Control over plasmonic properties and local electric field enhancement has become an essential aspect of many modern technologies. Here we investigate these phenomena in graphene / hexagonal boron nitride (G/h-BN) heterostructures positioned on silicon (Si) and silicon dioxide (SiO2) substrates. Using finite element method for physics-based simulations of radio-frequency (RF) fields in optical range, we analyze electric field at the edges, on the flakes, and in the surrounding regions of the G/h-BN heterostructures. The results demonstrate that the electric field distribution around and within the heterostructure is strongly dependent on the thickness of graphene and h-BN flakes. The highest electric field amplification and focusing occurs at the G/h-BN edge for h-BN thicknesses between 80 and 100 nm on the Si substrate. In contrast, the SiO2 substrate substantially reduces overall field intensity in the G/h-BN heterostructures in comparison to the Si and reference structure without h-BN. These findings provide a consistent theoretical explanation for previously reported experimental Raman spectroscopy data on G/h-BN heterostructures and corroborate the model of localized charge carrier accumulation at the nanoscale G/h-BN edges on Si substrates. Furthermore, the study provides predictions for optimal excitation frequencies and for tailoring graphene plasmonic features in visible spectral range with the use of diamond and other CMOS compatible materials.
{"title":"Computational analysis of visible frequency plasmonic properties of graphene on wide band gap heterostructures.","authors":"Muhammad Qamar, Ghulam Abbas, Meiyong Liao, Satoshi Koizumi, Takatoshi Yamada, Bohuslav Rezek","doi":"10.1038/s41598-026-40039-y","DOIUrl":"https://doi.org/10.1038/s41598-026-40039-y","url":null,"abstract":"<p><p>Control over plasmonic properties and local electric field enhancement has become an essential aspect of many modern technologies. Here we investigate these phenomena in graphene / hexagonal boron nitride (G/h-BN) heterostructures positioned on silicon (Si) and silicon dioxide (SiO<sub>2</sub>) substrates. Using finite element method for physics-based simulations of radio-frequency (RF) fields in optical range, we analyze electric field at the edges, on the flakes, and in the surrounding regions of the G/h-BN heterostructures. The results demonstrate that the electric field distribution around and within the heterostructure is strongly dependent on the thickness of graphene and h-BN flakes. The highest electric field amplification and focusing occurs at the G/h-BN edge for h-BN thicknesses between 80 and 100 nm on the Si substrate. In contrast, the SiO<sub>2</sub> substrate substantially reduces overall field intensity in the G/h-BN heterostructures in comparison to the Si and reference structure without h-BN. These findings provide a consistent theoretical explanation for previously reported experimental Raman spectroscopy data on G/h-BN heterostructures and corroborate the model of localized charge carrier accumulation at the nanoscale G/h-BN edges on Si substrates. Furthermore, the study provides predictions for optimal excitation frequencies and for tailoring graphene plasmonic features in visible spectral range with the use of diamond and other CMOS compatible materials.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146202646","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 : 2026-02-15DOI: 10.1038/s41598-026-36477-3
Heba M Khalil, Ahmed Elrefaiy, Mostafa Elbaz, Amira A Elsonbaty
<p><p>This paper presents a novel dual metaheuristic loss function framework integrated within Generative Adversarial Networks (GANs) for enhanced thermal image augmentation, specifically designed to improve paddy leaf disease detection through intelligent data quality enhancement and diversity generation. The proposed methodology revolutionizes traditional GAN training by replacing conventional loss functions with two bio-inspired metaheuristic algorithms: the Chaoborus algorithm, which serves as an innovative generator loss function implementing intelligent missing pixel imputation through phantom midge larvae hunting behavior simulation, and the Australian Crayfish algorithm, which functions as an advanced discriminator loss function optimizing adaptive 8-pixel connectivity through foraging and territorial behavior modeling. The framework incorporates strategically positioned identity blocks to preserve critical thermal signatures during adversarial training, ensuring disease-specific thermal patterns remain intact throughout the image enhancement process while maintaining diagnostic integrity. The proposed dual metaheuristic GAN achieves superior image generation quality with 31.47 ± 0.52 dB Peak Signal-to-Noise Ratio (PSNR) and 0.923 ± 0.008 Structural Similarity Index Measure (SSIM), representing significant improvements over state-of-the-art methods including StyleGAN2 (26.89 dB PSNR), Progressive GAN (27.34 dB PSNR), and BigGAN (28.12 dB PSNR). Disease classification performance