Large Number Of Control Variables Research Articles

Study on the Impact of RMB Exchange Rate on China's Industrial Structure Adjustment

This paper firstly summarizes the current situation of China's current industrial structure as well as the change of exchange rate system, systematically describes the research object of this paper, and outlines its main features. After that, this paper analyzes the impact of the real effective exchange rate of RMB on China's secondary and tertiary industries for the purpose of the study, takes into full consideration the main factors affecting the industrial structure, selects a large number of control variables, and completes the empirical analysis of China's overall as well as the eastern, middle and western regions by using the panel data of the country's 30 provinces in the 16-year period from 2005 to 2020. In addition, a variety of robustness tests were conducted to fully ensure the stability and interpretability of the model. It is finally concluded that the appreciation of RMB exchange rate will significantly benefit the development of China's tertiary industry, promote the industrial structure from the secondary industry to the tertiary industry, and complete the upgrade of industrial structure. Specifically, for every 1% increase in the real effective exchange rate of RMB, the proportion of tertiary industry in the GDP of the whole country, the east, the center and the west will increase by 0.7850%, 0.7171%, 1.0186% and 0.4873% respectively. Besides, this paper also verifies the indirect impact of the exchange rate on the industrial structure through the three paths of price effect, foreign trade openness and FDI, and verifies the existence of the intermediary effect. According to the empirical results, the price effect, FDI, and the increase of foreign trade openness will promote the advanced industrial structure of China. But both FDI and foreign trade openness are negatively correlated with the exchange rate, which is the core variable of this paper.

MPC-Based Decentralized Voltage Control in Power Distribution Systems With EV and PV Coordination

Distribution systems are growing rapidly in size and complexity with increased penetrations of distributed energy resources (DER) and electric vehicles (EVs), leading to operational challenges. Reactive power compensation from photovoltaic (PV) inverters and active power curtailment of PV output are commonly used to mitigate voltage-related problems. However, the charging/discharging flexibility of EVs can be used to avoid PV output curtailment and save energy motivating methods to coordinate EV charging with PVs. Unfortunately, a large number of control variables makes the centralized voltage control computationally expensive. In this work, a decentralized voltage control algorithm which considers the active and reactive power compensation from PV inverters and EVs is presented. The proposed approach helps to solve the voltage issues in a more effective way. The approach involves clustering of the distribution network based on modified modularity, an index that considers EV flexibility and prediction time period. A model predictive control (MPC)-based algorithm is proposed for each cluster to solve the voltage problem using the respective PVs and EVs, while ensuring that the EV charging demand is satisfied while contributing to the voltage regulation. The proposed algorithm is validated using the IEEE 123 node test systems under two PV and EV penetration levels and shown to be effective in solving the voltage regulation problem.

Variable Switching Point Model Predictive Control for DC-Link Voltage Regulation of Back-to-Back Converters

In this article, a novel control method for back-to-back converters used in grid-to-motor connections is explored. To increase the robustness of low dc-link capacitances, a control method based on variable switching point model predictive control is proposed. While previous model predictive control methods for the back-to-back converter selected a certain switching state to fulfill all control goals, we use the switching time in addition to the switching state in order to minimize deviations from the target voltage. Choosing a variable switching point provides an additional degree of freedom to the control framework and allows the system to cope with the large number of control variables. In this case, the variable switching point is used to minimize the effects of low dc-link capacitances on the system. This can either be achieved by selecting a switching point that yields low dc-link capacitor charging or by selecting a switching point that aims to keep the dc-link voltage close to the reference. The proposed method is verified through numerical simulations and hardware-in-the-loop experiments and compared to existing approaches. The results show that it is possible to control the dc-link using only the switching point of the converter.

Finite Element Analysis and Experimental Investigation of Cut Surface Formation of Magnetic Silicon Steel in Shear Cutting.

