Sensors最新文献

{"title":"Research on the Quantitative Inversion of Soil Iron Oxide Content Using Hyperspectral Remote Sensing and Machine Learning Algorithms in the Lufeng Annular Structural Area of Yunnan, China.","authors":"Yingtao Qi, Shu Gan, Xiping Yuan, Lin Hu, Jiankai Hu, Hailong Zhao, Chengzhuo Lu","doi":"10.3390/s24217039","DOIUrl":"10.3390/s24217039","url":null,"abstract":"<p><p>This study used hyperspectral remote sensing to rapidly, economically, and non-destructively determine the soil iron oxide content of the Dinosaur Valley annular tectonic region of Lufeng, Yunnan Province. The laboratory determined the iron oxide content and original spectral reflectance (OR) in 138 surface soil samples. We first subjected the OR data to Savizky-Golay smoothing, followed by four spectral transformations-continuum removal reflectance, reciprocal logarithm reflectance, standard normal variate reflectance, and first-order differential reflectance-which improved the signal-to-noise ratio of the spectral curves and highlighted the spectral features. Then, we combined the correlation coefficient method (CC), competitive adaptive reweighting algorithm, and Boruta algorithm to screen out the characteristic wavelength. From this, we constructed the linear partial least squares regression model, nonlinear random forest, and XGBoost machine learning algorithms. The results show that the CC-Boruta method can effectively remove any noise and irrelevant information to improve the model's accuracy and stability. The XGBoost nonlinear machine learning algorithm model better captures the complex nonlinear relationship between the spectra and iron oxide content, thus improving its accuracy. This provides a relevant reference for the rapid and accurate inversion of iron oxide content in soil using hyperspectral data.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"24 21","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11548619/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142627431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel Applications in Controlled Drug Delivery Systems by Integrating Osmotic Pumps and Magnetic Nanoparticles.","authors":"David Navarro-Tumar, Belén García-Merino, Cristina González-Fernández, Inmaculada Ortiz, Ma-Fresnedo San-Román, Eugenio Bringas","doi":"10.3390/s24217042","DOIUrl":"10.3390/s24217042","url":null,"abstract":"<p><p>The alarming rise in chronic diseases worldwide highlights the urgent need to overcome the limitations of conventional drug delivery systems. In this context, osmotic pumps are able to release drugs by differential osmotic pressure, achieving a controlled rate independent of physiological factors and reducing the dosing frequency. As osmotic pumps are based on the phenomenon of osmosis, the choice of high osmolality draw solutions (DSs) is a critical factor in the successful delivery of the target drug. Therefore, one alternative that has received particular attention is the formulation of DSs with magnetic nanoparticles (MNPs) due to their easy recovery, negligible reverse solute flux (RSF), and their possible tailor-made functionalization to generate high osmotic gradients. In this work, the possible integration of DSs formulated with MNPs in controlled drug delivery systems is discussed for the first time. In particular, the main potential advantages that these novel medical devices could offer, including improved scalability, regeneration, reliability, and enhanced drug delivery performance, are provided and discussed. Thus, the results of this review may demonstrate the potential of MNPs as osmotic agents, which could be useful for advancing the design of osmotic pump-based drug delivery systems.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"24 21","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11548579/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142626339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ZnO Nanowires/Self-Assembled Monolayer Mediated Selective Detection of Hydrogen.","authors":"Mandeep Singh, Navpreet Kaur, Elisabetta Comini","doi":"10.3390/s24217011","DOIUrl":"10.3390/s24217011","url":null,"abstract":"<p><p>We are proposing a novel self-assembled monolayer (SAM) functionalized ZnO nanowires (NWs)-based conductometric sensor for the selective detection of hydrogen (H₂). The modulation of the surface electron density of ZnO NWs due to the presence of negatively charged terminal amine groups (-NH₂) of monolayers leads to an enhanced electron donation from H₂ to ZnO NWs. This, in turn, increases the relative change in the conductance (response) of functionalized ZnO NWs as compared to bare ones. In contrast, the sensing mechanism of bare ZnO NWs is determined by the chemisorbed oxygen ions. The functionalized ZnO NWs exhibit an eight times higher response compared to bare ZnO NWs at an optimal working temperature of 200 °C. Finally, in comparison to studies in the literature involving strategies to enhance the sensing performance of metal oxides toward H₂, like decoration with metal nanoparticles, heterostructures, and functionalization with a metal-organic framework, etc., SAM functionalization showed superior sensing results.