Groundwater is a crucial water source for urban areas in Africa, particularly where surface water is insufficient to meet demand. This study analyses the water quality of five shallow wells (WW1-WW5) in Half-London Wa...Groundwater is a crucial water source for urban areas in Africa, particularly where surface water is insufficient to meet demand. This study analyses the water quality of five shallow wells (WW1-WW5) in Half-London Ward, Tunduma Town, Tanzania, using Principal Component Analysis (PCA) to identify the primary factors influencing groundwater contamination. Monthly samples were collected over 12 months and analysed for physical, chemical, and biological parameters. The PCA revealed between four and six principal components (PCs) for each well, explaining between 84.61% and 92.55% of the total variance in water quality data. In WW1, five PCs captured 87.53% of the variability, with PC1 (33.05%) dominated by pH, EC, TDS, and microbial contamination, suggesting significant influences from surface runoff and pit latrines. In WW2, six PCs explained 92.55% of the variance, with PC1 (36.17%) highlighting the effects of salinity, TDS, and agricultural runoff. WW3 had four PCs explaining 84.61% of the variance, with PC1 (39.63%) showing high contributions from pH, hardness, and salinity, indicating geological influences and contamination from human activities. Similarly, in WW4, six PCs explained 90.83% of the variance, where PC1 (43.53%) revealed contamination from pit latrines and fertilizers. WW5 also had six PCs, accounting for 92.51% of the variance, with PC1 (42.73%) indicating significant contamination from agricultural runoff and pit latrines. The study concludes that groundwater quality in Half-London Ward is primarily affected by a combination of surface runoff, pit latrine contamination, agricultural inputs, and geological factors. The presence of microbial contaminants and elevated nitrate and phosphate levels underscores the need for improved sanitation and sustainable agricultural practices. Recommendations include strengthening sanitation infrastructure, promoting responsible farming techniques, and implementing regular groundwater monitoring to safeguard water resources and public health in the region.展开更多
Due to the complexity of underground engineering geology,the tunnel boring machine(TBM)usually shows poor adaptability to the surrounding rock mass,leading to machine jamming and geological hazards.For the TBM project...Due to the complexity of underground engineering geology,the tunnel boring machine(TBM)usually shows poor adaptability to the surrounding rock mass,leading to machine jamming and geological hazards.For the TBM project of Lanzhou Water Source Construction,this study proposed a neural network called PCA-GRU,which combines principal component analysis(PCA)with gated recurrent unit(GRU)to improve the accuracy of predicting rock mass classification in TBM tunneling.The input variables from the PCA dimension reduction of nine parameters in the sample data set were utilized for establishing the PCA-GRU model.Subsequently,in order to speed up the response time of surrounding rock mass classification predictions,the PCA-GRU model was optimized.Finally,the prediction results obtained by the PCA-GRU model were compared with those of four other models and further examined using random sampling analysis.As indicated by the results,the PCA-GRU model can predict the rock mass classification in TBM tunneling rapidly,requiring about 20 s to run.It performs better than the previous four models in predicting the rock mass classification,with accuracy A,macro precision MP,and macro recall MR being 0.9667,0.963,and 0.9763,respectively.In Class II,III,and IV rock mass prediction,the PCA-GRU model demonstrates better precision P and recall R owing to the dimension reduction technique.The random sampling analysis indicates that the PCA-GRU model shows stronger generalization,making it more appropriate in situations where the distribution of various rock mass classes and lithologies change in percentage.展开更多
In this research,the performance of regular rapeseed oil(RSO)and modified low-linolenic rapeseed oil(LLRO)during frying was assessed using a frying procedure that commonly found in fast-food restaurants.Key physicoche...In this research,the performance of regular rapeseed oil(RSO)and modified low-linolenic rapeseed oil(LLRO)during frying was assessed using a frying procedure that commonly found in fast-food restaurants.Key physicochemical attributes of these oils were investigated.RSO and LLRO differed for initial linolenic acid(12.21%vs.2.59%),linoleic acid(19.15%vs.24.73%).After 6 successive days frying period of French fries,the ratio of linoleic acid to palmitic acid dropped by 54.49%in RSO,higher than that in LLRO(51.54%).The increment in total oxidation value for LLRO(40.46 unit)was observed to be significantly lower than those of RSO(42.58 unit).The changes in carbonyl group value and iodine value throughout the frying trial were also lower in LLRO compared to RSO.The formation rate in total polar compounds for LLRO was 1.08%per frying day,lower than that of RSO(1.31%).In addition,the formation in color component and degradation in tocopherols were proportional to the frying time for two frying oils.Besides,a longer induction period was also observed in LLRO(8.87 h)compared to RSO(7.68 h)after frying period.Overall,LLRO exhibited the better frying stability,which was confirmed by principal component analysis(PCA).展开更多
