Objective:To analyse the prevalence of serotypes,antibiotic resistance,and virulence genes of Group B Streptococcus(GBS)strains isolated from pregnant women at 35-37 weeks of gestation in Ho Chi Minh City,Vietnam,from...Objective:To analyse the prevalence of serotypes,antibiotic resistance,and virulence genes of Group B Streptococcus(GBS)strains isolated from pregnant women at 35-37 weeks of gestation in Ho Chi Minh City,Vietnam,from January 2022 to January 2023.Methods:GBS strains were isolated through selective culture methods and confirmed by PCR.Serotyping,virulence gene detection,and antibiotic susceptibility testing were performed using PCR,gel electrophoresis techniques and Kirby-Bauer test.Results:Totally,61 GBS isolated from 300 participants have been identified including seven GBS serotypes(Ⅰa,Ⅰb,Ⅱ,Ⅲ,Ⅳ,Ⅴ,andⅥ).SerotypesⅦ,Ⅷ,andⅨwere not detected in the study population.Antibiotic resistance patterns varied:13.1%of isolates were fully susceptible,while the majority showed multi-drug resistance,with 34.4%resistant to three antibiotics.SerotypeⅠa demonstrated high susceptibility(35.7%),while serotypeⅢshowed extensive resistance,with 87.5%being resistant to at least three antibiotics.All strains are susceptible to vancomycin andβ-lactams susceptibility also remained high,but resistance to clindamycin,erythromycin,and tetracycline was high(>65%).The virulence genes scpB,cylB,fbsB,and cfb were highly prevalent(90%-100%),indicating their potential for vaccine and diagnostic development.Conclusions:Our findings provide valuable insights into GBS serotypes,resistance,and virulence factors,contributing to community monitoring,preventive measures,diagnostics,and vaccine development.However,the limited sample size necessitates further research.展开更多
The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermo...The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.展开更多
Background:The medicinal material known as Os Draconis(Longgu)originates from fossilized remains of ancient mammals and is widely used in treating emotional and mental conditions.However,fossil resources are nonrenewa...Background:The medicinal material known as Os Draconis(Longgu)originates from fossilized remains of ancient mammals and is widely used in treating emotional and mental conditions.However,fossil resources are nonrenewable,and clinical demand is increasingly difficult to meet,leading to a proliferation of counterfeit products.During prolonged geological burial,static pressure from the surrounding strata severely compromises the microstructural integrity of osteons in Os Draconis,but Os Draconis still largely retains the structural features of mammalian bone.Methods:Using verified authentic Os Draconis samples over 10,000 years old as a baseline,this study summarizes the ultrastructural characteristics of genuine Os Draconis.Employing electron probe microanalysis and optical polarized light microscopy,we examined 28 batches of authentic Os Draconis and 31 batches of counterfeits to identify their ultrastructural differences.Key points for ultrastructural identification of Os Draconis were compiled,and a new identification approach was proposed based on these differences.Results:Authentic Os Draconis exhibited distinct ultrastructural markers:irregularly shaped osteons with traversing fissures,deformed/displaced Haversian canals,and secondary mineral infill(predominantly calcium carbonate).Counterfeits showed regular osteon arrangements,absent traversal fissures,and homogeneous hydroxyapatite composition.Lab-simulated samples lacked structural degradation features.EPMA confirmed calcium carbonate infill in fossilized Haversian canals,while elemental profiles differentiated lacunae types(void vs.mineral-packed).Conclusion:The study established ultrastructural criteria for authentic Os Draconis identification:osteon deformation,geological fissures penetrating bone units,and heterogenous mineral deposition.These features,unattainable in counterfeits or modern processed bones,provide a cost-effective,accurate identification method.This approach bridges gaps in TCM material standardization and supports quality control for clinical applications.展开更多
Root rot is a prevalent soil-borne fungal disease in citrus.Citron C-05(Citrus medica)stands out as a germplasm within Citrus spp.due to its complete resistance to citrus canker and favorable characteristics such as s...Root rot is a prevalent soil-borne fungal disease in citrus.Citron C-05(Citrus medica)stands out as a germplasm within Citrus spp.due to its complete resistance to citrus canker and favorable characteristics such as single embryo and easy rooting.However,Citron C-05 was found to be highly susceptible to root rot during cultivation,with the specific pathogens previously unknown.In this study,four candidate fungal species were isolated from Citron C-05 roots.Sequence analysis of ITS,EF-1a,RPB1,and RPB2 identified two Fusarium solani strains,Rr-2 and Rr-4,as the candidates causing root rot in Citron C-05.Resistance tests showed these two pathogens increased root damage rate from 10.30%to 35.69%in Citron C-05,sour orange(Citrus aurantium),sweet orange(Citrus sinensis)and pummelo(Citrus grandis).F.solani exhibited the weak pathogenicity towards trifoliate orange(Poncirus trifoliata).DAB staining revealed none of reddish-brown precipitation in the four susceptible citrus germplasm after infection with F.solani,while trifoliate orange exhibited significant H2O2 accumulation.Trypan blue staining indicated increased cell death in the four susceptible citrus germplasm following infection with these two pathogens but not in trifoliate orange.These findings provide a comprehensive understanding of citrus root rot and support future research on the mechanisms of root rot resistance in citrus.展开更多
[Objectives]This study aimed to investigate the pathogenicity,growth characteristics and drug resistance of Streptococcus suis type 2.[Methods]Bacterial isolation and identification,biochemical experiments,determinati...[Objectives]This study aimed to investigate the pathogenicity,growth characteristics and drug resistance of Streptococcus suis type 2.[Methods]Bacterial isolation and identification,biochemical experiments,determination of growth curve and correlation curve between OD 600 values and viable counts,drug susceptibility tests,pathogenicity analysis,and histopathological observations were carried out.[Results]The Streptococcus strain isolated from infected pigs was identified as Streptococcus suis type 2,which was named TA01 strain.TA01 strain reached the growth peak at 6-8 h post-incubation,and viable counts gradually declined after 8 h of incubation.The correlation equation between OD 600 values and viable counts is y=24.659 x-1.076 1,R^2=0.996 7.TA01 strain was sensitive to penicillin,erythromycin,florfenicol and oxacillin,and resistant to ciprofloxacin,polymyxin B and clindamycin.According to the results of pathogenicity analysis,all the mice in 3.6×10^9 cfu/mouse group died within 48,and these dead mice exhibited acute pyaemia septica.Based on the Reed-Muench formula,it was calculated that LD 50 of TA01 strain was 1.137×10^8 cfu/mouse.Pathological examination showed obvious blue-stained bacteria clusters,accompanied by neutrophil infiltration.[Conclusions]TA01 strain was a virulent strain of Streptococcus suis type 2.Compared with Streptococcus strains which were isolated and reported in China,TA01 strain exhibited strong virulence and rapid proliferation.展开更多
Dental caries is one of the most common chronic diseases and is caused by acid fermentation of bacteria adhered to the teeth. Streptococcus mutans （S. mutans） utilizes sortase A （SrtA） to anchor surface proteins t...Dental caries is one of the most common chronic diseases and is caused by acid fermentation of bacteria adhered to the teeth. Streptococcus mutans （S. mutans） utilizes sortase A （SrtA） to anchor surface proteins to the cell wall and forms a biofilm to facilitate its adhesion to the tooth surface. Some plant natural products, especially several flavonoids, are effective inhibitors of SrtA. However, given the limited number of inhibitors and the development of drug resistance, the discovery of new inhibitors is urgent. Here, the high-throughput virtual screening approach was performed to identify new potential inhibitors of S. mutans SrtA. Two libraries were used for screening, and nine compounds that had the lowest scores were chosen for further molecular dynamics simulation, binding free energy analysis and absorption, distribution, metabolism, excretion and toxicity （ADMET） properties analysis. The results revealed that several similar compounds composed of benzofuran, thiadiazole and pyrrole, which exhibited good affinities and appropriate pharmacokinetic In addition, the carbonyl of these compounds can have a strategy for microbial infection disease therapy. parameters, were potential inhibitors to impede the catalysis of SrtA. key role in the inhibition mechanism. These findings can provide a new展开更多
