Assessing healthcare workers’knowledge and confidence in the diagnosis,management and prevention of Monkeypox

BACKGROUND Monkeypox(Mpox),is a disease of global public health concern,as it does not affect only countries in western and central Africa.AIM To assess Burundi healthcare workers(HCWs)s’level of knowledge and confidence in the diagnosis and management of Mpox.METHODS We conducted a cross-sectional study via an online survey designed mainly from the World Health Organization course distributed among Burundi HCWs from June-July 2023.The questionnaire comprises 8 socioprofessional-related questions,22 questions about Mpox disease knowledge,and 3 questions to assess confidence in Mpox diagnosis and management.The data were analyzed via SPSS software version 25.0.A P value<0.05 was considered to indicate statistical significance.RESULTS The study sample comprised 471 HCWs who were mainly medical doctors(63.9%)and nurses(30.1%).None of the 22 questions concerning Mpox knowledge had at least 50%correct responses.A very low number of HCWs(17.4%)knew that Mpox has a vaccine.The confidence level to diagnose(21.20%),treat(18.00%)or prevent(23.30%)Mpox was low among HCWs.The confidence level in the diagnosis of Mpox was associated with the HCWs’age(P value=0.009),sex(P value<0.001),work experience(P value=0.002),and residence(P value<0.001).The confidence level to treat Mpox was significantly associated with the HCWs’age(P value=0.050),sex(P value<0.001),education(P value=0.033)and occupation(P value=0.005).The confidence level to prevent Mpox was associated with the HCWs’education(P value<0.001),work experience(P value=0.002),residence(P value<0.001)and type of work institution(P value=0.003).CONCLUSION This study revealed that HCWs have the lowest level of knowledge regarding Mpox and a lack of confidence in the ability to diagnose,treat or prevent it.There is an urgent need to organize continuing medical education programs on Mpox epidemiology and preparedness for Burundi HCWs.We encourage future researchers to assess potential hesitancy toward Mpox vaccination and its associated factors.

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.

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.

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.

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.

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.

PMMA和Confidence高粘度骨水泥在老年胸腰椎骨折椎体成形术中的应用效果对比分析

目的比较PMMA和Confidence高粘度骨水泥在老年胸腰椎骨折椎体成形术中的应用效果。方法选取2014年12月~2016年5月我院收治的116例老年胸腰椎骨折行椎体成形术患者,按照随机数字表法将患者分为研究组和对照组,每组58例。研究组使用Confidence高粘度骨水泥填充,对照组使用PMMA骨水泥填充。比较两组手术时间、术中出血量、住院时间、手术前后VAS评分、生活质量水平、椎体前缘高度及Cobb角的差异。结果研究组手术时间、术中出血量、住院时间分别为（73.36±14.83）min、（25.82±6.15）ml、（6.82±2.17）d,与对照组的（78.12±15.19）min、（28.10±6.39）ml、（7.08±2.09）d比较均无显著差异（P〉0.05）。术前,两组VAS评分比较无统计学差异（P〉0.05）。术后3 d、术后10 d、术后30 d,研究组VAS评分分别为（3.06±0.53）分、（1.87±0.42）分、（1.04±0.25）分,均显著低于对照组的（3.56±0.57）分、（2.33±0.51）分、（1.80±0.31）分（P〈0.05）。研究组手术后SF-36各维度评分均较对照组的为优（P〈0.05）;两组椎体前缘高度及Cobb角比较无显著差异（P〉0.05）。结论 Confidence高粘度骨水泥用于老年胸腰椎骨折椎体成形术效果好,在减轻疼痛和提高生活质量方面比PMMA骨水泥更优,二者在改善椎体前缘高度及Cobb角方面效果相当。

基于All-confidence的正负关联分类

针对分类中如何有效利用负关联模式提高分类准确率,提出了一种基于正负关联模式的分类算法.利用类Apriori算法挖掘包含正项或/和负项且项与项之间互相关联的正负关联模式来产生分类规则.为提高挖掘效率,先找出能覆盖训练集的信息熵最小k个正/负项.然后,把这k个正/负项分别与其他项进行连接得到相应的正负关联模式.实验表明,该算法有效减少了挖掘的规则数,极大减少了挖掘时间,并提高了分类准确率.

On the Confidence Limits of Bernoulli Parameter in Any Sequential Case

Let x 1,x 2,… be independent identically distributed (i.i.d.) random variables, in which x n=0 or 1 and the probability of ｛x n=1｝ is p. Here p is unknown. Let τ be any finite stopping time for (x n,n1). For any sequential sample (x 1,x 2,…,x τ ) and γ∈(0,1), we have given an optimal confidence limit of p with confidence level γ . Some related problems are also discussed.

