Wastewater treatment using coconut fibre ash as an adsorbent for removal of heavy metals

The study aimed at evaluating the performance of coconut fibre ash as an alternative low-cost adsorbent to the synthetic adsorbents used in wastewater treatment. This research aims to identify the optimum condition for the adsorption process, considering the effect of particle size, adsorbent dosage, and contact time of adsorbents of coconut fibre ash in removing lead (Pb), copper (Cu), and zinc (Zn) metal ions from electroplating wastewater. The adsorbents coconut fibre ash was prepared through activation of carbon at 450º C after following proper cleaning and drying process. The experiments were conducted at varying adsorbent dosages (0.2 g, 0.6 g, and 1 g), particle size (50 to 200 microns), and contact times (40 minutes, 80 minutes, and 120 minutes). The result shows that adsorbents show less efficiency in removing Zn metal ions, which is not more than 34% in the case of 1g adsorbent dosage, particle size ranges 100-200 microns, and 120 minute contact time. The maximum removal efficiency of 95.04% and 80% was obtained at the optimum amount (1g) of adsorbent dosage for Pb and Cu respectively. In the case of contact time, it was identified that the optimum condition for maximum removal efficiency is 120 minutes with a 1g adsorbent dosage both for Pb and Cu ions. To ensure maximum removal of metal avoiding any desorption of the metal ion from the adsorbent surface, it was identified that a maximum contact time of 120 minutes should be allowed for adsorption. However, it could be concluded that adsorbents of coconut fibre ash can be used in treating wastewater facilitating good adsorption capacity in removing heavy metals, low cost and availability

Modeling Credit Approval Data with Neural Networks: An Experimental Investigation and Optimization

This study proposes an investigation and optimization of Multi-Layer Perceptron (MLP) based artificial neural networks (ANN) credit prediction model, combine with the effect of different ratios of training to testing instances over five real-world credit databases. As an outcome from the alteration procedure, three different types of hidden units [K = 9 (ANN–1), K = 10 (ANN–2), K = 23 (ANN–3)] are chosen through the pilot experiments and execute, therefore, 45 (5×3×3) unique neural models. Experimental results indicate that “the neural architecture with ten hidden units” is proposed as an optimal approach to classifying the credit information. With these contributions, therefore, we complement previous evidence and modernize the methods of credit prediction modeling. This study, however, has realistic implications for bank managers and other stakeholders to delineate the risk profile of the credit customers

Credit default prediction using a support vector machine and a probabilistic neural network

The design of consistent classifiers to forecast credit-granting choices is critical for many financial decision-making practices. Although a number of artificial and statistical techniques have been developed to predict customer insolvency, how to provide an inclusive appraisal of prediction models and recommend adequate classifiers is still an imperative and understudied area in credit default prediction (CDP) modeling. Previous evidence demonstrates that the ranking of classifiers varies for different criteria with measures under different circumstances. In this study, we address this methodological flaw by proposing the simultaneous application of support vector machine and probabilistic neural network (PNN)-based CDP algorithms, together with frequently used high-performance models. We fill the gap by introducing a set of multidimensional evaluation measures combined with some novel metrics that are helpful in discovering unseen features of the model’s performance. For effectiveness and feasibility purposes, six real-world credit data sets have been applied. Our empirical study shows that the PNN model is more robust than its rivals, and traditional performance evaluations are more or less consistent with their original counterparts. With these contributions, therefore, our investigations offer several advantages to practitioners of financial risk management

Iron-containing micronutrient powder provided to children with moderate-to-severe malnutrition increases hemoglobin concentrations but not the risk of infectious morbidity:a randomized, double-blind, placebo-controlled, noninferiority safety trial

A link between the provision of iron and infectious morbidity has been suggested, particularly in children with malnutrition. Two meta-analyses concluded that iron is not harmful, but malnourished children were underrepresented in most available studies