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In this article the aim is to propose a new form to densify parallelepipeds of R^N by sequences of α‐dense curves with accumulated densities.

This will be done by using a basic α‐densification technique and adding the new concept of sequence of α‐dense curves with accumulated density to improve the resolution of some global optimization problems.

It is found that the new technique based on sequences of α‐dense curves with accumulated densities allows to simplify considerably the process consisting on the exploration of the set of optimizer points of an objective function with feasible set a parallelepiped K of R^N. Indeed, since the sequence of the images of the curves of a sequence of α‐dense curves with accumulated density is expansive, in each new step of the algorithm it is only necessary to explore a residual subset. On the other hand, since the sequence of their densities is decreasing and tends to zero, the convergence of the algorithm is assured.

The results of this new technique of densification by sequences of α‐dense curves with accumulated densities will be applied to densify the feasible set of an objective function which minimizes the quadratic error produced by the adjustment of a model based on a beta probability density function which is largely used in studies on the transition‐time of forest vegetation.

A sequence of α‐dense curves with accumulated density represents an original concept to be added to the set of techniques to optimize a multivariable function by the reduction to only one variable as a new application of α‐dense curves theory to the global optimization.

There has been growing interest in recent years in modelling various poverty‐related issues. However, there have not been many attempts at empirical estimation of best‐fit income distribution functions with an objective of subsequent use in poverty focused models. The purpose of this paper is to fill this gap by empirically estimating best‐fit income distribution functions for different household income groups and computing poverty and inequality indices for Sri Lanka.

The authors empirically estimated a number of popular distribution functions found in the income distribution literature to find the best‐fit income distribution using household income and expenditure survey data for Sri Lanka and subsequently estimated various poverty and inequality measures.

The results show that the income distributions of all low‐income household groups follow the beta general probability distribution. The poverty measures derived using these distributions show that among the different income groups, the estate low‐income group has the highest incidence of poverty, followed by the rural low‐income group.

According to the best of the authors' knowledge, empirical estimation of income distribution functions for South Asia has never been attempted. The results of this study, even though based on Sri Lankan data, will be relevant to most developing countries in South Asia and will be very useful in developing poverty alleviation strategies.

This study aims to investigate the insights of soot formation such as rate of soot coagulation, rate of soot nucleation, rate of soot surface growth and soot surface oxidation in ethylene/hydrogen/nitrogen diffusion jet flame at standard atmospheric conditions, which is very challenging to capture even with highly sophisticated measuring systems such as Laser Induced Incandescence and Planar laser-induced fluorescence. The study also aims to investigate the volume of soot in the flame using soot volume fraction and to understand the global correlation effect in the formation of soot in ethylene/hydrogen/nitrogen diffusion jet flame.

A large eddy simulation (LES) was performed using box filtered subgrid-scale tensor. A filtered and residual component of the governing equations such as continuity, momentum, energy and species are resolved and modeled, respectively. All the filtered and residual components are numerically solved using the ILU method by considering PISO pressure–velocity solver. All the hyperbolic flux uses the QUICK algorithm, and an elliptic flux uses SOU to evaluate face values. In all the cases, Courant–Friedrichs–Lewy (CFL) conditions are maintained unity.

The findings are as follows: soot volume fraction (SVF) as a function of a flame-normalized length for three different Reynolds number configurations (Re = 15,000, Re = 8,000 and Re = 5,000) using LES; soot gas phase and particulate phase insights such as rate of soot nucleation, rate of soot coagulation, rate of soot surface growth and soot surface oxidation for three different Reynolds number configurations (Re = 15,000, Re = 8,000 and Re = 5,000); and soot global correction using total soot volume in the flame volume as a function of Reynolds number and Froude number.

The originality of this study includes the following: coupling LES turbulent model with chemical equilibrium diffusion combustion conjunction with semi-empirical Brookes Moss Hall (BMH) soot model by choosing C6H6 as a soot precursor kinetic pathway; insights of soot formations such as rate of soot nucleation, soot coagulation rate, soot surface growth rate and soot oxidation rate for ethylene/hydrogen/nitrogen co-flow flame; and SVF and its insights study for three inlet fuel port configurations having the three different Reynolds number (Re = 15,000, Re = 8,000 and Re = 5,000).

