MethodsX最新文献

{"title":"Construction and applications of iterative methods for finding approximate solutions of nonlinear equations having unknown zeros of multiplicity with fractal geometry and dynamical behavior","authors":"Farooq Ahmed Shah ,&nbsp;Iftikhar Haider ,&nbsp;Muhammad Waseem ,&nbsp;Alexey Mikhaylov ,&nbsp;Nora Baranyai","doi":"10.1016/j.mex.2025.103778","DOIUrl":"10.1016/j.mex.2025.103778","url":null,"abstract":"<div><div>In this study, several new iterative schemes are developed to compute the multiple zeros of nonlinear equations. The construction of these methods is based on the variational iteration approach which provides a systematic framework for formulating efficient and flexible algorithms. The proposed schemes generalize and encompass well-known classical methods such as Newton’s and Halley’s methods, along with their modified versions as special cases. This generality enhances their adaptability to a broader class of nonlinear problems involving both known and unknown multiplicities. To assess the effectiveness of the proposed iterative schemes, extensive numerical experiments are conducted, comparing their convergence speed and accuracy with existing methods. The results demonstrate that the newly developed methods exhibit superior performance in terms of stability, precision, and computational efficiency. Furthermore, to visualize and analyse the global convergence behaviour fractal basin plots are presented. These fractals illustrate the basins of attraction in the complex plane, providing deeper insight into the dynamical behaviour, convergence regions and boundary structures associated with each iterative process.<ul><li><span>•</span><span><div>Developing efficient and flexible iterative methods using the variational iteration method.</div></span></li><li><span>•</span><span><div>Generalizing classical methods to tackle nonlinear problems with known and unknown multiplicity.</div></span></li><li><span>•</span><span><div>Validating performance of various methods through detailed numerical experiments and fractal basin plots.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103778"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926358","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":"Automated quantitative analysis of osteoclast numbers and areas in cell culture plates using the YOLOv8 instance segmentation model","authors":"Jia Huang ,&nbsp;Zefeng Li ,&nbsp;Junyu Ouyang ,&nbsp;Jialing Li ,&nbsp;Yichen Fan ,&nbsp;Die Huang ,&nbsp;Mong Dong ,&nbsp;Fujun Jin ,&nbsp;YuJing Lu","doi":"10.1016/j.mex.2026.103977","DOIUrl":"10.1016/j.mex.2026.103977","url":null,"abstract":"<div><div>Quantitative assessment of osteoclasts by counting and measuring area proportions is a fundamental approach for analyzing in vitro osteoclastogenesis. However, there is a lack of user-friendly automated analysis tools that do not require programming expertise, leading researchers to rely on manual operations for quantitative analysis. To address this limitation, the present study developed an offline software application utilizing the YOLOv8 instance segmentation model. This software facilitates the automated detection of osteoclasts in single images and batches, achieving an inference speed of less than one second per image. Following detection, the system promptly generates a visualized prediction report for user reference, providing essential data support for subsequent analyses. To ensure optimal model performance, the model was trained on a high-quality dataset meticulously annotated by experts in bone histology. The model ultimately achieved a mean average precision of 95.3% in detecting osteoclasts derived from mouse bone marrow macrophages, facilitating swift feedback of quantitative analysis results and significantly enhancing analysis efficiency. In conclusion, this study developed an accessible quantitative analysis tool for osteoclasts, intended for researchers without programming expertise.<ul><li><span>•</span><span><div>An automated quantitative analysis system for osteoclasts has been developed, enabling precise quantification of osteoclast number and area. This system demonstrates high accuracy and rapid detection, significantly improving research efficiency.</div></span></li><li><span>•</span><span><div>The model is packaged as a standalone offline application, enabling researchers without programming experience to use it effortlessly.</div></span></li><li><span>•</span><span><div>The YOLOv8l-seg model was evaluated on both segmentation and detection tasks to verify its performance.