With the rapid advancement of visual generative models such as Generative Adversarial Networks(GANs)and stable Diffusion,the creation of highly realistic Deepfake through automated forgery has significantly progressed...With the rapid advancement of visual generative models such as Generative Adversarial Networks(GANs)and stable Diffusion,the creation of highly realistic Deepfake through automated forgery has significantly progressed.This paper examines the advancements inDeepfake detection and defense technologies,emphasizing the shift from passive detection methods to proactive digital watermarking techniques.Passive detection methods,which involve extracting features from images or videos to identify forgeries,encounter challenges such as poor performance against unknown manipulation techniques and susceptibility to counter-forensic tactics.In contrast,proactive digital watermarking techniques embed specificmarkers into images or videos,facilitating real-time detection and traceability,thereby providing a preemptive defense againstDeepfake content.We offer a comprehensive analysis of digitalwatermarking-based forensic techniques,discussing their advantages over passivemethods and highlighting four key benefits:real-time detection,embedded defense,resistance to tampering,and provision of legal evidence.Additionally,the paper identifies gaps in the literature concerning proactive forensic techniques and suggests future research directions,including cross-domain watermarking and adaptive watermarking strategies.By systematically classifying and comparing existing techniques,this review aims to contribute valuable insights for the development of more effective proactive defense strategies in Deepfake forensics.展开更多
The Internet of Medical Things(IoMT)is transforming healthcare by enabling real-time data collection,analysis,and personalized treatment through interconnected devices such as sensors and wearables.The integration of ...The Internet of Medical Things(IoMT)is transforming healthcare by enabling real-time data collection,analysis,and personalized treatment through interconnected devices such as sensors and wearables.The integration of Digital Twins(DTs),the virtual replicas of physical components and processes,has also been found to be a game changer for the ever-evolving IoMT.However,these advancements in the healthcare domain come with significant cybersecurity challenges,exposing it to malicious attacks and several security threats.Intrusion Detection Systems(IDSs)serve as a critical defense mechanism,yet traditional IDS approaches often struggle with the complexity and scale of IoMT networks.With this context,this paper follows a systematic approach to analyze the existing literature and highlight the current trends and challenges related to IDS in the IoMT domain.We leveraged techniques like bibliographic and keyword analysis to collect 832 research works published from 2007 to 2025,aligned with the theme“Digital Twins and IDS in IoMT.”It was found that by simulating device behaviours and network interactions in IoMT,DTs not only provide a proactive platform for early threat detection,but also offer a scalable and adaptive approach to mitigating evolving security threats in IoMT.Overall,this review provides a closer look into the role of IDS and DT in securing IoMT systems and sheds light on the possible research directions for developers and the research community.展开更多
Diabetes imposes a substantial burden on global healthcare systems.Worldwide,nearly half of individuals with diabetes remain undiagnosed,while conventional diagnostic techniques are often invasive,painful,and expensiv...Diabetes imposes a substantial burden on global healthcare systems.Worldwide,nearly half of individuals with diabetes remain undiagnosed,while conventional diagnostic techniques are often invasive,painful,and expensive.In this study,we propose a noninvasive approach for diabetes detection using photoplethysmography(PPG),which is widely integrated into modern wearable devices.First,we derived velocity plethysmography(VPG)and acceleration plethysmography(APG)signals from PPG to construct multi-channel waveform representations.Second,we introduced a novel multiset spatiotemporal feature fusion framework that integrates hand-crafted temporal,statistical,and nonlinear features with recursive feature elimination and deep feature extraction using a one-dimensional statistical convolutional neural network(1DSCNN).Finally,we developed an interpretable diabetes detection method based on XGBoost,with explainable artificial intelligence(XAI)techniques.Specifically,SHapley Additive exPlanations(SHAP)and Local InterpretableModel-agnostic Explanations(LIME)were employed to identify and interpret potential digital biomarkers associated with diabetes.To validate the proposed method,we extended the publicly available Guilin People’s Hospital dataset by incorporating in-house clinical data from ten subjects,thereby enhancing data diversity.A subject-independent cross-validation strategy was applied to ensure that the testing subjects remained independent of the training data for robust generalization.Compared with existing state-of-the-art methods,our approach achieved superior performance,with an area under the curve(AUC)of 80.5±15.9%,sensitivity of 77.2±7.5%,and specificity of 64.3±18.2%.These results demonstrate that the proposed approach provides a noninvasive,interpretable,and accessible solution for diabetes detection using PPG signals.展开更多
This research aims to address the challenges of fault detection and isolation(FDI)in digital grids,focusing on improving the reliability and stability of power systems.Traditional fault detection techniques,such as ru...This research aims to address the challenges of fault detection and isolation(FDI)in digital grids,focusing on improving the reliability and stability of power systems.Traditional fault detection techniques,such as rule-based fuzzy systems and conventional FDI methods,often struggle with the dynamic nature of modern grids,resulting in delays and inaccuracies in fault classification.To overcome these limitations,this study introduces a Hybrid NeuroFuzzy Fault Detection Model that combines the adaptive learning capabilities of neural networks with the reasoning strength of fuzzy logic.The model’s performance was evaluated through extensive simulations on the IEEE 33-bus test system,considering various fault scenarios,including line-to-ground faults(LGF),three-phase short circuits(3PSC),and harmonic distortions(HD).The quantitative results show that the model achieves 97.2%accuracy,a false negative rate(FNR)of 1.9%,and a false positive rate(FPR)of 2.3%,demonstrating its high precision in fault diagnosis.The qualitative analysis further highlights the model’s adaptability and its potential for seamless integration into smart grids,micro grids,and renewable energy systems.By dynamically refining fuzzy inference rules,the model enhances fault detection efficiency without compromising computational feasibility.These findings contribute to the development of more resilient and adaptive fault management systems,paving the way for advanced smart grid technologies.展开更多
