In order to further improve the utility of unmanned aerial vehicle(UAV)remote-sensing for quickly and accurately monitoring the growth of winter wheat under film mulching, this study examined the treatments of ridge m...In order to further improve the utility of unmanned aerial vehicle(UAV)remote-sensing for quickly and accurately monitoring the growth of winter wheat under film mulching, this study examined the treatments of ridge mulching,ridge–furrow full mulching, and flat cropping full mulching in winter wheat.Based on the fuzzy comprehensive evaluation (FCE) method, four agronomic parameters (leaf area index, above-ground biomass, plant height, and leaf chlorophyll content) were used to calculate the comprehensive growth evaluation index (CGEI) of the winter wheat, and 14 visible and near-infrared spectral indices were calculated using spectral purification technology to process the remote-sensing image data of winter wheat obtained by multispectral UAV.Four machine learning algorithms, partial least squares, support vector machines, random forests, and artificial neural network networks(ANN), were used to build the winter wheat growth monitoring model under film mulching, and accuracy evaluation and mapping of the spatial and temporal distribution of winter wheat growth status were carried out.The results showed that the CGEI of winter wheat under film mulching constructed using the FCE method could objectively and comprehensively evaluate the crop growth status.The accuracy of remote-sensing inversion of the CGEI based on the ANN model was higher than for the individual agronomic parameters, with a coefficient of determination of 0.75,a root mean square error of 8.40, and a mean absolute value error of 6.53.Spectral purification could eliminate the interference of background effects caused by mulching and soil, effectively improving the accuracy of the remotesensing inversion of winter wheat under film mulching, with the best inversion effect achieved on the ridge–furrow full mulching area after spectral purification.The results of this study provide a theoretical reference for the use of UAV remote-sensing to monitor the growth status of winter wheat with film mulching.展开更多
To provide new insights into the development and utilization of Douchi artificial starters,three common strains(Aspergillus oryzae,Mucor racemosus,and Rhizopus oligosporus)were used to study their influence on the fer...To provide new insights into the development and utilization of Douchi artificial starters,three common strains(Aspergillus oryzae,Mucor racemosus,and Rhizopus oligosporus)were used to study their influence on the fermentation of Douchi.The results showed that the biogenic amine contents of the three types of Douchi were all within the safe range and far lower than those of traditional fermented Douchi.Aspergillus-type Douchi produced more free amino acids than the other two types of Douchi,and its umami taste was more prominent in sensory evaluation(P<0.01),while Mucor-type and Rhizopus-type Douchi produced more esters and pyrazines,making the aroma,sauce,and Douchi flavor more abundant.According to the Pearson and PLS analyses results,sweetness was significantly negatively correlated with phenylalanine,cysteine,and acetic acid(P<0.05),bitterness was significantly negatively correlated with malic acid(P<0.05),the sour taste was significantly positively correlated with citric acid and most free amino acids(P<0.05),while astringency was significantly negatively correlated with glucose(P<0.001).Thirteen volatile compounds such as furfuryl alcohol,phenethyl alcohol,and benzaldehyde caused the flavor difference of three types of Douchi.This study provides theoretical basis for the selection of starting strains for commercial Douchi production.展开更多
As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crud...As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.展开更多
The proliferation of intelligent,connected Internet of Things(IoT)devices facilitates data collection.However,task workers may be reluctant to participate in data collection due to privacy concerns,and task requesters...The proliferation of intelligent,connected Internet of Things(IoT)devices facilitates data collection.However,task workers may be reluctant to participate in data collection due to privacy concerns,and task requesters may be concerned about the validity of the collected data.Hence,it is vital to evaluate the quality of the data collected by the task workers while protecting privacy in spatial crowdsourcing(SC)data collection tasks with IoT.To this end,this paper proposes a privacy-preserving data reliability evaluation for SC in IoT,named PARE.First,we design a data uploading format using blockchain and Paillier homomorphic cryptosystem,providing unchangeable and traceable data while overcoming privacy concerns.Secondly,based on the uploaded data,we propose a method to determine the approximate correct value region without knowing the exact value.Finally,we offer a data filtering mechanism based on the Paillier cryptosystem using this value region.The evaluation and analysis results show that PARE outperforms the existing solution in terms of performance and privacy protection.展开更多
1) Background: Rapid and acurate diagnostic testing for case identification, quarantine, and contact tracing is essential for managing the COVID 19 pandemic. Rapid antigen detection tests are available, however, it is...1) Background: Rapid and acurate diagnostic testing for case identification, quarantine, and contact tracing is essential for managing the COVID 19 pandemic. Rapid antigen detection tests are available, however, it is important to evaluate their performances before use. We tested a rapid antigen detection of SARS-CoV-2, based on the immunochromatography (Boson Biotech SARS-CoV-2 Ag Test (Xiamen Boson Biotech Co., Ltd., China)) and the results were compared with the real time reverse transcriptase-Polymerase chain reaction (RT-PCR) (Gold standard) results;2) Methods: From November 2021 to December 2021, samples were collected from symptomatic patients and asymptomatic individuals referred for testing in a hospital during the second pandemic wave in Gabon. All these participants attending “CTA Angondjé”, a field hospital set up as part of the management of COVID-19 in Gabon. Two nasopharyngeal swabs were collected in all the patients, one for Ag test and the other for RT-PCR;3) Results: A total of 300 samples were collected from 189 symptomatic and 111 asymptomatic individuals. The sensitivity and specificity of the antigen test were 82.5% [95%CI 73.8 - 89.3] and 97.9 % [95%CI 92.2 - 98.2] respectively, and the diagnostic accuracy was 84.4% (95% CI: 79.8 - 88.3%). The antigen test was more likely to be positive for samples with RT-PCR Ct values ≤ 32, with a sensitivity of 89.8%;4) Conclusions: The Boson Biotech SARS-CoV-2 Ag Test has good sensitivity and can detect SARS-CoV-2 infection, especially among symptomatic individuals with low viral load. This test could be incorporated into efficient testing algorithms as an alternative to PCR to decrease diagnostic delays and curb viral transmission.展开更多
First,we propose a cross-domain authentication architecture based on trust evaluation mechanism,including registration,certificate issuance,and cross-domain authentication processes.A direct trust evaluation mechanism...First,we propose a cross-domain authentication architecture based on trust evaluation mechanism,including registration,certificate issuance,and cross-domain authentication processes.A direct trust evaluation mechanism based on the time decay factor is proposed,taking into account the influence of historical interaction records.We weight the time attenuation factor to each historical interaction record for updating and got the new historical record data.We refer to the beta distribution to enhance the flexibility and adaptability of the direct trust assessment model to better capture time trends in the historical record.Then we propose an autoencoder-based trust clustering algorithm.We perform feature extraction based on autoencoders.Kullback leibler(KL)divergence is used to calculate the reconstruction error.When constructing a convolutional autoencoder,we introduce convolutional neural networks to improve training efficiency and introduce sparse constraints into the hidden layer of the autoencoder.The sparse penalty term in the loss function measures the difference through the KL divergence.Trust clustering is performed based on the density based spatial clustering of applications with noise(DBSCAN)clustering algorithm.During the clustering process,edge nodes have a variety of trustworthy attribute characteristics.We assign different attribute weights according to the relative importance of each attribute in the clustering process,and a larger weight means that the attribute occupies a greater weight in the calculation of distance.Finally,we introduced adaptive weights to calculate comprehensive trust evaluation.Simulation experiments prove that our trust evaluation mechanism has excellent reliability and accuracy.展开更多