evaluation across four distinct neural network architectures (ResNet-50, EfficientNet-B7, Vision Transformer, and DenseNet-201) reveals substantial accuracy improvements, with the Vision Transformer achieving 97.89 ± 0.63% accuracy using the proposed augmentation compared to 83.45 ± 1.76% on original datasets and 87.23 ± 1.54% with standard augmentation techniques. Statistical significance analysis confirms the robustness of improvements with p-values less than 0.001 for all comparative metrics and Cohen's d effect sizes exceeding 1.2, indicating large practical significance. Rigorous tenfold cross-validation yields consistent performance with 96.85% mean accuracy and low standard deviation (0.674%), while Leave-One-Out Cross-Validation demonstrates minimal bias (< 0.0012) and low variance (< 0.0055). Generalization studies across five different datasets show robust transferability with direct transfer accuracies ranging from 84.12% to 91.45%, improving to 89.67-95.89% with minimal fine-tuning. Environmental robustness evaluation reveals excellent stability under varying temperature (15-35 °C), humidity (40-80%), and temporal conditions, with performance drops limited to 6.07% under extreme conditions. Comprehensive ablation studies validate the synergistic contribution of each framework component, with individual algorithms providing 5.68 dB and 3.76 dB PSNR improvements respectively, while their combination with identity blocks achieves the full 31.47 dB perform
本文提出了一种新的双元启发式损失函数框架，集成在生成对抗网络（GANs）中，用于增强热图像增强，专门设计用于通过智能数据质量增强和多样性生成来提高水稻叶片病害检测。提出的方法通过用两种生物启发的元启发式算法取代传统的损失函数，彻底改变了传统的GAN训练：Chaoborus算法，作为一种创新的生成器损失函数，通过模拟幻影蚊幼虫的狩猎行为来实现智能缺失像素的输入；澳大利亚小龙虾算法，作为一种先进的判别器损失函数，通过觅食和领土行为建模来优化自适应8像素连接。该框架结合了战略性定位的识别块，以在对抗训练期间保留关键的热特征，确保在整个图像增强过程中保持疾病特定的热模式完整，同时保持诊断的完整性。该方法的峰值信噪比（PSNR）为31.47±0.52 dB，结构相似指数（SSIM）为0.923±0.008 dB，图像生成质量优于StyleGAN2 （26.89 dB PSNR）、Progressive GAN （27.34 dB PSNR）和BigGAN （28.12 dB PSNR）。通过四种不同的神经网络架构（ResNet-50、EfficientNet-B7、Vision Transformer和DenseNet-201）对疾病分类性能进行评估，结果显示准确率有了显著提高，使用所提出的增强方法，Vision Transformer的准确率达到97.89±0.63%，而使用原始数据集的准确率为83.45±1.76%，使用标准增强技术的准确率为87.23±1.54%。统计显著性分析证实了所有比较指标的p值小于0.001和Cohen's d效应值超过1.2的改进的稳健性，表明具有较大的实际意义。严格的十倍交叉验证产生了一致的性能，平均准确率为96.85%，标准偏差低（0.674%），而留一交叉验证显示最小的偏差(
{"title":"Enhanced paddy leaf disease detection using novel dual metaheuristic loss functions in generative adversarial networks with identity block preservation for thermal image augmentation.","authors":"Heba M Khalil, Ahmed Elrefaiy, Mostafa Elbaz, Amira A Elsonbaty","doi":"10.1038/s41598-026-36477-3","DOIUrl":"https://doi.org/10.1038/s41598-026-36477-3","url":null,"abstract":"<p><p>This paper presents a novel dual metaheuristic loss function framework integrated within Generative Adversarial Networks (GANs) for enhanced thermal image augmentation, specifically designed to improve paddy leaf disease detection through intelligent data quality enhancement and diversity generation. The proposed methodology revolutionizes traditional GAN training by replacing conventional loss functions with two bio-inspired metaheuristic algorithms: the Chaoborus algorithm, which serves as an innovative generator loss function implementing intelligent missing pixel imputation through phantom midge larvae hunting behavior simulation, and the Australian Crayfish algorithm, which functions as an advanced discriminator loss function optimizing adaptive 8-pixel connectivity through foraging and territorial behavior modeling. The framework incorporates strategically positioned identity blocks to preserve critical thermal signatures during adversarial training, ensuring disease-specific thermal patterns remain intact throughout the image enhancement process while maintaining diagnostic integrity. The proposed dual metaheuristic GAN achieves superior image generation quality with 31.47 ± 0.52 dB Peak Signal-to-Noise Ratio (PSNR) and 0.923 ± 0.008 Structural Similarity Index Measure (SSIM), representing significant improvements over state-of-the-art methods including StyleGAN2 (26.89 dB PSNR), Progressive GAN (27.34 dB PSNR), and BigGAN (28.12 dB PSNR). Disease classification performance evaluation across four distinct neural network architectures (ResNet-50, EfficientNet-B7, Vision Transformer, and DenseNet-201) reveals substantial accuracy improvements, with the Vision Transformer achieving 97.89 ± 0.63% accuracy using the proposed augmentation compared to 83.45 ± 1.76% on original datasets and 87.23 ± 1.54% with standard augmentation techniques. Statistical significance analysis confirms the robustness of improvements with p-values less than 0.001 for all comparative metrics and Cohen's d effect sizes exceeding 1.2, indicating large practical significance. Rigorous tenfold cross-validation yields consistent performance with 96.85% mean accuracy and low standard deviation (0.674%), while Leave-One-Out Cross-Validation demonstrates minimal bias (< 0.0012) and low variance (< 0.0055). Generalization studies across five different datasets show robust transferability with direct transfer accuracies ranging from 84.12% to 91.45%, improving to 89.67-95.89% with minimal fine-tuning. Environmental robustness evaluation reveals excellent stability under varying temperature (15-35 °C), humidity (40-80%), and temporal conditions, with performance drops limited to 6.07% under extreme conditions. Comprehensive ablation studies validate the synergistic contribution of each framework component, with individual algorithms providing 5.68 dB and 3.76 dB PSNR improvements respectively, while their combination with identity blocks achieves the full 31.47 dB perform","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146198074","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 : 2026-02-15DOI: 10.1038/s41598-026-40143-z
