Alzheimer’s Disease (AD), the most common form of dementia, is an incurable neurological condition that results in a progressive mental deterioration. Although definitive diagnosis of AD is difficult, in practice, AD...Alzheimer’s Disease (AD), the most common form of dementia, is an incurable neurological condition that results in a progressive mental deterioration. Although definitive diagnosis of AD is difficult, in practice, AD diagnosis is largely based on clinical history and neuropsychological data including magnetic resource imaging (MRI). Increasing research has been reported on applying machine learning to AD recognition in recent years. This paper presents our latest contribution to the advance. It describes an automatic AD recognition algorithm that is based on deep learning on 3D brain MRI. The algorithm uses a convolutional neural network (CNN) to fulfil AD recognition. It is unique in that the three dimensional topology of brain is considered as a whole in AD recognition, resulting in an accurate recognition. The CNN used in this study consists of three consecutive groups of processing layers, two fully connected layers and a classification layer. In the structure, every one of the three groups is made up of three layers, including a convolutional layer, a pooling layer and a normalization layer. The algorithm was trained and tested using the MRI data from Alzheimer’s Disease Neuroimaging Initiative. The data used include the MRI scanning of about 47 AD patients and 34 normal controls. The experiment had shown that the proposed algorithm delivered a high AD recognition accuracy with a sensitivity of 1 and a specificity of 0.93.展开更多
To enable precision medicine and remote patient monitoring,internet of healthcare things(IoHT)has gained significant interest as a promising technique.With the widespread use of IoHT,nonetheless,privacy infringements ...To enable precision medicine and remote patient monitoring,internet of healthcare things(IoHT)has gained significant interest as a promising technique.With the widespread use of IoHT,nonetheless,privacy infringements such as IoHT data leakage have raised serious public concerns.On the other side,blockchain and distributed ledger technologies have demonstrated great potential for enhancing trustworthiness and privacy protection for IoHT systems.In this survey,a holistic review of existing blockchain-based IoHT systems is conducted to indicate the feasibility of combining blockchain and IoHT in privacy protection.In addition,various types of privacy challenges in IoHT are identified by examining general data protection regulation(GDPR).More importantly,an associated study of cutting-edge privacy-preserving techniques for the identified IoHT privacy challenges is presented.Finally,several challenges in four promising research areas for blockchain-based IoHT systems are pointed out,with the intent of motivating researchers working in these fields to develop possible solutions.展开更多
A new image enhancement algorithm based on Retinex theory is proposed to solve the problem of bad visual effect of an image in low-light conditions. First, an image is converted from the RGB color space to the HSV col...A new image enhancement algorithm based on Retinex theory is proposed to solve the problem of bad visual effect of an image in low-light conditions. First, an image is converted from the RGB color space to the HSV color space to get the V channel. Next, the illuminations are respectively estimated by the guided filtering and the variational framework on the V channel and combined into a new illumination by average gradient. The new reflectance is calculated using V channel and the new illumination. Then a new V channel obtained by multiplying the new illumination and reflectance is processed with contrast limited adaptive histogram equalization(CLAHE). Finally, the new image in HSV space is converted back to RGB space to obtain the enhanced image. Experimental results show that the proposed method has better subjective quality and objective quality than existing methods.展开更多
Assessing plant water status is important for monitoring plant physiology. Radio signals are attenuated when passing through vegetation. Both analytical and empirical models developed for radio frequency (RF) loss thr...Assessing plant water status is important for monitoring plant physiology. Radio signals are attenuated when passing through vegetation. Both analytical and empirical models developed for radio frequency (RF) loss through vegetation have been dependent on experimental measurements and those measurements have been completed in specific situations. However, for models to be more broadly applicable across a broad range of vegetation types and constructs, basic electrical properties of the vegetation need to be characterised. Radio waves are affected especially by water and the relationship between water content in vegetation expressed as effective water path (EWP) in mm and measured RF loss (dB) at 2.4 GHz was investigated in this work. The EWP of eucalyptus leaves of varying amounts of leaf moisture (0% - 41.5%) ranged from 0 - 14 mm, respectively. When the model was compared with the actual RF loss there was a systematic offset equivalent to a residual leaf moisture content of 6.5% that was unaccounted for in the leaf moisture content determination (oven drying). This was attributed to bound water. When the model was adjusted for this amount of additional leaf water, the average RMSE in predicted RF loss was ±2.2 dB and was found to explain 89% of the variance in measured RF loss.展开更多
