The following is a short interview with the winners:

1. Congratulations on winning the 2023 Best Paper Award! Could you please briefly introduce the subject of your awarded paper?
I am a fixed-term Assistant Professor at the Department of Mechanical and Aerospace Engineering at Politecnico di Torino. My research focuses on predictive maintenance and fault diagnosis for rotating machinery using state-of-the-art artificial intelligence models, now with a growing interest in large language models to interface with unstructured data. The paper is about the interpretability of black box models based on machine learning for rolling element bearing diagnosis. In the paper, we adopted a game-theory perspective in order to treat the diagnostic features as players of a cooperative game and quantify each feature’s contribution to the final prediction that we have for diagnosis by means of Shapley values. This could help to explain the models’ predictions and, in the case of having more sensors, to understand which sensor is more important for the models to make a decision about the diagnosis.

2. What benefits do you think authors can gain when publishing their articles in Applied Sciences? What appealed to you about submitting to this journal?
Mostly, the interdisciplinarity of the journal fits well with the cross-domain nature of my work. I also value the reasonable review timelines, which are important in a fast-moving research field like mine.

3. Why did you choose this research field?
I chose this field to develop methods that are deployable in real industrial settings, ideally without relying on extensive damage data that are rarely available.

4. Which research topics do you think will be of particular interest to the community in the coming years?
I believe that generative AI, together with discriminative AI approaches for AI models, will open new research directions across many engineering domains, including maintenance and diagnostics.
 
5. Have you encountered difficulties in conducting your research? How did you overcome them?
Yes, obtaining reliable experimental data can be challenging, and data quality is sometimes questionable. Collaborative analysis can bring multiple perspectives to the same data that we obtain.
Collaboration with colleagues can be crucial to detecting issues that we might find in the data that we obtained. That was the case that I faced.

6. What advice would you give to young researchers seeking to make a meaningful impact?
Even though I am still an early career researcher, I would say to be persistent, continuously validate your ideas, and collaborate with others to find solutions. I hope that this will have an impact on my research.

7. What do you think about open access publishing, especially in the context of Applied Sciences?
Open access accelerates dissemination, benefiting both the scientific community and readers. The main drawback is APCs, which can be a burden on research budgets. However, institutional support and MDPI waivers can make a big difference.

8. Do you have suggestions for how Applied Sciences could further support researchers?
I would suggest targeted free waivers or substantial discounts for early career researchers to encourage participation and help grow the journal.