Pub Date : 2025-01-29DOI: 10.1016/j.mex.2025.103193
Hayati Mukti Asih , Agung Sutrisno , Cynthia E.A. Wuisang , Muhammad Faishal
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.
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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.
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Hygiene and sanitation, market competitiveness, and waste minimization are the three highest priorities for sustainable CSH operations across scenarios.
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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.
{"title":"Sustainability decision-making in poultry slaughterhouses: A comparative analysis of AHP and fuzzy AHP","authors":"Hayati Mukti Asih , Agung Sutrisno , Cynthia E.A. Wuisang , 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}
Pub Date : 2025-01-29DOI: 10.1016/j.mex.2025.103194
Marcel Dossow, Mengxi Chen, Hartmut Spliethoff, Sebastian Fendt
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.
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.
The methodology encompasses a comprehensive suitability analysis, …
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Providing a recommended list of suitability and exclusion criteria, categorized into ``requisite,'' ``infrastructure,'' and ``environmental'' criteria, tailored for sustainable fuel production site selection.
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Offering a structured workflow for deriving suitability maps through a combination of GIS-based FAHP with exclusion analysis.
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Providing a practical, replicable algorithm that can guide users through the process, making it easier to apply in various geographic and project contexts.
{"title":"Advancing GIS-based suitability analysis of BtX, PtX, PBtX, and eBtX facilities using the fuzzy analytic hierarchy process","authors":"Marcel Dossow, Mengxi Chen, Hartmut Spliethoff, 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}
Pub Date : 2025-01-28DOI: 10.1016/j.mex.2025.103189
Jawad Rafiq, Israa S. Abu-Mahfouz, Konstantinos Chavanidis, Pantelis Soupios
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.
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The straightforward nature of the approach enables its versatile application for predicting various geological processes beyond geothermal exploration.
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The application of this protocol is accessible to a broader audience of researchers, as it does not require knowledge of programming language.
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The results obtained from this approach demonstrate high predictive performance, underscoring reliability in identifying and analyzing structural elements in geothermal fields.
{"title":"Integrated structural analysis for geothermal exploration: A new protocol combining remote sensing and aeromagnetic geophysical data","authors":"Jawad Rafiq, Israa S. Abu-Mahfouz, Konstantinos Chavanidis, 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}
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.
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Combines Isolation Forest for anomaly labeling and supervised models for anomaly prediction.
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Scalable and adaptable for diverse IoT applications for environmental monitoring.
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Provides actionable insights through anomaly visualization, revealing patterns in sensor performance.
{"title":"A two-step machine learning approach for predictive maintenance and anomaly detection in environmental sensor systems","authors":"Saiprasad Potharaju , Ravi Kumar Tirandasu , Swapnali N. Tambe , Devyani Bhamare Jadhav , Dudla Anil Kumar , 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}
Pub Date : 2025-01-28DOI: 10.1016/j.mex.2025.103188
María Borrego-Ramos , Raquel Viso , Saúl Blanco , Begoña Sánchez-Astráin , Camino F. de la Hoz , José A. Juanes
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 Gelidium corneum, 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 rbcL 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.
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A polyphasic method was developed for studying epiphytic diatoms on Gelidium corneum, combining morphological and molecular tools.
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The approach overcomes challenges in diatom characterization, including intricate host morphology and cryptic species identification.
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Standardized protocols enhance reproducibility and offer insights into diatom-macroalgal ecological interactions.
{"title":"A polyphasic method for the characterization of epiphytic diatoms growing on Gelidium corneum","authors":"María Borrego-Ramos , Raquel Viso , Saúl Blanco , Begoña Sánchez-Astráin , Camino F. de la Hoz , 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}
Colorectal cancer, arising from adenomatous polyps, is a leading cause of cancer-related mortality, making early detection and removal crucial for preventing cancer progression. Machine learning is increasingly used to enhance polyp detection during colonoscopy, the gold standard for colorectal cancer screening, despite its operator-dependent miss rates. This study explores the impact of RGB color adjustment on Convolutional Neural Network (CNN) models for improving polyp detection and localization in colonoscopic images. Using datasets from Harvard Dataverse for training and internal validation, and LDPolypVideo-Benchmark for external validation, RGB color adjustments were applied, and YOLOv8s was used to develop models. Bayesian optimization identified the best RGB adjustments, with performance assessed using mean average precision (mAP) and F1-scores. Results showed that RGB adjustment with 1.0 R-1.0 G-0.8 B improved polyp detection, achieving an mAP of 0.777 and an F1-score of 0.720 on internal test sets, and localization performance with an F1-score of 0.883 on adjusted images. External validation showed improvement but with a lower F1-score of 0.556. While RGB adjustments improved performance in our study, their generalizability to diverse datasets and clinical settings has yet to be validated. Thus, although RGB color adjustment enhances CNN model performance for detecting and localizing colorectal polyps, further research is needed to verify these improvements across diverse datasets and clinical settings.
