MethodsX最新文献

{"title":"Industrial wastewater treatment and reuse: Heckman probit sample selection model","authors":"Urgessa Tilahun Bekabil ,&nbsp;M.K. Jayamohan ,&nbsp;Amsalu Bedemo Beyene","doi":"10.1016/j.mex.2025.103192","DOIUrl":"10.1016/j.mex.2025.103192","url":null,"abstract":"<div><div>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&amp;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&amp;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&amp;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.<ul><li><span>•</span><span><div>The Heckman Probit Selection model effectively identifies and analyzes the factors influencing wastewater treatment and reuse in the industrial sector.</div></span></li><li><span>•</span><span><div>Implementing wastewater treatment solutions is crucial for minimizing water shortages in urban areas of emerging economies.</div></span></li><li><span>•</span><span><div>The estimated model highlights the essential actions to be taken for achieving a sustainable environment.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103192"},"PeriodicalIF":1.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of smart electricity meter data analysis in driving sustainable development","authors":"Archana Y. Chaudhari ,&nbsp;Preeti Mulay ,&nbsp;Shradha Chavan","doi":"10.1016/j.mex.2025.103196","DOIUrl":"10.1016/j.mex.2025.103196","url":null,"abstract":"<div><div>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.<ul><li><span>•</span><span><div>The proposed Closeness-based Gaussian Mixture Incremental Clustering (CGMIC) Algorithm, updating load patterns incrementally, avoiding the need to re-cluster all data from scratch.</div></span></li><li><span>•</span><span><div>The CGMIC algorithm is validated using IITB Indian Residential Energy Dataset. Effectiveness is measured using metrics like the silhouette score and Davis Bouldin index.</div></span></li><li><span>•</span><span><div>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.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103196"},"PeriodicalIF":1.6,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of the remaining useful life of a milling machine using machine learning","authors":"Abbas Al-Refaie ,&nbsp;Majd Al-atrash ,&nbsp;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}

{"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 ,&nbsp;Elfatih M. Abdel-Rahman ,&nbsp;Daisy Salifu ,&nbsp;Brian Kanji ,&nbsp;Frank T. Ndjomatchoua ,&nbsp;Ritter A.Y. Guimapi ,&nbsp;Sunday Ekesi ,&nbsp;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}

{"title":"Cubic-quartic optical solitons with polarization-mode dispersion by the improved adomian decomposition scheme","authors":"Afrah M. Almalki ,&nbsp;A.A. AlQarni ,&nbsp;H.O. Bakodah ,&nbsp;A.A. Alshaery ,&nbsp;Ahmed H. Arnous ,&nbsp;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}

{"title":"Sustainability decision-making in poultry slaughterhouses: A comparative analysis of AHP and fuzzy AHP","authors":"Hayati Mukti Asih ,&nbsp;Agung Sutrisno ,&nbsp;Cynthia E.A. Wuisang ,&nbsp;Muhammad Faishal","doi":"10.1016/j.mex.2025.103193","DOIUrl":"10.1016/j.mex.2025.103193","url":null,"abstract":"<div><div>The chicken meat industry is vital for global food security and economic growth but faces significant sustainability challenges, especially in balancing economic, environmental, and social aspects. Addressing these challenges in chicken slaughterhouses (CSH) in the Special Region of Yogyakarta, Indonesia, is crucial. This study aims to prioritize criteria for developing strategies to enhance CSH sustainability by comparing the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (Fuzzy AHP) using different fuzzy numbers. The findings emphasize the need for a strategy that merges stakeholder engagement, technological innovation, and circular economy principles to advance sustainability. This study fills a research gap by applying multi-criteria decision-making in the poultry industry, which provides a deeper understanding of the robustness and sensitivity of sustainability assessments.<ul><li><span>•</span><span><div>Employing AHP and Fuzzy AHP with different fuzzy numbers enriches sustainability evaluations by balancing precise judgments and expert uncertainties, which enhancing assessment robustness in the poultry industry.</div></span></li><li><span>•</span><span><div>Hygiene and sanitation, market competitiveness, and waste minimization are the three highest priorities for sustainable CSH operations across scenarios.</div></span></li><li><span>•</span><span><div>These findings highlight the need for strategies that integrate stakeholder engagement, innovation, and circular economy principles, addressing a gap in decision-making research for the poultry industry in developing regions.