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This study aims to optimize electrical systems represented by ordinary differential equations and events, using their frequency spectrum is an important purpose for designers, especially to calculate harmonics.

This paper presents a methodology to achieve this, by using a gradient-based optimization algorithm. The paper proposes to use a time simulation of the electrical system, and then to compute its frequency spectrum in the optimization loop.

The paper shows how to proceed efficiently to compute the frequency spectrum of an electrical system to include it in an optimization loop. Derivatives of the frequency spectrum such as the optimization inputs can also be calculated. This is possible even if the sized system behavior cannot be defined a priori, e.g. when there are static converters or electrical devices with natural switching.

Using an efficient sequential quadratic programming optimizer, automatic differentiation is used to compute the model gradients. Frequency spectrum derivatives with respect to the optimization inputs are calculated by an analytical formula. The methodology uses a “white-box” approach so that automatic differentiation and the differential equations simulator can be used, unlike most state-of-the-art simulators.

A few earlier studies presented infeasible heatline trajectories for natural convection within annular domains involving an inner circular cylinder and outer square/circular enclosure. The purpose of this paper is to revisit and illustrate the correct heatline trajectories for various test cases.

Galerkin finite element based methodology and space adaptive grid have been used to simulate natural convective flows within the annular domains. The prediction of heatlines involves derivatives at the nodes, which are evaluated based on finite element basis functions and contributions from neighboring elements.

The heatlines in the earlier work indicate infeasible heat flow paths such as heat flow from one portion to the other of isothermal hot walls and heat flow across the adiabatic walls. Current results illustrate physically consistent heat flow paths involving perpendicularly emerging heatlines from hot to cold walls for conductive transport, long heat flow paths around the closed-loop heatline cells for convective transport and parallel layout of heatlines to the adiabatic walls. Results also demonstrate complex heatlines involving multiple flow vortices and complex flow structures.

Current work translates heatfunctions from energy flux vectors, which are determined by using basis sets. This work demonstrates the expected heatline trajectories for various scenarios involving conductive and convective heat transport within enclosures with an inner hot object as a first attempt, and the results are precursors for the understanding of energy flow estimates.

Considering the frequency of extreme events, enhancing the global financial system's stability has become crucial. This study aims to investigate the contagion effects of extreme risk events in the international commodity market on China's financial industry. It highlights the significance of comprehending the origins, severity and potential impacts of extreme risks within China's financial market.

This study uses the tail-event driven network risk (TENET) model to construct a tail risk spillover network between China's financial market and the international commodity market. Combining with the characteristics of the network, this study employs an autoregressive distributed lag (ARDL) model to examine the factors influencing systemic risks in China's financial market and to explore the early identification of indicators for systemic risks in China's financial market.

The research reveals a strong tail risk contagion effect between China's financial market and the international commodity market, with a more pronounced impact from the latter to the former. Industrial raw materials, food, metals, oils, livestock and textiles notably influence China's currency market. The systemic risk in China's financial market is driven by systemic risks in the international commodity market and network centrality and can be accurately predicted with the ARDL-error correction model (ECM) model. Based on these, Chinese regulatory authorities can establish a monitoring and early warning mechanism to promptly identify contagion signs, issue timely warnings and adjust regulatory measures.

This study provides new insights into predicting systemic risk in China's financial market by revealing the tail risk spillover network structure between China's financial and international commodity markets.

The present study aims at quantifying the likely impacts of an environmental tax on macroeconomic variables and pollution in Iran.

The computable general equilibrium model, which allows the prediction of the economy-wide effects of any change in policy instruments, is applied.

The main findings reveal that gross domestic product, private consumption and income in both urban and rural areas will follow a declining trend as a result of environmental tax imposition. In a scenario with the highest level of tax, the predicted percentage change to the gross domestic product and private consumption is estimated at −21.32 and −40.96, respectively. In the same scenario, pollution emissions would decrease by 12.4–22.6% for CO2, CH4 and N2O.

