Franklin Open最新文献

{"title":"Advanced model order reduction for high-dimensional aerospace control systems","authors":"Md. Shariful Islam,&nbsp;Md. Sumon Hossain,&nbsp;Md. Mobin Hossain","doi":"10.1016/j.fraope.2025.100412","DOIUrl":"10.1016/j.fraope.2025.100412","url":null,"abstract":"<div><div>This paper presents the two-sided iterative algorithm (TSIA) and its frequency-limited extension for <span><math><msub><mrow><mi>H</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> optimal, a robust framework for model order reduction of a high-dimensional nonlinear aerospace model. The methods derive a surrogate reduced-order model that preserves critical flight dynamics for flight control system design across a flight envelope of Mach numbers, altitudes, and angles of attack. By iteratively solving Sylvester equations and leveraging frequency-limited Gramians, the methods ensure stability and accuracy in targeted frequency bands, capturing low-frequency and mid-frequency short-period modes. In particular, frequency-limited TSIA achieves superior alignment at resonance peaks with minimal absolute and relative errors. Numerical simulations demonstrate the reduced model’s efficiency in maintaining dynamic fidelity, enabling rapid simulations and precise control adjustments. This framework significantly enhances the efficiency of flight control system design, improving aircraft stability, safety, and operational performance, and offering a scalable approach for large-scale aerospace systems.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"13 ","pages":"Article 100412"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A fractional order model for transmission dynamics of TB with funding-driven vaccination and social processes","authors":"Chiganga S. Ruoja ,&nbsp;Nkuba Nyerere ,&nbsp;Maranya Mayengo ,&nbsp;Farai Nyabadza","doi":"10.1016/j.fraope.2025.100413","DOIUrl":"10.1016/j.fraope.2025.100413","url":null,"abstract":"<div><div>This study highlights the role of human preventive behavior, positive attitudes towards hospital-based treatment, and funding of vaccination programs in understanding the transmission dynamics of tuberculosis. To effectively account for human experiences, a Caputo-based fractional-order mathematical model is formulated. The well-posedness of the proposed model is examined using the Generalized Mean Value Theorem, Mittag-Leffler functions, and the Banach contraction mapping principle. To examine the robustness of the solutions, the Ulam–Hyers approach is employed. The next-generation matrix technique is adopted to derive the socioeconomic reproduction number, denoted as <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>S</mi><mi>E</mi></mrow></msub></math></span>. Estimation of parameter values is done by fitting the model to real tuberculosis data reported by the World Health Organization for Tanzania, covering the years 2000 to 2023. The system of equations is solved using the Predict-Evaluate-Correct-Evaluate Adams–Bashforth–Moulton scheme. Both analytical and numerical results indicate that increasing funding for vaccination programs, raising the level of disease-induced fear, and higher proportion of patients with positive attitudes towards hospital-based treatment can potentially reduce the disease burden in the community. Furthermore, incorporating human behavioral responses and experiences improves the accuracy of future disease dynamics predictions rather than ignoring them.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"13 ","pages":"Article 100413"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainable renewable energy source selection with novel fuzzy entropy based extended TOPSIS method","authors":"Manish Garg ,&nbsp;Satish Kumar","doi":"10.1016/j.fraope.2025.100398","DOIUrl":"10.1016/j.fraope.2025.100398","url":null,"abstract":"<div><div>As the world transition towards a low-carbon economy, the selection of sustainable renewable energy has become a critical component of mitigating climate change, ensuring energy security, and promoting economic growth. With the increasing demand to reduce greenhouse gas emission and reliance on fossil fuels, it is essential to evaluate and compare different renewable energy sources such as solar, wind, hydro, and biomass power to determine the most suitable options relative to various conflicting parameters such as cost, efficiency, scalability, energy density and environmental impact. The selection of sustainable renewable energy source is a complex process. For this, opinions of experts regarding the performance of different sources relative to different criteria are taken into consideration. In real-world decision making processes, decision makers often rely on linguistic assessment, which cannot be precisely quantified using classical (crisp) approaches. This kind of qualitative information introduces epistemic uncertainty, which fuzzy set theory is specifically designed to handle. This paper presents a modified TOPSIS (Technique for Order Preference by Similarity to Ideal Solutions) method in fuzzy environment for the selection of suitable renewable energy source. Fuzzy entropy quantify the fuzziness or vagueness within a fuzzy set. This study presents a new fuzzy entropy to overcome the limitations of several existing fuzzy entropies. A novel fuzzy distance measure is proposed to more accurately quantify the dissimilarity between fuzzy sets, enhancing the precision of comparative analysis. The effectiveness of the proposed distance measure in pattern recognition is demonstrated through comprehensive numerical illustrations. This research aims to enhance the stability of the TOPSIS method by integrating the proposed entropy and distance measures into its framework.