The spread of the Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)has already taken on pandemic extents,influencing even more than 200 nations in a couple of months.Although,regulation measures in China hav...The spread of the Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)has already taken on pandemic extents,influencing even more than 200 nations in a couple of months.Although,regulation measures in China have decreased new cases by over 98%,this decrease is not the situation everywhere,and most of the countries still have been affected by it.The objective of this research work is to make a comparative analysis of the top 5 most populated countries namely United States,India,China,Pakistan and Indonesia,from 1st January 2020 to 31st July 2020.This research work also targets to predict an increase in the number of deaths and total infected cases in these five countries.In our research,the performance of the proposed framework is determined by using three Machine Learning(ML)regression algorithms namely Linear Regression(LR),Support Vector Regression(SVR),andRandom Forest(RF)Regression.The proposed model is also validated upon the infected and death cases of further dates.The performance of these three algorithms is compared using the RootMean Square Error(RMSE)metrics.Random Forest algorithm shows best performance as compared to other proposed algorithms,with the lowest RMSE value in the prediction of total infected and total deaths cases for all the top five most populated countries.展开更多
Rapid and sensitive detection of dissolved gases in seawater is quite essential for the investigation of the global carbon cycle.Large quantities of in situ optical detection techniques showed restricted measurement e...Rapid and sensitive detection of dissolved gases in seawater is quite essential for the investigation of the global carbon cycle.Large quantities of in situ optical detection techniques showed restricted measurement efficiency,owing to the single gas sensor without the identification ability of multiple gases.In this work,a novel gas-liquid Raman detection method of monitoring the multi-component dissolved gases was proposed based on a continuous gas-liquid separator under a large difference of partial pressure.The limit of detection(LOD)of the gas Raman spectrometer could arrive at about 14 μl·L^(-1)for N_(2)gas.Moreover,based on the continuous gas-liquid separation process,the detection time of the dissolved gases could be largely decreased to about 200 s compared with that of the traditional detection method(30 min).Effect of equilibrium time on gas-liquid separation process indicated that the extracted efficiency and decay time of these dissolved gases was CO_(2)>O_(2)>N_(2).In addition,the analysis of the relationship between equilibrium time and flow speed indicated that the decay time decreased with the increase of the flow speed.The validation and application of the developed system presented its great potential for studying the components and spatiotemporal distribution of dissolved gases in seawater.展开更多
As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power pla...As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power plants’carbon management systems can no longer meet the demands of high-precision,real-time monitoring.Smart power plants now offer innovative solutions for carbon emission tracking and intelligent analysis by integrating IoT,big data,and AI technologies.Current research predominantly focuses on optimizing individual processes,lacking systematic exploration of comprehensive dynamic monitoring and intelligent decision-making across the entire workflow.To address this gap,we propose a smart carbon emission monitoring and analysis platform for power plants that integrates IoT sensing,multimodal data analytics,and AI-driven decision-making.The platform establishes a multi-source sensor network to collect emissions data throughout the fuel combustion,auxiliary equipment operation,and waste treatment processes.Combining carbon emission factor analysis with machine learning models enables real-time emission calculations and utilizes long short-term memory networks to predict future emission trends.展开更多
Actuator dynamics introduce a synchronization disparity between commanded displacements transmitted to the actuator and the actual displacements generated by the actuator,thereby affecting its precision and potentiall...Actuator dynamics introduce a synchronization disparity between commanded displacements transmitted to the actuator and the actual displacements generated by the actuator,thereby affecting its precision and potentially leading to instability in real-time hybrid simulation(RTHS).This study aims to elucidate the relationship between calculated and measured displacements by analyzing their magnitude and phase in the frequency domain via transformations.The physical implications of these relationships are explored in the context of frequency domain evaluation indices(FEI),the transfer function of actuator dynamics,and delay compensation.Formulations for achieving perfect compensation of actuator dynamics are developed,and an enhanced compensation approach,termed improved windowed frequency domain evaluation index-based compensation(IWFEI),is introduced.The efficacy of IWFEI is assessed using a RTHS benchmark model,with perturbed simulations conducted to validate its robustness.Uncertainties inherent in actuator dynamics are represented as random variables in these simulations.Comparative analysis of the mean values and variances of evaluation criteria demonstrates that IWFEI enables more accurate and robust compensation.Furthermore,strong correlations observed among criteria in the time and frequency domains underscore the effectiveness of the proposed frequency domain-based compensation method in mitigating amplitude errors and phase delays in RTHS.展开更多
In the food production sector,quickly identifying potential hazards is crucial due to the resilience of many pathogens,which could lead to wasted production results and,more severely,epidemic outbreaks.E.coli monitori...In the food production sector,quickly identifying potential hazards is crucial due to the resilience of many pathogens,which could lead to wasted production results and,more severely,epidemic outbreaks.E.coli monitoring is essential;however,traditional quality control methods in fish farming are often slow and intrusive,thus promoting an increase in fish stress and mortality rates.This paper presents an alternative method by utilizing a prototype inspired by polarized optical microscopy(POM),constructed with a Raspberry Pi microprocessor to assess pixel patterns and calculate analyte levels.展开更多
[Objective]To investigate the expression of zebrafish vascular endothelial growth factor-2（VEGFR-2） at different developmental stages.[Method]Total RNAs were extracted from 12,24,48,72 and 96 hpf stage zebrafish emb...[Objective]To investigate the expression of zebrafish vascular endothelial growth factor-2（VEGFR-2） at different developmental stages.[Method]Total RNAs were extracted from 12,24,48,72 and 96 hpf stage zebrafish embryos and larvae.Real-time quantitative RT-PCR was performed to examine the expression of VEGFR-2.The data were analyzed by 2^-△△Ct method.[Result]The expression level of VEGFR-2 gene increased gradually from 12 to 72 hpf,and subsequently decreased at 96 hpf.The expression level was lowest at 12 hpf,highest at 72 hpf,and had significant differences when compared with that of other developmental stages.[Conclusion]The expression level of VEGFR-2 increases gradually before blood vessel maturation and decreases as blood vessels mature.展开更多
