The determination of rate parameters of gas-phase elementary reactions is usually based on direct measurements. The rate parameters obtained in many independent direct measurements are then used in reaction mechanisms, which are tested against the results of indirect experiments, like time-to-ignition or laminar flame velocity measurements. We suggest a new approach that takes into account both direct and indirect measurements and optimizes all influential rate parameters. First, the domain of feasibility of the Arrhenius parameters is determined from all of the available direct measurements. Thereafter, the optimal Arrhenius parameters are sought within this domain to reproduce the selected direct and indirect measurements. Other parameters of a complex mechanism (third-body efficiencies, enthalpies of formation, parameters of pressure dependence, etc.) can also be taken into account in a similar way. A new fitting algorithm and a new method for error calculation were developed to determine the optimal mean values and the covariance matrix of all parameters. The approach is demonstrated on the calculation of Arrhenius parameters of reactions (R1): H + O 2 = OH + O and (R2): H + O 2 + M = HO 2 + M (low-pressure limit, M = N 2 or Ar). In total, 9 direct measurements for reaction (R1) (745 data points), 10 direct measurements for reaction (R2) (258 data points), and 11 ignition time measurements (79 data points) were taken into account. The application of the method resulted in the following rate parameters for the investigated reactions-(R1): A = 3.003 × 10 10 cm 3 mol −1 s −1 , n = 0.965, E/R = 6158 K (T = 950-3550 K) DETERMINATION OF RATE PARAMETERS BASED ON DIRECT AND INDIRECT MEASUREMENTS 285and (R2): A = 7.856 × 10 18 cm 6 mol −2 s −1 , n = −1.100, E/R = 0 K (low-pressure limit, M = N 2 , T = 300-1850 K). The optimized third-body efficiency of Ar relative to N 2 is m = 0.494 (standard deviation σ = 0.010). The uncertainty parameter f as a function of temperature was also calculated. Average uncertainty parameter values are f = 0.025 and 0.049 for reactions (R1) and (R2) (corresponding to 6% and 12%), respectively, which are much lower than those of the previous evaluations. C 2012 Wiley Periodicals, Inc. Int J Chem Kinet 44: 284-302, 2012

Learning object names after few exposures, is thought to be a typically human capacity. Previous accounts of similar skills in dogs did not include control testing procedures, leaving unanswered the question whether this ability is uniquely human. To investigate the presence of the capacity to rapidly learn words in dogs, we tested object-name learning after four exposures in two dogs with knowledge of multiple toy-names. The dogs were exposed to new object-names either while playing with the objects with the owner who named those in a social context or during an exclusion-based task similar to those used in previous studies. The dogs were then tested on the learning outcome of the new object-names. Both dogs succeeded after exposure in the social context but not after exposure to the exclusion-based task. Their memory of the object-names lasted for at least two minutes and tended to decay after retention intervals of 10 min and 1 h. This reveals that rapid object-name learning is possible for a non-human species (dogs), although memory consolidation may require more exposures. We suggest that rapid learning presupposes learning in a social context. To investigate whether rapid learning of object names in a social context is restricted to dogs that have already shown the ability to learn multiple object-names, we used the same procedure with 20 typical family dogs. These dogs did not demonstrate any evidence of learning the object names. This suggests that only a few subjects show this ability. Future studies should investigate whether this outstanding capacity stems from the exceptional talent of some individuals or whether it emerges from previous experience with object name learning.

SUMMARYIn this paper we describe an approach for establishing control limits and sampling times which derives from economic performance criteria and a model for random shifts. The total cost related to both production and control is calculated, based on cost estimates for false alarms, for not identifying a true out of control situation, and for obtaining a data record through sampling. We describe the complete process for applying the method and compare with conventional procedures to real data from a Portuguese pulp and paper industrial plant. It turns out that substantial cost-reductions may be obtained.

Control charts have traditionally been used in industrial statistics but are constantly seeing new areas of application, especially in the age of Industry 4.0. This paper introduces a new method, which is suitable for applications in the health care sector, especially for monitoring the health characteristic of a patient. We adopt a Markov chain-based approach and develop a method in which not only the shift size (ie, the degradation of the patient's health) can be random, but the effect of the repair (ie, treatment) and time between samplings (ie, visits)too. This means that we do not use many often-present assumptions that are usually not applicable for medical treatments. The average cost of the protocol, which is determined by the time between samplings and the control limit, can be estimated using the stationary distribution of the Markov chain.Furthermore, we incorporate the standard deviation of the cost into the optimisation procedure, which is often very important from a process control viewpoint. The sensitivity of the optimal parameters and the resulting average cost and cost standard deviation on different parameter values is investigated. We demonstrate the usefulness of the approach for real-life data of patients treated in Hungary, namely the monitoring of cholesterol level of patients with cardiovascular event risk. The results showed that the optimal parameters from our approach can be somewhat different from the original medical parameters.