BACKGROUND Group B Streptococcus(GBS)is a normal component of the gastrointestinal and genital microbiota in humans and can lead to important infections in newborns.AIM To compare GBS isolation and identification meth...BACKGROUND Group B Streptococcus(GBS)is a normal component of the gastrointestinal and genital microbiota in humans and can lead to important infections in newborns.AIM To compare GBS isolation and identification methods as well as to assess the antibiotic susceptibility and to identify resistance genes in GBS strains from pregnant women attended in healthcare services from the city of Vitória da Conquista,in Bahia State,Brazil.METHODS From January 2017 to February 2018,vaginorectal swabs were obtained from 186 participants and the samples were seeded onto chromogenic agar for GBS before and after inoculation in selective broth.Confirmatory identification using 3 CAMP and latex tests was performed in samples with GBS-suggestive colonies.Then,disk diffusion antibiograms were performed in GBS-positive samples,and the detection of the resistance genes ermB,ermTR,mefA,and linB in the clindamycin and/or erythromycin-resistant samples was carried out.RESULTS Thirty-two samples(17.2%)were GBS-positive.The culture in chromogenic agar after sample incubation in selective broth was the most sensitive method(96.9%)for GBS detection.All isolates were susceptible to penicillin,ampicillin,cefotaxime,and vancomycin.Clindamycin resistance was observed in 6 samples(18.8%),while 8 samples(25%)were erythromycin-resistant.All erythromycin and/or clindamycin-resistant GBS strains had negative D-tests.Two strains(25%)presented an M phenotype and 6 isolates(75%)presented a cMLSB phenotype.The ermB gene was identified in 4 samples(44.4%),the mefA gene was also found in 4 samples(44.4%),the ermTR gene was identified in 1 isolate(11.1%),and the linB gene was not found in any isolate.CONCLUSION This study evidenced that the screening for SGB can be performed by means of various methods,including chromogenic media,and that the chemoprophylaxis for pregnant women who cannot use penicillin must be susceptibility-guided.展开更多
Streptococcus suis is a Gram-positive pathogen that causes serious diseases in pigs. In addition to S. suis serotype 2 （SS2）, S. suis serotype 9 （SS9） is another prevalent serotype, which is frequently isolated fr...Streptococcus suis is a Gram-positive pathogen that causes serious diseases in pigs. In addition to S. suis serotype 2 （SS2）, S. suis serotype 9 （SS9） is another prevalent serotype, which is frequently isolated from the organs of diseased pigs in China. An immunoproteomic-based approach was developed to identify antigens common to SS2 and SS9 for vaccine development. Cell wall proteins extracted from SS2 strain HA9801 were screened by two-dimensional Western blot using anti-SS2 sera, anti-SS9 sera, or pre-immune sera pooled from specific pathogen free （SPF） mice. Protein spots on preparative gels were excised and identified by matrix-assisted laser desorption ionization time-of-flight mass spectrometry, which led to the identification of four shared immunogenic proteins （arginine deiminase, translation elongation factor-Ts, o-acetylserine lyase, and 1-phosphofructokinase）. The genes encoding these four proteins from SS9 strain GZ0565 were cloned and their proteins were overexpressed in Escherichia coli BL21. Western blot analysis of these recombinant proteins using the convalescent serum of an SPF mini-pig inoculated with the SS2 strain, anti-SS2 sera, and anti-SS9 sera pooled from SPF mice further confirmed the immunogenicity of these proteins. These immunogenic proteins, which are encoded by genes that are reasonably conserved among SS2 and SS9 strains, could be developed as vaccine candidates.展开更多
Suppression subtractive hybridization （SSH） was performed with virulent strain ATCC35246 and avirulent strain ST171 to identify novel genes associated with virulence in Streptococcus equi ssp. zooepidemicus （SEZ）....Suppression subtractive hybridization （SSH） was performed with virulent strain ATCC35246 and avirulent strain ST171 to identify novel genes associated with virulence in Streptococcus equi ssp. zooepidemicus （SEZ）. There were fourteen genomic regions that only presented in virulent strain ATCC35246. These regions encoded 14 proteins, some of them were homologous to proteins associated with cellular surface structure, molecular synthesis, energy metabolism, regulation, transport systems, and other unknown functions. Primers for 6 particular regions were designed from the already published SEZ sequence. Then, we used PCR to evaluate the distribution and conservation of these 6 DNA fragments in various SEZ strains collected from different sources, regions, groups, and times. The results showed that these 6 DNA fragments were widely distributed in SEZ strains, yet they were not existence in the avirulent strain ST171. Moreover, these fragments could not be detected in other Streptococcus groups.展开更多
Background: Streptococcus pyogenes (Group A streptococcus) is an important Gram-positive human pathogen affected the upper respiratory tract, such as the tonsils and pharynx, and is also induces post-infection disease...Background: Streptococcus pyogenes (Group A streptococcus) is an important Gram-positive human pathogen affected the upper respiratory tract, such as the tonsils and pharynx, and is also induces post-infection diseases such as rheumatic fever and glomerulonephritis. This study aim to isolate Streptococcus pyogenes from children with pharyngitis and to evaluate the molecular identification of S. pyogenes compared with conventional methods. Methods: A cross sectional study was conducted on total of 200 throat swab samples which were collected from children with pharyngitis referred to Wad medani Pediatric Teaching Hospital and Wad medani ENT hospital from January to November 2021. Demographic and clinical data were collected by questionnaire. Throat swabs were tested with the standard microbiological techniques to isolated Group A streptococcus (GAS). Antimicrobial susceptibility testing was performed to all GAS isolates using the Kirby Bauer disk diffusion method according to clinical laboratory standard institute (CLSI) guidelines. Additionally, PCR was used to identify Spy 1258 gene of isolated bacteria. Results: From all throat swab samples screened, 51 isolates (25.5%) were identified as GAS. Antibiotic susceptibility testing revealed that all the GAS isolates were sensitive to Penicillin and Azithromycin. Sensitivity to Erythromycin, Gentamicin, Clarithromycin, Amoxicillin and Cephalexin were 88.2%, 86.3%, 45.1%, 41.2%, 13.7%, respectively. Based on PCR identification of Spy 1258 gene the percentage of isolated bacteria was 21%. Conclusion: The rate of isolated Streptococcus pyogenes was 25.5% by conventional methods and 21% by PCR. The bacteria were sensitive to Penicillin and Azithromycin. The Spy 1258 gene was specific for detection of Streptococcus pyogenes.展开更多
