MethodsX最新文献

{"title":"Sustainable metal recovery from spent lithium-ion battery using electrochemical technique: A comprehensive method review","authors":"Sayali Apte ,&nbsp;Aparna Mukherjee ,&nbsp;Preeti Mishra","doi":"10.1016/j.mex.2025.103506","DOIUrl":"10.1016/j.mex.2025.103506","url":null,"abstract":"<div><div>The pervasive presence of Spent Lithium-Ion Batteries (S-LIB) poses a significant environmental threat due to their hazardous components and the resource-intensive mining of scarce metals like lithium, cobalt, and nickel. Efficient recycling offers a solution by fostering a circular economy and mitigating the environmental impact of disposal and primary resource extraction. Electrochemical recycling (ECR) has emerged as a promising sustainable technology for recovering scarce metals and promoting the circular economy.</div><div>The paper discusses methods for ECR of S-LIBs, emphasizing the latest developments targeting the increasing demand for critical metals. The U.S. patent data available through January 2025 is accessed from the Lens database using different keywords. Focusing on the latest and highly cited patents, the paper establishes benefits, drawbacks, research gaps, and future scopes in the domain of ECR.</div><div>Higher selectivity, reduced energy usage, less environmental footprint, increased recovery rates, and possibilities of electrolyte regeneration appeared as some strengths of electrochemical techniques. However, challenges like process complexity due to multi-element systems, employment of strongly corrosive solvents, membrane fouling, and scalability are also witnessed. Future work must aim to improvise the electrochemical recovery system using high-performance anodes, decrease corrosiveness to enhance the electrode durability, and improve membrane performance to make scalable, cost-efficient, and environmentally friendly electrochemical recovery of high-purity metals from S-LIBs.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103506"},"PeriodicalIF":1.6,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144631808","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":"Residual-based multivariate exponentially weighted moving average control chart for statistical process control of water quality in Surabaya city utilizing generative adversarial network","authors":"Muhammad Ahsan ,&nbsp;Raditya Widi Indarsanto ,&nbsp;Kevin Agung Fernanda Rifki ,&nbsp;Muhammad Hisyam Lee","doi":"10.1016/j.mex.2025.103504","DOIUrl":"10.1016/j.mex.2025.103504","url":null,"abstract":"<div><div>This study proposes novel framework to enhance statistical process control (SPC) of water quality by addressing the pervasive issue of autocorrelation in time-series data. We investigate the characteristics of pH, turbidity, and KMnO₄ in Surabaya city's water, revealing significant autocorrelation that compromises statistical independence assumption crucial for reliable SPC. To overcome this, Generative Adversarial Network (GAN) model was developed to generate decorrelated residual time-series. The efficacy of GAN model in reducing autocorrelation was quantitatively validated, achieving Mean Squared Error (MSE) of 0.0054, Root Mean Squared Error (RMSE) of 0.0738, and Mean Absolute Error (MAE) of 0.0556. Subsequently, these GAN-derived residuals were integrated into Multivariate Exponentially Weighted Moving Average (MEWMA) control chart for process monitoring. Phase I analysis detected 33 out-of-control signals; after identifying and removing outliers, process was brought under statistical control with no further out-of-control signals detected. However, subsequent Phase II online monitoring detected eight statistically significant out-of-control signals, indicating a potential loss of process stability over time. Our findings underscore the significant utility of GAN-based residual analysis as a robust strategy for mitigating autocorrelation effects in environmental water quality data. This approach leads to improved process monitoring and enables early anomaly detection, crucial for proactive water quality management.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103504"},"PeriodicalIF":1.6,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654437","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 ‘A quick-to-implement and optimized CRISPR-Cas9 protocol to obtain insertional and small indel mutants in Chlamydomonas reinhardtii’ [MethodsX 15 (2025) 103416]","authors":"Mariano A. De Silvio ,&nbsp;Camila Sánchez-Retuerta ,&nbsp;M. Águila Ruiz-Sola ,&nbsp;Olga Baidukova ,&nbsp;Elena Monte","doi":"10.1016/j.mex.2025.103501","DOIUrl":"10.1016/j.mex.2025.103501","url":null,"abstract":"","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103501"},"PeriodicalIF":1.6,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670900","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":"Segmentation and quantification of testicular