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103193"},"PeriodicalIF":1.6,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing GIS-based suitability analysis of BtX, PtX, PBtX, and eBtX facilities using the fuzzy analytic hierarchy process","authors":"Marcel Dossow,&nbsp;Mengxi Chen,&nbsp;Hartmut Spliethoff,&nbsp;Sebastian Fendt","doi":"10.1016/j.mex.2025.103194","DOIUrl":"10.1016/j.mex.2025.103194","url":null,"abstract":"<div><div>To address the urgent need for sustainable fuel production, this study proposes a novel methodology that integrates Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) techniques to identify optimal sites for Biomass-to-X (BtX), Power-to-X (PtX), or hybrid (e-/PBtX) facilities. The proposed methodology provides a systematic and quantitative approach to evaluate location suitability, offering valuable insights for spatial decision-making in sustainable fuel production from BtX, PtX, or e-/PBtX.</div><div>The CES-GIS-SAFAHP methodology uses selected and relevant geospatial data, which is processed to derive criteria-specific datasets, such as spatially resolved energy density maps for biomass-based systems and combined wind and solar energy datasets for hybrid processes. These data are then subjected to a Fuzzy Analytic Hierarchy Process (FAHP), which involves the use of pairwise comparisons and Fuzzy normalization to assign weights to the criteria, ultimately resulting in the generation of weighted overlay maps. The results of both the weighed overlay and a concurrently performed exclusion analysis, delineating areas that fail to meet key conditions or constraints, are combined to produce a final suitability map enabling the identification of optimal plant locations based on their overall suitability index. The proposed approach offers a robust, quantitative framework for spatial optimization in the siting of sustainable fuel production facilities with significant applications for policy-makers, industry, and researchers involved in BtX, PtX, and e-/PBtX scale-up.</div><div>The methodology encompasses a comprehensive suitability analysis, …<ul><li><span>•</span><span><div>Providing a recommended list of suitability and exclusion criteria, categorized into ``requisite,'' ``infrastructure,'' and ``environmental'' criteria, tailored for sustainable fuel production site selection.</div></span></li><li><span>•</span><span><div>Offering a structured workflow for deriving suitability maps through a combination of GIS-based FAHP with exclusion analysis.</div></span></li><li><span>•</span><span><div>Providing a practical, replicable algorithm that can guide users through the process, making it easier to apply in various geographic and project contexts.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103194"},"PeriodicalIF":1.6,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated structural analysis for geothermal exploration: A new protocol combining remote sensing and aeromagnetic geophysical data","authors":"Jawad Rafiq,&nbsp;Israa S. Abu-Mahfouz,&nbsp;Konstantinos Chavanidis,&nbsp;Pantelis Soupios","doi":"10.1016/j.mex.2025.103189","DOIUrl":"10.1016/j.mex.2025.103189","url":null,"abstract":"<div><div>Geothermal energy holds significant potential as a sustainable and clean source, yet efficient exploration methodologies remain critical for identifying viable sites. This paper presents a novel protocol for the identification and analysis of structural lineaments in geothermal fields, crucial for coherent geothermal exploration. The approach integrates surface data from remote sensing and data from airborne magnetic geophysical surveys that provide information on the subsurface structures, to analyze structural lineament density analysis, orientation, and high permeable zones, and assess geothermal potential. By combining information from these two sources, the study demonstrates the relationships between structural lineaments and areas of high permeability, shedding light on geothermal resource distribution. This twofold structural analysis not only enhances our ability to identify potential geothermal sites but also contributes to a deeper understanding of the geological factors influencing geothermal reservoirs. This integrated approach advances geothermal exploration in line with the global shift towards sustainable energy.<ul><li><span>•</span><span><div>The straightforward nature of the approach enables its versatile application for predicting various geological processes beyond geothermal exploration.</div></span></li><li><span>•</span><span><div>The application of this protocol is accessible to a broader audience of researchers, as it does not require knowledge of programming language.</div></span></li><li><span>•</span><span><div>The results obtained from this approach demonstrate high predictive performance, underscoring reliability in identifying and analyzing structural elements in geothermal fields.