To overcome the limitations of low efficiency and reliance on manual processes in the measurement of geometric parameters for bridge prefabricated components,a method based on deep learning and computer vision is deve...To overcome the limitations of low efficiency and reliance on manual processes in the measurement of geometric parameters for bridge prefabricated components,a method based on deep learning and computer vision is developed to identify the geometric parameters.The study utilizes a common precast element for highway bridges as the research subject.First,edge feature points of the bridge component section are extracted from images of the precast component cross-sections by combining the Canny operator with mathematical morphology.Subsequently,a deep learning model is developed to identify the geometric parameters of the precast components using the extracted edge coordinates from the images as input and the predefined control parameters of the bridge section as output.A dataset is generated by varying the control parameters and noise levels for model training.Finally,field measurements are conducted to validate the accuracy of the developed method.The results indicate that the developed method effectively identifies the geometric parameters of bridge precast components,with an error rate maintained within 5%.展开更多
As artificial intelligence(AI)technology has continued to develop,its efficient data processing and pattern recognition capabilities have significantly improved the precision and speed of decision-making processes,and...As artificial intelligence(AI)technology has continued to develop,its efficient data processing and pattern recognition capabilities have significantly improved the precision and speed of decision-making processes,and it has been widely applied across various fields.In the field of astronomy,AI techniques have demonstrated unique advantages,particularly in the identification of pulsars and their candidates.AI is able to address the challenges of pulsar celestial body identification and classification because of its accuracy and efficiency.This paper systematically surveys commonly used AI models for pulsar candidate identification,analyzing and discussing the typical applications of machine learning,artificial neural networks,convolutional neural networks,and generative adversarial networks in candidate identification.Furthermore,it explores how th.e introduction of AI techniques not only enhances the efficiency and accuracy of pulsar identification but also provides new perspectives and tools for pulsar survey data processing,thus playing a significant role in advancing pulsar research and the field of astronomy.展开更多
Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on ...Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on machine learning of rock visible and near-infrared spectral data.First,the rock spectral data are preprocessed using Savitzky-Golay(SG)smoothing to remove the noise of the spectral data;then,the preprocessed rock spectral data are downscaled using Principal Component Analysis(PCA)to reduce the redundancy of the data,optimize the effective discriminative information,and obtain the rock spectral features;finally,a Bayesian-optimized lithology identification model is established based on rock spectral features,optimize the model hyperparameters using Bayesian optimization(BO)algorithm to avoid the combination of hyperparameters falling into the local optimal solution,and output the predicted type of rock,so as to realize the Bayesian-optimized lithology identification.In addition,this paper conducts comparative analysis on models based on Artificial Neural Network(ANN)/Random Forest(RF),dimensionality reduction/full band,and optimization algorithms.It uses the confusion matrix,accuracy,Precison(P),Recall(R)and F_(1)values(F_(1))as the evaluation indexes of model accuracy.The results indicate that the lithology identification model optimized by the BO-ANN after dimensionality reduction achieves an accuracy of up to 99.80%,up to 99.79%and up to 99.79%.Compared with the BO-RF model,it has higher identification accuracy and better stability for each type of rock identification.The experiments and reliability analysis show that the Bayesian-optimized lithology identification method proposed in this paper has good robustness and generalization performance,which is of great significance for realizing fast,accurate and Bayesian-optimized lithology identification in tunnel site.展开更多
In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant ...In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant statistical fluctuations.These issues can lead to potential failures in peak-searching-based identification methods.To address the low precision associated with short-duration measurements of radionuclides,this paper proposes an identification algorithm that leverages heterogeneous spectral transfer to develop a low-count energy spectral identification model.Comparative experiments demonstrated that transferring samples from 26 classes of simulated heterogeneous gamma spectra aids in creating a reliable model for measured gamma spectra.With only 10%of target domain samples used for training,the accuracy on real low-count spectral samples was 95.56%.This performance shows a significant improvement over widely employed full-spectrum analysis methods trained on target domain samples.The proposed method also exhibits strong generalization capabilities,effectively mitigating overfitting issues in low-count energy spectral classification under short-duration measurements.展开更多
Ocean energy has progressively gained considerable interest due to its sufficient potential to meet the world’s energy demand,and the blade is the core component in electricity generation from the ocean current.Howev...Ocean energy has progressively gained considerable interest due to its sufficient potential to meet the world’s energy demand,and the blade is the core component in electricity generation from the ocean current.However,the widened hydraulic excitation frequency may satisfy the blade resonance due to the time variation in the velocity and angle of attack of the ocean current,even resulting in blade fatigue and destructively interfering with grid stability.A key parameter that determines the resonance amplitude of the blade is the hydrodynamic damping ratio(HDR).However,HDR is difficult to obtain due to the complex fluid-structure interaction(FSI).Therefore,a literature review was conducted on the hydrodynamic damping characteristics of blade-like structures.The experimental and simulation methods used to identify and obtain the HDR quantitatively were described,placing emphasis on the experimental processes and simulation setups.Moreover,the accuracy and efficiency of different simulation methods were compared,and the modal work approach was recommended.The effects of key typical parameters,including flow velocity,angle of attack,gap,rotational speed,and cavitation,on the HDR were then summarized,and the suggestions on operating conditions were presented from the perspective of increasing the HDR.Subsequently,considering multiple flow parameters,several theoretical derivations and semi-empirical prediction formulas for HDR were introduced,and the accuracy and application were discussed.Based on the shortcomings of the existing research,the direction of future research was finally determined.The current work offers a clear understanding of the HDR of blade-like structures,which could improve the evaluation accuracy of flow-induced vibration in the design stage.展开更多
It is of great significance to accurately and rapidly identify shale lithofacies in relation to the evaluation and prediction of sweet spots for shale oil and gas reservoirs.To address the problem of low resolution in...It is of great significance to accurately and rapidly identify shale lithofacies in relation to the evaluation and prediction of sweet spots for shale oil and gas reservoirs.To address the problem of low resolution in logging curves,this study establishes a grayscale-phase model based on high-resolution grayscale curves using clustering analysis algorithms for shale lithofacies identification,working with the Shahejie For-mation,Bohai Bay Basin,China.The grayscale phase is defined as the sum of absolute grayscale and relative amplitude as well as their features.The absolute grayscale is the absolute magnitude of the gray values and is utilized for evaluating the material composition(mineral composition+total organic carbon)of shale,while the relative amplitude is the difference between adjacent gray values and is used to identify the shale structure type.The research results show that the grayscale phase model can identify shale lithofacies well,and the accuracy and applicability of this model were verified by the fitting relationship between absolute grayscale and shale mineral composition,as well as corresponding re-lationships between relative amplitudes and laminae development in shales.Four lithofacies are iden-tified in the target layer of the study area:massive mixed shale,laminated mixed shale,massive calcareous shale and laminated calcareous shale.This method can not only effectively characterize the material composition of shale,but also numerically characterize the development degree of shale laminae,and solve the problem that difficult to identify millimeter-scale laminae based on logging curves,which can provide technical support for shale lithofacies identification,sweet spot evaluation and prediction of complex continental lacustrine basins.展开更多