The evaluation model was established to estimate the number of houses collapsed during typhoon disaster for Zhejiang Province.The factor leading to disaster,the environment fostering disaster and the exposure of build...The evaluation model was established to estimate the number of houses collapsed during typhoon disaster for Zhejiang Province.The factor leading to disaster,the environment fostering disaster and the exposure of buildings were processed by Principal Component Analysis.The key factor was extracted to support input of vector machine model and to build an evaluation model;the historical fitting result kept in line with the fact.In the real evaluation of two typhoons landed in Zhejiang Province in 2008 and 2009,the coincidence of evaluating result and actual value proved the feasibility of this model.展开更多
Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challe...Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.展开更多
Popular descriptive multivariate statistical method currently employed is the principal component analyses （PCA） method. PCA is used to develop linear combinations that successively maximize the total variance of a ...Popular descriptive multivariate statistical method currently employed is the principal component analyses （PCA） method. PCA is used to develop linear combinations that successively maximize the total variance of a sample where there is no known group structure. This study aimed at demonstrating the performance evaluation of pilot activated sludge treatment system by inoculating a strain of Pseudomonas capable of degrading malathion which was isolated by enrichment technique. An intensive analytical program was followed for evaluating the efficiency of biosimulator by maintaining the dissolved oxygen （DO） concentration at 4.0 mg/L. Analyses by high performance liquid chromatographic technique revealed that 90% of malathion removal was achieved within 29 h of treatment whereas COD got reduced considerably during the treatment process and mean removal efficiency was found to be 78%. The mean pH values increased gradually during the treatment process ranging from 7.36-8.54. Similarly the mean ammonia-nitrogen （NH3-N） values were found to be fluctuating between 19.425-28.488 mg/L, mean nitrite-nitrogen （NO3-N） ranging between 1.301- 2.940 mg/L and mean nitrate-nitrogen （NO3-N） ranging between 0.0071-0.0711 mg/L. The study revealed that inoculation of bacterial culture under laboratory conditions could be used in bioremediation of environmental pollution caused by xenobiotics. The PCA analyses showed that pH, COD, organic load and total malathion concentration were highly correlated and emerged as the variables controlling the first component, whereas dissolved oxygen, NO3-N and NH3-N governed the second component. The third component repeated the trend exhibited by the first two components.展开更多
Continued innovation in screening methodologies remains important for the discovery of high-quality multiactive fungi,which have been of great significance to the development of new drugs.Mangrove-derived fungi,which ...Continued innovation in screening methodologies remains important for the discovery of high-quality multiactive fungi,which have been of great significance to the development of new drugs.Mangrove-derived fungi,which are well recognized as prolific sources of natural products,are worth sustained attention and further study.In this study,118 fungi,which mainly included Aspergillus spp.(34.62%)and Penicillium spp.(15.38%),were isolated from the mangrove ecosystem of the Maowei Sea,and 83.1%of the cultured fungi showed at least one bioactivity in four antibacterial and three antioxidant assays.To accurately evaluate the fungal bioactivities,the fungi with multiple bioactivities were successfully evaluated and screened by principal component analysis(PCA),and this analysis provided a dataset for comparing and selecting multibioactive fungi.Among the 118 mangrove-derived fungi tested in this study,Aspergillus spp.showed the best comprehensive activity.Fungi such as A.clavatonanicus,A.flavipes and A.citrinoterreus,which exhibited high comprehensive bioactivity as determined by the PCA,have great potential in the exploitation of natural products and the development of new drugs.This study demonstrated the first use of PCA as a time-saving,scientific method with a strong ability to evaluate and screen multiactive fungi,which indicated that this method can affect the discovery and development of new drugs.展开更多
