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Mathematical models such as Fitzhugh–Nagoma and Hodgkin–Huxley models have been used to understand complex nervous systems. Still, due to their complexity, these models have made it challenging to analyze neural function. The discrete Rulkov model allows the analysis of neural function to facilitate the investigation of neuronal dynamics or others. This paper introduces a fractional memristor Rulkov neuron model and analyzes its dynamic effects, investigating how to improve neuron models by combining discrete memristors and fractional derivatives. These improvements include the more accurate generation of heritable properties compared to full-order models, the treatment of dynamic firing activity at multiple time scales for a single neuron, and the better performance of firing frequency responses in fractional designs compared to integer models. Initially, we combined a Rulkov neuron model with a memristor and evaluated all system parameters using bifurcation diagrams and the 0–1 chaos test. Subsequently, we applied a discrete fractional-order approach to the Rulkov memristor map. We investigated the impact of all parameters and the fractional order on the model and observed that the system exhibited various behaviors, including tonic firing, periodic firing, and chaotic firing. We also found that the more I tend towards the correct order, the more chaotic modes in the range of parameters. Following this, we coupled the proposed model with a similar one and assessed how the fractional order influences synchronization. Our results demonstrated that the fractional order significantly improves synchronization. The results of this research emphasize that the combination of memristor and discrete neurons provides an effective tool for modeling and estimating biophysical effects in neurons and artificial neural networks. [ABSTRACT FROM AUTHOR]

This article presents the topology and material optimization of an underactuated lower limb robot for gait rehabilitation of stroke survivors. Robot aided lower limb rehabilitation has been studied for the last three decades and a few over-actuated and underactuated robot designs have been proposed in the literature. Over-actuated robots give more controlled motions, whereas underactuated designs allow unconstrained naturalistic motions. Designing an underactuated robot is difficult as it must be lightweight and yet strong enough to scaffold human lower limbs during gait. In this research, a Stephenson III six-bar linkage is modified to be used as single-actuated lower limb robot. The proposed robot design couples two four-bar linkages in a driver-driven mode to provide motions, which are equivalent to the motions from a six-bar linkage. A digital twin of the underactuated robot is developed to conduct in-silico experiments and evaluate the use of three different materials namely, aluminum alloy, structural steel, and fiber reinforced carbon composite. Finite element analysis (FEA) modeling is carried out using the Ansys workbench to assess mechanical performance indices. In order to minimize the overall weight of the robot, structural optimization is carried out using a multi-mode single objective genetic algorithm. Functional and nonfunctional design requirements are formulated as constraints to be used during the optimization experiments. The topology and material optimization presented in this article provides insights into the robot design requirements and the optimization process. As a result of this process, a significant reduction in the robot weight is achieved without compromising the mechanical performance. [ABSTRACT FROM AUTHOR]

Background: Artificial Intelligence (AI) has become a ubiquitous phenomenon in recent times, with most organizations today attempting to maneuver their way around developing AI systems with the aim of improving the products and services they provide. However, what complicates developing AI systems is the paucity in frameworks to support organizations with AI System Development (SD). As a result, many organizations are using existing approaches which have been previously applied in earlier emerging Information Technology endeavors. This study explored how a framework can promote effective organizational AI SD. To achieve this a holistic framework for AI SD was conceptualized and examined from an organizational perspective. Method: This study has examined the conceptualized framework People-Process-Data-Technology (2PDT) in AI, through a case study research design. The empirical data was analyzed based on 12 case studies within Australia including 39 interviews with AI experts. We have applied thematic analysis to investigate requirements of organizational AI SD. Results: The results demonstrate that organizations are challenged by key factors, which inhibits their ability to effectively develop AI systems. For example, organizations are not achieving successful delivery of AI systems due to a lack of required skills. Additionally, a plethora of AI technology which is constantly evolving, poor data quality, and the paucity of AI SD frameworks are all contributing to unpredictable delivery outcomes. Conclusion: This paper investigated requirements for effective organizational AI SD by examining the 2PDT framework. The results contribute to AI phenomenon by developing the requirements for AI SD, in terms of people, process, data and technology. It contributes to theory by evaluating and developing AI requirements for effective AI systems. The examination of the framework and case study approach added valuable knowledge to the AI domain. In addition, we contributed to practice by identifying requirements that organizations should consider in achieving better AI SD outcomes. [ABSTRACT FROM AUTHOR]