No abstract

Smartphones are attracting increasing interest due to how they are revolutionizing our lives. On the other hand, hardware and software failures that occur in them are continually present. This work aims to investigate these failures in a typical smartphone by collecting data from a class of people. Concerns have been raised that call into question the efficiency of applied methods for identifying and prioritizing the potential defects. The widely used hybridized engineering method, Fuzzy Failure Mode and Effect Analysis (F-FMEA), is an excellent approach to solving these problems. The F-FMEA method was applied to prioritize the potential failures based on their Severity (S), expected Occurrence (O), and the likelihood of Detectability (D). After collecting failure data from different users on a selected smartphone, two well-known defuzzification methods facing the Risk Priority Number (RPN) in F-FMEA were applied. Despite this interest, to the best of our knowledge, no one has studied smartphone failures with a technique that combines the results of different fuzzy applications. Thus, to combine the results of the derived fuzzy subsystems for the average value, we suggest a summative defuzzification method. Our findings indicate that F-FMEA with a summative defuzzification procedure is a clear improvement on the F-FMEA method. Even though the summation method modifies close results of the defuzzification one, it was shown that it provides more accurate results.

Mobile devices are well-known communication tools. People, especially young people, cannot go even one step without them. Technological advancements provide better features, but at the same time, such systems still face security risks. Protective layers do exist, but some systems are automated and engineered, while others rely on humans. This work begins with examining some critical points related to the weakest link in the security chain: the human factor. Errors are given in the view of the Swiss Cheese Model by emphasizing the role of latent conditions in "holes".We found that the Swiss Cheese Model has some limitations. In order to enhance it, we have used the Failure Mode and Effect Analysis risk matrix methodology. Thus, we represent its application on mobile devices to demonstrate that it can give us more accurate results by identifying the most critical points where manufacturers should focus on. This work is based on qualitative data, and it provides the basis for quantitative research. In the end, we suggest that in order to obtain more accurate findings, the Failure Mode and Effect Analysis can be further extended.