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103977"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148184120","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":"Quantum federated learning for autonomous vehicle cybersecurity: An analytical review of architectures and threat landscapes","authors":"Sowmya T,&nbsp;Raghavendra M Devadas","doi":"10.1016/j.mex.2026.103990","DOIUrl":"10.1016/j.mex.2026.103990","url":null,"abstract":"<div><div>With the rapid rollout of autonomous vehicles (AVs), many AV criminal attacks form one of the biggest cybersecurity attack surfaces in contemporary infrastructure. But there are also the AV subsystems—LiDAR, radar, cameras, GPS, CAN buses, and V2X communications—all of which expose separate exploit vectors that would threaten both passenger safety as well as traffic. Centralized machine learning has been utilised for intrusion detection, but privacy issues, scalability limitations, and susceptibility to quantum-enabled adversaries have motivated interest in Federated Learning (FL) as a distributed approach. On the one hand, vehicles operating on RSA- and ECC-based protocols will become insecure due to quantum effects, motivating the need for Quantum Key Distribution (QKD), Post-Quantum Cryptography (PQC) [7], or any alternative to current systems. This seminal work proposes a summarization of Quantum Federated Learning (QFL) research specifically for AV cybersecurity. The contributions are: (i) a multi-dimensional risk matrix for AV attack priorities; (ii) a cross-study synthesis of gaps in FL-IDS deployment; (iii) comparative analysis across QKD, PQC and hybrid models of quantum security transition; (iv) construction of a QFL reference architecture with seven gaps agenda and (v) structured 2025–2030 road-map. The analysis shows that neither FL nor quantum security of it alone is strictly sufficient, and their togetherness in the hinge point between them — QFL — is the most urgent way forward for securing autonomous vehicles.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103990"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148184130","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 rapid and simplified technique for inducing Ganoderma lucidum infections in coconut and arecanut seedlings","authors":"Daliyamol ,&nbsp;K.K. Greena ,&nbsp;V.H. Prathibha ,&nbsp;M. Amruthalekshmi ,&nbsp;M.K. Rajesh ,&nbsp;Y. Diwakar ,&nbsp;Vinayaka Hegde","doi":"10.1016/j.mex.2026.103969","DOIUrl":"10.1016/j.mex.2026.103969","url":null,"abstract":"<div><div><em>Ganoderma lucidum</em> is a soil borne fungi known to cause Basal stem rot (BSR) or Thanjavur wilt or <em>Ganoderma</em> wilt disease in coconut as well as Foot rot or <em>Anabe roga</em> disease in arecanut plantations. Due to its soil borne nature, effectively managing the disease in field conditions is highly challenging. To combat this issue, the development of resistant or tolerant seedlings is crucial for sustainable production of coconut and arecanut palms. Therefore, a reliable and rapid method to assess resistance of palms to <em>Ganoderma</em> disease is essential. Here we report a rapid inoculation technique wherein seedlings were soaked in 5 days old mycelia cultures supplemented with 0.002% Tween 20 for a period of 60 min. The initial symptoms could be noticed 15 days after inoculation in arecanut and 30 days after inoculation in coconut. The technique ensures consistent infection, allowing disease evaluation as early as two weeks post inoculation with <em>G. lucidum</em>. Our results indicate that this new inoculation technique can be used routinely to screen coconut and arecanut seedlings for BSR disease and to evaluate the resistance of different cultivars to <em>G. lucidum.</em><ul><li><span>•</span><span><div>Developed a rapid, simple and efficient inoculation technique.</div></span></li><li><span>•</span><span><div>The technique was evaluated for coconut and arecanut seedlings as well as tissue culture plantlets.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103969"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148184242","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":"Knowledge, support, and networking for Phelan-McDermid syndrome: a study protocol","authors":"Luca Colnaghi ,&nbsp;Giulia Villa ,&nbsp;Ilaria Marcomini ,&nbsp;Andrea Poliani ,&nbsp;Maya Fedeli ,&nbsp;Claudio Losi ,&nbsp;Debora Rosa ,&nbsp;Duilio Fiorenzo Manara","doi":"10.1016/j.mex.2025.103771","DOIUrl":"10.1016/j.mex.2025.103771","url":null,"abstract":"<div><h3>Background</h3><div>Phelan-McDermid syndrome (PMS) is a rare neurodevelopmental disorder caused by deletions in chromosome 22q13.3 or pathogenic variants in the SHANK3 gene. Individuals present with intellectual disability, autism-spectrum traits, seizures, gastrointestinal and motor issues, and sleep disturbances, requiring lifelong multidisciplinary care. In Italy, PMS care is fragmented and unevenly distributed, with families often providing intensive home-based support at high personal, financial, and social costs.