Instantaneous Detection Research Articles

Research on underwater target signal orientation estimation based on smoothness priors approach

Purpose This paper aims to address the challenges in hydroacoustic signal detection, signal distortion and target localization caused by baseline drift. The authors propose a combined algorithm that integrates short-time Fourier transform (STFT) detection, smoothness priors approach (SPA), attitude calibration and direction of arrival (DOA) estimation for micro-electro-mechanical system vector hydrophones. Design/methodology/approach Initially, STFT method screens target signals with baseline drift in low signal-to-noise ratio environments, facilitating easier subsequent processing. Next, SPA is applied to the screened target signal, effectively removing the baseline drift, and combined with filtering to improve the signal-to-noise ratio. Then, vector channel amplitudes are corrected using attitude correction with 2D compass data. Finally, the absolute target azimuth is estimated using the minimum variance distortion-free response beamformer. Findings Simulation and experimental results demonstrate that the SPA outperforms high-pass filtering in removing baseline drift and is comparable to the effectiveness of variational mode decomposition, with significantly shorter processing times, making it more suitable for real-time applications. The detection performance of the STFT method is superior to instantaneous correlation detection and sample entropy methods. The final DOA estimation achieves an accuracy within 2°, enabling precise target azimuth estimation. Originality/value To the best of the authors’ knowledge, this study is the first to apply SPA to baseline drift removal in hydroacoustic signals, significantly enhancing the efficiency and accuracy of signal processing. It demonstrates the method’s outstanding performance in the field of underwater signal processing. In addition, it confirms the reliability and feasibility of STFT for signal detection in the presence of baseline drift.

Instantaneous detection and location of contaminant sources in intelligent buildings: A distributed Kalman filter scheme

Indoor Air Quality (IAQ), as a common concern of Intelligent Buildings (IBs), is easily compromised by contaminants which may result from accidents, pathogens or terrorist attacks. Once some contaminant occurs in one zone, it will be quickly diffused to other zones through windows, doors or Heating, Ventilation and Air Conditioning (HVAC) systems, which makes detecting and locating the contaminant source a significant challenge. In this paper, a distributed Kalman filter (DKF) based scheme for detecting and locating the multiple contaminant sources is developed. Firstly, a distributed state-space model of contaminant diffusion is established with the help of the multizone simulation program-CONTAM and the Multi-Chamber theory. Then, a multi-agent based distributed monitoring framework is constructed, where each agent equipped for each zone runs a Kalman filter using both the information of the local zone and the residuals of the interconnected zones. The decoupling strategy is employed to determine the filter gains to guarantee the optimal contaminant concentration estimations. Compared with existing methods, the proposed scheme can detect and locate multiple contaminant sources instantaneously. Meanwhile, it has the advantages of low computation and communication consumptions. A model of a realistic 11-zone experimental center created on CONTAM is used to demonstrate the effectiveness and superiority of the proposed scheme.

HCO-RLF: Hybrid classification optimization using recurrent learning and fuzzy for COVID-19 detection on CT images

COVID-19 infection detection through initial lesion classification provides early diagnosis and prevents breathing difficulties. Detecting the infectious part of the lungs using computerized tomography (CT) images has become an instantaneous detection method. In this article, a Hybrid Classification Optimization (HCO) using Recurrent Leaning and Fuzzy (RLF) is proposed. The neural network classifies infected and non-infected regions using pixel distributions and their variations. This is performed by identifying missing features and training the recurrent network using regional differences. Based on the feature availability, recurrent learning classifies the region through the input dataset and recurrent training correlation. The fuzzy predicts missing features through substituted derivatives obtained between wide ranges of variations. In this process, the maximum fuzzy derivatives for feature substitution are used for infected region prediction. The least fuzzy derivatives are prevented from the training layer to reduce false rates in region classification. This joint process improves the training consistency to leverage the detection and region classification accuracies. The proposed HCO-RLF improves detection and classification accuracy, and precision by 11.96 %, 9.98 %, and 13.42 % for the varying classification rates. Besides, the results are obtained in comparison with the existing DR-MIL, DSAE, and BS-FSA methods discussed later in the article.

Phosphate Ion-Selective Electrode Based on Electrochemically Modified Iron.