Actuator faults can be critical in turbofan engines as they can lead to stall,surge,loss of thrust and failure of speed control.Thus,fault diagnosis of gas turbine actuators has attracted considerable attention,from b...Actuator faults can be critical in turbofan engines as they can lead to stall,surge,loss of thrust and failure of speed control.Thus,fault diagnosis of gas turbine actuators has attracted considerable attention,from both academia and industry.However,the extensive literature that exists on this topic does not address identifying the severity of actuator faults and focuses mainly on actuator fault detection and isolation.In addition,previous studies of actuator fault identification have not dealt with multiple concurrent faults in real time,especially when these are accompanied by sudden failures under dynamic conditions.This study develops component-level models for fault identification in four typical actuators used in high-bypass ratio turbofan engines under both dynamic and steady-state conditions and these are then integrated with the engine performance model developed by the authors.The research results reported here present a novel method of quantifying actuator faults using dynamic effect compensation.The maximum error for each actuator is less than0.06%and 0.07%,with average computational time of less than 0.0058 s and 0.0086 s for steady-state and transient cases,respectively.These results confirm that the proposed method can accurately and efficiently identify concurrent actuator fault for an engine operating under either transient or steady-state conditions,even in the case of a sudden malfunction.The research results emonstrate the potential benefit to emergency response capabilities by introducing this method of monitoring the health of aero engines.展开更多
The precise identification of quartz minerals is crucial in mineralogy and geology due to their widespread occurrence and industrial significance.Traditional methods of quartz identification in thin sections are labor...The precise identification of quartz minerals is crucial in mineralogy and geology due to their widespread occurrence and industrial significance.Traditional methods of quartz identification in thin sections are labor-intensive and require significant expertise,often complicated by the coexistence of other minerals.This study presents a novel approach leveraging deep learning techniques combined with hyperspectral imaging to automate the identification process of quartz minerals.The utilizied four advanced deep learning models—PSPNet,U-Net,FPN,and LinkNet—has significant advancements in efficiency and accuracy.Among these models,PSPNet exhibited superior performance,achieving the highest intersection over union(IoU)scores and demonstrating exceptional reliability in segmenting quartz minerals,even in complex scenarios.The study involved a comprehensive dataset of 120 thin sections,encompassing 2470 hyperspectral images prepared from 20 rock samples.Expert-reviewed masks were used for model training,ensuring robust segmentation results.This automated approach not only expedites the recognition process but also enhances reliability,providing a valuable tool for geologists and advancing the field of mineralogical analysis.展开更多
In a recent case report in the World Journal of Clinical Cases,emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes.Laboratory-on-a-chip(LOC)technology has...In a recent case report in the World Journal of Clinical Cases,emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes.Laboratory-on-a-chip(LOC)technology has emerged as a transformative tool in health care,offering rapid,sensitive,and specific identification of microorganisms.This editorial provides a comprehensive overview of LOC technology,highlighting its principles,advantages,applications,challenges,and future directions.Success studies from the field have demonstrated the practical benefits of LOC devices in clinical diagnostics,epidemiology,and food safety.Comparative studies have underscored the superiority of LOC technology over traditional methods,showcasing improvements in speed,accuracy,and portability.The future integration of LOC with biosensors,artificial intelligence,and data analytics promises further innovation and expansion.This call to action emphasizes the importance of continued research,investment,and adoption to realize the full potential of LOC technology in improving healthcare outcomes worldwide.展开更多
Radio Frequency Fingerprint Identification(RFFI)technology provides a means of identifying spurious signals.This technology has been widely used in solving Automatic Dependent Surveillance–Broadcast(ADS-B)signal spoo...Radio Frequency Fingerprint Identification(RFFI)technology provides a means of identifying spurious signals.This technology has been widely used in solving Automatic Dependent Surveillance–Broadcast(ADS-B)signal spoofing problems.However,the effects of circuit changes over time often lead to a decline in identification accuracy within open-time set.This paper proposes an ADS-B transmitter identification method to solve the degradation of identification accuracy.First,a real-time data processing system is established to receive and store ADS-B signals to meet the conditions for open-time set.The system possesses the following functionalities:data collection,data parsing,feature extraction,and identity recognition.Subsequently,a two-dimensional TimeFrequency Feature Diagram(TFFD)is proposed as a signal pre-processing method.The TFFD is constructed from the received ADS-B signal and the reconstructed signal for input to the recognition model.Finally,incorporating a frequency offset layer into the Swin Transformer architecture,a novel recognition network framework is proposed.This integration can enhance the network recognition accuracy and robustness by tailoring to the specific characteristics of ADSB signals.Experimental results indicate that the proposed recognition architecture achieves recognition accuracy of 95.86%in closed-time set and 84.33%in open-time set,surpassing other algorithms.展开更多
文摘Objective:To analyse the prevalence of serotypes,antibiotic resistance,and virulence genes of Group B Streptococcus(GBS)strains isolated from pregnant women at 35-37 weeks of gestation in Ho Chi Minh City,Vietnam,from January 2022 to January 2023.Methods:GBS strains were isolated through selective culture methods and confirmed by PCR.Serotyping,virulence gene detection,and antibiotic susceptibility testing were performed using PCR,gel electrophoresis techniques and Kirby-Bauer test.Results:Totally,61 GBS isolated from 300 participants have been identified including seven GBS serotypes(Ⅰa,Ⅰb,Ⅱ,Ⅲ,Ⅳ,Ⅴ,andⅥ).SerotypesⅦ,Ⅷ,andⅨwere not detected in the study population.Antibiotic resistance patterns varied:13.1%of isolates were fully susceptible,while the majority showed multi-drug resistance,with 34.4%resistant to three antibiotics.SerotypeⅠa demonstrated high susceptibility(35.7%),while serotypeⅢshowed extensive resistance,with 87.5%being resistant to at least three antibiotics.All strains are susceptible to vancomycin andβ-lactams susceptibility also remained high,but resistance to clindamycin,erythromycin,and tetracycline was high(>65%).The virulence genes scpB,cylB,fbsB,and cfb were highly prevalent(90%-100%),indicating their potential for vaccine and diagnostic development.Conclusions:Our findings provide valuable insights into GBS serotypes,resistance,and virulence factors,contributing to community monitoring,preventive measures,diagnostics,and vaccine development.However,the limited sample size necessitates further research.
