Lineament is a unique geological structure. The study of Lunar lineament structure has great significance on understanding its history and evolution of Lunar surface. However, the existing geographic feature extraction methods are not suitable for the extraction of Lunar lineament structure. In this paper, a new lineament extraction method is proposed based on improved-UNet++ and YOLOv5. Firstly, new lineament dataset is created containing lineaments structure based on CCD data from LROC. At same time the residual blocks are replaced with the VGG blocks in the down sample part of the UNet++ with adding the attention block between each layer. Secondly, the improved-UNet++ and YOLO networks are trained to execute the object detection and semantic segmentation of lineament structure respectively. Finally, a polygon-match strategy is proposed to combine the results of object detection and semantic segmentation. The experiment result indicate that this new method has relatively better and more stable performance compared with current mainstream networks and the original UNet++ network in the instance segmentation of lineament structure. Additionally, the polygon-match strategy is able to perform preciser edge detail in the instance segmentation of lineament structure result.
{"title":"A New Lunar Lineament Extraction Method Based on Improved UNet++ and YOLOv5","authors":"Pengcheng Yan, Jiarui Liang, Xiaolin Tian, Yikui Zhai","doi":"10.3390/s24072256","DOIUrl":"https://doi.org/10.3390/s24072256","url":null,"abstract":"Lineament is a unique geological structure. The study of Lunar lineament structure has great significance on understanding its history and evolution of Lunar surface. However, the existing geographic feature extraction methods are not suitable for the extraction of Lunar lineament structure. In this paper, a new lineament extraction method is proposed based on improved-UNet++ and YOLOv5. Firstly, new lineament dataset is created containing lineaments structure based on CCD data from LROC. At same time the residual blocks are replaced with the VGG blocks in the down sample part of the UNet++ with adding the attention block between each layer. Secondly, the improved-UNet++ and YOLO networks are trained to execute the object detection and semantic segmentation of lineament structure respectively. Finally, a polygon-match strategy is proposed to combine the results of object detection and semantic segmentation. The experiment result indicate that this new method has relatively better and more stable performance compared with current mainstream networks and the original UNet++ network in the instance segmentation of lineament structure. Additionally, the polygon-match strategy is able to perform preciser edge detail in the instance segmentation of lineament structure result.","PeriodicalId":221960,"journal":{"name":"Sensors (Basel, Switzerland)","volume":"197 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140792290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gold nanoparticles (AuNPs) are good candidates for donor material in energy transfer systems and can easily be functionalized with various ligands on the surface with Au–S bonding. Cyclodextrin (CD) forms inclusion complexes with fluorophores due to its unique structure for host–guest interaction. In this study, we fabricated βCD-functionalized AuNPs using different lengths of thiol ligands and recognized cholesterol to confirm the energy-transfer-based turn-on fluorescence mechanism. AuNP–βCD conjugated with various thiol ligands and quenched the fluorescein (Fl) dye, forming βCD-Fl inclusion complexes. As the distance between AuNPs and βCD decreased, the quenching efficiency became higher. The quenched fluorescence was recovered when the cholesterol replaced the Fl because of the stronger binding affinity of the cholesterol with βCD. The efficiency of cholesterol recognition was also affected by the energy transfer effect because the shorter βCD ligand had a higher fluorescence recovery. Furthermore, we fabricated a liposome with cholesterol embedded in the lipid bilayer membrane to mimic the cholesterol coexisting with lipids in human serum. These cellular cholesterols accelerated the replacement of the Fl molecules, resulting in a fluorescence recovery higher than that of pure lipid. These discoveries are expected to give guidance towards cholesterol sensors or energy-transfer-based biosensors using AuNPs.