Landslide hazard susceptibility evaluation takes on critical significance in early warning and disaster prevention and reduction.In order to solve the problems of poor effectiveness of landslide data and complex calcu...Landslide hazard susceptibility evaluation takes on critical significance in early warning and disaster prevention and reduction.In order to solve the problems of poor effectiveness of landslide data and complex calculation of weights for multiple evaluation factors in the existing landslide susceptibility evaluation models,in this study,a method of landslide hazard susceptibility evaluation is proposed by combining SBAS-InSAR(Small Baseline Subsets-Interferometric Synthetic Aperture Radar)and SSA-BP(Sparrow Search Algorithm-Back Propagation)neural network algorithm.The SBAS-InSAR technology is adopted to identify potential landslide hazards in the study area,update the cataloging data of landslide hazards,and 11 evaluation factors are chosen for constructing the SSA-BP model for training and validation.Baihetan Reservoir area is selected as a case study for validation.As indicated by the results,the application of SBAS-InSAR technology,combined with both ascending and descending orbit data,effectively addresses the incomplete identification of landslide hazards caused by geometric distortion of single orbit SAR data(e.g.,shadow,overlay,and perspective contraction)in deep canyon areas,thereby enabling the acquisition of up-to-date landslide hazard data.Moreover,in comparison to the conventional BP(Back Propagation)algorithm,the accuracy of the model constructed by the SSA-BP algorithm exhibits a significant increase,with mean squared error and mean absolute error reduced by 0.0142 and 0.0607,respectively.Additionally,during the process of susceptibility evaluation,the SSA-BP model effectively circumvents the issue of considerable manual interventions in calculating the weight of evaluation factors.The area under the curve of this model reaches 0.909,surpassing BP(0.835),random forest(0.792),and the information value method(0.699).The risk of landslide occurrence in the Baihetan Reservoir area is positively correlated with slope,surface temperature,and deformation rate,while it is negatively correlated with fault distance and normalized difference vegetation index.Geological lithology exerts minimal influence on the occurrence of landslides,with the risk being low in forest land and high in grassland.The method proposed in this study provides a useful reference for disaster prevention and mitigation departments to perform landslide hazard susceptibility evaluations in deep canyon areas under complex geological conditions.展开更多
Metal-ion batteries(MIBs),including alkali metal-ion(Li^(+),Na^(+),and K^(3)),multi-valent metal-ion(Zn^(2+),Mg^(2+),and Al^(3+)),metal-air,and metal-sulfur batteries,play an indispensable role in electrochemical ener...Metal-ion batteries(MIBs),including alkali metal-ion(Li^(+),Na^(+),and K^(3)),multi-valent metal-ion(Zn^(2+),Mg^(2+),and Al^(3+)),metal-air,and metal-sulfur batteries,play an indispensable role in electrochemical energy storage.However,the performance of MIBs is significantly influenced by numerous variables,resulting in multi-dimensional and long-term challenges in the field of battery research and performance enhancement.Machine learning(ML),with its capability to solve intricate tasks and perform robust data processing,is now catalyzing a revolutionary transformation in the development of MIB materials and devices.In this review,we summarize the utilization of ML algorithms that have expedited research on MIBs over the past five years.We present an extensive overview of existing algorithms,elucidating their details,advantages,and limitations in various applications,which encompass electrode screening,material property prediction,electrolyte formulation design,electrode material characterization,manufacturing parameter optimization,and real-time battery status monitoring.Finally,we propose potential solutions and future directions for the application of ML in advancing MIB development.展开更多
A dissertation is a research report or scientific paper written by an author to obtain a certain degree. It reflects postgraduates’ research achievements and the educational quality of an institute, even a country. T...A dissertation is a research report or scientific paper written by an author to obtain a certain degree. It reflects postgraduates’ research achievements and the educational quality of an institute, even a country. To construct an optimized quality evaluation system for postgraduate dissertation (QESPD), we summarized the influencing factors and invited 10 experienced specialists to rate and prioritize them based on fuzzy analytic hierarchy process. Four primary indicators (innovation, integrity, scientificity and normativity) and 16 sub-indicators were selected to form the evaluation system. The order of primary indicators by weight, was innovation (0.4269), scientificity (0.2807), integrity (0.1728) and normativity (0.1196). The top five sub-dimensions were theoretical originality, scientific value, data reliability, design rationality and evidence credibility. To demonstrate the effectiveness of the proposed system, a case study was performed. In the case study, it was demonstrated that the established two-index-hierarchy QESPD in this study was a more scientific and reasonable evaluation system worthy of promotion and application.展开更多
To effectively quantify the impact of distributed photovoltaic(PV)access on the distribution network,this paper proposes a comprehensive evaluation method of distributed PV grid connection combining subjective and obj...To effectively quantify the impact of distributed photovoltaic(PV)access on the distribution network,this paper proposes a comprehensive evaluation method of distributed PV grid connection combining subjective and objective combination of assignment and technique for order preference by similarity to an ideal solution(TOPSIS)—rank sum ratio(RSR)(TOPSIS-RSR)method.Based on the traditional distribution network evaluation system,a comprehensive evaluation system has been constructed.It fully considers the new development requirements of distributed PV access on the environmental friendliness and absorptive capacity of the distribution grid and comprehensively reflects the impact of distributed PV grid connection.The analytic hierarchy process(AHP)was used to determine the subjective weights of the primary indicators,and the Spearman consistency test was combined to determine the weights of the secondary indicators based on three objective assignment methods.The subjective and objective combination weights of each assessment indicator were calculated through the principle of minimum entropy.Calculate the distance between the indicators to be evaluated and the positive and negative ideal solutions,the relative closeness ranking,and qualitative binning by TOPSIS-RSR method to obtain the comprehensive evaluation results of different scenarios.By setting up different PV grid-connected scenarios and utilizing the IEEE33 node simulation algorithm,the correctness and effectiveness of the proposed subject-object combination assignment and integrated assessment method are verified.展开更多
With the rise of blockchain technology,the security issues of smart contracts have become increasingly critical.Despite the availability of numerous smart contract vulnerability detection tools,many face challenges su...With the rise of blockchain technology,the security issues of smart contracts have become increasingly critical.Despite the availability of numerous smart contract vulnerability detection tools,many face challenges such as slow updates,usability issues,and limited installation methods.These challenges hinder the adoption and practicality of these tools.This paper examines smart contract vulnerability detection tools from 2016 to 2023,sourced from the Web of Science(WOS)and Google Scholar.By systematically collecting,screening,and synthesizing relevant research,38 open-source tools that provide installation methods were selected for further investigation.From a developer’s perspective,this paper offers a comprehensive survey of these 38 open-source tools,discussing their operating principles,installation methods,environmental dependencies,update frequencies,and installation challenges.Based on this,we propose an Ethereum smart contract vulnerability detection framework.This framework enables developers to easily utilize various detection tools and accurately analyze contract security issues.To validate the framework’s stability,over 1700 h of testing were conducted.Additionally,a comprehensive performance test was performed on the mainstream detection tools integrated within the framework,assessing their hardware requirements and vulnerability detection coverage.Experimental results indicate that the Slither tool demonstrates satisfactory performance in terms of system resource consumption and vulnerability detection coverage.This study represents the first performance evaluation of testing tools in this domain,providing significant reference value.展开更多