The diagnosis of multiple sclerosis(MS)is based on accurate detection of lesions on magnetic resonance imaging(MRI)which also provides ongoing essential information about the progression and status of the disease.Manu...The diagnosis of multiple sclerosis(MS)is based on accurate detection of lesions on magnetic resonance imaging(MRI)which also provides ongoing essential information about the progression and status of the disease.Manual detection of lesions is very time consuming and lacks accuracy.Most of the lesions are difficult to detect manually,especially within the grey matter.This paper proposes a novel and fully automated convolution neural network(CNN)approach to segment lesions.The proposed system consists of two 2D patchwise CNNs which can segment lesions more accurately and robustly.The first CNN network is implemented to segment lesions accurately,and the second network aims to reduce the false positives to increase efficiency.The system consists of two parallel convolutional pathways,where one pathway is concatenated to the second and at the end,the fully connected layer is replaced with CNN.Three routine MRI sequences T1-w,T2-w and FLAIR are used as input to the CNN,where FLAIR is used for segmentation because most lesions on MRI appear as bright regions and T1-w&T2-w are used to reduce MRI artifacts.We evaluated the proposed system on two challenge datasets that are publicly available from MICCAI and ISBI.Quantitative and qualitative evaluation has been performed with various metrics like false positive rate(FPR),true positive rate(TPR)and dice similarities,and were compared to current state-of-the-art methods.The proposed method shows consistent higher precision and sensitivity than other methods.The proposed method can accurately and robustly segment MS lesions from images produced by different MRI scanners,with a precision up to 90%.展开更多
A filament is an important structure for studying star formation,especially intersections of filaments which are believed to be more dense than other regions.Identifying filament intersections is the first step in stu...A filament is an important structure for studying star formation,especially intersections of filaments which are believed to be more dense than other regions.Identifying filament intersections is the first step in studying them.Current methods can only extract two-dimensional intersections without considering the velocity dimension.In this paper,we propose a method to identify three-dimensional(3 D)intersections by combining Harris Corner Detection and Hough Line Transform,which achieve a precision of 98%.We apply this method for extracting intersection structures of the OMC-2/3 molecular cloud and to study its physical properties and obtain the associated PDF distribution.Results show denser gas is concentrated in those 3 D intersections.展开更多
Detection of plant water status is important for monitoring plant physiology. Previous studies showed that radio waves are attenuated when passing through vegetation such as trees, and models (both empirical and analy...Detection of plant water status is important for monitoring plant physiology. Previous studies showed that radio waves are attenuated when passing through vegetation such as trees, and models (both empirical and analytical) were developed. However, for models to be more broadly applicable across a broad range of vegetation types and constructs, basic electrical properties of the vegetation need to be characterised. In our previous work, a model was developed to calculate the RF loss through vegetation with varying water content. In this paper, the model was extended to calculate RF loss through tree canopies with or without an air gap. When the model was compared with the actual RF loss acquired using Eucalyptus <em>blakelyi</em> trees (with and without leaves), there was a systematic offset equivalent to a residual moisture content of 13% that was attributed to bound water. When the model was adjusted for the additional water content, the effective water path (EWP) was found to explain 72% of the variance in the measured RF loss.展开更多
Assessing plant water status is important for monitoring plant physiology. Previous studies showed that radio waves are attenuated when passing through vegetation such as trees. The degree of radio frequency (RF) loss...Assessing plant water status is important for monitoring plant physiology. Previous studies showed that radio waves are attenuated when passing through vegetation such as trees. The degree of radio frequency (RF) loss has previously been measured for various tree types but the relationship between water content and RF loss has not been quantified. In this study, the amount of water inside leaves was expressed as an effective water path (EWP), the thickness of a hypothetical sheet of 100% water with the same mass. A 2.4331 GHz radio wave was transmitted through a wooden frame covered on both sides with 5 mm clear acrylic sheets and filled with <em>Eucalyptus laevopinea</em> leaves. The RF loss through the leaves was measured for different stages of drying. The results showed that there is a nonlinear relationship between effective water path (EWP) in mm and RF loss in dB. It can be concluded that 2.4 GHz frequency radio waves can be used to predict the water content inside eucalyptus leaves (0 < EWP < 14 mm;RMSE ± 0.87 mm) and demonstrates the potential to measure the water content of whole trees.展开更多
