Real-time detection of surface defects on cables is crucial for ensuring the safe operation of power systems.However,existing methods struggle with small target sizes,complex backgrounds,low-quality image acquisition,...Real-time detection of surface defects on cables is crucial for ensuring the safe operation of power systems.However,existing methods struggle with small target sizes,complex backgrounds,low-quality image acquisition,and interference from contamination.To address these challenges,this paper proposes the Real-time Cable Defect Detection Network(RC2DNet),which achieves an optimal balance between detection accuracy and computational efficiency.Unlike conventional approaches,RC2DNet introduces a small object feature extraction module that enhances the semantic representation of small targets through feature pyramids,multi-level feature fusion,and an adaptive weighting mechanism.Additionally,a boundary feature enhancement module is designed,incorporating boundary-aware convolution,a novel boundary attention mechanism,and an improved loss function to significantly enhance boundary localization accuracy.Experimental results demonstrate that RC2DNet outperforms state-of-the-art methods in precision,recall,F1-score,mean Intersection over Union(mIoU),and frame rate,enabling real-time and highly accurate cable defect detection in complex backgrounds.展开更多
Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and...Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and biosafety concerns.In this study,we propose a high-precision automated colony extraction and separation system that combines large-field imaging and artificial intelligence(AI)to facilitate intelligent screening and localization of colonies.Firstly,a large-field imaging system was developed to capture high-resolution images of 90 mm Petri dishes,achieving a physical resolution of 13.2μm and an imaging speed of 13 frames per second.Subsequently,AI technology was employed for the automatic recognition and localiza-tion of colonies,enabling the selection of target colonies with diameters ranging from 1.9 to 2.3 mm.Next,a three-axis motion control platform was designed,accompanied by a path planning algorithm for the efficient extraction of colonies.An electronic pipette was employed for accurate colony collection.Additionally,a bacterial suspension concentration measurement module was developed,incorporating a 650 nm laser diode as the light source,achieving a measurement accuracy of 0.01 McFarland concentration(MCF).Finally,the system’s performance was validated through the preparation of an Esckerichia coli(E.coli)suspension.After 17 hours of cultivation,E.coli was extracted four times,achieving the target concentration set by the system.This work is expected to enable rapid and accurate microbial sample preparation,significantly reducing de-tection cycles and alleviating the workload of healthcare personnel.展开更多
As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic e...As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.展开更多
Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements ...Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring.展开更多
The lack of macro-continuity and mechanical strength of covalent organic frameworks(COFs)has significantly limited their practical applications.Here,we propose an“alcohol-triggered defect cleavage”strategy to precis...The lack of macro-continuity and mechanical strength of covalent organic frameworks(COFs)has significantly limited their practical applications.Here,we propose an“alcohol-triggered defect cleavage”strategy to precisely regulate the growth and stacking of COF grains through a moderate reversed Schiff base reaction,realizing the direct synthesis of COF nanofibers(CNFs)with high aspect ratio(L/D=103.05)and long length(>20μm).An individual CNF exhibits a biomimetic scale-like architecture,achieving superior flexibility and fatigue resistance under dynamic bending via a multiscale stress dissipation mechanism.Taking advantages of these structural features,we engineer CNF aerogels(CNF-As)with programmable porous structures(e.g.,honeycomb,lamellar,isotropic)via directional ice-template methodology.CNF-As demonstrate 100%COF content,high specific surface area(396.15 m^(2)g^(-1))and superelasticity(~0%elastic deformation after 500 compression cycles at 50%strain),outperforming most COF-based counterparts.Compared with the conventional COF aerogels,the unique structural features of CNF-A enable it to perform outstandingly in uranium extraction,with an 11.72-fold increment in adsorption capacity(920.12 mg g^(-1))and adsorption rate(89.9%),and a 2.48-fold improvement in selectivity(U/V=2.31).This study provides a direct strategy for the development of next-generation COF materials with outstanding functionality and structural robustness.展开更多
As an important class of phenanthroline derivatives containing soft N and hard O donor atoms,the laborious syntheses of unsymmetrical 1,10-phenanthroline-derived diamide ligands(DAPhen) have hindered its extensive stu...As an important class of phenanthroline derivatives containing soft N and hard O donor atoms,the laborious syntheses of unsymmetrical 1,10-phenanthroline-derived diamide ligands(DAPhen) have hindered its extensive study.In this work,we first report a convenient synthetic method for the construction of DAPhen using Friedländer reaction by two facile steps(vs.previous 12 steps).A variety of DAPhen ligands are readily available,especially unsymmetrical ones,which give us a platform to systematically study the substituent effect on f-block elements extraction performance.The performance of unsymmetrical extractants is experimentally confirmed to falls between that of their corresponding symmetrical extractants by extracting UO_(2)^(2+) as the representative f-block element.This work provides a direct and versatile method to synthesize symmetrical and unsymmetrical DAPhen,which paves way for the investigations on their coordination properties with metal ions and other applications.展开更多