The extremely high concentrations of PM2.5(particulate matter with an aerodynamic meter≤2.5 mm)during severe and persistent haze events in China have been closely related to the formation of secondary aerosols(SA).Ne...The extremely high concentrations of PM2.5(particulate matter with an aerodynamic meter≤2.5 mm)during severe and persistent haze events in China have been closely related to the formation of secondary aerosols(SA).New particle formation(NPF)is the critical initial step of SA formation.New particles are commonly formed from gas-phase precursors(e.g.,SO2,volatile organic compounds)via nucleation and initial growth,in which molecular clusters with a mobility diameter smaller than 3 nm(hereafter referred to nanoscale molecular clusters)will be involved throughout the whole process.Recently,significant breakthroughs have been obtained on NPF studies,which are mostly attributed to the technical development in the real-time analysis of size-resolved number concentration and chemical composition of nanoscale molecular clusters.Regarding the detection of size-resolved number concentrations of nanoscale molecular clusters,both methods and instruments have been well built up;practical application in laboratory-scale experiments and field measurements have also been successfully demonstrated.In contrast,real-time analysis of chemical composition of nanoscale molecular clusters has still encountered the great challenges caused by the complex organic compositions of the clusters,and improvement of present analytical strategies is urgently required.The better understanding in NPF will not only benefit the atmospheric modeling and climate predictions but also the source control of SA.展开更多
Analysis of a disaster event can identify strengths and weaknesses of the response implemented by the disaster management system;however, analysis does not typically occur until after the response phase is over.The re...Analysis of a disaster event can identify strengths and weaknesses of the response implemented by the disaster management system;however, analysis does not typically occur until after the response phase is over.The result is that knowledge gained can only benefit future responses rather than the response under investigation. This article argues that there is an opportunity to conduct analysis while the response is operational due to the increasing availability of information within hours and days of a disaster event. Hence, this article introduces a methodology for analyzing publicly communicated disaster response information in near-real-time. A classification scheme for the disaster information needs of the public has been developed to facilitate analysis and has led to the establishment of best observed practice standards for content and timeliness. By comparing the information shared with the public within days of a disaster to these standards,information gaps are revealed that can be investigated further. The result is identification of potential deficiencies in communicating critical disaster response information to the public at a time when they can still be corrected.展开更多
Background:Epidemiological studies have confirmed that longer exposure to insecticides like cypermethrin(CYP)significantly increases the risk of male reproductive toxicity.Crocus sativus L.has been recognized due to i...Background:Epidemiological studies have confirmed that longer exposure to insecticides like cypermethrin(CYP)significantly increases the risk of male reproductive toxicity.Crocus sativus L.has been recognized due to its therapeutic properties,but its exact role and molecular mechanisms in treatment of reproductive dysfunction remain unclear.Methods:During this study,36 rats were randomly divided into six groups(n=6):control,CYP-induced(60 mg/kg),standard(leuprolide 3 mg/kg)and three treatment groups receiving aqueous,ethanolic,and oil extracts(50 mg/kg or 20 mL/kg)for post-toxicity induction.Results:The finding represented that exposure of CYP significantly increased oxidative stress,disrupted testicular architecture,and markedly reduced testosterone levels(P<0.05).Importantly,Crocus sativus L.treatment alleviated these changes by increasing the expression of Nrf2(nuclear factor erythroid 2-related factor 2),restoring the activity of antioxidant enzymes,and enhancing testicular histomorphology.Surprisingly,molecular docking established a high binding affinity of Crocus sativus L.phytoconstituents such as gallic acid,cinnamic acid and quercetin to the Nrf2-Keap1 complex.It is worth noting that,Crocus sativus L.exhibited a high level of protection against reproductive toxicity caused by CYP in male rats,which was mediated by the activation of Nrf2 pathway,reduction of oxidative damage,and favorable ADMET characteristics.Conclusion:Notably,this research provides a more valid,safe,and effective method of developing new drugs for reproductive disorders,however,further investigation is needed to support the research findings and implement it in clinical practice.展开更多
Chlorine, chlorine dioxide, and ozone are widely used as disinfectants in drinking water treatments. However, the combined use of different disinfectants can result in the formation of various organic and inorganic di...Chlorine, chlorine dioxide, and ozone are widely used as disinfectants in drinking water treatments. However, the combined use of different disinfectants can result in the formation of various organic and inorganic disinfection byproducts(DBPs). The toxic interactions, including synergism, addition, and antagonism, among the complex DBPs are still unclear. In this study, we established and verified a real-time cell analysis(RTCA) method for cytotoxicity measurement on Chinese hamster ovary(CHO) cell. Using this convenient and accurate method, we assessed the cytotoxicity of a series of binary combinations consisting of one of the 3 inorganic DBPs(chlorite, chlorate, and bromate) and one of the 32 regulated and emerging organic DBPs. The combination index(CI) of each combination was calculated and evaluated by isobolographic analysis to reflect the toxic interactions. The results confirmed the synergistic effect on cytotoxicity in the binary combinations consisting of chlorite and one of the 5 organic DBPs(2 iodinated DBPs(I-DBPs) and 3 brominated DBPs(Br-DBPs)), chlorate and one of the 4 organic DBPs(3 aromatic DBPs and dibromoacetonitrile), and bromate and one of the 3 organic DBPs(2 I-DBPs and dibromoacetic acid). The possible synergism mechanism of organic DBPs on the inorganic ones may be attributed to the influence of organic DBPs on cell membrane and cell antioxidant system. This study revealed the toxic interactions among organic and inorganic DBPs, and emphasized the latent adverse outcomes in the combined use of different disinfectants.展开更多
