Two-dimensional perovskite ferroelectric which strongly couple ferroelectricity with semiconducting properties are promising candidates for optoelectronic applications.However,it is still a great challenge to fabricat...Two-dimensional perovskite ferroelectric which strongly couple ferroelectricity with semiconducting properties are promising candidates for optoelectronic applications.However,it is still a great challenge to fabricate self-powered broadband photodetectors with low detection limit.Herein,we successfully realized self-powered broadband photodetection with low detection limit by using a trilayered perovskite ferroelectric(BA)_(2)EA_(2)Pb_(3)I_(10)(1,BA=n-butylamine,EA=ethylamine).Giving to its large spontaneous polarization(5.6μC/cm^(2)),1 exhibits an open-circuit voltage of 0.25 V which provide driving force to separate carriers.Combining with its low dark current(~10^(-14)A)and narrow bandgap(Eg=1.86 e V),1 demonstrates great potential on detecting the broadband weak lights.Thus,a prominent photodetection performance with high open-off ratio(~10^(5)),outstanding responsivity(>10 m A/W),and promising detectivity(>1011Jones),as well as the low detecting limit(~nW/cm^(2))among the wide wavelength from 377 nm to637 nm was realized based on the single crystal of 1.This work demonstrates the great potential of 2D perovskite ferroelectric on self-powered broadband photodetectors.展开更多
Visible and near-infrared photodetectors are widely used in intelligent driving,health monitoring,and other fields.However,the application of photodetectors in the near-infrared region is significantly impacted by hig...Visible and near-infrared photodetectors are widely used in intelligent driving,health monitoring,and other fields.However,the application of photodetectors in the near-infrared region is significantly impacted by high dark current,which can greatly reduce their performance and sensitivity,thereby limiting their effectiveness in certain applications.In this work,the introduction of a C60 back interface layer successfully mitigated back interface reactions to decrease the thickness of the Mo(S,Se)_(2)layer,tailoring the back-contact barrier and preventing reverse charge injection,resulting in a kesterite photodetector with an ultralow dark current density of 5.2×10^(-9)mA/cm^(2)and ultra-weak-light detection at levels as low as 25 pW/cm^(2).Besides,under a self-powered operation,it demonstrates outstanding performance,achieving a peak responsivity of 0.68 A/W,a wide response range spanning from 300 to 1600 nm,and an impressive detectivity of 5.27×10^(14)Jones.In addition,it offers exceptionally rapid response times,with rise and decay times of 70 and 650 ns,respectively.This research offers important insights for developing high-performance self-powered near-infrared photodetectors that have high responsivity,rapid response times,and ultralow dark current.展开更多
Low power consumption,high responsivity,and self-powering are key objectives for photoelectrochemical ultravio-let detectors.In this research,In-dopedα-Ga_(2)O_(3) nanowire arrays were fabricated on fluorine-doped ti...Low power consumption,high responsivity,and self-powering are key objectives for photoelectrochemical ultravio-let detectors.In this research,In-dopedα-Ga_(2)O_(3) nanowire arrays were fabricated on fluorine-doped tin oxide(FTO)substrates through a hydrothermal approach,with subsequent thermal annealing.These arrays were then used as photoanodes to con-struct a ultraviolet(UV)photodetector.In doping reduced the bandgap ofα-Ga_(2)O_(3),enhancing its absorption of UV light.Conse-quently,the In-dopedα-Ga_(2)O_(3) nanowire arrays exhibited excellent light detection performance.When irradiated by 255 nm deep ultraviolet light,they obtained a responsivity of 38.85 mA/W.Moreover,the detector's response and recovery times are 13 and 8 ms,respectively.The In-dopedα-Ga_(2)O_(3) nanowire arrays exhibit a responsivity that is about three-fold higher than the undoped one.Due to its superior responsivity,the In-doped device was used to develop a photoelectric imaging system.This study demonstrates that dopingα-Ga_(2)O_(3) nanowire with indium is a potent approach for optimizing their photoelectrochemi-cal performance,which also has significant potential for optoelectronic applications.展开更多
Polar semiconductors,particularly the emerging polar two-dimensional(2D)halide perovskites,have motivated immense interest in diverse photoelectronic devices due to their distinguishing polarizationgenerated photoelec...Polar semiconductors,particularly the emerging polar two-dimensional(2D)halide perovskites,have motivated immense interest in diverse photoelectronic devices due to their distinguishing polarizationgenerated photoelectric effects.However,the constraints on the organic cation's choice are still subject to limitations of polar 2D halide perovskites due to the size of the inorganic pocket between adjacent corner-sharing octahedra.Herein,a mixed spacer cation ordering strategy is employed to assemble a polar 2D halide perovskite NMAMAPb Br_(4)(NMPB,NMA is N-methylbenzene ammonium,MA is methylammonium)with alternating cation in the interlayer space.Driven by the incorporation of a second MA cation,the perovskite layer transformed from a 2D Pb_(7)Br_(24)anionic network with corner-and face-sharing octahedra to a flat 2D PbBr_(4)perovskite networks only with corner-sharing octahedra.In the crystal structure of NMPB,the asymmetric hydrogen-bonding interactions between ordered mixed-spacer cations and 2D perovskite layers give rise to a second harmonic generation response and a large polarization of 1.3μC/cm^(2).More intriguingly,the ordered 2D perovskite networks endow NMPB with excellent self-powered polarization-sensitive detection performance,showing a considerable polarization-related dichroism ratio up to 1.87.The reconstruction of an inorganic framework within a crystal through mixed cation ordering offers a new synthetic tool for templating perovskite lattices with controlled properties,overcoming limitations of conventional cation choice.展开更多
Quantum dot(QD)-based infrared photodetector is a promising technology that can implement current monitoring,imaging and optical communication in the infrared region. However, the photodetection performance of self-po...Quantum dot(QD)-based infrared photodetector is a promising technology that can implement current monitoring,imaging and optical communication in the infrared region. However, the photodetection performance of self-powered QD devices is still limited by their unfavorable charge carrier dynamics due to their intrinsically discrete charge carrier transport process. Herein, we strategically constructed semiconducting matrix in QD film to achieve efficient charge transfer and extraction.The p-type semiconducting CuSCN was selected as energy-aligned matrix to match the n-type colloidal PbS QDs that was used as proof-of-concept. Note that the PbS QD/CuSCN matrix not only enables efficient charge carrier separation and transfer at nano-interfaces but also provides continuous charge carrier transport pathways that are different from the hoping process in neat QD film, resulting in improved charge mobility and derived collection efficiency. As a result, the target structure delivers high specific detectivity of 4.38 × 10^(12)Jones and responsivity of 782 mA/W at 808 nm, which is superior than that of the PbS QD-only photodetector(4.66 × 10^(11)Jones and 338 mA/W). This work provides a new structure candidate for efficient colloidal QD based optoelectronic devices.展开更多