Text semantic extraction has been envisioned as a promising solution to improve the data transmission efficiency with the limited radio resources for the autonomous interactions among machines and things in the future...Text semantic extraction has been envisioned as a promising solution to improve the data transmission efficiency with the limited radio resources for the autonomous interactions among machines and things in the future sixth-generation(6G)wireless networks.In this paper,we propose a Chinese text semantic extraction model,namely T-Pointer,to improve the quality of semantic extraction by integrating the Transformer with the pointer-generator network.The proposed T-Pointer model consists of a semantic encoder and a semantic decoder.In the encoding stage,we use the multi-head attention mechanism of the Transformer to extract semantic features from the input Chinese text.In the decoding stage,we first use the Transformer to extract multi-level global text features.Then,we introduce the pointer-generator network model to directly copy the keyword information from the source text.The simulation results demonstrate that the T-Pointer model can improve the bilingual evaluation understudy(BLEU)and recalloriented understudy for gisting evaluation(ROUGE)by 14.69%and 14.87%on average in comparison with the state-of-the-art models,respectively.Also,we implement the T-Pointer model on a semantic communication system based on the universal software radio peripheral(USRP)platform.The result shows that the packet delay of semantic transmission can be reduced by 52.05%on average,compared to traditional information transmission.展开更多
This article presents a new synergistic extraction system composed of Cyanex 272(C272,bis(2,4,4-trimethylpentyl)phosphinic acid)and iso-octanol for Sc_(3+) separation.The proposed synergistic system possessed an Sc^(3...This article presents a new synergistic extraction system composed of Cyanex 272(C272,bis(2,4,4-trimethylpentyl)phosphinic acid)and iso-octanol for Sc_(3+) separation.The proposed synergistic system possessed an Sc^(3+) extraction efficiency of 93.5%and a back-extraction efficiency of 82.7%,with selectivity coefficients of β_(Sc/Fe)=459 and β_(Sc/Al)=4241,which are considerably higher as compared to the current extraction systems.The extraction mechanism was studied and interpreted.The enhanced extraction efficiency is attributed to the increased hydrophobicity of the ternary complex,whereas the back-extraction efficiency can be ascribed to the attenuated stability of the complex.C272 and C272–iso-octanol systems also possess considerable surface activity,which is beneficial for the phase separation in solvent extraction.Based on the solvent extraction results,a preliminary study was conducted on polymer inclusion membranes(PIMs)using the binary system for Sc^(3+) separation to avoid the formation of the third phase,achieving an optimal initial flux of PIM of 6.71×10^(−4)mol·m^(−2)·h^(−1).Our results provide valuable information on highly efficient Sc^(3+) separation,and the study on PIM extraction has shown a green alternative to solvent extraction.展开更多
Deep learning has made significant progress in the field of oriented object detection for remote sensing images.However,existing methods still face challenges when dealing with difficult tasks such as multi-scale targ...Deep learning has made significant progress in the field of oriented object detection for remote sensing images.However,existing methods still face challenges when dealing with difficult tasks such as multi-scale targets,complex backgrounds,and small objects in remote sensing.Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot.Therefore,we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture,specifically optimized for the characteristics of large target scale variations,diverse orientations,and numerous small objects in remote sensing images.Our innovations lie in two main aspects:First,a dynamic snake convolution(DSC)is introduced into the backbone network to enhance the model’s feature extraction capability for oriented targets.Second,an innovative focusing-diffusion module is designed in the feature fusion neck to effectively integrate multi-scale feature information.Finally,we introduce Layer-Adaptive Sparsity for magnitude-based Pruning(LASP)method to perform lightweight network pruning to better complete tasks in resource-constrained scenarios.Experimental results on the lightweight platform Orin demonstrate that the proposed method significantly outperforms the original YOLOv8s model in oriented remote sensing object detection tasks,and achieves comparable or superior performance to state-of-the-art methods on three authoritative remote sensing datasets(DOTA v1.0,DOTA v1.5,and HRSC2016).展开更多
An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of a...An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering.展开更多
Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representati...Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representation,modeling,fusion,computation,and storage.Within this framework,knowledge extraction,as the core component,directly determines KG quality.In military domains,traditional manual curation models face efficiency constraints due to data fragmentation,complex knowledge architectures,and confidentiality protocols.Meanwhile,crowdsourced ontology construction approaches from general domains prove non-transferable,while human-crafted ontologies struggle with generalization deficiencies.To address these challenges,this study proposes an OntologyAware LLM Methodology for Military Domain Knowledge Extraction(LLM-KE).This approach leverages the deep semantic comprehension capabilities of Large Language Models(LLMs)to simulate human experts’cognitive processes in crowdsourced ontology construction,enabling automated extraction of military textual knowledge.It concurrently enhances knowledge processing efficiency and improves KG completeness.Empirical analysis demonstrates that this method effectively resolves scalability and dynamic adaptation challenges in military KG construction,establishing a novel technological pathway for advancing military intelligence development.展开更多
