MethodsX最新文献_第4页

A novel hybrid CLARA and fuzzy time series Markov chain model for predicting air pollution in Jakarta

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-02-05 DOI: 10.1016/j.mex.2025.103202

Nurtiti Sunusi , Ankaz As Sikib , Sumanta Pasari

Air pollution poses a significant challenge to public health and the global environment. The Industrial Revolution, advancing technology and society, led to elevated air pollution levels, contributing to acid rain, smog, ozone depletion, and global warming. Poor air quality increases risks of respiratory inflammation, tuberculosis, asthma, chronic obstructive pulmonary disease (COPD), pneumoconiosis, and lung cancer.

In this context, developing reliable air pollution forecasting models is imperative for guiding effective mitigation strategies and policy interventions. This study presents a daily air pollution prediction model focusing on Jakarta's sulfur dioxide (SO₂) and carbon monoxide (CO) levels, leveraging a hybrid methodology that integrates Clustering Large Applications (CLARA) with the Fuzzy Time Series Markov Chain (FTSMC) approach.

The analysis revealed five distinct clusters, with medoid selection refined iteratively to ensure stabilization. A 5 × 5 Markov transition probability matrix was subsequently constructed for modeling the data. Predicted values for SO₂ and CO in Jakarta using the CLARA-FTSMC hybrid method showed strong alignment with the actual data. Forecasting accuracy results for SO₂ and CO in Jakarta, based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), showed excellent performance, underscoring the efficacy of the CLARA-FTSMC hybrid approach in predicting air pollution levels.

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The CLARA-FTSMC hybrid method demonstrates high effectiveness in analyzing large datasets, addressing the limitations of previous hybrid clustering fuzzy time series methods.
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The number of fuzzy time series partitions is optimally determined based on clustering results obtained through the gap statistic approach, ensuring robust partitioning.
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The forecasting accuracy of the CLARA-FTSMC hybrid method, evaluated using MAE and RMSE, showed excellent performance in predicting daily air pollution levels of SO₂ and CO in Jakarta.

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引用次数: 0

A protocol to optimize non-invasive brain stimulation for post-stroke rehabilitation 优化脑卒中后康复的非侵入性脑部刺激方案

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-02-05 DOI: 10.1016/j.mex.2025.103209

Ayesha Juhi , Manul Das , Dinesh Bhatia , Suman Dhaka , Rajesh Kumar , Deepak Kumar , Shreya Sharma , Pritam Kumar Chaudhary , Chanchal Goyal , Md Asif Khan , Himel Mondal

This randomized controlled trial investigates the optimal dosing for post-stroke rehabilitation using repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS). Previous studies demonstrated improvements in cognitive and motor functions with specific intensities of rTMS and tDCS, but this trial explores various frequencies and currents to optimize therapeutic outcomes. A total of 128 post-stroke patients (within 1–6 months of stroke) with paraplegia or hemiplegia are recruited. Patients are divided into four groups for both rTMS (n = 49) and tDCS (n = 49): three groups with different stimulation intensities (1 Hz, 5 Hz, 10 Hz for rTMS; 0.5 mA, 1 mA, 2 mA for tDCS) and a sham control group. Along with this, there is a standard therapy group (n = 30) as control. Participants receive 20 min sessions, five days a week, over six weeks. Cognitive and motor assessments are conducted at 4 weeks, 6 weeks, and 6 months to measure short-term and sustained effects.

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Hemodynamically stable post-stroke patients randomized in four groups in rTMS and tDCS each and their baseline cognitive and motor function assessed
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Application of the two types of therapy for 6 weeks
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Checking improvement of cognitive and motor function and compare the improvement among subgroups of recipient of various frequencies and currents

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引用次数: 0

A methodology for evaluating the fracture width of a notched RC multiple-layer flat slab under cyclic loading

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-02-04 DOI: 10.1016/j.mex.2025.103184

Nawir Rasidi, Taufiq Rochman

This paper introduces an innovative method for assessing crack width in notched reinforced concrete (RC) slabs under cyclic loads, considering both precast and in-situ layers in composite slab configurations. The study addresses gaps in current research on crack behaviour under fatigue, providing a method that evaluates stress distribution and crack spacing around flexural notches. By employing empirical curvature values and comparing predicted crack widths with experimental data and fracture mechanics standards, this approach accounts effectively for tension stiffening effects.

