MethodsX最新文献

{"title":"Advancements in epilepsy classification: Current trends and future directions","authors":"Rajani Rai B ,&nbsp;Karunakara Rai B ,&nbsp;Mamatha A S ,&nbsp;Kavitha Sooda","doi":"10.1016/j.mex.2025.103257","DOIUrl":"10.1016/j.mex.2025.103257","url":null,"abstract":"<div><div>This paper presents a comprehensive survey on categorizing focal and non-focal epilepsy using Electroencephalogram (EEG) signals. It emphasizes how recent advances in machine learning and deep learning methodologies assists in overcoming the existing challenges in classification. The paper synthesizes cutting-edge techniques with the focus on the application of hybrid models that combine traditional signal processing techniques with machine learning algorithms. By highlighting key breakthroughs in the field, the paper aims to propose novel directions for improving classification precision. Furthermore, the paper delves into the challenges faced by current methods and the possible solutions. The paper concludes with the discussion on potential future research directions, especially in areas of multimodal data integration and real-time seizure prediction, and emphasizes the potential for AI-driven personalized epilepsy treatment techniques.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103257"},"PeriodicalIF":1.6,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637642","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":"Automating sentinel-1 SLC product processing: Parallelization and optimization for efficient polarimetric parameter extraction","authors":"Hansanee Fernando ,&nbsp;Kwabena Nketia ,&nbsp;Thuan Ha ,&nbsp;Sarah van Steenbergen ,&nbsp;Heather McNairn ,&nbsp;Steve Shirtliffe","doi":"10.1016/j.mex.2025.103253","DOIUrl":"10.1016/j.mex.2025.103253","url":null,"abstract":"<div><div>Processing Sentinel-1 (S1) Single Look Complex (SLC) data is time-consuming, even with software like SNAP or PolSARpro. Command line processing on Windows provides an automated alternative, enabling R-based processing of multiple S1-SLC files without manual interaction. Here we demonstrate a user friendly automated process, to process an unlimited number of S1-SLC images, tailored for users with minimal SAR or programming competence. The proposed workflow integrates RStudio, SNAP, and PolSARpro software libraries to implement the same processes a user can achieve via the corresponding graphic user interfaces (GUI). The workflow includes bulk S1-SLC imagery downloads, installation and configuration of dependent software applications. Within the SNAP GUI, a base-graph was constructed, encompassing crucial processing steps such as data import, sub-swath extraction, orbit determination, calibration, speckle filtering, debursting, and terrain correction, which acts as a template for generating customized SNAP graphs for individual S1 imagery. These graphs are batch processed with R, using parallel computing to run multiple graphs simultaneously. In the subsequent PolSARpro processing phase, outputs from the SNAP processing pipeline are made interoperable with PolSARpro tools for onward post-processing. Similarly, we leverage the parallelization mechanisms of R for user specific parameter extraction, which maximizes resource utilization while maintaining computational performance.<ul><li><span>•</span><span><div>Automated Workflow for SAR Processing: Introduces an automated, user-friendly framework combining RStudio, SNAP, and PolSARpro to process unlimited Sentinel-1 Single Look Complex (S1-SLC) images, eliminating manual interaction and catering to users with minimal programming or SAR expertise.</div></span></li><li><span>•</span><span><div>Customizable and Scalable Processing: Leverages SNAP's base-graph templates for essential SAR processing steps (e.g., orbit determination, calibration, speckle filtering, and terrain correction) to enable batch processing and parallel computing for efficient handling of large datasets.</div></span></li><li><span>•</span><span><div>Interoperability and Enhanced Performance: Integrates outputs from SNAP into PolSARpro for advanced post-processing, employing R-based parallelization to optimize resource utilization and ensure efficient user-specific parameter extraction.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103253"},"PeriodicalIF":1.6,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143593727","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":"Determination of the method of induction of mutations by gamma radiation in soybeans (Glycine max L. Merrill) for tolerance to carbonic rot produced by the fungus Macrophomina phaseolina (Tassi Goid.)","authors":"Antonio Samudio-Oggero ,&nbsp;Héctor D. Nakayama N․ ,&nbsp;Gloría A. Resquín Romero ,&nbsp;Wilson D. Romero Vergara ,&nbsp;Oscar J. Vega Alvarenga ,&nbsp;Juan V. Benítez Núñez ,&nbsp;Benito Ortega Tórres ,&nbsp;Pablo C. Caballero Romero ,&nbsp;María C. Caridad González","doi":"10.1016/j.mex.2025.103251","DOIUrl":"10.1016/j.mex.2025.103251","url":null,"abstract":"<div><div>In recent years, there has been an increase in the appearance of charcoal