Consumption pattern and consumer preference for value-added fish and fish products in north zone of India

Present paper attempts to analyze consumption pattern and consumer preferences towards value-added fish and fish products in north zone of India. Results reveal that socio economic variables affect consumption of value-added fish and fish products. A total of 49 percent respondents were of middle age group (35 to 50 years). All were literates except 7% from the rural area. All were purchasing fish at least once in 15 days. A total of 90% respondents in rural, 11% in semi urban and 50% in urban area were unaware of value-added fish and fish products. About 10% of respondents had consumed it, out of which most were from urban area. Demand analysis by Cobb Douglas (CD) Demand function revealed that when price of fish, price of the substitutes, income of family and family size were used as independent variables, variation in demand of fish explained by CD Demand function was about 39% in urban area, 24% in semi urban area and 22% in rural area. From Garette ranking technique major problems in fish consumption found were irregular supply, lack of fresh fish, high price and presence of bones in fish. While lack of awareness, unavailability, no preference and unacceptable taste were major problems for consumption of value-added fish and fish products.

User responses to the learning demands of consumer products

Designers are typically male, under 35 years old and unimpaired. Users can be of any age and currently over 15% will have some form of impairment. As a result a vast array of consumer products suit youthful males and in many cases exclude other demographics (e.g. Keates and Clarkson, 2004). In studying the way a range of users learn how to use new products, key cognitive difficulties are revealed and linked back to the areas of the product causing the problems. The trials were structured so each user had to complete a specific set of tasks and were consistent across the user spectrum. The tasks set aimed to represent both everyday usage and less familiar functions. Whilst the knowledge gained could provide designers with valuable guidelines for the specific products examined, a more general abstraction provides knowledge of the pitfalls to avoid in the design of other product families.

Technology confidence in early stage development of medical devices

Innovation is a critical factor in ensuring commercial success within the area of medical technology. Biotechnology and Healthcare developments require huge financial and resource investment, in-depth research and clinical trials. Consequently, these developments involve a complex multidisciplinary structure, which is inherently full of risks and uncertainty. In this context, early technology assessment and 'proof of concept' is often sporadic and unstructured. Existing methodologies for managing the feasibility stage of medical device development are predominantly suited to the later phases of development and favour detail in optimisation, validation and regulatory approval. During these early phases, feasibility studies are normally conducted to establish whether technology is potentially viable. However, it is not clear how this technology viability is currently measured. This paper aims to redress this gap through the development of a technology confidence scale, as appropriate explicitly to the feasibility phase of medical device design. These guidelines were developed from analysis of three recent innovation studies within the medical device industry.

Combining information sources for confidence estimation with CRF models

Obtaining accurate confidence measures for automatic speech recognition (ASR) transcriptions is an important task which stands to benefit from the use of multiple information sources. This paper investigates the application of conditional random field (CRF) models as a principled technique for combining multiple features from such sources. A novel method for combining suitably defined features is presented, allowing for confidence annotation using lattice-based features of hypotheses other than the lattice 1-best. The resulting framework is applied to different stages of a state-of-the-art large vocabulary speech recognition pipeline, and consistent improvements are shown over a sophisticated baseline system. Copyright © 2011 ISCA.

Confidence in beliefs about pain predicts expectancy effects on pain perception and anticipatory processing in right anterior insula.

Psychological factors play a major role in exacerbating chronic pain. Effective self-management of pain is often hindered by inaccurate beliefs about the nature of pain which lead to a high degree of emotional reactivity. Probabilistic models of perception state that greater confidence (certainty) in beliefs increases their influence on perception and behavior. In this study, we treat confidence as a metacognitive process dissociable from the content of belief. We hypothesized that confidence is associated with anticipatory activation of areas of the pain matrix involved with top-down modulation of pain. Healthy volunteers rated their beliefs about the emotional distress that experimental pain would cause, and separately rated their level of confidence in this belief. Confidence predicted the influence of anticipation cues on experienced pain. We measured brain activity during anticipation of pain using high-density EEG and used electromagnetic tomography to determine neural substrates of this effect. Confidence correlated with activity in right anterior insula, posterior midcingulate and inferior parietal cortices during the anticipation of pain. Activity in the right anterior insula predicted a greater influence of anticipation cues on pain perception, whereas activity in right inferior parietal cortex predicted a decreased influence of anticipatory cues. The results support probabilistic models of pain perception and suggest that confidence in beliefs is an important determinant of expectancy effects on pain perception.

