Simultaneous determination of typical toxic aldehydes formed during food frying and digestion using isotope dilution UHPLC-QqQ-MS/MS

An isotope dilution ultra-performance liquid chromatography-triple quadrupole mass spectrometry method was developed to simultaneously detect two typical kinds ofα,β-unsaturated aldehydes,namely 4-hydroxy-2-hexenal(4-HHE)and 4-hydroxy-2-nonenal(4-HNE),in foods.The proposed method exhibited a linear range of 10-1000 ng/mL with a limit of detection of 0.1-2.0 ng/g and a limit of quantification of 0.3-5.0 ng/g.The recovery rates of these typical toxic aldehydes(i.e.,4-HHE,4-HNE)and their d3-labeled analogues were 91.54%-105.12%with a low matrix effect.Furthermore,this proposed method was successfully applied to a real frying system and a simulated digestion system,wherein the contents of 4-HHE and 4-HNE were determined for both.Overall,the obtained results provide strong support for further research into the production of 4-HHE and 4-HNE resulting from foods during oil digestion and frying.

Two-dimensional plane strain consolidation of unsaturated soils considering the depth-dependent stress

In practical engineering,the total vertical stress in the soil layer is not constant due to stress diffusion,and varies with time and depth.Therefore,the purpose of this paper is to investigate the effect of stress diffusion on the two-dimensional(2D)plane strain consolidation properties of unsaturated soils when the stress varies with time and depth.A series of semi-analytical solutions in terms of excess pore air and water pressures and settlement for 2D plane strain consolidation of unsaturated soils can be derived with the joint use of Laplace transform and Fourier sine series expansion.Then,the inverse Laplace transform of the semi-analytical solution is given in the time domain using a self-programmed code based on Crump’s method.The reliability of the obtained solutions is proved by the degeneration.Finally,the 2D plots of excess pore pressures and the curves of settlement varying with time,considering different physical parameters of unsaturated soil stratum and depth-dependent stress,are depicted and analyzed to study the 2D plane strain consolidation properties of unsaturated soils subjected to the depthdependent stress.

食品工程研究生导师团队培养模式研究

国内外发展趋势表明,构建研究生导师团队培养模式将成为高校研究生培养的主流。本文以昆明理工大学食品科学与工程学院食品功能因子研究与利用团队为例,阐述了传统单一导师培养模式存在的弊端,提出了研究生导师团队建设以及研究生培养的具体实践方案,以期为食品工程硕士研究生的培养提供借鉴。

Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data

Slope failures lead to catastrophic consequences in numerous countries and thus the stability assessment for slopes is of high interest in geotechnical and geological engineering researches.A hybrid stacking ensemble approach is proposed in this study for enhancing the prediction of slope stability.In the hybrid stacking ensemble approach,we used an artificial bee colony(ABC)algorithm to find out the best combination of base classifiers(level 0)and determined a suitable meta-classifier(level 1)from a pool of 11 individual optimized machine learning(OML)algorithms.Finite element analysis(FEA)was conducted in order to form the synthetic database for the training stage(150 cases)of the proposed model while 107 real field slope cases were used for the testing stage.The results by the hybrid stacking ensemble approach were then compared with that obtained by the 11 individual OML methods using confusion matrix,F1-score,and area under the curve,i.e.AUC-score.The comparisons showed that a significant improvement in the prediction ability of slope stability has been achieved by the hybrid stacking ensemble(AUC?90.4%),which is 7%higher than the best of the 11 individual OML methods(AUC?82.9%).Then,a further comparison was undertaken between the hybrid stacking ensemble method and basic ensemble classifier on slope stability prediction.The results showed a prominent performance of the hybrid stacking ensemble method over the basic ensemble method.Finally,the importance of the variables for slope stability was studied using linear vector quantization(LVQ)method.

Modelling the performance of EPB shield tunnelling using machine and deep learning algorithms

This paper introduces an intelligent framework for predicting the advancing speed during earth pressure balance(EPB)shield tunnelling.Five artificial intelligence(AI)models based on machine and deep learning techniques-back-propagation neural network(BPNN),extreme learning machine(ELM),support vector machine(SVM),long-short term memory(LSTM),and gated recurrent unit(GRU)-are used.Five geological and nine operational parameters that influence the advancing speed are considered.A field case of shield tunnelling in Shenzhen City,China is analyzed using the developed models.A total of 1000 field datasets are adopted to establish intelligent models.The prediction performance of the five models is ranked as GRU>LSTM>SVM>ELM>BPNN.Moreover,the Pearson correlation coefficient(PCC)is adopted for sensitivity analysis.The results reveal that the main thrust(MT),penetration(P),foam volume(FV),and grouting volume(GV)have strong correlations with advancing speed(AS).An empirical formula is constructed based on the high-correlation influential factors and their corresponding field datasets.Finally,the prediction performances of the intelligent models and the empirical method are compared.The results reveal that all the intelligent models perform better than the empirical method.

