Library Impact Data Project: hit, miss or maybe

Purpose In February 2011 the University of Huddersfield along with 7 partners were awarded JISC funding through the Activity Data programme to investigate the hypothesis that: “There is a statistically significant correlation across a number of universities between library activity data and student attainment” The Library Impact Data Project aimed to analyse users’ actions with regards to library usage and then linking those to final degree award. By identifying a positive correlation in this data those subject areas or courses which exhibit high usage of library resources can be used as models of good practice. Design, methodology or approach The overall approach of the project is to extract anonymised activity data from partners’ systems and analyse the findings. For each student who graduated in the sample years, the following data was required: final grade achieved; number of books borrowed; number of times e-resources were accessed; number of times each student entered the library and school or faculty. This data was then collated, normalised, and then analysed. In addition all partners were asked to hold a number of focus groups in order to secure qualitative data from students on library usage to provide a holistic picture of how students engage with library resources. Findings This paper will report on the findings of the project which ran from February to July 2011. It will consider whether the hypothesis was proven for the three indicators of library usage. Research or practical limitations or implications The main aim of the project was to support the hypothesis. The project acknowledges however, that the relationship between the two variables is not a causal relationship and there will be other factors which influence student attainment. Conclusions The paper will discuss the implications of the results and suggest further work that could result from the projects findings

Comparison of stimulation patterns for FES-cycling using measures of oxygen cost and stimulation cost

<b>Aim</b><p></p> The energy efficiency of FES-cycling in spinal cord injured subjects is very much lower than that of normal cycling, and efficiency is dependent upon the parameters of muscle stimulation. We investigated measures which can be used to evaluate the effect on cycling performance of changes in stimulation parameters, and which might therefore be used to optimise them. We aimed to determine whether oxygen cost and stimulation cost measurements are sensitive enough to allow discrimination between the efficacy of different activation ranges for stimulation of each muscle group during constant-power cycling. <p></p> <b>Methods</b><p></p> We employed a custom FES-cycling ergometer system, with accurate control of cadence and stimulated exercise workrate. Two sets of muscle activation angles (“stimulation patterns”), denoted “P1” and “P2”, were applied repeatedly (eight times each) during constant-power cycling, in a repeated measures design with a single paraplegic subject. Pulmonary oxygen uptake was measured in real time and used to determine the oxygen cost of the exercise. A new measure of stimulation cost of the exercise is proposed, which represents the total rate of stimulation charge applied to the stimulated muscle groups during cycling. A number of energy-efficiency measures were also estimated. <p></p> <b>Results</b><p></p> Average oxygen cost and stimulation cost of P1 were found to be significantly lower than those for P2 (paired <i>t</i>-test, <i>p</i> < 0.05): oxygen costs were 0.56 ± 0.03 l min<sup>−1</sup> and 0.61 ± 0.04 l min<sup>−1</sup>(mean ± S.D.), respectively; stimulation costs were 74.91 ± 12.15 mC min<sup>−1</sup> and 100.30 ± 14.78 mC min<sup>−1</sup> (mean ± S.D.), respectively. Correspondingly, all efficiency estimates for P1 were greater than those for P2. <p></p> <b>Conclusion</b><p></p> Oxygen cost and stimulation cost measures both allow discrimination between the efficacy of different muscle activation patterns during constant-power FES-cycling. However, stimulation cost is more easily determined in real time, and responds more rapidly and with greatly improved signal-to-noise properties than the ventilatory oxygen uptake measurements required for estimation of oxygen cost. These measures may find utility in the adjustment of stimulation patterns for achievement of optimal cycling performance. <p></p&gt

Hierarchical incremental class learning with reduced pattern training

Hierarchical Incremental Class Learning (HICL) is a new task decomposition method that addresses the pattern classification problem. HICL is proven to be a good classifier but closer examination reveals areas for potential improvement. This paper proposes a theoretical model to evaluate the performance of HICL and presents an approach to improve the classification accuracy of HICL by applying the concept of Reduced Pattern Training (RPT). The theoretical analysis shows that HICL can achieve better classification accuracy than Output Parallelism [1]. The procedure for RPT is described and compared with the original training procedure. RPT reduces systematically the size of the training data set based on the order of sub-networks built. The results from four benchmark classification problems show much promise for the improved model

Pattern matching and pattern discovery algorithms for protein topologies

We describe algorithms for pattern matching and pattern learning in TOPS diagrams (formal descriptions of protein topologies). These problems can be reduced to checking for subgraph isomorphism and finding maximal common subgraphs in a restricted class of ordered graphs. We have developed a subgraph isomorphism algorithm for ordered graphs, which performs well on the given set of data. The maximal common subgraph problem then is solved by repeated subgraph extension and checking for isomorphisms. Despite the apparent inefficiency such approach gives an algorithm with time complexity proportional to the number of graphs in the input set and is still practical on the given set of data. As a result we obtain fast methods which can be used for building a database of protein topological motifs, and for the comparison of a given protein of known secondary structure against a motif database

Generalised Pattern Matching Revisited

In the problem of Generalised Pattern Matching (GPM) [STOC'94, Muthukrishnan and Palem], we are given a text T of length n over an alphabet Σ_T, a pattern P of length m over an alphabet Σ_P, and a matching relationship ⊆ Σ_T × Σ_P, and must return all substrings of T that match P (reporting) or the number of mismatches between each substring of T of length m and P (counting). In this work, we improve over all previously known algorithms for this problem: - For ? being the maximum number of characters that match a fixed character, we show two new Monte Carlo algorithms, a reporting algorithm with time ?(? n log n log m) and a (1-ε)-approximation counting algorithm with time ?(ε^-1 ? n log n log m). We then derive a (1-ε)-approximation deterministic counting algorithm for GPM with ?(ε^-2 ? n log⁶ n) time. - For ? being the number of pairs of matching characters, we demonstrate Monte Carlo algorithms for reporting and (1-ε)-approximate counting with running time ?(√? n log m √{log n}) and ?(√{ε^-1 ?} n log m √{log n}), respectively, as well as a (1-ε)-approximation deterministic algorithm for the counting variant of GPM with ?(ε^-1 √{?} n log^{7/2} n) time. - Finally, for ℐ being the total number of disjoint intervals of characters that match the m characters of the pattern P, we show that both the reporting and the counting variants of GPM can be solved exactly and deterministically in ?(n√{ℐ log m} +n log n) time. At the heart of our new deterministic upper bounds for ? and ? lies a faster construction of superimposed codes, which solves an open problem posed in [FOCS'97, Indyk] and can be of independent interest. To conclude, we demonstrate first lower bounds for GPM. We start by showing that any deterministic or Monte Carlo algorithm for GPM must use Ω(?) time, and then proceed to show higher lower bounds for combinatorial algorithms. These bounds show that our algorithms are almost optimal, unless a radically new approach is developed

Memory Driven Pattern Formation

The diffusion equation is extended by including spatial-temporal memory in such a manner that the conservation of the concentration is maintained. The additional memory term gives rise to the formation of non-trivial stationary solutions. The steady state pattern in an infinite domain is driven by a competition between conventional particle current and a feedback current. We give a general criteria for the existence of a non-trivial stationary state. The applicability of the model is tested in case of a strongly localized, time independent memory kernel. The resulting evolution equation is exactly solvable in arbitrary dimensions and the analytical solutions are compared with numerical simulations. When the memory term offers an spatially decaying behavior, we find also the exact stationary solution in form of a screened potential.Comment: 14 pages, 12 figure