Traditional methods for designing concrete mixtures provide good results; however, they do not guarantee the optimum composition. Consequently, applying operational research techniques is motivated by an increasing need for designers to proportion the concrete's raw materials that satisfy the concrete performance requirements such as mechanical properties, chemical properties, workability, sustainability, and cost. For this reason, many authors have been looking for mathematical programming and machine learning solutions to predict concrete mix properties and optimise concrete mixtures. Therefore, a comprehensive review of operational research techniques concerning the design and proportioning of concrete mixtures and a classification framework are presented herein.

Information technology (IT) systems are known to promote improvements in quality and productivity of the work environments of complex and adaptive socio-technical systems that span hardware, community and software aspects. Systems development lies in eliciting and specifying requirements. However, current requirements of elicitation techniques are limited to correctly understanding the complexity involved in socio-technical systems. Therefore, approaches based on Resilience Engineering can provide concepts and methods for a better understanding of socio-technical systems’ functioning. This study aims to increase the application of the Functional Resonance Analysis Method (FRAM) in the requirements elicitation process. Specifically, understanding variability and its role in enhancing the requirements elicitation and specification process for the design/redesign of IT systems in complex socio-technical systems deployed in building maintenance is the main goal. This study proposes the merging of human factors and ergonomics (HFE) and Resilience Engineering concepts with Software Engineering. A case study was performed with workers to produce requirements specifications for work order issuing activity. This case study indicates the usefulness of the proposed approach for the specification of functional requirements to redesign the IT system examined. FRAM enables inferences to be made from hidden or fuzzy situations that are often not expressed by system users or are not detected by the system designer.

Corn is an example of an agricultural grain with a specific combustibility level and can promote smoldering fires during storage. This paper conducts an experimental design to numerically evaluate how three parameters, namely particle size, moisture, and air ventilation, influence the smoldering velocity. The work methodology is based on Minitab’s experimental design, which defined the number of experiments. First, a pile of corn is heated by a hot plate and a set of thermocouples registers all temperature variations. Then, a full-factorial experiment is implemented in Minitab to analyze the smoldering, which provides a mathematical equation to represent the smoldering velocity. The results indicate that particle size is the most influential factor in the reaction, with 35% and 45% variation between the dried and wet samples. Moreover, comparing the influence of moisture between corn flour and corn powder samples, a variation of 19% and 31% is observed; additionally, analyzing the ventilation as the only variant, we noticed variations of 15% and 17% for dried and wet corn flour, and 27% and 10% for dried and wet corn powder. Future studies may use the experimental design of this work to standardize the evaluation methodology and more effectively evaluate the relevant influencing factors.