A Novel Combined Model for Predicting Humidity in Sheep Housing Facilities.

Accurately predicting humidity changes in sheep barns is important to ensure the healthy growth of the animals and to improve the economic returns of sheep farming. In this study, to address the limitations of conventional methods in establishing accurate mathematical models of dynamic changes in humidity in sheep barns, we propose a method to predict humidity in sheep barns based on a machine learning model combining a light gradient boosting machine with gray wolf optimization and support-vector regression (LightGBM-CGWO-SVR). Influencing factors with a high contribution to humidity were extracted using LightGBM to reduce the complexity of the model. To avoid the local extremum problem, the CGWO algorithm was used to optimize the required hyperparameters in SVR and determine the optimal hyperparameter combination. The combined algorithm was applied to predict the humidity of an intensive sheep-breeding facility in Manas, Xinjiang, China, in real time for the next 10 min. The experimental results indicated that the proposed LightGBM-CGWO-SVR model outperformed eight existing models used for comparison on all evaluation metrics. It achieved minimum values of 0.0662, 0.2284, 0.0521, and 0.0083 in terms of mean absolute error, root mean square error, mean squared error, and normalized root mean square error, respectively, and a maximum value of 0.9973 in terms of the R2 index.

Credible pigeon permissioned blockchain traceability platform integrated with IoT based on HACCP.

The pigeon food production industry from breeding to processing into food for market circulation involves many stages and people, which is prone to food safety issues and difficult to regulate. To address these problems, one possible solution is to establish a traceability system. However, in traditional traceability systems, a number of stages involved and each of them provides their own data accumulated in the database. Therefore, complex traceability data are compose of too many stages easily result in confusing information for customers. Besides, centralized data storage makes data vulnerable to be tampered with. To solve these problems, hazard analysis and critical control points (HACCP) principles have been utilized in our work which is a comprehensive traceability system. In this work, we analyze the pigeon food production industry through HACCP principles and determine some critical control points (CCPs), including incubation, breeding, transportation, slaughtering, processing, and logistics. With the help of these CCPs, we are able to build a traceability system with critical and abundant data but not too complicated for users. To further improve the system, there are different kinds of techniques integrated into it. Firstly, a permissioned blockchain, Hyperledger Fabric, is selected as blockchain module to enhance trustworthiness of data. Secondly, the system contains various IoT devices for automatically collecting environmental parameter data with the aim of reducing human interference. Besides, it is worth mentioning that the proposed information management device can decrease the data entry burden. Consequently, the implementation of the traceability system increase consumers' confidence in pigeon food production. To summarize, it is a new application of modern agricultural information technique in food safety and a bold experiment in the field of poultry, particularly pigeons.

A Hybrid Model for Temperature Prediction in a Sheep House.

Too high or too low temperature in the sheep house will directly threaten the healthy growth of sheep. Prediction and early warning of temperature changes is an important measure to ensure the healthy growth of sheep. Aiming at the randomness and empirical problem of parameter selection of the traditional single Extreme Gradient Boosting (XGBoost) model, this paper proposes an optimization method based on Principal Component Analysis (PCA) and Particle Swarm Optimization (PSO). Then, using the proposed PCA-PSO-XGBoost to predict the temperature in the sheep house. First, PCA is used to screen the key influencing factors of the sheep house temperature. The dimension of the input vector of the model is reduced; PSO-XGBoost is used to build a temperature prediction model, and the PSO optimization algorithm selects the main hyperparameters of XGBoost. We carried out a global search and determined the optimal hyperparameters of the XGBoost model through iterative calculation. Using the data of the Xinjiang Manas intensive sheep breeding base to conduct a simulation experiment, the results show that it is different from the existing ones. Compared with the temperature prediction model, the evaluation indicators of the PCA-PSO-XGBoost model proposed in this paper are root mean square error (RMSE), mean square error (MSE), coefficient of determination (R2), mean absolute error (MAE) , which are 0.0433, 0.0019, 0.9995, 0.0065, respectively. RMSE, MSE, and MAE are improved by 68, 90, and 94% compared with the traditional XGBoost model. The experimental results show that the model established in this paper has higher accuracy and better stability, can effectively provide guiding suggestions for monitoring and regulating temperature changes in intensive housing and can be extended to the prediction research of other environmental parameters of other animal houses such as pig houses and cow houses in the future.

DPD studies on mixed micelles self-assembled from MPEG-PDEAEMA and MPEG-PCL for controlled doxorubicin release.

In order to better understand and improve the drug loading capacity and release behavior of the pH-responsive mixed micelles in well controlled pH environments, dissipative particle dynamics (DPD) simulations are employed. This is performed by studying the co-micellization behavior of these materials produced from the two specific diblock polymers, poly(ethylene glycol) methyl ether-b-poly(N, N diethylamino ethyl methacrylate) (MPEG-PDEAEMA) and poly(ethylene glycol) methyl ether-b-polycaprolactone (MPEG-PCL) for doxorubicin (DOX) delivery. With the use of appropriate interaction parameters, the formation mechanism of (drug-loaded) mixed micelles, particle sizes, morphology, and composition are investigated. Simulation results show that compared with pure MPEG-PDEAEMA or MPEG-PCL, the mixed MPEG-PDEAEMA and MPEG-PCL system can combine to form multifunctional mixed micelles with larger particle sizes that lead to improved stability, higher drug loading capacity and better-controlled drug release performance. Simulations of the drug release process using the mixed micelles show that, when the environment is acidic, the tertiary amine group of PDEAEMA and DOX3 lead to rapid diffusion and release of the DOX in the aqueous solution. It is found that the presence of MPEG-PCL has a great influence in avoiding the fast release of the drug inside the core of micelles. Therefore, this study offers a deeper understanding of the mechanism on the co-micellization behaviors, the pH-responsive and drug controlled release behaviors of mixed micelles.