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Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

This study aims to investigate the corrosion behavior of stir-cast hybrid aluminum composite reinforced with CeO₂ and graphene nanoplatelets (GNPs) nanoparticulates used as cylinder liner material in the engines (automotive, aerospace and aircraft industries).

The composites were prepared using the stir-casting technique, and their microstructure and corrosion behavior was evaluated using scanning electron microscopy (SEM) and potentiodynamic polarization test, respectively.

The results showed that the addition of CeO₂ and GNPs improved the corrosion resistance of the composites, and the optimal combination of these two nanoparticles was found to be 3 wt.% CeO₂ and 3 wt.% GNPs. The enhanced corrosion resistance was attributed to the formation of a protective layer on the surface of the composite, as well as the effective dispersion and uniform distribution of nanoparticles in the matrix. The 0.031362 was noted as the lowest corrosion rate (mmpy) and was noticed in 94% Al-6061 alloy + (3 Wt.% CeO₂ + 3 Wt.% GNPs) sample at room temperature and at elevated temperatures; the corrosion rate (mmpy) was observed as 0.0601 and 0.0636 at 45 °C and 75 °C, respectively.

In the vast majority of the published research publications, either cerium oxide or graphene nanoplatelets were utilized as a single reinforcement or in conjunction with other types of reinforcement such as alumina, silicon carbide, carbon nano-tubes, tungsten carbide, etc., but on the combination of the CeO₂ and GNPs as reinforcements have very less literatures with 2 wt.% each only. The prepared hybrid aluminum composite (reinforcing 1 wt.% to 3 wt.% in Al-6061 alloy) was considered for replacing the cylinder liner material in the piston-cylinder arrangement of engines.

The purpose of this study is to offer a better and more effective hexacopter design for a 3 kg payload using finite element analysis (FEA), facilitating the use of different materials for different components that too without compromising strength.

A 3D computer-aided design (CAD) model of a hexacopter with a regular hexagonal frame is presented. Furthermore, a finite element model is developed to perform a structural analysis and determine Von Mises stress and strain values along with deformations of different components of the proposed hexacopter design.

The results establish that carbon fibre outperforms acrylonitrile butadiene (ABS) with respect to deformations. Within the permissible limits of the stress and strain values, both carbon fiber and ABS are suggested for different components. Thus, a proposed hexacopter offers lighter weight, high strength and low cost.

The use of different materials for different components is suggested by making use of static structural analysis. This encourages new research work and helps in developing new applications of hexacopter, and it has never been reported in literature. The suggested materials for the components of the hexacopter will prove to be suitable considering weight, strength and cost.