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.

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.

TARGET INFORMATION DIFFERENTIATING TECHNOLOGY IN INFRARED-RADAR DUAL-MODE GUIDANCE SYSTEM

An improved target tracking information differentiating system using the neural network to substitute for fuzzy rules is presented for the infrared-radar dual-mode guidance system. Since the neural network training based on the expert knowledge database is conducted off-line, the benefits for developing real-time tracking capabilities can be obtained. The network outputs the confidence degree denoted by the weight value of target information in the data fusion center according to two input variables of the measurement noise covariance and the tracking filter covariance. Simulation results show that the improved system can differentiate the target tracking information from the seeker fast and accurately.

Moving Ahead with Confidence

NEW quality productive forces were one of the most frequently mentioned keywords for China’s high-quality development in the past year.The annual Central Economic Work Conference(CEWC)in December that reviewed the economy in 2024 and gave directions for the economic work in 2025 called for balancing fostering new growth drivers and revitalizing old ones while developing new quality productive forces based on local conditions.

An Adaptive Vision Navigation Algorithm in Agricultural IoT System for Smart Agricultural Robots

As the agricultural internet of things(IoT)technology has evolved,smart agricultural robots needs to have both flexibility and adaptability when moving in complex field environments.In this paper,we propose the concept of a vision-based navigation system for the agricultural IoT and a binocular vision navigation algorithm for smart agricultural robots,which can fuse the edge contour and the height information of rows of crop in images to extract the navigation parameters.First,the speeded-up robust feature(SURF)extracting and matching algorithm is used to obtain featuring point pairs from the green crop row images observed by the binocular parallel vision system.Then the confidence density image is constructed by integrating the enhanced elevation image and the corresponding binarized crop row image,where the edge contour and the height information of crop row are fused to extract the navigation parameters(θ,d)based on the model of a smart agricultural robot.Finally,the five navigation network instruction sets are designed based on the navigation angleθand the lateral distance d,which represent the basic movements for a certain type of smart agricultural robot working in a field.Simulated experimental results in the laboratory show that the algorithm proposed in this study is effective with small turning errors and low standard deviations,and can provide a valuable reference for the further practical application of binocular vision navigation systems in smart agricultural robots in the agricultural IoT system.

Mining association rules in incomplete information systems

Based on the rough set theory which is a powerful tool in dealing with vagueness and uncertainty, an algorithm to mine association rules in incomplete information systems was presented and the support and confidence were redefined. The algorithm can mine the association rules with decision attributes directly without processing missing values. Using the incomplete dataset Mushroom from UCI machine learning repository, the new algorithm was compared with the classical association rules mining algorithm based on Apriori from the number of rules extracted, testing accuracy and execution time. The experiment results show that the new algorithm has advantages of short execution time and high accuracy.

A pre-warning system of abnormal energy consumption in lead smelting based on LSSVR-RP-CI

The pre-warning of abnormal energy consumption is important for energy conservation of industrial engineering. However, related studies on the lead smelting industries which usually have a huge energy consumption are rarely reported. Therefore, a pre-warning system was established in this study based on the intelligent prediction of energy consumption and the identification of abnormal energy consumption. A least square support vector regression (LSSVR) model optimized by the adaptive genetic algorithm was developed to predict the energy consumption in the process of lead smelting. A recurrence plots (RP) analysis and a confidence intervals (CI) analysis were conducted to quantitatively confirm the stationary degree of energy consumption and the normal range of energy consumption, respectively, to realize the identification of abnormal energy consumption. It is found the prediction accuracy of LSSVR model can exceed 90% based on the comparison between the actual and predicted data. The energy consumption is considered to be non-stationary if the correlation coefficient between the time series of periodicity and energy consumption is larger than that between the time series of periodicity and Lorenz. Additionally, the lower limit and upper limit of normal energy consumption are obtained.

Handling Label Noise in Air Traffic Complexity Evaluation Based on Confident Learning and XGBoost

Air traffic complexity is a critical indicator for air traffic operation,and plays an important role in air traffic management(ATM),such as airspace reconfiguration,air traffic flow management and allocation of air traffic controllers(ATCos).Recently,many machine learning techniques have been used to evaluate air traffic complexity by constructing a mapping from complexity related factors to air traffic complexity labels.However,the low quality of complexity labels,which is named as label noise,has often been neglected and caused unsatisfactory performance in air traffic complexity evaluation.This paper aims at label noise in air traffic complexity samples,and proposes a confident learning and XGBoost-based approach to evaluate air traffic complexity under label noise.The confident learning process is applied to filter out noisy samples with various label probability distributions,and XGBoost is used to train a robust and high-performance air traffic complexity evaluation model on the different label noise filtered ratio datasets.Experiments are carried out on a real dataset from the Guangzhou airspace sector in China,and the results prove that the appropriate label noise removal strategy and XGBoost algorithm can effectively mitigate the label noise problem and achieve better performance in air traffic complexity evaluation.

Establishment of an index system for evaluating outstanding biomedical scientists for science foundation of Shanghai

Objective:To establish a scientific,objective and applicable index system for evaluating out- standing biomedical scientists for science foundation of Shanghai.Methods:According to the principal in- dices that have been used in the developed countries for evaluating their talented personnel and the reality of our country,an index system was set up to evaluate the outstanding biomedical scientists for Shanghai science foundation.The following parameters were used to simplify the indices.,correlation coefficient, multiple correlation coefficient,partial correlation coefficient,creditability,and discriminatory power. And analytic hierarchy process was used to determine the weights of each index.Results and Conclusions: The established index system is scientific and applicable;it is helpful for cultivating and evaluating out- standing biomedical scientists.

