Safety of Coach Based School Transport in the UK: A Study of Safety Compliance of Coach Operators and Trust of Stakeholders

Coaches are considered as the safest mode of transport for children, but coach crashes result in a high number of fatalities per crash. In the United Kingdom (UK) alone 1218 children were injured in 381 coach crashes between 2005 and 2016. Schools in the UK rely on coach operators to provide vehicles for school trips. Between 2016 and 2017 alone, 78 coach operators’ licenses have been revoked without public inquiries in the UK due to operator’s non-compliance. There are only limited studies available, which examined the safety of children travelling by hired coaches in the UK. The safety of children travelling using hired coaches in the UK is investigated to identify the safety related issues. This is achieved through the analysis of existing literature, the national crash statistics, traffic commissioner reports and the views of relevant stakeholders. Sequential mixed-method exploratory research was used for data gathering and analysis. The results show that there is a critical knowledge gap within the stakeholders. The most significant safety issue identified is the stakeholders’ unawareness of drivers and coaches safety condition before and during school trips. This requires immediate attention before more children lives are put at risk

Big Data and IoT Opportunities for Small and Medium-Sized Enterprises (SMEs)

The advancement of technology and emergence of internet of things (IoT) has exponentially caused a data explosion in the 21st century era. As such, the arrival of IoT is set to revolutionize the development of the small and medium-sized enterprise (SME) organizations by shaping it into a more universal and integrated ecosystem. Despite evidential studies of the potential of advanced technologies for businesses, the SMEs are apprehensive towards new technologies adoption such as big data analytics and IoT. Therefore, the aim of this chapter is to provide a holistic study of big data and IoT opportunities, challenges, and applications within the SMEs context. The authors hope that the outcome of this study would provide foundational information on how the SMEs can partake with the new wave technological advancement and in turn, spurring more SMEs for adoption

Growth and morphological responses of Andrographis paniculate to varying shade and nitrogen fertilization

Andrographis paniculata (Burm. f.) Nees is a traditional medicinal plant with valuable phytochemical and pharmacological potential. Growth and morphological responses to light and N can be useful measurements to determine favorable growing conditions for A. paniculata. Despite numerous findings on other medicinal and aromatic plants, there is little information about how light and N affect growth and morphology A. paniculata. The objective of this study was to determine the effects of shade and N on growth and morphological responses of A. paniculata. Plants were grown under two shade levels, 0% and 40%, and fertilized with five N rates, 90, 135, 180, 225 and 270kg ha-¹ in a nested design. Shaded plants grew taller with greater total leaf area, specific leaf area, leaf area ratio and net assimilation rate than sun-grown plants. Fertilizing plants with increasing rate of N has increased their height, leaf area index, total leaf area, shoot and root dry mass, leaf mass ratio and root shoot ratio. There was a quadratic relationship between N rate and total dry mass of plants. The goal in commercial A. paniculata cultivation is to produce high yielding high quality plants. Results showed that A. paniculata could adapt to varying levels of shade and N by altering its growth and morphology. Shading at 40% and fertilizing with 225kg N ha-¹ can increase growth and yield of A. paniculata

UTMCrawler : crawling the E-business social network using genetic algorithm for relevant document searching

The increasing of online social network in the Internet has caused the explosion of search results from the search engines. According to the Google search engine statistics, until 2008 almost 1 trillion web pages have been indexing including the online social network website. Thus, how can we retrieve the massive online social network information with the exploded information accessible in the web? In this paper, we have designed the internet agent! crawler based genetic algorithm to retrieve the e-business web pages from the lelong.com.my, the Malaysia online auction website. We used genetic operation in order to retrieve the information connected between the users by expanding the keywords. Our result shows that the genetic algorithm can be a promising technique in terms of accuracy of the retrieval results

Malaysian business community social network mapping on the web based on improved genetic algorithm

