This work developed a machine learning (ML) model for predicting the minimum film thickness ($h_0$) in plain journal bearings. A reference dataset comprising 376,349 datapoints was generated from the approximated pressure journal design method developed by Reason and Narang. The inputs in the dataset included geometric and operational features: Society of Automative Engineers (SAE) oil grades, speed (500–10,000 rpm), bearing load (1,500–9,500 N), bearing length/diameter (0.01–0.08 m), clearance (0.00001–0.00006 m), and initial oil temperature (40–70 ℃). Four supervised learning models, i.e., Artificial Neural Network ($h_0$_ANN), Gaussian Process Regression ($h_0$_GPR), Ordinary Least Squares Regression ($h_0$_OLS), and Ridge Regression ($h_0$_Ridge) were developed and evaluated. The results indicated that Artificial Neural Network (ANN) provided the most accurate predictions via achieving an $R^2$ value of 0.99. The GPR model obtained an $R^2$ value of 0.98 while those of the OLS and Ridge Regression achieved $R^2$ of 0.95 and 0.93, respectively. Having shown superior predictive capabilities, the ANN model was selected for further statistical evaluation. Analysis from Local Interpretable Model-agnostic Explanations (LIME), Shapley Additive exPlanations (SHAP), and radar plots all proposed that the ANN model offered stable predictions within the considered parametric space. The model was presented in an explicit mathematical form in an Excel spreadsheet; its computational efficiency was recommended for the design of plain journal bearings.

Large language models (LLMs) are increasingly used to turn natural-language knowledge into downstream executable rules, raising two lifecycle questions: whether a compiled rule faithfully preserves its source fragment's intended semantics, and whether later textual edits change rule behavior. We address these through a controlled benchmark based on inverted compilation: formal rules are generated programmatically, rendered into wiki-style fragments by an LLM, and reconstructed by an independent LLM pipeline, so the original rules provide automatic ground truth. The benchmark contains 2,000 rules across four business domains and 9,000 typed drift triples. For compilation verification, a slot-matched structural verifier reached 0.763 commit precision at a 32.1% commit rate, far exceeding a paraphrase-similarity baseline (0.729 precision, 2.4% commit rate). For drift classification, a slot-difference classifier was the only method that separated multiple impact categories, with F1 scores of 0.684 (condition), 0.601 (exception), and 0.419 (boundary), whereas token-level baselines collapsed to a coarse impactful-versus-cosmetic split. A complementary readiness-assessment experiment returned a negative result: surface-feature classifiers matched a majority-class baseline on synthesized fragments, indicating that readiness estimation needs authentic human-authored text or controlled degradations. Overall, slot-level structural analysis offers an effective signal for verifying and maintaining LLM-compiled rule systems, while exception extraction, cosmetic-edit discrimination, and the synthetic-to-real gap remain key limitations for future neuro-symbolic knowledge engineering.

In mobility-aware scenarios such as vehicular networks, mobile augmented reality (AR)/virtual reality (VR) services, and other latency-sensitive Multi-access Edge Computing (MEC) applications, continuous user movement leads to frequent migrations of service function chains (SFCs). Traditional approaches typically rely on global deployment comparisons, which fail to accurately identify the specific virtual network functions (VNFs) that require migration and their optimal target nodes. This limitation often results in redundant migrations, inefficient resource utilization, and an increased risk of service disruption, thus hindering the balance between latency assurance and resource efficiency. To overcome these limitations, this paper proposed a graph-enhanced deep reinforcement learning–based adaptive migration optimization (DRL-GAMO) framework. By integrating the topological representation capability of graph neural networks (GNNs) with the decision-making efficiency of deep reinforcement learning (DRL), DRL-GAMO established a topology–resource–decision mapping that jointly optimized VNF selection and determination of target nodes. This pre-migration decision process effectively reduced redundant operations and directed migration behaviors toward resource-efficient strategies. The designed reward function minimized migration overhead under service-level agreement (SLA) latency constraints and penalized downtime to maintain service continuity. Simulation results demonstrated that DRL-GAMO achieved stable service latency, lower resource consumption, and shorter migration time while reducing migration volume by more than 40% compared with DRL-ADMO, thereby improving the migration success rate and validating its effectiveness in MEC environments.

