Factor Retention Decisions in Exploratory Factor Analysis Results: A Study Type of Knowledge Management Process at Malaysian University Libraries

Structural equation modeling (SEM) is a versatile statistical modeling tool which uses in the social sciences research. Recently, in Library and Information Science (LIS) environment, structural equation modeling has gained popularity across many disciplines, due to its generality and flexibility. Its estimation techniques, modeling capabilities and breadth of application are expanding rapidly.This paper reported a structural equation modeling through an Exploratory Factor Analysis (EFA) result, which involves 300 lead users at six selected Malaysian university libraries through survey. The decision of how many factors to retain is a critical component of exploratory factor analysis. Evidence is presented that parallel analysis is one of the most accurate factor retention methods. SPSS 20 was utilized to analyze the factor analysis data. In this regards, the results of EFA could provide empirical evidence of each hypotheses construct. It is hoped that the EFA results could be used to level Confirmatory Factor Analysis (CFA) to perform full Structural Equation Modeling.© 2013 The Authors. Copyright for this article is retained by the author(s), with first publication rights granted to the journal. This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).fi=vertaisarvioitu|en=peerReviewed

Effect of site of lactate infusion on regional lactate exchange in pigs

Background The rate of extra-hepatic lactate production and the route of influx of lactate to the liver may influence both hepatic and extra-hepatic lactate exchange. We assessed the dose-response of hepatic and extra-hepatic lactate exchange during portal and central venous lactate infusion. Methods Eighteen pigs randomly received either portal (n=5) or central venous (n=7) lactate infusion or saline (n=6). Sodium lactate was infused at 33, 66, 99, and 133 µmol kg−1 min−1 for 20 min each. Systemic and regional abdominal blood flows and plasma lactate were measured at 20 min intervals until 1 h post-infusion, and regional lactate exchange was calculated (area under lactate uptake-time curve). Results Total hepatic lactate uptake [median (95% confidence interval)] during the experimental protocol (140 min) was higher during portal [8198 (5487-12 798) µmol kg−1] than during central venous lactate infusion [4530 (3903-5514) µmol kg−1, P<0.05]. At a similar hepatic lactate delivery (∼400 µmol kg−1 min−1), hepatic lactate uptake [mean and standard deviation (sd)] was higher during portal [118 (sd 55) µmol kg−1 min−1] than during central venous lactate infusion [44 (12) µmol kg−1 min−1, P<0.05]. Time courses of arterial lactate concentrations and lactate uptake at other measured regions were similar in both groups. Conclusions Higher hepatic lactate uptake during portal compared with central venous lactate infusion at a similar total hepatic lactate influx underlines the role of portal vein lactate concentration in total hepatic lactate uptake capacity. Arterial lactate concentration does not depend on the site of lactate infusion. At higher arterial lactate concentrations, all regions participated in lactate uptak

An integrated hardware/software design methodology for signal processing systems

This paper presents a new methodology for design and implementation of signal processing systems on system-on-chip (SoC) platforms. The methodology is centered on the use of lightweight application programming interfaces for applying principles of dataflow design at different layers of abstraction. The development processes integrated in our approach are software implementation, hardware implementation, hardware-software co-design, and optimized application mapping. The proposed methodology facilitates development and integration of signal processing hardware and software modules that involve heterogeneous programming languages and platforms. As a demonstration of the proposed design framework, we present a dataflow-based deep neural network (DNN) implementation for vehicle classification that is streamlined for real-time operation on embedded SoC devices. Using the proposed methodology, we apply and integrate a variety of dataflow graph optimizations that are important for efficient mapping of the DNN system into a resource constrained implementation that involves cooperating multicore CPUs and field-programmable gate array subsystems. Through experiments, we demonstrate the flexibility and effectiveness with which different design transformations can be applied and integrated across multiple scales of the targeted computing system