MethodsX最新文献_第6页

An integrated, tiered microplastic workflow, supporting rapid broadscale detection options 集成的、分层的微塑料工作流程，支持快速的大规模检测选项

IF 1.9

MethodsX Pub Date : 2025-08-05 DOI: 10.1016/j.mex.2025.103536

Samantha K Lynch , Colin L Johnson , Shivanesh Rao , Jaimie Loa-Kum-Cheung , Edwina L Foulsham , Alessandra L Suzzi , Lachlan Hill , Neil Doszpot , Rajitha Athukorala , Uthpala Pinto , Keegan Vickers , Maddison Carbery , Marina F.M. Santana

{"title":"An integrated, tiered microplastic workflow, supporting rapid broadscale detection options","authors":"Samantha K Lynch , Colin L Johnson , Shivanesh Rao , Jaimie Loa-Kum-Cheung , Edwina L Foulsham , Alessandra L Suzzi , Lachlan Hill , Neil Doszpot , Rajitha Athukorala , Uthpala Pinto , Keegan Vickers , Maddison Carbery , Marina F.M. Santana","doi":"10.1016/j.mex.2025.103536","DOIUrl":"10.1016/j.mex.2025.103536","url":null,"abstract":"<div><div>With growing concerns regarding microplastic pollution, there is an urgent need to improve understanding of their presence, distribution, and environmental impacts. This necessitates more coordinated and harmonised large-scale microplastic monitoring initiatives. However, such assessments are traditionally expensive, labour-intensive, and hindered by a lack of standardised sampling and analytical protocols, which impede rapid, yet accurate identification of microplastic sources and ecological risks. To improve environmental microplastic contamination estimates, this study proposes a rapid, cost-effective, and bulk-processing approach within a criteria-driven Tiered Microplastics Workflow (TMW). This approach enables the efficient quantification of microplastic contamination in estuarine surface waters, offering adaptable levels of analytical resolution, that is scalable for environmental monitoring. Key features of the TMW include:<ul><li><span>•</span><span><div><strong>Streamlined processing</strong>: sieving, digestion, density separation, vacuum degassing, size-classed filtration, Nile Red staining, and automated fluorescent particle counts via a Python script, enabling 24 samples to be processed in five days.</div></span></li><li><span>•</span><span><div><strong>Rapid Count Method:</strong> Enabling microplastic identification in broadscale monitoring within a 20 % error margin. Script-based microplastic counts align with FTIR results (R² = 0.83).</div></span></li><li><span>•</span><span><div><strong>Flexible resolution:</strong> Sample processing can be paused and switched to other analytical methods while maintaining data comparability ensuring data harmonisation.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103536"},"PeriodicalIF":1.9,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771578","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}

引用次数: 0

Improving the prediction of bitumen’s density and thermal expansion by optimizing artificial neural networks with Optuna and TensorFlow 通过Optuna和TensorFlow优化人工神经网络，改进沥青密度和热膨胀的预测