DISTRIBUTED MONITORING SYSTEM RELIABILITY ESTIMATION WITH CONSIDERATION OF STATISTICAL UNCERTAINTY

Taking into account the whole system structure and the component reliability estimation uncertainty, a system reliability estimation method based on probability and statistical theory for distributed monitoring systems is presented. The variance and confidence intervals of the system reliability estimation are obtained by expressing system reliability as a linear sum of products of higher order moments of component reliability estimates when the number of component or system survivals obeys binomial distribution. The eigenfunction of binomial distribution is used to determine the moments of component reliability estimates, and a symbolic matrix which can facilitate the search of explicit system reliability estimates is proposed. Furthermore, a case of application is used to illustrate the procedure, and with the help of this example, various issues such as the applicability of this estimation model, and measures to improve system reliability of monitoring systems are discussed.

A Non-Asymptotic Confidence Region with a Fixed Size for a Scalar Function Value: Applications in C-OTDR Monitoring Systems

In this paper we will investigate some non-asymptotic properties of the modified least squares estimates for the non-linear function f(λ*) by observations that nonlinearly depend on the parameter λ*. Non-asymptotic confidence regions with fixed sizes for the modified least squares estimate are used. The obtained confidence region is valid for a finite number of data points when the distributions of the observations are unknown. Asymptotically the suggested estimates represent usual estimates of the least squares. The paper presents the results of practical applications of the proposed method in C-OTDR monitoring systems.

Fuzzy and Reflection in the Construction of a Medical Expert System

The fuzzy of symptoms (including visual images), representations and assessments in medicine correspond to the peculiarities of the picture of the world of the patient and the physician taking into account the influence of reflection. The continuum of intermediate characteristics of the signs creates serious difficulty for their assessment by physicians. Experts’ confidence factors not only for linguistic features, but also for visual images can help increase the hypothesis quality in intelligent medical diagnostic systems.

Effect of BRCA2 Mutation on Familial Breast Cancer Survival:A Systematic Review and Meta-analysis

Reports of BRCA2 genetic mutations on the prognosis of familial breast cancer（BC） patients have been contradictory. True difference in survival,if it exists,would have important implications for genetic counseling and in treatment of hereditary BC. The purpose of this study was to compare overall survival rate（OSR） among BRCA2 mutation carriers,non-carriers and sporadic BC patients. We searched the PUBMED and EMBASE databases and retrieved 4529 articles using keywords that included breast cancer,BRCA,prognosis and survival. Nine articles were selected for systematic review and among them 6 were included in our meta-analysis. We used the fixed and random effect models to calculate the summary odds ratio（OR） and corresponding 95% confidence interval（CI）. BRCA2 mutation carriers had significantly higher long-term OSR than non-carriers（OR=0.69 [95% CI=0.5–0.95]）,while both short-term and long-term OSR of BRCA2 mutation carriers did not differ from those of patients with sporadic disease（OR=1.11 [95% CI=0.74–1.65]; 0.85 [95% CI=0.38–1.94],respectively）. For BC-specific survival rate（BCSSR）,BRCA2 mutation carriers had a similar BCSSR to the non-carriers（OR=0.61 [95% CI=0.28–1.34]）. There was no significant difference in disease-free survival（DFS） between BRCA2 mutation carriers and patients with sporadic disease. Our results suggest that BRCA2 mutation increases long-term OSR in hereditary BC,which reminds us a new prospect of management of the disease.

Official Confident of Eastern China Province's Foreign Trade

Customs statistics show the foreign tradevolume of eastern China’s Zhejiang Provincein 1998 amounted to US$14.85 billion,anannual increase of 4.3 percent.The exportswas US$10.8 billion,up 7.7 percent from1997;the imports ranked the fourth in thenation.This was hard won in the difficultiescaused by the Asian financial crisis.

European Businesses Remain Confident about China

On June 30th, 2009, the European Union Chamber of Commerce in China launches its sixth annual European Chamber Business Confidence Survey, which is published in partnership with Roland Berger Strategy Consultants.

Confidence Level of System Spares OptimizationUsing Fuzzy Theory

To obtain a higher readiness for a complex system, the common method is to store some spares. Confidence level of system spares is an important parameter to control the probability of the spares required. By introducing fuzzy theory, this paper allocates the confidence level of system spares to its subsystems, thus to achieve a rational management of system spares,

Study on Early Warning Signals of Report on Adverse Reactions of Spontaneous Reporting System of Runzao Zhiyang Capsule

Spontaneous reporting system(SRS) is an important way to monitor the adverse drug reaction(ADR) and discover the ADR signal for marketed drugs. It can detect adverse reaction signals timely and effectively, and prevent the occurrence of drug damage. Runzao Zhiyang Capsule is mainly composed of Radix Polygoni Multiflor, Radix Polygoni Multiflori Preparata, Radix Rehmanniae Recens, Radix Sophorae Flavescentis, Folium Mori and Urtica dentata Hand.-Mazz. It has the functions of nourishing blood, nourishing yin, expelling wind to relieve itching and moistening the intestines to relieve constipation. It is mainly used for skin itching, acne, constipation and other diseases caused by blood deficiency and wind dryness. The large, national SRS database of ADRs needs effective evaluation methods. We reported on the use of Bayesian confidence propagation neural network method(BCPNN) and reporting rate ratio method(PRR) with propensity score to control confounding variables. The tendency score method was used to control the hybrid bias produced by SRS data analysis. After the calculation of PRR and BCPNN, the score of "diarrhea", "rash" and "gastric dysfunction" showed that there was an early warning before and after matching. To sum up, it indicated that diarrhea, rash and gastric dysfunction were early warning signs.