Suicide prevention is an important global topic, since suicide is being a worldwide serious health issue for decades. In the meantime, people start to pay more attention to mental health issues and Artificial Intelligence is developed and applied in many different fields. Recently, there are many studies been done on using Conversational Agents for mental illness. There are also applications of preventing suicide with Conversational Agents. In the Netherlands, 10.4 suicides happened per 100 thousand inhabitants in 2019. 113 Suicide Prevention is a Dutch national organization with a mission of pursing zero suicides. They are providing multiple services to help people with suicidal thoughts, including a self-test for evaluating suicidal risks anonymously. Even though, people who have taken the self-test receive some suggestions from the 113 websites, they might not contact the 113 helpline for further help. However, it is significant for people with suicide ideation to disclose their thoughts in order to reduce their intention of committing suicide. Therefore, a conversational agent was designed and developed to upgrade the suicide self-test service, which was aiming to resolve test-takers barriers towards contacting the helpline for human help. With the service, the conversational agent interacts with users to help them take the self-test and persuade them to reach for further help by relieving their worriers. A double-blind mixed designed experiment was conducted with 147 healthy general participants and two conditions (traditional questionnaire and interactive service) to verify the hypotheses and evaluate the service. It was hypothesised that participants would have more motivation to contact a helpline for further professional help after taking the self-test in an interactive way than taking the self-test in a traditional questionnaire way. The results showed a significant increase in the traditional questionnaire condition comparing to the interactive service condition. For participants' satisfaction and perceived usefulness, these are higher in the traditional questionnaire condition than the ones in the interactive service condition. In conclusion, the proposed interactive self-test service did not show an expected potential of increasing people's motivation to contact a helpline for professional help. However, the traditional self-test with a textual barrier intervention section showed the potential to motivate people’s help-seeking behavior comparing to the proposed interactive self-test service based on the experiment.

The magnitude of leakage in the dam body and its foundation can be used as an important indicator in dam risk management. This study presents a data mining and monitoring framework for safety control of the dam leakage flow. First, the influencing factors in dam leakage flow are investigated. Second, a kernel extreme learning machine (KELM) is trained to predict dam leakage, where the parameters are optimized adaptively by parallel multi-population Jaya algorithm. Finally, a novel global sensitivity analysis is proposed to evaluate the relative importance of each input variable based on the KELM. Monitoring data of leakage flow from the concrete face rockfill dam in a pumped-storage power station is used for modeling and verification. The simulated results of the case study reveal that KELM achieves a satisfactory prediction of the leakage flow. It is also found that the water level fluctuation and rainfall have a significant impact on leakage magnitude. The sensitivity analysis provides a useful qualitative metric of dam leakage, which is of great value for dam safety monitoring and operation.

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