Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different events.It is challenging to determine the approximate crowd size from an ima...Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different events.It is challenging to determine the approximate crowd size from an image of the crowd’s density.Therefore in this research study,we proposed a multi-headed convolutional neural network architecture-based model for crowd counting,where we divided our proposed model into two main components:(i)the convolutional neural network,which extracts the feature across the whole image that is given to it as an input,and(ii)the multi-headed layers,which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the crowd.We employed the available public benchmark crowd-counting datasets UCF CC 50 and ShanghaiTech parts A and B for model training and testing to validate the model’s performance.To analyze the results,we used two metrics Mean Absolute Error(MAE)and Mean Square Error(MSE),and compared the results of the proposed systems with the state-of-art models of crowd counting.The results show the superiority of the proposed system.展开更多
The effects of disinfectants and plasmid-based antibiotic resistance genes(ARGs)on the growth of microorganisms and the plasmid-mediated transfer of ARGs in the water and biofilm of the drinkingwater distribution syst...The effects of disinfectants and plasmid-based antibiotic resistance genes(ARGs)on the growth of microorganisms and the plasmid-mediated transfer of ARGs in the water and biofilm of the drinkingwater distribution system under simulated conditionswere explored.The heterotrophic plate count of the water in reactors with 0.1 mg/L NaClO and NH_(2)Cl was higher than in the control groups.Therewas no similar phenomenon in biofilm.In thewater of reactors containing NaClO,the aphA and bla geneswere lower than in the antibiotic resistant bacteria group,while both genes were higher in the water of reactors with NH_(2)Cl than in the control group.Chloramine may promote the transfer of ARGs in the water phase.Both genes in the biofilm of the reactors containing chlorine were lower than the control group.Correlation analysis between ARGs and water quality parameters revealed that the copy numbers of the aphA gene were significantly positively correlated with the copy numbers of the bla gene in water and significantly negatively correlated in biofilm(p<0.05).The results of the sequencing assay showed that bacteria in the biofilm,in the presence of disinfectant,were primarily Gram-negative.1.0 mg/L chlorine decreased the diversity of the community in the biofilm.The relative abundance of some bacteria that may undergo transfer increased in the biofilm of the reactor containing 0.1 mg/L chlorine.展开更多
Environmental DNA(e DNA)methods have emerged as a promising tool for studying a broad spectrum of biological taxa.However,metabarcoding studies of avian biodiversity using e DNA have received little attention.In this ...Environmental DNA(e DNA)methods have emerged as a promising tool for studying a broad spectrum of biological taxa.However,metabarcoding studies of avian biodiversity using e DNA have received little attention.In this study,we compared waterbird biodiversity derived from e DNA metabarcoding with that obtained from traditional point counting surveys at 23 sites in Tai Lake of eastern China and evaluated the accuracy of e DNA metabarcoding for waterbird community studies.The point counting method recorded a higher total number of waterbird species(22)compared to the e DNA technique(16).While e DNA achieved a 74.5%detection rate for waterbird species and was able to identify a significantly greater number of species(12.48±1.97)at each sampling site than point counting method(6.13±2.69),particularly highlighting several rare and elusive species,it failed to detect some species commonly observed by the point counting method.The alpha diversity analysis revealed no significant differences in waterbird diversity between the e DNA method and the point counting method,except that the e DNA method exhibited lower Pielou evenness.Waterbird e DNA sequencing abundance correlated significantly with species occurrence,whereas Spearman's analysis indicated no significant difference between e DNA sequence abundance and species abundance from the point counting method.e DNA method detected no significant difference in waterbird composition between sampling sites,while the point counting method revealed significant differences.Consequently,e DNA is an effective complementary tool for assessing the diversity of wintering waterbirds in lakes,though it is unable to capture the full diversity of waterbird communities.It is crucial to develop sampling strategies that comprehensively monitor species composition and integrate e DNA with traditional survey methods for accurate evaluation of community structure.展开更多
According to the World Health Organization(WHO)manual,sperm concentration should be measured using an improved Neubauer hemocytometer,while sperm motility should be measured by manual assessment.However,in China,thous...According to the World Health Organization(WHO)manual,sperm concentration should be measured using an improved Neubauer hemocytometer,while sperm motility should be measured by manual assessment.However,in China,thousands of laboratories do not use the improved Neubauer hemocytometer or method;instead,the Makler counting chamber is one of the most widely used chambers.To study sources of error that could impact the measurement of the apparent concentration and motility of sperm using the Makler counting chamber and to verify its accuracy for clinical application,67 semen samples from patients attending the Department of Andrology,West China Second University Hospital,Sichuan University(Chengdu,China)between 13 September 2023 and 27 September 2023,were included.Compared with applying the cover glass immediately,delaying the application of the cover glass for 5 s,10 s,and 30 s resulted in average increases in the sperm concentration of 30.3%,74.1%,and 107.5%,respectively(all P<0.0001)and in the progressive motility(PR)of 17.7%,30.8%,and 39.6%,respectively(all P<0.0001).However,when the semen specimens were fixed with formaldehyde,a delay in the application of the cover glass for 5 s,10 s,and 30 s resulted in an average increase in the sperm concentration of 6.7%,10.8%,and 14.6%,respectively,compared with immediate application of the cover glass.The accumulation of motile sperm due to delays in the application of the cover glass is a significant source of error with the Makler counting chamber and should be avoided.展开更多
