Structural parameterization and functional prediction of antigenic polypeptome sequences with biological activity through quantitative sequence-activity models (QSAM) by molecular electronegativity edge-distance vector (VMED)

Only from the primary structures of peptides, a new set of descriptors called the molecular electro-negativity edge-distance vector (VMED) was proposed and applied to describing and characterizing the molecular structures of oligopeptides and polypeptides, based on the electronegativity of each atom or electronic charge index (ECI) of atomic clusters and the bonding distance between atom-pairs. Here, the molecular structures of antigenic polypeptides were well expressed in order to propose the auto-mated technique for the computerized identification of helper T lymphocyte (Th) epitopes. Furthermore, a modified MED vector was proposed from the primary structures of polypeptides, based on the ECI and the relative bonding distance of the fundamental skeleton groups. The side-chains of each amino acid were here treated as a pseudo-atom. The developed VMED was easy to calculate and able to work. Some quantitative model was established for 28 immunogenic or antigenic polypeptides (AGPP) with 14 (1― 14) Ad and 14 other restricted activities assigned as "1"(+) and "0"(-), respectively. The latter comprised 6 Ab(15-20), 3 Ak(21-23), 2 Ek(24-26), 2 H-2k(27 and 28) restricted sequences. Good results were obtained with 90% correct classification (only 2 wrong ones for 20 training samples) and 100% correct prediction(none wrong for 8 testing samples); while con-trastively 100% correct classification (none wrong for 20 training samples) and 88% correct classification (1 wrong for 8 testing samples). Both stochastic samplings and cross valida-tions were performed to demonstrate good performance. The described method may also be suitable for estimation and prediction of classes I and II for major histocompatibility an-tigen (MHC) epitope of human. It will be useful in immune identification and recognition of pro-teins and genes and in the design and devel-opment of subunit vaccines. Several quantitative structure activity relationship (QSAR) models were developed for various oligopeptides and polypeptides including 58 dipeptides and 31 pentapeptides with angiotensin converting enzyme (ACE) inhibition by multiple linear regression (MLR) method. In order to explain the ability to characterize molecular structure of polypeptides, a molecular modeling investigation on QSAR was performed for functional prediction of polypeptide sequences with anti-genic activity and heptapeptide sequences with tachykinin activity through quantitative se-quence-activity models (QSAMs) by the molecular electronegativity edge-distance vector (VMED). The results showed that VMED exhibited both excellent structural selectivity and good activity prediction. Moreover, the results showed that VMED behaved quite well for both QSAR and QSAM of poly-and oli-gopeptides, which exhibited both good estimation ability and prediction power, equal to or better than those reported in the previous references. Finally, a preliminary conclusion was drwan: both classical and modified MED vectors were very useful structural descriptors. Some suggestions were proposed for further studies on QSAR/QSAM of proteins in various fields.

Solubility study of hydrogen in direct coal liquefaction solvent based on quantitative structure–property relationships model

Direct coal liquefaction(DCL)is an important and effective method of converting coal into high-valueadded chemicals and fuel oil.In DCL,heating the direct coal liquefaction solvent(DCLS)from low to high temperature and pre-hydrogenation of the DCLS are critical steps.Therefore,studying the dissolution of hydrogen in DCLS under liquefaction conditions gains importance.However,it is difficult to precisely determine hydrogen solubility only by experiments,especially under the actual DCL conditions.To address this issue,we developed a prediction model of hydrogen solubility in a single solvent based on the machine-learning quantitative structure–property relationship(ML-QSPR)methods.The results showed that the squared correlation coefficient R^(2)=0.92 and root mean square error RMSE=0.095,indicating the model’s good statistical performance.The external validation of the model also reveals excellent accuracy and predictive ability.Molecular polarization(a)is the main factor affecting the dissolution of hydrogen in DCLS.The hydrogen solubility in acyclic alkanes increases with increasing carbon number.Whereas in polycyclic aromatics,it decreases with increasing ring number,and in hydrogenated aromatics,it increases with hydrogenation degree.This work provides a new reference for the selection and proportioning of DCLS,i.e.,a solvent with higher hydrogen solubility can be added to provide active hydrogen for the reaction and thus reduce the hydrogen pressure.Besides,it brings important insight into the theoretical significance and practical value of the DCL.