β-Ga_(2)O_(3),as one of the important 4th generation semiconductors,is widely used in solar-blind ultraviolet(UV)detectors with a short detection range of 200-280 nm benefiting from its ultra-wide bandgap,strong radi...β-Ga_(2)O_(3),as one of the important 4th generation semiconductors,is widely used in solar-blind ultraviolet(UV)detectors with a short detection range of 200-280 nm benefiting from its ultra-wide bandgap,strong radiation resistance,and excellent chemical and thermal stabilities.Here,a self-powered photodetector(PD)based on an Ag/β-Ga_(2)O_(3) Schottky heterojunction was designed and fabricated.Through a subtle design of electrodes,the pyro-phototronic effect was discovered,which can be coupled to the common photovoltaic effect and further enhance the performance of the PD.Compared to traditional Ga_(2)O_(3)-based PD,the as-used PD exhibited a self-driving property and a broadband response beyond the bandgap lim-itations,ranging from 200 nm(deep UV)to 980 nm(infrared).Moreover,the photoresponse time was greatly shrunk owing to the coupling effect.Under laser irradiation,with a wavelength of 450 nm and a power density of 8 mW cm-2,the photocurrent could be improved by around 41 times compared with the sole photovoltaic effect.Besides,the performances of the Schottky PD were enhanced at both high and low temperatures.The device also possessed long-term working stability.This paper not only re-veals basic physics lying in the 4th generation semiconductor Ga_(2)O_(3) but also sheds light on the multi-encryption transmission of light information using this PD.展开更多
Recently,self-powered ultraviolet photodetectors(UV PDs)based on SnO_(2)have gained increasing interest due to its feature of working continuously without the need for external power sources.Nevertheless,the productio...Recently,self-powered ultraviolet photodetectors(UV PDs)based on SnO_(2)have gained increasing interest due to its feature of working continuously without the need for external power sources.Nevertheless,the production of the majority of these existing UV PDs necessitates additional manufacturing stages or intricate processes.In this work,we present a facile,cost-effective approach for the fabrication of a self-powered UV PD based on p-Si/n-SnO_(2)junction.The self-powered device was achieved simply by integrating a p-Si substrate with a n-type SnO_(2)microbelt,which was synthesized via the chemical vapor deposition(CVD)method.The high-quality feature,coupled with the belt-like shape of the SnO_(2)microbelt enables the favorable contact between the n-type SnO_(2)and p-type silicon.The built-in electric field created at the interface endows the self-powered performance of the device.The p-Si/n-SnO_(2)junction photodetector demonstrated a high responsivity(0.12 mA/W),high light/dark current ratio(>103),and rapid response speed at zero bias.This method offers a practical way to develop cost-effective and high-performance self-powered UV PDs.展开更多
The rise of smart wearable devices has driven the demand for flexible,high-performance optoelectronic devices with low power and easy high-density integration.Emerging Two-dimensional(2D)materials offer promising solu...The rise of smart wearable devices has driven the demand for flexible,high-performance optoelectronic devices with low power and easy high-density integration.Emerging Two-dimensional(2D)materials offer promising solutions.However,the use of 3D metal in traditional 2D devices often leads to Fermi-level pinning,compromising device performance.2D metallic materials,such as graphene and 2H-phase NbSe_(2),present a new avenue for addressing this issue and constructing high-performance,low-power photodetectors.In this work,we designed an all-2D asymmetric contacts photodetector using Gr and NbSe_(2)as electrodes for the 2D semiconductor WSe_(2).The asymmetric Schottky barriers and built-in electric fields facilitated by this architecture resulted in outstanding photovoltaic characteristics and self-powered photodetection.Under zero bias,the device exhibited a responsivity of 287 mA/W,a specific detectivity of 5.3×10^(11)Jones,and an external quantum efficiency of 88%.It also demonstrated an ultra-high light on/offratio(1.8×10^(5)),ultra-fast photoresponse speeds(80/72μs),broad-spectrum responsiveness(405980 nm),and exceptional cycling stability.The applications of the Gr/WSe_(2)/NbSe_(2)heterojunction in imaging and infrared optical communication have been explored,underscoring its significant potential.This work offers an idea to construct all-2D ultrathin optoelectronic devices.展开更多
Self-powered neutron detectors(SPNDs)play a critical role in monitoring the safety margins and overall health of reactors,directly affecting safe operation within the reactor.In this work,a novel fault identification ...Self-powered neutron detectors(SPNDs)play a critical role in monitoring the safety margins and overall health of reactors,directly affecting safe operation within the reactor.In this work,a novel fault identification method based on graph convolutional networks(GCN)and Stacking ensemble learning is proposed for SPNDs.The GCN is employed to extract the spatial neighborhood information of SPNDs at different positions,and residuals are obtained by nonlinear fitting of SPND signals.In order to completely extract the time-varying features from residual sequences,the Stacking fusion model,integrated with various algorithms,is developed and enables the identification of five conditions for SPNDs:normal,drift,bias,precision degradation,and complete failure.The results demonstrate that the integration of diverse base-learners in the GCN-Stacking model exhibits advantages over a single model as well as enhances the stability and reliability in fault identification.Additionally,the GCN-Stacking model maintains higher accuracy in identifying faults at different reactor power levels.展开更多
