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Natural flood disasters frequently happen in Malaysia especially during monsoon season and Kuala Kangsar, Perak, is one of the cities with the frequent record of natural flood disasters. Previous flood disaster faced by this city showed the failure in notifying the citizen with sufficient time for preparation and evacuation. The authority in charge of the flood disaster in Kuala Kangsar depends on the real-time monitoring from the hydrological sensor located at several stations along the main river. The real-time information from hydrological sensor failed to provide early notification and warning to the public. Although many hydrological sensors are available at the stations, only water level sensors and rainfall sensors are used by authority for flood monitoring. This study developed a flood prediction model using artificial intelligence to predict the incoming flood in Kuala Kangsar area based on artificial neural network (ANN). The flood prediction model is expected to predict the incoming flood disaster by using information from the variety of hydrological sensors. The study finds that the proposed ANN model based on nonlinear autoregressive network with exogenous inputs (NARX) has better performance than other models with the correlation coefficient that is equal to 0.98930. The NARX model of flood prediction developed in this study can be referred to as the future flood prediction model in Kuala Kangsar, Perak.

This study illuminates how organizational actors use images in their struggle to define a contested industry. By leveraging social semiotics and visual rhetoric, we examine how multimodal texts (combining words and images) are used to label and reframe an industry using technical, environmental, human-rights, and preservation-of-life criteria. Building on theories of legitimation, we find that for this industry, contesting attempts at legitimacy work are escalated along a moral hierarchy. We offer an approach for examining how actors draw from broader meaning systems, use visual rhetoric in multimodal texts, and employ dual processes of legitimation and de-legitimation.

Are the learning procedures of genetic algorithms (GAs) able to generate optimal architectures for artificial neural networks (ANNs) in high frequency data? In this experimental study, GAs are used to identify the best architecture for ANNs. Additional learning is undertaken by the ANNs to forecast daily excess stock returns. No ANN architectures were able to outperform a random walk, despite the finding of non-linearity in the excess returns. This failure is attributed to the absence of suitable ANN structures and further implies that researchers need to be cautious when making inferences from ANN results that use high frequency data.

This chapter offers an overview of the applications of artificial intelligence (AI) in the textile industry and in particular, the textile colouration and finishing industry. The advent of new technologies such as AI and the Internet of Things (IoT) has changed many businesses and one area AI is seeing growth in is the textile industry. It is estimated that the AI software market shall reach a new high of over US$60 billion by 2022, and the largest increase is projected to be in the area of machine learning (ML). This is the area of AI where machines process and analyse vast amount of data they collect to perform tasks and processes. In the textile manufacturing industry, AI is applied to various areas such as colour matching, colour recipe formulation, pattern recognition, garment manufacture, process optimisation, quality control and supply chain management for enhanced productivity, product quality and competitiveness, reduced environmental impact and overall improved customer experience. The importance and success of AI is set to grow as ML algorithms become more sophisticated and smarter, and computing power increases.

Using the case of the Deepwater Horizon blowout in the Gulf of Mexico in 2010, I argue that the catastrophe was less an example of a low probability-high catastrophe event than an instance of socially produced risks and insecurities associated with deepwater oil and gas production during the neoliberal period after 1980. The disaster exposes the deadly intersection of the aggressive enclosure of a new technologically risky resource frontier (the deepwater continental shelf) with what I call a frontier of neoliberalized risk, a lethal product of cut-throat corporate cost-cutting, the collapse of government oversight and regulatory authority and the deepening financialization and securitization of the oil market. These two local pockets of socially produced risk and wrecklessness have come to exceed the capabilities of what passes as risk management and energy security. In this sense, the Deepwater Horizon disaster was produced by a set of structural conditions, a sort of rogue capitalism, not unlike those which precipitated the financial meltdown of 2008. The forms of accumulation unleashed in the Gulf of Mexico over three decades rendered a high-risk enterprise yet more risky, all the while accumulating insecurities and radical uncertainties which made the likelihood of a Deepwater Horizon type disaster highly overdetermined.

We develop and validate measurement instruments for the business partner, watchdog, and scorekeeper roles of controllers. This study addresses calls to enhance the quality of survey research in management accounting by devoting more attention to scale development and especially to construct validity. By focusing on the activity sets of the controllers’ roles, we provide a theoretically and empirically grounded picture of their current roles. The measurement instruments presented in this study enable systematic research progress on controller roles, their relationships, antecedents, and performance outcomes.

In this chapter, the post-disaster handling of the British Petroleum Oil Spill in the Gulf of Mexico is analyzed according to the concept of “Public Reserve.” Public Reserve extends the theory of privacy from the individual into the context of corporate behavior and environmental regulation and management by government. Secrecy is viewed as a form of privacy.