Modeling Carbon Release of Brazilian Highest Economic Pole and Major Urban Emitter: Comparing Classical Methods and Artificial Neural Networks

Despite the concern about climate change and the associated negative impacts, fossil fuels continue to prevail in the global energy consumption. This paper aimed to propose the first model that relates CO2 emissions of Sao Paulo, the main urban center emitter in Brazil, with gross national product and energy consumption. Thus, we investigated the accuracy of three different methods: multivariate linear regression, elastic-net regression, and multilayer perceptron artificial neural networks. Comparing the results, we clearly demonstrated the superiority of artificial neural networks when compared with the other models. They presented better results of mean absolute percentage error (MAPE = 0.76%) and the highest possible coefficient of determination (R2 = 1.00). This investigation provides an innovative integrated climate-economic approach for the accurate prediction of carbon emissions. Therefore, it can be considered as a potential valuable decision-support tool for policymakers to design and implement effective environmental policies.

Application of the QFD-fuzzy-SERVQUAL methodology as a quality planning tool at the surgical centre of a public teaching hospital

Background In Brazil, many public hospitals face constant problems related to high demand vis-à-vis an overall scarcity of resources, which hinders the operations of different sectors such as the surgical centre, as it is considered one of the most relevant pillars for the proper hospital functioning, due to its complexity, criticality as well as economic and social importance. Proper asset management based on well-founded decisions is, therefore, a sine-qua-non condition for addressing such demands. However, subjectivity and other difficulties present in decisions make the management of hospital resources a constant challenge. Methods Thus, the present work proposes the application of a hybrid approach, formed by the QFD tools, fuzzy logic and SERVQUAL as a decision support tool for the quality planning of the surgical centre of the Onofre Lopes Teaching Hospital (Hospital Universitário Onofre Lopes—HUOL). To accomplish such objective, it was necessary to discover and analyse the main needs of the medical team working in the operating room, through the application of the SERVQUAL questionnaire, associated with fuzzy logic. Results Then, the most relevant deficiencies were transformed into entries for the QFD-fuzzy, where they were translated into project requirements. Soon after, the analysis of the existing relationships between the inputs and these requirements was carried out, generating the ranking of actions with the greatest impact on the improvement of the surgical centre overall quality. Conclusions As a result, it was found that the proposed methodology can optimize the decision process to which hospital managers are submitted, improving the surgical centre operation efficiency.

Modeling of Risk Aversion Linked to Renewable Energy Policy and Decision-Maker Behavior

This paper presents the behavior of decision makers, the possible choices and the strategies 1 resulting from the uncertainties related to the integration of renewable energies. Its uncertainties 2 are the risks associated with the volatility of renewable sources, the dynamics of energy production 3 as well as the planning and operation of the electricity grid. The goal is to model the risk-averse 4 decision-maker’s behavior and the choice of integrating renewable energies into the electrical system. 5 Following a bibliographic approach, we expose a methodology to model the decision-maker’s 6 behavior(risk aversion and predilection for risk) to risk taking. The risk-averse decision maker may 7 adopt nonlinear utility functions. Risk aversion is a behavior that reflects the desire to avoid risk 8 decisions and thus reduces the risk of adverse consequences. A decision support tool is provided to 9 the decision-maker to choose a best-fit strategy based on his preferences. The rational and risk-averse 10 decision-maker would seek to maximize a concave utility function instead of seeking to minimize its 11 cost. Taste or aversion to risk can be modeled by a thematic function of utility.

Risk of Injury to Others: The Development of an Algorithm to Identify Children and Youth at High-Risk of Aggressive Behaviours

Youth violence is considered one of the most preventable causes of morbidity and premature mortality. Various risk factors have previously been identified, however, there is presently a crucial need to develop effective decision-support tools in order to identify children and youth at increased risk for violence. The current study utilised data collected from the interRAI Child and Youth Mental Health Screener (ChYMH-S), within the province of Ontario, to develop and validate a methodology for the purpose of identifying young persons who were at greater risk of harm to others. Additional data from 59 mental health agencies validated the algorithm, and it was found to be a strong predictor of harmful behaviour toward others. The RIO algorithm provides a valuable decision-support tool with strong psychometric properties that may be used to identify young persons who exhibit signs or symptoms associated with increased likelihood of harm toward others, in order to provide early intervention efforts for these vulnerable youth, thereby reducing the likelihood of future aggressive behaviours.

Assessing the Risks and Success Factors of Telehealth Development Projects in an Academic Setting

The development of a telehealth technology in an academic setting is a complex project that faces several obstacles. The early assessment of the project risks plays a critical role in the translation of promising telehealth innovations into healthcare practice. This paper presents a decision support tool based on Failure Mode and Effects Analysis (FMEA) and Quality Function Deployment (QFD) techniques to associate the project risks to relevant success factors. Certain modifications in both techniques are applied to deploy them for project risk assessment. The project risks and success factors used in the tool are identified from the literature. The proposed decision support tool enables researchers to manage the risks in their telehealth development projects and identify action items to overcome such risks. The application of the proposed tool is illustrated with a telehealth development project for virtual physical therapy.

Optimising the Preventive Maintenance Interval Using a Semi-Markov Process, Z-Transform, and Finite Planning Horizon

This chapter uses a semi-Markov process and the z transform to find the optimal preventive maintenance interval when dealing with maintenance decision making for a finite time planning horizon. The result is a method that can be easily implemented to assets for which a Weibull reliability analysis exists. The suggested preventive interval formulation is simple and practical. The requirements to apply this simple formula are related to the existence of asset´s reliability data as well as cost/rewards that the assets have when remaining or transitioning to a given state. The application of this method can be very straightforward, and the tool can become a good decision support tool allowing “what if” analysis for different time horizon and maintenance policies.