Research Trends on the Application of Electrolyzed Water in Food Preservation and Sanitation

Electrolyzed water (EW) has been proposed as a novel promising sanitizer and cleaner in recent years. It is an effective antimicrobial and antibiofilm agent that has several advantages of being on the spot, environmentally friendly, cheap, and safe for human beings. Therefore, EW has been applied widely in various fields, including agriculture, food sanitation, livestock management, medical disinfection, clinical, and other fields using antibacterial technology. Currently, EW has potential significance for high-risk settings in hospitals and other clinical facilities. The research focus has been shifted toward the application of slightly acidic EW as more effective with some supplemental chemical and physical treatment methods such as ultraviolet radiations and ultrasound. This review article summarizes the possible mechanism of action and highlights the latest research studies in antimicrobial applications.

Climate change impacts and adaptation strategies for pasture-based industries: Australian perspective

In recent decades Australia has experienced warmer temperatures and, in southern Australia declining rainfall, and climate change projections indicate that these trends are likely to continue. In southern Australia pasture growth patterns have changed with increased winter production but a contraction of the spring growing season and increased inter-annual variability of production. A range of options have been investigated to adapt farm businesses to the changing climate including feedbase, livestock management and diversification. The challenge for adaptation research is to better understand impacts and adaptation options for increases in extreme climate events, such as heatwaves, drought and intense rainfall events.

Anomaly Detection of Operating Equipment in Livestock Farms Using Deep Learning Techniques

In order to establish a smart farm, many kinds of equipment are built and operated inside and outside of a pig house. Thus, the environment for livestock (limited to pigs in this paper) in the barn is properly maintained for its growth conditions. However, due to poor environments such as closed pig houses, lack of stable power supply, inexperienced livestock management, and power outages, the failure of these environment equipment is high. Thus, there are difficulties in detecting its malfunctions during equipment operation. In this paper, based on deep learning, we provide a mechanism to quickly detect anomalies of multiple equipment (environmental sensors and controllers, etc.) in each pig house at the same time. In particular, environmental factors (temperature, humidity, CO2, ventilation, radiator temperature, external temperature, etc.) to be used for learning were extracted through the analysis of data accumulated for the generation of predictive models of each equipment. In addition, the optimal recurrent neural network (RNN) environment was derived by analyzing the characteristics of the learning RNN. In this way, the accuracy of the prediction model can be improved. In this paper, the real-time input data (only in the case of temperature) was intentionally induced above the threshold, and 93% of the abnormalities were detected to determine whether the equipment was abnormal.