Amirkabir University of TechnologyAUT Journal of Modeling and Simulation2588-295358220261001Prompt Engineering for Biomedical NLI: An Exploratory Study.591810.22060/miscj.2025.24382.5421ENHasan Fadhil QasimAlkhawafAlborz Campus, University of Tehran, Tehran, Iran0009-0001-8762-8276HeshaamFailiCollege of Engineering, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran0000-0003-0443-3762Journal Article20250711Abstract<br><br><br><br>Biomedical Natural Language Inference (BioNLI) is a core task in biomedical NLP that looks to identify whether the biomedical premise entails the hypothesis from the premise. Prompt-based methods are gaining traction as one of the simplest and fastest approaches for effectively using large language models (LLMs) for these sorts of inference tasks without the need for complex, time-consuming fine-tuning of the model. However, the inherent difficulty of biomedical NLI problems represents a significant challenge for prompt engineering given the heavy use of terminology specific to the biomedical domain. During in-context prompting or using predefined examples for zero-shot or few-shot prompting, there is often a lack of contextual information or generalizability to the heterogeneity found in biomedical texts for determining entailment decisions. In this work, we present a comprehensive evaluation of a variety of prompting methods (zero-shot, few-shot static, few-shot dynamic, Chain-of-Thought, self-consistency, and Tree-of-Thought) with two LLMs, DeepSeek-R1-Distill-Qwen-14B and LLaMA-3.1-8B-Instruct, from the prompt-engineering perspective. We applied these methods to the BioNLI dataset and reported on key evaluation metrics across all methods. Our results show that dynamic contextual in-context prompting, together with structured reasoning, produces high-quality inference in our context. Between all of the models and configurations, few-shot ToT prompting using the DeepSeek model produced the best results, scoring a macro-F1 score of 71.05, even outperforming retrieval-augmented models reported on in prior studies. These findings show that prompt engineering alone can handle complex biomedical reasoning effectively, without needing retrieval or full fine-tuning.Amirkabir University of TechnologyAUT Journal of Modeling and Simulation2588-295358220261001Packet-Based Predictive Control Framework for Rotary inverted pendulum benchmark with Round-Trip Time-Delay compensation611010.22060/miscj.2026.23654.5392ENMostafaNasiriIsfahan University of TechnologyEhsanZeinaliIsfahan University of Technology, Isfahan, IranJournal Article20241105Time delays in data transmission are a critical factor that significantly influence the stability and performance of pocket-based control systems, especially under realistic network conditions where delays are unpredictable and variable. These delays, stemming from communication constraints, often include challenges such as packet disorder and loss, which can compromise control accuracy and system robustness. To address this issue, this paper introduces a novel round-trip delay compensation strategy that explicitly considers both feedback and feedforward delays. These delays are modeled as stochastic but constrained entities, accurately capturing the uncertainties present in real-world network environments. The impact of these delays on system stability is rigorously analyzed using spectral radius methods, providing a quantitative assessment of stability margins and robustness. Additionally, the effects of key parameters such as sampling time and the design of controllers based on linear quadratic regulation are investigated through comprehensive simulations, highlighting their influence on system stability and performance. Practical validation is performed on a pocket-based inverted pendulum system, demonstrating that the proposed approach reduces overshoot by approximately 20%, shortens the settling time by about 25%, and decreases peak motor torque by 23%. These results underscore the enhanced robustness and reliability achieved through the proposed delay compensation, making it a practical and effective solution for networked control systems operating under variable communication delays and packet irregularities.Amirkabir University of TechnologyAUT Journal of Modeling and Simulation2588-295358220261001Sentence Splitter: Uncovering Latent Factual Structure for Self-Supervised Learning611410.22060/miscj.2026.25717.5489ENAhmadPouraminiMahsaAfsharizadehDepartment of Computer Engineering, Sirjan University of Technology, Sirjan, IranJournal Article20260422This paper introduces Sentence Splitter, a self-supervised framework built upon a T5-based encoder--decoder architecture for uncovering the latent factual structure of natural language sentences. The proposed method identifies the semantic boundary between a descriptive prefix (head) and its factual completion (tail) by formulating sentence splitting as a discrete segmentation problem, where a sentence of length $N$ admits $N$ possible split points but only one recovers the intended head--tail structure. Rather than explicitly searching over all candidate boundaries, the model learns to recover the factual completion through probabilistic sequence generation. To eliminate the need for manual annotation, symbolic head--tail pairs are first verbalized into natural-language templates that provide supervision for training the Sentence Splitter. The trained splitter is then applied to raw text to extract aligned prefix--tail pairs, which are subsequently used to train a generative model that proposes additional plausible completions through a lightweight bootstrapping process. This unified pipeline provides a scalable and structure-aware approach to constructing self-supervised training data while bridging symbolic knowledge and natural language. Experiments on both structured and naturally occurring text demonstrate that the proposed splitter generalizes beyond synthetic templates and that the resulting structure-aware supervision consistently improves downstream performance on knowledge graph completion and commonsense question answering, highlighting the effectiveness of recovering latent factual structure for knowledge-centric NLP.Amirkabir University of TechnologyAUT Journal of Modeling and Simulation2588-295358220261001MBFANet: A lightweight network with multi-branch fusion and attention modules for real-time segmentation611610.22060/miscj.2026.25626.5483ENZainabRawan AbdulraheemFaculty of Computer Engineering, Shahrood University of Technology, Shahrood, IranMansoorFatehShahrood University of Technology- Iran - ShahroodSaidehFerdowsiSchool of Mathematics, Statistics and Actuarial Science, University of Essex, Colchester, EnglandSadjadRezvaniFaculty of Computer Engineering, Shahrood University of Technology, Shahrood, IranJournal Article20260315Masked face segmentation plays a crucial role in face recognition, security, and medical applications, yet remains challenging due to occlusions, varying lighting conditions, and diverse mask patterns. To address these issues, we propose MBFANet, a lightweight and efficient deep learning model that integrates multi-branch feature fusion and attention mechanisms for real-time segmentation. Our architecture is built upon RepVGG, an efficient convolutional backbone, and is enhanced by two key modules: Channel Aggregation and Coordinate Refinement (CACR) for improving spatial and channel-wise feature representation, and the Atrous Pyramid Attention Module (APAM) for capturing multi-scale contextual information. Additionally, a Multi-Branch Feature Fusion (MBFF) module is employed in the decoder to refine segmentation outputs. To optimize segmentation performance, we introduce a hybrid loss function that combines Dice Loss and Focal Loss, effectively addressing class imbalance while improving boundary precision. Experimental evaluations on the Masked Face Segmentation Dataset (MFSD) demonstrate that MBFANet achieves superior segmentation accuracy, outperforming existing models in IoU, F1-score, recall, and precision while maintaining a low parameter count (10M) and high inference speed (153 FPS). Ablation studies confirm the effectiveness of the proposed modules, showing consistent improvements across all evaluation metrics. With its balance between accuracy, efficiency, and computational cost, MBFANet presents a highly effective solution for real-time masked face segmentation, making it well-suited for practical deployment in security, biometric authentication, and mobile applications.