This study aims to investigate the responses of a perovskite-based direct-conversion dual-layer flat-panel detector(DL-FPD)numerically.To this end,the X-ray sensitivity,spatial resolution quantified by the modulation ...This study aims to investigate the responses of a perovskite-based direct-conversion dual-layer flat-panel detector(DL-FPD)numerically.To this end,the X-ray sensitivity,spatial resolution quantified by the modulation transfer function(MTF),and detective quantum efficiency(DQE)of the DL-FPD are evaluated numerically using a linear cascade model.In addition,both the single-crystal(SC)and polycrystalline(PC)structures of MAPbI_(3)are investigated,along with various other key parameters such as the material thickness,electric field strength,X-ray beam spectrum,and electronic readout noise.The results demonstrate that SC perovskite consistently exhibits better performance than PC perovskite owing to fewer material defects.Increasing the layer thickness may decrease the MTF,but can also enhance the sensitivity and DQE.Moreover,appropriately increasing the external electric field within the material can improve the sensitivity,MTF,and DQE.Finally,reducing the electronic readout noise can significantly enhance the DQE for low-dose imaging.This study demonstrates the potential of high-quality dual-energy X-ray imaging using direct-conversion perovskite DL-FPDs.展开更多
The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduce...The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.展开更多
Formamidinium lead iodide(FAPbI_(3))perovskite exhibits an impressive X-ray absorption coefficient and a large carrier mobility-lifetime product(μτ),making it as a highly promising candidate for X-ray detection appl...Formamidinium lead iodide(FAPbI_(3))perovskite exhibits an impressive X-ray absorption coefficient and a large carrier mobility-lifetime product(μτ),making it as a highly promising candidate for X-ray detection application.However,the presence of larger FA^(+)cation induces to an expansion of the Pb-I octahedral framework,which unfortunately affects both the stability and charge carrier mobility of the corresponding devices.To address this challenge,we develop a novel low-dimensional(HtrzT)PbI_(3) perovskite featuring a conjugated organic cation(1H-1,2,4-Triazole-3-thiol,HtrzT^(+))which matches well with theα-FAPbI_(3) lattices in two-dimensional plane.Benefiting from the matched lattice between(HtrzT)PbI_(3) andα-FAPbI_(3),the anchored lattice enhances the Pb-I bond strength and effectively mitigates the inherent tensile strain of theα-FAPbI_(3) crystal lattice.The X-ray detector based on(HtrzT)PbI_(3)(1.0)/FAPbI_(3) device achieves a remarkable sensitivity up to 1.83×10^(5)μC Gy_(air)^(−1) cm^(−2),along with a low detection limit of 27.6 nGy_(air) s^(−1),attributed to the release of residual stress,and the enhancement in carrier mobility-lifetime product.Furthermore,the detector exhibits outstanding stability under X-ray irradiation with tolerating doses equivalent to nearly 1.17×10^(6) chest imaging doses.展开更多
Soft X-ray detectors play a vital role in materials science,high-energy physics and medical imaging.Cs_(2)AgBiBr_(6),a lead-free double perovskite,has gained attention for its excellent optoelectronic properties,stabi...Soft X-ray detectors play a vital role in materials science,high-energy physics and medical imaging.Cs_(2)AgBiBr_(6),a lead-free double perovskite,has gained attention for its excellent optoelectronic properties,stability,and nontoxicity.However,its fast crystallization and requirement for high-temperature annealing(>250℃)often lead to inferior film quality,limiting its application in flexible devices.This study introduces an alloying strategy that significantly improves the quality of Cs_(2)AgBiBr_(6)thin films annealed at a reduced temperature of 150℃.Devices based on the alloyed thin films exhibit an ultra-low dark current of 0.32 nA·cm^(-2)and a quantum efficiency of 725%.Furthermore,the first successful integration of Cs_(2)AgBiBr_(6)with a thinfilm transistor backplane demonstrates its superior imaging performance,indicating that Cs_(2)AgBiBr_(6)is a promising material for next-generation soft X-ray sensors.展开更多
Background Frailty is common and significantly impacts prognosis in heart failure(HF). The Vulnerable Elders Survey-13(VES-13), widely used in oncogeriatrics and public health, remains unexplored as a frailty screenin...Background Frailty is common and significantly impacts prognosis in heart failure(HF). The Vulnerable Elders Survey-13(VES-13), widely used in oncogeriatrics and public health, remains unexplored as a frailty screening tool in HF outpatients. In this study, we prospectively evaluated VES-13 against a multimodal screening assessment in detecting frailty and predicting individual risk of adverse prognosis.Methods Frailty was assessed at the initial visit using both a multimodal approach, incorporating Barthel Index, Older American Resources and Services scale, Pfeiffer Test, abbreviated Geriatric Depression Scale, age > 85 years, lacking support systems,and VES-13. Patients scoring ≥ 3 on VES-13 or meeting at least one multimodal criterion were classified as frail. Endpoints included all-cause mortality, a composite of death or HF hospitalization, and recurrent HF hospitalizations.Results A total of 301 patients were evaluated. VES-13 identified 40.2% as frail and the multimodal assessment 33.2%. In Cox regression analyses, frailty identified by VES-13 showed greater prognostic significance than the multimodal assessment for allcause mortality(HR = 3.70 [2.15–6.33], P < 0.001 vs. 2.40 [1.46–4.0], P = 0.001) and the composite endpoint(HR = 3.13 [2.02–4.84], P< 0.001 vs. 1.96 [1.28–2.99], P = 0.002). Recurrent HF hospitalizations were four times more frequent in VES-13 frail patients while two times in those identified as frail by the multimodal assessment. Additionally, stratifying patients by VES-13 tertiles provided robust risk differentiation.Conclusions VES-13, a simple frailty tool, outperformed a comprehensive multimodal assessment and could be easily integrated into routine HF care, highlighting its clinical utility in identifying patients at risk for poor outcomes.展开更多
The detrimental phase transformations of sodium layered transition metal oxides(Na_(x)TMO_(2))during desodiation/sodiation seriously suppress their practical applications for sodium ion batteries(SIBs).Undoubtedly,com...The detrimental phase transformations of sodium layered transition metal oxides(Na_(x)TMO_(2))during desodiation/sodiation seriously suppress their practical applications for sodium ion batteries(SIBs).Undoubtedly,comprehensively investigating of the dynamic crystal structure evolutions of Na_(x)TMO_(2)associating with Na ions extraction/intercalation and then deeply understanding of the relationships between electrochemical performances and phase structures drawing support from advanced characterization techniques are indispensable.In-situ high-energy X-ray diffraction(HEXRD),a powerful technology to distinguish the crystal structure of electrode materials,has been widely used to identify the phase evolutions of Na_(x)TMO_(2)and then profoundly revealed the electrochemical reaction processes.In this review,we begin with the descriptions of synchrotron characterization techniques and then present the advantages of synchrotron X-ray diffraction(XRD)over conventional XRD in detail.The optimizations of structural stability and electrochemical properties for P2-,O3-,and P2/O3-type Na_(x)TMO_(2)cathodes through single/dual-site substitution,high-entropy design,phase composition regulation,and surface engineering are summarized.The dynamic crystal structure evolutions of Na_(x)TMO_(2)polytypes during Na ion extraction/intercalation as well as corresponding structural enhancement mechanisms characterizing by means of HEXRD are concluded.The interior relationships between structure/component of Na_(x)TMO_(2)polytypes and their electrochemical properties are discussed.Finally,we look forward the research directions and issues in the route to improve the electrochemical properties of Na_(x)TMO_(2)cathodes for SIBs in the future and the combined utilizations of multiple characterization techniques.This review will provide significant guidelines for rational designs of high-performance Na_(x)TMO_(2)cathodes.展开更多
