Particle size fraction(clay, silt, and sand) is an important characteristic that influences several soil functions. The laser-diffraction method(LDM) provides a fast and cost-effective measurement of particle size dis...Particle size fraction(clay, silt, and sand) is an important characteristic that influences several soil functions. The laser-diffraction method(LDM) provides a fast and cost-effective measurement of particle size distribution, but the results usually differ from those obtained by the traditional sieve-pipette method(SPM). This difference can persist even when calibration is applied between the two methods. This partly relates to the different size ranges of particles measured by the two methods as a result of different operational principles, i.e., particle sedimentation according to Stokes’ Law vs. Mie theory for laser beam scattering. The objective of this study was to identify particle size ranges of LDM equivalent to those measured by SPM and evaluate whether new calibration models based on size range correction can be used to improve LDM-estimated particle size fractions, using 51 soil samples with various texture collected from five soil orders in New Zealand. Particle size distribution was determined using both LDM and SPM. Compared with SPM, original data from LDM underestimated the clay fraction(< 2 μm), overestimated the silt fraction(2–53 μm), but provided a good estimation of the sand fraction(53–2 000 μm).Results from three statistical indices, including Pearson’s correlation coefficient, slope, and Lin’s concordance correlation coefficient, showed that the size ranges of < 2 and 2–53 μm defined by SPM corresponded with the < 5 and 5–53 μm size ranges by LDM, respectively. Compared with the traditional calibration(based on the same particle size ranges), new calibration models(based on the corrected size ranges of these two methods) improved the estimation of clay and silt contents by LDM. Compared with soil-specific models(i.e., different models were developed for different soils), a universal model may be more parsimonious for estimating particle size fractions if the samples to be assessed represent multiple soil orders.展开更多
Industrial Internet of Things(IIoT)represents the expansion of the Internet of Things(IoT)in industrial sectors.It is designed to implicate embedded technologies in manufacturing fields to enhance their operations.How...Industrial Internet of Things(IIoT)represents the expansion of the Internet of Things(IoT)in industrial sectors.It is designed to implicate embedded technologies in manufacturing fields to enhance their operations.However,IIoT involves some security vulnerabilities that are more damaging than those of IoT.Accordingly,Intrusion Detection Systems(IDSs)have been developed to forestall inevitable harmful intrusions.IDSs survey the environment to identify intrusions in real time.This study designs an intrusion detection model exploiting feature engineering and machine learning for IIoT security.We combine Isolation Forest(IF)with Pearson’s Correlation Coefficient(PCC)to reduce computational cost and prediction time.IF is exploited to detect and remove outliers from datasets.We apply PCC to choose the most appropriate features.PCC and IF are applied exchangeably(PCCIF and IFPCC).The Random Forest(RF)classifier is implemented to enhance IDS performances.For evaluation,we use the Bot-IoT and NF-UNSW-NB15-v2 datasets.RF-PCCIF and RF-IFPCC show noteworthy results with 99.98%and 99.99%Accuracy(ACC)and 6.18 s and 6.25 s prediction time on Bot-IoT,respectively.The two models also score 99.30%and 99.18%ACC and 6.71 s and 6.87 s prediction time on NF-UNSW-NB15-v2,respectively.Results prove that our designed model has several advantages and higher performance than related models.展开更多
基金completed as part of the Manaaki Whenua–Landcare Research-led MBIE Program,Soil Health and Resilience—A Pathway to Prosperity and Wellbeing(No.P/442062/01)Next Generation S-Map—Smarter Decisions(No.P/443063/01)+1 种基金the Plant&Food Research-led Strategic Science Investment Fund Program,Sustainable Agro-Ecosystemsfunded by the New Zealand Ministry of Business,Innovation and Employment。
文摘Particle size fraction(clay, silt, and sand) is an important characteristic that influences several soil functions. The laser-diffraction method(LDM) provides a fast and cost-effective measurement of particle size distribution, but the results usually differ from those obtained by the traditional sieve-pipette method(SPM). This difference can persist even when calibration is applied between the two methods. This partly relates to the different size ranges of particles measured by the two methods as a result of different operational principles, i.e., particle sedimentation according to Stokes’ Law vs. Mie theory for laser beam scattering. The objective of this study was to identify particle size ranges of LDM equivalent to those measured by SPM and evaluate whether new calibration models based on size range correction can be used to improve LDM-estimated particle size fractions, using 51 soil samples with various texture collected from five soil orders in New Zealand. Particle size distribution was determined using both LDM and SPM. Compared with SPM, original data from LDM underestimated the clay fraction(< 2 μm), overestimated the silt fraction(2–53 μm), but provided a good estimation of the sand fraction(53–2 000 μm).Results from three statistical indices, including Pearson’s correlation coefficient, slope, and Lin’s concordance correlation coefficient, showed that the size ranges of < 2 and 2–53 μm defined by SPM corresponded with the < 5 and 5–53 μm size ranges by LDM, respectively. Compared with the traditional calibration(based on the same particle size ranges), new calibration models(based on the corrected size ranges of these two methods) improved the estimation of clay and silt contents by LDM. Compared with soil-specific models(i.e., different models were developed for different soils), a universal model may be more parsimonious for estimating particle size fractions if the samples to be assessed represent multiple soil orders.
文摘Industrial Internet of Things(IIoT)represents the expansion of the Internet of Things(IoT)in industrial sectors.It is designed to implicate embedded technologies in manufacturing fields to enhance their operations.However,IIoT involves some security vulnerabilities that are more damaging than those of IoT.Accordingly,Intrusion Detection Systems(IDSs)have been developed to forestall inevitable harmful intrusions.IDSs survey the environment to identify intrusions in real time.This study designs an intrusion detection model exploiting feature engineering and machine learning for IIoT security.We combine Isolation Forest(IF)with Pearson’s Correlation Coefficient(PCC)to reduce computational cost and prediction time.IF is exploited to detect and remove outliers from datasets.We apply PCC to choose the most appropriate features.PCC and IF are applied exchangeably(PCCIF and IFPCC).The Random Forest(RF)classifier is implemented to enhance IDS performances.For evaluation,we use the Bot-IoT and NF-UNSW-NB15-v2 datasets.RF-PCCIF and RF-IFPCC show noteworthy results with 99.98%and 99.99%Accuracy(ACC)and 6.18 s and 6.25 s prediction time on Bot-IoT,respectively.The two models also score 99.30%and 99.18%ACC and 6.71 s and 6.87 s prediction time on NF-UNSW-NB15-v2,respectively.Results prove that our designed model has several advantages and higher performance than related models.
