Diabetes remains a major global health challenge and requires diagnostic systems capable of handling uncertainty and sometimes conflicting clinical evidence. In this study, a Disc T-Spherical Fuzzy (DT-SF) TOPSIS framework is proposed for diabetes risk assessment, where the radius parameter is used to encode the confidence associated with each diagnostic attribute. The methodology also integrates the Analytic Hierarchy Process (AHP) to determine the relative importance of several key risk factors, including blood glucose, body mass index, family history, lifestyle factors, and clinical symptoms. One important feature of the proposed approach is the ternary classification scheme, which categorizes patients as Non-diabetic (N), Prediabetic (P), or Diabetic (D). In particular, this scheme allows the explicit identification of patients located in a grey zone (Class P), where early monitoring and preventive intervention may be beneficial. The proposed framework is evaluated using the Pima Indians Diabetes Dataset (PIDD). The obtained results show that the ternary DT-SF TOPSIS model achieves 89.14% accuracy, while the conventional binary thresholding method reaches 75.91%. Further analysis of the Closeness Coefficient (CC) distributions, together with threshold sensitivity examination, supports the robustness and interpretability of the proposed framework. Overall, the findings indicate that the DT-SF TOPSIS model provides a practical, confidence-weighted, and uncertainty-aware tool for multi-criteria diabetes risk assessment, with possible applications to other chronic diseases.

Urban transportation planning involves evaluating multiple conflicting criteria such as accessibility, cost-effectiveness, and environmental impact, often under uncertainty and incomplete information. These complex decisions require input from various stakeholders, including planners, policymakers, engineers, and community representatives, whose opinions may differ or contradict. Traditional decision-making approaches struggle to effectively handle such bipolar and multivalued expert evaluations. To address these challenges, we propose a novel decision-making framework based on Pythagorean fuzzy N-bipolar soft expert sets. This model allows experts to express both positive and negative opinions on a multinary scale, capturing nuanced judgments with higher accuracy. It introduces algebraic operations and a structured aggregation algorithm to systematically integrate and resolve conflicting expert inputs. Applied to a real-world case study, the framework evaluated five urban transport strategies based on key criteria, producing final scores as follows: improving public transit (−0.70), optimizing traffic signal timing (1.86), enhancing pedestrian infrastructure (3.10), expanding bike lanes (0.59), and implementing congestion pricing (0.77). The results clearly identify enhancing pedestrian infrastructure as the most suitable option, having obtained the highest final score of 3.10. Comparative analysis demonstrates the framework’s superior capability in modeling expert consensus, managing uncertainty, and supporting transparent multi-criteria group decision-making.

In this study, we introduce the notion of soft Oxtoby–Rose operators within the framework of abstract measurable soft spaces, extend the classical concept of lower-density operators, and explore their essential characteristics. Subsequently, we delve into the so-called soft Oxtoby–Rose topologies (soft OR-topologies), the soft topologies generated by soft Oxtoby–Rose operators. We examine the key features and definitions associated with soft OR-topologies. Specifically, we demonstrate that within soft OR-topologies, Baire category soft sets, soft locally closed sets, and Borel soft sets are all equivalent. We wrap up this research by analyzing various soft topological properties linked to soft OR-topologies.