The healthy condition of the milling tool has a very high impact on the machining quality of the titanium components.Therefore,it is important to recognize the healthy condition of the tool and replace the damaged cut...The healthy condition of the milling tool has a very high impact on the machining quality of the titanium components.Therefore,it is important to recognize the healthy condition of the tool and replace the damaged cutter at the right time.In order to recognize the health condition of the milling cutter,a method based on the long short term memory(LSTM)was proposed to recognize tool health state in this paper.The various signals collected in the tool wear experiments were analyzed by time-domain statistics,and then the extracted data were generated by principal component analysis(PCA)method.The preprocessed data extracted by PCA is transmitted to the LSTM model for recognition.Compared with back propagation neural network(BPNN)and support vector machine(SVM),the proposed method can effectively utilize the time-domain regulation in the data to achieve higher recognition speed and accuracy.展开更多
Multi-way principal component analysis (MPCA) is the most widely utilized multivariate statistical process control method for batch processes. Previous research on MPCA has commonly agreed that it is not a suitable me...Multi-way principal component analysis (MPCA) is the most widely utilized multivariate statistical process control method for batch processes. Previous research on MPCA has commonly agreed that it is not a suitable method for multiphase batch process analysis. In this paper, abundant phase information is revealed by way of partitioning MPCA model, and a new phase identification method based on global dynamic information is proposed. The application to injection molding shows that it is a feasible and effective method for multiphase batch process knowledge understanding, phase division and process monitoring.展开更多
We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were use...We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response.展开更多
With recent advances in biotechnology, genome-wide association study （GWAS） has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistica...With recent advances in biotechnology, genome-wide association study （GWAS） has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistical strategy is traditional logistical regression （LR） based on single-locus analysis. However, such a single-locus analysis leads to the well-known multiplicity problem, with a risk of inflating type I error and reducing power. Dimension reduction-based techniques, such as principal component-based logistic regression （PC-LR）, partial least squares-based logistic regression （PLS-LR）, have recently gained much attention in the analysis of high dimensional genomic data. However, the perfor- mance of these methods is still not clear, especially in GWAS. We conducted simulations and real data application to compare the type I error and power of PC-LR, PLS-LR and LR applicable to GWAS within a defined single nucleotide polymorphism （SNP） set region. We found that PC-LR and PLS can reasonably control type I error under null hypothesis. On contrast, LR, which is corrected by Bonferroni method, was more conserved in all simulation settings. In particular, we found that PC-LR and PLS-LR had comparable power and they both outperformed LR, especially when the causal SNP was in high linkage disequilibrium with genotyped ones and with a small effective size in simulation. Based on SNP set analysis, we applied all three methods to analyze non-small cell lung cancer GWAS data.展开更多
A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the mai...A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.展开更多
