Pub Date : 2026-03-25DOI: 10.1016/j.eng.2026.01.031
Weijie Tang, Ruomei Zhao, Hong Sun, Minzan Li, Lang Qiao, Mingjia Liu, Guohui Liu, Yang Liu, Di Song
Low spatial resolution (LR) remote sensing data is widely adopted because of its lower cost, although its limited analytical precision constrains its full use in precision agriculture. By contrast, the acquisition of high spatial resolution (HR) data often requires substantial expense. To address this limitation, this study proposes an unsupervised degradation-aware multi-channel super-resolution network (UDAMSR) to enhance LR spectral images without requiring paired HR–LR training data. The main contributions are as follows: ① the original framework is extended with dedicated queue and reconstruction layers to process multispectral and hyperspectral image (HIS) cubes, and a contrast-learning-based degradation-aware module is integrated to address unknown real-world degradation; ② comprehensive evaluation is conducted using image quality metrics, spectral consistency analysis, and performance in crop remote sensing tasks, such as chlorophyll content estimation; ③ the generalization capability of the model is assessed using data from three imaging devices, two spatial scales (near-ground and unmanned aerial vehicle (UAV)), and two geographic regions. The results show that the proposed method achieves the best overall performance in the comprehensive evaluation, with a mean peak signal-to-noise ratio (<span><span style=""></span><span data-mathml='<math xmlns="http://www.w3.org/1998/Math/MathML"><mover accent="true" is="true"><mrow is="true"><mi mathvariant="normal" is="true">P</mi><mi mathvariant="normal" is="true">S</mi><mi mathvariant="normal" is="true">N</mi><mi mathvariant="normal" is="true">R</mi></mrow><mrow is="true"><mo stretchy="false" is="true">¯</mo></mrow></mover></math>' role="presentation" style="font-size: 90%; display: inline-block; position: relative;" tabindex="0"><svg aria-hidden="true" focusable="false" height="2.317ex" role="img" style="vertical-align: -0.235ex;" viewbox="0 -896.2 2725 997.6" width="6.329ex" xmlns:xlink="http://www.w3.org/1999/xlink"><g fill="currentColor" stroke="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><g is="true"><g is="true"><g is="true"><use xlink:href="#MJMAIN-50"></use></g><g is="true" transform="translate(681,0)"><use xlink:href="#MJMAIN-53"></use></g><g is="true" transform="translate(1238,0)"><use xlink:href="#MJMAIN-4E"></use></g><g is="true" transform="translate(1988,0)"><use xlink:href="#MJMAIN-52"></use></g></g><g is="true" transform="translate(1112,237)"><g is="true"><use xlink:href="#MJMAIN-AF"></use></g></g></g></g></svg><span role="presentation"><math xmlns="http://www.w3.org/1998/Math/MathML"><mover accent="true" is="true"><mrow is="true"><mi is="true" mathvariant="normal">P</mi><mi is="true" mathvariant="normal">S</mi><mi is="true" mathvariant="normal">N</mi><mi is="true" mathvariant="normal">R</mi></mrow><mrow is="true"><mo is="true" stretchy="false">¯
{"title":"UDAMSR Net: An Unsupervised Degradation-Aware Network for Enhancing the Spatial Resolution of Spectral Images for Crop Sensing","authors":"Weijie Tang, Ruomei Zhao, Hong Sun, Minzan Li, Lang Qiao, Mingjia Liu, Guohui Liu, Yang Liu, Di Song","doi":"10.1016/j.eng.2026.01.031","DOIUrl":"https://doi.org/10.1016/j.eng.2026.01.031","url":null,"abstract":"Low spatial resolution (LR) remote sensing data is widely adopted because of its lower cost, although its limited analytical precision constrains its full use in precision agriculture. By contrast, the acquisition of high spatial resolution (HR) data often requires substantial expense. To address this limitation, this study proposes an unsupervised degradation-aware multi-channel super-resolution network (UDAMSR) to enhance LR spectral images without requiring paired HR–LR training data. The main contributions are as follows: ① the original framework is extended with dedicated queue and reconstruction layers to process multispectral and hyperspectral image (HIS) cubes, and a contrast-learning-based degradation-aware module is integrated to address unknown real-world degradation; ② comprehensive evaluation is