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In response to the challenge of low accuracy in load forecasting for the steel industry， a load forecasting approach is introduced based on ICEEMDAN （improved complete ensemble empirical mode decomposition with adaptive noise）. Firstly， ICEEMDAN is employed to decompose the electricity load in the steel industry into high and low-frequency modal components. Subsequently， the genetic algorithm （GA） is used to identify the primary influencing factors of the components. The PSO-BP （particle swarm optimization-backpropagation neural network） algorithm is then applied to construct prediction models for the high and low-frequency components. Finally， the forecasted results of each component are iteratively recombined to derive the ultimate load forecasting outcomes. Case analysis results indicate that this approach exhibits minimal prediction errors and higher precision.

Accurate forecasting of short-term power load for industrial sectors plays a pivotal role in ensuring the safe and economic operation of regional power grids. To address this critical need， a short-term power load forecasting model in industries based on correlation analysis and convolutional neural network （CNN） is proposed. First， the k-means clustering algorithm is optimized， and the raw data of industry load and external influencing factors are clustered to improve the accuracy of the subsequent correlation analysis. Then， an improved correlation analysis method based on normalized mutual information （NMI） is proposed to quantitatively assess the correlation between various external influencing factors and industrial loads. Last， a load forecasting network considering the correlation of external influencing factors based on the CNN is designed， which can be used for the short-term power load forecasting of industrial sectors after training. The results of a controlled experiment demonstrate the superiority of the proposed model in terms of both accuracy and stability when it comes to short-term power load forecasting for industrial sectors.

Accurate forecasting of short-term power load is an important condition for the safe and economic operation of the power system. To improve the accuracy of short-term load forecasting for the power system， a short-term load forecasting method based on key feature optimization is proposed. Firstly， the construction method of the meteorological features， daily type features and historical load features affecting the short-term load of the power system is optimized， which can provide more prior knowledge for the load forecasting model. Then， considering the characteristics of the input features and the output prediction vector， a short-term power load forecasting model combining the convolutional neural network and the fully connected layer is constructed. Finally， the effect of the short-term load forecasting method for the power system based on the key feature optimization in the actual load forecasting task is validated by a numerical example. The example result shows that the key feature optimization of meteorological features， daily type features and historical load features is conducive to improving the accuracy of the short-term load forecasting for the power system.

Neural information is encoded by action potentials delivered by neurons. Which component of neural activity constitutes the basic unit carrying information is still a controversial issue. In this paper, stimulation experiments using a network of hippocampal neurons cultured on a multi-electrode array are used to investigate this issue. The results show that for a set of pulse stimuli with varying voltage amplitude, the neuronal response to the fronto-potential sequence encodes more information through the moment of fronto-potential delivery than the number of fronto-potential deliveries, and that neurons at each locus are encoded independently of each other. After the addition of bicuculline inhibited the GABAA receptors, the information capacity decreased and the temporal resolution decreased, but the neurons at each site were still encoded independently. The results suggest that the encoding of stimulus amplitude in the cultured hippocampal neuronal network is better with spike timing than with count, and the effect of timing encoding is dependent on GABAA receptors.

Background: Human immunodeficiency virus (HIV) is spreading rapidly among men who have sex with men (MSM) in China. Anonymous questionnaires or direct interviews have been frequently used to study their behavior. The aim of the study was to describe the behavioral risk profile of the MSM in Beijing using the randomized response techniques (RRTs). Methods: A cross-sectional survey of sexual behavior among a sample of MSM was conducted in two HIV counseling and testing clinics in Beijing. The survey was carried out with an anonymous questionnaire containing sensitive questions on sexual behavior. To obtain the honest responses to the sensitive questions, three distinctive RRTs were used in the questionnaire: (1) Additive randomized response model for quantitative questions, (2) randomized response model for multiple choice questions, and (3) Simmons randomized response model for binomial questions. Formulae for the point estimate, variance, and confidence interval (CI) were provided for each specific model. Results: Using RRTs in a sample of 659 participants, the mean age at first homosexual encounter was estimated to be 21.7 years (95% CI: 21.2–22.2), and each had sex with about three (2.9, 95% CI: 2.4–3.4) male partners on average in the past month. The estimated rate for consistent condom use was 56.4% (95% CI: 50.1–62.8%). In addition, condom was estimated to be used among 80.0% (95% CI: 74.1–85.9%) of the population during last anal sex with a male partner. Conclusions: Our study employed RRTs in a survey containing questions on sexual behavior among MSM, and the results showed that RRT might be a useful tool to obtain truthful feedback on sensitive information such as sexual behavior from the respondents, especially in traditional Chinese cultural settings.

Background: B7-H3 is an important immune checkpoint molecule that plays a negative role in immune regulation. This study was aimed to explore B7-H3 expression in HIV-infected patients and its clinical significance. Methods: To explore the expression and clinical significance of B7-H3 in HIV-infected patients, we investigated the B7-H3 expression pattern and the correlation of B7-H3 expression with clinical parameters of HIV-infected patients with different levels of CD4+ T cells. To assess the role of B7-H3 in regulating the function of T cells in HIV infection, we performed a proliferation assay and T cell function test in vitro. Results: B7-H3 expression in HIV-infected patients was significantly higher than that in healthy controls. mB7-H3 expression on CD4+CD25high T cells and CD14+ monocytes increased with disease progression. mB7-H3 expression on CD4+CD25high T cells and monocytes was negatively correlated with lymphocyte count, CD4+T cell count, and positively correlated with HIV viral load in HIV-infected patients. when the number of CD4+ T cells in HIV-infected patients was ≥ 200/µL, sB7-H3 and mB7-H3 expression levels on CD4+CD25high T cells and monocytes were negatively correlated with lymphocyte count, CD4+T cell count. sB7-H3 and mB7-H3 expression on monocytes were positively correlated with HIV viral load. B7-H3 inhibited the proliferation of lymphocytes and the secretion of IFN-γ in vitro, especially the ability of CD8+ T cells to secrete IFN-γ. Conclusions: B7-H3 played an important negative regulatory role in anti-HIV infection immunity. It could be used as a potential biomarker for the progression of HIV infection and a novel target for the treatment of HIV infection. [ABSTRACT FROM AUTHOR]