Franklin Open最新文献

{"title":"Mathematical model for trypanosomiasis management integrating treatment enhancement, vector control, and host management strategies","authors":"Akintayo Emmanuel Akinsunmade ,&nbsp;Ahmed Oluseun Afolabi","doi":"10.1016/j.fraope.2026.100500","DOIUrl":"10.1016/j.fraope.2026.100500","url":null,"abstract":"<div><div>Human African Trypanosomiasis (HAT) remains a persistent public health challenge due to its complex zoonotic transmission cycle and high endemic potential. This study introduces a mathematical model that explicitly integrates stage-specific human clinical progression with a dynamic cattle reservoir and mechanistic vector control to evaluate intervention strategies within a One Health framework. Comprehensive analysis using the normalized forward sensitivity index and Latin Hypercube Sampling-Partial Rank Correlation Coefficient (LHS-PRCC) identifies the cattle cycle as the dominant transmission pathway, contributing approximately 97.8% of the overall epidemic risk. Notably, cattle treatment was identified as the most influential single lever for reducing the basic reproduction number, while the tsetse biting rate and mortality remain the primary environmental drivers of infection. Numerical simulations validate these theoretical findings, demonstrating that single interventions are insufficient for sustained elimination. While human treatment provides essential clinical benefits by rapidly reducing morbidity, its impact on the population-level epidemic threshold is negligible. Conversely, integrated cattle treatment and vector control are highly effective at clearing the primary animal reservoir and infectious vector pools. The model decisively demonstrates that only a combined strategy, leveraging animal reservoir clearance alongside immediate clinical care, can successfully drive disease prevalence to a near-zero state. Consequently, this study recommends a policy shift from human-centric care to an integrated host-vector management framework. Prioritizing interventions at the cattle-vector interface is essential to meet the World Health Organization (WHO) 2030 targets and achieve sustainable local elimination.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"14 ","pages":"Article 100500"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977462","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":"Securing wireless sensor networks: A survey of challenges and innovations","authors":"R Sudha ,&nbsp;M Premkumar","doi":"10.1016/j.fraope.2025.100470","DOIUrl":"10.1016/j.fraope.2025.100470","url":null,"abstract":"<div><div>WSNs are a significant component of the modern technology systems. They find application in numerous sectors such as environmental monitoring, health care, agriculture and intelligent constructions. Although they possess low power, have low processing capacity and can readily be influenced by unsafe environments, they are still very important. They are therefore usually the victims of various forms of security threats as a result of that. The more advanced these threats become, the higher is the need to have strong and flexible security measures. The survey examines a broad spectrum of studies on the security of WSN. It encompasses numerous fields, such as how to transmit data safely, how to deal with keys, trust and reputation management systems and how to check and authenticate users. It also considers novel concepts such as applying blockchain and machine learning to identify security problems. Some of the studies in the research are based on straightforward solutions that can be applied effectively with the available limited resources of WSNs, whereas others attempt to achieve the balance between security and efficiency. Despite these developments, there are issues. The solutions are mostly energy consuming or are rigid to adapt to changing network conditions. The disconnect between the concepts in studies and their practical implementation is also present, in particular, the use of complex technologies, such as AI, in practice. This review aims to unite significant conclusions of the recent research, to emphasize the main issues that are not resolved yet, and to propose the useful directions in the further investigations. This survey provides an up-to-date and clear image of the current trends in WSN security, as it allows the researcher and developers to develop more efficient and profound strategies on how to make the system stronger and more reliable.