Pub Date : 2026-02-20DOI: 10.1038/s41598-026-39119-w
M Sankush Krishna, Aruru Sai Kumar, Srinivas Kankanala, Anil Kumar Nayak
The variation in the properties of MgO Nanoribbons towards Arsenic (As) atoms is discussed in the current work. To evaluate the MgONRs behavior towards the As atoms, the first principles approach within the context of density functional theory is deployed to evaluate the electronic and transport characteristics of MgONRs. Results revealed that As-termination is found to improve the stability of the MgONRs compared to hydrogenated MgONRs (H-MgO-H). The electronic characteristics of MgONRs are significantly altered with As passivation. Further, the current-voltage (I-V) characteristics reveal a significantly enhanced current conductivity for the As-terminated MgONRs (As-MgO-As). This determines their transport characteristics are significantly enahnced with As termination. Further, the local device density of states showcase that the carrier transmission majorly occurs through the edges. From the acquired results, it can be concluded that MgONRs can be efficiently utilized as an effective material for the future nanoelectronic applications.
{"title":"First principles investigation of arsenic functionalized MgO nanoribbons.","authors":"M Sankush Krishna, Aruru Sai Kumar, Srinivas Kankanala, Anil Kumar Nayak","doi":"10.1038/s41598-026-39119-w","DOIUrl":"https://doi.org/10.1038/s41598-026-39119-w","url":null,"abstract":"<p><p>The variation in the properties of MgO Nanoribbons towards Arsenic (As) atoms is discussed in the current work. To evaluate the MgONRs behavior towards the As atoms, the first principles approach within the context of density functional theory is deployed to evaluate the electronic and transport characteristics of MgONRs. Results revealed that As-termination is found to improve the stability of the MgONRs compared to hydrogenated MgONRs (H-MgO-H). The electronic characteristics of MgONRs are significantly altered with As passivation. Further, the current-voltage (I-V) characteristics reveal a significantly enhanced current conductivity for the As-terminated MgONRs (As-MgO-As). This determines their transport characteristics are significantly enahnced with As termination. Further, the local device density of states showcase that the carrier transmission majorly occurs through the edges. From the acquired results, it can be concluded that MgONRs can be efficiently utilized as an effective material for the future nanoelectronic applications.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146259146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-20DOI: 10.1038/s41598-026-38078-6
Amr Ismail, Walid Hamdy, Ali H Ibrahim, Wael A Awad
Agriculture and global food security are critically dependent on accurate and timely identification of plant diseases and pests. Traditional approaches to disease identification rely heavily on visual inspection and expert knowledge, which frequently lack the accuracy, speed, and scalability needed to address growing agricultural challenges. Early and precise disease detection enables proactive interventions that can prevent widespread crop damage and reduce excessive pesticide use, thereby supporting sustainable agricultural practices. Artificial intelligence, particularly deep learning methods, has emerged as a transformative solution for automated plant disease diagnosis. Convolutional neural networks (CNNs) have demonstrated remarkable capabilities in image classification tasks, evolving from individual architectures to sophisticated ensembles and transferring learning models. However, existing CNN-based research on rice disease identification has typically focused on a limited number of disease classes, restricting their practical applicability in real-world agricultural settings. This study addresses these limitations by implementing DenseNet121, an advanced CNN architecture known for its efficient feature reuse and gradient flow, for comprehensive rice disease classification. We utilized a dataset comprising seven of the most common rice diseases, significantly expanding the scope beyond previous studies. The model employs transfer learning with pre-trained ImageNet weights and is optimized using the Adam optimizer with carefully tuned hyperparameters. The experimental evaluation on an independent test set demonstrates that our proposed model achieves an overall accuracy of 97.9%, with individual disease classification accuracy ranging from 94% to 99.67%. The model exhibits balanced performance across multiple metrics, including precision (96.2%), recall (97.97%), and F1-score (97%), confirming its robustness and generalizability. These results establish DenseNet121 as a highly effective framework for automated rice disease diagnosis, offering a practical tool for enhancing agricultural productivity and food security.
