Considerations of Area Parameter of the Deflection Basin on the Structural Evaluation of Flexible Pavements

By using the concept of modified structural number(SNC)and deflection measurements,a simplified calculation methodology,that permits the structural condition evaluation of an existing pavement,is being proposed.The values of SNC and the curvature parameters were first determined through simulations using the ELSYM-5 software.Deflection measurements were carried out in experimental segments of Brazilian highways.The resilient moduli of each layer were determined from backcalculation using the ELMOD program for a three-layer system.Theoretical correlation models between SNC and the basin deformation parameter were determined and later,calibrated with the results of experimental sections.Utilizing the studied models,a good correlation was found between SNC,area parameter and maximum deflection,enabling the determination of SNC through deflection measurements and assisting in the diagnostic of structural condition of asphalt pavements.

Structural Evaluation for Maputo Airport Pavement

Maputo Airport was initially constructed to serve mixed traffic of light and medium aircrafts. With its opening to heavier aircrafts such as B727, DC10, Airbus 340, etc. , structural improvements have become necessary. For this purpose, structural evaluation were described and performed using falling weight deflectometer. Results show that while subgrade response to loads appears more consistent with depth, surface layer of the pavement is significantly influenced by the layer thickness as well as mechanical properties of pavement materials. Load magnitude also affects pavement performance. But loading conditions show an equivalent or even greater influence on pavement performance.

Biomarkers and Ultra Structural Evaluation of Marine Pollution by Polycyclic Aromatic Hydrocarbons

The present study is endeavored to study the oxidative stress and antioxidant response as well as the ultrastructural change of both liver and muscle tissues of Siganus rivulatus collected from polluted region (El-Mex Bay) and control region (Marsa Matrouh) to identify the significance of biomarkers. The results revealed that, El-Mex Bay was polluted by different types of chemicals including hydrocarbons comparing with the reference area (Matrouh area). Where, the concentration range of total hydrocarbons in sediment samples collected from Matrouh coast was 409.24 - 521.26 ng/g, dry weight, where as this range in sediment samples collected from El-MexBay was 4159.77 - 4589.81 ng/g, dry weight. In this context, the induction of antioxidant systems in fish collected from polluted area in response to oxidative stress should be considered as a clear indication of the presence of pollution and environmental health degradation. Also, The increase in lipid peroxidation was a useful indicator of the pollution load in the present study. The results suggest that chemical pollution is capable of inducing morphological alteration in liver offish collected from polluted area. The present study indicated that ultra-structural changes serveas biomarker of stress in aquatic environment.

Statistical potentials for 3D structure evaluation:From proteins to RNAs

Structure evaluation is critical to in silico 3-dimensional structure predictions for biomacromolecules such as proteins and RNAs.For proteins,structure evaluation has been paid attention over three decades along with protein folding problem,and statistical potentials have been shown to be effective and efficient in protein structure prediction and evaluation.In recent two decades,RNA folding problem has attracted much attention and several statistical potentials have been developed for RNA structure evaluation,partially with the aid of the progress in protein structure prediction.In this review,we will firstly give a brief overview on the existing statistical potentials for protein structure evaluation.Afterwards,we will introduce the recently developed statistical potentials for RNA structure evaluation.Finally,we will emphasize the perspective on developing new statistical potentials for RNAs in the near future.

Evaluation Method of Web Site Structure Based on Web Structure Mining

The structure of Web site became more complex than before. During the design period of a Web site, the lack of model and method results in improper Web structure, which depend on the designer's experience. From the point of view of software engineering, every period in the software life must be evaluated before starting the next period's work. It is very important and essential to search relevant methods for evaluating Web structure before the site is completed. In this work, after studying the related work about the Web structure mining and analyzing the major structure mining methods (Page\|rank and Hub/Authority), a method based on the Page\|rank for Web structure evaluation in design stage is proposed. A Web structure modeling language WSML is designed, and the implement strategies for evaluating system of the Web site structure are given out. Web structure mining has being used mainly in search engines before. It is the first time to employ the Web structure mining technology to evaluate a Web structure in the design period of a Web site. It contributes to the formalization of the design documents for Web site and the improving of software engineering for large scale Web site, and the evaluating system is a practical tool for Web site construction.

