Probabilistic Harmonic Calculation in Distribution Networks with Electric Vehicle Charging Stations

Integrating EV charging station into power grid will bring impacts on power system, among which the most significant one is the harmonic pollution on distribution networks. Due to the uncertainty of the EV charging process, the harmonic currents brought by EV charging stations have a random nature. This paper proposed a mathematical simulation method for studying the working status of charging stations, which considers influencing factors including random leaving factor, electricity price, and waiting time. Based on the proposed simulation method, the probability distribution of the harmonic currents of EV charging stations is obtained and used in the calculation of the probability harmonic power flow. Then the impacts of EVs and EV charging stations on distribution networks can be analyzed. In the case study, the proposed simulation and analysis method is implemented on the IEEE-34 distribution network. The influences of EV arrival rates, the penetration rate, and the accessing location of EV charging station are also investigated. Results show that this research has good potential in guiding the planning and construction of charging station

Time series study on the effect of low air pollution level NO2 On the death of residents from cardiovascular and cerebrovascular diseases

Objective: To explore the impact of low-level atmospheric nitrogen dioxide (NO2) on the death risk of cardiovascular and cerebrovascular diseases in Enshi City, so as to provide scientific basis for locating sensitive populations and formulating population health policiesmethods the monitoring of air pollutants, meteorological factors and death data of residents from cardiovascular and cerebrovascular diseases in Enshi City from 2015 to 2018 were collected. The generalized additive model based on Poisson distribution was used to analyze the impact of low air pollution level NO2 on the death risk of cardiovascular and cerebrovascular diseases in Enshi City, and subgroup analysis was carried out on age, gender and seasonresults the average concentrations of major gaseous pollutants in Enshi from 2015 to 2018 were NO2 (21.40 μg/m3), sulfur dioxide (so, 9.68 μg/m3). Carbon oxide (CO, 0.88 mg/m3) and ozone (O, 61.21 μg/m3). The results of single pollutant model analysis show that every increase of NO2 concentration in the total population μg/m, the risk of death from cardiovascular and cerebrovascular diseases on the same day (lag0) will increase by 0.33% (-0.06%~0.72%) (P>0.05); In the female population, every 1% increase in NO2 concentration μg/m, the death risk of cardiovascular and cerebrovascular diseases with cumulative lag of 1 day (lag01) will increase by 0.92% (0.26%~1.56%) (P < 0.05); In the cold season, every increase of NO2 concentration μg/m, the death risk of cardiovascular and cerebrovascular diseases in the whole population on the same day (lag0) will increase by 0.62% (0.12%~1.12%) (P < 0.05). The results of the two pollutant model show that after controlling other gaseous pollutants (SO2, Co or O3), the impact of NO2 on the death risk of cardiovascular and cerebrovascular diseases in women and the whole population in cold season still exists. Conclusion: Low pollution level of NO2 in Enshi City will increase the death risk of cardiovascular and cerebrovascular diseases among women and the whole population in cold season. Attention should be paid to the health protection of special populations in low pollution areas and in special seasons

AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models

Evaluating the general abilities of foundation models to tackle human-level tasks is a vital aspect of their development and application in the pursuit of Artificial General Intelligence (AGI). Traditional benchmarks, which rely on artificial datasets, may not accurately represent human-level capabilities. In this paper, we introduce AGIEval, a novel benchmark specifically designed to assess foundation model in the context of human-centric standardized exams, such as college entrance exams, law school admission tests, math competitions, and lawyer qualification tests. We evaluate several state-of-the-art foundation models, including GPT-4, ChatGPT, and Text-Davinci-003, using this benchmark. Impressively, GPT-4 surpasses average human performance on SAT, LSAT, and math competitions, attaining a 95% accuracy rate on the SAT Math test and a 92.5% accuracy on the English test of the Chinese national college entrance exam. This demonstrates the extraordinary performance of contemporary foundation models. In contrast, we also find that GPT-4 is less proficient in tasks that require complex reasoning or specific domain knowledge. Our comprehensive analyses of model capabilities (understanding, knowledge, reasoning, and calculation) reveal these models' strengths and limitations, providing valuable insights into future directions for enhancing their general capabilities. By concentrating on tasks pertinent to human cognition and decision-making, our benchmark delivers a more meaningful and robust evaluation of foundation models' performance in real-world scenarios. The data, code, and all model outputs are released in https://github.com/microsoft/AGIEval.Comment: 19 page