Time-varying confidence interval forecasting of travel time for urban arterials using ARIMA-GARCH model

To improve the forecasting reliability of travel time, the time-varying confidence interval of travel time on arterials is forecasted using an autoregressive integrated moving average and generalized autoregressive conditional heteroskedasticity （ARIMA-GARCH） model. In which, the ARIMA model is used as the mean equation of the GARCH model to model the travel time levels and the GARCH model is used to model the conditional variances of travel time. The proposed method is validated and evaluated using actual traffic flow data collected from the traffic monitoring system of Kunshan city. The evaluation results show that, compared with the conventional ARIMA model, the proposed model cannot significantly improve the forecasting performance of travel time levels but has advantage in travel time volatility forecasting. The proposed model can well capture the travel time heteroskedasticity and forecast the time-varying confidence intervals of travel time which can better reflect the volatility of observed travel times than the fixed confidence interval provided by the ARIMA model.

The Effect of Self-confidence in SLA

Krashen's affective filter theory drew researchers' attention to the fresh field of internal factors of learners.But researchers seem to pay too much attention to the other factors while tending to neglect a factor that we cannot afford to play down in language learning research-self-confidence.This paper is designed to discuss the close relationship between self-confidence and some of the other important affective factors,and furthermore raise appropriate concern over self-confidence.

基于最大频繁all-confidence模式的二次挖掘探讨药物模式组配规律

方剂配伍规律研究是方剂学难点和重点课题之一.利用all-confidence度量,挖掘最大频繁all-confidence模式.提出基于最大频繁all-confidence模式的二次挖掘方法,挖掘药物模式之间的组配规则,从而探寻药物模式之间的组配规律,辅助方剂配伍规律研究,并为临床组方提供规则参考.首先给出最大频繁all-confidence模式等相关的概念,其次给出基于最大频繁all-confidence模式的二次挖掘方法,最后在方剂数据库上进行实验,实验结果表明基于最大频繁all-confidence模式的二次挖掘能有效挖掘大量药物模式之间的组配规则,有利于药物模式之间组配规律的探寻.

Regaining of morning erection and sexual confidence in patients with erectile dysfunction

Aim： To investigate how erectile dysfunction （ED） medications affect morning erection in patients with ED and how they respond to the return of morning erection. Methods： This study was conducted in 120 patients who experienced successful intercourse with either tadalafil or sildenafil. Using a random face-to-face interview and a questionnaire （about the quality and number of days getting morning erection after using the two medications）, the impact of the medications on the morning erections was investigated, and the participants were asked about their feelings on regaining morning erection. Results： Of the respondents, 81% （68% of those with sildenafil and 99% of those with tadalafil） experienced morning erections after taking an oral ED medication. The men who took tadalafil mainly for 2 days with one dose, while those who took sildenafil experienced morning erections mainly for 1 day. The major sentiment upon regaining a morning erection was, ＂having more confidence as a man＂ （74%）. Among the 96 respondents who experienced morning erections with tadalafil, 52% preferred tadalafil over sildenafil, not only because of freedom from concerns about a specific time to have relations, but also regaining morning erection. Conclusion： Regaining a morning erection affects the recovery of confidence as a man and influences the preference for tadalafil over sildenafil.

How to Strengthen Child Learners' Self-confidence

Self-confidence is closely related to L2 learning.In order to make their learners enjoy English learning,English teach ers need to strengthen the learners’English learning confidence.This article is to find out English teachers’role to strengthening learners’self-confidence in ELL by means of class observation.The writer concludes improving speaking ability;giving praise and offering gentle error correcting can help to strengthen the learners’confidence.

Consumer confidence and consumers’ preferences for infant formulas in China

A series of safety incidents related to domestically-produced infant formulas(DIFs) almost destroyed Chinese consumer confidence in domestic dairy products. Understanding consumer confidence and its effect on consumption behavior is important to restore consumer confidence and enhance the competitiveness of domestic dairy industry. This article first measures Chinese consumer confidence in DIFs safety using a two-dimension scale(optimism and pessimism) and then investigates its effect on consumers’ preferences for DIFs through a choice experiment. Involving 450 consumers residing in Beijing, Tianjin and Shijiazhuang, the experiment elicited information on their preferences for DIFs with six attributes(organic raw milk, farm-to-table traceability, region of origin, quality certification, animal welfare, and price). Our analysis yields four findings. First, Chinese consumers still lack confidence in DIFs safety. Second, quality certification is the most preferred product characteristic, followed by organic raw milk and farm-to-table traceability. Third, serving as a signal of high quality, price exerts a positive impact on choices of DIFs for pessimistic consumers. Finally, consumer preferences for farm-to-table traceability and region of origin are significantly influenced by consumer confidence.