Strategies to reduce the carbon footprint of consumer goods by influencing stakeholders

Consumer goods contribute to anthropogenic climate change across their product life cycles through carbon emissions arising from raw materials extraction, processing, logistics, retail and storage, through to consumer use and disposal. How can consumer goods manufacturers make stepwise reductions in their product life cycle carbon emissions by engaging with, and influencing their main stakeholders? A semi-structured interview approach was used: to identify strategies and actions, stakeholders in the consumer goods industry (suppliers, manufacturers, retailers and NGOs) were interviewed about carbon emissions reduction projects. Based on this, a summarising presentation was made, which was shared during a second round of interviews to validate and refine the results. The results demonstrate several opportunities that have not yet been exploited by companies. These include editing product choice in stores to remove products with higher carbon footprints, using marketing competences for environmental benefits, and bundling competences to create winewinewin business models. Governments and NGOs have important enabling roles to accelerate industry change. Although this work was initially developed to explore how companies can reduce life cycle carbon emissions of their products, these strategies and actions also give insights on how companies can influence and anticipate stakeholder actions in general. © 2012 Elsevier Ltd. All rights reserved.

N-gram posterior probability confidence measures for statistical machine translation: An empirical study

We report an empirical study of n-gram posterior probability confidence measures for statistical machine translation (SMT). We first describe an efficient and practical algorithm for rapidly computing n-gram posterior probabilities from large translation word lattices. These probabilities are shown to be a good predictor of whether or not the n-gram is found in human reference translations, motivating their use as a confidence measure for SMT. Comprehensive n-gram precision and word coverage measurements are presented for a variety of different language pairs, domains and conditions. We analyze the effect on reference precision of using single or multiple references, and compare the precision of posteriors computed from k-best lists to those computed over the full evidence space of the lattice. We also demonstrate improved confidence by combining multiple lattices in a multi-source translation framework. © 2012 The Author(s).

Using sub-word-level information for confidence estimation with conditional random field models

The task of word-level confidence estimation (CE) for automatic speech recognition (ASR) systems stands to benefit from the combination of suitably defined input features from multiple information sources. However, the information sources of interest may not necessarily operate at the same level of granularity as the underlying ASR system. The research described here builds on previous work on confidence estimation for ASR systems using features extracted from word-level recognition lattices, by incorporating information at the sub-word level. Furthermore, the use of Conditional Random Fields (CRFs) with hidden states is investigated as a technique to combine information for word-level CE. Performance improvements are shown using the sub-word-level information in linear-chain CRFs with appropriately engineered feature functions, as well as when applying the hidden-state CRF model at the word level.

A confidence-based approach for improving keyword hypothesis scores

The task in keyword spotting (KWS) is to hypothesise times at which any of a set of key terms occurs in audio. An important aspect of such systems are the scores assigned to these hypotheses, the accuracy of which have a significant impact on performance. Estimating these scores may be formulated as a confidence estimation problem, where a measure of confidence is assigned to each key term hypothesis. In this work, a set of discriminative features is defined, and combined using a conditional random field (CRF) model for improved confidence estimation. An extension to this model to directly address the problem of score normalisation across key terms is also introduced. The implicit score normalisation which results from applying this approach to separate systems in a hybrid configuration yields further benefits. Results are presented which show notable improvements in KWS performance using the techniques presented in this work. © 2013 IEEE.