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The social aspect of sustainable and ‘clean’ production/manufacturing technologies is researched and understood by means of Social Life Cycle Assessment (SLCA), a Life Cycle Sustainability Assessment (LCSA) tool, which is still in its infancy. In this paper, a search for all peer-reviewed publications on applied Social LCA, which have appeared in scientific journals, between O’Brien et al (1996) and the latest one at the time of writing (April 2018), was carried out, using Scopus as the repository and using “S-LCA” OR “SLCA” OR “Social LCA” OR “Social Life Cycle Assessment” as search-phrases in title, abstract and keywords of publications, separately. Overall, 213 publications were unearthed, and the trend shows that there has been a near-exponential increase over time. A little over 55% of these publications – 121 to be precise - were applications of S-LCA – often in combination with environmental-LCA and life cycle costing analysis, in an LCSA. This paper discusses the contributions of a selected subset of these 121 publications to the body of S-LCA knowledge, with the focus being restricted to applications in developing and transition economies of the world, on the premise that there is a more urgent need to understand social aspects of production and manufacturing in these parts of the world. A SWOT analysis of S-LCA has been carried out towards the end. There is a consensus among many researchers that while LCC and E-LCA have matured a lot over time, S-LCA, the newest of the trio, is evolving slowly to become a harmonised tool which can serve as an effective complement to the aforesaid two, in life cycle sustainability assessments of products and processes in industry.

Elemental diffusion drives the microstructure development in the MCrAlY-superalloy systems at high temperature. In this paper, two diffusion models were built to simulate the diffusion behavior of elements in the coating or in the coating–substrate system. Firstly, a core–shell model was set up to investigate the thermodynamic and kinetic behavior of the localized microstructure. The results of the simulation successfully explained the mechanism of the formation of α(core)–γ′(shell) structure at lower temperature (750 °C) and γ(core)–β(shell) structure at higher temperature (1100 °C). Secondly, a coating–substrate planner model was used to simulate the interdiffusion of elements between the MCrAlY coating and the superalloy substrate. The simulation results in the Ni22Cr10AlY-superalloy system semiquantitatively agreed with the experimental observation. Furthermore, by applying the planner diffusion model, the effect of the MCrAlY coatings on the formation of TCP phases in the substrate was studied, and a GOODMAN map for designing TCP-limited MCrAlY coatings can be provided.

Malaria is a leading cause of death in Nigeria and remains a public health concern because of the increasing resistance of the disease to antimalarial drugs. Pregnant women and children under five years of age are the most vulnerable. Efforts to eradicate malaria is often frustrated due to some various sociodemographic factors and medical factors. One of the vital therapeutic factors is misdiagnosis. Hence, the paper applied different data mining models to diagnose malaria using fifteen symptoms of patients that attended a hospital in Nigeria. The data were obtained from a peer reviewed data article that comprises 337 subjects at Federal Polytechnic Ilaro Medical Centre, Ogun State. The independent variables are 15 symptoms, age, and sex, while the target or dependent is the outcome. The outcome is the result of the diagnosis, which is positive for negative for malaria. Eight machine learning tools were applied to the data on the Orange Software platform. Weak non-significant correlations were obtained between the 15 symptoms and the outcome, and hence no pattern was observed. However, the application of data mining tools revealed a hidden pattern that correctly predicted the outcome using the subjects' symptoms, age, and sex. 6 out of the 8 machine learning models were adjudged to perform well using different performance metrics. The Adaptive boosting model gave a percent 100% precision in the classification, and logistic regression was the least. Furthermore, a percent performance of Adaboost implies that the model correctly predicted all the 221 true negatives and 116 true positives with a misclassification (misdiagnosis) of zero. Classify using only the 15 symptoms reduced the predictive accuracy of the 6 models. Nevertheless, Adaboost performance was the best with a classification accuracy of 98.2%, precision of 96.6%, and an error rate of just 1.8%. Again, logistic regression performance was the least. The present work has presented a strong relationship between age and sex and the outcome. Adaboost model can be used to design decision support systems or rapid diagnostic tools that utilise the internet or mobile devices as platforms in the diagnosis of malaria. The application of the present work as potentials in reduction of misdiagnosis incidences, reducing the mortality due to malaria and improving the overall public health of people residing in malaria endemic areas.