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Fractional-order grey prediction models have gained wide recognition for their computational efficiency and straightforward modeling mechanisms. However, their performance in seasonal forecasting tasks still needs improvement. To address this, this paper designs a novel optimization scheme and applies it to the representative fractional-order grey GM(1,1) model (FGM(1,l)) to advance research in this area. In this optimization scheme, the dummy variable is used to enable the model to directly handle seasonal time series, the discretization technique is employed to simplify the computational steps, and the Bernoulli parameter and the linearly weighted hybrid fractional-order accumulation strategy are used to enhance the model's fitting capability. To verify the effectiveness of the proposed method, the optimized model and some benchmark algorithms are used to model three quarterly data sets. The experimental results show that the optimized model can produce better performance, which verifies the effectiveness of this optimization scheme. [ABSTRACT FROM AUTHOR]

Forest biomass and carbon storage models are crucial for inventorying, monitoring, and assessing forest resources. This study develops models specific to China's diverse forests, offering a methodological foundation for national carbon storage estimation and a quantitative basis for national, regional, and global carbon sequestration projections. Utilizing data from 52,700 permanent plots obtained during China's 9th national forest inventory, we calculated biomass and carbon storage per hectare for 35 tree species groups using respective individual tree biomass models and carbon factors. We then constructed a three-level volume-based model system for forest biomass and carbon storage, applying weighted regression, dummy variable modeling, and simultaneous equations with error-in-variables. This system encompasses one population of forests, three forest categories (level I), 20 forest types (level II), and 74 forest sub-types (level III). Finally, the assessment of these models was carried out with six evaluation indices, and comparative analyses with previously established biomass models of three major forest types were conducted. Determination coefficients (R2) for the population average model, and three dummy models on levels I, II, and III, exceed 0.78, 0.85, 0.92, and 0.95, respectively, with corresponding mean prediction errors (MPEs) of 0.42%, 0.34%, 0.24%, and 0.19%, and mean percent standard errors (MPSEs) of approximately 22%, 21%, 15%, and 12%. Models for 20 forest types and 74 sub-types yield R2 values above 0.87 and 0.85, with MPE values below 3% and 5%, respectively. Notably, the estimates of previous biomass models of three major forest types demonstrated considerable uncertainty, with TRE ranging from −20% to 74%. However, accuracy has improved with larger sample sizes. In total biomass and carbon storage estimations, the R2 values of dummy models for levels I, II, and III progressively increase and MPSE and MPE values decrease, whereas TRE approximates zero. The tiered model system of simultaneous equations developed herein offers a quantitative framework for precise evaluations of biomass and carbon storage on different scales. For enhanced accuracy in such estimations, applying level III models is recommended whenever feasible, especially for national estimation. [ABSTRACT FROM AUTHOR]

Energy, as one of the most important factors of production, plays a decisive role in the economic life and development of the civilization of societies. Along with population growth, industrial development and technological progress, humans demand for energy resources is becoming more and more intense. Energy consumption, along with energy production, is one of the important criteria for measuring the economic progress of countries. The effects of the Covid-19 pandemic on a strategic field such as the energy sector as the infrastructure of the economic artery are impressive. The resulting shock is so deep and effective that it changes energy consumption by affecting production activities and demand. The purpose of this study is to investigate and evaluate the impact of the Covid-19 pandemic on non-renewable energy consumption in the member countries of the Organisation for Economic Co-operation and Development (OECD), during the period of 2010-2020. In order to estimate the model, the Fully Modified Ordinary Least Square (FMOLS) estimation technique is used. The obtained results show that the covid-19 epidemic has a negative and significant effect on the consumption of non-renewable energy. In other words, the spread of the corona virus reduces the consumption of non-renewable energy. The global recession caused a demand and supply shock in the energy sector. On the demand side, some actions to curb the disease and economic disruptions related to the spread of Covid-19 led to a decrease in the speed of production and mobility around the world. The negative shock on the supply side also caused by disruptions in the flow of goods and services, and reinforced the demand shock. The result of these two shocks caused a significant decrease in global demand for fossil fuels. The covid-19 pandemic the consumption of fossil energy through various channels. The implementation of social distancing measures and public quarantine during the epidemic limited production, transportation, trade and financial markets globally. This resulted in a drop in energy demand.Also, the findings of the study show the positive impact of financial development, commercial freedom and economic growth on non-renewable energy consumption. The development of the financial system can provide financial resources for companies with much lower costs and facilitate the expansion of their production scale and thus increases energy consumption. In advanced and industrialized countries like OECD, the activities of capital markets first cause more energy production and consumption, then after a period of energy consumption, due to environmental considerations, capital market and money market resources are transferred to clean energies.