Border-associated macrophages are located at the interface between the brain and the periphery, including the perivascular spaces, choroid plexus, and meninges. Until recently, the functions of border-associated macro...Border-associated macrophages are located at the interface between the brain and the periphery, including the perivascular spaces, choroid plexus, and meninges. Until recently, the functions of border-associated macrophages have been poorly understood and largely overlooked. However, a recent study reported that border-associated macrophages participate in stroke-induced inflammation, although many details and the underlying mechanisms remain unclear. In this study, we performed a comprehensive single-cell analysis of mouse border-associated macrophages using sequencing data obtained from the Gene Expression Omnibus(GEO) database(GSE174574 and GSE225948). Differentially expressed genes were identified, and enrichment analysis was performed to identify the transcription profile of border-associated macrophages. CellChat analysis was conducted to determine the cell communication network of border-associated macrophages. Transcription factors were predicted using the ‘pySCENIC' tool. We found that, in response to hypoxia, borderassociated macrophages underwent dynamic transcriptional changes and participated in the regulation of inflammatory-related pathways. Notably, the tumor necrosis factor pathway was activated by border-associated macrophages following ischemic stroke. The pySCENIC analysis indicated that the activity of signal transducer and activator of transcription 3(Stat3) was obviously upregulated in stroke, suggesting that Stat3 inhibition may be a promising strategy for treating border-associated macrophages-induced neuroinflammation. Finally, we constructed an animal model to investigate the effects of border-associated macrophages depletion following a stroke. Treatment with liposomes containing clodronate significantly reduced infarct volume in the animals and improved neurological scores compared with untreated animals. Taken together, our results demonstrate comprehensive changes in border-associated macrophages following a stroke, providing a theoretical basis for targeting border-associated macrophages-induced neuroinflammation in stroke treatment.展开更多
The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information ...The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information flow.To ensure effective transmission of wide-frequency electrical information by the communication protocol of a WAMS,this study performs real-time traffic monitoring and analysis of the data network of a power information system,and establishes corresponding network optimization strategies to solve existing transmission problems.This study utilizes the traffic analysis results obtained using the current real-time dynamic monitoring system to design an optimization strategy,covering the optimization in three progressive levels:the underlying communication protocol,source data,and transmission process.Optimization of the system structure and scheduling optimization of data information are validated to be feasible and practical via tests.展开更多
Real-time performance and reliability are two most important issues in applications of time-triggered controller area network （CAN） bus systems at present. A scheduling matrix of time-triggered CAN-bus system is est...Real-time performance and reliability are two most important issues in applications of time-triggered controller area network （CAN） bus systems at present. A scheduling matrix of time-triggered CAN-bus system is established using average-loading algorithm. Periodic messages are guaranteed to transmit without delay by distributing independent transmission windows within the system matrix. Considering the traditional CAN-bus transmission mechanism and the time-triggered feature, an algorithm is improved to calculate the worst-case delay of event-triggered messages in time-triggered CAN-bus systems. The failure probability is calculated for event-triggered messages whose worst-case delay exceeds their deadlines. Different levels of redundant structures of CAN-bus circuits are analyzed and the maintenance management is proposed to improve the system reliability. Finally, the reliabilities of different structures are calculated and the influences of maintenance on the system reliability are analyzed.展开更多
A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and...A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.展开更多
In order to optimize the embedded system implementation for Ethernet-based computer numerical control （CNC） system, it is very necessary to establish the performance analysis model and further adopt the codesign met...In order to optimize the embedded system implementation for Ethernet-based computer numerical control （CNC） system, it is very necessary to establish the performance analysis model and further adopt the codesign method from the control, communication and computing perspectives. On the basis of analyzing real-time Ethemet, system architecture, time characteristic parameters of control-loop ere, a performance analysis model for real-time Ethemet-based CNC system was proposed, which is able to include the timing effects caused by the implementation platform in the simulation. The key for establishing the model is accomplished by designing the error analysis module and the controller nodes. Under the restraint of CPU resource and communication bandwidth, the experiment with a case study was conducted, and the results show that if the deadline miss ratio of data packets is 0.2%, then the percentage error is 1.105%. The proposed model can be used at several stages of CNC system development.展开更多
As multimedia data sharing increases,data security in mobile devices and its mechanism can be seen as critical.Biometrics combines the physiological and behavioral qualities of an individual to validate their characte...As multimedia data sharing increases,data security in mobile devices and its mechanism can be seen as critical.Biometrics combines the physiological and behavioral qualities of an individual to validate their character in real-time.Humans incorporate physiological attributes like a fingerprint,face,iris,palm print,finger knuckle print,Deoxyribonucleic Acid(DNA),and behavioral qualities like walk,voice,mark,or keystroke.The main goal of this paper is to design a robust framework for automatic face recognition.Scale Invariant Feature Transform(SIFT)and Speeded-up Robust Features(SURF)are employed for face recognition.Also,we propose a modified Gabor Wavelet Transform for SIFT/SURF(GWT-SIFT/GWT-SURF)to increase the recognition accuracy of human faces.The proposed scheme is composed of three steps.First,the entropy of the image is removed using Discrete Wavelet Transform(DWT).Second,the computational complexity of the SIFT/SURF is reduced.Third,the accuracy is increased for authentication by the proposed GWT-SIFT/GWT-SURF algorithm.A comparative analysis of the proposed scheme is done on real-time Olivetti Research Laboratory(ORL)and Poznan University of Technology(PUT)databases.When compared to the traditional SIFT/SURF methods,we verify that the GWT-SIFT achieves the better accuracy of 99.32%and the better approach is the GWT-SURF as the run time of the GWT-SURF for 100 images is 3.4 seconds when compared to the GWT-SIFT which has a run time of 4.9 seconds for 100 images.展开更多
