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The present study aims to analyse the literature on capital structure theories for the last 21 years to identify the existing gaps and themes for prospective researchers in this domain.

A sample of 183 articles published from 1999 to 2019 in the Scopus database using “capital structure theory” and “leverage” as keywords was analysed on various basis. A citation analysis was also performed to recognize impactful authors and papers.

The findings revealed that though the capital structure research studies were highly focussed on developed economies, with time, research studies in developing markets are increasing. Further, the capital structure research studies were largely conducted by considering all the industries together, whereas the focus on a particular industrial sector was meagre. Almost all the studies were empirical, thus providing scope for primary research. Various forms of regression were popular econometric techniques used in this area of late. This review highlighted the dominance of trade-off theory to elucidate the capital structure of firms, irrespective of the status of the economy. The comprehensive review uncovered the existing gaps and identified major themes evolving in the capital structure domain.

Unlike a traditional review paper, this study classifies sample articles based on several parameters and depicts a graphical presentation of the findings to cover research gaps, avenues, evolving themes, key aspects, impactful authors and their papers, etc. in the capital structure domain. It provides ready-made information available for prospective research studies in this field.

The purpose of this study is to investigate the dynamics of capital structure for businesses in China and India. Whether and how they adjust their capital structures to witness the trade-off behaviour in the light of different macro-level factors.

Firms listed on the National Stock Exchange and Shanghai Stock Exchange over the period of 2009-2018 are used for the study. System generalized method of moments proposed by Blundell and Bond (1998) is deployed due to the use of dynamic short panel data.

Indian firms revert to their target leverage ratios at a higher rate as compared to Chinese firms (30 and 20 per cent, respectively). Further, the inflation rate, bond market and stock market development are significant factors impacting leverage in the case of India, whereas bond market development significantly impacts leverage in the case of China. These results are robust across various definitions of leverage and other firm and institutional control variables.

This study has implications for various stakeholders. The study highlights that development in financial markets and economy impact the financing decisions and should be a cause for concern for the financial managers and policymakers. Thus, managers can use the findings of the study if they desire to maintain their target capital structures for better firm valuation and the policymakers can support them in achieving the same. Even, the investors can make informed investment decisions considering macro-level factors impacting firms’ financing choices.

It is believed to be the first piece of research effort to consider the novel paradigm of the macro-level factors impacting the target leverage to estimate the adjustment speed. Secondly, it is a pioneering study, which attempts to compare the trade-off behaviour of the top two emerging economies of the world.

The three-dimensional porous scaffold is an important concept in tissue engineering and helps to restore or regenerate a damaged tissue. Additive manufacturing (AM) technology makes the production of custom-designed scaffolds possible. However, modeling scaffolds with intricate architecture and customized pore size and spatial distribution presents a challenge. This paper aims to achieve coupling control of pore size and spatial distribution in bone scaffolds for AM.

First, the proposed method assumes that pore size and spatial distribution have already been transformed from the requirements of scaffolds as inputs. Second, the structural characteristics of scaffolds are explicitly correlated with an all-hexahedron meshing method for scaffold design so that the average pore size could be controlled. Third, the highly coupled internal mesh vertices are adjusted based on a random strategy so that the pore size and spatial distribution conform to their respective desired values. Fourth, after the adjustment, the unit pore cell based on a triply periodic minimal surface was mapped into the hexahedrons through a shape function, thereby ensuring the interconnectivity of the porous scaffold.

The case studies of three bone scaffolds demonstrate that the proposed approach is feasible and effective to simultaneously control pore size and spatial distribution in porous scaffolds.

The proposed method may make it more flexible to design scaffolds with controllable internal pore architecture for AM.

In the control approach, the highly coupled mesh vertices are adjusted through a random strategy, which can determine the moving direction and range of a vertex dynamically and biasedly, thus ensuring the feasibility and efficiency of the proposed method.

The purpose of this paper is to cover an experimental investigation of the impulse response of the foam-mass system (FMS) to unveil some of the foam dynamic behavior features needed to optimize the impact comfort of seat-occupant system. The equation of motion of the studied system is modeled as a sum of a linear elastic, pneumatic damping and viscoelastic residual forces. An identification methodology based on two separated calibration processes of the viscoelastic parameters was developed.

The viscoelastic damping force representing the foam short memory effects was modeled through the hereditary formulation. Its parameters were predicted from the free vibrational response of the FMS using iterative Prony method for autoregressive–moving–average model. However, the viscoelastic residual force resulting in the long memory effects of the material was modeled with fractional derivative term and its derivative order was predicted from previous cyclic compression standards.

The coefficients of the motion law were determined using closed form solution approach. The predictions obtained from the simulations of the impulse and cyclic tests are reasonably accurate. The physical interpretations as well as the mathematical correlations between the system parameters were discussed in details.

The prediction model combines hereditary and fractional derivative formulations resulting in short and long physical memory effects, respectively. Simulation of impulse and cyclic behavior yields good correlation with experimental findings.

Clinical coding is a process of accurate translation of written medical terms into codes. The Payment by Results initiative has focused attention on the quality of clinical coded data as all income for in patient services is derived from coded clinical data. The aim of this study was to evaluate the quality of clinical coded data by making comparisons between the information held on the dialect encoder system and the information recorded in the clinical case notes.

The 50 episodes for this study were randomly selected from a list of all episodes ending August 2005 within the ENT specialty in a teaching hospital.

There were only 17 (34 per cent) episodes with a structured summary within the case notes. Of the 50 recorded primary diagnoses 42 (84 per cent) were correctly coded. Of the 43 recorded primary procedures, 37 (86 per cent) were correctly coded.