Smart farming with outdoor monitoring systems is critical to address food shortages and sustainability challenges.These systems facilitate informed decisions that enhance efficiency in broader environmental management...Smart farming with outdoor monitoring systems is critical to address food shortages and sustainability challenges.These systems facilitate informed decisions that enhance efficiency in broader environmental management.Existing outdoor systems equipped with energy harvesters and self-powered sensors often struggle with fluctuating energy sources,low durability under harsh conditions,non-transparent or non-biocompatible materials,and complex structures.Herein,a multifunctional hydrogel is developed,which can fulfill all the above requirements and build selfsustainable outdoor monitoring systems solely by it.It can serve as a stable energy harvester that continuously generates direct current output with an average power density of 1.9 W m^(-3)for nearly 60 days of operation in normal environments(24℃,60%RH),with an energy density of around 1.36×10^(7)J m^(-3).It also shows good self-recoverability in severe environments(45℃,30%RH)in nearly 40 days of continuous operation.Moreover,this hydrogel enables noninvasive and self-powered monitoring of leaf relative water content,providing critical data on evaluating plant health,previously obtainable only through invasive or high-power consumption methods.Its potential extends to acting as other self-powered environmental sensors.This multifunctional hydrogel enables self-sustainable outdoor systems with scalable and low-cost production,paving the way for future agriculture.展开更多
Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable...Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.展开更多
The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approac...The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum(Al^(3+))and fluoride(F^(−))ions in aqueous solutions.The proposed method involves the synthesis of sulfur-functionalized carbon dots(C-dots)as fluorescence probes,with fluorescence enhancement upon interaction with Al^(3+)ions,achieving a detection limit of 4.2 nmol/L.Subsequently,in the presence of F^(−)ions,fluorescence is quenched,with a detection limit of 47.6 nmol/L.The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python,followed by data preprocessing.Subsequently,the fingerprint data is subjected to cluster analysis using the K-means model from machine learning,and the average Silhouette Coefficient indicates excellent model performance.Finally,a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions.The results demonstrate that the developed model excels in terms of accuracy and sensitivity.This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment,making it a valuable tool for safeguarding our ecosystems and public health.展开更多
In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,par...In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,particularly in snowy environments,remains a challenge.Snow-covered roads introduce unpredictable surface conditions,occlusions,and reduced visibility,that require robust and adaptive path detection algorithms.This paper presents an enhanced road detection framework for snowy environments,leveraging Simple Framework forContrastive Learning of Visual Representations(SimCLR)for Self-Supervised pretraining,hyperparameter optimization,and uncertainty-aware object detection to improve the performance of YouOnly Look Once version 8(YOLOv8).Themodel is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø,Norway,which covers a range of snow textures,illumination conditions,and road geometries.The proposed framework achieves scores in terms of mAP@50 equal to 99%and mAP@50–95 equal to 97%,demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions.The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments,enabling robust decision-making in hazardous weather conditions.This research lays the groundwork for more resilient perceptionmodels in self-driving systems,paving the way for the future development of intelligent and adaptive transportation networks.展开更多
With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a c...With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a convenient and visual technique with low equipment requirements and high sensitivity for the field detection of GM plants is still lacking.On the basis of the existing recombinase polymerase amplification(RPA)technique,we developed a multiplex RPA(multi-RPA)method that can simultaneously detect three transgenic elements,including the cauliflower mosaic virus 35S gene(CaMV35S)promoter,neomycin phosphotransferaseⅡgene(NptⅡ)and hygromycin B phosphotransferase gene(Hyg),thus improving the detection rate.Moreover,we coupled this multi-RPA technique with the CRISPR/Cas12a reporter system,which enabled the detection results to be clearly observed by naked eyes under ultraviolet(UV)light(254 nm;which could be achieved by a portable UV flashlight),therefore establishing a multi-RPA visual detection technique.Compared with the traditional test strip detection method,this multi-RPA-CRISPR/Cas12a technique has the higher specificity,higher sensitivity,wider application range and lower cost.Compared with other polymerase chain reaction(PCR)techniques,it also has the advantages of low equipment requirements and visualization,making it a potentially feasible method for the field detection of GM plants.展开更多
2D Ruddlesden-Popper(RP)polar perovskite,displaying the intrinsic optical anisotropy and structural polarity,has a fantastic application perspective in self-powered polarized light detection.However,the weak van der W...2D Ruddlesden-Popper(RP)polar perovskite,displaying the intrinsic optical anisotropy and structural polarity,has a fantastic application perspective in self-powered polarized light detection.However,the weak van der Waals interaction between the organic spacing bilayers is insufficient to preserve the stability of RP-type materials.Hence,it is of great significance to explore new stable 2D RP-phase candidates.In this work,we have successfully constructed a highly-stable polar 2D perovskite,(t-ACH)_(2)PbI_(4)(1,where t-ACH^(+)is HOOC_(8)H_(12)NH_(3)^(+)),by adopting a hydrophobic carboxylate trans-isomer of tranexamic acid as the spacing component.Strikingly,strong O-H…O hydrogen bonds between t-ACH^(+)organic bilayers compose the dimer,thus decreasing van der Waals gap and enhancing structural stability.Besides,such orientational hydrogen bonds contribute to the formation of structural polarity and generate an obvious bulk photovoltaic effect in 1,which facilitates its self-powered photodetection.As predicted,the combination of inherent anisotropy and polarity leads to self-powered polarized-light detection with a high ratio of around∼5.3,superior to those of inorganic 2D counterparts.This work paves a potential way to design highly-stable 2D perovskites for high-performance optoelectronic devices.展开更多
Conventional superconducting nanowire single-photon detectors(SNSPDs)have been typically limited in their applications due to their size,weight,and power consumption,which confine their use to laboratory settings.Howe...Conventional superconducting nanowire single-photon detectors(SNSPDs)have been typically limited in their applications due to their size,weight,and power consumption,which confine their use to laboratory settings.However,with the rapid development of remote imaging,sensing technologies,and long-range quantum communication with fewer topographical constraints,the demand for high-efficiency single-photon detectors integrated with avionic platforms is rapidly growing.We herein designed and manufactured the first drone-based SNSPD system with a system detection efficiency(SDE)as high as 91.8%.This drone-based system incorporates high-performance NbTiN SNSPDs,a self-developed miniature liquid helium dewar,and custom-built integrated electrical setups,making it capable of being launched in complex topographical conditions.Such a drone-based SNSPD system may open the use of SNSPDs for applications that demand high SDE in complex environments.展开更多