{"title":"Energy Transfer-Based Recognition of Membrane Cholesterol by Controlling Intradistance of Linker","authors":"Yong Ho Cho, Tae Kyung Won, Dong June Ahn","doi":"10.3390/s24072315","DOIUrl":"https://doi.org/10.3390/s24072315","url":null,"abstract":"Gold nanoparticles (AuNPs) are good candidates for donor material in energy transfer systems and can easily be functionalized with various ligands on the surface with Au–S bonding. Cyclodextrin (CD) forms inclusion complexes with fluorophores due to its unique structure for host–guest interaction. In this study, we fabricated βCD-functionalized AuNPs using different lengths of thiol ligands and recognized cholesterol to confirm the energy-transfer-based turn-on fluorescence mechanism. AuNP–βCD conjugated with various thiol ligands and quenched the fluorescein (Fl) dye, forming βCD-Fl inclusion complexes. As the distance between AuNPs and βCD decreased, the quenching efficiency became higher. The quenched fluorescence was recovered when the cholesterol replaced the Fl because of the stronger binding affinity of the cholesterol with βCD. The efficiency of cholesterol recognition was also affected by the energy transfer effect because the shorter βCD ligand had a higher fluorescence recovery. Furthermore, we fabricated a liposome with cholesterol embedded in the lipid bilayer membrane to mimic the cholesterol coexisting with lipids in human serum. These cellular cholesterols accelerated the replacement of the Fl molecules, resulting in a fluorescence recovery higher than that of pure lipid. These discoveries are expected to give guidance towards cholesterol sensors or energy-transfer-based biosensors using AuNPs.","PeriodicalId":221960,"journal":{"name":"Sensors (Basel, Switzerland)","volume":"222 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140790239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Parth Kadav, Sachin Sharma, Johan Fanas Rojas, Pritesh Patil, C. Wang, A. R. Ekti, Richard T. Meyer, Zachary D. Asher
Safe autonomous vehicle (AV) operations depend on an accurate perception of the driving environment, which necessitates the use of a variety of sensors. Computational algorithms must then process all of this sensor data, which typically results in a high on-vehicle computational load. For example, existing lane markings are designed for human drivers, can fade over time, and can be contradictory in construction zones, which require specialized sensing and computational processing in an AV. But, this standard process can be avoided if the lane information is simply transmitted directly to the AV. High definition maps and road side units (RSUs) can be used for direct data transmission to the AV, but can be prohibitively expensive to establish and maintain. Additionally, to ensure robust and safe AV operations, more redundancy is beneficial. A cost-effective and passive solution is essential to address this need effectively. In this research, we propose a new infrastructure information source (IIS), chip-enabled raised pavement markers (CERPMs), which provide environmental data to the AV while also decreasing the AV compute load and the associated increase in vehicle energy use. CERPMs are installed in place of traditional ubiquitous raised pavement markers along road lane lines to transmit geospatial information along with the speed limit using long range wide area network (LoRaWAN) protocol directly to nearby vehicles. This information is then compared to the Mobileye commercial off-the-shelf traditional system that uses computer vision processing of lane markings. Our perception subsystem processes the raw data from both CEPRMs and Mobileye to generate a viable path required for a lane centering (LC) application. To evaluate the detection performance of both systems, we consider three test routes with varying conditions. Our results show that the Mobileye system failed to detect lane markings when the road curvature exceeded ±0.016 m−1. For the steep curvature test scenario, it could only detect lane markings on both sides of the road for just 6.7% of the given test route. On the other hand, the CERPMs transmit the programmed geospatial information to the perception subsystem on the vehicle to generate a reference trajectory required for vehicle control. The CERPMs successfully generated the reference trajectory for vehicle control in all test scenarios. Moreover, the CERPMs can be detected up to 340 m from the vehicle’s position. Our overall conclusion is that CERPM technology is viable and that it has the potential to address the operational robustness and energy efficiency concerns plaguing the current generation of AVs.