Calibration of quantitative rescattering model for simulating vortex high-order harmonic generation driven by Laguerre–Gaussian beam with nonzero orbital angular momentum

We calibrate the macroscopic vortex high-order harmonic generation(HHG)obtained by the quantitative rescattering(QRS)model to compute single-atom induced dipoles against that by solving the time-dependent Schr?dinger equation(TDSE).We show that the QRS perfectly agrees with the TDSE under the favorable phase-matching condition,and the QRS can accurately predict the main features in the spatial profiles of vortex HHG if the phase-matching condition is not good.We uncover that harmonic emissions from short and long trajectories are adjusted by the phase-matching condition through the time-frequency analysis and the QRS can simulate the vortex HHG accurately only when the interference between two trajectories is absent.This work confirms that it is an efficient way to employ the QRS model in the single-atom response for precisely simulating the macroscopic vortex HHG.

Quantitative Modelling of Multiphase Lithospheric Stretching and Deep Thermal History of Some Tertiary Rift Basins in Eastern China

The stretching process of some Tertiary rift basins in eastern China is characterized by multiphase rifting. A multiple instantaneous uniform stretching model is proposed in this paper to simulate the formation of the basins as the rifting process cannot be accurately described by a simple (one episode) stretching model. The study shows that the multiphase stretching model, combined with the back-stripping technique, can be used to reconstruct the subsidence history and the stretching process of the lithosphere, and to evaluate the depth to the top of the asthenosphere and the deep thermal evolution of the basins. The calculated results obtained by applying the quantitative model to the episodic rifting process of the Tertiary Qiongdongnan and Yinggehai basins in the South China Sea are in agreement with geophysical data and geological observations. This provides a new method for quantitative evaluation of the geodynamic process of multiphase rifting occurring during the Tertiary in eastern China.

Quantitative Damage Detection for Planetary Gear Sets Based on Physical Models

Planetary gear set is the critical component in helicopter transmission train, and an important problem in condition monitoring and health management of planetary gear set is quantitative damage detection. In order to resolve this problem, an approach based on physical models is presented to detect damage quantitatively in planetary gear set. A particular emphasis is put on a feature generation and selection method, which is used for sun gear tooth breakage damage detection quantitatively in planetary gear box of helicopter transmission system. In this feature generation procedure, the pure torsional dynamical models of 2K-H planetary gear set is established for healthy case and sun gear tooth-breakage case. Then, a feature based on the spectrum of simulation signals of the dynamical models is generated. Aiming at selecting the best feature suitable for quantitative damage detection, a two-sample Z-test procedure is used to analyze the performance of features on damage evolution tracing. A feature named SR, which had better performance in tracking damage, is proposed to detect damage in planetary gear set. Meanwhile, the sun gear tooth-chipped seeded experiments with different severity are designed to validate the method above, and then the test vibration signal is picked up and used for damage detection. With the results of several experiments for quantitative damage detection, the feasibility and the effect of this approach are verified. The proposed method can supply an effective tool for degradation state identification in condition monitoring and health management of helicopter transmission system.

A Mathematical Calculation Model Using Biomarkers to Quantitatively Determine the Relative Source Proportion of Mixed Oils

It is difficult to identify the source（s） of mixed oils from multiple source rocks, and in particular the relative contribution of each source rock. Artificial mixing experiments using typical crude oils and ratios of different biomarkers show that the relative contribution changes are non-linear when two oils with different concentrations of biomarkers mix with each other. This may result in an incorrect conclusion if ratios of biomarkers and a simple binary linear equation are used to calculate the contribution proportion of each end-member to the mixed oil. The changes of biomarker ratios with the mixing proportion of end-member oils in the trinal mixing model are more complex than in the binary mixing model. When four or more oils mix, the contribution proportion of each end-member oil to the mixed oil cannot be calculated using biomarker ratios and a simple formula. Artificial mixing experiments on typical oils reveal that the absolute concentrations of biomarkers in the mixed oil cause a linear change with mixing proportion of each end-member. Mathematical inferences verify such linear changes. Some of the mathematical calculation methods using the absolute concentrations or ratios of biomarkers to quantitatively determine the proportion of each end-member in the mixed oils are deduced from the results of artificial experiments and by theoretical inference. Ratio of two biomarker compounds changes as a hyperbola with the mixing proportion in the binary mixing model, as a hyperboloid in the trinal mixing model, and as a hypersurface when mixing more than three end- members. The mixing proportion of each end-member can be quantitatively determined with these mathematical models, using the absolute concentrations and the ratios of biomarkers. The mathematical calculation model is more economical, convenient, accurate and reliable than conventional artificial mixing methods.

