Pool Model Research Articles

Market valuation and corporate investment in India

Purpose This study aims to examine the impact of stock market valuation on corporate investment. Specifically, it attempts to understand the influence of both the fundamental and non-fundamental components of stock price on firms’ investment decisions. Design/methodology/approach The study decomposes the market-to-book (MB) ratio into three components, namely, firm-level mispricing, industry mispricing and growth component to examine the effect of each of these components on corporate investment decisions. Based on the literature review, four testable hypotheses concerning the relationship between market valuation and corporate investment have been generated. These hypotheses have been tested on the panel data of 1,311 Indian Public Limited Manufacturing Firms using a pooled data regression model. Findings The study finds that both the fundamental and non-fundamental components of stock price influence the investment decisions along with the cash flow variable. The market valuation–investment nexus is more pronounced in the case of equity-dependent firms, which shows that stock valuation affects corporate investment predominantly through the equity transaction channel. Further, the positive relationship between industry mispricing and corporate investment demonstrates that the market sentiment also affects firms’ investment decisions. Originality/value The relationship between the different components of market value and corporate investment decisions has not been explored in India. Hence, the present study is unique because it breaks the MB ratio down into growth and mispricing components and examines the impact of each of these components on corporate investment.

Comparison of Multiple Linear Regression and Biotic Ligand Models to Predict the Toxicity of Nickel to Aquatic Freshwater Organisms.

Toxicity-modifying factors can be modeled either empirically with linear regression models or mechanistically, such as with the biotic ligand model (BLM). The primary factors affecting the toxicity of nickel to aquatic organisms are hardness, dissolved organic carbon (DOC), and pH. Interactions between these terms were also considered. The present study develops multiple linear regressions (MLRs) with stepwise regression for 5 organisms in acute exposures, 4 organisms in chronic exposures, and pooled models for acute, chronic, and all data and compares the performance of the Pooled All MLR model to the performance of the BLM. Independent validation data were used for evaluating model performance, which for pooled models included data for organisms and endpoints not present in the calibration data set. Hardness and DOC were most often selected as the explanatory variables in the MLR models. An attempt was also made at evaluating the uncertainty of the predictions for each model; predictions that showed the most error tended to show the highest levels of uncertainty as well. The performances of the 2 models were largely equal, with differences becoming more apparent when looking at the performance within subsets of the data. Environ Toxicol Chem 2021;40:2189-2205. © 2021 SETAC.

Theoretical adequacy, methodological quality and efficacy of online interventions targeting resilience: a systematic review and meta-analysis.

BackgroundThere is a growing interest in the promotion of mental health, and concepts as resilience are re-emerging and taking relevance. In addition, Information and Communication Technologies can provide potential benefits in the field of mental health, and the treatment of mental disorders in particular. This study aims to synthesize the evidence of internet-based resilience interventions, analyzing the theoretical adequacy, methodological quality and efficacy.MethodsA systematic search was performed. The eligibility criteria stated for this article were: randomized controlled trials targeted at adults or adolescents and including any psychological intervention focussing on resilience in its rationale or design. Studies with direct (e.g. resilience scales) and proximal resilience measures (e.g. scales on well-being) were included. Risk of bias was assessed for each trial using Cochrane’s Collaboration Tool. Two reviewers worked independently in order to identify potential articles. A total of 11 articles were selected. A random-effects pooling model using the Hartung–Knapp–Sidik–Jonkman method based on direct and proximal resilience measures at post-test was used.ResultsThe overall effects of online resilience training compared to control groups at post-test were not significant; the effect size concerning the improvement of resilience was g=0.12 (95% CI: −0.14 to 0.38). In addition, a potential association between the type of outcome and the effect size could be revealed.ConclusionsThe results of the present meta-analysis showed that the overall effect of online resilience trainings was not significant. Nonetheless, a tendency for a higher benefit for resilience was found in the studies with a clear assessment theory, indicating some promising effects.Registration NumberPROSPERO CRD42018083339.