IF 1.9

MethodsX Pub Date : 2025-07-30 DOI: 10.1016/j.mex.2025.103524

Eli I. Assaf , Xueyan Liu , Sandra Erkens

{"title":"Improving the prediction of bitumen’s density and thermal expansion by optimizing artificial neural networks with Optuna and TensorFlow","authors":"Eli I. Assaf , Xueyan Liu , Sandra Erkens","doi":"10.1016/j.mex.2025.103524","DOIUrl":"10.1016/j.mex.2025.103524","url":null,"abstract":"<div><div>Previous work demonstrated that Random Forest Regressors (RFRs) could estimate the physical properties of bitumen using molecular descriptors derived from Molecular Dynamics (MD) simulations, thereby reducing the need for computationally intensive simulations. However, due to their decision-tree structure, RFRs lack true predictive capabilities, particularly for interpolation and extrapolation beyond the training data.</div><div>This study advances that foundation by employing Artificial Neural Networks (ANNs), which—when properly trained—can capture complex relationships with greater continuity and generalizability. Beyond simply replacing RFRs, we develop a fully automated framework for constructing Machine Learning Models (MLMs) to predict density and thermal expansion coefficients of bitumen. Using Optuna for hyperparameter optimization, we ensure that the information extracted from MD simulations is utilized effectively.</div><div>The resulting ANN models accurately reproduce MD-predicted densities, achieving R<sup>2</sup>>0.99, MSEs below 0.1 %, and maximum absolute errors below 5 % on test data. In addition to reducing computational cost, the models exhibit improved interpolation and extrapolation capabilities, enabling reliable predictions for properties, ranges, and compositions not explicitly simulated.</div><div>Key aspects of our approach include:<ul><li><span>•</span><span><div>Transitioning from RFRs to ANNs, improving generalization, interpolation, and predictive accuracy.</div></span></li><li><span>•</span><span><div>Automated hyperparameter optimization, leveraging Optuna to maximize model efficiency.</div></span></li><li><span>•</span><span><div>Expanding applicability, enabling property prediction for unseen compositions without additional MD simulations.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103524"},"PeriodicalIF":1.9,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144770792","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}

引用次数: 0

Bayesian generalized dissimilarity model for marine biodiversity analysis 海洋生物多样性分析的贝叶斯广义不相似模型

IF 1.9

MethodsX Pub Date : 2025-07-29 DOI: 10.1016/j.mex.2025.103532

Evellin Dewi Lusiana , Suci Astutik , Nurjannah , Abu Bakar Sambah

引用次数: 0

When thermal risk indices work and when they don’t: A case study of two maize insect pests 热风险指数何时起作用，何时不起作用：两种玉米害虫的案例研究

IF 1.9

MethodsX Pub Date : 2025-07-28 DOI: 10.1016/j.mex.2025.103537

Komi Mensah Agboka , Frank Thomas Ndjomatchoua , Luca Rossini , Ritter A. Guimapi , Elfatih M. Abdel-Rahman

{"title":"When thermal risk indices work and when they don’t: A case study of two maize insect pests","authors":"Komi Mensah Agboka , Frank Thomas Ndjomatchoua , Luca Rossini , Ritter A. Guimapi , Elfatih M. Abdel-Rahman","doi":"10.1016/j.mex.2025.103537","DOIUrl":"10.1016/j.mex.2025.103537","url":null,"abstract":"<div><div>The biological life cycle of terrestrial arthropods, using temperature as the primary driving factor has a large interest for insect pests in agriculture, forestry, urban ecosystems, as constitutes the basics for the development of mathematical models for decision making. A recent study proposed a physiologically-based risk index (<em>RI</em>) which finds large applications in the definition of risk maps; however, further case studies are needed to better explore its strengths and limitations. This study aims to extend this knowledge by presenting an application of the <em>RI</em> on two economically significant pests: the fall armyworm <em>Spodoptera frugiperda</em> and the stem borer <em>Busseola fusca</em>, major treats for maize production.<ul><li><span>•</span><span><div>While the case of <em>S. frugiperda</em> follows the theoretical expectations, providing values <span><math><mrow><mi>R</mi><mi>I</mi><mo>></mo><mn>1</mn></mrow></math></span> for temperature ranges typical of the regions of its confirmed persistence, the model fails for <em>B. fusca</em>, as <span><math><mrow><mi>R</mi><mi>I</mi><mo><</mo><mn>1</mn></mrow></math></span> for weather conditions where field presence and damage are well-documented.</div></span></li><li><span>•</span><span><div>Accordingly, we trace the breakdown to limiting model assumptions, particularly temperature-only drivers, linear cause-and-effect biodemographic parameters, omission of seasonal dynamics, and reliance on laboratory parameters.</div></span></li><li><span>•</span><span><div>This dual-case contrast highlights both the potential and limitations of <span><math><mrow><mi>R</mi><mi>I</mi></mrow></math></span> and calls for refinements that include a broader ecological realism and data availability.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103537"},"PeriodicalIF":1.9,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144770791","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}