Accurate estimation of tree stem form and merchantable volume is an important prerequisite for forest management and economic evaluation. A system of compatible taper-volume equations was constructed for estimating the upper stem diameter and merchantable volume of different larch species in northeast China using Larix olgensis (LO) and Larix kaempferi (LK) as examples. Five common compatible taper-volume systems were evaluated by using the diameter, height, and cumulative volume data of 262 LO and 86 LK trees. The seemingly unrelated regression (SUR) was used for parameter estimation of the nonlinear simultaneous equations, and the power function was applied to eliminate the heteroscedasticity of the volume equations. Subsequently, the tree species were used as dummy variables to construct generalized equations suitable for the two species. Finally, the adjusted coefficient of determination ( R a 2 ), root mean square error (RMSE), Akaike information criterion (AIC), RMSE%, condition number (CN), mean absolute bias (MAB) and mean percentage of bias (MPB) were used to evaluate the performance of the model. The segmented model outperformed the simple model, and in particular, the model presented by Fang (FS 46:1–12, 2000) exhibited the best performance in predicting diameter and volume. The Fang (FS 46:1–12, 2000) model explained the difference in stem form between both larch species, that is, LO had a higher upper inflection point and a lower inflection point than LK. The dummy variable model provides an effective approach to accurately predict stem diameter and volume variables for both species. The generalized equation can be used for simultaneous estimation of stem taper and merchantable volume for LO and LK in northeast China. Under the same conditions, LO has longer middle segments and better stem form than LK. [ABSTRACT FROM AUTHOR]

AH stock premium and its price linkage have always been the key problems of concern for scholars and investors. Taking the stock price yield of AH stock as the explanatory variable, this paper adopts the panel quantile model to probe into the linkage mechanism of AH stock price from five aspects such as risk difference, liquidity difference, demand elasticity difference, information asymmetry and new regulation policy of reduction. Due to the herding effect, information asymmetry has a negative effect on the H-share yield in the income and loss intervals, and a positive effect in the stable interval. The negative effect of liquidity difference on the H-share yield in the loss interval is converted into the positive effect in the income interval, but it is the most sensitive in high risk areas. The influence of demand elasticity on H-share yield is characterized by the typical characteristics of Chinese stock market "chasing after go up and killing cheapen" or "buying the winners". Risk difference is not obvious because the behavior of risk lover and risk averter is offset by each other. The introduction of a 2017-year reduction in the new rules has significantly reduced the share price yield of H-shares. Finally, some suggestions are put forward for investors to invest and avoid risk in AH stocks and some specific policy implications for financial regulators are also provided based on these results. [ABSTRACT FROM AUTHOR]

The article considers the main characteristics of the Russian Federation subjects’ reserve funds analyzes the factors of their formation and assesses their effectiveness through the lens of the regions’ countercyclical fiscal policy. In theory, reserve funds serve not only as a source of additional budgetary funds, but also as an instrument of anti-crisis policy and financial stability. In practice, the reserve funds of Russian regions are believed not to fulfill the designated tasks, although the number of relevant studies is extremely limited. The latter determines the relevance of this study. The authors establish that the regional reserve fund of the subject as a public-law entity was established only in about half of the Russian regions and in many respects their creation coincided with the recovery growth after the crisis of 2009–2010. The theoretical provisions are also consistent with the fact that on average the reserve fund is owned by the entities whose economy is more dependent on the mining industry, has greater fiscal capacity, is less subsidized and has a lower level of debt. At the same time, greater fiscal capacity or debt sustainability can hardly be considered as factors in the creation of reserve funds. A number of models are constructed to assess the effectiveness of reserve funds of Russian regions from the point of view of countercyclical fiscal policy. It is concluded that reserve funds in their current form are ineffective for smoothing regional expenditures and maintaining overall fiscal stability. Nevertheless, many questions remain in this topic, including alternative model specifications and evaluation techniques. Based on the results obtained, the directions for further research are formulated.