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"14 ","pages":"Article 100470"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798985","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":"Prabhakar fractional-order modeling of malaria transmission with vaccination and waning immunity","authors":"Akanksha Singh ,&nbsp;Rutu Raval ,&nbsp;Anil Chavada ,&nbsp;Nimisha Pathak ,&nbsp;Rajesh K. Pandey","doi":"10.1016/j.fraope.2025.100484","DOIUrl":"10.1016/j.fraope.2025.100484","url":null,"abstract":"<div><div>In this study, we develop a classical malaria transmission model by extending an existing framework to incorporate partial vaccination and waning immunity, introducing compartments for vaccinated susceptible, exposed, infected, and recovered individuals. To enhance biological realism and capture complex memory and hereditary effects, we fractionalize the model using the Caputo-type Prabhakar derivative. This generalized fractional operator, with its non-local Mittag–Leffler kernel, enables a more flexible representation of disease progression than classical derivatives. Furthermore, we show that by taking appropriate parameter limits in the Prabhakar operator, the model reduces to a special case involving the Atangana–Baleanu fractional derivative in the Caputo sense (ABC type), thereby connecting the generalized framework to a widely used non-singular fractional operator. We prove that the fractional model retains positivity and boundedness, and we determine the equilibrium points and basic reproduction number (<span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>) using the next-generation matrix method. Sensitivity analysis reveals the most influential parameters affecting <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>, offering practical insights for targeted control strategies. Numerical simulations are performed via a fractional fourth-order Runge–Kutta method. Results show that decreasing the fractional order <span><math><msub><mrow><mi>a</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> delays infection peaks, smooths transitions between compartments, and slows disease spread, reflecting the influence of long-term memory in moderating epidemic dynamics. Phase portraits indicate that trajectories approach equilibrium more gradually for smaller <span><math><msub><mrow><mi>a</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> values, while 3D time-dependent plots illustrate the evolution of each compartment under varying memory effects. Additionally, 3D interaction surfaces between vaccinated and unvaccinated subgroups highlight the interplay between immunity and transmission. These findings confirm the versatility of Prabhakar fractional-order modeling for realistic malaria dynamics.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"14 ","pages":"Article 100484"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939513","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 novel four-term conjugate gradient method for large-scale optimization problems involving formulation and application","authors":"Ahmad Alhawarat ,&nbsp;Sulaiman M. Ibrahim ,&nbsp;Abdullah S. Al-Jawarneh ,&nbsp;Mohammad N Al Smadi ,&nbsp;Shahrina Ismail","doi":"10.1016/j.fraope.2025.100478","DOIUrl":"10.1016/j.fraope.2025.100478","url":null,"abstract":"<div><div>The conjugate gradient (CG) method is widely employed for solving unconstrained optimization problems due to its independence from second derivatives or their approximations. It has found extensive applications in fields such as image restoration, neural networks, deep learning, and machine learning. In this study, we propose a novel four-term CG method designed to improve both convergence and descent properties. The proposed method builds upon the WYL and Dai-Liao CG frameworks, with the latter preserving the conjugacy condition under inexact line searches. Numerical experiments demonstrate that the proposed method outperforms several well-established CG methods, including CG-Descent, Dai-Liao, and the WYL family, in terms of iteration count, function and gradient evaluations, and CPU time. Furthermore, the method is extended to address real-world application problems, highlighting its practical effectiveness.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"14 ","pages":"Article 100478"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939504","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":"Dynamic analysis of stochastic SE2I2T model with saturation incidence rate","authors":"Kazi Hafizur Rahman ,&nbsp;Chandra N. Podder","doi":"10.1016/j.fraope.2025.100458","DOIUrl":"10.1016/j.fraope.2025.100458","url":null,"abstract":"<div><div>Tuberculosis (TB) remains one of the world’s deadliest infectious diseases, responsible for numerous deaths annually despite being preventable and treatable. In this paper, we propose and analyze a novel stochastic SE2I2T epidemiological model that accounts for two strains of tuberculosis (TB): drug-sensitive and drug-resistant under a saturation incidence rate. This model reflects realistic transmission dynamics by incorporating treatment inefficacy and environmental fluctuations modeled via Gaussian white noise. By creating an appropriate Lyapunov function, we first establish the existence of a unique global positive solution to the underlying problem. Sufficient conditions for extinction and persistence of the disease are investigated. Conditions for the existence of a stationary distribution is also found. The theoretical findings are then validated through numerical simulations using the Milstein scheme. Notably, the results indicate that higher noise intensities and reduced contact between treated individuals and drug-sensitive strains significantly increase the likelihood of disease eradication. Our threshold analysis reveals that increased environmental noise can drive the system toward extinction even in the presence of high transmission, while elevated resistance probabilities can undermine treatment success, leading to the persistence of the resistant strain.