Deep-seated toppling in the upper reaches of the Lancang River,southwest China involves deformations exceeding 100 m in depth.The slope deformation is initiated by river downcutting and evolves distinctive characteris...Deep-seated toppling in the upper reaches of the Lancang River,southwest China involves deformations exceeding 100 m in depth.The slope deformation is initiated by river downcutting and evolves distinctive characteristics with a depth of river incision.In this study,we propose a system for evaluating the stability of deep-seated toppled slopes in different evolutionary stages.This system contains identification criteria for each evolutionary stage and provides the corresponding stability evaluation methods.Based on the mechanical and kinematic analysis of slope blocks,the specific stage of slope movement can be identified in the field through outcrop mapping,in situ tests,surface displacement monitoring,and adit and borehole explorations.The stability evaluation methods are established based on the limiting equilibrium theory and the strain compatibility between the undisturbed zone and the toppled zone.Finally,several sample slopes in different evolution stages have been investigated to verify the applicability and accuracy of the proposed stability evaluation system.The results indicate that intense tectonic activity and rapid river incision lead to a maximum principal stress ratio exceeding 10 near the slope surface,thus triggering widespread toppling deformations along the river valley.When considering the losses of joint cohesion during the further rotation process,the safety factor of the slope drops by 7%e28%.The self-stabilization of toppling deformation can be recognized by the layer symmetry configuration after the free rotation of the deflected layers.Intensely toppled rock blocks mainly suffer sliding failures beyond the layer symmetry condition.The factor of safety of the K73 rockslide decreased from 1.17 to 0.87 by considering the development of the potential sliding surface and the toesaturated zone.展开更多
Background: International research and innovation efforts for neglected tropical diseases have increased in recent decades due to disparities in overall health research funding in relation to global burden of disease....Background: International research and innovation efforts for neglected tropical diseases have increased in recent decades due to disparities in overall health research funding in relation to global burden of disease. However, within the field of neglected tropical diseases some seem far more neglected than others. In this research the aim is to investigate the distribution of resources and efforts, as well as the mechanisms that underpin funding allocation for neglected tropical diseases. Methodology: A systematic literature review was conducted to establish a comprehensive overview of known indicators for innovation efforts related to a wide range of neglected tropical diseases. Articles were selected based on a subjective evaluation of their relevance, the presence of original data, and the breadth of their scope. This was followed by thirteen in-depth open-ended interviews with representatives of private, public and philanthropic funding organizations, concerning evaluation criteria for funding research proposals. Results: The findings reveal a large difference in the extent to which the individual diseases are neglected with notable differences between absolute and relative efforts. Criteria used in the evaluation of research proposals relate to potential impact, the probability of success and strategic fit. Private organizations prioritize strategic fit and economic impact;philanthropic organizations prioritize short-term societal impact;and public generally prioritize the probability of success by accounting for follow-up funding and involvement of industry. Funding decisions of different types of organizations are highly interrelated. Conclusions: This study shows that the evaluation of funding proposals introduces and retains unequal funding distribution, reinforcing the relative neglect of diseases. Societal impact is the primary rationale for funding but application of it as a funding criterion is associated with significant challenges. Furthermore, current application of evaluation criteria leads to a primary focus on short-term impact. Through current practice, the relatively most neglected diseases will remain so, and a long-term strategy is needed to resolve this.展开更多
Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning.This task is very complex,as the behaviour of road agents depends on many factors and the number of po...Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning.This task is very complex,as the behaviour of road agents depends on many factors and the number of possible future trajectories can be consid-erable(multi-modal).Most prior approaches proposed to address multi-modal motion prediction are based on complex machine learning systems that have limited interpret-ability.Moreover,the metrics used in current benchmarks do not evaluate all aspects of the problem,such as the diversity and admissibility of the output.The authors aim to advance towards the design of trustworthy motion prediction systems,based on some of the re-quirements for the design of Trustworthy Artificial Intelligence.The focus is on evaluation criteria,robustness,and interpretability of outputs.First,the evaluation metrics are comprehensively analysed,the main gaps of current benchmarks are identified,and a new holistic evaluation framework is proposed.Then,a method for the assessment of spatial and temporal robustness is introduced by simulating noise in the perception system.To enhance the interpretability of the outputs and generate more balanced results in the proposed evaluation framework,an intent prediction layer that can be attached to multi-modal motion prediction models is proposed.The effectiveness of this approach is assessed through a survey that explores different elements in the visualisation of the multi-modal trajectories and intentions.The proposed approach and findings make a significant contribution to the development of trustworthy motion prediction systems for autono-mous vehicles,advancing the field towards greater safety and reliability.展开更多
Kiwifruit canker and brown spot are significant diseases affecting kiwis,caused by Pseudomonas syringae patho-genic variations(Pseudomonas syringae pv.Actinidiae(Psa))and Corynesporapolytica(Corynespora cassiicola).At ...Kiwifruit canker and brown spot are significant diseases affecting kiwis,caused by Pseudomonas syringae patho-genic variations(Pseudomonas syringae pv.Actinidiae(Psa))and Corynesporapolytica(Corynespora cassiicola).At present,the research on canker disease and brown spot disease mainly focuses on the isolation and identification of pathogenic bacteria,drug control,resistance gene mining and functional verification.Practice has proved that breeding disease resistant varieties are an effective method to control canker disease and brown spot disease.However,most existing cultivars lack genes for canker and brown spot resistance.Wild kiwifruit resources in nat-ure exhibit extensive genetic diversity due to prolonged natural selection,containing numerous resistance genes.But,due to insufficient understanding of the resistance of most kiwifruit varieties(lines)to canker disease and brown spot disease,some high-quality resources have not been fully utilized.The incidence of canker and brown spot of 18 kiwifruit cultivars(lines)was measured by inoculating isolated branches and leaves,and their resistance to canker and brown spot was analyzed according to the length,disease index,mean diameter,and systematic clustering.The results were as follows:Among 18 different kiwifruit varieties(lines)for canker disease,there were two highly resistant materials,eight disease-resistant materials,four disease-susceptible materials,and two highly susceptible materials.Moreover,regarding brown spot disease,there were one highly resistant material,five dis-ease-resistant materials,four susceptible materials,and three highly susceptible materials.Furthermore,four resources were resistant to both diseases.The outcomes provided a theoretical basis for breeding kiwifruit against canker and brown spot.展开更多