{"title":"Classification of rice plant diseases using efficient DenseNet121.","authors":"Amr Ismail, Walid Hamdy, Ali H Ibrahim, Wael A Awad","doi":"10.1038/s41598-026-38078-6","DOIUrl":"https://doi.org/10.1038/s41598-026-38078-6","url":null,"abstract":"<p><p>Agriculture and global food security are critically dependent on accurate and timely identification of plant diseases and pests. Traditional approaches to disease identification rely heavily on visual inspection and expert knowledge, which frequently lack the accuracy, speed, and scalability needed to address growing agricultural challenges. Early and precise disease detection enables proactive interventions that can prevent widespread crop damage and reduce excessive pesticide use, thereby supporting sustainable agricultural practices. Artificial intelligence, particularly deep learning methods, has emerged as a transformative solution for automated plant disease diagnosis. Convolutional neural networks (CNNs) have demonstrated remarkable capabilities in image classification tasks, evolving from individual architectures to sophisticated ensembles and transferring learning models. However, existing CNN-based research on rice disease identification has typically focused on a limited number of disease classes, restricting their practical applicability in real-world agricultural settings. This study addresses these limitations by implementing DenseNet121, an advanced CNN architecture known for its efficient feature reuse and gradient flow, for comprehensive rice disease classification. We utilized a dataset comprising seven of the most common rice diseases, significantly expanding the scope beyond previous studies. The model employs transfer learning with pre-trained ImageNet weights and is optimized using the Adam optimizer with carefully tuned hyperparameters. The experimental evaluation on an independent test set demonstrates that our proposed model achieves an overall accuracy of 97.9%, with individual disease classification accuracy ranging from 94% to 99.67%. The model exhibits balanced performance across multiple metrics, including precision (96.2%), recall (97.97%), and F1-score (97%), confirming its robustness and generalizability. These results establish DenseNet121 as a highly effective framework for automated rice disease diagnosis, offering a practical tool for enhancing agricultural productivity and food security.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146259150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-20DOI: 10.1038/s41598-026-38222-2
José A R Monteiro, Dora N Marques, João M M Linhares, Sérgio M C Nascimento
{"title":"Rapid test for detecting red-green color vision deficiencies using a neural network-assisted color-naming task.","authors":"José A R Monteiro, Dora N Marques, João M M Linhares, Sérgio M C Nascimento","doi":"10.1038/s41598-026-38222-2","DOIUrl":"https://doi.org/10.1038/s41598-026-38222-2","url":null,"abstract":"","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146259229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-20DOI: 10.1038/s41598-026-39070-w
Matthew Coleman, Lucius Caviola, Joshua Lewis, Geoffrey P Goodwin
{"title":"Lay beliefs about the badness, likelihood, and importance of human extinction.","authors":"Matthew Coleman, Lucius Caviola, Joshua Lewis, Geoffrey P Goodwin","doi":"10.1038/s41598-026-39070-w","DOIUrl":"https://doi.org/10.1038/s41598-026-39070-w","url":null,"abstract":"","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146259243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-20DOI: 10.1038/s41598-026-40505-7
Xilla T Ussery, Nivedita Bhatt, Ian A Critchley, Shekman L Wong, John C Pottage, David Melnick, Kamal A Hamed
{"title":"Randomized study of the efficacy, safety, and pharmacokinetics of SPR720 for the treatment of Mycobacterium avium complex pulmonary disease.","authors":"Xilla T Ussery, Nivedita Bhatt, Ian A Critchley, Shekman L Wong, John C Pottage, David Melnick, Kamal A Hamed","doi":"10.1038/s41598-026-40505-7","DOIUrl":"https://doi.org/10.1038/s41598-026-40505-7","url":null,"abstract":"","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146259304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-20DOI: 10.1038/s41598-026-40225-y
Won-Joon Wang, Hung Soo Kim
{"title":"Trend analysis of dam inflow data using the trend accuracy index and the potential-evapotranspiration correction factor.","authors":"Won-Joon Wang, Hung Soo Kim","doi":"10.1038/s41598-026-40225-y","DOIUrl":"https://doi.org/10.1038/s41598-026-40225-y","url":null,"abstract":"","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146259468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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