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RGB Color Adjustment: Applied RGB color adjustments to colonoscopic images to enhance the performance of Convolutional Neural Network (CNN) models.
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Model Development: Used YOLOv8s for polyp detection and localization, with Bayesian optimization to identify the best RGB adjustments.
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Performance Evaluation: Assessed model performance using mAP and F1-scores on both internal and external validation datasets.
{"title":"Optimizing colorectal polyp detection and localization: Impact of RGB color adjustment on CNN performance","authors":"Jirakorn Jamrasnarodom , Pharuj Rajborirug , Pises Pisespongsa , Kitsuchart Pasupa","doi":"10.1016/j.mex.2025.103187","DOIUrl":"10.1016/j.mex.2025.103187","url":null,"abstract":"<div><div>Colorectal cancer, arising from adenomatous polyps, is a leading cause of cancer-related mortality, making early detection and removal crucial for preventing cancer progression. Machine learning is increasingly used to enhance polyp detection during colonoscopy, the gold standard for colorectal cancer screening, despite its operator-dependent miss rates. This study explores the impact of RGB color adjustment on Convolutional Neural Network (CNN) models for improving polyp detection and localization in colonoscopic images. Using datasets from Harvard Dataverse for training and internal validation, and LDPolypVideo-Benchmark for external validation, RGB color adjustments were applied, and YOLOv8s was used to develop models. Bayesian optimization identified the best RGB adjustments, with performance assessed using mean average precision (mAP) and F<sub>1</sub>-scores. Results showed that RGB adjustment with 1.0 R-1.0 G-0.8 B improved polyp detection, achieving an mAP of 0.777 and an F<sub>1</sub>-score of 0.720 on internal test sets, and localization performance with an F<sub>1</sub>-score of 0.883 on adjusted images. External validation showed improvement but with a lower F<sub>1</sub>-score of 0.556. While RGB adjustments improved performance in our study, their generalizability to diverse datasets and clinical settings has yet to be validated. Thus, although RGB color adjustment enhances CNN model performance for detecting and localizing colorectal polyps, further research is needed to verify these improvements across diverse datasets and clinical settings.<ul><li><span>•</span><span><div><strong>RGB Color Adjustment</strong>: Applied RGB color adjustments to colonoscopic images to enhance the performance of Convolutional Neural Network (CNN) models.</div></span></li><li><span>•</span><span><div><strong>Model Development</strong>: Used YOLOv8s for polyp detection and localization, with Bayesian optimization to identify the best RGB adjustments.</div></span></li><li><span>•</span><span><div><strong>Performance Evaluation</strong>: Assessed model performance using mAP and F<sub>1</sub>-scores on both internal and external validation datasets.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103187"},"PeriodicalIF":1.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096629","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}
Pub Date : 2025-01-25DOI: 10.1016/j.mex.2025.103185
Manish Bali , Ved Prakash Mishra , Anuradha Yenkikar , Diptee Chikmurge
Diabetic Retinopathy (DR), a diabetes-related eye condition, damages retinal blood vessels and can lead to vision loss if undetected early. Precise diagnosis is challenging due to subtle, varied symptoms. While classical deep learning (DL) models like CNNs and ResNet's are widely used, they face resource and accuracy limitations. Quantum computing, leveraging quantum mechanics, offers revolutionary potential for faster problem-solving across fields like cryptography, optimization, and medicine. This research introduces QuantumNet, a hybrid model combining classical DL and quantum transfer learning to enhance DR detection. QuantumNet demonstrates high accuracy and resource efficiency, providing a transformative solution for DR detection and broader medical imaging applications. The method is as follows:
•
Evaluate three classical deep learning models—CNN, ResNet50, and MobileNetV2—using the APTOS 2019 blindness detection dataset on Kaggle to identify the best-performing model for integration.
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QuantumNet combines the best-performing classical DL model for feature extraction with a variational quantum classifier, leveraging quantum transfer learning for enhanced diagnostics, validated statistically and on Google Cirq using standard metrics.
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QuantumNet achieves 94.11 % accuracy, surpassing classical DL models and prior research by 11.93 percentage points, demonstrating its potential for accurate, efficient DR detection and broader medical imaging applications.
