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I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

Sharding of blockchains consists of partitioning a blockchain network into several sub-networks called “shards,” each shard processing and storing disjoint sets of transactions in parallel. Sharding has recently been applied to public blockchains to improve scalability through parallelism. The throughput of sharded blockchain is optimized when the workload among the shards is approximately the same. The purpose of this paper is to investigate the problem of balancing workload of account-based blockchains such as Ethereum.

Two known consensus-based distributed load-balancing algorithms have been adapted to sharded blockchains. These algorithms migrate accounts across shards to balance transaction processing times. Two methods to predict transaction processing times are proposed.

The authors identify some challenging aspects for solving the load-balancing problem in sharded blockchains. Experiments conducted with Ethereum transactions show that the two load-balancing algorithms are challenged by accounts often created to process a single transaction to optimize anonymity, while existing accounts sparsely generate transactions.

Tests in this work have been conducted on transactions originating from a blockchain platform rather than using artificially generated data distributions. They show the specificity of the load-balancing problem for sharded blockchains, which were hidden in artificial data sets.

A three‐dimensional thermal finite element model including the effect of the powder‐to‐solid transition has been developed to investigate the transient temperature distribution and effects of substrate preheating during laser densification of dental powder bed for the layer‐by‐layer fabrication. The model encompasses the effects of the temperature‐ and porosity‐dependent thermal conduction and radiation as well as the temperature‐dependent natural convection. Substrate preheating is found to be important in preventing the formation of cracks in the dental porcelain body during laser densification. The simulation results are found to match the experiments very well.

A 3D finite element model was developed that simulates selective area laser deposition vapor infiltration (SALDVI) of silicon carbide. The model predicts the laser input power history needed to maintain constant surface temperature and the distribution of vapor deposited SiC within the powder bed as well as on the surface of the powder bed. The model considers a moving Gaussian distribution laser beam, temperature‐ and pore‐dependent thermal conductivity, specific heat and temperature‐dependent deposition rate. Furthermore, the model also includes closed‐loop control of the laser power to achieve a desired target processing temperature on the surface of the power bed. Effects of laser scanning rates have been investigated. The simulated solid fraction and SALD distributions are also consistent in the trend with the experimental data.

Residual thermal stresses and distortion are frequently present in the parts built using a layer‐by‐layer solid freeform fabrication techniques assisted with a moving laser source. This study uses finite element analysis to investigate the effect of laser scanning patterns on residual thermal stresses and distortion. It is shown that the out‐of‐plane distortion of a layer, processed by a moving laser beam can be minimized with a proper selection of the laser scanning pattern. A scanning pattern having changes in its scanning direction frequently by 90° at every turn can lead to the cancellation of concave upward and downward distortions. As a result of this cancellation, very small distortion is present in the laser processed plate. It is also found that distortion is mainly caused by transient thermal stresses rather than residual thermal stresses.

This paper considers a maintenance problem for a queueing system. The arrival rate decreases as the server state becomes worse. The system can be recovered by maintenance, though the system is closed until the end of maintenance. A semi‐Markov decision process is formulated to find the optimal policy that maximizes the total expected discounted income from customers.

This paper aimed to examine the relationship between 360° assessment of leadership derailment factors and leadership effectiveness, differences across position levels, and impact of self‐other agreement.

The data were from an archive of 360° feedback (N=523). Boss ratings of leadership effectiveness were regressed on leadership derailment factors as rated by other rater sources (e.g. peers and direct reports). Polynomial regressions were conducted to examine the impact of self‐other agreement.

As hypothesized, derailment factors had statistically significant negative correlations with leadership effectiveness. Higher‐level managers were rated higher on derailment factors than lower‐level managers. In‐agreement high ratings of derailment factors (i.e. rated high by both self and others) were associated with lower effectiveness than in‐agreement low ratings (i.e. rated low by both self and others). Self under‐ratings of derailment factors (i.e. self ratings lower than others’ ratings) were related to lower effectiveness than self over‐ratings (i.e. self ratings higher than others’ ratings). It also was found that self ratings were less accurate than ratings from other rater sources.

Leadership derailment induces significant direct as well as indirect costs to organizations. The 360° feedback process can be used to help managers enhance their self‐awareness of derailment potential. Findings of this study can be used to help interpret 360° assessment results.

Past research on 360° feedback has focused primarily on positive leadership characteristics. This study represents one of the few in the literature that empirically has examined the assessment of negative leadership characteristics in 360° feedback.

The meshfree node-based smoothed point interpolation method (NS-PIM) is extended to the forward and inversion analysis of a high gravelly soil core rock-fill dam during construction periods.

As one member of the meshfree methods, the NS-PIM has the advantages of “softer” stiffness and adaptability to large deformations which is quite indispensable for the stability analysis of rock-fill dams. In this work, the present method contains a reconstruction procedure to deal with the existence or nonexistence of the construction layers. After verifying the validity of the NS-PIM method for nonlinear elastic model during construction period, the convergence features of the NS-PIM and FEM methods are further investigated with different mesh schemes. Furthermore, the NS-PIM and FEM methods are applied for the forward analysis of a high gravelly soil core rock-fill dam and the convergence features under complex stress conditions are also studied using the rock-fill dam model. Finally, the NS-PIM method is used to calculate the Duncan–Chang parameters of the deep overburden under the high gravelly soil core rock-fill dam based on the back-propagation neural network method.