The significance of the detection of phosphate ions is immense in the realms of chemistry, biology, medicine, environment, and industry. The detection of phosphate ions is currently mainly reliant on blue molybdenum colorimetry, which is accurate but requires sample pretreatment, intricate operation, and a high price tag. Consequently, it is essential to create a sensor with superior efficiency, precision, straightforward functioning, and instantaneous online detection. This study has designed and created an electrochemical modification based on an iron metal electrode for this purpose. Cyclic voltammetry was used to initially ascertain the potential (-0.57 V) necessary for constant potential electrolysis. Employing a constant potential electrolysis method, the iron oxide and its phosphate were modified onto the surface of the iron electrode to enable reaction with phosphate ions. Scanning electron microscopy and energy dispersive X-ray spectroscopy were used to characterize and analyze the morphology and elemental composition of Fe-PME, elucidating how it responds to phosphate ionation. The two-electrode system was then utilized for the evaluation of the phosphate ion response of Fe-PME at pH 4. Fe-PME's response to phosphate ions is demonstrated by the results, ranging from 10-5 to 0.1 M and with a slope of -52.8 mV dec-1. Fe-PME exhibited satisfactory results when compared to the conventional blue colorimetry of molybdenum.

Experimental and theoretical studies for instantaneous detection of l-cysteine and l-histidine using a simple Cu(II)-dppy complex

Developing simple and effective receptors for instantaneous detection of l-amino acids is vital for the recognition of life-threatening diseases in their early stage. Here, we report the synthesis and characterization of 2,6-di(pyrazin-2-yl)pyridine (dppy) based ligand L (L = 2,2′-(4-(3,4-diethoxyphenyl)pyridine-2,6-diyl)dipyrazine) and its copper (II) complex, [Cu(NO3)2L]. The in-situ prepared [Cu(H2O)2L](NO3)2 receptor in aqueous acetonitrile (4:1 v/v, 10 mM HEPES buffer, pH 7.4) medium displayed instantaneous responses towards biologically important bio-thiol l-Cysteine (Cys) and essential amino acid l-Histidine (His) over other l-amino acids through UV–Visible absorption spectral studies. Additionally, the competitive experiments confirmed that in-situ prepared [Cu(H2O)2L](NO3)2 receptor selectively detects Cys (1 equivalent) in the presence of other l-amino acids. The detection limits of Cys and His were calculated to be 8.23 × 10−7 M and 2.55 × 10−6 M, respectively. The density functional theory (DFT) calculation shows that selectivity of [Cu(H2O)2L](NO3)2 towards Cys is due to the reduction of Cu(II) to Cu(I) in the presence of Cys, while Cys is oxidized to cystine (Cys-Cys). For His, selectivity observed due to the formation of [Cu(His)2] moiety between Cu(II) ion with one His via N,O coordination mode and another His via N,N,O-coordination mode.

Research on the Key Technology of a Fluorescence Detection Device Using the RT-LAMP Method for Instant Detection.

As of 31 October 2023, there have been 771,795,258 confirmed cases of COVID-19 globally. Developing simple, portable, and reliable testing devices has become increasingly important. This paper presents a point-of-care testing (POCT) device for COVID-19 based on the dual-excitation fluorescence RT-LAMP method, which is derived from the principles of RT-LAMP-based COVID-19 detection kits available in the market. The key design solutions of the device were simulated and modeled. Key performance metrics such as detection repeatability and linearity were validated. Comparative experiments with the RT-qPCR detection method were conducted to verify the accuracy and reliability of the device. Additionally, the device's detection sensitivity and accuracy were assessed. Experimental results show that the repeatability coefficient of variation (CV) value is ≤0.09%; the linearity R2 for the FAM channel is 0.9977 and that for the HEX channel is 0.9899; it exhibits good anti-interference performance, with negligible cross-channel interference; the temperature stability is ±0.062 °C, the temperature accuracy is less than 0.2 °C, and there is no significant temperature overshoot during the heating process. Compared with the real-time quantitative PCR (RT-qPCR) instrument, the positive agreement rate is 100% and the negative agreement rate is 95.0%. This research provides a foundational basis for the development of equipment for the prevention of infectious diseases and clinical diagnostics.

