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This paper introduces a model for analysis of spatiotemporal distribution of temperature between the wall of a building and the facing of a facade with possible convection air movement. In the framework of the considered model one can take into account native convection of air and possibility of forced convection. This paper aims to analyze the considered distribution of temperature. Also an analytical approach for analysis of the considered distribution to increase predictability of the heat transport with native and forced convection is considered.

In this paper we introduce a model for analysis of spatio-temporal distribution of temperature between the wall of a building and the facing of a facade with account possible convection air movement has been analyzed. In the framework of the considered model one can take into account native convection of air and possibility to take into account forced convection. We analyzed the considered distribution of temperature. Also we consider an analytical approach for analysis of the considered distribution to increase of predictability of the heat transport with account native and forced convection.

In this paper we introduce a model for analysis of spatio-temporal distribution of temperature between the wall of a building and the facing of a facade with account possible convection air movement has been analyzed. In the framework of the considered model one can take into account native convection of air and possibility to take into account forced convection. We analyzed the considered distribution of temperature. Also we consider an analytical approach for analysis of the considered distribution to increase of predictability of the heat transport with account native and forced convection.

This paper is original.

This study aims to explore the influence of Fourier-feature enhanced physics-informed neural networks (PINNs) on effectively solving two-dimensional local time-fractional anomalous diffusion equations with nonlinear thermal diffusivity. By tackling the shortcomings of conventional numerical methods in managing fractional derivatives and nonlinearities, this research addresses a significant gap in the literature regarding efficient solution strategies for complex diffusion processes.

This study uses a quantitative methodology featuring a feed-forward neural network architecture combined with a Fourier feature layer. Automatic differentiation is implemented to ensure precise gradient calculations for fractional derivatives. The effectiveness of the proposed approach is showcased through numerical simulations across various sub-diffusion and super-diffusion scenarios, with fractal space parameters adjusted to examine behavior. In addition, the training process is assessed using the Fisher information matrix to analyze the loss landscape.

The results demonstrate that the Fourier-feature enhanced PINNs effectively capture the dynamics of the anomalous diffusion equation, achieving greater solution accuracy than traditional methods. The analysis using the Fisher information matrix underscores the importance of hyperparameter tuning in optimizing network performance. These findings support the hypothesis that Fourier features improve the model’s capacity to represent complex solution behaviors, providing the relationship between model architecture and diffusion dynamics.

This research presents a novel approach to solving fractional anomalous diffusion equations through Fourier-feature enhanced PINNs. The results contribute to the advancement of computational methods in areas such as thermal engineering, materials science and biological diffusion modeling, while also providing a foundation for future investigations into training dynamics within neural networks.

This study aims to explore the relationship between social capital and real earnings management (REM).

Using the social capital index from 1990 to 2014, this study investigates whether managers are less likely to carry out real earnings management when firms headquartered in a county with greater social capital and whether this impact will differ according to firm characteristics and the external environment.

Social capital is negatively linked to a firm’s REM, as a manager’s mindset toward misconduct might be more constrained by a better social environment and a lower tendency to undertake real earnings manipulation. Furthermore, we find that the effect of social capital on real earnings management is stronger for firms with geographically concentrated structures, weaker external monitoring, Sarbanes-Oxley Act adoption and greater pressure to meet earnings targets.

This study sheds light on the relation between social capital and accounting decisions by exploring whether social capital can influence real earnings management and provides evidence that social capital has a beneficial impact on reducing certain misbehaviors in financial reporting and that the effect is stronger when a firm has a geographically concentrated structure, weaker external monitoring, SOX adoption and less pressure to meet earnings targets.

This study aims to enhance singularity detection in non-stationary signals by introducing the frugal wavelet transform (FrugWT), a novel variation of the wavelet transform.

The frugal wavelet transform, based on a modified first-level discrete wavelet transform decomposition, is compared with traditional discrete wavelet transform. The performance of these transforms is evaluated using signals derived from finite element analysis of a functionally graded tapered beam made of porous material.

The frugal wavelet transform significantly outperforms the discrete wavelet transform in detecting singularities within the analyzed signals. It offers more accurate detection of singularities and local abrupt changes, demonstrating its effectiveness for signal analysis.

This paper contributes to the field by proposing the relative frugal wavelet transform as a novel enhancement of the frugal wavelet transform. It provides a significant improvement in detecting subtle singularities in one-dimensional signals, with potential applications in advanced signal processing and analysis across various scientific domains such as electrical engineering, automotive, aerospace engineering, civil engineering, marine engineering and medical signal processing.