The increasing global prevalence of mild cognitive impairment(MCI)necessitates a paradigm shift in early detection strategies.Conventional neuropsychological assessment methods,predominantly paper-and-pencil tests suc...The increasing global prevalence of mild cognitive impairment(MCI)necessitates a paradigm shift in early detection strategies.Conventional neuropsychological assessment methods,predominantly paper-and-pencil tests such as the Mini-Mental State Examination and the Montreal Cognitive Assessment,exhibit inherent limitations with respect to accessibility,administration burden,and sensitivity to subtle cognitive decline,particularly among diverse populations.This commentary critically examines a recent study that champions a novel approach:The integration of gait and handwriting kinematic parameters analyzed via machine learning for MCI screening.The present study positions itself within the broader landscape of MCI detection,with a view to comparing its advantages against established neuropsychological batteries,advanced neuroimaging(e.g.,positron emission tomography,magnetic resonance imaging),and emerging fluid biomarkers(e.g.,cerebrospinal fluid,blood-based assays).While the study demonstrates promising accuracy(74.44%area under the curve 0.74 with gait and graphic handwriting)and addresses key unmet needs in accessibility and objectivity,we highlight its cross-sectional nature,limited sample diversity,and lack of dual-task assessment as areas for future refinement.This commentary posits that kinematic biomarkers offer a distinctive,scalable,and ecologically valid approach to widespread MCI screening,thereby complementing existing methods by providing real-world functional insights.Future research should prioritize longitudinal validation,expansion to diverse cohorts,integration with multimodal data including dual-tasking,and the development of highly portable,artificial intelligence-driven solutions to achieve the democratization of early MCI detection and enable timely interventions.展开更多
Objective Shellfish are recognized as important vehicles of norovirus-associated gastroenteritis. The present study aimed to monitor norovirus contamination in oysters along the farm-to-fork continuum in Guangxi, a ma...Objective Shellfish are recognized as important vehicles of norovirus-associated gastroenteritis. The present study aimed to monitor norovirus contamination in oysters along the farm-to-fork continuum in Guangxi, a major oyster production area in Southwestern China. Methods Oyster samples were collected monthly from farms, markets, and restaurants, from January to December 2016. Norovirus was detected and quantified by one-step reverse transcription-droplet digital polymerase chain reaction（RT-ddPCR）. Results A total of 480 oyster samples were collected and tested for norovirus genogroups I and II. Norovirus was detected in 20.7% of samples, with genogroup II predominating. No significant difference was observed in norovirus prevalence among different sampling sites. The norovirus levels varied widely, with a geometric mean of 19,300 copies/g in digestive glands. Both norovirus prevalence and viral loads showed obvious seasonality, with a strong winter bias. Conclusion This study provides a systematic analysis of norovirus contamination ‘from the farm to the fork＇ in Guangxi. RT-ddPCR can be a useful tool for detection and quantification of low amounts of norovirus in the presence of inhibitors found particularly in foodstuffs. This approach will contribute to the development of strategies for controlling and reducing the risk of human illness resulting from shellfish consumption.展开更多
The digital coherent detection technique has been investigated without any frequency-scanning device in the Brillouin optical time domain reflectometry （BOTDR）, where the simplex pulse codes are applied in the sensi...The digital coherent detection technique has been investigated without any frequency-scanning device in the Brillouin optical time domain reflectometry （BOTDR）, where the simplex pulse codes are applied in the sensing system. The time domain signal of every code sequence is collected by the data acquisition card （DAQ）. A shift-averaging technique is applied in the frequency domain for the reason that the local oscillator （LO） in the coherent detection is fix-frequency deviated from the primary source. With the 31-bit simplex code, the signal-to-noise ratio （SNR） has 3.5-dB enhancement with the same single pulse traces, accordant with the theoretical analysis. The frequency fluctuation for simplex codes is 14.01 MHz less than that for a single pulse as to 4-m spatial resolution. The results are believed to be beneficial for the BOTDR performance improvement.展开更多
Bacterial fruit blotch caused by Acidovorax citrulli is a serious threat to cucurbit industry worldwide.The pathogen is seedtransmitted,so seed detection to prevent distribution of contaminated seed is crucial in dise...Bacterial fruit blotch caused by Acidovorax citrulli is a serious threat to cucurbit industry worldwide.The pathogen is seedtransmitted,so seed detection to prevent distribution of contaminated seed is crucial in disease management.In this study,we adapted a quantitative real-time PCR(qPCR)assay to droplet digital PCR(ddPCR)format for A.citrulli detection by optimizing reaction conditions.The performance of ddPCR in detecting A.citrulli pure culture,DNA,infested watermelon/melon seed and commercial seed samples were compared with multiplex PCR,qPCR,and dilution plating method.The lowest concentrations detected(LCD)by ddPCR reached up to 2 fg DNA,and 102 CFU mL–1 bacterial cells,which were ten times more sensitive than those of the qPCR.When testing artificially infested watermelon and melon seed,0.1%infestation level was detectable using ddPCR and dilution plating method.The 26 positive samples were identified in 201 commercial seed samples through ddPCR,which was the highest positive number among all the methods.High detection sensitivity achieved by ddPCR demonstrated a promising technique for improving seed-transmitted pathogen detection threshold in the future.展开更多
Traffic monitoring is of major importance for enforcing traffic management policies.To accomplish this task,the detection of vehicle can be achieved by exploiting image analysis techniques.In this paper,a solution is ...Traffic monitoring is of major importance for enforcing traffic management policies.To accomplish this task,the detection of vehicle can be achieved by exploiting image analysis techniques.In this paper,a solution is presented to obtain various traffic parameters through vehicular video detection system(VVDS).VVDS exploits the algorithm based on virtual loops to detect moving vehicle in real time.This algorithm uses the background differencing method,and vehicles can be detected through luminance difference of pixels between background image and current image.Furthermore a novel technology named as spatio-temporal image sequences analysis is applied to background differencing to improve detection accuracy.Then a hardware implementation of a digital signal processing (DSP) based board is described in detail and the board can simultaneously process four-channel video from different cameras. The benefit of usage of DSP is that images of a roadway can be processed at frame rate due to DSP′s high performance.In the end,VVDS is tested on real-world scenes and experiment results show that the system is both fast and robust to the surveillance of transportation.展开更多
