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Articles de revues sur le sujet « RAG Optimization Framework »

Auteur : Grafiati
Publié le 7 juin 2025
Mis à jour le 2 août 2025

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1

Yumuşak, Semih. "An Information-Theoretic Framework for Retrieval-Augmented Generation Systems." Electronics 14, no. 15 (2025): 2925. https://doi.org/10.3390/electronics14152925.

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Retrieval-Augmented Generation (RAG) systems have emerged as a critical approach for enhancing large language models with external knowledge, yet the field lacks systematic theoretical analysis for understanding their fundamental characteristics and optimization principles. A novel information-theoretic approach for analyzing and optimizing RAG systems is introduced in this paper by modeling them as cascading information channel systems where each component (query encoding, retrieval, context integration, and generation) functions as a distinct information-theoretic channel with measurable cap
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Vaibhav Fanindra Mahajan. "Retrieval-augmented generation: The technical foundation of intelligent AI Chatbots." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 4093–99. https://doi.org/10.30574/wjarr.2025.26.1.1571.

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Retrieval-Augmented Generation (RAG) has emerged as a transformative approach in conversational AI technology, addressing fundamental limitations of traditional chatbot systems. This technical article explores the architecture, mechanisms, and advantages of RAG implementations. Traditional AI chatbots suffer from outdated knowledge bases, hallucination tendencies, and limited context awareness - constraints that RAG effectively overcomes by combining dynamic information retrieval with sophisticated text generation capabilities. The RAG framework operates through a multi-stage process encompass
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Kwon, Mincheol, Jimin Bang, Seyoung Hwang, Junghoon Jang, and Woosin Lee. "A Dynamic-Selection-Based, Retrieval-Augmented Generation Framework: Enhancing Multi-Document Question-Answering for Commercial Applications." Electronics 14, no. 4 (2025): 659. https://doi.org/10.3390/electronics14040659.

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Commercial multi-document question-answering (QA) applications require a high multi-document retrieval performance, while simultaneously minimizing Application Programming Interface (API) usage costs of large language models (LLMs) and system complexity. To address this need, we designed the Dynamic-Selection-based, Retrieval-Augmented Generation (DS-RAG) framework, which consists of two key modules: an Entity-Preserving Question Decomposition (EPQD) module that effectively decomposes questions while preserving the entities of the original user’s question to reduce unnecessary retrieval and en
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Dhami, Aatishkumar, and Lagan Goel. "Optimizing retrieval augmented generation pipelines for domain specific applications." International Journal of Research in Modern Engineering & Emerging Technology 13, no. 3 (2025): 55–72. https://doi.org/10.63345/ijrmeet.org.v13.i3.4.

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Retrieval Augmented Generation (RAG) pipelines have emerged as a transformative approach in integrating external knowledge into generative models. However, tailoring these systems to domain-specific applications presents unique challenges, including the handling of specialized vocabularies and intricate contextual nuances. This paper introduces a novel optimization framework for RAG pipelines, emphasizing adaptive retrieval strategies, customized knowledge bases, and fine-tuned generative components. By incorporating domain-tailored filtering mechanisms and dynamically adjusting retrieval para
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Ievgen, Gartman. "Architectural Features of Extended Retrieval Generation with External Memory." International Journal of Engineering and Computer Science 14, no. 06 (2025): 27355–61. https://doi.org/10.18535/ijecs.v14i06.5163.

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This article examines the RoCR framework, a Retrieval-Augmented Generation (RAG) system optimized for edge deployment in latency-sensitive environments such as real-time search, product recommendation, and dynamic content generation in eCommerce platforms. RoCR leverages Compute-in-Memory (CiM) architectures to enable fast, energy-efficient inference at scale. At the core of the solution is the CiM-Retriever, a module optimized for performing max inner product search (MIPS). Two architectural variants of the generator are analyzed—decoder-only (RA-T) and encoder–decoder with kNN cross-attentio
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Researcher. "OPTIMIZING AI ALGORITHMS: AN EMPIRICAL STUDY OF FEATURE ENGINEERING, FINE-TUNING, AND EVALUATION STRATEGIES." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 2183–96. https://doi.org/10.5281/zenodo.14370672.

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This comprehensive article explores cutting-edge techniques for optimizing machine learning models, with a particular focus on advanced strategies for enhancing AI algorithms and large language models (LLMs). We begin by examining the critical role of feature engineering and selection in model performance, emphasizing the importance of word embeddings in natural language processing tasks. The article then delves into hyperparameter optimization methods, including grid search, random search, and Bayesian optimization, alongside tools that automate these processes. We introduce Spectrum, a
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Sezgin, Anıl, and Aytuğ Boyacı. "Real-Time Drone Command Processing: A Large Language Model Approach for IoD Systems." Turkish Journal of Science and Technology 20, no. 1 (2025): 281–97. https://doi.org/10.55525/tjst.1623326.

