Fifty-six multiparous Holstein cows were assigned at 3 wk prepartum to rations based on grass silage with 1) corn distillers grains to provide 86 and 90% of estimated required metabolizable Lys and Met, respectively; 2) a blend of blood meal, fish meal, and meat and bone meal as amino acid (AA) sources to provide 112 and 103% of required metabolizable Lys and Met, respectively; 3) ruminally protected Lys and Met added as a top-dressing to ration 1 to provide 27 g/d of Lys and 8 g/d of Met as available AA at the duodenum postpartum; and 4) ruminally protected AA for 8 wk postpartum as a top-dressing to ration 1 to provide 40 g/d of Lys and 13 g/d of Met as available AA at the duodenum. Cows fed rations 3 and 4 were offered 13.5 g/d of duodenally available Lys and 4 g/d of duodenally available Met for 3 wk prepartum. The total length of the study was 43 wk. Cows fed ration 4 consumed 3 to 4 kg more dry matter than did cows fed the other three rations, and milk yield and the percentage of milk protein and fat were significantly increased during the first 8 wk of lactation. In early lactation, cows fed ration 3 had a greater milk fat percentage but similar dry matter intake, protein percentage, and yield of 4% fat-corrected milk compared with cows fed ration 2. The concentrations of blood serum glutamic oxaloacetic transaminase, serum glutamic pyruvic transaminase, triglyceride, and nonesterified fatty acids were lower for cows fed ration 4 during the first 8 wk of lactation than they were for cows fed the other three rations. The mammary arteriovenous difference of whole blood AA indicated that Met along with His and Arg may be the most limiting AA for milk yield.
Taking time to identify expected products and waiting for the checkout in a retail store are common scenes we all encounter in our daily lives. The realization of automatic product recognition has great significance for both economic and social progress because it is more reliable than manual operation and time-saving. Product recognition via images is a challenging task in the field of computer vision. It receives increasing consideration due to the great application prospect, such as automatic checkout, stock tracking, planogram compliance, and visually impaired assistance. In recent years, deep learning enjoys a flourishing evolution with tremendous achievements in image classification and object detection. This article aims to present a comprehensive literature review of recent research on deep learning-based retail product recognition. More specifically, this paper reviews the key challenges of deep learning for retail product recognition and discusses potential techniques that can be helpful for the research of the topic. Next, we provide the details of public datasets which could be used for deep learning. Finally, we conclude the current progress and point new perspectives to the research of related fields.
In e-commerce, user reviews can play a significant role in determining the revenue of an organisation.Online users rely on reviews before making decisions about any product and service. As such, the credibility of online reviews is crucial for businesses and can directly affect companies' reputation and profitability. That is why some businesses are paying spammers to post fake reviews. These fake reviews exploit consumer purchasing decisions. Consequently, the techniques for detecting fake reviews have extensively been explored in the past twelve years. However, there still lacks a survey that can analyse and summarise the existing approaches. To bridge up the issue, this survey paper details the task of fake review detection, summing up the existing datasets and their collection methods. It analyses the existing feature extraction techniques. It also summarises and analyses the existing techniques critically to identify gaps based on two groups: traditional statistical machine learning and deep learning methods. Further, we conduct a benchmark study to investigate the performance of different neural network models and transformers that have not been used for fake review detection yet. The experimental results on two benchmark datasets show that RoBERTa performs about 7% better than the state-of-the-art methods in a mixed domain for the deception dataset with the highest accuracy of 91.2%, which can be used as a baseline for future studies. Finally, we highlight the current gaps in this research area and the possible future directions.
INDEX TERMSFake review; Fake review detection; Feature engineering, Machine learning; Deep learning.
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