Adjustment Coefficient Research Articles

Ion Thruster Discharge Modeling with Adjustment of Coefficient of Anomalous Electron Conductivity

The results of plasma simulations in a discharge chamber of a Direct Current gridded ion thruster are dependent on the choice of anomalous electron conductivity model. Usually, additional electron diffusion is used to reproduce the experimental conductivity level. Up-to-date, the existing numerical models use different values of the additional diffusion coefficient, and this choice is often nonrigorous or insufficiently justified. In this research, the influence of the additional diffusion coefficient on the results of an axisymmetrical Full-Particle in Cell simulation of plasma in the discharge chamber of a Direct Current gridded ion thruster was investigated. The modeled thruster had a rated power of 3 kW, a thrust of 85 mN, and a specific impulse of 4500 s. The magnitude of the additional diffusion was varied from zero to the value of Bohm diffusion . gave the best agreement of the simulation results with the experimental data for the chosen thruster. A qualitative analysis of the plasma parameter distributions inside the discharge chamber at various anomalous electron conductivity coefficients is also provided. The profile of the ion current density through the ion-optical system obtained in the simulation is in sufficient agreement with experimental data.

Brand new norms for a good old test: Northern Italy normative study of MiniMental State Examination

AimMini-Mental State Examination (MMSE) is one of the most used tests for the screening of global cognition in patients with neurological and medical disorders. Norms for the Italian version of the test were published in the 90 s; more recent norms were published in 2020 for Southern Italy only. In the present study, we computed novel adjustment coefficients, equivalent scores and cut-off value for Northern Italy (Lombardia and Veneto) and Italian speaking Switzerland.MethodsWe recruited 361 healthy young and old (range: 20–95 years) individuals of both sexes (men: 156, women: 205) and from different educational levels (range: 4–22 years). Neuropsychiatric disorders and severe medical conditions were excluded with a questionnaire and cognitive deficits and were ruled out with standardized neuropsychological tests assessing the main cognitive domains. We used a slightly modified version of MMSE: the word ‘fiore’ was replaced with ‘pane’ in verbal recalls to reduce the common interference error ‘casa, cane, gatto’. The effect of socio-demographic features on performance at MMSE was assessed via multiple linear regression, with test raw score as dependent variable and sex, logarithm of 101—age and square root of schooling as predictors.ResultsMean raw MMSE score was 28.8 ± 1.7 (range: 23–30). Multiple linear regression showed a significant effect of all socio-demographic variables and reported a value of R2 = 0.26. The new cut off was ≥ 26 /30.ConclusionWe provide here updated norms for a putatively more accurate version of Italian MMSE, produced in a Northern population but potentially valid all over Italy.

INCENTIVE MECHANISM FOR PERFORMANCE-BASED PAYMENT OF INFRASTRUCTURE PPP PROJECTS: COUPLING OF REPUTATION AND RATCHET EFFECTS

The performance-based payment PPP model has been widely used in the infrastructure projects. However, the ratchet effect derived from performance-based reputation incentives has been largely overlooked. To overcome this shortcoming, ratchet effect is considered in the performance-based payment incentive process. A multi-period dynamic incentive mechanism is developed by coupling the reputation and ratchet effect. The main results show that: (1) Under the coupling of reputation and ratchet effects, the optimal incentive coefficient in the last performance assessment period is always greater than that of the first period. The bargaining power can replace part of the incentive effect; (2) Due to the ratchet effect, if the government improve performance targets through performance adjustment coefficients, it needs to increase incentives to overcome the decreasing effort of the private sector; (3) When the bargaining power and punishment coefficient are small, the reputation incentive is replacing the explicit incentive. The increasing incentive coefficient would make the ratchet effect dominant the reputation effect; (4) To prevent the incentive incompatibility derived from the ratchet effect, the government should increase the incentive while increasing the punishment to achieve the “penalties and rewards”. This study provides theoretical and methodological guidance to design incentive contracts for infrastructure PPP projects.