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One of the most critical steps toward autonomous capabilities, where natural language instructions can be successfully converted into executable API calls, is integrating Large Language Models (LLMs) into the ecosystem of the Internet of Drones (IoD). This study introduces an end-to-end LLM-based framework for enhancing real-time drone operation and problem handling in intent recognition, parameter extraction, and ambiguity resolution. It has resorted to a spectrum of methodologies in the form of Retrieval-Augmented Generation (RAG) and customized fine-tuning specific to each domain, towards a
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Narendra Kumar Reddy Choppa and Mark Knipp. "Advancing Generative AI with GraphQL API: Unified Data Access in Microsoft Fabric Ecosystem." Journal of Computer Science and Technology Studies 7, no. 5 (2025): 438–50. https://doi.org/10.32996/jcsts.2025.7.5.54.

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GraphQL API integration within the Microsoft Fabric ecosystem represents a transformative advancement in how organizations manage and access data across diverse data sources unified by OneLake, including Lakehouses, Data Warehouses, SQL Databases, Mirrored Databases, and Datamarts. This integration enables efficient data retrieval, optimized query processing, and seamless connectivity across the Microsoft Fabric ecosystem. This unified data access approach is particularly advantageous for generative AI applications, as it simplifies the process of gathering and integrating diverse datasets req
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Xu, Sheng. "Algorithm Optimization and Performance Improvement of Debt Enterprise Information Retrieval System in the Big Data Environment." Frontiers in Computing and Intelligent Systems 13, no. 1 (2025): 23–25. https://doi.org/10.54097/gqqkaf43.

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Against the backdrop of the big data era, the debt enterprise information retrieval system, as the core tool for financial risk management, is confronted with the challenge of processing massive heterogeneous data. The multi-source heterogeneity, high-frequency dynamics and concealed correlations of debt information lead to high data integration costs, difficult timeliness guarantee and insufficient penetration of deep risks, causing deviations in risk assessment and errors in the prediction of innovation potential. This paper reviews the existing technical solutions, deeply analyzes the chara
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Twinkle Joshi. "Architecting Agentic AI for Modern Software Testing: Capabilities, Foundations, and a Proposed Scalable Multi-Agent System for Automated Test Generation." Journal of Information Systems Engineering and Management 10, no. 52s (2025): 625–38. https://doi.org/10.52783/jisem.v10i52s.10768.

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The progression of software testing has evolved from manual processes to automated systems. However, the emergence of Agentic AI-driven testing represents the next transformative leap. These intelligent agents autonomously generate, execute, and optimize tests, redefining the quality assurance (QA) landscape. Agentic AI—defined by its capacity to independently perceive, plan, execute, and learn—has emerged as a transformative force in software testing. This article examines the impact of Agentic AI on the software testing lifecycle, highlighting its core capabilities, such as dynamic test gene
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Gaduš, J. "Optimization of frameworks by means of fem use." Research in Agricultural Engineering 49, No. 1 (2012): 32–36. http://dx.doi.org/10.17221/4949-rae.

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The paper presents an application of an optimization procedure for a mass optimization of a welded framework of a special tractor trailer designed for transport of seeding machines. The used optimization procedure, so-called Fully Stressed Design (FSD), is based on an indirect approach utilizing optimum criteria. The aim of the optimization was to achieve the lowest possible mass of the construction taking into consideration the allowed resistance. As the paper shows, on the basis of the optimization procedure we achieved more than 35% savings of the material mass. In this wa
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Şenol, Niyazi, Hasan U. Akay, and Şahin Yiğit. "A Gradient Enhanced Efficient Global Optimization-Driven Aerodynamic Shape Optimization Framework." Aerospace 12, no. 7 (2025): 644. https://doi.org/10.3390/aerospace12070644.

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The aerodynamic optimization of airfoil shapes remains a critical research area for enhancing aircraft performance under various flight conditions. In this study, the RAE 2822 airfoil was selected as a benchmark case to investigate and compare the effectiveness of surrogate-based methods under an Efficient Global Optimization (EGO) framework and an adjoint-based approach in both single-point and multi-point optimization settings. Prior to optimization, the computational fluid dynamics (CFD) model was validated against experimental data to ensure accuracy. For the surrogate-based methods, Krigi
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Knollmeyer, Simon, Oğuz Caymazer, and Daniel Grossmann. "Document GraphRAG: Knowledge Graph Enhanced Retrieval Augmented Generation for Document Question Answering Within the Manufacturing Domain." Electronics 14, no. 11 (2025): 2102. https://doi.org/10.3390/electronics14112102.

