A reliability based consistent fuzzy preference relations for risk assessment in oil and gas industry

In decision making, linguistic variables tend to be complex to handle but they make more sense than classical fuzzy numbers. Fuzziness is not sufficient enough to deal with information and degree of reliability of information is critical. Z-numbers is proposed to model the uncertainty produced by human judgment when eliciting information. Therefore, the implementation of z-numbers is taken into consideration, where it has more authority to describe the knowledge of human being and extensively used in the uncertain information development. This issue has motivated the authors to propose fuzzy multi criteria decision making methodology using z-numbers. The proposed methodology is demonstrated the capability to handle knowledge of human being and uncertain information for risk assessment in oil and gas industry. This assessment is due to periodic basis, which will give insights from the operational until the strategic level of decision making process that is capable of dealing with uncertainty in human judgment. The consistent fuzzy preference relations is developed to calculate the preference-weights of the criteria related based on the derived network structure and to resolve conflicts arising from differences in information and opinions provided by the decision makers. The proposed methodology is constructed without losing the generality of the consistent fuzzy preference relations under fuzzy environment

Obstacle and collision avoidance system for AI-enabled multi-robot system

Multi-robot coordination has a wide range of applications in autonomous vehicles, logistics planning, and cooperative inspection, etc. This project aims to design an obstacle and collision avoidance module for a multi-robot system consisting of multiple mobile robots.Bachelor of Engineering (Electrical and Electronic Engineering

A reliability based consistent fuzzy preference relations – fuzzy similarity for risk assessment in oil and gas industry

In decision making, linguistic variables tend to be complex to handle but they make more sense than classical fuzzy numbers. Fuzziness is not sufficient enough to deal with information and degree of reliability of information is critical. Znumbers is proposed to model the uncertainty produced by human judgment when eliciting information. Therefore, the implementation of z-numbers is taken into consideration, where it has more authority to describe the knowledge of human being and extensively used in the uncertain information development. This issue has motivated the authors to propose fuzzy multi criteria group decision making methodology using z-numbers. The proposed methodology is demonstrated the capability to handle knowledge of human being and uncertain information for risk assessment in oil and gas industry. This assessment is due to periodic basis, which will give insights from the operational until the strategic level of decision making process that is capable of dealing with uncertainty in human judgment. The consistent fuzzy preference relations is developed to calculate the preference-weights of the criteria related based on the derived network structure. The fuzzy similarity measure method is applied to resolve conflicts arising from differences in information and opinions provided by the decision makers. The proposed methodology is constructed without losing the generality of the consistent fuzzy preference relations and fuzzy similarity under fuzzy environmen

Fuzzy analytic hierarchy process using intuitive vectorial centroid for eco-friendly car selection

Eco-friendly car is expected to be the next driving market force for global transportation and technology due to its paramount importance towards the sustainability of the environment and society. However, the actual sales of eco-friendly car are not that convincing and it is even decreasing because the consumer is still uncertain to consider eco-friendly as one of the criteria for them to buy their cars. This situation is worsen by the lack of information and awareness regarding sustainability transportation initiatives. Due to the uncertainty and vague understanding of the consumer about this problem, this paper attempts to investigate the current preference of consumer to buy their cars, and whether they really need to buy the eco-friendly car by using the Fuzzy Analytic Hierarchy Process (FAHP) which implements the Intuitive Vectorial Centroid (IVC). Based on FAHP, the imprecise or fuzzy judgment from the decision maker can be incorporated, to anticipate a better decision for eco-friendly car selection. The outcome of FAHP is compared with crisp Analytic Hierarchy Process (AHP), and the findings shows that FAHP can provide an accurate and consistent result with AHP, although it deals with fuzzy judgment inputs from multiple decision makers

Teaching Adolescents With Type 1 Diabetes Self-Compassion (TADS) to Reduce Diabetes Distress: Protocol for a Randomized Controlled Trial

BackgroundAdolescents living with type 1 diabetes (T1D) often experience diabetes distress (DD), a construct distinct from depression or anxiety that refers to the negative emotions that arise from living with and managing diabetes. Self-compassion, which involves being open to one’s own suffering and treating oneself with the same care one would show to loved ones, is associated with better psychological and clinical outcomes among individuals with T1D. Self-compassion is a skill that can be taught and therefore represents an opportunity for intervention. ObjectiveThe overall aim of this study is to assess the effectiveness of a web-based mindful self-compassion for teens (MSC-T) intervention on improving DD, anxiety, depression, diabetes-related disordered eating, and suicidal ideation experienced by youth with T1D (aged between 12 and 17 years) compared with a waitlist control group (standard of care). We will also explore (1) if the effect of the MSC-T intervention changes over time, (2) if the MSC-T intervention has a positive impact on measures of glycemic control, and (3) if the effect of the MSC-T intervention differs based on self-reported gender. MethodsWe will conduct a single-center, parallel-group randomized controlled trial of 140 adolescents with T1D followed for 12 months. Participants will be randomly allocated (using hidden allocation) in a 1:1 ratio to either the MSC-T intervention or the waitlist control group. Our primary outcome is DD, as measured by the Problem Areas in Diabetes-Teen (PAID-T) version at 3 months. Secondary outcomes, assessed at 3 and 12 months, include anxiety (Generalized Anxiety Disorder 7-item [GAD-7] scale), depression (Patient Health Questionnaire-9 [PHQ-9]), diabetes-related disordered eating (Diabetes Eating Problem Survey-Revised [DEPS-R] version), and suicidal ideation (using 1 question from the PHQ-9). ResultsStudy recruitment began in October 2022 and was completed in March 2023, with a total of 141 participants enrolling. Data collection will be ongoing until March 2024. The first results are expected in June 2024. ConclusionsThis study will be the first randomized trial to assess the effectiveness of the web-based MSC-T intervention on adolescents with T1D. Given that adolescence is a period where individuals are typically required to assume more responsibility for their diabetes care, providing adolescents with the tools they need to better manage the stress that often accompanies T1D management is paramount. Trial RegistrationClinicalTrials.gov NCT05463874; https://clinicaltrials.gov/study/NCT05463874 International Registered Report Identifier (IRRID)DERR1-10.2196/5393