Quantitative Metal Magnetic Memory Reliability Modeling for Welded Joints

Metal magnetic memory（MMM） testing has been widely used to detect welded joints. However, load levels, environmental magnetic field, and measurement noises make the MMM data dispersive and bring difficulty to quantitative evaluation. In order to promote the development of quantitative MMM reliability assessment, a new MMM model is presented for welded joints. Steel Q235 welded specimens are tested along the longitudinal and horizontal lines by TSC-2M-8 instrument in the tensile fatigue experiments. The X-ray testing is carried out synchronously to verify the MMM results. It is found that MMM testing can detect the hidden crack earlier than X-ray testing. Moreover, the MMM gradient vector sum K_（vs） is sensitive to the damage degree, especially at early and hidden damage stages. Considering the dispersion of MMM data, the K_（vs） statistical law is investigated, which shows that K_（vs） obeys Gaussian distribution. So K_（vs） is the suitable MMM parameter to establish reliability model of welded joints. At last, the original quantitative MMM reliability model is first presented based on the improved stress strength interference theory. It is shown that the reliability degree R gradually decreases with the decreasing of the residual life ratio T, and the maximal error between prediction reliability degree R_1 and verification reliability degree R_2 is 9.15%. This presented method provides a novel tool of reliability testing and evaluating in practical engineering for welded joints.

Quantitative Model for the Surface-related Electron Transfer in CdS Quantum Dots

The influence of surface S^2- dangling bonds and surface doped ions（Se^2-, Cu^2＋, and Hg^2＋） on the photoluminescence of Cd^2＋-rich CdS QDs was investigated. A quantitative model was proposed to understand the complex transfer processes of excited electrons in CdS QDs. The transfer of excited electrons from either the conduction band or the Cd^2＋-related trap-state to the surface S^2-related shallow hole trap-state is effective. However, the trap of excited electrons by surface doped ion trap-states from the Cd^2＋-related trap-state is more effective than that from the conduction band. The efficiency of trapping electrons from both the conduction band and the Cd^2＋-related trap-state can be quantitatively understood with the help of the proposed model. The results show that the transfer efficiency of excited electrons is dependent on the location of the energy-level of the relevant surface-related trap-state. The trap of excited electrons by the surface trap-state with energy-level closer to that of the conduction band is more effective, especially for the trap of excited electrons from Cd^2＋-related trap-state.

Quantitative models for microstructure and thermal conductivity of vermicular graphite cast iron cylinder block based on cooling rate

The relationships of cooling rate with microstructure and thermal conductivity of vermicular graphite cast iron(VGI) cylinder block were studied, which are important for design and optimization of the casting process of VGI cylinder blocks. Cooling rates at different positions in the cylinder block were calculated based on the cooling curves recorded with a solidification simulation software. The metallographic structure and thermal conductivity were observed and measured using optical microscopy(OM), scanning electrical microscopy(SEM) and laser flash diffusivity apparatus, respectively. The effects of the cooling rate on the vermicularity, total and average areas of all graphite particles, and the pearlite fraction in the VGI cylinder block were investigated. It is found that the vermicularity changes in parabola trend with the increase of cooling rate. The total area of graphite particles and the cooling rate at eutectoid stage can be used to predict pearlite fraction well. Moreover, it is found that the thermal conductivity at room temperature is determined by the average area of graphite particles and pearlite fraction when the range of vermicularity is from 80% to 93%. Finally, the quantitative models are established to calculate the vermicularity, pearlite fraction, and thermal conductivity of the VGI cylinder block.

Quantitative modeling, optimization, and verification of ^(63)Nipowered betavoltaic cells based on three-dimensional ZnO nanorod arrays

Betavoltaic cells(BCs)are promising self-generating power cells with long life and high power density.However,the low energy conversion efficiency(ECE)has limitations in practical engineering applications.Widebandgap semiconductors(WBGSs)with three-dimensional(3-D)nanostructures are ideal candidates for increasing the ECE of BCs.This paper proposes hydrothermally grown ZnO nanorod arrays(ZNRAs)for ^(63)Ni-powered BCs.A quantitative model was established for simulation using the parameter values of the dark characteristics,which were obtained from the experimental measurements for a simulated BC based on a Ni-incorporated ZNRAs structure.Monte Carlo(MC)modeling and simulation were conducted to obtain the values of the β energy deposited in ZNRAs with different nanorod spacings and heights.Through the simulation and optimization of the 3-D ZNRAs and 2-D ZnO bulk structures,the performance of the ^(63)Ni-powered BCs based on both structures was evaluated using a quantitative model.The BCs based on the 3-D ZNRAs structure and 2-D ZnO bulk structure achieved a maximum ECE of 10.1%and 4.69%,respectively,which indicates the significant superiority of 3-D nanostructured WBGSs in increasing the ECE of BCs.

