Prediction of sedimentation and bank erosion due to the construction of Kahang Dam

River impoundments continue to cause changes to the hydrological regimes of its host river. Thus, assessment and development of tools for better understanding of the sediment dynamics and riverbank erosion downstream the dam will be of great benefit to researchers and policymakers. The present research employs the use of field techniques and estimation models to improve the (i) prediction of suspended sediment concentration, (ii) monitoring riverbank erosion, and (iii) development of Riverbank Erosion Index (RbEI) for downstream Kahang Dam. This research used the Artificial Neural Network (ANN) and ANN with Autoregressive (AR) (NNETAR) in predicting suspended sediment concentration using sediment concentration, discharge and water level as inputs. Similarly, erosion pins were installed on four transects to monitor the riverbank for thirteen months. The results obtained for sediment concentration prediction clearly show that the R2 for NNETAR (0.885) have better value compared to ANN (0.695) even though the relationship between discharge and sediment concentration was weak, it outperforms the ANN. While based on the sediment rating curve (SRC) results, the same pattern was exhibited where the R2 for NNETAR show a greater value than ANN and SRC with R2 values of 0.695 and 0.451, respectively. Based on the observed results of quantified riverbank erosion, the most active transect eroded 1.747 mm/yr- while 0.657 mm/yr- is the least eroded. furthermore, the result reveals the maximum and minimum sediment contribution to the fluvial system from riverbank eroded to be 0.00743 tonnes/yr and 0.00148 tonnes/yr respectively. Lastly, by using discharge and percentage soil composition (sand and clay), a RbEI was developed by the adopted Equation 4.7 to estimate the status of riverbank erosion of River Kahang. Moreover, five classifications of erosion status were proposed, which can be used to describe the status and severity of the riverbank erosion. In conclusion, the estimates by the RbEI is expected to serve as basis for analysing and adopting river stabilisation and restoration design, which will be of importance to dam operators in making informed decisions regarding early warnings on the riverbank stability. Also, reliable sediment concentration estimation will assist in the development of catchment sediment budget which will give an insight into the effect of situating a dam on a river in terms of sediment supply and riverbank erosio

Guidance on the scientific requirements for health claims related to muscle function and physical performance (Revision 1)

EFSA has asked the Panel on Nutrition, Novel Foods and Food Allergens (NDA) to update the guidance on the scientific requirements for health claims related to physical performance published in 2012. The update takes into account the experience gained by the NDA Panel with the evaluation of additional health claim applications, changes introduced to the general scientific guidance for stakeholders for health claims applications and information collected from a grant launched in 2014 which aimed at gathering information in relation to claimed effects, outcome variables and methods of measurement in the context of the scientific substantiation of health claims. The guidance is intended to assist applicants in preparing applications for the authorisation of health claims related to muscle function and physical performance. The draft guidance was subject to public consultation from 16 July to 2 September 2018. This document supersedes the guidance on the scientific requirements for health claims related to physical performance published in 2012. It is intended that the guidance will be further updated as appropriate in the light of experience gained from the evaluation of health claims

Critical Perspectives Sustainability of the on South African Civil Society Sector

This report presents the findings of a research and advocacy process that included consultative workshops with CSOs in all nine of South Africa's provinces, interviews with CSOs, politicians, government departments, the NLB, NDA and local funders. The report highlights the successes and ongoing problems associated with the NLB and the NDA. It locates them within a broader context of government unevenness, inefficiency and corruption

RESEARCH REPORT ON THE NATIONAL DEVELOPMENT AGENCY

This Report attempts to examine how an institution like the NDA, which was established to meet the short-term needs of NGOs in development and the long-term need to promote partnership between government and these organisations, is assisting in creating favourable conditions for the sustainability of the non-profit sector and to what extent the legislative framework appears to have enabled or hindered the NDA in the execution of its mandate

Reporting bias in drug trials submitted to the Food and Drug Administration: review of publication and presentation.

BackgroundPrevious studies of drug trials submitted to regulatory authorities have documented selective reporting of both entire trials and favorable results. The objective of this study is to determine the publication rate of efficacy trials submitted to the Food and Drug Administration (FDA) in approved New Drug Applications (NDAs) and to compare the trial characteristics as reported by the FDA with those reported in publications.Methods and findingsThis is an observational study of all efficacy trials found in approved NDAs for New Molecular Entities (NMEs) from 2001 to 2002 inclusive and all published clinical trials corresponding to the trials within the NDAs. For each trial included in the NDA, we assessed its publication status, primary outcome(s) reported and their statistical significance, and conclusions. Seventy-eight percent (128/164) of efficacy trials contained in FDA reviews of NDAs were published. In a multivariate model, trials with favorable primary outcomes (OR = 4.7, 95% confidence interval [CI] 1.33-17.1, p = 0.018) and active controls (OR = 3.4, 95% CI 1.02-11.2, p = 0.047) were more likely to be published. Forty-one primary outcomes from the NDAs were omitted from the papers. Papers included 155 outcomes that were in the NDAs, 15 additional outcomes that favored the test drug, and two other neutral or unknown additional outcomes. Excluding outcomes with unknown significance, there were 43 outcomes in the NDAs that did not favor the NDA drug. Of these, 20 (47%) were not included in the papers. The statistical significance of five of the remaining 23 outcomes (22%) changed between the NDA and the paper, with four changing to favor the test drug in the paper (p = 0.38). Excluding unknowns, 99 conclusions were provided in both NDAs and papers, nine conclusions (9%) changed from the FDA review of the NDA to the paper, and all nine did so to favor the test drug (100%, 95% CI 72%-100%, p = 0.0039).ConclusionsMany trials were still not published 5 y after FDA approval. Discrepancies between the trial information reviewed by the FDA and information found in published trials tended to lead to more favorable presentations of the NDA drugs in the publications. Thus, the information that is readily available in the scientific literature to health care professionals is incomplete and potentially biased

Non-Data-Aided Parameter Estimation in an Additive White Gaussian Noise Channel

Non-data-aided (NDA) parameter estimation is considered for binary-phase-shift-keying transmission in an additive white Gaussian noise channel. Cramer-Rao lower bounds (CRLBs) for signal amplitude, noise variance, channel reliability constant and bit-error rate are derived and it is shown how these parameters relate to the signal-to-noise ratio (SNR). An alternative derivation of the iterative maximum likelihood (ML) SNR estimator is presented together with a novel, low complexity NDA SNR estimator. The performance of the proposed estimator is compared to previously suggested estimators and the CRLB. The results show that the proposed estimator performs close to the iterative ML estimator at significantly lower computational complexity

Big Data Management in Education Sector: an Overview

The advancement in technological innovation has given rise to a new trend known as Big Data today. Given the soaring popularity of big data technology, organisations are profoundly attracted to and interested in it to transform their organisation by improving their businesses. Big data is enabling organisations to outpace their competitors and save cost. Similarly, the application of Big Data management in Universities is an essential aspect to institutions that have Big Data to manage; as the use of Big Data in the higher education sector is increasing day by day. Many studies have been carried out on big data and analytics with little interest in its management. Big Data management is a reality that represents a set of challenges involving Big Data modeling, storage, and retrieval, analysis, and visualization for several areas in organizations. This paper introduces and contributes to the conceptual and theoretical understanding of Big Data management within higher education as it outlines its relevance to higher education institutions. It describes the opportunities this growing research area brings to higher education as well as major challenges associated with it