Future development strategies for KODISA journals: overview of 2016 and strategic plans for the future

Purpose – With the rise of the fourth industrial revolution, it has converged with the existing industrial revolution to give shape to increased accessibility of knowledge and information. As a result, it has become easier for scholars to actively pursue and compile research in various fields. This current study aims to focus and assess the current standing of KODISA: the Journal of Distribution Science (JDS), International Journal of Industrial Distribution & Business (IJIDB), the East Asian Journal of Business Management (EAJBM), the Journal of Asian Finance, Economics and Business (JAFEB) in a rapidly evolving era. Novel strategies for creating the future vision of KODISA 2020 will also be examined. Research design, data, and methodology – The current research will analyze published journals of KODISA in order to offer a vision for the KODISA 2020 future. In part 1, this paper will observe the current address of the KODISA journal and its overview of past achievements. Next, part 2 will discuss the activities that will be needed for journals of KODISA, JDS, IJIDB, EAJBM, JAFEB to branch out internationally and significant journals will be statistically analyzed in part 3. The last part 4 will offer strategies for the continued growth of KODISA and visions for KODISA 2020. Results – Among the KODISA publications, IJIDB was second, JDS was 23rd (in economic publications of 54 journals), and EAJBM was 22nd (out of 79 publications in management field journals). This shows the high quality of the KODISA publication journals. According to 2016 publication analysis, JDS, IJIDB, etc. each had 157 publications, 15 publications, 16 publications, and 28 publications. In the case of JDS, it showed an increase of 14% compared to last year. Additionally, JAFEB showed a significant increase of 68%. This shows that compared to other journals, it had a higher rate of paper submission. IJIDB and EAJBM did not show any significant increases. In JDS, it showed many studies related to the distribution, management of distribution, and consumer behavior. In order to increase the status of the KODISA journal to a SCI status, many more international conferences will open to increase its international recognition levels. Second, the systematic functions of the journal will be developed further to increase its stability. Third, future graduate schools will open to foster future potential leaders in this field and build a platform for innovators and leaders. Conclusions – In KODISA, JDS was first published in 1999, and has been registered in SCOPUS February 2017. Other sister publications within the KODISA are preparing for SCOPUS registration as well. KODISA journals will prepare to be an innovative journal for 2020 and the future beyond

Cases of ethical violation in research publications: through editorial decision making process

Purpose – To improve and strengthen existing publication and research ethics, KODISA has identified and presented various cases which have violated publication and research ethics and principles in recent years. The editorial office of KODISA has been providing and continues to provide advice and feedback on publication ethics to researchers during peer review and editorial decision making process. Providing advice and feedback on publication ethics will ensure researchers to have an opportunity to correct their mistakes or make appropriate decisions and avoid any violations in research ethics. The purpose of this paper is to identify different cases of ethical violation in research and inform and educate researchers to avoid any violations in publication and research ethics. Furthermore, this article will demonstrate how KODISA journals identify and penalize ethical violations and strengthens its publication ethics and practices. Research design, data and methodology – This paper examines different types of ethical violation in publication and research ethics. The paper identifies and analyzes all ethical violations in research and combines them into five general categories. Those five general types of ethical violations are thoroughly examined and discussed. Results – Ethical violations of research occur in various forms at regular intervals; in other words, unethical researchers tend to commit different types of ethical violations repeatedly at same time. The five categories of ethical violation in research are as follows: (1) Arbitrary changes or additions in author(s) happen frequently in thesis/dissertation related publications. (2) Self plagiarism, submitting same work or mixture of previous works with or without using proper citations, also occurs frequently, but the most common type of plagiarism is changing the statistical results and using them to present as the results of the empirical analysis; (3) Translation plagiarism, another ethical violation in publication, is difficult to detect but occurs frequently; (4) Fabrication of data or statistical analysis also occurs frequently. KODISA requires authors to submit the results of the empirical analysis of the paper (the output of the statistical program) to prevent this type of ethical violation; (5) Mashup or aggregator plagiarism, submitting a mix of several different works with or without proper citations without alterations, is very difficult to detect, and KODISA journals consider this type of plagiarism as the worst ethical violation. Conclusions – There are some individual cases of ethical violation in research and publication that could not be included in the five categories presented throughout the paper. KODISA and its editorial office should continue to develop, revise, and strengthen their publication ethics, to learn and share different ways to detect any ethical violations in research and publication, to train and educate its editorial members and researchers, and to analyze and share different cases of ethical violations with the scholarly community

Formation characteristics and photoluminescence of Ge nanocrystals in HfO[sub 2]

Genanocrystals (NCs) are shown to form within HfO₂ at relatively low annealing temperatures (600–700 °C) and to exhibit characteristic photoluminescence(PL) emission consistent with quantum confinement effects. After annealing at 600 °C, sample implanted with 8.4×10¹⁵ Ge cm⁻² show two major PL peaks, at 0.94 and 0.88 eV, which are attributed to no-phonon and transverse-optical phonon replica of Ge NCs, respectively. The intensity reaches a maximum for annealing temperatures around 700 °C and decreases at higher temperatures as the NC size continues to increase. The no-phonon emission also undergoes a significant redshift for temperatures above 800 °C. For fluences in the range from 8.4×1015 to 2.5×10¹⁶ cm⁻², the average NC size increases from ∼13.5±2.6 to ∼20.0±3.7 nm. These NC sizes are much larger than within amorphous SiO₂. Implanted Ge is shown to form Ge NCs within the matrix of monoclinic (m)-HfO₂ during thermal annealing with the orientation relationship of [101]m-HfO₂//[110]Ge NC.S.H.C. and R.G.E. acknowledge supports from the Korea Research Foundation Grant Grant No. KRF-2007-521- C00094 and from the Australian Research Council Discovery Project, respectively

An Energy-Efficient Algorithm for Classification of Fall Types Using a Wearable Sensor

Objective: To mitigate damage from falls, it is essential to provide medical attention expeditiously. Many previous studies have focused on detecting falls and have shown that falls can be accurately detected at least in a laboratory setting. However, a very few studies have classified the different types of falls. To this end, in this paper, a novel energy-efficient algorithm that can discriminate the five most common fall types was developed for wearable systems. Methods: A wearable system with an inertial measurement unit sensor was first developed. Then, our novel algorithm, temporal signal angle measurement (TSAM), was used to classify the different types of falls at various sampling frequencies, and the results were compared with those from three different machine learning algorithms. Results: The overall performance of the TSAM and that of the machine learning algorithms were similar. However, the TSAM outperformed the machine learning algorithms at frequencies in the range of 10-20 Hz. As the sampling frequency dropped from 200 to 10Hz, the accuracy of the TSAM ranged from 93.3% to 91.8%. The sensitivity and specificity ranges from 93.3% to 91.8%, and 98.3% to 97.9%, respectively for the same frequency range. Conclusion: Our algorithm can be utilized with energy-efficient wearable devices at low sampling frequencies to classify different types of falls. Significance: Our system can expedite medical assistance in emergency situations caused by falls by providing the necessary information to medical doctors or clinicians.1