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Retrieval-Augmented Generation (RAG) systems have shown significant potential for domain-specific Question Answering (QA) tasks, although persistent challenges in retrieval precision and context selection continue to hinder their effectiveness. This study introduces Document Graph RAG (GraphRAG), a novel framework that bolsters retrieval robustness and enhances answer generation by incorporating Knowledge Graphs (KGs) built upon a document’s intrinsic structure into the RAG pipeline. Through the application of the Design Science Research methodology, we systematically design, implement, and ev
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Chun, Asaph Young, Steven G. Heeringa, and Barry Schouten. "Responsive and Adaptive Design for Survey Optimization." Journal of Official Statistics 34, no. 3 (2018): 581–97. http://dx.doi.org/10.2478/jos-2018-0028.

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Abstract We discuss an evidence-based approach to guiding real-time design decisions during the course of survey data collection. We call it responsive and adaptive design (RAD), a scientific framework driven by cost-quality tradeoff analysis and optimization that enables the most efficient production of high-quality data. The notion of RAD is not new; nor is it a silver bullet to resolve all the difficulties of complex survey design and challenges. RAD embraces precedents and variants of responsive design and adaptive design that survey designers and researchers have practiced over decades. I
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Proctor, Philippe, Christof Teuscher, Adam Hecht, and Marek Osiński. "Proximal Policy Optimization for Radiation Source Search." Journal of Nuclear Engineering 2, no. 4 (2021): 368–97. http://dx.doi.org/10.3390/jne2040029.

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Rapid search and localization for nuclear sources can be an important aspect in preventing human harm from illicit material in dirty bombs or from contamination. In the case of a single mobile radiation detector, there are numerous challenges to overcome such as weak source intensity, multiple sources, background radiation, and the presence of obstructions, i.e., a non-convex environment. In this work, we investigate the sequential decision making capability of deep reinforcement learning in the nuclear source search context. A novel neural network architecture (RAD-A2C) based on the advantage
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Chen, Xiao, Chen Xi, Min Xiao, and Lang Wu. "Spilt Tensile Strength of Fiber-Reinforced Recycled Aggregate Concrete Simulation Employing Tunned Random Forests Trees." Electronic Journal of Structural Engineering 25, no. 2 (2025): 17–25. https://doi.org/10.56748/ejse.24756.

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A significant quantity of waste concrete is produced each year due to the demand for concrete manufacturing, which drives the yearly need for raw materials. Recycled aggregate concrete has become a viable remedy as a result. It is vulnerable to breaking and has less strength since the hardened mortar is affixed to natural aggregates, which presents a problem. The goal of this research is to employ random forests (RF) frameworks to project the split tensile strength (STS) of fiber-reinforced recycled aggregate concrete (RAC). The RF framework uses the Chimp optimization algorithm (CHOA) and art
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Lingareddy Alva. "Generative AI for self-optimizing and autonomous data pipelines." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 1071–79. https://doi.org/10.30574/wjarr.2025.26.2.1667.

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Generative AI technologies offer transformative potential for addressing fundamental challenges in data pipeline management across enterprise environments. This comprehensive exploration details how artificial intelligence can create self-optimizing, autonomous data pipelines capable of adapting to evolving data ecosystems without human intervention. The integration of machine learning techniques—including anomaly detection, reinforcement learning, and large language models—enables unprecedented capabilities in pipeline orchestration, from predictive failure prevention to dynamic resource allo
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Liu, Shuxin. "Exploring Optimal Prefetching Sizes in RaLMSpec to Enhance Retrieval-Augmented Generation Efficiency." Highlights in Science, Engineering and Technology 138 (May 11, 2025): 24–31. https://doi.org/10.54097/hhff9g78.

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Retrieval-augmented generation (RAG) frameworks like RaLMSpec enhance language model performance by integrating external knowledge. A key method to accelerate RaLMSpec efficiency is the prefetching, which determines the number of documents to retrieve in advance to balance retrieval speed and cache utilization. This study introduces and tests both static and dynamic prefetching strategies to optimize performance in RaLMSpec. Static prefetching uses fixed sizes, while dynamic prefetching adjusts based on real-time factors including task complexity, cache hit rates, and retrieval latency. Experi
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Sofyan, Yusuf, Wirda Fitriani, and Heri Kurniawan. "Optimization of Transaction Processing System (TPS) Using RAD With FAST Method." International Journal of Science, Technology & Management 3, no. 6 (2022): 1784–90. http://dx.doi.org/10.46729/ijstm.v3i6.638.

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The use of computer-based information systems today has been understood by most companies in order to survive and succeed well. The transaction processing system (TPS) is a computerized system that runs and records daily routine business transactions that serve the operational level in the company. Information systems of purchase, inventory, sales are part of the TPS widely used by companies. The problem here is how to ensure that the system is connected to the organization's business plan and information requirements, where often a system that is built is not in accordance with the wishes of
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Nasir, Sheharyar, Shumail Sahibzada, and Farrukh Sher Malik. "Adjoint-Based Optimization for Enhanced Aerodynamic Performance Using Multi-Parameterization Techniques." International Journal of Innovative Research in Computer Science and Technology 13, no. 2 (2025): 42–53. https://doi.org/10.55524/ijircst.2025.13.2.7.

