Variation Regulation and Models of Raw Milk Composition of Chinese Holstein Cattle in Tianjin

Based on raw milk DHI data of Chinese Holstein cattle in northern China,milk composition (milk protein percentage and milk fat percentage) of lactating cow is grouped into parity 1 to 4. After preprocessing original data,6114 data records of milk protein percentage and 5871 data records of milk fat percentage were obtained. This study discusses effects of natural months,lactation parity and their interaction on changes of milk protein percentage and milk fat percentage,and the model is established using GLM procedure of SAS software. At last,results are as follows: (i) Duncan multiple comparison of natural months,regardless of parity (only parity 1 to 4) ,indicates that milk composition takes on significant difference between different months (P < 0. 05) . And milk protein percentage reaches highest in September (3. 187%), drops to the lowest in July (3. 016%); the milk fat percentage reaches highest in February (4. 137%),and drops to the lowest in July (3.845%) . (ii) Duncan multiple comparison of different parity,regardless months (January to December) ,shows that milk composition of different parity also takes on significant difference (P < 0. 05) although the difference between parities are not significant; milk protein percentage reaches highest in the 2nd parity (3. 114%)and drops to the lowest in the 4th parity (3. 066%); milk fat percentage reaches highest in the 2nd and 3rd parity (3. 983% and 3. 973%),and drops to the lowest in the 4th parity (3. 923%). (iii) Using Wood model,the relational expression between milk protein percentage (MPP,%)and milk fat percentage (MFP,%)of different parity and natural month,i. e. MPP = 3. 094x - 0. 046 4 × e 0. 011 7x and MFP = 4. 211 6x - 0. 034 4 × e 0. 027 6x (x stands for month) . According to the above results,it is concluded that natural months,lactation parity and their interaction significantly influence milk protein percentage and milk fat percentage (P < 0. 001) ,and milk protein percentage and milk fat percentage take on Wood model change characteristics with natural months respectively. This study is intended to explore change regulation of milk composition,and to provide decision reference for properly regulating feeding management and nutrition supply of cattle,and thereby guaranteeing the quality of raw milk in certain month reach sales standard.

Quantification of how mechanical ventilation influences the airborne infection risk of COVID-19 and HVAC energy consumption in office buildings

This paper presents an EnergyPlus-based parametric analysis to investigate the infection risk of Coronavirus Disease 2019(COVID-19)under different mechanical ventilation scenarios for a typical medium-sized office building in various climate zones.A Wells-Riley(WR)based Gammaitoni-Nucci(GN)model was employed to quantitatively calculate the airborne infection risk.The selected parameters for the parametric analysis include the climate zone,outdoor air fraction,fraction of infectors,quanta generation rate,and exposure time.The loss and deposition of particles are not considered.The results suggest that the COVID-19 infection risk varies significantly with climate and season under different outdoor air fraction scenarios since the building heating and cooling load fundamentally impacts the supply airflow rate and thus directly influences the amount of mechanical ventilation,which determines the dilution ratio of contaminants.This risk assessment identified the climate zones that benefit the most and the least from increasing the outdoor air fraction.The climate zones such as 1A(Honolulu,HI),2B(Tucson,AZ),3A(Atlanta,GA),and 7(International Falls,MN)are the most energy-efficient locations when it comes to increasing the outdoor air fraction to reduce the COVID-19 infection risk.In contrast,the climate zones such as 6A(Rochester,MN)and 6B(Great Falls,MT)are the least energy-efficient ones.This paper facilitates understanding a widely recommended COVID-19 risk mitigation strategy(i.e.,increase the outdoor airflow rate)from the perspective of energy consumption.

Extracting typical occupancy schedules from social media (TOSSM) and its integration with building energy modeling

Building occupancy,one of the most important consequences of occupant behaviors,is a driving influencer for building energy consumption and has been receiving increasing attention in the building energy modeling community.With the vast development of information technologies in the era of the internet-of-things,occupant sensing and data acquisition are not limited to a single node or traditional approaches.The prevalence of social networks provides a myriad of publically available social media data that might contain occupancy information in the space for a given time.In this paper,we explore two approaches to extract the typical occupancy schedules for the input to the building energy simulation based on the data from social networks.The first approach uses text classification algorithms to identify whether people are present in the space where they are posting on social media.On top of that,the typical building occupancy schedules are extracted with assumed people counting rules.The second approach utilizes the processed Global Positioning System(GPS)tracking data provided by social networking service companies such as Facebook and Google Maps.Web scraping techniques are used to obtain and post-process the raw data to extract the typical building occupancy schedules.The results show that the extracted building occupancy schedules from different data sources(Twitter,Facebook,and Google Maps)share a similar trend but are slightly distinct from each other and hence may require further validation and corrections.To further demonstrate the application of the extracted Typical Occupancy Schedules from Social Media(TOSSM),data-driven models for predicting hourly energy usage prediction of a university museum are developed with the integration of TOSSM.The results indicate that the incorporation of TOSSM could improve the hourly energy usage prediction accuracy to a small extent regarding the four adopted evaluation metrics for this museum building.