One of the major concerns of the constrained-surface stereolithography (SLA) process is that the built-up part may break because of the force resulting from the pulling-up process. This resultant force may become significant if the interface mechanism between the two contact surfaces (i.e. newly cured layer and the bottom of the resin vat) produces a strong bonding between them. The purpose of this paper is to characterize the separation process between the cured part and the resin vat by adopting an appropriate and simple mechanics-based model that can be used to probe the pulling-up process.

In this paper, the time-histories of the pulling-up forces are measured using FlexiForce® force sensors. The experimental data are analyzed and used to estimate the constitutive parameters of the separation mechanism. Here, the separation mechanism is modeled based on the concept of cohesive zone model (CZM) that is well-studied in the field of fracture mechanics. By using the experimentally measured pulling-up force, this paper proposes a very efficient inverse technique to estimate the constitutive parameters for the CZM. The constitutive laws for the CZM facilitate in relating the separation force at the interface between the cured part and the resin vat in terms of the pulling-up velocity. Unlike work proposed earlier, computationally expensive full-scale finite element runs are not essential in the current work while estimating the required parameters of the constitutive laws. Instead, mechanics-based computationally efficient surrogate model is proposed to readily estimate these constitutive parameters.

Two constitutive laws are compared on the basis of their predictions of the separation force profile. Excellent match is obtained between the measured and the predicted separation force profiles.

This paper selects a suitable mechanics-based model that can characterize the separation process and proposes a computationally efficient scheme to estimate the required constitutive parameters. The proposed scheme can be used to reliably predict the separation force for the constrained-surface SLA process, leading to improved productivity and reliability of the SLA processes in fabricating the built-up parts.

This paper aims to enhance the predictability of stock returns. Existing studies have used investor sentiment to forecast stock returns. However, it is unclear whether high-frequency intraday investor sentiment can enhance the forecasting performance of low-frequency stock returns.

Thus, we employ the MIDAS model and the high-frequency intraday sentiment extracted from the Internet stock forum to forecast Chinese A-shares returns at daily frequency.

The results illustrate that high-frequency sentiment data are better than daily sentiment data in predicting daily stock returns, and the sentiment in non-trading hours has been proved superior to those in trading hours.

First, our study adds to the growing literature on investor sentiment. We are the first to construct a proxy for high-frequency investor sentiment using intraday postings collected from Chinese Internet stock forum. Second, we confirm that sentiment in non-trading hours has a stronger predictive ability than those in trading hours. Third, we also contribute to the performance comparison of MIDAS-class models. The good performance of U-MIDAS is confirmed in our empirical applications.

Bayesian networks (BNs) are implemented for monitoring a process via statistical process control (SPC) where attribute data are available on output from the system. The paper aims to discuss this issue.

The BN provides a graphical and numerical tool to help a manager understand the effect of sample observations on the probability that the process is out-of-control and requires investigation. The parameters for the BN SPC model are statistically designed to minimize the out-of-control average run length (ARL) of the process at a specified in-control ARL and sample size.

The BN model outperforms adaptive np control charts in all experiments, except for some cases where only a large change in the proportion of sample defects is relevant. The BN is particularly useful when small sample sizes are available and when managers need to detect small changes in the proportion of defects produced by the process.

The BN model is statistically designed and parameters are chosen to minimize out-of-control ARL. Future advancements will address the economic design of BNs for SPC with attribute data.

The BNs allow qualitative knowledge to be combined with sample data, and the average percentage of defects can be modeled as a continuous random variable. The framework of the BN easily permits classification of the system operation into two or more states, so diagnostic analysis can be performed simultaneously with statistical inference.

The purpose of this paper is to investigate the development process of the fire whirl in the fixed-frame facility and focus on the impacts of the fire whirl’s vortex core on the formation and flame structure of the fire whirl.