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Airfoil shape optimization is imperative for enhancing the aerodynamic performance of the aircraft. In the shape optimization process, geometry parameterization holds a pivotal role; directly influencing its robustness and efficiency. In this study, Adjoint-based shape optimization of the airfoil RAE-2822 was performed at transonic Mach while employing two parameterization methods – Hicks-Henne and FFD. The prime objective is to compare the efficiency of parameterization techniques and form comparison metrics based on their five fundamental characteristics - Parsimony, Intuitiveness, Orthogona
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Chen, Xiaofei, Jie Fang, and Jiandong Li. "Robust Control Design and Optimization for Under-Actuated Mechanical Systems Considering Fuzzy Uncertainties." Processes 13, no. 3 (2025): 609. https://doi.org/10.3390/pr13030609.

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This paper addresses the robust control problem for under-actuated mechanical systems subject to uncertainties. The key challenge lies in achieving precise control with insufficient degrees of freedom while maintaining robustness against system uncertainties. We propose a novel control framework that characterizes bounded, time-varying uncertainties through fuzzy set theory, leading to a fuzzy dynamical system formulation. The main contributions are threefold: (1) the development of a deterministic robust controller that eschews traditional IF-THEN rules while guaranteeing system stability thr
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Zulfiqar, M., Kelum A. A. Gamage, M. Kamran, and M. B. Rasheed. "Hyperparameter Optimization of Bayesian Neural Network Using Bayesian Optimization and Intelligent Feature Engineering for Load Forecasting." Sensors 22, no. 12 (2022): 4446. http://dx.doi.org/10.3390/s22124446.

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This paper proposes a new hybrid framework for short-term load forecasting (STLF) by combining the Feature Engineering (FE) and Bayesian Optimization (BO) algorithms with a Bayesian Neural Network (BNN). The FE module comprises feature selection and extraction phases. Firstly, by merging the Random Forest (RaF) and Relief-F (ReF) algorithms, we developed a hybrid feature selector based on grey correlation analysis (GCA) to eliminate feature redundancy. Secondly, a radial basis Kernel function and principal component analysis (KPCA) are integrated into the feature-extraction module for dimensio
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Cihan, Pınar. "Bayesian Hyperparameter Optimization of Machine Learning Models for Predicting Biomass Gasification Gases." Applied Sciences 15, no. 3 (2025): 1018. https://doi.org/10.3390/app15031018.

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Predicting biomass gasification gases is crucial for energy production and environmental monitoring but poses challenges due to complex relationships and variability. Machine learning has emerged as a powerful tool for optimizing and managing these processes. This study uses Bayesian optimization to tune parameters for various machine learning methods, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), Elastic Net, Adaptive Boosting (AdaBoost), Gradient-Boosting Regressor (GBR), K-nearest Neighbors (KNN), and Decision Tree (DT), aimin
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Shashikala, J., and N. Thangadurai. "Evaluating spatial and frequency domain enhancement techniques on dental images to assist dental implant therapy." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5019–33. https://doi.org/10.11591/ijece.v11i6.pp5019-5033.

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Dental imaging provides the patient's anatomical details for the dental implant based on the maxillofacial structure and the two-dimensional geometric projection, helping clinical experts decide whether the implant surgery is suitable for a particular patient. Dental images often suffer from problems associated with random noise and low contrast factors, which need effective preprocessing operations. However, each enhancement technique comes with some advantages and limitations. Therefore, choosing a suitable image enhancement method always a difficult task. In this paper, a universal fram
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Pramanik, Subhadip, Abdalla Alameen, Hitesh Mohapatra, Debanjan Pathak, and Adrijit Goswami. "AdaMoR-DDMOEA: Adaptive Model Selection with a Reliable Individual-Based Model Management Framework for Offline Data-Driven Multi-Objective Optimization." Mathematics 13, no. 1 (2025): 158. https://doi.org/10.3390/math13010158.

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Many real-world expensive industrial and engineering multi-objective optimization problems (MOPs) are driven by historical, experimental, or simulation data. In such scenarios, due to the expensive cost and time required, we are only left with a small amount of labeled data to perform the optimization. These offline data-driven MOPs are usually solved by multi-objective evolutionary algorithms (MOEAs) with the help of surrogate models constructed from offline historical data. The key challenge in developing these data-driven MOEAs is that they have to replace multiple conflicting fitness funct
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Khan, Rezwan Al Islam, Chenyun Zhang, Zhongxiao Deng, et al. "Multi-Agent Reinforcement Learning Tracking Control of a Bionic Wheel-Legged Quadruped." Machines 12, no. 12 (2024): 902. https://doi.org/10.3390/machines12120902.