The 9 th Conference of the International Society for Integrated Disaster Risk Management(IDRiM)was held in Sydney on 2–4 October 2018.The event was hosted by Data61,the data innovation hub of Australia’s National Sc...The 9 th Conference of the International Society for Integrated Disaster Risk Management(IDRiM)was held in Sydney on 2–4 October 2018.The event was hosted by Data61,the data innovation hub of Australia’s National Science Agency CSIRO.The IDRiM annual conference series traditionally brings together researchers and practitioners across all disciplines of disaster risk management(DRM)and the Australian instalment was no exception.展开更多
Ore sorting is a preconcentration technology and can dramatically reduce energy and water usage to improve the sustainability and profitability of a mining operation.In porphyry Cu deposits,Cu is the primary target,wi...Ore sorting is a preconcentration technology and can dramatically reduce energy and water usage to improve the sustainability and profitability of a mining operation.In porphyry Cu deposits,Cu is the primary target,with ores usually containing secondary‘pay’metals such as Au,Mo and gangue elements such as Fe and As.Due to sensing technology limitations,secondary and deleterious materials vary in correlation type and strength with Cu but cannot be detected simultaneously via magnetic resonance(MR)ore sorting.Inferring the relationships between Cu and other elemental abundances is particularly critical for mineral processing.The variations in metal grade relationships occur due to the transition into different geological domains.This raises two questions-how to define these geological domains and how the metal grade relationship is influenced by these geological domains.In this paper,linear relationship is assumed between Cu grade and other metal grades.We applies a Bayesian hierarchical(partial-pooling)model to quantify the linear relationships between Cu,Au,and Fe grades from geochemical bore core data.The hierarchical model was compared with two other models-‘complete-pooling’model and‘nopooling’model.Mining blocks were split based on spatial domain to construct hierarchical model.Geochemical bore core data records metal grades measured from laboratory assay with spatial coordinates of sample location.Two case studies from different porphyry Cu deposits were used to evaluate the performance of the hierarchical model.Markov chain Monte Carlo(MCMC)was used to sample the posterior parameters.Our results show that the Bayesian hierarchical model dramatically reduced the posterior predictive variance for metal grades regression compared to the no-pooling model.In addition,the posterior inference in the hierarchical model is insensitive to the choice of prior.The data is wellrepresented in the posterior which indicates a robust model.The results show that the spatial domain can be successfully utilised for metal grade regression.Uncertainty in estimating the relationship between pay metals and both secondary and gangue elements is quantified and shown to be reduced with partial-pooling.Thus,the proposed Bayesian hierarchical model can offer a reliable and stable way to monitor the relationship between metal grades for ore sorting and other mineral processing options.展开更多
Collections of biological specimens are essential in entomology laboratories for scientific knowledge and the characterization of natural varieties.It is vital to liberate useful information from physical collections ...Collections of biological specimens are essential in entomology laboratories for scientific knowledge and the characterization of natural varieties.It is vital to liberate useful information from physical collections by digitizing specimens,allowing them to be shared,examined,annotated,and compared more readily.As a result,current research has concentrated on developing 3D modeling machine systems to digitize insect specimens.Despite many great outcomes,these systems have certain drawbacks.In this research,a new scanning machine is proposed for creating 3D virtual models of insects.Our method has overcome certain previous constraints by aiding in the automation of the entire imaging process at a low cost,lowering shooting time,and generating 3D models with accurate color,high resolution,and high accuracy of insect samples with small sizes and complicated structures.Because of its ease of installation and modification,our system may be expanded and utilized in a variety of settings and areas.展开更多
Integrated water quantity and quality simulations have become a popular tool in investigations on global water crisis.For integrated and complex models,conventional uncertainty estimations focus on the uncertainties o...Integrated water quantity and quality simulations have become a popular tool in investigations on global water crisis.For integrated and complex models,conventional uncertainty estimations focus on the uncertainties of individual modules,e.g.,module parameters and structures,and do not consider the uncertainties propagated from interconnected modules.Therefore,this study investigated all the uncertainties of integrated water system simulations using the GLUE(i.e.,generalized likelihood uncertainty estimation)method,including uncertainties associated with individual modules,propagated uncertainties associated with interconnected modules,and their combinations.The changes in both acceptability thresholds of GLUE and the uncertainty estimation results were also investigated for different fixed percentages of total number of iterations(100000).Water quantity and quality variables(i.e.,runoff and ammonium nitrogen)were selected for the case study.The results showed that module uncertainty did not affect the runoff simulation performance,but remarkably weakened the water quality responses as the fixed percentage increased during calibration and validation periods.The propagated uncertainty from hydrological modules could not be ignored for water quality simulations,particularly during validation.The combination of module and propagated uncertainties further weakened the water quality simulation performance.The uncertainty intervals became wider owing to an increase in the fixed percentages and introduction of more uncertainty sources.Moreover,the acceptability threshold had a negative nonlinear relationship with the fixed percentage.The fixed percentages(20.0%-30.0%)were proposed as the acceptability thresholds owing to the satisfactory simulation performance and noticeably reduced uncertainty intervals they produced.This study provided methodological foundations for estimating multiple uncertainty sources of integrated water system models.展开更多
