Preparation and characterization of activated carbon from palm kernel shell

In Malaysia, the production of activated carbons is still coconut-based although Malaysia has long shifted from coconut into palm oil plantation. Huge amount of waste Palm Kernel Shells (PKS) are being generated and disposed off into the landfill with little known of their usage on large scale. In this study, the potential of production of activated carbon from raw palm kernel shells are studied. Activated carbon was prepared from raw palm kernel shells using chemical activation with potassium hydroxide (KOH) as an activating agent. The effects of different process parameters: KOH concentration, activation temperature and time on physicochemical properties of the prepared activated carbon were investigated. The activated carbon was analyzed using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), Fourier Transform Infrared (FTIR) spectroscopy, proximate analysis and methylene blue adsorption study. FTIR analysis indicates that raw palm kernel shell has successfully been converted into activated carbon. SEM photograph revealed that prepared activated carbons have numerous burn-off pores with extensive surface area for adsorption. Activated carbon sample prepared at 700 ºC and 1 hour activation with 30 wt % KOH impregnation showed greatest extend of methylene blue removal of 6.932 mg/g equivalent to 69.324 %RE with largest specific surface area of 21.137 x 10-3 km2kg-1 have been reported. This study shows that palm kernel shells can be used as a good source for the production of activated carbon

The mediation between participative leadership and employee exploratory innovation: Examining intermediate knowledge mechanisms

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.We examine mediation effects of coworker knowledge sharing and absorptive capacity on the participative leadership–employee exploratory innovation relationship in R&D units of Taiwanese technology firms. Deploying a time-lagged questionnaire method implemented over four business quarters, data is generated from 1600 paired samples (managers and employees) in R&D units of Taiwanese technology firms. The structural equation modeling results reveal that (1) participative leadership is positively related to employee exploratory innovation; (2) coworker knowledge and (3) absorptive capacity partially mediate the relationship between participative leadership and employee exploratory innovation independently; and, (4) coworker knowledge sharing in combination with absorptive capacity partially mediates this relationship. The results extend previous research on participative leadership and innovation by demonstrating that participative leadership is related to employee exploratory innovation (Lee and Meyer-Doyle, 2017; Mom et al., 2009).Results also confirm that participative leadership drives employee exploratory innovation through employee absorptive capacity. This reinforces the need highlighted by Lane et al. (2006) to investigate the role of absorptive capacity at the individual-level. Collectively, while participative leadership is important for employee exploratory innovation it is the knowledge mechanisms existing and interacting at the employee-level that are central to generating increased employee exploratory innovation from this leadership approach

Degree correlation effect of bipartite network on personalized recommendation

In this paper, by introducing a new user similarity index base on the diffusion process, we propose a modified collaborative filtering (MCF) algorithm, which has remarkably higher accuracy than the standard collaborative filtering. In the proposed algorithm, the degree correlation between users and objects is taken into account and embedded into the similarity index by a tunable parameter. The numerical simulation on a benchmark data set shows that the algorithmic accuracy of the MCF, measured by the average ranking score, is further improved by 18.19% in the optimal case. In addition, two significant criteria of algorithmic performance, diversity and popularity, are also taken into account. Numerical results show that the presented algorithm can provide more diverse and less popular recommendations, for example, when the recommendation list contains 10 objects, the diversity, measured by the hamming distance, is improved by 21.90%.Comment: 9 pages, 3 figure