Shear cutting allows for shaping materials with any length of cutting line with high efficiency and without negative thermal effects, but it causes stresses and deformations in the cutting zone of the material. This has a negative effect on the magnetic properties of the sheet in the areas of the cut edge. The main problem on production lines is to ensure appropriate control of the process so as to obtain the appropriate technological quality of the cut edge, free of not only defects in the form of burrs and shape deviations, but also the minimum deformed zone. This task is difficult due to the large number of control variables, the influence of which on the shaping of the material and the formation of the cut edge is not fully understood. The article attempts to determine the course of the cutting process and to examine the influence of control variables on the formation of the cut edge in the shear-slitting process in which the tools perform a rotary motion. For this purpose, FEM modeling, vision techniques and experimental studies were used. A 3D model of the process was developed, which enables a detailed analysis of the states of stresses, strains, displacements and fracture mechanisms of the material. The simulation results were verified using vision techniques, which were used in the work to observe the flow and cracking mechanisms of the material. Parametric analyses were performed for the process control variables. The research showed a significant influence of the cutting velocity and the clearance between the tools on the formation of the cut edge. The most homogeneous surface of the cut edge with the minimum burr height was obtained for the following parameters: rake angle α = 15–30°, horizontal clearance hc = 0.03 mm and slitting velocity v2 = 15 m/min. The developed results can be useful for controlling the cutting process on production lines in terms of maximum process efficiency while maintaining the appropriate technological quality of the cut edge.

Model-based optimisation of agricultural profitability and nutrient management: a practical approach for dealing with issues of scale.

To manage agricultural landscapes more sustainably, we must understand and quantify the synergies and trade-offs between environmental impact, production, and other ecosystem services. Models play an important role in this type of analysis as generally it is infeasible to test multiple scenarios by experiment. These models can be linked with algorithms that optimise for multiple objectives by searching a space of allowable management interventions (the control variables). Optimisation of landscapes for multiple objectives can be computationally challenging, however, particularly if the scale of management is typically smaller (e.g. field scale) than the scale at which the objective is quantified (landscape scale) resulting in a large number of control variables whose impacts do not necessarily scale linearly. In this paper, we explore some practical solutions to this problem through a case study. In our case study, we link a relatively detailed, agricultural landscape model with a multiple-objective optimisation algorithm to determine solutions that both maximise profitability and minimise greenhouse gas emissions in response to management. The optimisation algorithm combines a non-dominated sorting routine with differential evolution, whereby a 'population' of 100 solutions evolves over time to a Pareto optimal front. We show the advantages of using a hierarchical approach to the optimisation, whereby it is applied to finer-scale units first (i.e. fields), and then the solutions from each optimisation are combined in a second step to produce landscape-scale outcomes. We show that if there is no interaction between units, then the solution derived using such an approach will be the same as the one obtained if the landscape is optimised in one step. However, if there is spatial interaction, or if there are constraints on the allowable sets of solutions, then outcomes can be quite different. In these cases, other approaches to increase the efficiency of the optimisation may be more appropriate-such as initialising the control variables for half of the population of solutions with values expected to be near optimal. Our analysis shows the importance of aligning a policy or management recommendation with the appropriate scale.

Small Signal Modeling and Decoupled Controller Design for a Triple Active Bridge Multiport DC–DC Converter

This article proposes modeling and two controller design techniques for a triple active bridge (TAB) three-port dc-dc converter comprising of fuel cell, battery, and load. The sources are integrated, employing boost interleaved full-bridge converters in order to get smoother source current profile. The converter is a nonlinear multi-input multi-output (MIMO) system with a large number of control variables. Moreover, a high degree of coupling among the control variables makes its modeling and control system design quite cumbersome and complex. To overcome the complexity of analysis in a higher order system like TAB, a generalized frequency-domain modeling technique is introduced in this article. A new decoupling matrix-based proportional-integral controller design method is also proposed. It reduces the design complexity and improves the system dynamic performance (lower settling time, overshoot/undershoot in the controlled variables) in comparison to similar three-port converters reported in the literature. Further, the performance of the proposed controller is compared by simulation with another popular MIMO system controller design technique, namely the state feedback control. A 1-kW laboratory prototype is built and tested to verify the system performance during dynamic load changes, source current variation, and battery charging/discharging operation.

Bridging the gap from welfare to education: Propensity score matching evaluation of a bridging intervention.

We evaluate a bridging intervention for a group of young people aged 18–29, with no formal educational qualifications, who are not in employment, education or training. The bridging intervention consisted of classroom training, educational internships and mentoring. Based on Danish register data with a large number of control variables, a propensity score matching estimator was applied to assess the effectiveness of the bridging intervention. The results show that the bridging intervention was effective in increasing educational enrollment and completion for all participants. The effects of the intervention were particularly large for participants assessed to be ‘not ready for education’ and those diagnosed with psychiatric disorders suggesting that the bridging intervention may be especially beneficial for these subgroups.