</div></div><div><h3>Methods</h3><div>This national participatory Citizen-Science study, conducted with the Italian Phelan-McDermid Syndrome Association (AISPHEM), will engage informal caregivers of individuals with genetically confirmed PMS across Italy. A qualitative phase using semi-structured online interviews will explore caregiving experiences, unmet needs, barriers to care, coping strategies, and social isolation. Insights will guide the creation of the first Italian PMS registry, capturing longitudinal clinical, socio-demographic, and caregiver-related data.</div></div><div><h3>Expected Results &amp; Conclusions</h3><div>The project will generate novel evidence on caregiver needs, develop the first national PMS registry, and produce a service map to support equitable, coordinated PMS care and a stronger national caregiver network in Italy.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103771"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926288","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":"Field boundary delineation with seasonal sentinel 2 imagery using Segment Anything Model (SAM)","authors":"Thuan Ha,&nbsp;Kwabena Abrefa Nketia,&nbsp;Hansanee Fernando,&nbsp;Sarah van Steenbergen,&nbsp;Shawn Neudorf,&nbsp;Steve J. Shirtliffe","doi":"10.1016/j.mex.2026.103794","DOIUrl":"10.1016/j.mex.2026.103794","url":null,"abstract":"<div><div>Accurate field boundary delineation is critical for accurate modelling on crop yields and for precision agriculture (PA), enabling site-specific management to optimize resource use and crop productivity. Traditional boundary mapping methods, such as manual digitization and semi-automated extraction from farm machinery, are labor-intensive and challenging to apply at large scales. Advances in high-resolution land cover data and satellite imagery offer scalable solutions for automated field boundary extraction. In this study, we propose a fully automated workflow that integrates a pre-trained foundation model, the Segment Anything Model - SAM [<span><span>1</span></span>] with time-series Sentinel-2 imagery. Seasonal composites of Red, Green, and Blue bands were generated at different phenological stages to support segmentation. The method was applied across over 32 million hectares (79 million acres) of cultivated land in the Canadian Prairies, achieving an intersection-over-union (IoU) accuracy of 0.86 compared to manual segmentation. The workflow consists of four main steps: (1) setting the python working environment, (2) seasonal image acquisition and preprocessing using Google Earth Engine via Python API; (3) field boundary segmentation using SAM; and (4) post-processing and feature cleaning using ArcGIS Pro. This approach demonstrates a scalable, efficient solution for large-scale field boundary mapping to support PA applications.<ul><li><span>•</span><span><div>Integrates a foundation segmentation model (SAM) with Sentinel-2 seasonal imagery</div></span></li><li><span>•</span><span><div>Demonstrates high-accuracy, large-scale automated field boundary delineation</div></span></li><li><span>•</span><span><div>Provides a reproducible workflow adaptable to other regions and datasets</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103794"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034293","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 practical workflow for applying validation strategies in tree height-diameter modelling.","authors":"Santosh Ayer, Kishor Prasad Bhatta","doi":"10.1016/j.mex.2026.103982","DOIUrl":"10.1016/j.mex.2026.103982","url":null,"abstract":"<p><p>Accurate validation of height-diameter (H-D) models is important for forest modelling applications, yet validation approaches are often applied inconsistently across forestry studies. This may produce different estimates of model performance depending on data structure and validation conditions. Therefore, the objective of this study was to present a practical workflow for applying internal, grouped, and external validation approaches in H-D modelling. Leave-one-out cross-validation (LOOCV), K-fold cross-validation, leave-one-district-out (LODO), leave-one-species-out (LOSO), and independent external validation strategies were included. Publicly available forest inventory datasets from Nepal were used to demonstrate the workflow, including a national multi-species dataset and two independent <i>Shorea robusta</i> datasets from western and far-western Nepal. The workflow demonstrated how different testing structures influence estimated model performance under within-dataset, grouped, and independent prediction conditions. This method provides a reproducible framework for validation in forest inventory and allometric modelling studies.