Prediction of Disc Cutter Life During Shield Tunneling with AI via the Incorporation of a Genetic Algorithm into a GMDH-Type Neural Network

Disc cutter consumption is a critical problem that influences work performance during shield tunneling processes and directly affects the cutter change decision.This study proposes a new model to estimate the disc cutter life(Hf)by integrating a group method of data handling(GMDH)-type neural network(NN)with a genetic algorithm(GA).The efficiency and effectiveness of the GMDH network structure are optimized by the GA,which enables each neuron to search for its optimum connections set from the previous layer.With the proposed model,monitoring data including the shield performance database,disc cutter consumption,geological conditions,and operational parameters can be analyzed.To verify the performance of the proposed model,a case study in China is presented and a database is adopted to illustrate the excellence of the hybrid model.The results indicate that the hybrid model predicts disc cutter life with high accuracy.The sensitivity analysis reveals that the penetration rate(PR)has a significant influence on disc cutter life.The results of this study can be beneficial in both the planning and construction stages of shield tunneling.

Real-time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural network

An accurate prediction of earth pressure balance(EPB)shield moving performance is important to ensure the safety tunnel excavation.A hybrid model is developed based on the particle swarm optimization(PSO)and gated recurrent unit(GRU)neural network.PSO is utilized to assign the optimal hyperparameters of GRU neural network.There are mainly four steps:data collection and processing,hybrid model establishment,model performance evaluation and correlation analysis.The developed model provides an alternative to tackle with time-series data of tunnel project.Apart from that,a novel framework about model application is performed to provide guidelines in practice.A tunnel project is utilized to evaluate the performance of proposed hybrid model.Results indicate that geological and construction variables are significant to the model performance.Correlation analysis shows that construction variables(main thrust and foam liquid volume)display the highest correlation with the cutterhead torque(CHT).This work provides a feasible and applicable alternative way to estimate the performance of shield tunneling.

Hybrid ensemble soft computing approach for predicting penetration rate of tunnel boring machine in a rock environment

This study implements a hybrid ensemble machine learning method for forecasting the rate of penetration(ROP) of tunnel boring machine(TBM),which is becoming a prerequisite for reliable cost assessment and project scheduling in tunnelling and underground projects in a rock environment.For this purpose,a sum of 185 datasets was collected from the literature and used to predict the ROP of TBM.Initially,the main dataset was utilised to construct and validate four conventional soft computing(CSC)models,i.e.minimax probability machine regression,relevance vector machine,extreme learning machine,and functional network.Consequently,the estimated outputs of CSC models were united and trained using an artificial neural network(ANN) to construct a hybrid ensemble model(HENSM).The outcomes of the proposed HENSM are superior to other CSC models employed in this study.Based on the experimental results(training RMSE=0.0283 and testing RMSE=0.0418),the newly proposed HENSM is potential to assist engineers in predicting ROP of TBM in the design phase of tunnelling and underground projects.

Calculation of groundwater head distribution with a close barrier during excavation dewatering in confined aquifer

When pumping is conducted in confined aquifer inside excavation pit(waterproof curtain),the direction of the groundwater seepage outside the excavation changes from horizontal to vertical owing to the existence of the curtain barrier.There is no analytical calculation method for the groundwater head distribution induced by dewatering inside excavation.This paper first analyses the mechanism of the blocking effects from a close barrier in confined aquifer.Then,a simple equation based on analytical solution is proposed to calculate groundwater heads inside and outside of the excavation pit with waterproof curtain(hereafter refer to close barrier)in a confined aquifer.The distribution of groundwater head is derived according to two conditions:(i)pumping with a constant water head,and(ii)pumping with a constant flow rate.The proposed calculation equation is verified by both numerical simulation and experimental results.The comparisons demonstrate that the proposed model can be applied in engineering practice of excavation.