Job Satisfaction and Bad Jobs: Why Are Cleaners So Happy at Work?

Our analysis is based on the French DARES Working Conditions survey which contains a very large sample of individuals representative of the French working population. We demonstrate that employees working in the cleaning sector report significantly higher levels of satisfaction than the other employees. This statistical result is robust; it persists when we introduce a large number of control variables. This result is puzzling insofar as it is generally agreed that these workers hold ‘poor quality’ jobs: low pay, an abnormal pattern of work, arduous working conditions. We suggest that cleaners’ expectations and standards are influenced by an adaptation process. Their job satisfaction needs to be considered in the light of their past experience. Their employment history shapes their wants and needs and thus affects the way they evaluate their work.

Effects of ski lift ticket discounts on local tourism demand

PurposeThe purpose of this paper is to provide a first evaluation of the effectiveness of the early bird discount on ski lift tickets by estimating the impact on hotel overnight stays of the Saas-Fee destination.Design/methodology/approachThe difference-in-differences (DID) approach is used to compare winter sport destinations with and without the price reduction before and after the introduction of the price discount. The sample is composed of the 54 largest Swiss winter sport destinations for the seasons 2013/2014 and 2016/2017.FindingsDID estimations show an increase in overnight stays of Swiss residents by 50 per cent as compared to the control group. Quantile regression estimations for the conditional upper part of the overnight stays distribution reveal a lower average treatment effect of 38 per cent. However, DID estimates for total overnight stays (domestic and foreign) are much smaller – about 17 per cent – indicating that the price reductions are not effective in attracting foreign visitors. Results are not sensitive when taking into account a large number of control variables (elevation, size and snow making capacity).Research limitations/implicationsAs tourists visiting winter sport destinations are interested in a mix of activities, lift ticket revenues or number of skier days should be used as an alternative outcome measure.Practical implicationsAs positive effects on local tourism demand are mainly limited to Swiss tourists, such price strategies should be carefully considered. In the long term, the skiing market will stagnate or even shrink for several reasons (population ageing, climate change and changes in leisure preferences).Originality valueThis paper provides a first quantitative evaluation of price discounts in tourism research. Knowledge about the discounts and consumers reactions to sales promotions are of great interest to marketing managers in today’s competitive ski market.

Can Direct Regulations Spur Innovations in Environmental Technologies? A Study on Firm‐Level Patenting

AbstractUsing a rich Norwegian panel dataset that includes information about the type and number of patent applications, direct environmental regulations, and a large number of control variables, we analyze the effects of direct regulations on environmental patenting. We use inspection violation status as a measure of regulatory stringency, while controlling for risk class. Violation status captures the probability that a firm might be sanctioned for violating its emission permit. Controlling for risk class captures firm heterogeneity related to dirtiness and inspection frequency. We empirically identify strong and significant effects on innovations resulting from the implicit regulatory costs of direct regulations.

Education and democracy: New evidence from 161 countries

This study reinvestigates the hypothesis that education has a significant effect on democracy. It adopts a panel causality methodological approach and data from 161 countries, spanning the period 1970–2013. The empirical analysis detects the presence of democracy dividend driven by education. The results survive a number of robustness checks pertaining geographical differentiation, educational stages and the inclusion of a large number of control variables. It is the first paper that makes use of an extended country sample, time period, while it provides a number of robustness checks not previously reported in the literature.

Price Elasticity of Alcohol Demand in India.

Using a household survey conducted in 2014, this study estimates price elasticity of demand (PED) for beer, country liquor and spirits in India. Ordinary least-square models were used to estimate the responsiveness in alcohol demand due to price change. A large number of control variables were included to adjust for potential confounding in the model. Inter-district variation in alcohol consumption is adjusted for by including district fixed effects. Alcohol prices are negatively associated with demand for alcoholic beverages. The PED ranged from -0.14 for spirits to -0.46 for country liquor. Low level of education was positively associated with spirits consumption. The magnitude of elasticity varied by rural-urban, education and gender. Results indicate that a policy mix of price controls and awareness campaigns would be most effective in tackling the adverse effects of harmful drinking in India. The demand for beer, country liquor and spirits is negatively associated with its own price. The elasticity estimates ranged from -0.14 for spirits to -0.44 for country liquor. The elasticity estimates varied by rural-urban, gender and by education levels of the drinkers.

