A Rapid Detection of Meat Spoilage using an Electronic Nose and Fuzzy-Wavelet systems

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid detection of meat spoilage microorganisms during aerobic or modified atmosphere storage, an electronic nose with the aid of fuzzy wavelet network has been considered in this research. The proposed model incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results against neural networks and neurofuzzy systems indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology

Neuro-Fuzzy based Identification of Meat Spoilage using an Electronic Nose

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). An adaptive fuzzy logic system model that utilizes a prototype defuzzification scheme has been developed to classify beef samples in their respective quality class and to predict their associated microbiological population directly from volatile compounds fingerprints. Results confirmed the superiority of the adopted methodology and indicated that volatile information in combination with an efficient choice of a modeling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage

Methods, Models & Systems for Dealing with Some Business, Management & Engineering Problems

This keynote presentation will report some of our research work and experience on the development and applications of relevant methods, models, systems and simulation techniques in support of different types and various levels of decision making for business, management and engineering. In particular, the following topics will be covered. Modelling, multi-agent-based simulation and analysis of the allocation management of carbon dioxide emission permits in China (Nanfeng Liu & Shuliang Li Agent-based simulation of the dynamic evolution of enterprise carbon assets (Yin Zeng & Shuliang Li) A framework & system for extracting and representing project knowledge contexts using topic models and dynamic knowledge maps: a big data perspective (Jin Xu, Zheng Li, Shuliang Li & Yanyan Zhang) Open innovation: intelligent model, social media & complex adaptive system simulation (Shuliang Li & Jim Zheng Li) A framework, model and software prototype for modelling and simulation for deshopping behaviour and how companies respond (Shawkat Rahman & Shuliang Li) Integrating multiple agents, simulation, knowledge bases and fuzzy logic for international marketing decision making (Shuliang Li & Jim Zheng Li) A Web-based hybrid intelligent system for combined conventional, digital, mobile, social media and mobile marketing strategy formulation (Shuliang Li & Jim Zheng Li) A hybrid intelligent model for Web & social media dynamics, and evolutionary and adaptive branding (Shuliang Li) A hybrid paradigm for modelling, simulation and analysis of brand virality in social media (Shuliang Li & Jim Zheng Li) Network configuration management: attack paradigms and architectures for computer network survivability (Tero Karvinen & Shuliang Li)

A quarter century of creative chaos: Xinhua News Agency 1980-2005

Chinese media in the context of China's rise have puzzled many scholars who used to understand media and communications phenomena by employing the theories generated from a few affluent Western democracies, notably the US. As a result, a complex but more accurate picture has been ignored. Under numerous theoretical polarizations, the contemporary social world seems little changed but polarized. This thesis aims to propose a different approach endeavoring to 'de-Westernize' or 'internationalize' media and communications studies. As a starting point, this study focuses on the globalization debate, Chinese media and news agency studies. The thesis has investigated the Chinese news agency, Xinhua, by employing Fuzzy Logic which captures the complexity of the change in the agency's business structure and journalistic practices over last 25 years. The change is also examined by scrutinizing the role of journalists in the interrelations of Xinhua with its news sources, media and nonmedia clients, and other news agencies. A combination of archive study and 94 semistructured interviews conducted in Beijing, Shanghai, Guangzhou, Hong Kong, Macau and London provides an inclusive account of the Chinese news institution. The key research findings drawn from the empirical research into Xinhua have justified the central argument of this thesis: Crisp Logic or the 'either/or' approach has failed to explain the dynamics of the change to the media system based in a 'non-Western' society. The numerous theoretical polarizations generated by Crisp Logic to a large extent have distorted the understanding of the contemporary social world by polarizing it. Fuzzy Logic serves better(though it is not the only choice)than the traditional approach to reflect on the set of variables existing between the two poles created by Crisp Logic. This thesis is the first doctorate research in the UK and other English-speaking countries to investigate Xinhua by 'going inside' the news institution's headquarters, local branches and overseas bureaus. This is the first comprehensive academic study of the agency, which not only examines the agency's recent change in business structure and journalistic practices, but also provides a historical account of the agency and its relationship with other social institutions. This is the first media study that employs Fuzzy Logic to understand the globalization theory, Chinese media and news agencies.

A Fuzzy-Wavelet Neural Network Model for the Detection of Meat Spoilage using an Electronic Nose

Food product safety is one of the most promising areas for the application of electronic noses. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). This paper proposes a fuzzy-wavelet neural network model which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modeling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results indicated that the proposed modeling scheme could be considered as a valuable detection methodology in food microbiology