{"title":"QuantumNet: An enhanced diabetic retinopathy detection model using classical deep learning-quantum transfer learning","authors":"Manish Bali , Ved Prakash Mishra , Anuradha Yenkikar , Diptee Chikmurge","doi":"10.1016/j.mex.2025.103185","DOIUrl":"10.1016/j.mex.2025.103185","url":null,"abstract":"<div><div>Diabetic Retinopathy (DR), a diabetes-related eye condition, damages retinal blood vessels and can lead to vision loss if undetected early. Precise diagnosis is challenging due to subtle, varied symptoms. While classical deep learning (DL) models like CNNs and ResNet's are widely used, they face resource and accuracy limitations. Quantum computing, leveraging quantum mechanics, offers revolutionary potential for faster problem-solving across fields like cryptography, optimization, and medicine. This research introduces QuantumNet, a hybrid model combining classical DL and quantum transfer learning to enhance DR detection. QuantumNet demonstrates high accuracy and resource efficiency, providing a transformative solution for DR detection and broader medical imaging applications. The method is as follows:<ul><li><span>•</span><span><div>Evaluate three classical deep learning models—CNN, ResNet50, and MobileNetV2—using the APTOS 2019 blindness detection dataset on Kaggle to identify the best-performing model for integration.</div></span></li><li><span>•</span><span><div>QuantumNet combines the best-performing classical DL model for feature extraction with a variational quantum classifier, leveraging quantum transfer learning for enhanced diagnostics, validated statistically and on Google Cirq using standard metrics.</div></span></li><li><span>•</span><span><div>QuantumNet achieves 94.11 % accuracy, surpassing classical DL models and prior research by 11.93 percentage points, demonstrating its potential for accurate, efficient DR detection and broader medical imaging applications.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103185"},"PeriodicalIF":1.6,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096628","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}
Pub Date : 2025-01-25DOI: 10.1016/j.mex.2025.103183
Pishun Tantivangphaisal , David M.G. Taborda , Stavroula Kontoe
A modification of the high-cycle accumulation (HCA) framework coupled with a practical constitutive model for sands and its numerical implementation as a user-defined soil model in PLAXIS is presented. The implemented model is compared against data from the original high-cyclic tests in Karlsruhe fine sand and more recent laboratory tests in Dunkirk sand. A reference 15 MW offshore wind turbine monopile foundation subject to lateral cyclic wave loading is used in an engineering design scenario at three different load levels to verify the current numerical implementation.
Details include:
•
Modifications made to the HCA framework to couple it with a practical sand constitutive model,
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Implementation of an efficient workflow to switch between low and high cycle constitutive equations in PLAXIS, and
•
Verification of the implementation at single element and boundary value problem scales.
{"title":"Implementation of a practical sand constitutive model coupled with the high cycle accumulation framework in PLAXIS","authors":"Pishun Tantivangphaisal , David M.G. Taborda , Stavroula Kontoe","doi":"10.1016/j.mex.2025.103183","DOIUrl":"10.1016/j.mex.2025.103183","url":null,"abstract":"<div><div>A modification of the high-cycle accumulation (HCA) framework coupled with a practical constitutive model for sands and its numerical implementation as a user-defined soil model in PLAXIS is presented. The implemented model is compared against data from the original high-cyclic tests in Karlsruhe fine sand and more recent laboratory tests in Dunkirk sand. A reference 15 MW offshore wind turbine monopile foundation subject to lateral cyclic wave loading is used in an engineering design scenario at three different load levels to verify the current numerical implementation.</div><div>Details include:<ul><li><span>•</span><span><div>Modifications made to the HCA framework to couple it with a practical sand constitutive model,</div></span></li><li><span>•</span><span><div>Implementation of an efficient workflow to switch between low and high cycle constitutive equations in PLAXIS, and</div></span></li><li><span>•</span><span><div>Verification of the implementation at single element and boundary value problem scales.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103183"},"PeriodicalIF":1.6,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096989","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}
Retinopathy of prematurity (ROP) is a retinal disorder that can cause blindness in premature infants with low birth weight. Early detection and timely treatment are crucial to prevent blindness associated with ROP. It's essential to identify the stage and presence of Plus disease accurately when examining retinal images of at-risk infants. We are developing an explainable automated ROP screening system for the HVDROPDB datasets. The fundus images were classified as without stage (Normal)/with Stage (ROP) by segmenting the ridge. Stages 1–3 were classified using machine Learning (ML) models.
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This study aims to improve accuracy of Stages 1–3 classification and identify Pre-plus/ Plus disease using MultiCNN_LSTM networks. This is accomplished by using multiple CNNs (Convolutional Neural Networks) to extract features and LSTM (Long Short-Term Memory) classifier to classify images.
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Cropped STAGE dataset and HVDROPDB-PLUS dataset are constructed with RetCam and Neo images.
