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Four-protein model for predicting prognostic risk of lung cancer

Frontiers of Medicine 2022, Volume 16, Issue 4,   Pages 618-626 doi: 10.1007/s11684-021-0867-0

Abstract: Patients with lung cancer at the same stage may have markedly different overall outcome and a lack of specific biomarker to predict lung cancer outcome. Heat-shock protein 90 β (HSP90β) is overexpressed in various tumor cells. In this study, the ELISA results of HSP90β combined with CEA, CA125, and CYFRA21-1 were used to construct a recursive partitioning decision tree model to establish a four-protein diagnostic model and predict the survival of patients with lung cancer. Survival analysis showed that the recursive partitioning decision tree could distinguish the prognosis between high- and low-risk groups. Results suggested that the joint detection of HSP90β, CEA, CA125, and CYFRA21-1 in the peripheral blood of patients with lung cancer is plausible for early diagnosis and prognosis prediction of lung cancer.

Keywords： lung cancer     HSP90β     decision tree model     prognosis    

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Assessment of novel nature-inspired fuzzy models for predicting long contraction scouring and related

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 3,   Pages 665-681 doi: 10.1007/s11709-021-0713-0

Abstract: Thus, the proposed ANFIS-BBO is a capable cost-effective method for predicting long contraction scouring

Keywords： long contraction scour     prediction     uncertainty     ANFIS model     meta-heuristic algorithm    

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An exploratory study for predicting component reliability with new load conditions

Zhengwei HU, Xiaoping DU

Frontiers of Mechanical Engineering 2019, Volume 14, Issue 1,   Pages 76-84 doi: 10.1007/s11465-018-0522-x

Abstract: Reliability is important to design innovation. A new product should be not only innovative, but also reliable. For many existing components used in the new product, their reliability will change because the applied Loads are different from the ones for which the components are originally designed and manufactured. Then the new reliability must be re-evaluated. The system designers of the new product, however, may not have enough information to perform this task. With a beam problem as a case study, this study explores a feasible way to re-evaluate the component reliability with new Loads given the following information: The original reliability of the component with respect to the component Loads and the distributions of the new component Loads. Physics-based methods are employed to build the equivalent component limit-state function that can predict the component failure under the new Loads. Since the information is limited, the re-evaluated component reliability is given by its maximum and minimum values. The case study shows that good accuracy can be obtained even though the new reliability is provided with the aforementioned interval.

Keywords： reliability     component     failure mode     prediction     random variable    

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High-precision standard enthalpy of formation for polycyclic aromatic hydrocarbons predicting from general

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 12,   Pages 1743-1750 doi: 10.1007/s11705-022-2184-9

Abstract: The standard enthalpy of formation is an important predictor of the reaction heat of a chemical reaction. In this work, a high-precision method was developed to calculate accurate standard enthalpies of formation for polycyclic aromatic hydrocarbons based on the general connectivity based hierarchy (CBH) with the discrete correction of atomization energy. Through a comparison with available experimental findings and other high-precision computational results, it was found that the present method can give a good description of enthalpy of formation for polycyclic aromatic hydrocarbons. Since CBH schemes can broaden the scope of application, this method can be used to investigate the energetic properties of larger polycyclic aromatic hydrocarbons to achieve a high-precision calculation at the CCSD(T)/CBS level. In addition, the energetic properties of CBH fragments can be accurately calculated and integrated into a database for future use, which will increase computational efficiency. We hope this work can give new insights into the energetic properties of larger systems.

Keywords： standard enthalpy of formation     polycyclic aromatic hydrocarbons     connectivity based hierarchy     high-precision calculation    

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A method for predicting consolidation settlements of floating column improved clayey subsoil

Jinchun CHAI, Supasit PONGSIVASATHIT,

Frontiers of Structural and Civil Engineering 2010, Volume 4, Issue 2,   Pages 241-251 doi: 10.1007/s11709-010-0024-3

Abstract: A method of predicting the consolidation settlement-time curve of floating soil-cement column on improved

Keywords： cement depth mixing     ground improvement     consolidation     settlement    

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Assessment of different machine learning techniques in predicting the compressive strength of self-compacting

Van Quan TRAN; Hai-Van Thi MAI; Thuy-Anh NGUYEN; Hai-Bang LY

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 7,   Pages 928-945 doi: 10.1007/s11709-022-0837-x

Abstract: Therefore, the XGB model can be used as a practical tool for engineers in predicting the CS of SCC.