Water quality monitoring has one of the highest priorities in surface water protection policy. Many variety approaches are being used to interpret and analyze the concealed variables that determine the variance of obs...Water quality monitoring has one of the highest priorities in surface water protection policy. Many variety approaches are being used to interpret and analyze the concealed variables that determine the variance of observed water quality of various source points. A considerable proportion of these approaches are mainly based on statistical methods, multivariate statistical techniques in particular. In the present study, the use of multivariate techniques is required to reduce the large variables number of Nile River water quality upstream Cairo Drinking Water Plants (CDWPs) and determination of relationships among them for easy and robust evaluation. By means of multivariate statistics of principal components analysis (PCA), Fuzzy C-Means (FCM) and K-means algorithm for clustering analysis, this study attempted to determine the major dominant factors responsible for the variations of Nile River water quality upstream Cairo Drinking Water Plants (CDWPs). Furthermore, cluster analysis classified 21 sampling stations into three clusters based on similarities of water quality features. The result of PCA shows that 6 principal components contain the key variables and account for 75.82% of total variance of the study area surface water quality and the dominant water quality parameters were: Conductivity, Iron, Biological Oxygen Demand (BOD), Total Coliform (TC), Ammonia (NH3), and pH. However, the results from both of FCM clustering and K-means algorithm, based on the dominant parameters concentrations, determined 3 cluster groups and produced cluster centers (prototypes). Based on clustering classification, a noted water quality deteriorating as the cluster number increased from 1 to 3. However the cluster grouping can be used to identify the physical, chemical and biological processes creating the variations in the water quality parameters. This study revealed that multivariate analysis techniques, as the extracted water quality dominant parameters and clustered information can be used in reducing the number of sampling parameters on the Nile River in a cost effective and efficient way instead of using a large set of parameters without missing much information. These techniques can be helpful for decision makers to obtain a global view on the water quality in any surface water or other water bodies when analyzing large data sets especially without a priori knowledge about relationships between them.展开更多
AIM: To investigate the metabolic profiles of xenograft pancreatic cancer before and after radiotherapy by high-resolution magic angle spinning proton magnetic resonance spectroscopy (HRMAS 1H NMR) combined with princ...AIM: To investigate the metabolic profiles of xenograft pancreatic cancer before and after radiotherapy by high-resolution magic angle spinning proton magnetic resonance spectroscopy (HRMAS 1H NMR) combined with principal components analysis (PCA) and evaluate the radiotherapeutic effect. METHODS: The nude mouse xenograft model of human pancreatic cancer was established by injecting human pancreatic cancer cell SW1990 subcutaneously into the nude mice. When the tumors volume reached 800 mm3 , the mice received various radiation doses. Two weeks later, tumor tissue sections were prepared for running the NMR measurements. 1H NMR and PCA were used to determine the changes in the metabolic profiles of tumor tissues after radiotherapy. Metabolic profiles of normal pancreas, pancreatic tumor tissues, and radiationtreated pancreatic tumor tissues were compared. RESULTS: Compared with 1H NMR spectra of the normal nude mouse pancreas, the levels of choline, taurine, alanine, isoleucine, leucine, valine, lactate, and glutamic acid of the pancreatic cancer group were increased, whereas an opposite trend for phosphocholine, glycerophosphocholine, and betaine was observed. The ratio of phosphocholine to creatine, and glycerophosphocholine to creatine showed noticeable decrease in the pancreatic cancer group. After further evaluation of the tissue metabolic profile after treatment with three different radiation doses, no significant change in metabolites was observed in the 1H NMR spectra, while the inhibition of tumor growth was in proportion to the radiation doses. However, PCA results showed that the levels of choline and betaine were decreased with the increased radiation dose, and conversely, the level of acetic acid was dramatically increased. CONCLUSION: The combined methods were demonstrated to have the potential for allowing early diagnosis and assessment of pancreatic cancer response to radiotherapy.展开更多
5 critical quality characteristics must be controlled in the surface mount and wire-bond process in semiconductor packaging. And these characteristics are correlated with each other. So the principal components analy...5 critical quality characteristics must be controlled in the surface mount and wire-bond process in semiconductor packaging. And these characteristics are correlated with each other. So the principal components analysis（PCA） is used in the analysis of the sample data firstly. And then the process is controlled with hotelling T^2 control chart for the first several principal components which contain sufficient information. Furthermore, a software tool is developed for this kind of problems. And with sample data from a surface mounting device（SMD） process, it is demonstrated that the T^2 control chart with PCA gets the same conclusion as without PCA, but the problem is transformed from high-dimensional one to a lower dimensional one, i.e., from 5 to 2 in this demonstration.展开更多
