The effects of earning quality on sustainable reports: an empirical study from Vietnam

Sustainable Reports have been specifically concerned by related parties over the period, firstly by researchers in the developed countries and spreading to the developing jurisdictions. The aim of the research is to analyze the impact of the Earning Quality to Sustainable Reports of non-financial firms listed on Vietnam financial market. The authors have used the combining method to measure the Sustainable Reports disclosure level according to the Global Reporting Initiatives 4 (GRI4) standard for 312 enterprises in Vietnam during 2015- 2019. The research result has recognized the positive effect of Earning Quality to Sustainable Reports while measuring Earning Quality via the aspects of earnings management and the stability of earnings. Besides, the research has considered the impact of the state-owned, foreign-owned factors, the audit quality and how the legislation policy relates to Sustainable Reports in interaction with Earning Quality. The result of the study suggests a number of cognitive enhancement recommendations in the Sustainable Reports for Vietnam and similar countries

A study on the effects of plasma spraying parameters on the adhesion strength of Cr3C2-NiCr coating on 16Mn steel

This paper experimentally studied the adhesion strength of Cr3C2-30 %NiCr coating created on 16Mn steel substrate by plasma thermal coating technique in relation to spraying parameters. Experiments were carried out according to the central composite design (CCD) experimental matrix with three parameters: current intensity, powder feeding rate, and spray distance. Samples consisting of an annular disc and a latch made of 16Mn were fabricated according to the JIS H8664-1977 standard. Cr3C2-30 %NiCr coating was then created on the top surface of the disc including end of the latch. Adhesion strength of the coating to the substrate was measured through the tensile test. ANOVA analysis of variance was performed to evaluate the influence of the spraying parameters on adhesion strength and to build an empirical regression function representing the relationship between those parameters and the adhesion. Optimization problem was solved by ANOVA method and genetic algorithm (GA) to determine the value of the spraying parameters at which the coating has the greatest adhesion strength to the substrate. The results showed that the spraying parameters greatly affected the adhesion of the Cr3C2-30 %NiCr coating to the 16Mn substrate. Among them the spray distance has the greatest influence while the powder feeding rate has the least. Secondly, the regression function was well reflected the relationship between the three parameters and adhesion strength of the coating on the substrate. Using the values of spray parameter obtained from the GA optimization to create Cr3C2-30 %NiCr coating on 16Mn steel, the adhesion strength of the coating to the substrate reached a value of 98.4 % compared to the predictio

Self-adaptation of mutation rates in non-elitist populations

The runtime of evolutionary algorithms (EAs) depends critically on their parameter settings, which are often problem-specific. Automated schemes for parameter tuning have been developed to alleviate the high costs of manual parameter tuning. Experimental results indicate that self-adaptation, where parameter settings are encoded in the genomes of individuals, can be effective in continuous optimisation. However, results in discrete optimisation have been less conclusive. Furthermore, a rigorous runtime analysis that explains how self adaptation can lead to asymptotic speedups has been missing. This paper provides the first such analysis for discrete, population-based EAs. We apply level-based analysis to show how a self-adaptive EA is capable of fine-tuning its mutation rate, leading to exponential speedups over EAs using fixed mutation rates

Populations can be essential in tracking dynamic optima

Real-world optimisation problems are often dynamic. Previously good solutions must be updated or replaced due to changes in objectives and constraints. It is often claimed that evolutionary algorithms are particularly suitable for dynamic optimisation because a large population can contain different solutions that may be useful in the future. However, rigorous theoretical demonstrations for how populations in dynamic optimisation can be essential are sparse and restricted to special cases. This paper provides theoretical explanations of how populations can be essential in evolutionary dynamic optimisation in a general and natural setting. We describe a natural class of dynamic optimisation problems where a sufficiently large population is necessary to keep track of moving optima reliably. We establish a relationship between the population-size and the probability that the algorithm loses track of the optimum

On the choice of the parameter control mechanism in the (1+(λ, λ)) genetic algorithm

The self-adjusting (1 + (λ, λ)) GA is the best known genetic algorithm for problems with a good fitness-distance correlation as in OneMax. It uses a parameter control mechanism for the parameter λ that governs the mutation strength and the number of offspring. However, on multimodal problems, the parameter control mechanism tends to increase λ uncontrollably. We study this problem and possible solutions to it using rigorous runtime analysis for the standard Jumpk benchmark problem class. The original algorithm behaves like a (1+n) EA whenever the maximum value λ = n is reached. This is ineffective for problems where large jumps are required. Capping λ at smaller values is beneficial for such problems. Finally, resetting λ to 1 allows the parameter to cycle through the parameter space. We show that this strategy is effective for all Jumpk problems: the (1 + (λ, λ)) GA performs as well as the (1 + 1) EA with the optimal mutation rate and fast evolutionary algorithms, apart from a small polynomial overhead. Along the way, we present new general methods for bounding the runtime of the (1 + (λ, λ)) GA that allows to translate existing runtime bounds from the (1 + 1) EA to the self-adjusting (1 + (λ, λ)) GA. Our methods are easy to use and give upper bounds for novel classes of functions

Level-Based Analysis of the Univariate Marginal Distribution Algorithm

Estimation of Distribution Algorithms (EDAs) are stochastic heuristics that search for optimal solutions by learning and sampling from probabilistic models. Despite their popularity in real-world applications, there is little rigorous understanding of their performance. Even for the Univariate Marginal Distribution Algorithm (UMDA) -- a simple population-based EDA assuming independence between decision variables -- the optimisation time on the linear problem OneMax was until recently undetermined. The incomplete theoretical understanding of EDAs is mainly due to lack of appropriate analytical tools. We show that the recently developed level-based theorem for non-elitist populations combined with anti-concentration results yield upper bounds on the expected optimisation time of the UMDA. This approach results in the bound $\mathcal{O}(n\lambda\log \lambda+n^2)$ on two problems, LeadingOnes and BinVal, for population sizes $\lambda>\mu=\Omega(\log n)$, where $\mu$ and $\lambda$ are parameters of the algorithm. We also prove that the UMDA with population sizes $\mu\in \mathcal{O}(\sqrt{n}) \cap \Omega(\log n)$ optimises OneMax in expected time $\mathcal{O}(\lambda n)$, and for larger population sizes $\mu=\Omega(\sqrt{n}\log n)$, in expected time $\mathcal{O}(\lambda\sqrt{n})$. The facility and generality of our arguments suggest that this is a promising approach to derive bounds on the expected optimisation time of EDAs.Comment: To appear in Algorithmica Journa

Effects of financial statements information on firms’ value: evidence from Vietnamese listed firms

The paper studies the effects of information reporting in financial statements on values of Vietnamese firms. The study uses panel data with 1,070 observations from 214 firms, which are listed in the stock market of Vietnam in the period from 2012 to 2016. Multiple regression results show that the growth, firm size, profitability, auditing quality and timelineness are positively related to firm values, whereas the capital structure, auditing explanation negatively affect that indicator. The paper also indicates the inconsistency in measuring firms’ value by different measures including EV, Tobin’s Q or share price. Moreover, the research results reflect that measuring firms’ value by EV is more appropriate. The results of empirical research are instructive for enterprises to improve the usefulness of information in financial statements, thereby enhancing enterprises’ values