Organic photovoltaic(OPV)materials are promising candidates for cheap,printable solar cells.However,there are a very large number of potential donors and acceptors,making selection of the best materials difficult.Here...Organic photovoltaic(OPV)materials are promising candidates for cheap,printable solar cells.However,there are a very large number of potential donors and acceptors,making selection of the best materials difficult.Here,we show that machine-learning approaches can leverage computationally expensive DFT calculations to estimate important OPV materials properties quickly and accurately.We generate quantitative relationships between simple and interpretable chemical signature and one-hot descriptors and OPV power conversion efficiency(PCE),open circuit potential(Voc),short circuit density(Jsc),highest occupied molecular orbital(HOMO)energy,lowest unoccupied molecular orbital(LUMO)energy,and the HOMO–LUMO gap.The most robust and predictive models could predict PCE(computed by DFT)with a standard error of±0.5 for percentage PCE for both the training and test set.This model is useful for pre-screening potential donor and acceptor materials for OPV applications,accelerating design of these devices for green energy applications.展开更多
In Australia,the proportion of forest area that burns in a typical fire season is less than for other vegetation types.However,the 2019-2020 austral spring-summer was an exception,with over four times the previous max...In Australia,the proportion of forest area that burns in a typical fire season is less than for other vegetation types.However,the 2019-2020 austral spring-summer was an exception,with over four times the previous maximum area burnt in southeast Australian temperate forests.Temperate forest fires have extensive socio-economic,human health,greenhouse gas emissions,and biodiversity impacts due to high fire intensities.A robust model that identifies driving factors of forest fires and relates impact thresholds to fire activity at regional scales would help land managers and fire-fighting agencies prepare for potentially hazardous fire in Australia.Here,we developed a machine-learning diagnostic model to quantify nonlinear relationships between monthly burnt area and biophysical factors in southeast Australian forests for 2001-2020 on a 0.25°grid based on several biophysical parameters,notably fire weather and vegetation productivity.Our model explained over 80%of the variation in the burnt area.We identified that burnt area dynamics in southeast Australian forest were primarily controlled by extreme fire weather,which mainly linked to fluctuations in the Southern Annular Mode(SAM)and Indian Ocean Dipole(IOD),with a relatively smaller contribution from the central Pacific El Niño Southern Oscillation(ENSO).Our fire diagnostic model and the non-linear relationships between burnt area and environmental covariates can provide useful guidance to decision-makers who manage preparations for an upcoming fire season,and model developers working on improved early warning systems for forest fires.展开更多
Terrestrial analogues can provide essential scientific information and technology validation to assist future crewed missions to the Martian surface.This paper analyses the recent literature since 2010 in this area,hi...Terrestrial analogues can provide essential scientific information and technology validation to assist future crewed missions to the Martian surface.This paper analyses the recent literature since 2010 in this area,highlighting key topics,authors,and research groups.It reviews analogue locations,missions,the scientific impact from research activities.The findings indicate that permanent analogue sites enable reproducible science and objective comparison between studies.A standard,open registry of analogue facilities,and associated peer-reviewed research may lead to accelerated and better targeted analogue research.展开更多
This paper presents a pseudopotential lattice Boltzmann analysis to show the deficiency of previous pseudopotential models,i.e.,inconsistency between equilibrium velocity and mixture velocity.To rectify this problem,t...This paper presents a pseudopotential lattice Boltzmann analysis to show the deficiency of previous pseudopotential models,i.e.,inconsistency between equilibrium velocity and mixture velocity.To rectify this problem,there are two strategies:decoupling relaxation time and kinematic viscosity or introducing a system mixture relaxation time.Then,we constructed two modified models:a two-relaxationtime(TRT)scheme and a triple-relaxation-time(TriRT)scheme to decouple the relaxation time and kinematic viscosity.Meanwhile,inspired by the idea of a system mixture relaxation time,we developed three mixture models under different collision schemes,viz.mix-SRT,mix-TRT,and mix-TriRT models.Afterwards,we derived the advection-diffusion equation for the multicomponent system and derived the mutual diffusivity in a binarymixture.Finally,we conducted several numerical simulations to validate the analysis on these models.The numerical results show that these models can obtain smaller spurious currents than previous models and have a wider range for the accessible viscosity ratio with fourth-order isotropy.Compared to previous models,presentmodels avoid complex matrix operations and only fourth-order isotropy is required.The increased simplicity and higher computational efficiency of these models make them easy to apply to engineering and industrial applications.展开更多
