Non-structural Parameters Research Articles

Damped accumulation-based volatility multivariable grey prediction power model and its application

PurposeIn this paper, a new grey Cosine New Structured Grey Model (CNSGM(1,N)) prediction power model is constructed for the small-sample modeling and prediction problem with complex nonlinearity and insignificant volatility.Design/methodology/approachFirstly, the weight of some relevant factors is determined by the grey comprehensive correlation degree, and the data are preprocessed. Secondly, according to the principle of “new information priority” and the volatility characteristics of the sequence growth rate, the ideas of damping accumulation power index and trigonometric function are integrated into the New Structured Grey Model (NSGM(1,N)) model. Finally, the non-structural parameters are optimized by the genetic algorithm, and the structural parameters are calculated by the least squares method, so a new CNSGM(1,N) predictive power model is constructed.FindingsUnder the principle of “new information priority,” through the combination with the genetic algorithm, the traditional first-order accumulation generation is transformed into damping accumulation generation, and the trigonometric function with the idea of integer is introduced to further simulate the phenomenon that the volatility is not obvious in the real system. It is applied to the simulation and prediction of China’s carbon dioxide emissions, and compared with other comparison models; it is found that the model has a better simulation effect and excellent performance.Originality/valueThe main contribution of this paper is to propose a new grey CNSGM(1,N) prediction power model, which can not only be applied to complex nonlinear cases but also reflect the differences between the old and new data and can reflect the volatility characteristics of the characteristic behavior sequence of the system.

Damping accumulative NDAGM(1,N, α) power model and its applications

PurposeIn this essay, a new NDAGM(1,N,α) power model is recommended to resolve the hassle of the distinction between old and new information, and the complicated nonlinear traits between sequences in real behavior systems.Design/methodology/approachFirstly, the correlation aspect sequence is screened via a grey integrated correlation degree, and the damped cumulative generating operator and power index are introduced to define the new model. Then the non-structural parameters are optimized through the genetic algorithm. Finally, the pattern is utilized for the prediction of China’s natural gas consumption, and in contrast with other models.FindingsBy altering the unknown parameters of the model, theoretical deduction has been carried out on the newly constructed model. It has been discovered that the new model can be interchanged with the traditional grey model, indicating that the model proposed in this article possesses strong compatibility. In the case study, the NDAGM(1,N,α) power model demonstrates superior integrated performance compared to the benchmark models, which indirectly reflects the model’s heightened sensitivity to disparities between new and old information, as well as its ability to handle complex linear issues.Practical implicationsThis paper provides a scientifically valid forecast model for predicting natural gas consumption. The forecast results can offer a theoretical foundation for the formulation of national strategies and related policies regarding natural gas import and export.Originality/valueThe primary contribution of this article is the proposition of a grey multivariate prediction model, which accommodates both new and historical information and is applicable to complex nonlinear scenarios. In addition, the predictive performance of the model has been enhanced by employing a genetic algorithm to search for the optimal power exponent.

Simulation on a new reverse circulation fishing tool: Design and evaluation of the salvage capacity and efficiency

AbstractTo solve the insufficient energy for reverse circulation fishing tools in complex structure wells, it is necessary to further optimize the design of the tools to improve the impurity salvage efficiency. In this work, a three‐dimensional (3D) flow field simulation model of reverse circulation fishing tool with venturi negative pressure is employed to identify the vortex zone of circulation channel and to investigate the negative pressure and jet velocity distribution around the spray nozzle based on the ANSYS‐CFX. The effects of the spray angle, diameter of the suction nozzle, and the nonstructural parameters on negative pressure and jet velocity are investigated through sensitivity analysis. As revealed from the results, there may be an optimum value for the spray angle, and the nozzle jet direction can indeed affect the propagation and attenuation of the bottom hole flow field greatly. The combination of the inner diameter of the nozzle outlet and that of the nozzle inlet (Φ + Φ′ = 3 mm + 6 mm) is the best design solution when the negative pressure at the nozzle outlet can be the highest. The negative pressure and jet velocity increase with the increasement of the inlet flow rate. The comparative analysis of different bottom hole impurity mass in the cases of the borehole inner diameter 5′ and 8.5″ on the salvage efficiency are conducted numerically. It is observed that the impurity mass salvaged and salvage time are not directly related to its total amount, but only related to the flow rate of the sucked fluid flow. The impurity salvage efficiency can be as high as 95.4%, which meets the engineering requirements in the field. This work improves the working performance of the reverse circulation fishing tool and provides theoretical guidance for well washing.

