477 research outputs found
Analysing Knowledge-Sharing Practices Using Activity Theory in the SME Organisation
An activity theory method is used to analyse the knowledge-sharing practices. The activity theory emphasises the necessity of analysing the SME organisation as a whole. In the context of knowledge-sharing practices, activity theory is used to collect interconnected parts of SME practices. A cross-sectional design was used to study the relationship among relationship commitment, knowledge-sharing practices, employee development, team performance, and a moderating role of social identification. The majority of the SMEs were established 3–5 years ago (46.3%), and 84.4% were private, with an employee range of less than 50 (73.1%). Furthermore, 82.1% of the SMEs in this study were in the growth stage. Knowledge-sharing practices have a significant positive effect on team performance (0.278, [Formula: see text]), with a moderating impact of role and behaviour on knowledge-sharing practices and team performance (0.178, [Formula: see text]). The findings have confirmed the significant and positive effects of knowledge-sharing practices on the mediation of employee development (0.045, [Formula: see text]). The activity theory models for knowledge-sharing practices emphasise the contextual nature of knowledge sharing and ensure systematic evaluation
Predictive Analysis of Students’ Learning Performance Using Data Mining Techniques: A Comparative Study of Feature Selection Methods
The utilization of data mining techniques for the prompt prediction of academic success has gained significant importance in the current era. There is an increasing interest in utilizing these methodologies to forecast the academic performance of students, thereby facilitating educators to intervene and furnish suitable assistance when required. The purpose of this study was to determine the optimal methods for feature engineering and selection in the context of regression and classification tasks. This study compared the Boruta algorithm and Lasso regression for regression, and Recursive Feature Elimination (RFE) and Random Forest Importance (RFI) for classification. According to the findings, Gradient Boost for the regression part of this study had the least Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE) of 12.93 and 18.28, respectively, in the case of the Boruta selection method. In contrast, RFI was found to be the superior classification method, yielding an accuracy rate of 78% in the classification part. This research emphasized the significance of employing appropriate feature engineering and selection methodologies to enhance the efficacy of machine learning algorithms. Using a diverse set of machine learning techniques, this study analyzed the OULA dataset, focusing on both feature engineering and selection. Our approach was to systematically compare the performance of different models, leading to insights about the most effective strategies for predicting student success
Towards Designing a Knowledge Sharing System for Higher Learning Institutions in the UAE Based on the Social Feature Framework
Numerous ICT instruments, such as communication tools, social media platforms, and collaborative software, bolster and facilitate knowledge sharing activities. Determining the vital success factors for knowledge sharing within its unique context is argued to be essential before implementing it. Therefore, it is imperative to define domain-specific critical success factors when envisioning the design of a knowledge sharing system. This research paper introduces the blueprint for an Academic Knowledge Sharing System (AKSS), rooted in an essential success framework tailored to knowledge sharing to deploy within an academic institution. In this regard, an extensive exploration of the relevant literature led to the formulation of the research hypothesis that guided the construction of a questionnaire targeting university students through the online platform Pollfish, utilizing a quantitative approach to investigate, while the data collected was analyzed using SPSS version 22. The study unveils critical factors, including encouragement, acknowledgment, a reward system, fostering a knowledge sharing culture, and leading by example, contributing to developing the knowledge sharing framework. Furthermore, the study illustrates how this framework seamlessly integrated into the design, implementation, and execution of the Academic Knowledge Sharing System (AKSS)
Intelligent Fault-Tolerant Mechanism for Data Centers of Cloud Infrastructure
Fault tolerance in cloud computing is considered as one of the most vital issues to deliver reliable services. Checkpoint/restart is one of the methods used to enhance the reliability of the cloud services. However, many existing methods do not focus on virtual machine (VM) failure that occurs due to the higher response time of a node, byzantine fault, and performance fault, and existing methods also ignore the optimization during the recovery phase. This paper proposes a checkpoint/restart mechanism to enhance reliability of cloud services. Our work is threefold: (1) we design an algorithm to identify virtual machine failure due to several faults; (2) an algorithm to optimize the checkpoint interval time is designed; (3) lastly, the asynchronous checkpoint/restart with log-based recovery mechanism is used to restart the failed tasks. The valuation results obtained using a real-time dataset shows that the proposed model reduces power consumption and improves the performance with a better fault tolerance solution compared to the nonoptimization method
Neural Modeling and Control of Diesel Engine with Pollution Constraints
The paper describes a neural approach for modelling and control of a
turbocharged Diesel engine. A neural model, whose structure is mainly based on
some physical equations describing the engine behaviour, is built for the
rotation speed and the exhaust gas opacity. The model is composed of three
interconnected neural submodels, each of them constituting a nonlinear
multi-input single-output error model. The structural identification and the
parameter estimation from data gathered on a real engine are described. The
neural direct model is then used to determine a neural controller of the
engine, in a specialized training scheme minimising a multivariable criterion.
