Market-oriented new product development of health promoting foods for the ageing population

Major factors influencing food development and food marketing strategies in global market places at present can be attributable to the changing age structure of the population. The significant shifts in global age structure will inevitably lead to the number of people aged 60 reaching an all-time high of one billion by the year 2020. The rapidly growing population of ageing people globally represents a large, neglected and very much under-developed category within the Food Industry. The primary focus of this study was the integration of knowledge creation techniques at early NPD stages, for the development of market-oriented new health promoting foods for the ageing population. The methodology of this study was centered on an exploratory sequential mixed methods strategy. Stage one of the study involved in-depth semi-structured interviews with 16 Stakeholders to facilitate the need identification stage of the NPD process. The main outputs identified were the need for: the fortification of foods for a preventative nutrition approach, the development of foods that targeted age-related conditions such as cognitive, heart, gut and bone health, the integration of ageing compensatory packaging adaptations and the creation of marketing messages with an active lifestyle message. Stage two consisted of a market-oriented computer assisted NPD technique, a user centered design interaction (UCD) to integrate consumers as co-creators throughout the idea generation stage of the NPD process. The most important product attributes identified in this stage included: products targeted at brain and cognitive health, liquid based beverages, easy to use packaging with environmentally friendly elements, simplistic marketing with a clear focus on health not age and realistic health claims constructed with consumer friendly terminology. Finally, Stage three used an abbreviated means-end chain (MEC) analysis to complete the concept development stage of the NPD process. This stage identified commercial information that could be used by food firms for the development of positioning and communication strategies. Equally, the information generated could be of high strategic importance to governments, policy makers, health professionals and medical professionals. The values and goals listed in this stage included: better overall health, active lifestyle, optimum nutrition and wellbeing feelings. Overall, this research illustrated that knowledge creation techniques can assist firms in the development of market-oriented health promoting foods for the ageing population.

A probabilistic approach to user mobility prediction for wireless services.

Mobile and wireless networks have long exploited mobility predictions, focused on predicting the future location of given users, to perform more efficient network resource management. In this paper, we present a new approach in which we provide predictions as a probability distribution of the likelihood of moving to a set of future locations. This approach provides wireless services a greater amount of knowledge and enables them to perform more effectively. We present a framework for the evaluation of this new type of predictor, and develop 2 new predictors, HEM and G-Stat. We evaluate our predictors accuracy in predicting future cells for mobile users, using two large geolocation data sets, from MDC [11], [12] and Crawdad [13]. We show that our predictors can successfully predict with as low as an average 2.2% inaccuracy in certain scenarios.

Improving cross language information retrieval using corpus based query suggestion approach

Users seeking information may not find relevant information pertaining to their information need in a specific language. But information may be available in a language different from their own, but users may not know that language. Thus users may experience difficulty in accessing the information present in different languages. Since the retrieval process depends on the translation of the user query, there are many issues in getting the right translation of the user query. For a pair of languages chosen by a user, resources, like incomplete dictionary, inaccurate machine translation system may exist. These resources may be insufficient to map the query terms in one language to its equivalent terms in another language. Also for a given query, there might exist multiple correct translations. The underlying corpus evidence may suggest a clue to select a probable set of translations that could eventually perform a better information retrieval. In this paper, we present a cross language information retrieval approach to effectively retrieve information present in a language other than the language of the user query using the corpus driven query suggestion approach. The idea is to utilize the corpus based evidence of one language to improve the retrieval and re-ranking of news documents in the other language. We use FIRE corpora - Tamil and English news collections in our experiments and illustrate the effectiveness of the proposed cross language information retrieval approach.