Industrial operators need reliable communication in high-noise,safety-critical environments where speech or touch input is often impractical.Existing gesture systems either miss real-time deadlines on resourceconstrai...Industrial operators need reliable communication in high-noise,safety-critical environments where speech or touch input is often impractical.Existing gesture systems either miss real-time deadlines on resourceconstrained hardware or lose accuracy under occlusion,vibration,and lighting changes.We introduce Industrial EdgeSign,a dual-path framework that combines hardware-aware neural architecture search(NAS)with large multimodalmodel(LMM)guided semantics to deliver robust,low-latency gesture recognition on edge devices.The searched model uses a truncated ResNet50 front end,a dimensional-reduction network that preserves spatiotemporal structure for tubelet-based attention,and localized Transformer layers tuned for on-device inference.To reduce reliance on gloss annotations and mitigate domain shift,we distill semantics from factory-tuned vision-language models and pre-train with masked language modeling and video-text contrastive objectives,aligning visual features with a shared text space.OnML2HP and SHREC’17,theNAS-derived architecture attains 94.7% accuracywith 86ms inference latency and about 5.9W power on Jetson Nano.Under occlusion,lighting shifts,andmotion blur,accuracy remains above 82%.For safetycritical commands,the emergency-stop gesture achieves 72 ms 99th percentile latency with 99.7% fail-safe triggering.Ablation studies confirm the contribution of the spatiotemporal tubelet extractor and text-side pre-training,and we observe gains in translation quality(BLEU-422.33).These results show that Industrial EdgeSign provides accurate,resource-aware,and safety-aligned gesture recognition suitable for deployment in smart factory settings.展开更多
Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning appr...Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.展开更多
The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,th...The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.展开更多
To ease the scarcity of lithium(Li)resource and cut down on environmental pollution,an efficient,selective,inexpensive and sustainable Li recycling process from waste batteries is needed,which is yet to be achieved.He...To ease the scarcity of lithium(Li)resource and cut down on environmental pollution,an efficient,selective,inexpensive and sustainable Li recycling process from waste batteries is needed,which is yet to be achieved.Here,we report a low-potential photoelectrochemical(PEC)system that selectively and efficiently extracts Li metals from multi-cation electrolytes under 1 sun illumination.Based on the difference of redox potential,we can get rid of the disturbance of other cations(i.e.,Fe,Co and Ni ions)by a bias-free PEC device to realize the extraction of high-purity Li metals on a coplanar Si-based photocathode-TiO_(2) photoanode tandem device at 2 V of applied bias(far less than the redox potentials of Li^(+)/Li).In such system,the extraction rate of Li metals(purity>99.5%)exceeds 1.35 g h^(-1)m^(-2)with 90%of Faradaic efficiency.Long-term experiments,different electrode/electrolyte tests,and various price assessments further demonstrate the stability,compatibility and economy of PEC extraction system,enabling a solar-driven pathway for the recycling of critical metal resources.展开更多
As a key low-carbon energy source,nuclear power plays a vital role in the global transition toward sustainable energy.Photocatalytic uranium extraction from seawater(UES)offers a promising solution to ensure long-term...As a key low-carbon energy source,nuclear power plays a vital role in the global transition toward sustainable energy.Photocatalytic uranium extraction from seawater(UES)offers a promising solution to ensure long-term uranium supply but is challenged by ultra-low uranium concentrations and ion interference.To overcome these issues,we design three diketopyrrolopyrrole-based covalent organic frameworks(COFs)via a synergisticπ-extended lock and carboxyl-functionalized anchor molecular engineering strategy.Among them,TPy-DPP-COF features a covalently lockedπ-conjugated structure that enhances planarity,optimizes energy alignment,and minimizes exciton binding energy,thereby promoting charge transfer and suppressing recombination.Concurrently,carboxyl groups enable uranyl-specific coordination and create local electric fields to facilitate charge separation.These features contribute to the outstanding performance of TPy-DPP-COF,which achieves a high uranium adsorption capacity of 16.33 mg g−1 in natural seawater under irradiation,with only 29.3%capacity loss after 10 cycles,surpassing industrial benchmarks.Density functional theory(DFT)calculations and experimental studies reveal a synergistic photocatalysis-adsorption pathway,with DPP units acting as active sites for uranium reduction.This work highlights a molecular design strategy for developing efficient COF-based photocatalysts for practical marine uranium recovery.展开更多
