Sustainable concepts and practices have taken a centre-stage in different fields of studies and professions. This is because human activities continue to threaten the carrying capacity of earth resources as well as life basic needs such as shelter. Ghana, a developing nation, has been characterized with a boom in construction activities. In almost every constructional work, Portland cement remains the main binding agent that is used to bind aggregates together in a monolithic unit. The overdependence of the Ghanaian construction industry on Portland cement has contributed to huge sums of foreign exchange used to import cement ingredients, high cost of buildings and annual artificial shortages of cement which leads to high cost of the product. In this work, alternative binding agent—pozzolana cement, is reported with regards to the theory behind its utilization, laboratory results and practical applications. Results obtained from both the laboratory and the field works have shown that the future binder for the Ghanaian construction industry is pozzolana cement. CSIR-Building and Road Research Institute recommends it for the construction industry for use in various forms of construction such as block making, concrete and mortar works.

Inference for the mean of a normal distribution with known coefficient of variation is of special theoretical interest be- cause the model belongs to the curved exponential family with a scalar parameter of interest and a two-dimensional minimal sufficient statistic. Therefore, standard inferential methods cannot be directly applied to this problem. It is also of practical interest because this problem arises naturally in many environmental and agriculture studies. In this paper we proposed a modified signed log likelihood ratio method to obtain inference for the normal mean with known coeffi- cient of variation. Simulation studies show the remarkable accuracy of the proposed method even for sample size as small as 2. Moreover, a new point estimator for the mean can be derived from the proposed method. Simulation studies show that new point estimator is more efficient than most of the existing estimators.