root rot disease in soybeans crops (<em>Glycine max</em> L. Merril). Charcoal rot is caused by the soil-borne fungus <em>Macrophomina phaseolina</em>. This disease is typically exacerbated by water deficiency and high temperatures. To evaluate the soybean genotypes' response to this pathogen, novel genotypes developed through gamma irradiation of 150 Gy and 200 Gy were tested under, in field and greenhouse conditions. Additionally, total phenol content was analyzed as a potential indicator of plant tolerance. The results indicate that the incidence of disease in non-irradiated genotypes was 100 %, in genotypes irradiated with a dose of 150 Gy it was 87 %, and those irradiated with a dose of 200 Gy a 100 %. An increase in the level of total phenols was observed in the tolerant genotypes as well as some mutant genotypes with characteristics that show tolerance to the charcoal root rot disease. The results suggest that gamma radiation-induced mutation may be an effective method for breeding disease-resistant soybean varieties.<ul><li><span>•</span><span><div>This variability method can be applied to any plant species.</div></span></li><li><span>•</span><span><div>This method can cause mutations in any part of the genome, this allows its application to be unlimited.</div></span></li><li><span>•</span><span><div>It is a method that can be used in a complementary way to other plant breeding methods.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103251"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548375","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":"A new method and information system based on artificial intelligence for black flight identification","authors":"Arwin Datumaya Wahyudi Sumari ,&nbsp;Rosa Andrie Asmara ,&nbsp;Ika Noer Syamsiana","doi":"10.1016/j.mex.2025.103250","DOIUrl":"10.1016/j.mex.2025.103250","url":null,"abstract":"<div><div>Identification of aircraft entering a country's sovereign airspace if it shuts down its identification system, either the Identification Friend or Foe system and/or the Automatic Dependent Surveillance Broadcast system, has long been a challenge for the National Air Operations Command. Aircraft that do not want their identities to be revealed are called black flights and generally have certain missions that can interfere with the sovereignty of a country's airspace. Military radar units that have the task of monitoring airspace are generally equipped with Primary Surveillance Radar that detects the presence of aircraft in their operating area and Secondary Surveillance Radar which functions to identify the aircraft. In the case of black flight, data from the radar in the form of airspeed, altitude, and position are not able to help identify the identity of the black flight. The contributions of this research are:<ul><li><span>•</span><span><div>a new method of black flight identification that combines air speed data and altitude with Radar Cross Section (RCS) data using machine learning,</div></span></li><li><span>•</span><span><div>a new information system that combines the display of the Plan Position Indicator (PPI) of military radar and ADS-B to accelerate decision-making on black flight,</div></span></li><li><span>•</span><span><div>a new approach to national air defense procedures.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103250"},"PeriodicalIF":1.6,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548376","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":"Simple DNA extraction for museum beetle specimens to unlock genetic data from historical collections","authors":"Hathal M. Al-Dhafer ,&nbsp;Raju Balaji ,&nbsp;Mahmoud S. Abdel-Dayem ,&nbsp;Iftekhar Rasool ,&nbsp;Amr Mohamed ,&nbsp;Senthilkumar Palanisamy","doi":"10.1016/j.mex.2025.103236","DOIUrl":"10.1016/j.mex.2025.103236","url":null,"abstract":"<div><div>Museum beetle specimens are valuable resources for genetic analyses; however, obtaining DNA from aged specimens remains challenging due to degradation, desiccation, and contamination. In this study, we present a simple, low-cost protocol for extracting DNA from museum beetles, optimized using cetyltrimethylammonium bromide (CTAB). This method effectively addresses common issues such as DNA fragmentation and contamination, enabling the recovery of DNA suitable for downstream applications such as PCR and next-generation sequencing. It provides a reproducible, non-destructive approach to extracting genetic material from fragile beetle specimens, thereby facilitating molecular investigations in fields such as taxonomy and conservation biology. The protocol is summarized as follows:<ul><li><span>•</span><span><div>A method for DNA extraction is optimized for museum beetle specimens preserved for over 45 years.</div></span></li><li><span>•</span><span><div>The protocol is non-destructive and compatible with PCR and next-generation sequencing.</div></span></li><li><span>•</span><span><div>Multiple extractions can be pooled to increase yields, particularly when DNA concentrations are low.