The analysis of the ejaculate,better known as spermiogram,represents the first and main step to identify whether a series of sperm quality parameters are within the norm and therefore are consistent with normal sperm ...The analysis of the ejaculate,better known as spermiogram,represents the first and main step to identify whether a series of sperm quality parameters are within the norm and therefore are consistent with normal sperm fertilizing capacity.Among these,sperm concentration and motility are the first parameters to be evaluated through an estimation carried out by expert examiners.展开更多
Aiming at problems such as large errors and low efficiency in manual counting of drill pipes during drilling depth measurement,an intelligent detection and counting method for the small targets at the end of drill pip...Aiming at problems such as large errors and low efficiency in manual counting of drill pipes during drilling depth measurement,an intelligent detection and counting method for the small targets at the end of drill pipes based on the improved YOLO11n is proposed.This method realizes the high-precision detection of targets at drill pipe ends in the image by optimizing the target detection model,and combines a post-processing correction mechanism to improve the drill pipe counting accuracy.In order to alleviate the low-precision problem of YOLO11n algorithm for small target recognition in the complex underground background,the YOLO11n algorithm is improved.First,the key module C3k2 in the backbone network was improved,and Poly Kernel Inception(PKI)Block was introduced to replace Bottleneck in it to fully integrate the target context information and the model’s capability of feature extraction;Second,within the model’s neck network,a new feature fusion pyramid ISOP(Improved Small Object Pyramid)is proposed,SPDConv is introduced to strengthen the P2 feature,and CSP and OmniKernel are combined to integrate multi-scale features;Finally,the default loss function is substituted with Powerful-IoU(PIoU)to solve the anchor box expansion problem.On the self-built dataset,experimental verification was conducted.The findings showed that the Recall rose by 6.4%,mAP@0.5 increased by 4.5%,and mAP@0.5:0.95 improved by 6%compared with the baseline model,effectively solving the issues of false detection and missed detection problems in small target detection task.Meanwhile,we conducted counting tests on drilling videos from 5 different scenarios,achieving an average accuracy of 97.3%,which meets the accuracy needs for drill pipe recognition and counting in coal mine drilling sites.The research findings offer theoretical basis and technical backing for promoting the intelligent development of coal mine gas extraction drilling sites.展开更多
The particle identification(PID)of hadrons plays a crucial role in particle physics experiments,especially in flavor physics and jet tagging.The cluster counting method,which measures the number of primary ionizations...The particle identification(PID)of hadrons plays a crucial role in particle physics experiments,especially in flavor physics and jet tagging.The cluster counting method,which measures the number of primary ionizations in gaseous detectors,is a promising breakthrough in PID.However,developing an effective reconstruction algorithm for cluster counting remains challenging.To address this challenge,we propose a cluster counting algorithm based on long short-term memory and dynamic graph convolutional neural networks for the CEPC drift chamber.Experiments on Monte Carlo simulated samples demonstrate that our machine learning-based algorithm surpasses traditional methods.It improves the K/πseparation of PID by 10%,meeting the PID requirements of CEPC.展开更多
In this paper,we study composition operators on weighted Bergman spaces of Dirichlet series.We first establish some Littlewood-type inequalities for generalized mean counting functions.Then we give sufficient conditio...In this paper,we study composition operators on weighted Bergman spaces of Dirichlet series.We first establish some Littlewood-type inequalities for generalized mean counting functions.Then we give sufficient conditions for a composition operator with zero characteristic to be bounded or compact on weighted Bergman spaces of Dirichlet series.The corresponding sufficient condition for compactness in the case of positive characteristics is also obtained.展开更多
Background:One-third of veterans returning from the 1990–1991 Gulf War reported a myriad of symptoms including cognitive dysfunction,skin rashes,musculoskeletal discomfort,and fatigue.This symptom cluster is now refe...Background:One-third of veterans returning from the 1990–1991 Gulf War reported a myriad of symptoms including cognitive dysfunction,skin rashes,musculoskeletal discomfort,and fatigue.This symptom cluster is now referred to as Gulf War Illness(GWI).As the underlying mechanisms of GWI have yet to be fully elucidated,diagnosis and treatment are based on symptomatic presentation.One confounding factor tied to the illness is the high presence of post-traumatic stress disorder(PTSD).Previous research efforts have demonstrated that both GWI and PTSD are associated with immunological dysfunction.As such,this research endeavor aimed to provide insight into the complex relationship between GWI symptoms,cytokine presence,and immune cell populations to pinpoint the impact of PTSD on these measures in GWI.Methods:Symptom measures were gathered through the Multidimensional fatigue inventory(MFI)and 36-item short form health survey(SF-36)scales and biological measures were obtained through cytokine&cytometry analysis.Subgrouping was conducted using Davidson Trauma Scale scores and the Structured Clinical Interview for Diagnostic and statistical manual of mental disorders(DSM)-5,into GWI with high probability of PTSD symptoms(GWIH)and GWI with low