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103189"},"PeriodicalIF":1.6,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A two-step machine learning approach for predictive maintenance and anomaly detection in environmental sensor systems","authors":"Saiprasad Potharaju ,&nbsp;Ravi Kumar Tirandasu ,&nbsp;Swapnali N. Tambe ,&nbsp;Devyani Bhamare Jadhav ,&nbsp;Dudla Anil Kumar ,&nbsp;Shanmuk Srinivas Amiripalli","doi":"10.1016/j.mex.2025.103181","DOIUrl":"10.1016/j.mex.2025.103181","url":null,"abstract":"<div><div>Environmental sensor systems are essential for monitoring infrastructure and environmental quality but are prone to unreliability caused by sensor faults and environmental anomalies. Using Environmental Sensor Telemetry Data, this study introduces a novel methodology that combines unsupervised and supervised machine learning approaches to detect anomalies and predict sensor failures. The dataset consisted of sensor readings such as temperature, humidity, CO, LPG, and smoke, with no class labels available. This research is novel in seamlessly blending unsupervised anomaly detection using Isolation Forest to create labels for data points that were previously unlabeled. Finally, these generated labels were used to train the supervised learning models such as Random Forest, Neural Network (MLP Classifier), and AdaBoost to predict anomalies in new sensor data as soon as it gets recorded. The models confirmed the proposed framework's accuracy, whereas Random Forest 99.93 %, Neural Network 99.05 %, and AdaBoost 98.04 % validated the effectiveness of the suggested framework. Such an approach addresses a critical gap, transforming raw, unlabeled IoT sensor data into actionable insights for predictive maintenance. This methodology provides a scalable and robust real-time anomaly detection and sensor fault prediction methodology that greatly enhances the reliability of the environmental monitoring systems and advances the intelligent infrastructure management.<ul><li><span>•</span><span><div>Combines Isolation Forest for anomaly labeling and supervised models for anomaly prediction.</div></span></li><li><span>•</span><span><div>Scalable and adaptable for diverse IoT applications for environmental monitoring.</div></span></li><li><span>•</span><span><div>Provides actionable insights through anomaly visualization, revealing patterns in sensor performance.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103181"},"PeriodicalIF":1.6,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A polyphasic method for the characterization of epiphytic diatoms growing on Gelidium corneum","authors":"María Borrego-Ramos ,&nbsp;Raquel Viso ,&nbsp;Saúl Blanco ,&nbsp;Begoña Sánchez-Astráin ,&nbsp;Camino F. de la Hoz ,&nbsp;José A. Juanes","doi":"10.1016/j.mex.2025.103188","DOIUrl":"10.1016/j.mex.2025.103188","url":null,"abstract":"<div><div>Epiphytic diatoms associated with marine macroalgae play vital ecological roles in nutrient cycling and primary production, yet their study remains limited due to the lack of standardized methodologies. This study focuses on diatom communities growing on <em>Gelidium corneum</em>, a key red alga in the Cantabrian coast (Spain). Samples were collected from two depths along the northern coast of Spain and processed using both morphological and molecular approaches. Morphological analysis involved diatom frustule preparation using hydrogen peroxide digestion, acid treatments, and permanent slide mounting, enabling identification through light microscopy. Molecular analysis employed DNA extraction and <em>rbcL</em> marker-based metabarcoding, allowing detailed taxonomic characterization. Results highlight the efficacy of combining morphological and molecular techniques to overcome the limitations of either approach individually. By standardizing procedures, we enhance the reproducibility and comparability of studies focused on diatom epiphytes. Our results highlight the ecological significance of diatom-macroalgal interactions and provide a framework for future investigations into these essential but underexplored communities.<ul><li><span>•</span><span><div>A polyphasic method was developed for studying epiphytic diatoms on <em>Gelidium corneum</em>, combining morphological and molecular tools.</div></span></li><li><span>•</span><span><div>The approach overcomes challenges in diatom characterization, including intricate host morphology and cryptic species identification.</div></span></li><li><span>•</span><span><div>Standardized protocols enhance reproducibility and offer insights into diatom-macroalgal ecological interactions.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103188"},"PeriodicalIF":1.6,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096908","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}