AIM:To investigate the long-term outcomes in acute primary angle closure(APAC)patients treated with lens extraction(LE)surgery and to identify risk factors for glaucomatous optic neuropathy(GON).METHODS:In this longit...AIM:To investigate the long-term outcomes in acute primary angle closure(APAC)patients treated with lens extraction(LE)surgery and to identify risk factors for glaucomatous optic neuropathy(GON).METHODS:In this longitudinal observational study,detailed medical histories of APAC patients and comprehensive ophthalmic examinations at final followup were collected.Logistic regression analysis was performed to identify predictors of blindness.Univariate and multivariate linear regression analyses were conducted to determine risk factors associated with visual outcomes.RESULTS:This study included 39 affected eyes of 31 subjects(26 females)with an average age of 74.1±8.0y.At 6.7±4.2y after APAC attack,2(5.7%)eyes had bestcorrected visual acuity(VA)worse than 3/60.Advanced glaucomatous visual field loss was observed in 15(39.5%)affected eyes and 5(25.0%)fellow eyes.Nine affected eyes(23.7%)had GON,and 11(28.9%)were blind.Six(15.4%)affected eyes and 2(9.1%)fellow eyes had suspicious progression.A significantly higher blindness rate in factory workers compared to office workers.Logistic regression identified that worse VA at attack(OR 10.568,95%CI 1.288-86.695;P=0.028)and worse early postoperative VA(OR 13.214,95%CI 1.157-150.881;P=0.038)were risk factors for blindness.Multivariate regression showed that longer duration of elevated intraocular pressure(P=0.004)and worse early postoperative VA(P=0.009)were associated with worse visual outcomes.CONCLUSION:Despite LE surgery,some APAC patients experience continued visual function deterioration.Lifelong monitoring is necessary.Target pressure and progression rates should be re-evaluated during follow-up.展开更多
AIM： To rapidly quantify hepatitis B virus （HBV） DNA by real-time PCR using efficient TaqMan probe and extraction methods of virus DNA. METHODS： Three standards were prepared by cloning PCR products which targeted...AIM： To rapidly quantify hepatitis B virus （HBV） DNA by real-time PCR using efficient TaqMan probe and extraction methods of virus DNA. METHODS： Three standards were prepared by cloning PCR products which targeted S, C and X region of HBV genome into pGEM-T vector respectively. A pair of primers and matched TaqMan probe were selected by comparing the copy number and the Ct values of HBV serum samples derived from the three different standard curves using certain serum DNA. Then the efficiency of six HBV DNA extraction methods including guanidinium isothiocyanate, proteinase K, NaI, NaOH lysis, alkaline lysis and simple boiling was analyzed in sample A, B and C by real-time PCR. Meanwhile, 8 clinical HBV serum samples were quantified. RESULTS： The copy number of the same HBV serum sample originated from the standard curve of S, C and X regions was 5.7 × 10^4/mL, 6.3 × 10^2/mL and 1.6 × 10^3/ mL respectively. The relative Ct value was 26.6, 31.8 and 29.5 respectively. Therefore, primers and matched probe from S region were chosen for further optimization of six extraction methods. The copy number of HBV serum samples A, B and C was 3.49 × 10^9/mL, 2.08 × 10^6/mL and 4.40 × 10^7/mL respectively, the relative Ct value was 19.9, 30 and 26.2 in the method of NaOH lysis, which was the efficientest among six methods. Simple boiling showed a slightly lower efficiency than NaOH lysis. Guanidinium isothiocyanate, proteinase K and NaI displayed that the copy number of HBV serum sample A, B and C was around 10^S/mL, meanwhile the Ct value was about 30. Alkaline failed to quantify the copy number of three HBV serum samples. Standard deviation （SD） and coefficient variation （CV） were very low in all 8 clinical HBV serum samples, showing that quantification of HBV DNA in triplicate was reliable and accurate. CONCLUSION： Real-time PCR based on optimized primers and TaqMan probe from S region in combination with NaOH lysis is a simple, rapid and accurate method for quantification of HBV serum DNA.展开更多
An effective approach is presented to extract welds from real-time radiographs, Firstly an algorithm based on an adaptive bidirectional threshold was proposed to segment the gradient image into ternary image, and then...An effective approach is presented to extract welds from real-time radiographs, Firstly an algorithm based on an adaptive bidirectional threshold was proposed to segment the gradient image into ternary image, and then the bidirectional accumulator Hough Transform was developed to extract weld edges from the ternary image. Different values of the coefficient proposed in the threshold algorithm were tested, and the proposed approach was applied to extract welds from real-time radiographic images of different types of welds with defects. Results show that the proposed method is adaptive and effective to extract welds from real-time radiographs of linear welds.展开更多
Violence recognition is crucial because of its applications in activities related to security and law enforcement.Existing semi-automated systems have issues such as tedious manual surveillances,which causes human err...Violence recognition is crucial because of its applications in activities related to security and law enforcement.Existing semi-automated systems have issues such as tedious manual surveillances,which causes human errors and makes these systems less effective.Several approaches have been proposed using trajectory-based,non-object-centric,and deep-learning-based methods.Previous studies have shown that deep learning techniques attain higher accuracy and lower error rates than those of other methods.However,the their performance must be improved.This study explores the state-of-the-art deep learning architecture of convolutional neural networks(CNNs)and inception V4 to detect and recognize violence using video data.In the proposed framework,the keyframe extraction technique eliminates duplicate consecutive frames.This keyframing phase reduces the training data size and hence decreases the computational cost by avoiding duplicate frames.For feature selection and classification tasks,the applied sequential CNN uses one kernel size,whereas the inception v4 CNN uses multiple kernels for different layers of the architecture.For empirical analysis,four widely used standard datasets are used with diverse activities.The results confirm that the proposed approach attains 98%accuracy,reduces the computational cost,and outperforms the existing techniques of violence detection and recognition.展开更多
基金supported by the National Natural Science Foundation of China under Grant 62306128the Basic Science Research Project of Jiangsu Provincial Department of Education under Grant 23KJD520003the Leading Innovation Project of Changzhou Science and Technology Bureau under Grant CQ20230072.