Objective: To evaluate the role of prevention and control strategies for nosocomial infection in a tertiary teaching hospital during the sudden outbreak of Corona Virus Disease 2019 (COVID-19). Methods: The hospital i...Objective: To evaluate the role of prevention and control strategies for nosocomial infection in a tertiary teaching hospital during the sudden outbreak of Corona Virus Disease 2019 (COVID-19). Methods: The hospital initiated an emergency plan involving multi-departmental defense and control. It adopted a series of nosocomial infection prevention and control measures, including strengthening pre-examination and triage, optimizing the consultation process, improving the hospital’s architectural composition, implementing graded risk management, enhancing personal protection, and implementing staff training and supervision. Descriptive research was used to evaluate the short-term effects of these in-hospital prevention and control strategies. The analysis compared changes in related evaluation indicators between January 24, 2020 and February 12, 2020 (Chinese Lunar New Year’s Eve 2020 to lunar January 19) and the corresponding lunar period of the previous year. Results: Compared to the same period last year, the outpatient fever rate increased by 1.85-fold (P P Conclusion: The nosocomial infection prevention and control strategies implemented during this specific period improved the detection and control abilities for the COVID-19 source of infection and enhanced the compliance with measures. This likely contributed significantly to avoiding the occurrence of nosocomial infection.展开更多
This paper realizes the full-domain collaborative deployment of multiple interference sources of the global satellite navigation system(GNSS)and evaluates the deployment effect to enhance the ability to disturb the at...This paper realizes the full-domain collaborative deployment of multiple interference sources of the global satellite navigation system(GNSS)and evaluates the deployment effect to enhance the ability to disturb the attacker and the capability to defend the GNSS during navigation countermeasures.Key evaluation indicators for the jamming effect of GNSS suppressive and deceptive jamming sources are first created,their evaluation models are built,and their detection procedures are sorted out,as the basis for determining the deployment principles.The principles for collaboratively deploying multi-jamming sources are developed to obtain the deployment structures(including the required number,structures in demand,and corresponding positions)of three single interference sources required by collaboratively deploying.Accordingly,simulation and hardware-in-loop testing results are presented to determine a rational configuration of the collaborative deployment of multi-jamming sources in the set situation and further realize the full-domain deployment of an interference network from ground,air to space.Varied evaluation indices for the deployment effect are finally developed to evaluate the deployment effect of the proposed configuration and further verify its reliability and rationality.展开更多
The intelligent detection technology driven by X-ray images and deep learning represents the forefront of advanced techniques and development trends in flaw detection and automated evaluation of light alloy castings.H...The intelligent detection technology driven by X-ray images and deep learning represents the forefront of advanced techniques and development trends in flaw detection and automated evaluation of light alloy castings.However,the efficacy of deep learning models hinges upon a substantial abundance of flaw samples.The existing research on X-ray image augmentation for flaw detection suffers from shortcomings such as poor diversity of flaw samples and low reliability of quality evaluation.To this end,a novel approach was put forward,which involves the creation of the Interpolation-Deep Convolutional Generative Adversarial Network(I-DCGAN)for flaw detection image generation and a comprehensive evaluation algorithm named TOPSIS-IFP.I-DCGAN enables the generation of high-resolution,diverse simulated images with multiple appearances,achieving an improvement in sample diversity and quality while maintaining a relatively lower computational complexity.TOPSIS-IFP facilitates multi-dimensional quality evaluation,including aspects such as diversity,authenticity,image distribution difference,and image distortion degree.The results indicate that the X-ray radiographic images of magnesium and aluminum alloy castings achieve optimal performance when trained up to the 800th and 600th epochs,respectively.The TOPSIS-IFP value reaches 78.7%and 73.8%similarity to the ideal solution,respectively.Compared to single index evaluation,the TOPSIS-IFP algorithm achieves higher-quality simulated images at the optimal training epoch.This approach successfully mitigates the issue of unreliable quality associated with single index evaluation.The image generation and comprehensive quality evaluation method developed in this paper provides a novel approach for image augmentation in flaw recognition,holding significant importance for enhancing the robustness of subsequent flaw recognition networks.展开更多
Soybean is one of the important crops in China. Soymilk, a traditional neutral plant-based protein drink, is rich in high quality proteins. Although soybean milk is rich in nutrients, its marketing among consumers, es...Soybean is one of the important crops in China. Soymilk, a traditional neutral plant-based protein drink, is rich in high quality proteins. Although soybean milk is rich in nutrients, its marketing among consumers, especially those in Western countries who are used to peaceful flavor, has been limited due to the adverse flavor impact brought by its special composition. In recent years, with the increasing attention to the nutritional value of soymilk, the flavor of soymilk has become a popular research object for scholars at home and abroad. The flavor components of soymilk are mainly volatile small molecular compounds produced by enzymatic reactions catalyzed by lipoxygenase(LOX). After formation, they interact with protein macromolecules to form the overall flavor of soymilk. At present, there are many methods to control the off-odor of soymilk at home and abroad, including physical heating methods, chemical methods, biological enzymatic digestion methods, mask methods, and a variety of breeding methods. These methods effectively reduce the off-odor of soymilk, but all of them have shortcomings. Currently, the sensory characteristics of the beany odor in soymilk are evaluated mainly by traditional human sensory scoring along with the assistance of modern instrument analysis of volatile flavor substances using headspace solid phase microextraction(SPME) gas chromatography coupled with-mass spectrometry(GC-MS). This paper summarized the research results of volatile flavor substances in soymilk in recent years and the sensory evaluation methods of soymilk at home and abroad, and looked forward to the future development direction, hoping to provide some theoretical bases and reference detection methods for solving the problem of soymilk flavor in the future.展开更多
The instability of slope blocks occurred frequently along traffic corridor in Southeastern Tibet(TCST),which was primarily controlled by the rock mass structures.A rapid method evaluating the control effects of rock m...The instability of slope blocks occurred frequently along traffic corridor in Southeastern Tibet(TCST),which was primarily controlled by the rock mass structures.A rapid method evaluating the control effects of rock mass structures was proposed through field statistics of the slopes and rock mass structures along TCST,which combined the stereographic projection method,modified M-JCS model,and limit equilibrium theory.The instabilities of slope blocks along TCST were then evaluated rapidly,and the different control factors of instability were analyzed.Results showed that the probabilities of toppling(5.31%),planar(16.15%),and wedge(35.37%)failure of slope blocks along TCST increased sequentially.These instability modes were respectively controlled by the anti-dip joint,the joint parallel to slope surface with a dip angle smaller than the slope angle(singlejoint),and two groups of joints inclined out of the slope(double-joints).Regarding the control effects on slope block instability,the stabilization ability of doublejoints(72.7%),anti-dip joint(67.4%),and single-joint(57.6%)decreased sequentially,resulting in different probabilities of slope block instability.Additionally,nearby regional faults significantly influenced the joints,leading to spatial heterogeneity and segmental clustering in the stabilization ability provided by joints to the slope blocks.Consequently,the stability of slope blocks gradually weakened as they approached the fault zones.This paper can provide guidance and assistance for investigating the development characteristics of rock mass structures and the stability of slope blocks.展开更多
基金This study was funded by the National Key R&D Program of China(2021YFD1900700)the National Natural Science Foundation of China(51909221)the China Postdoctoral Science Foundation(2020T130541 and 2019M650277).