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"24 21","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11548179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142627705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smartphone Light Sensors as an Innovative Tool for Solar Irradiance Measurements.","authors":"José Luis Di Laccio, Andrés Monetta, Rodrigo Alonso-Suárez, Martín Monteiro, Arturo C Marti","doi":"10.3390/s24217051","DOIUrl":"10.3390/s24217051","url":null,"abstract":"<p><p>In recent years, the teaching of experimental science and engineering has been revolutionized by the integration of smartphone sensors, which are widely used by a large portion of the population. Concurrently, interest in solar energy has surged. This raises the important question of how smartphone sensors can be harnessed to incorporate solar energy studies into undergraduate education. We provide comprehensive guidelines for using smartphone sensors in various conditions, along with detailed instructions on how to calibrate them with widely accessible clear-sky satellite data. This smartphone-based method is also compared with professional reference measurements to ensure consistency. This experiment can be easily conducted with most smartphones, basic materials, and a clear, open location over a few hours (methods). The findings demonstrate that smartphones, combined with simple resources, can accurately measure solar irradiance and support experiments on solar radiation physics, atmospheric interactions, and variations in solar energy across locations, cloud cover, and time scales. This approach provides a practical and accessible tool for studying solar energy, offering an innovative and engaging method for measuring solar resources.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"24 21","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11548245/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142627500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ChartLine: Automatic Detection and Tracing of Curves in Scientific Line Charts Using Spatial-Sequence Feature Pyramid Network.","authors":"Wenjin Yang, Jie He, Qian Li","doi":"10.3390/s24217015","DOIUrl":"10.3390/s24217015","url":null,"abstract":"<p><p>Line charts are prevalent in scientific documents and commercial data visualization, serving as essential tools for conveying data trends. Automatic detection and tracing of line paths in these charts is crucial for downstream tasks such as data extraction, chart quality assessment, plagiarism detection, and visual question answering. However, line graphs present unique challenges due to their complex backgrounds and diverse curve styles, including solid, dashed, and dotted lines. Existing curve detection algorithms struggle to address these challenges effectively. In this paper, we propose ChartLine, a novel network designed for detecting and tracing curves in line graphs. Our approach integrates a Spatial-Sequence Attention Feature Pyramid Network (SSA-FPN) in both the encoder and decoder to capture rich hierarchical representations of curve structures and boundary features. The model incorporates a Spatial-Sequence Fusion (SSF) module and a Channel Multi-Head Attention (CMA) module to enhance intra-class consistency and inter-class distinction. We evaluate ChartLine on four line chart datasets and compare its performance against state-of-the-art curve detection, edge detection, and semantic segmentation methods. Extensive experiments demonstrate that our method significantly outperforms existing algorithms, achieving an F-measure of 94% on a synthetic dataset.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"24 21","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11548359/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142627350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Review of Potential Exoskeletons for the Prevention of Work-Related Musculoskeletal Disorders in Agriculture.","authors":"Sanura Dunu Arachchige, Lasitha Piyathilaka, Jung-Hoon Sul, D M G Preethichandra","doi":"10.3390/s24217026","DOIUrl":"10.3390/s24217026","url":null,"abstract":"<p><p>Exoskeletons possess a high potential for assisting the human workforce while eliminating or reducing the risk of Work-Related Musculoskeletal Disorders (WMSDs). However, their usage in agricultural work, where there is a plethora of reported WMSD cases, seems limited. Since agricultural tasks are