Miner revenue optimization algorithm based on Pareto artificial bee colony in blockchain network

In order to improve the revenue of attacking mining pools and miners under block withholding attack, we propose the miner revenue optimization algorithm (MROA) based on Pareto artificial bee colony in blockchain network. MROA establishes the revenue optimization model of each attacking mining pool and revenue optimization model of entire attacking mining pools under block withholding attack with the mathematical formulas such as attacking mining pool selection, effective computing power, mining cost and revenue. Then, MROA solves the model by using the modified artificial bee colony algorithm based on the Pareto method. Namely, the employed bee operations include evaluation value calculation, selection probability calculation, crossover operation, mutation operation and Pareto dominance method, and can update each food source. The onlooker bee operations include confirmation probability calculation, crowding degree calculation, neighborhood crossover operation, neighborhood mutation operation and Pareto dominance method, and can find the optimal food source in multidimensional space with smaller distribution density. The scout bee operations delete the local optimal food source that cannot produce new food sources to ensure the diversity of solutions. The simulation results show that no matter how the number of attacking mining pools and the number of miners change, MROA can find a reasonable miner work plan for each attacking mining pool, which increases minimum revenue, average revenue and the evaluation value of optimal solution, and reduces the spacing value and variance of revenue solution set. MROA outperforms the state of the arts such as ABC, NSGA2 and MOPSO.

PEAB: A pool-based distributed evolutionary algorithm model with buffer

Pool Model is an asynchronous, loosely coupled distributed evolutionary algorithm (dEA) design architecture. However, the classical Pool Model face some design problems, such as population control, work redundancy, rough selection/replacement strategies, and unreliable connections, etc. In this paper, a novel distributed pool evolutionary algorithm (EA) model with buffer (PEAB) is proposed. PEAB can solve the inherent problems of the Pool Model by using the buffer setting, the Reunion mechanism, and the Migration in Pool (MP) strategy. Besides, PEAB provides stronger population control and more global population selection/replacement strategies. In the experimental part, we compared PEAB with another Pool Model named EvoSpace using a common benchmark. The experiments showed that the convergence rate of PEAB is 59.7% faster than that of EvoSpace under the respective fastest conditions. PEAB also has a faster reception rate of the first generation and stronger population control. Besides, this paper also tests and analyzes the scalability of PEAB using two other benchmarks. The overall trend of the experiment results suggested that PEAB would be faster with more Workers. Last but not least, this paper studies the effect of the MP strategy on the performance of PEAB, and the results showed that the MP strategy can effectively improve the convergence efficiency.

Simulating organ biomass variability and carbohydrate distribution in perennial fruit crops: a comparison between the common assimilate pool and phloem carbohydrate transport models

AbstractVariability in fruit quality greatly impedes the profitability of an orchard. Modelling can help find the causes of quality variability. However, studies suggest that the common assimilate pool model is inadequate in terms of describing variability in organ biomass. The aim of the current study was to compare the performances of the common assimilate pool and phloem carbohydrate transport models in simulating phloem carbohydrate concentration and organ biomass variability within the whole-plant functional–structural grapevine (Vitis vinifera) model that we developed previously. A statistical approach was developed for calibrating the model with a detailed potted experiment that entails three levels of leaf area per vine during the fruit ripening period. Global sensitivity analysis illustrated that carbohydrate allocation changed with the amount of leaf area as well as the limiting factors for organ biomass development. Under a homogeneous canopy architecture where all grape bunches were equally close to the carbohydrate sources, the common assimilate pool and phloem transport models produced very similar results. However, under a heterogeneous canopy architecture with variable distance between bunches and carbohydrate sources, the coefficient of variation for fruit biomass rose from 0.01 to 0.17 as crop load increased. These results indicate that carbohydrate allocation to fruits is affected by both the size of crop load and fruit distribution, which is not adequately described by the common assimilate pool model. The new grapevine model can also simulate dynamic canopy growth and be adapted to help optimize canopy architecture and quality variability of other perennial fruit crops.