The Impacts of Alternative Policy Instruments on Environmental Performance: A Firm Level Study of Temporary and Persistent Effects

Using a rich Norwegian panel data set that includes information about environmental regulations such as environmental taxes, non-tradable emission quotas and technology standards, all kinds of polluting emissions, and a large number of control variables, we analyze the effects of direct and indirect environmental regulations on environmental performance. We identify positive and significant effects of both direct and indirect policy instruments. Moreover, we test whether the two types of regulations lead to positive and persistent effects on environmental performance. We find evidence that direct regulations promote such effects. Indirect regulations, on the other hand, will only have potential persistent effects if environmental taxes are increasing over time.

Application of Regression Trees in Optimization of Metal Forming Process

Application of sensitivity analysis in optimization of process parameters of production processes for innovative materials, e.g. dual phase steel, requires deterministic model of thermomechanical processes and large datasets that covers whole surface of results. Difficulties in optimization of process parameters correspond with large number of control variables, which should be considered in the technology design. Furthermore, conduction of such analysis takes the great computational cost. Presented work concerns possibility of application of regression trees, especially CART model, in preliminary analysis for sensitivity analysis. Use of data mining algorithms enables acquiring of preliminary, rough results: relationships among parameters of the hot rolling process of dual phase steel strips and rules of optimization of this process, it also does not require any apriori knowledge about thermomechanical processes.

Data Exploration Approach <i>Versus</i> Sensitivity Analysis for Optimization of Metal Forming Processes

Product properties for innovative materials, e.g. dual phase steels, require precise control of production processes. Difficulties in optimization of process parameters correspond with large number of control variables, which should be considered in the technology design. Sensitivity analysis allows evaluating the importance of all process inputs on the final properties of material. Information on the most important inputs is crucial for further design of the process. Application of sensitivity analysis requires detailed knowledge of the process phenomena as well as the definition of the mathematical model of the thermomechanical process. Furthermore, some sensitivity analysis algorithms are of the high computational cost. Presented work concerns possibility of the application of data exploration approach in evaluation of the importance of process inputs as the alternative for sensitivity analysis. Use of data mining algorithms eliminates necessity of mathematical model development, it also does not require any apriori knowledge about the process. Authors presents the comparison of sensitivity analysis and data exploration approach in evaluating relationships between inputs and outputs of the hot rolling for dual phase steel strips. The presented approach and the perspectives of the practical application could lead to significant decrease of time necessary for the computations of process design. The theoretical considerations are supplemented with the results of both types of analysis.

Combustion Noise and Pollutants Prediction for Injection Pattern and Exhaust Gas Recirculation Tuning in an Automotive Common-Rail Diesel Engine

In the last years, emissions standards for internal combustion engines are becoming more and more restrictive, particularly for NOx and soot emissions from Diesel engines. In order to comply with these requirements, OEMs have to face with innovative combustion concepts and/or sophisticate after-treatment devices. In both cases, the role of the Engine Management System (EMS) is increasingly essential, following the large number of actuators and sensors introduced and the need to meet customer expectations on performance and comfort. On the other hand, the large number of control variables to be tuned imposes a massive recourse to the experimental testing which is poorly sustainable in terms of time and money. In order to reduce the experimental effort and the time to market, the application of simulation models for EMS calibration has become fundamental. Predictive models, validated against a limited amount of experimental data, allow performing detailed analysis on the influence of engine control variables on pollutants, comfort and performance.In this paper, a simulation analysis on the impact of injection pattern and Exhaust Gas Recirculation (EGR) rate on fuel consumption, combustion noise, NO and soot emissions is presented for an automotive Common-Rail Diesel engine. Simulations are accomplished by means of a quasi-dimensional multi-zone model of in-cylinder processes. Furthermore a methodology for in-cylinder pressure processing is presented to estimate combustion noise contribution to radiated noise.Model validation is carried out by comparing simulated in-cylinder pressure traces and exhaust emissions with experimental data measured at the test bench in steady-state conditions. Effects of control variables on engine performance, noise and pollutants are analyzed by imposing significant deviation of EGR rate and injection pattern (i.e. rail pressure, start-of-injection, number of injections). The results evidence that quasi-dimensional in-cylinder models can be effective in supporting the engine control design toward the optimal tuning of EMS with significant saving of time and money.