文摘Alzheimer’s Disease (AD), the most common form of dementia, is an incurable neurological condition that results in a progressive mental deterioration. Although definitive diagnosis of AD is difficult, in practice, AD diagnosis is largely based on clinical history and neuropsychological data including magnetic resource imaging (MRI). Increasing research has been reported on applying machine learning to AD recognition in recent years. This paper presents our latest contribution to the advance. It describes an automatic AD recognition algorithm that is based on deep learning on 3D brain MRI. The algorithm uses a convolutional neural network (CNN) to fulfil AD recognition. It is unique in that the three dimensional topology of brain is considered as a whole in AD recognition, resulting in an accurate recognition. The CNN used in this study consists of three consecutive groups of processing layers, two fully connected layers and a classification layer. In the structure, every one of the three groups is made up of three layers, including a convolutional layer, a pooling layer and a normalization layer. The algorithm was trained and tested using the MRI data from Alzheimer’s Disease Neuroimaging Initiative. The data used include the MRI scanning of about 47 AD patients and 34 normal controls. The experiment had shown that the proposed algorithm delivered a high AD recognition accuracy with a sensitivity of 1 and a specificity of 0.93.
文摘To enable precision medicine and remote patient monitoring,internet of healthcare things(IoHT)has gained significant interest as a promising technique.With the widespread use of IoHT,nonetheless,privacy infringements such as IoHT data leakage have raised serious public concerns.On the other side,blockchain and distributed ledger technologies have demonstrated great potential for enhancing trustworthiness and privacy protection for IoHT systems.In this survey,a holistic review of existing blockchain-based IoHT systems is conducted to indicate the feasibility of combining blockchain and IoHT in privacy protection.In addition,various types of privacy challenges in IoHT are identified by examining general data protection regulation(GDPR).More importantly,an associated study of cutting-edge privacy-preserving techniques for the identified IoHT privacy challenges is presented.Finally,several challenges in four promising research areas for blockchain-based IoHT systems are pointed out,with the intent of motivating researchers working in these fields to develop possible solutions.
基金supported by the China Scholarship CouncilPostgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX17_0776)the Natural Science Foundation of NUPT(No.NY214039)
文摘A new image enhancement algorithm based on Retinex theory is proposed to solve the problem of bad visual effect of an image in low-light conditions. First, an image is converted from the RGB color space to the HSV color space to get the V channel. Next, the illuminations are respectively estimated by the guided filtering and the variational framework on the V channel and combined into a new illumination by average gradient. The new reflectance is calculated using V channel and the new illumination. Then a new V channel obtained by multiplying the new illumination and reflectance is processed with contrast limited adaptive histogram equalization(CLAHE). Finally, the new image in HSV space is converted back to RGB space to obtain the enhanced image. Experimental results show that the proposed method has better subjective quality and objective quality than existing methods.
文摘Assessing plant water status is important for monitoring plant physiology. Radio signals are attenuated when passing through vegetation. Both analytical and empirical models developed for radio frequency (RF) loss through vegetation have been dependent on experimental measurements and those measurements have been completed in specific situations. However, for models to be more broadly applicable across a broad range of vegetation types and constructs, basic electrical properties of the vegetation need to be characterised. Radio waves are affected especially by water and the relationship between water content in vegetation expressed as effective water path (EWP) in mm and measured RF loss (dB) at 2.4 GHz was investigated in this work. The EWP of eucalyptus leaves of varying amounts of leaf moisture (0% - 41.5%) ranged from 0 - 14 mm, respectively. When the model was compared with the actual RF loss there was a systematic offset equivalent to a residual leaf moisture content of 6.5% that was unaccounted for in the leaf moisture content determination (oven drying). This was attributed to bound water. When the model was adjusted for this amount of additional leaf water, the average RMSE in predicted RF loss was ±2.2 dB and was found to explain 89% of the variance in measured RF loss.
基金Thanks to research training program(RTP)of University of Newcastle,Australia and PGRSS,UON for providing funding.APC of CMC will be paid by PGRSS,UON funding.
