Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving

Risk assessment is a crucial component of collision warning and avoidance systems for intelligent vehicles.Reachability-based formal approaches have been developed to ensure driving safety to accurately detect potential vehicle collisions.However,they suffer from over-conservatism,potentially resulting in false–positive risk events in complicated real-world applications.In this paper,we combine two reachability analysis techniques,a backward reachable set(BRS)and a stochastic forward reachable set(FRS),and propose an integrated probabilistic collision–detection framework for highway driving.Within this framework,we can first use a BRS to formally check whether a two-vehicle interaction is safe;otherwise,a prediction-based stochastic FRS is employed to estimate the collision probability at each future time step.Thus,the framework can not only identify non-risky events with guaranteed safety but also provide accurate collision risk estimation in safety-critical events.To construct the stochastic FRS,we develop a neural network-based acceleration model for surrounding vehicles and further incorporate a confidence-aware dynamic belief to improve the prediction accuracy.Extensive experiments were conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving data.The efficiency and effectiveness of the framework with infused confidence beliefs were tested in both naturalistic and simulated highway scenarios.The proposed risk assessment framework is promising for real-world applications.

A Brief Overview of the Development and Significance of Modern Scientific Medicine in China:Take Chinese Academy of Medical Science and Peking Union Medical College as an Example

Since the early 20th century,China has gradually established a clinical,educational,and research system centered around modern scientific medicine,which has now become the dominant force in China's medical and health system and services,with the construction and development of the Chinese Academy of Medical Sciences and Peking Union Medical College as the most prominent symbol.The scientific medicine in the new era requires close cooperation across multiple disciplines and fields to build a high-quality and efficient medical and health service system.It also involves combining the excellent traditional Chinese culture with Western medicine to explore a unique path of modern scientific medicine with Chinese characteristics.

Low-Carbon Dispatch of an Integrated Energy System Considering Confidence Intervals for Renewable Energy Generation

Addressing the insufficiency in down-regulation leeway within integrated energy systems stemming from the erratic and volatile nature of wind and solar renewable energy generation,this study focuses on formulating a coordinated strategy involving the carbon capture unit of the integrated energy system and the resources on the load storage side.A scheduling model is devised that takes into account the confidence interval associated with renewable energy generation,with the overarching goal of optimizing the system for low-carbon operation.To begin with,an in-depth analysis is conducted on the temporal energy-shifting attributes and the low-carbon modulation mechanisms exhibited by the source-side carbon capture power plant within the context of integrated and adaptable operational paradigms.Drawing from this analysis,a model is devised to represent the adjustable resources on the charge-storage side,predicated on the principles of electro-thermal coupling within the energy system.Subsequently,the dissimilarities in the confidence intervals of renewable energy generation are considered,leading to the proposition of a flexible upper threshold for the confidence interval.Building on this,a low-carbon dispatch model is established for the integrated energy system,factoring in the margin allowed by the adjustable resources.In the final phase,a simulation is performed on a regional electric heating integrated energy system.This simulation seeks to assess the impact of source-load-storage coordination on the system’s low-carbon operation across various scenarios of reduction margin reserves.The findings underscore that the proactive scheduling model incorporating confidence interval considerations for reduction margin reserves effectively mitigates the uncertainties tied to renewable energy generation.Through harmonized orchestration of source,load,and storage elements,it expands the utilization scope for renewable energy,safeguards the economic efficiency of system operations under low-carbon emission conditions,and empirically validates the soundness and efficacy of the proposed approach.

China Continues to Bring Confidence to Global Economy Recovery

Looking forward to 2024,what opportunities and challenges will global development face?In particular,how can the global economy get rid of the“sequelae”of the pandemic?At the 170th“Monthly Talk on the Economy”held recently by the China Center for International Economic Exchange,experts analyzed the world’s economy since the outbreak of COVID-19,and made clear the context of and provided suggestions for global development in 2024.

Wang Yi Addresses Symposium on International Situation and China's Foreign Relations in 2023

On January 9,2024,the Symposium on the International Situation and China’s Foreign Relations in 2023 was held in Beijing.Member of the Political Bureau of the CPC Central Committee and Foreign Minister Wang Yi attended the symposium and delivered a keynote speech entitled“Self-Confidence and SelfReliance,Openness and Inclusiveness,Fairness and Justice,and Win-Win Cooperation.”