histology images using machine learning bioimage analysis tools; Ilastik and Fiji software","authors":"Elna Owembabazi,&nbsp;Ibe Michael Usman,&nbsp;Wusa Makena","doi":"10.1016/j.mex.2025.103503","DOIUrl":"10.1016/j.mex.2025.103503","url":null,"abstract":"<div><div>Histomorphological and histochemical techniques are widely used in infertility studies to assess testicular damage, determine the mechanisms involved, investigate potential interventions strategies, monitor treatment response and prognosis. Testis, a primary male reproductive organ is a compartmentalized organ made up of several seminiferous tubules and supporting tissue. Hence, focal damage is common, and accordingly, making accurate and insightful deductions require careful analysis of almost the entire testis section. However, manual analysis of testis histology sections to extract quantifiable data is hectic, time-consuming, liable to bias and undetected patchy damages and inter-personal variability. To circumvent these challenges, we present a step-by-step workflow using free, open-source interactive machine learning-based bioimage analysis tools; ilastik and Fiji. Ilastik uses a random forest classifier to compute generic pixel or object features for image segmentation. The segmented images exported from ilastik are subsequently quantified in FIJI to extract data for statistical analysis.<ul><li><span>•</span><span><div>A step-by-step workflow using free, open-source interactive machine learning-based bioimage analysis tools; ilastik and Fiji.</div></span></li><li><span>•</span><span><div>A semiautomated, reproducible, time saving, unbiased, and broad scope method for analysis and heterogeneous tissue images.</div></span></li><li><span>•</span><span><div>Extraction of quantifiable data from images for statistical analysis to make comprehensive conclusions.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103503"},"PeriodicalIF":1.6,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654405","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":"GeoBM: A Python-based tool for integrated visualization of global bibliometric data","authors":"Chun Chong Fu ,&nbsp;Jorge Fleta-Asín ,&nbsp;Fernando Muñoz ,&nbsp;Carlos Sáenz-Royo ,&nbsp;Loo Keat Wei","doi":"10.1016/j.mex.2025.103497","DOIUrl":"10.1016/j.mex.2025.103497","url":null,"abstract":"<div><div>The rapid proliferation of scientometric and bibliometric analyses has emphasized the need for robust, scalable methods to visualize complex, large-scale research data. Conventional geospatial visualization techniques—most notably choropleth maps—often introduce significant distortions due to their inability to adequately account for spatial heterogeneity and overdispersion in bibliometric distributions. To address these methodological shortcomings, we propose GeoBM (Geographic Bibliometric Mapping), a computational framework that enables enhanced geovisualization of global scientific output and collaboration patterns. GeoBM integrates normalized country-level publication volumes with bilateral collaboration frequencies to produce high-resolution, interpretable geographic maps that reflect both research intensity and international connectivity. Implemented in Python, the framework leverages modular, algorithmically optimized routines for real-time data processing and visualization, incorporating statistical controls to mitigate overdispersion and enhance visual fidelity. The system supports extensive customization and is deployed via open-source platforms such as Google Colab and GitHub, facilitating broad accessibility and reproducibility. By providing a dual-focus representation of publication density and collaborative strength, GeoBM offers a powerful tool for the spatial analysis of global research networks, contributing to more nuanced evaluations in science policy, research management, and innovation studies.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103497"},"PeriodicalIF":1.6,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670899","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":"Enhanced SqueezeNet model for detecting IoT-Bot attacks: A comprehensive approach","authors":"Balaganesh Bojarajulu ,&nbsp;Sarvesh Tanwar ,&nbsp;Thipendra Pal Singh","doi":"10.1016/j.mex.2025.103499","DOIUrl":"10.1016/j.mex.2025.103499","url":null,"abstract":"<div><div>The exponential growth of Internet of Things (abbreviated as IoT) has led to a surge in cyber threats, especially botnet attacks that compromise network security. Although machine learning (abbreviated ML) &amp; deep learning (abbreviated as DL) approaches have shown promise in detecting these attacks, they often struggle with limited accuracy &amp; high computational requirements, making them unsuitable for real-time detection in resource-constrained IoT environments. To overcome these limitations, this research proposes an enhanced detection framework based