Bright yellow fluorescent carbon dots as selective nanoprobe for detection of 2-nitrophenol and 4-nitrophenol in aqueous medium

Different types of phenolic pollutants pose a major risk to the environment and the health of living things. It is therefore crucial for their selective recognition especially nitrophenols. In this work, yellow fluorescent carbon dots (C-dots) were used as selective nanoprobe to efficiently detect 2-nitrophenol (2-NP) and 4-nitrophenol (4-NP). One-pot synthetic method was adopted for making C-dots from o-phenylenediamine and oleic acid mixture. The synthesized C-dots showed an excitation wavelength of 420 nm with Stokes shift of up to 135 nm. These C-dots exhibited instantaneous selective detection of 2-NP and 4-NP in aqueous solution with a detection limit of 6.4 nM and 4.8 nM respectively. Upon incubated with 2-NP and 4-NP, the C-dots’ fluorescence underwent quenching with a maximum extent of respective 80 % and 83 %. By comparing with other analogs of analyte, it was discovered that the emission intensity of the C-dots remained unchanged. Static quenching was proposed behind the changes of fluorescence after the treatment with 2-NP and 4-NP. Such type of C-dots will demonstrate the selective sensing platform in real field application.

A segmented trajectory planning and guidance method for hypersonic glide vehicles considering target detection performance

This paper proposes a closed-loop detection and guidance method for optimizing the detection effectiveness of Radio Frequency (RF) stealth targets by terminal-guided hypersonic glide vehicles (HGV) during the terminal-guidance phase. This method divides the guidance process into two stages based on the detectability of the target. In the search stage, the Twin Delayed Deep Deterministic Policy Gradient (TD3) coupled with Successive Convex Optimization (SCO) guidance method is employed. This approach separates the detection performance from the numerical solution process of the optimization problem, enabling online adjustment of performance indicator functions to refine trajectory configurations. In the tracking phase, the dynamic characteristics of the target's Radar Cross Section (RCS) are convexified and discretized, incorporated into the objective function of the optimization problem as performance indicators. The Higher-Order Soft-Trust-Region Sequential Convex Programming (HSSCP) method is employed to improve the convergence of the algorithm. Additionally, the online trajectory planning problem is integrated into the Model Predictive Control (MPC) framework to achieve closed-loop guidance. Simulation results indicate that this method can improve the detection performance of RF stealthy targets during the terminal guidance phase, the cumulative detection information for the target increased by approximately 20.6%, and the maximum instantaneous detection probability improved by about 14.3%. Moreover, it can achieve convergence of complex multi-constraint problems within a limited number of iterations.

π-π conjugated PDI supramolecular regulating the photoluminescence of imine-COFs for sensitive smartphone visual detection of levofloxacin

As the contamination and enrichment in food chain of levofloxacin (LV) antibiotics have caused a significant threat to life safety, the instant detection of LV has become an urgent need. Here, a PDI-functionalized imine-based covalent organic framework (PDI-COF300) was prepared by the electrostatic self-assembly method as fluorescent probe for smartphone visual detection of LV, which exhibited excellent fluorescence quantum yield (82.68%), greater stability, high sensitivity with detection limit of 0.303 μM. Based on the results of molecular docking and Stern-Volmer equation, the LV detection by PDI-COF300 was mainly a static quenching process through π-π stacked hydrophobic interactions and fluorescence resonance energy transfer. Besides, PDI-COF300 was applied to LV detection in environmental medium and milk samples with recoveries from 85.56% to 108.34% and relative standard deviations <2.70%. This work also provided a new general strategy for using PDI-COF in smartphone devices and fluorescent papers for LV fluorescence detection and microanalysis.

Attenuation of non-stationary random noise in ground penetrating radar data based on time-varying filtering

The attenuation of random noise is significant in the enhancement of the signal-to-noise ratio in ground penetrating radar (GPR) data. However, the non-stationary nature of random noise poses significant challenges to most conventional denoising methods. We propose a time-varying bandpass filter (TVBF) based on the local maxima multiple synchrosqueezing transform (LMMSST), referred to as TVBFLMMSST, for the attenuation of non-stationary random noise in GPR data. The LMMSST method is introduced to achieve highly compressed time–frequency representation (TFR). By integrating an instantaneous frequency trajectory detection algorithm, we design the TVBF to successfully separate useful signals from random noise. The efficacy of the TVBFLMMSST is validated through synthetic and field experiments, demonstrating its capability to effectively isolate components of useful signals and noise signals. Compared to classical denoising methods, TVBFLMMSST exhibits robust performance in attenuating random noise while preserving the amplitude of useful signals.

