Integrating knowledge management with project management for project success

This paper aims to study the improvement of project success in organizations by integrating knowledge management strategies with project management practices in a typical project lifecycle.  According to the Standish Group’s Chaos Report for 2009, only 32% of all surveyed projects are considered to be successful and are delivered on time, on budget, with the required features and functions. This could be an indication that project management practitioners have not fully acquired and transferred knowledge learned from past projects to ensure a higher success rate for current and future projects.  Knowledge management is an emerging discipline and practice in organizations. This paper proposes an integrated model that combines knowledge management with project management to improve project success and thus contribute towards competitiveness and sustainability in organizations. &nbsp

Detector-Device-Independent Quantum Key Distribution

Recently, a quantum key distribution (QKD) scheme based on entanglement swapping, called measurement-device-independent QKD (mdiQKD), was proposed to bypass all detector side-channel attacks. While mdiQKD is conceptually elegant and offers a supreme level of security, the experimental complexity is challenging for practical systems. For instance, it requires interference between two widely separated independent single-photon sources, and the rates are dependent on detecting two photons - one from each source. Here we experimentally demonstrate a QKD scheme that removes the need for a two-photon system and instead uses the idea of a two-qubit single-photon (TQSP) to significantly simplify the implementation and improve the efficiency of mdiQKD in several aspects.Comment: 5 pages + 3 figure

Blending Mathematics Teaching with Kindness

Mathematics can be intellectually demanding, engaging, and fulfilling. Learning mathematical concepts adequately warrants an environment where students can err without penalty, shame, or hurtful consequences. Teaching mathematics efficaciously depends on the trusting relationship between the teacher and the students. We advocate blending mathematics teaching with kindness because it benefits the teacher, the students, and society. Kindness, niceness, caring, and benevolence are interrelated but not synonymous. We outline four progressive levels of kindness: conditional, superficial, optimal, and genuine. Blending mathematics teaching and kindness effectively requires the teacher to decenter from their own perspectives and adopt the student’s perspective as the student struggles through a challenging math problem. The efficacy of blending teaching and kindness depends on the teacher’s inner cultivation of benevolence. In one’s journey towards teaching with genuine kindness, one would need self-knowledge, unwavering commitment, continual practice, collegial support, spiritual guidance, and mindful awareness

Detector-device-independent QKD: security analysis and fast implementation

One of the most pressing issues in quantum key distribution (QKD) is the problem of detector side- channel attacks. To overcome this problem, researchers proposed an elegant "time-reversal" QKD protocol called measurement-device-independent QKD (MDI-QKD), which is based on time-reversed entanglement swapping. However, MDI-QKD is more challenging to implement than standard point- to-point QKD. Recently, an intermediary QKD protocol called detector-device-independent QKD (DDI-QKD) has been proposed to overcome the drawbacks of MDI-QKD, with the hope that it would eventually lead to a more efficient detector side-channel-free QKD system. Here, we analyze the security of DDI-QKD and elucidate its security assumptions. We find that DDI-QKD is not equivalent to MDI-QKD, but its security can be demonstrated with reasonable assumptions. On the more practical side, we consider the feasibility of DDI-QKD and present a fast experimental demonstration (clocked at 625 MHz), capable of secret key exchange up to more than 90 km.Comment: 9 pages, 4 figure

Maximizing bidder’s profit in online auctions using grey system theory predictor agent

Purpose – As the demand for online auctions increases, the process of monitoring multiple auction houses, deciding which auction to participate in and making the right bids, become challenging tasks for consumers. Hence, knowing the closing price of a given auction would be an advantage, since this information will ensure a win in a given auction. However, predicting a closing price for an auction is not easy, since it is dependent on many factors. The purpose of this paper is to report on a predictor agent that utilises grey system theory to predict the closing price for a given auction. Design/methodology/approach – The focus of the research is on grey system agent. This paper reports on the development of a predictor agent that attempts to predict the online auction closing price in order to maximise the bidder's profit. The performance of this predictor agent is compared with two well‐known techniques, the Simple Exponential Function and the Time Series, in a simulated auction environment and in the eBay auction. Findings – The grey theory agent gives a better result when less input data are made, while the Time Series Agent can be used with the availability of a lot of information. Although the Simple Exponential Function Agent is able to predict well with less input data, it is not an appropriate method to be applied in the prediction model since its formula is not realistic and applicable in predicting the online auction closing price. The experimental results also showed that using moving historical data produces a higher accuracy rate than using fixed historical data for all three agents. Originality/value – Grey system theory prediction model, GM(1, 1) has not been applied in online auction prediction. In this paper the authors have applied grey theory into an agent to predict the closing price of an online auction, in order to increase the profit of bidders in the bidding stage. The experimental results show that the accuracy of the grey prediction model is more then 90 per cent, with less then eight historical data inputs