{"title":"Automated Lane Centering: An Off-the-Shelf Computer Vision Product vs. Infrastructure-Based Chip-Enabled Raised Pavement Markers","authors":"Parth Kadav, Sachin Sharma, Johan Fanas Rojas, Pritesh Patil, C. Wang, A. R. Ekti, Richard T. Meyer, Zachary D. Asher","doi":"10.3390/s24072327","DOIUrl":"https://doi.org/10.3390/s24072327","url":null,"abstract":"Safe autonomous vehicle (AV) operations depend on an accurate perception of the driving environment, which necessitates the use of a variety of sensors. Computational algorithms must then process all of this sensor data, which typically results in a high on-vehicle computational load. For example, existing lane markings are designed for human drivers, can fade over time, and can be contradictory in construction zones, which require specialized sensing and computational processing in an AV. But, this standard process can be avoided if the lane information is simply transmitted directly to the AV. High definition maps and road side units (RSUs) can be used for direct data transmission to the AV, but can be prohibitively expensive to establish and maintain. Additionally, to ensure robust and safe AV operations, more redundancy is beneficial. A cost-effective and passive solution is essential to address this need effectively. In this research, we propose a new infrastructure information source (IIS), chip-enabled raised pavement markers (CERPMs), which provide environmental data to the AV while also decreasing the AV compute load and the associated increase in vehicle energy use. CERPMs are installed in place of traditional ubiquitous raised pavement markers along road lane lines to transmit geospatial information along with the speed limit using long range wide area network (LoRaWAN) protocol directly to nearby vehicles. This information is then compared to the Mobileye commercial off-the-shelf traditional system that uses computer vision processing of lane markings. Our perception subsystem processes the raw data from both CEPRMs and Mobileye to generate a viable path required for a lane centering (LC) application. To evaluate the detection performance of both systems, we consider three test routes with varying conditions. Our results show that the Mobileye system failed to detect lane markings when the road curvature exceeded ±0.016 m−1. For the steep curvature test scenario, it could only detect lane markings on both sides of the road for just 6.7% of the given test route. On the other hand, the CERPMs transmit the programmed geospatial information to the perception subsystem on the vehicle to generate a reference trajectory required for vehicle control. The CERPMs successfully generated the reference trajectory for vehicle control in all test scenarios. Moreover, the CERPMs can be detected up to 340 m from the vehicle’s position. Our overall conclusion is that CERPM technology is viable and that it has the potential to address the operational robustness and energy efficiency concerns plaguing the current generation of AVs.","PeriodicalId":221960,"journal":{"name":"Sensors (Basel, Switzerland)","volume":"43 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140756527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rosa Maset-Roig, Jordi Caplliure-Llopis, N. de Bernardo, Jesús Privado, Jorge Alarcón-Jiménez, Julio Martín-Ruiz, Marta Botella-Navas, Carlos Villarón-Casales, D. Sancho-Cantus, J. E. de la Rubia Ortí
Introduction: Amyotrophic lateral sclerosis (ALS) produces alterations in the autonomic nervous system (ANS), which explains the cardiac manifestations observed in patients. The assessment of heart rate variability (HRV) is what best reflects the activity of the ANS on heart rate. The Polar H7 Bluetooth® device proves to be a non-invasive and much faster technology than existing alternatives for this purpose. Objective: The goal of this study is to determine HRV using Polar H7 Bluetooth technology in ALS patients, comparing the obtained measurements with values from healthy individuals. Method: The sample consisted of 124 participants: 68 diagnosed with ALS and 56 healthy individuals. Using Polar H7 Bluetooth technology and the ELITE HRV application, various HRV measurements were determined for all participants, specifically the HRV index, RMSSD, RMSSD LN, SDNN index, PNN50, LF, HF, LF/HF ratio, HR average, and HF peak frequency. Results: Statistically significant differences were observed between ALS patients and healthy individuals in the HRV index, RMSSD, RMSSD LN, SDNN index, PNN50, HF, and LF, where healthy individuals exhibited higher scores. For the HR average, the ALS group showed a higher value. Values were similar when comparing men and women with ALS, with only a higher HF peak frequency observed in women. Conclusion: The Polar H7 Bluetooth® device is effective in determining heart rate variability alterations in ALS, being a promising prognostic tool for the disease.
