In-cylinder pressure measurements and indicating diagrams have proven to be a valuable research tool for the analysis of combustion in spark-ignition or compression-ignition engines. With the use of thermodynamic models, the rate of heat release and mass fraction burned curves are calculated, and from the latter the CA50 parameter (crank angle fifty), which is the angle in which 50% of the total fuel has been burned. The empirical process of obtaining the optimum start of combustion typically leads to a value of CA50 from 8° to 10° after top dead center. This paper attempts to numerically investigate which properties have an influence on this optimum CA50. A simple thermodynamic model was implemented which used the Wiebe function for the rate heat release. The CA50 was then evaluated for combustion duration in the base configuration and in a theoretical adiabatic engine. Results showed that the CA50 is mildly sensitive to combustion duration and highly sensitive to the wall heat transfer coefficient, reaching very low values (still always located after top dead center). These lower values provided higher work output in the model. The main conclusion is that the optimal combustion timing of 8° to 10° is due to a compromise between power, wall heat transfer and exhaust gas energy, which prevents lower values.

Mechanical properties of welded joints depend on the way heat flows through the welding passes. In multipass welding the reheating of the heat affected zone (HAZ) can form local brittle zones that need to be delimited for evaluation. The difficulty lies in the choice of a model that can simulate multipass welding. This study evaluated Rosenthal’s Medium Thick Plate (MTP) and the Distributed heat Sources (DHS) of Mhyr and Gröng models. Two assumptions were considered for both models: constant and temperature-dependent physical properties. It was carried out on a multipass welding of an API 5L X80 tube, with 1016 mm (42″) external diameter, 16 mm thick and half V-groove bevel, in the 3G up position. The root pass was welded with Gas Metal Arc Welding (GMAW) process with controlled short-circuit transfer. The Flux Cored Arc Welding (FCAW) process was used in the filling and finishing passes, using filler metal E111T1-K3M-JH4. The evaluation criteria used were overlapping the simulated isotherms on the marks revealed in the macrographs and the comparison between the experimental thermal cycle and those simulated by the proposed models. The DHS model with the temperature-dependent properties presented the best results and simulated with accuracy the HAZ of root and second welding passes. In this way, it was possible to delimit the HAZ heated sub-regions.

The Amazon Basin is undergoing rapid climatic and environmental changes, with direct impacts on infectious disease transmission and public health. Rising temperatures, altered rainfall patterns, and more frequent extreme events are reshaping vector and pathogen dynamics. Additionally, deforestation and land-use change increase human and animal exposure. These combined factors intensify the risk of disease transmission in the region. This narrative review synthesizes evidence generated by researchers from a reference center in tropical medicine in the Amazon, integrating epidemiological analyses, experimental studies, ecological and predictive modelling, and health systems research conducted over the past decade. The evidence indicates that temperature and hydrological variability affect vector competence, pathogen development, and host-vector contact rates, producing nonlinear and context-dependent transmission outcomes. Extreme hydroclimatic events additionally disrupt healthcare delivery, hinder surveillance, and compromise continuity of care for infections requiring sustained treatment, such as tuberculosis and HIV. Emerging advances, including species distribution modelling, remote sensing, and integrated climate-epidemiological surveillance, provide predictive capacity to anticipate outbreaks and identify emerging hotspots. However, substantial research gaps persist, particularly regarding multi-stressor interactions, mechanistic pathways of climate-pathogen-vector adaptation, and health system resilience under climatic extremes. From the perspective of a reference center that integrates education, research, and healthcare delivery in tropical medicine, this article articulates a research agenda for climate-informed tropical medicine, highlighting priorities for interdisciplinary research, surveillance innovation, and adaptive public health strategies in the Amazon and other climate-sensitive regions.

A transferência de calor na soldagem é um fenômeno regido pela Equação de Fourier, e ocorre entre a poça de fusão, sua vizinhança e as condições de co...

A obtenção de raizes ou zero de função de equações transcendentais na forma reduzida, depende do comportamento destas equações próximo à essas raizes,...

At the beginning of any electric arc welding process, a regime called transient regime will always occur, characterized by the continuous variation of the energy flow, also resulting in the variation of the cooling rate of the welded joint. Consequently, the weld bead hardness varies from a maximum value to an approximately constant value, and this phenomenon can be represented by an asymptotic exponential function. For several welded specimens, it would be possible to fit a non-linear regression for each specimen, however, it would be a difficult task to combine different regressions in a single analysis. The objective of this study is to evaluate the applicability of mixed-effects models and artificial neural networks to predict the longitudinal hardness of the weld bead in the initial transient regime. To this end, linearized and nonlinear mixed-effects models and artificial neural networks with different architectures were fitted. Finally, valid models were obtained with all approaches, with artificial neural network model slightly better fitted. However, the explainability capacity of the linearized mixed-effects model was superior, showing that the relative thickness, one of the predictor variables, was responsible for 48% of the variation in longitudinal hardness of weld bead.