文摘In order to further improve the utility of unmanned aerial vehicle(UAV)remote-sensing for quickly and accurately monitoring the growth of winter wheat under film mulching, this study examined the treatments of ridge mulching,ridge–furrow full mulching, and flat cropping full mulching in winter wheat.Based on the fuzzy comprehensive evaluation (FCE) method, four agronomic parameters (leaf area index, above-ground biomass, plant height, and leaf chlorophyll content) were used to calculate the comprehensive growth evaluation index (CGEI) of the winter wheat, and 14 visible and near-infrared spectral indices were calculated using spectral purification technology to process the remote-sensing image data of winter wheat obtained by multispectral UAV.Four machine learning algorithms, partial least squares, support vector machines, random forests, and artificial neural network networks(ANN), were used to build the winter wheat growth monitoring model under film mulching, and accuracy evaluation and mapping of the spatial and temporal distribution of winter wheat growth status were carried out.The results showed that the CGEI of winter wheat under film mulching constructed using the FCE method could objectively and comprehensively evaluate the crop growth status.The accuracy of remote-sensing inversion of the CGEI based on the ANN model was higher than for the individual agronomic parameters, with a coefficient of determination of 0.75,a root mean square error of 8.40, and a mean absolute value error of 6.53.Spectral purification could eliminate the interference of background effects caused by mulching and soil, effectively improving the accuracy of the remotesensing inversion of winter wheat under film mulching, with the best inversion effect achieved on the ridge–furrow full mulching area after spectral purification.The results of this study provide a theoretical reference for the use of UAV remote-sensing to monitor the growth status of winter wheat with film mulching.
基金supported by Special key project of technological innovation and application development in Yongchuan District,Chongqing(2021yc-cxfz20002)the special funds of central government for guiding local science and technology developmentthe funds for the platform projects of professional technology innovation(CSTC2018ZYCXPT0006).
文摘To provide new insights into the development and utilization of Douchi artificial starters,three common strains(Aspergillus oryzae,Mucor racemosus,and Rhizopus oligosporus)were used to study their influence on the fermentation of Douchi.The results showed that the biogenic amine contents of the three types of Douchi were all within the safe range and far lower than those of traditional fermented Douchi.Aspergillus-type Douchi produced more free amino acids than the other two types of Douchi,and its umami taste was more prominent in sensory evaluation(P<0.01),while Mucor-type and Rhizopus-type Douchi produced more esters and pyrazines,making the aroma,sauce,and Douchi flavor more abundant.According to the Pearson and PLS analyses results,sweetness was significantly negatively correlated with phenylalanine,cysteine,and acetic acid(P<0.05),bitterness was significantly negatively correlated with malic acid(P<0.05),the sour taste was significantly positively correlated with citric acid and most free amino acids(P<0.05),while astringency was significantly negatively correlated with glucose(P<0.001).Thirteen volatile compounds such as furfuryl alcohol,phenethyl alcohol,and benzaldehyde caused the flavor difference of three types of Douchi.This study provides theoretical basis for the selection of starting strains for commercial Douchi production.
基金This work was financially supported by the National Natural Science Foundation of China(52074089 and 52104064)Natural Science Foundation of Heilongjiang Province of China(LH2019E019).
文摘As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.
基金This work was supported by the National Natural Science Foundation of China under Grant 62233003the National Key Research and Development Program of China under Grant 2020YFB1708602.
文摘The proliferation of intelligent,connected Internet of Things(IoT)devices facilitates data collection.However,task workers may be reluctant to participate in data collection due to privacy concerns,and task requesters may be concerned about the validity of the collected data.Hence,it is vital to evaluate the quality of the data collected by the task workers while protecting privacy in spatial crowdsourcing(SC)data collection tasks with IoT.To this end,this paper proposes a privacy-preserving data reliability evaluation for SC in IoT,named PARE.First,we design a data uploading format using blockchain and Paillier homomorphic cryptosystem,providing unchangeable and traceable data while overcoming privacy concerns.Secondly,based on the uploaded data,we propose a method to determine the approximate correct value region without knowing the exact value.Finally,we offer a data filtering mechanism based on the Paillier cryptosystem using this value region.The evaluation and analysis results show that PARE outperforms the existing solution in terms of performance and privacy protection.
文摘1) Background: Rapid and acurate diagnostic testing for case identification, quarantine, and contact tracing is essential for managing the COVID 19 pandemic. Rapid antigen detection tests are available, however, it is important to evaluate their performances before use. We tested a rapid antigen detection of SARS-CoV-2, based on the immunochromatography (Boson Biotech SARS-CoV-2 Ag Test (Xiamen Boson Biotech Co., Ltd., China)) and the results were compared with the real time reverse transcriptase-Polymerase chain reaction (RT-PCR) (Gold standard) results;2) Methods: From November 2021 to December 2021, samples were collected from symptomatic patients and asymptomatic individuals referred for testing in a hospital during the second pandemic wave in Gabon. All these participants attending “CTA Angondjé”, a field hospital set up as part of the management of COVID-19 in Gabon. Two nasopharyngeal swabs were collected in all the patients, one for Ag test and the other for RT-PCR;3) Results: A total of 300 samples were collected from 189 symptomatic and 111 asymptomatic individuals. The sensitivity and specificity of the antigen test were 82.5% [95%CI 73.8 - 89.3] and 97.9 % [95%CI 92.2 - 98.2] respectively, and the diagnostic accuracy was 84.4% (95% CI: 79.8 - 88.3%). The antigen test was more likely to be positive for samples with RT-PCR Ct values ≤ 32, with a sensitivity of 89.8%;4) Conclusions: The Boson Biotech SARS-CoV-2 Ag Test has good sensitivity and can detect SARS-CoV-2 infection, especially among symptomatic individuals with low viral load. This test could be incorporated into efficient testing algorithms as an alternative to PCR to decrease diagnostic delays and curb viral transmission.
基金This work is supported by the 2022 National Key Research and Development Plan“Security Protection Technology for Critical Information Infrastructure of Distribution Network”(2022YFB3105100).
文摘First,we propose a cross-domain authentication architecture based on trust evaluation mechanism,including registration,certificate issuance,and cross-domain authentication processes.A direct trust evaluation mechanism based on the time decay factor is proposed,taking into account the influence of historical interaction records.We weight the time attenuation factor to each historical interaction record for updating and got the new historical record data.We refer to the beta distribution to enhance the flexibility and adaptability of the direct trust assessment model to better capture time trends in the historical record.Then we propose an autoencoder-based trust clustering algorithm.We perform feature extraction based on autoencoders.Kullback leibler(KL)divergence is used to calculate the reconstruction error.When constructing a convolutional autoencoder,we introduce convolutional neural networks to improve training efficiency and introduce sparse constraints into the hidden layer of the autoencoder.The sparse penalty term in the loss function measures the difference through the KL divergence.Trust clustering is performed based on the density based spatial clustering of applications with noise(DBSCAN)clustering algorithm.During the clustering process,edge nodes have a variety of trustworthy attribute characteristics.We assign different attribute weights according to the relative importance of each attribute in the clustering process,and a larger weight means that the attribute occupies a greater weight in the calculation of distance.Finally,we introduced adaptive weights to calculate comprehensive trust evaluation.Simulation experiments prove that our trust evaluation mechanism has excellent reliability and accuracy.