ON CONFIDENCE REGIONS OF SEMIPARAMETRIC NONLINEAR REGRESSION MODELS(A GEOMETRIC APPROACH)

A geometric framework is proposed for semiparametric nonlinear regression models based on the concept of least favorable curve, introduced by Severini and Wong (1992). The authors use this framework to drive three kinds of improved approximate confidence regions for the parameter and parameter subset in terms of curvatures. The results obtained by Hamilton et al. (1982), Hamilton (1986) and Wei (1994) are extended to semiparametric nonlinear regression models.

Confidence support vector domain description

To accelerate the training of support vector domain description （SVDD）, confidence support vector domain description （CSVDD） is proposed based on the observation that the description boundary is determined by a small subset of training data called support vectors. Namely, the number of training samples in the userdefined sphere is calculated and taken as the confidence measure, according to which the training samples are ranked in ascending order. Those former ranked ones are selected as the boundary targets for the SVDD training. Simulations on UCI data demonstrate the effectiveness and superiority of CSVDD： the number of training targets and the training time are reduced without any loss of accuracy.

Statistical damage detection method for frame structures using a confidence interval

A novel damage detection method is applied to a 3-story frame structure, to obtain statistical quantification control criterion of the existence, location and identification of damage. The mean, standard deviation, and exponentially weighted moving average （EWMA） are applied to detect damage information according to statistical process control （SPC） theory. It is concluded that the detection is insignificant with the mean and EWMA because the structural response is not independent and is not a normal distribution. On the other hand, the damage information is detected well with the standard deviation because the influence of the data distribution is not pronounced with this parameter. A suitable moderate confidence level is explored for more significant damage location and quantification detection, and the impact of noise is investigated to illustrate the robustness of the method.

Deep learning-based evaluation of factor of safety with confidence interval for tunnel deformation in spatially variable soil

The random finite difference method(RFDM) is a popular approach to quantitatively evaluate the influence of inherent spatial variability of soil on the deformation of embedded tunnels.However,the high computational cost is an ongoing challenge for its application in complex scenarios.To address this limitation,a deep learning-based method for efficient prediction of tunnel deformation in spatially variable soil is proposed.The proposed method uses one-dimensional convolutional neural network(CNN) to identify the pattern between random field input and factor of safety of tunnel deformation output.The mean squared error and correlation coefficient of the CNN model applied to the newly untrained dataset was less than 0.02 and larger than 0.96,respectively.It means that the trained CNN model can replace RFDM analysis for Monte Carlo simulations with a small but sufficient number of random field samples(about 40 samples for each case in this study).It is well known that the machine learning or deep learning model has a common limitation that the confidence of predicted result is unknown and only a deterministic outcome is given.This calls for an approach to gauge the model’s confidence interval.It is achieved by applying dropout to all layers of the original model to retrain the model and using the dropout technique when performing inference.The excellent agreement between the CNN model prediction and the RFDM calculated results demonstrated that the proposed deep learning-based method has potential for tunnel performance analysis in spatially variable soils.

Similarity quantification of soil parametric data and sites using confidence ellipses

This paper presents a confidence ellipse-based method to evaluate the similarity of soil parametric data using the database from the site investigation reports.Then,the obtained similarity assessment results of parametric data are used to further estimate the site similarity via two proposed strategies,namely the mean and weighted mean approaches.The former referred to the average of parametric data similarity degrees,while the latter was the weighted average,and the weight was calculated using the coefficient of variation(COV)of each parameter.For illustration,the liquidity index(LI)dataset was firstly used to explore the performance of the presented method in the evaluation of parametric data similarity.Subsequently,the site similarity was assessed and the effects of numbers and weights of selected parameters for study were systematically studied.Lastly,the transformation models about the relationships between Cc and x as well as between Cc and e0 were constructed to illustrate the application of the similarity analysis in reduction of transformation uncertainty.Results show that the greatest site similarity degree is at about 0.76 in this study,and the maximum decrease of transformation uncertainty can reach up to 18%and 25.5%as union parametric data similarity degree increases.Moreover,the site similarity degree represents the whole similarity between two different sites,and the presented union parameter similarity degree maintains a good agreement with transformation uncertainty.