In this paper, a mathematical model of real-time simulation is given, and the problem of convergence on real-time Runge-Kutta algorithms is analysed. At last a theorem on the relation between the order of compensation...In this paper, a mathematical model of real-time simulation is given, and the problem of convergence on real-time Runge-Kutta algorithms is analysed. At last a theorem on the relation between the order of compensation and the convergent order of real-time algorithm is proved.展开更多
With the flourishing development of Unmanned Aerial Vehicles(UAVs), the mission tasks of UAVs have become more and more complex. Consequently, a Real-Time Operating System(RTOS) that provides operating environments fo...With the flourishing development of Unmanned Aerial Vehicles(UAVs), the mission tasks of UAVs have become more and more complex. Consequently, a Real-Time Operating System(RTOS) that provides operating environments for various mission services on these UAVs has become crucial, which leads to the necessity of having a deep understanding of an RTOS. In this paper, an empirical study is conducted on FreeRTOS, a commonly used RTOS for UAVs, from a complex network perspective. A total of 85 releases of FreeRTOS, from V2.4.2 to V10.0.0, are modeled as directed networks, in which the nodes represent functions and the edges denote function calls. It is found that the size of the FreeRTOS network has grown almost linearly with the evolution of the versions, while its main core has evolved steadily. In addition, a k-core analysis-based metric is proposed to identify major functionality changes of FreeRTOS during its evolution.The result shows that the identified versions are consistent with the version change logs. Finally,it is found that the clustering coefficient of the Linux OS scheduler is larger than that of the FreeRTOS scheduler. In conclusion, the empirical results provide useful guidance for developers and users of UAV RTOSs.展开更多
Opinion (sentiment) analysis on big data streams from the constantly generated text streams on social media networks to hundreds of millions of online consumer reviews provides many organizations in every field with o...Opinion (sentiment) analysis on big data streams from the constantly generated text streams on social media networks to hundreds of millions of online consumer reviews provides many organizations in every field with opportunities to discover valuable intelligence from the massive user generated text streams. However, the traditional content analysis frameworks are inefficient to handle the unprecedentedly big volume of unstructured text streams and the complexity of text analysis tasks for the real time opinion analysis on the big data streams. In this paper, we propose a parallel real time sentiment analysis system: Social Media Data Stream Sentiment Analysis Service (SMDSSAS) that performs multiple phases of sentiment analysis of social media text streams effectively in real time with two fully analytic opinion mining models to combat the scale of text data streams and the complexity of sentiment analysis processing on unstructured text streams. We propose two aspect based opinion mining models: Deterministic and Probabilistic sentiment models for a real time sentiment analysis on the user given topic related data streams. Experiments on the social media Twitter stream traffic captured during the pre-election weeks of the 2016 Presidential election for real-time analysis of public opinions toward two presidential candidates showed that the proposed system was able to predict correctly Donald Trump as the winner of the 2016 Presidential election. The cross validation results showed that the proposed sentiment models with the real-time streaming components in our proposed framework delivered effectively the analysis of the opinions on two presidential candidates with average 81% accuracy for the Deterministic model and 80% for the Probabilistic model, which are 1% - 22% improvements from the results of the existing literature.展开更多
Sensor-based ore sorting is a technology used to classify high-grade mineralized rocks from low-grade waste rocks to reduce operation costs.Many ore-sorting algorithms using color images have been proposed in the past...Sensor-based ore sorting is a technology used to classify high-grade mineralized rocks from low-grade waste rocks to reduce operation costs.Many ore-sorting algorithms using color images have been proposed in the past,but only some validate their results using mineral grades or optimize the algorithms to classify rocks in real-time.This paper presents an ore-sorting algorithm based on image processing and machine learning that is able to classify rocks from a gold and silver mine based on their grade.The algorithm is composed of four main stages:(1)image segmentation and partition,(2)color and texture feature extraction,(3)sub-image classification using neural networks,and(4)a voting system to determine the overall class of the rock.The algorithm was trained using images of rocks that a geologist manually classified according to their mineral content and then was validated using a different set of rocks analyzed in a laboratory to determine their gold and silver grades.The proposed method achieved a Matthews correlation coefficient of 0.961 points,higher than other classification algorithms based on support vector machines and convolutional neural networks,and a processing time under 44 ms,promising for real-time ore sorting applications.展开更多
文摘The spread of the Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)has already taken on pandemic extents,influencing even more than 200 nations in a couple of months.Although,regulation measures in China have decreased new cases by over 98%,this decrease is not the situation everywhere,and most of the countries still have been affected by it.The objective of this research work is to make a comparative analysis of the top 5 most populated countries namely United States,India,China,Pakistan and Indonesia,from 1st January 2020 to 31st July 2020.This research work also targets to predict an increase in the number of deaths and total infected cases in these five countries.In our research,the performance of the proposed framework is determined by using three Machine Learning(ML)regression algorithms namely Linear Regression(LR),Support Vector Regression(SVR),andRandom Forest(RF)Regression.The proposed model is also validated upon the infected and death cases of further dates.The performance of these three algorithms is compared using the RootMean Square Error(RMSE)metrics.Random Forest algorithm shows best performance as compared to other proposed algorithms,with the lowest RMSE value in the prediction of total infected and total deaths cases for all the top five most populated countries.
