Yuming Guan, Jingbo Mu, Hongwei Che, Xiaoliang Zhang and Zhixiao Zhang
The purpose of this study is to design carbon electrode materials for high performance electric double-layer capacitors (EDLCs) with pores that are large enough and have suitable…
Abstract
Purpose
The purpose of this study is to design carbon electrode materials for high performance electric double-layer capacitors (EDLCs) with pores that are large enough and have suitable pore size distribution for the electrolyte to access completely to improve EDLCs’ electrochemical performance.
Design/methodology/approach
This study develop an improved traditional KOH activation method, and a series of micro-meso hierarchical porous carbons have been successfully prepared from phenol formaldehyde resin by combining polyethylene glycol (PEG) and conventional KOH activation.
Findings
As evidenced by N2 adsorption/desorption tests, the obtained samples present Types IV and I-IV hybrid shape isotherms compared with KOH-activated resin (typical of Type I). The sample AC2-7-1, which the addition quantity of PEG is 25 per cent PF (weight ration) activated at 700? For 1 h is considered as the optimum preparation condition. It exhibits the highest specific capacitance value of 240 F/g in 30 wt% KOH aqueous electrolytes because of its higher specific surface area (2085 m2/g), greater pore volume (1.08 cm3/g) and the maximum mesoporosity (43 per cent). In addition, the capacity decay of this material is only 3.1 per cent after 1000 cycles.
Originality/value
The materials that are rich in micropores and mesopores show great potential in EDLC capacitors, particularly for applications where high power output and good high-frequency capacitive performances are required.
Details
Keywords
Ruan Wang, Jun Deng, Xinhui Guan and Yuming He
With the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data…
Abstract
Purpose
With the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data visualization techniques to genealogical data is limited. This paper aims to fill this research gap by providing a systematic framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy.
Design/methodology/approach
Based on a literature review of genealogy's current knowledge reasoning research, the authors constructed an integrated framework for knowledge inference and visualization application using a knowledge graph. Additionally, the authors applied this framework in a case study using “Manchu Clan Genealogy” as the data source.
Findings
The case study shows that the proposed framework can effectively decompose and reconstruct genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting the intricate relationships among people, places, and time entities.
Originality/value
This study proposed a framework for genealogy knowledge reasoning and visual analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and establish a data network with semantic correlations while establishing reasoning rules to enable inference discovery and visualization of hidden relationships.
Details
Keywords
Muneer Shaik and Maheswaran S.
The purpose of this paper is twofold: first, to propose a new robust volatility ratio (RVR) that compares the intraday high–low volatility with that of the intraday open–close…
Abstract
Purpose
The purpose of this paper is twofold: first, to propose a new robust volatility ratio (RVR) that compares the intraday high–low volatility with that of the intraday open–close volatility estimator; and second, to empirically test the proposed RVR on the cross-sectional (CS) average of the constituent stocks of India’s BSE Sensex and US’s Dow Jones Industrial Average index to find the evidence of “excess volatility.”
Design/methodology/approach
The authors model the proposed RVR by assuming the logarithm of the price process to follow the Brownian motion. The authors have theoretically shown that the RVR is unbiased in the case of zero drift parameter. Moreover, the RVR is found to be an even function of the non-zero drift parameter.
Findings
The empirical results show that the analysis based on the RVR supports the existence of “excess volatility” in the CS average of the constituent stocks of India’s BSE Sensex and US’s Dow Jones index. In particular, the authors have observed that the CS average of individual constituent stocks of BSE Sensex is found to be more excessively volatile than the US’s Dow Jones index during the period of the study from January 2008 to September 2016, based on multiple k-day time window analysis.
Practical implications
The study has implications for the policy makers and practitioners who would like to understand the volatility behavior in the asset returns based on the RVR of this study. In general, the proposed model can be used as a specification tool to find whether the stock prices follow the random walk behavior or excessively volatile.
Originality/value
The authors contribute to the existing volatility literature in finance by proposing a new RVR based on extreme values of asset prices and absolute returns. The authors implement the bootstrap technique on RVR to find the estimates of mean and standard error for multiple k-day time windows. The RVR can capture the excess volatility by comparing two independent volatility estimators. This is possibly the first study to find the CS average of all the constituent stocks of BSE Sensex based on the RVR.