X-ray detection is of great importance for computed tomography scanning,security inspection,non-destructive testing of industrial products and other detection applications[1,2].Due to the potential cancer risk caused ...X-ray detection is of great importance for computed tomography scanning,security inspection,non-destructive testing of industrial products and other detection applications[1,2].Due to the potential cancer risk caused by X-ray inspection,there is always room to achieve X-ray detectors with even better sensitivity and detection limit.There are two available approaches detecting X-rays,the first is indirect conversion using scintillators to convert X-ray photons into light and then a photodiode to展开更多
This study explores the application of X-ray-induced photochromism and photoluminescence in optical storage,anti-counterfeiting,non-destructive testing,and high-resolution X-ray detection and imaging.Ba_(2)LaNbO_(6):B...This study explores the application of X-ray-induced photochromism and photoluminescence in optical storage,anti-counterfeiting,non-destructive testing,and high-resolution X-ray detection and imaging.Ba_(2)LaNbO_(6):Bi,Eu phosphors were synthesized,with Bi enhancing X-ray-induced photochromic prop-erties.Under X-ray irradiation,the phosphors transfer from white to red in bright field conditions and emit red photoluminescence in dark field conditions.Exposure to 470 nm ultraviolet light induces rapid bleaching.The mechanisms of photochromism and photoluminescence,particularly Bi's role as a colorant,were systematically investigated.The Ba_(2)LaNbO_(6):Bi,Eu phosphors film achieves high resolution,high-lighting its potential for X-ray imaging and non-destructive testing.Furthermore,the flexible Ba_(2)LaNbO_(6):Bi,Eu film supports dual-mode imaging and detection,addressing the limitations of traditional flat dis-plays in 3D imaging.展开更多
An x-ray scintillator screen with a special structure, functioning as detector and analyser grating, was proposed for collecting the interferogram of differential phase contrast imaging without absorption grating and ...An x-ray scintillator screen with a special structure, functioning as detector and analyser grating, was proposed for collecting the interferogram of differential phase contrast imaging without absorption grating and difficulty of fabrication by a state of the art technique. On the basis of phase grating diffraction, a detecting model of the scintillator screen was built for analysing the phase and absorption information of objects. According to the analysis, a new method of phase retrievals based on two-images and the optimal structure of screen were presented.展开更多
With the rapid growth of the autonomous system,deep learning has become integral parts to enumerate applications especially in the case of healthcare systems.Human body vertebrae are the longest and complex parts of t...With the rapid growth of the autonomous system,deep learning has become integral parts to enumerate applications especially in the case of healthcare systems.Human body vertebrae are the longest and complex parts of the human body.There are numerous kinds of conditions such as scoliosis,vertebra degeneration,and vertebrate disc spacing that are related to the human body vertebrae or spine or backbone.Early detection of these problems is very important otherwise patients will suffer from a disease for a lifetime.In this proposed system,we developed an autonomous system that detects lumbar implants and diagnoses scoliosis from the modified Vietnamese x-ray imaging.We applied two different approaches including pre-trained APIs and transfer learning with their pre-trained models due to the unavailability of sufficient x-ray medical imaging.The results show that transfer learning is suitable for the modified Vietnamese x-ray imaging data as compared to the pre-trained API models.Moreover,we also explored and analyzed four transfer learning models and two pre-trained API models with our datasets in terms of accuracy,sensitivity,and specificity.展开更多
INTRODUCTION.On May 1st,2024,around 2:10 a.m.,a catastrophic collapse occurred along the Meilong Expressway near Meizhou City,Guangdong Province,China,at coordinates 24°29′24″N and 116°40′25″E.This colla...INTRODUCTION.On May 1st,2024,around 2:10 a.m.,a catastrophic collapse occurred along the Meilong Expressway near Meizhou City,Guangdong Province,China,at coordinates 24°29′24″N and 116°40′25″E.This collapse resulted in a pavement failure of approximately 17.9 m in length and covering an area of about 184.3 m^(2)(Chinanews,2024).展开更多
Detection of ore waste is crucial for achieving automation in mineral metallurgy production.However,deep learning-based target detection algorithms still face several challenges in iron waste screening,including poor ...Detection of ore waste is crucial for achieving automation in mineral metallurgy production.However,deep learning-based target detection algorithms still face several challenges in iron waste screening,including poor lighting conditions in underground mining environments,dust disturbances,platform vibrations during operation,and limited resources for large-scale computing equipment.These factors contribute to extended computation times and unsatisfactory detection accuracy.To address these challenges,this paper proposes an ore waste detection algorithm based on an improved version of YOLOv5.To enhance feature extraction capabilities,the RepLKNet module is incorporated into the YOLOv5 backbone and neck networks.This module enhances the deformation information of feature extraction with the maximum effective Receptive Field to increase the model's accuracy.The Normalizationbased Attention Module(NAM)was introduced to enhance the attention mechanism by focusing on the most relevant features.This improves accuracy in detecting objects against noisy or unclear backgrounds,thereby further enhancing detection performance while reducing model parameters.Additionally,the loss function is optimized to constrain angular deviation using the SIOU loss function,which prevents the training frame from drifting during training and enhances convergence speed.To validate the performance of the proposed method,we tested it using a self-constructed dataset comprising 1,328 images obtained from the crushing station at Jinchuan Group's No.2 mine.The results indicate that,compared to YOLOv5s on the self-constructed dataset,the proposed algorithm achieves an 18.3%improvement in mAP(0.5),a 54%reduction in FLOPs,and a 52.53%decrease in model parameters.The effectiveness and superiority of the proposed algorithm are demonstrated through case studies and comparative analyses.展开更多