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"14 ","pages":"Article 100458"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885081","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 similarity measure for intuitionistic fuzzy sets with improved clustering accuracy and applications in pattern recognition","authors":"Savita ,&nbsp;Niteesh Kumar ,&nbsp;Anjul Siwch","doi":"10.1016/j.fraope.2026.100518","DOIUrl":"10.1016/j.fraope.2026.100518","url":null,"abstract":"<div><div>Modern systems often encounter uncertainties arising from both randomness and fuzziness, which pose significant challenges to effective data analysis. To address these challenges, researchers have increasingly focused on fuzzy clustering techniques as a unified approach to managing dual uncertainties. This manuscript proposes a modified variant of the intuitionistic fuzzy c-means clustering technique coupled with an improved genetic algorithm to refine clustering precision. The proposed method mitigates noise sensitivity by developing a robust similarity metric, while Yager’s negation function effectively handles uncertainty in membership value assignments. Additionally, the improved genetic algorithm employs normalized crossover and mutation operators to mitigate sensitivity to initial centroid selection. The method demonstrates superior adaptability compared to existing approaches, effectively addressing key clustering challenges. Its efficacy is validated through experiments on benchmark datasets, with comparative analyses against existing state-of-the-art techniques by delivering higher accuracy, maximizing inter-cluster distance, reducing objective function value and minimizing sensitivity analysis. These findings demonstrate that the proposed IGAAIFCM technique successfully addresses key limitations of conventional fuzzy clustering by delivering a robust and uncertainty-adaptive clustering framework with improved stability and accuracy.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"14 ","pages":"Article 100518"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187861","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":"Plastic bottle defect detection based on convolutional neural network with MobileNetV2 architecture","authors":"Herman Yuliansyah ,&nbsp;Riyan Adi Saputro ,&nbsp;Itsnaini Irvina Khoirunnisa ,&nbsp;Wan Nur Syamilah Wan Ali ,&nbsp;Yohani Setiya Rafika Nur ,&nbsp;Nabilah Filzah Mohd Radzuan","doi":"10.1016/j.fraope.2026.100522","DOIUrl":"10.1016/j.fraope.2026.100522","url":null,"abstract":"<div><div>Solid waste generated from human activities can be categorized into two main types: organic and inorganic waste. Plastic is one type of inorganic waste that can be recycled to reduce the negative impact of plastic waste and provide economic value. However, manual sorting of plastic bottles is time-consuming and requires a more efficient system to distinguish between recyclable and non-recyclable. This research proposes a plastic bottle defect detection model using a Convolutional Neural Network (CNN) based on the MobileNetV2 architecture with transfer learning. The model was trained using data augmentation, systematic hyperparameter tuning, and fine-tuning with dropout regularization, and evaluated using accuracy, precision, recall, and F1-score metrics. The proposed model was trained and evaluated using a dataset of 1074 plastic bottle images consisting of recyclable and non-recyclable bottle classes. The model used is MobileNetV2 with hyperparameter tuning to achieve optimal performance. The results showed that the best combination of hyperparameters was the Adam optimizer with a learning rate of 0.001 and 20 epochs, achieving an accuracy, precision, recall, and F1-score of 0.98 each . The applied method is effective in classifying images of recyclable and non-recyclable bottles, which provides an experimental contribution toward the development of an artificial intelligence-based waste sorting system and demonstrates its potential for real-world deployment, as well as opening up opportunities for further research in model optimization.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"14 ","pages":"Article 100522"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188037","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":"Advancing brain tumor classification through pre-trained transformer and transfer learning models","authors":"Vergin Raja Sarobin M ,&nbsp;Siddhi Gupta ,&nbsp;Azrina Abd Aziz","doi":"10.1016/j.fraope.2026.100493","DOIUrl":"10.1016/j.fraope.2026.100493","url":null,"abstract":"<div><div>Brain tumor classification by MRI images remains an important yet difficult issue in the field of medical diagnosis. The accuracy of tumor identification significantly influences treatment results; however, the process is hard because of the range of tumor shapes and textures and differences in MRI scans. Since timely and precise diagnosis is paramount for patient care, precise segmentation of brain lesions becomes pivotal as the premise for the next medical decision. Traditional CNNs have registered notable success in medical image analysis. Their natural weakness, though, - a limited receptive field - will