引用次数: 0

Method for identifying the locations of longitudinal seams based on LiDAR data detected inside shield tunnel 基于盾构隧道激光雷达探测数据的纵向煤层位置识别方法

IF 1.9

MethodsX Pub Date : 2025-07-28 DOI: 10.1016/j.mex.2025.103541

Yu-Lin Chen , Hao-Yuan Liang , Jia-Xuan Zhang

引用次数: 0

Solving age-dependent infectious diseases and tumor growth models using the contraction approach 使用收缩方法解决年龄依赖性传染病和肿瘤生长模型

IF 1.9

MethodsX Pub Date : 2025-07-28 DOI: 10.1016/j.mex.2025.103505

Syed Khayyam Shah , Muhammad Sarwar , Kamal Shah , Manel Hleili , Thabet Abdeljawad

{"title":"Solving age-dependent infectious diseases and tumor growth models using the contraction approach","authors":"Syed Khayyam Shah , Muhammad Sarwar , Kamal Shah , Manel Hleili , Thabet Abdeljawad","doi":"10.1016/j.mex.2025.103505","DOIUrl":"10.1016/j.mex.2025.103505","url":null,"abstract":"<div><div>This study establishes existence and uniqueness theorems for solution sets in three domains of biological modeling: age-dependent diseases infectiousness, infectious disease transmission, and tumor growth dynamics. We illustrate that fixed-point theory, using contraction mapping concepts, offers solid mathematical foundations for model stability and solution consistency. Our principal contribution is to develop generalized contraction techniques that ensure the existence and uniqueness of solutions for the differential equations describing these biological systems. This mathematical framework improves the mathematical proficiency of epidemiological and oncological modeling and offers computational techniques for model validation. These findings address significant deficiencies in the scientific literature by employing fixed-point methodologies from classical analysis to manage the intricate nonlinearities present in biological systems, thereby paving emerging paths for the investigation of disease dynamics and treatment effectiveness.<ul><li><span>•</span><span><div>Purpose: In this work, we will look for the criteria of existence of unique solutions of the equations in the models like, tumor growth, infectious diseases dependency and spread.</div></span></li><li><span>•</span><span><div>Methodology: Utilizing contraction principle and using different contractions from the literature like, F-contraction<strong>,</strong> α-F-contraction, rational type (ψ, φ)-contraction, and Geraghty-type contraction we come up with the conditions where the mentioned biological models possesses unique solutions.</div></span></li><li><span>•</span><span><div>Findings: Imposing different conditions we established novel results which help us ensure the stability by analyzing the existence and uniqueness of the solution of the problems arising in the aforementioned biological models.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103505"},"PeriodicalIF":1.9,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780123","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}

引用次数: 0

An open-source workflow for identifying hydrodynamic water quality events in rivers by continuous water quality monitoring and time-series data processing using R and US EPA CANARY 通过使用R和美国环保局CANARY进行连续水质监测和时间序列数据处理，用于识别河流中水动力水质事件的开源工作流

