We synthesized isolated Cr­(III) sites on SiO2−Al2O3 and Al2O3 by grafting and subsequent controlled thermal treatment of Cr­(OSi­(OtBu)3)3(THF)2 and Cr­(Al­(OiPr)4)3 on alumina. CrO x /Al2O3 was obtained from incipient wetness impregnation of Al2O3 with CrO3 in H2O followed by calcination, as carried out for the synthesis of industrial Cr-based dehydrogenation catalysts. These materials were characterized by IR, EPR, XAS, and by the adsorption of the probe molecules CO and pyridine, and the results were compared to previously reported isolated Cr­(III)/SiO2. All of these materials were active in propane dehydrogenation at 550 °C, where higher TOFs were obtained for Cr­(III)/SiO2-Al2O3 and Cr­(III)/Al2O3 than for CrO x /Al2O3 and Cr­(III)/SiO2. For mechanistic studies the reverse reaction, propene hydrogenation, was studied. Here, the order of reactivity was opposite that of dehydrogenation, with CrO x /Al2O3 and Cr­(III)/SiO2 being more active in hydrogenation than Cr­(III)/SiO2-Al2O3 and Cr­(III)/Al2O3. Kinetic analysis and labeling studies with D2 showed that the rate law is in all cases first order in H2 partial pressure but had different dependence on propene partial pressure from catalyst to catalyst. We found small normal kinetic isotope effects of 1 ≤ KIE ≤ 2, activation enthalpies up to 40 kJ mol–1, and large negative activation entropies between −100 and −194 J K–1 mol–1 for the different Cr catalysts. We also performed parahydrogen-induced polarization (PHIP) experiments, which showed that H2 addition to propene proceeds, at least in part, via a pairwise mechanism. The experimental data for propene hydrogenation suggests adsorption/desorption pre-equilibria of H2 (or D2) and propene followed by H2 activation and insertion of propene. DFT computations for propane dehydrogenation and propene hydrogenation on Cr­(III) on a periodic alumina model show that a mechanism involving X–H activation (X = R, H) is energetically feasible with activation enthalpies and entropies that are comparable to the experimentally determined values.

Identifying a precise anatomic skeleton is important in order to ensure high quality motion capture. In this paper we discuss two skeleton fitting techniques based on 3D optical marker data. First a local technique is proposed based on relative marker trajectories. Then it is compared to a global optimization of a skeleton model. Various proposals are made to handle the skin deformation problem. Index Terms-skeleton fitting, motion capture, optical markers

[12,7,3,4,11]. In [7]

No abstract

Forming effective coalitions is a major research challenge in AI and multi-agent systems. Coalition Structure Generation (CSG) involves partitioning a set of agents into coalitions so that social surplus (the sum of the rewards of all coalitions) is maximized. A partition is called a coalition structure (CS). In traditional works, the value of a coalition is given by a black box function called a characteristic function. In this paper, we propose a novel formalization of CSG, i.e., we assume that the value of a characteristic function is given by an optimal solution of a distributed constraint optimization problem (DCOP) among the agents of a coalition. A DCOP is a popular approach for modeling cooperative agents, since it is quite general and can formalize various application problems in MAS. At first glance, this approach sounds like a very bad idea considering the computational costs, since we need to solve an NP-hard problem just to obtain the value of a single coalition. To optimally solve a CSG, we might need to solve O(2 n ) DCOP problem instances, where n is the number of agents. However, quite surprisingly, we show that an approximation algorithm, whose computational cost is about the same as solving just one DCOP, can find a CS whose social surplus is at least max(1/ n/2 , 1/(w * + 1)) of the optimal CS, where w * is the tree width of a constraint graph. Furthermore, we can generalize this approximation algorithm with a parameter k, i.e., the generalized algorithm can find a CS whose social surplus is at least max(k/ n/2 , k/(w * + 1)) of the optimal CS by exploring more search space. These results illustrate that the locality of interactions among agents, which is explicitly modeled in the DCOP formalization, is quite useful in developing efficient CSG algorithms with quality guarantees.