Keywords： compressive strength     self-compacting concrete     machine learning techniques     particle swarm optimization     extreme gradient boosting    

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Online gasoline blending with EPA Complex Model for predicting emissions

Stefan JANAQI, Mériam CHÈBRE, Guillaume PITOLLAT

Frontiers of Engineering Management 2018, Volume 5, Issue 2,   Pages 214-226 doi: 10.15302/J-FEM-2017022

Abstract: The empirical Complex Model developed by the US Environmental Protection Agency (EPA) is used by refiners to predict the toxic emissions of reformulated gasoline with respect to gasoline properties. The difficulty in implementing this model in the blending process stems from the implicit definition of Complex Model through a series of disjunctions assembled by the EPA in the form of spreadsheets. A major breakthrough in the refinery-based Complex Model implementation occurred in 2008 and 2010 through the use of generalized disjunctive and mixed-integer nonlinear programming (MINLP). Nevertheless, the execution time of these MINLP models remains prohibitively long to control emissions with our online gasoline blender. The first objective of this study is to present a new model that decreases the execution time of our online controller. The second objective is to consider toxic thresholds as hard constraints to be verified and search for blends that verify them. Our approach introduces a new way to write the Complex Model without any binary or integer variables. Sigmoid functions are used herein to approximate step functions until the measurement precision for each blend property is reached. By knowing this level of precision, we are able to propose an extremely good and differentiable approximation of the Complex Model. Next, a differentiable objective function is introduced to penalize emission values higher than the threshold emissions. Our optimization module has been implemented and tested with real data. The execution time never exceeded 1 s, which allows the online regulation of emissions the same way as other traditional properties of blended gasoline.

Keywords： emissions     reformulated gasoline     online control     global optimization    

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Clinical factors associated with composition of lung microbiota and important taxa predicting clinical

Frontiers of Medicine 2022, Volume 16, Issue 3,   Pages 389-402 doi: 10.1007/s11684-021-0856-3

Abstract: Few studies have described the key features and prognostic roles of lung microbiota in patients with severe community-acquired pneumonia (SCAP). We prospectively enrolled consecutive SCAP patients admitted to ICU. Bronchoscopy was performed at bedside within 48 h of ICU admission, and 16S rRNA gene sequencing was applied to the collected bronchoalveolar lavage fluid. The primary outcome was clinical improvements defined as a decrease of 2 categories and above on a 7-category ordinal scale within 14 days following bronchoscopy. Sixty-seven patients were included. Multivariable permutational multivariate analysis of variance found that positive bacteria lab test results had the strongest independent association with lung microbiota (R2=0.033; P=0.018), followed by acute kidney injury (AKI; R2=0.032; P=0.011) and plasma MIP-1β level (R2=0.027; P=0.044). Random forest identified that the families Prevotellaceae, Moraxellaceae, and Staphylococcaceae were the biomarkers related to the positive bacteria lab test results. Multivariable Cox regression showed that the increase in α-diversity and the abundance of the families Prevotellaceae and Actinomycetaceae were associated with clinical improvements. The positive bacteria lab test results, AKI, and plasma MIP-1β level were associated with patients’ lung microbiota composition on ICU admission. The families Prevotellaceae and Actinomycetaceae on admission predicted clinical improvements.

Keywords： severe community-acquired pneumonia     lung microbiota     clinical improvements     7-category ordinal scale     Prevotellaceae    

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Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 2,   Pages 284-305 doi: 10.1007/s11709-022-0901-6

Abstract: This study developed different ML models for predicting the CS of FRSCC to address these limitations.

Keywords： compressive strength     self-compacting concrete     artificial neural network     decision tree     CatBoost    

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Genetic and clinical markers for predicting treatment responsiveness in rheumatoid arthritis

Xin Wu, Xiaobao Sheng, Rong Sheng, Hongjuan Lu, Huji Xu

Frontiers of Medicine 2019, Volume 13, Issue 4,   Pages 411-419 doi: 10.1007/s11684-018-0659-3

Abstract: Although many drugs and therapeutic strategies have been developed for rheumatoid arthritis (RA) treatment, numerous patients with RA fail to respond to currently available agents. In this review, we provide an overview of the complexity of this autoimmune disease by showing the rapidly increasing number of genes associated with RA. We then systematically review various factors that have a predictive value (predictors) for the response to different drugs in RA treatment, especially recent advances. These predictors include but are certainly not limited to genetic variations, clinical factors, and demographic factors. However, no clinical application is currently available. This review also describes the challenges in treating patients with RA and the need for personalized medicine. At the end of this review, we discuss possible strategies to enhance the prediction of drug responsiveness in patients with RA.

