Research opportunities in production engineering: a diagnosis instrument proposal / Oportunidades de pesquisa em engenharia de produção: uma proposta de instrumento de diagnóstico

The University-Industry Relationship presents itself as an important factor for the socio-economic development, whether municipal, regional or national. So the goal of this article is to propose an instrument for the diagnosis of research opportunities in Production Engineering in the commercial sector at the Brazilian city of Itabira (MG). The research method was the theoretic-conceptual, through a bibliometrics analysis, obtained on the basis of Web of Science. Articles were assessed from knowledge areas required by the Brazilian Association of Production Engineering (ABEPRO) and published in 2014 and 2015. The diagnostic instrument was drawn up from a bibliometrics analysis and validated by experts’ opinion, professors from Federal University of Itajubá (UNIFEI). A pilot test has also been accomplished with four companies, members of an Association of Itabira. As the main result, the test has indicated that the priority area for research is the Economic Engineering in the commercial sector of Itabira

Systems dynamics simulation in hospital logistics : stock and capacity management integrated vision

Orientador: Orlando Fontes Lima JuniorDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Civil, Arquitetura e UrbanismoResumo: Este estudo teve como objetivo desenvolver um modelo de simulação, utilizando sistemas dinâmicos, para a análise de diferentes políticas de logística hospitalar. O modelo permite analisar de forma integrada como a gestão de diferentes políticas de estoque e de capacidades influenciam o custo do atendimento no hospital. Na modelagem consideraram-se como principais fatores a gestão de estoque de medicamentos e a capacidade de atendimento, que é definida pela disponibilidade de mão-de-obra especializada (médicos). Dois outros importantes fatores foram considerados como restrições de capacidade: a disponibilidade de macas e de equipamentos. Para demonstrar a aplicabilidade do modelo, um estudo de caso foi desenvolvido no pronto socorro do Hospital de Clínicas da Unicamp (Universidade Estadual de Campinas), onde foi realizada a análise dos diferentes cenários, integrando as políticas de estoque, capacidade e seus custos. Após análise destes cenários, estratégias integradas de políticas de estoque e capacidades que reduzissem o custo de atendimento foram propostas.Abstract: The main objective of this dissertation is to develop a simulation model, using systems dynamics, to analyze different hospital logistic policies. The model allows an integrated analysis of how the management of different stock and capacities policies impacts the hospital attendance costs. The main factors of this process considered in the modeling are the medicine stock management and attendance capacity, defined as the specialized work source availability (physicians). Other important factors in this process were considered as capacity restrictions: hammock and equipments availability. In order to show the model suitability, the Emergency Room of Unicamp Clínicas Hospital case was studied. Different scenarios analyses were done combining stock policies, capacities and their costs. Using these results, integrated strategies of stock policies and capacities were proposed in order to reduce costs.MestradoTransportesMestre em Engenharia Civi

Quantifying the impact of particle matter on mortality and hospitalizations in four Brazilian metropolitan areas

Air quality management involves investigating areas where pollutant concentrations are above guideline or standard values to minimize its effect on human health. Particulate matter (PM) is one of the most studied pollutants, and its relationship with health has been widely outlined. To guide the construction and improvement of air quality policies, the impact of PM on the four Brazilian southeast metropolitan areas was investigated. One-year long modeling of PM10 and PM2.5 was performed with the WRF-Chem model for 2015 to quantify daily and annual PM concentrations in 102 cities. Avoidable mortality due to diverse causes and morbidity due to respiratory and circular system diseases were estimated concerning WHO guidelines, which was adopted in Brazil as a final standard to be reached in the future; although there is no deadline set for its implementation yet. Results showed satisfactory representation of meteorology and ambient PM concentrations. An overestimation in PM concentrations for some monitoring stations was observed, mainly in São Paulo metropolitan area. Cities around capitals with high modelled annual PM2.5 concentrations do not monitor this pollutant. The total avoidable deaths estimated for the region, related to PM2.5, were 32,000±5,300 due to all-cause mortality, between 16,000±2,100 and 51,000±3,000 due non-accidental causes, between 7,300±1,300 and 16,700±1,500 due to cardiovascular disease, between 4,750±900 and 10,950±870 due ischemic heart diseases and 1,220±330 avoidable deaths due to lung cancer. Avoidable respiratory hospitalizations were greater for PM2.5 among ‘children’ age group than for PM10 (all age group) except in São Paulo metropolitan area. For circulatory system diseases, 9,840±3,950 avoidable hospitalizations in the elderly related to a decrease in PM2.5 concentrations were estimated. This study endorses that more restrictive air quality standards, human exposure, and health effects are essential factors to consider in urban air quality management

A citizen science approach for enhancing public understanding of air pollution

The deterioration in air quality is a challenging problem worldwide. There is a need to raise awareness among the people and support informed decision making. Over the years, citizen science activities have been implemented for environmental monitoring and raising awareness but most of such works are contributory in nature, i.e. task design, planning and analysis are performed by professional researchers and citizens act as participants. Our objective is to demonstrate that citizen science can be used as a 'tool' to enhance public understanding of air pollution by engaging communities and local stakeholders. We present a co-creation based citizen science approach which incorporates the ideas of inclusion, where citizens are involved in most of the steps of the scientific process; collaboration, where the citizen scientists define research problems and methodologies, and reciprocation, where citizen scientists share their observations through storytelling. We integrate the use of interactive air quality quizzes, offline questionnaires and low-cost air quality monitoring sensors. The results show that such methods can generate insightful data which can assist in understanding people's perception and exposure levels at a fine-grained level. It was also observed that community engagement in air quality monitoring can enhance partnerships between the community and research fraternity

