At present, sustainability and emerging technology are the main issues in any supply chain management (SCM) sector. At the same time, the ongoing pandemic is increasing consumers’ concerns about food safety, processing, and distribution, which should meet sustainability requirements. Thus, supervision and monitoring of product quality with symmetric information traceability are important in fresh food and fishery SCM. Food safety and traceability systems based on blockchain, Internet of Things (IoT), wireless sensor networks (WSN), and radio frequency identification (RFID) provide reliability from production to consumption. This review focuses on RFID-based traceability systems in fisheries’ SCM, which have been employed globally to ensure fish quality and security, and summarizes their advantages in real-time applications. The results of this study will help future researchers to improve consumers’ trust in fisheries SCM. Thus, this review aims to provide guidelines and solutions for enhancing the reliability of RFID-based traceability in food SCM systems so to ensure the integrity and transparency of product information.

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The cross-coupled circuit mechanism based dynamic latch comparator is presented in this research. The comparator is designed using differential input stages with regenerative S-R latch to achieve lower offset, lower power, higher speed and higher resolution. In order to decrease circuit complexity, a comparator should maintain power, speed, resolution and offset-voltage properly. Simulations show that this novel dynamic latch comparator designed in 0.18 µm CMOS technology achieves 3.44 mV resolution with 8 bit precision at a frequency of 50 MHz while dissipating 158.5 µW from 1.8 V supply and 88.05 µA average current. Moreover, the proposed design propagates as fast as 4.2 nS with energy efficiency of 0.7 fJ/conversion-step. Additionally, the core circuit layout only occupies 0.008 mm2.

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Smart home or home automation has become widely popular especially in the case of easing the lives of people with special needs, for instance the elderly and handicapped people. In every home, a specific user has a unique pattern or sequence of using the functions of that house. Recognizing that unique pattern is the key to ensuring an intelligently and properly automated household where the house will remember the behavior of a user and predict the next service required by the user successfully. In this research, a recently developed algorithm named as sequence prediction via enhanced episode discovery (SPEED) is considered for modification by inclusion of location agents. A smart home prototype consisting of two rooms is designed as a testbed for verification. The results show that the accuracy of this algorithm is more than 40%, which is better than the previous SPEED. Moreover, the algorithm detects the location of next predicted event. Since human activity can be distinguished by their existing locations, predicting the next event as well as the location helps to determine the next action more accurately.