MethodsX最新文献

{"title":"Urea water bath: A novel treatment for monogenean parasite infections in hagfish (Agnatha: Myxiniformes: Myxinidae)","authors":"Taketeru Tomita ,&nbsp;Hiroko Takaoka ,&nbsp;Nozomi Hanahara","doi":"10.1016/j.mex.2025.103518","DOIUrl":"10.1016/j.mex.2025.103518","url":null,"abstract":"<div><div>Monogenean flatworms are pathogenic parasites that infect the skin of marine fish and pose a major problem in aquaculture and aquarium industries. Freshwater baths are the most commonly used method to treat these infections. However, this method is not suitable for certain fish taxa, including hagfish (Agnatha: Myxiniformes: Myxinidae), which have low tolerance to hypoosmotic environments.</div><div>In the present study, we developed a treatment fluid specifically for use in hagfish. The fluid was prepared by dissolving urea in a mixture of chlorine-free tap water and seawater. A 5-min bath of three captive purple hagfish in the fluid successfully eliminated the parasites, with no apparent side effects. To date, this is the only known effective treatment for monogenean infections in hagfish.</div><div><ul><li><span>•</span><span><div>In this study, we developed a method to treat monogenean infections in hagfish.</div></span></li><li><span>•</span><span><div>This method uses a fluid containing a high concentration of urea.</div></span></li><li><span>•</span><span><div>Bathing infected hagfish in the fluid successfully eliminated the parasites without apparent side effects.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103518"},"PeriodicalIF":1.6,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711929","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":"Bayesian MCMC with Gibbs sampling for saturation flow rate estimation in heterogeneous traffic at pretimed signalized intersections","authors":"Lulusi Lulusi ,&nbsp;Sugiarto Sugiarto ,&nbsp;Sofyan M. Saleh ,&nbsp;Muhammad Isya ,&nbsp;Muhammad Rusdi ,&nbsp;Roudhia Rahma","doi":"10.1016/j.mex.2025.103507","DOIUrl":"10.1016/j.mex.2025.103507","url":null,"abstract":"<div><div>Pretimed signalized intersections significantly contribute to traffic congestion, especially under the heterogeneous traffic conditions commonly observed in emerging economies such as Indonesia. Accurate estimation of the base saturation flow rate (BSFR) is essential for reliable capacity assessment, which influences effective intersection design and operation. However, the current BSFR estimation methods outlined in the Indonesian Highway Capacity Guidelines (IHCG, 2023) rely on outdated linear models derived from the Indonesian Highway Capacity Manual (IHCM, 1997), which are inadequate for addressing contemporary heterogeneous traffic complexities. This study introduces a Bayesian Markov Chain Monte Carlo (MCMC) model employing Gibbs sampling to improve BSFR estimation accuracy. The Bayesian MCMC model achieved a Root Mean Square Error Approximation (RMSEA) of 8.638 % compared to the existing IHCG method, which produced an RMSEA of up to 51.428 %, enabling a more precise intersection capacity design. Additionally, the developed model reduced the BSFR overestimation associated with the IHCG method by approximately 42.79 %, highlighting the potential of Bayesian MCMC methods to effectively address heterogeneous traffic challenges, enhance traffic management strategies, and optimize intersection operations.</div><div>The Bayesian approach provides a probabilistic framework for quantifying uncertainty, allows for the incorporation of prior knowledge to enhance parameter estimation flexibility, and effectively mitigates model overfitting.</div><div>The developed model demonstrates robust statistical validity, characterized by a mean beta parameter value of 403.30, standard deviation of 8.66, and Monte Carlo Standard Error (MCSE) of 0.0008, confirming high reliability and predictive precision.</div><div>The proposed BSFR model exhibited superior performance in fitting empirical data, as evidenced by an RMSE of 240.403 PCU/g/h/We and RMSEA of 8.638 %, indicating an excellent model fit within acceptable thresholds (&lt;10 %).