Artificial neural network optimized by differential evolution for predicting diameters of jet grouted columns

A novel and effective artificial neural network(ANN) optimized using differential evolution(DE) is first introduced to provide a robust and reliable forecasting of jet grouted column diameters.The proposed computational method adopts the DE algorithm to tackle the difficulties in the training and performance of neural networks and optimize the four quintessential hyper-parameters(i.e.the epoch size,the number of neurons in a hidden layer,the number of hidden layers,and the regularization parameter) that govern the neural network efficacy.This approach is further enhanced by a stochastic gradient optimization algorithm to allow ’expensive’ computation efforts.The ANN-DE is first trained using a prepared jet grouting dataset,then verified and compared with the prevalent machine learning tools,i.e.neural networks and support vector machine(SVM).The results show that,the ANN-DE outperforms the existing methods for predicting the diameter of jet grouting columns since it well balances training efficiency and model performance.Specifically,the ANN-DE achieved root mean square error(RMSE)values of 0.90603 and 0.92813 for the training and testing phases,respectively.The corresponding values were 0.8905 and 0.9006 for the optimized ANN,then,0.87569 and 0.89968 for the optimized SVM,respectively.The proposed paradigm is bound to be useful for solving various geotechnical engineering problems regardless of multi-dimension and nonlinearity.

Prediction of geological characteristics from shield operational parameters by integrating grid search and K-fold cross validation into stacking classification algorithm

This study presents a framework for predicting geological characteristics based on integrating a stacking classification algorithm(SCA) with a grid search(GS) and K-fold cross validation(K-CV). The SCA includes two learner layers: a primary learner’s layer and meta-classifier layer. The accuracy of the SCA can be improved by using the GS and K-CV. The GS was developed to match the hyper-parameters and optimise complicated problems. The K-CV is commonly applied to changing the validation set in a training set. In general, a GS is usually combined with K-CV to produce a corresponding evaluation index and select the best hyper-parameters. The torque penetration index(TPI) and field penetration index(FPI) are proposed based on shield parameters to express the geological characteristics. The elbow method(EM) and silhouette coefficient(Si) are employed to determine the types of geological characteristics(K) in a Kmeans++ algorithm. A case study on mixed ground in Guangzhou is adopted to validate the applicability of the developed model. The results show that with the developed framework, the four selected parameters, i.e. thrust, advance rate, cutterhead rotation speed and cutterhead torque, can be used to effectively predict the corresponding geological characteristics.

Numerical evaluation of the ground response induced by dewatering in a multi-aquifer system

This study presents a numerical investigation of dewatering-induced settlement and wall deflection during pumping tests in Tianjin,China.Based on the measured groundwater head and building settlement during the pumping test,a three-dimensional liquid-solid coupling model is established by using the finite element method(FEM).The void ratio,hydraulic conductivity,and elastic modulus of each layer are back-calculated through the numerical model.The groundwater drawdown,seepage field,ground settlement,horizontal ground displacement,and diaphragm wall lateral deflection are analyzed using the FEM model.The simulated results demonstrate that(i)the maximum ground settlement outside of the excavation reaches to 82 mm due to the leakage effect of aquitards;(ii)large horizontal displacement occurs in the soil during the pumping test with a maximum value of 28.3 mm,and the installation of the diaphragm wall in the aquifer can reduce the horizontal displacement of the ground;(iii)long-term pumping causes a large lateral deflection of the diaphragm wall,and a maximum value of 23.2 mm occurs at the layer where the screens of the wells are located;and(iv)long-term large-scale pumping should be avoided before excavation.

Use of recycled gypsum in the cement-based stabilization of very soft clays and its micro-mechanism

This paper presents an experimental study and micro-mechanism discussion on gypsum role in the mechanical improvements of cement-based stabilized clay(CBSC).A soft marine clay at two initial water contents(i.e.50%and 70%)was treated by reconstituted cementitious binders with varying gypsum to clinker(G/C)ratios and added metakaolin to facilitate the formation of ettringite,followed by the measurements of final water contents,dry densities and strengths in accordance with ASTM standards as well as microstructure by mercury intrusion porosimetry(MIP)and scanning electron microscopy(SEM).Results reveal that the gypsum fraction has a significant influence on the index and mechanical properties of the CBSC,and there exists a threshold of the G/C ratio,which is 10%and 15%for clays with 50%and 70%initial water contents,respectively.Beyond which adding excessive gypsum cannot improve the strength further,eliminating the beneficial role.At these thresholds of the G/C ratio,the unconfined compressive strength(UCS)values for clays with 50%and 70%initial water contents are 1.74 MPa and 1.53 MPa at 60 d of curing,respectively.Microstructure characterization shows that,besides the common cementation-induced strengthening,newly formed ettringite also acts as significant pore infills,and the associated remarkable volumetric expansion is responsible,and may be the primary factor,for the beneficial strength gain due to the added gypsum.Moreover,pore-filling ettringite also leads to the conversion of relatively large inter-aggregate to smaller intra-aggregate pores,thereby causing a more homogeneous matrix or solid skeleton with higher strength.Overall,added gypsum plays a vital beneficial role in the strength development of the CBSC,especially for very soft clays.