conducted using image quality metrics, spectral consistency analysis, and performance in crop remote sensing tasks, such as chlorophyll content estimation; ③ the generalization capability of the model is assessed using data from three imaging devices, two spatial scales (near-ground and unmanned aerial vehicle (UAV)), and two geographic regions. The results show that the proposed method achieves the best overall performance in the comprehensive evaluation, with a mean peak signal-to-noise ratio (<span><span style=\"\"></span><span data-mathml='<math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mover accent=\"true\" is=\"true\"><mrow is=\"true\"><mi mathvariant=\"normal\" is=\"true\">P</mi><mi mathvariant=\"normal\" is=\"true\">S</mi><mi mathvariant=\"normal\" is=\"true\">N</mi><mi mathvariant=\"normal\" is=\"true\">R</mi></mrow><mrow is=\"true\"><mo stretchy=\"false\" is=\"true\">&#xAF;</mo></mrow></mover></math>' role=\"presentation\" style=\"font-size: 90%; display: inline-block; position: relative;\" tabindex=\"0\"><svg aria-hidden=\"true\" focusable=\"false\" height=\"2.317ex\" role=\"img\" style=\"vertical-align: -0.235ex;\" viewbox=\"0 -896.2 2725 997.6\" width=\"6.329ex\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g fill=\"currentColor\" stroke=\"currentColor\" stroke-width=\"0\" transform=\"matrix(1 0 0 -1 0 0)\"><g is=\"true\"><g is=\"true\"><g is=\"true\"><use xlink:href=\"#MJMAIN-50\"></use></g><g is=\"true\" transform=\"translate(681,0)\"><use xlink:href=\"#MJMAIN-53\"></use></g><g is=\"true\" transform=\"translate(1238,0)\"><use xlink:href=\"#MJMAIN-4E\"></use></g><g is=\"true\" transform=\"translate(1988,0)\"><use xlink:href=\"#MJMAIN-52\"></use></g></g><g is=\"true\" transform=\"translate(1112,237)\"><g is=\"true\"><use xlink:href=\"#MJMAIN-AF\"></use></g></g></g></g></svg><span role=\"presentation\"><math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mover accent=\"true\" is=\"true\"><mrow is=\"true\"><mi is=\"true\" mathvariant=\"normal\">P</mi><mi is=\"true\" mathvariant=\"normal\">S</mi><mi is=\"true\" mathvariant=\"normal\">N</mi><mi is=\"true\" mathvariant=\"normal\">R</mi></mrow><mrow is=\"true\"><mo is=\"true\" stretchy=\"false\">¯","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"1 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147507275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-25DOI: 10.1016/j.eng.2026.03.009
Ziyang Wang, Shen Yang, Junqi Wang, Shi-Jie Cao
Managing municipal living plastic waste (MLPW) entails complex system-level challenges across collection, recycling, and treatment, necessitating simultaneous optimization of resource, environmental, and economic objectives. However, robust assessment is frequently impeded by data scarcity and measurement inaccuracies, which undermine the transferability of evaluation models. To address these limitations, an artificial intelligence (AI)-enhanced evaluation framework is proposed. Baseline material flows were derived directly from field measurements, while machine learning algorithms were employed for independent validation and data imputation, thereby enhancing the credibility of environmental and economic assessments. The framework was applied in a megacity as the case study. Results indicated that MLPW management should focus on upgrading recycling and advancing the substitution of biodegradable plastics while rigorously enforcing source reduction. The recycling/treatment yields a 96.3% reduction in annual emissions by 2060 relative to the baseline. Cumulatively, this optimal trajectory achieves a reduction of 22.22 Mt CO2-eq between 2020 and 2060, generating economic benefits of approximately 197.7 billion CNY. Given the current technological conditions, mechanical recycling is identified as the priority pathway, offering superior mitigation potential (emission intensity of about 108 kg CO2-eq·t−1) and cost-effectiveness (economic return of around 613.9 CNY·t−1). By leveraging AI to ensure evaluation completeness and credibility even under data-constrained conditions, this framework offers a transferable tool for providing quantitative evidence to support policy prioritization in zero-waste city initiatives globally.