The results reveal that composite slabs exhibit controlled crack propagation and improved resistance under cyclic loading, demonstrating the effectiveness of the composite action. This model not only bridges the gap between theory and practical application in crack width prediction but also contributes to optimizing durability in RC structures exposed to cyclic stresses. The methodology aids in extending structural service life and refining design criteria in fatigue and fracture engineering.

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Measures crack width and spacing near flexural cracks using empirical curvature data.
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Validates predictions with established standards, demonstrating method accuracy.
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Offers a model for crack behaviour that aligns with fatigue and fracture mechanics in RC slabs.

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引用次数: 0

Microplastics and nanoplastics detection using flow cytometry: Challenges and methodological advances with fluorescent dye application 使用流式细胞术检测微塑料和纳米塑料：应用荧光染料的挑战和方法学进展

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-02-04 DOI: 10.1016/j.mex.2025.103200

Lucas Ainé , Justine Jacquin , Colette Breysse , Catherine Colin , Jean-Michel Andanson , Florence Delor-Jestin

Flow cytometry (FC) enables the precise quantification of specific types of microparticles and larger nanoparticles (>200 nm) in liquid media. Initially developed for biological applications, this technique has recently been adapted to the environmental field for the measurement of microplastics and nanoplastics (MNPs). Nile Red, a fluorochrome extensively used in MNP analysis due to its effectiveness and accessibility, has been applied to significantly enhance the sensitivity and specificity of MNP detection of this technique. Additionally, flow cytometry offers the advantage of automated detection, allowing the quantification of smaller particles, including those under 1 µm, which are often missed by traditional spectroscopic methods. However, despite its promise, the presence of undissolved dye in aqueous media presents a significant challenge for accurate quantification. In recent years, various methodologies have been developed to overcome these limitations, including the use of co-solvents, surfactants, and pre-filtration or pre-sonication techniques to enhance quantification accuracy. This review examines recent literature on MNPs detection via FC, with a focus on technical improvements made and the remaining metrological challenges, offering insights into how this method can be further refined for future investigations.

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引用次数: 0

Effects of ZnO nanoparticles concentration on the morphology and textural properties of ZnO/NiFe2O4 nanocomposite

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-02-03 DOI: 10.1016/j.mex.2025.103199

Jimoh Oladejo Tijani , Augustine Innalegwu Daniel , Sarah Udenyi Onogwu , Ambali Saka Abdulkareem , Usman Ahmed Aminu , Marshall Keyster , Ashwil Klein

The aim of this study is to synthesize and characterize ZnO and NiFe₂O₄ nanoparticles via green route and co-precipitation of ZnO/NiFe₂O₄. X-ray diffraction (XRD) data show no extra diffraction peaks belonging to other phases except wurtzite. High resolution transmission electron microscopy (HRTEM) images showed that the average interplanar distance of wurtzite phase at 3, 5, and 7 % dopant concentration were about 0.28, 0.44 and 0.33 nm respectively. X-ray photoelectron spectroscopy (XPS) results show difference in binding energies of the elements present in different concentration of the dopants. Electron Energy Loss Spectroscopy (EELS) spectra show similarities in the shape of Zn, Fe and Ni from zero loss, low loss and core loss region with a little shift in energy. All the elements exhibit multiple oxidation state; +2 and +3 for Fe and +1 and +2 for Zn and Ni. Brunauer-Emmett-Teller (BET) plot shows that ZnO belongs to the type II isotherm curve while NiFe₂O₄ and 3, 5 and 7 % ZnO/NiFe₂O₄ all belong to type IV isotherm curve indicating ZnO as macroporous while NiFe₂O₄ and different dopant concentration of ZnO/NiFe₂O₄ are mesoporous. The study shows the complete synthesis of ternary ZnO/NiFe₂O₄ nanocomposites using green synthesis and sol-gel approach.

