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In this chapter it is shown how economic evaluation algorithms of water use can be integrated into a long-term water management model such that surface-water availability and economic evaluation of various levels of water availability to different uses can be modeled simultaneously. This approach makes it possible to include essential features of economic analyses of water use into water resource modeling and thus improves the capability of such models to support decision making in water management. This is especially relevant for the implementation of the Water Framework Directive, which requires economic analyses to be included in the decision process about future water management strategies.

The water management simulation model WBalMo is presented and the integration of economic-evaluation algorithms is demonstrated for the examples of surface-water use for fish farming and for filling open-cast mining pits in order to achieve acceptable water-quality levels in the emerging pit lakes. Results of applying this integrated evaluation approach are shown for different water management scenarios under conditions of global change in the East German Spree and Schwarze Elster river basins, where water scarcity is an urgent issue. Among the lessons which are drawn by the authors one lesson reads that integrating economic evaluation algorithms into a pre-existing model might bring enormous problems. Therefore, such model approaches should be developed together by water engineers and economists in an interdisciplinary endeavor right from the start.

In empirical research, panel (and multinomial) probit models are leading examples for the use of maximum simulated likelihood estimators. The Geweke–Hajivassiliou–Keane (GHK) simulator is the most widely used technique for this type of problem. This chapter suggests an algorithm that is based on GHK but uses an adaptive version of sparse-grids integration (SGI) instead of simulation. It is adaptive in the sense that it uses an automated change-of-variables to make the integration problem numerically better behaved along the lines of efficient importance sampling (EIS) and adaptive univariate quadrature. The resulting integral is approximated using SGI that generalizes Gaussian quadrature in a way such that the computational costs do not grow exponentially with the number of dimensions. Monte Carlo experiments show an impressive performance compared to the original GHK algorithm, especially in difficult cases such as models with high intertemporal correlations.

Failure to rescue events, or events involving preventable deaths from complications, are a significant contributor to inpatient mortality. While many interventions have been designed and implemented over several decades, this patient safety issue remains at the forefront of concern for most hospitals. In the first part of this study, the development and implementation of one type of highly studied and widely adopted rescue intervention, algorithm-based patient assessment tools, is examined. The analysis summarizes how a lack of systems-oriented approaches in the design and implementation of these tools has resulted in suboptimal understanding of patient risk of mortality and complications and the early recognition of patient deterioration. The gaps identified impact several critical aspects of excellent patient care, including information-sharing across care settings, support for the development of shared mental models within care teams, and access to timely and accurate patient information.

This chapter describes the use of several system-oriented design and implementation activities to establish design objectives, model clinical processes and workflows, and create an extensible information system model to maximize the benefits of patient state and risk assessment tools in the inpatient setting. A prototype based on the product of the design activities is discussed along with system-level considerations for implementation. This study also demonstrates the effectiveness and impact of applying systems design principles and practices to real-world clinical applications.

Survival (default) data are frequently encountered in financial (especially credit risk), medical, educational, and other fields, where the “default” can be interpreted as the failure to fulfill debt payments of a specific company or the death of a patient in a medical study or the inability to pass some educational tests.

This paper introduces the basic ideas of Cox's original proportional model for the hazard rates and extends the model within a general framework of statistical data mining procedures. By employing regularization, basis expansion, boosting, bagging, Markov chain Monte Carlo (MCMC) and many other tools, we effectively calibrate a large and flexible class of proportional hazard models.

The proposed methods have important applications in the setting of credit risk. For example, the model for the default correlation through regularization can be used to price credit basket products, and the frailty factor models can explain the contagion effects in the defaults of multiple firms in the credit market.

Estimating parameters of constrained optimization models in a consistent way requires a different set of methods than what is available in a typical econometric toolkit. We identify three complications likely to arise in this context, and suggest solutions to those complications: (i) the bi-level programming character, (ii) ill-posedness, and (iii) derivation of estimator properties. The solutions suggested involve a combination of numerical techniques and utilization of out-of-sample information through Bayesian techniques. The proposed framework is also suitable for typical empirical problems arising in trade analysis such as the estimation of trade equilibrium models and data balancing exercises.

This chapter examines strategies deployed at individual and institutional levels to combat fake news in two media houses in Uganda. Grounded in the hierarchy of influences model, we examine journalists' and editors' perspectives on how Vision Group and Nation Media Group newsrooms respond to fake news. Journalists' and editors' responses, obtained through semi-structured interviews as well as document review enabled us to underscore the centrality of professional standards, training and technology in combating fake news. We found that technology plays a key role in fact-checking, although newsrooms are yet to adopt advanced digital tools such as artificial intelligence (AI) and algorithms. The newsrooms under investigation deploy conventional hardware and software to detect, flag and debunk fake news. We posit that for the strategies adopted at the organisational level to succeed, they ought to appeal to individual reporters' goals and interests. Further, discourses on adoption of newer technology ought to reflect the contexts in which the news organisations operate in addition to their financial standing.

Cities’ population growth goes in hand with the development of new technologies that are becoming the key factor of the Smart City (SC) concept. It allows the implementation of efficient management solutions, operation, and sustainable development of a city to face the challenges of urbanization and improve the services for the citizens and visitors.

The concept of the SC 5.0 was first presented in Svítek, Skobelev, and Kozhevnikov (2020), where the problems of the complexity of current cities due to rigid management processes, variety of infrastructure, and SC modules, systems, subsystems, and applications were described.

To prove the concept, several practical examples were developed to cover the topics: modeling in SCs, practical implementation of multiagent technologies, the approach of creating city ontology and the city knowledge base as the instrument of semantic interoperability, and visualization possibilities of Smart Evropská as a SC Testbed used for teaching purposes.

The new organizational structure is proposed based on knowledge graphs, and practical examples are shown. The applicability of knowledge graphs to be used in combination with data management platforms for monitoring SC key performance indicators (KPIs) and providing interoperability of services is presented.