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There are many complex networks like World-Wide Web, internet and social networks have been reported to be scale-free. The major property of scale-free networks is their degree distributions are in power law form. Generally, the degree exponents of scale-free networks fall into the range of (2, 3). The purpose of this paper is to investigate other situations where the degree exponents may lie outside the range.

In this paper, analysis has been carried out by varying the degree exponents in the range of (0.5, 4.5). In total, 243 scenarios have been generated with varying network size of 1,000, 2,000 and 4,000, and degree exponents in the range of (0.5, 4.5) using interval of 0.05.

The following five indicators have been investigated: average density, average clustering coefficient, average path length, average diameter and average node degree. These indicators vary with the network size and degree exponent. If certain indicators do not satisfy with the user requirement using degree exponents of (2, 3), one can further increase or decrease the value with tradeoff. Results recommend that for degree exponents in (0.5, 2), 26 possible scale-free networks can be selected whereas for (3, 4.5), 41 possible scale-free networks can be selected, assuming a 100 percent deviation on the network parameters.

A tolerance analysis is given for the tradeoff and guideline is drawn to help better design of scale-free network for degree exponents in range of (0.5, 2) and (3, 4.5) using network size 1,000, 2,000 and 4,000. The methodology is applicable to any network size.

Concentration is the key to safer driving. Ideally, drivers should focus mainly on front views and side mirrors. Typical distractions are eating, drinking, cell phone use, using and searching things in car as well as looking at something outside the car. In this paper, distracted driving detection algorithm is targeting on nine scenarios nodding, head shaking, moving the head 45° to upper left and back to position, moving the head 45° to lower left and back to position, moving the head 45° to upper right and back to position, moving the head 45° to lower right and back to position, moving the head upward and back to position, head dropping down and blinking as fundamental elements for distracted events. The purpose of this paper is preliminary study these scenarios for the ideal distraction detection, the exact type of distraction.

The system consists of distraction detection module that processes video stream and compute motion coefficient to reinforce identification of distraction conditions of drivers. Motion coefficient of the video frames is computed which follows by the spike detection via statistical filtering.

The accuracy of head motion analyzer is given as 98.6 percent. With such satisfactory result, it is concluded that the distraction detection using light computation power algorithm is an appropriate direction and further work could be devoted on more scenarios as well as background light intensity and resolution of video frames.

The system aimed at detecting the distraction of the public transport driver. By providing instant response and timely warning, it can lower the road traffic accidents and casualties due to poor physical conditions. A low latency and lightweight head motion detector has been developed for online driver awareness monitoring.

In this chapter, we present an introductory and definitive discussion of transformative leadership as a holistic and bold approach for the next generation of higher education. We integrate this concept with the idea of sustainable innovation. The chapter is divided into four sections, each addressing essential aspects of transformative leadership in higher education. In Section 1, we introduce a high-level integrated approach to transformative leadership in higher education institutions. We define and discuss the diverse pillars that form the foundation of this leadership style. In Section 2, we propose a contextual framework for transformative leadership as a value space. This framework provides guidelines and principles for crafting a transformative leadership strategy, and we offer indicative actions and initiatives for its deployment in higher education. To support the documentation of the transformative leadership strategy, Section 3 outlines simple designs for tools and instruments, including the transformative leadership scorecard and the systematic overview of the portfolio of transformative educational programs. We also emphasize the significance of social impact, research, innovation, and sustainability aspects within the strategy. In Section 4, we summarize the key takeaways from this chapter. Our contribution is manifold, as this chapter can serve as a valuable reference for administrators seeking to design and execute transformative leadership in universities and colleges. Additionally, it offers guiding principles for researchers interested in making further contributions in this domain.

This paper looks at one relatively less‐visited issue in market timing: switching investments on common stocks between different stock markets, namely, “intermarket timing”. By employing the stock price data for the period of 1992‐2002 from a developed market, Hong Kong, and two emerging markets, Shanghai and Shenzhen, this paper examines potential gains and the required predictive accuracy for intermarket timing between Hong Kong and Shanghai, and between Hong Kong and Shenzhen from Hong Kong investors’ perspective. Potential gains could be obtained from such timing strategy, and the non‐high minimum forecasting ability required for successful timing is fairly attainable for Hong Kong investors, even after taking into account the assumed transaction costs.