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The use of weather derivatives is impaired with a basis risk which diminishes the hedging effectiveness and hinders the distribution of these risk management instruments in the agricultural sector. A frequently suggested approach to reduce the basis risk is the use of mixed indices composed of several weather variables. The purpose of this paper is to compare the hedging effectiveness of a simple temperature‐based and a simple precipitation‐based weather derivative with that of a derivative based on a mixed index of two weather variables.

The basis of this comparison are empirical yield time series of the winter wheat production of 32 farms located in central Germany, as well as daily temperature and precipitation data collected by selected weather stations over several years. Insurance is structured as an option on an accumulated weather index and priced by index‐value simulation. In addition, the bootstrapping method is used to improve statistical reliability. The hedging effectiveness is measured non‐parametrically regarding the relative reduction of the standard deviation of winter wheat revenues caused by using weather derivatives.

The results reveal that mixed index‐based weather derivatives have a significantly higher potential to reduce the risk of winter wheat revenues than simple index‐based weather derivatives. However, using mixed index‐based weather derivatives does not lead to a significantly higher hedging effectiveness than the simultaneous use of several simple index‐based weather derivatives. Moreover, simple index‐based weather derivatives may more easily raise the interest of other industries which could serve as potential trading partners for the agricultural sector.

The authors analyzed the hedging effectiveness of weather derivatives based on simple and mixed indices with regard to the production of winter wheat in Central Germany. To confirm that the present results are generalizable, further research is required for other types of production apart from winter wheat cultivation and with respect to other regions besides Germany.

The focus and results of the present study are very relevant for farmers as well as for potential providers of weather derivatives. The results reconfirm that weather derivative providers should better offer different weather derivatives based on a simple index than complex derivatives that are based on a mixed index.

To the best of the authors' knowledge, this paper is the first that provides a comparative impact analysis of simple and mixed index‐based weather derivatives conducted for real individual farms with regard to their hedging effectiveness.

The purpose of this paper is to investigate a novel generalised grey target decision method (GGTDM) with index and weight involving mixed attribute values.

The mixed attribute values are transformed into binary connection numbers and also comprised of two-tuple (determinacy, uncertainty) numbers to fulfil the decision-making task. The proposed method constructs the weight function to convert the mixed attribute-based weights into the certain number-based weights and determines the alternatives ranking by the comprehensive weighted Gini–Simpson indices (CWGSIs).

The result of decision making regarding the numerical example by the proposed approach is somewhat different from that obtained by the reported vector-based method. The reasons for this are threefold: the decision-making bases are different, the target centre indices are determined by different mechanisms and certain number-based weights are calculated in different ways.

The proposed method ranks an alternative based on the Gini–Simpson index, as derived from the viewpoint of measuring the uncertainty (heterogeneity): however, the vector-based GGTDM makes a decision based on proximity, as is the case when measuring the similarities between index vectors.

The proposed approach is admissible to solving mixed attribute-based decision making especially for alternative indices and attribute weights containing both uncertain numbers.

The proposed method provides a new perspective on measuring the difference of alternatives to the target centre via the CWGSI: the CWGSI is obtained by relying on the pseudo-probabilities achieved by the ratios of the alternative indices to the target centre indices. It also builds a weight function converting the mixed attribute-based weights into certain number-based weights. This method provides a framework that should be tested in terms of its effective decision making using real data and an actual problem.

This paper is to propose a novel generalized grey target decision method (GGTDM) with index and weight both containing mixed types of data.

The decision-making steps of the proposed approach are as follows. First, all mixed attribute values of alternatives and weights are transformed into binary connection numbers and also comprised two-tuple (determinacy, uncertainty) numbers. Then, the two-tuple (determinacy, uncertainty) numbers of target center indices are calculated. Next, the certain weights are determined by the Gini–Simpson (G–S) index-based method. Following this, the comprehensive-weighted Kullback–Leibler distances (CWKLDs) of all alternatives and the target center are obtained. Finally, the alternative ranking relies on the CWKLD considering the smaller value as the better option.

The certain weights determined by the improved Gini–Simpson index (IGSI) based method are more accurate in compared with that by the proximity-based method and the weight function method. The discrimination ability of alternatives ranking of the proposed approach is stronger than that of the compared comprehensive-weighted proximity (CWP) based method and comprehensive-weighted Gini–Simpson index (CWGSI) based method.

