Urban bus electrification is a complex multi-criteria decision under deep uncertainty. We integrate Symmetry Point of Criterion (SPC) and weighting with Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS) within a q-rung orthopair fuzzy set (q-ROFS) environment and introduce a dual-aggregation scheme that linearly combines q-ROFWFA and q-ROFEWA via a tuneable parameter λ to mitigate aggregation bias. We further establish theoretical properties (boundary, monotonicity, idempotency) and show that the Lance distance–based CRADIS-L yields scale-invariant ranking and formal resistance to rank reversal. On a real Istanbul case, the unit energy cost emerges as the dominant criterion (0.2975), and AB Volvo 7900 ranks first overall; extensive ablation, robustness, and statistical significance tests confirm stability and rank-order reliability. Beyond offering a reproducible decision pipeline with Bayesian fusion of user-centric and technical attributes, our framework generalizes to IFS/PyFS/FFS in the limit q→ {1,2,3} and to single-operator cases when λ→ {0,1}.
This paper presents the design, fabrication, and measurement of a high-gain, wideband foldable reflectarray antenna optimized for 3U CubeSat deep-space communication at an 8.4 GHz downlink frequency. The antenna employs custom intertwined square loops and convoluted strips as unit elements on an FR-3703 substrate with an air gap, enabling a 360° phase shift across 240 elements. Its three-piece mechanically foldable structure deploys from a compact 10 cm × 10 cm × 30 cm stowed volume to a 30 cm × 30 cm aperture (8.4λ × 8.4λ). The design achieves a simulated gain of 26.2 dBi, sidelobe level of − 21.1 dB, and an 18
Unloading processes denote the extraction of finished goods and raw materials from transport units and their subsequent conveyance to designated locations. The efficiency of unloading processes is vital in supply chain and logistics management, regarded as an essential component. Delays in unloading operations result in numerous challenges, including heightened operational expenses, diminished labour efficiency, and supply chain bottlenecks. Consequently, it is essential to ascertain unloading times beforehand to mitigate these challenges, resulting in diminished idle time, enhanced overall efficiency, and optimized scheduling. Therefore, precise prediction of unloading times is critically significant. The novelty of this study lies in the application of machine learning techniques to improve operational efficiency by accurately predicting unloading time. To that end, this study employed LightGBM and XGBoost to predict the unloading time in a real case. The unloading time can be predicted with R2 score greater than 0.99 utilizing both models. Subsequently, the SHapley Additive exPlanations (SHAP) methodology was used to ascertain how each input feature contributed to the model’s output. The load of leg significantly influences the unloading time more than the gross weight of truck and the leg distance.
Medical insurance is designed to reimburse employees for monetary losses brought on by illness risks. However, the increasing costs of medical insurance make people tend to commit fraud. Medical insurance fraud (MIF) leads to losses for individuals, businesses, and governments, while also threatening the global sustainability of medical insurance systems. For the sake of classifying the severity of MIF, this study investigates a unique sorting method by integrating cloud model with technique for order preference by similarity to ideal solution (TOPSIS), i.e., Cloud-TOPSIS-Sort, to tackle multi-criteria sorting (MCS) problem. Firstly, given uncertainty in MCS, this study will adopt probabilistic interval-valued hesitant fuzzy set (PIVHFS) for handling uncertainty and propose a technique for objectively ascertaining the probability information in PIVHFS. Secondly, a method of transforming PIVHFS into cloud model is proposed for aggregating probabilistic interval-valued hesitant fuzzy elements (PIVHFEs). Thirdly, considering the important role of divergence in uncertainty theory, f-divergence is extended for cloud model to realize the effective processing of uncertain information. Then, a Cloud-TOPSIS-Sort method is raised, and a case study concerning MIF is used for illustrating the application of the suggested technique.
Medical insurance is an essential component of social insurance. However, the recent surge in medical insurance fraud (MIF) has posed challenges to medical insurance supervision, and how to accurately identify MIF is currently the focus of research. Considering that the existing methods of MIF identification are mostly utilized for binary classification problems and the issue of class imbalance arises in data samples, this paper proposes a preference disaggregation-based multiclass Mahalanobis-Taguchi system (PDMMTS) for effective identification of MIF. Firstly, instead of directly learning from training samples, a continuous measurement scale is constructed, which is not influenced by data distribution and can overcome the problem of classification imbalance. Secondly, the identification of MIF is extended from binary classification to multi-class classification, providing more effective references for MIF supervision. Furthermore, a model for optimization, grounded in PDMMTS, has been formulated to ascertain the boundary profiles and feature variable weights, and then the classification thresholds are derived, which can avoid the impact of subjective determination of thresholds. Finally, comparative experiments with four multiclass methods suitable for imbalanced data were conducted on the Tianchi health insurance dataset. The results show that PDMMTS achieves a 6.85 % improvement in macro F1-score over the best-performing baseline model, i.e., eXtreme Gradient Boosting (XGBoost), validating the effectiveness of the proposed approach. This research can provide effective support for the detection of healthcare fraud in the era of artificial intelligence, demonstrating broad application prospects.