文摘Real-time detection of surface defects on cables is crucial for ensuring the safe operation of power systems.However,existing methods struggle with small target sizes,complex backgrounds,low-quality image acquisition,and interference from contamination.To address these challenges,this paper proposes the Real-time Cable Defect Detection Network(RC2DNet),which achieves an optimal balance between detection accuracy and computational efficiency.Unlike conventional approaches,RC2DNet introduces a small object feature extraction module that enhances the semantic representation of small targets through feature pyramids,multi-level feature fusion,and an adaptive weighting mechanism.Additionally,a boundary feature enhancement module is designed,incorporating boundary-aware convolution,a novel boundary attention mechanism,and an improved loss function to significantly enhance boundary localization accuracy.Experimental results demonstrate that RC2DNet outperforms state-of-the-art methods in precision,recall,F1-score,mean Intersection over Union(mIoU),and frame rate,enabling real-time and highly accurate cable defect detection in complex backgrounds.
文摘Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and biosafety concerns.In this study,we propose a high-precision automated colony extraction and separation system that combines large-field imaging and artificial intelligence(AI)to facilitate intelligent screening and localization of colonies.Firstly,a large-field imaging system was developed to capture high-resolution images of 90 mm Petri dishes,achieving a physical resolution of 13.2μm and an imaging speed of 13 frames per second.Subsequently,AI technology was employed for the automatic recognition and localiza-tion of colonies,enabling the selection of target colonies with diameters ranging from 1.9 to 2.3 mm.Next,a three-axis motion control platform was designed,accompanied by a path planning algorithm for the efficient extraction of colonies.An electronic pipette was employed for accurate colony collection.Additionally,a bacterial suspension concentration measurement module was developed,incorporating a 650 nm laser diode as the light source,achieving a measurement accuracy of 0.01 McFarland concentration(MCF).Finally,the system’s performance was validated through the preparation of an Esckerichia coli(E.coli)suspension.After 17 hours of cultivation,E.coli was extracted four times,achieving the target concentration set by the system.This work is expected to enable rapid and accurate microbial sample preparation,significantly reducing de-tection cycles and alleviating the workload of healthcare personnel.
基金supported by the National Natural Science Foundation of China 62402171.
文摘As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.
基金the support of the Major Science and Technology Project of Yunnan Province,China(Grant No.202502AD080007)the National Natural Science Foundation of China(Grant No.52378288)。
文摘Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring.
基金supported by the National Natural Science Foundation of China(No.52403035)the Shanghai Sailing Program(23YF1400300)+1 种基金the Fundamental Research Funds for the Central Universities(2232023D-05)the Weiqiao Teaching and Research Innovation Program.
文摘The lack of macro-continuity and mechanical strength of covalent organic frameworks(COFs)has significantly limited their practical applications.Here,we propose an“alcohol-triggered defect cleavage”strategy to precisely regulate the growth and stacking of COF grains through a moderate reversed Schiff base reaction,realizing the direct synthesis of COF nanofibers(CNFs)with high aspect ratio(L/D=103.05)and long length(>20μm).An individual CNF exhibits a biomimetic scale-like architecture,achieving superior flexibility and fatigue resistance under dynamic bending via a multiscale stress dissipation mechanism.Taking advantages of these structural features,we engineer CNF aerogels(CNF-As)with programmable porous structures(e.g.,honeycomb,lamellar,isotropic)via directional ice-template methodology.CNF-As demonstrate 100%COF content,high specific surface area(396.15 m^(2)g^(-1))and superelasticity(~0%elastic deformation after 500 compression cycles at 50%strain),outperforming most COF-based counterparts.Compared with the conventional COF aerogels,the unique structural features of CNF-A enable it to perform outstandingly in uranium extraction,with an 11.72-fold increment in adsorption capacity(920.12 mg g^(-1))and adsorption rate(89.9%),and a 2.48-fold improvement in selectivity(U/V=2.31).This study provides a direct strategy for the development of next-generation COF materials with outstanding functionality and structural robustness.
基金financial support from the National Natural Science Foundation of China (Nos.22476178,U2067213)Natural Science Foundation of Zhejiang Province (No.LRG25B060002)。
文摘As an important class of phenanthroline derivatives containing soft N and hard O donor atoms,the laborious syntheses of unsymmetrical 1,10-phenanthroline-derived diamide ligands(DAPhen) have hindered its extensive study.In this work,we first report a convenient synthetic method for the construction of DAPhen using Friedländer reaction by two facile steps(vs.previous 12 steps).A variety of DAPhen ligands are readily available,especially unsymmetrical ones,which give us a platform to systematically study the substituent effect on f-block elements extraction performance.The performance of unsymmetrical extractants is experimentally confirmed to falls between that of their corresponding symmetrical extractants by extracting UO_(2)^(2+) as the representative f-block element.This work provides a direct and versatile method to synthesize symmetrical and unsymmetrical DAPhen,which paves way for the investigations on their coordination properties with metal ions and other applications.