This study uses a general equilibrium model to examine the effects of the carbon tax on environmental issues and household welfare, considering the unique conditions and regulations of Iran. While the related literature examines the CO2 tax, the current study covers more pollutants, including CO2, CH4, N2O, CO, SO2 and NOx. In addition, a distinguishing feature of the current study is that it applies a modified version of the social accounting matrix (SAM) database, which includes the heavy subsidies of energy products. Another significant feature of the current study is that it examines tax policy while tax rates are exerted endogenously (compared to previous studies).

Recent evidence from a linear econometric framework infers that housing search activity, captured from Google Trends data, can predict housing returns for the USA at a national and regional (metropolitan statistical area [MSA]) level. Based on search theory, the authors, however, postulate that search activity can also predict housing returns volatility. This study aims to explore the possibility of using online search activity to predict both housing returns and volatility.

Using a k-th order non-parametric causality-in-quantiles test allows us to test for predictability in a robust manner over the entire conditional distribution of both housing price returns and its volatility (i.e. squared returns) by controlling for nonlinearity and structural breaks that exist in the data.

The analysis over the monthly period of 2004:01 to 2021:01 produces results indicating that while housing search activity continues to predict aggregate US house price returns, barring the extreme ends of the conditional distribution, volatility is relatively strongly predicted over the entire quantile range considered. The results carry over to an alternative (the generalized autoregressive conditional heteroskedasticity-based) metric of volatility, higher (weekly)-frequency data (over January 2018–March 2021) and to over 84% of the 77 MSAs considered.

To the best of the authors’ knowledge, this is the first study regarding predictability of overall and regional US housing price returns and volatility using search activity, based on a non-parametric higher-order causality-in-quantiles framework, which is insightful to investors, policymakers and academics.

Improving the competitiveness of East Africa's rice industries necessitates increased and viable production of rice of the quality desired by consumers. This paper aims to understand consumer preferences for rice quality attributes in Uganda and Kenya to inform the countries' rice breeding programs and value chain development interventions.

Rice samples are obtained from retail markets in various districts/counties across the two countries. The samples are analyzed in a grain quality laboratory for the rice's physicochemical characteristics and the resulting data are used to non-parametrically estimate hedonic price functions. District/county dummies are included to account for potential heterogeneity in consumer preferences.

Ugandan consumers are willing to pay a price premium for rice with a relatively high proportion of intact grains, but the consumers discount chalkiness. Kenyan consumers discount high amylose content and impurities. There is evidence of heterogeneity in consumer preferences for rice in Mbale, Butaleja and Arua districts of Uganda and in Kericho and Busia counties of Kenya.

The study makes a novel contribution to the literature on consumer preferences for rice in East Africa by applying a hedonic pricing model to the data generated from a laboratory analysis of the physicochemical characteristics of rice samples obtained from the market. Rather than base our analysis on consumers' subjective sensory assessment of the quality characteristics of rice, standard laboratory methods are used to generate the data, which enables a more objective assessment of the relationship between market prices and the quantities of attributes present in the rice samples.

A parabolic trough solar collector is an advanced concentrated solar power technology that significantly captures radiant energy. Solar power will help different sectors reach their energy needs in areas where traditional fuels are in use. This study aims to examine the sensitivity analysis for optimizing the heat transfer and entropy generation in the Jeffrey magnetohydrodynamic hybrid nanofluid flow under the influence of motile gyrotactic microorganisms with solar radiation in the parabolic trough solar collectors. The influences of viscous dissipation and Ohmic heating are also considered in this investigation.

Governing partial differential equations are derived via boundary layer assumptions and nondimensionalized with the help of suitable similarity transformations. The resulting higher-order coupled ordinary differential equations are numerically investigated using the Runga-Kutta fourth-order numerical approach with the shooting technique in the computational MATLAB tool.

The numerical outcomes of influential parameters are presented graphically for velocity, temperature, entropy generation, Bejan number, drag coefficient and Nusselt number. It is observed that escalating the values of melting heat parameter and the Prandl number enhances the Nusselt number, while reverse effect is observed with an enhancement in the magnetic field parameter and bioconvection Lewis number. Increasing the magnetic field and bioconvection diffusion parameter improves the entropy and Bejan number.