The establishment of highly sensitive diagnostic methods is critical in the early diagnosis and control of Zika virus(ZIKV)and in preventing serious neurological complications of ZIKV infection. In this study, we esta...The establishment of highly sensitive diagnostic methods is critical in the early diagnosis and control of Zika virus(ZIKV)and in preventing serious neurological complications of ZIKV infection. In this study, we established micro-droplet digital polymerase chain reaction(ddPCR) and real-time quantitative PCR(RT-qPCR) protocols for the detection of ZIKV based on the amplification of the NS5 gene. For the ZIKV standard plasmid, the RT-qPCR results showed that the cycle threshold(Ct) value was linear from 10~1 to 10~8 copy/l L, with a standard curve R^2 of 0.999 and amplification efficiency of 92.203%;however, a concentration as low as 1 copy/l L could not be detected. In comparison with RT-qPCR, the dd PCR method resulted in a linear range of 10~1–10~4 copy/l L and was able to detect concentrations as low as 1 copy/l L. Thus, for detecting ZIKV from clinical samples, RT-qPCR is a better choice for high-concentration samples(above 10~1 copy/l L),while ddPCR has excellent accuracy and sensitivity for low-concentration samples. These results indicate that the ddPCR method should be of considerable use in the early diagnosis, laboratory study, and monitoring of ZIKV.展开更多
A difficult problem in forestry is tree inventory.In this study, a GoProHero attached to a small unmanned aerial vehicle was used to capture images of a small area covered by pinus pinea trees. Then, a digital surface...A difficult problem in forestry is tree inventory.In this study, a GoProHero attached to a small unmanned aerial vehicle was used to capture images of a small area covered by pinus pinea trees. Then, a digital surface model was generated with image matching. The elevation model representing the terrain surface, a ‘digital terrain model’,was extracted from the digital surface model using morphological filtering. Individual trees were extracted by analyzing elevation flow on the digital elevation model because the elevation reached the highest value on the tree peaks compared to the neighborhood elevation pixels. The quality of the results was assessed by comparison with reference data for correctness of the estimated number of trees. The tree heights were calculated and evaluated with ground truth dataset. The results showed 80% correctness and 90% completeness.展开更多
With increasing need of high quality movie, more and more standard resolution films are upconverted to the high-resolution films. After this operation, the defects exist in the old movie are more obvious because they ...With increasing need of high quality movie, more and more standard resolution films are upconverted to the high-resolution films. After this operation, the defects exist in the old movie are more obvious because they are enlarged in size, therefore, an efficient artifacts detection method with more precise result and lower computational complexity is in need. This paper provided a line scratch mathematical model, which derives from the Kokaram model and Bruni model, and then gave a detection method to meet the requirements of the high-resolution video application.展开更多
A cross-correlation detection method to process backscatter signals of multi-laser beams measuring （MLBM） is presented, which can be firstly filtered by the digital filter composed of average median filter and finit...A cross-correlation detection method to process backscatter signals of multi-laser beams measuring （MLBM） is presented, which can be firstly filtered by the digital filter composed of average median filter and finite impulse response （FIR） digital filter. The processing of backscatter signals using single-pulse and three-pulse cross-correlation detection methods is depicted in detail. From calculation results, the multi-pulse cross-correlation detection could effectively improve signal-to-noise ratio （SNR）. Finally, both wind velocity and direction are determined by the peak-delay method based on the correlation function which shows high measuring precision and high SNR of the MLBM system with the assistance of the digital cross- correlation detection.展开更多
The purpose of this work is to analyze the feasibility of using the wavelet transform in the edge detection of digital terrain models (DTM) obtained by Laser Scanner. The Haar wavelet transform and the edge detection ...The purpose of this work is to analyze the feasibility of using the wavelet transform in the edge detection of digital terrain models (DTM) obtained by Laser Scanner. The Haar wavelet transform and the edge detection method called Wavelet Transform Modulus Maxima (WTMM), both implemented in Matlab language, were used. In order to validate and verify the efficiency of WTMM, the edge detection of the same DTM was performed by the Roberts, Sobel-Feldman and Canny methods, chosen due to the wide use in the scientific community in the area of Image Processing and Remote Sensing. The comparison of the results showed superior performance of WTMM in terms of processing time.展开更多
The paper analyzed a new watermarking detection paradigm including double detection thresholds based on sequential hypothesis testing. A joint design of watermarking encoding and detection was proposed. The paradigm h...The paper analyzed a new watermarking detection paradigm including double detection thresholds based on sequential hypothesis testing. A joint design of watermarking encoding and detection was proposed. The paradigm had good immunity to noisy signal attacks and high detection probability. Many experiments proved that the above algorithm can detect watermarks about 66% faster than popular detectors, which could have significant impact on many applications such as video watermarking detection and watermark-searching in a large database of digital contents.展开更多
Owing to the rapid increase in the interchange of text information through internet networks,the reliability and security of digital content are becoming a major research problem.Tampering detection,Content authentica...Owing to the rapid increase in the interchange of text information through internet networks,the reliability and security of digital content are becoming a major research problem.Tampering detection,Content authentication,and integrity verification of digital content interchanged through the Internet were utilized to solve a major concern in information and communication technologies.The authors’difficulties were tampering detection,authentication,and integrity verification of the digital contents.This study develops an Automated Data Mining based Digital Text Document Watermarking for Tampering Attack Detection(ADMDTW-TAD)via the Internet.The DM concept is exploited in the presented ADMDTW-TAD technique to identify the document’s appropriate characteristics to embed larger watermark information.The presented secure watermarking scheme intends to transmit digital text documents over the Internet securely.Once the watermark is embedded with no damage to the original document,it is then shared with the destination.The watermark extraction process is performed to get the original document securely.The experimental validation of the ADMDTW-TAD technique is carried out under varying levels of attack volumes,and the outcomes were inspected in terms of different measures.The simulation values indicated that the ADMDTW-TAD technique improved performance over other models.展开更多
