Identifying functional stakeholder clusters to maximise communication for the ecosystem approach to fisheries management

Interaction with ecological models can improve stakeholder participation in fisheries management. Problems exist in efficiently communicating outputs to stakeholders and an objective method of structuring stakeholder differences is lacking. This paper aims to inform the design of a multi-user communication interface for fisheries management by identifying functional stakeholder groups. Intuitive categorisation of stakeholders, derived from survey responses, is contrasted with an Evidence-Based method derived from analysis of stakeholder literature. Intuitive categorisation relies on interpretation and professional judgement when categorising stakeholders among conventional stakeholder groups. Evidence-Based categorisation quantitatively characterises each stakeholder with a vector of four management objective interest-strength values (Yield, Employment, Profit and Ecosystem Preservation). Survey respondents agreed little in forming intuitive groups and the groups were poorly defined and heterogeneous in interests. In contrast the Evidence-Based clusters were well defined and largely homogeneous, so more useful for identifying functional relations with model outputs. The categorisations lead to two different clusterings of stakeholders and suggest unhelpful stereotyping of stakeholders may occur with the Intuitive categorisation method. Stakeholder clusters based on literature-evidence show a high degree of common interests among clusters and is encouraging for those seeking to maximise dialogue and consensus forming. © 2013 Elsevier Ltd.

A Corpus-Based Approach to Speech Enhancement from Nonstationary Noise

Temporal dynamics and speaker characteristics are two important features of speech that distinguish speech from noise. In this paper, we propose a method to maximally extract these two features of speech for speech enhancement. We demonstrate that this can reduce the requirement for prior information about the noise, which can be difficult to estimate for fast-varying noise. Given noisy speech, the new approach estimates clean speech by recognizing long segments of the clean speech as whole units. In the recognition, clean speech sentences, taken from a speech corpus, are used as examples. Matching segments are identified between the noisy sentence and the corpus sentences. The estimate is formed by using the longest matching segments found in the corpus sentences. Longer speech segments as whole units contain more distinct dynamics and richer speaker characteristics, and can be identified more accurately from noise than shorter speech segments. Therefore, estimation based on the longest recognized segments increases the noise immunity and hence the estimation accuracy. The new approach consists of a statistical model to represent up to sentence-long temporal dynamics in the corpus speech, and an algorithm to identify the longest matching segments between the noisy sentence and the corpus sentences. The algorithm is made more robust to noise uncertainty by introducing missing-feature based noise compensation into the corpus sentences. Experiments have been conducted on the TIMIT database for speech enhancement from various types of nonstationary noise including song, music, and crosstalk speech. The new approach has shown improved performance over conventional enhancement algorithms in both objective and subjective evaluations.

A quality-scalable and energy-efficient approach for spectral analysis of heart rate variability

Today there is a growing interest in the integration of health monitoring applications in portable devices necessitating the development of methods that improve the energy efficiency of such systems. In this paper, we present a systematic approach that enables energy-quality trade-offs in spectral analysis systems for bio-signals, which are useful in monitoring various health conditions as those associated with the heart-rate. To enable such trade-offs, the processed signals are expressed initially in a basis in which significant components that carry most of the relevant information can be easily distinguished from the parts that influence the output to a lesser extent. Such a classification allows the pruning of operations associated with the less significant signal components leading to power savings with minor quality loss since only less useful parts are pruned under the given requirements. To exploit the attributes of the modified spectral analysis system, thresholding rules are determined and adopted at design- and run-time, allowing the static or dynamic pruning of less-useful operations based on the accuracy and energy requirements. The proposed algorithm is implemented on a typical sensor node simulator and results show up-to 82% energy savings when static pruning is combined with voltage and frequency scaling, compared to the conventional algorithm in which such trade-offs were not available. In addition, experiments with numerous cardiac samples of various patients show that such energy savings come with a 4.9% average accuracy loss, which does not affect the system detection capability of sinus-arrhythmia which was used as a test case. 

An iterative longest matching segment approach to speech enhancement with additive noise and channel distortion

This paper presents a new approach to speech enhancement from single-channel measurements involving both noise and channel distortion (i.e., convolutional noise), and demonstrates its applications for robust speech recognition and for improving noisy speech quality. The approach is based on finding longest matching segments (LMS) from a corpus of clean, wideband speech. The approach adds three novel developments to our previous LMS research. First, we address the problem of channel distortion as well as additive noise. Second, we present an improved method for modeling noise for speech estimation. Third, we present an iterative algorithm which updates the noise and channel estimates of the corpus data model. In experiments using speech recognition as a test with the Aurora 4 database, the use of our enhancement approach as a preprocessor for feature extraction significantly improved the performance of a baseline recognition system. In another comparison against conventional enhancement algorithms, both the PESQ and the segmental SNR ratings of the LMS algorithm were superior to the other methods for noisy speech enhancement.