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This paper presents a novel approach to developing control strategies for mobile robots, specifically the Pegasus, a bionic wheel-legged quadruped robot with unique chassis mechanics that enable four-wheel independent steering and diverse gaits. A multi-agent (MA) reinforcement learning (RL) controller is proposed, treating each leg as an independent agent with the goal of autonomous learning. The framework involves a multi-agent setup to model torso and leg dynamics, incorporating motion guidance optimization signal in the policy training and reward function. By doing so, we address leg sched
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Kommineni, Vamsi Krishna, Waqas Ahmed, Birgitta Koenig-Ries, and Sheeba Samuel. "Automating Information Retrieval from Biodiversity Literature Using Large Language Models: A Case Study." Biodiversity Information Science and Standards 8 (September 10, 2024): e136735. https://doi.org/10.3897/biss.8.136735.

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Recently, Large Language Models (LLMs) have transformed information retrieval, becoming widely adopted across various domains due to their ability to process extensive textual data and generate diverse insights. Biodiversity literature, with its broad range of topics, is no exception to this trend (Boyko et al. 2023, Castro et al. 2024). LLMs can help in information extraction and synthesis, text annotation and classification, and many other natural language processing tasks. We leverage LLMs to automate the information retrieval task from biodiversity publications, building upon data sourced
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Travadi, Mohammad. "AI-Powered Customer Support Automation: Transforming Ticket Creation and Management." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40922.

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The increasing frequency of customer inquiries and complaints in the digital era has put a strain on traditional support systems, resulting in inefficiencies and delayed responses. This paper describes an artificial intelligence- driven ticket automation system that uses advanced Natural Language Processing (NLP) techniques to accelerate ticket production, classification, and resolution. Using frameworks like LangChain, LangGraph, and Retrieval-Augmented Generation (RAG), the system automates operations, increases response accuracy, and interacts smoothly with current support structures. The m
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Setiawan, Andi, Ahmad Fauzi, and Ade Irma Purnamasari. "Optimalisasi Aplikasi CyReborn dengan HttpURLConnection API Berbasis Framework dan Android Untuk Autentifikasi Peserta PKKMB." Jurnal Teknologi Informasi dan Ilmu Komputer 8, no. 3 (2021): 495. http://dx.doi.org/10.25126/jtiik.0813243.

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<p class="Abstrak">Aplikasi <em>CyReborn</em> merupakan sistem untuk memudahkan autentifikasi data peserta pengenalan kehidupan kampus mahasiswa baru atau PKKMB berbasis <em>framework</em>, namun mengalami kendala pada saat pengoperasinya, yaitu sulitnya menguraikan kepadatan antrian pada proses autentifikasi peserta terutama pada saat absensi pagi, absensi istirahat, dan absensi pulang. Tujuan penelitian dari penelitian ini adalah optimalisasi aplikasi <em>CyReborn</em> dengan <em>HttpURLConnection API</em> agar dapat menjalankan <em&gt
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Teoh, Yee Chuen, Mohammed Sakib Noor, Sina Aghakhani, Jack Girton, Guiping Hu, and Ratul Chowdhury. "Viral escape-inspired framework for structure-guided dual bait protein biosensor design." PLOS Computational Biology 21, no. 4 (2025): e1012964. https://doi.org/10.1371/journal.pcbi.1012964.

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A generalizable computational platform, CTRL-V (Computational TRacking of Likely Variants), is introduced to design selective binding (dual bait) biosensor proteins. The iteratively evolving receptor binding domain (RBD) of SARS-CoV-2 spike protein has been construed as a model dual bait biosensor which has iteratively evolved to distinguish and selectively bind to human entry receptors and avoid binding neutralizing antibodies. Spike RBD prioritizes mutations that reduce antibody binding while enhancing/ retaining binding with the ACE2 receptor. CTRL-V’s through iterative design cycles was sh
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Rosca, Cosmina-Mihaela, Adrian Stancu, and Ionuț-Adrian Gortoescu. "Advanced Sensor Integration and AI Architectures for Next-Generation Traffic Navigation." Applied Sciences 15, no. 8 (2025): 4301. https://doi.org/10.3390/app15084301.

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Traffic congestion represents an urban challenge that authorities are trying to solve through various means. Current traffic management systems do not solve these challenges, which is why the research presents a new proposal for a traffic optimization system. The proposed solution integrates small-sized equipment (ESP32 equipped with accelerometers, gyroscopes, and cameras), cloud-based AI services (Azure Content Safety), and a multi-parametric analytical framework for real-time navigation. The system uses the Traffic Optimization Algorithm (TOA) proposed by the authors to calculate the Global
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Zhang, Yong, Feng Gao, and Fengkui Zhao. "Research on Path Planning and Tracking Control of Autonomous Vehicles Based on Improved RRT* and PSO-LQR." Processes 11, no. 6 (2023): 1841. http://dx.doi.org/10.3390/pr11061841.