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Green synthesis of ZnO and NiFe₂O₄ using leaf extract of Anacardium occidentale
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Co-precipitation method at different concentration of ZnO and NiFe₂O₄ for the synthesis of ZnO/NiFe₂O₄.
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Nanocomposites was characterized using different analytical tools

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引用次数: 0

Industrial wastewater treatment and reuse: Heckman probit sample selection model

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-02-01 DOI: 10.1016/j.mex.2025.103192

Urgessa Tilahun Bekabil , M.K. Jayamohan , Amsalu Bedemo Beyene

Wastewater treatment and reuse help emerging economies to minimize their water scarcity. This study examines the factors influencing wastewater treatment and reuse by manufacturing firms in Shaggar City. The Heckman Probit Sample Selection model is used to analyse the data collected from 303 randomly selected manufacturing firms using structured questionnaires. Results revealed that R&D costs, innovation practices, the availability of purpose-driven vehicles for waste transport, energy costs, and the scale of operations significantly affect the reuse of treated wastewater. The result also shown that presence of wastewater treatment facilities, energy costs, R&D costs, and existence of solid waste disposal facilities were found to be statistically significant in determining wastewater treatment decision. The result implies that firms with treatment and disposal facilities take a more active approach to adopting sustainable techniques as their energy costs and R&D investment rise. Policymakers and industrial firms should think about ways to educate and encourage firms and the surrounding community to reuse treated wastewater, which promotes water conservation and reduces urban water scarcity.

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The Heckman Probit Selection model effectively identifies and analyzes the factors influencing wastewater treatment and reuse in the industrial sector.
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Implementing wastewater treatment solutions is crucial for minimizing water shortages in urban areas of emerging economies.
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The estimated model highlights the essential actions to be taken for achieving a sustainable environment.

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引用次数: 0

The role of smart electricity meter data analysis in driving sustainable development

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-01-31 DOI: 10.1016/j.mex.2025.103196

Archana Y. Chaudhari , Preeti Mulay , Shradha Chavan

The analysis of Smart Electricity Meter (SEM) data, which plays an important role in sustainability of the electricity system. The widespread use SEM generates a substantial volume of data. However, when faced with an influx of new data, traditional clustering methods require re-clustering all the data from scratch. To address the challenge of handling the ever-increasing data, an incremental clustering algorithm proves to be the most suitable choice. Proposed Closeness-based Gaussian Mixture Incremental Clustering (CGMIC) Algorithm updates load patterns without relying on overall daily load curve clustering. The CGMIC algorithm first extracts load patterns from new data and then either intergrades the existing load patterns or forms new ones. The IITB Indian Residential Energy Dataset,is utilized to validate the proposed system. The performance of CGMIC compared with DBSCAN on silhouette score and Davis Bouldin index metrics. The insight of this research contributes directly to sustainable development goals. By effectively identifies changes in residential electricity consumption behavior.

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The proposed Closeness-based Gaussian Mixture Incremental Clustering (CGMIC) Algorithm, updating load patterns incrementally, avoiding the need to re-cluster all data from scratch.
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The CGMIC algorithm is validated using IITB Indian Residential Energy Dataset. Effectiveness is measured using metrics like the silhouette score and Davis Bouldin index.
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The insights from the CGMIC algorithm help identify changes in residential electricity consumption behavior, providing valuable information for utility companies to optimize electricity load management, thereby contributing to sustainable development goals.

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引用次数: 0

Prediction of the remaining useful life of a milling machine using machine learning

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-01-31 DOI: 10.1016/j.mex.2025.103195

Abbas Al-Refaie , Majd Al-atrash , Natalija Lepkova

The cutting tool is a key component of the milling machine that decides productivity. Hence, an adequate predictive maintenance (PdM) strategy for the cutting tools becomes necessary. This research seeks to develop a smart maintenance web application that utilizes Machine Learning (ML) supervised models to predict the Remaining Useful Life (RUL) for milling operations. The ML models were developed using a four-stage process including data pre-processing, training, evaluation, and deployment. Several ML algorithms were applied and the results were evaluated using five measures involving Accuracy, Mean Absolute Error (MAE), Mean Squared Error (MSE), R-squared, and R-squared adjusted. It was found that the Multi-Layer Perceptron Regressor provided the largest accuracies, adjusted R-squared, MAE, and MSE of 99 %, 0.99, 3.7, and 23.13, respectively. A web application for maintenance was finally developed with several ML algorithms at the evaluation stage. Maintenance engineers can utilize the developed smart web application to monitor the machine's health state and predict failure occurrence. In conclusion, the developed web application assists engineers in developing reliable predictions of maintenance activities, which may save costly production and maintenance losses.

