Applied System Innovation

Melanoma remains one of the most aggressive forms of skin cancer, and its early detection is critical for improving patient survival. Vision Transformers (ViTs) have recently shown strong performance in dermoscopic image analysis; however, their effectiveness often relies on stacking multiple transformer encoder blocks, resulting in large numbers of trainable parameters and increased model complexity. In this study, we propose TRM-ViT, a parameter-efficient recursive Vision Transformer designed for binary melanoma classification. Instead of using multiple independent encoder blocks, TRM-ViT applies a single transformer encoder block recursively with shared weights, enabling effective depth while substantially reducing the number of trainable parameters. Experiments conducted on the HAM10000 dataset demonstrate that TRM-ViT achieves a ROC–AUC of 0.7952, comparable to a standard Vision Transformer (0.7951), while using approximately seven times fewer parameters (2.15 M vs. 14.57 M). Notably, the proposed model maintains high melanoma sensitivity, making it particularly suitable for screening-oriented applications. These results indicate that recursive weight sharing can provide an effective trade-off between diagnostic performance and model compactness, supporting the development of efficient decision-support tools for melanoma screening in resource-constrained environments.

Pipeline integrity is crucial for ensuring the safe and efficient transportation of hydrocarbons. One of the essential methods for maintaining pipeline integrity is periodic inspection using Pipeline Inspection Gauges (PIGs). These PIGs traverse extensive pipeline networks, collecting critical data related to inertial navigation and inspection technologies, such as geometric, ultrasonic, or magnetic flux inspection. Following an inspection, data is downloaded for post-processing to identify and accurately locate pipeline anomalies. Accurate positioning of indications is crucial for effective repair or maintenance of the identified pipeline section. Thus, ongoing efforts aim to improve the precision of indication positioning. This study introduces an innovative method and model for deriving pipeline trajectory characteristics to enhance positioning accuracy. The method is based on distance sampling of odometers, improving the PIG displacement measurement by implementing multiple odometries. Using the method described in this work can compensate for odometer slip, since the distance measurement error was reduced from 15.67% to 1.38%. The model simulates (three and four) odometer trajectories in curvature and calculates the curvature along the pipeline based on odometer data. The curvature model is evaluated with real data obtained from a test circuit, demonstrating that the proposed method and model technique can yield trajectory characteristics such as curvature detection; we can differentiate linear sections from bend sections in the test circuit. However, the curvature measurement error remains considerable due to odometer slippage. Therefore, future work proposes using additional odometers to improve measurement accuracy.

In elite sports, discovering interdisciplinary causal relationships from public data is critical for gaining a competitive edge. However, the causal knowledge required for these practices is difficult to obtain through either existing intervention-based sports science methods or computational techniques focused on statistical association. This paper formalizes a multi-domain collaborative framework, which involves three roles: (1) the elite sports team; (2) the sport science expert; and (3) the causal inference expert. Our nine-step workflow, which processes three core elements of problem, data, and computing, guides these experts through a cycle that systematically transforms practical problems into computational models and, crucially, translates complex analytical outputs back into actionable strategies. The framework also introduces a dual-dimensional “field evaluation” method, encompassing both process and outcome, to quantify the trustworthiness of knowledge in practical settings where a “gold standard” is absent. This framework was applied in an illustrative case study prior to the Paris 2024 Olympics, providing one additional evidence-informed input for the national team. The success was observed and interpreted as contextual consistency rather than causal validation. This framework ensures the practical application of causal discovery in elite sports, offering a repeatable and explainable pathway for generating credible, evidence-based insights from public data for elite sports decision-making.