Multi-Criteria Usability Evaluation of mHealth Applications on Type 2 Diabetes Mellitus Using Two Hybrid MCDM Models: CODAS-FAHP and MOORA-FAHP

Digital twin and fuzzy framework for supply chain sustainability risk assessment and management in supplier selection

Risks in the supply chain can damage many companies and organizations due to sustainability risk factors. This study evaluates the supply chain risk assessment and management and then selects the best supplier in a gas company in Egypt. A comprehensive methodology can use the experts' opinions who use the linguistic variables in the spherical fuzzy numbers (SFNs) to evaluate the criteria and suppliers in this study based on their views. Selecting the best supplier is a complex task due to various criteria related to supply chain risk assessment, such as supply risks, environmental risks, financial risks, regularity risks, political risk, ethical risks, and technology risks and their sub-criteria. This study suggested a new combined model with multi-criteria decision-making (MCDM) under a spherical fuzzy set (SFS) environment to overcome uncertainty and incomplete data in the assessment process. The MCDM methodology has two methods: the Entropy and COmbinative Distance-based Assessment (CODAS) methods. The SFS-Entropy is used to compute supply chain risk assessment and management criteria weights. The SFS-CODAS method is used to rank the supplier. The main results show that supply risks have the highest importance, followed by financial and environmental risks, and ethical risks have the lowest risk importance. The criteria weights were changed under sensitivity analysis to show the stability and validation of the results obtained from the suggested methodology. The comparative analysis is implemented with other MCDM methods named TOPSIS, VIKOR, MARCOS, COPRAS, WASPAS, and MULTIMOORA methods under the SFS environment. This study can help managers and organizations select the best supplier with the lowest sustainability risks.

Multicriteria Decision-Making in Diabetes Management and Decision Support: Systematic Review

BACKGROUND

Diabetes mellitus prevalence is increasing among adults and children around the world. Diabetes care is complex; examining the diet, type of medication, diabetes recognition, and willingness to use self-management tools are just a few of the challenges faced by diabetes clinicians who should make decisions about them. Making the appropriate decisions will reduce the cost of treatment, decrease the mortality rate of diabetes, and improve the life quality of patients with diabetes. Effective decision-making is within the realm of multicriteria decision-making (MCDM) techniques.

OBJECTIVE

The central objective of this study is to evaluate the effectiveness and applicability of MCDM methods and then introduce a novel categorization framework for their use in this field.

METHODS

The literature search was focused on publications from 2003 to 2023. Finally, by applying the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method, 63 articles were selected and examined.

RESULTS

The findings reveal that the use of MCDM methods in diabetes research can be categorized into 6 distinct groups: the selection of diabetes medications (19 publications), diabetes diagnosis (12 publications), meal recommendations (8 publications), diabetes management (14 publications), diabetes complication (7 publications), and estimation of diabetes prevalence (3 publications).

CONCLUSIONS

Our review showed a significant portion of the MCDM literature on diabetes. The research highlights the benefits of using MCDM techniques, which are practical and effective for a variety of diabetes challenges.

Usability Evaluation and Classification of mHealth Applications for Type 2 Diabetes Mellitus Using MARS and ID3 Algorithm

The rapid growth of mHealth applications for Type 2 Diabetes Mellitus (T2DM) patients’ self-management has motivated the evaluation of these applications from both the usability and user point of view. The objective of this study was to identify mHealth applications that focus on T2DM from the Android store and rate them from the usability perspective using the MARS tool. Additionally, a classification of these mHealth applications was conducted using the ID3 algorithm to identify the most preferred application. The usability of the applications was assessed by two experts using MARS. A total of 11 mHealth applications were identified from the initial search, which fulfilled our inclusion criteria. The usability of the applications was rated using the MARS scale, from 1 (inadequate) to 5 (excellent). The Functionality (3.23) and Aesthetics (3.22) attributes had the highest score, whereas Information (3.1) had the lowest score. Among the 11 applications, “mySugr” had the highest average MARS score for both Application Quality (4.1/5) as well as Application Subjective Quality (4.5/5). Moreover, from the classification conducted using the ID3 algorithm, it was observed that 6 out of 11 mHealth applications were preferred for the self-management of T2DM.

Optimal Design of Ceramic Based Hip Implant Composites Using Hybrid AHP-MOORA Approach

Designing excellent hip implant composite material with optimal physical, mechanical and wear properties is challenging. Improper hip implant composite design may result in a premature component and product failure. Therefore, a hybrid decision-making tool was proposed to select the optimal hip implant composite according to several criteria that are probably conflicting. In varying weight proportions, a series of hip implant composite materials containing different ceramics (magnesium oxide, zirconium oxide, chromium oxide, silicon nitride and aluminium oxide) were fabricated and evaluated for wear and physicomechanical properties. The density, void content, hardness, indentation depth, elastic modulus, compressive strength, wear, and fracture toughness values were used to rank the hip implant composites. It was found that the density and void content of the biocomposites remain in the range of 3.920-4.307 g/cm3 and 0.0021-0.0089%, respectively. The composite without zirconium oxide exhibits the lowest density (3.920 g/cm3), while the void content remains lowest for the composite having no chromium oxide content. The highest values of hardness (28.81 GPa), elastic modulus (291 GPa) and fracture toughness (11.97 MPa.m1/2) with the lowest wear (0.0071 mm3/million cycles) were exhibited by the composites having 83 wt.% of aluminium oxide and 10 wt.% of zirconium oxide. The experimental results are compositional dependent and without any visible trend. As a result, selecting the best composites among a group of composite alternatives becomes challenging. Therefore, a hybrid AHP-MOORA based multi-criteria decision-making approach was adopted to choose the best composite alternative. The AHP (analytic hierarchy process) was used to calculate the criteria weight, and MOORA (multiple objective optimisation on the basis of ratio analysis) was used to rank the composites. The outcomes revealed that the hip implant composite with 83 wt.% aluminium oxide, 10 wt.% zirconium oxide, 5 wt.% silicon nitride, 3 wt.% magnesium oxide, and 1.5 wt.% chromium oxide had the best qualities. Finally, sensitivity analysis was conducted to determine the ranking's robustness and stability concerning the criterion weight.