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This chapter extends the work of Baltagi, Bresson, Chaturvedi, and Lacroix (2018) to the popular dynamic panel data model. The authors investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, the authors consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner’s (1986) g-priors for the variance–covariance matrices. The authors propose a general “toolbox” for a wide range of specifications which includes the dynamic panel model with random effects, with cross-correlated effects à la Chamberlain, for the Hausman–Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using a Monte Carlo simulation study, the authors compare the finite sample properties of the proposed estimator to those of standard classical estimators. The chapter contributes to the dynamic panel data literature by proposing a general robust Bayesian framework which encompasses the conventional frequentist specifications and their associated estimation methods as special cases.

The purpose of this paper is to address both the evolutionary and control aspects associated with the management of artificial superintelligence. Through empirical analysis, the authors examine the diffusion pattern of those high technologies that can be considered as forerunners to the adoption of artificial superintelligence (ASI).

The evolutionary perspective is divided into three parts, based on major developments in this area, namely, robotics, automation and artificial intelligence (AI). The authors then provide several dynamic models of the possible future evolution of superintelligence. These include diffusion modeling, predator–prey models and hostility models. The problem of control in superintelligence is reviewed next, where the authors discuss Asimov’s Laws and IEEE initiative. The authors also provide an empirical analysis of the application of diffusion modeling to three technologies from the industries of manufacturing, communication and energy, which can be considered as potential precursors to the evolution of the field of ASI. The authors conclude with a case study illustrating emerging solutions in the form of long-term social experiments to address the problem of control in superintelligence.

The results from the empirical analysis of the manufacturing, communication and energy sectors suggest that the technology diffusion model fits well with the data of robotics, telecom and solar installations till date. The results suggest a gradual diffusion process, like any other high technology. Thus, there appears to be no threat of “existential catastrophe” (Bostrom, 2014). The case study indicates that any future threat can be pre-empted by some long-term social measures.

This paper contributes to the emerging stream of artificial superintelligence. As humanity comes closer to grappling with the important question of the management and control of this technology for the future, it is important that modeling efforts be made to understand the extant perspective of the development of the high-technology diffusion. Presently, there are relatively few such efforts available in the literature.

India began gas imports since 2004 through liquified natural gas (LNG) route. Imports through trans‐country gas pipelines could help in bringing gas directly into the densely populated Northern part of India, which are far from domestic gas resources as well as coastal LNG terminals. The purpose of this paper is to report scenarios, which quantify the impacts for India of regional cooperation to materialize trans‐country pipelines. The analysis covers time period from 2005 to 2030.

The long‐term energy system model ANSWER‐MARKAL is used for the analysis.

Trans‐country pipelines could deliver direct economic benefit of US$310 billion for the period 2010‐2030. Besides these, there are positive externalities in terms of lower greenhouse gas emissions and improved local environment, and enhanced energy security. However, the benefits are sensitive to global gas prices as higher gas prices would reduce the demand for gas and also the positive externalities from using gas.

Trans‐country pipelines are of great importance to India as they add 0.4 per cent to gross domestic product over the period besides yielding positive environmental externalities and improved energy security.

Quantification of benefits from trans‐country pipeline proposals till 2030.

This paper aims to present a hierarchical multiscale model to evaluate the effect of fused deposition modeling (FDM) process parameters on mechanical properties. Asymptotic homogenization mathematical theory is developed into two scales (micro and macro scales) to compute the effective elastic and shear modulus of the printed parts. Four parameters, namely, raster orientation, layer height, build orientation and porosity are studied.

The representative volume elements (RVEs) are generated by mimicking the microstructure of the printed parts. The RVEs subjected to periodic boundary conditions were solved using finite element. The experimental characterization according to ASTM D638 was conducted to validate the computational modeling results.

The computational model reports reduction (E1, ∼>38%) and (G12, ∼>50%) when porosity increased. The elastic modulus increases (1.31%–47.68%) increasing the orthotropic behavior in parts. Quasi-solids parts (100% infill) possess 10.71% voids. A reduction of 11.5% and 16.5% in elastic modulus with layer height is reported. In total, 45–450 oriented parts were highly orthotropic, and 0–00 parts were strongest. The order of parameters affecting the mechanical properties is porosity > layer height > raster orientation > build orientation.

This study adds value to the state-of-the-art terms of construction of RVEs using slicing software, discarding the necessity of image processing and study of porosity in FDM parts, reporting that the infill density is not the only measure of porosity in these parts.