</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103507"},"PeriodicalIF":1.6,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696759","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":"Shewhart type simultaneous univariate control chart based on ranked set sampling scheme","authors":"Sukma Adi Perdana ,&nbsp;Muhammad Mashuri ,&nbsp;Muhammad Ahsan","doi":"10.1016/j.mex.2025.103513","DOIUrl":"10.1016/j.mex.2025.103513","url":null,"abstract":"<div><div>The control charts with the Ranked Set Sampling (RSS) scheme performs better in detecting shifts in mean and variance of the process compared to the control charts with the Simple Random Sampling scheme (SRS). In this article, we develop a Novel Shewhart type simultaneous univariate control chart (Max Chart) based on the RSS scheme. Since the distribution of the statistics <span><math><mrow><mi>M</mi><mo>(</mo><msub><mi>n</mi><mi>i</mi></msub><mo>)</mo></mrow></math></span> based on the RSS scheme is not known, an alternative approach is taken by using an improved bootstrap method in developing a novel Shewhart type simultaneous control chart based on RSS. The results of the Monte Carlo simulation study show that, when compared to the conventional Max chart based on the SRS scheme, the average run length under out-of-control conditions (ARL₁) obtained from that the Improved Bootstrap Max Chart based on the RSS scheme is superior in detecting shifts in mean and variance of the process. A real case example is also given to illustrate the operation and performance of the proposed control chart. Key points:<ul><li><span>1.</span><span><div>A novel Shewhart type simultaneous univariate control chart (Max Chart) based on the RSS scheme is proposed to improve the performance of the of control chart in detecting process shifts.</div></span></li><li><span>2.</span><span><div>The Control limit of Shewhart type simultaneous univariate control chart (Max Chart) based on the RSS scheme was constructed using an Improved Bootstrap method.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103513"},"PeriodicalIF":1.6,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685758","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":"Hybrid genetic algorithms-driven optimization of machine learning models for heart disease prediction","authors":"Sherko H. Murad ,&nbsp;Noor Bahjat Tayfor ,&nbsp;Nozad H. Mahmood ,&nbsp;Lawson Arman","doi":"10.1016/j.mex.2025.103510","DOIUrl":"10.1016/j.mex.2025.103510","url":null,"abstract":"<div><div>Machine learning (ML) models, such as K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), play a vital role in predicting heart disease. However, their performance is often limited by poor hyperparameter selection. This study presents a novel hybrid approach that uses a Genetic Algorithm (GA) to systematically optimize the hyperparameters of KNN and SVM models, leading to improved classification outcomes. The optimization driven by the GA resulted in significant performance improvements, increasing the classification accuracy of KNN to 95.38 % and SVM to 90 %. Furthermore, there were significant improvements in precision, recall, and F-score. Our findings demonstrate that GA-based hyperparameter tuning is an effective strategy for improving the predictive power and clinical relevance of ML models used for heart disease classification.<ul><li><span>•</span><span><div><strong>GA Driven Optimization:</strong> Genetic algorithm was utilized to fine-tune the hyperparameters of K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) classifiers for improved performance.</div></span></li><li><span>•</span><span><div><strong>Significant Performance Gains:</strong> The optimization process aimed to maximize classification across metrics such as accuracy, precision, recall, and F-score.</div></span></li><li><span>•</span><span><div><strong>Improved Accuracy:</strong> The GA-based tuning significantly increased the classification accuracy, improving KNN to 95.38 % and SVM to 90 %.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103510"},"PeriodicalIF":1.6,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685757","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":"Satellite imagery pre-processing and feature extraction for the mapping of coastal ecosystems using Google Earth Engine: A workflow for practitioners","authors":"Ahmad Badruzzaman ,&nbsp;Prawesti Wulandari ,&nbsp;Sainal Sainal ,&nbsp;Matthew Ashley ,&nbsp;Susan Jobling ,&nbsp;Melanie C. Austen ,&nbsp;Radisti A. Praptiwi","doi":"10.1016/j.mex.2025.103516","DOIUrl":"10.1016/j.mex.2025.103516","url":null,"abstract":"<div><div>The use of Google Earth Engine (GEE) is increasingly common in geospatial analysis of satellite images for various environmental management purposes due to its easy accessibility and capabilities to support complex pre-processing and mining of geographic data. In the context of coastal management, GEE provides opportunities for cost-efficient mapping of coastal habitats and their ecosystem service potentials. Understanding the extent of coastal habitats and the spatial and temporal variabilities of their ecosystem services can be useful for management and intervention purposes. GEE is well-suited for this due to its user-friendliness, particularly for non-experts of programming languages, such as area managers and other practitioners. However, there is no specific methodological guideline for the pre-processing and feature extraction of satellite images in GEE that can be readily adopted by these practitioners. This study develops general methodological steps to perform those processes that can be adapted to different management needs. Highlights of this study:<ul><li><span>•</span><span><div>Steps detailed in this method paper will produce processed satellite images readily applicable for machine learning to classify coastal ecosystems.