{"title":"Analysis of Heart Rate Variability in Individuals Affected by Amyotrophic Lateral Sclerosis","authors":"Rosa Maset-Roig, Jordi Caplliure-Llopis, N. de Bernardo, Jesús Privado, Jorge Alarcón-Jiménez, Julio Martín-Ruiz, Marta Botella-Navas, Carlos Villarón-Casales, D. Sancho-Cantus, J. E. de la Rubia Ortí","doi":"10.3390/s24072355","DOIUrl":"https://doi.org/10.3390/s24072355","url":null,"abstract":"Introduction: Amyotrophic lateral sclerosis (ALS) produces alterations in the autonomic nervous system (ANS), which explains the cardiac manifestations observed in patients. The assessment of heart rate variability (HRV) is what best reflects the activity of the ANS on heart rate. The Polar H7 Bluetooth® device proves to be a non-invasive and much faster technology than existing alternatives for this purpose. Objective: The goal of this study is to determine HRV using Polar H7 Bluetooth technology in ALS patients, comparing the obtained measurements with values from healthy individuals. Method: The sample consisted of 124 participants: 68 diagnosed with ALS and 56 healthy individuals. Using Polar H7 Bluetooth technology and the ELITE HRV application, various HRV measurements were determined for all participants, specifically the HRV index, RMSSD, RMSSD LN, SDNN index, PNN50, LF, HF, LF/HF ratio, HR average, and HF peak frequency. Results: Statistically significant differences were observed between ALS patients and healthy individuals in the HRV index, RMSSD, RMSSD LN, SDNN index, PNN50, HF, and LF, where healthy individuals exhibited higher scores. For the HR average, the ALS group showed a higher value. Values were similar when comparing men and women with ALS, with only a higher HF peak frequency observed in women. Conclusion: The Polar H7 Bluetooth® device is effective in determining heart rate variability alterations in ALS, being a promising prognostic tool for the disease.","PeriodicalId":221960,"journal":{"name":"Sensors (Basel, Switzerland)","volume":"88 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140763165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gianmarco Alfieri, M. Modesti, Riccardo Riggi, A. Bellincontro
Electronic nose devices stand out as pioneering innovations in contemporary technological research, addressing the arduous challenge of replicating the complex sense of smell found in humans. Currently, sensor instruments find application in a variety of fields, including environmental, (bio)medical, food, pharmaceutical, and materials production. Particularly the latter, has seen a significant increase in the adoption of technological tools to assess food quality, gradually supplanting human panelists and thus reshaping the entire quality control paradigm in the sector. This process is happening even more rapidly in the world of wine, where olfactory sensory analysis has always played a central role in attributing certain qualities to a wine. In this review, conducted using sources such as PubMed, Science Direct, and Web of Science, we examined papers published between January 2015 and January 2024. The aim was to explore prevailing trends in the use of human panels and sensory tools (such as the E-nose) in the wine industry. The focus was on the evaluation of wine quality attributes by paying specific attention to geographical origin, sensory defects, and monitoring of production trends. Analyzed results show that the application of E-nose-type sensors performs satisfactorily in that trajectory. Nevertheless, the integration of this type of analysis with more classical methods, such as the trained sensory panel test and with the application of destructive instrument volatile compound (VOC) detection (e.g., gas chromatography), still seems necessary to better explore and investigate the aromatic characteristics of wines.
电子鼻设备是当代技术研究的开创性创新，解决了复制人类复杂嗅觉的艰巨挑战。目前，传感仪器已应用于环境、（生物）医疗、食品、制药和材料生产等多个领域。特别是在后者，采用技术工具评估食品质量的情况显著增加，逐渐取代了人类专家，从而重塑了该行业的整个质量控制模式。在葡萄酒领域，这一进程的发展更为迅速，因为嗅觉感官分析一直是葡萄酒品质鉴定的核心。在本综述中，我们利用 PubMed、Science Direct 和 Web of Science 等资料来源，对 2015 年 1 月至 2024 年 1 月期间发表的论文进行了研究。目的是探讨葡萄酒行业使用人类评审团和感官工具（如电子鼻）的流行趋势。重点是对葡萄酒质量属性的评估，特别关注地理原产地、感官缺陷和生产趋势监测。分析结果表明，电子鼻型传感器在该领域的应用效果令人满意。然而，为了更好地探索和研究葡萄酒的芳香特征，似乎仍有必要将这种分析方法与更经典的方法相结合，如训练有素的感官小组测试和应用破坏性仪器挥发性化合物（VOC）检测（如气相色谱法）。