Forest biomass and carbon storage models are crucial for inventorying, monitoring, and assessing forest resources. This study develops models specific to China’s diverse forests, offering a methodological foundation for national carbon storage estimation and a quantitative basis for national, regional, and global carbon sequestration projections. Utilizing data from 52,700 permanent plots obtained during China’s 9th national forest inventory, we calculated biomass and carbon storage per hectare for 35 tree species groups using respective individual tree biomass models and carbon factors. We then constructed a three-level volume-based model system for forest biomass and carbon storage, applying weighted regression, dummy variable modeling, and simultaneous equations with error-in-variables. This system encompasses one population of forests, three forest categories (level I), 20 forest types (level II), and 74 forest sub-types (level III). Finally, the assessment of these models was carried out with six evaluation indices, and comparative analyses with previously established biomass models of three major forest types were conducted. Determination coefficients (R2) for the population average model, and three dummy models on levels I, II, and III, exceed 0.78, 0.85, 0.92, and 0.95, respectively, with corresponding mean prediction errors (MPEs) of 0.42%, 0.34%, 0.24%, and 0.19%, and mean percent standard errors (MPSEs) of approximately 22%, 21%, 15%, and 12%. Models for 20 forest types and 74 sub-types yield R2 values above 0.87 and 0.85, with MPE values below 3% and 5%, respectively. Notably, the estimates of previous biomass models of three major forest types demonstrated considerable uncertainty, with TRE ranging from −20% to 74%. However, accuracy has improved with larger sample sizes. In total biomass and carbon storage estimations, the R2 values of dummy models for levels I, II, and III progressively increase and MPSE and MPE values decrease, whereas TRE approximates zero. The tiered model system of simultaneous equations developed herein offers a quantitative framework for precise evaluations of biomass and carbon storage on different scales. For enhanced accuracy in such estimations, applying level III models is recommended whenever feasible, especially for national estimation.

The use of logit and probit models when examining binary dependent variables including those in the form 0/1 (i.e., dummy variables), yes/no, and true/false (hereafter binary DVs) is commonplace. Yet, the appropriateness and effectiveness of such models are challenged when the event rate of a binary DV is rare or common. To better understand the impact on the field of strategy, we undertook a literature review and assessed recently published research in the Strategic Management Journal. We then utilized Monte Carlo simulations with results showing that as event rates become rarer or more common, issues including biased coefficients, standard error inflation, low statistical power to detect significant effects, and model convergence failure increasingly arise. In addition, small sample sizes amplified these empirical issues. Using a strategy example study, we also show how various analytic tools can lead to different findings when empirical models face an extreme event rate with small sample sizes. Based on our findings, we provide step-by-step guidance for strategy researchers going forward. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Solar energy systems which is one of the renewable energy sources takes more interest and gains prevalence day by day. A significant problem in solar energy systems as in other many renewable energy sources is the instability of the energy that the system will provide. Forecasting of the energy to be obtained is very important in this respect. In this study, solar radiation has been forecasted using meteorological data taken from the General Directorate of Meteorology for Isparta province. Random Forest (RF), k-Nearest Neighbor (k-NN), Artificial Neural Network (ANN) and Deep Learning (DL) methods have been used for forecasting. In addition, the results of dummy variable usage for time data have been examined with these different methods. According to the findings obtained, it is seen that the dummy variable usage increases performance for ANN and DL methods but decreases performance for RF and k-NN methods. Best results have been obtained with ANN and DL for the forecasting of the solar radiation. [ABSTRACT FROM AUTHOR]

In order to clarify the combined impact of tree species composition, site quality, and stand age on the growth and harvest of mixed forests, the prediction models of average DBH and stand volume for mixed forests were established, respectively. The interval period and tree species composition coefficient (TSCC) were considered as independent variables. These models were then optimized by using the particle swarm optimization algorithm for reparameterization and evaluating their applicability. It was found that after introducing the site quality grade and TSCC, the average stand height prediction model showed a better fitting result. The fit accuracy of the average DBH prediction model and the stand volume prediction model were both improved with the help of the TSCC, mainly because the tree species composition affects the growth rate of the average stand height and average DBH and the maximum growth rate of the stand volume. The degree of the impact can be sorted as Cunninghamia lanceolata > Pinus massoniana > hard broad-leaved tree species (group). Overall, the established growth and harvest prediction models for mixed forests with the interval period and TSCC as independent variables have high fit accuracy and applicability. [ABSTRACT FROM AUTHOR]

Copyright of Stable Economy Journal is the property of University of Sistan & Baluchestan and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