</div></span></li><li><span>•</span><span><div>The development of this adaptable workflow can benefit and empower local area managers, particularly in low-resource settings, to conduct monitoring of their area.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103516"},"PeriodicalIF":1.6,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663266","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":"Methodological approach to assess cardiovascular dynamics in elite cyclists","authors":"Manuel Mateo-March,&nbsp;Alejandro Javaloyes,&nbsp;Iván Peña-González,&nbsp;Manuel Moya-Ramón","doi":"10.1016/j.mex.2025.103515","DOIUrl":"10.1016/j.mex.2025.103515","url":null,"abstract":"<div><div>This study introduces a novel time-series method to optimize performance in professional cycling, analyzing cardiovascular reactivity and power output in elite cyclists during monument races. Integrating power meter and heart rate data, we derive critical power (CP), assess effort intensity (% CP), and track heart rate dynamics across race quartiles (Q1, Q2, Q3, Q4), revealing heart rate dynamics in elite cyclist. Preliminary testing of this method showed that Top 10 cyclists show significantly higher heart rate increase rates in Q1 (<em>p</em> &lt; 0.05) and greater heart rate modulation in Q1 and Q3 (<em>p</em> &lt; 0.05) than non-top cyclists (Top 11–30), indicating superior cardiovascular responsiveness. Key features include:</div><div>• Thorough raw data preprocessing for reliability.</div><div>• Power output normalization to CP for consistent assessment.</div><div>• Sigmoid modeling of heart rate rise to gauge cardiovascular reactivity.</div><div>This R-based, reproducible method empowers sports scientists and coaches to boost cyclist performance, with potential use in other endurance sports.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103515"},"PeriodicalIF":1.6,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670877","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":"Bridging spatiotemporal wildfire prediction and decision modeling using transformer networks and fuzzy inference systems","authors":"Parul Dubey ,&nbsp;Pushkar Dubey","doi":"10.1016/j.mex.2025.103498","DOIUrl":"10.1016/j.mex.2025.103498","url":null,"abstract":"<div><div>Wildfires present a growing threat to ecosystems, human settlements, and climate stability, necessitating accurate and interpreted prediction systems. Existing AI-based models often prioritize performance over explainability, limiting their utility in real-time decision-making contexts. Current wildfire forecasting models struggle to incorporate uncertainty and offer transparent response strategies. Moreover, many models fail to integrate domain knowledge in a way that supports actionable interventions. This study utilizes the Canadian Fire Spread Dataset, augmented with Sentinel, ERA5, and SRTM data, encompassing vegetation, meteorological, and topographic variables. The suggested system uses a Transformer-based model to predict fires over time and space, along with a Fuzzy Rule-Based System (FRBS) to create rules for responding to those predictions. This integration allows for both high accuracy and interpretability in decision-making under uncertain environmental conditions. The novelty lies in the use of symbolic fuzzy reasoning layered onto a deep attention-based architecture. Performance was evaluated using metrics such as accuracy, precision, recall, F1-score, and AUC. The model achieved an F1-score of 92.9 % and accuracy of 94.8 %, significantly outperforming baseline and deep learning alternatives.</div><div>• Integrates deep learning with fuzzy logic for both accurate forecasting and interpretable response planning.</div><div>• Enables uncertainty-aware reasoning by translating predictions into actionable fire management rules.</div><div>• Demonstrates superior performance across diverse environmental datasets using multi-source satellite and climate inputs.