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It has long been challenged that the distributions of empirical returns do not follow the log-normal distribution upon which many celebrated results of finance are based including the Black–Scholes Option-Pricing model. Borland (2002) succeeds in obtaining alternate closed form solutions for European options based on Tsallis distribution, which allow for statistical feedback as a model of the underlying stock returns. Motivated by this, we simulate two distinct time series based on initial data from NIFTY daily close values, one based on the Gaussian return distribution and the other on non-Gaussian distribution. Using techniques of non-linear dynamics, we examine the underlying dynamic characteristics of both the simulated time series and compare them with the characteristics of actual data. Our findings give a definite edge to the non-Gaussian model over the Gaussian one.

The malfunction variables of power stations are related to the areas of weather, physical structure, control and load behaviour. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behaviour into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms are used to perform the Short-term estimation. The environment, the operation and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a data set. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, for any future power grid, there is a testbed ready to estimate the future failures.

Changing ecosystems with digitalisation, the value of information affects all sub-ecosystems, and due to societal changes, management approaches have started to be implemented. This new administration digitalisation, which is the basis of their strategy, made it mandatory for them to adapt to technology. This article details the theoretical basis of the business ecosystem to investigate the integration of human-computer interaction (HCI) technology and platform business ecosystem into the artificial intelligence (AI) environment and to create practicality for the intellectual development of platform business strategy ecosystem management. Then, in the context of textile e-commerce, the intelligent clothing ecosystem is recommended, and the structure of the smart clothing ecosystem is created. A flexible production planning model with multiple constraints and maximum cost savings has been developed within system constraints and objective function scope. The standard genetic algorithm after optimisation was used to solve the aim of model function (based on double-layer integer coding). In the context of the parameters of the algorithm and textile layout, the algorithm was calculated with the MATLAB simulation tool, and then a textile experiment was carried out to solve the problem of system management. Finally, the audio, interactive customer survey results were analysed with structural equation modelling, and a human-computer interactive customer trust model was proposed. The results indicate that the calculation results of the objective function with 6 order sizes and 40 order sizes tend to be good and that the 6th order, respectively, tends to be good. The Gantt chart, calculated by the optimisation algorithm, can show the equipment selection strategy, start time and end time of each order in different workshops. The combination of each operation planning strategy is the production planning chart that corresponds to the order. It is thought that this study, AI-oriented HCI technology for future studies, can solve the strategic management problem of the platform business ecosystem, realise the efficient ecological functioning of the system and benefit businesses in this context.

Nowadays, the increase in the capacity of batteries has laid the foundations for a broader diffusion of electric mobility. However, electric mobility is causing a growing electricity demand as well as the need to increase the diffusion of suitable charging stations. Within these last challenges, drawing on the recent literature, this chapter provides a critical and wide-ranging review of papers dealing with the formulation of the problem of the localisation of electric vehicle (EV) charging points. This problem is approached considering the electric charging infrastructure technologies, localisation criteria and related methodologies. This review shows how the ‘electric mobility revolution’ applies the technological innovations provided by the energy supply systems, and the location of these systems within the urban contexts. Since the technological innovations have different options, achieving an international standard of charging systems is still far away. Moreover, as there are several criteria, parameters and methodologies, and some analytical approaches for the localisation of electric vehicle charging points, the formulation of the ‘localisation’ problem should require the application of multi-criteria analysis to be addressed. Finally, the results show that there is no consensus on technologies, criteria, and methodologies to be adopted. Therefore, this wide-ranging analysis of the literature would be useful to support possible benchmarking and systematisation accordingly.

Climate control needs have reached momentum. While scientists call for stabilizing climate and regulators structure climate change mitigation and adaptation efforts around the globe, economists are concerned with finding proper and fair financing mechanisms. In an overlapping-generations framework, Sachs (2014) solves the climate change predicament that seems to pit today’s against future generations. Sachs (2014) proposes that the current generation mitigates climate change financed through bonds to remain financially as well-off as without mitigation while improving environmental well-being of future generations through ensured climate stability. This intergenerational tax-and-transfer policy turns climate change mitigation into a Pareto improving strategy. Sachs’ (2014) discrete model is integrated in contemporary growth and resource theories. The following article analyzes how climate bonds can be phased-in, in a model for a socially optimal solution and a laissez-faire economy. Optimal trajectories are derived partially analytically (e.g., by using the Pontryagin maximum principle to define the optimal equilibrium), partially data driven (e.g., by the use of modern big market data), and partially by using novel cutting-edge methods – for example, nonlinear model predictive control (NMPC), which solves complex dynamic optimization problems with different nonlinearities for infinite and finite decision horizons. NMPC will be programed with terminal condition in order to determine appropriate numeric solutions converging to some optimal equilibria. The analysis tests if the climate change debt adjusted growth model stays within the bounds of a sustainable fiscal policy by employing NMPC, which solves complex dynamic systems with different nonlinearities.

Global warming is a process that takes place 11,500 years after the end of the last Ice Age. The main identified reason is the increased emissions of greenhouse gases (GHGs). Since the nineteenth century, GHG evolution has recorded a quantum leap from the previous linear development. Human is the main factor behind this evolution, through industrialization and the exponential increase of population. Based on these, the chapter’s primary goal was to highlight an original method of predicting the future evolution of GHG emissions in the domains of Energy (including Transportation), Industry Processes and Product Use, Agriculture, and Waste Management. The novelty of the research consisted of testing several variants of functions (power, exponential, inverse trigonometric) to identify, from a group of variants. This optimal function would generate those predictions, which are closest to the real values. The causes that create GHG emissions in each of the four domains were the foundation for the analysis. This chapter focuses on two main subjects: first, the identification of a smooth function to predict the evolution of GHG emissions, and second, the function’s use to estimate the projections of GHG emissions in the coming years for the four domains: Energy (including Transportation), Industry Processes and Product Use, Agriculture, and Waste Management. An observation was that the weights of these four domains remain relatively the same despite the reductions in the total GHG emissions.

The purpose of this chapter is to outline new methodological developments in business valuation, with particular attention to how those developments are being used in litigation involving lost profits and the value of operating businesses. In addition to methodological developments, the chapter also includes a discussion of recent legal developments, particularly selected cases that affect the use and standards for business valuation techniques within litigation settings. Finally, the chapter includes a mathematical appendix.

This paper aims to provide decision support for precast concrete contractors about both precast concrete supply chain strategies and construction configurations.

This paper proposes a simulation-based optimisation for supply chain and construction (SOSC) during the planning phase of PC building projects. The discrete event simulation is used to capture the characteristics of supply chain and construction processes, and calculate construction objectives under different plans. Particle swarm optimisation is combined with simulation to find optimal supply chain strategies and construction configurations.

The efficiency of SOSC is compared with the parametric simulation approach. Over 70 per cent of time and effort used to simulate and compare alternative plans is saved owing to SOSC.

Building simulation model costs a lot of time and effort. The data requirement of the proposed method is high.

The proposed SOSC approach can provide decision support for PC contractors by optimising supply chain strategies and construction configurations.

This paper has two contributions: one is in providing a decision support tool SOSC to optimise both supply chain strategies and construction configurations, while the other is in building a prototype of SOSC and testing it in a case study.