Analysis of the Significance of Key Points of Patient Interventional Psychological Care and Pain Care

Interventional therapy has become increasingly popular in clinical practice due to advancements in medical technology.However,patients often experience psychological and physiological pressure due to its invasive nature.The management of patient discomfort and tension is crucial to ensure effective treatment.Psychological and pain management are essential components of interventional therapy,as they significantly impact patient recovery and prognosis.This article discussed the importance of interventional psychological and pain care for patients,starting with the development and spread of interventional therapy.The significance of providing high-quality nursing services to patients and improving their quality of life was also discussed.

DM Code Key Point Detection Algorithm Based on CenterNet

Data Matrix(DM)codes have been widely used in industrial production.The reading of DM code usually includes positioning and decoding.Accurate positioning is a prerequisite for successful decoding.Traditional image processing methods have poor adaptability to pollution and complex backgrounds.Although deep learning-based methods can automatically extract features,the bounding boxes cannot entirely fit the contour of the code.Further image processing methods are required for precise positioning,which will reduce efficiency.Because of the above problems,a CenterNet-based DM code key point detection network is proposed,which can directly obtain the four key points of the DM code.Compared with the existing methods,the degree of fitness is higher,which is conducive to direct decoding.To further improve the positioning accuracy,an enhanced loss function is designed,including DM code key point heatmap loss,standard DM code projection loss,and polygon Intersection-over-Union(IoU)loss,which is beneficial for the network to learn the spatial geometric characteristics of DM code.The experiment is carried out on the self-made DM code key point detection dataset,including pollution,complex background,small objects,etc.,which uses the Average Precision(AP)of the common object detection metric as the evaluation metric.AP reaches 95.80%,and Frames Per Second(FPS)gets 88.12 on the test set of the proposed dataset,which can achieve real-time performance in practical applications.

SIX KEY POINTS FOR ACUPUNCTURE THERAPY

In the present paper, the author puts forward six key points for acupuncture treatment of diseases, namely, (1) careful examination, (2) definite diagnosis, (3) precise and appropriate identification of syndromes, (4) accurate location of the acupoint, (5) flexible application of needling manipulations, and (6) 'Deqi'. The first three aspects are the foundation, accurate location and flexible needling manipulations are also the prerequisite for effective treatment of diseases. In addition, sound theoretical basic knowledge of both traditional Chinese medicine (TCM) and modern medicine, and flexibly applying suitable needling maneuvers, stimulating quantity and duration of needle retaining in accordance with the concrete state of disease and the patient's conditions are also very important in clinical practice of acupuncture.

CPC Central Committee and the State Council Issued the Decision to Specify the “Key Points” of the Universal Two-child Policy

The Decision of CPC Central Committee and the State Council on Full Implementation of the Universal Two-child policy and Innovation and Improvement of Family Planning Service Management（Hereinafterreferred to as the Decision）was announced on January 5,2016.China will implement a birth registration service system.

Key Points of Supervision of Third-Party Payment Business:A Comparative Analysis of China's Mainland and Macao

China's Mainland has witnessed remarkable achievements in payment innovations based on internet and financial technologies in recent years,whereas Macao has made little progress in financial technologies,especially in payment technologies.Based on the concept and types of third-party payment business in China's Mainland and Macao,as well as to investigate the causes for Macao’s lack of innovation in third-party payment,this study compares their differences from two aspects:business licensing authorities and key points of supervision.By comparison,although the classification and methods of third-party payment businesses are different between China's Mainland and Macao,they are all managed and licensed by a unified supervision department.Moreover,the key points of supervision in both places are similar,but unlike China's Mainland,which takes financial risk prevention as the principle and financial technology as the means to encourage innovation,Macao showed obvious deficiencies.In order to further deepen the connection between the financial markets of China's Mainland and Macao as well as boost financial technologies in Macao,this study aims to provide some suggestions and references for the development of cross-border payment systems.

Real-Time Safety Behavior Detection Technology of Indoors Power Personnel Based on Human Key Points

Safety production is of great significance to the development of enterprises and society.Accidents often cause great losses because of the particularity environment of electric power.Therefore,it is important to improve the safety supervision and protection in the electric power environment.In this paper,we simulate the actual electric power operation scenario by monitoring equipment and propose a real-time detection method of illegal actions based on human body key points to ensure safety behavior in real time.In this method,the human body key points in video frames were first extracted by the high-resolution network,and then classified in real time by spatial-temporal graph convolutional network.Experimental results show that this method can effectively detect illegal actions in the simulated scene.