Effect of Capital Structure, Liquidity on Performance: Evidence from Deposit Money Banks in Nigeria

This study examined the effect of capital structure and liquidity on the performance of deposit money banks (DMB’s) in Nigeria using 10 quoted DMB’s in Nigeria from the year 2009 to 2019. The dependent variables are return on asset (ROA) and return on equity (ROE) while the independent variables are short term debt (STD), long term debt (LTD), debt equity ratio (D/E), current ratio (CA) and quick cash ratio (CASH). STD, LTD and D/E are proxies for capital structure while CA and CASH as proxies for liquidity. The study also controls for asset tangibility and firm size. The data were analyzed using the panel estimator based on the pooled effect model, fixed effect model and random effect model.The results revealed that debt equity ratio has significant negative effect on return on asset and return on equity. Short term debt and long-term debt have negative effect on return on asset but has positive effect on return on equity. The findings also revealed that current ratio and quick cash ratio has negative effect on return on asset but have positive effect on return on equity. Tangibility of asset has positive significant effect on return on asset but negative effect on return on equity. Firm size has positive effect on both return on asset and return on equity.Based on the findings, the study recommends that business managers should keep debt equity ratio at the point that is optimal, any excess increase in debt will have negative impact on profitability. Business managers tend to find a meeting point for debt structure that will favourably affect both equity provider and the business at large. Banks should invest the excess liquidity on value increasing activities to drive profitability. Policy marker should install more policies to carter for the excess liquidity. Keywords: Short Term Debt, Long Term Debt, Debt-Equity, Current ratio, Quick Ratio, Cash. Profitability DOI: 10.7176/RJFA/12-14-01 Publication date: July 31 st 2021

La estructura sectorial-regional de los salarios en México

The present study analyses the sectoral and the regional structure of the wages in Mexico. We exploit a pooled data model with information from the National Occupation and Employment Survey (ENOE) from 2005 to 2018. The results show generalized discrimination against women in all economic activities and regions of the country. The education return on wage differentiates among economic activities, and overall, it is more profitable for women. In professional servi-ces, an additional year of education increases 8.4 per cent the salary; in restaurants and lodging services, this return is 3.9 per cent. This study also shows that education is more profitable in the northeast than in other parts of the country. Wages in agriculture in the north double the wages in agriculture in the south. One of the main implications of this study is that education is a crucial instrument for narrowing the gap between women’s and men’s wages.

Model-Based Analysis of Muscle Strength and EMG-Force Relation with respect to Different Patterns of Motor Unit Loss

This study presents a model-based sensitivity analysis of the strength of voluntary muscle contraction with respect to different patterns of motor unit loss. A motor unit pool model was implemented including simulation of a motor neuron pool, muscle force, and surface electromyogram (EMG) signals. Three different patterns of motor unit loss were simulated, including (1) motor unit loss restricted to the largest ones, (2) motor unit loss restricted to the smallest ones, and (3) motor unit loss without size restriction. The model outputs including muscle force amplitude, variability, and the resultant EMG-force relation were quantified under two different motor neuron firing strategies. It was found that motor unit loss restricted to the largest ones had the most dominant impact on muscle strength and significantly changed the EMG-force relation, while loss restricted to the smallest motor units had a pronounced effect on force variability. These findings provide valuable insight toward our understanding of the neurophysiological mechanisms underlying experimental observations of muscle strength, force control, and EMG-force relation in both normal and pathological conditions.

Who can take advantage of medically assisted reproduction in Germany?

Compared with other European societies, access to medically assisted reproduction (MAR) in Germany is rather restrictive. Surprisingly little is known about the actual utilization of MAR in Germany and its social selectivity, primarily due to a lack of appropriate data. This article studies the use of MAR by drawing on 11 waves of the German Family Panel (pairfam), an annual survey that collects data on partnership and family dynamics in Germany. In estimating pooled logistic regression models that take into account whether or not MAR has been used, the following treatments were included: (i) in-vitro fertilization or micro-fertilization; (ii) intrauterine insemination; and (iii) any type of surgery performed to treat infertility. The highest odds of seeking medical help for infertility were found among married, childless couples who had a higher income, a strong desire to have children, a poor self-assessed health status, and who reported having problems conceiving a child prior to the year of the interview. Among these married couples, those with an above-average income were particularly likely to use MAR. These findings indicate that social selectivity in the uptake of MAR is based on economic resources and on the importance of marriage as the cultural ideal for a ‘normal family’.

