It is well known that determining the exact values of crossing number for circulant graphs is very difficult. Even so, some important results in this field are still proved. D.J. Ma was proved that the crossing number of C(2m + 2, m) is m + 1[8]. Then such problem for C(n, 3) was further solved [7]. Pak Tung Ho and X. Lin obtained accurate values for the crossover numbers of C (3m, m) and C (3m + 1, m)[4][5]. In this paper, as a complement, we show that the edges from the principal cycle of C(9, 3) do not cross each other in an optimal drawing.

The continuous development of emerging economies represented by China and Russia has exacerbated the changes in the world political and economic landscape, and international organizations represented by the United Nations have led to inefficient dispute resolution mechanisms in international affairs due to their formalism and pluralism. On the contrary, G-groups has shown its flexibility and efficiency in global governance. However, the international community has been questioned G-group’s legitimacy for many years. This paper will take the G7 and G20 as examples, analyze the legitimacy problems in G-groups, explain their reform measures, and propose future reform directions to promote the development of G-groups, so as to help the international community to conduct global governance more effectively.

This paper discusses the dawn of cognitive neuroscience in management and organizational research. The study does that in two tiers: first, it reviews the interdisciplinary field of organizational cognitive neuroscience, and second, it analyzes the role organizational cognitive neuroscience (OCN) could play in reducing counterproductive workplace behaviors (CWB). Theoretically, the literature has established the benefits of a neuro-scientific approach to understanding various organizational behaviors, but no research has been done on using organizational neuroscience techniques to study counterproductive work behaviors. This paper, however, has taken the first step towards this research avenue. The study will shed light on this interdisciplinary field of organizational cognitive neuroscience (OCN) and the benefits that organizations can reap from it with respect to understanding employee behavior. A research agenda for future studies is provided to scholars who are interested in advancing the investigation of cognition in counterproductive work behaviors, also by using neuroscience techniques. The study concludes by providing evidence drawn from the literature in favor of adopting an OCN approach in organizations.

Online transportation is a new type of service equipped with an internet network, and its presence in Indonesia is considered a service that disrupts the transportation sector. The government is faced with a complex policy problem to regulate online transportation. This article aims to reveal the role of policy actors in the media regarding policy issues and online transportation policy solutions. This article used qualitative analysis and the NPF policy narrative framework approach. This study found that licensing issues and Permenhub were problems that the DIY and Riau governments shared. More specific problems in Riau Province are related to violence issues, and that in DIY are related to congestion problems. The policy solution recommended by policy actors to the media is to make regional level regulations that technically regulate online transportation according to the area conditions.

This issue offers an in-depth examination of several facets of human resource management along with an engaging discourse on a wide range of issues pertaining to human resource management across several sectors, including the healthcare, services, and educational sectors. More specifically, the papers in this issue highlight common and contemporary issues in human resource management and offer solutions. They also elaborate on the impact of numerous elements on different behaviors or performances related to human resource management.

This paper provides a comprehensive review of SURF (speeded up robust features) feature descriptor, commonly used technique for image feature extraction. The SURF algorithm has obtained significant popularity because to its robustness, efficiency, and invariance to various image transformations. In this paper, an in-depth analysis of the underlying principles of SURF, its key components, and its use in computer vision tasks such as object recognition, image matching, and 3D reconstruction are proposed. Furthermore, we discuss recent advancements and variations of the SURF algorithm and compare it with other popular feature descriptors. Through this review, the aim is to provide a clear understanding of the SURF feature descriptor and its significance in the area of computer vision.