{"title":"Recent Advances and Future Perspectives in the E-Nose Technologies Addressed to the Wine Industry","authors":"Gianmarco Alfieri, M. Modesti, Riccardo Riggi, A. Bellincontro","doi":"10.3390/s24072293","DOIUrl":"https://doi.org/10.3390/s24072293","url":null,"abstract":"Electronic nose devices stand out as pioneering innovations in contemporary technological research, addressing the arduous challenge of replicating the complex sense of smell found in humans. Currently, sensor instruments find application in a variety of fields, including environmental, (bio)medical, food, pharmaceutical, and materials production. Particularly the latter, has seen a significant increase in the adoption of technological tools to assess food quality, gradually supplanting human panelists and thus reshaping the entire quality control paradigm in the sector. This process is happening even more rapidly in the world of wine, where olfactory sensory analysis has always played a central role in attributing certain qualities to a wine. In this review, conducted using sources such as PubMed, Science Direct, and Web of Science, we examined papers published between January 2015 and January 2024. The aim was to explore prevailing trends in the use of human panels and sensory tools (such as the E-nose) in the wine industry. The focus was on the evaluation of wine quality attributes by paying specific attention to geographical origin, sensory defects, and monitoring of production trends. Analyzed results show that the application of E-nose-type sensors performs satisfactorily in that trajectory. Nevertheless, the integration of this type of analysis with more classical methods, such as the trained sensory panel test and with the application of destructive instrument volatile compound (VOC) detection (e.g., gas chromatography), still seems necessary to better explore and investigate the aromatic characteristics of wines.","PeriodicalId":221960,"journal":{"name":"Sensors (Basel, Switzerland)","volume":"93 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140763893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jitong Kang, Mei Li, Shanjun Mao, Yingbo Fan, Zheng Wu, Ben Li
In recent years, the deformation detection technology for underground tunnels has played a crucial role in coal mine safety management. Currently, traditional methods such as the cross method and those employing the roof abscission layer monitoring instrument are primarily used for tunnel deformation detection in coal mines. With the advancement of photogrammetric methods, three-dimensional laser scanners have gradually become the primary method for deformation detection of coal mine tunnels. However, due to the high-risk confined spaces and distant distribution of coal mine tunnels, stationary three-dimensional laser scanning technology requires a significant amount of labor and time, posing certain operational risks. Currently, mobile laser scanning has become a popular method for coal mine tunnel deformation detection. This paper proposes a method for detecting point cloud deformation of underground coal mine tunnels based on a handheld three-dimensional laser scanner. This method utilizes SLAM laser radar to obtain complete point cloud information of the entire tunnel, while projecting the three-dimensional point cloud onto different planes to obtain the coordinates of the tunnel centerline. By using the calculated tunnel centerline, the three-dimensional point cloud data collected at different times are matched to the same coordinate system, and then the tunnel deformation parameters are analyzed separately from the global and cross-sectional perspectives. Through on-site collection of tunnel data, this paper verifies the feasibility of the algorithm and compares it with other centerline fitting and point cloud registration algorithms, demonstrating higher accuracy and meeting practical needs.
近年来，地下巷道变形检测技术在煤矿安全管理中发挥着至关重要的作用。目前，煤矿巷道变形检测主要采用交叉法、顶板剥离层监测仪等传统方法。随着摄影测量方法的发展，三维激光扫描仪逐渐成为煤矿巷道变形检测的主要方法。然而，由于煤矿巷道的密闭空间风险高、分布距离远，固定式三维激光扫描技术需要耗费大量人力和时间，存在一定的操作风险。目前，移动激光扫描已成为煤矿巷道变形检测的常用方法。本文提出了一种基于手持式三维激光扫描仪的煤矿井下巷道点云变形检测方法。该方法利用 SLAM 激光雷达获取整个隧道的完整点云信息，同时将三维点云投影到不同的平面上，从而获得隧道中心线的坐标。利用计算出的隧道中心线，将不同时间采集的三维点云数据匹配到同一坐标系，然后分别从全局和断面角度分析隧道变形参数。通过现场采集隧道数据，本文验证了该算法的可行性，并与其他中心线拟合和点云注册算法进行了比较，证明了该算法具有更高的精度，满足了实际需求。