The landscape of financial transactions has grown increasingly complex due to the expansion of global economic integration and advancements in information technology.This complexity poses greater challenges in detecti...The landscape of financial transactions has grown increasingly complex due to the expansion of global economic integration and advancements in information technology.This complexity poses greater challenges in detecting and managing financial fraud.This review explores the role of Graph Neural Networks(GNNs)in addressing these challenges by proposing a unified framework that categorizes existing GNN methodologies applied to financial fraud detection.Specifically,by examining a series of detailed research questions,this review delves into the suitability of GNNs for financial fraud detection,their deployment in real-world scenarios,and the design considerations that enhance their effectiveness.This review reveals that GNNs are exceptionally adept at capturing complex relational patterns and dynamics within financial networks,significantly outperforming traditional fraud detection methods.Unlike previous surveys that often overlook the specific potentials of GNNs or address them only superficially,our review provides a comprehensive,structured analysis,distinctly focusing on the multifaceted applications and deployments of GNNs in financial fraud detection.This review not only highlights the potential of GNNs to improve fraud detection mechanisms but also identifies current gaps and outlines future research directions to enhance their deployment in financial systems.Through a structured review of over 100 studies,this review paper contributes to the understanding of GNN applications in financial fraud detection,offering insights into their adaptability and potential integration strategies.展开更多
The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more e...The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.展开更多
Plants play a crucial role in maintaining ecological balance and biodiversity.However,plant health is easily affected by environmental stresses.Hence,the rapid and precise monitoring of plant health is crucial for glo...Plants play a crucial role in maintaining ecological balance and biodiversity.However,plant health is easily affected by environmental stresses.Hence,the rapid and precise monitoring of plant health is crucial for global food security and ecological balance.Currently,traditional detection strategies for monitoring plant health mainly rely on expensive equipment and complex operational procedures,which limit their widespread application.Fortunately,near-infrared(NIR)fluorescence and surface-enhanced Raman scattering(SERS)techniques have been recently highlighted in plants.NIR fluorescence imaging holds the advantages of being non-invasive,high-resolution and real-time,which is suitable for rapid screening in large-scale scenarios.While SERS enables highly sensitive and specific detection of trace chemical substances within plant tissues.Therefore,the complementarity of NIR fluorescence and SERS modalities can provide more comprehensive and accurate information for plant disease diagnosis and growth status monitoring.This article summarizes these two modalities in plant applications,and discusses the advantages of multimodal NIR fluorescence/SERS for a better understanding of a plant’s response to stress,thereby improving the accuracy and sensitivity of detection.展开更多
Deep learning-based object detection has revolutionized various fields,including agriculture.This paper presents a systematic review based on the PRISMA 2020 approach for object detection techniques in agriculture by ...Deep learning-based object detection has revolutionized various fields,including agriculture.This paper presents a systematic review based on the PRISMA 2020 approach for object detection techniques in agriculture by exploring the evolution of different methods and applications over the past three years,highlighting the shift from conventional computer vision to deep learning-based methodologies owing to their enhanced efficacy in real time.The review emphasizes the integration of advanced models,such as You Only Look Once(YOLO)v9,v10,EfficientDet,Transformer-based models,and hybrid frameworks that improve the precision,accuracy,and scalability for crop monitoring and disease detection.The review also highlights benchmark datasets and evaluation metrics.It addresses limitations,like domain adaptation challenges,dataset heterogeneity,and occlusion,while offering insights into prospective research avenues,such as multimodal learning,explainable AI,and federated learning.Furthermore,the main aim of this paper is to serve as a thorough resource guide for scientists,researchers,and stakeholders for implementing deep learning-based object detection methods for the development of intelligent,robust,and sustainable agricultural systems.展开更多
基金financially supported by the National Natural Science Foundation of China(Nos.22435005,22193042,21921001,22305105,52202194,22201284)Natural Science Foundation of Jiangxi Province(No.20224BAB213003)+1 种基金the Natural Science Foundation of Fujian Province(No.2023J05076)Jiangxi Provincial Education Department Science and Technology Research Foundation(No.GJJ2200384)。
文摘Two-dimensional perovskite ferroelectric which strongly couple ferroelectricity with semiconducting properties are promising candidates for optoelectronic applications.However,it is still a great challenge to fabricate self-powered broadband photodetectors with low detection limit.Herein,we successfully realized self-powered broadband photodetection with low detection limit by using a trilayered perovskite ferroelectric(BA)_(2)EA_(2)Pb_(3)I_(10)(1,BA=n-butylamine,EA=ethylamine).Giving to its large spontaneous polarization(5.6μC/cm^(2)),1 exhibits an open-circuit voltage of 0.25 V which provide driving force to separate carriers.Combining with its low dark current(~10^(-14)A)and narrow bandgap(Eg=1.86 e V),1 demonstrates great potential on detecting the broadband weak lights.Thus,a prominent photodetection performance with high open-off ratio(~10^(5)),outstanding responsivity(>10 m A/W),and promising detectivity(>1011Jones),as well as the low detecting limit(~nW/cm^(2))among the wide wavelength from 377 nm to637 nm was realized based on the single crystal of 1.This work demonstrates the great potential of 2D perovskite ferroelectric on self-powered broadband photodetectors.