Climate change, driven by global warming, poses a significant threat to humanity and disrupts the ecological balance. In Europe, concentrations of air pollutants remain very high, and problems related to air quality and the acceleration of the phenomenon of global warming persist. As a result, carbon taxation has emerged as a key strategy to mitigate climate change. In Romania, environmental taxes are an important instrument of environmental policy as an economic instrument for environmental protection and natural resource management. Using 1990–2021 time series data and an Autoregressive Distributed Lag (ARDL) Bounds cointegration for long-run analysis and the Toda–Yamamoto test for causality analysis, we investigated whether environmental taxes, renewable energy consumption, urbanization and economic growth significantly impact CO₂ emissions in Romania.

This paper differs from the assessment of the Environmental Kuznets Curve (EKC) hypothesis (Grossman and Krueger 1991) and instead aims to determine the impact of environmental taxes, renewable energy consumption, per capita GDP and urbanization on CO₂ emissions in Romania. The study investigates both short- and long-term effects, as well as Toda–Yamamoto causality linkages (Toda and Yamamoto 1995) between these variables. We adopt an ARDL estimation technique with Bound cointegration test and error correction models (Pesaran et al., 2001) to examine the short- and long-term effects.

The findings revealed that environmental taxes positively and significantly reduce CO₂ emissions, while urbanization induces CO₂ emissions, in the long run. Moreover, in the short run, environmental taxes and renewable energy consumption significantly reduce CO₂ emissions while per capita GDP and urbanization significantly increase CO₂ emissions. A unidirectional causality exists between renewable energy consumption and CO₂ emissions. Thus, to realize its 34% target of renewable energy consumption in 2030, Romania should prioritize the implementation of the Casa Verde Plus program and enforce sustainable urban planning to meet near-zero energy standards. Consequently, the government should continue to enforce carbon taxes to promote environmental sustainability.

Empirical evidence supports the cointegration relationship between environmental taxes and CO₂ emissions, with carbon taxes effectively reducing CO₂ emissions and improving environmental quality (Allan et al., 2014; Polat and Polat, 2018; Kiuila et al., 2019, etc.). While existing research (Floros and Vlachou, 2005; Wissema and Dellink, 2007; Aydin and Esen, 2018; Lin and Li, 2011) primarily focuses on country-specific or regional analyses, limited research has been conducted on the impact of carbon taxation on CO₂ emissions in Romania. However, to the best of our knowledge, limited research on this phenomenon in Romania exists in response to recommendations for climate change mitigation. Furthermore, urbanization has significantly contributed to rising atmospheric carbon levels and subsequent global warming and climate change (Woldu, 2021). As economic growth, particularly in countries like Romania, drives urbanization, it leads to increased energy demand, expanding urban areas and mounting environmental concerns. This process involves industrial restructuring, and the development of new infrastructure, all of which exert pressure on energy consumption and CO₂ emissions (Niu and Lekse, 2018). While economic growth is a primary objective, industrialization and urbanization inevitably generate unintended consequences, including CO₂ emissions. However, limited research exists on the impact of urbanization patterns on CO₂ emissions in Romania. This study investigates the dynamic causal relationships among urbanization, per capita GDP, carbon taxes, renewable energy consumption and CO₂ emissions, considering both short-run and long-run effects in Romania.

While the average cost of debt capital can be calculated from historical financial statement data by dividing the interest paid each year by the total level of debt, this average cost of debt provides little information regarding the true cost of acquiring additional debt capital, and hence, its use is potentially problematic in financial decision-making. This study focuses on the linkage between observed changes in the average interest rates calculated from financial statements (balance sheet and income statement) and the marginal cost of borrowing or the cost of acquiring new debt. Motivated by the capital asset pricing model (CAPM), the marginal cost of capital is modeled as a function of a risk-free interest rate (the return on Moody’s Aaa bonds), returns on the S\&P stock index capturing overall market returns and a portfolio of agricultural stocks to represent farm sector-specific risks.

Using a unique dataset constructed from United States Department of Agriculture (USDA) state-level Financial Performance of the Farm Sector data for the years 1960 through 2003 and state-level Agricultural Resource Management Survey (ARMS) data for the years 2003–2014 and Bayesian methods, we model the observed interest rate as an autoregressive function controlling for changes in debt and key rates of return in the general economy.

The results indicate that the marginal interest rate is a function of the Aaa corporate bond rate and the stock market. We also find evidence of a negative relationship between returns to a portfolio of agricultural stocks and the marginal interest rate. Overall, the findings suggest that the imputed interest rate frequently misrepresents the marginal cost of debt capital.

Most farm financial datasets allow for the analysis of the farm firm’s average interest rate. However, farmers make decisions based on the marginal cost of credit – the interest rate on a newly issued note. This study estimates this marginal interest rate for the 15 states for which the ARMS data are representative for the years 1960 through 2014 and compares the estimated marginal interest rate with the imputed average interest rate.