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Path planning and tracking control are essential parts of autonomous vehicle research. Regarding path planning, the Rapid Exploration Random Tree Star (RRT*) algorithm has attracted much attention due to its completeness. However, the algorithm still suffers from slow convergence and high randomness. Regarding path tracking, the Linear Quadratic Regulator (LQR) algorithm is widely used in various control applications due to its efficient stability and ease of implementation. However, the relatively empirical selection of its weight matrix can affect the control effect. This study suggests a pa
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Macchia, Gianluca, Emanuele De Angelis, and Michele Vitagliano. "The link between MiFID and Risk Appetite Framework as an application of best practices for wealth management and the entire value chain of the financial industry." Risk Management Magazine 18, no. 3 (2023): 62–77. http://dx.doi.org/10.47473/2020rmm0134.

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After a short review of the MiFID regulations and the RAF, the paper identifies the link between them which allows to mitigate a balance sheet risk sustained by the financial intermediary and, at the same time, to improve its stability and value creation, through a maximization of customer loyalty. The client’s attitude towards risk can be summarized in these terms: "I don't like risk, but I like to win". Thus, a three-dimensional approach towards expected utility is suggested for estimating risk tolerance: risk aversion, loss aversion and reflection. In addition, a definition of the client's
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Chotikunnan, Phichitphon, Yutthana Pititheeraphab, Thanate Angsuwatanakul, et al. "Enhancing MG996R Servo Motor Performance Using PSO-Tuned PID and Feedforward Control." International Journal of Robotics and Control Systems 5, no. 2 (2025): 1120–38. https://doi.org/10.31763/ijrcs.v5i2.1854.

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The aim of this research is to improve the precision of factory-locked MG996R servo motors, which are frequently employed in biomedical and robotic applications. These motors are characterized by the absence of inherent feedback channels and adjustable internal settings. The proposed technique proposes a non-invasive control strategy that utilizes externally obtained feedback to enable closed-loop control without requiring any modifications to the interior circuitry. The scientific contribution consists of the development of an outer-loop PID control framework that has been optimized using Par
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Leon, Vasileios, George Lentaris, Evangelos Petrongonas, et al. "Improving Performance-Power-Programmability in Space Avionics with Edge Devices: VBN on Myriad2 SoC." ACM Transactions on Embedded Computing Systems 20, no. 3 (2021): 1–23. http://dx.doi.org/10.1145/3440885.

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The advent of powerful edge devices and AI algorithms has already revolutionized many terrestrial applications; however, for both technical and historical reasons, the space industry is still striving to adopt these key enabling technologies in new mission concepts. In this context, the current work evaluates an heterogeneous multi-core system-on-chip processor for use on-board future spacecraft to support novel, computationally demanding digital signal processors and AI functionalities. Given the importance of low power consumption in satellites, we consider the Intel Movidius Myriad2 system-
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Bennani, Fatima Ezzahra, Khalid Karrouchi, Latifa Doudach, et al. "In Silico Identification of Promising New Pyrazole Derivative-Based Small Molecules for Modulating CRMP2, C-RAF, CYP17, VEGFR, C-KIT, and HDAC—Application towards Cancer Therapeutics." Current Issues in Molecular Biology 44, no. 11 (2022): 5312–51. http://dx.doi.org/10.3390/cimb44110361.

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Despite continual efforts being made with multiple clinical studies and deploying cutting-edge diagnostic tools and technologies, the discovery of new cancer therapies remains of severe worldwide concern. Multiple drug resistance has also emerged in several cancer cell types, leaving them unresponsive to the many cancer treatments. Such a condition always prompts the development of next-generation cancer therapies that have a better chance of inhibiting selective target macromolecules with less toxicity. Therefore, in the present study, extensive computational approaches were implemented combi
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Wang, Wujie, Qihao Hu, Lina Ma, Fan Shang, Hongze Leng, and Junqiang Song. "Optimal Coherence Length Control in Interferometric Fiber Optic Hydrophones via PRBS Modulation: Theory and Experiment." Sensors 25, no. 15 (2025): 4711. https://doi.org/10.3390/s25154711.

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Interferometric fiber optic hydrophones (IFOHs) are highly sensitive for underwater acoustic detection but face challenges owing to the trade-off between laser monochromaticity and coherence length. In this study, we propose a pseudo-random binary sequence (PRBS) phase modulation method for laser coherence length control, establishing the first theoretical model that quantitatively links PRBS parameter to coherence length, elucidating the mechanism underlying its suppression of parasitic interference noise. Furthermore, our research findings demonstrate that while reducing the laser coherence
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Artuso, Anna, Elena Cossu, Liang He, and Qirui She. "REHABILITATION OF LANDFILLS. NEW FUNCTIONS AND NEW SHAPES FOR THE LANDFILL OF GUIYANG, CHINA." Detritus, no. 11 (July 23, 2020): 57–67. http://dx.doi.org/10.31025/2611-4135/2020.13971.