Tourism during COVID-19 Pandemic: Evidence from Top 20 Visited Country

Purpose of the Study: The study aims to examine the impact of government actions and country-specific factors during the COVID-19 on the tourism industry around the globe. Design/Methodology/Approach: The sample includes data of the 20 highly visited countries worldwide according to the UNWTO (United Nations World Tourism Organization). This study uses the pooled panel ordinary least squares regression model to test the impact of COVID-19 on tourism industries. Findings: This study finds that out of government action variable like social distancing significantly decreases the income from the tourism industry and the number of tourists. Public transport mobility and infrastructural facilities have a significant positive relation with tourism. We have found no significant relationship between people’s awareness and the tourism industry. Research limitations/implications: The study's sample size is only restricted to a sample of 20 most visited countries, which may exclude the holistic effects of the pandemic around the world. On another note, we have not included any primary data which may provide essential findings to the international regulators and organizers during the pandemic. We have failed to do so as we have resource limitation and communication breakdown during this pandemic. Practical Implications: Findings of the study may provide a dynamic perspective of the pandemic to the governments of various countries and international regulators. Such findings can guide the concerned stakeholders to tackle with much more resilience and caution. Originality/Value- Various studies are undertaken and are still exploring the impact of this pandemic on the tourism industry day by day. Our research is different in the sense that it covers the sensitive aspects of the pandemic, like actions of the government and differing impact due to various country-specific factors that influence the industry.

Rising housing costs and income poverty among the elderly in Germany

Housing costs have been increasing rapidly in Germany in recent years. Given the importance of housing for the elderly, one may expect many to be forced to dedicate ever-larger shares of their income to housing costs. Using longitudinal data from the German Socio-Economic Panel (GSOEP), we examine how changes in housing costs between 1996 and 2017 have affected income poverty among Germany’s over-65s. Our results reveal that higher costs have indeed contributed to increased income poverty in old age in that period. Our pooled probit regression models as applied to Germany’s elderly show that increased housing costs mean tenants, homeowners with outstanding mortgages, single-person households and people with migration background all suffer a higher risk of poverty, while the risk remains lower for outright homeowners. Since the relative income position of the elderly is expected to further deteriorate in future, our study suggests a need for policy action to avoid a worsening in figures for old-age income poverty in Germany.

Code Clone Detection with Hierarchical Attentive Graph Embedding

Code clone serves as a typical programming manner that reuses the existing code to solve similar programming problems, which greatly facilitates software development but recurs program bugs and maintenance costs. Recently, deep learning-based detection approaches gradually present their effectiveness on feature representation and detection performance. Among them, deep learning approaches based on abstract syntax tree (AST) construct models relying on the node embedding technique. In AST, the semantic of nodes is obviously hierarchical, and the importance of nodes is quite different to determine whether the two code fragments are cloned or not. However, some approaches do not fully consider the hierarchical structure information of source code. Some approaches ignore the different importance of nodes when generating the features of source code. Thirdly, when the tree is very large and deep, many approaches are vulnerable to the gradient vanishing problem during training. In order to properly address these challenges, we propose a hierarchical attentive graph neural network embedding model-HAG for the code clone detection. Firstly, the attention mechanism is applied on nodes in AST to distinguish the importance of different nodes during the model training. In addition, the HAG adopts graph convolutional network (GCN) to propagate the code message on AST graph and then exploits a hierarchical differential pooling GCN to sufficiently capture the code semantics at different structure level. To evaluate the effectiveness of HAG, we conducted extensive experiments on public clone dataset and compared it with seven state-of-the-art clone detection models. The experimental results demonstrate that the HAG achieves superior detection performance compared with baseline models. Especially, in the detection of moderately Type-3 or Type-4 clones, the HAG particularly outperforms baselines, indicating the strong detection capability of HAG for semantic clones. Apart from that, the impacts of the hierarchical pooling, attention mechanism and critical model parameters are systematically discussed.

