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The study introduces a new approach to leverage-value relationship. Besides applying the classical regression models, the study deals with leverage as a continuous treatment variable implemented on the firm’s value using the dose-response function (DFR).

After proper model calibration and splitting the treatment (leverage) into ten doses, a response function is generated, which enables the realization of the dose level at which the firm’s value is maximized. Furthermore, the study tests the pecking order theory (POT) and the trade-off theory (TOT) using the threshold model to see whether firms are under or over-indebted. The analysis is carried out on panel data from small-medium enterprises (SMEs), providing more valuable insights than large and mature companies.

The study used two leverage measures: total liabilities ratio and bank debt ratio. Value is measured by the market capitalization and Tobin’s Q. In general, the study finds a positive relationship between leverage and value; POT is not strongly supported, firms are below their optimal leverage and there is a certain leverage dose that would maximize firms’ value.

Since the threshold model and DRF show that SMEs are under-indebted, firms could benefit from extra leverage doses without affecting the firm’s risk profile, especially in a low-interest rate regime, and the potential increase in public-private expenditure after Italy obtained the European Recovery Funds.

The study contributes to new knowledge and understanding of financial leverage from new methodological perspectives, offering valuable insights from SMEs using novel approaches.

This study aims to model and investigate Basic Yellow 28 (BY28) adsorption onto activated carbon in batch and continuous process.

Batch adsorption experiments were carried out at 25 °C with 50 mg/L BY28 solution at pH 6 with different amounts of activated carbon. Freundlich and Langmuir adsorption isotherm models were used to model batch data. Pseudo-first-order and pseudo-second-order kinetic models were applied with linear regression. The changes of the breakthrough curve with the column height, flow rate, column diameter and adsorbent amount were examined in fixed bed column at room temperature. BY28 adsorption data were modelled by using different adsorption column models (Adams & Bohart, Thomas, Yoon & Nelson, Clark and modified dose–response) with non-linear regression.

Freundlich model and pseudo-second-order kinetic model expressed the experimental data with high compatibility. Modified dose-response model corresponded to the fixed bed column data very well.

Adsorption of Basic Yellow 28 on activated carbon in a fixed bed column was studied for the first time. Continuous adsorption process was modelled with theoretical adsorption models using non-linear regression.

A quantitative microbiological risk assessment (QMRA) was developed to estimate the probability of getting listeriosis as a consequence of chicken and beef consumption in Chile. The paper aims to discuss these issues.

As a first step a deterministic retail-to-home model was constructed for the Chilean susceptible population, including storage, cross-contamination and cooking. Next, two probabilistic models were developed, including variability and/or the uncertainty of some of the parameters. The probabilistic models were analyzed by Monte Carlo simulations with 100,000 iterations.

Of the total susceptible population used in the model (2.81 million people), the deterministic model estimated 11 and two listeriosis cases because of beef and poultry consumption, respectively and the variability model estimated a mean of 322 and 7,546 cases for beef and poultry consumption, respectively. The uncertainty analysis showed large ranges, with realistic estimates made with an initial concentration of Listeria monocytogenes of 0.04-1 CFU/g and a dose-response parameter r ranging from 10-14 to 10-10.

The lack of information was the major limitation of the model, so the generation of it has to be a priority in Chile for developing less uncertain risk assessments in the future.

Raw animal products can be the cause of listeriosis cases if they are not stored, cooked and/or handled properly. Consumer education seems to be an essential factor for disease prevention.

This is the first QMRA made in Chile, and also the first study of listeriosis in non-processed meat.

The authors performed a systematic review and meta-analysis of all published randomized controlled trials with the aim to determine and quantify the anti-hyperglycemic effects of glutamine (Gln) in acute and chronic clinical settings.

The authors conducted a comprehensive search of all randomized clinical trials performed up to December 2018, to identify those investigating the impact of Gln supplementation on fasting blood sugar (FBS), insulin levels and homeostatic model assessment-insulin resistance (HOMA-IR) via ISI Web of Science, Cochrane library PubMed and SCOPUS databases. A meta-analysis of eligible studies was conducted using random effects model to estimate the pooled effect size. Fractional polynomial modeling was used to explore the dose–response relationships between Gln supplementation and diabetic indices.

The results of the present meta-analysis suggest that of Gln supplementation had a significant effect on FBS (weighted mean difference (WMD): –2.868 mg/dl, 95 per cent CI: –5.467, –0.269, p = 0.031). However, the authors failed to observe that Gln supplementation affected insulin levels (WMD: 1.06 units, 95 per cent CI: –1.13, 3.26, p = 0.34) and HOMA-IR (WMD: 0.001 units, 95 per cent CI: –2.031, 2.029, p = 0.999). Subgroup analyses showed that the highest decrease in FBS levels was observed when the duration of intervention was less than two weeks (WMD: –4.064 mg/dl, 95 per cent CI: –7.428, –0.700, p = 0.01) and when Gln was applied via infusion (WMD: –5.334 mg/dl, 95 per cent CI: –10.48, 0.17, p = 0.04).