Measures of Variability for Qualitative Variables Using the R Software

Although there are many measures of variability for qualitative variables, they are little used in social research, nor are they included in statistical software. The aim of this article is to present six measures of variation for qualitative variables of simple calculation, as well as to facilitate their use by means of the R software. The measures considered are, on the one hand, Freemans variation ratio, Morals universal variation ratio, Kvalseths standard deviation from the mode, and Wilcoxs variation ratio which are most affected by proximity to a constant random variable, where the measures of variability for qualitative variables reach their minimum value of 0. On the other hand, the Gibbs-Poston index of qualitative variation and Shannons relative entropy are included, which are more affected by the proximity to a uniform distribution, where the measures of variability for qualitative variables reach their maximum value of 1. Point and interval estimation are addressed. Bootstrap by the percentile and bias-corrected and accelerated percentile methods are used to obtain confidence intervals. Two calculation situations are presented: with a sample mode and with two or more modes. The standard deviation from the mode among the six considered measures, and the universal variation ratio among the three variation ratios, are particularly recommended for use.

The Application of the Domestication and Foreignization Translation Strategies in the Context of the Dissemination of Chinese Culture:A Case Study of the English Translation of Long

In January 2024,the Chinese Year of the Loong,how to translate the Chinese character Long went viral on the Internet.Taking the English translation of Long as an example,this study tends to make a tentative analysis of the application of translation strategies in the macro context of the dissemination of Chinese culture.Moreover,it probes into the phenomenon of Chinese cultural aphasia by analyzing the domestication and foreignization of Long in three aspects.The three attitudes provided ought to be addressed in the Chinese-English translations.This study emphasizes enhancing translators’cultural awareness and their cultural confidence in the process of translating the source language to the target language.

Ministry of Commerce:Full of confidence,clear direction,strong motivation

On March 12,the official account of the International Business Daily of the Ministry of Commerce published an article titled“Foreign trade enterprises:full of confidence,clear direction,strong motivation”.The content is as follows:This year,China is still facing a tough foreign trade situation.On the one hand,many international organizations predict that global economic growth in 2024 will still be lower than the historical average and the pressure on external demand will increase;on the other hand,trade protectionism and unilateralism are on the rise,geopolitical conflicts,the global“super election year”and other spillover factors have created many uncertainties.

The Dynamic Interplay Between Language Motivation and English Speaking Fluency:Implications for Effective Teaching Strategies

In this study,we aim to investigate the reciprocal influence between language motivation and English speaking fluency among language learners,and to draw implications for effective teaching methodologies.By analyzing multiple cases of language learners in conjunction with relevant theories and practical insights,the study uncovers a dynamic correlation between language motivation and speaking fluency.The research findings indicate that heightened language motivation can positively impact learners’speaking fluency,while improved oral skills,in turn,bolster learners’language confidence and motivation.Building on these insights,the study proposes impactful teaching approaches,such as cultivating learners’enthusiasm for language acquisition,providing diverse opportunities for oral practice,and fostering active engagement in language communication.These strategies are designed to enhance language motivation and speaking fluency among learners,offering valuable guidance and reference for educators.

Analysis of the Reform of Ideological and Political Education in College English Curriculum from the Perspective of“Cultural Confidence”

With the rapid development of the education industry,higher requirements have been put forward for college English education and teaching,accelerating the pace of college English reform.Issues such as how to integrate knowledge transmission,language expression skill improvement,cultural element extraction,and ideological and political education,and present the role of curriculum education function have attracted attention.In the era of informatization,college students’thinking has undergone certain changes and gradually developed towards diversification.Therefore,universities should start from the perspective of cultural confidence,extract valuable ideological and political elements,and explore effective tourism ideological and political teaching methods.While helping college students enhance their cultural confidence,it is also beneficial for them to better inherit traditional Chinese culture.This article conducts an in-depth exploration of ideological and political education in college English courses from the perspective of“cultural confidence”and uses scientific and effective measures to solve the problems encountered,aiming to provide valuable guidelines for relevant researchers.