IF 1.9

MethodsX Pub Date : 2025-07-27 DOI: 10.1016/j.mex.2025.103538

L. Cronin , C.M. Taylor , C. Briciu Burghina , F.E. Lucy , F. Regan

{"title":"An open-source workflow for identifying hydrodynamic water quality events in rivers by continuous water quality monitoring and time-series data processing using R and US EPA CANARY","authors":"L. Cronin , C.M. Taylor , C. Briciu Burghina , F.E. Lucy , F. Regan","doi":"10.1016/j.mex.2025.103538","DOIUrl":"10.1016/j.mex.2025.103538","url":null,"abstract":"<div><div>Improving European surface water quality requires urgent action to address diffuse pollution sources particularly from agriculture, with increased frequency and intensity of hydroclimatic events also a key driver of pollutant export to waters and water quality decline worldwide.</div><div>However, the need for comprehensive, practical protocols for sensor deployment, sensor maintenance and data management for the adoption of high frequency water quality monitoring has been highlighted, along with the challenges for citizen scientists in analyzing millions of water quality data points and sharing metadata. The practical method presented, with reproducibility built into the workflow, is designed for multiple users and a step-by-step application of the workflow is demonstrated including:<ul><li><span>•</span><span><div>Deployment arrangement for water quality sondes in two temporary monitoring stations with different site characteristics.</div></span></li><li><span>•</span><span><div>Data collection and data validation methods.</div></span></li><li><span>•</span><span><div>Concise, reproducible, open-source workflow detailing the use of R, R markdown and US EPA CANARY software for data import, data cleaning, data visualization, data integrity, along with site-specific CANARY event system configuration for the detection of potential water quality events.</div></span></li></ul>Results for two monitoring stations on different rivers show CANARY successfully identified 100 % (n 47) and 97 % (n 39) of the manually identified turbidity events.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103538"},"PeriodicalIF":1.9,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780121","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}

引用次数: 0

Sustainable management of coffee berry disease and leaf rust co-infection: a systematic review of deterministic models 咖啡莓病和叶锈病共同感染的可持续管理：确定性模型的系统综述

IF 1.9

MethodsX Pub Date : 2025-07-25 DOI: 10.1016/j.mex.2025.103511

Usa Wannasingha Humphries , Porntip Dechpichai , Alhassan Ibrahim , Muhammad Waqas , Boobphachard Chansawang , Gabor Kiss , Angkool Wangwongchai

{"title":"Sustainable management of coffee berry disease and leaf rust co-infection: a systematic review of deterministic models","authors":"Usa Wannasingha Humphries , Porntip Dechpichai , Alhassan Ibrahim , Muhammad Waqas , Boobphachard Chansawang , Gabor Kiss , Angkool Wangwongchai","doi":"10.1016/j.mex.2025.103511","DOIUrl":"10.1016/j.mex.2025.103511","url":null,"abstract":"<div><div>Colletotrichum kahawae, the cause of Coffee Berry Disease (CBD), poses a significant threat to global coffee production, especially in Africa, with potential implications for Latin America and Asia.</div><div>This systematic review evaluates 24 models of CBD, Coffee Leaf Rust (CLR), Coffee Berry Borer (CBB), and co-infection of CBD with CLR, as well as strategies for optimal control.</div><div>It concludes the relevance of the basic reproduction number (<span><math><msub><mi>R</mi><mn>0</mn></msub></math></span>) for forecasting outbreaks and the benefits of employing a combination of control measures.</div><div>Despite advancements, limitations still exist, including the need for empirical validation and the consideration of vector-mediated transmission in co-infection models. Future research should integrate GIS, climate data, and field trials for validation, model vector-host-pathogen interactions, and assess cost-effective strategies for smallholder farmers to ensure sustainable coffee production.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103511"},"PeriodicalIF":1.9,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750123","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}

引用次数: 0

Investigation of concrete durability enhancement using supplementary cementitious materials 补充胶凝材料增强混凝土耐久性的研究

