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This paper presents an innovative approach to enhance the querying capability of ChatGPT, a conversational artificial intelligence model, by incorporating voice-based interaction and a convolutional neural network (CNN)-based impaired vision detection model. The proposed system aims to improve user experience and accessibility by allowing users to interact with ChatGPT using voice commands. Additionally, a CNN-based model is employed to detect impairments in user vision, enabling the system to adapt its responses and provide appropriate assistance. This research tackles head-on the challenges of user experience and inclusivity in artificial intelligence (AI). It underscores our commitment to overcoming these obstacles, making ChatGPT more accessible and valuable for a broader audience. The integration of voice-based interaction and impaired vision detection represents a novel approach to conversational AI. Notably, this innovation transcends novelty; it carries the potential to profoundly impact the lives of users, particularly those with visual impairments. The modular approach to system design ensures adaptability and scalability, critical for the practical implementation of these advancements. Crucially, the solution places the user at its core. Customizing responses for those with visual impairments demonstrates AI’s potential to not only understand but also accommodate individual needs and preferences.

This paper presents the design and analysis of different rectifier configurations using the SMS7630 Schottky diode in RF energy harvesting applications. This study focuses on achieving maximum power transfer between the antenna and the rectifier by employing a reconfigurable impedance matching circuit. The designed impedance matching circuit successfully achieved a reflection coefficient (S11) below -10dB across a range of frequencies from 1.5GHz to 5.6GHz. The performance and effectiveness of the rectifier designs and the impedance matching circuit were evaluated using advanced design system (ADS) software. The results demonstrate the feasibility and effectiveness of the proposed approach in optimising power transfer efficiency for RF energy harvesting systems within the specified frequency range.

Millimeter-wave 5G signals require accurate path loss predictions due to the low spectral and energy efficiencies of pre5G networks. This paper proposes a hybrid machine learning technique comprising an environment classifier that determines the propagation environment using a convolutional neural network (CNN) in the TensorFlow machine learning framework and a path loss model using the XGboost model. The results of the evaluation demonstrate the model's exceptional accuracy in predicting path loss for the 5G n258 standard (24.25-27.5 GHz) band. Through extensive training and testing using a carefully constructed dataset, the model achieves a root mean square error (RMSE) under 1 dB when compared with the empirical 26 GHz band measurements. Moreover, the machine learning model demonstrates a low computational latency with the parameters sweep predictions of 0.23 seconds, yielding a 99.65 % decrease in execution time compared with the conventional methods.

This work aims to explore the possibilities of using a robotic emotion recognition model to help people with autism spectrum disorder improve their social skills. An OAK-D lite camera and Lenovo IdeaPad L340 Gaming were used to run a Python algorithm to estimate emotions in a stage neural network hybrid deep learning system. The data was gathered from 30 Manchester Metropolitan University engineering students. Results showed that the idea of robotic emotion recognition works, but the algorithm could be improved, resulting in better accuracy with the hardware also due for enhancement. However, the project can expand to other areas, such as public security and mood monitoring services and combine it with other indicators, such as heart rate sensors and rapid movement detection.

The rapid growth of the Internet of Things (IoT) has led to increased demand for low-power energy harvesting solutions to power the vast number of interconnected devices. In- this context, the combination of wireless radio frequency (RF) energy harvesting, and perovskite photovoltaic has emerged as a promising approach for efficient energy harvesting in low-power IoT applications. This paper presents a comprehensive analysis of the design considerations for a hybrid wireless RF-perovskite photovoltaic energy harvester tailored specifically for low-power IoT devices. For the first time, combined mathematical modelling of the system architecture is presented. The achieved efficiency of the combined system is 78% and this holds a great promise for next generation 5G/6G smart IoT passive electronics.

Consumer knowledge of the goods produced or processed by the numerous suppliers and processors is still relatively low due to the growing complexity of the structure of pharmaceutical supply chains. Information asymmetry in the pharmaceutical sector has an effect on welfare, sustainability, and health. (1) Background: In this respect, we wanted to develop a productive structure for a pharmaceutical supply chain that satisfies the consumer information needs and fosters consumer confidence in the pharmacy goods they buy. By using blockchain technology, the main goals were to develop and implement a pharmaceutical supply chain. (2) Objectives: The main objectives of this work were to leverage an Ethereum platform for the development of a tractability system in a pharmaceutical supply chain environment and to analyze the efficiency of MSMAChain with respect to the cost and execution of transactions based on our designed smart contracts. (3) Results: This research looked into a variety of issues related to the value, viability, and effects of blockchain technology for use in supply chain applications. The methods and creations in this environment were monitored and researched. It is vital to identify a number of crucial subjects including future research areas, in order to achieve the widespread acceptance of the supply chain traceability provided by blockchain technology. (4) Conclusions: MSMAChain, an Ethereum blockchain-based approach, leverages smart contracts and decentralized off-chain storage for efficient product traceability in terms of the cost and execution of transaction for a health care supply chain.