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A Web application based on machine learning techniques was developed for RUL predictions for the milling cutting tool.
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A comparison between the prediction results from various machine learning techniques was conducted.
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The web application is found to be valuable for maintenance prediction and planning.

{"title":"Prediction of the remaining useful life of a milling machine using machine learning","authors":"Abbas Al-Refaie , Majd Al-atrash , Natalija Lepkova","doi":"10.1016/j.mex.2025.103195","DOIUrl":"10.1016/j.mex.2025.103195","url":null,"abstract":"<div><div>The cutting tool is a key component of the milling machine that decides productivity. Hence, an adequate predictive maintenance (PdM) strategy for the cutting tools becomes necessary. This research seeks to develop a smart maintenance web application that utilizes Machine Learning (ML) supervised models to predict the Remaining Useful Life (RUL) for milling operations. The ML models were developed using a four-stage process including data pre-processing, training, evaluation, and deployment. Several ML algorithms were applied and the results were evaluated using five measures involving Accuracy, Mean Absolute Error (MAE), Mean Squared Error (MSE), R-squared, and R-squared adjusted. It was found that the Multi-Layer Perceptron Regressor provided the largest accuracies, adjusted R-squared, MAE, and MSE of 99 %, 0.99, 3.7, and 23.13, respectively. A web application for maintenance was finally developed with several ML algorithms at the evaluation stage. Maintenance engineers can utilize the developed smart web application to monitor the machine's health state and predict failure occurrence. In conclusion, the developed web application assists engineers in developing reliable predictions of maintenance activities, which may save costly production and maintenance losses.<ul><li><span>•</span><span><div>A Web application based on machine learning techniques was developed for RUL predictions for the milling cutting tool.</div></span></li><li><span>•</span><span><div>A comparison between the prediction results from various machine learning techniques was conducted.</div></span></li><li><span>•</span><span><div>The web application is found to be valuable for maintenance prediction and planning.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103195"},"PeriodicalIF":1.6,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096988","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}

引用次数: 0

Towards combining self-organizing maps (SOM) and convolutional neural network (CNN) for improving model accuracy: Application to malaria vectors phenotypic resistance

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-01-30 DOI: 10.1016/j.mex.2025.103198

Komi Mensah Agboka , Elfatih M. Abdel-Rahman , Daisy Salifu , Brian Kanji , Frank T. Ndjomatchoua , Ritter A.Y. Guimapi , Sunday Ekesi , Landmann Tobias

This study introduces a hybrid approach that combines unsupervised self-organizing maps (SOM) with a supervised convolutional neural network (CNN) to enhance model accuracy in vector-borne disease modeling. We applied this method to predict insecticide resistance (IR) status in key malaria vectors across Africa. Our results show that the combined SOM/CNN approach is more robust than a standalone CNN model, achieving higher overall accuracy and Kappa scores among others. This confirms the potential of the SOM/CNN hybrid as an effective and reliable tool for improving model accuracy in public health applications.

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The hybrid model, combining SOM and CNN, was implemented to predict IR status in malaria vectors, providing enhanced accuracy across various validation metrics.
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Results indicate a notable improvement in robustness and predictive accuracy over traditional CNN models.
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The combined SOM/CNN approach demonstrated higher Kappa scores and overall model accuracy.