One of the most important indirect economic consequences of dam failure (DF) is decrease the employment of agricultural sector (EOAS) downstream of the dam, its accurate estimation is difficult due to multi-layer effects of (DF). The main purpose of this study is to predict rate of employment by considering qualitative and quantitative impacts of DF by using dummy variable (DV) regression models technique in estimating income functions (IF) and production of crops functions (PFs) in the AS and using the functions in linear programming model (LPM) for optimal allocation of labour. The results of model showed that with 36% decrease in accessible water resource after DF, proportionate with the decreasing trend, the level of labour employment has decreased about 23% in downstream area of the dam. The results of this research have good conformity with former findings such as simulation method for failure embankment which is equal to 25%. So, combination of LPM with DV regression for predicting unemployment rate originated from DF and managing social and economic crisis in line with sustainable development is a realistic and accurate method. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Labour productivity is an ongoing topic in literature, specifically in developed economies where labour costs are higher. The main aim of this study is to examine the effects of labour productivity on firm performance. Using a sample of 311 Czech firms, including 18 certified firms from the European Foundation for Quality Management (EFQM) Excellence Model, the two-step system generalised method of moments (GMM) is performed to test the hypotheses. The current study results revealed that the quality certificates have a positive impact on the firm performance. The relationship between labour productivity and firm performance of certified firms is negative. However, the labour productivity of non-certified firms has a positive impact on the firm's performance. The present study's findings offer deeper insight into how labour productivity of the certified and non-certified firms relates to firm performance. [ABSTRACT FROM AUTHOR]

This article presents a mixed finite element method to find the resonant frequencies of 3-D closed cavity resonators filled with both electric and magnetic lossy, anisotropic media. By introducing a dummy variable with zero value in the 3-D linear vector Maxwell’s eigenvalue problem for the electric field, the divergence-free condition for electric flux density is enforced in a weak sense. In addition, by introducing a dummy variable with an arbitrary constant in the 3-D linear vector Maxwell’s eigenvalue problem for the magnetic field, the divergence-free condition for magnetic flux density is enforced in a weak sense. At the same time, we prove that the eigenmodes of the second-order linear Maxwell’s eigenvalue problems with introduced the dummy variables are equivalent to the ones of the original second-order Maxwell’s eigenvalue problems. As a consequence, the novel method of introducing dummy variables can be free of all the spurious modes in solving eigenmodes of 3-D closed cavity problems. Finally, two numerical experiments are carried out to show that the numerical eigensolver supported by this article can eliminate all the spurious modes, including spurious zero modes.

Aim of study: This paper introduces comparative evaluations of artificial neural network models and regression modeling techniques based on some fitting statistics and desirable characteristics for predicting dominant height. Area of study: The data of this study were obtained from Oriental spruce (Picea orientalis L.) felled trees in even-aged and mixed Oriental spruce and Scotch pine (Pinus sylvestris L.) stands in the northeast of Türkiye. Material and methods: A total of 873 height-age pairs were obtained from Oriental spruce trees in a mixed forest stand. Nonlinear mixed-effects models (NLMEs), autoregressive models (ARM), dummy variable method (DVM), and artificial neural networks (ANNs) were compared to predict dominant height growth. Main results: The best predictive model was NLME with a single random parameter (root mean square error, RMSE: 0.68 m). The results showed that NLMEs outperformed ARM (RMSE: 1.09 m), DVM in conjunction with ARM (RMSE: 1.09 m), and ANNs (RMSE: from 1.11 to 2.40 m) in the majority of the cases. Whereas considering variations among observations by random parameter(s) significantly improved predictions of dominant height, considering correlated error terms by autoregressive correlation parameter(s) enhanced slightly the predictions. ANNs generally underperformed compared to NLMEs, ARM, and DVM with ARM. Research highlights: All regression techniques fulfilled the desirable characteristics such as sigmoidal pattern, polymorphism, multiple asymptotes, base-age invariance, and inflection point. However, ANNs could not replicate most of these features, excluding the sigmoidal pattern. Accordingly, ANNs seem insufficient to assure biological growth assumptions regarding dominant height growth.