基金funded by the National Natural Science Foundation of China(Grant No.41861134008)Muhammad Asif Khan academician workstation of Yunnan Province(Grant No.202105AF150076)+6 种基金General program of Yunnan Province Science and Technology Department(Grant No.202105AF150076)Key Project of Natural Science Foundation of Yunnan Province(Grant No.202101AS070019)Key R&D Program of Yunnan Province(Grant No.202003AC100002)General Program of basic research plan of Yunnan Province(Grant No.202001AT070059)Major scientific and technological projects of Yunnan Province:Research on Key Technologies of ecological environment monitoring and intelligent management of natural resources in Yunnan(No:202202AD080010)“Study on High-Level Hidden Landslide Identification Based on Multi-Source Data”of Key Laboratory of Early Rapid Identification,Prevention and Control of Geological Diseases in Traffic Corridor of High Intensity Earthquake Mountainous Area of Yunnan Province(KLGDTC-2021-02)Guizhou Scientific and Technology Fund(QKHJ-ZK[2023]YB 193).
文摘Landslide hazard susceptibility evaluation takes on critical significance in early warning and disaster prevention and reduction.In order to solve the problems of poor effectiveness of landslide data and complex calculation of weights for multiple evaluation factors in the existing landslide susceptibility evaluation models,in this study,a method of landslide hazard susceptibility evaluation is proposed by combining SBAS-InSAR(Small Baseline Subsets-Interferometric Synthetic Aperture Radar)and SSA-BP(Sparrow Search Algorithm-Back Propagation)neural network algorithm.The SBAS-InSAR technology is adopted to identify potential landslide hazards in the study area,update the cataloging data of landslide hazards,and 11 evaluation factors are chosen for constructing the SSA-BP model for training and validation.Baihetan Reservoir area is selected as a case study for validation.As indicated by the results,the application of SBAS-InSAR technology,combined with both ascending and descending orbit data,effectively addresses the incomplete identification of landslide hazards caused by geometric distortion of single orbit SAR data(e.g.,shadow,overlay,and perspective contraction)in deep canyon areas,thereby enabling the acquisition of up-to-date landslide hazard data.Moreover,in comparison to the conventional BP(Back Propagation)algorithm,the accuracy of the model constructed by the SSA-BP algorithm exhibits a significant increase,with mean squared error and mean absolute error reduced by 0.0142 and 0.0607,respectively.Additionally,during the process of susceptibility evaluation,the SSA-BP model effectively circumvents the issue of considerable manual interventions in calculating the weight of evaluation factors.The area under the curve of this model reaches 0.909,surpassing BP(0.835),random forest(0.792),and the information value method(0.699).The risk of landslide occurrence in the Baihetan Reservoir area is positively correlated with slope,surface temperature,and deformation rate,while it is negatively correlated with fault distance and normalized difference vegetation index.Geological lithology exerts minimal influence on the occurrence of landslides,with the risk being low in forest land and high in grassland.The method proposed in this study provides a useful reference for disaster prevention and mitigation departments to perform landslide hazard susceptibility evaluations in deep canyon areas under complex geological conditions.
基金supported by the National Natural Science Foundation of China(52203364,52188101,52020105010)the National Key R&D Program of China(2021YFB3800300,2022YFB3803400)+2 种基金the Strategic Priority Research Program of Chinese Academy of Science(XDA22010602)the China Postdoctoral Science Foundation(2022M713214)the China National Postdoctoral Program for Innovative Talents(BX2021321)。
文摘Metal-ion batteries(MIBs),including alkali metal-ion(Li^(+),Na^(+),and K^(3)),multi-valent metal-ion(Zn^(2+),Mg^(2+),and Al^(3+)),metal-air,and metal-sulfur batteries,play an indispensable role in electrochemical energy storage.However,the performance of MIBs is significantly influenced by numerous variables,resulting in multi-dimensional and long-term challenges in the field of battery research and performance enhancement.Machine learning(ML),with its capability to solve intricate tasks and perform robust data processing,is now catalyzing a revolutionary transformation in the development of MIB materials and devices.In this review,we summarize the utilization of ML algorithms that have expedited research on MIBs over the past five years.We present an extensive overview of existing algorithms,elucidating their details,advantages,and limitations in various applications,which encompass electrode screening,material property prediction,electrolyte formulation design,electrode material characterization,manufacturing parameter optimization,and real-time battery status monitoring.Finally,we propose potential solutions and future directions for the application of ML in advancing MIB development.
文摘A dissertation is a research report or scientific paper written by an author to obtain a certain degree. It reflects postgraduates’ research achievements and the educational quality of an institute, even a country. To construct an optimized quality evaluation system for postgraduate dissertation (QESPD), we summarized the influencing factors and invited 10 experienced specialists to rate and prioritize them based on fuzzy analytic hierarchy process. Four primary indicators (innovation, integrity, scientificity and normativity) and 16 sub-indicators were selected to form the evaluation system. The order of primary indicators by weight, was innovation (0.4269), scientificity (0.2807), integrity (0.1728) and normativity (0.1196). The top five sub-dimensions were theoretical originality, scientific value, data reliability, design rationality and evidence credibility. To demonstrate the effectiveness of the proposed system, a case study was performed. In the case study, it was demonstrated that the established two-index-hierarchy QESPD in this study was a more scientific and reasonable evaluation system worthy of promotion and application.
基金support of the project“State Grid Corporation Headquarters Science and Technology Program(5108-202299258A-1-0-ZB)”.
文摘To effectively quantify the impact of distributed photovoltaic(PV)access on the distribution network,this paper proposes a comprehensive evaluation method of distributed PV grid connection combining subjective and objective combination of assignment and technique for order preference by similarity to an ideal solution(TOPSIS)—rank sum ratio(RSR)(TOPSIS-RSR)method.Based on the traditional distribution network evaluation system,a comprehensive evaluation system has been constructed.It fully considers the new development requirements of distributed PV access on the environmental friendliness and absorptive capacity of the distribution grid and comprehensively reflects the impact of distributed PV grid connection.The analytic hierarchy process(AHP)was used to determine the subjective weights of the primary indicators,and the Spearman consistency test was combined to determine the weights of the secondary indicators based on three objective assignment methods.The subjective and objective combination weights of each assessment indicator were calculated through the principle of minimum entropy.Calculate the distance between the indicators to be evaluated and the positive and negative ideal solutions,the relative closeness ranking,and qualitative binning by TOPSIS-RSR method to obtain the comprehensive evaluation results of different scenarios.By setting up different PV grid-connected scenarios and utilizing the IEEE33 node simulation algorithm,the correctness and effectiveness of the proposed subject-object combination assignment and integrated assessment method are verified.
基金supported by the Major Public Welfare Special Fund of Henan Province(No.201300210200)the Major Science and Technology Research Special Fund of Henan Province(No.221100210400).
文摘With the rise of blockchain technology,the security issues of smart contracts have become increasingly critical.Despite the availability of numerous smart contract vulnerability detection tools,many face challenges such as slow updates,usability issues,and limited installation methods.These challenges hinder the adoption and practicality of these tools.This paper examines smart contract vulnerability detection tools from 2016 to 2023,sourced from the Web of Science(WOS)and Google Scholar.By systematically collecting,screening,and synthesizing relevant research,38 open-source tools that provide installation methods were selected for further investigation.From a developer’s perspective,this paper offers a comprehensive survey of these 38 open-source tools,discussing their operating principles,installation methods,environmental dependencies,update frequencies,and installation challenges.Based on this,we propose an Ethereum smart contract vulnerability detection framework.This framework enables developers to easily utilize various detection tools and accurately analyze contract security issues.To validate the framework’s stability,over 1700 h of testing were conducted.Additionally,a comprehensive performance test was performed on the mainstream detection tools integrated within the framework,assessing their hardware requirements and vulnerability detection coverage.Experimental results indicate that the Slither tool demonstrates satisfactory performance in terms of system resource consumption and vulnerability detection coverage.This study represents the first performance evaluation of testing tools in this domain,providing significant reference value.
