Pancreatic pathologies, including pancreatitis and adenocarcinoma, frequently disrupt exocrine-endocrine synergy, leading to pancreatogenic (type 3c) diabetes. Existing computational models often treat these systems in isolation, failing to capture the mechanistic transition to endocrine failure. This study proposes a hybrid modeling framework integrating physiologically based pharmacokinetics (PBPK) with Neural Ordinary Differential Equations (Neural ODEs). The PBPK backbone defines anatomical compartments, including acinar tissue and the islet microenvironment, to govern concentration-balance kinetics. At the same time, Neural ODE residuals are introduced on two specified coupling channels to represent latent nonlinearities such as inflammation-mediated β-cell decline. Designed to synthesize transcriptomic, enzymatic, and clinical datasets, this architecture maintains mechanistic interpretability while capturing complex dynamical interactions. In addition, this work establishes a foundation for predictive simulation and systematic analysis of the pathways linking exocrine injury to endocrine dysfunction. By merging physiological modeling with machine learning, the proposed framework enables robust investigation into the progression of multi-system pancreatic diseases.
This paper proposes ImageTrust, a detection system which addresses some of the shortcomings usually associated with AI media detection, namely, poor performance after recompression and the missing calibrated uncertainty estimates along with predictions. ImageTrust concatenates embeddings from ResNet-50 (2,048-d), EfficientNet-B0 (1,280-d) and ViT-B/16 (768-d) into a single 4,096-dimensional embedding, which is classified by both an XGBoost and MLP meta-classifier. We employ temperature scaling to decrease calibration error (ECE = 0.016 for XGBoost) and use conformal prediction to provide 95.2
Tourism is a valuable financial support for Governments worldwide. Therefore, Governments promote it by undertaking new facility construction projects for tourist spots. These projects are to be selected from a project pool that satisfies a budget constraint. To make this selection process transparent, the Government often involves the public using technological platforms available in existing and upcoming smart cities. Several mechanisms have been proposed to date to either optimize or extend the process’s functionality. This article discusses the project selection process in the smart city context, then formalizes the problem, and provides a detailed analysis and performance evaluation of recent works that either optimize or extend its functionality. This article concludes by summarizing the challenges, including over-tourism, open issues, and directions for future work in this domain.
Thanks to its fully customizable stack and its alignment with European initiatives toward technological autonomy, RISC-V is emerging as a viable alternative to other more mature architectures. Yet despite this momentum, deploying cloud environments and running cloud-native workloads on RISC-V remains an open challenge. This paper reports on our experience deploying experimental Kubernetes cluster on RISC-V hardware and evaluating two representative workloads: Apache Spark for large-scale data analytics and PyTorch for convolutional neural network training, both running inside containerized environments. To enable a meaningful cross-architecture comparison against an x86 baseline, we adopt two throughput-normalized metrics, Queries Per Cycle (QPC) for Spark and Frames Per Cycle (FPC) for PyTorch. Our results show that Kubernetes operates correctly on RISC-V with no fundamental orchestration-level barriers, while a significant performance gap with x86 persists, driven primarily by memory hierarchy limitations and the relative immaturity of the software stack. These results provide a reproducible baseline for evaluating future, higher-performance RISC-V platforms as the ecosystem matures.
With the rapid growth of the logistics and delivery industry, there has been a growing demand in recent years for delivery planning that balances safety and efficiency. In this paper, the authors propose a safe and efficient delivery planning method based on road characteristics, taking into account the risk level and distance of each road segment calculated using telematics data. The proposed method models the actual road network as an undirected graph, assigns distance and risk levels to each link, and constructs a mathematical model that optimizes the actual routes traveled by delivery vehicles. To reduce computational load, the authors introduce delivery area segmentation via clustering and perform route optimization using a hybrid genetic algorithm (GA). Simulation results on a large-scale road network covering central Fukui City showed that the proposed method can formulate delivery plans appropriately considering both driving distance and risk levels by adjusting the weighting coefficients. Furthermore, through comparison with the previous method, the authors confirmed that the proposed method can reduce a computation time even for large-scale networks.