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The enlargement of a modern landfill may provide an opportunity to intervene with large-scale projects and thus restore spaces for community use and potentially providing an added value. Based on these premises, the intervention on the Guiyang landfill in China has been developed focussing on the possibility of future reclamation of the site under construction during the design stage by applying an approach that takes into account future use from a technical and, more importantly, economical perspective from the outset. The design proposal has been developed following analysis of these element
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Elmitwalli, Sherif, John Mehegan, Sophie Braznell, and Allen Gallagher. "Scalable evaluation framework for retrieval augmented generation in tobacco research using large Language models." Scientific Reports 15, no. 1 (2025). https://doi.org/10.1038/s41598-025-05726-2.

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Abstract Retrieval-augmented generation (RAG) systems show promise in specialized knowledge domains, but the tobacco research field lacks standardized assessment frameworks for comparing different large language models (LLMs). This gap impacts public health decisions that require accurate, domain-specific information retrieval from complex tobacco industry documentation. To develop and validate a tobacco domain-specific evaluation framework for assessing various LLMs in RAG systems that combines automated metrics with expert validation. Using a Goal-Question-Metric paradigm, we evaluated two d
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Muniyandi, Venkatesh. "RAG Architecture Design Patterns :Balancing Retrieval Depth and Generative Coherence." May 7, 2025. https://doi.org/10.5281/zenodo.15377243.

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Retrieval-Augmented Generation (RAG) architectures represent a hybrid approach that blends information retrieval with generative modeling to tackle complex natural language processing (NLP) tasks. A key challenge in these systems is optimizing the balance between retrieval depth and generative coherence. Retrieval depth refers to the number of documents retrieved and utilized by the generative model, while generative coherence is the degree to which the generated output is relevant, contextually accurate, and logically consistent with the retrieved information. This paper proposes the RAG Opti
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Soman, Karthik, Peter W. Rose, John H. Morris, et al. "Biomedical knowledge graph-optimized prompt generation for large language models." Bioinformatics, September 17, 2024. http://dx.doi.org/10.1093/bioinformatics/btae560.

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Abstract Motivation Large Language Models (LLMs) are being adopted at an unprecedented rate, yet still face challenges in knowledge-intensive domains like biomedicine. Solutions such as pre-training and domain-specific fine-tuning add substantial computational overhead, requiring further domain-expertise. Here, we introduce a token-optimized and robust Knowledge Graph-based Retrieval Augmented Generation (KG-RAG) framework by leveraging a massive biomedical KG (SPOKE) with LLMs such as Llama-2-13b, GPT-3.5-Turbo and GPT-4, to generate meaningful biomedical text rooted in established knowledge.
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Zhou, Qingqing, Can Liu, Yuchen Duan, et al. "GastroBot: a Chinese gastrointestinal disease chatbot based on the retrieval-augmented generation." Frontiers in Medicine 11 (May 22, 2024). http://dx.doi.org/10.3389/fmed.2024.1392555.

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IntroductionLarge Language Models (LLMs) play a crucial role in clinical information processing, showcasing robust generalization across diverse language tasks. However, existing LLMs, despite their significance, lack optimization for clinical applications, presenting challenges in terms of illusions and interpretability. The Retrieval-Augmented Generation (RAG) model addresses these issues by providing sources for answer generation, thereby reducing errors. This study explores the application of RAG technology in clinical gastroenterology to enhance knowledge generation on gastrointestinal di
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Kresevic, Simone, Mauro Giuffrè, Milos Ajcevic, Agostino Accardo, Lory S. Crocè, and Dennis L. Shung. "Optimization of hepatological clinical guidelines interpretation by large language models: a retrieval augmented generation-based framework." npj Digital Medicine 7, no. 1 (2024). http://dx.doi.org/10.1038/s41746-024-01091-y.

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AbstractLarge language models (LLMs) can potentially transform healthcare, particularly in providing the right information to the right provider at the right time in the hospital workflow. This study investigates the integration of LLMs into healthcare, specifically focusing on improving clinical decision support systems (CDSSs) through accurate interpretation of medical guidelines for chronic Hepatitis C Virus infection management. Utilizing OpenAI’s GPT-4 Turbo model, we developed a customized LLM framework that incorporates retrieval augmented generation (RAG) and prompt engineering. Our fr
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Pal, Harikrishna. "Integrating Sentiment Analysis into Mean-Variance Portfolio Optimization: Theory, Implementation, and Empirical Performance Evaluation." International Journal For Multidisciplinary Research 7, no. 2 (2025). https://doi.org/10.36948/ijfmr.2025.v07i02.41758.

