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Infertility is known to occur frequently worldwide, and the incidence is continuing to rise in China. It is known that semenogelin (SEMG) protein secreted by human seminal vesicles plays an important role in male reproductive system function. However, an association between alterations in SEMG gene functions and idiopathic male infertility occurrence in Chinese-Han population has not been examined. The aim of this study was thus to investigate the inherent relationship between SEMG gene alterations and idiopathic male infertility using a method of variant genotyping selection and semen quality analysis. A population of 484 males with clinically diagnosed idiopathic male infertility and 246 fertile controls were selected after signing consent forms. Results demonstrated a significantly increased frequency of idiopathic infertility with abnormal semen parameters such as semen volume, sperm concentration, sperm number per ejaculate, and sperm motility in variants carrying the rs2301366 TA genotype. Combined association analysis from target single-nucleotide polymorphisms (SNPs) was selected from the genotype database of unrelated Chinese-Han in Beijing individuals from the Hap Map. SNP array analysis in blood samples in each group was carried out by TaqMan Universal PCR Master Mix and TaqMan SNP Genotyping Assays. In addition, the interaction between SEMG SNPs and binding protein epididymal protease inhibitor (EPPIN) SNPs was determined. Our findings demonstrated that the presence of SEMG SNPs and EPPIN SNPs increased the frequency of idiopathic male infertility in Chinese-Han population. It is proposed that measurement of SEMG SNPs and EPPIN SNPs in carriers may thus be utilized to identify idiopathic male infertility. [ABSTRACT FROM AUTHOR]

Window operation is an important occupant behavior, and has significant impacts on building energy consumption. Recently, various stochastic and non-stochastic models have been proposed, aiming to describe occupant window behavior based on several influencing factors. However, most of the employed methods are logit regression and Markov chain techniques, and the application of machine learning to model occupants' window behavior is rarely investigated. In addition, most published studies referring to occupants' window behavior have been carried out within European countries, where the influence of outdoor air quality is rarely considered. This study compares different models of occupants' window behavior, including models based on logistic regression, Markov processes, and an artificial neural network. An artificial neural network model is proposed to explore the application and optimization of an artificial neural network algorithm under a condition of having less samples. Moreover, the outdoor fine inhalable particles ( PM 2.5 ) concentration is considered as an influencing factor for building a window opening model for office buildings during the transition season in China. From this work, it is generally concluded that the PM 2.5 concentration and outdoor humidity should be considered in the modeling of occupant window behavior in Beijing, China. In addition, more true estimations can be obtained from artificial neural network models than from logistic regression models and Markov models. This result demonstrates that the proposed artificial neural network yields a prediction model of office window states with higher accuracy and better interpretability of highly correlated factors as compared to logistic regression models and Markov models. The proposed approaches provide a new and detailed way for engineers and building operators to better understand occupant window behaviors and their impacts on energy use in office buildings. • Logistic regression model, Markov model and ANN are presented to model window state. • PM 2.5 concentration should be considered in modeling window behavior in China area. • ANN models have extreme higher prediction accuracy than the other two models. [ABSTRACT FROM AUTHOR]

Highlights • The concentration of PM10 and PM2.5 were monitored in real-time at subway stations in Beijing. • The characteristics were analysed using General linear model (GLM) and correlation approaches. • The average overall PM concentration ratio inside subway station is about 68.7%, lower than that in outdoor condition. • In the station hall and platform, the real-time PM concentrations varied periodically. • Outdoor environment is mutually correlated with PM concentrations. Abstract The particulate matters (PM10 and PM2.5) inside urban subway stations greatly influence indoor air quality and passenger comfort. This study aims to analyze and interpret the concentrations of PM10 and PM2.5, measured in several subway stations from October 9th to 22nd, 2016 in Beijing, China. The overall methodology was based on the Statistical Package for Social Science (SPSS) software while General linear model (GLM) and correlation analysis were further applied to examine the sensitivities of different variables to the particle concentrations. The data analysis showed the average overall mass ratio of PM concentrations inside subway station is about 68.7%, much lower than outdoor condition (79.6%). In the areas of the station hall and platform, the real-time PM10 and PM2.5 concentrations varied periodically. In working and operation offices, all rooms had much higher PM concentrations than the outdoor environment when its pollution level was level 3, in which the facility room reached the highest level, while the closed meeting room had the lowest. Correlation analysis results indicated that PM10 and PM2.5 concentrations were mutually correlated (average R2 = 0.854), and a strong linear correlation (R2 = 0.897) of the subway-station PM concentrations to the outdoor PM conditions, regardless of the outdoor atmospheric PM concentrations pollution level was. Nevertheless, the impact of passenger number and temperature & humidity on the station PM concentrations was less, when compared to the outdoor environment. This paper is expected to provide useful information for further research and design of effective prevention measures on PM in local subway stations, towards a more sustainable and healthier built environment in the city underground. [ABSTRACT FROM AUTHOR]

• The model-free RL method is developed for controlling windows to improve the indoor comfort. • Representative factors of thermal comfort and indoor air quality are considered for training agents and evaluating policies. • LSTM RNN is able to predict indoor temperature and simulate the building environment. • The feasibility of Q-learning and SARSA algorithms is tested. An occupant's window opening and closing behaviour can significantly influence the level of comfort in the indoor environment. Such behaviour is, however, complex to predict and control conventionally. This paper, therefore, proposes a novel reinforcement learning (RL) method for the advanced control of window opening and closing. The RL control aims at optimising the time point for window opening/closing through observing and learning from the environment. The theory of model-free RL control is developed with the objective of improving occupant comfort, which is applied to historical field measurement data taken from an office building in Beijing. Preliminary testing of RL control is conducted by evaluating the control method's actions. The results show that the RL control strategy improves thermal and indoor air quality by more than 90% when compared with the actual historically observed occupant data. This methodology establishes a prototype for optimally controlling window opening and closing behaviour. It can be further extended by including more environmental parameters and more objectives such as energy consumption. The model-free characteristic of RL avoids the disadvantage of implementing inaccurate or complex models for the environment, thereby enabling a great potential in the application of intelligent control for buildings. [ABSTRACT FROM AUTHOR]

With the massive increase of subway passengers in large Chinese cities, thermal comfort in the subway has become an important topic. The thermal comfort of subway passengers is different from that in above-ground buildings and has its own particularities, as the subway comprises of underground semi-closed buildings. Moreover, the thermal environment experienced by passengers is dynamic. Considering these reasons, this study adopts a dynamic thermal comfort study method for investigating thermal sensations in the subway. The field study was conducted in two Beijing subway stations in May 2016 and June–July 2017, and a total of 628 samples were obtained. Variations in thermal sensation and thermal comfort were analyzed. The results showed that the thermal sensations of subway passengers change dramatically through the entire process, and alternate between hot and cold. For the station hall and platform, although the temperature and the relative humidity were beyond the standard range of the "Code for Design of Metro in China" (GB50157- 2013), passengers find them acceptable. The study also provides reliable basic data for the design and operation management of ventilation and air-conditioning systems in the subway station, so as to create a comfortable thermal environment for subway passengers. [ABSTRACT FROM AUTHOR]