管理城市生活塑料垃圾（MLPW）涉及复杂的系统级挑战，涉及收集、回收和处理，需要同时优化资源、环境和经济目标。然而，可靠的评估经常受到数据缺乏和测量不准确的阻碍，这破坏了评估模型的可转移性。为了解决这些限制，提出了一个人工智能（AI）增强的评估框架。基线物质流直接来自现场测量，而机器学习算法用于独立验证和数据输入，从而提高了环境和经济评估的可信度。该框架以某特大城市为例进行了应用研究。结果表明，在严格执行源头减量的同时，应注重提高回收利用水平，推进生物降解塑料的替代。到2060年，回收/处理将使年排放量较基线减少96.3%。在2020年至2060年间，这一最优轨迹累计实现了2222万吨二氧化碳当量的减排，产生了约1977亿元人民币的经济效益。鉴于目前的技术条件，机械回收被确定为优先途径，具有卓越的减排潜力（排放强度约为108 kg CO2-eq·t−1）和成本效益（经济回报约为613.9元人民币·t−1）。通过利用人工智能确保评估的完整性和可信度，即使在数据受限的条件下，该框架也提供了一种可转移的工具，可提供定量证据，以支持全球零废物城市倡议的政策优先级。
{"title":"AI-Enhanced Assessment Framework for City-Scale Management of Municipal Living Plastic Waste Towards Zero-Waste Cities","authors":"Ziyang Wang, Shen Yang, Junqi Wang, Shi-Jie Cao","doi":"10.1016/j.eng.2026.03.009","DOIUrl":"https://doi.org/10.1016/j.eng.2026.03.009","url":null,"abstract":"Managing municipal living plastic waste (MLPW) entails complex system-level challenges across collection, recycling, and treatment, necessitating simultaneous optimization of resource, environmental, and economic objectives. However, robust assessment is frequently impeded by data scarcity and measurement inaccuracies, which undermine the transferability of evaluation models. To address these limitations, an artificial intelligence (AI)-enhanced evaluation framework is proposed. Baseline material flows were derived directly from field measurements, while machine learning algorithms were employed for independent validation and data imputation, thereby enhancing the credibility of environmental and economic assessments. The framework was applied in a megacity as the case study. Results indicated that MLPW management should focus on upgrading recycling and advancing the substitution of biodegradable plastics while rigorously enforcing source reduction. The recycling/treatment yields a 96.3% reduction in annual emissions by 2060 relative to the baseline. Cumulatively, this optimal trajectory achieves a reduction of 22.22 Mt CO<sub>2</sub>-eq between 2020 and 2060, generating economic benefits of approximately 197.7 billion CNY. Given the current technological conditions, mechanical recycling is identified as the priority pathway, offering superior mitigation potential (emission intensity of about 108 kg CO<sub>2</sub>-eq·t<sup>−1</sup>) and cost-effectiveness (economic return of around 613.9 CNY·t<sup>−1</sup>). By leveraging AI to ensure evaluation completeness and credibility even under data-constrained conditions, this framework offers a transferable tool for providing quantitative evidence to support policy prioritization in zero-waste city initiatives globally.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"112 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147507276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-19DOI: 10.1016/j.eng.2026.01.030
Tianhai Wang, Ning Wang, Shunda Li, Zhiwen Jin, Jianxing Xiao, Yanlong Miao, Yifan Sun, Han Li, Man Zhang
Deep learning (DL) methods, particularly those that combine camera and light detection and ranging (LiDAR) data, have demonstrated remarkable accuracy in three-dimensional (3D) obstacle detection. This is crucial for achieving rigorous and reliable autonomous navigation of agricultural machinery. However, recent approaches heavily rely on large-scale labeled datasets during training, which creates challenges for their application in agriculture because of presence of scarce and distinct agricultural samples. To overcome this limitation, this paper proposes a novel 3D detection method for agricultural obstacles with few or zero samples based on a multimodal feature representation mechanism. Image and point cloud attitude adjusters are integrated to increase the accuracy, reliability, and uniformity of multimodal data. Semantic and geometry-intensity feature encoders are integrated to capture essential relationships among categories. The Bird’s Eye View (BEV) fusion decoder is designed to discern intracategory similarities and intercategory distinctions. Multicategory experiments in various field scenarios reveal that the proposed method reduces the dependence on training samples by 30%–40%, and the precision rate, recall rate, F1 score, and detection speed are 95.03%, 97.01%, 96.01%, and 16.56 frames per second (FPS), respectively. Even in completely unknown scenarios (i.e., obstacle categories that lack any corresponding training samples), the proposed method still achieves an acceptable F1 score of 81.63%. As indicated by the results, the proposed method achieves a sophisticated trade-off among detection performance, operational efficiency, and data dependency, providing an effective safety guarantee for the autonomous navigation of agricultural machinery.