FINESSE: Field Investigations to Enable Solar System Science and Exploration

The FINESSE (Field Investigations to Enable Solar System Science and Exploration) team is focused on a science and exploration field-based research program aimed at generating strategic knowledge in preparation for the human and robotic exploration of the Moon, near-Earth asteroids (NEAs) and Phobos and Deimos. We follow the philosophy that "science enables exploration and exploration enables science." 1) FINESSE Science: Understand the effects of volcanism and impacts as dominant planetary processes on the Moon, NEAs, and Phobos & Deimos. 2) FINESSE Exploration: Understand which exploration concepts of operations (ConOps) and capabilities enable and enhance scientific return. To accomplish these objectives, we are conducting an integrated research program focused on scientifically-driven field exploration at Craters of the Moon National Monument and Preserve in Idaho and at the West Clearwater Lake Impact Structure in northern Canada. Field deployments aimed at reconnaissance geology and data acquisition were conducted in 2014 at Craters of the Moon National Monument and Preserve. Targets for data acquisition included selected sites at Kings Bowl eruptive fissure, lava field and blowout crater, Inferno Chasm vent and outflow channel, North Crater lava flow and Highway lava flow. Field investigation included (1) differential GPS (dGPS) measurements of lava flows, channels (and ejecta block at Kings Bowl); (2) LiDAR imaging of lava flow margins, surfaces and other selected features; (3) digital photographic documentation; (4) sampling for geochemical and petrographic analysis; (5) UAV aerial imagery of Kings Bowl and Inferno Chasm features; and (6) geologic assessment of targets and potential new targets. Over the course of the 5-week field FINESSE campaign to the West Clearwater Impact Structure (WCIS) in 2014, the team focused on several WCIS research topics, including impactites, central uplift formation, the impact-generated hydrothermal system, multichronometer dating of impact products, and using WCIS as an analog test site for crew studies of sampling protocols. The FINESSE team visited and mapped all of the major islands within West Clearwater Lake. Excellent cliff exposures around the coasts of many of the islands allowed a general stratigraphy of impactites to be defined. Notable differences to previous work includes the discovery of a monomict lithic breccia and a medium to coarse grained impact melt rock. In addition, ample rock samples were returned from West Clearwater for geochronology study. Geochronology work centers around laboratory analyses of these samples (and samples collected in the future or obtained from archives housed at the Canadian Geological Survey). Samples returned from the FINESSE field season have been evaluated for suitability for geochronologic analysis, and selected samples have been crushed for mineral separation and/or sawed for the preparation of polished petrologic thin sections. Heavy minerals (e.g., zircon, titanite, and apatite) will be separated from the crushed material for (U-Th)/He geochronology. The sections will be used for laser ablation 40Ar/39Ar research after neutron irradiation. This presentation will highlight the exciting science and exploration work conducted by FINESSE, as well as future plans for continued research

A multi-species functional embedding integrating sequence and network structure

A key challenge to transferring knowledge between species is that different species have fundamentally different genetic architectures. Initial computational approaches to transfer knowledge across species have relied on measures of heredity such as genetic homology, but these approaches suffer from limitations. First, only a small subset of genes have homologs, limiting the amount of knowledge that can be transferred, and second, genes change or repurpose functions, complicating the transfer of knowledge. Many approaches address this problem by expanding the notion of homology by leveraging high-throughput genomic and proteomic measurements, such as through network alignment. In this work, we take a new approach to transferring knowledge across species by expanding the notion of homology through explicit measures of functional similarity between proteins in different species. Specifically, our kernel-based method, HANDL (Homology Assessment across Networks using Diffusion and Landmarks), integrates sequence and network structure to create a functional embedding in which proteins from different species are embedded in the same vector space. We show that inner products in this space and the vectors themselves capture functional similarity across species, and are useful for a variety of functional tasks. We perform the first whole-genome method for predicting phenologs, generating many that were previously identified, but also predicting new phenologs supported from the biological literature. We also demonstrate the HANDL embedding captures pairwise gene function, in that gene pairs with synthetic lethal interactions are significantly separated in HANDL space, and the direction of separation is conserved across species. Software for the HANDL algorithm is available at http://bit.ly/lrgr-handl.Published versio