MethodsX最新文献

{"title":"A simple method for generating long-term Holocene climate data with future climate projections from meteorological observation data","authors":"Jake Tuuli ,&nbsp;Andy J. Baird ,&nbsp;Dylan M. Young ,&nbsp;Andrew Duncan ,&nbsp;Roxane Andersen","doi":"10.1016/j.mex.2025.103265","DOIUrl":"10.1016/j.mex.2025.103265","url":null,"abstract":"<div><div>Peatlands play a crucial role in global carbon storage, yet their resilience to climate change remains uncertain. This study presents a novel method for generating long-term (&gt;1000 years) site-specific climate data to drive peatland ecohydrological models. Using meteorological observations, we employ the Long Ashton Research Station Weather Generator (LARS-WG) to produce stochastic climate series for precipitation and temperature. The method integrates Holocene climate reconstructions from the EPOCH-2 database to simulate paleoclimate trends and interpolates climate projections based on Shared Socioeconomic Pathways (SSP) from CMIP6 models. Finally, a time series of potential evapotranspiration is calculated using a modified version of the Thornthwaite equation. This approach ensures continuity in climate inputs for peatland modelling, aiding in the assessment of long-term climate impacts on carbon dynamics. Our method provides a replicable framework for other regions, supporting improved climate-driven peatland simulations.<ul><li><span>•</span><span><div>Long-term paleoclimate data with climate projections tailored to specific sites are scarcely available</div></span></li><li><span>•</span><span><div>This research outlines a simple method for generating climate series for driving ecosystem models</div></span></li><li><span>•</span><span><div>Uses open-source resources and databases that are applicable across Europe</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103265"},"PeriodicalIF":1.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714992","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":"Analysing learning behaviour: A data-driven approach to improve time management and active listening skills in students","authors":"Vinayak Hegde ,&nbsp;Vishrutha M ,&nbsp;Pallavi M. Shanthappa ,&nbsp;Rekha Bhat ,&nbsp;Nisha Raveendran ,&nbsp;Roshin C","doi":"10.1016/j.mex.2025.103262","DOIUrl":"10.1016/j.mex.2025.103262","url":null,"abstract":"<div><div>Learning behavior refers to the actions, attitudes, and strategies individuals employ when acquiring new knowledge. Time management is a perennial challenge in modern life, where individuals often juggle multiple responsibilities and commitments. Active listening is an indispensable skill in both educational and interpersonal contexts. Methodologically, the study began with comprehensive data collection through a survey, data preprocessing tasks and feature selection, followed by training and evaluating predictive models using various ML algorithms. With concerns rising over student failures, we conducted a survey with 350 participants, utilizing Google Forms. After testing multiple ML models with datasets, Random Forest was determined to be the most dependable model by emphasizing algorithms. It demonstrated remarkable durability (0.811012) and accuracy during cross-validation. The significance of addressing the effective abilities that students should absorb and the possibility of ML approaches in comprehending and reducing its negative impacts on academic success are both highlighted by these findings. By analyzing learning behavior, researchers and educators can gain insights into effective learning strategies, identify barriers to learning, and develop interventions to support learners in achieving their educational goals. The findings emphasize the pivotal role of non-cognitive skills like time management and active listening in fostering academic achievement.<ul><li><span>•</span><span><div>Significance of Non-Cognitive Skills: The study underscores the critical role of non-cognitive skills, such as time management and active listening, in fostering academic success and overall learning effectiveness.</div></span></li><li><span>•</span><span><div>Survey and Dataset Analysis: The survey, conducted with 350 participants using Google Forms, provided valuable data for training models and highlighted challenges like student failures, which can be addressed through predictive analysis.</div></span></li><li><span>•</span><span><div>Impact on Academic Success: By analyzing learning behaviors and identifying barriers, the findings emphasize the potential of machine learning approaches in understanding and mitigating factors that negatively affect academic performance.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103262"},"PeriodicalIF":1.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685172","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 quality assessment framework for transdisciplinary research design, monitoring, and evaluation: Guidance for application","authors":"Brian Belcher,&nbsp;Rachel Claus","doi":"10.1016/j.mex.2025.103264","DOIUrl":"10.1016/j.mex.2025.103264","url":null,"abstract":"<div><div>Appropriate definitions and measures of quality are needed to guide research design and evaluation. Traditional disciplinary research approaches have well-established evaluation criteria and processes in which research quality is often narrowly defined. In contrast, emerging change-oriented transdisciplinary research (TDR) approaches integrate disciplines and include societal actors in the research process in multiple ways and contexts. Standard research assessment criteria are simply inadequate for TDR, and inappropriate use of standard criteria may disadvantage TDR proposals and impede the development of TDR. This paper presents a Quality Assessment Framework (QAF) designed for TDR, along with guidance for its application. The background outlines the origin of the framework in a systematic review of literature on the definition and assessment of transdisciplinary research quality, and the subsequent application, testing and refinement of the framework, including discussion of key revisions. It also compares the QAF with two other similar evaluation frameworks. The QAF is designed for a range of users, including: research funders and research managers assessing proposals; researchers designing, planning, and monitoring a research project; and research evaluators assessing projects <em>ex-post</em>. The framework is organized as:<ul><li><span>•</span><span><div>Four principles of TDR quality</div></span></li><li><span>•</span><span><div>Specific criteria aligned with each principle</div></span></li><li><span>•</span><span><div>Standardized four-point scoring</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103264"},"PeriodicalIF":1.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746800","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":"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}