{"title":"Multimodal Feature Representation Mechanism for 3D Detection of Agricultural Obstacles with Few or Zero Samples","authors":"Tianhai Wang, Ning Wang, Shunda Li, Zhiwen Jin, Jianxing Xiao, Yanlong Miao, Yifan Sun, Han Li, Man Zhang","doi":"10.1016/j.eng.2026.01.030","DOIUrl":"https://doi.org/10.1016/j.eng.2026.01.030","url":null,"abstract":"Deep learning (DL) methods, particularly those that combine camera and light detection and ranging (LiDAR) data, have demonstrated remarkable accuracy in three-dimensional (3D) obstacle detection. This is crucial for achieving rigorous and reliable autonomous navigation of agricultural machinery. However, recent approaches heavily rely on large-scale labeled datasets during training, which creates challenges for their application in agriculture because of presence of scarce and distinct agricultural samples. To overcome this limitation, this paper proposes a novel 3D detection method for agricultural obstacles with few or zero samples based on a multimodal feature representation mechanism. Image and point cloud attitude adjusters are integrated to increase the accuracy, reliability, and uniformity of multimodal data. Semantic and geometry-intensity feature encoders are integrated to capture essential relationships among categories. The Bird’s Eye View (BEV) fusion decoder is designed to discern intracategory similarities and intercategory distinctions. Multicategory experiments in various field scenarios reveal that the proposed method reduces the dependence on training samples by 30%–40%, and the precision rate, recall rate, F<sub>1</sub> score, and detection speed are 95.03%, 97.01%, 96.01%, and 16.56 frames per second (FPS), respectively. Even in completely unknown scenarios (i.e., obstacle categories that lack any corresponding training samples), the proposed method still achieves an acceptable F<sub>1</sub> score of 81.63%. As indicated by the results, the proposed method achieves a sophisticated trade-off among detection performance, operational efficiency, and data dependency, providing an effective safety guarantee for the autonomous navigation of agricultural machinery.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"77 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147489531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Spatial frequency domain imaging (SFDI) has been widely applied in fruit quality inspection because of its noncontact and wide-field advantages. However, conventional multispectral SFDI systems remain constrained by low transmission efficiency, limited spectral range, and reliance on mechanical scanning. To overcome these limitations, we developed a continuously tunable wavelength SFDI system (450–1040 nm) that enables both continuous-spectrum and selectable-band imaging through patterned monochromatic illumination. The system adopts a modular design that integrates a monochromatic light generation module, a projection module, an imaging module, and a motorized imaging platform. This configuration allows flexible coupling and replacement of light sources and projection modules, enabling automated measurement of optical properties across different wavelength ranges according to application needs. With its high tunability, the system supports customized measurements at specific wavelengths via dedicated acquisition software, and it also provides the potential for spectral extension into longer infrared bands by simply upgrading the light source and infrared-sensitive projection module. Leveraging its wavelength tunability, we further demonstrated the system’s capability for depth-resolved imaging by jointly regulating the spatial frequency and wavelength. The results showed that the system achieved an imaging depth of 3–4 mm. The optical property measurements of various fruits obtained using our system were in close agreement with the reference values provided by the integrating sphere (IS). The mean measurement error of the absorption coefficient (<span><span style=""></span><span data-mathml='<math xmlns="http://www.w3.org/1998/Math/MathML"><msub is="true"><mi is="true">μ</mi><mi is="true">a</mi></msub></math>' role="presentation" style="font-size: 90%; display: inline-block; position: relative;" tabindex="0"><svg aria-hidden="true" focusable="false" height="1.855ex" role="img" style="vertical-align: -0.697ex;" viewbox="0 -498.8 1077.9 798.9" width="2.504ex" xmlns:xlink="http://www.w3.org/1999/xlink"><g fill="currentColor" stroke="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><g is="true"><g is="true"><use xlink:href="#MJMATHI-3BC"></use></g><g is="true" transform="translate(603,-150)"><use transform="scale(0.707)" xlink:href="#MJMATHI-61"></use></g></g></g></svg><span role="presentation"><math xmlns="http://www.w3.org/1998/Math/MathML"><msub is="true"><mi is="true">μ</mi><mi is="true">a</mi></msub></math></span></span><script type="math/mml"><math><msub is="true"><mi is="true">μ</mi><mi is="true">a</mi></msub></math></script></span>) was approximately 0.002 mm<sup>−1</sup>, while that of the reduced scattering coefficient (<span><span style=""></span><span data-mathml='<math xmlns="http://www.w3.org/1998/Math/MathML"><mrow is="true"><msub is="true"><