Nanotechnology has captured the interest of researchers due to its engrossing performance and wide range of applications in heat transfer and solar energy storage. There are numerous advantages of hybrid nanofluids over traditional heat transfer fluids. In addition, the upswing suspension of the motile gyrotactic microorganisms improves the hybrid nanofluid stability, enhancing the performance of the solar collector. The use of solar energy reduces the industry’s dependency on fossil fuels.

This study aims to examine the effect of corporate derivatives use on firm value and how the corporate governance index modifies this relationship.

The sample consists of 219 nonfinancial firms on the Pakistan Stock Exchange (PSX) from 2011 to 2019. The study used ordinary least square regression with year and industry dummies for estimations. Multiple estimation models such as fixed/random effect, Fama–MacBeth and two-stage least squares (2SLS) are used for robustness. Finally, the PROCESS macro tool is used to estimate the effect of moderating the role of corporate governance (CG) as robustness.

The findings show that derivatives use has an inverse influence on firm value. The firms did not use derivatives as a risk management tool but for speculation motives. However, the corporate governance index significantly weakens this relationship. However, strong governance forces the managers to use derivatives for hedging purposes. The firm-specific factors, including size, age, leverage, cash, financial distress cost, dividend and growth opportunities, also significantly influence firm value. The findings are robust to the other estimation models.

The findings indicate that emerging economies like Pakistan are more prone to agency problems. The strong corporate governance structure helps firms turn the speculative motive of derivatives use into hedging purposes and mitigate the agency issues.

This empirical evidence suggests that good governance structures can help improve the impact of derivative usage on firm value.

To the best of the author's knowledge, this is the first study that examines the conditional role of corporate governance on the derivatives–value relationship from the viewpoint of agency problem/speculative motive.

This study aims to enhance the fuel efficiency of jet transport aircraft based on mathematical models and flight crew operating manual (FCOM) for the purpose to assist the civil aviation industry in improving flight safety and operational efficiency.

The research applies flight data mining and fuzzy logic modeling technologies to set up lift-to-drag ratio (L/D) models and nine models of thrust, Mach number, engine pressure ratio and fuel flow rate to estimate the deviation of each flight parameter. All performance deviations are calculated based on the values of flight data recorded in the quick access recorder and FCOM at the observed flight conditions. The L/D model can obtain the influence of each flight parameter and estimate the insufficient amount of each parameter by averaging it with the least square method. In the estimation of optimal altitude, nine models are built based on data from FCOM to estimate the optimal altitude and complete comparative analysis of the airspeed, Mach number and fuel flow rate at the optimal altitude.

Analyze 11 relevant parameters from the sensitivity derivative of L/D model to obtain how each parameter affected fuel consumption and explore the causes of additional fuel consumption. Complete the estimation of the optimal cruise altitude of the aircraft, and calculate the comparative analysis of the altitude, speed, Mach number and other parameters with the sensitivity derivative of the L/D. The estimation of the optimal cruise altitude of the aircraft can meet the analysis of the sensitivity derivative.

This study is to enhance the fuel efficiency of jet commercial transport based on mathematical model and FCOM. FCOM is required to conduct this study. The estimation of the optimal cruise altitude through the nine models of the aircraft could meet the analysis of the sensitivity derivative.

The object of present research is to demonstrate the effectiveness of optimization of flight conditions through model analysis to get knowledge of the effects of each influencing flight variable to L/D for future flight operations’ reference.

The model-based derivative analysis had the ability to perform derivative prediction analysis on any input parameters, more flight parameters could be optimized in future research to help airlines improve flight safety and operational efficiency.

The present enhancement method of fuel efficiency is an innovation to examine the abnormal aircraft performance and its flight operations, thereby to explore the causes of additional fuel consumption. The present method can become an auxiliary tool for flight operations quality assurance to improve fuel efficiency for the airlines.