IMPACT OF SSTA OF SOUTHERN HEMISPHERE ON FLOOD SEASON PRECIPITATION ANOMALIES IN YUNNAN

Based on the reanalysis data of monthly mean global SST and wind from the NCEP/NCAR and the observation data of rain seasons in 124 stations of Yunnan province from 1961 to 2006, we applied the analytical methods of correlation analysis and composite analysis and a significance testing method to two sets of samples of average differences. The goal is to investigate into the influence of the Southern Hemispheric(SH) SST on the summer precipitation in Yunnan from January to May so as to identify the key time and marine regions. Physical mechanisms are obtained by analyzing the influence of sea level wind and the key marine regions on the precipitation during Yunnan's rain season.Results show that there is indeed significant relationship between the SST in SH and summer precipitation in Yunnan.The key areas for influencing the summer precipitation are mainly distributed in a region called "West Wind Drift" in the SH, including the Southeast Indian, southern Australia, west coast of eastern Pacific off Chile, Peru and the southwest Atlantic Magellan. Besides, the most significant marine region is the west coast of Chile and Peru(cold-current areas of the eastern Pacific). Diagnostic analysis results also showed that monsoons in the Bay of Bengal, a cross-equatorial flow in the Indian Ocean near the equator and southwest monsoon in India weaken during the warm phase of the Peruvian cold current in the eastern Pacific. Otherwise, they strengthen.

Exploration of Key Points in Evaluation Criteria of Chinese Medicine in the Treatment of Cancer

Although tumor node metastasis （TNM） staging in WM has been accepted and used with no reserve in the criteria of CM diagnosis and treatment of cancer, some differences still exist between CM and WM in the evaluation criterion. The reason for this focuses on evaluation criterion of effects on solid tumor made by the World Health Organization （WHO）, which does not reflect some aspects such as survival time of tumor patients or improvement in quality of life （QOL）. Therefore, forerunners in the CM field have been making efforts to try to find an evaluation criteria that would integrate CM and WM, and thus could be approved by CM and WM specialists.

Human steering angle estimation in video based on key point detection and Kalman filter

Human pose recognition and estimation in video is pervasive.However,the process noise and local occlusion bring great challenge to pose recognition.In this paper,we introduce the Kalman filter into pose recognition to reduce noise and solve local occlusion problem.The core of pose recognition in video is the fast detection of key points and the calculation of human steering angles.Thus,we first build a human key point detection model.Frame skipping is performed based on the Hamming distance of the hash value of every two adjacent frames in video.Noise reduction is performed on key point coordinates with the Kalman filter.To calculate the human steering angle,current state information of key points is predicted using the optimal estimation of key points at the previous time.Then human steering angle can be calculated based on current and previous state information.The improved SENet,NLNet and GCNet modules are integrated into key point detection model for improving accuracy.Tests are also given to illustrate the effectiveness of the proposed algorithm.

Key Points of UN Resolution 1701

Emphasizes the need for the unconditional release of the abducted Israeli soldiers Encourages efforts aimed at urgently settling the issue of the Lebanese prisoners detained in Israel Calls for a full cessation of

Key Points of the Initial Actions for the Implementation of the Joint Statement

I. The parties held serious and productive discussions onthe actions each party will take in the initial phase for theimplementation of the Joint Statement of September 19,2005. The parties reaffirmed their common goal and will

Key Points in President Hu Jintao’s Speech

Ten "observances":1. We must adhere to the fundamental principles of Marxism while combining Marxism with the reality of China. We must liberalize our thoughts, be pragmatic, advance with time, update theories according

Identification of the key thermal points on machine tools by grouping and optimizing variables

The grouping and optimization approach to identify the key thermal points on machine tools is studied.To solve the difficulty in grouping because of the high correlated variables from distinct groups,the variables grouping technique is improved.Temperature variables are sorted according to their relativities with the thermal errors.The representative temperature variables are determined by analyzing the variable correlation in sort order and removing the other variables in the same group.Considering the diverse effect of importing the different variables on thermal error model,the method of variable combination optimization is improved.Regression models made up of different combination of representative temperature variables are evaluated by the index of both the determined coefficient and the average residual squares to select the combination of the temperature variables.For the machine tools with complicated structures which need more initial temperature measuring points the improvement is demanded.The improved approach is applied to a precision horizontal machining center to identify the key thermal points.Experimental results show that the proposed approach is capable of avoiding the high correlation among the different groups' variables,effectively reducing the number of the key thermal points without depressing the prediction accuracy of the thermal error model for machine tools.