INCENTIVE MECHANISM FOR PERFORMANCE-BASED PAYMENT OF INFRASTRUCTURE PPP PROJECTS: COUPLING OF REPUTATION AND RATCHET EFFECTS

The performance-based payment PPP model has been widely used in the infrastructure projects. However, the ratchet effect derived from performance-based reputation incentives has been largely overlooked. To overcome this shortcoming, ratchet effect is considered in the performance-based payment incentive process. A multi-period dynamic incentive mechanism is developed by coupling the reputation and ratchet effect. The main results show that: (1) Under the coupling of reputation and ratchet effects, the optimal incentive coefficient in the last performance assessment period is always greater than that of the first period. The bargaining power can replace part of the incentive effect; (2) Due to the ratchet effect, if the government improve performance targets through performance adjustment coefficients, it needs to increase incentives to overcome the decreasing effort of the private sector; (3) When the bargaining power and punishment coefficient are small, the reputation incentive is replacing the explicit incentive. The increasing incentive coefficient would make the ratchet effect dominant the reputation effect; (4) To prevent the incentive incompatibility derived from the ratchet effect, the government should increase the incentive while increasing the punishment to achieve the “penalties and rewards”. This study provides theoretical and methodological guidance to design incentive contracts for infrastructure PPP projects.

Hydraulic Planning in Insular Urban Territories: The Case of Madeira Island—São Vicente

This study aims to examine the flood propensity of the main watercourse of São Vicente drainage basin and, if relevant, to propose two methodologies to alleviate the impacts, i.e., detention basin sizing and riverbed roughness coefficient adjustment. Geomorphological data were obtained from the watershed characterization process and used through the SIG ArcGIS software for the flood propensity assessment and then for the calculation of the expected peak flow rate for a return period of 100 years through the Gumbel Distribution. Subsequently, the drainage capacity of the river mouth was verified using the Manning-Strickler equation, in order to establish whether the river mouth of the watershed has the capacity to drain the entire volume of rainwater in a severe flood event. In summary, it was possible to conclude that São Vicente’s watershed river mouth is not able to completely drain the rain flow for the established return period. Thus, its drainage capacity was guaranteed by modifying the walls and streambed roughness coefficient and by sizing the detention basin using the Dutch and the Simplified Triangular Hydrograph methods.

Multi-objective imperfect selective maintenance optimization for series-parallel systems with stochastic mission duration

To improve the probability that an engineering system successfully completes its next mission, it is crucial to implement timely maintenance activities, especially when maintenance time or maintenance resources are limited. Taking series-parallel system as the object of study, this paper develops a multi-objective imperfect selective maintenance optimization model. Among it, during the scheduled breaks, potential maintenance actions are implemented for the components, ranging from minimal repair to replacement. Considering that the level of maintenance actions is closely related to the maintenance cost, age reduction coefficient and hazard rate adjustment coefficient are taken into account. Moreover, improved hybrid hazard rate approach is adopted to describe the reliability improvement of the components, and the mission duration is regarded as a random variable. On this basis, a nonlinear stochastic optimization model is established with dual objectives to minimize the total maintenance cost and maximize the system reliability concurrently. The fast elitist non-dominated sorting genetic algorithm (NSGA-II) is adopted to solve the model. Numerical experiments are conducted to verify the effectiveness of the proposed approach. The results indicate that the proposed model can obtain better scheduling schemes for the maintenance resources, and more flexible maintenance plans are gained.

Loudness-Level-Chasing Algorithm for Multiformat Live Audio Production

This paper proposes a loudness-level-chasing algorithm for live audio production to manage an integrated loudness level of a downmixed (DMX) signal produced from an immersive audio (IA) signal. The purpose of this study is to establish a method to simulcast the IA and DMX signals at the same integrated loudness level without manual operation. The proposed algorithm derives an adjustment coefficient applied to the amplitude of the DMX signal, taking the difference of the temporary integrated loudness levels of the IA and DMX signals and the remaining time of the program into consideration. The adjustment coefficient is iteratively updated throughout the program and is sequentially applied to the incoming DMX signal. The effectiveness of the adjustment in accordance with the proposed algorithm is verified using four 22.2 multichannel (22.2 ch) sound programs as the IA signals. The DMX signals are produced in the stereo format from the 22.2 ch sound signals using a downmixing method. Simulation experiments confirm that the proposed algorithm adjusts the integrated loudness level of the DMX signal within a deviation of <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula>0.1 dB from that of the 22.2 ch sound signal. In addition, the results of subjective evaluation carried out by professional sound engineers proves that the adjustment in accordance with the proposed algorithm is non-annoying for listeners if a transition rate of the adjustment coefficient is in the range of 0.1 to 3 dB/s. In summary, the proposed algorithm can effectively manage the integrated loudness level of multiformat audio in live productions.