基金the National Natural Science Foundation of China(52304236)the Natural Science Foundation of Shandong Province(ZR2021QE076)for the financial support to this research extracted from the project.
文摘Rapid and sensitive detection of dissolved gases in seawater is quite essential for the investigation of the global carbon cycle.Large quantities of in situ optical detection techniques showed restricted measurement efficiency,owing to the single gas sensor without the identification ability of multiple gases.In this work,a novel gas-liquid Raman detection method of monitoring the multi-component dissolved gases was proposed based on a continuous gas-liquid separator under a large difference of partial pressure.The limit of detection(LOD)of the gas Raman spectrometer could arrive at about 14 μl·L^(-1)for N_(2)gas.Moreover,based on the continuous gas-liquid separation process,the detection time of the dissolved gases could be largely decreased to about 200 s compared with that of the traditional detection method(30 min).Effect of equilibrium time on gas-liquid separation process indicated that the extracted efficiency and decay time of these dissolved gases was CO_(2)>O_(2)>N_(2).In addition,the analysis of the relationship between equilibrium time and flow speed indicated that the decay time decreased with the increase of the flow speed.The validation and application of the developed system presented its great potential for studying the components and spatiotemporal distribution of dissolved gases in seawater.
文摘As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power plants’carbon management systems can no longer meet the demands of high-precision,real-time monitoring.Smart power plants now offer innovative solutions for carbon emission tracking and intelligent analysis by integrating IoT,big data,and AI technologies.Current research predominantly focuses on optimizing individual processes,lacking systematic exploration of comprehensive dynamic monitoring and intelligent decision-making across the entire workflow.To address this gap,we propose a smart carbon emission monitoring and analysis platform for power plants that integrates IoT sensing,multimodal data analytics,and AI-driven decision-making.The platform establishes a multi-source sensor network to collect emissions data throughout the fuel combustion,auxiliary equipment operation,and waste treatment processes.Combining carbon emission factor analysis with machine learning models enables real-time emission calculations and utilizes long short-term memory networks to predict future emission trends.
基金Ministry of Science and Technology of China under Grant No.2023YFC3804300National Science Foundation of China under Grant No.52178114。
文摘Actuator dynamics introduce a synchronization disparity between commanded displacements transmitted to the actuator and the actual displacements generated by the actuator,thereby affecting its precision and potentially leading to instability in real-time hybrid simulation(RTHS).This study aims to elucidate the relationship between calculated and measured displacements by analyzing their magnitude and phase in the frequency domain via transformations.The physical implications of these relationships are explored in the context of frequency domain evaluation indices(FEI),the transfer function of actuator dynamics,and delay compensation.Formulations for achieving perfect compensation of actuator dynamics are developed,and an enhanced compensation approach,termed improved windowed frequency domain evaluation index-based compensation(IWFEI),is introduced.The efficacy of IWFEI is assessed using a RTHS benchmark model,with perturbed simulations conducted to validate its robustness.Uncertainties inherent in actuator dynamics are represented as random variables in these simulations.Comparative analysis of the mean values and variances of evaluation criteria demonstrates that IWFEI enables more accurate and robust compensation.Furthermore,strong correlations observed among criteria in the time and frequency domains underscore the effectiveness of the proposed frequency domain-based compensation method in mitigating amplitude errors and phase delays in RTHS.
基金European Commission(CZ.10.03.01/00/22-003/0000048)Fundacao para a Ciencia e a Tecnologia(PTDC/EEI-EEE/0415/2021),CICECO(UIDB/50011/2020,UIDP/50011/2020,LA/P/0006/2020)+1 种基金VSB-Technical University of Ostrava(SP2025/039)FCT/MCTES(UI/BD/153066/2022)。
文摘In the food production sector,quickly identifying potential hazards is crucial due to the resilience of many pathogens,which could lead to wasted production results and,more severely,epidemic outbreaks.E.coli monitoring is essential;however,traditional quality control methods in fish farming are often slow and intrusive,thus promoting an increase in fish stress and mortality rates.This paper presents an alternative method by utilizing a prototype inspired by polarized optical microscopy(POM),constructed with a Raspberry Pi microprocessor to assess pixel patterns and calculate analyte levels.