complex and performed in harsh environments, developing novel exoskeleton-based solutions could be challenging. However, commercial exoskeletons are already being used in various other industries, such as logistics, military, medicine, and manufacturing. Thus, it is expected that those existing exoskeleton solutions could be applied to agricultural tasks. Nevertheless, prior to implementation, assessing the feasibility, efficacy, and necessary modifications for these exoskeletons is imperative to supporting agricultural activities prone to WMSDs. In this review, prevalent exoskeletons documented in scientific literature are identified, and their potential relevance to agricultural tasks with elevated WMSD risks is evaluated. The review further highlights and deliberates on exoskeletons that could be applicable in an agricultural context. This comprehensive examination serves as a foundational step towards the conceptualization and development of exoskeleton-based approaches tailored explicitly for agricultural tasks.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"24 21","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11548226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142626082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Portable Detection of Copper Ion Using Personal Glucose Meter.","authors":"Bin Du, Taoying Chen, Anqi Huang, Haijun Chen, Wei Liu","doi":"10.3390/s24217002","DOIUrl":"10.3390/s24217002","url":null,"abstract":"<p><p>A simple and sensitive method for Cu²⁺ detection was developed using the Cu⁺-catalyzed alkyne-azide cycloaddition reaction, Fe₃O₄ magnetic nanoparticles (MNPs) as the reaction platform, and a portable blood glucose meter (PGM) as the detection method. Gold nanoparticles (AuNPs) were labeled with glucose oxidase (GOx) and alkyne-functionalized, terminally thiolated ssDNA (C2). In the presence of Cu²⁺ and ascorbate, the functionalized AuNPs were captured onto MNPs modified with azide-functionalized ssDNA (C1) via the Cu⁺-catalyzed alkyne-azide cycloaddition reaction. The GOx on the AuNPs' surface oxidized glucose (Glu) into gluconic acid and H₂O₂, reducing the Glu content in the reaction solution, which was quantitatively detected by the PGM. Under optimal conditions, the PGM response of the system showed a good linear relationship with the logarithm of Cu²⁺ concentration in the range of 0.05 to 10.00 μmol/L, with a detection limit of 0.03 μmol/L (3σ). In actual tap water samples, the spiked recovery rate of Cu²⁺ was between 92.30% and 113.33%, and the relative standard deviation was between 0.14% and 0.34%, meeting the detection requirements for Cu²⁺ in real water samples.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"24 21","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11548145/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142627208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating MIR and NIR Spectroscopy Coupled with Multivariate Analysis for Detection and Quantification of Additives in Tobacco Products.","authors":"Zeb Akhtar, Michaël Canfyn, Céline Vanhee, Cédric Delporte, Erwin Adams, Eric Deconinck","doi":"10.3390/s24217018","DOIUrl":"10.3390/s24217018","url":null,"abstract":"<p><p>The detection and quantification of additives in tobacco products are critical for ensuring consumer safety and compliance with regulatory standards. Traditional analytical techniques, like gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and others, although effective, suffer from drawbacks, including complex sample preparation, high costs, lengthy analysis times, and the requirement for skilled operators. This study addresses these challenges by evaluating the efficacy of mid-infrared (MIR) spectroscopy and near-IR (NIR) spectroscopy, coupled with multivariate analysis, as potential solutions for the detection and quantification of additives in tobacco products. So, a representative set of tobacco products was selected and spiked with the targeted additives, namely caffeine, menthol, glycerol, and cocoa. Multivariate analysis of MIR and NIR spectra consisted of principal component analysis (PCA), hierarchical clustering analysis (HCA), partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) to classify samples based on targeted additives. Based on the unsupervised techniques (PCA and HCA), a distinction could be made between spiked and non-spiked samples for all four targeted additives based on both MIR and NIR spectral data. During supervised analysis, SIMCA achieved 87-100% classification accuracy for the different additives and for both spectroscopic techniques. PLS-DA models showed classification rates of 80% to 100%, also demonstrating robust performance. Regression studies, using PLS, showed that it is possible to effectively estimate the concentration levels of the targeted molecules. The results also highlight the necessity of optimizing data pretreatment for accurate quantification of the target additives. Overall, NIR spectroscopy combined with SIMCA provided the most accurate and robust classification models for all target molecules, indicating that it is the most effective single technique for this type of analysis. MIR, on the other hand, showed the overall best performance for quantitative estimation.