probability of PTSD symptoms(GWIL).Data was analyzed using analysis of variance(ANOVA)statistical analysis along with correlation graph analysis.We mapped correlations between immune cells and cytokine signaling measures,hormones and GWI symptom measures to identify patterns in regulation between the GWIH,GWIL,and healthy control groups.Results:GWI with comorbid PTSD symptoms resulted in poorer health outcomes compared with both healthy control(HC)and the GWIL subgroup.Significant differences were found in basophil levels of GWI compared with HC at peak exercise regardless of PTSD symptom comorbidity(ANOVA F=4.7,P=0.01)indicating its potential usage as a biomarker for general GWI from control.While the unique identification of GWI with PTSD symptoms was less clear,the GWIL subgroup was found to be delineated from both GWIH and HC on measures of IL-15 across an exercise challenge(ANOVA F>3.75,P<0.03).Additional differences in natural killer(NK)cell numbers and function highlight IL-15 as a potential biomarker of GWI in the absence of PTSD symptoms.Conclusions:We conclude that disentangling GWI and PTSD by defining trauma-based subgroups may aid in the identification of unique GWI biosignatures that can help to improve diagnosis and target treatment of GWI more effectively.展开更多
Conventional superconducting nanowire single-photon detectors(SNSPDs)have been typically limited in their applications due to their size,weight,and power consumption,which confine their use to laboratory settings.Howe...Conventional superconducting nanowire single-photon detectors(SNSPDs)have been typically limited in their applications due to their size,weight,and power consumption,which confine their use to laboratory settings.However,with the rapid development of remote imaging,sensing technologies,and long-range quantum communication with fewer topographical constraints,the demand for high-efficiency single-photon detectors integrated with avionic platforms is rapidly growing.We herein designed and manufactured the first drone-based SNSPD system with a system detection efficiency(SDE)as high as 91.8%.This drone-based system incorporates high-performance NbTiN SNSPDs,a self-developed miniature liquid helium dewar,and custom-built integrated electrical setups,making it capable of being launched in complex topographical conditions.Such a drone-based SNSPD system may open the use of SNSPDs for applications that demand high SDE in complex environments.展开更多
Because human immunodeficiency virus(HIV)-associated Burkitt lymphoma(BL)has a poor prognosis new therapeutic approaches need to be developed1.Axicabtagene ciloleucel(axi-cel)is an anti-CD19 CAR-T cell commercially av...Because human immunodeficiency virus(HIV)-associated Burkitt lymphoma(BL)has a poor prognosis new therapeutic approaches need to be developed1.Axicabtagene ciloleucel(axi-cel)is an anti-CD19 CAR-T cell commercially available FDA-approved product for patients with relapsed or refractory(R/R)large B-cell lymphoma(LBCL).However,axi-cel has not been approved by the FDA for use in patients with R/R BL.展开更多
Identifying factors that exert more influence on system output from data is one of the most challenging tasks in science and engineering.In this work,a sensitivity analysis of the generalized Gaussian process regressi...Identifying factors that exert more influence on system output from data is one of the most challenging tasks in science and engineering.In this work,a sensitivity analysis of the generalized Gaussian process regression(SA-GGPR)model is proposed to identify important factors of the nonlinear counting system.In SA-GGPR,the GGPR model with Poisson likelihood is adopted to describe the nonlinear counting system.The GGPR model with Poisson likelihood inherits the merits of nonparametric kernel learning and Poisson distribution,and can handle complex nonlinear counting systems.Nevertheless,understanding the relationships between model inputs and output in the GGPR model with Poisson likelihood is not readily accessible due to its nonparametric and kernel structure.SA-GGPR addresses this issue by providing a quantitative assessment of how different inputs affect the system output.The application results on a simulated nonlinear counting system and a real steel casting-rolling process have demonstrated that the proposed SA-GGPR method outperforms several state-of-the-art methods in identification accuracy.展开更多
The Cramer–Rao lower bound on range error is modeled for pseudo-random ranging systems using Geiger-mode avalanche photodiodes. The theoretical results are shown to agree with the Monte Carlo simulation, satisfying b...The Cramer–Rao lower bound on range error is modeled for pseudo-random ranging systems using Geiger-mode avalanche photodiodes. The theoretical results are shown to agree with the Monte Carlo simulation, satisfying boundary evaluations. Experimental tests prove that range errors caused by the fluctuation of the number of photon counts in the laser echo pulse leads to the range drift of the time point spread function. The function relationship between the range error and the photon counting ratio is determined by using numerical fitting.Range errors due to a different echo energy is calibrated so that the corrected range root mean square error is improved to 1 cm.展开更多
To realize automatic counting of urediospores of Puccinia striiformis f.sp.tritici(Pst)(causal agent of wheat stripe rust),an automatic counting system for urediospores of wheat stripe rust pathogen based on image pro...To realize automatic counting of urediospores of Puccinia striiformis f.sp.tritici(Pst)(causal agent of wheat stripe rust),an automatic counting system for urediospores of wheat stripe rust pathogen based on image processing was developed using MATLAB GUIDE platform in combination with Local C Compiler(LCC).The system is independent of the MATLAB environment and can be run on a computer without the MATLAB software.Using this system,automatic counting of Pst urediospores in a microscopic image can be implemented via image processing technologies including image scaling,clustering segmentation,morphological modification,watershed transformation,connected region labeling,etc.Structure design of the automatic counting system,the key algorithms used in the system and realization of the main functions of the system were described in detail.Spore counting tests were conducted using microscopic digital images of Pst urediospores and the high accuracies more than 95%were obtained.The results indicated that it is feasible to count Pst urediospores automatically using the developed system based on image processing.展开更多
