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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.

Computational modeling brings unique and critical contributions to behavioral and social research. Computational modeling can transform logic models into dynamic models and helps formalize complex theory construction. Computational modeling opens new vistas in data designs and data analysis. Computational models allow us to explore systems not in dynamic equilibrium, to understand the implications of different initialization conditions, to examine complex system synergies through process decomposition, and to provide policy-related tools such as counterfactual simulations.

In this paper we show, by means of an example of its application to the problem of house price forecasting, an approach to attribute selection and dependence modelling utilising the Gamma Test (GT), a non-linear analysis algorithm that is described. The GT is employed in a two-stage process: first the GT drives a Genetic Algorithm (GA) to select a useful subset of features from a large dataset that we develop from eight economic statistical series of historical measures that may impact upon house price movement. Next we generate a predictive model utilising an Artificial Neural Network (ANN) trained to the Mean Squared Error (MSE) estimated by the GT, which accurately forecasts changes in the House Price Index (HPI). We present a background to the problem domain and demonstrate, based on results of this methodology, that the GT was of great utility in facilitating a GA based approach to extracting a sound predictive model from a large number of inputs in a data-point sparse real-world application.

One crucial but sometimes overlooked fact regarding the difference between observation in the cross-section and observation over time must be stated before proceeding further. Tempting though it is to draw conclusions about the dynamics of a process from cross-sectional observations taken as a snapshot of that process, it is a fallacious practice except under a very precise condition that is highly unlikely to obtain in processes of interest to the social scientist. That condition is known as ergodicity.

In the 1990s, individuals aged 18–64 were eligible for disability insurance, if their work capacity was reduced by at least 25 percent (50 percent before 1993). In the beginning of the period, before 1991, disability insurance could also be granted for labor market reasons (i.e., if unemployed had been compensated long enough to exhaust their benefits – obtained benefits for 300 days). This possibility was gradually phased out after 1991. In 1995, the enforcement of the rules was tightened. When evaluating applications for disability pensions, local insurance offices now had to request a medical certificate and a work-related test of the applicant's degree of work capacity. Local offices also had to consult the applicant's employer, physician, or other qualified personnel, and even pay personal visits to the applicant. The possibilities for rehabilitating the applicant should also be investigated. From 1997, work incapacity should be evaluated in relation to all possible employment opportunities. Potential income changes resulting from changes in employment should not affect the evaluation⁴ (National Social Insurance Board, 2005).

Sustainable urban development implies a process by which sustainability can be attained, emphasizing improvement, progress and positive change, incorporating both environmental and social dimensions. Thus, sustainable urban development highlights the need to reform the market mechanism to achieve environmental goals with the attainment of a balance with social and economic considerations. The large and emerging economies of BRICS have experienced fast-paced urbanization process in recent decades. Along with it the problem of environmental degradation occurs as fast urbanization requires more and more energy consumption as well as carbon emission. Here comes the problem of sustainable urbanization which is a matter of serious concern to deal with. In this chapter, five BRICS nations have taken up to see their respective progress in the fields of per capita energy consumption and carbon emission in their urban areas. To show this it investigates the relationship between urbanization, energy consumption, and CO₂ emissions over the period 1992–2014, using a panel data model. The relative potentials of the countries to reduce respective CO₂ emissions are also analyzed. The results indicate that per capita CO₂ emissions are characterized by conspicuous regional imbalances during the study period. Nations are found to present a long run bidirectional positive relationship, the importance of which is found to vary between the respective regions of the countries.