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The proposed networks outperform individual CNNs and CNN_LSTM networks in terms of accuracy and F1 score.
{"title":"Classification of Stages 1,2,3 and Preplus, Plus disease of ROP using MultiCNN_LSTM classifier","authors":"Ranjana Agrawal , Sucheta Kulkarni , Madan Deshpande , Anita Gaikwad , Rahee Walambe , Ketan V. Kotecha","doi":"10.1016/j.mex.2025.103182","DOIUrl":"10.1016/j.mex.2025.103182","url":null,"abstract":"<div><div>Retinopathy of prematurity (ROP) is a retinal disorder that can cause blindness in premature infants with low birth weight. Early detection and timely treatment are crucial to prevent blindness associated with ROP. It's essential to identify the stage and presence of Plus disease accurately when examining retinal images of at-risk infants. We are developing an explainable automated ROP screening system for the HVDROPDB datasets. The fundus images were classified as without stage (Normal)/with Stage (ROP) by segmenting the ridge. Stages 1–3 were classified using machine Learning (ML) models.<ul><li><span>•</span><span><div>This study aims to improve accuracy of Stages 1–3 classification and identify Pre-plus/ Plus disease using MultiCNN_LSTM networks. This is accomplished by using multiple CNNs (Convolutional Neural Networks) to extract features and LSTM (Long Short-Term Memory) classifier to classify images.</div></span></li><li><span>•</span><span><div>Cropped STAGE dataset and HVDROPDB-PLUS dataset are constructed with RetCam and Neo images.</div></span></li><li><span>•</span><span><div>The proposed networks outperform individual CNNs and CNN_LSTM networks in terms of accuracy and F1 score.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103182"},"PeriodicalIF":1.6,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135711","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}
Pub Date : 2025-01-22DOI: 10.1016/j.mex.2025.103179
Dias Tina Thomas , Charu Eapen , Atmananda S. Hegde , Prajwal Prabhudev Mane , Ajit R. Mahale
Knee osteoarthritis (KOA)is a degenerative joint condition affecting about 240 million people worldwide with rising incidences in India. The progressive nature of the disease leads to pain, reduced mobility, and diminished quality of life. Despite extensive global research, there is a lack of normative data on the cartilage thickness specific to the Indian population, which is crucial to understanding the nature of the disease progression. Thereby this study aims to establish normative cartilage thickness values in healthy Indian adults and correlate these values to the knee injury and osteoarthritis outcome score (KOOS). Using ultrasonography, the cartilage thickness will be measured in 100 healthy individuals. Baseline cartilage values will be linked to the various domains of the KOOS score to evaluate early cartilage degeneration and its impact on function. This research will address the gap in Indian-specific data, including early detection and management of KOA and improving clinical decision-making for better outcomes and quality of life.
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Establish normative knee cartilage thickness in healthy Indian population using USG.
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Helps identify KOA in early stages through USG-based cartilage thickness evaluation
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Enables clinicians to target rehabilitation efforts effectively and potentially improve patients' outcomes and quality of life.
{"title":"A protocol to assess the Knee cartilage thickness in healthy older adults and analyze its correlation with patient-reported outcomes","authors":"Dias Tina Thomas , Charu Eapen , Atmananda S. Hegde , Prajwal Prabhudev Mane , Ajit R. Mahale","doi":"10.1016/j.mex.2025.103179","DOIUrl":"10.1016/j.mex.2025.103179","url":null,"abstract":"<div><div>Knee osteoarthritis (KOA)is a degenerative joint condition affecting about 240 million people worldwide with rising incidences in India. The progressive nature of the disease leads to pain, reduced mobility, and diminished quality of life. Despite extensive global research, there is a lack of normative data on the cartilage thickness specific to the Indian population, which is crucial to understanding the nature of the disease progression. Thereby this study aims to establish normative cartilage thickness values in healthy Indian adults and correlate these values to the knee injury and osteoarthritis outcome score (KOOS). Using ultrasonography, the cartilage thickness will be measured in 100 healthy individuals. Baseline cartilage values will be linked to the various domains of the KOOS score to evaluate early cartilage degeneration and its impact on function. This research will address the gap in Indian-specific data, including early detection and management of KOA and improving clinical decision-making for better outcomes and quality of life.<ul><li><span>•</span><span><div>Establish normative knee cartilage thickness in healthy Indian population using USG.</div></span></li><li><span>•</span><span><div>Helps identify KOA in early stages through USG-based cartilage thickness evaluation</div></span></li><li><span>•</span><span><div>Enables clinicians to target rehabilitation efforts effectively and potentially improve patients' outcomes and quality of life.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103179"},"PeriodicalIF":1.6,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096553","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}
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