{"title":"Design, Characterization, and Application of a Continuously Tunable Wavelength Spatial Frequency Domain Imaging System for Measuring the Optical Properties of Fruits","authors":"Yuan Gao, Zhizhong Sun, Xuan Luo, Dong Hu, Benhui Dai, Yingjie Zheng, Yibin Ying, Lijuan Xie","doi":"10.1016/j.eng.2026.01.029","DOIUrl":"https://doi.org/10.1016/j.eng.2026.01.029","url":null,"abstract":"Spatial frequency domain imaging (SFDI) has been widely applied in fruit quality inspection because of its noncontact and wide-field advantages. However, conventional multispectral SFDI systems remain constrained by low transmission efficiency, limited spectral range, and reliance on mechanical scanning. To overcome these limitations, we developed a continuously tunable wavelength SFDI system (450–1040 nm) that enables both continuous-spectrum and selectable-band imaging through patterned monochromatic illumination. The system adopts a modular design that integrates a monochromatic light generation module, a projection module, an imaging module, and a motorized imaging platform. This configuration allows flexible coupling and replacement of light sources and projection modules, enabling automated measurement of optical properties across different wavelength ranges according to application needs. With its high tunability, the system supports customized measurements at specific wavelengths via dedicated acquisition software, and it also provides the potential for spectral extension into longer infrared bands by simply upgrading the light source and infrared-sensitive projection module. Leveraging its wavelength tunability, we further demonstrated the system’s capability for depth-resolved imaging by jointly regulating the spatial frequency and wavelength. The results showed that the system achieved an imaging depth of 3–4 mm. The optical property measurements of various fruits obtained using our system were in close agreement with the reference values provided by the integrating sphere (IS). The mean measurement error of the absorption coefficient (<span><span style=\"\"></span><span data-mathml='<math xmlns=\"http://www.w3.org/1998/Math/MathML\"><msub is=\"true\"><mi is=\"true\">&#x3BC;</mi><mi is=\"true\">a</mi></msub></math>' role=\"presentation\" style=\"font-size: 90%; display: inline-block; position: relative;\" tabindex=\"0\"><svg aria-hidden=\"true\" focusable=\"false\" height=\"1.855ex\" role=\"img\" style=\"vertical-align: -0.697ex;\" viewbox=\"0 -498.8 1077.9 798.9\" width=\"2.504ex\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g fill=\"currentColor\" stroke=\"currentColor\" stroke-width=\"0\" transform=\"matrix(1 0 0 -1 0 0)\"><g is=\"true\"><g is=\"true\"><use xlink:href=\"#MJMATHI-3BC\"></use></g><g is=\"true\" transform=\"translate(603,-150)\"><use transform=\"scale(0.707)\" xlink:href=\"#MJMATHI-61\"></use></g></g></g></svg><span role=\"presentation\"><math xmlns=\"http://www.w3.org/1998/Math/MathML\"><msub is=\"true\"><mi is=\"true\">μ</mi><mi is=\"true\">a</mi></msub></math></span></span><script type=\"math/mml\"><math><msub is=\"true\"><mi is=\"true\">μ</mi><mi is=\"true\">a</mi></msub></math></script></span>) was approximately 0.002 mm<sup>−1</sup>, while that of the reduced scattering coefficient (<span><span style=\"\"></span><span data-mathml='<math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mrow is=\"true\"><msub is=\"true\"><","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"60 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147506580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-18DOI: 10.1016/j.eng.2026.03.007
Xinxue Zhang, Pengfei Dong, Youfa Wang, Shuang Wu, Lili Zhang, Jie Han, Geoffrey I.N. Waterhouse, Quancai Sun, Jing Huang, Jin Wang
MicroRNAs (miRNAs) are promising biomarkers for cancer, offering a minimally invasive approach for liquid biopsy. However, their low abundance in clinical samples poses a major challenge for detection sensitivity and analytical reliability. Herein, this study report a fluorescence-electrochemical dual-mode sensor that integrates multiple signal amplification strategies for the ultrasensitive detection of miRNA-21. MXene–gold nanoparticles (AuNPs) composites were first modified on the electrode surface to enable efficient probe loading and enhanced electron transfer to amplify signal. The urchin-shaped covalent organic frameworks (COFs) facilitated more efficient methylene blue loading, with the unique structure further enhancing signal response. Combined with the miRNA-21 triggered entropy-driven catalysis (EDC)–DNA catalysis (DNAzyme) cascade amplification, the sensor realized a multi-level synergistic signal enhancement, demonstrating remarkable sensitivity. Notably, the urchin-shaped COFs served dual roles as both fluorescent reporters and nanocarriers, facilitating dual-mode signal readout. Under optimized conditions, the sensor exhibited a linear response to miRNA-21 in the range of 1–105 fmol∙L–1, with detection limits of 0.285 and 0.342 fmol∙L–1 for the electrochemical and fluorescence modes, respectively. The platform enabled accurate detection of miRNA-21 in human serum and cancer cells. It provides a viable pathway for early cancer diagnosis with sensitivity and reliability.