The proposed method fulfills the decision-making task relying on CWKLD, which solves the uncertain measurement from the viewpoint of entropy.

The proposed approach adopts the IGSI to transform uncertain weights into certain ones and takes the CWKLD as the basis for the decision-making.

Available information for evaluating the possibility of hospitality firm failure in emerging countries is often deficient. Oversampling can compensate for this but can also yield mixed samples, which limit prediction models’ effectiveness. This research aims to provide a feasible approach to handle possible mixed information caused by oversampling.

This paper uses mixed sample modelling (MSM) when evaluating the possibility of firm failure on enlarged hospitality firms. The mixed sample is filtered out with a mixed sample index through control of the noisy parameter and outliner parameter and meta-models are used to build MSM models for hospitality firm failure prediction, with performances compared to traditional models.

The proposed models are helpful in predicting hospitality firm failure in the mixed information situation caused by oversampling, whereas MSM significantly improves the performance of traditional models. Meanwhile, only partial mixed hospitality samples matter in predicting firm failure in both rich- and poor-information situations.

This research is helpful for managers, investors, employees and customers to reduce their hospitality-related risk in the emerging Chinese market. The two-dimensional sample collection strategies, three-step prediction process and five MSM modelling principles are helpful for practice of hospitality firm failure prediction.

This research provides a means of processing mixed hospitality firm samples through the early definition and proposal of MSM, which addresses the ranking information within samples in deficient information environments and improves forecasting accuracy of traditional models. Moreover, it provides empirical evidence for the validation of sample selection and sample pairing strategy in evaluating the possibility of hospitality firm failure.

The purpose of this paper is to present a computational fluid dynamic simulation for the investigation of the particles segregation phenomenon in the gas–solid fluidized beds.

These particles have the same size and different densities. The k–ε model and multiphase particle-in-cell method have been utilized for modeling the turbulent fluid flow and solid particles behaviors, respectively. The coupled equations of the velocity and pressure have been solved by using a combination of SIMPLE and PISO algorithms. After validating the simulation, different mixing indices, with different calculation bases, have been investigated, and it has been found that the Lacey mixing index, which was defined based on statistical concepts, is suitable to investigate the segregation/mixing phenomena of this bed in different conditions. Finally, the effects of parameters such as velocity, particle density ratio, jetsam concentration, and initial arrangement on the segregation/mixing behaviors of the bed have been studied.

The results show that the increase in the superficial gas velocity decreases the mixing index to a minimum value and then increases this index in the beds with mixed initial condition, unlike the beds with separated initial condition. Moreover, an increase in the particle density ratio increases the minimum fluidization velocity of the bed, and also the amount of segregation, and increase in the jetsam concentration increases the value of the mixing index.

A computational fluid dynamics simulation has been presented for the particles segregation phenomenon in the gas–solid fluidized beds.

Research organisations and individual researchers increasingly choose to share their research findings by providing lists of their published works on the World Wide Web. To facilitate the exchange of ideas, the lists often include links to published papers in portable document format (PDF) or Postscript (PS) format. Generally, these publication Web sites are updated regularly to include new works. While manual monitoring of relevant Web sites is tedious, commercial search engines and information monitoring systems are ineffective in finding and tracking scholarly publications. Analyses the characteristics of publication index pages and describes effective automatic extraction techniques that the authors have developed. The authors’ techniques combine lexical and syntactic analyses with heuristics. The proposed techniques have been implemented and tested for more than 14,000 Web pages and achieved consistently high success rates of around 90 percent.

Total factor productivity (TFP) change is an important driver of long-run economic growth in the construction sector. However, examining TFP alone is insufficient to identify the cause of TFP changes. Therefore, this paper employs the infrequently used Geometric Young Index (GYI) and stochastic frontier analysis (SFA) to measure and decompose the TFP Index (TFPI) at the firm-level from 2009 to 2018 based on Malaysian construction firms' data.

To improve the TFPI estimation, normally unobserved environmental variables were included in the GYI-TFPI model. These are the physical operation of the firm (inland versus marine operation) and regional locality (West Malaysia versus East Malaysia). Consequently, the complete components of TFPI (i.e. technological, environmental, managerial, and statistical noise) can be accurately decomposed.

