to meta-analyze the experimental conclusions. RR = 1.26, 94% CI is 1.24, 1.71,
P
= 0.0001 < 0.05, it is considered that the combined effect size is statistically significant, and the effectiveness of acupuncture.
The up-dip extent of slip during large megathrust earthquakes is important for both tsunami excitation and subsequent tsunami earthquake potential, but it is unclear whether frictional properties ...and/or fault structure determine the up-dip limit. A finite-fault slip model for the 2021 MW 8.2 Chignik, Alaska Peninsula earthquake obtained by joint inversion of seismic-geodetic data with model spatial extent constraints from the tsunami waves provides unusually good constraints on the up-dip edge of coseismic slip. Rupture initiated ∼35 km deep and propagated unilaterally northeastward with large-slip (up to 8.4 m) distributed over a depth range of 26 to 42 km beneath the continental shelf. Aftershocks concentrate up-dip of the coseismic slip around a strong megathrust reflector with high Coulomb stress change. The ∼25 km deep up-dip edge of slip strongly correlates with a change in plate interface reflectivity apparent in reflection profiles, indicating that a structural and frictional transition provided a barrier to shallower rupture.
•Joint analysis of extensive observations to resolve the slip extent of the 2021 Chignik earthquake.•High-resolution slip is determined by iteration of the finite-fault inversion and tsunami predictions.•The 2021 earthquake ruptured a deeper portion of the Semidi segment with no shallow slip.•Complex physical state of the subduction zone controlled the up-dip limits of the rupture.•The 2021 rupture prompts us to reevaluate the rupture zone of the 1938 earthquake.
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