X-ray digital imaging technology has found wide application owing to its advantages of real-time, visualization and rapid imaging. In substations the substantial electromagnetic interference has some influence on the ...X-ray digital imaging technology has found wide application owing to its advantages of real-time, visualization and rapid imaging. In substations the substantial electromagnetic interference has some influence on the live detection by the X-ray digital imaging technology, hindering the promotion of the technology in the detection of electric equipment. Based on a large number of field tests, the author carded out a series of researches on electromagnetic interference protection measures, image de-noising, and image enhancement algorithms.展开更多
Pancreatic cancer(PC) is a leading cause of cancerrelated death worldwide. Clinical symptoms typically present late when treatment options are limited and survival expectancy is very short. Metastatic mutations are he...Pancreatic cancer(PC) is a leading cause of cancerrelated death worldwide. Clinical symptoms typically present late when treatment options are limited and survival expectancy is very short. Metastatic mutations are heterogeneous and can accumulate up to twenty years before PC diagnosis. Given such genetic diversity, detecting and managing the complex states of disease progression may be limited to imaging modalities and markers present in circulation. Recent developments in digital pathology imaging show potential for early PC detection, making a differential diagnosis, and predicting treatment sensitivity leading to long-term survival in advanced stage patients. Despite large research efforts, the only serum marker currently approved for clinical use is CA 19-9. Utility of CA 19-9 has been shown to improve when it is used in combination with PC-specific markers. Efforts are being made to develop early-screening assays that can detect tumor-derived material, present in circulation, before metastasis takes a significant course. Detection of markers that identify circulating tumor cells and tumor-derived extracellular vesicles(EVs) in biofluid samples offers a promising non-invasive method for this purpose. Circulating tumor cells exhibit varying expression of epithelial and mesenchymal markers depending on the state of tumor differentiation. This offers a possibility for monitoring disease progression using minimally invasive procedures. EVs also offer the benefit of detecting molecular cargo of tumor origin and add the potential to detect circulating vesicle markers from tumors that lack invasive properties. This review integrates recent genetic insights of PC progression with developments in digitalpathology and early detection of tumor-derived circulating material.展开更多
Ultrasensitive detection of nucleic acids is of great significance for precision medicine.Digital polymerase chain reaction(dPCR)is the most sensitive method but requires sophisticated and expensive instruments and a ...Ultrasensitive detection of nucleic acids is of great significance for precision medicine.Digital polymerase chain reaction(dPCR)is the most sensitive method but requires sophisticated and expensive instruments and a long reaction time.Digital PCR-free technologies,which mean the digital assay not relying on thermal cycling to amplify the signal for quantitative detection of nucleic acids at the singlemolecule level,include the digital isothermal amplification techniques(d IATs)and the digital clustered regularly interspaced short palindromic repeats(CRISPR)technologies.They combine the advantages of d PCR and IATs,which could be fast and simple,enabling absolute quantification of nucleic acids at a single-molecule level with minimum instrument,representing the next-generation molecular diagnostic technology.Herein,we systematically summarized the strategies and applications of various dIATs,including the digital loop-mediated isothermal amplification(dLAMP),the digital recombinase polymerase amplification(dRPA),the digital rolling circle amplification(dRCA),the digital nucleic acid sequencebased amplification(d NASBA)and the digital multiple displacement amplification(d MDA),and evaluated the pros and cons of each method.The emerging digital CRISPR technologies,including the detection mechanism of CRISPR and the various strategies for signal amplification,are also introduced comprehensively in this review.The current challenges as well as the future perspectives of the digital PCR-free technology were discussed.展开更多
As smart contracts,represented by Solidity,become deeply integrated into the manufacturing industry,blockchain-based Digital Twins(DT)has gained momentum in recent years.Most of the blockchain infrastructures in wides...As smart contracts,represented by Solidity,become deeply integrated into the manufacturing industry,blockchain-based Digital Twins(DT)has gained momentum in recent years.Most of the blockchain infrastructures in widespread use today are based on the Proof-of-Work(PoW)mechanism,and the process of creating blocks is known as“mining”.Mining becomes increasingly difficult as the blockchain grows in size and the number of on-chain business systems increases.To lower the threshold of participation in the mining process,“mining pools”have been created.Miners can cooperate and share the mining rewards according to the hashrate they contributed to the pool.Stratum is the most widely used communication protocol between miners and mining pools.Its security is essential for the participants.In this paper,we propose two novel Man-In-The-Middle(MITM)attack schemes against Stratum,which allow attackers to steal miners'hashrate to any mining pool using hijacked TCP connections.Compared with existing attacks,our work is more secretive,more suitable for the real-world environment,and more harmful.The Proof-of-Concept(PoC)shows that our schemes work perfectly on most mining softwares and pools.Furthermore,we present a lightweight AI-driven approach based on protocol-level feature analysis to detect Stratum MITM for blockchain-based DTs.Its detection model consists of three layers:feature extraction layer,vectorization layer,and detection layer.Experiments prove that our detection approach can effectively detect Stratum MITM traffic with 98%accuracy.Our work alerts the communities and provides possible mitigation against these more hidden and profitable attack schemes.展开更多
基金supported by the National Fund Cultivation Project from China People’s Police University(Grant Number:JJPY202402)National Natural Science Foundation of China(Grant Number:62172165).