Tomato plant diseases often first manifest on the leaves,making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry.However,conventional deep learning models face challenges ...Tomato plant diseases often first manifest on the leaves,making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry.However,conventional deep learning models face challenges such as large model sizes and slow detection speeds when deployed on resource-constrained platforms and agricultural machinery.This paper proposes a lightweight model for detecting tomato leaf diseases,named LT-YOLO,based on the YOLOv8n architecture.First,we enhance the C2f module into a RepViT Block(RVB)with decoupled token and channel mixers to reduce the cost of feature extraction.Next,we incorporate a novel Efficient Multi-Scale Attention(EMA)mechanism in the deeper layers of the backbone to improve detection of critical disease features.Additionally,we design a lightweight detection head,LT-Detect,using Partial Convolution(PConv)to significantly reduce the classification and localization costs during detection.Finally,we introduce a Receptive Field Block(RFB)in the shallow layers of the backbone to expand the model’s receptive field,enabling effective detection of diseases at various scales.The improved model reduces the number of parameters by 43%and the computational load by 50%.Additionally,it achieves a mean Average Precision(mAP)of 90.9%on a publicly available dataset containing 3641 images of tomato leaf diseases,with only a 0.7%decrease compared to the baseline model.This demonstrates that the model maintains excellent accuracy while being lightweight,making it suitable for rapid detection of tomato leaf diseases.展开更多
X-ray detectors show potential applications in medical imaging,materials science,and nuclear energy.To achieve high detection efficiency and spatial resolution,many conventional semiconductor materials,such as amorpho...X-ray detectors show potential applications in medical imaging,materials science,and nuclear energy.To achieve high detection efficiency and spatial resolution,many conventional semiconductor materials,such as amorphous selenium,cadmium telluride zinc,and perovskites,have been utilized in direct conversion X-ray detectors.However,these semiconductor materials are susceptible to temperature-induced performance degradation,crystallization,delamination,uneven lattice growth,radiation damage,and high dark current.This study explores a new approach by coupling an FC40 electronic fluorinated liquid with a specialized high-resolution and high-readout-speed complementary metal-oxide-semiconductor(CMOS)pixel array,specifically the Topmetal II−chip,to fabricate a direct conversion X-ray detector.The fluorinated liquid FC40(molecular formula:C_(21)F_(48)N_(2))is an electronic medium that is minimally affected by temperature and displays no issues with uniform conductivity.It exhibits a low dark current and minimal radiation damage and enables customizable thickness in X-ray absorption.This addresses the limitations inherent in conventional semiconductor-based detectors.In this study,simple X-ray detector imaging tests were conducted,demonstrating the excellent coupling capability between FC40 electronic fluorinated liquid and CMOS chips by the X-ray detector.A spatial resolution of 4.0 lp/mm was measured using a striped line par card,and a relatively clear image of a cockroach was displayed in the digital radiography imaging results.Preliminary test results indicated the feasibility of fabricating an X-ray detector by combining FC40 electronic fluorinated liquid and CMOS chips.Owing to the absence of issues related to chip-material coupling,a high spatial resolution could be achieved by reducing the chip pixel size.This method presents a new avenue for studies on novel liquid-based direct conversion X-ray detectors.展开更多
Deep learning created a sharp rise in the development of autonomous image recognition systems,especially in the case of the medical field.Among lung problems,tuberculosis,caused by a bacterium called Mycobacterium tub...Deep learning created a sharp rise in the development of autonomous image recognition systems,especially in the case of the medical field.Among lung problems,tuberculosis,caused by a bacterium called Mycobacterium tuberculosis,is a dangerous disease because of its infection and damage.When an infected person coughs or sneezes,tiny droplets can bring pathogens to others through inhaling.Tuberculosis mainly damages the lungs,but it also affects any part of the body.Moreover,during the period of the COVID-19(coronavirus disease 2019)pandemic,the access to tuberculosis diagnosis and treatment has become more difficult,so early and simple detection of tuberculosis has been more and more important.In our study,we focused on tuberculosis diagnosis by using the chestX-ray image,the essential input for the radiologist’s profession,and researched the effectiveness of the transfer learning approach in the case study of Vietnamese chest X-ray images.We proposed four strategies to clarify our hypothesis in different ways of applying transfer learning and different training set types.We also prepared a Vietnamese X-ray image dataset with the support of the VRPACS team to provide the basis for training and testing deep learning models.Our experiments were carried out by applying three different architectures,Alexnet,Resnet,and Densenet,on international,Vietnamese,and combined X-ray image datasets.After training,all models were verified on a pure Vietnamese X-rays set.The results show that transfer learning is suitable in the case study of Vietnamese chest X-ray images with high evaluating metrics in terms of AUC(Area under the Receiver Operating Characteristic Curve),sensitivity,specificity,and accuracy.In the best strategy,most of the scores were more than 0.93,and all AUCs were more than 0.98.展开更多
With the growing demand for higher product quality in manufacturing,X-ray non-destructive testing has found widespread application not only in industrial quality control but also in a wide range of industrial applicat...With the growing demand for higher product quality in manufacturing,X-ray non-destructive testing has found widespread application not only in industrial quality control but also in a wide range of industrial applications,owing to its unique capability to penetrate materials and reveal both internal and surface defects.This paper presents a systematic review of recent advances and current applications of X-ray-based defect detection in industrial components.It begins with an overview of the fundamental principles of X-ray imaging and typical inspection workflows,followed by a review of classical image processing methods for defect detection,segmentation,and classification,with particular emphasis on their limitations in feature extraction and robustness.The focus then shifts to recent developments in deep learning techniques—particularly convolutional neural networks,object detection,and segmentation algorithms—and their innovative applications in X-ray defect analysis,which demonstrate substantial advantages in terms of automation and accuracy.In addition,the paper summarizes newly released public datasets and performance evaluation metrics reported in recent years.Finally,it discusses the current challenges and potential solutions in X-ray-based defect detection for industrial components,outlines key directions for future research,and highlights the practical relevance of these advances to real-world industrial applications.展开更多
Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is propose...Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is proposed based on the improved fully convolutional one-stage object detection(FCOS)algorithm.The regression performance of bounding boxes was optimized by introducing the complete intersection over union loss function into the improved algorithm.The feature fusion network structure is modified by adding adaptive fusion paths to the feature fusion network structure,which makes full use of the features of accurate localization and semantics of multi-scale feature fusion networks.Finally,the network structure was trained and validated by using the X-ray image dataset of damages in conveyor belts with steel rope cores provided by a flaw detection equipment manufacturer.In addition,the data enhancement methods such as rotating,mirroring,and scaling,were employed to enrich the image dataset so that the model is adequately trained.Experimental results showed that the improved FCOS algorithm promoted the precision rate and the recall rate by 20.9%and 14.8%respectively,compared with the original algorithm.Meanwhile,compared with Fast R-CNN,Faster R-CNN,SSD,and YOLOv3,the improved FCOS algorithm has obvious advantages;detection precision rate and recall rate of the modified network reached 95.8%and 97.0%respectively.Furthermore,it demonstrated a higher detection accuracy without affecting the speed.The results of this work have some reference significance for the automatic identification and detection of steel core conveyor belt damage.展开更多
Halide perovskites have emerged as promising materials for X-ray detection with exceptional properties and reasonable costs.Among them,heterostructures between 3D perovskites and low-dimensional perovskites attract in...Halide perovskites have emerged as promising materials for X-ray detection with exceptional properties and reasonable costs.Among them,heterostructures between 3D perovskites and low-dimensional perovskites attract intensive studies of their advantages due to low-level ion migration and decent stability.However,there is still a lack of methods to precisely construct heterostructures and a fundamental understanding of their structure-dependent optoelectronic properties.Herein,a gas-phase method was developed to grow 2D perovskites directly on 3D perovskites with nanoscale accuracy.In addition,the larger steric hindrance of organic layers of 2D perovskites was proved to enable slower ion migration,which resulted in reduced trap states and better stability.Based on MAPbBr_(3)single crystals with the(PA)_(2)PbBr_(4)capping layer,the X-ray detector achieved a sensitivity of 22,245μC Gy_(air)^(−1)cm^(−2),a response speed of 240μs,and a dark current drift of 1.17.10^(–4)nA cm^(−1)s^(−1)V^(−1),which were among the highest reported for state-of-the-art perovskite-based X-ray detectors.This study presents a precise synthesis method to construct perovskite-based heterostructures.It also brings an in-depth understanding of the relationship between lattice structures and properties,which are beneficial for advancing high-performance and cost-effective X-ray detectors.展开更多
Listeria monocytogenes(LM)is a dangerous foodborne pathogen for humans.One emerging and validated method of indirectly assessing LM in food is detecting 3-hydroxy-2-butanone(3H2B)gas.In this study,the synthesis of 3-(...Listeria monocytogenes(LM)is a dangerous foodborne pathogen for humans.One emerging and validated method of indirectly assessing LM in food is detecting 3-hydroxy-2-butanone(3H2B)gas.In this study,the synthesis of 3-(2-aminoethylamino)propyltrimethoxysilane(AAPTMS)functionalized hierarchical hollow TiO_(2)nanospheres was achieved via precise controlling of solvothermal reaction temperature and post-grafting route.The sensors based on as-prepared materials exhibited excellent sensitivity(480 Hz@50 ppm),low detection limit(100 ppb),and outstanding selectivity.Moreover,the evaluation of LM with high sensitivity and specificity was achieved using the sensors.Such stable three-dimensional spheres,whose distinctive hierarchical and hollow nanostructure simultaneously improved both sensitivity and response/recovery speed dramatically,were spontaneously assembled by nanosheets.Meanwhile,the moderate loadings of AAPTMS significantly improved the selectivity of sensors.Then,the gas-sensing mechanism was explored by utilizing thermodynamic investigation,Gaussian 16 software,and in situ diffuse reflectance infrared transform spectroscopy,illustrating the weak chemisorption between the-NHgroup and 3H2B molecules.These portable sensors are promising for real-time assessment of LM at room temperature,which will make a magnificent contribution to food safety.展开更多
Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptibl...Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptible to security and privacy threats due to hardware and architectural issues. Although small drones hold promise for expansion in both civil and defense sectors, they have safety, security, and privacy threats. Addressing these challenges is crucial to maintaining the security and uninterrupted operations of these drones. In this regard, this study investigates security, and preservation concerning both the drones and Internet of Drones (IoD), emphasizing the significance of creating drone networks that are secure and can robustly withstand interceptions and intrusions. The proposed framework incorporates a weighted voting ensemble model comprising three convolutional neural network (CNN) models to enhance intrusion detection within the network. The employed CNNs are customized 1D models optimized to obtain better performance. The output from these CNNs is voted using a weighted criterion using a 0.4, 0.3, and 0.3 ratio for three CNNs, respectively. Experiments involve using multiple benchmark datasets, achieving an impressive accuracy of up to 99.89% on drone data. The proposed model shows promising results concerning precision, recall, and F1 as indicated by their obtained values of 99.92%, 99.98%, and 99.97%, respectively. Furthermore, cross-validation and performance comparison with existing works is also carried out. Findings indicate that the proposed approach offers a prospective solution for detecting security threats for aerial systems and satellite systems with high accuracy.展开更多
基金supported in part by the National Natural Science Foundation of China(Nos.12305349,12235006,12027812)Shenzhen Science and Technology Program(No.JSGGKQTD20210831174329010)Guangdong Basic and Applied Basic Research Foundation(No.2021TQ06Y108).