{"title":"Urchin-Shaped COFs Empower Dual-Mode Sensor Integrating Multiple Signal Amplification for Ultrasensitive miRNA-21 Detection","authors":"Xinxue Zhang, Pengfei Dong, Youfa Wang, Shuang Wu, Lili Zhang, Jie Han, Geoffrey I.N. Waterhouse, Quancai Sun, Jing Huang, Jin Wang","doi":"10.1016/j.eng.2026.03.007","DOIUrl":"https://doi.org/10.1016/j.eng.2026.03.007","url":null,"abstract":"MicroRNAs (miRNAs) are promising biomarkers for cancer, offering a minimally invasive approach for liquid biopsy. However, their low abundance in clinical samples poses a major challenge for detection sensitivity and analytical reliability. Herein, this study report a fluorescence-electrochemical dual-mode sensor that integrates multiple signal amplification strategies for the ultrasensitive detection of miRNA-21. MXene–gold nanoparticles (AuNPs) composites were first modified on the electrode surface to enable efficient probe loading and enhanced electron transfer to amplify signal. The urchin-shaped covalent organic frameworks (COFs) facilitated more efficient methylene blue loading, with the unique structure further enhancing signal response. Combined with the miRNA-21 triggered entropy-driven catalysis (EDC)–DNA catalysis (DNAzyme) cascade amplification, the sensor realized a multi-level synergistic signal enhancement, demonstrating remarkable sensitivity. Notably, the urchin-shaped COFs served dual roles as both fluorescent reporters and nanocarriers, facilitating dual-mode signal readout. Under optimized conditions, the sensor exhibited a linear response to miRNA-21 in the range of 1–10<sup>5</sup> fmol∙L<sup>–1</sup>, with detection limits of 0.285 and 0.342 fmol∙L<sup>–1</sup> for the electrochemical and fluorescence modes, respectively. The platform enabled accurate detection of miRNA-21 in human serum and cancer cells. It provides a viable pathway for early cancer diagnosis with sensitivity and reliability.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"84 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147471174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-18DOI: 10.1016/j.eng.2026.03.006
Bo Chen, Yimin Xuan
Hydrogen-powered engines generate carbon-free flue gases with water vapor as their main combustion product. Under extreme temperature and pressure conditions, water vapor exhibits pronounced non-ideal gas behavior, resulting in thermophysical properties with significant pressure dependency. Concurrently, radiative heat transfer is strongly influenced by pressure-induced spectral line broadening and the high optical thickness that results from elevated water vapor concentrations. Existing thermophysical correlations are generally valid only within limited operating envelopes and lack the theoretical justification needed to reliably extend predictions to high-temperature regimes. Furthermore, conventional weighted-sum-of-gray-gases (WSGG) models rarely account for specific hydrogen-powered engine conditions. Most existing WSGG models employ coupled variables, in which pressure and concentration effects are lumped into a single pressure-path-length product, making them inadequate for capturing complex independent dependencies. In this study, sensitivity analyses of key thermophysical parameters were conducted to identify the parameters’ functional dependence on temperature and pressure, yielding predictive models for specific enthalpy, heat capacities, and viscosity that enable physically consistent extrapolation to temperatures exceeding 2000 K. For radiative characteristics, a unified hydrogen-oriented WSGG (H-WSGG) framework was developed that decouples the nonlinear influences of water vapor concentration and total pressure, treating temperature, pressure, concentration, and path length as fully independent variables. Unlike conventional methods, these nonlinear effects are explicitly integrated into the absorption coefficients and weighting factors. Based on this framework and the Brayton cycle characteristics, two practical models were derived: the constant-pressure H-WSGG-C model and the constant-concentration H-WSGG-T model. Validation against original data and line-by-line (LBL) calculations under non-isothermal and non-uniform conditions demonstrate that the proposed thermophysical models achieve high accuracy and the H-WSGG models exhibit strong agreement with LBL benchmarks.