A novel knowledge-based potential for RNA 3D structure evaluation

Ribonucleic acids(RNAs)play a vital role in biology,and knowledge of their three-dimensional(3D)structure is required to understand their biological functions.Recently structural prediction methods have been developed to address this issue,but a series of RNA 3D structures are generally predicted by most existing methods.Therefore,the evaluation of the predicted structures is generally indispensable.Although several methods have been proposed to assess RNA 3D structures,the existing methods are not precise enough.In this work,a new all-atom knowledge-based potential is developed for more accurately evaluating RNA 3D structures.The potential not only includes local and nonlocal interactions but also fully considers the specificity of each RNA by introducing a retraining mechanism.Based on extensive test sets generated from independent methods,the proposed potential correctly distinguished the native state and ranked near-native conformations to effectively select the best.Furthermore,the proposed potential precisely captured RNA structural features such as base-stacking and base-pairing.Comparisons with existing potential methods show that the proposed potential is very reliable and accurate in RNA 3D structure evaluation.

Low Carbon Building Design Optimization Based on Intelligent Energy Management System

The construction of relevant standards for building carbon emission assessment in China has just started,and the quantitative analysis method and evaluation system are still imperfect,which hinders the development of low-carbon building design.Therefore,the use of intelligent energy management system is very necessary.The purpose of this paper is to explore the design optimization of low-carbon buildings based on intelligent energy management systems.Based on the proposed quantitative method of building carbon emission,this paper establishes the quota theoretical system of building carbon emission analysis,and develops the quota based carbon emission calculation software.Smart energy management system is a low-carbon energy-saving system based on the reference of large-scale building energy-saving system and combined with energy consumption.It provides a fast and effective calculation tool for the quantitative evaluation of carbon emission of construction projects,so as to realize the carbon emission control and optimization in the early stage of architectural design and construction.On this basis,the evaluation,analysis and calculation method of building structure based on carbon reduction target is proposed,combined with the carbon emission quota management standard proposed in this paper.Taking small high-rise residential buildings as an example,this paper compares and analyzes different building structural systems from the perspectives of structural performance,economy and carbon emission level.It provides a reference for the design and evaluation of low-carbon building structures.The smart energy management system collects user energy use parameters.It uses time period and time sequence to obtain a large amount of data for analysis and integration,which provides users with intuitive energy consumption data.Compared with the traditional architectural design method,the industrialized construction method can save 589.22 megajoules(MJ)per square meter.Based on 29270 megajoules(MJ)per ton of standard coal,the construction area of the case is about 8000 m2,and the energy saving of residential buildings is 161.04 tons of standard coal.This research is of great significance in reducing the carbon emission intensity of buildings.

Nonlinear Electrical Impedance Tomography Method Using a Complete Electrode Model for the Characterization of Heterogeneous Domains

This paper presents an electrical impedance tomography(EIT)method using a partial-differential-equationconstrained optimization approach.The forward problem in the inversion framework is described by a complete electrodemodel(CEM),which seeks the electric potential within the domain and at surface electrodes considering the contact impedance between them.The finite element solution of the electric potential has been validated using a commercial code.The inverse medium problem for reconstructing the unknown electrical conductivity profile is formulated as an optimization problem constrained by the CEM.The method seeks the optimal solution of the domain’s electrical conductivity to minimize a Lagrangian functional consisting of a least-squares objective functional and a regularization term.Enforcing the stationarity of the Lagrangian leads to state,adjoint,and control problems,which constitute the Karush-Kuhn-Tucker(KKT)first-order optimality conditions.Subsequently,the electrical conductivity profile of the domain is iteratively updated by solving the KKT conditions in the reduced space of the control variable.Numerical results show that the relative error of the measured and calculated electric potentials after the inversion is less than 1%,demonstrating the successful reconstruction of heterogeneous electrical conductivity profiles using the proposed EIT method.This method thus represents an application framework for nondestructive evaluation of structures and geotechnical site characterization.

