Bézout rings with almost stable range 1

Elementary divisor domains were defined by Kaplansky [I. Kaplansky, Elementary divisors and modules, Trans. Amer. Math. Soc. 66 (1949) 464-491] and generalized to rings with zero-divisors by Gillman and Henriksen [L. Gillman, M. Henriksen, Some remarks about elementary divisor rings, Trans. Amer. Math. Soc. 82 (1956) 362-365]. In [M.D. Larsen, W.J. Lewis, T.S. Shores, Elementary divisor rings and finitely presented modules, Trans. Amer. Math. Soc. 187 (1) (1974) 231-248], it was also proved that if a Hermite ring satisfies (N), then it is an elementary divisor ring. The aim of this article is to generalize this result (as well as others) to a much wider class of rings. Our main result is that Bézout rings whose proper homomorphic images all have stable range 1 (in particular, neat rings) are elementary divisor rings.     

Side-chain oxidation of some phenyl-substituted alcohols in aqueous solution: Formation and fragmentation of radical-cations

The reactions of the hydroxyl radical (at low pH) and SO₄^−. have been employed to oxidize a number of phenyl-substituted alcohols in aqueous solution; ESR spectroscopy has been employed to study directly the radicals formed by fragmentation of first-formed radical-cations. Examples of deprotonation (to give benzyl radicals), C_α−C_β bond scission (e.g. PhCH₂CH₂OH to PhCH₂ ^.) and longer-range fragmentation (e.g. PhCH₂CH₂CMe₂OH to PhCH₂CH₂ ^.) are described and discussed: evidence is obtained for intramolecular nucleophilic attack as a route for overall electron-transfer from side-chain to aromatic ring. Solvation of the proton and of oxygen-conjugated carbonium ions is thought, at least in part, to account for differences between fragmentations induced in the gaseous and aqueous phases.     

The S.M.A.R.T. (small mass,affordable, rapid,transfer-less) digestion method for heavy metal determinations

The S.M.A.R.T. (small mass, affordable, rapid, transfer-less) digestion method was developed to determine heavy metal concentrations in small sample masses. The S.M.A.R.T. digestion method is a hot water bath digestion where sample digestion and dilution are performed in the original sample tube. This method is faster than the typical methods used and reduces potential sources of error. Masses as small as 0.01 g have been digested and analysed using this method. The preparation and digestion time is reduced from 10 h to less than 4 h. Acid volumes are reduced from millilitres to microlitres and the only disposable supplies needed are sample tubes and pipette tips. Method accuracy was determined by digesting seven replicates of two standard reference materials using the S.M.A.R.T. method and analysing samples by inductively coupled plasma mass spectrometry. The S.M.A.R.T. digestion method was found to provide excellent recoveries for Al (76 ± 2.7%), Mn (99 ± 11%), Co (92 ± 17%), Ni (93 ± 28%), Cu (109 ± 33%), Zn (97 ± 7.1%), As (108 ± 20%), Sr (90 ± 12%), Mo (84 ± 23%), Ag (91 ± 1.8%), Cd (95 ± 6.2%), Sn (139 ± 52%) and Pb (95 ± 22%). This study has successfully developed an efficient and reproducible digestion method for heavy metal determination in limited biomass samples.     

Optimal Control of Non-convex Measure-driven Differential Inclusions

Necessary conditions for optimality in control problems with differential-inclusion dynamics have recently been developed in the non-convex case by Clarke, Vinter, and others. Using appropriate reparametrizations of the time variable, we extend these results to systems whose dynamics involve a differential inclusion where a vector-valued measure appears. An auxiliary result central to our proof is an extension of existing free end-time necessary conditions to Clarke’s stratified framework.     

Isomorphisms of Brin-Higman-Thompson groups

Let m, m′, r, r′, t, t′ be positive integers with r, r′ ? 2. Let \(\mathbb{L}_r \) denote the ring that is universal with an invertible 1×r matrix. Let \(M_m (\mathbb{L}_r^{ \otimes t} )\) denote the ring of m × m matrices over the tensor product of t copies of \(\mathbb{L}_r \) . In a natural way, \(M_m (\mathbb{L}_r^{ \otimes t} )\) is a partially ordered ring with involution. Let \(PU_m (\mathbb{L}_r^{ \otimes t} )\) denote the group of positive unitary elements. We show that \(PU_m (\mathbb{L}_r^{ \otimes t} )\) is isomorphic to the Brin-Higman-Thompson group tV _r,m ; the case t=1 was found by Pardo, that is, \(PU_m (\mathbb{L}_r )\) is isomorphic to the Higman-Thompson group V _r,m . We survey arguments of Abrams, Ánh, Bleak, Brin, Higman, Lanoue, Pardo and Thompson that prove that t′V _r′,m′ ≌ tV _r,m if and only if r′ =r, t′ =t and gcd(m′, r′?1) = gcd(m, r?1) (if and only if \(M_{m'} (\mathbb{L}_{r'}^{ \otimes t'} )\) and \(M_m (\mathbb{L}_r^{ \otimes t} )\) are isomorphic as partially ordered rings with involution).     

Some extensions of Brioschi’s double alternant

We evaluate some determinants related to Brioschi’s extension of the classical double alternant of Cauchy, and Scott’s extension of Brioschi’s.     

Optimal learning for sequential sampling with non-parametric beliefs

We propose a sequential learning policy for ranking and selection problems, where we use a non-parametric procedure for estimating the value of a policy. Our estimation approach aggregates over a set of kernel functions in order to achieve a more consistent estimator. Each element in the kernel estimation set uses a different bandwidth to achieve better aggregation. The final estimate uses a weighting scheme with the inverse mean square errors of the kernel estimators as weights. This weighting scheme is shown to be optimal under independent kernel estimators. For choosing the measurement, we employ the knowledge gradient policy that relies on predictive distributions to calculate the optimal sampling point. Our method allows a setting where the beliefs are expected to be correlated but the correlation structure is unknown beforehand. Moreover, the proposed policy is shown to be asymptotically optimal.     

Volatility estimation for stochastic project value models

One of the key parameters in modeling capital budgeting decisions for investments with embedded options is the project volatility. Most often, however, there is no market or historical data available to provide an accurate estimate for this parameter. A common approach to estimating the project volatility in such instances is to use a Monte Carlo simulation where one or more sources of uncertainty are consolidated into a single stochastic process for the project cash flows, from which the volatility parameter can be determined. Nonetheless, the simulation estimation method originally suggested for this purpose systematically overstates the project volatility, which can result in incorrect option values and non-optimal investment decisions. Examples that illustrate this issue numerically have appeared in several recent papers, along with revised estimation methods that address this problem. In this article, we extend that work by showing analytically the source of the overestimation bias and the adjustment necessary to remove it. We then generalize this development for the cases of levered cash flows and non-constant volatility. In each case, we use an example problem to show how a revised estimation methodology can be applied.