基金supported by the State Grid Southwest Branch Project“Research on Defect Diagnosis and Early Warning Technology of Relay Protection and Safety Automation Devices Based on Multi-Source Heterogeneous Defect Data”.
文摘The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.
基金supported by the Scientific and Technological Innovation Project of the China Academy of Chinese Medical Sciences(CI2021A04013)the National Natural Science Foundation of China(82204610)+1 种基金the Qihang Talent Program(L2022046)the Fundamental Research Funds for the Central Public Welfare Research Institutes(ZZ15-YQ-041 and L2021029).
文摘Background:The medicinal material known as Os Draconis(Longgu)originates from fossilized remains of ancient mammals and is widely used in treating emotional and mental conditions.However,fossil resources are nonrenewable,and clinical demand is increasingly difficult to meet,leading to a proliferation of counterfeit products.During prolonged geological burial,static pressure from the surrounding strata severely compromises the microstructural integrity of osteons in Os Draconis,but Os Draconis still largely retains the structural features of mammalian bone.Methods:Using verified authentic Os Draconis samples over 10,000 years old as a baseline,this study summarizes the ultrastructural characteristics of genuine Os Draconis.Employing electron probe microanalysis and optical polarized light microscopy,we examined 28 batches of authentic Os Draconis and 31 batches of counterfeits to identify their ultrastructural differences.Key points for ultrastructural identification of Os Draconis were compiled,and a new identification approach was proposed based on these differences.Results:Authentic Os Draconis exhibited distinct ultrastructural markers:irregularly shaped osteons with traversing fissures,deformed/displaced Haversian canals,and secondary mineral infill(predominantly calcium carbonate).Counterfeits showed regular osteon arrangements,absent traversal fissures,and homogeneous hydroxyapatite composition.Lab-simulated samples lacked structural degradation features.EPMA confirmed calcium carbonate infill in fossilized Haversian canals,while elemental profiles differentiated lacunae types(void vs.mineral-packed).Conclusion:The study established ultrastructural criteria for authentic Os Draconis identification:osteon deformation,geological fissures penetrating bone units,and heterogenous mineral deposition.These features,unattainable in counterfeits or modern processed bones,provide a cost-effective,accurate identification method.This approach bridges gaps in TCM material standardization and supports quality control for clinical applications.
基金supported by Joint Funds of the National Natural Science Foundation of China(Grant No.U21A20228).
文摘Root rot is a prevalent soil-borne fungal disease in citrus.Citron C-05(Citrus medica)stands out as a germplasm within Citrus spp.due to its complete resistance to citrus canker and favorable characteristics such as single embryo and easy rooting.However,Citron C-05 was found to be highly susceptible to root rot during cultivation,with the specific pathogens previously unknown.In this study,four candidate fungal species were isolated from Citron C-05 roots.Sequence analysis of ITS,EF-1a,RPB1,and RPB2 identified two Fusarium solani strains,Rr-2 and Rr-4,as the candidates causing root rot in Citron C-05.Resistance tests showed these two pathogens increased root damage rate from 10.30%to 35.69%in Citron C-05,sour orange(Citrus aurantium),sweet orange(Citrus sinensis)and pummelo(Citrus grandis).F.solani exhibited the weak pathogenicity towards trifoliate orange(Poncirus trifoliata).DAB staining revealed none of reddish-brown precipitation in the four susceptible citrus germplasm after infection with F.solani,while trifoliate orange exhibited significant H2O2 accumulation.Trypan blue staining indicated increased cell death in the four susceptible citrus germplasm following infection with these two pathogens but not in trifoliate orange.These findings provide a comprehensive understanding of citrus root rot and support future research on the mechanisms of root rot resistance in citrus.
基金Supported by National Key Basic Research Program of China(973 Program)(2017YFD0500605)
文摘[Objectives]This study aimed to investigate the pathogenicity,growth characteristics and drug resistance of Streptococcus suis type 2.[Methods]Bacterial isolation and identification,biochemical experiments,determination of growth curve and correlation curve between OD 600 values and viable counts,drug susceptibility tests,pathogenicity analysis,and histopathological observations were carried out.[Results]The Streptococcus strain isolated from infected pigs was identified as Streptococcus suis type 2,which was named TA01 strain.TA01 strain reached the growth peak at 6-8 h post-incubation,and viable counts gradually declined after 8 h of incubation.The correlation equation between OD 600 values and viable counts is y=24.659 x-1.076 1,R^2=0.996 7.TA01 strain was sensitive to penicillin,erythromycin,florfenicol and oxacillin,and resistant to ciprofloxacin,polymyxin B and clindamycin.According to the results of pathogenicity analysis,all the mice in 3.6×10^9 cfu/mouse group died within 48,and these dead mice exhibited acute pyaemia septica.Based on the Reed-Muench formula,it was calculated that LD 50 of TA01 strain was 1.137×10^8 cfu/mouse.Pathological examination showed obvious blue-stained bacteria clusters,accompanied by neutrophil infiltration.[Conclusions]TA01 strain was a virulent strain of Streptococcus suis type 2.Compared with Streptococcus strains which were isolated and reported in China,TA01 strain exhibited strong virulence and rapid proliferation.