Abbas Ghaedi Haghighi, Amir Reza Zarrati, Mojtaba Karimaei Tabarestani, Seyed Mohammad Fattahi
Bridge scour remains one of the leading causes of hydraulic failure in bridge foundations, posing severe economic and safety risks. Conventional countermeasures such as riprap may be costly, while Portland cement raises sustainability concerns. This study investigates the use of Alkaline-Activated Cement (AAC) as an innovative and eco-friendly stabilization method to determine the optimal extent of treated streambeds around cylindrical and rectangular piers, as well as wing-wall and vertical-wall abutments. Laboratory flume experiments were conducted under flow intensities of 0.75 and 0.9, with trial-and-error testing applied to establish effective protection geometries. Results show that with the AAC optimal extent found in each case, maximum scour depths reduced by 70-80% compared to untreated conditions and successfully shifted scour holes downstream without compromising stability. The findings highlight AAC-treated streambeds as a practical and sustainable countermeasure for bridge scour, while also underscoring the need for further research on the influence of flow angle of attack, Froude number, and live-bed conditions to refine design guidelines.
{"title":"Extent of stabilized streambed region by alkaline activated cement around bridge piers and abutments in clear water condition.","authors":"Abbas Ghaedi Haghighi, Amir Reza Zarrati, Mojtaba Karimaei Tabarestani, Seyed Mohammad Fattahi","doi":"10.1038/s41598-026-40143-z","DOIUrl":"https://doi.org/10.1038/s41598-026-40143-z","url":null,"abstract":"<p><p>Bridge scour remains one of the leading causes of hydraulic failure in bridge foundations, posing severe economic and safety risks. Conventional countermeasures such as riprap may be costly, while Portland cement raises sustainability concerns. This study investigates the use of Alkaline-Activated Cement (AAC) as an innovative and eco-friendly stabilization method to determine the optimal extent of treated streambeds around cylindrical and rectangular piers, as well as wing-wall and vertical-wall abutments. Laboratory flume experiments were conducted under flow intensities of 0.75 and 0.9, with trial-and-error testing applied to establish effective protection geometries. Results show that with the AAC optimal extent found in each case, maximum scour depths reduced by 70-80% compared to untreated conditions and successfully shifted scour holes downstream without compromising stability. The findings highlight AAC-treated streambeds as a practical and sustainable countermeasure for bridge scour, while also underscoring the need for further research on the influence of flow angle of attack, Froude number, and live-bed conditions to refine design guidelines.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146198140","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 : 2026-02-15DOI: 10.1038/s41598-025-34703-y
Song Li, Wenfen Liu, Yan Wu, Xianglin Wu, Lihui Li
The secure and efficient sharing of geographic spatial data is crucial for applications in urban planning, disaster management, and environmental monitoring. However, conventional access control systems face scalability, security, and transparency problems in a distributed environment. This paper proposes a new framework that marries attribute-based access control with blockchain technology and smart contracts for fine-grained, decentralized, and tamper-proof data sharing. This paper introduces a new framework which combines Attribute-Based Access Control (ABAC), blockchain technology, smart contracts, and an upgraded Black-winged Kite (UBK) algorithm. Access regulations and audit logs are stored on a private blockchain using a Proof-of-Authority consensus mechanism for immutability and transparency. Experimental results show that the proposed method reduces evaluation policy time by 70% and storage overhead by 52% compared to the traditional attribute-based access control, while achieving 98.2% accuracy in access decisions. The performance test shows evaluation time and storage increase linearly, thus proving appropriate large-scale deployment. The combination of blockchain and smart contracts guarantees security-auditable and automated enforcement of access policies without needing a central authority.
