On absolute summability factors of infinite series

In this paper a general theorem on |A,δ|_k-summability methods has been proved. This theorem includes, as a special case, a known result in [E. Savas, Factors for |A|_k Summability of infinite series, Comput. Math. Appl. 53 (2007) 1045–1049].     

Characterization of general summability factors and applications

By (A,B), we denote the set of all sequences ? such that Σ?_nx_n is summable by B whenever Σx_n is summable by A, where A and B are some summability methods. In this paper, we established a simple set of necessary and sufficient conditions for summability factor of type ?∈(A,B), and also deduced various known and some new results as special cases.     

A converse Lyapunov theorem for almost sure stabilizability

We prove a converse Lyapunov theorem for almost sure stabilizability and almost sure asymptotic stabilizability of controlled diffusions: given a stochastic system a.s. stochastic open-loop stabilizable at the origin, we construct a lower semicontinuous positive definite function whose level sets form a local basis of viable neighborhoods of the equilibrium. This result provides, with the direct Lyapunov theorems proved in a companion paper, a complete Lyapunov-like characterization of the a.s. stabilizability.     

A new delay-dependent absolute stability criterion for a class of nonlinear neutral systems

This paper deals with absolute stability for a class of nonlinear neutral systems using a discretized Lyapunov functional approach. A delay-dependent absolute stability criterion is obtained and formulated in the form of linear matrix inequalities (LMIs). The criterion is valid not only for systems with small delay, but also for systems with non-small delay. Neither model transformation nor bounding technique for cross terms, nor free weighting matrix method is involved through derivation of the stability criterion. Numerical examples show that for small delay case, the results obtained in this paper significantly improve the estimate of the stability limit over some existing result in the literature; for non-small delay case, the ideal results can also be achieved.     

A constrained optimization approach for an adaptive generalized subspace tracking algorithm

In this paper, we present an orthonormal version of the generalized signal subspace tracking. It is based on an interpretation of the generalized signal subspace as the solution of a constrained minimization task. This algorithm, referred to as the CGST algorithm, guarantees the C_x-orthonormality of the estimated generalized signal subspace basis at each iteration which C_x denotes the correlation matrix of the sequence x(t). An efficient implementation of the proposed algorithm enhances applicability of it in real time applications.     

7 Themes for guiding situated ergonomic assessments of medical devices: A case study of an inpatient glucometer

There is relatively little guidance on the situated ergonomic assessment of medical devices, and few case studies that detail this type of evaluation. This paper reports results of a detailed case study that focuses on the design and use of a modern blood glucose meter on an oncology ward. We spent approximately 150 h in-situ, over 11 days and 4 nights, performing observations and interviews with users. This was complemented by interviews with two staff with oversight and management responsibility related to the device. We identified 19 issues with the design and use of this device. These issues were grouped into 7 themes which can help guide the situated study of medical devices: usability, knowledge gaps and mental models, workarounds, wider tasks and equipment, the patient, connection between services, and policy.     

A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor

Support vector machines (SVM) and other machine-learning (ML) methods have been explored as ligand-based virtual screening (VS) tools for facilitating lead discovery. While exhibiting good hit selection performance, in screening large compound libraries, these methods tend to produce lower hit-rate than those of the best performing VS tools, partly because their training-sets contain limited spectrum of inactive compounds. We tested whether the performance of SVM can be improved by using training-sets of diverse inactive compounds. In retrospective database screening of active compounds of single mechanism (HIV protease inhibitors, DHFR inhibitors, dopamine antagonists) and multiple mechanisms (CNS active agents) from large libraries of 2.986 million compounds, the yields, hit-rates, and enrichment factors of our SVM models are 52.4–78.0%, 4.7–73.8%, and 214–10,543, respectively, compared to those of 62–95%, 0.65–35%, and 20–1200 by structure-based VS and 55–81%, 0.2–0.7%, and 110–795 by other ligand-based VS tools in screening libraries of ≥1 million compounds. The hit-rates are comparable and the enrichment factors are substantially better than the best results of other VS tools. 24.3–87.6% of the predicted hits are outside the known hit families. SVM appears to be potentially useful for facilitating lead discovery in VS of large compound libraries.