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The purpose of this study is to explore the use of silver submicron-scale wire (AgSMW) additives in filament feedstock for fused filament fabrication (FFF) additive manufacturing technologies. The antibacterial effect of the additive on printed objects is assessed and its impact on mechanical behavior is determined.

AgSMW-PLA composite FFF filaments were fabricated by solution processing, granulation and extrusion. The reduction in the growth of Escherichia coli (E. coli) is measured after exposure to FFF-printed composite test specimens with AgSMW additive concentrations ranging from 0.0 to 10.0 weight per cent. The effect of the additive addition on the thermal properties and tensile mechanical performance was measured. Scanning electron microscopy (SEM) was used to analyze the composite microstructure and fracture behavior.

E. coli growth was reduced by approximately 50 per cent at the highest additive concentration of 10.0 weight per cent. This is attributed to the release of silver ions through water diffusion into the bulk of the composite. The ultimate tensile strength declined with increasing AgSMW concentration with a moderate reduction of 18 per cent at 10.0 weight per cent. The elastic modulus did not vary significantly at any of the concentrations studied. The ductility of the composite was only notably reduced at the highest concentration. The reduction in mechanical strength and strain at break is attributed to an increase in void defects in the composite with increasing additive concentration.

This study demonstrates the successful incorporation of AgSMWs into FFF-compatible filaments for use in commercially available printing systems. The results demonstrate significant reduction of bacteria growth when using these materials. While the mechanical performance degrades slightly, the results indicate the material’s efficacy for a variety of potential biomedical applications. As a proof of concept, surgical tools were printed using the composite.

While deficits for students with learning disabilities (LD) are prevalent in almost all aspects of mathematics, difficulty in the application and understanding of problem-solving tasks are much more challenging to remediate than computational and procedural skills. Given the complexities involved in authentic problem-solving activities emphasized in current mathematics standards and the inherent challenges presented to students with LD, the importance of using strategies and techniques guided by evidence-based practices is paramount. Yet, ineffective instructional strategies for problem solving are still widespread in both mathematics curricula and available teacher resources. In this chapter, we provide a description of a commonly used ineffective problem-solving strategy (i.e., the keyword strategy), an overview of the keyword research, and an explanation for its ineffectiveness. We conclude with a description of three evidenced-based problem-solving approaches and practices that significantly improve the mathematical performance of students with LD.

Most countries around the world base their old‐age pension programs largely on the pay‐as‐you‐go defined benefit (PAYGO DB) model. However, due to a number of factors including population aging, the maturing of these schemes, rapidly increasing old‐age pension costs, and the perceived need to become more competitive in international markets, many nations have become increasingly concerned about the present (or projected future) economic burden of paying for the pension benefits promised by these schemes. This concern has led policy makers to look for alternative models. One of the most innovative alternatives to emerge during the past ten years is the notional defined contribution (NDC) model. In this article we describe this model and discuss some of the implications of a shift to this model for women and low‐wage workers. We conclude that in the industrial nations women and low‐wage workers are likely to do less well with schemes based all or in part on the NDC model because such schemes are typically designed to be less redistributive (from higher to lower income groups) than the PAYGO DB schemes they will be replacing. However, in developing countries the reverse will often be true as the NDC schemes are likely to be replacing PAYGO DB schemes that tend to redistribute from low‐income groups to higher income groups. Relative to funded DC schemes a major advantage of the NDC model is that it does not subject individual pension benefits to the volatility of financial markets. This issue is relevant to workers in both developed and developing nations, but it is a particularly important consideration in developing nations.

Since the development of the COVID-19 vaccinations, questions surrounding race have been prominent in the literature on vaccine uptake. Early in the vaccine rollout, public health officials were concerned with the relatively lower rates of uptake among certain racial/ethnic minority groups. We suggest that this may also be patterned by racial/ethnic residential segregation, which previous work has demonstrated to be an important factor for both health and access to health care.

In this study, we examine county-level vaccination rates, racial/ethnic composition, and residential segregation across the U.S. We compile data from several sources, including the American Community Survey (ACS) and Centers for Disease Control (CDC) measured at the county level.

We find that just looking at the associations between racial/ethnic composition and vaccination rates, both percent Black and percent White are significant and negative, meaning that higher percentages of these groups in a county are associated with lower vaccination rates, whereas the opposite is the case for percent Latino. When we factor in segregation, as measured by the index of dissimilarity, the patterns change somewhat. Dissimilarity itself was not significant in the models across all groups, but when interacted with race/ethnic composition, it moderates the association. For both percent Black and percent White, the interaction with the Black-White dissimilarity index is significant and negative, meaning that it deepens the negative association between composition and the vaccination rate.

The analysis is only limited to county-level measures of racial/ethnic composition and vaccination rates, so we are unable to see at the individual-level who is getting vaccinated.

We find that segregation moderates the association between racial/ethnic composition and vaccination rates, suggesting that local race relations in a county helps contextualize the compositional effects of race/ethnicity.