文摘With the rapid advancement of visual generative models such as Generative Adversarial Networks(GANs)and stable Diffusion,the creation of highly realistic Deepfake through automated forgery has significantly progressed.This paper examines the advancements inDeepfake detection and defense technologies,emphasizing the shift from passive detection methods to proactive digital watermarking techniques.Passive detection methods,which involve extracting features from images or videos to identify forgeries,encounter challenges such as poor performance against unknown manipulation techniques and susceptibility to counter-forensic tactics.In contrast,proactive digital watermarking techniques embed specificmarkers into images or videos,facilitating real-time detection and traceability,thereby providing a preemptive defense againstDeepfake content.We offer a comprehensive analysis of digitalwatermarking-based forensic techniques,discussing their advantages over passivemethods and highlighting four key benefits:real-time detection,embedded defense,resistance to tampering,and provision of legal evidence.Additionally,the paper identifies gaps in the literature concerning proactive forensic techniques and suggests future research directions,including cross-domain watermarking and adaptive watermarking strategies.By systematically classifying and comparing existing techniques,this review aims to contribute valuable insights for the development of more effective proactive defense strategies in Deepfake forensics.
基金This research is conducted as part of the project titled“Digital Twin-based Intrusion Detection System Using Federated Learning for IoMT”(2024-2027),supported by C3iHub,IIT Kanpur,India,under Sanction Order No.:IHUB-NTIHAC/2024/01/3.
文摘The Internet of Medical Things(IoMT)is transforming healthcare by enabling real-time data collection,analysis,and personalized treatment through interconnected devices such as sensors and wearables.The integration of Digital Twins(DTs),the virtual replicas of physical components and processes,has also been found to be a game changer for the ever-evolving IoMT.However,these advancements in the healthcare domain come with significant cybersecurity challenges,exposing it to malicious attacks and several security threats.Intrusion Detection Systems(IDSs)serve as a critical defense mechanism,yet traditional IDS approaches often struggle with the complexity and scale of IoMT networks.With this context,this paper follows a systematic approach to analyze the existing literature and highlight the current trends and challenges related to IDS in the IoMT domain.We leveraged techniques like bibliographic and keyword analysis to collect 832 research works published from 2007 to 2025,aligned with the theme“Digital Twins and IDS in IoMT.”It was found that by simulating device behaviours and network interactions in IoMT,DTs not only provide a proactive platform for early threat detection,but also offer a scalable and adaptive approach to mitigating evolving security threats in IoMT.Overall,this review provides a closer look into the role of IDS and DT in securing IoMT systems and sheds light on the possible research directions for developers and the research community.
基金funded by the National Science and Technology Major Project under Grant No.2024ZD0532000 and Grant No.2024ZD0532002the National Natural Science Foundation of China under Grant No.62173318+2 种基金the Shenzhen Basic Research Program under Grant No.JCYJ20250604182831042the Key Laboratory of Biomedical Imaging Science and System,Chinese Academy of Sciencesthe Alliance of International Science Organization(ANSO)under Grant No.2021A8017729010.
文摘Diabetes imposes a substantial burden on global healthcare systems.Worldwide,nearly half of individuals with diabetes remain undiagnosed,while conventional diagnostic techniques are often invasive,painful,and expensive.In this study,we propose a noninvasive approach for diabetes detection using photoplethysmography(PPG),which is widely integrated into modern wearable devices.First,we derived velocity plethysmography(VPG)and acceleration plethysmography(APG)signals from PPG to construct multi-channel waveform representations.Second,we introduced a novel multiset spatiotemporal feature fusion framework that integrates hand-crafted temporal,statistical,and nonlinear features with recursive feature elimination and deep feature extraction using a one-dimensional statistical convolutional neural network(1DSCNN).Finally,we developed an interpretable diabetes detection method based on XGBoost,with explainable artificial intelligence(XAI)techniques.Specifically,SHapley Additive exPlanations(SHAP)and Local InterpretableModel-agnostic Explanations(LIME)were employed to identify and interpret potential digital biomarkers associated with diabetes.To validate the proposed method,we extended the publicly available Guilin People’s Hospital dataset by incorporating in-house clinical data from ten subjects,thereby enhancing data diversity.A subject-independent cross-validation strategy was applied to ensure that the testing subjects remained independent of the training data for robust generalization.Compared with existing state-of-the-art methods,our approach achieved superior performance,with an area under the curve(AUC)of 80.5±15.9%,sensitivity of 77.2±7.5%,and specificity of 64.3±18.2%.These results demonstrate that the proposed approach provides a noninvasive,interpretable,and accessible solution for diabetes detection using PPG signals.
