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.

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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.

Electrical impedance tomography (EIT) is an emerging medical imaging technique, which has already been investigated in several clinical applications due to its low-cost, non-invasiveness, non-radioactivity, high temporal resolution, and great sensitivity to impedance changes. One potential use of EIT is to perform long-term continuous imaging monitoring of brain for patients who suffer from severe cerebral diseases. However, this application requires a demanding performance of electrodes because of the characteristics of cerebral EIT measurements. Although Ag/AgCl bio-electrodes are widely used for clinical practices or EIT research at the moment, influences of different types of Ag/AgCl electrodes on cerebral EIT measurements have not been investigated. In this study, five common types of Ag/AgCl bio-electrodes were put into comparison by measuring the forearm and the brain of 10 healthy adult volunteers and evaluating those data in frequency or time domain in terms of contact impedance, uniformity, signal-to-noise ratio, and stability. Results show that Ag/AgCl powder electrode has an overall best performance with as low contact impedance as commercial ECG electrodes (p > 0.05), high SNR (60.3 ± 4.5 dB), better uniformity (coefficient of correlation 0.95 ± 0.03), and greater stability (slope 0.68 ± 0.03). After further improvement in design and instrumentation, Ag/AgCl powder electrode is likely to become the optimal choice for cerebral EIT measurements and provide feasible technical support for further research or application in cerebral EIT.

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Cardiovascular related diseases are the most significant health concern around the globe. The most crucial health indicator is blood pressure because it gives essential information about the health of a patient's heart. Cardiovascular diseases can be detected early and prevented if blood pressure is monitored continuously and regularly. Blood pressure cuffs, which are widely used to control blood flow in the arm or wrist when measuring blood pressure, are not practical for continuous blood pressure measurement. However, biosignals can be used for blood pressure estimation; but it is still critical and challenging. In this paper, we conducted a comprehensive analysis of feature extraction techniques for blood pressure estimation by using PPG signals. The feature extraction techniques were further divided into three subgroups to analyse the significance of each group. Group A includes timebased features; group B presents statistical feature extraction, and group C presents frequency domain-based features. The analysis employed several machine learning algorithms and compared their performance from many perspectives for the first time, to the best of our knowledge. The experimental results from two publicly available datasets demonstrated that the set of features belonging to group A were more reliable than other techniques for blood pressure estimation. We found that deep learning models achieved better performance than all traditional machine learning methods. We also found that the GRU model and Bi-LSTM achieved the best performance for time-domain features for blood pressure estimation. We believe the findings of this benchmark study will help researchers choose the most appropriate method for feature extraction and machine learning algorithms.