Tobacco is addictive, primarily because of the presence of nicotine.¹ Although nicotine itself is not the direct cause of most smoking-related diseases, addiction to nicotine in tobacco is the proximate cause of these diseases because it sustains smoking behavior.^2,3 Thus, the magnitude of public health harm that is caused by tobacco is inextricably linked to its addictive nature.

Recent declines in US cigarette smoking prevalence have coincided with increases in use of other tobacco products. Multiple product tobacco models can help assess the population health impacts associated with use of a wide range of tobacco products. We present a multi-state, dynamical systems population structure model that can be used to assess the effects of tobacco product use behaviors on population health. The model incorporates transition behaviors, such as initiation, cessation, switching, and dual use, related to the use of multiple products. The model tracks product use prevalence and mortality attributable to tobacco use for the overall population and by sex and age group. The model can also be used to estimate differences in these outcomes between scenarios by varying input parameter values. We demonstrate model capabilities by projecting future cigarette smoking prevalence and smoking-attributable mortality and then simulating the effects of introduction of a hypothetical new lower-risk tobacco product under a variety of assumptions about product use. Sensitivity analyses were conducted to examine the range of population impacts that could occur due to differences in input values for product use and risk. We demonstrate that potential benefits from cigarette smokers switching to the lower-risk product can be offset over time through increased initiation of this product. Model results show that population health benefits are particularly sensitive to product risks and initiation, switching, and dual use behaviors. Our model incorporates the variety of tobacco use behaviors and risks that occur with multiple products. As such, it can evaluate the population health impacts associated with the introduction of new tobacco products or policies that may result in product switching or dual use. Further model development will include refinement of data inputs for non-cigarette tobacco products and inclusion of health outcomes such as morbidity and disability.

Resilience in hospitals ‐ their ability to withstand, adapt to, and rapidly recover from disruptive events ‐ is vital to their role as part of national critical infrastructure. This paper presents a model to provide planning guidance to decision makers about how to make hospitals more resilient against possible disruption scenarios. This model represents a hospital’s adaptive capacities that are leveraged to care for patients during loss of infrastructure services (power, water, etc.). The model is an optimization that reallocates and substitutes resources to keep patients in a high care state or allocates resources to allow evacuation if necessary. An illustrative example demonstrates how the model might be used in practice.

Neural-inspired spike-based computing machines often claim to achieve considerable advantages in terms of energy and time efficiency by using spikes for computation and communication. However, fundamental questions about spike-based computation remain unanswered. For instance, how much advantage do spike-based approaches have over conventional methods, and under what circumstances does spike-based computing provide a comparative advantage? Simply implementing existing algorithms using spikes as the medium of computation and communication is not guaranteed to yield an advantage. Here, we demonstrate that spike-based communication and computation within algorithms can increase throughput, and they can decrease energy cost in some cases. We present several spiking algorithms, including sorting a set of numbers in ascending/descending order, as well as finding the maximum or minimum or median of a set of numbers. We also provide an example application: a spiking median-filtering approach for image processing providing a low-energy, parallel implementation. The algorithms and analyses presented here demonstrate that spiking algorithms can provide performance advantages and offer efficient computation of fundamental operations useful in more complex algorithms.

We present a modification to the fuzzy ARTMAP neural network architecture for conducting boosted learning in a probabilistic setting. We call this new architecture boosted ARTMAP (BARTMAP). Performance comparison with fuzzy ARTMAP, PROBART and ART-EMAP on some simple two-class problems is discussed. Experimental results indicate that BARTMAP gives better generalization results on some problems involving classification overlap. In addition BARTMAP requires fewer resources, i.e., network nodes, to achieve performance levels comparable to those in fuzzy ARTMAP.