•A structured workflow is developed to select appropriate validation strategies based on data availability and modelling objectives.•Internal, structured, and external validation are applied to assess model performance under different scenarios.•A framework for the practical interpretation of validation results is provided to guide their use in general and site-specific modelling contexts.</p>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"103982"},"PeriodicalIF":2.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13251202/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148226591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of multiple parallel mineralization method to grow lettuce, Swiss chard, and peppers in a simplified deep flow technique hydroponic system","authors":"Jada Tarvin-Imeokparia ,&nbsp;Adeyemi Abejoye ,&nbsp;Isabella Vega ,&nbsp;Max Abel Alphonse Mendoza ,&nbsp;Campbell Moses ,&nbsp;Myalia Durno ,&nbsp;Meghan S. Cahill ,&nbsp;Christian O. Dimkpa ,&nbsp;Jason C. White ,&nbsp;Shelley Durocher ,&nbsp;Frederick Pettit ,&nbsp;Mark Meyering ,&nbsp;Jonathan M. Jacobs ,&nbsp;Maria Soledad Benitez Ponce ,&nbsp;Nathaniel Heiden","doi":"10.1016/j.mex.2025.103749","DOIUrl":"10.1016/j.mex.2025.103749","url":null,"abstract":"<div><div>Organic fertilizers are not widely used in hydroponics due to an absence of mineralizing microbes that process organic inputs into plant available forms. The Multiple Parallel Mineralization (MPM) method enables this microbial processing to occur in hydroponic systems. The objective of this method is to provide an accessible protocol for use of the MPM method in simplified deep flow technique hydroponic systems without a need for laboratory testing of nutrient content. The method described here provides hydroponic growers with an alternative option to mineral fertilizers. We provide information about yields and the affordability of this method that growers can use to determine the economic viability of this method in their respective markets.</div><div>Swiss chard, lettuce, and bell peppers were grown with this method</div><div>Aquarium nitrate test kits are used to replace lab testing typically used with the MPM method to validate the presence of nitrate as an indicator of microbial activity</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103749"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749534","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 approach to in vivo low-magnification optical coherence tomography for selective monitoring of tissue glucose concentration","authors":"Akitoshi Seiyama ,&nbsp;Fengcheng Wei ,&nbsp;Masato Ohmi","doi":"10.1016/j.mex.2025.103752","DOIUrl":"10.1016/j.mex.2025.103752","url":null,"abstract":"<div><div>The low-magnification optical coherence tomography (LM-OCT) is expected to enable the quantification of tissue glucose concentration (TGC) by maintaining high spatial resolution while reducing artifacts caused by tissue heterogeneity. In the present study, we report the practical application of LM-OCT using a conventional OCT system, i.e., without compromising spatial resolution, for the selective monitoring of changes in TGC through <em>in vivo</em> experiments using a hairless rat model.</div><div>A commercially available high-spatial resolution OCT device with a center wavelength of 1300 nm was used to detect the three-dimensional (3D) skin surface structure and the scattering coefficient related to the OCT slope.</div><div>The grand-averaged OCT signal was employed to estimate the OCT slope, while the original high-resolution OCT signals were used to reconstruct the 3D-structural image.</div><div>A strong linear correlation was observed between changes in the OCT slope and that in TGC, but not with 2-deoxy-glucose, lactate, or Intralipos (soybean oil).</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103752"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798172","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":"Corrigendum to “From detection to grading: A hybrid KOA-YOLOv5-RF model for knee osteoarthritis diagnosis” [MethodsX, Volume 15, December 2025, 103725]","authors":"Manikandaprabhu Perumalsamy ,&nbsp;Priya Govindarajan ,&nbsp;Rinhas Bran ,&nbsp;Adarsh Krishna KP ,&nbsp;Niranjan V Jyothi ,&nbsp;M Batumalay","doi":"10.1016/j.mex.2025.103751","DOIUrl":"10.1016/j.mex.2025.103751","url":null,"abstract":"","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103751"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798205","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}