基金Supported by National Natural Science Foundation of Shandong Province (No. SY2008C179)~~
文摘[Objective]To investigate the expression of zebrafish vascular endothelial growth factor-2（VEGFR-2） at different developmental stages.[Method]Total RNAs were extracted from 12,24,48,72 and 96 hpf stage zebrafish embryos and larvae.Real-time quantitative RT-PCR was performed to examine the expression of VEGFR-2.The data were analyzed by 2^-△△Ct method.[Result]The expression level of VEGFR-2 gene increased gradually from 12 to 72 hpf,and subsequently decreased at 96 hpf.The expression level was lowest at 12 hpf,highest at 72 hpf,and had significant differences when compared with that of other developmental stages.[Conclusion]The expression level of VEGFR-2 increases gradually before blood vessel maturation and decreases as blood vessels mature.
基金supported by the National Natural Science Foundation of China(No.21107066)National Instrumentation Program(No.2011YQ170067)Young Teachers Program of Universities in Shanghai(2012).
文摘The extremely high concentrations of PM2.5(particulate matter with an aerodynamic meter≤2.5 mm)during severe and persistent haze events in China have been closely related to the formation of secondary aerosols(SA).New particle formation(NPF)is the critical initial step of SA formation.New particles are commonly formed from gas-phase precursors(e.g.,SO2,volatile organic compounds)via nucleation and initial growth,in which molecular clusters with a mobility diameter smaller than 3 nm(hereafter referred to nanoscale molecular clusters)will be involved throughout the whole process.Recently,significant breakthroughs have been obtained on NPF studies,which are mostly attributed to the technical development in the real-time analysis of size-resolved number concentration and chemical composition of nanoscale molecular clusters.Regarding the detection of size-resolved number concentrations of nanoscale molecular clusters,both methods and instruments have been well built up;practical application in laboratory-scale experiments and field measurements have also been successfully demonstrated.In contrast,real-time analysis of chemical composition of nanoscale molecular clusters has still encountered the great challenges caused by the complex organic compositions of the clusters,and improvement of present analytical strategies is urgently required.The better understanding in NPF will not only benefit the atmospheric modeling and climate predictions but also the source control of SA.
文摘Analysis of a disaster event can identify strengths and weaknesses of the response implemented by the disaster management system;however, analysis does not typically occur until after the response phase is over.The result is that knowledge gained can only benefit future responses rather than the response under investigation. This article argues that there is an opportunity to conduct analysis while the response is operational due to the increasing availability of information within hours and days of a disaster event. Hence, this article introduces a methodology for analyzing publicly communicated disaster response information in near-real-time. A classification scheme for the disaster information needs of the public has been developed to facilitate analysis and has led to the establishment of best observed practice standards for content and timeliness. By comparing the information shared with the public within days of a disaster to these standards,information gaps are revealed that can be investigated further. The result is identification of potential deficiencies in communicating critical disaster response information to the public at a time when they can still be corrected.
文摘Background:Epidemiological studies have confirmed that longer exposure to insecticides like cypermethrin(CYP)significantly increases the risk of male reproductive toxicity.Crocus sativus L.has been recognized due to its therapeutic properties,but its exact role and molecular mechanisms in treatment of reproductive dysfunction remain unclear.Methods:During this study,36 rats were randomly divided into six groups(n=6):control,CYP-induced(60 mg/kg),standard(leuprolide 3 mg/kg)and three treatment groups receiving aqueous,ethanolic,and oil extracts(50 mg/kg or 20 mL/kg)for post-toxicity induction.Results:The finding represented that exposure of CYP significantly increased oxidative stress,disrupted testicular architecture,and markedly reduced testosterone levels(P<0.05).Importantly,Crocus sativus L.treatment alleviated these changes by increasing the expression of Nrf2(nuclear factor erythroid 2-related factor 2),restoring the activity of antioxidant enzymes,and enhancing testicular histomorphology.Surprisingly,molecular docking established a high binding affinity of Crocus sativus L.phytoconstituents such as gallic acid,cinnamic acid and quercetin to the Nrf2-Keap1 complex.It is worth noting that,Crocus sativus L.exhibited a high level of protection against reproductive toxicity caused by CYP in male rats,which was mediated by the activation of Nrf2 pathway,reduction of oxidative damage,and favorable ADMET characteristics.Conclusion:Notably,this research provides a more valid,safe,and effective method of developing new drugs for reproductive disorders,however,further investigation is needed to support the research findings and implement it in clinical practice.