Simulations show the effect of the pollution constraint weighting on a
trajectory tracking of the engine speed. Neural networks, which are flexible
and parsimonious nonlinear black-box models, with universal approximation
capabilities, can accurately describe or control complex nonlinear systems,
with little a priori theoretical knowledge. The presented work extends optimal
neuro-control to the multivariable case and shows the flexibility of neural
optimisers. Considering the preliminary results, it appears that neural
networks can be used as embedded models for engine control, to satisfy the more
and more restricting pollutant emission legislation. Particularly, they are
able to model nonlinear dynamics and outperform during transients the control
schemes based on static mappings.Comment: 15 page
Caustics of Compensated Spherical Lens Models
We consider compensated spherical lens models and the caustic surfaces they
create in the past light cone. Examination of cusp and crossover angles
associated with particular source and lens redshifts gives explicit lensing
models that confirm previous claims that area distances can differ by
substantial factors from angular diameter distances even when averaged over
large angular scales. `Shrinking' in apparent sizes occurs, typically by a
factor of 3 for a single spherical lens, on the scale of the cusp caused by the
lens; summing over many lenses will still leave a residual effect.Comment: 21 pages, 5 ps figures, eps
Lensing and caustic effects on cosmological distances
We consider the changes which occur in cosmological distances due to the
combined effects of some null geodesics passing through low-density regions
while others pass through lensing-induced caustics. This combination of effects
increases observed areas corresponding to a given solid angle even when
averaged over large angular scales, through the additive effect of increases on
all scales, but particularly on micro-angular scales; however angular sizes
will not be significantly effected on large angular scales (when caustics
occur, area distances and angular-diameter distances no longer coincide). We
compare our results with other works on lensing, which claim there is no such
effect, and explain why the effect will indeed occur in the (realistic)
situation where caustics due to lensing are significant. Whether or not the
effect is significant for number counts depends on the associated angular
scales and on the distribution of inhomogeneities in the universe. It could
also possibly affect the spectrum of CBR anisotropies on small angular scales,
indeed caustics can induce a non-Gaussian signature into the CMB at small
scales and lead to stronger mixing of anisotropies than occurs in weak lensing.Comment: 28 pages, 6 ps figures, eps
Dark Energy or Apparent Acceleration Due to a Relativistic Cosmological Model More Complex than FLRW?
We use the Szekeres inhomogeneous relativistic models in order to fit
supernova combined data sets. We show that with a choice of the spatial
curvature function that is guided by current observations, the models fit the
supernova data almost as well as the LCDM model without requiring a dark energy
component. The Szekeres models were originally derived as an exact solution to
Einstein's equations with a general metric that has no symmetries and are
regarded as good candidates to model the true lumpy universe that we observe.
The null geodesics in these models are not radial. The best fit model found is
also consistent with the requirement of spatial flatness at CMB scales. The
first results presented here seem to encourage further investigations of
apparent acceleration using various inhomogeneous models and other constraints
from CMB and large structure need to be explored next.Comment: 6 pages, 1 figure, matches version published in PR
Scalar field and electromagnetic perturbations on Locally Rotationally Symmetric spacetimes
We study scalar field and electromagnetic perturbations on Locally
Rotationally Symmetric (LRS) class II spacetimes, exploiting a recently
developed covariant and gauge-invariant perturbation formalism. From the
Klein-Gordon equation and Maxwell's equations, respectively, we derive
covariant and gauge-invariant wave equations for the perturbation variables and
thereby find the generalised Regge-Wheeler equations for these LRS class II
spacetime perturbations. As illustrative examples, the results are discussed in
detail for the Schwarzschild and Vaidya spacetime, and briefly for some classes
of dust Universes.Comment: 22 pages; v3 has minor changes to match published versio
Risk in the "Red Zone": Outcomes for Children Admitted to Ebola Holding Units in Sierra Leone Without Ebola Virus Disease.
We collected data on 1054 children admitted to Ebola Holding Units in Sierra Leone and describe outcomes of 697/1054 children testing negative for Ebola virus disease (EVD) and accompanying caregivers. Case-fatality was 9%; 3/630 (0.5%) children discharged testing negative were readmitted EVD-positive. Nosocomial EVD transmission risk may be lower than feared
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