{"title":"Attribute based access control of geographic spatial data sharing using blockchain and smart contracts.","authors":"Song Li, Wenfen Liu, Yan Wu, Xianglin Wu, Lihui Li","doi":"10.1038/s41598-025-34703-y","DOIUrl":"https://doi.org/10.1038/s41598-025-34703-y","url":null,"abstract":"<p><p>The secure and efficient sharing of geographic spatial data is crucial for applications in urban planning, disaster management, and environmental monitoring. However, conventional access control systems face scalability, security, and transparency problems in a distributed environment. This paper proposes a new framework that marries attribute-based access control with blockchain technology and smart contracts for fine-grained, decentralized, and tamper-proof data sharing. This paper introduces a new framework which combines Attribute-Based Access Control (ABAC), blockchain technology, smart contracts, and an upgraded Black-winged Kite (UBK) algorithm. Access regulations and audit logs are stored on a private blockchain using a Proof-of-Authority consensus mechanism for immutability and transparency. Experimental results show that the proposed method reduces evaluation policy time by 70% and storage overhead by 52% compared to the traditional attribute-based access control, while achieving 98.2% accuracy in access decisions. The performance test shows evaluation time and storage increase linearly, thus proving appropriate large-scale deployment. The combination of blockchain and smart contracts guarantees security-auditable and automated enforcement of access policies without needing a central authority.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146202513","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 : 2026-02-15DOI: 10.1038/s41598-026-39573-6
Majid Taheri, Zohreh Feizabadi
{"title":"Effect of thermal and gold nanoparticles on the optoelectronic properties of graphene oxide.","authors":"Majid Taheri, Zohreh Feizabadi","doi":"10.1038/s41598-026-39573-6","DOIUrl":"https://doi.org/10.1038/s41598-026-39573-6","url":null,"abstract":"","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146202638","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 : 2026-02-15DOI: 10.1038/s41598-026-36984-3
Andrea Andrade, Javier Vega-Reyes, Gonzalo Yáñez-Durán, Sergio Henríquez-Gallegos, Camilo Torres, Clara Villalba-Yepez, Johanna Castaño, L F Montoya, Miguel Pereira, Oscar Valerio
{"title":"Effect of drying method on the surface properties of cellulose nanofibril films.","authors":"Andrea Andrade, Javier Vega-Reyes, Gonzalo Yáñez-Durán, Sergio Henríquez-Gallegos, Camilo Torres, Clara Villalba-Yepez, Johanna Castaño, L F Montoya, Miguel Pereira, Oscar Valerio","doi":"10.1038/s41598-026-36984-3","DOIUrl":"https://doi.org/10.1038/s41598-026-36984-3","url":null,"abstract":"","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146202662","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 : 2026-02-15DOI: 10.1038/s41598-025-02219-0
Zahabul Islam, Mohammed Mayyas
{"title":"Reinforcing role of graphene in high entropy alloy matrix composites.","authors":"Zahabul Islam, Mohammed Mayyas","doi":"10.1038/s41598-025-02219-0","DOIUrl":"https://doi.org/10.1038/s41598-025-02219-0","url":null,"abstract":"","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146202759","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 : 2026-02-15DOI: 10.1038/s41598-026-40037-0
Mohd Ashraf Mohammad Rafee, Khairul Shafiq Ibrahim, Roqiah Fatmawati Abdul Kadir, Mohamed-Syarif Mohamed-Yassin, Noorhida Baharudin
{"title":"Validity of the 2019 European Society of Cardiology Pre-Test Probability (2019 ESC-PTP) for predicting obstructive coronary artery disease among Malaysians.","authors":"Mohd Ashraf Mohammad Rafee, Khairul Shafiq Ibrahim, Roqiah Fatmawati Abdul Kadir, Mohamed-Syarif Mohamed-Yassin, Noorhida Baharudin","doi":"10.1038/s41598-026-40037-0","DOIUrl":"https://doi.org/10.1038/s41598-026-40037-0","url":null,"abstract":"","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146202770","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 : 2026-02-15DOI: 10.1038/s41598-026-40387-9
Tahereh Nayerifard, Haleh Amintoosi, Abbas Ghaemi Bafghi
{"title":"Source camera attribution using a rule-based explainable convolutional neural network.","authors":"Tahereh Nayerifard, Haleh Amintoosi, Abbas Ghaemi Bafghi","doi":"10.1038/s41598-026-40387-9","DOIUrl":"https://doi.org/10.1038/s41598-026-40387-9","url":null,"abstract":"","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146202785","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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