Academic Adaptation Among International Students from East Asian Countries: A Consensual Qualitative Research

This study used a consensual qualitative research method to explore academic adaptation experiences of international students (N = 13) from East Asia countries at a U.S. university. The analysis yielded five domains from the data (challenges, feelings, strategies, suggestions, and self-reflections). Implications for college counselors, university administrators, and future research are discussed

Isolation and Characterization of Clinical Listeria monocytogenes in Beijing, China, 2014–2016

Listeria monocytogenes is an important foodborne pathogen with a significant impact on public health worldwide. A great number of outbreaks caused by L. monocytogenes has been reported, especially in the United States, and European countries. However, listeriosis has not yet been included in notifiable disease in China, and thus information on this infection has been scarce among the Chinese population. In this study, we described a 3-year surveillance of listeriosis in Beijing, China. Fifty-six L. monocytogenes strains isolated from 49 clinical infectious cases (27 pregnancy-associated infections and 22 non-pregnancy-associated infections) were analyzed by serotyping, pulsed field gel electrophoresis (PFGE), multilocus sequence typing (MLST), and antimicrobial susceptibility testing between 2014 and 2016 in Beijing. The predominant serogroups were 1/2a,3a and 1/2b,3b,7 which accounted for 92% of the overall isolates. Four strains were serogroup 4b,4d,4e, isolated from patients with pregnancy-associated infections. Based on PFGE, these isolates were divided into 32 pulsotypes (PTs) and 3 clusters associated with serogroups. Ten PTs were represented by more than one isolate with PT09 containing the most number of isolates. MLST differentiated the isolates into 18 STs, without new ST designated. The three most common STs were ST8 (18.4%), ST5 (16.3%), and ST87 (12.2%), accounting for 46.9% of the isolates. STs prevalent in other parts of the world were also present in China such as ST1, ST2, ST5, ST8, and ST9 which caused maternal fetal infections or outbreaks. However, the STs and serogroup distribution of clinical L. monocytogenes in Beijing, China was different from those in other countries. Strains of ST1 and ST2 were isolated from patients with pregnancy-associated infection, whereas none of ST155 isolates caused pregnancy-associated cases. Surveillance of molecular characterization will provide important information for prevention of listeriosis. This study also enhances our understanding of genetic diversity of clinical L. monocytogenes in China

Establishment and Verification of a Bagged-Trees-Based Model for Prediction of Sentinel Lymph Node Metastasis for Early Breast Cancer Patients

Purpose: Lymph node metastasis is a multifactorial event. Several scholars have developed nomograph models to predict the sentinel lymph nodes (SLN) metastasis before operation. According to the clinical and pathological characteristics of breast cancer patients, we use the new method to establish a more comprehensive model and add some new factors which have never been analyzed in the world and explored the prospect of its clinical application.Materials and methods: The clinicopathological data of 633 patients with breast cancer who underwent SLN examination from January 2011 to December 2014 were retrospectively analyzed. Because of the imbalance in data, we used smote algorithm to oversample the data to increase the balanced amount of data. Our study for the first time included the shape of the tumor and breast gland content. The location of the tumor was analyzed by the vector combining quadrant method, at the same time we use the method of simply using quadrant or vector for comparing. We also compared the predictive ability of building models through logistic regression and Bagged-Tree algorithm. The Bagged-Tree algorithm was used to categorize samples. The SMOTE-Bagged Tree algorithm and 5-fold cross-validation was used to established the prediction model. The clinical application value of the model in early breast cancer patients was evaluated by confusion matrix and the area under receiver operating characteristic (ROC) curve (AUC).Results: Our predictive model included 12 variables as follows: age, body mass index (BMI), quadrant, clock direction, the distance of tumor from the nipple, morphology of tumor molybdenum target, glandular content, tumor size, ER, PR, HER2, and Ki-67.Finally, our model obtained the AUC value of 0.801 and the accuracy of 70.3%.We used logistic regression to established the model, in the modeling and validation groups, the area under the curve (AUC) were 0.660 and 0.580.We used the vector combining quadrant method to analyze the original location of the tumor, which is more precise than simply using vector or quadrant (AUC 0.801 vs. 0.791 vs. 0.701, Accuracy 70.3 vs. 70.3 vs. 63.6%).Conclusions: Our model is more reliable and stable to assist doctors predict the SLN metastasis in breast cancer patients before operation