Evaluation of Volumetric Properties of Cassava Peel Ash Modified Asphalt Mixtures

In continuance to providing a reliable and cost-efficient road construction material that would aid the development of sustainable pavements while also eradicating agricultural wastes to protect the environment, Cassava Peel Ash (CPA) modified asphalt mixture is seen to be one of the most viable options. This study aimed to determine the suitability of Cassava Peel Ash (CPA) in hot mix asphalt for improved pavement performance. Using response surface methodology, a central composite design was employed for the mix design parameters, namely coarse aggregate (CA), fine aggregate (FA), mineral filler (MF), bitumen content (BC), and cassava peel ash (CPA). CPA was used as a partial replacement for filler and varied between 0% and 20%. The BC varied between 4% and 8%, the MF varied between 15% and 20%, the FA varied between 10% and 14%, and the CA varied between 46% and 52%. The interactive effect between the mix design parameters on the volumetric properties of the asphalt mixtures was evaluated. The results obtained showed the Marshall stability, flow, density, volume of the void, and void in mineral aggregates of the asphalt mixtures at 1.8037–8.045 kN, 2.7-8.22 mm, 2.0426–2.3909%, 1.094–7.966% and 55.5105–93.1393% respectively. These results indicate that the interaction of CA, FA, MF, BC, and CPA influences the volumetric properties of asphalt mixtures. From the RSM analysis, a prediction model and an optimal condition of 4.018% asphalt content, 20% cassava peel ash, 46% coarse aggregate, 10% fine aggregate, and 15% mineral filler were achieved for the asphalt mixtures. Doi: 10.28991/CEJ-2022-08-10-07 Full Text: PD

Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases

The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular "reactive oxygen species" (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation. We review the considerable and wide-ranging evidence for the involvement of this combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause (i.e. iron-catalysed free radical and especially hydroxyl radical generation). The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological (and potentially therapeutic) importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the simultaneous combination of multiple factors that is responsible. This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, etc...Comment: 159 pages, including 9 Figs and 2184 reference

Exploiting Genomic Knowledge in Optimising Molecular Breeding Programmes: Algorithms from Evolutionary Computing

Comparatively few studies have addressed directly the question of quantifying the benefits to be had from using molecular genetic markers in experimental breeding programmes (e.g. for improved crops and livestock), nor the question of which organisms should be mated with each other to best effect. We argue that this requires in silico modelling, an approach for which there is a large literature in the field of evolutionary computation (EC), but which has not really been applied in this way to experimental breeding programmes. EC seeks to optimise measurable outcomes (phenotypic fitnesses) by optimising in silico the mutation, recombination and selection regimes that are used. We review some of the approaches from EC, and compare experimentally, using a biologically relevant in silico landscape, some algorithms that have knowledge of where they are in the (genotypic) search space (G-algorithms) with some (albeit well-tuned ones) that do not (F-algorithms). For the present kinds of landscapes, F- and G-algorithms were broadly comparable in quality and effectiveness, although we recognise that the G-algorithms were not equipped with any 'prior knowledge' of epistatic pathway interactions. This use of algorithms based on machine learning has important implications for the optimisation of experimental breeding programmes in the post-genomic era when we shall potentially have access to the full genome sequence of every organism in a breeding population. The non-proprietary code that we have used is made freely available (via Supplementary information)