The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scatt...The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.展开更多
文摘Groundwater is a crucial water source for urban areas in Africa, particularly where surface water is insufficient to meet demand. This study analyses the water quality of five shallow wells (WW1-WW5) in Half-London Ward, Tunduma Town, Tanzania, using Principal Component Analysis (PCA) to identify the primary factors influencing groundwater contamination. Monthly samples were collected over 12 months and analysed for physical, chemical, and biological parameters. The PCA revealed between four and six principal components (PCs) for each well, explaining between 84.61% and 92.55% of the total variance in water quality data. In WW1, five PCs captured 87.53% of the variability, with PC1 (33.05%) dominated by pH, EC, TDS, and microbial contamination, suggesting significant influences from surface runoff and pit latrines. In WW2, six PCs explained 92.55% of the variance, with PC1 (36.17%) highlighting the effects of salinity, TDS, and agricultural runoff. WW3 had four PCs explaining 84.61% of the variance, with PC1 (39.63%) showing high contributions from pH, hardness, and salinity, indicating geological influences and contamination from human activities. Similarly, in WW4, six PCs explained 90.83% of the variance, where PC1 (43.53%) revealed contamination from pit latrines and fertilizers. WW5 also had six PCs, accounting for 92.51% of the variance, with PC1 (42.73%) indicating significant contamination from agricultural runoff and pit latrines. The study concludes that groundwater quality in Half-London Ward is primarily affected by a combination of surface runoff, pit latrine contamination, agricultural inputs, and geological factors. The presence of microbial contaminants and elevated nitrate and phosphate levels underscores the need for improved sanitation and sustainable agricultural practices. Recommendations include strengthening sanitation infrastructure, promoting responsible farming techniques, and implementing regular groundwater monitoring to safeguard water resources and public health in the region.
基金State Key Laboratory of Hydroscience and Hydraulic Engineering of Tsinghua University,Grant/Award Number:2019-KY-03Key Technology of Intelligent Construction of Urban Underground Space of North China University of Technology,Grant/Award Number:110051360022XN108-19+3 种基金Research Start-up Fund Project of North China University of Technology,Grant/Award Number:110051360002Yujie Project of North China University of Technology,Grant/Award Number:216051360020XN199/006National Natural Science Foundation of China,Grant/Award Numbers:51522903,51774184National Key R&D Program of China,Grant/Award Numbers:2018YFC1504801,2018YFC1504902。
文摘Due to the complexity of underground engineering geology,the tunnel boring machine(TBM)usually shows poor adaptability to the surrounding rock mass,leading to machine jamming and geological hazards.For the TBM project of Lanzhou Water Source Construction,this study proposed a neural network called PCA-GRU,which combines principal component analysis(PCA)with gated recurrent unit(GRU)to improve the accuracy of predicting rock mass classification in TBM tunneling.The input variables from the PCA dimension reduction of nine parameters in the sample data set were utilized for establishing the PCA-GRU model.Subsequently,in order to speed up the response time of surrounding rock mass classification predictions,the PCA-GRU model was optimized.Finally,the prediction results obtained by the PCA-GRU model were compared with those of four other models and further examined using random sampling analysis.As indicated by the results,the PCA-GRU model can predict the rock mass classification in TBM tunneling rapidly,requiring about 20 s to run.It performs better than the previous four models in predicting the rock mass classification,with accuracy A,macro precision MP,and macro recall MR being 0.9667,0.963,and 0.9763,respectively.In Class II,III,and IV rock mass prediction,the PCA-GRU model demonstrates better precision P and recall R owing to the dimension reduction technique.The random sampling analysis indicates that the PCA-GRU model shows stronger generalization,making it more appropriate in situations where the distribution of various rock mass classes and lithologies change in percentage.
基金This work was financially supported by the Science and Technology Research Project of Jiangxi Provincial Education Department(GJJ210322)the National Natural Science Foundation of China(No.32260635).
