Trying to adopt systems development methodologies — a case-based exploration of business users' interests

Information systems methodologies are an important component of the IS infrastructure and a primary device for organizing systems development work. Evidence suggests that methodology adoption and use are problematic. This research seeks to generate insight into business users' interest in adoption through detailed examination of a case. A framework is developed for organizing relevant research findings. The field research methods are described and details of the case reported. The case highlights the role of business managers in methodology adoption and the influence of business pressures originating in the strategic environment. Analysis shows the organizing framework to require extension to include a more direct role for business decision makers. it is argued that previous research has obscured the legitimate concern of business with systems development methodologies. As business increasingly asserts its interest in and control over IS, it will be necessary to give greater consideration to the needs of business in the selection and adoption of methodologies.     

Towards quality requirements via animated formal specifications

Assuring a high quality requirements specification document involves both an early validation process and an increased level of participation. An approach and its supporting environment which combines the benefits of a formal system specification and its subsequent execution via a rapid prototype is reported. The environment assists in the construction, clarification, validation and visualisation of a formal specification. An illustrative case study demonstrates the consequences of assertions about system properties at this early stage of software development. Our approach involves the pragmatic combination of technical benefits of formal systems engineering based techniques with the context‐sensitive notions of increased participation of both developer and user stakeholders to move us closer towards a quality requirements specification document.     

Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality.

A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules. Based on a Takagi-Sugeno (T-S) inference mechanism a one to one mapping between a fuzzy rule base and a model matrix feature subspace is established. This link enables rule based knowledge to be extracted from matrix subspace to enhance model transparency. In order to achieve maximized model robustness and sparsity, a new robust extended Gram-Schmidt (G-S) method has been introduced via two effective and complementary approaches of regularization and D-optimality experimental design. Model rule bases are decomposed into orthogonal subspaces, so as to enhance model transparency with the capability of interpreting the derived rule base energy level. A locally regularized orthogonal least squares algorithm, combined with a D-optimality used for subspace based rule selection, has been extended for fuzzy rule regularization and subspace based information extraction. By using a weighting for the D-optimality cost function, the entire model construction procedure becomes automatic. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.     

Nonlinearity Measurements of High-Power Laser Detectors at NIST

We briefly explain the fundamentals of detector nonlinearity applicable to both electrical and optical nonlinearity measurements. We specifically discuss the attenuation method for optical nonlinearity measurement that the NIST system is based upon, and we review the possible sources of nonlinearity inherent to thermal detectors used with high-power lasers. We also describe, in detail, the NIST nonlinearity measurement system, in which detector responsivity can be measured at wavelengths of 1.06 µm and 10.6 µm, over a power range from 1 W to 1000 W. We present the data processing method used and show measurement results depicting both positive and negative nonlinear behavior. The expanded uncertainty of a typical NIST high-power laser detector calibration including nonlinearity characterization is about 1.3 %.     

Dissipation of Turbulent Kinetic Energy near a Bubble Plume

Profiles of the rate of dissipation of turbulent kinetic energy were inferred from temperature microstructure measurements near a bubble plume at the center of a tank with diameter of 13.7 m and maximum depth of 8.3 m. Six sets of between 18 and 51 profiles were collected at airflow rates of 0.1–0.6 L/s, measured at atmospheric pressure, and ensemble-averaged dissipation profiles were calculated. The dissipation in all cases was between 10?8 and 10?6?m2/s3 in most of the profile, but it increased sharply near the water surface. Energy considerations are used to discuss the experimental results in terms of previous numerical models of bubble plume turbulence. Two previous numerical studies show that the turbulence dissipates between 15 and 30% of the available power. In the experiments, the fraction is less than 1% because some of the energy of the plume is used to generate waves on the water surface and the profiles used to compute the volume-averaged dissipation were relatively far from the bubble plume.     