基金supported by the National Natural Science Foundation of China(No.52472225)the Science and Technology Plan Project of Shenzhen(No.20220808165025003),China。
文摘Visible and near-infrared photodetectors are widely used in intelligent driving,health monitoring,and other fields.However,the application of photodetectors in the near-infrared region is significantly impacted by high dark current,which can greatly reduce their performance and sensitivity,thereby limiting their effectiveness in certain applications.In this work,the introduction of a C60 back interface layer successfully mitigated back interface reactions to decrease the thickness of the Mo(S,Se)_(2)layer,tailoring the back-contact barrier and preventing reverse charge injection,resulting in a kesterite photodetector with an ultralow dark current density of 5.2×10^(-9)mA/cm^(2)and ultra-weak-light detection at levels as low as 25 pW/cm^(2).Besides,under a self-powered operation,it demonstrates outstanding performance,achieving a peak responsivity of 0.68 A/W,a wide response range spanning from 300 to 1600 nm,and an impressive detectivity of 5.27×10^(14)Jones.In addition,it offers exceptionally rapid response times,with rise and decay times of 70 and 650 ns,respectively.This research offers important insights for developing high-performance self-powered near-infrared photodetectors that have high responsivity,rapid response times,and ultralow dark current.
基金supported by the National Key Research and Development Program of China(2023YFB3610500)National Natural Science Foundation of China(62104110,62374094)+1 种基金the Project funded by China Postdoctoral Science Foundation(2023T160332)Natural Science Foundation of Nanjing University of Posts and Telecommunications(NY224084,NY224131).
文摘Low power consumption,high responsivity,and self-powering are key objectives for photoelectrochemical ultravio-let detectors.In this research,In-dopedα-Ga_(2)O_(3) nanowire arrays were fabricated on fluorine-doped tin oxide(FTO)substrates through a hydrothermal approach,with subsequent thermal annealing.These arrays were then used as photoanodes to con-struct a ultraviolet(UV)photodetector.In doping reduced the bandgap ofα-Ga_(2)O_(3),enhancing its absorption of UV light.Conse-quently,the In-dopedα-Ga_(2)O_(3) nanowire arrays exhibited excellent light detection performance.When irradiated by 255 nm deep ultraviolet light,they obtained a responsivity of 38.85 mA/W.Moreover,the detector's response and recovery times are 13 and 8 ms,respectively.The In-dopedα-Ga_(2)O_(3) nanowire arrays exhibit a responsivity that is about three-fold higher than the undoped one.Due to its superior responsivity,the In-doped device was used to develop a photoelectric imaging system.This study demonstrates that dopingα-Ga_(2)O_(3) nanowire with indium is a potent approach for optimizing their photoelectrochemi-cal performance,which also has significant potential for optoelectronic applications.
基金supported by the National Natural Science Foundation of China(Nos.22193042,22125110,22075285,52473283,21921001,U21A2069)the Key Research Program of Frontier Sciences of the Chinese Academy of Sciences(No.ZDBS-LY-SLH024)the Youth Innovation Promotion of Chinese Academy of Sciences(No.2020307)。
文摘Polar semiconductors,particularly the emerging polar two-dimensional(2D)halide perovskites,have motivated immense interest in diverse photoelectronic devices due to their distinguishing polarizationgenerated photoelectric effects.However,the constraints on the organic cation's choice are still subject to limitations of polar 2D halide perovskites due to the size of the inorganic pocket between adjacent corner-sharing octahedra.Herein,a mixed spacer cation ordering strategy is employed to assemble a polar 2D halide perovskite NMAMAPb Br_(4)(NMPB,NMA is N-methylbenzene ammonium,MA is methylammonium)with alternating cation in the interlayer space.Driven by the incorporation of a second MA cation,the perovskite layer transformed from a 2D Pb_(7)Br_(24)anionic network with corner-and face-sharing octahedra to a flat 2D PbBr_(4)perovskite networks only with corner-sharing octahedra.In the crystal structure of NMPB,the asymmetric hydrogen-bonding interactions between ordered mixed-spacer cations and 2D perovskite layers give rise to a second harmonic generation response and a large polarization of 1.3μC/cm^(2).More intriguingly,the ordered 2D perovskite networks endow NMPB with excellent self-powered polarization-sensitive detection performance,showing a considerable polarization-related dichroism ratio up to 1.87.The reconstruction of an inorganic framework within a crystal through mixed cation ordering offers a new synthetic tool for templating perovskite lattices with controlled properties,overcoming limitations of conventional cation choice.
基金supported by the National Natural Science Foundation of China (No. 62204079)the Science and Technology Development Project of Henan Province (Nos.202300410048, 202300410057)+2 种基金the China Postdoctoral Science Foundation (No. 2022M711037)the Intelligence Introduction Plan of Henan Province in 2021 (No. CXJD2021008)Henan University Fund。
文摘Quantum dot(QD)-based infrared photodetector is a promising technology that can implement current monitoring,imaging and optical communication in the infrared region. However, the photodetection performance of self-powered QD devices is still limited by their unfavorable charge carrier dynamics due to their intrinsically discrete charge carrier transport process. Herein, we strategically constructed semiconducting matrix in QD film to achieve efficient charge transfer and extraction.The p-type semiconducting CuSCN was selected as energy-aligned matrix to match the n-type colloidal PbS QDs that was used as proof-of-concept. Note that the PbS QD/CuSCN matrix not only enables efficient charge carrier separation and transfer at nano-interfaces but also provides continuous charge carrier transport pathways that are different from the hoping process in neat QD film, resulting in improved charge mobility and derived collection efficiency. As a result, the target structure delivers high specific detectivity of 4.38 × 10^(12)Jones and responsivity of 782 mA/W at 808 nm, which is superior than that of the PbS QD-only photodetector(4.66 × 10^(11)Jones and 338 mA/W). This work provides a new structure candidate for efficient colloidal QD based optoelectronic devices.
