Nonlinear control techniques for an atomic force microscope system

Two nonlinear control techniques are proposed for an atomic force microscope system. Initially, a learning-based control algorithm is developed for the microcantilever-sample system that achieves asymptotic cantilever tip tracking for periodic trajectories. Specifically, the control approach utilizes a learning-based feedforward term to compensate for periodic dynamics and high-gain terms to account for non-periodic dynamics. An adaptive control algorithm is then developed to achieve asymptotic cantilever tip tracking for bounded tip trajectories despite uncertainty throughout the system parameters. Simulation results are provided to illttstrate the efficacy and performance of the control strategies.     

Tumor-Localized Administration of α-GalCer to Recruit Invariant Natural Killer T Cells and Enhance Their Antitumor Activity against Solid Tumors

Invariant natural killer T (iNKT) cells have the capacity to mount potent anti-tumor reactivity and have therefore become a focus in the development of cell-based immunotherapy. iNKT cells attack tumor cells using multiple mechanisms with a high efficacy; however, their clinical application has been limited because of their low numbers in cancer patients and difficulties in infiltrating solid tumors. In this study, we aimed to overcome these critical limitations by using α-GalCer, a synthetic glycolipid ligand specifically activating iNKT cells, to recruit iNKT to solid tumors. By adoptively transferring human iNKT cells into tumor-bearing humanized NSG mice and administering a single dose of tumor-localized α-GalCer, we demonstrated the rapid recruitment of human iNKT cells into solid tumors in as little as one day and a significantly enhanced tumor killing ability. Using firefly luciferase-labeled iNKT cells, we monitored the tissue biodistribution and pharmacokinetics/pharmacodynamics (PK/PD) of human iNKT cells in tumor-bearing NSG mice. Collectively, these preclinical studies demonstrate the promise of an αGC-driven iNKT cell-based immunotherapy to target solid tumors with higher efficacy and precision.     

Green leaf phenology at Landsat resolution: Scaling from the field to the satellite

Despite the large number of in situ, plot-level phenological measurements and satellite-derived phenological studies, there has been little success to date in merging these records temporally or spatially. In this research, we bridge this scale gap through higher resolution satellite records (Landsat) and quantify the accuracy of satellite-derived metrics with direct field measurements. We compiled fifty-seven Landsat scenes from southern New England (P12 R51) from 1984 to 2002. Green vegetation areal abundance for each scene was derived from spectral mixture analysis and a single set of endmembers. The leaf area signal was fit with a logistic-growth simulating sigmoid curve to derive phenological markers (half-maximum leaf-onset and offset). Spring leaf-onset dates in homogenous stands of deciduous forests displayed significant and persistent local variability. The local variability was validated with multiple springtime ground observations (r² = 0.91). The highest degree of verified small-scale variation occurred where contiguous forests displayed leaf-onset gradients of 10-14 days over short distances (< 500 m). These dramatic gradients occur in of low-relief (< 40 m) upland regions. The patterns suggest that microclimates resulting from springtime cold-air drainage may be influential in governing the start of leaf growth; every 4.16 m loss in elevation delayed spring leaf onset by 1 day. These microclimates may be of crucial importance in interpreting in situ records and interpolating phenology from satellite data. Regional patterns from the Landsat analyses suggest topographic, coastal, and land-use controls on phenology. Our results indicate that deciduous forests in the Providence, RI metropolitan area leaf out 5-7 days earlier than comparable rural areas. The platform-independent curve-fit methodology may be extended across platforms and field data. The methodologically consistent approach, in tandem with Landsat data, allows an effective scaling from plot to satellite phenological observations.     

Analysis of input-to-state stability for discrete time nonlinear systems via dynamic programming

The input-to-state stability (ISS) property for systems with disturbances has received considerable attention over the past decade or so, with many applications and characterizations reported in the literature. The main purpose of this paper is to present analysis results for ISS that utilize dynamic programming techniques to characterize minimal ISS gains and transient bounds. These characterizations naturally lead to computable necessary and sufficient conditions for ISS. Our results make a connection between ISS and optimization problems in nonlinear dissipative systems theory (including L₂-gain analysis and nonlinear H_∞ theory). As such, the results presented address an obvious gap in the literature.     