{"title":"Towards combining self-organizing maps (SOM) and convolutional neural network (CNN) for improving model accuracy: Application to malaria vectors phenotypic resistance","authors":"Komi Mensah Agboka , Elfatih M. Abdel-Rahman , Daisy Salifu , Brian Kanji , Frank T. Ndjomatchoua , Ritter A.Y. Guimapi , Sunday Ekesi , Landmann Tobias","doi":"10.1016/j.mex.2025.103198","DOIUrl":"10.1016/j.mex.2025.103198","url":null,"abstract":"<div><div>This study introduces a hybrid approach that combines unsupervised self-organizing maps (SOM) with a supervised convolutional neural network (CNN) to enhance model accuracy in vector-borne disease modeling. We applied this method to predict insecticide resistance (IR) status in key malaria vectors across Africa. Our results show that the combined SOM/CNN approach is more robust than a standalone CNN model, achieving higher overall accuracy and Kappa scores among others. This confirms the potential of the SOM/CNN hybrid as an effective and reliable tool for improving model accuracy in public health applications.<ul><li><span>•</span><span><div>The hybrid model, combining SOM and CNN, was implemented to predict IR status in malaria vectors, providing enhanced accuracy across various validation metrics.</div></span></li><li><span>•</span><span><div>Results indicate a notable improvement in robustness and predictive accuracy over traditional CNN models.</div></span></li><li><span>•</span><span><div>The combined SOM/CNN approach demonstrated higher Kappa scores and overall model accuracy.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103198"},"PeriodicalIF":1.6,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135804","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}

引用次数: 0

Cubic-quartic optical solitons with polarization-mode dispersion by the improved adomian decomposition scheme

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-01-29 DOI: 10.1016/j.mex.2025.103191

Afrah M. Almalki , A.A. AlQarni , H.O. Bakodah , A.A. Alshaery , Ahmed H. Arnous , Anjan Biswas

This research investigates the numerical computation of cubic-quartic optical solitons in birefringent fibers in accordance with Kerr's law. Utilizing the Improved Adomian Decomposition Method (IADM), the study improves the solution of complex-valued nonlinear evolution equations. It identifies a strong correlation between numerical results and earlier analytical soliton expressions from Zahran and Bekir. The analysis highlights impressively low computational errors, confirming IADM's effectiveness in delivering accurate solutions. This method decomposes both linear and nonlinear differential equations into simpler sub-problems, enabling the extraction of approximate analytical solutions without the need for linearization or perturbation techniques. IADM's adaptability suggests its potential for application in various domains, particularly in the optimization and design of optical communication systems.

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The research utilizes both the Adomian Decomposition Method (ADM) and its enhanced version (IADM) to solve the Gerdjikov-Ivanov equation.
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Numerical simulations validate the accuracy and stability of these methods, with IADM showing superior convergence.
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The study underscores the importance of these methods in improving optical communication systems and other nonlinear applications.

{"title":"Cubic-quartic optical solitons with polarization-mode dispersion by the improved adomian decomposition scheme","authors":"Afrah M. Almalki , A.A. AlQarni , H.O. Bakodah , A.A. Alshaery , Ahmed H. Arnous , Anjan Biswas","doi":"10.1016/j.mex.2025.103191","DOIUrl":"10.1016/j.mex.2025.103191","url":null,"abstract":"<div><div>This research investigates the numerical computation of cubic-quartic optical solitons in birefringent fibers in accordance with Kerr's law. Utilizing the Improved Adomian Decomposition Method (IADM), the study improves the solution of complex-valued nonlinear evolution equations. It identifies a strong correlation between numerical results and earlier analytical soliton expressions from Zahran and Bekir. The analysis highlights impressively low computational errors, confirming IADM's effectiveness in delivering accurate solutions. This method decomposes both linear and nonlinear differential equations into simpler sub-problems, enabling the extraction of approximate analytical solutions without the need for linearization or perturbation techniques. IADM's adaptability suggests its potential for application in various domains, particularly in the optimization and design of optical communication systems.<ul><li><span>•</span><span><div>The research utilizes both the Adomian Decomposition Method (ADM) and its enhanced version (IADM) to solve the Gerdjikov-Ivanov equation.</div></span></li><li><span>•</span><span><div>Numerical simulations validate the accuracy and stability of these methods, with IADM showing superior convergence.</div></span></li><li><span>•</span><span><div>The study underscores the importance of these methods in improving optical communication systems and other nonlinear applications.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103191"},"PeriodicalIF":1.6,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135802","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}

引用次数: 0