generally make it difficult for them to comprehend general spatial relationships in an image, particularly when abnormal areas cover extensive space. In order to overcome this, we investigate Vision Transformers (ViTs), which provide a more comprehensive comprehension of MRI scans by modeling long-range dependencies among picture patches by utilizing self-attention mechanisms. A comparison between a transfer learning strategy utilizing VGG16 and a ViT-based model is presented in this paper. We trained our models on a combined dataset sourced from Figshare, SARTAJ, and Br35H, comprising 7023 brain MRI images across four classes: glioma, meningioma, no tumor, and pituitary. Experimental results show that the Vision Transformer consistently outperforms the VGG16 model, achieving a test accuracy of 99.6 %, with both precision and F1-score reaching 99.59 %. Our findings suggest that ViTs hold strong promise for integration into clinical workflows, with potential for generalization across MRI scanners and hospital environments.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"14 ","pages":"Article 100493"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977534","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":"(μ1,μ2)-Pseudo almost periodic dynamics of hematopoiesis model with mixed delays and nonlinear harvesting term","authors":"Moez Ayachi","doi":"10.1016/j.fraope.2025.100450","DOIUrl":"10.1016/j.fraope.2025.100450","url":null,"abstract":"<div><div>In this paper, we are interested to the dynamics of a generalized Hematopoiesis model involving mixed delays, nonlinear harvesting term and doubly-measure pseudo-almost periodic (<span><math><mrow><mrow><mo>(</mo><msub><mrow><mi>μ</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>,</mo><msub><mrow><mi>μ</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>)</mo></mrow><mo>−</mo><mi>PAP</mi></mrow></math></span>) parameters. By using the properties of <span><math><mrow><mrow><mo>(</mo><msub><mrow><mi>μ</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>,</mo><msub><mrow><mi>μ</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>)</mo></mrow><mo>−</mo><mi>PAP</mi></mrow></math></span> functions and the contraction mapping principle, suitable sufficient conditions were established for the existence, uniqueness and stability of <span><math><mrow><mrow><mo>(</mo><msub><mrow><mi>μ</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>,</mo><msub><mrow><mi>μ</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>)</mo></mrow><mo>−</mo><mi>PAP</mi></mrow></math></span> solution. Finally, a numerical example is presented to illustrate the theoretical findings. The results obtained in this paper provide a better understanding of the dynamics of hematopoiesis models with mixed delays and could be useful in further studies on hematopoiesis models.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"14 ","pages":"Article 100450"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798984","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":"Modelling and design optimization of switched reluctance motors based on machine learning methods: Review","authors":"Jackson Oloo,&nbsp;Laszlo Szamel","doi":"10.1016/j.fraope.2025.100474","DOIUrl":"10.1016/j.fraope.2025.100474","url":null,"abstract":"<div><div>Accurate modelling of Switched Reluctance Motors (SRM) is a complex and time consuming task that involves a lot of tradeoffs and assumptions. Numerical and analytical modelling techniques have been extensively used in literature. Due to recent advancements in artificial intelligence and machine learning (ML) methods, there is need to explore nonlinear modelling of SRMs using machine learning surrogate models. This paper presents a comprehensive review of machine learning techniques of optimizing Switched Reluctance Motor (SRM) models. Several machine learning models have been developed to optimize SRM parameters such as stator pole arc<strong>,</strong> rotor pole arc<strong>,</strong> stack length<strong>,</strong> turns per pole<strong>,</strong> and phase current<strong>.</strong> The SRM parameter values have been obtained from Finite Element Analysis data of an 8/6, 600 V machine. The design parameters are optimized based on weighted combination objective function. The objective is to minimize torque ripples while maximizing efficiency. The ML techniques considered in this work include the Bayesian algorithm, Feed Forward Neural Network (FNN), Back Propagation Neural Network (BPNN), Artificial Neural Network (ANN), Radial Basis Function Neural Network (RBFNN), Sequential Least Squares Programming (SLSP) and Gaussian Process Regressor (GPR). The BPNN is trained with various techniques such as Levenberg Marquardt (LM), Differential Evolution (DE), Gradient Descent Algorithm (GDA), Quasi-Newton algorithm, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Best parameters, percentage torque ripple reduction and percentage efficiency improvement are then used to evaluate the performance of the machine learning optimization models.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"14 ","pages":"Article 100474"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885156","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}