Artificial neural network applied on sintered BaTiO3-ceramic density

It is very important to determine microstructure parameters of consolidated ceramic samples, because it opens new frontiers for further microelectronics miniaturization and integrations. Therefore, controlling, predicting and designing the ceramic materials? properties are the objectives in ceramic materials consolidating process, within the science of sintering. In order to calculate the precise values of desired microstructure parameter at the level of the grains? coating layers based on the measurements on the bulk samples, we applied the artificial neural networks, as a powerful mathematical tool for mapping input-output data. Input signals are propagated forward, as well as the adjustable coefficients that contribute the calculated output signal, denoted as error, which is propagated backwards and replaced by examined parameter. In our previous research, we used neural networks to calculate different electrophysical parameters at the nano level of the grain boundary, like relative capacitance, breakdown voltage or tangent loss, and now we extend the research on sintered material?s density calculation. Errors on the network output were substituted by different consolidated samples density values measured on the bulk, thus enabling the calculation of precise material?s density values between the layers. We performed the neural network theoretical experiments for different number of neurons in hidden layers, according to experimental ceramics material?s density of ?=5.4x103[kg/m3], but it opens the possibility for neural networks application within other density values, as well.

Statistics and Analysis of the Relationship between Strength and Age of Coral Concrete

In this study, based on the experimental results of this study and from the previous research, it is concluded that the compressive strength of coral concrete at 3, 7, 14, 21, 60, and 90 days reaches about 71%, 84%, 92%, 96%, 104%, and 110% of that at 28 days, respectively, indicating that the early strength development of coral concrete is significantly faster than that of ordinary concrete but slower in the later stage. A new model to characterize the strength‐age relationship of coral concrete is proposed in this study, which is in good agreement with the experimental result, but for the long‐term strength prediction of coral concrete in actual engineering, it is recommended to adjust the prediction results according to the environment and working conditions of coral concrete, and the adjustment coefficient could be approximately taken as 0.88. In addition, the effects of water‐binder ratio (w/b), total dosage of cementitious materials (b), supplementary cementitious material (SCM) content, and aggregate properties, on the age strength development of coral concrete are studied.

Makroekonomske determinante stope neizmirenja kredita u bankarskom sektoru Republike Srbije

In literature, there is inconsistent view of how the size of a company affects the level of credit risk. The aim of this research is to to determine whether the exposure to systemic risk increases with the size of the borrower. Empirical analysis of the time series of loan default rates, as dependent variable, on the one hand, and macroeconomic factors, as regressors, on the other hand, is based on the error correction model. Parallel to this, a panel data analysis was applied where panel units are defined at the level of the risk segment of the loan portfolio. Research results confirm that there is statistically significant impact of macroecnomic determinants on loan default rate in banking sector of Republic of Serbia. However, in the segment of small and medium-sized enterprises, the adjustment coefficient is not statistically significant. Along with this, in the short run, there is a statistically significant negative impact of the one-quarter lagged default rate on the default rate in the SMEs segment. Based on the research results, it can be inferred that the credit risk of the SMEs segment is the most resistant to the influence of macroeconomic factors. SME are the most flexible because they are not burdened by size, and on the other hand, they are not endangered as micro businesses by the risk of concentration of one large customer and weak negotiating position in relation to creditors and suppliers.

Noise detection and suppression in ECG using adaptive filter algorithm

A novel Power Line Interference (PLI) detection and suppression algorithm is proposed to preprocess Electrocardiogram (ECG) signals. A distinctive feature of this algorithm is the ability to detect the presence or absence of PLI in the ECG signal before applying the PLI suppression algorithm in the ECG signal. Only after the detection of PLI, it can be suppressed. We propose a PLI detector employing a classifier designed based on observations of the Root Mean Square (RMS) values of noise and reference ECG signals. An efficient recursive Least Mean Square (LMS) adaptive notch filter was also developed to serve the purpose of PLI suppression. An adaptive filter consists of a digital filter with adjustable coefficients and an LMS algorithm to modify the coefficient values to filter each sample. Experimental results demonstrate superior performance of this proposed algorithm.

