## 4 Results
### Analysis of the Selected Studies
The architecture's structure incorporated interlinked components: (i) remote and wearable sensors for collecting data from the animals; (ii) cloud servers for interacting with the sensors as well as for receiving, storing, and processing the sensory data, resulting in changes in the states of the sensors; (iii) AI models for identifying data patterns and ML models for making predictions; (iv) a user interface through which theusers can monitor the information and predictions of the DTs as well as interact with the platform. Particularly, in [64], the sensor-based DTs animal emotion modeling process is presented while the development of the DTs processing pipeline for classifying and estimating affective state and behavior in real-time is overviewed. This solution, however, appears to still be in a theoretical stage and has not yet been evaluated by obtaining real results. A 3D real-time virtual model of a barn and the animals husband in it (showing their exact location) was developed by [PERSON] and [PERSON] [65]. The model was developed based on data collected from IoT devices according to the five-domain model for animal welfare assessment, involving the physical environment, health, mental state, behavioral interactions, and nutrition. AI software was also developed to monitor the collected data and alert when values appear to be outside the preset intervals. In addition, the software provides information on possible problems that may arise from the measured deviations and on the actions that can be taken as soon as possible to solve the problem. Augmented reality (AR) and virtual reality (VR) resources provide faster learning in a safe environment and can be used for different purposes by veterinarians, breeders, and students. This solution, as it appears, has not yet presented any evaluation results. Finally, [PERSON] et al. [66] presented a conceptual framework for a circular meat supply chain that integrates the key technological aspects of the DTs concept across various areas of the meat supply chain towards \"zero-waste\". Animal management, as part of the meat supply chain, is achieved by incorporating DTs, which can provide insights into animal health and welfare by monitoring, predicting, and influencing animal behavior and environmental conditions. In this context, the physical objects of the framework range from those concerning individual animals (e.g., exercise and rest level, grazing patterns, and location) to those about environmental and housing conditions (e.g., temperature, humidity, luminosity, etc.). Since this framework appears to be at a starting point, its implementation, as well as the generation of a simulation model, form part of future research considerations. Another domain of DTs application in livestock animal production systems appears to be real-time environmental and energy control. In this context, [PERSON] et al. [67] proposed a DTs framework for supporting real-time environmental control in a pig house and presented several scenarios regarding energy efficiency for a DTs framework deployment. Considering that energy is a major concern in the operation of pig houses while creating a comfortable feeding environment, in this work, a virtual pig house was implemented, and several conditions were simulated by applying actual data so as to find the best possible solutions for energy efficiency. In this regard, a number of entities incorporated in an actual enclosed pig house were modeled in order to develop its digital counterpart. The DT-based dynamic simulation analysis for the pig house indicated not only an energy-efficient solution that can be applied in the design stage but also resulted in information related to the optimal control of the heating, ventilation, and air conditioning (HVAC) system during the actual operation stage. Moreover, in their work, [PERSON] et al.