文摘This study aims to investigate the responses of a perovskite-based direct-conversion dual-layer flat-panel detector(DL-FPD)numerically.To this end,the X-ray sensitivity,spatial resolution quantified by the modulation transfer function(MTF),and detective quantum efficiency(DQE)of the DL-FPD are evaluated numerically using a linear cascade model.In addition,both the single-crystal(SC)and polycrystalline(PC)structures of MAPbI_(3)are investigated,along with various other key parameters such as the material thickness,electric field strength,X-ray beam spectrum,and electronic readout noise.The results demonstrate that SC perovskite consistently exhibits better performance than PC perovskite owing to fewer material defects.Increasing the layer thickness may decrease the MTF,but can also enhance the sensitivity and DQE.Moreover,appropriately increasing the external electric field within the material can improve the sensitivity,MTF,and DQE.Finally,reducing the electronic readout noise can significantly enhance the DQE for low-dose imaging.This study demonstrates the potential of high-quality dual-energy X-ray imaging using direct-conversion perovskite DL-FPDs.
基金supported by Ho Chi Minh City Open University,Vietnam under grant number E2024.02.1CD and Suan Sunandha Rajabhat University,Thailand.
文摘The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.
基金supports from the National Natural Science Foundation of China(22375220,U2001214,22471302)the Guangdong Basic and Applied Basic Research Foundation(2024B1515020101)Open Project Fund from State Key Laboratory of Optoelectronic Materials and Technologies(OEMT-2024-KF-08).
文摘Formamidinium lead iodide(FAPbI_(3))perovskite exhibits an impressive X-ray absorption coefficient and a large carrier mobility-lifetime product(μτ),making it as a highly promising candidate for X-ray detection application.However,the presence of larger FA^(+)cation induces to an expansion of the Pb-I octahedral framework,which unfortunately affects both the stability and charge carrier mobility of the corresponding devices.To address this challenge,we develop a novel low-dimensional(HtrzT)PbI_(3) perovskite featuring a conjugated organic cation(1H-1,2,4-Triazole-3-thiol,HtrzT^(+))which matches well with theα-FAPbI_(3) lattices in two-dimensional plane.Benefiting from the matched lattice between(HtrzT)PbI_(3) andα-FAPbI_(3),the anchored lattice enhances the Pb-I bond strength and effectively mitigates the inherent tensile strain of theα-FAPbI_(3) crystal lattice.The X-ray detector based on(HtrzT)PbI_(3)(1.0)/FAPbI_(3) device achieves a remarkable sensitivity up to 1.83×10^(5)μC Gy_(air)^(−1) cm^(−2),along with a low detection limit of 27.6 nGy_(air) s^(−1),attributed to the release of residual stress,and the enhancement in carrier mobility-lifetime product.Furthermore,the detector exhibits outstanding stability under X-ray irradiation with tolerating doses equivalent to nearly 1.17×10^(6) chest imaging doses.
基金supported by the NSFC under Grant No.62474169the National Key Research and Development Program of China under Grant No.2024YFB3212200the funding from USTC under Grant Nos.WK2100000025,KY2190000003,and KY2190000006。
文摘Soft X-ray detectors play a vital role in materials science,high-energy physics and medical imaging.Cs_(2)AgBiBr_(6),a lead-free double perovskite,has gained attention for its excellent optoelectronic properties,stability,and nontoxicity.However,its fast crystallization and requirement for high-temperature annealing(>250℃)often lead to inferior film quality,limiting its application in flexible devices.This study introduces an alloying strategy that significantly improves the quality of Cs_(2)AgBiBr_(6)thin films annealed at a reduced temperature of 150℃.Devices based on the alloyed thin films exhibit an ultra-low dark current of 0.32 nA·cm^(-2)and a quantum efficiency of 725%.Furthermore,the first successful integration of Cs_(2)AgBiBr_(6)with a thinfilm transistor backplane demonstrates its superior imaging performance,indicating that Cs_(2)AgBiBr_(6)is a promising material for next-generation soft X-ray sensors.
文摘Background Frailty is common and significantly impacts prognosis in heart failure(HF). The Vulnerable Elders Survey-13(VES-13), widely used in oncogeriatrics and public health, remains unexplored as a frailty screening tool in HF outpatients. In this study, we prospectively evaluated VES-13 against a multimodal screening assessment in detecting frailty and predicting individual risk of adverse prognosis.Methods Frailty was assessed at the initial visit using both a multimodal approach, incorporating Barthel Index, Older American Resources and Services scale, Pfeiffer Test, abbreviated Geriatric Depression Scale, age > 85 years, lacking support systems,and VES-13. Patients scoring ≥ 3 on VES-13 or meeting at least one multimodal criterion were classified as frail. Endpoints included all-cause mortality, a composite of death or HF hospitalization, and recurrent HF hospitalizations.Results A total of 301 patients were evaluated. VES-13 identified 40.2% as frail and the multimodal assessment 33.2%. In Cox regression analyses, frailty identified by VES-13 showed greater prognostic significance than the multimodal assessment for allcause mortality(HR = 3.70 [2.15–6.33], P < 0.001 vs. 2.40 [1.46–4.0], P = 0.001) and the composite endpoint(HR = 3.13 [2.02–4.84], P< 0.001 vs. 1.96 [1.28–2.99], P = 0.002). Recurrent HF hospitalizations were four times more frequent in VES-13 frail patients while two times in those identified as frail by the multimodal assessment. Additionally, stratifying patients by VES-13 tertiles provided robust risk differentiation.Conclusions VES-13, a simple frailty tool, outperformed a comprehensive multimodal assessment and could be easily integrated into routine HF care, highlighting its clinical utility in identifying patients at risk for poor outcomes.