文摘In this research,the performance of regular rapeseed oil(RSO)and modified low-linolenic rapeseed oil(LLRO)during frying was assessed using a frying procedure that commonly found in fast-food restaurants.Key physicochemical attributes of these oils were investigated.RSO and LLRO differed for initial linolenic acid(12.21%vs.2.59%),linoleic acid(19.15%vs.24.73%).After 6 successive days frying period of French fries,the ratio of linoleic acid to palmitic acid dropped by 54.49%in RSO,higher than that in LLRO(51.54%).The increment in total oxidation value for LLRO(40.46 unit)was observed to be significantly lower than those of RSO(42.58 unit).The changes in carbonyl group value and iodine value throughout the frying trial were also lower in LLRO compared to RSO.The formation rate in total polar compounds for LLRO was 1.08%per frying day,lower than that of RSO(1.31%).In addition,the formation in color component and degradation in tocopherols were proportional to the frying time for two frying oils.Besides,a longer induction period was also observed in LLRO(8.87 h)compared to RSO(7.68 h)after frying period.Overall,LLRO exhibited the better frying stability,which was confirmed by principal component analysis(PCA).
基金Supported by Scientific Research Project for Commonwealth (GYHY200806017)Innovation Project for Graduate of Jiangsu Province (CX09S-018Z)
文摘The evaluation model was established to estimate the number of houses collapsed during typhoon disaster for Zhejiang Province.The factor leading to disaster,the environment fostering disaster and the exposure of buildings were processed by Principal Component Analysis.The key factor was extracted to support input of vector machine model and to build an evaluation model;the historical fitting result kept in line with the fact.In the real evaluation of two typhoons landed in Zhejiang Province in 2008 and 2009,the coincidence of evaluating result and actual value proved the feasibility of this model.
基金This work was supported by the Pilot Seed Grant(Grant No.RES0049944)the Collaborative Research Project(Grant No.RES0043251)from the University of Alberta.
文摘Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.
文摘Popular descriptive multivariate statistical method currently employed is the principal component analyses （PCA） method. PCA is used to develop linear combinations that successively maximize the total variance of a sample where there is no known group structure. This study aimed at demonstrating the performance evaluation of pilot activated sludge treatment system by inoculating a strain of Pseudomonas capable of degrading malathion which was isolated by enrichment technique. An intensive analytical program was followed for evaluating the efficiency of biosimulator by maintaining the dissolved oxygen （DO） concentration at 4.0 mg/L. Analyses by high performance liquid chromatographic technique revealed that 90% of malathion removal was achieved within 29 h of treatment whereas COD got reduced considerably during the treatment process and mean removal efficiency was found to be 78%. The mean pH values increased gradually during the treatment process ranging from 7.36-8.54. Similarly the mean ammonia-nitrogen （NH3-N） values were found to be fluctuating between 19.425-28.488 mg/L, mean nitrite-nitrogen （NO3-N） ranging between 1.301- 2.940 mg/L and mean nitrate-nitrogen （NO3-N） ranging between 0.0071-0.0711 mg/L. The study revealed that inoculation of bacterial culture under laboratory conditions could be used in bioremediation of environmental pollution caused by xenobiotics. The PCA analyses showed that pH, COD, organic load and total malathion concentration were highly correlated and emerged as the variables controlling the first component, whereas dissolved oxygen, NO3-N and NH3-N governed the second component. The third component repeated the trend exhibited by the first two components.
基金the Key R&D Program of Shandong Province(No.2020CXGC010703)the Key Project of the Natural Science Foundation of Shandong Province(No.ZR2020 KB021)。
文摘Continued innovation in screening methodologies remains important for the discovery of high-quality multiactive fungi,which have been of great significance to the development of new drugs.Mangrove-derived fungi,which are well recognized as prolific sources of natural products,are worth sustained attention and further study.In this study,118 fungi,which mainly included Aspergillus spp.(34.62%)and Penicillium spp.(15.38%),were isolated from the mangrove ecosystem of the Maowei Sea,and 83.1%of the cultured fungi showed at least one bioactivity in four antibacterial and three antioxidant assays.To accurately evaluate the fungal bioactivities,the fungi with multiple bioactivities were successfully evaluated and screened by principal component analysis(PCA),and this analysis provided a dataset for comparing and selecting multibioactive fungi.Among the 118 mangrove-derived fungi tested in this study,Aspergillus spp.showed the best comprehensive activity.Fungi such as A.clavatonanicus,A.flavipes and A.citrinoterreus,which exhibited high comprehensive bioactivity as determined by the PCA,have great potential in the exploitation of natural products and the development of new drugs.This study demonstrated the first use of PCA as a time-saving,scientific method with a strong ability to evaluate and screen multiactive fungi,which indicated that this method can affect the discovery and development of new drugs.