In order to clarify the combined impact of tree species composition, site quality, and stand age on the growth and harvest of mixed forests, the prediction models of average DBH and stand volume for mixed forests were established, respectively. The interval period and tree species composition coefficient (TSCC) were considered as independent variables. These models were then optimized by using the particle swarm optimization algorithm for reparameterization and evaluating their applicability. It was found that after introducing the site quality grade and TSCC, the average stand height prediction model showed a better fitting result. The fit accuracy of the average DBH prediction model and the stand volume prediction model were both improved with the help of the TSCC, mainly because the tree species composition affects the growth rate of the average stand height and average DBH and the maximum growth rate of the stand volume. The degree of the impact can be sorted as Cunninghamia lanceolata > Pinus massoniana > hard broad-leaved tree species (group). Overall, the established growth and harvest prediction models for mixed forests with the interval period and TSCC as independent variables have high fit accuracy and applicability.

Carbon dioxide has a significant impact on global climate change due to its natural greenhouse effect. The objective and credible forecast of carbon emissions is very important for the government to formulate and implement the corresponding emission reduction targets. For controlling the growth of carbon emissions, Chinese government has put forward the low-carbon pilot policy and carbon trading policy. However, the existing grey models cannot measure the impact of policies and their interactions. In order to remedy the defect, a novel grey multivariable model based on dummy variables and their interactions is established. Two kinds of grey multivariable models and back propagation neural network model are chosen as comparison models to highlight that the introduction of dummy variables and their interactions plays an important part in improving the model performance. To verify the effectiveness, these four models are selected to simulate and predict the carbon emissions generated from primary energy consumption in Guangdong Province of China. The empirical results indicate that the mean absolute percentage errors of the novel model are 2.87% and 0.86%, respectively, which is significantly better than these three competing models. Finally, based on the outstanding performance of the novel model, it is chosen to forecast the fluctuating tendency of carbon emissions in the next 5 years. [ABSTRACT FROM AUTHOR]

Accurate knowledge of individual tree ages is critical for forestry and ecological research. However, previous methods suffer from flaws such as tree damage, low efficiency, or ignoring autocorrelation among residuals. In this paper, an approach for estimating the ages of individual trees is proposed based on the diameter series of Cinnamomum camphora (Cinnamomum camphora (L.) Presl), Schima superba (Schima superba Gardn. et Champ.), and Liquidambar formosana (Liquidambar formosana Hance). Diameter series were obtained by stem analysis. Panel data contains more information, more variability, and more efficiency than pure time series data or cross-sectional data, which is why diameter series at stump and breast heights were chosen to form the panel data. After choosing a base growth equation, a constraint was added to the equation to improve stability. The difference method was used to reduce autocorrelation and the parameter classification method was used to improve model suitability. Finally, the diameter increment equation of parameter a-classification was developed. The mean errors of estimated ages based on the panel data at breast height for C. camphora, S. superba, and L. formosana were 0.47, 2.46, and −0.56 years and the root mean square errors were 2.04, 3.15 and 2.47 years, respectively. For C. camphora and L. formosana, the estimated accuracy based on the panel data was higher at breast height than at stump height. This approach to estimating individual tree ages is highly accurate and reliable, and provides a feasible way to obtain tree ages by field measurement. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of the electrical safety operations of private electric facilities, the use of AI and IoT is expected. In this paper, we propose a leakage current alarm prediction model using a random forest and an artificial neural network. Customer information, periodic inspection history, alarm occasions on the previous day, and weather information are used as explanatory variables. A grid search was performed for hyperparameter optimization of each model, and generalization performance was evaluated using OOB verification and cross-validation. As a result of comparing the performances of the two models by the PR curve, it was found that the random forest had a larger PR curve and had better prediction performance.