基金supported by the State Grid Corporation Science and Technology Project(No.5419-202158503A-0-5-ZN)。
文摘The detrimental phase transformations of sodium layered transition metal oxides(Na_(x)TMO_(2))during desodiation/sodiation seriously suppress their practical applications for sodium ion batteries(SIBs).Undoubtedly,comprehensively investigating of the dynamic crystal structure evolutions of Na_(x)TMO_(2)associating with Na ions extraction/intercalation and then deeply understanding of the relationships between electrochemical performances and phase structures drawing support from advanced characterization techniques are indispensable.In-situ high-energy X-ray diffraction(HEXRD),a powerful technology to distinguish the crystal structure of electrode materials,has been widely used to identify the phase evolutions of Na_(x)TMO_(2)and then profoundly revealed the electrochemical reaction processes.In this review,we begin with the descriptions of synchrotron characterization techniques and then present the advantages of synchrotron X-ray diffraction(XRD)over conventional XRD in detail.The optimizations of structural stability and electrochemical properties for P2-,O3-,and P2/O3-type Na_(x)TMO_(2)cathodes through single/dual-site substitution,high-entropy design,phase composition regulation,and surface engineering are summarized.The dynamic crystal structure evolutions of Na_(x)TMO_(2)polytypes during Na ion extraction/intercalation as well as corresponding structural enhancement mechanisms characterizing by means of HEXRD are concluded.The interior relationships between structure/component of Na_(x)TMO_(2)polytypes and their electrochemical properties are discussed.Finally,we look forward the research directions and issues in the route to improve the electrochemical properties of Na_(x)TMO_(2)cathodes for SIBs in the future and the combined utilizations of multiple characterization techniques.This review will provide significant guidelines for rational designs of high-performance Na_(x)TMO_(2)cathodes.
文摘X-ray detection is of great importance for computed tomography scanning,security inspection,non-destructive testing of industrial products and other detection applications[1,2].Due to the potential cancer risk caused by X-ray inspection,there is always room to achieve X-ray detectors with even better sensitivity and detection limit.There are two available approaches detecting X-rays,the first is indirect conversion using scintillators to convert X-ray photons into light and then a photodiode to
基金supported by the Key Project of the National Natural Science Foundation of China-Yunnan Joint Fund(No.U2102215)National Natural Science Foundation(No.52472002)+5 种基金Science and Technology Project of Southwest Joint Graduate School of Yunnan Province(No.202302A0370008)2024 Industrial Innovation Talent Support Project(Preparation of luminous materials,performance control and application in plateau agriculture,No.YFGRC202407)National Natural Science Foundation of High-End Foreign Expert Introduction Plan(No.G2022039008L)Academician Workstation of Cherkasova Tatiana in Yunnan Province(No.202305AF150099)Yunnan Province Major Science and Technology Special Plan(No.202302AB080005)and UTS Chancellor’s Research Fellowship Program(No.J.L.,PRO22-15457)the National Health and Medical Research Council(No.J.L.,2025442).
文摘This study explores the application of X-ray-induced photochromism and photoluminescence in optical storage,anti-counterfeiting,non-destructive testing,and high-resolution X-ray detection and imaging.Ba_(2)LaNbO_(6):Bi,Eu phosphors were synthesized,with Bi enhancing X-ray-induced photochromic prop-erties.Under X-ray irradiation,the phosphors transfer from white to red in bright field conditions and emit red photoluminescence in dark field conditions.Exposure to 470 nm ultraviolet light induces rapid bleaching.The mechanisms of photochromism and photoluminescence,particularly Bi's role as a colorant,were systematically investigated.The Ba_(2)LaNbO_(6):Bi,Eu phosphors film achieves high resolution,high-lighting its potential for X-ray imaging and non-destructive testing.Furthermore,the flexible Ba_(2)LaNbO_(6):Bi,Eu film supports dual-mode imaging and detection,addressing the limitations of traditional flat dis-plays in 3D imaging.
基金supported by the National Natural Science Foundation of China (Grant Nos. 60232090 and 10774102)the Science & Technology Project from Shenzhen Government of China (Grant Nos. 2008340 and 200717)
文摘An x-ray scintillator screen with a special structure, functioning as detector and analyser grating, was proposed for collecting the interferogram of differential phase contrast imaging without absorption grating and difficulty of fabrication by a state of the art technique. On the basis of phase grating diffraction, a detecting model of the scintillator screen was built for analysing the phase and absorption information of objects. According to the analysis, a new method of phase retrievals based on two-images and the optimal structure of screen were presented.
文摘With the rapid growth of the autonomous system,deep learning has become integral parts to enumerate applications especially in the case of healthcare systems.Human body vertebrae are the longest and complex parts of the human body.There are numerous kinds of conditions such as scoliosis,vertebra degeneration,and vertebrate disc spacing that are related to the human body vertebrae or spine or backbone.Early detection of these problems is very important otherwise patients will suffer from a disease for a lifetime.In this proposed system,we developed an autonomous system that detects lumbar implants and diagnoses scoliosis from the modified Vietnamese x-ray imaging.We applied two different approaches including pre-trained APIs and transfer learning with their pre-trained models due to the unavailability of sufficient x-ray medical imaging.The results show that transfer learning is suitable for the modified Vietnamese x-ray imaging data as compared to the pre-trained API models.Moreover,we also explored and analyzed four transfer learning models and two pre-trained API models with our datasets in terms of accuracy,sensitivity,and specificity.
基金supported by the National Natural Science Foundation of China(Nos.42371094,41907253)partially supported by the Interdisciplinary Cultivation Program of Xidian University(No.21103240005)the Postdoctoral Fellowship Program of CPSF(No.GZB20240589)。
文摘INTRODUCTION.On May 1st,2024,around 2:10 a.m.,a catastrophic collapse occurred along the Meilong Expressway near Meizhou City,Guangdong Province,China,at coordinates 24°29′24″N and 116°40′25″E.This collapse resulted in a pavement failure of approximately 17.9 m in length and covering an area of about 184.3 m^(2)(Chinanews,2024).
基金supported by the Department of science and technology of Shaanxi Province(NO.2023-ZDLGY-24).