基金National Natural Science Foundation of China under Grants 62122069,62071431,62072490,62301490Science and Technology Development Fund of Macao,Macao,China under Grant 0158/2022/A+2 种基金Guangdong Basic and Applied Basic Research Foundation(2022A1515011287)MYRG2020-00107-IOTSCFDCT SKL-IOTSC(UM)-2021-2023。
文摘Text semantic extraction has been envisioned as a promising solution to improve the data transmission efficiency with the limited radio resources for the autonomous interactions among machines and things in the future sixth-generation(6G)wireless networks.In this paper,we propose a Chinese text semantic extraction model,namely T-Pointer,to improve the quality of semantic extraction by integrating the Transformer with the pointer-generator network.The proposed T-Pointer model consists of a semantic encoder and a semantic decoder.In the encoding stage,we use the multi-head attention mechanism of the Transformer to extract semantic features from the input Chinese text.In the decoding stage,we first use the Transformer to extract multi-level global text features.Then,we introduce the pointer-generator network model to directly copy the keyword information from the source text.The simulation results demonstrate that the T-Pointer model can improve the bilingual evaluation understudy(BLEU)and recalloriented understudy for gisting evaluation(ROUGE)by 14.69%and 14.87%on average in comparison with the state-of-the-art models,respectively.Also,we implement the T-Pointer model on a semantic communication system based on the universal software radio peripheral(USRP)platform.The result shows that the packet delay of semantic transmission can be reduced by 52.05%on average,compared to traditional information transmission.
基金support from the National Natural Science Foundation of China Regional Innovation and Development Joint Fund(U24A20557)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDC0230403)+3 种基金the National Natural Science Foundation of China(22378393,22208356)“Hundred Talents Program”of the Chinese Academy of Sciencesthe Chinese Academy of Sciences stably supports the youth team plan in the field of basic research(YSBR 038)Key Research&Development projects in Qinghai Province(2023-HZ-805).
文摘This article presents a new synergistic extraction system composed of Cyanex 272(C272,bis(2,4,4-trimethylpentyl)phosphinic acid)and iso-octanol for Sc_(3+) separation.The proposed synergistic system possessed an Sc^(3+) extraction efficiency of 93.5%and a back-extraction efficiency of 82.7%,with selectivity coefficients of β_(Sc/Fe)=459 and β_(Sc/Al)=4241,which are considerably higher as compared to the current extraction systems.The extraction mechanism was studied and interpreted.The enhanced extraction efficiency is attributed to the increased hydrophobicity of the ternary complex,whereas the back-extraction efficiency can be ascribed to the attenuated stability of the complex.C272 and C272–iso-octanol systems also possess considerable surface activity,which is beneficial for the phase separation in solvent extraction.Based on the solvent extraction results,a preliminary study was conducted on polymer inclusion membranes(PIMs)using the binary system for Sc^(3+) separation to avoid the formation of the third phase,achieving an optimal initial flux of PIM of 6.71×10^(−4)mol·m^(−2)·h^(−1).Our results provide valuable information on highly efficient Sc^(3+) separation,and the study on PIM extraction has shown a green alternative to solvent extraction.
基金funded by the Hainan Province Science and Technology Special Fund under Grant ZDYF2024GXJS292.
文摘Deep learning has made significant progress in the field of oriented object detection for remote sensing images.However,existing methods still face challenges when dealing with difficult tasks such as multi-scale targets,complex backgrounds,and small objects in remote sensing.Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot.Therefore,we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture,specifically optimized for the characteristics of large target scale variations,diverse orientations,and numerous small objects in remote sensing images.Our innovations lie in two main aspects:First,a dynamic snake convolution(DSC)is introduced into the backbone network to enhance the model’s feature extraction capability for oriented targets.Second,an innovative focusing-diffusion module is designed in the feature fusion neck to effectively integrate multi-scale feature information.Finally,we introduce Layer-Adaptive Sparsity for magnitude-based Pruning(LASP)method to perform lightweight network pruning to better complete tasks in resource-constrained scenarios.Experimental results on the lightweight platform Orin demonstrate that the proposed method significantly outperforms the original YOLOv8s model in oriented remote sensing object detection tasks,and achieves comparable or superior performance to state-of-the-art methods on three authoritative remote sensing datasets(DOTA v1.0,DOTA v1.5,and HRSC2016).
基金support of the National Natural Science Foundation of China(No.52274176)the Guangdong Province Key Areas R&D Program(No.2022B0101070001)+5 种基金Chongqing Elite Innovation and Entrepreneurship Leading talent Project(No.CQYC20220302517)the Chongqing Natural Science Foundation Innovation and Development Joint Fund(No.CSTB2022NSCQ-LZX0079)the National Key Research and Development Program Young Scientists Project(No.2022YFC2905700)the Chongqing Municipal Education Commission“Shuangcheng Economic Circle Construction in Chengdu-Chongqing Area”Science and Technology Innovation Project(No.KJCX2020031)the Fundamental Research Funds for the Central Universities(No.2024CDJGF-009)the Key Project for Technological Innovation and Application Development in Chongqing(No.CSTB2025TIAD-KPX0029).
文摘An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering.