文摘This research aims to address the challenges of fault detection and isolation(FDI)in digital grids,focusing on improving the reliability and stability of power systems.Traditional fault detection techniques,such as rule-based fuzzy systems and conventional FDI methods,often struggle with the dynamic nature of modern grids,resulting in delays and inaccuracies in fault classification.To overcome these limitations,this study introduces a Hybrid NeuroFuzzy Fault Detection Model that combines the adaptive learning capabilities of neural networks with the reasoning strength of fuzzy logic.The model’s performance was evaluated through extensive simulations on the IEEE 33-bus test system,considering various fault scenarios,including line-to-ground faults(LGF),three-phase short circuits(3PSC),and harmonic distortions(HD).The quantitative results show that the model achieves 97.2%accuracy,a false negative rate(FNR)of 1.9%,and a false positive rate(FPR)of 2.3%,demonstrating its high precision in fault diagnosis.The qualitative analysis further highlights the model’s adaptability and its potential for seamless integration into smart grids,micro grids,and renewable energy systems.By dynamically refining fuzzy inference rules,the model enhances fault detection efficiency without compromising computational feasibility.These findings contribute to the development of more resilient and adaptive fault management systems,paving the way for advanced smart grid technologies.
文摘The increasing global prevalence of mild cognitive impairment(MCI)necessitates a paradigm shift in early detection strategies.Conventional neuropsychological assessment methods,predominantly paper-and-pencil tests such as the Mini-Mental State Examination and the Montreal Cognitive Assessment,exhibit inherent limitations with respect to accessibility,administration burden,and sensitivity to subtle cognitive decline,particularly among diverse populations.This commentary critically examines a recent study that champions a novel approach:The integration of gait and handwriting kinematic parameters analyzed via machine learning for MCI screening.The present study positions itself within the broader landscape of MCI detection,with a view to comparing its advantages against established neuropsychological batteries,advanced neuroimaging(e.g.,positron emission tomography,magnetic resonance imaging),and emerging fluid biomarkers(e.g.,cerebrospinal fluid,blood-based assays).While the study demonstrates promising accuracy(74.44%area under the curve 0.74 with gait and graphic handwriting)and addresses key unmet needs in accessibility and objectivity,we highlight its cross-sectional nature,limited sample diversity,and lack of dual-task assessment as areas for future refinement.This commentary posits that kinematic biomarkers offer a distinctive,scalable,and ecologically valid approach to widespread MCI screening,thereby complementing existing methods by providing real-world functional insights.Future research should prioritize longitudinal validation,expansion to diverse cohorts,integration with multimodal data including dual-tasking,and the development of highly portable,artificial intelligence-driven solutions to achieve the democratization of early MCI detection and enable timely interventions.
文摘Objective Shellfish are recognized as important vehicles of norovirus-associated gastroenteritis. The present study aimed to monitor norovirus contamination in oysters along the farm-to-fork continuum in Guangxi, a major oyster production area in Southwestern China. Methods Oyster samples were collected monthly from farms, markets, and restaurants, from January to December 2016. Norovirus was detected and quantified by one-step reverse transcription-droplet digital polymerase chain reaction（RT-ddPCR）. Results A total of 480 oyster samples were collected and tested for norovirus genogroups I and II. Norovirus was detected in 20.7% of samples, with genogroup II predominating. No significant difference was observed in norovirus prevalence among different sampling sites. The norovirus levels varied widely, with a geometric mean of 19,300 copies/g in digestive glands. Both norovirus prevalence and viral loads showed obvious seasonality, with a strong winter bias. Conclusion This study provides a systematic analysis of norovirus contamination ‘from the farm to the fork＇ in Guangxi. RT-ddPCR can be a useful tool for detection and quantification of low amounts of norovirus in the presence of inhibitors found particularly in foodstuffs. This approach will contribute to the development of strategies for controlling and reducing the risk of human illness resulting from shellfish consumption.
基金supported by the National High Technology Research and Development Program of China(Grant No.2012AA041203)the National Natural Science Foundation of China(Grant Nos.61377062 and 31201377)+1 种基金the Program of Shanghai Excellent Technical Leaders,China(Grant No.13XD1425400)the Doctorial Fund of Zhengzhou University of Light Industry,China(Grant No.2013BSJJ012)
文摘The digital coherent detection technique has been investigated without any frequency-scanning device in the Brillouin optical time domain reflectometry （BOTDR）, where the simplex pulse codes are applied in the sensing system. The time domain signal of every code sequence is collected by the data acquisition card （DAQ）. A shift-averaging technique is applied in the frequency domain for the reason that the local oscillator （LO） in the coherent detection is fix-frequency deviated from the primary source. With the 31-bit simplex code, the signal-to-noise ratio （SNR） has 3.5-dB enhancement with the same single pulse traces, accordant with the theoretical analysis. The frequency fluctuation for simplex codes is 14.01 MHz less than that for a single pulse as to 4-m spatial resolution. The results are believed to be beneficial for the BOTDR performance improvement.
基金supported by the the National Key Research and Development Program of China (2017YFD0201602)the National Natural Science Foundation of China (31401704)the Beijing Academy of Agriculture and Forestry Foundation, China (KJCX20180203)
文摘Bacterial fruit blotch caused by Acidovorax citrulli is a serious threat to cucurbit industry worldwide.The pathogen is seedtransmitted,so seed detection to prevent distribution of contaminated seed is crucial in disease management.In this study,we adapted a quantitative real-time PCR(qPCR)assay to droplet digital PCR(ddPCR)format for A.citrulli detection by optimizing reaction conditions.The performance of ddPCR in detecting A.citrulli pure culture,DNA,infested watermelon/melon seed and commercial seed samples were compared with multiplex PCR,qPCR,and dilution plating method.The lowest concentrations detected(LCD)by ddPCR reached up to 2 fg DNA,and 102 CFU mL–1 bacterial cells,which were ten times more sensitive than those of the qPCR.When testing artificially infested watermelon and melon seed,0.1%infestation level was detectable using ddPCR and dilution plating method.The 26 positive samples were identified in 201 commercial seed samples through ddPCR,which was the highest positive number among all the methods.High detection sensitivity achieved by ddPCR demonstrated a promising technique for improving seed-transmitted pathogen detection threshold in the future.