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"24 21","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11548177/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142626831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Apple Detection and Counting with AD-YOLO and MR-SORT.","authors":"Xueliang Yang, Yapeng Gao, Mengyu Yin, Haifang Li","doi":"10.3390/s24217012","DOIUrl":"10.3390/s24217012","url":null,"abstract":"<p><p>In the production management of agriculture, accurate fruit counting plays a vital role in the orchard yield estimation and appropriate production decisions. Although recent tracking-by-detection algorithms have emerged as a promising fruit-counting method, they still cannot completely avoid fruit occlusion and light variations in complex orchard environments, and it is difficult to realize automatic and accurate apple counting. In this paper, a video-based multiple-object tracking method, MR-SORT (Multiple Rematching SORT), is proposed based on the improved YOLOv8 and BoT-SORT. First, we propose the AD-YOLO model, which aims to reduce the number of incorrect detections during object tracking. In the YOLOv8s backbone network, an Omni-dimensional Dynamic Convolution (ODConv) module is used to extract local feature information and enhance the model's ability better; a Global Attention Mechanism (GAM) is introduced to improve the detection ability of a foreground object (apple) in the whole image; a Soft Spatial Pyramid Pooling Layer (SSPPL) is designed to reduce the feature information dispersion and increase the sensory field of the network. Then, the improved BoT-SORT algorithm is proposed by fusing the verification mechanism, SURF feature descriptors, and the Vector of Local Aggregate Descriptors (VLAD) algorithm, which can match apples more accurately in adjacent video frames and reduce the probability of ID switching in the tracking process. The results show that the mAP metrics of the proposed AD-YOLO model are 3.1% higher than those of the YOLOv8 model, reaching 96.4%. The improved tracking algorithm has 297 fewer ID switches, which is 35.6% less than the original algorithm. The multiple-object tracking accuracy of the improved algorithm reached 85.6%, and the average counting error was reduced to 0.07. The coefficient of determination R2 between the ground truth and the predicted value reached 0.98. The above metrics show that our method can give more accurate counting results for apples and even other types of fruit.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"24 21","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11548465/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142627228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Open Radio Access Network Systems with LLAMA V2 for Enhanced Mobile Broadband, Ultra-Reliable Low-Latency Communications, and Massive Machine-Type Communications: A Framework for Efficient Network Slicing and Real-Time Resource Allocation.","authors":"H Ahmed Tahir, Walaa Alayed, Waqar Ul Hassan, Thuan Dinh Do","doi":"10.3390/s24217009","DOIUrl":"10.3390/s24217009","url":null,"abstract":"<p><p>This study presents an advanced framework integrating LLAMA_V2, a large language model, into Open Radio Access Network (O-RAN) systems. The focus is on efficient network slicing for various services. Sensors in IoT devices generate continuous data streams, enabling resource allocation through O-RAN's dynamic slicing and LLAMA_V2's optimization. LLAMA_V2 was selected for its superior ability to capture complex network dynamics, surpassing traditional AI/ML models. The proposed method combines sophisticated mathematical models with optimization and interfacing techniques to address challenges in resource allocation and slicing. LLAMA_V2 enhances decision making by offering explanations for policy decisions within the O-RAN framework and forecasting future network conditions using a lightweight LSTM model. It outperforms baseline models in key metrics such as latency reduction, throughput improvement, and packet loss mitigation, making it a significant solution for 5G network applications in advanced industries.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"24 21","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11548582/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142626736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}