Profit Optimization with a Power-Law Adjustable Coefficient for Zero Ending Inventories Dynamic Pricing Model, Stochastic Demand, and Fixed Lifetime Product

We consider a single product with a fixed lifetime and price sensitive intensity of a Poisson customers flow. A model with a power adjustable coefficient of a dynamic retail price control through the intensity of the demand is considered providing almost surely zero ending inventories at the end of the product's lifetime. To obtain probabilistic characteristics of the selling process and the expected profit, the diffusion approximation of the stock level process is used. The task of the expected profit maximization with respect to the adjustable coefficient for a linear approximation of the intensity-of-price dependence is solved.

AC-IBFGS-Based Inversion Method for Estimating the Quasi-Viscoelastic Parameters of Arch Dams

This article investigates an inverse problem for variable physical and mechanical comprehensive parameters. A back analysis method based on an improved BFGS (Broyden-Fletcher-Goldfarb Shanno-Newtonian iteration) and AC (adjustment coefficient) is proposed to estimate the physical and mechanical comprehensive parameters of complex concrete arch dam structures. These quasi-viscoelastic variable physical and mechanical comprehensive parameters of concrete arch dam structures are established based on in situ monitoring data and test results. By studying the constitutive model of concrete arch dam structures and the corresponding characterization method of the gradual changes of those parameters, a method utilizing adjustment coefficient is established to determine the initial values of the physical and mechanical comprehensive parameters and an adjoin method combined with BFGS Newtonian iteration method is proposed to inverse the variable physical and mechanical comprehensive parameters of concrete arch dam structures and analyze the variation regulation of these parameters. The proposed inversion analysis method was successfully implemented for the second-highest concrete double curvature arch dam. The results indicate that the proposed method has high precision and strong generalizability.

Adaptive pid regulation method of uninterruptible power supply battery charge current based on artificial neural network

The paper considers the issue of improving the methods of uninterruptible power supply intelligent control based on adaptive algorithms with the use of artificial neural network technologies. An adaptive PID regulation method of the UPS battery charge current is proposed. A neural network based control scheme with adjustment of PID regulation coefficients has been developed. The simulation modeling was used to search for the artificial neural network topology, which would be the most effective according to the criteria of the battery charge current regulation accuracy. The use of artificial neural networks in the uninterruptible power supply control system made it possible to obtain more effective results for maintaining a stable battery charge current in the transient.

ПРОЕКТНЫЙ РАСЧЕТ ЗУБЧАТОЙ ПЕРЕДАЧИ ГРУЗОВОГО ЭЛЕКТРОВОЗА С ОПТИМАЛЬНЫМИ ПАРАМЕТРАМИ КОРРИГИРОВАНИЯ

In result of the study the authors have established that mistakes in design a gearwheel are among main reasons that lead to a failure of a gearwheel for the 2ES6 freight electric locomotive. In order to increase life of the gearwheel they suggest a method for its redesign with adjustment of offset coefficients. The authors have developed a cylindrical involute helical gearwheel. With the use of modeling in the «Shafts and mechanical gears» application within the framework of the KOMPAS-3D system the authors have got strength and geometrical parameters of the redesigned gearwheel that meets all reliability requirements. Moreover, they have carried out a durability calculation that has confirmed correctness of gained values. As a result, the use of the developed model with optimal parameters of correlation adjustment will contribute to a decrease of a number of traction gearbox failures.

Incentive and Game of Information Sharing Based on Blockchain Technology

With the development of the information age, the efficiency of information resource sharing has not been fully released, and the willingness of participants to share information needs to be improved. It is of great significance to promote the willingness of participants to share information and increase the value of information utilization. Aiming at the problem of low information sharing rate of participants, this paper constructs an evolutionary game model between information sharing and non-sharing participants under the blockchain technology platform and introduces the critical value corresponding to the initial proportion of participants under the condition of a stable strategy as a trigger Conditions, dynamic adjustment of incentive coefficients to maintain a stable strategy of information sharing, simulation with MATLAB. Research shows that the use of blockchain smart contract technology to dynamically adjust the evolutionary stability strategy is shown as: When the initial proportion of participants is lower than the critical value, the incentive parameters are transformed into shared incentive benefits to motivate the participants to actively participate in information sharing; when it is higher than the critical value, the incentive parameters are transformed into sharing costs and dynamically adjusted to reach the initial participants. The proportion is maximized, which provides a strategy for participants to choose information-sharing strategies.