Contrast Enhancement Based Image Detection Using Edge Preserved Key Pixel Point Filtering

In existing methods for segmented images,either edge point extraction or preservation of edges,compromising contrast images is so sensitive to noise.The Degeneration Threshold Image Detection(DTID)framework has been proposed to improve the contrast of edge filtered images.Initially,DTID uses a Rapid Bilateral Filtering process for filtering edges of contrast images.This filter decomposes input images into base layers in the DTID framework.With minimal filtering time,Rapid Bilateral Filtering handles high dynamic contrast images for smoothening edge preservation.In the DTID framework,Rapid Bilateral Filtering with Shift-Invariant Base Pass Domain Filter is insensitive to noise.This Shift-Invariant Filtering estimates value across edges for removing outliers(i.e.,noise preserving base layers of the contrast image).The intensity values are calculated in the base layer of the contrast image for accurately detecting nearby spatial locations using Shift-Invariant base Pass Domain Filter(SIDF).At last,Affine Planar Transformation is applied to detect edge filtered contrast images in the DTID framework for attaining a high quality of the image.It normalizes the translation and rotation of images.With this,Degeneration Threshold Image Detection maximizes average contrast enhancement quality and performs an experimental evaluation of factors such as detection accuracy,rate,and filtering time on contrast images.Experimental analysis shows that the DTID framework reduces the filtering time taken on contrast images by 54%and improves average contrast enhancement quality by 27%compared to GUMA,HMRF,SWT,and EHS.It provides better performance on the enhancement of average contrast enhancement quality by 28%,detection accuracy rate by 26%,and reduction in filtering time taken on contrast images by 30%compared to state-of-art methods.

The Research on the Methods to Confirm the Key Control Points of the Internal Accounting Control

The control points are the key issue of the internal control, and the key control points mean the control joints that play an important part in the process of the operation. If these key control points are not well controlled, the process of the operation disposal will be mistaken and cannot reach the goal. However, for the confirmation of the control points, many studies only do qualitative research from the important aspect instead of quantificational research. They are devoid of a series of scientific methods. This paper advances quantificational methods to confirm the key control points from two aspects of job evaluation and mathematics model.

Multi-Branch High-Dimensional Guided Transformer-Based 3D Human Posture Estimation

The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional(3D)method that takes into account self-occlusion,badly posedness,and a lack of depth data in the per-frame 3D posture estimation from two-dimensional(2D)mapping to 3D mapping.Firstly,by examining the relationship between the movements of different bones in the human body,four virtual skeletons are proposed to enhance the cyclic constraints of limb joints.Then,multiple parameters describing the skeleton are fused and projected into a high-dimensional space.Utilizing a multi-branch network,motion features between bones and overall motion features are extracted to mitigate the drift error in the estimation results.Furthermore,the estimated relative depth is projected into 3D space,and the error is calculated against real 3D data,forming a loss function along with the relative depth error.This article adopts the average joint pixel error as the primary performance metric.Compared to the benchmark approach,the estimation findings indicate an increase in average precision of 1.8 mm within the Human3.6M sample.

Human Interaction Recognition in Surveillance Videos Using Hybrid Deep Learning and Machine Learning Models

Human Interaction Recognition(HIR)was one of the challenging issues in computer vision research due to the involvement of multiple individuals and their mutual interactions within video frames generated from their movements.HIR requires more sophisticated analysis than Human Action Recognition(HAR)since HAR focuses solely on individual activities like walking or running,while HIR involves the interactions between people.This research aims to develop a robust system for recognizing five common human interactions,such as hugging,kicking,pushing,pointing,and no interaction,from video sequences using multiple cameras.In this study,a hybrid Deep Learning(DL)and Machine Learning(ML)model was employed to improve classification accuracy and generalizability.The dataset was collected in an indoor environment with four-channel cameras capturing the five types of interactions among 13 participants.The data was processed using a DL model with a fine-tuned ResNet(Residual Networks)architecture based on 2D Convolutional Neural Network(CNN)layers for feature extraction.Subsequently,machine learning models were trained and utilized for interaction classification using six commonly used ML algorithms,including SVM,KNN,RF,DT,NB,and XGBoost.The results demonstrate a high accuracy of 95.45%in classifying human interactions.The hybrid approach enabled effective learning,resulting in highly accurate performance across different interaction types.Future work will explore more complex scenarios involving multiple individuals based on the application of this architecture.