This study promotes a better awareness of the impact of poor coding and gives recommendations that will be helpful to those involved in coding processes.

This study aims to explore the impact of capital structure (CS), including total debts, short-term debt, long-term debt and total shareholder equity, on environmental, social and governance (ESG) performance in the context of Jordan.

To achieve the study’s objectives, the authors used the content analysis approach and the longitudinal data generated from the annual reports of 51 industrial companies listed on the Amman Stock Exchange for the period 2012–2020.

The findings show that debt financing enhances ESG performance in all dimensions, while financing by equity did not affect ESG. Consequently, Jordanian companies’ managers are trying to reduce agency costs by investing in ESG activities. In addition, companies are focusing on debt financing instead of equity to achieve their financial as well as nonfinancial goals. This is because the opportunism of new shareholders will likely lead to a focus on maximizing their value at the expense of the broader group of stakeholders, and this will adversely affect companies’ ESG performance. Therefore, debt financing limits shareholder control.

To the best of the authors’ knowledge, this is the first examination of the impact of CS financing choices on ESG performance. Thus, this study has important implications for the decisions of executives, policymakers, shareholders and lenders, as it enables them to better understand the linkage between CS and ESG.

In order to prevent accidents during driving, driver drowsiness detection systems have become a hot topic for researchers. There are various types of features that can be used to detect drowsiness. Detection can be done by utilizing behavioral data, physiological measurements and vehicle-based data. The existing deep convolutional neural network (CNN) models-based ensemble approach analyzed the behavioral data comprises eye or face or head movement captured by using a camera images or videos. However, the developed model suffered from the limitation of high computational cost because of the application of approximately 140 million parameters.

The proposed model uses significant feature parameters from the feature extraction process such as ReliefF, Infinite, Correlation, Term Variance are used for feature selection. The features that are selected are undergone for classification using ensemble classifier.

The output of these models is classified into non-drowsiness or drowsiness categories.

In this research work higher end camera are required to collect videos as it is cost-effective. Therefore, researches are encouraged to use the existing datasets.

This paper overcomes the earlier approach. The developed model used complex deep learning models on small dataset which would also extract additional features, thereby provided a more satisfying result.

Drowsiness can be detected at the earliest using ensemble model which restricts the number of accidents.

Currently, human hearts destined for transplantation can be used for 4.5 hours which is often insufficient to test the heart, the purpose of this paper is to find a compatible recipient and transport the heart to larger distances. Cooling systems with simultaneous internal and external liquid cooling were numerically simulated as a method to extend the usable life of human hearts.

Coolant was pumped inside major veins and through the cardiac chambers and also between the heart and cooling container walls. In Case 1, two inlets and two outlets on the container walls steadily circulated the coolant. In the Case 2, an additional inlet was specified on the container wall thus creating a steady jet impinging one of the thickest parts of the heart. Laminar internal flow and turbulent external flow were used in both cases. Unsteady periodic inlet velocities at two frequencies were applied in Case 3 and Case 4 that had four inlets and four outlets on walls with turbulent flows used for internal and external circulations.

Computational results show that the proposed cooling systems are able to reduce the heart temperature from +37°C to almost uniform +5°C within 25 min of cooling, thus reducing its metabolic rate of decay by 95 percent. Calculated combined thermal and hydrodynamic stresses were below the allowable threshold. Unsteady flows did not make any noticeable difference in the speed of cooling and uniformity of temperature field.

This is the pioneering numerical study of conjugate convective cooling schemes capable of cooling organs much faster and more uniformly than currently practiced.

The purpose of this paper is to examine the effect of ownership structure and corporate governance on capital structure decisions of firms on the Ghana Stock Exchange (GSE).

To analyze the impact of ownership structure and corporate governance on firms' financing decisions, unbalanced panel data covering a period from 2002 to 2007 is employed using the seemingly unrelated regression approach to mitigate the effects of multicollinearity among the regressors.

The regression results reveal that managerial shareholding significantly positively influences the choice of long‐term debt over equity. Among the corporate governance variables, board size is found to be positively and statistically significantly related to capital structure choices. Firm level factors such as volatility in earnings, asset tangibility, dividend payout ratio and profitability are significant determinants of corporate capital structure decisions on the GSE. The findings are largely consistent with theories of capital structure decisions observed in the literature.

The main value of this paper is to provide a comprehensive understanding of the impact of forms of ownership and other governance practices on capital structure decisions of firms from an emerging market perspective.

The purpose of this paper is to investigate the determinants of capital structure using a cross-section sample of 1,481 non-financial firms listed on the Chinese stock exchanges in 2011.

Employing four leverage measures (total leverage and long-term leverage in terms of both book value and market value, respectively) this study examines the effects of factors with proven influences on capital structure in literature, along with industry effect and ownership effect.

The authors find that large firms favour debt financing while profitable firms rely more on internal capital accumulation. Intangibility and business risk increase the level of debt financing but tax has little impact on capital structure. The authors also observe strong industrial effect and ownership effect. Real estate firms borrow considerably more and firms from utility and manufacturing industries use more long-term debt despite compared with commercial firms. On the other hand, firms with state ownership tend to borrow more, while firms with foreign ownership choose more equity financing.

The study uses cross-section data to avoid any potential time effects, which allows the authors to focus on their main research question – to identify the determinants of capital structure for Chinese firms. Future research may gain more insights using panel data and considering other factors such as crisis and financial reforms.

These results may provide important implications to investors in making investment decision and to firms in making financing decisions.

This paper uses by far the largest and latest cross-section sample from the Chinese stock markets, offering a more complete picture of the financing behaviours in the Chinese firms, with known characters and the impact of ownerships.