Calibrating Survey Data using Iterative Proportional Fitting (Raking)

In this article, I introduce the ipfraking package, which implements weight-calibration procedures known as iterative proportional fitting, or raking, of complex survey weights. The package can handle a large number of control variables and trim the weights in various ways. It also provides diagnostic tools for the weights it creates. I provide examples of its use and a suggested workflow for creating raked replicate weights.

Price elasticities of alcohol demand: evidence from Russia

In this paper, we estimate price elasticities of demand of several types of alcoholic drinks, using 14 rounds of data from the Russia Longitudinal Monitoring Survey-HSE, collected from 1994 until 2009. We deal with potential confounding problems by taking advantage of a large number of control variables, as well as by estimating community fixed effect models. All in all, although alcohol prices do appear to influence consumption behaviour in Russia, in most cases the size of effect is modest. The finding that two particularly problematic drinks-cheap vodka and fortified wine-are substitute goods also suggests that increasing their prices may not lead to smaller alcohol consumption. Therefore, any alcohol pricing policies in Russia must be supplemented with other measures, such as restrictions on numbers of sales outlets or their opening times.

Is life satisfaction hump-shaped with alcohol consumption? Evidence from Russian panel data

There has been a growing interest in the study of the shape of the relationship between alcohol consumption and psychological well-being in recent years. Overall, evidence is however still mixed and debated, the type of measures and methods of analysis having been emphasized as key elements in these studies. This paper contributes to this debate by providing new evidence relying on a large-scale population-based study. We used the Russia Longitudinal Monitoring Survey to build an unbalanced panel of 17,953 individuals providing 97,973 observations throughout 10 rounds. We studied the shape of the relationship between alcohol consumption (defined in grams of pure alcohol consumed in the last 30days) and life satisfaction (measured by a five-item scale) by running a set of regressions. We successively introduced a large number of control variables (age, gender, marital status, occupation, income, health condition, education, living area, smoking status, and body mass index) and individual fixed effects in order to take both potential confounders and unobserved individual heterogeneity into account. Unadjusted analyses indicated a clear hump-shaped relationship between life satisfaction and alcohol use. The association was inverse J-shaped among men and inverse U-shaped among women. When control variables and individual fixed effects were introduced, the hump-shaped curve became increasingly flattened in all samples. Among women, all specifications (linear, quadratic and based on quartile dummies) turned non-significant. The quadratic specification for alcohol use remained however significant in the full sample and among men. In addition, in these two samples, being a fourth quartile drinker was negatively associated with satisfaction.

Combustion Noise and Pollutants Prediction for Injection Pattern and EGR Tuning in an Automotive Common-Rail Diesel Engine

In the last years emissions standards for internal combustion engines are becoming more and more restrictive, particularly for NOx and soot emissions from Diesel engines. In order to comply with these requirements, OEMs have to face with innovative combustion concepts (PCCI) and/or sophisticate after-treatment devices. In both cases the role of the Engine Management System (EMS) is increasingly essential, following the large number of actuators and sensors introduced and the need to meet customer expectations on performance and comfort. On the other hand, the large number of control variables to be tuned imposes a massive recourse to the experimental testing which is poorly sustainable in terms of time and money. In order to reduce the experimental effort and the time to market, the application of simulation models for EMS calibration has become fundamental. Predictive models, validated against a limited amount of experimental data, allow performing detailed analysis on the influence of engine control variables on pollutants, comfort and performance.In this paper a simulation analysis on the impact of injection pattern and Exhaust Gas Recirculation (EGR) rate on fuel consumption, combustion noise, NO and soot emissions is presented for an automotive Common-Rail Diesel engine. Simulations are accomplished by means of a quasi-dimensional multi-zone model of in-cylinder processes. Fuel spray evolution and interaction with surrounding gases (i.e. air and inert) is simulated by dividing the jet core into many parcels to account for the thermal gradient and the chemical composition within the combustion chamber. Furthermore a methodology for in-cylinder pressure processing is presented to estimate combustion noise contribution to radiated noise.Model validation is carried out by comparing simulated in-cylinder pressure traces and exhaust emissions with experimental data measured at the test bench in steady-state conditions. Effects of control variables on engine performance, noise and pollutants are analyzed by imposing significant deviation of EGR rate and injection pattern (i.e. rail pressure, start-of-injection, nr. of injections). The results evidence that quasi-dimensional in-cylinder models can be effective in supporting the engine control design toward the optimal tuning of EMS with significant saving of time and money.