The results reveal that TFP change is affected by technological stagnation and improvements in technical efficiency but a decline in scale-mix efficiency. Moreover, the effect of environmental efficiency on TFP is most profound. In this case, being a marine construction firm and operating in East Malaysia can reduce TFPI by up to 38%. The result, therefore, indicates the need for progressive policies to improve long-term productivity.

Monitoring and evaluating productivity change allows an informed decision to be made by managers/policy makers to improve firms' competitiveness. Incentives and policies to improve innovation, competition, training, removing unnecessary taxes and regulation on outputs (inputs) could enhance the technological, technical and scale-mix of resources. Furthermore, improving public infrastructure, particularly in East Malaysia could improve regionality locality in relation to the environmental index.

This study contributes to knowledge by demonstrating how TFP components can be completely modelled using an aggregator index with good axiomatic properties and SFA. In addition, this paper is the first to apply and include the GYI and environmental variables in modelling construction productivity, which is of crucial importance in formulating appropriate policies.

The purpose of this study is to establish an effective numerical simulation method to describe the flow pattern and optimize the strategy of noncontact mixing induced by alternating Gaussian light inside a nanofluid droplet and analyzing the influencing factors and flow mechanism of fluid mixing inside a droplet.

First, the heat converted by the alternating incident Gaussian light acting on the nanoparticles was considered as the bulk heat source distribution, and the equilibrium equation between the surface tension and the viscous force at the upper boundary force was established; then, the numerical simulation methods for multiple-physical-field coupling was established, and the mixing index was used to quantify the mixing degree inside a droplet. The effects of the incident position of alternating Gaussian light and the height of the droplet on the mixing characteristics inside a droplet were studied. Finally, the nondimensional Marangoni number was used to reveal the flow mechanism of the internal mixing of the droplet.

Noncontact alternating Gaussian light can induce asymmetric vortex motion inside a nanofluid droplet. The incident position of alternating Gaussian light is a significant factor affecting the mixing degree in the droplet. In addition, the heat transfer caused by the surface tension gradient promotes the convection effect, which significantly enhances the mixing of the fluid in the droplet.

This study demonstrates the possibility of the chaotic mixing phenomenon induced by noncontact Gaussian light that occurs within a tiny droplet and provides a feasible method to achieve efficient mixing inside droplets at the microscale.

The purpose of this paper is to describe development of Russian mutual fund (MF) market, to suggest and apply methodology of statistical analysis of management quality, to evaluate profitability and risks of the market. Design/methodology/approach – Statistical research applies: descriptive statistics, correlation analysis, regression analysis, cluster analysis, ratings, transition probability, optimal portfolio theory.

Russian mutual market is growing both in number and in aggregate value of net assets. Profitability and risks of the market are high.

Statistical research was fulfilled on the data for 2004‐2006.

The results of the investigation are useful for investors and for managers of the funds.

The paper represents an attempt of statistical investigation of Russian MF market, its profitability and risks, classification and rating of MFs and analysis of rating stability.

The purpose of this paper is to analyse the performance of the Indian non-life (general) insurance sector in terms of total factor productivity (TFP) over the period 2005–2016.

This study utilises Färe‒Primont index (FPI) to access the change in TFP and its components: technical change, technical efficiency and mix and scale efficiency over the observation period. Moreover, it employs the Mann–Whitney U-test to scrutinise the difference between the public and the private insurers in terms of growth in productivity.

The results reveal that the insurance sector possesses a very low level of TFP. Also, the results divulge an improvement of 11.98 per cent in TFP of the insurance sector at an annual average rate of 12.41 per cent over the observation period. The growth in productivity is mainly attributable to the improvement of 10.81 per cent in the scale‒mix efficiency. The progress in scale‒mix efficiency is mainly the result of improvements in residual scale and residual mix efficiency. The results also show that the privately owned insurers have experienced a high productivity growth rate than the state-owned insurers.

The results hold practical implications for the regulators, policymakers and decision makers of the Indian non-life insurance companies.

This study is the first of its kind to use FPI, which satisfies all economically relevant axioms and tests defined by the index number theory to comprehensively access the change in TFP of the Indian non-life insurance sector.