Kriging method application and traffic behavior profiles from local radar network database: A proposal to support traffic solutions and air pollution control strategies

Vehicles are commonly the main source of outdoor air pollution in urban areas and vehicular emission inventory is a tool to identify the emissions contribution from mobile sources. In this study, we developed an emission inventory to Belo Horizonte, a densely populated urban city in Brazil, with approximately 2.0 million vehicles. The vehicular emission inventory was developed applying the National Vehicle Emission Inventory model (VEIN) using emission factor from São Paulo State Environmental Protection Agency, different traffic behavior profile (constant and different diurnal cycle per vehicle type) established from local radar data and kriging interpolation method considering four different scenarios with reductions in fleet composition. The scenarios were described as according the combination between traffic behavior profiles, vehicle flow, vehicle type and a fuel consumption. The comparison between scenarios showed reductions of emissions around 8.5 % (CO), 8.8 % (CO2), when it was considered 10 % of reduction in fleet composition of passenger cars and light commercial vehicles. Considering 20 % reduction in diesel fleet composition (trucks and buses), a decrease of 8.4 % (NOx) and 8.6 % (PM) was observed. Furthermore, this work presented that the kriging method to define a spatial/temporal distributing using radar traffic data is an alternative low- cost method to investigate the effect of real traffic data on the vehicular emissions modeling. This study is pioneer in Brazil and reinforced the importance of detailing traffic activities using real data to estimate vehicular emissions in an urban area. Transportation management strategies to reduce air pollution and to assist users to reduce air pollution exposure are mandatory to create a collaborative network and build sustainable cities in the future. It is necessary more investigations methods to generate an accuracy spatial distribution aggregated coupled with different traffic behavior profile to develop actions to reduce vehicular emissions in urban areas, investigate air pollution exposure, perform project-level emissions and hot-spot analysis

Coupled models using radar network database to assess vehicular emissions in current and future scenarios

Vehicles are one of the most significant sources of air pollutant emissions in urban areas, and their real contribution always needs to be updated to predict impacts on air quality. Radar databases and traffic counts using statistical modeling is an alternative and low-cost approach to produce traffic activities data in each urban street to be used as input to predict vehicular emissions. In this work, we carried out a spatial statistical analysis of local radar data and calculated traffic flow using local radar data combined with different statistical models. Future scenarios about vehicle emission inventory to define public policies were also proposed and analyzed for Belo Horizonte (BH), a Brazilian State capital, with the third-largest metropolitan region in the country. The Normal-Neighborhood Model (i.e., the mixed effect model with random effect in the neighborhood, radar type, and in the regional area) was used to calculate traffic flow in each urban street. Results showed average reductions in CO (4.5%), NMHC (3.0%), NOx (3.0%) and PM2.5 (6.2%) emissions even with an increase in fleet composition (25% in average). The decrease is a result of the implementation of emission control programs by the government, improvements vehicles technologies, and the quality of fuels. Prediction of traffic data from radar databases has proven to be useful for avoiding the high costs of performing origin-destination surveys and traffic modeling using commercial software. Radar databases can provide many potential benefits for research and analysis in environmental and transportation planning. These findings can be incorporated in future investigations to implement public policies on vehicular emission reduction in urban areas and to advance environmental health effects research and human health risk assessment.[Display omitted]•The model Normal-Neighborhood was more suitable to perform a spatial distribution of vehicle flow.•Different fleet reduction combinations generate up to a 40% reduction in vehicle emissions.•Mobility and transportation solutions can be proposed using radar data

Traffic data in air quality modeling: a review of key variables, improvements in results, open problems and challenges in current research

Outdoor air pollution was responsible for approximately 4.2 million deaths around the world in 2016, with the emissions from road vehicles being the main source of air pollution in urban areas. To fulfill the need to identify the contribution of pollutants emitted by on-road vehicles and examine the limitations of various air quality models (boundary conditions, wind behavior representations, chemical mechanisms and reactions), a systematic review of the main traffic variables used in emissions and air quality modeling was performed. The discussion of their relationships, connections, and relevance showed a consistent sequence to generate traffic data using different traffic models. A list of key traffic variables to use as input data for vehicle emissions modeling and consequently to improve the accuracy of air quality modeling was proposed. A revision over 125 published articles was realized approaching methods to integrate traffic, emissions, air quality models, and detailing how these data can improve the results generated by the air quality model. Traffic models (macroscopic, mesoscopic, and microscopic) require variables at different levels of detail, such as traffic flow, speed, fuel consumption, and fleet composition. The emissions models (static and dynamic) are the key inputs to regional air quality models, but there is a tradeoff between the accuracy in emission estimates and the level of detail in model inputs. Meteorological data also influence the results. The conclusions showed that gaps remain on consistent emissions factors, spatial and temporal distributions, allocations of emissions on grid cells, and performance of the meteorological models. The average link-based traffic parameters are a persistent limitation. The proposed key traffic variables list point to flow per vehicle type as the most important variable. There is a need for scientific efforts to integrate traffic engineering data into emissions models to improve air quality modeling results using better traffic flow representations. Uncertainties in traffic data must first be analyzed, and accordingly a guidance with an accuracy reference for distinctive applications in different regions should be proposed