The complex turbulent reacting flame surface is captured by the large eddy simulation turbulence closure coupled with two sub-grid scale (SGS) kinetic schemes (i.e. the chemistry equilibrium and steady diffusion flamelet). Numerical predictions are validated thoroughly against the measurements by Lei et al. (2015) with excellent agreements. A double maximum tangential velocity refinement approach is proposed to quantify the vortex cores’ instantaneous location and region, addressing the missing definition in other studies.

The numerical results show that the transition process of the fire whirl is dominated by the vortex core movement, which is related to the centripetal force. The unsteadiness of the fully developed fire whirl was found depending on the instantaneous fluctuation of heat release rate. The steady diffusion flamelet scheme is essential to capture the instantaneous fluctuation. Furthermore, the axial velocity inside the vortex core is the key to determining the state of fire whirl.

Due to intensive interactions between buoyant fires and ambient rotating flow, the on-set and formation of fire whirl still remain largely elusive. This paper focused on the transition process of fire whirl between different development stages. This paper provides insights into the transition process from the inclined flame to the fire whirls based on the centripetal force.

This paper presented and compared two SGS kinetic schemes to resolve the fire whirl development process and the unsteadiness of its vortical structures. The modelling framework addresses the shortcoming of previous numerical studies where RANS turbulence closure and simplified combustion kinetics was adopted. Numerical results also revealed the fire whirl transition process and its relationship to centripetal force.

The purpose of this paper is to formulate a structured approach to design an annular diffusion flame combustion chamber for use in the development of a 1,400 kW range aero turbo shaft engine. The purpose is extended to perform numerical combustion modeling by solving transient Favre Averaged Navier Stokes equations using realizable two equation k-e turbulence model and Discrete Ordinate radiation model. The presumed shape β-Probability Density Function (β-PDF) is used for turbulence chemistry interaction. The experiments are conducted on the real engine to validate the combustion chamber performance.

The combustor geometry is designed using the reference area method and semi-empirical correlations. The three dimensional combustor model is made using a commercial software. The numerical modeling of the combustion process is performed by following Eulerian approach. The functional testing of combustor was conducted to evaluate the performance.

The results obtained by the numerical modeling provide a detailed understanding of the combustor internal flow dynamics. The transient flame structures and streamline plots are presented. The velocity profiles obtained at different locations along the combustor by numerical modeling mostly go in-line with the previously published research works. The combustor exit temperature obtained by numerical modeling and experiment are found to be within the acceptable limit. These results form the basis of understanding the design procedure and opens-up avenues for further developments.

Internal flow and combustion dynamics obtained from numerical simulation are not experimented owing to non-availability of adequate research facilities.

This study contributes toward the understanding of basic procedures and firsthand experience in the design aspects of combustors for aero-engine applications. This work also highlights one of the efficient, faster and economical aero gas turbine annular diffusion flame combustion chamber design and development.

The main novelty in this work is the incorporation of scoops in the dilution zone of the numerical model of combustion chamber to augment the effectiveness of cooling of combustion products to obtain the desired combustor exit temperature. The use of polyhedral cells for computational domain discretization in combustion modeling for aero engine application helps in achieving faster convergence and reliable predictions. The methodology and procedures presented in this work provide a basic understanding of the design aspects to the beginners working in the gas turbine combustors particularly meant for turbo shaft engines applications.

Users often struggle to select choosing among similar online services. To help them make informed decisions, it is important to establish a service reputation measurement mechanism. User-provided feedback ratings serve as a primary source of information for this mechanism, and ensuring the credibility of user feedback is crucial for a reliable reputation measurement. Most of the previous studies use passive detection to identify false feedback without creating incentives for honest reporting. Therefore, this study aims to develop a reputation measure for online services that can provide incentives for users to report honestly.

In this paper, the authors present a method that uses a peer prediction mechanism to evaluate user credibility, which evaluates users’ credibility with their reports by applying the strictly proper scoring rule. Considering the heterogeneity among users, the authors measure user similarity, identify similar users as peers to assess credibility and calculate service reputation using an improved expectation-maximization algorithm based on user credibility.

Theoretical analysis and experimental results verify that the proposed method motivates truthful reporting, effectively identifies malicious users and achieves high service rating accuracy.

The proposed method has significant practical value in evaluating the authenticity of user feedback and promoting honest reporting.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.