文摘Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representation,modeling,fusion,computation,and storage.Within this framework,knowledge extraction,as the core component,directly determines KG quality.In military domains,traditional manual curation models face efficiency constraints due to data fragmentation,complex knowledge architectures,and confidentiality protocols.Meanwhile,crowdsourced ontology construction approaches from general domains prove non-transferable,while human-crafted ontologies struggle with generalization deficiencies.To address these challenges,this study proposes an OntologyAware LLM Methodology for Military Domain Knowledge Extraction(LLM-KE).This approach leverages the deep semantic comprehension capabilities of Large Language Models(LLMs)to simulate human experts’cognitive processes in crowdsourced ontology construction,enabling automated extraction of military textual knowledge.It concurrently enhances knowledge processing efficiency and improves KG completeness.Empirical analysis demonstrates that this method effectively resolves scalability and dynamic adaptation challenges in military KG construction,establishing a novel technological pathway for advancing military intelligence development.
文摘Industrial operators need reliable communication in high-noise,safety-critical environments where speech or touch input is often impractical.Existing gesture systems either miss real-time deadlines on resourceconstrained hardware or lose accuracy under occlusion,vibration,and lighting changes.We introduce Industrial EdgeSign,a dual-path framework that combines hardware-aware neural architecture search(NAS)with large multimodalmodel(LMM)guided semantics to deliver robust,low-latency gesture recognition on edge devices.The searched model uses a truncated ResNet50 front end,a dimensional-reduction network that preserves spatiotemporal structure for tubelet-based attention,and localized Transformer layers tuned for on-device inference.To reduce reliance on gloss annotations and mitigate domain shift,we distill semantics from factory-tuned vision-language models and pre-train with masked language modeling and video-text contrastive objectives,aligning visual features with a shared text space.OnML2HP and SHREC’17,theNAS-derived architecture attains 94.7% accuracywith 86ms inference latency and about 5.9W power on Jetson Nano.Under occlusion,lighting shifts,andmotion blur,accuracy remains above 82%.For safetycritical commands,the emergency-stop gesture achieves 72 ms 99th percentile latency with 99.7% fail-safe triggering.Ablation studies confirm the contribution of the spatiotemporal tubelet extractor and text-side pre-training,and we observe gains in translation quality(BLEU-422.33).These results show that Industrial EdgeSign provides accurate,resource-aware,and safety-aligned gesture recognition suitable for deployment in smart factory settings.
文摘Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.
基金supported by the National Natural Science Foundation of China(Grant No.62403486)。
文摘The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.
基金the National Natural Science Foundation of China(22479047,22409058)the Outstanding Youth Scientist Foundation of Hunan Province(2022JJ10023)the Provincial Natural Science Foundation of Guangdong(2023A1515011745)for financial support of this research。
文摘To ease the scarcity of lithium(Li)resource and cut down on environmental pollution,an efficient,selective,inexpensive and sustainable Li recycling process from waste batteries is needed,which is yet to be achieved.Here,we report a low-potential photoelectrochemical(PEC)system that selectively and efficiently extracts Li metals from multi-cation electrolytes under 1 sun illumination.Based on the difference of redox potential,we can get rid of the disturbance of other cations(i.e.,Fe,Co and Ni ions)by a bias-free PEC device to realize the extraction of high-purity Li metals on a coplanar Si-based photocathode-TiO_(2) photoanode tandem device at 2 V of applied bias(far less than the redox potentials of Li^(+)/Li).In such system,the extraction rate of Li metals(purity>99.5%)exceeds 1.35 g h^(-1)m^(-2)with 90%of Faradaic efficiency.Long-term experiments,different electrode/electrolyte tests,and various price assessments further demonstrate the stability,compatibility and economy of PEC extraction system,enabling a solar-driven pathway for the recycling of critical metal resources.
基金the Young Elite Scientists Sponsorship Program by JXAST(2024QT11)the National Natural Science Foundation of China(22465001,22309003)the Jiangxi Provincial Natural Science Foundation(20232BAB203042,20242BAB22002).
文摘As a key low-carbon energy source,nuclear power plays a vital role in the global transition toward sustainable energy.Photocatalytic uranium extraction from seawater(UES)offers a promising solution to ensure long-term uranium supply but is challenged by ultra-low uranium concentrations and ion interference.To overcome these issues,we design three diketopyrrolopyrrole-based covalent organic frameworks(COFs)via a synergisticπ-extended lock and carboxyl-functionalized anchor molecular engineering strategy.Among them,TPy-DPP-COF features a covalently lockedπ-conjugated structure that enhances planarity,optimizes energy alignment,and minimizes exciton binding energy,thereby promoting charge transfer and suppressing recombination.Concurrently,carboxyl groups enable uranyl-specific coordination and create local electric fields to facilitate charge separation.These features contribute to the outstanding performance of TPy-DPP-COF,which achieves a high uranium adsorption capacity of 16.33 mg g−1 in natural seawater under irradiation,with only 29.3%capacity loss after 10 cycles,surpassing industrial benchmarks.Density functional theory(DFT)calculations and experimental studies reveal a synergistic photocatalysis-adsorption pathway,with DPP units acting as active sites for uranium reduction.This work highlights a molecular design strategy for developing efficient COF-based photocatalysts for practical marine uranium recovery.