基金supported by the National Natural Science Foundation of China (No. 21876210)。
文摘Chlorine, chlorine dioxide, and ozone are widely used as disinfectants in drinking water treatments. However, the combined use of different disinfectants can result in the formation of various organic and inorganic disinfection byproducts(DBPs). The toxic interactions, including synergism, addition, and antagonism, among the complex DBPs are still unclear. In this study, we established and verified a real-time cell analysis(RTCA) method for cytotoxicity measurement on Chinese hamster ovary(CHO) cell. Using this convenient and accurate method, we assessed the cytotoxicity of a series of binary combinations consisting of one of the 3 inorganic DBPs(chlorite, chlorate, and bromate) and one of the 32 regulated and emerging organic DBPs. The combination index(CI) of each combination was calculated and evaluated by isobolographic analysis to reflect the toxic interactions. The results confirmed the synergistic effect on cytotoxicity in the binary combinations consisting of chlorite and one of the 5 organic DBPs(2 iodinated DBPs(I-DBPs) and 3 brominated DBPs(Br-DBPs)), chlorate and one of the 4 organic DBPs(3 aromatic DBPs and dibromoacetonitrile), and bromate and one of the 3 organic DBPs(2 I-DBPs and dibromoacetic acid). The possible synergism mechanism of organic DBPs on the inorganic ones may be attributed to the influence of organic DBPs on cell membrane and cell antioxidant system. This study revealed the toxic interactions among organic and inorganic DBPs, and emphasized the latent adverse outcomes in the combined use of different disinfectants.
基金supported by Qingdao Key Medical and Health Discipline ProjectThe Intramural Research Program of the Affiliated Hospital of Qingdao University,No. 4910Qingdao West Coast New Area Science and Technology Project,No. 2020-55 (all to SW)。
文摘Border-associated macrophages are located at the interface between the brain and the periphery, including the perivascular spaces, choroid plexus, and meninges. Until recently, the functions of border-associated macrophages have been poorly understood and largely overlooked. However, a recent study reported that border-associated macrophages participate in stroke-induced inflammation, although many details and the underlying mechanisms remain unclear. In this study, we performed a comprehensive single-cell analysis of mouse border-associated macrophages using sequencing data obtained from the Gene Expression Omnibus(GEO) database(GSE174574 and GSE225948). Differentially expressed genes were identified, and enrichment analysis was performed to identify the transcription profile of border-associated macrophages. CellChat analysis was conducted to determine the cell communication network of border-associated macrophages. Transcription factors were predicted using the ‘pySCENIC' tool. We found that, in response to hypoxia, borderassociated macrophages underwent dynamic transcriptional changes and participated in the regulation of inflammatory-related pathways. Notably, the tumor necrosis factor pathway was activated by border-associated macrophages following ischemic stroke. The pySCENIC analysis indicated that the activity of signal transducer and activator of transcription 3(Stat3) was obviously upregulated in stroke, suggesting that Stat3 inhibition may be a promising strategy for treating border-associated macrophages-induced neuroinflammation. Finally, we constructed an animal model to investigate the effects of border-associated macrophages depletion following a stroke. Treatment with liposomes containing clodronate significantly reduced infarct volume in the animals and improved neurological scores compared with untreated animals. Taken together, our results demonstrate comprehensive changes in border-associated macrophages following a stroke, providing a theoretical basis for targeting border-associated macrophages-induced neuroinflammation in stroke treatment.
文摘The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information flow.To ensure effective transmission of wide-frequency electrical information by the communication protocol of a WAMS,this study performs real-time traffic monitoring and analysis of the data network of a power information system,and establishes corresponding network optimization strategies to solve existing transmission problems.This study utilizes the traffic analysis results obtained using the current real-time dynamic monitoring system to design an optimization strategy,covering the optimization in three progressive levels:the underlying communication protocol,source data,and transmission process.Optimization of the system structure and scheduling optimization of data information are validated to be feasible and practical via tests.
基金supported by Fundamental Research Funds for the Central Universities and National Science and Technology Major Project (No. 2010ZX04014-017)
文摘Real-time performance and reliability are two most important issues in applications of time-triggered controller area network （CAN） bus systems at present. A scheduling matrix of time-triggered CAN-bus system is established using average-loading algorithm. Periodic messages are guaranteed to transmit without delay by distributing independent transmission windows within the system matrix. Considering the traditional CAN-bus transmission mechanism and the time-triggered feature, an algorithm is improved to calculate the worst-case delay of event-triggered messages in time-triggered CAN-bus systems. The failure probability is calculated for event-triggered messages whose worst-case delay exceeds their deadlines. Different levels of redundant structures of CAN-bus circuits are analyzed and the maintenance management is proposed to improve the system reliability. Finally, the reliabilities of different structures are calculated and the influences of maintenance on the system reliability are analyzed.
基金Project(51175159)supported by the National Natural Science Foundation of ChinaProject(2013WK3024)supported by the Science andTechnology Planning Program of Hunan Province,ChinaProject(CX2013B146)supported by the Hunan Provincial InnovationFoundation for Postgraduate,China
文摘A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.
基金Projects(50875090,50905063) supported by the National Natural Science Foundation of ChinaProject(2009AA04Z111) supported by the National High Technology Research and Development Program of China+2 种基金Project(20090460769) supported by China Postdoctoral Science FoundationProject(2011ZM0070) supported by the Fundamental Research Funds for the Central Universities in ChinaProject(S2011010001155) supported by the Natural Science Foundation of Guangdong Province,China
文摘In order to optimize the embedded system implementation for Ethernet-based computer numerical control （CNC） system, it is very necessary to establish the performance analysis model and further adopt the codesign method from the control, communication and computing perspectives. On the basis of analyzing real-time Ethemet, system architecture, time characteristic parameters of control-loop ere, a performance analysis model for real-time Ethemet-based CNC system was proposed, which is able to include the timing effects caused by the implementation platform in the simulation. The key for establishing the model is accomplished by designing the error analysis module and the controller nodes. Under the restraint of CPU resource and communication bandwidth, the experiment with a case study was conducted, and the results show that if the deadline miss ratio of data packets is 0.2%, then the percentage error is 1.105%. The proposed model can be used at several stages of CNC system development.