基金National Natural Science Foundation of China(No.51805079)Shanghai Natural Science Foundation,China(No.17ZR1400600)Fundamental Research Funds for the Central Universities,China(No.16D110309)
文摘The healthy condition of the milling tool has a very high impact on the machining quality of the titanium components.Therefore,it is important to recognize the healthy condition of the tool and replace the damaged cutter at the right time.In order to recognize the health condition of the milling cutter,a method based on the long short term memory(LSTM)was proposed to recognize tool health state in this paper.The various signals collected in the tool wear experiments were analyzed by time-domain statistics,and then the extracted data were generated by principal component analysis(PCA)method.The preprocessed data extracted by PCA is transmitted to the LSTM model for recognition.Compared with back propagation neural network(BPNN)and support vector machine(SVM),the proposed method can effectively utilize the time-domain regulation in the data to achieve higher recognition speed and accuracy.
基金Supported by the Guangzhou Scientific and Technological Project (2012J5100032)Nansha District Independent Innovation Project (201103003)
文摘Multi-way principal component analysis (MPCA) is the most widely utilized multivariate statistical process control method for batch processes. Previous research on MPCA has commonly agreed that it is not a suitable method for multiphase batch process analysis. In this paper, abundant phase information is revealed by way of partitioning MPCA model, and a new phase identification method based on global dynamic information is proposed. The application to injection molding shows that it is a feasible and effective method for multiphase batch process knowledge understanding, phase division and process monitoring.
文摘We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response.
基金founded by the National Natural Science Foundation of China(81202283,81473070,81373102 and81202267)Key Grant of Natural Science Foundation of the Jiangsu Higher Education Institutions of China(10KJA330034 and11KJA330001)+1 种基金the Research Fund for the Doctoral Program of Higher Education of China(20113234110002)the Priority Academic Program for the Development of Jiangsu Higher Education Institutions(Public Health and Preventive Medicine)
文摘With recent advances in biotechnology, genome-wide association study （GWAS） has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistical strategy is traditional logistical regression （LR） based on single-locus analysis. However, such a single-locus analysis leads to the well-known multiplicity problem, with a risk of inflating type I error and reducing power. Dimension reduction-based techniques, such as principal component-based logistic regression （PC-LR）, partial least squares-based logistic regression （PLS-LR）, have recently gained much attention in the analysis of high dimensional genomic data. However, the perfor- mance of these methods is still not clear, especially in GWAS. We conducted simulations and real data application to compare the type I error and power of PC-LR, PLS-LR and LR applicable to GWAS within a defined single nucleotide polymorphism （SNP） set region. We found that PC-LR and PLS can reasonably control type I error under null hypothesis. On contrast, LR, which is corrected by Bonferroni method, was more conserved in all simulation settings. In particular, we found that PC-LR and PLS-LR had comparable power and they both outperformed LR, especially when the causal SNP was in high linkage disequilibrium with genotyped ones and with a small effective size in simulation. Based on SNP set analysis, we applied all three methods to analyze non-small cell lung cancer GWAS data.
基金Project(70671039) supported by the National Natural Science Foundation of China
文摘A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.
