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The first book to address the underlying premises of systems integration and how to exposit them into a practical and productive manner, this book prepares systems managers and systems engineers to consider their decisions in light of systems integration metrics. The book addresses two questions: Is there a way to express the interplay of human actions and the result of system interactions of a product with its environment, and are there methods that combine to improve the integration of systems? The systems integration theory and integration frameworks proposed in the book tie General Systems Theory with practice.

With the burgeoning of remote sensing and space technology, multi-satellite collaborative mission planning, which is the key to achieving efficient Earth observation, has become increasingly intricate due to the expanding complexity and volume of observation missions. Addressing the multi-satellite collaborative mission planning problem, which is characterized by its two-stage decision-making process involving mission assignment and resource scheduling, this study investigates a comprehensive joint decision making that encompasses both mission assignment and resource scheduling and comprehensively optimizes the mission completion rate, the mission profit rate, and the satellite resource utilization rate. Considering the interaction of these decisions, we formulate the problem as a bilevel programming model from a game-theoretic perspective and propose a nested bilevel improved genetic algorithm (NBIGA) for its solution. Simulation experiments substantiate the applicability of the bilevel programming model in joint decision making for the stages of mission assignment and resource scheduling in multi-satellite collaborative mission planning, as well as the robustness of the NBIGA. A comparative analysis with the nested bilevel genetic algorithm (NBGA) confirms that the algorithm proposed in this study can achieve superior optimization outcomes and higher solving efficiency.

In regional climate modelling, it is well known that domains should be neither too large to avoid a large departure from the driving data, nor too small to provide a sufficient distance from the lateral inflow boundary to allow the full development of the small-scale (SS) features permitted by the finer resolution. Although most practitioners of dynamical downscaling are well aware that the jump of resolution between the lateral boundary condition (LBC) driving data and the nested regional climate model affects the simulated climate, this issue has not been fully investigated. In principle, as the jump of resolution becomes larger, the region of interest in the limited-area domain should be located further away from the lateral inflow boundary to allow the full development of the SS features. A careless choice of domain might result in a suboptimal use of the full finer resolution potential to develop fine-scale features. To address this issue, regional climate model (RCM) simulations using various resolution driving data are compared following the perfect-prognostic Big-Brother protocol. Several experiments were carried out to evaluate the width of the spin-up region (i.e. the distance between the lateral inflow boundary and the domain of interest required for the full development of SS transient eddies) as a function of the RCM and LBC resolutions, as well as the resolution jump. The spin-up distance turns out to be a function of the LBC resolution only, independent of the RCM resolution. When varying the RCM resolution for a given resolution jump, it is found that the spin-up distance corresponds to a fixed number of RCM grid points that is a function of resolution jump only. These findings can serve a useful purpose to guide the choice of domain and RCM configuration for an optimal development of the small scales allowed by the increased resolution of the nested model.

Environmental sustainability is a common requirement on the development of various real-world systems, especially on road transportation systems. Motorized vehicles generate a large amount of harmful emissions, which have adverse effects to the environment and human health. Environmental sustainability requires more promotions of ‘ go - green ’ transportation modes such as public transit and bicycle to realize the increasing travel demands while keeping the environmental expenses low. In this paper, we make use of recent advances in discrete choice modeling to develop equivalent mathematical programming formulations for the combined modal split and traffic assignment (CMSTA) problem that explicitly considers mode and route similarities under congested networks. Specifically, a nested logit model is adopted to model the modal split problem by accounting for mode similarity among the available modes, and a cross-nested logit model is used to account for route overlapping in the traffic assignment problem. This new CMSTA model has the potential to enhance the behavioral modeling of travelers’ mode shift between private motorized mode and ‘ go - green ’ modes as well as their mode-specific route choices, and to assist in quantitatively evaluating the effectiveness of different ‘go-green’ promotion policies.

Computational thinking (CT) is a multidimensional term that encompasses a wide variety of problem-solving skills related to the field of computer science. Unfortunately, standardized, valid, and reliable methods to assess CT skills in preschool children are lacking, compromising the reliability of the results reported in CT interventions. To surpass this limitation, we validated in a sample of 700 preschool students (5–6 years old) the Beginners Computational Thinking test Short-Form (BCTt-SF), an unplugged 12-item instrument that measures three of the most common computational concepts assessed in preschool research: sequences, loops, and conditionals. The theoretical model underpinning the BCTt-SF was supported by dimensionality assessment, which suggested that preschool students can be distinguished in terms of four specific abilities (i.e., sequences, simple loops, nested loops, and conditionals) and that all of these abilities were related by a general factor. We modeled this hierarchical structure with a bi-factor model that presented excellent psychometric properties, from good statistical fit indices to adequate reliability of the general ability. To take full advantage of this model, we created an online application in the Shiny platform ( https://computationalthinkingtests.shinyapps.io/SF-BCTt/ ) for the seamless scoring of examinees by any teacher or researcher who uses the BCTt-SF to assess CT skills in preschool children. Finally, we demonstrated how the BCTt-SF can be used to test the impact of educational interventions for improving CT skills in preschoolers.