基金supported by the National Natural Science Foundation of China(Grant Nos.42307220 and 42090055)the Postdoctoral Research Project Funding of Shaanxi Province(Grant No.2023BSHEDZZ210).
文摘Deep-seated toppling in the upper reaches of the Lancang River,southwest China involves deformations exceeding 100 m in depth.The slope deformation is initiated by river downcutting and evolves distinctive characteristics with a depth of river incision.In this study,we propose a system for evaluating the stability of deep-seated toppled slopes in different evolutionary stages.This system contains identification criteria for each evolutionary stage and provides the corresponding stability evaluation methods.Based on the mechanical and kinematic analysis of slope blocks,the specific stage of slope movement can be identified in the field through outcrop mapping,in situ tests,surface displacement monitoring,and adit and borehole explorations.The stability evaluation methods are established based on the limiting equilibrium theory and the strain compatibility between the undisturbed zone and the toppled zone.Finally,several sample slopes in different evolution stages have been investigated to verify the applicability and accuracy of the proposed stability evaluation system.The results indicate that intense tectonic activity and rapid river incision lead to a maximum principal stress ratio exceeding 10 near the slope surface,thus triggering widespread toppling deformations along the river valley.When considering the losses of joint cohesion during the further rotation process,the safety factor of the slope drops by 7%e28%.The self-stabilization of toppling deformation can be recognized by the layer symmetry configuration after the free rotation of the deflected layers.Intensely toppled rock blocks mainly suffer sliding failures beyond the layer symmetry condition.The factor of safety of the K73 rockslide decreased from 1.17 to 0.87 by considering the development of the potential sliding surface and the toesaturated zone.
文摘Background: International research and innovation efforts for neglected tropical diseases have increased in recent decades due to disparities in overall health research funding in relation to global burden of disease. However, within the field of neglected tropical diseases some seem far more neglected than others. In this research the aim is to investigate the distribution of resources and efforts, as well as the mechanisms that underpin funding allocation for neglected tropical diseases. Methodology: A systematic literature review was conducted to establish a comprehensive overview of known indicators for innovation efforts related to a wide range of neglected tropical diseases. Articles were selected based on a subjective evaluation of their relevance, the presence of original data, and the breadth of their scope. This was followed by thirteen in-depth open-ended interviews with representatives of private, public and philanthropic funding organizations, concerning evaluation criteria for funding research proposals. Results: The findings reveal a large difference in the extent to which the individual diseases are neglected with notable differences between absolute and relative efforts. Criteria used in the evaluation of research proposals relate to potential impact, the probability of success and strategic fit. Private organizations prioritize strategic fit and economic impact;philanthropic organizations prioritize short-term societal impact;and public generally prioritize the probability of success by accounting for follow-up funding and involvement of industry. Funding decisions of different types of organizations are highly interrelated. Conclusions: This study shows that the evaluation of funding proposals introduces and retains unequal funding distribution, reinforcing the relative neglect of diseases. Societal impact is the primary rationale for funding but application of it as a funding criterion is associated with significant challenges. Furthermore, current application of evaluation criteria leads to a primary focus on short-term impact. Through current practice, the relatively most neglected diseases will remain so, and a long-term strategy is needed to resolve this.
基金European Commission,Joint Research Center,Grant/Award Number:HUMAINTMinisterio de Ciencia e Innovación,Grant/Award Number:PID2020‐114924RB‐I00Comunidad de Madrid,Grant/Award Number:S2018/EMT‐4362 SEGVAUTO 4.0‐CM。
文摘Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning.This task is very complex,as the behaviour of road agents depends on many factors and the number of possible future trajectories can be consid-erable(multi-modal).Most prior approaches proposed to address multi-modal motion prediction are based on complex machine learning systems that have limited interpret-ability.Moreover,the metrics used in current benchmarks do not evaluate all aspects of the problem,such as the diversity and admissibility of the output.The authors aim to advance towards the design of trustworthy motion prediction systems,based on some of the re-quirements for the design of Trustworthy Artificial Intelligence.The focus is on evaluation criteria,robustness,and interpretability of outputs.First,the evaluation metrics are comprehensively analysed,the main gaps of current benchmarks are identified,and a new holistic evaluation framework is proposed.Then,a method for the assessment of spatial and temporal robustness is introduced by simulating noise in the perception system.To enhance the interpretability of the outputs and generate more balanced results in the proposed evaluation framework,an intent prediction layer that can be attached to multi-modal motion prediction models is proposed.The effectiveness of this approach is assessed through a survey that explores different elements in the visualisation of the multi-modal trajectories and intentions.The proposed approach and findings make a significant contribution to the development of trustworthy motion prediction systems for autono-mous vehicles,advancing the field towards greater safety and reliability.
基金supported by the following grants:Science and Technology Support Plan of Guizhou Province:Breeding Research and Demonstration of all-Red Bud Transformation of“GH-1”Clone of“Hong yang”Kiwifruit(Guizhou Family Combination Support[2021]General 234)the National Key Research and Development Program“Quality and Efficiency Improvement Technology Integration and Demonstration of Advantageous Characteristic Industries in Guizhou Karst Mountain Area(2021YFD1100300)”Post-Subsidy FundTask 3 of National Key Research and Development Program,Green Prevention and Control Technology Integration and Demonstration of Main Diseases and Insect Pests of Kiwifruit in Shuicheng City,China(2022YFD1601710-3).
文摘Kiwifruit canker and brown spot are significant diseases affecting kiwis,caused by Pseudomonas syringae patho-genic variations(Pseudomonas syringae pv.Actinidiae(Psa))and Corynesporapolytica(Corynespora cassiicola).At present,the research on canker disease and brown spot disease mainly focuses on the isolation and identification of pathogenic bacteria,drug control,resistance gene mining and functional verification.Practice has proved that breeding disease resistant varieties are an effective method to control canker disease and brown spot disease.However,most existing cultivars lack genes for canker and brown spot resistance.Wild kiwifruit resources in nat-ure exhibit extensive genetic diversity due to prolonged natural selection,containing numerous resistance genes.But,due to insufficient understanding of the resistance of most kiwifruit varieties(lines)to canker disease and brown spot disease,some high-quality resources have not been fully utilized.The incidence of canker and brown spot of 18 kiwifruit cultivars(lines)was measured by inoculating isolated branches and leaves,and their resistance to canker and brown spot was analyzed according to the length,disease index,mean diameter,and systematic clustering.The results were as follows:Among 18 different kiwifruit varieties(lines)for canker disease,there were two highly resistant materials,eight disease-resistant materials,four disease-susceptible materials,and two highly susceptible materials.Moreover,regarding brown spot disease,there were one highly resistant material,five dis-ease-resistant materials,four susceptible materials,and three highly susceptible materials.Furthermore,four resources were resistant to both diseases.The outcomes provided a theoretical basis for breeding kiwifruit against canker and brown spot.