The Synchronous Approach to Designing Reactive Systems

Synchronous programming is available through several formally defined languages having very different characteristics: Esterel is imperative, while Lustre and Signal are declarative in style; Statecharts and Argos are graphical languages that allow one to program by constructing hierarchical automata. Our motivation for taking the synchronous design paradigm further, integrating imperative, declarative (or dataflow), and graphical programming styles, is that real systems typically have components that match each of these profiles. This paper motivates our interest in the mixed language programming of embedded software around a number of examples, and sketches the semantical foundation of the Synchronie toolset which ensures a coherent computational model. This toolset supports a design trajectory that incorporates rapid prototyping and systematic testing for early design validation, an object oriented development methodology for long term software management, and formal verification at the level of automatically generated object code.     

Anticipating infectious disease re-emergence and elimination: a test of early warning signals using empirically based models

Timely forecasts of the emergence, re-emergence and elimination of human infectious diseases allow for proactive, rather than reactive, decisions that save lives. Recent theory suggests that a generic feature of dynamical systems approaching a tipping point—early warning signals (EWS) due to critical slowing down (CSD)—can anticipate disease emergence and elimination. Empirical studies documenting CSD in observed disease dynamics are scarce, but such demonstration of concept is essential to the further development of model-independent outbreak detection systems. Here, we use fitted, mechanistic models of measles transmission in four cities in Niger to detect CSD through statistical EWS. We find that several EWS accurately anticipate measles re-emergence and elimination, suggesting that CSD should be detectable before disease transmission systems cross key tipping points. These findings support the idea that statistical signals based on CSD, coupled with decision-support algorithms and expert judgement, could provide the basis for early warning systems of disease outbreaks.     

Atomic Insights into the Enhanced Surface Stability in High Voltage Cathode Materials by Ultrathin Coating

Surface properties of electrode materials play a critical role in the function of batteries. Therefore, surface modifications, such as coatings, have been widely used to improve battery performance. Understanding how these coatings function to improve battery performance is crucial for both scientific research and applications. In this study the electrochemical performance of coated and uncoated LiNi_0.5Mn_1.5O₄ (LNMO) electrodes is correlated with ensemble‐averaged soft X‐ray absorption spectroscopy (XAS) and spatially resolved scanning transmission electron microscopy‐electron energy loss spectroscopy (STEM‐EELS) to illustrate the mechanism of how ultrathin layer Al₂O₃ coatings improve the cycle life of LiNi_0.5Mn_1.5O₄. Mn²⁺ evolution on the surface is clearly observed in the uncoated sample, which results from the reaction between the electrolytic solution and the surfaces of LiNi_0.5Mn_1.5O₄ particles, and also possibly atomic structure reconstructions and oxygen loss from the surface region in LiNi_0.5Mn_1.5O₄. The coating effectively suppresses Mn²⁺ evolution and improves the battery performance by decelerating the impedance buildup from the surface passivation. This study demonstrates the importance of combining ensemble‐averaged techniques (e.g., XAS) with localized techniques (e.g., STEM‐EELS), as the latter may yield unrepresentative information due to the limited number of studied particles, and sheds light on the design of future coating processes and materials.     

Unsuccessful retrieval attempts enhance subsequent learning.

Taking tests enhances learning. But what happens when one cannot answer a test question—does an unsuccessful retrieval attempt impede future learning or enhance it? The authors examined this question using materials that ensured that retrieval attempts would be unsuccessful. In Experiments 1 and 2, participants were asked fictional general-knowledge questions (e.g., “What peace treaty ended the Calumet War?”). In Experiments 3–6, participants were shown a cue word (e.g., whale) and were asked to guess a weak associate (e.g., mammal); the rare trials on which participants guessed the correct response were excluded from the analyses. In the test condition, participants attempted to answer the question before being shown the answer; in the read-only condition, the question and answer were presented together. Unsuccessful retrieval attempts enhanced learning with both types of materials. These results demonstrate that retrieval attempts enhance future learning; they also suggest that taking challenging tests—instead of avoiding errors—may be one key to effective learning. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Oxidation Behavior of Aerospace Materials in High Enthalpy Flows Using an Oxyacetylene Torch Facility

The oxyacetylene torch facility is used to measure the ablation rates of graphite and the surface temperatures of different aerospace materials. The free‐stream flame environment is characterized as a function of flame chemistry for heat flux, pO₂, and flow velocity. Measured ablation rates for graphite increase as a function of increasing heat flux and pO₂, which are validated by applying an oxygen diffusion based model. The model uses experimentally measured values for temperature, pO₂, and gas velocity in order to confirm torch testing results are reliable and reproducible. Surface temperatures of ultra‐high temperature ceramic composites are measured as a function of increasing heat flux and show an enthalpic cooling effect on the flame during oxidation testing.