基金supported by the National Natural Science Foundation of China(Grant Nos.52192610 and 52192613)the National Key R&D Project from the Minister of Science and Technology(No.2021YFA1201601)the CAS-TWAS President’s Fellow-ship(A.B).
文摘β-Ga_(2)O_(3),as one of the important 4th generation semiconductors,is widely used in solar-blind ultraviolet(UV)detectors with a short detection range of 200-280 nm benefiting from its ultra-wide bandgap,strong radiation resistance,and excellent chemical and thermal stabilities.Here,a self-powered photodetector(PD)based on an Ag/β-Ga_(2)O_(3) Schottky heterojunction was designed and fabricated.Through a subtle design of electrodes,the pyro-phototronic effect was discovered,which can be coupled to the common photovoltaic effect and further enhance the performance of the PD.Compared to traditional Ga_(2)O_(3)-based PD,the as-used PD exhibited a self-driving property and a broadband response beyond the bandgap lim-itations,ranging from 200 nm(deep UV)to 980 nm(infrared).Moreover,the photoresponse time was greatly shrunk owing to the coupling effect.Under laser irradiation,with a wavelength of 450 nm and a power density of 8 mW cm-2,the photocurrent could be improved by around 41 times compared with the sole photovoltaic effect.Besides,the performances of the Schottky PD were enhanced at both high and low temperatures.The device also possessed long-term working stability.This paper not only re-veals basic physics lying in the 4th generation semiconductor Ga_(2)O_(3) but also sheds light on the multi-encryption transmission of light information using this PD.
基金support for this research was provided by the High-Level Scientific Research Cultivation Project at Hubei Minzu University,with the grant identifier PY22001the Guiding Projects from the Department of Education in Hubei Province,identified by the grant number B2018088.
文摘Recently,self-powered ultraviolet photodetectors(UV PDs)based on SnO_(2)have gained increasing interest due to its feature of working continuously without the need for external power sources.Nevertheless,the production of the majority of these existing UV PDs necessitates additional manufacturing stages or intricate processes.In this work,we present a facile,cost-effective approach for the fabrication of a self-powered UV PD based on p-Si/n-SnO_(2)junction.The self-powered device was achieved simply by integrating a p-Si substrate with a n-type SnO_(2)microbelt,which was synthesized via the chemical vapor deposition(CVD)method.The high-quality feature,coupled with the belt-like shape of the SnO_(2)microbelt enables the favorable contact between the n-type SnO_(2)and p-type silicon.The built-in electric field created at the interface endows the self-powered performance of the device.The p-Si/n-SnO_(2)junction photodetector demonstrated a high responsivity(0.12 mA/W),high light/dark current ratio(>103),and rapid response speed at zero bias.This method offers a practical way to develop cost-effective and high-performance self-powered UV PDs.
基金Liming Gao acknowledges funding from the National Natural Science Foundation of China(No.61727805).
文摘The rise of smart wearable devices has driven the demand for flexible,high-performance optoelectronic devices with low power and easy high-density integration.Emerging Two-dimensional(2D)materials offer promising solutions.However,the use of 3D metal in traditional 2D devices often leads to Fermi-level pinning,compromising device performance.2D metallic materials,such as graphene and 2H-phase NbSe_(2),present a new avenue for addressing this issue and constructing high-performance,low-power photodetectors.In this work,we designed an all-2D asymmetric contacts photodetector using Gr and NbSe_(2)as electrodes for the 2D semiconductor WSe_(2).The asymmetric Schottky barriers and built-in electric fields facilitated by this architecture resulted in outstanding photovoltaic characteristics and self-powered photodetection.Under zero bias,the device exhibited a responsivity of 287 mA/W,a specific detectivity of 5.3×10^(11)Jones,and an external quantum efficiency of 88%.It also demonstrated an ultra-high light on/offratio(1.8×10^(5)),ultra-fast photoresponse speeds(80/72μs),broad-spectrum responsiveness(405980 nm),and exceptional cycling stability.The applications of the Gr/WSe_(2)/NbSe_(2)heterojunction in imaging and infrared optical communication have been explored,underscoring its significant potential.This work offers an idea to construct all-2D ultrathin optoelectronic devices.
基金the Industry-University Cooperation Project in Fujian Province University(No.2022H6020)。
文摘Self-powered neutron detectors(SPNDs)play a critical role in monitoring the safety margins and overall health of reactors,directly affecting safe operation within the reactor.In this work,a novel fault identification method based on graph convolutional networks(GCN)and Stacking ensemble learning is proposed for SPNDs.The GCN is employed to extract the spatial neighborhood information of SPNDs at different positions,and residuals are obtained by nonlinear fitting of SPND signals.In order to completely extract the time-varying features from residual sequences,the Stacking fusion model,integrated with various algorithms,is developed and enables the identification of five conditions for SPNDs:normal,drift,bias,precision degradation,and complete failure.The results demonstrate that the integration of diverse base-learners in the GCN-Stacking model exhibits advantages over a single model as well as enhances the stability and reliability in fault identification.Additionally,the GCN-Stacking model maintains higher accuracy in identifying faults at different reactor power levels.
