Challenges to Adopting Hybrid Methodology: Addressing Organizational Culture and Change Control Problems in Enterprise IT Infrastructure Projects

IT infrastructure projects have long been an overlooked field superseded by the more popular software development silos and cross-functional project teams when it comes to enterprise Agile transformations. This paper presents a systematic literature review by leveraging a qualitative research methodology based on empirical evidence provided in contemporary scholarly research articles to explore how certain variables such as organizational culture- including team structure, leadership hierarchy, geolocation, etc. along with an organization’s change management processes affect the adoption of a Hybrid/Agile project management methodology, focusing on reported challenges and critical success factors that define such large-scale enterprise transformations. The salient features from the conclusion of this preliminary research endeavor point to a direct relationship between certain aspects such as the size of the organization, stakeholder buy-in, and inherent resistance to change playing pivotal roles that define success within IT infrastructure teams and associated projects. This research endeavor and literature review also identifies a plethora of opportunities within IT infrastructure project management practices for future research based on the gaps that have been identified in contemporary literature

The sedimentation of flexible filaments

The dynamics of a flexible filament sedimenting in a viscous fluid are explored analytically and numerically. Compared to the well-studied case of sedimenting rigid rods, the introduction of filament compliance is shown to cause a significant alteration in the long-time sedimentation orientation and filament geometry. A model is developed by balancing viscous, elastic, and gravitational forces in a slender-body theory for zero-Reynolds-number flows, and the filament dynamics are characterized by a dimensionless elasto-gravitation number. Filaments of both non-uniform and uniform cross-sectional thickness are considered. In the weakly flexible regime, a multiple-scale asymptotic expansion is used to obtain expressions for filament translations, rotations, and shapes. These are shown to match excellently with full numerical simulations. Furthermore, we show that trajectories of sedimenting flexible filaments, unlike their rigid counterparts, are restricted to a cloud whose envelope is determined by the elasto-gravitation number. In the highly flexible regime we show that a filament sedimenting along its long axis is susceptible to a buckling instability. A linear stability analysis provides a dispersion relation, illustrating clearly the competing effects of the compressive stress and the restoring elastic force in the buckling process. The instability travels as a wave along the filament opposite the direction of gravity as it grows and the predicted growth rates are shown to compare favorably with numerical simulations. The linear eigenmodes of the governing equation are also studied, which agree well with the finite-amplitude buckled shapes arising in simulations

Spin readout via spin-to-charge conversion in bulk diamond nitrogen-vacancy ensembles

We demonstrate optical readout of ensembles of nitrogen-vacancy(NV) center spins in a bulk diamond sample via spin-to-charge conversion. A high power 594 nm laser is utilized to selectively ionize these paramagnetic defects in the spin state with a contrast of up to 12%. In comparison with the conventional 520 nm spin readout, spin-to-charge-conversion-based readout provides higher signal-to-noise ratio, with tenfold sensing measurement speedup for millisecond long pulse sequences. This level of performance was achieved for an NV- ionization of only 25%, limited by the ionization and readout laser powers. These observations pave the way to a range of high-sensitivity metrology applications where the use of NV- ensembles in bulk diamond has proven useful, including sensing and imaging of target materials overlaid on the diamond surface

Communicating uncertain information from deep learning models to users

“The use of Artificial Intelligence (AI) decision support systems is increasing in high-stakes contexts, such as healthcare, defense, and finance. Uncertainty information may help users better leverage AI predictions, especially when combined with domain knowledge. I conducted two human-subject experiments to examine the effects of uncertainty information with AI recommendations. The experimental stimuli are from an existing image recognition deep learning model, one popular approach to AI. In Paper I, I evaluated the effect of the number of AI recommendations and provision of uncertainty information. For a series of images, participants identified the subject and rated their confidence level. Results suggest that AI recommendations, especially multiple, increased accuracy and confidence. However, uncertainty information, which was represented visually with bars, did not significantly improve participants\u27 performance. In Paper II, I tested the effect of AI recommendations in a within-subject comparison and the effect of more salient uncertainty information in a between-subject comparison in the context of varying domain knowledge. The uncertainty information combined both numerical (percent) and visual (color-coded bar) formats to make the information easier to interpret and more noticeable. Consistent with Paper I, results suggest that AI recommendations improved participants’ accuracy and confidence. In addition, the more salient uncertainty information significantly increased accuracy, but not confidence. Based on a subjective measure of domain knowledge, participants had higher domain knowledge for animals. In general, AI recommendations and uncertainty information had less of an effect as domain knowledge increased. Results suggest that uncertainty information, can improve accuracy and potentially decrease over-confidence”--Abstract, page iv