The contact stress between the bump foil and the top foil of the gas foil bearing is relatively high during operation, and it is prone to stress concentration, which affects the formation of the gas film. The purpose of this paper is to find a bump structure that reduces stress concentration, accelerates gas film formation and adapts to fluctuating loads.

A flexible gas foil bearing with a flexible arch structure is proposed, and a hydrodynamic model containing mechanical characteristics of the foil contact as well as a thermal analysis model of the bearing system is established to predict and compare the effects of the two structures on the thermal characteristics: the stresses and deformations.

The findings indicate that under the same load and rotational speed, the flexible arch structure bearing exhibits significantly lower maximum stress and surface displacement compared to the initial structure. In addition, the flexible arch structure shows reduced stress concentration and a more uniform stress distribution However, the surface temperature with flexible arch structure is larger than that of the initial structure under the same working conditions due to friction formed between the foils, which may lead to performance decrease.

This paper proposes a new flexible arch structure that can reduce stress concentration.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2024-0225/

A combined approach of additive Holt–Winters, support vector regression, simple moving average and generalized simulated annealing with error correction and optimal parameter selection techniques emphasizing optimal smoothing period in residual adjustment is developed and proposed to predict datasets of container throughput at major ports.

The additive Holt–Winters model describes level, trend and seasonal patterns to provide smoothing values and residuals. In addition, the fitted additive Holt–Winters predicts a future smoothing value. Afterwards, the residual series is improved by using a simple moving average with the optimal period to provide a more obvious and steady series of the residuals. Subsequently, support vector regression formulates a nonlinear complex function with more obvious and steady residuals based on optimal parameters to describe the remaining pattern and predict a future residual value. The generalized simulated annealing searches for the optimal parameters of the proposed model. Finally, the future smoothing value and the future residual value are aggregated to be the future value.

The proposed model is applied to forecast two datasets of major ports in Thailand. The empirical results revealed that the proposed model outperforms all other models based on three accuracy measures for the test datasets. In addition, the proposed model is still superior to all other models with three metrics for the overall datasets of test datasets and additional unseen datasets as well. Consequently, the proposed model can be a useful tool for supporting decision-making on port management at major ports in Thailand.

The proposed model emphasizes smoothing residuals adjustment with optimal moving period based on error correction and optimal parameter selection techniques that is developed and proposed to predict datasets of container throughput at major ports in Thailand.

Although existing literature has highlighted the benefits of sustainability practices for business, few studies explore their potential downside. To address this gap, this study aims to examine the relationship between public sustainability mindset and financial misconduct in local firms.

Based on survey results from International City/County Management Association, the author aggregates data on public sustainability mindset at the metropolitan statistical area (MSA) level in the USA. The author uses linear regression analysis to investigate the hypotheses. Robustness tests are also performed using approaches such as propensity score matching, two-stage least squares, falsification test and alternative measure of sustainability mindset.

This study finds that in MSAs with a stronger public sustainability mindset, local firms are more likely to engage in financial misconduct. Moreover, this association is mitigated by the availability of employment opportunities in the area, indicating that job security concerns have a moderating effect. Additional test suggests that firms with more integrity culture are less likely to engage in financial misconduct, even in areas where residents have a strong sustainability mindset.

This paper could be of interest to both policymakers and managers as it illustrates an unexpected impact of public sustainability awareness on financial compliance issues. It also provides cautions when prompting sustainability mindset among the public and suggests potential solutions to address the problem.

This study aims to develop an accurate option pricing model for car leases by introducing a put option valuation framework based on the Weibull distribution. Traditional models typically assume asset values follow a lognormal distribution, failing to capture the left-skewed nature and bounded dynamics. To address this limitation, this study compares the performance of the Weibull distribution with that of the lognormal model using residual value data from two popular car models in South Korea, evaluating each model’s ability to reflect unique depreciation patterns. The findings demonstrate that the Weibull distribution provides a superior fit to the data, leading to more precise option pricing. This enhanced accuracy is crucial for auto finance companies navigating uncertainties in used car prices, particularly as the mobility services and car leasing markets continue to expand. Moreover, the practical implications of this research extend beyond the auto finance industry; insights from this study can inform sectors dealing with skewed or bounded assets, such as insurance products and financial derivatives, thereby enabling improved risk assessment and decision-making processes. This research introduces a novel approach to modeling put option values using the Weibull distribution, filling a significant gap in the existing literature on car lease option pricing. However, as this is the first study to model put option values using the Weibull distribution, further research is necessary. Specifically, investigating volatility patterns over the lifecycle of used cars could significantly enhance the value of this framework.