Land subsidence induced by groundwater extraction and building damage level assessment—a case study of Datun, China

As in many parts of the world, long-term excessive extraction of groundwater has caused significant land-surface sub- sidence in the residential areas of Datun coal mining district in East China. The recorded maximum level of subsidence in the area since 1976 to 2006 is 863 mm, and the area with an accumulative subsidence more than 200 mm has reached 33.1 km2 by the end of 2006. Over ten cases of building crack due to ground subsidence have already been observed. Spatial variation in ground subsi- dence often leads to a corresponding pattern of ground deformation. Buildings and underground infrastructures have been under a higher risk of damage in locations with greater differential ground deformation. Governmental guideline in China classifies build- ing damages into four different levels, based on the observable measures such as the width of wall crack, the degree of door and window deformation, the degree of wall inclination and the degree of structural destruction. Building damage level (BDL) is esti- mated by means of ground deformation analysis in terms of variations in slope gradient and curvature. Ground deformation analysis in terms of variations in slope gradient has shown that the areas of BDL III and BDL II sites account for about 0.013 km2 and 0.284 km2 respectively in 2006, and the predicted areas of BDL (define this first) III and II sites will be about 0.029 km2 and 0.423 km2 respectively by 2010. The situation is getting worse as subsidence continues. That calls for effective strategies for subsidence miti- gation and damage reduction, in terms of sustainable groundwater extraction, enhanced monitoring and the establishment of early warning systems.

Formulation of thermo-hydro-mechanical coupling behavior of unsaturated soils based on hybrid mixture theory

Thermo-Hydro-Mechanical （THM） coupling pro- cesses in unsaturated soils are very important in both theoretical researches and engineering applications. A coupled formulation based on hybrid mixture theory is derived to model the THM coupling behavior of unsaturated soils. The free-energy and dissipative functions for different phases are derived from Taylor＇s series expansions. Constitutive relations for THM coupled behaviors of unsaturated soils, which include deformation, entropy change, fluid flow, heat conduction, and dynamic compatibility conditions on the interfaces, are then established. The number of field equations is shown to be equal to the number of unknown variables; thus, a closure of this coupling problem is established. In addition to modifications of the physical conservation equations with coupling effect terms, the constitutive equations, which consider the coupling between elastoplastic deformation of the soil skeleton, fluid flow, and heat transfer, are also derived.

Novel integration of extreme learning machine and improved Harris hawks optimization with particle swarm optimization-based mutation for predicting soil consolidation parameter

The study proposes an improved Harris hawks optimization(IHHO) algorithm by integrating the standard Harris hawks optimization(HHO) algorithm and mutation-based search mechanism for developing a high-performance machine learning solution for predicting soil compression index. HHO is a newly introduced meta-heuristic optimization algorithm(MOA) used to solve continuous search problems.Compared to the original HHO, the proposed IHHO can evade trapping in local optima, which in turn raises the search capabilities and enhances the search mechanism relying on mutation. Subsequently, a novel meta-heuristic-based soft computing technique called ELM-IHHO was established by integrating IHHO and extreme learning machine(ELM) to estimate soil compression index. A sum of 688 consolidation test data was collected for this purpose from an ongoing dedicated freight corridor railway project. To evaluate the generalization capability of the proposed ELM-IHHO model, a detailed comparison between ELM-IHHO and other well-established MOAs, such as particle swarm optimization,genetic algorithm, and biogeography-based optimization integrated with ELM, was performed. Based on the outcomes, the ELM-IHHO model exhibits superior performance over the other MOAs in predicting soil compression index.