{"title":"A Coal Mine Tunnel Deformation Detection Method Using Point Cloud Data","authors":"Jitong Kang, Mei Li, Shanjun Mao, Yingbo Fan, Zheng Wu, Ben Li","doi":"10.3390/s24072299","DOIUrl":"https://doi.org/10.3390/s24072299","url":null,"abstract":"In recent years, the deformation detection technology for underground tunnels has played a crucial role in coal mine safety management. Currently, traditional methods such as the cross method and those employing the roof abscission layer monitoring instrument are primarily used for tunnel deformation detection in coal mines. With the advancement of photogrammetric methods, three-dimensional laser scanners have gradually become the primary method for deformation detection of coal mine tunnels. However, due to the high-risk confined spaces and distant distribution of coal mine tunnels, stationary three-dimensional laser scanning technology requires a significant amount of labor and time, posing certain operational risks. Currently, mobile laser scanning has become a popular method for coal mine tunnel deformation detection. This paper proposes a method for detecting point cloud deformation of underground coal mine tunnels based on a handheld three-dimensional laser scanner. This method utilizes SLAM laser radar to obtain complete point cloud information of the entire tunnel, while projecting the three-dimensional point cloud onto different planes to obtain the coordinates of the tunnel centerline. By using the calculated tunnel centerline, the three-dimensional point cloud data collected at different times are matched to the same coordinate system, and then the tunnel deformation parameters are analyzed separately from the global and cross-sectional perspectives. Through on-site collection of tunnel data, this paper verifies the feasibility of the algorithm and compares it with other centerline fitting and point cloud registration algorithms, demonstrating higher accuracy and meeting practical needs.","PeriodicalId":221960,"journal":{"name":"Sensors (Basel, Switzerland)","volume":"362 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140765037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Conventional spherical nucleic acid enzymes (SNAzymes), made with gold nanoparticle (AuNPs) cores and DNA shells, are widely applied in bioanalysis owing to their excellent physicochemical properties. Albeit important, the crowded catalytic units (such as G-quadruplex, G4) on the limited AuNPs surface inevitably influence their catalytic activities. Herin, a hybridization chain reaction (HCR) is employed as a means to expand the quantity and spaces of G4 enzymes for their catalytic ability enhancement. Through systematic investigations, we found that when an incomplete G4 sequence was linked at the sticky ends of the hairpins with split modes (3:1 and 2:2), this would significantly decrease the HCR hybridization capability due to increased steric hindrance. In contrast, the HCR hybridization capability was remarkably enhanced after the complete G4 sequence was directly modified at the non-sticky end of the hairpins, ascribed to the steric hindrance avoided. Accordingly, the improved SNAzymes using HCR were applied for the determination of AFB1 in food samples as a proof-of-concept, which exhibited outstanding performance (detection limit, 0.08 ng/mL). Importantly, our strategy provided a new insight for the catalytic activity improvement in SNAzymes using G4 as a signaling molecule.
{"title":"Improved Catalytic Activity of Spherical Nucleic Acid Enzymes by Hybridization Chain Reaction and Its Application for Sensitive Analysis of Aflatoxin B1","authors":"Wenjun Wang, Xuesong Li, Kun Zeng, Yanyan Lu, Boyuan Jia, Jianxia Lv, Chenghao Wu, Xinyu Wang, Xinshuo Zhang, Zhen Zhang","doi":"10.3390/s24072325","DOIUrl":"https://doi.org/10.3390/s24072325","url":null,"abstract":"Conventional spherical nucleic acid enzymes (SNAzymes), made with gold nanoparticle (AuNPs) cores and DNA shells, are widely applied in bioanalysis owing to their excellent physicochemical properties. Albeit important, the crowded catalytic units (such as G-quadruplex, G4) on the limited AuNPs surface inevitably influence their catalytic activities. Herin, a hybridization chain reaction (HCR) is employed as a means to expand the quantity and spaces of G4 enzymes for their catalytic ability enhancement. Through systematic investigations, we found that when an incomplete G4 sequence was linked at the sticky