基金supported in part by the National Natural Science Foundation of China (Nos 31300674,81173093,30970643,81373311 and J1103518)the Special Programme for Youth Science and the Technology Innovative Research Group of Sichuan Province,China (No 2011JTD0026)
文摘Dental caries is one of the most common chronic diseases and is caused by acid fermentation of bacteria adhered to the teeth. Streptococcus mutans （S. mutans） utilizes sortase A （SrtA） to anchor surface proteins to the cell wall and forms a biofilm to facilitate its adhesion to the tooth surface. Some plant natural products, especially several flavonoids, are effective inhibitors of SrtA. However, given the limited number of inhibitors and the development of drug resistance, the discovery of new inhibitors is urgent. Here, the high-throughput virtual screening approach was performed to identify new potential inhibitors of S. mutans SrtA. Two libraries were used for screening, and nine compounds that had the lowest scores were chosen for further molecular dynamics simulation, binding free energy analysis and absorption, distribution, metabolism, excretion and toxicity （ADMET） properties analysis. The results revealed that several similar compounds composed of benzofuran, thiadiazole and pyrrole, which exhibited good affinities and appropriate pharmacokinetic In addition, the carbonyl of these compounds can have a strategy for microbial infection disease therapy. parameters, were potential inhibitors to impede the catalysis of SrtA. key role in the inhibition mechanism. These findings can provide a new
文摘BACKGROUND Group B Streptococcus(GBS)is a normal component of the gastrointestinal and genital microbiota in humans and can lead to important infections in newborns.AIM To compare GBS isolation and identification methods as well as to assess the antibiotic susceptibility and to identify resistance genes in GBS strains from pregnant women attended in healthcare services from the city of Vitória da Conquista,in Bahia State,Brazil.METHODS From January 2017 to February 2018,vaginorectal swabs were obtained from 186 participants and the samples were seeded onto chromogenic agar for GBS before and after inoculation in selective broth.Confirmatory identification using 3 CAMP and latex tests was performed in samples with GBS-suggestive colonies.Then,disk diffusion antibiograms were performed in GBS-positive samples,and the detection of the resistance genes ermB,ermTR,mefA,and linB in the clindamycin and/or erythromycin-resistant samples was carried out.RESULTS Thirty-two samples(17.2%)were GBS-positive.The culture in chromogenic agar after sample incubation in selective broth was the most sensitive method(96.9%)for GBS detection.All isolates were susceptible to penicillin,ampicillin,cefotaxime,and vancomycin.Clindamycin resistance was observed in 6 samples(18.8%),while 8 samples(25%)were erythromycin-resistant.All erythromycin and/or clindamycin-resistant GBS strains had negative D-tests.Two strains(25%)presented an M phenotype and 6 isolates(75%)presented a cMLSB phenotype.The ermB gene was identified in 4 samples(44.4%),the mefA gene was also found in 4 samples(44.4%),the ermTR gene was identified in 1 isolate(11.1%),and the linB gene was not found in any isolate.CONCLUSION This study evidenced that the screening for SGB can be performed by means of various methods,including chromogenic media,and that the chemoprophylaxis for pregnant women who cannot use penicillin must be susceptibility-guided.
基金the fund of the Priority Academic Program Development of Jiangsu Higher Education Institutions, China (PAPD)the Natural Science Foundation of Jiangsu Province, China (BK2011644)+2 种基金the Youth Foundation of National Natural Science Foundation of China (31101828)the Youth Foundation of Nanjing Agricultural University, China (KJ2011012)the Specialized Research Funds for the Doctoral Program of Higher Education of China (20110097120016, 20110097130003)
文摘Streptococcus suis is a Gram-positive pathogen that causes serious diseases in pigs. In addition to S. suis serotype 2 （SS2）, S. suis serotype 9 （SS9） is another prevalent serotype, which is frequently isolated from the organs of diseased pigs in China. An immunoproteomic-based approach was developed to identify antigens common to SS2 and SS9 for vaccine development. Cell wall proteins extracted from SS2 strain HA9801 were screened by two-dimensional Western blot using anti-SS2 sera, anti-SS9 sera, or pre-immune sera pooled from specific pathogen free （SPF） mice. Protein spots on preparative gels were excised and identified by matrix-assisted laser desorption ionization time-of-flight mass spectrometry, which led to the identification of four shared immunogenic proteins （arginine deiminase, translation elongation factor-Ts, o-acetylserine lyase, and 1-phosphofructokinase）. The genes encoding these four proteins from SS9 strain GZ0565 were cloned and their proteins were overexpressed in Escherichia coli BL21. Western blot analysis of these recombinant proteins using the convalescent serum of an SPF mini-pig inoculated with the SS2 strain, anti-SS2 sera, and anti-SS9 sera pooled from SPF mice further confirmed the immunogenicity of these proteins. These immunogenic proteins, which are encoded by genes that are reasonably conserved among SS2 and SS9 strains, could be developed as vaccine candidates.
基金supported by the Program for New Century Excellent Talents (NCET) at the University of China(NCET-08-0794)the National Transgenic Major Program(2009ZX08009-154B)+3 种基金the Key Technology Program(R65286D)Jiangsu Province Science and Technology Support Program(BE2009388)the Fundamental Research Funds for the Central Universities,China(KYT 201003)the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions,China
文摘Suppression subtractive hybridization （SSH） was performed with virulent strain ATCC35246 and avirulent strain ST171 to identify novel genes associated with virulence in Streptococcus equi ssp. zooepidemicus （SEZ）. There were fourteen genomic regions that only presented in virulent strain ATCC35246. These regions encoded 14 proteins, some of them were homologous to proteins associated with cellular surface structure, molecular synthesis, energy metabolism, regulation, transport systems, and other unknown functions. Primers for 6 particular regions were designed from the already published SEZ sequence. Then, we used PCR to evaluate the distribution and conservation of these 6 DNA fragments in various SEZ strains collected from different sources, regions, groups, and times. The results showed that these 6 DNA fragments were widely distributed in SEZ strains, yet they were not existence in the avirulent strain ST171. Moreover, these fragments could not be detected in other Streptococcus groups.
