The human hand has so many degrees of freedom that it may seem impossible to control. A potential solution to this problem is “synergy control” which combines dimensionality reduction with great flexibility. With applicability to a wide range of tasks, this has become a very popular concept. In this review, we describe the evolution of the modern concept using studies of kinematic and force synergies in human hand control, neurophysiology of cortical and spinal neurons, and electromyographic (EMG) activity of hand muscles. We go beyond the often purely descriptive usage of synergy by reviewing the organization of the underlying neuronal circuitry in order to propose mechanistic explanations for various observed synergy phenomena. Finally, we propose a theoretical framework to reconcile important and still debated concepts such as the definitions of “fixed” vs. “flexible” synergies and mechanisms underlying the combination of synergies for hand control.

In this study, WRF-Chem is utilized at high resolution (1.333 km grid spacing for the innermost domain) to investigate impacts of southern California anthropogenic emissions (SoCal) on Phoenix ground-level ozone concentrations ([O³]) for a pair of recent exceedance episodes. First, WRF-Chem control simulations, based on the US Environmental Protection Agency (EPA) 2005 National Emissions Inventories (NEI05), are conducted to evaluate model performance. Compared with surface observations of hourly ozone, CO, NO^X, and wind fields, the control simulations reproduce observed variability well. Simulated [O³] are comparable with the previous studies in this region. Next, the relative contribution of SoCal and Arizona local anthropogenic emissions (AZ) to ozone exceedances within the Phoenix metropolitan area is investigated via a trio of sensitivity simulations: (1) SoCal emissions are excluded, with all other emissions as in Control; (2) AZ emissions are excluded with all other emissions as in Control; and (3) SoCal and AZ emissions are excluded (i.e., all anthropogenic emissions are eliminated) to account only for Biogenic emissions and lateral boundary inflow (BILB). Based on the USEPA NEI05, results for the selected events indicate the impacts of AZ emissions are dominant on daily maximum 8 h average (DMA8) [O³] in Phoenix. SoCal contributions to DMA8 [O³] for the Phoenix metropolitan area range from a few ppbv to over 30 ppbv (10–30 % relative to Control experiments). [O³] from SoCal and AZ emissions exhibit the expected diurnal characteristics that are determined by physical and photochemical processes, while BILB contributions to DMA8 [O³] in Phoenix also play a key role.

Many studies link the compositions of microbial communities to their environments, but the energetics of organism-specific biomass synthesis as a function of geochemical variables have rarely been assessed. We describe a thermodynamic model that integrates geochemical and metagenomic data for biofilms sampled at five sites along a thermal and chemical gradient in the outflow channel of the hot spring known as “Bison Pool” in Yellowstone National Park. The relative abundances of major phyla in individual communities sampled along the outflow channel are modeled by computing metastable equilibrium among model proteins with amino acid compositions derived from metagenomic sequences. Geochemical conditions are represented by temperature and activities of basis species, including pH and oxidation-reduction potential quantified as the activity of dissolved hydrogen. By adjusting the activity of hydrogen, the model can be tuned to closely approximate the relative abundances of the phyla observed in the community profiles generated from BLAST assignments. The findings reveal an inverse relationship between the energy demand to form the proteins at equal thermodynamic activities and the abundance of phyla in the community. The distance from metastable equilibrium of the communities, assessed using an equation derived from energetic considerations that is also consistent with the information-theoretic entropy change, decreases along the outflow channel. Specific divergences from metastable equilibrium, such as an underprediction of the relative abundances of phototrophic organisms at lower temperatures, can be explained by considering additional sources of energy and/or differences in growth efficiency. Although the metabolisms used by many members of these communities are driven by chemical disequilibria, the results support the possibility that higher-level patterns of chemotrophic microbial ecosystems are shaped by metastable equilibrium states that depend on both the composition of biomass and the environmental conditions.

Pure coconut oil, lanolin, and acetaminophen were vaporized at rates of 1–50 mg/min, using a porous network exhibiting a temperature gradient from 5000 to 5500 K/mm, without incurring noticeable chemical changes due to combustion, oxidation, or other thermally-induced chemical structural changes. The newly coined term “ereptiospiration” is used here to describe this combination of thermal transpiration at high temperature gradients since the process can force the creation of thermal aerosols by rapid heating in a localized zone. Experimental data were generated for these materials using two different supports for metering the materials to the battery powered coil: namely, a stainless steel fiber bundle and a 3-D printed steel cartridge. Heating coconut oil, lanolin, or acetaminophen in a beaker to lower temperatures than those achieved at the surface of the coil showed noticeable and rapid degradation in the samples, while visual and olfactory observations for ereptiospiration showed no noticeable degradation in lanolin and coconut oil while HPLC chromatograms along with visual observation confirm that within the limit of detection, acetaminophen remains chemically unaltered by ereptiospiration.

This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.