Search

In this study, we delve into the financial market to compare the performance of prominent AI and robotics-related stocks against traditional IT indices, such as the Nasdaq, and specialized AI and robotics ETFs. We evaluate the role of these stocks in diversifying portfolios, analyzing their return potential and risk profiles. Our analysis includes various investment scenarios, focusing on common AI-related stocks in the United States. We explore the influence of risk management strategies, ranging from “buy and hold” to daily rebalancing, on AI stock portfolios. This involves investigating long-term strategies like buy and hold, as well as short-term approaches, such as daily rebalancing. Our findings, covering the period from 30 April 2021, to 15 September 2023, show that AI-related stocks have not only outperformed in recent years but also highlight the growing “AI bubble” and the increasing significance of AI in investment decisions. The study reveals that these stocks have delivered superior performance, as indicated by metrics like Sharpe and Treynor ratios, providing insights into market trends and financial returns in the technology and robotics sectors. The results are particularly relevant for investors and traders in the AI sector, offering a balanced view of potential returns against the risks in this rapidly evolving market. This paper adds to the financial market literature by demonstrating that investing in emerging trends, such as AI, can be more advantageous in the short term compared to traditional markets like the Nasdaq.

This paper aims to optimize a pulsed electrophoretic deposition (EPD) process for TiO2 films. This is accomplished by determining the optimal configuration of the coating parameters from a robust optimization perspective. The experimental study uses a composite central design (CCD) with four control factors, i.e., the initial concentration (x1 in g/L), the deposition time (x2 in s), the duty cycle (x3 in %), and the voltage (x4 in V). The process responses that should all be maximized are the photocatalytic efficiency of the thin film (De) and three critical charges, which characterize the adhesion failure, i.e., LC1: the load at which the first cracks occurred; LC2: the load at which the film starts to delaminate at the edge level of the scratch track; and LC3: the load when the damage of the film exceeds 50%. This paper compares the robust optimization design of the EPD process using two methods: the robust design of processes and products using the stochastic frontier (RDPP-SF) and the surface response and desirability function methods. The findings show that the RDPP-SF method is superior to the response surface–desirability method for the process responses De and LC2 because of non-natural sources of variation; however, both methods perform comparably well while analyzing the LC1 and LC3 responses, which are subjected to pure random variability. The parameters setting for the process robust optimization are met in run 25 (x1 = 14 g/L, x2 = 150 s, x3 = 50%, and x4 40 V).

Wave energy is among the many renewable energy technologies being researched and developed to address the increasing demand for low-emissions energy. The unique design challenges for wave energy converter design—integrating complex and uncertain technological, economic, and ecological systems, overcoming the structural challenges of ocean deployment, and dealing with complex system dynamics—have lead to a disjointed progression of research and development. There is no common design practice across the wave energy industry and there is no published synthesis of the practices that are used by developers. In this paper, we summarize the methods being employed in WEC design as well as promising methods that have yet to be applied. We contextualize these methods within an overarching design process. We present results from a survey of WEC developers to identify methods that are common in industry. From the review and survey results, we conclude that the most common methods of WEC design are iterative methods in which design parameters are defined, evaluated, and then changed based on evaluation results. This leaves a significant space for improvement of methods that help designers make better-informed decisions prior to sophisticated evaluation, and methods of using the evaluation results to make better design decisions during iteration. Despite the popularity of optimization methods in academic research, they are less common in industry development. We end this paper with a summary of the areas of WEC design in which the testing and development of new methods is necessary, and where more research is required to fully understand the influence of design decisions on WEC performance.