文摘Background: Streptococcus pyogenes (Group A streptococcus) is an important Gram-positive human pathogen affected the upper respiratory tract, such as the tonsils and pharynx, and is also induces post-infection diseases such as rheumatic fever and glomerulonephritis. This study aim to isolate Streptococcus pyogenes from children with pharyngitis and to evaluate the molecular identification of S. pyogenes compared with conventional methods. Methods: A cross sectional study was conducted on total of 200 throat swab samples which were collected from children with pharyngitis referred to Wad medani Pediatric Teaching Hospital and Wad medani ENT hospital from January to November 2021. Demographic and clinical data were collected by questionnaire. Throat swabs were tested with the standard microbiological techniques to isolated Group A streptococcus (GAS). Antimicrobial susceptibility testing was performed to all GAS isolates using the Kirby Bauer disk diffusion method according to clinical laboratory standard institute (CLSI) guidelines. Additionally, PCR was used to identify Spy 1258 gene of isolated bacteria. Results: From all throat swab samples screened, 51 isolates (25.5%) were identified as GAS. Antibiotic susceptibility testing revealed that all the GAS isolates were sensitive to Penicillin and Azithromycin. Sensitivity to Erythromycin, Gentamicin, Clarithromycin, Amoxicillin and Cephalexin were 88.2%, 86.3%, 45.1%, 41.2%, 13.7%, respectively. Based on PCR identification of Spy 1258 gene the percentage of isolated bacteria was 21%. Conclusion: The rate of isolated Streptococcus pyogenes was 25.5% by conventional methods and 21% by PCR. The bacteria were sensitive to Penicillin and Azithromycin. The Spy 1258 gene was specific for detection of Streptococcus pyogenes.
基金The National Natural Science Foundation of China(No.52338011,52378291)Young Elite Scientists Sponsorship Program by CAST(No.2022-2024QNRC0101).
文摘To overcome the limitations of low efficiency and reliance on manual processes in the measurement of geometric parameters for bridge prefabricated components,a method based on deep learning and computer vision is developed to identify the geometric parameters.The study utilizes a common precast element for highway bridges as the research subject.First,edge feature points of the bridge component section are extracted from images of the precast component cross-sections by combining the Canny operator with mathematical morphology.Subsequently,a deep learning model is developed to identify the geometric parameters of the precast components using the extracted edge coordinates from the images as input and the predefined control parameters of the bridge section as output.A dataset is generated by varying the control parameters and noise levels for model training.Finally,field measurements are conducted to validate the accuracy of the developed method.The results indicate that the developed method effectively identifies the geometric parameters of bridge precast components,with an error rate maintained within 5%.
基金supported by the National Key R&D Program of China(2021YFC2203502 and 2022YFF0711502)the National Natural Science Foundation of China(NSFC)(12173077)+4 种基金the Tianshan Talent Project of Xinjiang Uygur Autonomous Region(2022TSYCCX0095 and 2023TSYCCX0112)the Scientific Instrument Developing Project of the Chinese Academy of Sciences(PTYQ2022YZZD01)China National Astronomical Data Center(NADC)the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China(MOF)and administrated by the Chinese Academy of Sciences(CAS)Natural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01A360)。
文摘As artificial intelligence(AI)technology has continued to develop,its efficient data processing and pattern recognition capabilities have significantly improved the precision and speed of decision-making processes,and it has been widely applied across various fields.In the field of astronomy,AI techniques have demonstrated unique advantages,particularly in the identification of pulsars and their candidates.AI is able to address the challenges of pulsar celestial body identification and classification because of its accuracy and efficiency.This paper systematically surveys commonly used AI models for pulsar candidate identification,analyzing and discussing the typical applications of machine learning,artificial neural networks,convolutional neural networks,and generative adversarial networks in candidate identification.Furthermore,it explores how th.e introduction of AI techniques not only enhances the efficiency and accuracy of pulsar identification but also provides new perspectives and tools for pulsar survey data processing,thus playing a significant role in advancing pulsar research and the field of astronomy.
基金support from the National Natural Science Foundation of China(Grant Nos:52379103 and 52279103)the Natural Science Foundation of Shandong Province(Grant No:ZR2023YQ049).
文摘Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on machine learning of rock visible and near-infrared spectral data.First,the rock spectral data are preprocessed using Savitzky-Golay(SG)smoothing to remove the noise of the spectral data;then,the preprocessed rock spectral data are downscaled using Principal Component Analysis(PCA)to reduce the redundancy of the data,optimize the effective discriminative information,and obtain the rock spectral features;finally,a Bayesian-optimized lithology identification model is established based on rock spectral features,optimize the model hyperparameters using Bayesian optimization(BO)algorithm to avoid the combination of hyperparameters falling into the local optimal solution,and output the predicted type of rock,so as to realize the Bayesian-optimized lithology identification.In addition,this paper conducts comparative analysis on models based on Artificial Neural Network(ANN)/Random Forest(RF),dimensionality reduction/full band,and optimization algorithms.It uses the confusion matrix,accuracy,Precison(P),Recall(R)and F_(1)values(F_(1))as the evaluation indexes of model accuracy.The results indicate that the lithology identification model optimized by the BO-ANN after dimensionality reduction achieves an accuracy of up to 99.80%,up to 99.79%and up to 99.79%.Compared with the BO-RF model,it has higher identification accuracy and better stability for each type of rock identification.The experiments and reliability analysis show that the Bayesian-optimized lithology identification method proposed in this paper has good robustness and generalization performance,which is of great significance for realizing fast,accurate and Bayesian-optimized lithology identification in tunnel site.
基金supported by the National Defense Fundamental Research Project(No.JCKY2022404C005)the Nuclear Energy Development Project(No.23ZG6106)+1 种基金the Sichuan Scientific and Technological Achievements Transfer and Transformation Demonstration Project(No.2023ZHCG0026)the Mianyang Applied Technology Research and Development Project(No.2021ZYZF1005)。
文摘In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant statistical fluctuations.These issues can lead to potential failures in peak-searching-based identification methods.To address the low precision associated with short-duration measurements of radionuclides,this paper proposes an identification algorithm that leverages heterogeneous spectral transfer to develop a low-count energy spectral identification model.Comparative experiments demonstrated that transferring samples from 26 classes of simulated heterogeneous gamma spectra aids in creating a reliable model for measured gamma spectra.With only 10%of target domain samples used for training,the accuracy on real low-count spectral samples was 95.56%.This performance shows a significant improvement over widely employed full-spectrum analysis methods trained on target domain samples.The proposed method also exhibits strong generalization capabilities,effectively mitigating overfitting issues in low-count energy spectral classification under short-duration measurements.
基金Supported by the National Natural Science Foundation of China(Nos.52222904 and 52309117)China Postdoctoral Science Foundation(Nos.2022TQ0168 and 2023M731895).