on an improved SqueezeNet model integrated with a Deep Convolutional Neural Network (abbreviated as DCNN) and an optimized stochastic mixed Lp layer. This model aims to improve detection accuracy while maintaining computational efficiency. Experimental evaluation using a large-scale intrusion detection dataset demonstrates that the proposed model significantly outperforms existing techniques such as Bi-GRU, CNN, PolyNet, and LinkNet, achieving a classification accuracy of 0.97 and a reduced false positive rate of 0.054. The complete research process is outlined below:<ul><li><span>•</span><span><div>Data Pre-processing: Min-max normalization is applied to the input dataset to ensure consistent data scaling and enhance model learning performance.</div></span></li><li><span>•</span><span><div>Feature Extraction and Classification: The improved SqueezeNet is integrated with DCNN &amp; a stochastic mixed Lp layer to extract meaningful features and classify attacks accurately.</div></span></li><li><span>•</span><span><div>Model Evaluation: Performance is validated through accuracy, precision, recall, and false positive rate using a benchmark intrusion detection dataset.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103499"},"PeriodicalIF":1.6,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654406","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":"Fabrication and characterization of electrospun nanofiber mats composed of biochar, sodium alginate, polyvinyl alcohol, and glyoxal","authors":"Solmaz Gholami ,&nbsp;Emad Dehghanifard ,&nbsp;Ali Partovinia ,&nbsp;Ahmad Jonidi Jafari ,&nbsp;Roshanak Rezaei Kalantary","doi":"10.1016/j.mex.2025.103500","DOIUrl":"10.1016/j.mex.2025.103500","url":null,"abstract":"<div><div>The aim of this study is to synthesize and characterize novel electrospun nanofibers composed of biochar, polyvinyl alcohol (PVA), sodium alginate (ALG), and glyoxal (GO). Biochar, known for its exceptional adsorption capacity, was integrated into the polymer matrix to enhance pollutant removal efficiency. Glyoxal was added to improve the nanofibers' electrical conductivity and mechanical strength. The electrospinning process was optimized to produce uniform, bead-free nanofibers by maintaining a 15 cm needle-to-collector distance, a 20 kV voltage, and a 0.8 mL/h feed rate. Characterization techniques such as FESEM, FTIR, XRD, and BET were employed to analyze the morphology, chemical composition, and surface properties of the nanofibers. The results confirmed the successful incorporation of biochar into the polymer matrix, revealing unique structural and morphological properties in the composite material. The research describes a comprehensive method for synthesizing biochar-loaded nanofibers and reports useful characterization information that could inform future research to optimize nanofiber composition for particular environmental applications.<ul><li><span>•</span><span><div>Biochar enhances pollutant adsorption capacity, while glyoxal improves electrical conductivity and mechanical strength. Sodium alginate (ALG) and polyvinyl alcohol (PVA) contribute to biocompatibility and processability, resulting in versatile composite materials.</div></span></li><li><span>•</span><span><div>Nanofiber mats were fabricated using varying concentrations of biochar, ALG, and PVA, with an ALG/PVA ratio of 20:80 % and biochar concentrations of 1 % (w/w).</div></span></li><li><span>•</span><span><div>Optimal electrospinning conditions included a 15 cm needle-to-collector distance, a 20 kV applied voltage, and a feed rate of 0.8 ml/h, ensuring the production of high-quality nanofibers.</div></span></li></ul><strong>Method name:</strong> Electrospinning.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103500"},"PeriodicalIF":1.6,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663268","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":"MentalRoBERTa-Caps: A capsule-enhanced transformer model for mental health classification","authors":"Faheem Ahmad Wagay ,&nbsp;Jahiruddin ,&nbsp;Yasir Altaf","doi":"10.1016/j.mex.2025.103483","DOIUrl":"10.1016/j.mex.2025.103483","url":null,"abstract":"<div><div>In recent years, the dominance of Large Language Models (LLMs) such as BERT and RoBERTa has led to remarkable improvements in NLP tasks, including mental illness detection from social media text. However, these models are often computationally intensive, requiring significant processing time and resources, which limits their applicability in real-time or resource-constrained environments. This paper proposes a lightweight yet effective hybrid model that integrates a 6-layer RoBERTa encoder with a capsule network architecture to balance performance, interpretability, and computational efficiency. The contextual embeddings generated by RoBERTa are transformed into primary capsules, and dynamic routing is employed to generate class capsule outputs that capture high-level abstractions.