We demonstrate a photon-counting chirped amplitude modulation （CAM） light detection and ranging （lidar） system incorporating a superconducting nanowire single-photon detector （SNSPD） and operated at a wavelength...We demonstrate a photon-counting chirped amplitude modulation （CAM） light detection and ranging （lidar） system incorporating a superconducting nanowire single-photon detector （SNSPD） and operated at a wavelength of 1550 nm. The distance accuracy of the lidar system was determined by the CAM bandwidth and signal-to-noise ratio （SNR） of an intermediate frequency （IF） signal. Owing to a short dead time （10 ns） and negligible dark count rate （70 Hz） of the SNSPD, the obtained IF signal attained an SNR of 42 dB and the direct distance accuracy was improved to 3 mm when the modulation bandwidth of the CAM signal was 240 MHz and the modulation period was 1 ms.展开更多
When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ...When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.展开更多
For accurate counting of alpha tracks on the polyallyl diglycol carbonate of CR-39-type track detectors,the size distributions of both artifact tracks and alpha tracks were investigated with an automatic counting syst...For accurate counting of alpha tracks on the polyallyl diglycol carbonate of CR-39-type track detectors,the size distributions of both artifact tracks and alpha tracks were investigated with an automatic counting system. At the same temperature and etchant concentration, the numbers and sizes of alpha tracks changed significantly with the etching time, and the artifact track changes were smaller. At the etching time of 5 h, the sizes of alpha tracks were evidently larger than those of the artifact tracks, and the deviation of its size distribution was much smaller than those of longer etching time. Based on the size distribution of alpha tracks etched for 5 h, the overlap effect and uncertainty of overlap correction were studied by the Monte Carlo simulations for different track densities. It was found that the counting uncertainty of the system could be less than 6% in a density range of 10–160 tracks mm^(-2) after taking the overlap correction into account.展开更多
In this paper, a deep learning-based method is proposed for crowdcountingproblems. Specifically, by utilizing the convolution kernel densitymap, the ground truth is generated dynamically to enhance the featureextracti...In this paper, a deep learning-based method is proposed for crowdcountingproblems. Specifically, by utilizing the convolution kernel densitymap, the ground truth is generated dynamically to enhance the featureextractingability of the generator model. Meanwhile, the “cross stage partial”module is integrated into congested scene recognition network (CSRNet) toobtain a lightweight network model. In addition, to compensate for the accuracydrop owing to the lightweight model, we take advantage of “structuredknowledge transfer” to train the model in an end-to-end manner. It aimsto accelerate the fitting speed and enhance the learning ability of the studentmodel. The crowd-counting system solution for edge computing is alsoproposed and implemented on an embedded device equipped with a neuralprocessing unit. Simulations demonstrate the performance improvement ofthe proposed solution in terms of model size, processing speed and accuracy.The performance on the Venice dataset shows that the mean absolute error(MAE) and the root mean squared error (RMSE) of our model drop by32.63% and 39.18% compared with CSRNet. Meanwhile, the performance onthe ShanghaiTech PartB dataset reveals that the MAE and the RMSE of ourmodel are close to those of CSRNet. Therefore, we provide a novel embeddedplatform system scheme for public safety pre-warning applications.展开更多
We investigate the influence of the field fluctuations to the emission photons of V-type three-level systems.The emission intensity I and Mandel＇s Q parameter show stochastic resonance with respect to the pure dephas...We investigate the influence of the field fluctuations to the emission photons of V-type three-level systems.The emission intensity I and Mandel＇s Q parameter show stochastic resonance with respect to the pure dephasing constantγp.The amplitude fluctuation of the field causes these systems to lose their coherence.On the other hand,the amplitude fluctuation provides a new interference method for these systems.The quantum beats are shown in the orthogonal system.展开更多
Inventory counting is crucial to manufacturing industries in terms of inventory management,production,and procurement planning.Many companies currently require workers to manually count and track the status of materia...Inventory counting is crucial to manufacturing industries in terms of inventory management,production,and procurement planning.Many companies currently require workers to manually count and track the status of materials,which are repetitive and non-value-added activities but incur significant costs to the companies as well as mental fatigue to the employees.This research aims to develop a computer vision system that can automate the material counting activity without applying any marker on the material.The type of material of interest is metal sheet,whose shape is simple,a large rectangular shape,yet difficult to detect.The use of computer vision technology can reduce the costs incurred fromthe loss of high-value materials,eliminate repetitive work requirements for skilled labor,and reduce human error.A computer vision system is proposed and tested on a metal sheet picking process formultiple metal sheet stacks in the storage area by using one video camera.Our results show that the proposed computer vision system can count the metal sheet picks under a real situation with a precision of 97.83%and a recall of 100%.展开更多
基金funded by Naif Arab University for Security Sciences under grant No.NAUSS-23-R10.