The results from this meta-analysis show that Gln supplementation did not have a significant effect on insulin levels and HOMA-IR. However, it did significantly reduce the levels of FBS, obtaining a higher effect when the duration of the intervention period was less than two weeks.

The purpose of this paper, based on first-hand data from 255 chairmen of planting cooperatives in Sichuan province, is threefold: to understand their social network heterogeneity; to understand the significance for members of marketing innovation in farmers' cooperatives and to understand the effects of chairmen's social network heterogeneity on cooperative marketing innovation.

The research employs an empirical survey of the chairmen of planting cooperatives in rural Sichuan province. The researchers use the ordinary least squares method to conduct regression on the data and the generalized linear model to process the data and avoid errors in the model setting. In the study, the following two hypotheses are examined: (1) The heterogeneity of chairmen's social networks has positive effects on cooperative marketing innovation; (2) The effects of heterogeneous external and internal social networks on cooperative marketing innovation are different.

The results show that both external and internal social network heterogeneity has positive effects on cooperative marketing innovation, and the effects of internal heterogeneity are greater than that of external heterogeneity.

This paper contributes to improving the income of farmers, the innovation of farmers' cooperatives and the development of agriculture in China. It provides a new way of managing and serving members to enable the long-term sustainable development of farmers' cooperatives.

Examining the impact of gender on various aspects of business performance has gained research and policy traction, although the empirical evidence remains inconclusive. This paper aims to focus on one type of business, namely, informal enterprises and one dimension of business performance, namely, product innovation, to better understand how product innovations affect employment in both female- and male-owned informal enterprises.

This paper relies on a unique data set of 513 informal enterprises located in two urban centres in Ghana (Accra and Tema), covering the period between 2013 and 2015 and the Dose-Response Model to examine the effect of product innovations on employment in informal enterprises in urban Ghana.

The findings suggest that product innovation has considerable beneficial impacts on the creation of employment in informal enterprises. The results do not show systematic differences in the factors affecting product innovation in female- and male-owned enterprises. However, they suggest that although female-owned enterprises are less likely to introduce product innovations, they do sell more innovative products.

These findings support the view that innovation is “gendered”, and therefore, requires a “gendered” policy lens.

The complexity of atmospheric corrosion, further compounded by the effects of climate change, makes existing models inappropriate for corrosion prediction. The commonly used kinetic model and dose-response functions are restricted in their capacity to represent the non-linear behaviour of corrosion phenomena. The application of artificial intelligence (AI)-driven machine learning algorithms to corrosion data can better represent the corrosion mechanism by considering the dynamic behaviour due to changing climatic conditions. Effective use of materials, coating systems and maintenance strategies can then be made with such a corrosivity model. Accurate corrosion prediction will help to improve climate change resilience of the social, economic and energy infrastructure in line with the UN Sustainable Development Goals (SDGs) 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure) and 13 (Climate Action). This chapter discusses atmospheric corrosion prediction in relation to the SDGs and the influence of AI in overcoming the challenges.

Discusses the measurement and conceptual difficulties of assessing the benefits of environmental improvements in monetary terms, in the context of a study undertaken to evaluate the health‐related benefits of improved air quality in a large, diverse urban population. Describes an interdisciplinary approach methodology to assess health benefits and uses the results from a study based on that approach to illustrate the practical and conceptual difficulties of comprehensively assessing such benefits. Ends with a discussion of the implications of these difficulties for using benefit‐cost analysis in deciding public policy for health‐related environmental improvements, particularly in the case of complex atmospheric changes.

The attack of September 11, 2001, demonstrated that terrorism is capable of inflicting damage and loss of life with a severity that is many multiples of the most extreme U.S. natural perils. This article addresses the need for a mathematical model for evaluating terrorism risk. The author compares and contrasts terrorism risk with other forms of catastrophe risk, and identifies human intelligence and intent as the distinguishing features. The author proceeds to propose that analytical techniques developed and applied within the discipline of wartime operations research (e.g., game theory, search theory), along with specialized statistical techniques, may be adopted to practically model the risk of terrorism. The article proceeds to demonstrate how even a highly simplified model can offer useful insights to the insurance industry with regard to this risk, although access to terrorism expertise is crucial.

Personal life experience is not sufficient for an adequate environmental risk evaluation. People cannot understand environmental danger without having necessary information. Once established, however, environmental awareness has a direct influence on people's evaluations and, consequently, on their lifestyles (Sjoberg, 1996).