Crimes against women in India have been continuously increasing lately as reported by the National Crime Records Bureau. Gender-based violence has become a serious issue to such an extent that it has been catalogued as a high impact health problem by the World Health Organization. However, there is a lack of spatiotemporal analyses to reveal a complete picture of the geographical and temporal patterns of crimes against women. We focus on analysing how the geographical pattern of ‘dowry deaths’changes over time in the districts of Uttar Pradesh during the period 2001–2014. The study of the geographical distribution of dowry death incidence and its evolution over time aims to identify specific regions that exhibit high risks and to hypothesize on potential risk factors. We also look into different spatial priors and their effects on final risk estimates. Various priors for the hyperparameters are also reviewed. The risk estimates seem to be robust in terms of the spatial prior and hyperprior choices and final results highlight several districts with extreme risks of dowry death incidence. Statistically significant associations are also found between dowry deaths, sex ratio and some forms of overall crime.

We study a decoupling iterative algorithm based on domain decomposition for the time-dependent nonlinear Stokes–Darcy model, in which different time steps can be used in the flow region and in the porous medium. The coupled system is formulated as a space-time interface problem based on the interface condition for mass conservation. The nonlinear interface problem is then solved by a nested iteration approach which involves, at each Newton iteration, the solution of a linearized interface problem and, at each Krylov iteration, parallel solution of time-dependent linearized Stokes and Darcy problems. Consequently, local discretizations in time (and in space) can be used to efficiently handle multiphysics systems of coupled equations evolving at different temporal scales. Numerical results with nonconforming time grids are presented to illustrate the performance of the proposed method.

In this paper, a telecommunication network situation is studied. Particularly, the problem considers an upper-level decision maker that enables different types of hubs in the network and another one that connects the users to the enabled hubs. The objective of the upper-level decision maker is to balance the number of users connected to the enabled hubs. However, the connection of users is performed by another decision maker with lower hierarchy, the one associated to the lower-level problem. Hence, decisions at the upper level are given as parameters to the lower level. So that, the lower level is solved to obtain feasible bi-level solutions. In this case, the rational reaction of the decision-maker assigned to the lower level is obtained. Due to the complexity of this problem, an evolutionary algorithm is proposed to obtain a good approximation. Numerical experimentation shows the efficiency and robustness of the proposed solution scheme. Based on the results, it can be concluded that the proposed algorithm provides good alternatives for the bi-level decision-making process. Also, some managerial insights are given regarding the different types of enabled hubs and the connection of the users.

The presentation of a Bayesian inference problem in terms of natural frequencies rather than probabilities has been shown to enhance performance. The effect of individual differences in cognitive processing on Bayesian reasoning has rarely been studied, despite enabling us to test process-oriented variants of the two main accounts of the facilitative effect of natural frequencies: The ecological rationality account (ERA), which postulates an evolutionarily shaped ease of natural frequency automatic processing, and the nested sets account (NSA), which posits analytical processing of nested sets. In two experiments, we found that cognitive reflection abilities predicted normative performance equally well in tasks featuring whole and arbitrarily parsed objects (Experiment 1 ) and that cognitive abilities and thinking dispositions (analytical vs. intuitive) predicted performance with single-event probabilities, as well as natural frequencies (Experiment 2 ). Since these individual differences indicate that analytical processing improves Bayesian reasoning, our findings provide stronger support for the NSA than for the ERA.

Space-filling designs are popular choices for computer experiments. A sliced design is a design that can be partitioned into several subdesigns. We propose a new type of sliced space-filling design called sliced rotated sphere packing designs. Their full designs and subdesigns are rotated sphere packing designs. They are constructed by rescaling, rotating, translating, and extracting the points from a sliced lattice. We provide two fast algorithms to generate such designs. Furthermore, we propose a strategy to use sliced rotated sphere packing designs adaptively. Under this strategy, initial runs are uniformly distributed in the design space, follow-up runs are added by incorporating information gained from initial runs, and the combined design is space-filling for any local region. Examples are given to illustrate its potential application.