A geographical similarity-based sampling method of non-fire point data for spatial prediction of forest fires

Understanding the mechanisms and risks of forest fires by building a spatial prediction model is an important means of controlling forest fires.Non-fire point data are important training data for constructing a model,and their quality significantly impacts the prediction performance of the model.However,non-fire point data obtained using existing sampling methods generally suffer from low representativeness.Therefore,this study proposes a non-fire point data sampling method based on geographical similarity to improve the quality of non-fire point samples.The method is based on the idea that the less similar the geographical environment between a sample point and an already occurred fire point,the greater the confidence in being a non-fire point sample.Yunnan Province,China,with a high frequency of forest fires,was used as the study area.We compared the prediction performance of traditional sampling methods and the proposed method using three commonly used forest fire risk prediction models:logistic regression(LR),support vector machine(SVM),and random forest(RF).The results show that the modeling and prediction accuracies of the forest fire prediction models established based on the proposed sampling method are significantly improved compared with those of the traditional sampling method.Specifically,in 2010,the modeling and prediction accuracies improved by 19.1%and 32.8%,respectively,and in 2020,they improved by 13.1%and 24.3%,respectively.Therefore,we believe that collecting non-fire point samples based on the principle of geographical similarity is an effective way to improve the quality of forest fire samples,and thus enhance the prediction of forest fire risk.

Bayesian Computation for the Parameters of a Zero-Inflated Cosine Geometric Distribution with Application to COVID-19 Pandemic Data

A new three-parameter discrete distribution called the zero-inflated cosine geometric(ZICG)distribution is proposed for the first time herein.It can be used to analyze over-dispersed count data with excess zeros.The basic statistical properties of the new distribution,such as the moment generating function,mean,and variance are presented.Furthermore,confidence intervals are constructed by using the Wald,Bayesian,and highest posterior density(HPD)methods to estimate the true confidence intervals for the parameters of the ZICG distribution.Their efficacies were investigated by using both simulation and real-world data comprising the number of daily COVID-19 positive cases at the Olympic Games in Tokyo 2020.The results show that the HPD interval performed better than the other methods in terms of coverage probability and average length in most cases studied.

Prediction of Uncertainty Estimation and Confidence Calibration Using Fully Convolutional Neural Network

Convolution neural networks(CNNs)have proven to be effective clinical imagingmethods.This study highlighted some of the key issues within these systems.It is difficult to train these systems in a limited clinical image databases,and many publications present strategies including such learning algorithm.Furthermore,these patterns are known formaking a highly reliable prognosis.In addition,normalization of volume and losses of dice have been used effectively to accelerate and stabilize the training.Furthermore,these systems are improperly regulated,resulting in more confident ratings for correct and incorrect classification,which are inaccurate and difficult to understand.This study examines the risk assessment of Fully Convolutional Neural Networks(FCNNs)for clinical image segmentation.Essential contributions have been made to this planned work:1)dice loss and cross-entropy loss are compared on the basis of segment quality and uncertain assessment of FCNNs;2)proposal for a group model for assurance measurement of full convolutional neural networks trained with dice loss and group normalization;And 3)the ability of the measured FCNs to evaluate the segment quality of the structures and to identify test examples outside the distribution.To evaluate the study’s contributions,it conducted a series of tests in three clinical image division applications such as heart,brain and prostate.The findings of the study provide significant insights into the predictive ambiguity assessment and a practical strategies for outside-distribution identification and reliable measurement in the clinical image segmentation.The approaches presented in this research significantly enhance the reliability and accuracy rating of CNNbased clinical imaging methods.

Monocular 3D object detection with Pseudo-LiDAR confidence sampling and hierarchical geometric feature extraction in 6G network

The high bandwidth and low latency of 6G network technology enable the successful application of monocular 3D object detection on vehicle platforms.Monocular 3D-object-detection-based Pseudo-LiDAR is a low-cost,lowpower solution compared to LiDAR solutions in the field of autonomous driving.However,this technique has some problems,i.e.,(1)the poor quality of generated Pseudo-LiDAR point clouds resulting from the nonlinear error distribution of monocular depth estimation and(2)the weak representation capability of point cloud features due to the neglected global geometric structure features of point clouds existing in LiDAR-based 3D detection networks.Therefore,we proposed a Pseudo-LiDAR confidence sampling strategy and a hierarchical geometric feature extraction module for monocular 3D object detection.We first designed a point cloud confidence sampling strategy based on a 3D Gaussian distribution to assign small confidence to the points with great error in depth estimation and filter them out according to the confidence.Then,we present a hierarchical geometric feature extraction module by aggregating the local neighborhood features and a dual transformer to capture the global geometric features in the point cloud.Finally,our detection framework is based on Point-Voxel-RCNN(PV-RCNN)with high-quality Pseudo-LiDAR and enriched geometric features as input.From the experimental results,our method achieves satisfactory results in monocular 3D object detection.