文摘The diagnosis of multiple sclerosis(MS)is based on accurate detection of lesions on magnetic resonance imaging(MRI)which also provides ongoing essential information about the progression and status of the disease.Manual detection of lesions is very time consuming and lacks accuracy.Most of the lesions are difficult to detect manually,especially within the grey matter.This paper proposes a novel and fully automated convolution neural network(CNN)approach to segment lesions.The proposed system consists of two 2D patchwise CNNs which can segment lesions more accurately and robustly.The first CNN network is implemented to segment lesions accurately,and the second network aims to reduce the false positives to increase efficiency.The system consists of two parallel convolutional pathways,where one pathway is concatenated to the second and at the end,the fully connected layer is replaced with CNN.Three routine MRI sequences T1-w,T2-w and FLAIR are used as input to the CNN,where FLAIR is used for segmentation because most lesions on MRI appear as bright regions and T1-w&T2-w are used to reduce MRI artifacts.We evaluated the proposed system on two challenge datasets that are publicly available from MICCAI and ISBI.Quantitative and qualitative evaluation has been performed with various metrics like false positive rate(FPR),true positive rate(TPR)and dice similarities,and were compared to current state-of-the-art methods.The proposed method shows consistent higher precision and sensitivity than other methods.The proposed method can accurately and robustly segment MS lesions from images produced by different MRI scanners,with a precision up to 90%.
基金supported by the National Natural Science Foundation of China(Grant Nos.11988101,11725313,11403041,11373038 and 11373045)CAS International Partnership Program(No.114A11KYSB20160008)the Young Researcher Grant of National Astronomical Observatories,Chinese Academy of Sciences.
文摘A filament is an important structure for studying star formation,especially intersections of filaments which are believed to be more dense than other regions.Identifying filament intersections is the first step in studying them.Current methods can only extract two-dimensional intersections without considering the velocity dimension.In this paper,we propose a method to identify three-dimensional(3 D)intersections by combining Harris Corner Detection and Hough Line Transform,which achieve a precision of 98%.We apply this method for extracting intersection structures of the OMC-2/3 molecular cloud and to study its physical properties and obtain the associated PDF distribution.Results show denser gas is concentrated in those 3 D intersections.
文摘Detection of plant water status is important for monitoring plant physiology. Previous studies showed that radio waves are attenuated when passing through vegetation such as trees, and models (both empirical and analytical) were developed. However, for models to be more broadly applicable across a broad range of vegetation types and constructs, basic electrical properties of the vegetation need to be characterised. In our previous work, a model was developed to calculate the RF loss through vegetation with varying water content. In this paper, the model was extended to calculate RF loss through tree canopies with or without an air gap. When the model was compared with the actual RF loss acquired using Eucalyptus <em>blakelyi</em> trees (with and without leaves), there was a systematic offset equivalent to a residual moisture content of 13% that was attributed to bound water. When the model was adjusted for the additional water content, the effective water path (EWP) was found to explain 72% of the variance in the measured RF loss.
文摘Assessing plant water status is important for monitoring plant physiology. Previous studies showed that radio waves are attenuated when passing through vegetation such as trees. The degree of radio frequency (RF) loss has previously been measured for various tree types but the relationship between water content and RF loss has not been quantified. In this study, the amount of water inside leaves was expressed as an effective water path (EWP), the thickness of a hypothetical sheet of 100% water with the same mass. A 2.4331 GHz radio wave was transmitted through a wooden frame covered on both sides with 5 mm clear acrylic sheets and filled with <em>Eucalyptus laevopinea</em> leaves. The RF loss through the leaves was measured for different stages of drying. The results showed that there is a nonlinear relationship between effective water path (EWP) in mm and RF loss in dB. It can be concluded that 2.4 GHz frequency radio waves can be used to predict the water content inside eucalyptus leaves (0 < EWP < 14 mm;RMSE ± 0.87 mm) and demonstrates the potential to measure the water content of whole trees.
文摘The 9 th Conference of the International Society for Integrated Disaster Risk Management(IDRiM)was held in Sydney on 2–4 October 2018.The event was hosted by Data61,the data innovation hub of Australia’s National Science Agency CSIRO.The IDRiM annual conference series traditionally brings together researchers and practitioners across all disciplines of disaster risk management(DRM)and the Australian instalment was no exception.
基金This research was funded by the CSIRO ResearchPlus Science Leader Grant Program.
