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   https://doi.org/10.1111/1462-2920.13825  

   Goldberg, S. J. ; Nelson, C. E. ; Viviani, D. A. ; Shulse, C. N. ; Church, M. J. ( September 2017 , Environmental Microbiology)
2. Tree-DOM: Dissolved organic matter in throughfall and stemflow: DOM in throughfall and stemflow

   https://doi.org/10.1002/lol2.10059  

   Van Stan, John T. ; Stubbins, Aron ( February 2018 , Limnology and Oceanography Letters)
3. Identifying labile DOM components in a coastal ocean through depleted bacterial transcripts and chemical signals: Labile DOM in a coastal ocean

   https://doi.org/10.1111/1462-2920.14344  

   Vorobev, Alexey ; Sharma, Shalabh ; Yu, Mengyun ; Lee, Juhyung ; Washington, Benjamin J. ; Whitman, William B. ; Ballantyne, IV, Ford ; Medeiros, Patricia M. ; Moran, Mary Ann ( August 2018 , Environmental Microbiology)
4. Dynamic Electricity Pricing – Modeling Manufacturer Response and an Application to Cement Processing

   https://doi.org/10.5539/eer.v9n2p1  

   Eryilmaz, Derya ; Apland, Jeffrey ; Smith, Timothy M. ( November 2019 , Energy and Environment Research)

   Dynamic pricing, also known as real-time pricing, provides electricity users with an economic incentive to adjust electricity use based on changing market conditions. This paper studies the economic implications of real-time pricing mechanisms in a cement manufacturing plant. Production for a representative cement manufacturing plant is modeled using stochastic mathematical programming. The results show that a cement plant can a) reduce electricity costs by shifting electricity load of certain processes to times when electricity prices are lower, and b) profitably reduce electricity use during peak prices through more efficient scheduling of production under real-time pricing compared to fixed pricing. Themore »results suggest that building scheduling flexibility into certain industrial manufacturing processes to reschedule electricity consumption when the electricity prices at their peak may be economical. The results also suggest that shifts in the production schedule of a cement manufacturer that result from real-time pricing may also influence environmental impacts. The modelling framework modeled real-time pricing as a source of risk in this study, which is also applicable to other energy intensive industries. As such, dynamic pricing strategies that include the non-market impacts of electricity generation should be further explored.« less
5. Processing-in-Memory Accelerator for Dynamic Neural Network with Run-Time Tuning of Accuracy, Power and Latency

   https://doi.org/10.1109/SOCC49529.2020.9524770  

   Yang, Li ; He, Zhezhi ; Angizi, Shaahin ; Fan, Deliang ( September 2020 , 2020 IEEE 33rd International System-on-Chip Conference (SOCC))

   With the widely deployment of powerful deep neural network (DNN) into smart, but resource limited IoT devices, many prior works have been proposed to compress DNN in a hardware-aware manner to reduce the computing complexity, while maintaining accuracy, such as weight quantization, pruning, convolution decomposition, etc. However, in typical DNN compression methods, a smaller, but fixed, network structure is generated from a relative large background model for resource limited hardware accelerator deployment. However, such optimization lacks the ability to tune its structure on-the-fly to best fit for a dynamic computing hardware resource allocation and workloads. In this paper, we mainlymore »review two of our prior works [1], [2] to address this issue, discussing how to construct a dynamic DNN structure through either uniform or non-uniform channel selection based sub-network sampling. The constructed dynamic DNN could tune its computing path to involve different number of channels, thus providing the ability to trade-off between speed, power and accuracy on-the-fly after model deployment. Correspondingly, an emerging Spin-Orbit Torque Magnetic Random-Access-Memory (SOT-MRAM) based Processing-In-Memory (PIM) accelerator will also be discussed for such dynamic neural network structure.« less