文摘Traffic monitoring is of major importance for enforcing traffic management policies.To accomplish this task,the detection of vehicle can be achieved by exploiting image analysis techniques.In this paper,a solution is presented to obtain various traffic parameters through vehicular video detection system(VVDS).VVDS exploits the algorithm based on virtual loops to detect moving vehicle in real time.This algorithm uses the background differencing method,and vehicles can be detected through luminance difference of pixels between background image and current image.Furthermore a novel technology named as spatio-temporal image sequences analysis is applied to background differencing to improve detection accuracy.Then a hardware implementation of a digital signal processing (DSP) based board is described in detail and the board can simultaneously process four-channel video from different cameras. The benefit of usage of DSP is that images of a roadway can be processed at frame rate due to DSP′s high performance.In the end,VVDS is tested on real-world scenes and experiment results show that the system is both fast and robust to the surveillance of transportation.
基金supported by the National Natural Science Foundation of China (Nos. 31470271 and 81730110)Guangzhou Science and Technology Program key projects (No. 201803040006)
文摘The establishment of highly sensitive diagnostic methods is critical in the early diagnosis and control of Zika virus(ZIKV)and in preventing serious neurological complications of ZIKV infection. In this study, we established micro-droplet digital polymerase chain reaction(ddPCR) and real-time quantitative PCR(RT-qPCR) protocols for the detection of ZIKV based on the amplification of the NS5 gene. For the ZIKV standard plasmid, the RT-qPCR results showed that the cycle threshold(Ct) value was linear from 10~1 to 10~8 copy/l L, with a standard curve R^2 of 0.999 and amplification efficiency of 92.203%;however, a concentration as low as 1 copy/l L could not be detected. In comparison with RT-qPCR, the dd PCR method resulted in a linear range of 10~1–10~4 copy/l L and was able to detect concentrations as low as 1 copy/l L. Thus, for detecting ZIKV from clinical samples, RT-qPCR is a better choice for high-concentration samples(above 10~1 copy/l L),while ddPCR has excellent accuracy and sensitivity for low-concentration samples. These results indicate that the ddPCR method should be of considerable use in the early diagnosis, laboratory study, and monitoring of ZIKV.
基金financially supported by the scientific research projects coordination unit of Akdeniz University,Project No.FBA-2015-446
文摘A difficult problem in forestry is tree inventory.In this study, a GoProHero attached to a small unmanned aerial vehicle was used to capture images of a small area covered by pinus pinea trees. Then, a digital surface model was generated with image matching. The elevation model representing the terrain surface, a ‘digital terrain model’,was extracted from the digital surface model using morphological filtering. Individual trees were extracted by analyzing elevation flow on the digital elevation model because the elevation reached the highest value on the tree peaks compared to the neighborhood elevation pixels. The quality of the results was assessed by comparison with reference data for correctness of the estimated number of trees. The tree heights were calculated and evaluated with ground truth dataset. The results showed 80% correctness and 90% completeness.
文摘With increasing need of high quality movie, more and more standard resolution films are upconverted to the high-resolution films. After this operation, the defects exist in the old movie are more obvious because they are enlarged in size, therefore, an efficient artifacts detection method with more precise result and lower computational complexity is in need. This paper provided a line scratch mathematical model, which derives from the Kokaram model and Bruni model, and then gave a detection method to meet the requirements of the high-resolution video application.
基金supported by National Natural Science Foundation of China under Grant No. 60425101-1Foundation for Innovative Research Groups of NSFC under Grant No. 60721001
文摘A cross-correlation detection method to process backscatter signals of multi-laser beams measuring （MLBM） is presented, which can be firstly filtered by the digital filter composed of average median filter and finite impulse response （FIR） digital filter. The processing of backscatter signals using single-pulse and three-pulse cross-correlation detection methods is depicted in detail. From calculation results, the multi-pulse cross-correlation detection could effectively improve signal-to-noise ratio （SNR）. Finally, both wind velocity and direction are determined by the peak-delay method based on the correlation function which shows high measuring precision and high SNR of the MLBM system with the assistance of the digital cross- correlation detection.
文摘The purpose of this work is to analyze the feasibility of using the wavelet transform in the edge detection of digital terrain models (DTM) obtained by Laser Scanner. The Haar wavelet transform and the edge detection method called Wavelet Transform Modulus Maxima (WTMM), both implemented in Matlab language, were used. In order to validate and verify the efficiency of WTMM, the edge detection of the same DTM was performed by the Roberts, Sobel-Feldman and Canny methods, chosen due to the wide use in the scientific community in the area of Image Processing and Remote Sensing. The comparison of the results showed superior performance of WTMM in terms of processing time.
基金This is work is supported by Shanghai Municipal Education Commission (NO.04DC33, NO. 2000SG46)
文摘The paper analyzed a new watermarking detection paradigm including double detection thresholds based on sequential hypothesis testing. A joint design of watermarking encoding and detection was proposed. The paradigm had good immunity to noisy signal attacks and high detection probability. Many experiments proved that the above algorithm can detect watermarks about 66% faster than popular detectors, which could have significant impact on many applications such as video watermarking detection and watermark-searching in a large database of digital contents.
基金funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Research Groups Program Grant No.(RGP-1443-0051).
