Body condition score (BCS) is a common tool for indirectly estimating the mobilization of energy reserves in the fat and muscle of cattle that meets the requirements of animal welfare and precision livestock farming for the effective monitoring of individual animals. However, previous studies on automatic BCS systems have used manual scoring for data collection, and traditional image extraction methods have limited model performance accuracy. In addition, the radio frequency identification device system commonly used in ranching has the disadvantages of misreadings and damage to bovine bodies. Therefore, the aim of this research was to develop and validate an automatic system for identifying individuals and assessing BCS using a deep learning framework. This work developed a linear regression model of BCS using ultrasound backfat thickness to determine BCS for training sets and tested a system based on convolutional neural networks with 3 channels, including depth, gray, and phase congruency, to analyze the back images of 686 cows. After we performed an analysis of image model performance, online verification was used to evaluate the accuracy and precision of the system. The results showed that the selected linear regression model had a high coefficient of determination value (0.976), and the correlation coefficient between manual BCS and ultrasonic BCS was 0.94. Although the overall accuracy of the BCS estimations was high (0.45, 0.77, and 0.98 within 0, 0.25, and 0.5 unit, respectively), the validation for actual BCS ranging from 3.25 to 3.5 was weak (the F1 scores were only 0.6 and 0.57, respectively, within the 0.25-unit range). Overall, individual identification and BCS assessment performed well in the online measurement , with accuracies of 0.937 and 0.409, respectively. A system for individual identification and BCS assessment was developed, and a convolutional neural network using depth, gray, and phase congruency channels to interpret image features exhibited advantages for monitoring thin cows.

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The ambient backscatter communication (AmBC) technology addresses connectivity, cost, and congestion bottlenecks for the Internet of Things (IoT) deployment. AmBC, by avoiding a dedicated power infrastructure and carrier emitter, allows tags to communicate by simply reflecting ambient radio frequency (RF) signals to the reader. Thus, the tag can operate in the low-maintenance, battery-free mode and be powered with energy harvesting. However, a fundamental bottleneck is the limited communication range. A novel solution is integrating tunnel diode amplifiers into tags to enhance their backscattered signal power and thus extend the communication range. This paper studies that solution and investigates the resulting capacity performance. Specifically, we first develop the system's mathematical model, elaborate on the tunnel diode's amplification mechanism, and derive its reflection gain. Subsequently, we derive the closed-form channel capacity and propose a reader-tag distance adjustment scheme to improve the system performance. Finally, simulation results corroborate our theoretical results. They show that the tunnel diode amplifier can significantly increase the system capacity by an order of magnitude when the tag receives less than -30 dBm of incident RF signal power. They also demonstrate a significant increase in the communication range.INDEX TERMS Ambient backscatter communication (AmBC), channel capacity, Internet of Things (IoT), reflection amplifier, tunnel diode (TD).However, the power level of the backscattered signal re-

Since the coronavirus disease 2019(COVID-19) has had brought severe impact on all aspects of the world. A series of interpersonal distancing methods such as ensuring effective and safe social distancing among people, wearing masks, and traffic lockdown measures are also continuing to take effect to curb the continuing outbreak of the COVID-19 (“Advice for the public on COVID-19”, 2020). In response to the globally spread of COVID-19, many advanced technologies in the field of Artificial Intelligence (AI) were applied rapidly and played an essential role in the operation for several months. There are many different leading technology categories in the field of artificial intelligence and many different sub-categories within each main technology categories. Moreover, since the AGI technology does not yet reach the basic human intelligence level, this study will focus on the impact of service robots, which are already widely used in the NAI application category, on hospitality marketing in the current situation in China. In this paper the aim is to assess the effectiveness of use of service robots in Marketing Hospitality Industry during the pandemic through a quantitative study.

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