Effective thermal conductivity of an intact heterogeneous limestone

The Cobourg limestone is a very low porosity rock consisting of lighter nodular regions that are predominantly calcite and darker regions consisting of calcite,quartz,dolomite,and an appreciable clay fraction.This paper presents the application of the theory of multi-phasic composites to estimate the possible maximum effective thermal conductivity of the heterogeneous rocks.The thermal conductivity estimates are expected to be representative of the intact rock,without fractures or fissures that can influence the heat conduction process.The estimates are therefore indicative of the thermal properties of the rock in undisturbed regions unaffected by the influences of stress relief and excavation damage during construction of deep ground repositories for the disposal of heat-emitting nuclear waste.

Origin and characteristics of grain dolomite of Ordovician Ma5^5 Member in the northwest of Ordos Basin, NW China

The origin of grain dolomite in M55 Member of Ordovician Majiagou Formation in northwestern Ordos Basin was studied by geochemical and petrological tests on core samples.Observation of cores,thin sections and casting thin sections,analysis of cathodoluminescence,X-ray diffraction,microscopic sampling of trace elements,laser samplingδ18O andδ13C,and fluid inclusion homogenization temperature were conducted.The results show that the dolomite is the product of recrystallization of micritic to crystal powder dolomite rather than the product of dolomitization of grain limestone.In the spherical grains are residual gypsum and halite pseudo crystals identical with those in the host micritic dolomite.The spherical particles of dolomite has similar trace elements andδ18O andδ13C characteristics to micritic dolomite.Furthermore,Mn/Sr ratio of the fine-medium dolomite between the dolomite grains is about 5-8,while Mn/Sr ratios of calcite in limestone,micritic dolostone in micritic dolomite,and micritic and powdery dolomite are about 0-2,indicating that the dolomite experienced strong diagenesis.Homogenization temperature of inclusions of fine-medium dolomite is about 148.19°C,higher than that of inclusions in micritic to crystal powder dolomite(about 122.60°C),which also supports the conclusion that the grain dolomite experienced burial diagenesis and negative shift ofδ18O andδ13C.Theδ18O,δ13C values of micritic to crystal powder dolomite match with the negative migration,but those of calcite in limestone don’t.It is of great significance to elucidate the genesis of"dolomite recrystallization"for the prediction of such dolomite reservoirs.

Application of Aquatic Plants for the Treatment of Selenium-Rich Mining Wastewater and Production of Renewable Fuels and Petrochemicals

Aquatic plants aggressively colonising wetlands are widely used for the biosorption of the soluble contaminants from wastewater and represent an attractive feedstock for biofuel production. Three common Australian aquatic plants, duckweed (Landoltia punctata), elodea, (Elodea canadensis) and water clover (Marsilea quadrifolia), colonizing different depths of wetlands were tested for their ability to treat the selenium-rich mining wastewater and for their potential for production of petrochemicals. The results showed that these plants could be effective at biofiltration of selenium and heavy metals from mining wastewater accumulating them in their fast growing biomass. Along with production of bio-gas and bio-solid components, pyrolysis of these plants produced a range of liquid petrochemicals including straight-chain C14-C20 alkanes, which can be directly used as a diesel fuel supplement or as a glycerine-free component of biodiesel. Other identified bio-oil components can be converted into petrochemicals using existing techniques such as catalytic hydrodeoxygenation. A dual application of aquatic plants for wastewater treatment and production of value-added chemicals offers an ecologically friendly and cost-effective solution for water pollution problems and renewable energy production.

An Investigation of Optimal Machine Learning Methods for the Prediction of ROTI

The rate of the total electron content(TEC)change index(ROTI)can be regarded as an effective indicator of the level of ionospheric scintillation,in particular in low and high latitude regions.An accurate prediction of the ROTI is essential to reduce the impact of the ionospheric scintillation on earth observation systems,such as the global navigation satellite systems.However,it is difficult to predict the ROTI with high accuracy because of the complexity of the ionosphere.In this study,advanced machine learning methods have been investigated for ROTI prediction over a station at high-latitude in Canada.These methods are used to predict the ROTI in the next 5 minutes using the data derived from the past 15 minutes at the same location.Experimental results show that the method of the bidirectional gated recurrent unit network(BGRU)outperforms the other six approaches tested in the research.It is also confirmed that the RMSEs of the predicted ROTI using the BGRU method in all four seasons of 2017 are less than 0.05 TECU/min.It is demonstrated that the BGRU method exhibits a high level of robustness in dealing with abrupt solar activities.