文摘Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different events.It is challenging to determine the approximate crowd size from an image of the crowd’s density.Therefore in this research study,we proposed a multi-headed convolutional neural network architecture-based model for crowd counting,where we divided our proposed model into two main components:(i)the convolutional neural network,which extracts the feature across the whole image that is given to it as an input,and(ii)the multi-headed layers,which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the crowd.We employed the available public benchmark crowd-counting datasets UCF CC 50 and ShanghaiTech parts A and B for model training and testing to validate the model’s performance.To analyze the results,we used two metrics Mean Absolute Error(MAE)and Mean Square Error(MSE),and compared the results of the proposed systems with the state-of-art models of crowd counting.The results show the superiority of the proposed system.
基金supported by the Natural Science Foundation of China(No.52070145,51778453).
文摘The effects of disinfectants and plasmid-based antibiotic resistance genes(ARGs)on the growth of microorganisms and the plasmid-mediated transfer of ARGs in the water and biofilm of the drinkingwater distribution system under simulated conditionswere explored.The heterotrophic plate count of the water in reactors with 0.1 mg/L NaClO and NH_(2)Cl was higher than in the control groups.Therewas no similar phenomenon in biofilm.In thewater of reactors containing NaClO,the aphA and bla geneswere lower than in the antibiotic resistant bacteria group,while both genes were higher in the water of reactors with NH_(2)Cl than in the control group.Chloramine may promote the transfer of ARGs in the water phase.Both genes in the biofilm of the reactors containing chlorine were lower than the control group.Correlation analysis between ARGs and water quality parameters revealed that the copy numbers of the aphA gene were significantly positively correlated with the copy numbers of the bla gene in water and significantly negatively correlated in biofilm(p<0.05).The results of the sequencing assay showed that bacteria in the biofilm,in the presence of disinfectant,were primarily Gram-negative.1.0 mg/L chlorine decreased the diversity of the community in the biofilm.The relative abundance of some bacteria that may undergo transfer increased in the biofilm of the reactor containing 0.1 mg/L chlorine.
基金funded by the National Key Research and Development Program of China(Award Number:2022YFC3202104)。
文摘Environmental DNA(e DNA)methods have emerged as a promising tool for studying a broad spectrum of biological taxa.However,metabarcoding studies of avian biodiversity using e DNA have received little attention.In this study,we compared waterbird biodiversity derived from e DNA metabarcoding with that obtained from traditional point counting surveys at 23 sites in Tai Lake of eastern China and evaluated the accuracy of e DNA metabarcoding for waterbird community studies.The point counting method recorded a higher total number of waterbird species(22)compared to the e DNA technique(16).While e DNA achieved a 74.5%detection rate for waterbird species and was able to identify a significantly greater number of species(12.48±1.97)at each sampling site than point counting method(6.13±2.69),particularly highlighting several rare and elusive species,it failed to detect some species commonly observed by the point counting method.The alpha diversity analysis revealed no significant differences in waterbird diversity between the e DNA method and the point counting method,except that the e DNA method exhibited lower Pielou evenness.Waterbird e DNA sequencing abundance correlated significantly with species occurrence,whereas Spearman's analysis indicated no significant difference between e DNA sequence abundance and species abundance from the point counting method.e DNA method detected no significant difference in waterbird composition between sampling sites,while the point counting method revealed significant differences.Consequently,e DNA is an effective complementary tool for assessing the diversity of wintering waterbirds in lakes,though it is unable to capture the full diversity of waterbird communities.It is crucial to develop sampling strategies that comprehensively monitor species composition and integrate e DNA with traditional survey methods for accurate evaluation of community structure.
基金supported by the Natural Science Foundation of China(No.32171264 and No.81974226)the Sichuan Science and Technology Program(2023NSFSC1609)。
文摘According to the World Health Organization(WHO)manual,sperm concentration should be measured using an improved Neubauer hemocytometer,while sperm motility should be measured by manual assessment.However,in China,thousands of laboratories do not use the improved Neubauer hemocytometer or method;instead,the Makler counting chamber is one of the most widely used chambers.To study sources of error that could impact the measurement of the apparent concentration and motility of sperm using the Makler counting chamber and to verify its accuracy for clinical application,67 semen samples from patients attending the Department of Andrology,West China Second University Hospital,Sichuan University(Chengdu,China)between 13 September 2023 and 27 September 2023,were included.Compared with applying the cover glass immediately,delaying the application of the cover glass for 5 s,10 s,and 30 s resulted in average increases in the sperm concentration of 30.3%,74.1%,and 107.5%,respectively(all P<0.0001)and in the progressive motility(PR)of 17.7%,30.8%,and 39.6%,respectively(all P<0.0001).However,when the semen specimens were fixed with formaldehyde,a delay in the application of the cover glass for 5 s,10 s,and 30 s resulted in an average increase in the sperm concentration of 6.7%,10.8%,and 14.6%,respectively,compared with immediate application of the cover glass.The accumulation of motile sperm due to delays in the application of the cover glass is a significant source of error with the Makler counting chamber and should be avoided.
文摘The analysis of the ejaculate,better known as spermiogram,represents the first and main step to identify whether a series of sperm quality parameters are within the norm and therefore are consistent with normal sperm fertilizing capacity.Among these,sperm concentration and motility are the first parameters to be evaluated through an estimation carried out by expert examiners.
基金Henan Province University Science and Technology Innovation Team Support Program Project(22IRTSTHN005)..