3D Modeling Quantitative Evaluation for the Underground Space Resources in Beijing City

Recently,underground space evaluation has become fundamental for a city’s long term sustainable development planning.This paper,based on the 3D-GIS tools,adopts"multi-factors comprehensive evaluation"models to establish a practicable underground space resources quantitative evaluation system.It sets up

Quantitative model for species distribution of hydroxy polyaluminum chloride

The species distribution of hydroxy polyaluminum chloride (PAC, Al T=0.1mol/L) solutions prepared through two different types of base injection was studied and compared quantitatively by Al Ferron timed complex colorimetric method(AFM) and 27 Al NMR spectroscopy method (ANM), and was simulated by using a quantitative calculating procedure of chemical equilibrium in MINEQL model. The results suggest that methodology of synthesis is very important for determining species distribution in the preparation of PAC solutions. In the PAC solution prepared by micro injection of base method(MIBM), there are at least five kinds of species including a kind of monomeric species Al 3+ , three kinds of polymeric species Al 2(OH) 4+ 2, Al 7(OH) 4+ 17 , Al 13 O 4(OH) (7- n )+ 24+ n ( n =0,2)and an aggregate of Al 13 or a solid phase Al(OH) 3 (aq.). Whereas in the PAC solution prepared by instantaneous injection of base method (IIBM), there are a kind of monomeric species Al 3+ , two kinds of polymeric species Al 2(OH) 4+ 2, Al 13 O 4(OH) (7- n )+ 24+ n ( n =0,2) and a solid phase Al(OH) 3(am). The change of species distribution in the PAC solution depends on preparing method, B(OH/Al) value and concentration.

An Approach for Integrating Quantitative Decision Model with Qualitative Judgment

In this paper, decision making in complex environment is considered and an approach integrating quantitative decision model with qualitative judgment is proposed. The concept of belief degree for quantitative decision model in a complex environment is presented. The integration in formulation and reasoning of quantitative model with qualitative judgment is studied. The combination of various belief degree generated by quantitative model and qualitative judgment is discussed. A decision rule of tradeoff between optimality and belief degree of optimality is proposed.

Quantitative versus Qualitative Geospatial Data in Spatial Modelling and Decision Making

In general, geospatial data can be divided into two formats, raster and vector formats. A raster consists of a matrix of cells where each cell contains a value representing quantitative information, such as temperature, vegetation intensity, land use/cover, elevation, etc. A vector data consists of points, lines and polygons representing location or distance or area of landscape features in graphical forms. Many raster data are derived from remote sensing techniques using sophisticated sensors by quantitative approach and many vector data are generated from GIS processes by qualitative approach. Among them, land use/cover data is frequently used in many GIS analyses and spatial modeling processes. However, proper use of quantitative and qualitative geospatial data is important in spatial modeling and decision making. In this article, we discuss common geospatial data formats, their origins and proper use in spatial modelling and decision making processes.

Multi-objective intelligent coordinating optimization blending system based on qualitative and quantitative synthetic model

A multi-objective intelligent coordinating optimization strategy based on qualitative and quantitative synthetic model for Pb-Zn sintering blending process was proposed to obtain optimal mixture ratio. The mechanism and neural network quantitative models for predicting compositions and rule models for expert reasoning were constructed based on statistical data and empirical knowledge. An expert reasoning method based on these models were proposed to solve blending optimization problem, including multi-objective optimization for the first blending process and area optimization for the second blending process, and to determine optimal mixture ratio which will meet the requirement of intelligent coordination. The results show that the qualified rates of agglomerate Pb, Zn and S compositions are increased by 7.1%, 6.5% and 6.9%, respectively, and the fluctuation of sintering permeability is reduced by 7.0%, which effectively stabilizes the agglomerate compositions and the permeability.