An unsupervised clustering method for nuclear magnetic resonance transverse relaxation spectrums based on the Gaussian mixture model and its application

To make the quantitative results of nuclear magnetic resonance(NMR) transverse relaxation(T;) spectrums reflect the type and pore structure of reservoir more directly, an unsupervised clustering method was developed to obtain the quantitative pore structure information from the NMR T;spectrums based on the Gaussian mixture model(GMM). Firstly, We conducted the principal component analysis on T;spectrums in order to reduce the dimension data and the dependence of the original variables. Secondly, the dimension-reduced data was fitted using the GMM probability density function, and the model parameters and optimal clustering numbers were obtained according to the expectation-maximization algorithm and the change of the Akaike information criterion. Finally, the T;spectrum features and pore structure types of different clustering groups were analyzed and compared with T;geometric mean and T;arithmetic mean. The effectiveness of the algorithm has been verified by numerical simulation and field NMR logging data. The research shows that the clustering results based on GMM method have good correlations with the shape and distribution of the T;spectrum, pore structure, and petroleum productivity, providing a new means for quantitative identification of pore structure, reservoir grading, and oil and gas productivity evaluation.

Prediction of the Residual Strength for Durability Failure of Concrete Structure in Acidic Environments

According to the results of accelerated tests of acidification corrosion depth and compressive strength of concretes subjected to sulfuric acid environments,the acidification depth laws of concretes were predicted based on the grey system theory.Thus,the remaining compressive strength was calculated when the acidification depth reached the protection layer thickness of concrete structures,which indicates that the limit state of durability failure can be defined based on strength degradation,and the calculation process was illustrated by an example.The calculated results show that the remaining compressive strength values in the durability failure limit state for the concrete structures exposed to p H=2 and 3 sulfuric acid water environments and wet-dry cyclic sulfuric acid environment with p H=2 are 74%,72%,and 80% of initialstrength,respectively.The method provides references for the durability evaluation of concrete structure design under the acidic environments.

System failure analysis based on DEMATEL–ISM and FMECA

A new method of system failure analysis was proposed. First, considering the relationships between the failure subsystems,the decision making trial and evaluation laboratory(DEMATEL) method was used to calculate the degree of correlation between the failure subsystems, analyze the combined effect of related failures, and obtain the degree of correlation by using the directed graph and matrix operations. Then, the interpretative structural modeling(ISM) method was combined to intuitively show the logical relationship of many failure subsystems and their influences on each other by using multilevel hierarchical structure model and obtaining the critical subsystems. Finally, failure mode effects and criticality analysis(FMECA) was used to perform a qualitative hazard analysis of critical subsystems, determine the critical failure mode, and clarify the direction of reliability improvement.Through an example, the result demonstrates that the proposed method can be efficiently applied to system failure analysis problems.

Application research of multivariate linkage fluctuation analysis on condition evaluation in process industry

Abnormal conditions are hazardous in complex process systems, and the aim of condition recognition is to detect abnormal conditions and thus avoid severe accidents. The relationship of linkage fluctuation between monitoring variables can characterize the operation state of the system. In this study,we present a straightforward and fast computational method, the multivariable linkage coarse graining(MLCG) algorithm, which converts the linkage fluctuation relationship of multivariate time series into a directed and weighted complex network. The directed and weighted complex network thus constructed inherits several properties of the series in its structure. Thereby, periodic series convert into regular networks, and random series convert into random networks. Moreover, chaotic time series convert into scale-free networks. It demonstrates that the MLCG algorithm permits us to distinguish, identify, and describe in detail various time series. Finally, we apply the MLCG algorithm to practical observations series, the monitoring time series from a compressor unit, and identify its dynamic characteristics. Empirical results demonstrate that the MLCG algorithm is suitable for analyzing the multivariable linkage fluctuation relationship in complex electromechanical system. This method can be used to detect specific or abnormal operation condition, which is relevant to condition identification and information quality control of complex electromechanical system in the process industry.