Calibration of AFM cantilever spring constants

In this paper we present two simple, reliable and readily applicable methods for calibrating cantilevers and measuring the thickness of thin gold films. The spring constant calibration requires knowledge of the Young's modulus, density of the cantilever and resonant frequency. The thickness of thin gold layers was determined by measuring changes in the resonant frequency and Q-factor of beam shaped AFM cantilevers before and after coating.The techniques for measuring the spring constant and thin film thickness provide accuracy on the order of 10-15%.     

Device-based decision-making for adaptation of three-dimensional content

The goal of this research was the creation of an adaptation mechanism for the delivery of three-dimensional content. The adaptation of content, for various network and terminal capabilities – as well as for different user preferences, is a key feature that needs to be investigated. Current state-of-the art research of the adaptation shows promising results for specific tasks and limited types of content, but is still not well-suited for massive heterogeneous environments. In this research, we present a method for transmitting adapted three-dimensional content to multiple target devices. This paper presents some theoretical and practical methods for adapting three-dimensional content, which includes shapes and animation. We also discuss practical details of the integration of our methods into MPEG-21 and MPEG-4 architectures.     

Collection of daily patient reported outcomes is feasible and demonstrates differential patient experience in chronic kidney disease

Introduction: Patient reported outcomes (PROs) are a critical metric documenting the impact of disease and treatment from the patient's perspective. A variety of generic and disease specific PRO measures (PROMs) are used in chronic kidney disease (CKD) but studies are primarily cross‐sectional. None of the available PROMs are designed for frequent iterative application. Methods: An online PROM for daily use in dialysis and CKD 4/5 patients was developed. The custom website utilised visual analogue scales to capture 6 PROs (general well being (GWB), pain, sleep, breathing, energy, and appetite). Outcomes of interest were uptake, response rates, intermodality variation, and change in PRO corresponding to predefined events. Findings: Forty‐three patients submitted at least once and 34 submitted beyond 30 days. Median follow‐up was 247 days, 64% male, age 62 ± 12 years. In individuals submitting for >30 days, dialysis patients had significantly worse median scores compared to CKD for sleep (47[32–80], 97[76–99], P = 0.003), appetite (66[50–96], 97[88–100], P = 0.008), energy (47[40–89], 84[67–96], P = 0.031), and GWB (63[49–94], 93[71–98], P = 0.026). Patients demonstrated a variety of stable bandwidths of response, deviations from this were associated with specific events e.g., acute admission, vascular procedures, disturbed fluid status, and dialysis start. Discussion: We successfully introduced an online, patient acceptable, iterative PROM that discriminates symptom burden, cross‐sectionally, and longitudinally. Further work will prospectively examine the predictive power of changes in PRO and more rigorously investigate the potential use of these methods to optimise patient care.     

Developing crash modification functions to assess safety effects of adding bike lanes for urban arterials with different roadway and socio-economic characteristics

Although many researchers have estimated crash modification factors (CMFs) for specific treatments (or countermeasures), there is a lack of studies that explored the heterogeneous effects of roadway characteristics on crash frequency among treated sites. Generally, the CMF estimated by before–after studies represents overall safety effects of the treatment in a fixed value. However, as each treated site has different roadway characteristics, there is a need to assess the variation of CMFs among the treated sites with different roadway characteristics through crash modification functions (CMFunctions). The main objective of this research is to determine relationships between the safety effects of adding a bike lane and the roadway characteristics through (1) evaluation of CMFs for adding a bike lane using observational before–after with empirical Bayes (EB) and cross-sectional methods, and (2) development of simple and full CMFunctions which are describe the CMF in a function of roadway characteristics of the sites. Data was collected for urban arterials in Florida, and the Florida-specific full SPFs were developed. Moreover, socio-economic parameters were collected and included in CMFunctions and SPFs (1) to capture the effects of the variables that represent volume of bicyclists and (2) to identify general relationship between the CMFs and these characteristics. In order to achieve better performance of CMFunctions, data mining techniques were used.