文摘Detection of ore waste is crucial for achieving automation in mineral metallurgy production.However,deep learning-based target detection algorithms still face several challenges in iron waste screening,including poor lighting conditions in underground mining environments,dust disturbances,platform vibrations during operation,and limited resources for large-scale computing equipment.These factors contribute to extended computation times and unsatisfactory detection accuracy.To address these challenges,this paper proposes an ore waste detection algorithm based on an improved version of YOLOv5.To enhance feature extraction capabilities,the RepLKNet module is incorporated into the YOLOv5 backbone and neck networks.This module enhances the deformation information of feature extraction with the maximum effective Receptive Field to increase the model's accuracy.The Normalizationbased Attention Module(NAM)was introduced to enhance the attention mechanism by focusing on the most relevant features.This improves accuracy in detecting objects against noisy or unclear backgrounds,thereby further enhancing detection performance while reducing model parameters.Additionally,the loss function is optimized to constrain angular deviation using the SIOU loss function,which prevents the training frame from drifting during training and enhances convergence speed.To validate the performance of the proposed method,we tested it using a self-constructed dataset comprising 1,328 images obtained from the crushing station at Jinchuan Group's No.2 mine.The results indicate that,compared to YOLOv5s on the self-constructed dataset,the proposed algorithm achieves an 18.3%improvement in mAP(0.5),a 54%reduction in FLOPs,and a 52.53%decrease in model parameters.The effectiveness and superiority of the proposed algorithm are demonstrated through case studies and comparative analyses.
文摘Tomato plant diseases often first manifest on the leaves,making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry.However,conventional deep learning models face challenges such as large model sizes and slow detection speeds when deployed on resource-constrained platforms and agricultural machinery.This paper proposes a lightweight model for detecting tomato leaf diseases,named LT-YOLO,based on the YOLOv8n architecture.First,we enhance the C2f module into a RepViT Block(RVB)with decoupled token and channel mixers to reduce the cost of feature extraction.Next,we incorporate a novel Efficient Multi-Scale Attention(EMA)mechanism in the deeper layers of the backbone to improve detection of critical disease features.Additionally,we design a lightweight detection head,LT-Detect,using Partial Convolution(PConv)to significantly reduce the classification and localization costs during detection.Finally,we introduce a Receptive Field Block(RFB)in the shallow layers of the backbone to expand the model’s receptive field,enabling effective detection of diseases at various scales.The improved model reduces the number of parameters by 43%and the computational load by 50%.Additionally,it achieves a mean Average Precision(mAP)of 90.9%on a publicly available dataset containing 3641 images of tomato leaf diseases,with only a 0.7%decrease compared to the baseline model.This demonstrates that the model maintains excellent accuracy while being lightweight,making it suitable for rapid detection of tomato leaf diseases.
基金supported by the National Natural Science Foundation of China(No.12235006)the National Key Research and Development Program of China(No.2020YFE0202002.
文摘X-ray detectors show potential applications in medical imaging,materials science,and nuclear energy.To achieve high detection efficiency and spatial resolution,many conventional semiconductor materials,such as amorphous selenium,cadmium telluride zinc,and perovskites,have been utilized in direct conversion X-ray detectors.However,these semiconductor materials are susceptible to temperature-induced performance degradation,crystallization,delamination,uneven lattice growth,radiation damage,and high dark current.This study explores a new approach by coupling an FC40 electronic fluorinated liquid with a specialized high-resolution and high-readout-speed complementary metal-oxide-semiconductor(CMOS)pixel array,specifically the Topmetal II−chip,to fabricate a direct conversion X-ray detector.The fluorinated liquid FC40(molecular formula:C_(21)F_(48)N_(2))is an electronic medium that is minimally affected by temperature and displays no issues with uniform conductivity.It exhibits a low dark current and minimal radiation damage and enables customizable thickness in X-ray absorption.This addresses the limitations inherent in conventional semiconductor-based detectors.In this study,simple X-ray detector imaging tests were conducted,demonstrating the excellent coupling capability between FC40 electronic fluorinated liquid and CMOS chips by the X-ray detector.A spatial resolution of 4.0 lp/mm was measured using a striped line par card,and a relatively clear image of a cockroach was displayed in the digital radiography imaging results.Preliminary test results indicated the feasibility of fabricating an X-ray detector by combining FC40 electronic fluorinated liquid and CMOS chips.Owing to the absence of issues related to chip-material coupling,a high spatial resolution could be achieved by reducing the chip pixel size.This method presents a new avenue for studies on novel liquid-based direct conversion X-ray detectors.
基金This research is funded by the project KC-4.0.14/19-25“Research on building a support system for diagnosis and prediction geo-spatial epidemiology of pulmonary tuberculosis by chest X-Ray images in Vietnam”.
文摘Deep learning created a sharp rise in the development of autonomous image recognition systems,especially in the case of the medical field.Among lung problems,tuberculosis,caused by a bacterium called Mycobacterium tuberculosis,is a dangerous disease because of its infection and damage.When an infected person coughs or sneezes,tiny droplets can bring pathogens to others through inhaling.Tuberculosis mainly damages the lungs,but it also affects any part of the body.Moreover,during the period of the COVID-19(coronavirus disease 2019)pandemic,the access to tuberculosis diagnosis and treatment has become more difficult,so early and simple detection of tuberculosis has been more and more important.In our study,we focused on tuberculosis diagnosis by using the chestX-ray image,the essential input for the radiologist’s profession,and researched the effectiveness of the transfer learning approach in the case study of Vietnamese chest X-ray images.We proposed four strategies to clarify our hypothesis in different ways of applying transfer learning and different training set types.We also prepared a Vietnamese X-ray image dataset with the support of the VRPACS team to provide the basis for training and testing deep learning models.Our experiments were carried out by applying three different architectures,Alexnet,Resnet,and Densenet,on international,Vietnamese,and combined X-ray image datasets.After training,all models were verified on a pure Vietnamese X-rays set.The results show that transfer learning is suitable in the case study of Vietnamese chest X-ray images with high evaluating metrics in terms of AUC(Area under the Receiver Operating Characteristic Curve),sensitivity,specificity,and accuracy.In the best strategy,most of the scores were more than 0.93,and all AUCs were more than 0.98.
基金supported in part by the Project of National Key Laboratory of Advanced Casting Technologies under Grant CAT2023-002.
文摘With the growing demand for higher product quality in manufacturing,X-ray non-destructive testing has found widespread application not only in industrial quality control but also in a wide range of industrial applications,owing to its unique capability to penetrate materials and reveal both internal and surface defects.This paper presents a systematic review of recent advances and current applications of X-ray-based defect detection in industrial components.It begins with an overview of the fundamental principles of X-ray imaging and typical inspection workflows,followed by a review of classical image processing methods for defect detection,segmentation,and classification,with particular emphasis on their limitations in feature extraction and robustness.The focus then shifts to recent developments in deep learning techniques—particularly convolutional neural networks,object detection,and segmentation algorithms—and their innovative applications in X-ray defect analysis,which demonstrate substantial advantages in terms of automation and accuracy.In addition,the paper summarizes newly released public datasets and performance evaluation metrics reported in recent years.Finally,it discusses the current challenges and potential solutions in X-ray-based defect detection for industrial components,outlines key directions for future research,and highlights the practical relevance of these advances to real-world industrial applications.