The issues of community social network mapping on the web have been intensively studied in recent years. Basically, we found that social networking among communities has become a popular issue within the virtual sphere. It relates to the practice of interacting with others online via blogsphere, forums, social media sites and other outlets. Surprisingly, Internet has caused great changes to the way people do business. In this chapter, we are focusing on the networks of business in the Internet since it has become an important way of spreading the information of a business via online. Business networking is a marketing method by which business opportunities are created through networks of like-minded business people. There are several popular businesses networking organization that create models of networking activity that, when followed, allow the business person to build new business relationship and generate business opportunities at the same time. Business that increased using the business social networks as a means of growing their circle of business contacts and promoting themselves online and at the same time develop such a “territory” in several regions in the country. Since businesses are expanding globally, social networks make it easier to keep in touch with other contacts around the world. Currently, searching and finding the relevant information become a high demand from the users. However, due to the rapid expansion of web pages available in the Internet lately, searching the relevant and up-to-date information has become an important issue especially for the industrial and business firms. Conventional search engines use heuristics to decide which web pages are the best match for the keyword. Results are retrieved from the repository which located at their local server to provide fast searched. As we know, search engine is an important component in searching information worldwide. However, the user is often facing an enormous result that inaccurate or not up-to-date. Sometimes, the conventional search engine typically returned the long lists of results that saddle the user to find the most relevant information needs. Google, Yahoo! and AltaVista are the examples of available search engine used by the users. However, the results obtain from the search engines sometimes misrelated to the users query. Moreover, 68% of the search engine users will click a search result within the first page of results and 92% of them will click a result within the first three pages of search results (iProspect, 2008). This statistic concluded that the users need to view page by pages to get the relevant result. Thus, this will consume the time to go through all the result provides by search engine. From our experienced, the relevant result also will not always promise found even after looking at page 5 and above. Internet also can create the abundance problem such as; limited coverage of the Web (hidden Web sources), limited query interface: keyword-oriented search and also a limited customisation to individual users. Thus, the result must be organized so that them looks more in effective and adapted way. In previous research, we present the model to evaluate the searched results using genetic algorithm (GA). In GA, we considered the user profiles and keywords of the web pages accessed by the crawler agents. Then we used the information in GA for retrieving the best web pages related to the business communities to invest at the Iskandar Malaysia in various sectors such as education, entertainment, medical healthcare etc. The main objective of this chapter is to provide the user with a searching interface that enabling them to quickly find the relevant information. In addition, we are using the crawler agent to make a fast crawling process and retrieve the web documents as many as it can and scalable. In the previous paper, we also using genetic algorithm (GA) to optimize the result search by the crawlers to overcome the problem mention above. We further improve the GA with relevance feedback to enhance the capabilities of the search system and to find more relevant results. From the experiments, we have found that a feedback mechanism will give the search system the user’s suggestions about the found documents, which leads to a new query using the proposed GA. In the new search stage, more relevant documents are retrieved by the agents to be judged by the user. From the experiments, the improved GA (IGA) has given a significant improvement in finding the related business communities to potentially invest at Iskandar Malaysia in comparison with the traditional GA model. This chapter is organized as follows. Section 2 defined the problem that related to this chapter. Section 3 is details on improved genetic algorithm and section 4 are the results and discussion. Section 5 explains the results and discussion of this chapter and Section 6 presented the case study. Finally, section 7 describes the conclusion

Query optimization in relevance feedback using hybrid GA-PSO for effective web information retrieval

Due to the rapid growth of web pages available on the Internet recently, searching a relevant and up-to-date information has become a crucial issue. Conventional search engines use heuristics to determine which web pages are the best match for a given keyword. Results are obtained from a database that is located at their local server to provide fast searching. However, to search for the relevant and related information needed is still difficult and tedious. By using the genetic algorithm (GA) in relevance feedback, this paper presents a model of hybrid GA-Particle Swarm Optimization (HGAPSO) based query optimization for Web information retrieval. We expanded the keywords to produce the new keywords that are related to the user search. Experimental results demonstrate that it is very effective to improve the search of the relevant web pages using the HGAPSO