{"title":"Prediction of the Thermodynamic Properties of Carbon-Free Flue Gases from Hydrogen-Powered Engines","authors":"Bo Chen, Yimin Xuan","doi":"10.1016/j.eng.2026.03.006","DOIUrl":"https://doi.org/10.1016/j.eng.2026.03.006","url":null,"abstract":"Hydrogen-powered engines generate carbon-free flue gases with water vapor as their main combustion product. Under extreme temperature and pressure conditions, water vapor exhibits pronounced non-ideal gas behavior, resulting in thermophysical properties with significant pressure dependency. Concurrently, radiative heat transfer is strongly influenced by pressure-induced spectral line broadening and the high optical thickness that results from elevated water vapor concentrations. Existing thermophysical correlations are generally valid only within limited operating envelopes and lack the theoretical justification needed to reliably extend predictions to high-temperature regimes. Furthermore, conventional weighted-sum-of-gray-gases (WSGG) models rarely account for specific hydrogen-powered engine conditions. Most existing WSGG models employ coupled variables, in which pressure and concentration effects are lumped into a single pressure-path-length product, making them inadequate for capturing complex independent dependencies. In this study, sensitivity analyses of key thermophysical parameters were conducted to identify the parameters’ functional dependence on temperature and pressure, yielding predictive models for specific enthalpy, heat capacities, and viscosity that enable physically consistent extrapolation to temperatures exceeding 2000 K. For radiative characteristics, a unified hydrogen-oriented WSGG (H-WSGG) framework was developed that decouples the nonlinear influences of water vapor concentration and total pressure, treating temperature, pressure, concentration, and path length as fully independent variables. Unlike conventional methods, these nonlinear effects are explicitly integrated into the absorption coefficients and weighting factors. Based on this framework and the Brayton cycle characteristics, two practical models were derived: the constant-pressure H-WSGG-C model and the constant-concentration H-WSGG-T model. Validation against original data and line-by-line (LBL) calculations under non-isothermal and non-uniform conditions demonstrate that the proposed thermophysical models achieve high accuracy and the H-WSGG models exhibit strong agreement with LBL benchmarks.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"191 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147496130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Powdery mildew poses a major threat to global wheat production, highlighting the urgent need to identify resistance genes. In this study, we report the cloning of PmNCA6, a powdery mildew resistance gene originating from Triticum boeoticum. Using bulked segregant exome capture sequencing (BSE-Seq) and genetic mapping, we mapped PmNCA6 to a 17-Mb recombination-suppressed interval (680.1–697.1 Mb) on chromosome 7AL. By applying a mutant exome sequencing (MutExomeSeq) approach, we analyzed six ethyl methanesulfonate (EMS)-induced susceptible mutants and identified non-synonymous mutations in a nucleotide-binding leucine-rich repeat (NLR) gene, NLR1. Functional validation through barley stripe mosaic virus-induced gene silencing (BSMV-VIGS) and transgenic complementation confirmed that two alternatively spliced NLR1 transcripts (NLR1_V1 and NLR1_V2) confer resistance to powdery mildew. Phylogenetic analysis revealed that PmNCA6 is orthologous to the stem rust resistance gene Sr22a. Domain-swapping experiments between PmNCA6 and Sr22a demonstrated that the leucine-rich repeat (LRR) domain of PmNCA6 is critical for powdery mildew specificity. Field trials of near-isogenic and recombinant inbred lines (RILs) indicated that PmNCA6-mediated resistance does not compromise yield performance. Screening of 553 Chinese wheat cultivars confirmed the absence of PmNCA6, emphasizing its potential for diversifying resistance sources in breeding programs. This study establishes MutExomeSeq as a robust tool for cloning genes in recombination-suppressed intervals and highlights the potential of engineering synthetic NLRs with tailored LRR domains to combat evolving pathogens.
