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This study aims to present a novel methodology for the evaluation of tribological properties of new nanocomposites with the A356 alloy matrix reinforced with aluminium oxide (Al₂O₃) nanoparticles.

Metal matrix nanocomposites (MMnCs) with varying amounts and sizes of Al₂O₃ particles were produced using a compocasting process. The influence of four factors, with different levels, on the wear rate, was analysed with the help of the design of experiments (DoE). A regression model was developed by using the response surface methodology (RSM) to establish a relationship between the observed factors and the wear rate. An artificial neural network was also applied to predict the value of wear rate. Adequacy of models was compared with experimental values. The extreme values of wear rate were determined with a genetic algorithm and particle swarm optimization using the RSM model.

The combination of optimization methods determined the values of the factors which provide the highest wear resistance, namely, reinforcement content of 0.44 wt.% Al₂O₃, sliding speed of 1 m/s, normal load of 100 N and particle size of 100 nm. Used methods proved as effective tools for modelling and predicting of the behaviour of aluminium matrix nanocomposites.

The specific combinations of the optimization methods has not been applied up to now in the investigation of MMnCs. In addition, using of small content of ceramic nanoparticles as reinforcement has been poorly investigated. It can be stated that the presented approach for testing and prediction of the wear rate of nanocomposites is a very good base for their future research.

The purpose of this paper is to intelligibly demonstrate the effectiveness of face mask wearing as a means to prevent COVID-19 transmission. Through understanding the benefits of wearing masks, it is hoped to facilitate the change of societal behavior and more people are willing to wear face mask.

The paper investigates the 50 states in the United States of America (U.S.) and Washington, D.C. that implemented the mask mandates before September 30, 2020, which are divided into four groups: (1) those implemented the statewide mask mandates before June 5, 2020 when World Health Organization (WHO) recommended mask wearing; (2) those implemented statewide mask mandates after June 5, 2020; (3) those implemented partial mandates affecting 30 percent or more of the state’s population; and (4) those implemented partial mandates affecting less than 30 percent. Simple descriptive statistics are analyzed.

For the 50 U.S. states and Washington, D.C., the higher the mask wearing rate, the lower the number of COVID-19 cases (correlation coefficient: −0.69 (p<0.001)). For the 23 states with mobility reduction of less than 15 percent, the higher the proportion of population required to wear masks, the lower the number of cases. This can be seen from the difference in the number of cases among the four groups by ANOVA (p = 0.013).

The positive effect of wearing masks is shown based on simple descriptive statistics for intuitive and intelligible understanding, which may lead people to comprehend the importance of wearing masks, and break through their custom, culture, and norms, and wear masks.

This paper aims to investigate the wear behaviour of different materials for cylinder liners and piston rings in a linear reciprocating tribometer with special focus on the wear of the cylinder liner in the boundary lubrication regime.

Conventional nitrided steel, as well as diamond-like carbon and chromium nitride-coated piston rings, were tested against cast iron, AlSi and Fe-coated AlSi cylinder liners. The experiments were carried out with samples produced from original engine parts to have the original surface topography available. Radioactive tracer isotopes were used to measure cylinder liner wear continuously, enabling separation of running-in and steady-state wear.

A ranking of the material pairings with respect to wear behaviour of the cylinder liner was found. Post-test inspection of the cylinder samples by scanning electron microscopy (SEM) revealed differences in the wear mechanisms for the different material combinations. The results show that the running-in and steady-state wear of the liners can be reduced by choosing the appropriate material for the piston ring.

The use of original engine parts in a closely controlled tribometer environment under realistic loading conditions, in conjunction with continuous and highly sensitive wear measurement methods and a detailed SEM analysis of the wear mechanisms, forms an intermediate step between engine testing and laboratory environment testing.

The purpose of this paper aims to design an optimization control for tunnel boring machine (TBM) based on geological identification. For unknown geological condition, the authors need to identify them before further optimization. For fully considering multiple crucial performance of TBM, the authors establish an optimization problem for TBM so that it can be adapted to varying geology. That is, TBM can operate optimally under corresponding geology, which is called geology-adaptability.

This paper adopted k-nearest neighbor (KNN) algorithm with modification to identify geological conditions. The modification includes adjustment of weights in voting procedure and similarity distance measurement, which at suitable for engineering and enhance accuracy of prediction. The authors also design several key performances of TBM during operation, and built a multi-objective function. Further, the multi-objective function has been transformed into a single objective function by weighted-combination. The reformulated optimization was solved by genetic algorithm in the end.

This paper provides a support for decision-making in TBM control. Through proposed optimization control, the advance speed of TBM has been enhanced dramatically in each geological condition, compared with the results before optimizing. Meanwhile, other performances are acceptable and the method is verified by in situ data.

This paper fulfills an optimization control of TBM considering several key performances during excavating. The optimization is conducted under different geological conditions so that TBM has geological-adaptability.

Improvement of reverse supply chains requires accurate and timely information about the patterns of consumption. In the consumer context, the ways to generate and access such use-visibility data are in their infancy. The purpose of this study is to demonstrate how the Internet of Things (IoT) may be operationalised in the domestic setting to capture data on a consumer’s use of products and the implications for reverse supply chains.

This study uses an explorative case approach drawing on data from studies of six UK households. “Horizontal” data, which reveals patterns in consumers’ use processes, is generated by combining “vertical” data from multiple sources. Use processes in the homes are mapped using IDEF0 and illustrated with the data. The quantitative data are generated using wireless sensors in the home, and qualitative data are drawn from online calendars, social media, interviews and ethnography.

The study proposes four generic measurement categories for operationalising the concept of use-visibility: experience, consumption, interaction and depletion, which together address the use of different household resources. The explorative case demonstrates how these measures can be operationalised to achieve visibility of the context of use in the home. The potential of such use-visibility for reverse supply chains is discussed.

This explorative case study is based on an in-depth study of the bathroom which illustrates the application of use-visibility measures (UVMs) but provides a limited use context. Further research is needed from a wider set of homes and a wider set of use processes and contexts.

The case demonstrates the operationalisation of the combination of data from different sources and helps answer questions of “why?”, “how?”, “when?” and “how much?”, which can inform reverse supply chains. The four UVMs can be operationalised in a way that can contribute to supply chain visibility, providing accurate and timely information of consumption, optimising resource use and eliminating waste.

IDEF0 framework and case analysis is used to identify and validate four UVMs available through IoT data – that of experience, consumption, interaction and depletion. The UVMs characterise IoT data generated from a given process and inform the primary reverse flow in the future supply chain. They provide the basis for future data collection and development of theory around their effect on reverse supply chain efficiency.