The results show that: the NS-PIM solution for construction analysis still possesses the property of upper bound solution even under complex stress conditions and can provide comparatively more conservative results for safety evaluation. Furthermore, it can be used to evaluate the accuracy of results and mesh quality together with the FEM solution which has the property of lower bound solution; the inversion analysis in this work provides a set of material parameters for the deep overburden under high rock-fill dam during construction period and the calculated results show good agreement with the measured displacement values and it is feasible to apply the NS-PIM to the forward and inversion analysis of high rock-fill dams on deep overburden during construction periods.

In further study, the feasibility of three-dimensional problems, elastic–plastic problems, contact problems and multipoint inversion can still be probed in the NS-PIM solution for the forward and inversion analysis of high rock-fill dams on deep overburden.

This paper introduced a method for the forward and inversion analysis of high rock-fill dams during construction period using the NS-PIM solution. The property of upper bound solution ensures that the NS-PIM can provide more conservative results for safety evaluation. The inversion analysis in this work provides a set of material parameters for the deep overburden under high rock-fill dam during construction periods.

First, the analysis from forward to inversion for high rock-fill dams during construction period using the NS-PIM solution is accomplished in this work. A procedure dealing with the existence or nonexistence of the construction layers is also developed for the construction analysis. Second, it is confirmed in this work that the NS-PIM still possesses the property of upper bound solution even under complex stress conditions (the forward analysis of high rock-fill dams during construction period). Thus, more conservative results can be provided for safety evaluation. Furthermore, it can be used to evaluate the accuracy of results and mesh quality together with the FEM solution which has the property of lower bound solution. Third, the calculated material parameters of the deep overburden in this work can be used for further studies of the high rock-fill dam.

The problem of motif discovery has become a significant challenge in the era of big data where there are hundreds of genomes requiring annotations. The importance of motifs has led many researchers to develop different tools and algorithms for finding them. The purpose of this paper is to propose a new algorithm to increase the speed and accuracy of the motif discovering process, which is the main drawback of motif discovery algorithms.

All motifs are sorted in a tree-based indexing structure where each motif is created from a combination of nucleotides: ‘A’, ‘C’, ‘T’ and ‘G’. The full motif can be discovered by extending the search around 4-mer nucleotides in both directions, left and right. Resultant motifs would be identical or degenerated with various lengths.

The developed implementation discovers conserved string motifs in DNA without having prior information about the motifs. Even for a large data set that contains millions of nucleotides and thousands of very long sequences, the entire process is completed in a few seconds.

Experimental results demonstrate the efficiency of the proposed implementation; as for a real-sequence of 1,270,000 nucleotides spread into 2,000 samples, it takes 5.9 s to complete the overall discovering process when the code ran on an Intel Core i7-6700 @ 3.4 GHz machine and 26.7 s when running on an Intel Xeon x5670 @ 2.93 GHz machine. In addition, the authors have improved computational performance by parallelizing the implementation to run on multi-core machines using the OpenMP framework. The speedup achieved by parallelizing the implementation is scalable and proportional to the number of processors with a high efficiency that is close to 100%.

Elevated temperature material properties are essential in predicting structural member's behavior in high-temperature exposures such as fire. Even though experimental methodologies are available to determine these properties, advanced equipment with high costs is required to perform those tests. Therefore, performing those experiments frequently is not feasible, and the development of numerical techniques is beneficial. A numerical technique is proposed in this study to determine the temperature-dependent thermal properties of the material using the fire test results based on the Artificial Neural Network (ANN)-based Finite Element (FE) model.

An ANN-based FE model was developed in the Matlab program to determine the elevated temperature thermal diffusivity, thermal conductivity and the product of specific heat and density of a material. The temperature distribution obtained from fire tests is fed to the ANN-based FE model and material properties are predicted to match the temperature distribution.

Elevated temperature thermal properties of normal-weight concrete (NWC), gypsum plasterboard and lightweight concrete were predicted using the developed model, and good agreement was observed with the actual material properties measured experimentally. The developed method could be utilized to determine any materials' elevated temperature material properties numerically with the adequate temperature distribution data obtained during a fire or heat transfer test.

Temperature-dependent material properties are important in predicting the behavior of structural elements exposed to fire. This research study developed a numerical technique utilizing ANN theories to determine elevated temperature thermal diffusivity, thermal conductivity and product of specific heat and density. Experimental methods are available to evaluate the material properties at high temperatures. However, these testing equipment are expensive and sophisticated; therefore, these equipment are not popular in laboratories causing a lack of high-temperature material properties for novel materials. However conducting a fire test to evaluate fire performance of any novel material is the common practice in the industry. ANN-based FE model developed in this study could utilize those fire testing results of the structural member (temperature distribution of the member throughout the fire tests) to predict the material's thermal properties.