文摘Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is proposed based on the improved fully convolutional one-stage object detection(FCOS)algorithm.The regression performance of bounding boxes was optimized by introducing the complete intersection over union loss function into the improved algorithm.The feature fusion network structure is modified by adding adaptive fusion paths to the feature fusion network structure,which makes full use of the features of accurate localization and semantics of multi-scale feature fusion networks.Finally,the network structure was trained and validated by using the X-ray image dataset of damages in conveyor belts with steel rope cores provided by a flaw detection equipment manufacturer.In addition,the data enhancement methods such as rotating,mirroring,and scaling,were employed to enrich the image dataset so that the model is adequately trained.Experimental results showed that the improved FCOS algorithm promoted the precision rate and the recall rate by 20.9%and 14.8%respectively,compared with the original algorithm.Meanwhile,compared with Fast R-CNN,Faster R-CNN,SSD,and YOLOv3,the improved FCOS algorithm has obvious advantages;detection precision rate and recall rate of the modified network reached 95.8%and 97.0%respectively.Furthermore,it demonstrated a higher detection accuracy without affecting the speed.The results of this work have some reference significance for the automatic identification and detection of steel core conveyor belt damage.
基金support from National Key Research and Development Program of China(2024YFE0217100)the National Natural Science Foundation of China(21905006,22261160370,and 62105075)+7 种基金the Guangdong Provincial Science and Technology Plan(2021A0505110003)the Natural Science Foundation of Hunan Province,China(2023JJ50132)Guangxi Department of Science and Technology(2020GXNSFBA159049 and AD19110030)the Shenzhen Science and Technology Program(SGDX20230116093205009,JCYJ20220818100211025 and 2022378670)the Natural Science Foundation of Top Talent of SZTU(GDRC202343)financial support of Innovation and Technology Fund(#GHP/245/22SZ)The University Grant Council of the University of Hong Kong(grant No.2302101786)General Research Fund(grant Nos.17200823 and 17310624)from the Research Grants Council.
文摘Halide perovskites have emerged as promising materials for X-ray detection with exceptional properties and reasonable costs.Among them,heterostructures between 3D perovskites and low-dimensional perovskites attract intensive studies of their advantages due to low-level ion migration and decent stability.However,there is still a lack of methods to precisely construct heterostructures and a fundamental understanding of their structure-dependent optoelectronic properties.Herein,a gas-phase method was developed to grow 2D perovskites directly on 3D perovskites with nanoscale accuracy.In addition,the larger steric hindrance of organic layers of 2D perovskites was proved to enable slower ion migration,which resulted in reduced trap states and better stability.Based on MAPbBr_(3)single crystals with the(PA)_(2)PbBr_(4)capping layer,the X-ray detector achieved a sensitivity of 22,245μC Gy_(air)^(−1)cm^(−2),a response speed of 240μs,and a dark current drift of 1.17.10^(–4)nA cm^(−1)s^(−1)V^(−1),which were among the highest reported for state-of-the-art perovskite-based X-ray detectors.This study presents a precise synthesis method to construct perovskite-based heterostructures.It also brings an in-depth understanding of the relationship between lattice structures and properties,which are beneficial for advancing high-performance and cost-effective X-ray detectors.
基金supported by the National Natural Science Foundation of China(No.32272399)the Shanghai Natural Science Foundation(No.21ZR1427500).
文摘Listeria monocytogenes(LM)is a dangerous foodborne pathogen for humans.One emerging and validated method of indirectly assessing LM in food is detecting 3-hydroxy-2-butanone(3H2B)gas.In this study,the synthesis of 3-(2-aminoethylamino)propyltrimethoxysilane(AAPTMS)functionalized hierarchical hollow TiO_(2)nanospheres was achieved via precise controlling of solvothermal reaction temperature and post-grafting route.The sensors based on as-prepared materials exhibited excellent sensitivity(480 Hz@50 ppm),low detection limit(100 ppb),and outstanding selectivity.Moreover,the evaluation of LM with high sensitivity and specificity was achieved using the sensors.Such stable three-dimensional spheres,whose distinctive hierarchical and hollow nanostructure simultaneously improved both sensitivity and response/recovery speed dramatically,were spontaneously assembled by nanosheets.Meanwhile,the moderate loadings of AAPTMS significantly improved the selectivity of sensors.Then,the gas-sensing mechanism was explored by utilizing thermodynamic investigation,Gaussian 16 software,and in situ diffuse reflectance infrared transform spectroscopy,illustrating the weak chemisorption between the-NHgroup and 3H2B molecules.These portable sensors are promising for real-time assessment of LM at room temperature,which will make a magnificent contribution to food safety.
文摘Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptible to security and privacy threats due to hardware and architectural issues. Although small drones hold promise for expansion in both civil and defense sectors, they have safety, security, and privacy threats. Addressing these challenges is crucial to maintaining the security and uninterrupted operations of these drones. In this regard, this study investigates security, and preservation concerning both the drones and Internet of Drones (IoD), emphasizing the significance of creating drone networks that are secure and can robustly withstand interceptions and intrusions. The proposed framework incorporates a weighted voting ensemble model comprising three convolutional neural network (CNN) models to enhance intrusion detection within the network. The employed CNNs are customized 1D models optimized to obtain better performance. The output from these CNNs is voted using a weighted criterion using a 0.4, 0.3, and 0.3 ratio for three CNNs, respectively. Experiments involve using multiple benchmark datasets, achieving an impressive accuracy of up to 99.89% on drone data. The proposed model shows promising results concerning precision, recall, and F1 as indicated by their obtained values of 99.92%, 99.98%, and 99.97%, respectively. Furthermore, cross-validation and performance comparison with existing works is also carried out. Findings indicate that the proposed approach offers a prospective solution for detecting security threats for aerial systems and satellite systems with high accuracy.