文摘Water quality monitoring has one of the highest priorities in surface water protection policy. Many variety approaches are being used to interpret and analyze the concealed variables that determine the variance of observed water quality of various source points. A considerable proportion of these approaches are mainly based on statistical methods, multivariate statistical techniques in particular. In the present study, the use of multivariate techniques is required to reduce the large variables number of Nile River water quality upstream Cairo Drinking Water Plants (CDWPs) and determination of relationships among them for easy and robust evaluation. By means of multivariate statistics of principal components analysis (PCA), Fuzzy C-Means (FCM) and K-means algorithm for clustering analysis, this study attempted to determine the major dominant factors responsible for the variations of Nile River water quality upstream Cairo Drinking Water Plants (CDWPs). Furthermore, cluster analysis classified 21 sampling stations into three clusters based on similarities of water quality features. The result of PCA shows that 6 principal components contain the key variables and account for 75.82% of total variance of the study area surface water quality and the dominant water quality parameters were: Conductivity, Iron, Biological Oxygen Demand (BOD), Total Coliform (TC), Ammonia (NH3), and pH. However, the results from both of FCM clustering and K-means algorithm, based on the dominant parameters concentrations, determined 3 cluster groups and produced cluster centers (prototypes). Based on clustering classification, a noted water quality deteriorating as the cluster number increased from 1 to 3. However the cluster grouping can be used to identify the physical, chemical and biological processes creating the variations in the water quality parameters. This study revealed that multivariate analysis techniques, as the extracted water quality dominant parameters and clustered information can be used in reducing the number of sampling parameters on the Nile River in a cost effective and efficient way instead of using a large set of parameters without missing much information. These techniques can be helpful for decision makers to obtain a global view on the water quality in any surface water or other water bodies when analyzing large data sets especially without a priori knowledge about relationships between them.
基金Supported by The Medical Imageology Special Purpose Foundation of Cancer Hospital/Cancer Institute Hehai University, No.YX200802
文摘AIM: To investigate the metabolic profiles of xenograft pancreatic cancer before and after radiotherapy by high-resolution magic angle spinning proton magnetic resonance spectroscopy (HRMAS 1H NMR) combined with principal components analysis (PCA) and evaluate the radiotherapeutic effect. METHODS: The nude mouse xenograft model of human pancreatic cancer was established by injecting human pancreatic cancer cell SW1990 subcutaneously into the nude mice. When the tumors volume reached 800 mm3 , the mice received various radiation doses. Two weeks later, tumor tissue sections were prepared for running the NMR measurements. 1H NMR and PCA were used to determine the changes in the metabolic profiles of tumor tissues after radiotherapy. Metabolic profiles of normal pancreas, pancreatic tumor tissues, and radiationtreated pancreatic tumor tissues were compared. RESULTS: Compared with 1H NMR spectra of the normal nude mouse pancreas, the levels of choline, taurine, alanine, isoleucine, leucine, valine, lactate, and glutamic acid of the pancreatic cancer group were increased, whereas an opposite trend for phosphocholine, glycerophosphocholine, and betaine was observed. The ratio of phosphocholine to creatine, and glycerophosphocholine to creatine showed noticeable decrease in the pancreatic cancer group. After further evaluation of the tissue metabolic profile after treatment with three different radiation doses, no significant change in metabolites was observed in the 1H NMR spectra, while the inhibition of tumor growth was in proportion to the radiation doses. However, PCA results showed that the levels of choline and betaine were decreased with the increased radiation dose, and conversely, the level of acetic acid was dramatically increased. CONCLUSION: The combined methods were demonstrated to have the potential for allowing early diagnosis and assessment of pancreatic cancer response to radiotherapy.
基金This project is supported by National Natural Science Foundation of China (No.70372062)Hi-Tech Program of Tianjin city,China (No.04310881R).
文摘5 critical quality characteristics must be controlled in the surface mount and wire-bond process in semiconductor packaging. And these characteristics are correlated with each other. So the principal components analysis（PCA） is used in the analysis of the sample data firstly. And then the process is controlled with hotelling T^2 control chart for the first several principal components which contain sufficient information. Furthermore, a software tool is developed for this kind of problems. And with sample data from a surface mounting device（SMD） process, it is demonstrated that the T^2 control chart with PCA gets the same conclusion as without PCA, but the problem is transformed from high-dimensional one to a lower dimensional one, i.e., from 5 to 2 in this demonstration.
基金supported by the National Key Research and Development Program of China(No.2018YFA0702800)the National Natural Science Foundation of China(No.12072056)supported by National Defense Fundamental Scientific Research Project(XXXX2018204BXXX).
文摘The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.