基金supported by the Research Platform for biomedical and Health Technology, NUS (Suzhou) Research Institute (RP-BHT-Prof. LEE Chengkuo)RIE Advanced Manufacturing and Engineering (AME) Programmatic Grant (Grant A18A4b0055)+1 种基金RIE 2025-Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) (Grant I2301E0027)Reimagine Research Scheme projects, National University of Singapore, A-0009037-03-00 and A-0009454-01-00 and Reimagine Research Scheme projects, National University of Singapore, A-0004772-00-00 and A-0004772-01-00。
文摘Smart farming with outdoor monitoring systems is critical to address food shortages and sustainability challenges.These systems facilitate informed decisions that enhance efficiency in broader environmental management.Existing outdoor systems equipped with energy harvesters and self-powered sensors often struggle with fluctuating energy sources,low durability under harsh conditions,non-transparent or non-biocompatible materials,and complex structures.Herein,a multifunctional hydrogel is developed,which can fulfill all the above requirements and build selfsustainable outdoor monitoring systems solely by it.It can serve as a stable energy harvester that continuously generates direct current output with an average power density of 1.9 W m^(-3)for nearly 60 days of operation in normal environments(24℃,60%RH),with an energy density of around 1.36×10^(7)J m^(-3).It also shows good self-recoverability in severe environments(45℃,30%RH)in nearly 40 days of continuous operation.Moreover,this hydrogel enables noninvasive and self-powered monitoring of leaf relative water content,providing critical data on evaluating plant health,previously obtainable only through invasive or high-power consumption methods.Its potential extends to acting as other self-powered environmental sensors.This multifunctional hydrogel enables self-sustainable outdoor systems with scalable and low-cost production,paving the way for future agriculture.
文摘Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.
基金supported by the National Natural Science Foundation of China(No.U21A20290)Guangdong Basic and Applied Basic Research Foundation(No.2022A1515011656)+2 种基金the Projects of Talents Recruitment of GDUPT(No.2023rcyj1003)the 2022“Sail Plan”Project of Maoming Green Chemical Industry Research Institute(No.MMGCIRI2022YFJH-Y-024)Maoming Science and Technology Project(No.2023382).
文摘The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum(Al^(3+))and fluoride(F^(−))ions in aqueous solutions.The proposed method involves the synthesis of sulfur-functionalized carbon dots(C-dots)as fluorescence probes,with fluorescence enhancement upon interaction with Al^(3+)ions,achieving a detection limit of 4.2 nmol/L.Subsequently,in the presence of F^(−)ions,fluorescence is quenched,with a detection limit of 47.6 nmol/L.The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python,followed by data preprocessing.Subsequently,the fingerprint data is subjected to cluster analysis using the K-means model from machine learning,and the average Silhouette Coefficient indicates excellent model performance.Finally,a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions.The results demonstrate that the developed model excels in terms of accuracy and sensitivity.This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment,making it a valuable tool for safeguarding our ecosystems and public health.
文摘In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,particularly in snowy environments,remains a challenge.Snow-covered roads introduce unpredictable surface conditions,occlusions,and reduced visibility,that require robust and adaptive path detection algorithms.This paper presents an enhanced road detection framework for snowy environments,leveraging Simple Framework forContrastive Learning of Visual Representations(SimCLR)for Self-Supervised pretraining,hyperparameter optimization,and uncertainty-aware object detection to improve the performance of YouOnly Look Once version 8(YOLOv8).Themodel is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø,Norway,which covers a range of snow textures,illumination conditions,and road geometries.The proposed framework achieves scores in terms of mAP@50 equal to 99%and mAP@50–95 equal to 97%,demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions.The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments,enabling robust decision-making in hazardous weather conditions.This research lays the groundwork for more resilient perceptionmodels in self-driving systems,paving the way for the future development of intelligent and adaptive transportation networks.
基金the Experimental Technology Research Project of Zhejiang University(SYB202138)National Natural Science Foundation of China(32000195)。
文摘With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a convenient and visual technique with low equipment requirements and high sensitivity for the field detection of GM plants is still lacking.On the basis of the existing recombinase polymerase amplification(RPA)technique,we developed a multiplex RPA(multi-RPA)method that can simultaneously detect three transgenic elements,including the cauliflower mosaic virus 35S gene(CaMV35S)promoter,neomycin phosphotransferaseⅡgene(NptⅡ)and hygromycin B phosphotransferase gene(Hyg),thus improving the detection rate.Moreover,we coupled this multi-RPA technique with the CRISPR/Cas12a reporter system,which enabled the detection results to be clearly observed by naked eyes under ultraviolet(UV)light(254 nm;which could be achieved by a portable UV flashlight),therefore establishing a multi-RPA visual detection technique.Compared with the traditional test strip detection method,this multi-RPA-CRISPR/Cas12a technique has the higher specificity,higher sensitivity,wider application range and lower cost.Compared with other polymerase chain reaction(PCR)techniques,it also has the advantages of low equipment requirements and visualization,making it a potentially feasible method for the field detection of GM plants.
基金supported by the National Natural Science Foundation of China(NSFC,Nos.22125110,U23A2094,22205233,22193042,21921001,22305248 and U21A2069)the Natural Science Foundation of Fujian Province(No.2023J02028)+3 种基金the Key Research Program of Frontier Sciences of Chinese Academy of Sciences(No.ZDBS-LY-SLH024)Fujian Science&Technology Innovation Laboratory for Optoelectronic Information of China(No.2021ZR126)the National Key Research and Development Program of China(No.2019YFA0210402)the China Postdoctoral Science Foundation(Nos.2022TQ0337 and 2023M733497).