Dairy is one of the main sources of food because it contains the basic components needed by the human body and play an important role in ensuring the quality of families' diet. Consumer preferences are signals for conveying information about the prices and characteristics of products, as well as directly affecting the decisions of agricultural producers. The consumer can use price as a means of comparing products, judging the relative value of money and the quality of the product. Although the development of healthy food production with increased health benefits and acceptable sensory properties has been one of the main goals of the dairy industry over the past two decades. However, the continuous rise in the price of milk, regardless of its quality, and the absence of a price mechanism. The research aims to identify the most important natural and environmental attributes or characteristics affecting consumer preferences and the retail price of milk in the study area. The analysis was based on the use of the Hedonic Price Analysis (HPA) model, which is a model used to estimate the natural and environmental characteristics that directly affect the market prices of a particular commodity, as it reflects the value of the commodity's characteristics. The research relied on two main sources of data, the first is secondary data, and the second is primary data for a random sample of milk consumers, collected from rural and urban areas of Zagazig Center in Al-Sharkia Governorate, to identify the impact of the most important natural and environmental characteristics on consumers’ preferences and the price of milk. The sample size was 120, which were randomly selected from the Zagazig Center. The laboratory test was also conducted for the types of milk purchased by consumers to identify the percentage of fat, solid-not-fat and water. The results of the research indicated that 50% of the study sample prefer to consume cow's milk, 50% of them prefer to consume buffalo milk, and about 63.3% prefer to buy bulk milk, and 36.7% prefer to buy milk packed in cartons. And that the price of milk, the packaged milk, is higher than the price of the bulk milk, and the reasons for this discrepancy in the price from the respondents' point of view were in manufacturing, packaging, the name of the producing company, characteristics of the milk, advertisement, the lifespan of the commodity. It was also found that there are significant differences between rural and urban residents according to the shape of the milk package, and that there is a relationship between the type of package and each of the taste and texture of milk, due to consumers' preference for the taste and texture of buffalo milk over cow's milk. By estimating the hedonic price model for milk in the linear form, it shows the significance of the model, and the signs of the regression coefficients for the independent variables are consistent with the economic logic and with the research expectations, as all the variables have a positive effect on the price, and that the natural characteristics have a statistically significant impact on the price of milk. and consumers are willing to spend extra money on improving the milk's freshness, aroma, taste, and texture. While it was found that the regression coefficient for color and milk content of water not significant. The results of the research confirmed that the characteristics of the quality of milk have a significant impact on the prices paid by the consumer, and that there is a significant effect of the characteristics of the good quality of milk in the study area, and there is also a statistically significant relationship between the natural characteristics of milk and consumer preference and the price of milk. The research recommends that the efforts of producers, manufacturers and marketers should target those characteristics of milk that consumers have shown sensitivity to enhance its market value, paying attention to the cleanliness of the tools used and those in the process of milking, transporting. [ABSTRACT FROM AUTHOR]

A joint determination of horizontal and vertical movement of water through porous medium is addressed in this study through fast multi-output relevance vector regression (FMRVR). To do this, an experimental data set conducted in a sand box with 300 × 300 × 150 mm dimensions made of Plexiglas is used. A random mixture of sand having size of 0.5-1 mm is used to simulate the porous medium. Within the experiments, 2, 3, 7, and 12 cm walls are used together with different injection locations as 130.7, 91.3, and 51.8 mm measured from the cutoff wall at the upstream. Then, the Cartesian coordinated of the tracer, time interval, length of the wall in each setup, and two dummy variables for determination of the initial point are considered as independent variables for joint estimation of horizontal and vertical velocity of water movement in the porous medium. Alternatively, the multi-linear regression, random forest, and the support vector regression approaches are used to alternate the results obtained by the FMRVR method. It was concluded that the FMRVR outperforms the other models, while the uncertainty in estimation of horizontal penetration is larger than the vertical one.

This paper has been empirically investigated the existence of the day‐of‐the‐week effect by using closing daily data for Nifty 50, Nifty 50 Midcap, Nifty 100, Nifty 100 Midcap, Nifty 100 Smallcap, and Nifty 200 for before and during the COVID‐19 health crisis. This study used secondary data for all indices over the period 1 April 2005–14 May 2020. The present study used both dummy variable regression and the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. The total study period is divided into two sub‐periods, that is, during and before the COVID‐19 health crisis. A negative return is found for Mondays when the during‐COVID‐19 health crisis period is examined; in contrast, it was positive for the before COVID‐19 period. Tuesday's effect on index return is found statistically significant and positive for all indices during the COVID‐19 crisis. [ABSTRACT FROM AUTHOR]