文摘AIM:To investigate the long-term outcomes in acute primary angle closure(APAC)patients treated with lens extraction(LE)surgery and to identify risk factors for glaucomatous optic neuropathy(GON).METHODS:In this longitudinal observational study,detailed medical histories of APAC patients and comprehensive ophthalmic examinations at final followup were collected.Logistic regression analysis was performed to identify predictors of blindness.Univariate and multivariate linear regression analyses were conducted to determine risk factors associated with visual outcomes.RESULTS:This study included 39 affected eyes of 31 subjects(26 females)with an average age of 74.1±8.0y.At 6.7±4.2y after APAC attack,2(5.7%)eyes had bestcorrected visual acuity(VA)worse than 3/60.Advanced glaucomatous visual field loss was observed in 15(39.5%)affected eyes and 5(25.0%)fellow eyes.Nine affected eyes(23.7%)had GON,and 11(28.9%)were blind.Six(15.4%)affected eyes and 2(9.1%)fellow eyes had suspicious progression.A significantly higher blindness rate in factory workers compared to office workers.Logistic regression identified that worse VA at attack(OR 10.568,95%CI 1.288-86.695;P=0.028)and worse early postoperative VA(OR 13.214,95%CI 1.157-150.881;P=0.038)were risk factors for blindness.Multivariate regression showed that longer duration of elevated intraocular pressure(P=0.004)and worse early postoperative VA(P=0.009)were associated with worse visual outcomes.CONCLUSION:Despite LE surgery,some APAC patients experience continued visual function deterioration.Lifelong monitoring is necessary.Target pressure and progression rates should be re-evaluated during follow-up.
基金Supported by the National Natural Science Foundation of China(No. 30371328), the Key Project of Natural Science Foundationof Shandong Province (No. Z2002C01), and the Key Project ofShandong Academy of Medical Sciences (No. 2005007)
文摘AIM： To rapidly quantify hepatitis B virus （HBV） DNA by real-time PCR using efficient TaqMan probe and extraction methods of virus DNA. METHODS： Three standards were prepared by cloning PCR products which targeted S, C and X region of HBV genome into pGEM-T vector respectively. A pair of primers and matched TaqMan probe were selected by comparing the copy number and the Ct values of HBV serum samples derived from the three different standard curves using certain serum DNA. Then the efficiency of six HBV DNA extraction methods including guanidinium isothiocyanate, proteinase K, NaI, NaOH lysis, alkaline lysis and simple boiling was analyzed in sample A, B and C by real-time PCR. Meanwhile, 8 clinical HBV serum samples were quantified. RESULTS： The copy number of the same HBV serum sample originated from the standard curve of S, C and X regions was 5.7 × 10^4/mL, 6.3 × 10^2/mL and 1.6 × 10^3/ mL respectively. The relative Ct value was 26.6, 31.8 and 29.5 respectively. Therefore, primers and matched probe from S region were chosen for further optimization of six extraction methods. The copy number of HBV serum samples A, B and C was 3.49 × 10^9/mL, 2.08 × 10^6/mL and 4.40 × 10^7/mL respectively, the relative Ct value was 19.9, 30 and 26.2 in the method of NaOH lysis, which was the efficientest among six methods. Simple boiling showed a slightly lower efficiency than NaOH lysis. Guanidinium isothiocyanate, proteinase K and NaI displayed that the copy number of HBV serum sample A, B and C was around 10^S/mL, meanwhile the Ct value was about 30. Alkaline failed to quantify the copy number of three HBV serum samples. Standard deviation （SD） and coefficient variation （CV） were very low in all 8 clinical HBV serum samples, showing that quantification of HBV DNA in triplicate was reliable and accurate. CONCLUSION： Real-time PCR based on optimized primers and TaqMan probe from S region in combination with NaOH lysis is a simple, rapid and accurate method for quantification of HBV serum DNA.
文摘An effective approach is presented to extract welds from real-time radiographs, Firstly an algorithm based on an adaptive bidirectional threshold was proposed to segment the gradient image into ternary image, and then the bidirectional accumulator Hough Transform was developed to extract weld edges from the ternary image. Different values of the coefficient proposed in the threshold algorithm were tested, and the proposed approach was applied to extract welds from real-time radiographic images of different types of welds with defects. Results show that the proposed method is adaptive and effective to extract welds from real-time radiographs of linear welds.
基金This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2018R1D1A1B07042967)the Soonchunhyang University Research Fund.
文摘Violence recognition is crucial because of its applications in activities related to security and law enforcement.Existing semi-automated systems have issues such as tedious manual surveillances,which causes human errors and makes these systems less effective.Several approaches have been proposed using trajectory-based,non-object-centric,and deep-learning-based methods.Previous studies have shown that deep learning techniques attain higher accuracy and lower error rates than those of other methods.However,the their performance must be improved.This study explores the state-of-the-art deep learning architecture of convolutional neural networks(CNNs)and inception V4 to detect and recognize violence using video data.In the proposed framework,the keyframe extraction technique eliminates duplicate consecutive frames.This keyframing phase reduces the training data size and hence decreases the computational cost by avoiding duplicate frames.For feature selection and classification tasks,the applied sequential CNN uses one kernel size,whereas the inception v4 CNN uses multiple kernels for different layers of the architecture.For empirical analysis,four widely used standard datasets are used with diverse activities.The results confirm that the proposed approach attains 98%accuracy,reduces the computational cost,and outperforms the existing techniques of violence detection and recognition.