</div></span></li></ul></div><div>This method broadens the possibilities for genetic analysis of historical specimens, offering new insights into long-term ecological and evolutionary processes.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103236"},"PeriodicalIF":1.6,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519402","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":"The methodological framework for DRIP: Drought representation index for CMIP model performance","authors":"Lucas Pereira de Almeida ,&nbsp;Ályson Brayner Sousa Estácio ,&nbsp;Rosa Maria Formiga-Johnsson ,&nbsp;Francisco de Assis de Souza Filho ,&nbsp;Victor Costa Porto ,&nbsp;Alexandra Nauditt ,&nbsp;Lars Ribbe ,&nbsp;Alfredo Akira Ohnuma Júnior","doi":"10.1016/j.mex.2025.103249","DOIUrl":"10.1016/j.mex.2025.103249","url":null,"abstract":"<div><div>This paper presents a <strong>methodological framework</strong> designed to evaluate the ability of CMIP climate models to simulate drought characteristics. The approach is based on the <strong>Drought Representation Index for CMIP Model Performance (DRIP)</strong>, which assesses models using three key drought parameters—average duration, severity, and return period—by comparing simulated outputs with historical observations. The methodology encompasses four main stages: data acquisition and preparation, drought characterization, DRIP calculation, and model ensemble generation (E-DRIP). This approach provides a systematic method to identify models that best represent regional drought dynamics and reduce uncertainty in climate projections. By leveraging DRIP as a selection criterion, E-DRIP ensembles outperform traditional CMIP ensembles in both reliability and precision. The method's flexibility allows adaptation to various drought indices and temporal scales, making it applicable across diverse climatic contexts. Validation in a climatically uncertain area, the Paraíba do Sul River Basin in Southeast Brazil, demonstrates DRIP's effectiveness in enhancing model performance assessment and improving drought scenario projections. This study contributes a replicable tool for climate modelling, supporting water resources management strategies amid increasing climate variability.<ul><li><span>•</span><span><div>DRIP index assesses CMIP models' performance in representing drought characteristics.</div></span></li><li><span>•</span><span><div>E-DRIP ensembles reduced drought projections uncertainties by up to 63 % in the validation study area.</div></span></li><li><span>•</span><span><div>DRIP enhances decision-making in climate model selection, improving its reliability for regional water planning.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103249"},"PeriodicalIF":1.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580062","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":"Multi-criteria evaluation and multi-method analysis for appropriately selecting renewable energy sources in Colombia","authors":"Christian M. Moreno-Rocha ,&nbsp;José R. Nuñez-Alvarez ,&nbsp;Juan Rivera-Alvarado ,&nbsp;Alfredo Ghisayz Ruiz ,&nbsp;Enderson A. Buelvas-Sanchez","doi":"10.1016/j.mex.2025.103248","DOIUrl":"10.1016/j.mex.2025.103248","url":null,"abstract":"<div><div>This research explores the implementation of renewable energy technologies for power generation using multi-criteria decision-making (MCDM) methods, including AHP, FAHP, TOPSIS, and FUZZY-TOPSIS. Ten renewable energy alternatives were evaluated across seven geographic regions in Colombia, revealing variability in preferences depending on the method and scenario. Alternatives 6 and 4 frequently stood out, while others showed varied rankings. This study significantly contributes to the energy sector by offering a rigorous framework for selecting renewable generation technologies, supporting sustainable energy planning, and providing a model for replication in global contexts, some key points are:<ul><li><span>•</span><span><div>The study applied systematic MCDM approaches to assess renewable energy sources.</div></span></li><li><span>•</span><span><div>Results demonstrated method-dependent variability and highlighted regional preferences.</div></span></li><li><span>•</span><span><div>It sets a benchmark for integrating sustainable practices into energy planning worldwide.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103248"},"PeriodicalIF":1.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548377","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":"EMI-LTI: An enhanced integrated model for lung tumor identification using Gabor filter and ROI","authors":"Jayapradha J ,&nbsp;Su-Cheng Haw ,&nbsp;Naveen Palanichamy ,&nbsp;Kok-Why Ng ,&nbsp;Muskan Aneja ,&nbsp;Ammar Taiyab","doi":"10.1016/j.mex.2025.103247","DOIUrl":"10.1016/j.mex.2025.103247","url":null,"abstract":"<div><div>In this work, the CT scans images of lung cancer patients are analysed to diagnose the disease at its early stage. The images are pre-processed using a series of steps such as the Gabor filter, contours to label the region of interest (ROI), increasing the sharpening and cropping of the image. Data augmentation is employed on the pre-processed images using two proposed architectures, namely (1) Convolutional Neural Network (CNN) and (2) Enhanced Integrated model for Lung Tumor Identification (EIM-LTI).