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Classical mean-variance (MV) portfolio optimization, while foundational in modern finance, assumes market efficiency and rational expectations, neglecting behavioral biases that significantly affect market outcomes. Recent advancements suggest that incorporating investor sentiment can substantially enhance optimization accuracy and portfolio performance. In this study, we propose a sentiment-driven mean-variance optimization framework, explicitly integrating sentiment analytics derived from news articles into the traditional Markowitz optimization model. We rigorously formulate a sentiment-adj
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Zhu, Hongli, Jian Xiang, and Zhichuang Yang. "UnrealMentor GPT: A System for Teaching Programming Based on a Large Language Model." Computer Applications in Engineering Education 33, no. 3 (2025). https://doi.org/10.1002/cae.70023.

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ABSTRACTThis paper introduces UnrealMentor GPT, a multiagent debugging framework that combines advanced large language model (LLM) capabilities with a dynamically updated knowledge base. Systems incorporating this framework are used in programming courses for university computer‐related majors. This teaching system based on Generative Pre‐training (GPT) technology guides students through a hierarchical learning process using multiple specialized agents (syntax checking, algorithm analysis, optimization) and retrieval‐augmented generation (RAG). Experimental results based on the effectiveness o
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Yang, Aimei. "Preparing Public Relations’ Practitioners for the AI Era: Advancing Pedagogical Principles in Public Relations’ Artificial Intelligence Education." Journalism & Mass Communication Educator, September 19, 2024. http://dx.doi.org/10.1177/10776958241277682.

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At the forefront of industries profoundly influenced by artificial intelligence (AI), public relations (PRs) are undergoing a transformative revolution. The increasing applications of AI in PRs are driving a demand for proficient practitioners. Recognizing this, PR educational institutions must adapt by delivering tailored AI education. Despite the growing importance of AI, a literature review reveals a lack of a well-designed AI curriculum in PRs. This essay draws insights from recent research on AI value alignment, dialogic communication, and PR ethics, articulating three foundational princi
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Kommineni, Vamsi Krishna, Waqas Ahmed, Birgitta Koenig-Ries, and Sheeba Samuel. "Automating Information Retrieval from Biodiversity Literature Using Large Language Models: A Case Study." Biodiversity Information Science and Standards 8 (September 10, 2024). http://dx.doi.org/10.3897/biss.8.136735.

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Recently, Large Language Models (LLMs) have transformed information retrieval, becoming widely adopted across various domains due to their ability to process extensive textual data and generate diverse insights. Biodiversity literature, with its broad range of topics, is no exception to this trend (Boyko et al. 2023, Castro et al. 2024). LLMs can help in information extraction and synthesis, text annotation and classification, and many other natural language processing tasks. We leverage LLMs to automate the information retrieval task from biodiversity publications, building upon data sourced
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-, Syed Arham Akheel. "Fine-Tuning Pre-Trained Language Models for Improved Retrieval in RAG Systems for Domain-Specific Use." International Journal For Multidisciplinary Research 6, no. 5 (2024). https://doi.org/10.36948/ijfmr.2024.v06i05.22581.

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Large Language Models (LLMs) have significantly advanced natural language understanding and generation capabilities, but domain-specific applications often necessitate supplementation with current, external information to mitigate knowledge gaps and reduce hallucinations. Retrieval-Augmented Generation (RAG) has emerged as an effective solution, dynamically integrating up-to-date information through retrieval mechanisms. Fine-tuning pre-trained LLMs with domain-specific data to optimize retrieval queries has become an essential strategy to enhance RAG systems, especially in ensuring the retrie
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Gießler, Maximilian, Bernd Waltersberger, Thomas Götz, and Robert Rockenfeller. "A multi-method framework for establishing an angular acceleration reference in sensor calibration and uncertainty quantification." Communications Engineering 4, no. 1 (2025). https://doi.org/10.1038/s44172-025-00384-8.

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Abstract Robots are increasingly being used across various sectors, from industry and healthcare to household applications. In practice, a pivotal challenge is the reaction to unexpected external disturbances, whose real-time feedback often relies on (noisy) sensor measurements. Subsequent inverse-dynamics calculations demand noise-amplifying numerical differentiation, leading to impracticable results. Although much effort has been spent on establishing direct measurement approaches, their measurement uncertainty quantification has not or yet insufficiently been tackled in the literature. Here
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Shi, Yayun, Chao Song, Yifu Chen, Hanyue Rao, and Tihao Yang. "Complex Standard Eigenvalue Problem Derivative Computation for Laminar–Turbulent Transition Prediction." AIAA Journal, May 26, 2023, 1–15. http://dx.doi.org/10.2514/1.j062212.

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As a high-fidelity approach to transition prediction, the coupled Reynolds-averaged Navier–Stokes (RANS) and linear stability theory (LST)-based [Formula: see text] method is widely used in engineering applications and is the preferred method for laminar flow optimization. However, the further development of gradient-based laminar flow wing optimization schemes is hindered by a lack of efficient and accurate derivative computation methods for LST-based eigenvalue problems with a large number of design variables. To address this deficiency and to compute the derivatives in the LST-based solutio
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