Role of Non-Structural Sugar Metabolism in Regulating Tuber Dormancy in White Yam (Dioscorea rotundata)

Changes in sugar composition occur continuously in plant tissues at different developmental stages. Tuber dormancy induction, stability, and breaking are very critical developmental transitions in yam crop production. Prolonged tuber dormancy after physiological maturity has constituted a great challenge in yam genetic improvement and productivity. In the present study, biochemical profiling of non-structural sugar in yam tubers during dormancy was performed to determine the role of non-structural sugar in yam tuber dormancy regulation. Two genotypes of the white yam species, one local genotype (Obiaoturugo) and one improved genotype (TDr1100873), were used for this study. Tubers were sampled at 42, 56, 87, 101, 115, and 143 days after physiological maturity (DAPM). Obiaoturugo exhibited a short dormant phenotype and sprouted at 101-DAPM, whereas TDr1100873 exhibited a long dormant phenotype and sprouted at 143-DAPM. Significant metabolic changes were observed in non-structural sugar parameters, dry matter, and moisture content in Obiaoturugo from 56-DAPM, whereas in TDr1100873, significant metabolic changes were observed from 101-DAPM. It was observed that the onset of these metabolic changes occurred at a point when the tubers of both genotypes exhibited a dry matter content of 60%, indicating that a dry matter content of 60% might be a critical threshold for white yam tuber sprouting. Non-reducing sugars increased by 9–10-fold during sprouting in both genotypes, which indicates their key role in tuber dormancy regulation in white yam. This result implicates that some key sugar metabolites can be targeted for dormancy manipulation of the yam crop.

Static contact mechanism between serrated contact wheel and rail in rail grinding with abrasive belt

Rail grinding with abrasive belt essentially appears as the complex nonlinear contact interaction between contact wheel, abrasive belt, and rail surface to realize flexible and efficient rail grinding, and in the grinding process, the higher grinding efficiency can be achieved with a serrated contact wheel. However, the contact mechanism between serrated contact wheel and rail is obscure. In this paper, a parametric contact model between serrated contact wheel and rail is established by setting the non-structural (grinding pressure and rotation angle) and structural parameters (helix angle, cogging ratio, etc.) of the serrated contact wheel. Then, the finite element simulation of contact behaviors under the changing conditions of various parameters is carried out to reveal the influences of the above parameters on the maximum contact stress, contact area, and rubber layer indentation depth. On this basis of the extension of the traditional Hertz contact theory and the calculation of the geometric structures of serrated contact wheel and rail, the mathematical model of the contact area is obtained and the ellipsoidal pressure distribution area and the stress concentration area are calculated and combined together to obtain the contact area pressure distribution model. The contact static test platform was designed and the errors between the theoretical and experimental lengths of the long and short axes of the contact area under a rotation angle of 0° were respectively determined as 7.3 % and 9.9 %, thus verifying the correctness of the established mathematical model. In addition, the theoretical and experimental results of the same points on the characteristic lines of the pressure distribution were obtained separately for the comparison. The comparison results proved the correctness of the mathematical model of pressure distribution.

Quasi-LPV system modeling and identification of a variable speed electro-mechanical and hydraulic MISO system

An accurate digital model is of great significance to system operation inversion and behavior prediction. The multi-energy domain coupling mechanism of the Electro-mechanical and Hydraulic (EMH) system is complex and has strong nonlinear characteristics. At present, the research mainly focuses on the mechanical-hydraulic coupling characteristics, while the research on the large operating range and the influence of electric motor and load characteristics on the nonlinear dynamics of the EMH system are less. Based on the first principle description, the nonlinear characteristics of the system components are described in this paper. Furthermore, the nonlinear dynamic model of the EMH system described in multi-state space is established based on the Quasi-LPV system. Combined with the experimental data, the structural and non-structural uncertain parameters of system are identified. Finally, the influence of the mechanical characteristics of electric motor, load on the nonlinear dynamics of the EMH system are discussed. Experiments show that the Quasi-LPV models proposed in this paper can accurately reproduce and predict system behavior. It provides technical support for rapid design, selection, and scheme optimization of hydraulic systems in general scenarios.

Influence of Non-Structural Parameters on Dual Parallel Jet Characteristics of Porous Nozzles.