Modeling ideological polarization in democratic party systems

There are mounting claims that increasing ideological polarization is reshaping democratic party systems with important effects on the functioning of electoral politics, the correlates of voting choice, turnout, and even the representativeness of government. Yet, our knowledge of what causes party system polarization is still developing. The primary research goal is to systematically combine and test existing theories predicting levels of party system polarization across 21 established democracies. Polarization levels have generally risen since the mid-1990s. A pooled model finds that characteristics of the electoral system and the party system largely determine the continuity of party system polarization. Polarization levels also appear linked to short-term factors such as citizens’ declining confidence in the economy and increasing concerns about immigration. The conclusion discusses the implications for party systems and politics in affluent democracies.

Model Validity and Transferability Informing Behavioral Energy Policies

A number of microeconomic choice models are currently applied to demonstrate systematic biases in energy consumer behavior. The models highlight the hidden potential of energy savings from policies that target the so-called behavioral anomalies. Nevertheless, whether these patterns are repeatable or not is not clear, because the efforts to determine the transferability or generalizability of these models are practically nonexistent. This paper uses a unique collection of empirical data from five EU countries collected within the CONSEED project to refine and develop further the standard consumer decision model, validate it for policy purposes, and elaborate on its transferability between countries. The pooled samples allow for a more reliable investigation of the relative importance of the factors influencing consumers’ attitudes and beliefs towards energy investment decisions. Based on the statistical tests conducted to evaluate the “transferability” of the pooled models (i.e., the possibility of creating a “universal” model of EE from the pooled model), it can be argued that the models are transferable in specific cases since attitudinal factors and demographic characteristics play a significant role. Although the pooled models are validated, any extrapolation of the above-mentioned findings to specific populations in terms of “space” (i.e., country) and “target” (e.g., sectors and technologies) should be approached with caution from a policy perspective.

Optimal uses of pooled testing for COVID-19 incorporating imperfect test performance and pool dilution effect: An application to congregate settings in Los Angeles County.

ABSTRACTIntroductionPooled testing is a potentially efficient alternative strategy for COVID‐19 testing in congregate settings. We evaluated the utility and cost‐savings of pooled testing based on imperfect test performance and potential dilution effect due to pooling and created a practical calculator for online use.MethodsWe developed a 2‐stage pooled testing model accounting for dilution. The model was applied to hypothetical scenarios of 100 specimens collected during a one‐week time‐horizon cycle for varying levels of COVID‐19 prevalence and test sensitivity and specificity, and to 338 skilled nursing facilities (SNFs) in Los Angeles County (Los Angeles) (data collected and analyzed in 2020).ResultsOptimal pool sizes ranged from 1 to 12 in instances where there is a least one case in the batch of specimens. 40% of Los Angeles SNFs had more than one case triggering a response‐testing strategy. The median number (minimum; maximum) of tests performed per facility were 56 (14; 356) for a pool size of 4, 64 (13; 429) for a pool size of 10, and 52 (11; 352) for an optimal pool size strategy among response‐testing facilities. The median costs of tests in response‐testing facilities were $8250 ($1100; $46,100), $6000 ($1340; $37,700), $6820 ($1260; $43,540), and $5960 ($1100; $37,380) when adopting individual testing, a pooled testing strategy using pool sizes of 4, 10, and optimal pool size, respectively.ConclusionsPooled testing is an efficient strategy for congregate settings with a low prevalence of COVID‐19. Dilution as a result of pooling can lead to erroneous false‐negative results.

Will Working Longer Enhance the Health of Older Adults? A Pooled Analysis of Repeated Cross-sectional Data in Japan.