文摘Ore sorting is a preconcentration technology and can dramatically reduce energy and water usage to improve the sustainability and profitability of a mining operation.In porphyry Cu deposits,Cu is the primary target,with ores usually containing secondary‘pay’metals such as Au,Mo and gangue elements such as Fe and As.Due to sensing technology limitations,secondary and deleterious materials vary in correlation type and strength with Cu but cannot be detected simultaneously via magnetic resonance(MR)ore sorting.Inferring the relationships between Cu and other elemental abundances is particularly critical for mineral processing.The variations in metal grade relationships occur due to the transition into different geological domains.This raises two questions-how to define these geological domains and how the metal grade relationship is influenced by these geological domains.In this paper,linear relationship is assumed between Cu grade and other metal grades.We applies a Bayesian hierarchical(partial-pooling)model to quantify the linear relationships between Cu,Au,and Fe grades from geochemical bore core data.The hierarchical model was compared with two other models-‘complete-pooling’model and‘nopooling’model.Mining blocks were split based on spatial domain to construct hierarchical model.Geochemical bore core data records metal grades measured from laboratory assay with spatial coordinates of sample location.Two case studies from different porphyry Cu deposits were used to evaluate the performance of the hierarchical model.Markov chain Monte Carlo(MCMC)was used to sample the posterior parameters.Our results show that the Bayesian hierarchical model dramatically reduced the posterior predictive variance for metal grades regression compared to the no-pooling model.In addition,the posterior inference in the hierarchical model is insensitive to the choice of prior.The data is wellrepresented in the posterior which indicates a robust model.The results show that the spatial domain can be successfully utilised for metal grade regression.Uncertainty in estimating the relationship between pay metals and both secondary and gangue elements is quantified and shown to be reduced with partial-pooling.Thus,the proposed Bayesian hierarchical model can offer a reliable and stable way to monitor the relationship between metal grades for ore sorting and other mineral processing options.
基金supported by the National Geographic Society Exploration Grants(NGS-KOR-59552T-19).
文摘Collections of biological specimens are essential in entomology laboratories for scientific knowledge and the characterization of natural varieties.It is vital to liberate useful information from physical collections by digitizing specimens,allowing them to be shared,examined,annotated,and compared more readily.As a result,current research has concentrated on developing 3D modeling machine systems to digitize insect specimens.Despite many great outcomes,these systems have certain drawbacks.In this research,a new scanning machine is proposed for creating 3D virtual models of insects.Our method has overcome certain previous constraints by aiding in the automation of the entire imaging process at a low cost,lowering shooting time,and generating 3D models with accurate color,high resolution,and high accuracy of insect samples with small sizes and complicated structures.Because of its ease of installation and modification,our system may be expanded and utilized in a variety of settings and areas.
基金supported by the National Natural Science Foundation of China(Grant Nos.42071041 and 41807171)the Outstanding Youth Science Foundation of the National Natural Science Foundation of China(Grant No.51822908)。
文摘Integrated water quantity and quality simulations have become a popular tool in investigations on global water crisis.For integrated and complex models,conventional uncertainty estimations focus on the uncertainties of individual modules,e.g.,module parameters and structures,and do not consider the uncertainties propagated from interconnected modules.Therefore,this study investigated all the uncertainties of integrated water system simulations using the GLUE(i.e.,generalized likelihood uncertainty estimation)method,including uncertainties associated with individual modules,propagated uncertainties associated with interconnected modules,and their combinations.The changes in both acceptability thresholds of GLUE and the uncertainty estimation results were also investigated for different fixed percentages of total number of iterations(100000).Water quantity and quality variables(i.e.,runoff and ammonium nitrogen)were selected for the case study.The results showed that module uncertainty did not affect the runoff simulation performance,but remarkably weakened the water quality responses as the fixed percentage increased during calibration and validation periods.The propagated uncertainty from hydrological modules could not be ignored for water quality simulations,particularly during validation.The combination of module and propagated uncertainties further weakened the water quality simulation performance.The uncertainty intervals became wider owing to an increase in the fixed percentages and introduction of more uncertainty sources.Moreover,the acceptability threshold had a negative nonlinear relationship with the fixed percentage.The fixed percentages(20.0%-30.0%)were proposed as the acceptability thresholds owing to the satisfactory simulation performance and noticeably reduced uncertainty intervals they produced.This study provided methodological foundations for estimating multiple uncertainty sources of integrated water system models.
基金This work was supported by the Australian Government through the Australian Research Council(ARC)under the Centre of Excellence scheme(project number CE170100026)This work was also supported by computational resources provided by the Australian Government through the National Computational Infrastructure National Facility and the Pawsey Supercomputer Centre.