Design Simulation and Test Research of a Balanced Single-Seat Control Valve with Adjustable Flow Coefficient Based on High-Pressure Sealing

Design Simulation and Test Research of a Balanced Single-Seat Control Valve with Adjustable Flow Coefficient Based on High-Pressure Sealing

Vertical Nonlinear Temperature Gradient and Temperature Load Mode of Ballastless Track in China

In the process of heat exchange with the external environment, the internal temperature of ballastless track structure presents a nonlinear distribution. The vertical temperature gradient will cause repeated warping and deformation of track slab, resulting in mortar layer separation, which will affect driving comfort and track durability. The traditional temperature field analysis method of concrete structure based on thermodynamics has the disadvantages of too many assumptions, difficult parameter selection and too much calculation of energy consumption. In this paper, based on the finite element software ANSYS, the heat exchange was transformed into the boundary condition of heat flux, which was applied to the thermodynamic analysis model to study the nonlinear temperature distribution law of ballastless track. The accuracy of the analysis method was verified by the measured data. On this basis, the regional distribution law of temperature gradient of ballastless track under different geographical coordinates and climatic conditions was studied. By adding a regional adjustment coefficient, the vertical temperature load model of ballastless track suitable for typical areas in China was proposed. The proposed temperature load model makes up for the lack of refinement of climate division and temperature load model in relevant specifications, and has strong engineering application and popularization value.

On the application of the LET-model for the approximation of core relative phase permeabilities

The article deals with the problem of adequate interpretation of core relative phase permeabilities and the use of the LET-model in application to this problem. Some existing analytical dependencies for the interpretation of laboratory studies of the OFP are briefly analyzed: the Corey model, the Sigmund and McCaffery model, the Chierici model, the LET-model. The advantage of the LET-model and the high evaluation of this model in independent studies of the authors are described. In the process of preliminary analysis, the authors of the article revealed that the non-trivial issue of determining the adjustable coefficients of the model using mathematical methods was not sufficiently covered, which became the central object of the work. Further, the application of the LET-model is formalized and the methodology for determining the adjustable coefficients of the model (L, E and T) is described. The task of selecting model parameters is interpreted as a multidimensional optimization problem. Also, a class of nonlinear least squares methods was defined to solve the problem. Several common numerical optimization methods for selecting the coefficients of the model are considered: the brute-force search method, the Levenberg-Marquardt algorithm method, the trust region method. Computational experiments were carried out to assess their applicability and comparative analysis of optimization algorithms. Based on the results of the experiments, it was concluded that the method of confidence domains is suitable for practical application, since they converge to the optimal solution quickly enough and demonstrates high accuracy in the selection of coefficients L, E, T. The final part of the article demonstrates the difference between the LET-model and the basic Corey model when approximating core relative phase permeabilities.

Dynamic Analysis and Chaos Control for a NEV Enterprise and a Competitive TEV Enterprise under the CAFC‐NEV Mandate

This paper studies the complexity analysis in production between competition and R&amp;D for a traditional energy vehicles enterprise and a new energy vehicles enterprise under the introduction of corporate average fuel consumption credits and new energy vehicle credits mandate. From the internal perspective of market complexity evolution, this paper analyzes the internal mechanism of market competition fluctuation and discusses the influence of different corresponding speeds of enterprises on market stability through the nonlinear dynamics’ theory. The research results show that the adjustment coefficient of R&amp;D investment and the price coefficient of credit transactions are the key factors affecting the competitive strategies of the two companies. The difference between these two factors will even change the relative R&amp;D investment intensity, relative profitability, and market stability of the two companies in a balanced state. In addition, the numerical analysis showed that the time‐delay feedback control method could control the unstable behavior of the dynamic system efficiently and quickly and make the market quickly recover a stable and orderly state, which provided a scientific basis for decision makers to effectively solve the market instability.