Speech Enhancement from Additive Noise and Channel Distortion - a Corpus-Based Approach

This paper presents a new approach to single-channel speech enhancement involving both noise and channel distortion (i.e., convolutional noise). The approach is based on finding longest matching segments (LMS) from a corpus of clean, wideband speech. The approach adds three novel developments to our previous LMS research. First, we address the problem of channel distortion as well as additive noise. Second, we present an improved method for modeling noise. Third, we present an iterative algorithm for improved speech estimates. In experiments using speech recognition as a test with the Aurora 4 database, the use of our enhancement approach as a preprocessor for feature extraction significantly improved the performance of a baseline recognition system. In another comparison against conventional enhancement algorithms, both the PESQ and the segmental SNR ratings of the LMS algorithm were superior to the other methods for noisy speech enhancement. Index Terms: corpus-based speech model, longest matching segment, speech enhancement, speech recognition

Multiloop robust H∞ control design based on the dynamic PLS approach to chemical processes

In this paper, a multiloop robust control strategy is proposed based on H∞ control and a partial least squares (PLS) model (H∞_PLS) for multivariable chemical processes. It is developed especially for multivariable systems in ill-conditioned plants and non-square systems. The advantage of PLS is to extract the strongest relationship between the input and the output variables in the reduced space of the latent variable model rather than in the original space of the highly dimensional variables. Without conventional decouplers, the dynamic PLS framework automatically decomposes the MIMO process into multiple single-loop systems in the PLS subspace so that the controller design can be simplified. Since plant/model mismatch is almost inevitable in practical applications, to enhance the robustness of this control system, the controllers based on the H∞ mixed sensitivity problem are designed in the PLS latent subspace. The feasibility and the effectiveness of the proposed approach are illustrated by the simulation results of a distillation column and a mixing tank process. Comparisons between H∞_PLS control and conventional individual control (either H∞ control or PLS control only) are also made

Challenging conventional risk assessment with respect to human exposure to multiple food contaminants in food: A case study using maize

Mycotoxins and heavy metals are ubiquitous in the environment and contaminate many foods. The widespread use of pesticides in crop production to control disease contributes further to the chemical contamination of foods. Thus multiple chemical contaminants threaten the safety of many food commodities; hence the present study used maize as a model crop to identify the severity in terms of human exposure when multiple contaminants are present. High Content Analysis (HCA) measuring multiple endpoints was used to determine cytotoxicity of complex mixtures of mycotoxins, heavy metals and pesticides. Endpoints included nuclear intensity (NI), nuclear area (NA), plasma membrane permeability (PMP), mitochondrial membrane potential (MMP) and mitochondrial mass (MM). At concentrations representing legal limits of each individual contaminant in maize (3. ng/ml ochratoxin A (OTA), 1. μg/ml fumonisin B1 (FB1), 2. ng/ml aflatoxin B1 (AFB1), 100. ng/ml cadmium (Cd), 150. ng/ml arsenic (As), 50. ng/ml chlorpyrifos (CP) and 5. μg/ml pirimiphos methyl (PM), the mixtures (tertiary mycotoxins plus Cd/As) and (tertiary mycotoxins plus Cd/As/CP/PM) were cytotoxic for NA and MM endpoints with a difference of up to 13.6% (. p≤. 0.0001) and 12% (. p≤. 0.0001) respectively from control values. The most cytotoxic mixture was (tertiary mycotoxins plus Cd/As/CP/PM) across all 4 endpoints (NA, NI, MM and MMP) with increases up to 61.3%, 23.0%, 61.4% and 36.3% (. p≤. 0.0001) respectively. Synergy was evident for two endpoints (NI and MM) at concentrations contaminating maize above legal limits, with differences between expected and measured values of (6.2-12.4% (. p≤. 0.05-. p≤. 0.001) and 4.5-12.3% (. p≤. 0.05-. p≤. 0.001) for NI and MM, respectively. The study introduces for the first time, a holistic approach to identify the impact in terms of toxicity to humans when multiple chemical contaminants are present in foodstuffs. Governmental regulatory bodies must begin to contemplate how to safeguard the population when such mixtures of contaminants are found in foods and this study starts to address this critical issue.

Wide Matching - An Approach to Improving Noise Robustness for Speech Enhancement

It is shown that under certain conditions it is possible to obtain a good speech estimate from noise without requiring noise estimation. We study an implementation of the theory, namely wide matching, for speech enhancement. The new approach performs sentence-wide joint speech segment estimation subject to maximum recognizability to gain noise robustness. Experiments have been conducted to evaluate the new approach with variable noises and SNRs from -5 dB to noise free. It is shown that the new approach, without any estimation of the noise, significantly outperformed conventional methods in the low SNR conditions while retaining comparable performance in the high SNR conditions. It is further suggested that the wide matching and deep learning approaches can be combined towards a highly robust and accurate speech estimator.

A Simultaneous Equation Approach to Estimating HIV Prevalence with Non-Ignorable Missing Responses

Estimates of HIV prevalence are important for policy in order to establish the health status of a country's population and to evaluate the effectiveness of population-based interventions and campaigns. However, participation rates in testing for surveillance conducted as part of household surveys, on which many of these estimates are based, can be low. HIV positive individuals may be less likely to participate because they fear disclosure, in which case estimates obtained using conventional approaches to deal with missing data, such as imputation-based methods, will be biased. We develop a Heckman-type simultaneous equation approach which accounts for non-ignorable selection, but unlike previous implementations, allows for spatial dependence and does not impose a homogeneous selection process on all respondents. In addition, our framework addresses the issue of separation, where for instance some factors are severely unbalanced and highly predictive of the response, which would ordinarily prevent model convergence. Estimation is carried out within a penalized likelihood framework where smoothing is achieved using a parametrization of the smoothing criterion which makes estimation more stable and efficient. We provide the software for straightforward implementation of the proposed approach, and apply our methodology to estimating national and sub-national HIV prevalence in Swaziland, Zimbabwe and Zambia.