文摘Owing to the rapid increase in the interchange of text information through internet networks,the reliability and security of digital content are becoming a major research problem.Tampering detection,Content authentication,and integrity verification of digital content interchanged through the Internet were utilized to solve a major concern in information and communication technologies.The authors’difficulties were tampering detection,authentication,and integrity verification of the digital contents.This study develops an Automated Data Mining based Digital Text Document Watermarking for Tampering Attack Detection(ADMDTW-TAD)via the Internet.The DM concept is exploited in the presented ADMDTW-TAD technique to identify the document’s appropriate characteristics to embed larger watermark information.The presented secure watermarking scheme intends to transmit digital text documents over the Internet securely.Once the watermark is embedded with no damage to the original document,it is then shared with the destination.The watermark extraction process is performed to get the original document securely.The experimental validation of the ADMDTW-TAD technique is carried out under varying levels of attack volumes,and the outcomes were inspected in terms of different measures.The simulation values indicated that the ADMDTW-TAD technique improved performance over other models.
文摘X-ray digital imaging technology has found wide application owing to its advantages of real-time, visualization and rapid imaging. In substations the substantial electromagnetic interference has some influence on the live detection by the X-ray digital imaging technology, hindering the promotion of the technology in the detection of electric equipment. Based on a large number of field tests, the author carded out a series of researches on electromagnetic interference protection measures, image de-noising, and image enhancement algorithms.
基金Supported by Division of Cancer Control and Population Sciences,National Cancer Institute,NIH,Rockville,MD 22805,United States
文摘Pancreatic cancer(PC) is a leading cause of cancerrelated death worldwide. Clinical symptoms typically present late when treatment options are limited and survival expectancy is very short. Metastatic mutations are heterogeneous and can accumulate up to twenty years before PC diagnosis. Given such genetic diversity, detecting and managing the complex states of disease progression may be limited to imaging modalities and markers present in circulation. Recent developments in digital pathology imaging show potential for early PC detection, making a differential diagnosis, and predicting treatment sensitivity leading to long-term survival in advanced stage patients. Despite large research efforts, the only serum marker currently approved for clinical use is CA 19-9. Utility of CA 19-9 has been shown to improve when it is used in combination with PC-specific markers. Efforts are being made to develop early-screening assays that can detect tumor-derived material, present in circulation, before metastasis takes a significant course. Detection of markers that identify circulating tumor cells and tumor-derived extracellular vesicles(EVs) in biofluid samples offers a promising non-invasive method for this purpose. Circulating tumor cells exhibit varying expression of epithelial and mesenchymal markers depending on the state of tumor differentiation. This offers a possibility for monitoring disease progression using minimally invasive procedures. EVs also offer the benefit of detecting molecular cargo of tumor origin and add the potential to detect circulating vesicle markers from tumors that lack invasive properties. This review integrates recent genetic insights of PC progression with developments in digitalpathology and early detection of tumor-derived circulating material.
基金supported by the National Key Research and Development Program of China(Nos.2023YFC2307305,2021YFF0703300)the Shenzhen Medical Research Fund(No.B2303003)+3 种基金Shenzhen Research Funding Program(Nos.JCYJ20220818102014028,RCBS20210609104339043)National Natural Science Foundation of China(No.22174167)Guangdong Basic and Applied Basic Research(No.2024A1515011281)Fundamental Research Funds for the Central Universities(No.24qnpy087)from Sun Yat-sen University。
文摘Ultrasensitive detection of nucleic acids is of great significance for precision medicine.Digital polymerase chain reaction(dPCR)is the most sensitive method but requires sophisticated and expensive instruments and a long reaction time.Digital PCR-free technologies,which mean the digital assay not relying on thermal cycling to amplify the signal for quantitative detection of nucleic acids at the singlemolecule level,include the digital isothermal amplification techniques(d IATs)and the digital clustered regularly interspaced short palindromic repeats(CRISPR)technologies.They combine the advantages of d PCR and IATs,which could be fast and simple,enabling absolute quantification of nucleic acids at a single-molecule level with minimum instrument,representing the next-generation molecular diagnostic technology.Herein,we systematically summarized the strategies and applications of various dIATs,including the digital loop-mediated isothermal amplification(dLAMP),the digital recombinase polymerase amplification(dRPA),the digital rolling circle amplification(dRCA),the digital nucleic acid sequencebased amplification(d NASBA)and the digital multiple displacement amplification(d MDA),and evaluated the pros and cons of each method.The emerging digital CRISPR technologies,including the detection mechanism of CRISPR and the various strategies for signal amplification,are also introduced comprehensively in this review.The current challenges as well as the future perspectives of the digital PCR-free technology were discussed.
文摘As smart contracts,represented by Solidity,become deeply integrated into the manufacturing industry,blockchain-based Digital Twins(DT)has gained momentum in recent years.Most of the blockchain infrastructures in widespread use today are based on the Proof-of-Work(PoW)mechanism,and the process of creating blocks is known as“mining”.Mining becomes increasingly difficult as the blockchain grows in size and the number of on-chain business systems increases.To lower the threshold of participation in the mining process,“mining pools”have been created.Miners can cooperate and share the mining rewards according to the hashrate they contributed to the pool.Stratum is the most widely used communication protocol between miners and mining pools.Its security is essential for the participants.In this paper,we propose two novel Man-In-The-Middle(MITM)attack schemes against Stratum,which allow attackers to steal miners'hashrate to any mining pool using hijacked TCP connections.Compared with existing attacks,our work is more secretive,more suitable for the real-world environment,and more harmful.The Proof-of-Concept(PoC)shows that our schemes work perfectly on most mining softwares and pools.Furthermore,we present a lightweight AI-driven approach based on protocol-level feature analysis to detect Stratum MITM for blockchain-based DTs.Its detection model consists of three layers:feature extraction layer,vectorization layer,and detection layer.Experiments prove that our detection approach can effectively detect Stratum MITM traffic with 98%accuracy.Our work alerts the communities and provides possible mitigation against these more hidden and profitable attack schemes.