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"13 ","pages":"Article 100398"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145366082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyperparameter optimized deep learning models for multiclass oral lesion classification in proprietary histopathological image Data","authors":"Hemashree HC ,&nbsp;Pradeep N ,&nbsp;Ahmed Mujib B-R ,&nbsp;Shashidhar R ,&nbsp;Vinayakumar Ravi","doi":"10.1016/j.fraope.2025.100437","DOIUrl":"10.1016/j.fraope.2025.100437","url":null,"abstract":"<div><div>Oral cancer remains an important health issue in worldwide because of its significant incidence and mortality rates, with Squamous Cell Carcinoma accounting for &gt;90 % of incidents. Early detection of oral lesions is critical to prevent progression to advanced stages, yet traditional biopsy methods are invasive and prone to human error. To solve this, the paper introduces several key innovations like novel multi source dataset strategy combining large public and proprietary clinical data, comprehensive six-class classification covering the entire oral lesion progression spectrum and rigorous hyperparameter optimization with clinical validation. Deep learning framework uses cutting-edge CNNs like InceptionV3, ResNet50, DenseNet121, and MobileNet which are currently enhanced by increasing the data and hyperparameter modification. The model was tested on both public and private datasets. On the public dataset 4988 images: 2494 normal, 2494 OSCC, InceptionV3 performed the best with 96.2 % accuracy and an AUC of 0.98. On the private dataset 2450 images: 1569 training, 489 testing and validation 392. DenseNet121 achieved 94.5 % train accuracy and 90.6 % validation accuracy, which shows strong generalizability. Our proposed method demonstrates a performance improvement of 4–6 % in accuracy over greater robustness compared to other deep learning frameworks (91 %), establishing new benchmark for automated OSCC classification. The model was optimized using the median filtering for reducing noise and augmentation techniques to increase its resilience. The framework presented here provides a non-invasive, an automated method of identifying OSCC early, minimizing diagnostic delay and leading to better patient results.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"13 ","pages":"Article 100437"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145750064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A robust and computationally efficient adaptive PI controller based on Takagi-Sugeno fuzzy model for improving the dynamic behavior of DFIG-based wind turbine systems","authors":"Abdellatif Kasbi ,&nbsp;Abderrafii Rahali ,&nbsp;Youcef Djeriri ,&nbsp;Nabil El Akchioui","doi":"10.1016/j.fraope.2025.100424","DOIUrl":"10.1016/j.fraope.2025.100424","url":null,"abstract":"<div><div>This paper presents a novel adaptive fuzzy proportional-integral (AFPI) controller to enhance the dynamic behavior and power quality of a doubly-fed induction generator (DFIG)-based wind turbine system in grid-connected mode. The proposed AFPI control method combines the design simplicity of the PI controller with the intelligent and adaptive structure of fuzzy logic control (FLC) to enhance the dynamic behavior of variable-speed DFIG-based wind turbine systems in various operating conditions. In this control system, the PI controller’s gains are dynamically adjusted by a fuzzy supervisory control system (FSCS) according to the system’s operating conditions, whose objective is to enhance the power quality, provide a fast dynamic response and a good robustness during uncertainties. This research paper has focused on the design of an implementable control algorithm on the programmable logic controllers (PLCs), which have stringent requirements for computational speed and memory capacity. In this context, the Takagi–Sugeno (TS) fuzzy model is proposed, as it is computationally more efficient than the Mamdani fuzzy model. Additionally, the Takagi–Sugeno (TS) fuzzy model simplifies the control system structure compared to the Mamdani type. Computer simulations were conducted using MATLAB/Simulink, and the results obtained for different operating conditions demonstrate the advantages and high control performance of the proposed control method over the Mamdani fuzzy models-based adaptive PI controller.