文摘Ocean energy has progressively gained considerable interest due to its sufficient potential to meet the world’s energy demand,and the blade is the core component in electricity generation from the ocean current.However,the widened hydraulic excitation frequency may satisfy the blade resonance due to the time variation in the velocity and angle of attack of the ocean current,even resulting in blade fatigue and destructively interfering with grid stability.A key parameter that determines the resonance amplitude of the blade is the hydrodynamic damping ratio(HDR).However,HDR is difficult to obtain due to the complex fluid-structure interaction(FSI).Therefore,a literature review was conducted on the hydrodynamic damping characteristics of blade-like structures.The experimental and simulation methods used to identify and obtain the HDR quantitatively were described,placing emphasis on the experimental processes and simulation setups.Moreover,the accuracy and efficiency of different simulation methods were compared,and the modal work approach was recommended.The effects of key typical parameters,including flow velocity,angle of attack,gap,rotational speed,and cavitation,on the HDR were then summarized,and the suggestions on operating conditions were presented from the perspective of increasing the HDR.Subsequently,considering multiple flow parameters,several theoretical derivations and semi-empirical prediction formulas for HDR were introduced,and the accuracy and application were discussed.Based on the shortcomings of the existing research,the direction of future research was finally determined.The current work offers a clear understanding of the HDR of blade-like structures,which could improve the evaluation accuracy of flow-induced vibration in the design stage.
基金supported by the National Natural Science Foundation of China(42122017,41821002)the Independent Innovation Research Program of China University of Petroleum(East China)(21CX06001A).
文摘It is of great significance to accurately and rapidly identify shale lithofacies in relation to the evaluation and prediction of sweet spots for shale oil and gas reservoirs.To address the problem of low resolution in logging curves,this study establishes a grayscale-phase model based on high-resolution grayscale curves using clustering analysis algorithms for shale lithofacies identification,working with the Shahejie For-mation,Bohai Bay Basin,China.The grayscale phase is defined as the sum of absolute grayscale and relative amplitude as well as their features.The absolute grayscale is the absolute magnitude of the gray values and is utilized for evaluating the material composition(mineral composition+total organic carbon)of shale,while the relative amplitude is the difference between adjacent gray values and is used to identify the shale structure type.The research results show that the grayscale phase model can identify shale lithofacies well,and the accuracy and applicability of this model were verified by the fitting relationship between absolute grayscale and shale mineral composition,as well as corresponding re-lationships between relative amplitudes and laminae development in shales.Four lithofacies are iden-tified in the target layer of the study area:massive mixed shale,laminated mixed shale,massive calcareous shale and laminated calcareous shale.This method can not only effectively characterize the material composition of shale,but also numerically characterize the development degree of shale laminae,and solve the problem that difficult to identify millimeter-scale laminae based on logging curves,which can provide technical support for shale lithofacies identification,sweet spot evaluation and prediction of complex continental lacustrine basins.
基金support by the National Natural Science Foundation of China(Grant No.52402520)。
文摘Actuator faults can be critical in turbofan engines as they can lead to stall,surge,loss of thrust and failure of speed control.Thus,fault diagnosis of gas turbine actuators has attracted considerable attention,from both academia and industry.However,the extensive literature that exists on this topic does not address identifying the severity of actuator faults and focuses mainly on actuator fault detection and isolation.In addition,previous studies of actuator fault identification have not dealt with multiple concurrent faults in real time,especially when these are accompanied by sudden failures under dynamic conditions.This study develops component-level models for fault identification in four typical actuators used in high-bypass ratio turbofan engines under both dynamic and steady-state conditions and these are then integrated with the engine performance model developed by the authors.The research results reported here present a novel method of quantifying actuator faults using dynamic effect compensation.The maximum error for each actuator is less than0.06%and 0.07%,with average computational time of less than 0.0058 s and 0.0086 s for steady-state and transient cases,respectively.These results confirm that the proposed method can accurately and efficiently identify concurrent actuator fault for an engine operating under either transient or steady-state conditions,even in the case of a sudden malfunction.The research results emonstrate the potential benefit to emergency response capabilities by introducing this method of monitoring the health of aero engines.
文摘The precise identification of quartz minerals is crucial in mineralogy and geology due to their widespread occurrence and industrial significance.Traditional methods of quartz identification in thin sections are labor-intensive and require significant expertise,often complicated by the coexistence of other minerals.This study presents a novel approach leveraging deep learning techniques combined with hyperspectral imaging to automate the identification process of quartz minerals.The utilizied four advanced deep learning models—PSPNet,U-Net,FPN,and LinkNet—has significant advancements in efficiency and accuracy.Among these models,PSPNet exhibited superior performance,achieving the highest intersection over union(IoU)scores and demonstrating exceptional reliability in segmenting quartz minerals,even in complex scenarios.The study involved a comprehensive dataset of 120 thin sections,encompassing 2470 hyperspectral images prepared from 20 rock samples.Expert-reviewed masks were used for model training,ensuring robust segmentation results.This automated approach not only expedites the recognition process but also enhances reliability,providing a valuable tool for geologists and advancing the field of mineralogical analysis.
文摘In a recent case report in the World Journal of Clinical Cases,emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes.Laboratory-on-a-chip(LOC)technology has emerged as a transformative tool in health care,offering rapid,sensitive,and specific identification of microorganisms.This editorial provides a comprehensive overview of LOC technology,highlighting its principles,advantages,applications,challenges,and future directions.Success studies from the field have demonstrated the practical benefits of LOC devices in clinical diagnostics,epidemiology,and food safety.Comparative studies have underscored the superiority of LOC technology over traditional methods,showcasing improvements in speed,accuracy,and portability.The future integration of LOC with biosensors,artificial intelligence,and data analytics promises further innovation and expansion.This call to action emphasizes the importance of continued research,investment,and adoption to realize the full potential of LOC technology in improving healthcare outcomes worldwide.
基金supported by the National Key Research and Development Program of China(No.2022YFB4300902)。
文摘Radio Frequency Fingerprint Identification(RFFI)technology provides a means of identifying spurious signals.This technology has been widely used in solving Automatic Dependent Surveillance–Broadcast(ADS-B)signal spoofing problems.However,the effects of circuit changes over time often lead to a decline in identification accuracy within open-time set.This paper proposes an ADS-B transmitter identification method to solve the degradation of identification accuracy.First,a real-time data processing system is established to receive and store ADS-B signals to meet the conditions for open-time set.The system possesses the following functionalities:data collection,data parsing,feature extraction,and identity recognition.Subsequently,a two-dimensional TimeFrequency Feature Diagram(TFFD)is proposed as a signal pre-processing method.The TFFD is constructed from the received ADS-B signal and the reconstructed signal for input to the recognition model.Finally,incorporating a frequency offset layer into the Swin Transformer architecture,a novel recognition network framework is proposed.This integration can enhance the network recognition accuracy and robustness by tailoring to the specific characteristics of ADSB signals.Experimental results indicate that the proposed recognition architecture achieves recognition accuracy of 95.86%in closed-time set and 84.33%in open-time set,surpassing other algorithms.