ends of the hairpins with split modes (3:1 and 2:2), this would significantly decrease the HCR hybridization capability due to increased steric hindrance. In contrast, the HCR hybridization capability was remarkably enhanced after the complete G4 sequence was directly modified at the non-sticky end of the hairpins, ascribed to the steric hindrance avoided. Accordingly, the improved SNAzymes using HCR were applied for the determination of AFB1 in food samples as a proof-of-concept, which exhibited outstanding performance (detection limit, 0.08 ng/mL). Importantly, our strategy provided a new insight for the catalytic activity improvement in SNAzymes using G4 as a signaling molecule.","PeriodicalId":221960,"journal":{"name":"Sensors (Basel, Switzerland)","volume":"1216 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140774187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Y. Rivera, Dorian Bascou, David Blanco, Lucas Álvarez-Piñeiro, C. Berna, J. Muñoz-Cobo, Alberto Escrivá
This paper investigates non-invasive techniques for annular two-phase flow analysis, focusing on liquid film characterization to understand the interfacial phenomena that are crucial for heat and mass transfer. Limited methods allow the study of the temporal and spatial evolution of liquid film, such as Planar Laser-Induced Fluorescence (PLIF). However, this method possesses optical challenges, leading to the need for improved techniques to mitigate refraction and reflection, such as Refractive Index Matching (RIM). This study utilizes an experimental annular flow facility to analyze both RIM and non-RIM PLIF over a range of liquid Reynolds numbers from 4200 to 10,400. Three configurations—PLIF RIM90, PLIF RIM40, and PLIF nRIM40—are compared from both qualitative and quantitative perspectives. In the quantitative analysis, key variables of the liquid film are measured, namely mean film thickness, disturbance wave height, and frequency. Variations in the analyzed variables indicate minor deviations, which are not likely to be caused by the technique used. However, all three methodologies exhibited errors that are estimated to be within a maximum of 10%, with a mean value of approximately 8%.
本文研究了用于环形两相流分析的非侵入式技术，重点是液膜表征，以了解对传热和传质至关重要的界面现象。研究液膜时空演变的方法有限，如平面激光诱导荧光（PLIF）。然而，这种方法在光学方面存在挑战，因此需要改进技术来减少折射和反射，如折射率匹配（RIM）。本研究利用环形流动实验设备，在 4200 到 10400 的液体雷诺数范围内分析了 RIM 和非 RIM PLIF。从定性和定量的角度对三种配置--PLIF RIM90、PLIF RIM40 和 PLIF nRIM40 进行了比较。在定量分析中，测量了液膜的关键变量，即平均膜厚、扰动波高度和频率。分析变量的变化表明存在微小的偏差，这些偏差不太可能是由所使用的技术造成的。不过，所有三种方法的误差估计最大在 10%以内，平均值约为 8%。
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Accurately identifying adulterants in agriculture and food products is associated with preventing food safety and commercial fraud activities. However, a rapid, accurate, and robust prediction model for adulteration detection is hard to achieve in practice. Therefore, this study aimed to explore deep-learning algorithms as an approach to accurately identify the level of adulterated coconut milk using two types of NIR spectrophotometer, including benchtop FT-NIR and portable Micro-NIR. Coconut milk adulteration samples came from deliberate adulteration with corn flour and tapioca starch in the 1 to 50% range. A total of four types of deep-learning algorithm architecture that were self-modified to a one-dimensional framework were developed and tested to the NIR dataset, including simple CNN, S-AlexNET, ResNET, and GoogleNET. The results confirmed the feasibility of deep-learning algorithms for predicting the degree of coconut milk adulteration by corn flour and tapioca starch using NIR spectra with reliable performance (R2 of 0.886–0.999, RMSE of 0.370–6.108%, and Bias of −0.176–1.481). Furthermore, the ratio of percent deviation (RPD) of all algorithms with all types of NIR spectrophotometers indicates an excellent capability for quantitative predictions for any application (RPD > 8.1) except for case predicting tapioca starch, using FT-NIR by ResNET (RPD < 3.0). This study demonstrated the feasibility of using deep-learning algorithms and NIR spectral data as a rapid, accurate, robust, and non-destructive way to evaluate coconut milk adulterants. Last but not least, Micro-NIR is more promising than FT-NIR in predicting coconut milk adulteration from solid adulterants, and it is portable for in situ measurements in the future.