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103498"},"PeriodicalIF":1.6,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680336","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":"Investigation into the determinants of occupational safety for achieving zero accidents in multi-storey building construction projects: A Bayesian belief network approach","authors":"Ryan Pramanda ,&nbsp;Sri Gunani Partiwi ,&nbsp;Adithya Sudiarno ,&nbsp;Hamdani","doi":"10.1016/j.mex.2025.103508","DOIUrl":"10.1016/j.mex.2025.103508","url":null,"abstract":"<div><div>The Indonesian construction sector is vital for economic development but faces high accident rates because of inadequate occupational health and safety (OHS) measures. This study examines causal factors in high-rise construction accidents, focusing on human factors, organization, work environment, and payment systems. A Bayesian Belief Network (BBN) was used to evaluate OHS implementation and identify key elements for achieving zero accidents. Data were collected through a structured questionnaire completed by 28 experts assessing 277 workers across 33 critical safety variable relationships. Analysis using RStudio’s bnlearn revealed that regulatory requirements strongly influence safety design, corporate culture, and OHS management, while overly complex safety designs hinder supervision and training. Findings show that workers’ physical and mental conditions are vital for zero accidents, whereas safety awareness alone is insufficient. Improved training enhances payment systems, boosting motivation and productivity. The study recommends simplifying safety design and refining OHS procedures to reduce accident risks. The main advantages of the proposed method are:<ul><li><span>•</span><span><div>Using Bayesian Belief Network to analyse work accidents at high-rise projects.</div></span></li><li><span>•</span><span><div>Examined the human, organisational, work environment, and payment system aspects.</div></span></li><li><span>•</span><span><div>Found that simplifying safety design and improving the training are important for achieving zero accidents.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103508"},"PeriodicalIF":1.6,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663174","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":"Health risks assessment of polycyclic aromatic hydrocarbons (PAHs) in meat kebabs through meta-analysis and Monte Carlo simulation","authors":"Fathollah Gholami-Borujeni ,&nbsp;Fatemeh Mortezazadeh ,&nbsp;Somayeh Hoseinvandtabar ,&nbsp;Anoushiravan Mohseni-Bandpei ,&nbsp;Hadi Niknejad","doi":"10.1016/j.mex.2025.103502","DOIUrl":"10.1016/j.mex.2025.103502","url":null,"abstract":"<div><div>Polycyclic aromatic hydrocarbons (PAHs) are included in various everyday food products, prompting worries over their possible detrimental effects on human health. This study aimed to estimate the health risks associated with PAH contamination in meat kebabs using a comprehensive meta-analysis and probabilistic risk assessment framework. The researchers collected information on PAH levels in meat kebabs (cooked by barbecuing, grilling, broiling, or smoking) from 42 studies found in well-known databases like Scopus, PubMed, Science Direct, Google Scholar, and Web of Science, covering the years from 2011 to October 2023. Furthermore, health risks were assessed through chronic daily intake (CDI), hazard quotient (HQ), and incremental lifetime cancer risk (LTCR), with uncertainty analyzed utilizing Monte Carlo simulation (MCS, 10,000 iterations). The investigation revealed that meat kebabs were made using barbecuing (7.4 %), broiling (11.2 %), smoking (25.7 %), and grilling (55.7 %) techniques. The highest average concentrations were observed for pyrene (Pyr: 359.32 µg/kg) and naphthalene (Nap: 263.20 µg/kg), while benzo[a]pyrene exhibited a lower mean concentration (BaP: 1.79 µg/kg). Risk assessment indicated that HQ values for some PAHs, particularly BaP (1.× 10²) and Nap (3.93), exceeded the threshold of 1, indicating possible non-carcinogenic effects at high intake levels. However, LTCR values for all PAHs remained below 1 × 10⁻⁶, indicating negligible lifetime cancer risk according to USEPA guidelines. According to the results, although meat kebabs may pose a non-carcinogenic concern at elevated intake levels, the lifetime cancer risk appears within acceptable limits. These findings highlight the importance of promoting safer cooking techniques and public awareness to mitigate exposure to PAHs through dietary sources.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103502"},"PeriodicalIF":1.6,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663269","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":"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}