文摘Aiming at problems such as large errors and low efficiency in manual counting of drill pipes during drilling depth measurement,an intelligent detection and counting method for the small targets at the end of drill pipes based on the improved YOLO11n is proposed.This method realizes the high-precision detection of targets at drill pipe ends in the image by optimizing the target detection model,and combines a post-processing correction mechanism to improve the drill pipe counting accuracy.In order to alleviate the low-precision problem of YOLO11n algorithm for small target recognition in the complex underground background,the YOLO11n algorithm is improved.First,the key module C3k2 in the backbone network was improved,and Poly Kernel Inception(PKI)Block was introduced to replace Bottleneck in it to fully integrate the target context information and the model’s capability of feature extraction;Second,within the model’s neck network,a new feature fusion pyramid ISOP(Improved Small Object Pyramid)is proposed,SPDConv is introduced to strengthen the P2 feature,and CSP and OmniKernel are combined to integrate multi-scale features;Finally,the default loss function is substituted with Powerful-IoU(PIoU)to solve the anchor box expansion problem.On the self-built dataset,experimental verification was conducted.The findings showed that the Recall rose by 6.4%,mAP@0.5 increased by 4.5%,and mAP@0.5:0.95 improved by 6%compared with the baseline model,effectively solving the issues of false detection and missed detection problems in small target detection task.Meanwhile,we conducted counting tests on drilling videos from 5 different scenarios,achieving an average accuracy of 97.3%,which meets the accuracy needs for drill pipe recognition and counting in coal mine drilling sites.The research findings offer theoretical basis and technical backing for promoting the intelligent development of coal mine gas extraction drilling sites.
基金supported by National Natural Science Foundation of China(NSFC)(Nos.12475200 and 12275296)Joint Fund of Research utilizing Large-Scale Scientific Facility of the NSFC and CAS(No.U2032114)Institute of High Energy Physics(Chinese Academy of Sciences)Innovative Project on Sciences and Technologies(Nos.E3545BU210 and E25456U210).
文摘The particle identification(PID)of hadrons plays a crucial role in particle physics experiments,especially in flavor physics and jet tagging.The cluster counting method,which measures the number of primary ionizations in gaseous detectors,is a promising breakthrough in PID.However,developing an effective reconstruction algorithm for cluster counting remains challenging.To address this challenge,we propose a cluster counting algorithm based on long short-term memory and dynamic graph convolutional neural networks for the CEPC drift chamber.Experiments on Monte Carlo simulated samples demonstrate that our machine learning-based algorithm surpasses traditional methods.It improves the K/πseparation of PID by 10%,meeting the PID requirements of CEPC.
基金supported by the National Natural Science Foundation of China(12171373)Chen's work also supported by the Fundamental Research Funds for the Central Universities of China(GK202207018).
文摘In this paper,we study composition operators on weighted Bergman spaces of Dirichlet series.We first establish some Littlewood-type inequalities for generalized mean counting functions.Then we give sufficient conditions for a composition operator with zero characteristic to be bounded or compact on weighted Bergman spaces of Dirichlet series.The corresponding sufficient condition for compactness in the case of positive characteristics is also obtained.
基金suppor ted by the US Depar tment of Defense Congressionally Directed Medical Research Program (CDMRP)awards (http://cdmrp.army.mil/) W81XWH-16-1-0632 (Craddock PI),W81XWH-16-1-0552 (Craddock PI),W81XWH-18-1-0549 (Sullivan PI),W81XWH-13-2-0072 (Sullivan PI),and W81XWH-09-2-0071 (Klimas PI)a Veterans Affairs Merit Award (4987.69) to Dr.Nancy Klimas。
文摘Background:One-third of veterans returning from the 1990–1991 Gulf War reported a myriad of symptoms including cognitive dysfunction,skin rashes,musculoskeletal discomfort,and fatigue.This symptom cluster is now referred to as Gulf War Illness(GWI).As the underlying mechanisms of GWI have yet to be fully elucidated,diagnosis and treatment are based on symptomatic presentation.One confounding factor tied to the illness is the high presence of post-traumatic stress disorder(PTSD).Previous research efforts have demonstrated that both GWI and PTSD are associated with immunological dysfunction.As such,this research endeavor aimed to provide insight into the complex relationship between GWI symptoms,cytokine presence,and immune cell populations to pinpoint the impact of PTSD on these measures in GWI.Methods:Symptom measures were gathered through the Multidimensional fatigue inventory(MFI)and 36-item short form health survey(SF-36)scales and biological measures were obtained through cytokine&cytometry analysis.Subgrouping was conducted using Davidson Trauma Scale scores and the Structured Clinical Interview for Diagnostic and statistical manual of mental disorders(DSM)-5,into GWI with high probability of PTSD symptoms(GWIH)and GWI with low probability of PTSD symptoms(GWIL).Data was analyzed using analysis of variance(ANOVA)statistical analysis along with correlation graph analysis.We mapped correlations between immune cells and cytokine signaling measures,hormones and GWI symptom measures to identify patterns in regulation between the GWIH,GWIL,and healthy control groups.Results:GWI with comorbid PTSD symptoms resulted in poorer health outcomes compared with both healthy control(HC)and the GWIL subgroup.Significant differences were found in basophil levels of GWI compared with HC at peak exercise regardless of PTSD symptom comorbidity(ANOVA F=4.7,P=0.01)indicating its potential usage as a biomarker for general GWI from control.While the unique identification of GWI with PTSD symptoms was less clear,the GWIL subgroup was found to be delineated from both GWIH and HC on measures of IL-15 across an exercise challenge(ANOVA F>3.75,P<0.03).Additional differences in natural killer(NK)cell numbers and function highlight IL-15 as a potential biomarker of GWI in the absence of PTSD symptoms.Conclusions:We conclude that disentangling GWI and PTSD by defining trauma-based subgroups may aid in the identification of unique GWI biosignatures that can help to improve diagnosis and target treatment of GWI more effectively.