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"13 ","pages":"Article 100424"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time sign language gesture recognition for Women's safety using dynamic time warping and voting algorithms","authors":"Akansha Tyagi ,&nbsp;Vaidya S. Prasanth ,&nbsp;Poonam Lunawat ,&nbsp;S. Ashok","doi":"10.1016/j.fraope.2025.100428","DOIUrl":"10.1016/j.fraope.2025.100428","url":null,"abstract":"<div><div>Advancements in computer vision techniques have spurred the development of sign language recognition systems, benefiting the deaf and mute community. However, the safety of deaf hard-of-hearing women remains a significant concern, particularly in the context of emerging threats. This paper presents a method for real-time recognition of women's safety sign language gestures using Dynamic Time Warping (DTW) and voting algorithms. The proposed approach leverages MediaPipe to extract keypoints from hand, body, and facial movements, representing gestures as feature matrices that encapsulate both spatial and temporal aspects. These matrices are compared using DTW to recognise gestures, with a voting algorithm determining the final gesture label. A novel dataset, WSISL-28, consisting of 28 women's safety-related gestures, was created to evaluate the method. Experimental results demonstrate an impressive accuracy of 98.45 %, affirming the feasibility and effectiveness of the proposed system for real-time sign language gesture recognition, which empowers deaf and mute women, enhancing their safety and security.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"13 ","pages":"Article 100428"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145693983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An extensive review of THz communication in 6G: Facilitating technologies with edge computing and native AI","authors":"Subhankar Shome ,&nbsp;Suman Das ,&nbsp;Saumya Das ,&nbsp;Debashish Pal","doi":"10.1016/j.fraope.2025.100434","DOIUrl":"10.1016/j.fraope.2025.100434","url":null,"abstract":"<div><div>The transition toward sixth-generation networks is expected to enable unprecedented capabilities such as immersive extended reality, holographic telepresence, large-scale digital twins, and ultra-reliable autonomous communications. Unlocking these services requires exploiting the terahertz spectrum, which provides massive bandwidth but also presents formidable challenges, including severe propagation loss, beam misalignment, blockage sensitivity, and the need for fine-grained synchronization in dynamic environments. To address these, this article advances a Native-AI paradigm where artificial intelligence and edge computing are embedded as intrinsic elements of the THz communication fabric rather than treated as external add-ons. Within this framework, AI methods such as deep reinforcement learning, graph neural networks, and federated learning are harnessed for proactive channel estimation, adaptive beamforming, RIS-assisted propagation control, and distributed mobility management across heterogeneous nodes, including base stations, UAV relays, and edge servers. The proposed architecture emphasizes hierarchical orchestration, fairness-aware resource allocation, robustness against adversarial threats, and zero-touch management to ensure reliable and secure operation in high-mobility and heterogeneous conditions. Our contribution lies in providing a unified architectural blueprint and technical mapping that connects native AI mechanisms to core THz communication functions, highlighting how synchronization, resilience, and scalability can be achieved through distributed coordination and intent-driven orchestration. Beyond architectural design, the paper discusses performance implications and practical deployment considerations, including challenges of non-IID data, energy and compute heterogeneity, and the vulnerability of distributed AI to adversarial manipulation. Strategies such as TinyML-based model compression, Byzantine-resilient federated protocols, and explainable AI are outlined as enablers of sustainable, trustworthy deployment. By synthesizing current research trends with original design insights, this work positions native AI as a foundational principle for 6G—transforming THz systems into intelligent, secure, and autonomous infrastructures capable of meeting the stringent demands of next-generation services.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"13 ","pages":"Article 100434"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145693982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BlockDrugChain: A blockchain and ML-driven framework for scalable drug discovery management","authors":"Dileep Kumar Murala","doi":"10.1016/j.fraope.2025.100438","DOIUrl":"10.1016/j.fraope.2025.100438","url":null,"abstract":"<div><div>The discovery of new drugs is a collaborative effort that involves researchers, pharmaceutical companies, and regulators. Cyberattacks, data tampering, and a lack of transparency are all common problems in today's centralised systems. This paper suggests using Hyperledger Fabric-based blockchain technology to increase the efficiency, transparency, and safety of drug discovery. The straightforward user interface makes it easy to upload, update, and validate data. Each contributor is granted a unique ID using SHA-256, which protects the data. With the use of machine learning, data is improved and people are empowered to make better decisions. To expedite verification, critical metadata is stored on-chain, while large files are stored off-chain. Custom smart contracts safeguard your data and transfer ownership to you. According to performance studies, it can process up to 365 transactions per second, making it ideal for large-scale collaboration. An alternative to traditional drug discovery approaches that is both modern and decentralised is offered by this idea.