As an important actuator of the dual parallel jet, the porous nozzle has some non-structural parameters (such as inlet pressure, nozzle spacing ratio, etc.) which have a significant influence on energy transport, chemical combustion and pollutant generation. The research on the microfluidic state of the porous nozzle dual parallel jet, however, remains insufficient because of its microjet pattern and complex intersection process. In this paper, the authors used numerical simulation and an experimental method to clarify the influence of porous nozzles’ non-structural parameters on dual parallel jet characteristics. The results show that the inlet pressure only changes the pressure peak value on the parallel jet axis; the starting point (SP) and peak point (PP) on the parallel jet axis, which are located at Xsp = 22 mm and Xpp = 75 mm, respectively, are not changed; and with the increase in the nozzle spacing ratio, the merging points (MPs) on the parallel jet axis are Xmp = 25 mm, 32 mm and 59 mm, respectively. The merging point and the combined point move to a farther distance and the inner deflection angle of the jet is weakened.

Development of the non-destructive monitoring methods of the pavement conditions via artificial neural networks

Non-structural parameters like surface defects and ride quality were frequently used, as a practical index for the rehabilitation selection process. The key purpose of this study was the assessment of using artificial network technology as a support for decision-makers about paving maintenance concerning the structural condition compared to the conventional, time-consuming, effort, and costly methods. The structural model was established based on the deflections from the FWD, (asphalt and base) layers thickness, surface temperature, precipitation rate, AADTT, traffic volume of class 9 and base layer type. The data used in building the developed ANN model is related to a previous study of flexible pavement structures on the M4 highway in the Russian highways network during the five years 2013-2017. The ANN model was built, trained, and tested by the Matlab program. The focus was on calculating roughness, fatigue, and rutting values as they are the most common pavement distress on site. We used the logistic model equations, developed by the Federal Highway Administration’s Long-Term pavement Performance (LTPP) to calculate the three pavement distress that will be used as output variables while training the ANN model. The ANN model presented a high performance in predicting the three pavement distress (fatigue, roughness, and rutting) where the R- squared value was equal (1, 0,999, and 1), respectively for the forecasting sections.

Bayesian estimation of a multivariate TAR model when the noise process follows a Student-t distribution

In this paper, we introduce a Bayesian methodology for the estimation of non-structural parameters (autoregressive matrices, covariance matrices and degrees of freedom) of a multivariate TAR model (MTAR) when noise process follows a multivariate Student-t distribution. For this, the use of non-informative prior distributions is proposed to obtain the full conditional distributions. MCMC methods are used to obtain samples of such distributions. The performance of the estimation is evaluated by means simulations. Finally, the model is applied to the returns data from the Bovespa, Colcap and Standard and Poor indexes.

The study of equivalent circuit model of battery based on parameters identification multi-dimensional look-up table method

Establishing reasonable and accurate battery model is important to battery energy storage system as well as parameter estimation for battery management system, such as SOC, SOH. In this paper, aiming at the equivalent circuit model, to improve its accuracy, the impact of the age, temperature, SOC, load current, self-discharge and other factors are considered, the experimental data is obtained under different conditions of reasonable experimental settings, using multiple linear regression method to get the model structure parameter off-line. Using the results establish an equivalent circuit model based on multi-dimensional look-up table, the model structure parameters can be interpolated lookup data based on conditions to suit the battery. Finally, design simulation examples verify the correctness and non-structural parameters of the model SOC, SOH implementation strategies.

Nonstructural Influence Factors of Dynamic Load Allowance for Concrete Beam Bridges

Studies show that nonstructural parameters, such as pavement conditions or load patterns, have greater influences on the dynamic load allowance (DLA) of bridges than structural parameters. For pavement roughness effects, the values of DLA caused by roughness profiles are calculated by a self-compiled program. The results showed that the values of DLA are discrete even if they are caused by roughness profiles that belong to the same power spectral density (PSD) grade. The PSD grade method for pavement conditions has limitations when it is used in the analysis of DLA. Statistical analysis was also carried out on these DLA results. The statistical analysis indicated that the values of DLA followed a normal distribution when they were excited by roughness profiles that belong to the same grade. For the influence of vehicle string loads on DLA, an improved optimization approach based on a genetic algorithm for the largest DLA is presented. A new method is used to calculate the fitness value in the genetic algorithm (GA) method, which could substantially reduce calculation time. The new approach is able to obtain the most unfavorable arrangement of the vehicle string and estimate the largest DLA caused by it.