The research mainly aimed to identify the importance of water user associations in increasing the productivity and improving the productivity of the water unit of the dry beans crop in the Nubaria region, by studying the most important features and economic and productive indicators, and estimating the indicators of economic and technical efficiency of the beans dry in the Water Users Associations. The study was based on field data collected from a random sample of dry beans farmers, members and non-members of the water users associations in Salah Al-Abd village. The sample observations were 100 farmers, representing about 18.28% of the total number of crop farmers in the village. The sample items were distributed according to the membership of water users associations, and the research relied on the use of simple regression models and cost functions, in addition to estimating some indicators of economic efficiency and productivity, Dummy variable was used in the regression model to reflect the impact of the membership of effect irrigation water users associations on these indicators. The most important results revealed the following: ➢ It was found that there was a significant effect on each of the productivity of the water unit, the productivity of feddan, the price of a ton, the total revenue, the variable production costs, the total costs of the ton, the total margin of the feddan, the net return per feddan, the net return on the kg, the return on the spent pound, the percentage of total revenue To variable costs, total revenue per unit of water, net revenue per unit of water, productivity per unit of water of the dry beans crop of the member of Water Users Associations. While there was an insignificant effect for each of the fixed costs per feddan, the total costs per feddan, the farm margin, and the amount of irrigation water used. ➢ By estimating the cost function, it was found that the indicators of the economic efficiency for the member farmers are better than the non-member farmers. It was also found that the optimum output of the members was about 1.13 tons/feddan, while it was for the non-members about 0.97 tons/feddan., and the volume of the maximum profit output for members amounted to about 1.21 tons/feddan, while for non-members it amounted to 1.01 tons/feddan [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This study aims to obtain a quantitative model that can be used to determine the factors that have a significant impact on loading and unloading productivity. Design/methodology/approach: The method used to solve the problem is multiple linear regression method and dummy variable. Findings: The number of samples for this analysis were all ships thats belong to PT Meratus Line which docked at the Mirah Terminal in Surabaya for 12 months, starting from January 2019 to December 2019. The response used is realization data in the number of containers per hour. The initial predictors that are thought to have an impact on productivity are operational personnel and loading and unloading workers, loading and unloading equipment readiness, work system, full empty ratio and total container weight. There are 4 steps to analyze the regression model that has been obtained. Simultaneous test (using P-value), individual test (t test), F test (simultaneous test), Glejser heteroscedasticity and heterocedasticity test, multicoreleation test, reliability test and validity test and residual test and the best final model obtained. Practical implications: The conclusion is that the weight and the order of the containers are the factors that most take influence the productivity of both the CY (Container Yard) and the ship. The value of the influence of the significance of determination is 957 or equal to 95.7%, so that means that one independent variable has a significant influence of 19% on the existing dependent variable. Paper type: Research paper.

One of the direct economic consequences of dam failure (DF) is that water supply for irrigation is affected and incomes of the agriculture sector (AS) are reduced. The main purpose of this study is to apply a linear programming model (LPM), which, the objective function of the model was set to maximize the income function of the region AS with accessible water sources and function of crops production before and after the DF by using dummy variable (DV) regression models to optimize water supply for irrigation. The results obtained indicate that the consumption of surface water(SW) and groundwaters (GW), before the DF has not been optimized, as there are 15.5 % source loss in SW and 14.5 % in GW. After the DF, the allocation of SW in the best possible situation of access to SW sources is independent of the model input. It has a fixed value equivalent to 86 million cubic meters (MCM), which indicates a 116% decrease in comparison with the optimized value. Total accessible water sources are decreased by 36 % and using GW is 15 % more than an average long period time. A based on the finding from this research and its comparison with previous studies, this model is appropriate for water supply programming after DF and for dry weather Conditions. [ABSTRACT FROM AUTHOR]

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The main aim of the current study is to explore the relationship between working capital (WC) and firm performance. We chose a sample of 326 Czech firms, including 20 certified firms from the EFQM (European Foundation for Quality Management) Excellence Model from the Albertina database. The sample of the Czech firms was taken from three sectors: manufacturing, automobile, and construction. We employed a two-step system generalized method of moment (GMM) technique to determine the results. The study results revealed a negative impact of WC on firm performance; moreover, the firms having a quality certificate from the EFQM Excellence Model perform better. The findings of previous research, which were held globally, and the current study results will encourage the directors, managers, and leaders of the Czech firms to participate in the quality award.