A comprehensive approach to detecting chemical adulteration in fruits using computer vision, deep learning, and chemical sensors

Contamination of harmful additives in fruits has become a concerning norm these days. Owing to the great popularity of fruits, dishonest vendors frequently use harmful chemicals to contaminate fruits to extend their shelf life, which is extremely dangerous for the general public's health. To mitigate this issue, machine-learning algorithms like Decision Tree Classifier, Naïve Bayes and a deep learning model named “DurbeenNet” are evaluated separately. Alongside, a computer vision-based detection method coupled with a hybrid model is proposed that combines deep learning and chemical sensor. Formaldehyde Detection Sensor is used in this experiment to take reading of the sensor data. Mango, Apple, Banana, and Malta are taken as sample fruits in this study. Sensor data for both fresh and chemical-mixed fruit is newly collected using Formaldehyde Detection Sensor. The above mentioned sensor data along with the previously captures images of both fresh and chemical-mixed state are being integrated to a hybrid model. Among two machine learning algorithms naïve bayes come up with 82 % accuracy. Using both sensor data and captured image data, the proposed model “SensorNet” provides highest accuracy of 97.03 % which is substantial than “DurbeenNet” model's accuracy. Through the utilization of these fruit samples, formaldehyde detection sensor provides instantaneous detection, identifying the specific toxic substances present in the contaminated fruits.

Bimetallic Ln-MOF nanofibrous membrane as colorimetric fluorescent test strip for 1, 4-dioxane-glycol mixture ratio sensing

A bimetallic Ln-MOF nanofibrous membrane (NFM) was successfully fabricated by in situ growth of Eu/Tb-MOF crystals onto electrospun polycaprolactam (PA6) nanofibers. For better combining with PA6 nanofibers, the Eu/Tb-MOFs were made into nanocrystals. Characterization with PXRD, FT-IR, EDS and XPS confirmed that the crystal structure of the Eu/Tb-MOFs maintained well on PA6 NFM. The as-prepared Eu/Tb-MOF@PA6 NFM can be used as a colorimetric fluorescent test strip for instant detection of the volume ratio of 1, 4-dioxane in 1, 4-dioxane-glycol mixture. With the increase of 1, 4-dioxane, the test strip presented color changes from green to red, which can be easily distinguished by the naked eye. In view of this, the conversion rate of the condensation reaction of glycol to 1, 4-dioxane will be preliminarily estimated without the help of equipment. More importantly, the test strip exhibited better convenience and reversibility compared with powdered Ln-MOFs because no separation process was needed for test strip before and after recovering. Benefiting from sufficient amide groups in PA6 for anchoring Eu/Tb-MOF particles, the test strip showed satisfactory stability in cycling experiments. This work will provide a new insight into the development of fluorescent test strip based on electrospun MOF NFM for quantitatively monitoring the reaction process.

Sending pictures over radio systems of trail cam in border security and directing uavs to the right areas

In this study, a method is proposed for the trail cams to send data via narrow band communication systems in border security and counter-terrorism areas and to direct drones to the right areas. The success of UAVs lies in scanning the correct areas for observation or detection. UAVs should be fed with data to observe the correct regions, and the probability of detecting border security or terrorist elements should be increased. Instantaneous detection is performed by trail cam, which generally operate dependent on GSM. However, these devices cannot provide real-time data in border areas with low population density and no GSM service, particularly in counter-terrorism operations. In this study, the dependence of trail cam devices on GSM was eliminated, and data transfer over the radio system was established to enable real-time data flow in a wide field. After the trail cam device makes a detection, the data is sent via the APCO-25 JEMUS radio system with a capacity of 9.6 KB. The resolution of the detection image is reduced, allowing it to be displayed on a remote-control computer in less than one minute. As a result of the study, when an intelligent trail cam with object recognition capability is developed, the device can assess what the image might be in real-time. Obtaining real time detection data from trail cams in border areas and counter-terrorism zones without GSM infrastructure can expedite the direction of UAVs to the correct regions for intervention by military units. Additionally, confirming that trail cam detects via narrowband communication systems in locations where units are temporarily stationed and without alpine terrain minimizes the surveillance vulnerability of UAVs unable to perform imaging due to adverse weather conditions. This also establishes a warning system against potential attacks by terrorist elements.