{"title":"MutExomeSeq Accelerates the Cloning of PmNCA6 Conferring Powdery Mildew Resistance from Triticum boeoticum","authors":"Wentao Wan, Renhui Zhao, Peize Zhao, Qiulian Tang, Guofeng Lv, Tiantian Chen, Ling Wang, Shujiang Zang, Ronglin Wu, Zunjie Wang, Shulin Chen, Zongkuan Wang, Xu Zhang, Jinghuang Hu, Hongya Wu, Datong Liu, Yong Zhang, Derong Gao, Hongjie Li, Huagang He, Tongde Bie","doi":"10.1016/j.eng.2026.02.027","DOIUrl":"https://doi.org/10.1016/j.eng.2026.02.027","url":null,"abstract":"Powdery mildew poses a major threat to global wheat production, highlighting the urgent need to identify resistance genes. In this study, we report the cloning of <em>PmNCA6</em>, a powdery mildew resistance gene originating from <em>Triticum boeoticum</em>. Using bulked segregant exome capture sequencing (BSE-Seq) and genetic mapping, we mapped <em>PmNCA6</em> to a 17-Mb recombination-suppressed interval (680.1–697.1 Mb) on chromosome 7AL. By applying a mutant exome sequencing (MutExomeSeq) approach, we analyzed six ethyl methanesulfonate (EMS)-induced susceptible mutants and identified non-synonymous mutations in a nucleotide-binding leucine-rich repeat (<em>NLR</em>) gene, <em>NLR1</em>. Functional validation through barley stripe mosaic virus-induced gene silencing (BSMV-VIGS) and transgenic complementation confirmed that two alternatively spliced <em>NLR1</em> transcripts (<em>NLR1_V1</em> and <em>NLR1_V2</em>) confer resistance to powdery mildew. Phylogenetic analysis revealed that <em>PmNCA6</em> is orthologous to the stem rust resistance gene <em>Sr22a</em>. Domain-swapping experiments between <em>PmNCA6</em> and <em>Sr22a</em> demonstrated that the leucine-rich repeat (LRR) domain of <em>PmNCA6</em> is critical for powdery mildew specificity. Field trials of near-isogenic and recombinant inbred lines (RILs) indicated that <em>PmNCA6</em>-mediated resistance does not compromise yield performance. Screening of 553 Chinese wheat cultivars confirmed the absence of <em>PmNCA6</em>, emphasizing its potential for diversifying resistance sources in breeding programs. This study establishes MutExomeSeq as a robust tool for cloning genes in recombination-suppressed intervals and highlights the potential of engineering synthetic NLRs with tailored LRR domains to combat evolving pathogens.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"52 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147466038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-14DOI: 10.1016/j.eng.2026.03.005
Xuan Mei, Xingchen Liu, Zaiyi Yang, Nigel Foo, Peter Wang, Ming Shao, Dean Ho, Yu Shrike Zhang
Aging is a systemic and progressive challenge of significant societal, medical, and scientific urgency. With global populations aging rapidly, age-related diseases, such as neurodegeneration and cardiovascular dysfunction, are accelerating, underscoring the urgent need to understand the mechanisms of aging and develop effective interventions. The trending organ-on-a-chip (OoC) technology offers a powerful solution by recapitulating organ- or tissue-level functions. However, the applications of OoC in aging research remain limited, since most current systems are confined to models that simulate endpoint phenotypes rather than the dynamic evolution of aging itself.
{"title":"The Present and Future of Aging-on-A-Chip","authors":"Xuan Mei, Xingchen Liu, Zaiyi Yang, Nigel Foo, Peter Wang, Ming Shao, Dean Ho, Yu Shrike Zhang","doi":"10.1016/j.eng.2026.03.005","DOIUrl":"https://doi.org/10.1016/j.eng.2026.03.005","url":null,"abstract":"Aging is a systemic and progressive challenge of significant societal, medical, and scientific urgency. With global populations aging rapidly, age-related diseases, such as neurodegeneration and cardiovascular dysfunction, are accelerating, underscoring the urgent need to understand the mechanisms of aging and develop effective interventions. The trending organ-on-a-chip (OoC) technology offers a powerful solution by recapitulating organ- or tissue-level functions. However, the applications of OoC in aging research remain limited, since most current systems are confined to models that simulate endpoint phenotypes rather than the dynamic evolution of aging itself.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"567 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147454709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-13DOI: 10.1016/j.eng.2026.03.004
Lu Li, Eva-Maria Dehne, Florian König-Huber, Uwe Marx
{"title":"Engineering Human Biology on Chips: How “Human-Like” Can We Become?","authors":"Lu Li, Eva-Maria Dehne, Florian König-Huber, Uwe Marx","doi":"10.1016/j.eng.2026.03.004","DOIUrl":"https://doi.org/10.1016/j.eng.2026.03.004","url":null,"abstract":"","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"1 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147448156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-13DOI: 10.1016/j.eng.2026.03.002
Chunyu Zou, Feiyang Deng, Bingjie Xiang, Kin Wa Kwan, Kwai Man Luk, Alfonso Hing Wan Ngan
{"title":"A Transition Metal Oxide Antenna Prototype Actuated by Light Stimuli","authors":"Chunyu Zou, Feiyang Deng, Bingjie Xiang, Kin Wa Kwan, Kwai Man Luk, Alfonso Hing Wan Ngan","doi":"10.1016/j.eng.2026.03.002","DOIUrl":"https://doi.org/10.1016/j.eng.2026.03.002","url":null,"abstract":"","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"36 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147448160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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