基金the Innovation Program for Quantum Science and Technology(Grant No.2023ZD0300100)the National Key Research and Development Program of China(Grant Nos.2023YFB3809600 and 2023YFC3007801)+1 种基金the National Natural Science Foundation of China(Grant Nos.62301543 and U24A20320)the Shanghai Sailing Program(Grant No.21YF1455700).
文摘Conventional superconducting nanowire single-photon detectors(SNSPDs)have been typically limited in their applications due to their size,weight,and power consumption,which confine their use to laboratory settings.However,with the rapid development of remote imaging,sensing technologies,and long-range quantum communication with fewer topographical constraints,the demand for high-efficiency single-photon detectors integrated with avionic platforms is rapidly growing.We herein designed and manufactured the first drone-based SNSPD system with a system detection efficiency(SDE)as high as 91.8%.This drone-based system incorporates high-performance NbTiN SNSPDs,a self-developed miniature liquid helium dewar,and custom-built integrated electrical setups,making it capable of being launched in complex topographical conditions.Such a drone-based SNSPD system may open the use of SNSPDs for applications that demand high SDE in complex environments.
基金supported by the Sponsored by Tianjin Health Research Project(Grant No.TJWJ2023ZD003)the Chinese Society of Clinical Oncology Beijing Xisike Clinical Oncology Research Foundation(Grant Nos.Y-NCJH202201-0027 and Y-2022YMJN/MS-0001).
文摘Because human immunodeficiency virus(HIV)-associated Burkitt lymphoma(BL)has a poor prognosis new therapeutic approaches need to be developed1.Axicabtagene ciloleucel(axi-cel)is an anti-CD19 CAR-T cell commercially available FDA-approved product for patients with relapsed or refractory(R/R)large B-cell lymphoma(LBCL).However,axi-cel has not been approved by the FDA for use in patients with R/R BL.
基金Project supported by the National Natural Science Foundation of China(Nos.62003301 and 61833014)the Natural Science Foundation of Zhejiang Province,China(No.LQ21F030018)。
文摘Identifying factors that exert more influence on system output from data is one of the most challenging tasks in science and engineering.In this work,a sensitivity analysis of the generalized Gaussian process regression(SA-GGPR)model is proposed to identify important factors of the nonlinear counting system.In SA-GGPR,the GGPR model with Poisson likelihood is adopted to describe the nonlinear counting system.The GGPR model with Poisson likelihood inherits the merits of nonparametric kernel learning and Poisson distribution,and can handle complex nonlinear counting systems.Nevertheless,understanding the relationships between model inputs and output in the GGPR model with Poisson likelihood is not readily accessible due to its nonparametric and kernel structure.SA-GGPR addresses this issue by providing a quantitative assessment of how different inputs affect the system output.The application results on a simulated nonlinear counting system and a real steel casting-rolling process have demonstrated that the proposed SA-GGPR method outperforms several state-of-the-art methods in identification accuracy.
基金supported by the National Natural Science Foundation of China(Nos.61101196 and 61271332)the Natural Science Research Foundation of Jiangsu Province(No.168JB510015)
文摘The Cramer–Rao lower bound on range error is modeled for pseudo-random ranging systems using Geiger-mode avalanche photodiodes. The theoretical results are shown to agree with the Monte Carlo simulation, satisfying boundary evaluations. Experimental tests prove that range errors caused by the fluctuation of the number of photon counts in the laser echo pulse leads to the range drift of the time point spread function. The function relationship between the range error and the photon counting ratio is determined by using numerical fitting.Range errors due to a different echo energy is calibrated so that the corrected range root mean square error is improved to 1 cm.
基金supported by International Research Exchange Scheme of the Marie Curie Program of the 7th Framework Program(Ref.PIRSES-GA-2013-612659)National Key Basic Research Program of China(2013CB127700)National Key Technologies Research and Development Program of China(2012BAD19BA04).
文摘To realize automatic counting of urediospores of Puccinia striiformis f.sp.tritici(Pst)(causal agent of wheat stripe rust),an automatic counting system for urediospores of wheat stripe rust pathogen based on image processing was developed using MATLAB GUIDE platform in combination with Local C Compiler(LCC).The system is independent of the MATLAB environment and can be run on a computer without the MATLAB software.Using this system,automatic counting of Pst urediospores in a microscopic image can be implemented via image processing technologies including image scaling,clustering segmentation,morphological modification,watershed transformation,connected region labeling,etc.Structure design of the automatic counting system,the key algorithms used in the system and realization of the main functions of the system were described in detail.Spore counting tests were conducted using microscopic digital images of Pst urediospores and the high accuracies more than 95%were obtained.The results indicated that it is feasible to count Pst urediospores automatically using the developed system based on image processing.