</div><div>To validate performance and explainability, we employ LIME (Local Interpretable Model-Agnostic Explanations) to provide insights into feature contributions and model decisions. Experimental results on benchmark mental health datasets demonstrate that our approach achieves high accuracy while significantly reducing inference time, making it suitable for deployment in real-world mental health monitoring systems.<ul><li><span>1.</span><span><div>To design a computationally efficient architecture for mental illness detection using a lightweight RoBERTa encoder integrated with capsule networks.</div></span></li><li><span>2.</span><span><div>To perform a detailed time complexity analysis highlighting the trade-offs between performance and efficiency.</div></span></li><li><span>3.</span><span><div>To enhance model interpretability through LIME-based feature attribution, supporting transparent and ex- plainable predictions.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103483"},"PeriodicalIF":1.6,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654407","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":"Simulation-based framework for stochastic multi-mode resource-constrained project scheduling","authors":"Ali Rahimifard ,&nbsp;Isa Nakhai-Kamalabadi ,&nbsp;Kaveh Khalili-Damghani ,&nbsp;Sadigh Raissi","doi":"10.1016/j.mex.2025.103496","DOIUrl":"10.1016/j.mex.2025.103496","url":null,"abstract":"<div><div>This paper introduces a new simulation-based framework designed to tackle the challenges of scheduling projects with uncertain activity durations and limited resources, known as the stochastic multi-mode resource-constrained project scheduling problem (SN-MMRCPSP). By combining Discrete Event Simulation (DES) and Multi-Agent Systems (MAS), the approach captures real-world uncertainties and complex interactions within projects. This model helps decision-makers plan more effectively in uncertain environments.</div><div>A Hybrid DES-MAS simulation architecture is proposed to model dynamic, uncertain scheduling environments.</div><div>The Taguchi Design of Experiments (DOE) is applied to determine optimal execution modes, enhancing robustness and performance.</div><div>Demonstrates the model’s practicality and effectiveness through comprehensive case studies and benchmark comparisons.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103496"},"PeriodicalIF":1.6,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144631809","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":"Performance analysis of neural network architectures for time series forecasting: A comparative study of RNN, LSTM, GRU, and hybrid models","authors":"Ariana Yunita ,&nbsp;MHD Iqbal Pratama ,&nbsp;Muhammad Zaki Almuzakki ,&nbsp;Hani Ramadhan ,&nbsp;Emelia Akashah P. Akhir ,&nbsp;Andi Besse Firdausiah Mansur ,&nbsp;Ahmad Hoirul Basori","doi":"10.1016/j.mex.2025.103462","DOIUrl":"10.1016/j.mex.2025.103462","url":null,"abstract":"<div><div>Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs) have gained significant popularity in time series forecasting across diverse domains including healthcare, astronomy, and engineering. However, the inherent variability in model performance due to random weight initialization raises questions about the reliability and consistency of these architectures for time series analysis. This study addresses this concern by conducting a comprehensive benchmark evaluation of nine neural network architectures: vanilla RNN, LSTM, GRU, and six hybrid configurations (RNN-LSTM, RNN-GRU, LSTM-RNN, GRU-RNN, LSTM-GRU, and GRU-LSTM). Performance evaluation was conducted using Monte Carlo simulation with 100 iterations across three real-world datasets: sunspot activity, Indonesian COVID-19 cases, and dissolved oxygen concentration measurements. Statistical analysis employed the Friedman test to assess performance differences across architectures. Results showed no statistically significant differences among the nine architectures. Despite the lack of statistical significance, consistent performance patterns emerged favoring LSTM-based hybrid architectures. The LSTM-GRU and LSTM-RNN configurations demonstrated superior performance across multiple evaluation metrics, with LSTM-RNN excelling in sunspot and dissolved oxygen forecasting, while standalone LSTM showed optimal performance for COVID-19 prediction. These findings provide evidence-based guidance for architecture selection in time series forecasting applications, suggesting that while statistical equivalence exists among architectures, LSTM-based hybrids offer practical advantages in terms of consistency and robustness across diverse temporal patterns.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103462"},"PeriodicalIF":1.9,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721635","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}