Instant Detection of Cerebral Blood Flow Changes in Infants with Congenital Heart Disease during Transcatheter Interventions.

Background: Transcatheter interventions are increasingly used in children with congenital heart disease. However, these interventions can affect cardiac output and cerebral circulation. In this pilot study, we aimed to investigate the use of NeoDoppler, a continuous transfontanellar cerebral Doppler monitoring system, to evaluate the impact of transcatheter interventions on cerebral circulation. Methods: Nineteen participants under one year of age (mean age 3.5 months) undergoing transcatheter cardiac interventions were prospectively included. Transfontanellar cerebral Doppler monitoring with the NeoDoppler system was initiated after intubation and continued until the end of the procedure. Results: Instant detection of changes in cerebral blood flow were observed across a spectrum of transcatheter interventions. Balloon aortic valvuloplasty demonstrated temporary cessation of cerebral blood flow during balloon inflation. Increase in cerebral diastolic blood flow velocity and decreased pulsatility were observed during patent ductus arteriosus occlusion. Changes in cerebral blood flow patterns were detected in two patients who encountered complications during their transcatheter interventions. There was no significant change in Doppler parameters before and after the interventions for the entire patient group. High quality recordings were achieved in 87.3% of the monitoring period. Conclusions: Continuous transfontanellar cerebral Doppler is feasible in monitoring cerebral hemodynamic trends and shows instantaneous changes associated with interventions and complications. It could become a useful monitoring tool during transcatheter interventions in infants.

Dual function Schiff-base as a selective fluorescence “Turn-on” sensor for Zn2+ and a colorimetric sensor for Cu2+ and Fe3+ ions

A simple and adaptable salen-based Schiff base receptor (L = 6,6′-((1E,1′E)-(cyclohexane-1,2-diylbis(azanylylidene))bis(methanylylidene))bis(2-methoxyphenol))was employed in the current study for colorimetric detection of Cu2+, Fe3+, and fluorescence detection of Zn2+ ions in a semi-aqueous solution. L could identify the three target ions among the many different cations and anions in the solution. The turn-on fluorescence signal of L to Zn2+ ions was boosted by the restriction of the intramolecular charge transfer process, and the resulting chelation enhances the fluorescence signal. The sensing mechanisms were validated using a Job’s plot, Electrospray Ionization Mass Spectrometry (ESI-MS) analysis, Nuclear Magnetic Resonance (NMR) titration experiment, and theoretical calculation investigations. The detection limits for Zn2+ were determined to be 0.038 μM, whereas Cu2+ and Fe3+ detection limits were 0.58 μM and 0.62 μM, respectively. Next, L was used to develop solid silica gel and test strips for instantaneous colorimetric and fluorescent detection of the three target ions in real time. The trace level of Cu2+ and Fe3+ in the tap and drinking water samples could be quantitatively measured using L. Eventually, applications for keypad locks and molecular logic gates were developed utilizing the receptor L.

A photoelectrochemical platform constructed with C-doped ZnIn2S4 /Bi2S3: Excellent for the visual detection of tumor marker

A photoelectrochemical (PEC) immunosensor based on a novel composite material was proposed for the highly sensitive and selective detection of Alpha-fetoprotein (AFP). C-doped ZnIn2S4/Bi2S3 (ZnIn2S4/Bi2S3/C) nanocomposites were synthesized in one pan by solvothermal method using glucose as carbon precursor and used as photoanode. ZnIn2S4/Bi2S3 displayed efficient visible light utilization and carrier separation efficiency. In addition, the high electrical conductivity of carbon materials also allowed ZnIn2S4/Bi2S3/C to show stronger photocurrent compared to ZnIn2S4/Bi2S3. The PEC sensor was successfully prepared in combination with a selective AFP antibody. The photocurrent decreased with the increase of AFP concentration in the range of 0.001–1000 ng mL−1, with a calculated detection limit of 0.12 ng mL−1. Besides, in order to eliminate the workstation's limitations, a colorimetric device that can display AFP levels nearly simultaneously was constructed by modifying Prussian blue on another FTO electrode and connecting the two electrodes by wires. This work provided a new approach for the instant detection of AFP in serum by developing a visualization device for rapid screening and identification of AFP by introducing the electrochromic principle and integrating the device from the viewpoint of improving the practicality of the sensing method.