Erratum to: Influence of Local Atomic and Electronic Structures of Magnetite on Subtle Effects in HERFD-XANES Spectra

Large iron oxide nanoparticles are studied by high-energy resolution fluorescence-detected X-ray absorption spectroscopy (HERFD XANES). A theoretical procedure within the finite-difference method framework is tested to calculate Fe K-edge X-ray absorption spectra for the magnetite structure. The influence of structural and non-structural calculation parameters on the subtle effects in the X-ray absorption spectra are considered. Tetrahedral positions of Fe3+ ions are shown to provide the main contribution to the spectral feature at 20–21 eV whose intensity grows together with the content of these ions.

Phenylalanine Ammonia-Lyase and Source-Flow-Sink Related Attributes in Rice Genotypes Subjected to High Night Temperatures

Phenylalanine ammonia-lyase (PAL) which is considered to be one of the main lines of cell acclimation against stress in plants, non-structural carbohydrates (NSC) accumulation and chlorophyll fluorescence parameters were quantified in two rice genotypes as a function of two temperature regimes: 22/30 °C (control) and 28/30 °C night/day (high night temperatures - HNT), imposed from heading to milk stage. The rice cultivars chosen were Nagina22 (N22) and BRS Querência (BRS-Quer), which are genotypes tolerant and sensitive to high temperatures, respectively. BRS-Quer genotype highlighted more sensitive responses maintaining higher PAL and peroxidase levels on seventh and twenty-first days after stress imposing. On the other hand, this genotype showed levels of fructose, glucose and sucrose decreasingly across stress period whether compared to N22. Both genotypes showed similarity for most of the chlorophyll fluorescence parameters. However, the photosynthesis induction curve highlighted that HNT caused decreases in some photochemical quenching of fluorescence as well as increases of non-phochemical quenching, affecting more prominently BRS-Quer genotype. N22 maintained unaltered the spikelet sterility and 1000-grain weight across temperature regimes showing a consistent trend with its stem NSC accumulation during stress period. The higher availability of soluble sugars shown by N22 at the end of stress period could be unloaded in spikelet formation and grain fillings contributing in their lower sterility rate and greater 1000-grain weight stability across the environments. These results indicate that selecting genotypes with higher capacity to stem NSC translocation beyond accumulation at HNT could lead to more grain yield stability in future climate scenarios.

Development of the pavement structural health index based on falling weight deflectometer testing

Non-structural parameters such as surface distresses and ride quality have commonly been used as functional indicators in the treatment selection process. However, transportation agencies have considered in recent years implementing structural capacity indicators into their pavement management system (PMS) and decision-making processes. The objective of this study was to develop a structural condition index for Louisiana, known as the Structural Health Index (SHI), on a scale from 0 to 100 that describes the structural integrity of the pavement structure based on the backcalculated layer moduli of in-service pavements as predicted from FWD testing. Based on the developed methodology, the SHI is estimated by first backcalculating the layer moduli using a backcalculation software. Then, a sigmodal function is used to calculate the SHI. Evaluation and validation of the SHI was successful and demonstrated that the new index responded realistically to sections in poor and in good structural conditions. Furthermore, it was shown that the new index provided additional information that complements existing functional indices in PMS by successfully identifying structurally deficient sections. The implementation of the SHI into the LADOTD decision matrix is demonstrated.

Forecasting new product diffusion with agent-based models

Agent-based model (ABM) has been widely used to explore the influence of complex interactions and individual heterogeneity on the diffusion of innovation, while it is seldom used as a forecasting tool in the innovation diffusion literature. This paper introduces a novel approach of forecasting new product diffusion with ABMs. The ABM is built on the hidden influence network (HIN) over which the innovation diffuses. An efficient method is presented to estimate non-structural parameters (i.e., p, q and m) and a multinomial logistic model is formulated to identify the type of the HIN for diffusion data. The simulation study shows that the trained logistic model performs well in inferring the HINs for most simulated diffusion data sets but poorly for those generated by ABMs with similar HINs. Therefore, to reduce the possible prediction loss arising from the misspecification of the HIN, three methods, namely, the predicted HIN, the weighted averaging and simple averaging, are developed to forecast new products diffusion. Their performances are evaluated by using a data set composed of 317 time series on consumer durables penetration. The results show that most identified HINs have moderate topology, and that our methods outperform four classical differential equation based diffusion models in both short-term and long-term prediction.