Post-evaluation of a water pollution control plan： methodology and case study

China has developed more than 20 water pollution control plans for river basins （RBWPs） since 1996. However, the implementation has generally lagged. This paper proposes a three-step, post-evaluation methodology to analyze the implementation result of a RBWP and its influential factors. First, a multi-attribute evaluation method based on an index system is established to score the enforcement results of a RBWP. Indicators measure how well a RBWP has achieved its objectives, which include water quality compliance, pollution load control, project construction, financial inputs, and related management requirements. Second, an interpretive structural model is used to detect the significant factors that affect RBWP implementation. This model can effectively analyze the cause-effect chain and hierarchical relationship among variables. Five groups of factors were identified, namely, plan preparation, water resource endowment, policy, institution, and management. Both qualitative and quantitative methods are employed in the third step to evaluate the extent to which these factors have influenced the execution result of a RBWP, including pre-post contrast, scenario analysis, and correlation analysis. This research then post-evaluated the implementation of the Huai River Basin water pollution control plans （H-RBWPs） over a period of 10 years as a case study. Results showed that the implementation of the H-RBWPs was unsatisfactory during 2001–2005, although it improved during 2006–2010. The poor execution of these plans was partially caused by the underestimation of regional economic development in combination with ineffective industrial structure adjustment policies. Therefore, this case study demonstrates the feasibility and flexibility of the proposed post-evaluation methodology.

cgRNASP-CN: a minimal coarse-grained representation-based statistical potential for RNA 3D structure evaluation

Knowledge of RNA 3-dimensional(3 D) structures is critical to understand the important biological functions of RNAs, and various models have been developed to predict RNA 3 D structures in silico. However, there is still lack of a reliable and efficient statistical potential for RNA 3 D structure evaluation. For this purpose, we developed a statistical potential based on a minimal coarse-grained representation and residue separation, where every nucleotide is represented by C4’ atom for backbone and N1(or N9) atom for base. In analogy to the newly developed all-atom rsRNASP, cgRNASP-CN is composed of short-ranged and long-ranged potentials, and the short-ranged one was involved more subtly. The examination indicates that the performance of cgRNASP-CN is close to that of the all-atom rsRNASP and is superior to other top all-atom traditional statistical potentials and scoring functions trained from neural networks, for two realistic test datasets including the RNA-Puzzles dataset. Very importantly,cgRNASP-CN is about 100 times more efficient than existing all-atom statistical potentials/scoring functions including rsRNASP. cgRNASP-CN is available at website: https://github.com/Tan-group/cgRNASP-CN.

Systematic Synthesis of Dedicated Hybrid Transmission

The great diversity of dedicated hybrid transmissions(DHTs)requires a method to identify solutions among all potential concepts involved in each structure.Therefore,a DHT synthesis tool is developed on the basis of general transmission synthesis.In the first synthesis step,transmission structures are generated with only conventional functions such as driving with only the internal combustion engine.Electric machines are then installed in the transmissions to achieve further hybrid functions,including boosting,eCVT and electric driving modes.The number of generated transmission concepts increases exponentially with each synthesis step.Various evaluations are carried out successively to identify the most suitable DHT concepts among the many possible solutions.The generated DHT concepts are evaluated in terms of structural feasibility,driving modes,drivability and load factors on transmission components.An example of DHT synthesis involving planetary gear sets is explained in detail.The best five DHT structures are identified out of more than 120 billion solutions.

Accelerating the detection of unfeasible hypothetical zeolites via symmetric local interatomic distance criteria

In silico prediction of potential synthetic targets is the prerequisite for function-led discovery of new zeolites. Millions of hypothetical zeolitic structures have been predicted via various computational methods, but most of them are experimentally inaccessible under conventional synthetic conditions.Screening out unfeasible structures is crucial for the selection of synthetic targets with desired functions.The local interatomic distance（LID） criteria are a set of structure rules strictly obeyed by all existing zeolite framework types. Using these criteria, many unfeasible hypothetical structures have been detected. However, to calculate their LIDs, all hypothetical structures need to be fully optimized without symmetry constraints. When evaluating a large number of hypothetical structures, such calculations may become too computationally expensive due to the forbiddingly high degree of freedom. Here, we propose calculating LIDs among structures optimized with symmetry constraints and using them as new structure evaluation criteria, i.e., the LIDsymcriteria, to screen out unfeasible hypothetical structures. We find that the LIDsymcriteria can detect unfeasible structures as many as the original non-symmetric LID criteria do, yet require at least one order of magnitude less computation at the initial geometry optimization stage.