{"title":"Exploring Deep Learning to Predict Coconut Milk Adulteration Using FT-NIR and Micro-NIR Spectroscopy","authors":"Agustami Sitorus, R. Lapcharoensuk","doi":"10.3390/s24072362","DOIUrl":"https://doi.org/10.3390/s24072362","url":null,"abstract":"Accurately identifying adulterants in agriculture and food products is associated with preventing food safety and commercial fraud activities. However, a rapid, accurate, and robust prediction model for adulteration detection is hard to achieve in practice. Therefore, this study aimed to explore deep-learning algorithms as an approach to accurately identify the level of adulterated coconut milk using two types of NIR spectrophotometer, including benchtop FT-NIR and portable Micro-NIR. Coconut milk adulteration samples came from deliberate adulteration with corn flour and tapioca starch in the 1 to 50% range. A total of four types of deep-learning algorithm architecture that were self-modified to a one-dimensional framework were developed and tested to the NIR dataset, including simple CNN, S-AlexNET, ResNET, and GoogleNET. The results confirmed the feasibility of deep-learning algorithms for predicting the degree of coconut milk adulteration by corn flour and tapioca starch using NIR spectra with reliable performance (R2 of 0.886–0.999, RMSE of 0.370–6.108%, and Bias of −0.176–1.481). Furthermore, the ratio of percent deviation (RPD) of all algorithms with all types of NIR spectrophotometers indicates an excellent capability for quantitative predictions for any application (RPD > 8.1) except for case predicting tapioca starch, using FT-NIR by ResNET (RPD < 3.0). This study demonstrated the feasibility of using deep-learning algorithms and NIR spectral data as a rapid, accurate, robust, and non-destructive way to evaluate coconut milk adulterants. Last but not least, Micro-NIR is more promising than FT-NIR in predicting coconut milk adulteration from solid adulterants, and it is portable for in situ measurements in the future.","PeriodicalId":221960,"journal":{"name":"Sensors (Basel, Switzerland)","volume":"93 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140778129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The verification of the correctness, adaptability, and robustness of software systems in modern precision measurement instruments is of great significance. Due to the difficulty in processing and calibrating high-precision fine-pitch gear artefacts, the function verification and accuracy calibration of vision measurement instruments for the fine-pitch gear have become a challenge. The calibration method of the gear vision measurement system based on the virtual gear artefact involves two steps, namely obtaining and applying the virtual artefact. The obtained virtual gear artefact has the same geometric features, error features, and image edge features as the real artefact. The calibration method based on the virtual artefact can complete the correctness verification of the gear vision measurement system, and is superior to the traditional methods in adaptability verification, robustness verification, and fault analysis. In a test, the characteristic error of the virtual gear artefact could be reproduced with the original shape in the evaluation results of the computer vision gear measurement (CVGM) system, while the reproduction error did not exceed 1.9 μm. This can meet the requirements of the verification of the gear vision measurement software. The application of the virtual gear artefact can significantly improve the accuracy and robustness of the computer vision measuring instrument of the fine-pitch gear.
{"title":"Calibration Method Based on Virtual Gear Artefact for Computer Vision Measuring Instrument of Fine Pitch Gear","authors":"Xiaoyi Wang, Tianyang Yao, Zhaoyao Shi","doi":"10.3390/s24072289","DOIUrl":"https://doi.org/10.3390/s24072289","url":null,"abstract":"The verification of the correctness, adaptability, and robustness of software systems in modern precision measurement instruments is of great significance. Due to the difficulty in processing and calibrating high-precision fine-pitch gear artefacts, the function verification and accuracy calibration of vision measurement instruments for the fine-pitch gear have become a challenge. The calibration method of the gear vision measurement system based on the virtual gear artefact involves two steps, namely obtaining and applying the virtual artefact. The obtained virtual gear artefact has the same geometric features, error features, and image edge features as the real artefact. The calibration method based on the virtual artefact can complete the correctness verification of the gear vision measurement system, and is superior to the traditional methods in adaptability verification, robustness verification, and fault analysis. In a test, the characteristic error of the virtual gear artefact could be reproduced with the original shape in the evaluation results of the computer vision gear measurement (CVGM) system, while the reproduction error did not exceed 1.9 μm. This can meet the requirements of the verification of the gear vision measurement software. The application of the virtual gear artefact can significantly improve the accuracy and robustness of the computer vision measuring instrument of the fine-pitch gear.","PeriodicalId":221960,"journal":{"name":"Sensors (Basel, Switzerland)","volume":"31 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140792580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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