文摘2D Ruddlesden-Popper(RP)polar perovskite,displaying the intrinsic optical anisotropy and structural polarity,has a fantastic application perspective in self-powered polarized light detection.However,the weak van der Waals interaction between the organic spacing bilayers is insufficient to preserve the stability of RP-type materials.Hence,it is of great significance to explore new stable 2D RP-phase candidates.In this work,we have successfully constructed a highly-stable polar 2D perovskite,(t-ACH)_(2)PbI_(4)(1,where t-ACH^(+)is HOOC_(8)H_(12)NH_(3)^(+)),by adopting a hydrophobic carboxylate trans-isomer of tranexamic acid as the spacing component.Strikingly,strong O-H…O hydrogen bonds between t-ACH^(+)organic bilayers compose the dimer,thus decreasing van der Waals gap and enhancing structural stability.Besides,such orientational hydrogen bonds contribute to the formation of structural polarity and generate an obvious bulk photovoltaic effect in 1,which facilitates its self-powered photodetection.As predicted,the combination of inherent anisotropy and polarity leads to self-powered polarized-light detection with a high ratio of around∼5.3,superior to those of inorganic 2D counterparts.This work paves a potential way to design highly-stable 2D perovskites for high-performance optoelectronic devices.
基金the Innovation Program for Quantum Science and Technology(Grant No.2023ZD0300100)the National Key Research and Development Program of China(Grant Nos.2023YFB3809600 and 2023YFC3007801)+1 种基金the National Natural Science Foundation of China(Grant Nos.62301543 and U24A20320)the Shanghai Sailing Program(Grant No.21YF1455700).
文摘Conventional superconducting nanowire single-photon detectors(SNSPDs)have been typically limited in their applications due to their size,weight,and power consumption,which confine their use to laboratory settings.However,with the rapid development of remote imaging,sensing technologies,and long-range quantum communication with fewer topographical constraints,the demand for high-efficiency single-photon detectors integrated with avionic platforms is rapidly growing.We herein designed and manufactured the first drone-based SNSPD system with a system detection efficiency(SDE)as high as 91.8%.This drone-based system incorporates high-performance NbTiN SNSPDs,a self-developed miniature liquid helium dewar,and custom-built integrated electrical setups,making it capable of being launched in complex topographical conditions.Such a drone-based SNSPD system may open the use of SNSPDs for applications that demand high SDE in complex environments.
基金supported by the National Key R&D Program of China(No.2022YFB4501704)the National Natural Science Foundation of China(Grant No.62102287)the Shanghai Science and Technology Innovation Action Plan Project(Nos.22YS1400600 and 22511100700).
文摘The landscape of financial transactions has grown increasingly complex due to the expansion of global economic integration and advancements in information technology.This complexity poses greater challenges in detecting and managing financial fraud.This review explores the role of Graph Neural Networks(GNNs)in addressing these challenges by proposing a unified framework that categorizes existing GNN methodologies applied to financial fraud detection.Specifically,by examining a series of detailed research questions,this review delves into the suitability of GNNs for financial fraud detection,their deployment in real-world scenarios,and the design considerations that enhance their effectiveness.This review reveals that GNNs are exceptionally adept at capturing complex relational patterns and dynamics within financial networks,significantly outperforming traditional fraud detection methods.Unlike previous surveys that often overlook the specific potentials of GNNs or address them only superficially,our review provides a comprehensive,structured analysis,distinctly focusing on the multifaceted applications and deployments of GNNs in financial fraud detection.This review not only highlights the potential of GNNs to improve fraud detection mechanisms but also identifies current gaps and outlines future research directions to enhance their deployment in financial systems.Through a structured review of over 100 studies,this review paper contributes to the understanding of GNN applications in financial fraud detection,offering insights into their adaptability and potential integration strategies.
文摘The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.
基金funded by the National Natural Science Foundation of China(Nos.22374055,22022404,22074050,82172055)the National Natural Science Foundation of Hubei Province(No.22022CFA033)the Fundamental Research Funds for the Central Universities(Nos.CCNU24JCPT001,CCNU24JCPT020)。
文摘Plants play a crucial role in maintaining ecological balance and biodiversity.However,plant health is easily affected by environmental stresses.Hence,the rapid and precise monitoring of plant health is crucial for global food security and ecological balance.Currently,traditional detection strategies for monitoring plant health mainly rely on expensive equipment and complex operational procedures,which limit their widespread application.Fortunately,near-infrared(NIR)fluorescence and surface-enhanced Raman scattering(SERS)techniques have been recently highlighted in plants.NIR fluorescence imaging holds the advantages of being non-invasive,high-resolution and real-time,which is suitable for rapid screening in large-scale scenarios.While SERS enables highly sensitive and specific detection of trace chemical substances within plant tissues.Therefore,the complementarity of NIR fluorescence and SERS modalities can provide more comprehensive and accurate information for plant disease diagnosis and growth status monitoring.This article summarizes these two modalities in plant applications,and discusses the advantages of multimodal NIR fluorescence/SERS for a better understanding of a plant’s response to stress,thereby improving the accuracy and sensitivity of detection.
文摘Deep learning-based object detection has revolutionized various fields,including agriculture.This paper presents a systematic review based on the PRISMA 2020 approach for object detection techniques in agriculture by exploring the evolution of different methods and applications over the past three years,highlighting the shift from conventional computer vision to deep learning-based methodologies owing to their enhanced efficacy in real time.The review emphasizes the integration of advanced models,such as You Only Look Once(YOLO)v9,v10,EfficientDet,Transformer-based models,and hybrid frameworks that improve the precision,accuracy,and scalability for crop monitoring and disease detection.The review also highlights benchmark datasets and evaluation metrics.It addresses limitations,like domain adaptation challenges,dataset heterogeneity,and occlusion,while offering insights into prospective research avenues,such as multimodal learning,explainable AI,and federated learning.Furthermore,the main aim of this paper is to serve as a thorough resource guide for scientists,researchers,and stakeholders for implementing deep learning-based object detection methods for the development of intelligent,robust,and sustainable agricultural systems.