Accurate knowledge of individual tree ages is critical for forestry and ecological research. However, previous methods suffer from flaws such as tree damage, low efficiency, or ignoring autocorrelation among residuals. In this paper, an approach for estimating the ages of individual trees is proposed based on the diameter series of Cinnamomum camphora (Cinnamomum camphora (L.) Presl), Schima superba (Schima superba Gardn. et Champ.), and Liquidambar formosana (Liquidambar formosana Hance). Diameter series were obtained by stem analysis. Panel data contains more information, more variability, and more efficiency than pure time series data or cross-sectional data, which is why diameter series at stump and breast heights were chosen to form the panel data. After choosing a base growth equation, a constraint was added to the equation to improve stability. The difference method was used to reduce autocorrelation and the parameter classification method was used to improve model suitability. Finally, the diameter increment equation of parameter a-classification was developed. The mean errors of estimated ages based on the panel data at breast height for C. camphora, S. superba, and L. formosana were 0.47, 2.46, and −0.56 years and the root mean square errors were 2.04, 3.15 and 2.47 years, respectively. For C. camphora and L. formosana, the estimated accuracy based on the panel data was higher at breast height than at stump height. This approach to estimating individual tree ages is highly accurate and reliable, and provides a feasible way to obtain tree ages by field measurement.

Key message: National and regional climate-sensitive biomass equations were developed for poplar plantations in China, with mean annual temperature and precipitation significantly affecting predicted biomass. In the context of global climate change, information on forest biomass becomes more and more important, and the impact of climate change on biomass estimation is also receiving increasing attention. Using the dummy variable modeling approach and the error-in-variable simultaneous equations approach, national and regional one- and two-variable individual tree biomass models were developed for poplar plantations in China. The models were based on above- and below-ground biomass data of 450 and 147 destructive sample trees, respectively, collected from poplar plantations in 15 provinces of three regions in China. In addition, combined with climate data of mean annual temperature (T) and mean annual precipitation (P), climate-sensitive individual tree biomass models were established, and the impact of climatic factors on biomass estimation was analyzed. The coefficients of determination of national above- and below-ground biomass models developed in this study were more than 0.90 and 0.82, whereas the mean prediction errors were less than 5% and 10%, respectively. For climate-sensitive biomass models, aboveground biomass was only impacted by T, while belowground biomass was affected by both T and P, and impact of the latter was greater than that of the former. Considering the comprehensive effect of climatic factors to above- and belowground estimation, the total biomass estimates of poplar trees with the same size would reach the maximum in the regions for T = 17 °C and P = 1200 mm, which might provide reference for the scientific management of poplar plantations in China. [ABSTRACT FROM AUTHOR]

Monitoring the mortality associated with a surgical procedure leads to the proper decision making in a healthcare system. However, the surgical outcomes depend not only on the risk factors of each patient but also on other categorical influential covariates which cannot be easily measured. Ignoring the unmeasured covariates leads to the poor performance of monitoring procedures. To deal with this significant issue, a general Phase-I risk-adjusted cumulative sum control chart is proposed using a combination of accelerated failure time and frailty models to monitor surgical outcomes. Extensive simulation studies are conducted which reveal that the proposed frailty-based CUSUM chart outperforms the simple CUSUM chart. Moreover, the proposed approach performs better than the existing dummy-based CUSUM chart in terms of detection power. Subsequently, the results of a real cardiac surgery dataset indicate that the inclusion of frailty variables in the risk-adjustment model can effectively model the heterogeneity of the surgical data. [ABSTRACT FROM AUTHOR]

The main aim of the current study is to explore the relationship between working capital (WC) and firm performance. We chose a sample of 326 Czech firms, including 20 certified firms from the EFQM (European Foundation for Quality Management) Excellence Model from the Albertina database. The sample of the Czech firms was taken from three sectors: manufacturing, automobile, and construction. We employed a two-step system generalized method of moment (GMM) technique to determine the results. The study results revealed a negative impact of WC on firm performance; moreover, the firms having a quality certificate from the EFQM Excellence Model perform better. The findings of previous research, which were held globally, and the current study results will encourage the directors, managers, and leaders of the Czech firms to participate in the quality award. [ABSTRACT FROM AUTHOR]

The prevalent political instability or uncertainty in numerous countries and its deleterious effects on economic attainment has attracted the attention of a considerable number of commentators and economists. By employing the Vector error correction model (VECM) approach, this study empirically investigated the ramifications of non-elite political uncertainty on the Zimbabwean economy. The paper is envisaged to add to the existing literature concerning the effects of non-elite political instability concerning the improvement in the Zimbabwean economy in the aftermath of prolonged instability. In the model, the dummy variable was used to capture the ramifications of non-elite political instability on economic performance. The study revealed that the Zimbabwean economy has been negatively affected by the higher propensity of a government collapse. This was found to be consistent with the theory and also in line with findings from previous studies. The recommendation is that political players need to encourage political stability to spur growth in the country. [ABSTRACT FROM AUTHOR]