The physical quality of seeds is a critical determinant of sorting efficiency and crop productivity, yet conventional assessment approaches are often labor-intensive, invasive, and time-consuming. To address these limitations, computer vision-based methods have been increasingly adopted; however, most existing techniques rely primarily on reflected visible light, thereby capturing only surface-level features and limiting the detection of internal defects. In this study, a low-cost imaging system integrating both reflection and transmission of visible light was developed to enhance the characterization of maize seed translucency. By enabling simultaneous acquisition of information from the two principal faces of white maize seeds, a more comprehensive representation of both external morphology and internal structural variations was achieved. A comparative analysis was conducted between the conventional reflection-based method and the proposed imaging approach, with correlation coefficients between seed faces determined as 0.62 and 0.84, respectively, indicating a substantial improvement in feature consistency and information richness. A dedicated dataset was subsequently constructed using both imaging techniques and employed to train a YOLOv5s-based detection model over 200 epochs. The classification performance demonstrated a marked enhancement, with the proposed method achieving an accuracy of 93.07%, compared to 81.5% obtained using the conventional approach. Furthermore, real-time detection capability was validated through the implementation of the optimized imaging system, in which improved inference stability and robustness were achieved under practical operating conditions. The results indicate that the integration of transmission with reflection imaging provides a cost-effective and reliable solution for non-destructive seed quality assessment, offering significant potential for scalable deployment in agricultural sorting systems.

Optical Character Recognition (OCR) plays a crucial role in the digitization and preservation of textual information; however, for low-resource languages such as Kashmiri, reliable OCR solutions remain largely unavailable. Kashmiri, primarily written in the Perso-Arabic (Nastaliq) script, poses significant challenges due to its cursive structure, extensive use of ligatures, complex diacritical marks, and limited availability of annotated datasets. This research aims to address these challenges by developing a functional OCR system specifically tailored for Kashmiri text. The proposed system is built using the open-source Kraken OCR engine and leverages deep learning techniques with transfer learning from a pre-trained Arabic OCR model. A synthetic dataset was generated using Unicode Kashmiri text, enriched with Kashmiri-specific diacritics and exclusive characters, and rendered into images through automated text-to-image pipelines. Extensive preprocessing, augmentation, and iterative fine-tuning were performed to improve recognition accuracy. Model performance was evaluated using standard metrics such as Character Error Rate (CER) and Word Error Rate (WER) on both seen and unseen data. Experimental results demonstrate a substantial improvement over the initial model, with character accuracy increasing from 54.91% to 79.91% and word accuracy improving from 4.65% to 44.19%. The final model shows strong recognition capability for common and Arabic script characters, while Kashmiri-specific inherited diacritics remain a challenging area. In addition, a cross-platform user interface developed using Flutter enables users to upload or capture images and obtain digitized Kashmiri text through a simple and accessible workflow. Rather than proposing a new recognition architecture, this work contributes empirical insights, reproducible methodology, and error characterization for OCR in a previously unsupported low-resource Nastaliq language. This work is positioned as a baseline OCR system for printed Kashmiri Nastaliq text at the line level and does not claim state-of-the-art performance.

Accurate prediction of the thermal ablation zone in hepatic radiofrequency ablation (RFA) is critical for preventing the recurrence of local tumor, yet it is complicated by the convective heat sink effect of blood perfusion. Traditional numerical solvers, such as the finite difference method (FDM), are inherently limited by time-step constraints which require greater computational cost and impede real-time clinical applications. This study proposed a mesh-free Physics-Informed Neural Network (PINN) framework to simulate the spatiotemporal dynamics of Pennes bioheat equation. By embedding the governing partial differential equation (PDE) directly into the loss function of the neural network, the model learnt the continuous temperature field without spatial discretization or labeled training data. A comparative analysis against an explicit FDM baseline yielded a relative L2 error norm of 1.9%. Although PINN’s continuous functional approximation slightly dampened the theoretical singularity at the tip of the electrode, it accurately resolved the critical 50 °C isotherm that defined the boundary of irreversible coagulative necrosis. Furthermore, the framework effectively decoupled computational cost from the time of physical simulation. While offline training required approximately 6 minutes, the optimized network executed online inference in milliseconds. This capability to provide physically consistent and near-instantaneous thermal predictions demonstrates the potential of the PINN framework for intraoperative decision support systems.