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"13 ","pages":"Article 100438"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145693980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid framework for symptom-based nerve weakness detection using machine learning and rule-based methods","authors":"Pawan Kumar Badhan","doi":"10.1016/j.fraope.2025.100414","DOIUrl":"10.1016/j.fraope.2025.100414","url":null,"abstract":"<div><div>Nerve weakness, marked by symptoms such as numbness, muscle stiffness, and memory loss, poses significant diagnostic challenges due to its diverse clinical manifestations. This study introduces a multimodal diagnostic framework that combines rule-based reasoning with advanced machine learning and deep learning techniques to improve the accuracy of detecting nerve-related disorders. The first objective is to develop a robust rule-based system that maps specific symptoms to underlying neurological conditions using a comprehensive and heterogeneous dataset. The second goal is to enhance this baseline model by integrating multimodal data inputs and leveraging ML/DL algorithms to boost diagnostic precision. The third aim is to enable early detection, timely intervention, and potential prevention of neurological and systemic disorders such as Multiple Sclerosis (MS), Parkinson’s Disease, Alzheimer’s Disease, and related conditions. The dataset includes over 1000 patient records collected from Punjab and other regions of India, capturing symptom onset and neurological indicators such as muscle weakness, headaches, and memory decline. Data sources include clinical interviews, audio/video recordings, wearable sensors, and diagnostic tools such as Electromyography (EMG), Magnetic Resonance Imaging (MRI), and Electroencephalography (EEG). Various machine learning and deep learning models—including Convolutional Neural Networks (CNNs)—were employed to process and analyze these multimodal inputs. The proposed hybrid diagnostic framework achieved accuracy levels between 91% and 97%, notably outperforming the standalone rule-based model, which achieved between 82% and 92%. Additionally, Extreme Gradient Boosting (XGBoost) was applied to further enhance predictive performance through gradient-based optimization.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"13 ","pages":"Article 100414"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BRCA-HM: A lightweight image encryption scheme using bit reversal, cellular automata, and Henon map in IoT application","authors":"Biswarup Yogi ,&nbsp;Ajoy Kumar Khan","doi":"10.1016/j.fraope.2025.100448","DOIUrl":"10.1016/j.fraope.2025.100448","url":null,"abstract":"<div><div>In this paper, the proposed method is BRCA-HM, a lightweight image encryption scheme that combines Bit Reversal, Cellular Automata, and the Henon map. The proposed scheme provides an efficient and robust encryption framework. In this work, we use Bit Reversal to improve diffusion, employ Cellular Automata Rule 90 for random-like transformation, and apply the Henon Map to introduce chaotic behaviour, thereby achieving high unpredictability and strong resistance to attacks. The proposed method demonstrates the validity of the proposed scheme through experimental results, where the information entropy is calculated as 7.9990, UACI is equal to 33.46%, and NPCR is equal to 99.68%, thereby proving the strong security properties of the proposed scheme, which is resistant to differential attacks. As a lightweight solution, BRCA-HM is attractive to data-hungry environments, such as IoT and those requiring borderline real-time multimedia. In the future, computational efficiency is expected to improve, and a multilayer encryption model will be extended to enhance the scheme’s stability. The proposed method opened up a potential approach for secure and efficient image encryption in modern digital communication systems. The findings highlight the potential for this hybrid approach to provide a safe, efficient, and durable solution for image encryption in IoT applications, maintaining data integrity and privacy while outperforming existing encryption techniques. The experimental results and comparison with state-of-the-art approaches indicate that the proposed scheme offers a high level of robustness against various unsafe attacks.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"13 ","pages":"Article 100448"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}