IF 1.9

MethodsX Pub Date : 2025-07-24 DOI: 10.1016/j.mex.2025.103527

Sajeev P S , Vijay Shankar Giri Rajagopal , Naveen Arasu A

{"title":"Investigation of concrete durability enhancement using supplementary cementitious materials","authors":"Sajeev P S , Vijay Shankar Giri Rajagopal , Naveen Arasu A","doi":"10.1016/j.mex.2025.103527","DOIUrl":"10.1016/j.mex.2025.103527","url":null,"abstract":"<div><div>This study investigates the influence of fly ash, metakaolin, and P. juliflora extract on the durability and performance of concrete. The research focuses on key durability tests, including Saturated Water Absorption, RCPT, Sulphuric Acid Resistance, Permeability, Sorptivity, and UPV tests. The experimental results indicate that incorporating fly ash and metakaolin significantly reduces water absorption, permeability, and chloride ion penetration, leading to improved resistance against corrosion and environmental deterioration. The results from the Sulphuric Acid Resistance Test showed that mixes containing metakaolin and fly ash exhibited lower weight and strength loss, demonstrating enhanced acid resistance. The Permeability and Sorptivity Tests further confirmed that blended cementitious materials contribute to a denser microstructure, reducing water ingress. Further, the UPV test suggested that long-term structural integrity improves with supplementary cementitious materials. The optimal combination of 10–15 % fly ash and 10–15 % metakaolin exhibited superior performance. This study concludes that utilizing industrial by-products and natural plant extracts enhances durability, sustainability, and eco-friendliness, making it a viable alternatives for virgin materials.</div><div>• Evaluates fly ash, metakaolin, and P. juliflora extract for improving concrete durability.</div><div>• Shows reduced chloride penetration, acid damage, and water absorption.</div><div>• Confirms denser microstructure and better integrity via UPV and permeability tests.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103527"},"PeriodicalIF":1.9,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771579","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}

引用次数: 0

GradCAM-PestDetNet: A deep learning-based hybrid model with explainable AI for pest detection and classification GradCAM-PestDetNet：一个基于深度学习的混合模型，具有可解释的人工智能，用于害虫检测和分类

IF 1.9

MethodsX Pub Date : 2025-07-24 DOI: 10.1016/j.mex.2025.103533

Ramitha Vimala , Saharsh Mehrotra , Satish Kumar , Pooja Kamat , Arunkumar Bongale , Ketan Kotecha

{"title":"GradCAM-PestDetNet: A deep learning-based hybrid model with explainable AI for pest detection and classification","authors":"Ramitha Vimala , Saharsh Mehrotra , Satish Kumar , Pooja Kamat , Arunkumar Bongale , Ketan Kotecha","doi":"10.1016/j.mex.2025.103533","DOIUrl":"10.1016/j.mex.2025.103533","url":null,"abstract":"<div><div>Pest detection is crucial for both agriculture and ecology. The growing global population demands an efficient pest detection system to ensure food security. Pests threaten agricultural productivity, sustainability, and economic development. They also cause damage to machinery, equipment and soil, making effective detection essential for commercial benefits. Traditional pest detection methods are often slow, less accurate and reliant on expert knowledge. With advancements in computer vision and AI, deep transfer learning models (DTLMs) have emerged as powerful solutions. The GradCAM-PestDetNet methodology utilizes object detection models like YOLOv8m, YOLOv8s and YOLOv8n, alongside transfer learning techniques such as VGG16, ResNet50, EfficientNetB0, MobileNetV2, InceptionV3 and DenseNet121 for feature extraction. Additionally, Vision Transformers (ViT) and Swim Transformers were explored for their ability to process complex data patterns. To enhance model interpretability, GradCAM-PestDetNet integrates Gradient-weighted Class Activation Mapping (Grad-CAM), allowing better visualization of model predictions.<ul><li><span>•</span><span><div>Uses YOLOv8 models (YOLOv8n for fastest inference at 1.86 ms/img) and transfer learning for pest detection ensuring that the system is viable for low-resource environments.</div></span></li><li><span>•</span><span><div>Employs an ensemble model (ResNet50, DenseNet, MobileNet) that achieved 67.07 % accuracy, 66.3 % F1-score and 68.1 % recall. This is an improvement over the baseline CNN which gave an accuracy of 21.5 %. This ensures a more generalized and robust model that is not biased towards the majority class.</div></span></li><li><span>•</span><span><div>Integrates Grad-CAM for improved interpretability in pest detection.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103533"},"PeriodicalIF":1.9,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771577","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}

引用次数: 0