Aggregation operators and CRITIC-VIKOR method for confidence complex q-rung orthopair normal fuzzy information and their applications

Supply chain management is an essential part of an organisation's sustainable programme.Understanding the concentration of natural environment,public,and economic influence and feasibility of your suppliers and purchasers is becoming progressively familiar as all industries are moving towards a massive sustainable potential.To handle such sort of developments in supply chain management the involvement of fuzzy settings and their generalisations is playing an important role.Keeping in mind this role,the aim of this study is to analyse the role and involvement of complex q-rung orthopair normal fuzzy(CQRONF)information in supply chain management.The major impact of this theory is to analyse the notion of confidence CQRONF weighted averaging,confidence CQRONF ordered weighted averaging,confidence CQRONF hybrid averaging,confidence CQRONF weighted geometric,confidence CQRONF ordered weighted geometric,confidence CQRONF hybrid geometric operators and try to diagnose various properties and results.Furthermore,with the help of the CRITIC and VIKOR models,we diagnosed the novel theory of the CQRONF-CRITIC-VIKOR model to check the sensitivity analysis of the initiated method.Moreover,in the availability of diagnosed operators,we constructed a multi-attribute decision-making tool for finding a beneficial sustainable supplier to handle complex dilemmas.Finally,the initiated operator's efficiency is proved by comparative analysis.

Validity and reliability of the evaluation tool of TCM confidence in students with TCM learning experience

Objective:This study aimed to design an evaluation scale of traditional Chinese medicine(TCM)confidence and then evaluate its validity and reliability.Methods:Three parallel samples with TCM learning experience were included in this current study.Randomly selected 500 nursing students were included factor analysis and reliability and validity analyses.After 4 weeks,200 nursing students were recruited to retest the reliability.The evaluation tool of TCM confidence included 6 dimensions and 25 entries.Results:Cronbach's a coefficient of the evaluation scale was 0.95 and that of the 6 observed aspects were higher than 0.90,and splithalf reliability was 0.81.For the retest reliability,Cronbach's a coefficient of the evaluation scale was 0.93 and that of the 6 examined aspects was all over 0.80.The index of content validity Scale Content Validity Index(S-CVI)was 0.92 and Item Content Validity Index(I-CVI)was 0.83–1.00.Conclusions:Observed results may indicate that the designed tool can be used to assess the TCM confidence level of students with TCM studied experience and also can evaluate results of TCM education during studying TCM lectures.

Current Situation and Implementation Paths of International Understanding Education in Local Application-oriented Universities

It is urgent and necessary to implement international understanding education in local application-oriented colleges and universities.Improving the international competitiveness of talents and cultivating global citizens is one of the goals of higher education.This paper discussed the problems of international understanding education in local application-oriented colleges and universities,including weak policy orientation,insufficient practical exploration,aphasia of national culture,etc.It is recommended to implement the international understanding education from two ways:subject penetration and project-based professional courses.In addition,the introduction of Chinese culture cannot be ignored to prevent the absence of national culture in cross-cultural communication.

Exploration on Ideological and Political Construction of Higher Vocational English Course in Southern Xinjiang from the Perspective of "Cultural Confidence"

At present,the lack of Chinese culture in higher vocational English teaching in southern Xinjiang is serious.The existing English curriculum system is not enough to support the goal of cultivating ethnic minority college students cultural confidence,and its curriculum ideological and political construction is still in the initial stage of exploration.The combination of the Chinese national culture and the ethnic culture of Xinjiang and the ideological and political elements organically integrated into cultural confidence have endowed the vocational English education in Xinjiang with the characteristics of the times.In this paper,from the perspective of China s"cultural confidence",ideological and political elements were explored,and the background of ideological and political construction in higher vocational English teaching course in southern Xinjiang was analyzed;it is expounded that the ideological and political construction is an inevitable requirement for the high-quality development of vocational English education in southern Xinjiang,and the content design and implementation path of higher vocational English course was explored to provide new ideas and new ways for the curriculum ideological and political construction of higher vocational English teaching in southern Xinjiang.

Constructing Confidence Regions for Autoregressive-Model Parameters

We discuss formulas and techniques for finding maximum-likelihood estimators of parameters of autoregressive (with particular emphasis on Markov and Yule) models, computing their asymptotic variance-covariance matrix and displaying the resulting confidence regions;Monte Carlo simulation is then used to establish the accuracy of the corresponding level of confidence. The results indicate that a direct application of the Central Limit Theorem yields errors too large to be acceptable;instead, we recommend using a technique based directly on the natural logarithm of the likelihood function, verifying its substantially higher accuracy. Our study is then extended to the case of estimating only a subset of a model’s parameters, when the remaining ones (called nuisance) are of no interest to us.