Quarterly Time-Series Forecasting With Neural Networks

Forecasting of time series that have seasonal and other variations remains an important problem for forecasters. This paper presents a neural network (NN) approach to forecasting quarterly time series. With a large data set of 756 quarterly time series from the M3 forecasting competition, we conduct a comprehensive investigation of the effectiveness of several data preprocessing and modeling approaches. We consider two data preprocessing methods and 48 NN models with different possible combinations of lagged observations, seasonal dummy variables, trigonometric variables, and time index as inputs to the NN. Both parametric and nonparametric statistical analyses are performed to identify the best models under different circumstances and categorize similar models. Results indicate that simpler models, in general, outperform more complex models. In addition, data preprocessing especially with deseasonalization and detrending is very helpful in improving NN performance. Practical guidelines are also provided.     

Evaluation of integrated software development environments: Challenges and results from three empirical studies

Evidence shows that integrated development environments (IDEs) are too often functionality-oriented and difficult to use, learn, and master. This article describes challenges in the design of usable IDEs and in the evaluation of the usability of such tools. It also presents the results of three different empirical studies of IDE usability. Different methods are sequentially applied across the empirical studies in order to identify increasingly specific kinds of usability problems that developers face in their use of IDEs. The results of these studies suggest several problems in IDE user interfaces with the representation of functionalities and artifacts, such as reusable program components. We conclude by making recommendations for the design of IDE user interfaces with better affordances, which may ameliorate some of most serious usability problems and help to create more human-centric software development environments.     

Serotyping of Streptococcus pneumoniae by coagglutination with 12 pooled antisera

We report on the performance of a recently introduced commercial chessboard method using 12 antisera, in comparison with that of the 55-antiserum panel used in determining the serogroups and types (SGTs) of Streptococcus pneumoniae, both of which were carried out by a coagglutination technique. Of a total of 150 strains of S. pneumoniae studied, 135 (90%) belonged to the SGTs represented in the 23-valent pneumococcal vaccine; of these, 130 (96.3%) were identified as the same SGTs by both typing methods. The remaining five strains showed cross-reactivity with more than two pools by the chessboard method, but could be assigned to a single SGT by the Quellung test. The 96.3% concordance of the chessboard method suggests it can be adopted for determination of the SGTs of S. pneumoniae in laboratories.     

A review of the groupware literature: Theories, methodologies, and a research agenda.

This paper brings the groupware literature to the attention of researchers and practitioners in order to validate it in terms of delivering computer technologies that actively facilitate groups of collaborating users. It reviews the theoretical perspectives and assumptions of group performance that are driving the present research. Also, the extant literature and outcome measures will be reviewed with an emphasis on the type of research conducted. The authors found that the theoretical frameworks used in the groupware research do not reflect recent developments in Industrial-Organizational Psychology (e.g., groupware research does not generally include critical contextual variables). Based on this review, the authors propose a comprehensive and integrated research agenda for future work in this area. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Sequence database searches via de novo peptide sequencing by tandem mass spectrometry

A method is described for searching protein sequence databases using tandem mass spectra of tryptic peptides. The approach uses a de novo sequencing algorithm to derive a short list of possible sequence candidates which serve as query sequences in a subsequent homology-based database search routine. The sequencing algorithm employs a graph theory approach similar to previously described sequencing programs. In addition, amino acid composition, peptide sequence tags and incomplete or ambiguous Edman sequence data can be used to aid in the sequence determinations. Although sequencing of peptides from tandem mass spectra is possible, one of the frequently encountered difficulties is that several alternative sequences can be deduced from one spectrum. Most of the alternative sequences, however, are sufficiently similar for a homology-based sequence database search to be possible. Unfortunately, the available protein sequence database search algorithms (e.g. Blast or FASTA) require a single unambiguous sequence as input. Here we describe how the publicly available FASTA computer program was modified in order to search protein databases more effectively in spite of the ambiguities intrinsic in de novo peptide sequencing algorithms.     

An Improved Method for Detection of Hemagglutination Using an Automated Microplate Reader

Several new automated microplate readers which can take multiple readings in each well have, been commercially developed. Data gathered from one of these readers have been analyzed to determine a parsimonious algorithm for discriminating positive hemagglutination reactions from negative ones. The resulting classification algorithm provides more accurate classification of weak positive reactions than the one point measurements normally used with only a moderate increase in computational complexity.