文摘As multimedia data sharing increases,data security in mobile devices and its mechanism can be seen as critical.Biometrics combines the physiological and behavioral qualities of an individual to validate their character in real-time.Humans incorporate physiological attributes like a fingerprint,face,iris,palm print,finger knuckle print,Deoxyribonucleic Acid(DNA),and behavioral qualities like walk,voice,mark,or keystroke.The main goal of this paper is to design a robust framework for automatic face recognition.Scale Invariant Feature Transform(SIFT)and Speeded-up Robust Features(SURF)are employed for face recognition.Also,we propose a modified Gabor Wavelet Transform for SIFT/SURF(GWT-SIFT/GWT-SURF)to increase the recognition accuracy of human faces.The proposed scheme is composed of three steps.First,the entropy of the image is removed using Discrete Wavelet Transform(DWT).Second,the computational complexity of the SIFT/SURF is reduced.Third,the accuracy is increased for authentication by the proposed GWT-SIFT/GWT-SURF algorithm.A comparative analysis of the proposed scheme is done on real-time Olivetti Research Laboratory(ORL)and Poznan University of Technology(PUT)databases.When compared to the traditional SIFT/SURF methods,we verify that the GWT-SIFT achieves the better accuracy of 99.32%and the better approach is the GWT-SURF as the run time of the GWT-SURF for 100 images is 3.4 seconds when compared to the GWT-SIFT which has a run time of 4.9 seconds for 100 images.
文摘In this paper, a mathematical model of real-time simulation is given, and the problem of convergence on real-time Runge-Kutta algorithms is analysed. At last a theorem on the relation between the order of compensation and the convergent order of real-time algorithm is proved.
基金supported by the National Natural Science Foundation of China (No. 61772055)Equipment Preliminary R&D Project of China (No. 41402020102)
文摘With the flourishing development of Unmanned Aerial Vehicles(UAVs), the mission tasks of UAVs have become more and more complex. Consequently, a Real-Time Operating System(RTOS) that provides operating environments for various mission services on these UAVs has become crucial, which leads to the necessity of having a deep understanding of an RTOS. In this paper, an empirical study is conducted on FreeRTOS, a commonly used RTOS for UAVs, from a complex network perspective. A total of 85 releases of FreeRTOS, from V2.4.2 to V10.0.0, are modeled as directed networks, in which the nodes represent functions and the edges denote function calls. It is found that the size of the FreeRTOS network has grown almost linearly with the evolution of the versions, while its main core has evolved steadily. In addition, a k-core analysis-based metric is proposed to identify major functionality changes of FreeRTOS during its evolution.The result shows that the identified versions are consistent with the version change logs. Finally,it is found that the clustering coefficient of the Linux OS scheduler is larger than that of the FreeRTOS scheduler. In conclusion, the empirical results provide useful guidance for developers and users of UAV RTOSs.
文摘Opinion (sentiment) analysis on big data streams from the constantly generated text streams on social media networks to hundreds of millions of online consumer reviews provides many organizations in every field with opportunities to discover valuable intelligence from the massive user generated text streams. However, the traditional content analysis frameworks are inefficient to handle the unprecedentedly big volume of unstructured text streams and the complexity of text analysis tasks for the real time opinion analysis on the big data streams. In this paper, we propose a parallel real time sentiment analysis system: Social Media Data Stream Sentiment Analysis Service (SMDSSAS) that performs multiple phases of sentiment analysis of social media text streams effectively in real time with two fully analytic opinion mining models to combat the scale of text data streams and the complexity of sentiment analysis processing on unstructured text streams. We propose two aspect based opinion mining models: Deterministic and Probabilistic sentiment models for a real time sentiment analysis on the user given topic related data streams. Experiments on the social media Twitter stream traffic captured during the pre-election weeks of the 2016 Presidential election for real-time analysis of public opinions toward two presidential candidates showed that the proposed system was able to predict correctly Donald Trump as the winner of the 2016 Presidential election. The cross validation results showed that the proposed sentiment models with the real-time streaming components in our proposed framework delivered effectively the analysis of the opinions on two presidential candidates with average 81% accuracy for the Deterministic model and 80% for the Probabilistic model, which are 1% - 22% improvements from the results of the existing literature.
文摘Sensor-based ore sorting is a technology used to classify high-grade mineralized rocks from low-grade waste rocks to reduce operation costs.Many ore-sorting algorithms using color images have been proposed in the past,but only some validate their results using mineral grades or optimize the algorithms to classify rocks in real-time.This paper presents an ore-sorting algorithm based on image processing and machine learning that is able to classify rocks from a gold and silver mine based on their grade.The algorithm is composed of four main stages:(1)image segmentation and partition,(2)color and texture feature extraction,(3)sub-image classification using neural networks,and(4)a voting system to determine the overall class of the rock.The algorithm was trained using images of rocks that a geologist manually classified according to their mineral content and then was validated using a different set of rocks analyzed in a laboratory to determine their gold and silver grades.The proposed method achieved a Matthews correlation coefficient of 0.961 points,higher than other classification algorithms based on support vector machines and convolutional neural networks,and a processing time under 44 ms,promising for real-time ore sorting applications.