Keywords： rheumatoid arthritis     gene     clinical markers     therapy    

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Role of stair-climbing test in predicting postoperative cardiopulmonary complications in elderly patients

Pei-Tu REN BM, Bao-Chun LU MM, Zhi-Liang CHEN MM, Hong FU MM,

Frontiers of Medicine 2010, Volume 4, Issue 1,   Pages 77-81 doi: 10.1007/s11684-010-0005-x

Abstract: This suggests that the stair-climbing test is an effective and simple method for predicting cardiopulmonary

Keywords： biliary disease     cardiopulmonary complication     stair-climbing test    

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Applying the multi-zone model in predicting the operating range of HCCI engines

Ming JIA, Maozhao XIE, Zhijun PENG,

Frontiers in Energy 2010, Volume 4, Issue 3,   Pages 414-423 doi: 10.1007/s11708-010-0108-8

Abstract: In this paper, a multi-zone model is developed to predict the operating range of homogeneous charge compression ignition (HCCI) engines. The boundaries of the operating range were determined by knock (presented by ringing intensity), partial burn (presented by combustion efficiency), and cycle-to-cycle variations (presented by the sensitivity of indicated mean effective pressure to initial temperature). By simulating an HCCI engine fueled with iso-octane, the knock and cycle-to-cycle variations predicted by the model showed satisfactory agreement with measurements made under different initial temperatures and equivalence ratios; the operating range was also well reproduced by the model. Furthermore, the model was applied to predict the operating range of the HCCI engine under different engine speeds by varying the intake temperatures and equivalence ratios. The potential to extend the operating range of the HCCI engine through two strategies, i.e., variable compression ratio and intake pressure boosting, was then investigated. Results indicate that the ignition point can be efficiently controlled by varying the compression ratio. A low load range can be extended by increasing the intake temperature while reducing the compression ratio. Higher intake temperatures and lower compression ratios can also extend the high load range. Boosting intake pressure is helpful in controlling the combustion of the HCCI engine, resulting in an extended high load range.

Keywords： homogeneous charge compression ignition (HCCI) engine     multi-zone     operating range    

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Predicting non-carcinogenic hazard quotients of heavy metals in pepper (

Marzieh Mokarram, Hamid Reza Pourghasemi, Huichun Zhang

Frontiers of Environmental Science & Engineering 2020, Volume 14, Issue 6, doi: 10.1007/s11783-020-1331-0

Abstract: Abstract • There was significant absorption of heavy metals by the pepper in contaminated soils. • The target hazard quotient (THQ) indices followed the order of Pb>Zn>>Cd » Ni. • Relationships exist between contaminated plants and electromagnetic wave. • PCA and random search can select the main spectra and predict THQ for each element. Given the tendency of heavy metals to accumulate in soil and plants, the purpose of this study was to determine the contamination levels of Cd, Ni, Pb, and Zn on peppers (leaves and fruit) grown in contaminated soils in industrial centers. For this purpose, we measured the uptake of the four heavy metals by peppers grown in the heavy metal contaminated soils throughout the four growth stages: two-leaf, growth, flowering, and fruiting, and calculated various vegetation indices to evaluate the heavy metal contamination potentials. Electromagnetic waves were also applied for analyzing the responses of the target plants to various heavy metals. Based on the relevant spectral bands identified by principal component analysis (PCA) and random search methods, a regression method was then employed to determine the most optimal spectral bands for estimating the target hazard quotient (THQ). The THQ was found to be the highest in the plants contaminated by Pb (THQ= 62) and Zn (THQ= 5.07). The results of PCA and random search indicated that the spectra at the bands of b570, b650, and b760 for Pb, b400 and b1030 for Ni, b400 and b880 for Cd, and b560, b910, and b1050 for Zn were the most optimal spectra for assessing THQ. Therefore, in future studies, instead of examining the amount of heavy metals in plants by chemical analysis in the laboratory, the responses of the plants to the electromagnetic waves in the identified bands can be readily investigated in the field based on the established correlations.