Encouraging older adults to continue working longer would be a realistic solution to the shrinking labor force, which is a result of the aging population. This study examined whether working longer improves the health of older adults. We used repeated cross-sectional data from 1,483,591 individuals aged 55-69 years collected from 11 waves of a nationwide population-based survey conducted in Japan from 1986 to 2016. We estimated pooled regression models to explain health outcomes by work status, controlling for potential endogeneity biases. Based on the estimation results, we conducted simulations to predict the health impact of policy measures that encourage older adults to participate in the labor force. The regression analysis showed that work status had a mixed health impact. For example, work reduced the probability of poor self-rated health by 6.7 (95% confidence interval [CI], 6.2-7.2) percentage points and increased that of psychological distress by 12.2 (95% CI, 11.3-13.1) percentage points. The simulation results showed that raising both the mandatory retirement age and eligibility age for claiming public pension benefits to 70 years would increase the employment rate by 27.8 (standard deviation [SD], 4.2) percentage points among those aged 65-69 years, which would reduce their probability of poor self-rated health by 1.8 (SD, 0.4) percentage points and raise that of psychological distress by 4.1 (SD, 0.8) percentage points for that age group. The results suggest the need to pay attention to the health outcomes of policy measures that encourage older adults to work longer.

Demographic dividend and economic growth in emerging economies: fresh evidence from the SAARC countries

PurposeThe study attempts to establish the relationship between demographic dividend and GDP growth rate by utilising panel data from 1990 to 2017 in Bangladesh, India, Pakistan, Nepal and Sri Lanka.Design/methodology/approachThis study employs the pooled OLS model, using data from the World Bank's database for the period 1990–2017 for five selected South Asia Association for Regional Cooperation (SAARC) countries.FindingsThe results reveal that demographic dividend affects economic growth in Bangladesh, India, Nepal, Sri Lanka and Pakistan, thereby supporting the demographic dividend hypothesis. For the country-specific analysis, it was also observed that demographic dividend impacts the economic growth of the five SAARC countries. In addition, growth of gross capital formation is highly significant for both aggregated and country-specific analyses. However, economic growth is unaffected by trade openness and unemployment rates. Moreover, the rate of labour force participation is negatively related to the GDP growth rate in the aggregated model.Originality/valueThis paper bestows insight into the fact that the impact of demographic dividend on the economic growth of the SAARC regions cannot be fully actualised if the workforce are underutilised. This region needs to adopt appropriate policies to strengthen the considerable benefits of demographic dividend on the economic growth.

Cloud detection of GF‐7 satellite laser footprint image

In November 2019, the GaoFen-7(GF-7) satellite was equipped with China's first laser altimeter with full waveform recording capability, which obtains high-precision long-range three-dimensional coordinates. The influence of clouds is noticeable for laser transmission, and a footprint camera is used to determine laser pointing and to image the ground. However, the cloud inevitably appears in the laser footprint image. In this study, the authors propose a cloud detection scheme for footprint images based on deep learning. First, an adaptive pooling model is proposed according to the characteristics of the cloud region. Next, model fusion was performed based on the SegNet and U-Net training results. Finally, test time augmentation was used to enhance the data and to improve cloud detection accuracy. The experimental results show that the fusion result of the model was approximately 5% better than that of the traditional cloud detection algorithm, which improved the shortcomings of the traditional algorithm, such as poor detection effect for thin clouds and complex underlying cloud surfaces. The related conclusions have certain reference significance for GF-7 data processing and related research on footprint images.

96MO A risk analysis of alpelisib (ALP)-induced hyperglycemia (HG) using baseline factors in patients (pts) with advanced solid tumours and breast cancer (BC): A pooled analysis of X2101 and SOLAR-1

ALP + fulvestrant is approved for PIK3CA-mutated, HR+/HER2– advanced BC in the US and EU. HG is an on-target adverse event (AE) of ALP and the most common all grade (G) and G3/4 AE in the phase III SOLAR-1 trial. Antihyperglycemic agents (AHAs) and ALP dose modifications (mods) and discontinuations (discs) were used to manage this AE (Table). We present a pooled risk factor model using safety sets of the phase I X2101 (NCT01219699) and SOLAR-1 (NCT02437318) trials and its application to SOLAR-1 data.