<ul><li><span>•</span><span><div>In this study, comparisons are made on non-pre-processed data, Haar and Gabor filters in CNN and the EIM-LTI models. The performance of the CNN and EIM-LTI models is evaluated through metrics such as precision, sensitivity, F1-score, specificity, training and validation accuracy.</div></span></li><li><span>•</span><span><div>The EIM-LTI model's training accuracy is 2.67 % higher than CNN, while its validation accuracy is 2.7 % higher. Additionally, the EIM-LTI model's validation loss is 0.0333 higher than CNN's.</div></span></li><li><span>•</span><span><div>In this study, a comparative analysis of model accuracies for lung cancer detection is performed. Cross-validation with 5 folds achieves an accuracy of 98.27 %, and the model was evaluated on unseen data and resulted in 92 % accuracy.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103247"},"PeriodicalIF":1.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563247","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":"Biogrouting with microbial-induced carbonate precipitation (MICP) for improving the physical and mechanical properties of granular soils potential liquefaction","authors":"Nur Ayu Diana ,&nbsp;Ria Asih Aryani Soemitro ,&nbsp;Januarti Jaya Ekaputri ,&nbsp;Trihanyndio Rendy Satrya ,&nbsp;Dwa Desa Warnana","doi":"10.1016/j.mex.2025.103246","DOIUrl":"10.1016/j.mex.2025.103246","url":null,"abstract":"<div><div>Biogrouting, a method to enhance soil properties using microorganisms and mechanical techniques, has shown great potential for soil improvement. Most studies focus on small sand columns in labs, but recent tests used 0.5 m plastic boxes filled with sand stabilized with microorganisms and fly ash. The experiments, conducted over 30 days, applied injection and infusion methods with microbial fluids, maintaining groundwater levels to simulate field conditions. Mechanical properties were analyzed through unconfined compressive strength (UCS) tests on extracted samples. Researchers also assessed calcium carbonate distribution and shear strength. Results showed water saturation significantly influenced vertical stress (qu), while UCS correlated with the permeability of sand containing varying calcium carbonate levels. Bacillus safensis, a resilient bacterium used in this process, can withstand extreme conditions. After completing its task, it enters a dormant state and reactivates when needed. The bacteria produce calcium carbonate by binding calcium with enzymes, which cements soil particles, enhancing strength and stability.<ul><li><span>•</span><span><div>Testing enzymes on microbes and natural soil</div></span></li><li><span>•</span><span><div>Installation settings for drip tools using infusion</div></span></li><li><span>•</span><span><div>Soil resistance testing after stabilization using UCS</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103246"},"PeriodicalIF":1.6,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580059","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":"Efficient scheduling of multiple software projects for work continuity and identical completion time","authors":"Abdulrahman Aldhubaiban,&nbsp;Ali AlMatouq","doi":"10.1016/j.mex.2025.103215","DOIUrl":"10.1016/j.mex.2025.103215","url":null,"abstract":"<div><div>In software development projects, it is desired to complete multiple projects at minimum cost and time while ensuring that the completion date is the same for all projects to meet certain operational and strategic objectives. Also, full-time employees assigned to projects should be reallocated smoothly to other tasks without any idle time during project execution to minimize costs even further. This study describes a model that enables the use of efficient continuous variable nonlinear solvers for finding the optimal schedule for possibly a large number of multiple software projects that make use of shared resources. The study validates the proposed solution using a random generator of multiple software project instances while interfacing to online optimization solvers to find a solution. Our continuous variable model was solved in the cloud for optimality for large instances of upto 40 different software projects and 100 employees in less than 21 min using nonlinear programming algorithms.<ul><li><span>•</span><span><div>A continuous variable nonlinear model is developed to efficiently schedule large-scale software projects.</div></span></li><li><span>•</span><span><div>The model enables scheduling for multiple projects with identical completion times while ensuring work continuity.</div></span></li><li><span>•</span><span><div>A cloud-based program architecture is designed to facilitate the testing of multiple solvers online.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103215"},"PeriodicalIF":1.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509558","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}