Keywords： Heavy metals     Plants     Target Hazard Quotient (THQ)     Principal Component Analysis (PCA)     Random search     Electromagnetic wave    

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An enhanced damage plasticity model for predicting the cyclic behavior of plain concrete under multiaxial

Mohammad Reza AZADI KAKAVAND, Ertugrul TACIROGLU

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 6,   Pages 1531-1544 doi: 10.1007/s11709-020-0675-7

Abstract: Some of the current concrete damage plasticity models in the literature employ a single damage variable for both the tension and compression regimes, while a few more advanced models employ two damage variables. Models with a single variable have an inherent difficulty in accounting for the damage accrued due to tensile and compressive actions in appropriately different manners, and their mutual dependencies. In the current models that adopt two damage variables, the independence of these damage variables during cyclic loading results in the failure to capture the effects of tensile damage on the compressive behavior of concrete and vice-versa. This study presents a cyclic model established by extending an existing monotonic constitutive model. The model describes the cyclic behavior of concrete under multiaxial loading conditions and considers the influence of tensile/compressive damage on the compressive/tensile response. The proposed model, dubbed the enhanced concrete damage plasticity model (ECDPM), is an extension of an existing model that combines the theories of classical plasticity and continuum damage mechanics. Unlike most prior studies on models in the same category, the performance of the proposed ECDPM is evaluated using experimental data on concrete specimens at the material level obtained under cyclic multiaxial loading conditions including uniaxial tension and confined compression. The performance of the model is observed to be satisfactory. Furthermore, the superiority of ECDPM over three previously proposed constitutive models is demonstrated through comparisons with the results of a uniaxial tension-compression test and a virtual test.

Keywords： damage plasticity model     plain concrete     cyclic loading     multiaxial loading conditions    

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A Method for Financial Crisis Predicting Based On Rough Neural Network

Liu Bingxiang,Sheng Zhaohan

Strategic Study of CAE 2002, Volume 4, Issue 8,   Pages 58-62

Abstract: The paper designs a method for enterprise financial crisis predicting based on rough neural network.An example in financial crisis predicting is given to validate the algorithm.supplies a basis for further study of applying rough neural network for enterprise financial crisis predicting

Keywords： financial crisis     index system     rough set     neural network     predicting    

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Title Author Date Type Operation

Four-protein model for predicting prognostic risk of lung cancer

Journal Article

Assessment of novel nature-inspired fuzzy models for predicting long contraction scouring and related

Journal Article

An exploratory study for predicting component reliability with new load conditions

Zhengwei HU, Xiaoping DU

Journal Article

High-precision standard enthalpy of formation for polycyclic aromatic hydrocarbons predicting from general

Journal Article

A method for predicting consolidation settlements of floating column improved clayey subsoil

Jinchun CHAI, Supasit PONGSIVASATHIT,

Journal Article

Assessment of different machine learning techniques in predicting the compressive strength of self-compacting

Van Quan TRAN; Hai-Van Thi MAI; Thuy-Anh NGUYEN; Hai-Bang LY

Journal Article

Online gasoline blending with EPA Complex Model for predicting emissions

Stefan JANAQI, Mériam CHÈBRE, Guillaume PITOLLAT

Journal Article

Clinical factors associated with composition of lung microbiota and important taxa predicting clinical

Journal Article

Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting

Journal Article

Genetic and clinical markers for predicting treatment responsiveness in rheumatoid arthritis

Xin Wu, Xiaobao Sheng, Rong Sheng, Hongjuan Lu, Huji Xu

Journal Article

Role of stair-climbing test in predicting postoperative cardiopulmonary complications in elderly patients

Pei-Tu REN BM, Bao-Chun LU MM, Zhi-Liang CHEN MM, Hong FU MM,

Journal Article

Applying the multi-zone model in predicting the operating range of HCCI engines

Ming JIA, Maozhao XIE, Zhijun PENG,

Journal Article

Predicting non-carcinogenic hazard quotients of heavy metals in pepper (

Marzieh Mokarram, Hamid Reza Pourghasemi, Huichun Zhang

Journal Article

An enhanced damage plasticity model for predicting the cyclic behavior of plain concrete under multiaxial

Mohammad Reza AZADI KAKAVAND, Ertugrul TACIROGLU

Journal Article

A Method for Financial Crisis Predicting Based On Rough Neural Network

Liu Bingxiang,Sheng Zhaohan

Journal Article