文摘Organic photovoltaic(OPV)materials are promising candidates for cheap,printable solar cells.However,there are a very large number of potential donors and acceptors,making selection of the best materials difficult.Here,we show that machine-learning approaches can leverage computationally expensive DFT calculations to estimate important OPV materials properties quickly and accurately.We generate quantitative relationships between simple and interpretable chemical signature and one-hot descriptors and OPV power conversion efficiency(PCE),open circuit potential(Voc),short circuit density(Jsc),highest occupied molecular orbital(HOMO)energy,lowest unoccupied molecular orbital(LUMO)energy,and the HOMO–LUMO gap.The most robust and predictive models could predict PCE(computed by DFT)with a standard error of±0.5 for percentage PCE for both the training and test set.This model is useful for pre-screening potential donor and acceptor materials for OPV applications,accelerating design of these devices for green energy applications.
基金supported by the National Natural Science Foundation of China(42088101 and 42030605)support from the research project:Towards an Operational Fire Early Warning System for Indonesia(TOFEWSI)+1 种基金The TOFEWSI project was funded from October 2017-October 2021 through the UK’s National Environment Research Council/Newton Fund on behalf of the UK Research&Innovation(NE/P014801/1)(UK Principal InvestigatorAllan Spessa)(https//tofewsi.github.io/)financial support from the Natural Science Foundation of Qinghai(2021-HZ-811)。
文摘In Australia,the proportion of forest area that burns in a typical fire season is less than for other vegetation types.However,the 2019-2020 austral spring-summer was an exception,with over four times the previous maximum area burnt in southeast Australian temperate forests.Temperate forest fires have extensive socio-economic,human health,greenhouse gas emissions,and biodiversity impacts due to high fire intensities.A robust model that identifies driving factors of forest fires and relates impact thresholds to fire activity at regional scales would help land managers and fire-fighting agencies prepare for potentially hazardous fire in Australia.Here,we developed a machine-learning diagnostic model to quantify nonlinear relationships between monthly burnt area and biophysical factors in southeast Australian forests for 2001-2020 on a 0.25°grid based on several biophysical parameters,notably fire weather and vegetation productivity.Our model explained over 80%of the variation in the burnt area.We identified that burnt area dynamics in southeast Australian forest were primarily controlled by extreme fire weather,which mainly linked to fluctuations in the Southern Annular Mode(SAM)and Indian Ocean Dipole(IOD),with a relatively smaller contribution from the central Pacific El Niño Southern Oscillation(ENSO).Our fire diagnostic model and the non-linear relationships between burnt area and environmental covariates can provide useful guidance to decision-makers who manage preparations for an upcoming fire season,and model developers working on improved early warning systems for forest fires.
文摘Terrestrial analogues can provide essential scientific information and technology validation to assist future crewed missions to the Martian surface.This paper analyses the recent literature since 2010 in this area,highlighting key topics,authors,and research groups.It reviews analogue locations,missions,the scientific impact from research activities.The findings indicate that permanent analogue sites enable reproducible science and objective comparison between studies.A standard,open registry of analogue facilities,and associated peer-reviewed research may lead to accelerated and better targeted analogue research.
基金supported by the Natural Science Foundation of Hunan Province,China(Grant No.2022JJ40466 and No.2020JJ4235)Research Foundation of Education Bureau of Hunan Province,China(Grant No.21C0213).
文摘This paper presents a pseudopotential lattice Boltzmann analysis to show the deficiency of previous pseudopotential models,i.e.,inconsistency between equilibrium velocity and mixture velocity.To rectify this problem,there are two strategies:decoupling relaxation time and kinematic viscosity or introducing a system mixture relaxation time.Then,we constructed two modified models:a two-relaxationtime(TRT)scheme and a triple-relaxation-time(TriRT)scheme to decouple the relaxation time and kinematic viscosity.Meanwhile,inspired by the idea of a system mixture relaxation time,we developed three mixture models under different collision schemes,viz.mix-SRT,mix-TRT,and mix-TriRT models.Afterwards,we derived the advection-diffusion equation for the multicomponent system and derived the mutual diffusivity in a binarymixture.Finally,we conducted several numerical simulations to validate the analysis on these models.The numerical results show that these models can obtain smaller spurious currents than previous models and have a wider range for the accessible viscosity ratio with fourth-order isotropy.Compared to previous models,presentmodels avoid complex matrix operations and only fourth-order isotropy is required.The increased simplicity and higher computational efficiency of these models make them easy to apply to engineering and industrial applications.