基金Project supported by National Key R&D Program of China(Grant No.2017YFA0304000)the National Natural Science Foundation of China(NSFC)(Grant Nos.61501442 and 61671438)the Joint Research Fund in Astronomy(U1631240)under Cooperative Agreement between the NSFC and Chinese Academy of Sciences(CAS)
文摘We demonstrate a photon-counting chirped amplitude modulation （CAM） light detection and ranging （lidar） system incorporating a superconducting nanowire single-photon detector （SNSPD） and operated at a wavelength of 1550 nm. The distance accuracy of the lidar system was determined by the CAM bandwidth and signal-to-noise ratio （SNR） of an intermediate frequency （IF） signal. Owing to a short dead time （10 ns） and negligible dark count rate （70 Hz） of the SNSPD, the obtained IF signal attained an SNR of 42 dB and the direct distance accuracy was improved to 3 mm when the modulation bandwidth of the CAM signal was 240 MHz and the modulation period was 1 ms.
文摘When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.
基金supported by the National Natural Science Foundation of China(No.11375048)
文摘For accurate counting of alpha tracks on the polyallyl diglycol carbonate of CR-39-type track detectors,the size distributions of both artifact tracks and alpha tracks were investigated with an automatic counting system. At the same temperature and etchant concentration, the numbers and sizes of alpha tracks changed significantly with the etching time, and the artifact track changes were smaller. At the etching time of 5 h, the sizes of alpha tracks were evidently larger than those of the artifact tracks, and the deviation of its size distribution was much smaller than those of longer etching time. Based on the size distribution of alpha tracks etched for 5 h, the overlap effect and uncertainty of overlap correction were studied by the Monte Carlo simulations for different track densities. It was found that the counting uncertainty of the system could be less than 6% in a density range of 10–160 tracks mm^(-2) after taking the overlap correction into account.
文摘In this paper, a deep learning-based method is proposed for crowdcountingproblems. Specifically, by utilizing the convolution kernel densitymap, the ground truth is generated dynamically to enhance the featureextractingability of the generator model. Meanwhile, the “cross stage partial”module is integrated into congested scene recognition network (CSRNet) toobtain a lightweight network model. In addition, to compensate for the accuracydrop owing to the lightweight model, we take advantage of “structuredknowledge transfer” to train the model in an end-to-end manner. It aimsto accelerate the fitting speed and enhance the learning ability of the studentmodel. The crowd-counting system solution for edge computing is alsoproposed and implemented on an embedded device equipped with a neuralprocessing unit. Simulations demonstrate the performance improvement ofthe proposed solution in terms of model size, processing speed and accuracy.The performance on the Venice dataset shows that the mean absolute error(MAE) and the root mean squared error (RMSE) of our model drop by32.63% and 39.18% compared with CSRNet. Meanwhile, the performance onthe ShanghaiTech PartB dataset reveals that the MAE and the RMSE of ourmodel are close to those of CSRNet. Therefore, we provide a novel embeddedplatform system scheme for public safety pre-warning applications.
基金supported by the National Natural Science Foundation of China(Grand Nos.91021009,21073110,and 11374191)the Natural Science Foundation of Shandong Province,China(Grant No.ZR2013AQ020)+1 种基金the Postdoctoral Science Foundation of China(Grant No.2013M531584)the Doctoral Program of Higher Education of China(Grant Nos.20130131110005 and 20130131120006)
文摘We investigate the influence of the field fluctuations to the emission photons of V-type three-level systems.The emission intensity I and Mandel＇s Q parameter show stochastic resonance with respect to the pure dephasing constantγp.The amplitude fluctuation of the field causes these systems to lose their coherence.On the other hand,the amplitude fluctuation provides a new interference method for these systems.The quantum beats are shown in the orthogonal system.
基金This work was jointly supported by the Excellent Research Graduate Scholarship-EreG Scholarship Program Under the Memorandum of Understanding between Thammasat University and National Science and Technology Development Agency(NSTDA),Thailand[No.MOU-CO-2562-8675]the Center of Excellence in Logistics and Supply Chain System Engineering and Technology(COE LogEn)+1 种基金Sirindhorn International Institute of Technology(SIIT)Thammasat University,Thailand.
文摘Inventory counting is crucial to manufacturing industries in terms of inventory management,production,and procurement planning.Many companies currently require workers to manually count and track the status of materials,which are repetitive and non-value-added activities but incur significant costs to the companies as well as mental fatigue to the employees.This research aims to develop a computer vision system that can automate the material counting activity without applying any marker on the material.The type of material of interest is metal sheet,whose shape is simple,a large rectangular shape,yet difficult to detect.The use of computer vision technology can reduce the costs incurred fromthe loss of high-value materials,eliminate repetitive work requirements for skilled labor,and reduce human error.A computer vision system is proposed and tested on a metal sheet picking process formultiple metal sheet stacks in the storage area by using one video camera.Our results show that the proposed computer vision system can count the metal sheet picks under a real situation with a precision of 97.83%and a recall of 100%.