Novel genetic algorithm search procedure for LEED surface structure determination

Low Energy Electron Diffraction (LEED) is one of the most powerful experimental techniques for surface structure analysis but until now only a trial-and-error approach has been successful. So far, fitting procedures developed to optimize structural and nonstructural parameters—by minimization of the R-factor—have had a fairly small convergence radius, suitable only for local optimization. However, the identification of the global minimum among the several local minima is essential for complex surface structures. Global optimization methods have been applied to LEED structure determination, but they still require starting from structures that are relatively close to the correct one, in order to find the final structure. For complex systems, the number of trial structures and the resulting computation time increase so rapidly that the task of finding the correct model becomes impractical using the present methodologies. In this work we propose a new search method, based on Genetic Algorithms, which is able to determine the correct structural model starting from completely random structures. This method—called here NGA-LEED for Novel Genetic Algorithm for LEED—utilizes bond lengths and symmetry criteria to select reasonable trial structures before performing LEED calculations. This allows a reduction of the parameter space and, consequently of the calculation time, by several orders of magnitude. A refinement of the parameters by least squares fit of simulated annealing is performed only at some intermediate stages and in the final step. The method was successfully tested for two systems, Ag(1 1 1)(4 × 4)-O and Au(1 1 0)-(1 × 2), both in theory versus theory and in theory versus experiment comparisons. Details of the implementation as well as the results for these two systems are presented.

Proposed Method for Probabilistic Estimation of Peak Component Acceleration Demands

A probabilistic method is proposed to quantify peak component acceleration (PCA) demands for nonstructural components attached to elastic and inelastic structures. Incremental dynamic analyses and site-specific ground-motion hazard information are used to estimate PCA hazard curves and component uniform hazard spectra (CUHS) based on various structural and nonstructural parameters. For a given structural system, the primary parameters of interest are the location of the component within the structure, the ratio of the period of the component to the modal periods of the primary structure, and the component damping ratio. Representative results for shear wall structures illustrate the value of applying the method to acceleration-sensitive components, as the quantification of CUHS facilitates the implementation of performance-based design and evaluation approaches. The variability in the component responses presented highlights the need for a robust probabilistic seismic demand estimation methodology for nonstructural components in which the major sources of variability are incorporated.

Multiple Filtering Devices for the Estimation of Cyclical DSGE Models

We propose a method to estimate time invariant cyclical DSGE models using the information provided by a variety of filtering approaches. We treat data filtered with alternative procedures as contaminated proxy of the relevant model-based quantities and estimate structural and nonstructural parameters jointly using an unobservable component structure. We employ simulated data to illustrate the properties of the procedure and compare our estimates with those obtained when just one filter is used. We revisit the role of money in the transmission of monetary business cycles.

Multiple filtering devices for the estimation of cyclical DSGE models

We propose a method to estimate time invariant cyclical DSGE models using the information provided by a variety of filtering approaches. We treat data filtered with alternative procedures as contaminated proxy of the relevant model-based quantities and estimate structural and nonstructural parameters jointly using an unobservable component structure. We employ simulated data to illustrate the properties of the procedure and compare our estimates with those obtained when just one filter is used. We revisit the role of money in the transmission of monetary business cycles.

Preliminary seismic vulnerability assessment of Mussoorie Town, Uttarakhand (India)

Rapid visual screening (RVS) can be effectively used to evaluate the vulnerability of a large number of buildings with less computational effort. In this study, a seismic vulnerability assessment is conducted for all municipal wards in Mussoorie town, Uttarakhand, India. Various structural parameters of the building, like structure type, number of stories, soil type, age and roof type and non-structural parameters like occupancy, use and owner name have been collected. Further, seismic vulnerability of every individual building in the town has been assessed. The data collection form of the Federal Emergency Management Agency, FEMA 154 for RVS was modified to suit local conditions and was utilized for this purpose. Using satellite image all of the buildings are mapped in a GIS and a damage probability matrix is used to estimate the effects of an earthquake. The buildings are classified in five categories of seismic vulnerability. The data of Mussoorie town show that a total of 623 buildings (about 20 per cent of those surveyed) show a high probability of Grade 5 damage and a very high probability of Grade 4 damage, whereas 587 buildings (about 19 per cent of those surveyed) show a high probability of Grade 4 damage and a very high probability of Grade 3 damage in case of earthquake shaking of Intensity VIII or more in MSK scale.