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2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Umair Saeed Bhutta ◽  
Aws AlHares ◽  
Yasir Shahab ◽  
Adeel Tariq

PurposeThis study aims to investigate two important research questions. First, this research examines the impact of real earnings management on investment inefficiency of the non-financial listed firms in Pakistan. Second, this research further explores the moderating role of short-term debt on the nexus between real earnings management and investment inefficiency. This study attempts to highlight an important research problem i.e. the jinx of real earnings management from the context of an emerging economy.Design/methodology/approachThis study employs the data from non-financial listed firms in Pakistan over the period from 2008 to 2018. The study uses panel data methodologies with firm and year fixed-effects to examine the proposed hypotheses. The results are robust to the use of sensitivity analysis, different estimation techniques and endogeneity issues (using two-stage least squares (2SLS) and generalized method of moments (GMM) techniques).FindingsThe results of the research are twofold. First, consistent with the theoretical arguments, the findings reveal that real earnings management increases investment inefficiency and results in over-investments by the firms. Second, short-term debt attenuates the relationship between real earnings management and investment inefficiency. It implies that a higher level of short-term debt weakens the adverse effects of real earnings management on the investment efficiency of the firm.Originality/valueThis study offers original findings on the issues pertaining to the quality of accounting and financial reporting in an emerging economy like Pakistan, where the implementation of regulations is weak in the corporate world and management frequently exploits shareholders' wealth for the short-term benefits.

2021 ◽  
Vol 6 (15) ◽  
pp. 299-312

The aim of this study is to examine the effects of economic growth and inflation on unemployment for the period 2005:1- 2020:9 in Turkey by using ARDL (Auto Regressive Distributed Lag) model. In the study, firstly unit root tests were carried out to determine whether economic growth (ind) and inflation (cpi) have long and short-term effects on unemployment (unemp). Then, the ARDL method was used to determine whether there is a long-term relationship between the series in the model where the unemployment rate is the dependent variable, the Industrial Production Index representing economic growth and the Consumer Price Index (CPI) representing inflation. Instead of GDP, the Industrial Production Index was preferred both to harmonize with the monthly data and to make a production-based analysis. As a result of the analysis, it was determined that there was a statistically significant cointegration relationship between the variables, and the short-term relationship was analyzed with the error correction model (ECM). As a result of the analysis, it has been determined that there is a cointegration relationship between unemployment, inflation rate and economic growth in Turkey. According to the results of the analysis, negative between unemployment and industrial production index; It is seen that there is a positive relationship between unemployment and inflation.

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-23
Vipin Kumar Kukkala ◽  
Sooryaa Vignesh Thiruloga ◽  
Sudeep Pasricha

Modern vehicles can be thought of as complex distributed embedded systems that run a variety of automotive applications with real-time constraints. Recent advances in the automotive industry towards greater autonomy are driving vehicles to be increasingly connected with various external systems (e.g., roadside beacons, other vehicles), which makes emerging vehicles highly vulnerable to cyber-attacks. Additionally, the increased complexity of automotive applications and the in-vehicle networks results in poor attack visibility, which makes detecting such attacks particularly challenging in automotive systems. In this work, we present a novel anomaly detection framework called LATTE to detect cyber-attacks in Controller Area Network (CAN) based networks within automotive platforms. Our proposed LATTE framework uses a stacked Long Short Term Memory (LSTM) predictor network with novel attention mechanisms to learn the normal operating behavior at design time. Subsequently, a novel detection scheme (also trained at design time) is used to detect various cyber-attacks (as anomalies) at runtime. We evaluate our proposed LATTE framework under different automotive attack scenarios and present a detailed comparison with the best-known prior works in this area, to demonstrate the potential of our approach.

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Lei Xie ◽  
Soo Jeoung Han ◽  
Michael Beyerlein ◽  
Jiacheng Lu ◽  
Lillian Vukin ◽  

Purpose This paper aims to conduct two studies to investigate shared leadership and team creativity (TC) in leaderless short-term project teams (STPTs). Design/methodology/approach To answer the research question, this paper used a multi-level mixed-methods design. This paper analyzed video recordings, transcripts of STPTs’ collaboration and self-report surveys from an international engineering competition. In Study 1, this paper attempted to connect relation-oriented shared leadership (ROSL) and task-oriented shared leadership (TOSL) with TC by coding video recordings. In Study 2, this paper further investigated the proposed positive relationship between shared leadership and TC by surveying a sample of 166 students in 51 teams. Findings In Study 1, this paper found that shared leadership covaries with TC following a similar behavioral pattern. In Study 2, multi-level structural equation modeling results suggested that both TOSL and ROSL are positively correlated with TC. Originality/value In this mixed-methods multi-level research, this paper found that when the team’s shared leadership increases, their TC activity becomes frequent. This paper confirmed the qualitative finding by quantitatively investigated the relationship between shared leadership and creativity at the team level.

André Alves de Castro Lopes ◽  
Júlio Cesar Bogiani ◽  
Cícero Célio de Figueiredo ◽  
Fábio Bueno dos Reis Junior ◽  
Djalma Martinhão Gomes de Sousa ◽  

Oecologia ◽  
2021 ◽  
Sarah Catto ◽  
Petra Sumasgutner ◽  
Arjun Amar ◽  
Robert L. Thomson ◽  
Susan J. Cunningham

AbstractThe provision of anthropogenic food undoubtedly influences urban bird fitness. However, the nature of the impact is unclear, with both benefits and costs of urban diets documented. Moreover, the influence of short-term fluctuations in food availability, linked to urban weekday/weekend cycles of human presence, is largely unknown. We explored whether breeding red-winged starlings Onychognathus morio in Cape Town, South Africa, altered foraging and provisioning behaviour between days with high human presence (HHP) and days with low human presence (LHP)—i.e. weekdays versus weekends and vacation days. We investigated the relationship between starling diet, adult body mass and nestling development. Breeding adults consumed and provisioned the same quantity of food, but a significantly greater proportion of anthropogenic food on HHP compared to LHP days. Adults apparently benefited from the anthropogenic diet, experiencing significantly greater mass gain on HHP days. However, nestlings experienced a cost, with the number of HHP days during the nestling period associated negatively with nestling size. Adults may, therefore, benefit from the high calorie content of anthropogenic food, while nestlings may be negatively affected by nutrient limitation. The quantity of food available in urban environments may, therefore, benefit adult survival, while its quality imposes a cost to nestling growth.

Zhongda Tian

In recent years, short-term wind power forecasting has proved to be an effective technology, which can promote the development of industrial informatization and play an important role in solving the control and utilization problems of renewable energy system. However, the application of short-term wind power prediction needs to deal with a large number of data to avoid the instability of forecasting, which is facing more and more difficulties. In order to solve this problem, this paper proposes a novel prediction approach based on kernel principal component analysis and echo state network optimized by improved particle swarm optimization algorithm. Short-term wind power generation is affected by many factors. The original multi-dimensional input variables are pre-processed by kernel principal component analysis to determine the principal components that affect wind power. The dimension of principal component is less than the original input data, which reduces the complexity of modeling. The convergence and stability of the echo state network can be improved by using the principal component of the input variable. The advantage is to reduce the input variables, eliminate the correlation between the input variables, and improve the prediction performance of the prediction model. Furthermore, an improved particle swarm optimization algorithm is proposed to optimize the dynamic reservoir parameters of echo state network. Compared with other state-of-the-art prediction models, the case studies show that the proposed approach has good prediction performance for actual wind power data.

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