Study on Quantitative Model of Karst Drainage Basin Water-Holding Based on Principal Component Analysis: A Case Study of Guizhou, China

In Karst drainage basins, there are the ground water and underground water exchanging frequently, and the shortage of water resources due to having the special double aquifer mediums and unique surface and subsurface river systematic structure. This paper is to select 20 research sampling areas coming fromGuizhouProvince, and according to the spectral characteristics of the catchment water-holding mediums and vegetations, and using the remote sensing technique, extract the watershed vegetation index. According to the principle of principal component analysis, using the software of Spss and Matlab is to analyze the impacts of watershed vegetation type on the catchment water-holding ability, and establish the principal component analysis function. Studies have shown that: 1) the watershed vegetation coverage rate plays an important role in Karst basin water-holding ability;2) the catchment water-holding ability is the comprehensive reflection and manifestation of the Catchment Water-storing Capacity (CWC);3) it is much better effects and higher accuracy to monitor/forecast the catchment water-holding volume by using the vegetation indices.

Comprehensive Evaluation of Talent Training Model for Modern Rehabilitation Therapy Technology:A Case Study of School Y

The purpose of this study is to comprehensively evaluate the modern training model of rehabilitation therapy technology talents.Selecting the third-year students of the rehabilitation therapy technology program in School Y as the research subject,300 questionnaires were collected and the effective response rate was 92%.The strengths and weaknesses of the modern training model were analyzed through a mixed qualitative and quantitative research method.It was found that 68%of the students thought that the modern model had obvious advantages in practical teaching,but 42%of the students thought that it still needed to be improved in personalized teaching.This study provides an empirical basis and specific suggestions for optimizing the cultivation of rehabilitation therapy technology talents.

Quantitative assessment model for gastric cancer screening

AIM: To set up a mathematic model for gastric cancer screening and to evaluate its function in mass screening for gastric cancer.METHODS: A case control study was carried on in 66patients and 198 normal people, then the risk and protective factors of gastric cancer were determined, including heavy manual work, foods such as small yellow-fin tuna, dried small shrimps, squills, crabs, mothers suffering from gastric diseases, spouse alive, use of refrigerators and hot food,etc. According to some principles and methods of probability and fuzzy mathematics, a quantitative assessment model was established as follows: first, we selected some factors significant in statistics, and calculated weight coefficient for each one by two different methods; second, population space was divided into gastric cancer fuzzy subset and non gastric cancer fuzzy subset, then a mathematic model for each subset was established, we got a mathematic expression of attribute degree (AD).RESULTS: Based on the data of 63 patients and 693 normal people, AD of each subject was calculated. Considering the sensitivity and specificity, the thresholds of AD values calculated were configured with 0.20 and 0.17, respectively.According to these thresholds, the sensitivity and specificity of the quantitative model were about 69% and 63%.Moreover, statistical test showed that the identification outcomes of these two different calculation methods were identical (P＞0.05).CONCLUSION: The validity of this method is satisfactory.It is convenient, feasible, economic and can be used to determine individual and population risks of gastric cancer.

Quantitative Stock Selection Model Based on Long-Short Term Memory(LSTM)Neural Network

This article attempted to construct a multi-factor quantitative stock selection model,analyze the financial indicators and transaction data of listed companies in detail via the big data statistical test method,and to find out the alpha excess return relative to the market in the case of short stock index futures as a hedge in the Chinese market.

Quantitative Assessment of Remotely Sensed Global Surface Models Using Various Land Classes Produced from Landsat Data in Istanbul

Digital elevation model(DEM) is the most popular product for three-dimensional(3D) digital representation of bare Earth surface and can be produced by many techniques with different characteristics and ground sampling distances(GSD). Space-borne optical and synthetic aperture radar(SAR) imaging are two of the most preferred and modern techniques for DEM generation. Using them, global DEMs that cover almost entire Earth are produced with low cost and time saving processing. In this study, we aimed to assess the Satellite pour l'observation de la Terre-5(SPOT-5), High Resolution Stereoscopic(HRS), the Advanced Space-borne Thermal Emission and Reflection Radiometer(ASTER), and the Shuttle Radar Topography Mission(SRTM) C-band global DEMs, produced with space-borne optical and SAR imaging. For the assessment, a reference DEM derived from 1∶1000 scaled digital photogrammetric maps was used. The study is performed in 100 km2 study area in Istanbul including various land classes such as open land, forest, built-up land, scrub and rough terrain obtained from Landsat data. The analyses were realized considering three vertical accuracy types as fundamental, supplemental, and consolidated, defined by national digital elevation program(NDEP) of USA. The results showed that, vertical accuracy of SRTM C-band DEM is better than optical models in all three accuracy types despite having the largest grid spacing. The result of SPOT-5 HRS DEM is very close by SRTM and superior in comparison with ASTER models.