文摘Objective: To evaluate the role of prevention and control strategies for nosocomial infection in a tertiary teaching hospital during the sudden outbreak of Corona Virus Disease 2019 (COVID-19). Methods: The hospital initiated an emergency plan involving multi-departmental defense and control. It adopted a series of nosocomial infection prevention and control measures, including strengthening pre-examination and triage, optimizing the consultation process, improving the hospital’s architectural composition, implementing graded risk management, enhancing personal protection, and implementing staff training and supervision. Descriptive research was used to evaluate the short-term effects of these in-hospital prevention and control strategies. The analysis compared changes in related evaluation indicators between January 24, 2020 and February 12, 2020 (Chinese Lunar New Year’s Eve 2020 to lunar January 19) and the corresponding lunar period of the previous year. Results: Compared to the same period last year, the outpatient fever rate increased by 1.85-fold (P P Conclusion: The nosocomial infection prevention and control strategies implemented during this specific period improved the detection and control abilities for the COVID-19 source of infection and enhanced the compliance with measures. This likely contributed significantly to avoiding the occurrence of nosocomial infection.
基金the National Natural Science Foundation of China(Grant No.42174047 and No.42174036)the National Science Foundation Project for Outstanding Youth(No.42104034).
文摘This paper realizes the full-domain collaborative deployment of multiple interference sources of the global satellite navigation system(GNSS)and evaluates the deployment effect to enhance the ability to disturb the attacker and the capability to defend the GNSS during navigation countermeasures.Key evaluation indicators for the jamming effect of GNSS suppressive and deceptive jamming sources are first created,their evaluation models are built,and their detection procedures are sorted out,as the basis for determining the deployment principles.The principles for collaboratively deploying multi-jamming sources are developed to obtain the deployment structures(including the required number,structures in demand,and corresponding positions)of three single interference sources required by collaboratively deploying.Accordingly,simulation and hardware-in-loop testing results are presented to determine a rational configuration of the collaborative deployment of multi-jamming sources in the set situation and further realize the full-domain deployment of an interference network from ground,air to space.Varied evaluation indices for the deployment effect are finally developed to evaluate the deployment effect of the proposed configuration and further verify its reliability and rationality.
基金funded by the National Key R&D Program of China(2020YFB1710100)the National Natural Science Foundation of China(Nos.52275337,52090042,51905188).
文摘The intelligent detection technology driven by X-ray images and deep learning represents the forefront of advanced techniques and development trends in flaw detection and automated evaluation of light alloy castings.However,the efficacy of deep learning models hinges upon a substantial abundance of flaw samples.The existing research on X-ray image augmentation for flaw detection suffers from shortcomings such as poor diversity of flaw samples and low reliability of quality evaluation.To this end,a novel approach was put forward,which involves the creation of the Interpolation-Deep Convolutional Generative Adversarial Network(I-DCGAN)for flaw detection image generation and a comprehensive evaluation algorithm named TOPSIS-IFP.I-DCGAN enables the generation of high-resolution,diverse simulated images with multiple appearances,achieving an improvement in sample diversity and quality while maintaining a relatively lower computational complexity.TOPSIS-IFP facilitates multi-dimensional quality evaluation,including aspects such as diversity,authenticity,image distribution difference,and image distortion degree.The results indicate that the X-ray radiographic images of magnesium and aluminum alloy castings achieve optimal performance when trained up to the 800th and 600th epochs,respectively.The TOPSIS-IFP value reaches 78.7%and 73.8%similarity to the ideal solution,respectively.Compared to single index evaluation,the TOPSIS-IFP algorithm achieves higher-quality simulated images at the optimal training epoch.This approach successfully mitigates the issue of unreliable quality associated with single index evaluation.The image generation and comprehensive quality evaluation method developed in this paper provides a novel approach for image augmentation in flaw recognition,holding significant importance for enhancing the robustness of subsequent flaw recognition networks.
基金Supported by the Youth Fund Project of the National Natural Science Foundation of China(32001570)the Post-doctorate Program Funding in Heilongjiang Province(LBH-Z19118)the Academic Backbone'Project of Northeast Agricultural University(20XG11)。
文摘Soybean is one of the important crops in China. Soymilk, a traditional neutral plant-based protein drink, is rich in high quality proteins. Although soybean milk is rich in nutrients, its marketing among consumers, especially those in Western countries who are used to peaceful flavor, has been limited due to the adverse flavor impact brought by its special composition. In recent years, with the increasing attention to the nutritional value of soymilk, the flavor of soymilk has become a popular research object for scholars at home and abroad. The flavor components of soymilk are mainly volatile small molecular compounds produced by enzymatic reactions catalyzed by lipoxygenase(LOX). After formation, they interact with protein macromolecules to form the overall flavor of soymilk. At present, there are many methods to control the off-odor of soymilk at home and abroad, including physical heating methods, chemical methods, biological enzymatic digestion methods, mask methods, and a variety of breeding methods. These methods effectively reduce the off-odor of soymilk, but all of them have shortcomings. Currently, the sensory characteristics of the beany odor in soymilk are evaluated mainly by traditional human sensory scoring along with the assistance of modern instrument analysis of volatile flavor substances using headspace solid phase microextraction(SPME) gas chromatography coupled with-mass spectrometry(GC-MS). This paper summarized the research results of volatile flavor substances in soymilk in recent years and the sensory evaluation methods of soymilk at home and abroad, and looked forward to the future development direction, hoping to provide some theoretical bases and reference detection methods for solving the problem of soymilk flavor in the future.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.41941019,42177142)the Second Tibetan Plateau Scientific Expedition and Research(STEP)program(Grant NO.2019QZKK0904)the Fundamental Research Funds for the Central Universities,CHD(Grant No.300102212213).
文摘The instability of slope blocks occurred frequently along traffic corridor in Southeastern Tibet(TCST),which was primarily controlled by the rock mass structures.A rapid method evaluating the control effects of rock mass structures was proposed through field statistics of the slopes and rock mass structures along TCST,which combined the stereographic projection method,modified M-JCS model,and limit equilibrium theory.The instabilities of slope blocks along TCST were then evaluated rapidly,and the different control factors of instability were analyzed.Results showed that the probabilities of toppling(5.31%),planar(16.15%),and wedge(35.37%)failure of slope blocks along TCST increased sequentially.These instability modes were respectively controlled by the anti-dip joint,the joint parallel to slope surface with a dip angle smaller than the slope angle(singlejoint),and two groups of joints inclined out of the slope(double-joints).Regarding the control effects on slope block instability,the stabilization ability of doublejoints(72.7%),anti-dip joint(67.4%),and single-joint(57.6%)decreased sequentially,resulting in different probabilities of slope block instability.Additionally,nearby regional faults significantly influenced the joints,leading to spatial heterogeneity and segmental clustering in the stabilization ability provided by joints to the slope blocks.Consequently,the stability of slope blocks gradually weakened as they approached the fault zones.This paper can provide guidance and assistance for investigating the development characteristics of rock mass structures and the stability of slope blocks.