Photonic-electronic integrated circuit-based coherent LiDAR engine

Chip-scale integration is a key enabler for the deployment of photonic technologies. Coherent laser ranging or FMCW LiDAR, a perception technology that benefits from instantaneous velocity and distance detection, eye-safe operation, long-range, and immunity to interference. However, wafer-scale integration of these systems has been challenged by stringent requirements on laser coherence, frequency agility, and the necessity for optical amplifiers. Here, we demonstrate a photonic-electronic LiDAR source composed of a micro-electronic-based high-voltage arbitrary waveform generator, a hybrid photonic circuit-based tunable Vernier laser with piezoelectric actuators, and an erbium-doped waveguide amplifier. Importantly, all systems are realized in a wafer-scale manufacturing-compatible process comprising III-V semiconductors, silicon nitride photonic integrated circuits, and 130-nm SiGe bipolar complementary metal-oxide-semiconductor (CMOS) technology. We conducted ranging experiments at a 10-meter distance with a precision level of 10 cm and a 50 kHz acquisition rate. The laser source is turnkey and linearization-free, and it can be seamlessly integrated with existing focal plane and optical phased array LiDAR approaches.

HDformer: A Higher-Dimensional Transformer for Detecting Diabetes Utilizing Long-Range Vascular Signals

Diabetes mellitus is a global concern, and early detection can prevent serious complications. 50% of those with diabetes live undiagnosed, disproportionately afflicting low-income groups. Non-invasive methods have emerged for timely detection; however, their limited accuracy constrains clinical usage. In this research, we present a novel Higher Dimensional Transformer (HDformer), the first Transformer-based architecture which utilizes long-range photoplethysmography (PPG) to detect diabetes. The long-range PPG maximizes signal contextual information when compared to the less-than 30 second signals commonly used in existing research. To increase the computational efficiency of HDformer’s long-range processing, a new attention module, Time Square Attention (TSA), is invented to achieve linear computational complexity with respect to the token volume while retaining the local/global dependencies. TSA converts the 1D inputs into 2D representations, grouping the adjacent points into a single 2D token. It then generates dynamic patches and feeds them into a gated mixture-of-experts (MoE) network, optimizing the learning on different attention areas. HDformer achieves state-of-the-art results (sensitivity 98.4, accuracy 97.3, specificity 92.8, AUC 0.929) on the standard MIMIC-III dataset, surpassing existing research. Furthermore, we develop an end-to-end solution where a low-cost wearable is prototyped to connect with the HDformer in the Cloud via a mobile app. This scalable, convenient, and affordable approach provides instantaneous detection and continuous monitoring for individuals. It aids doctors in easily screening for diabetes and safeguards underprivileged communities. The enhanced versatility of HDformer allows for efficient processing and learning of long-range signals in general one-dimensional time-series sequences, particularly for all biomedical waveforms.

Experimental Study on Attention Blink of Male Sanda Athletes of Different Sports Levels under Fatigue State

This article uses literature methods, experimental methods, mathematical statistics and other research methods to pay attention to instantaneous detection of male Sanda athletes of different sports levels under fatigue conditions. The results showed that male Sanda athletes' attentional blink phenomenon will be aggravated under fatigue state, but the position of the lowest point will not change. At the same time, excellent Sanda athletes will break away from the attention blinking phenomenon faster.

Establishment of RPA-Cas12a-Based Fluorescence Assay for Rapid Detection of Feline Parvovirus.

Feline parvovirus (FPV) is highly infectious for cats and other Felidae and often causes severe damage to young kittens. In this study, we incorporated recombinase polymerase amplification (RPA) and Cas12a-mediated detection and developed an RPA-Cas12a-based real-time or end-point fluorescence detection method to identify the NS1 gene of FPV. The total time of RPA-Cas12a-based fluorescence assay is approximately 25 min. The assay presented a limit of detection (LOD) of 1 copies/μl (25 copies/per reaction), with no cross-reactivity with several feline pathogens. The clinical performance of the assay was examined using total genomic DNA purified from 60 clinical specimens and then compared to results obtained with qPCR detection of FPV with 93.3% positive predictive agreement and 100% negative predictive agreement. Together, the rapid reaction, cost-effectiveness, and high sensitivity make the RPA-Cas12a-based fluorescence assay a fascinating diagnostic tool that will help minimize infection spread through instant detection of FPV.