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Cold weather and the subsequent demand for lightweight apparel have prompted research work in the field of thermal insulation clothing. In addition to natural fibres, such as wool and cashmere, hollow chemical fibres, such as polyester or polypropylene fibre, have been studied and used to make thermal-insulation clothing. Primarily, modifications are made to increase the hollow ratio; however, this results in the fibre becoming brittle and prone to collapse. Yet many animals survive in harsh and cold conditions, and their hair plays a key role in keeping the body warm.

In the research under discussion, 4 types of cold-resistant animals are selected for observation of their hair fibres based on optical and scanning electronic microscopies. The characteristics of the hair fibres of cold-resistant animals are examined, including colour, scale density, scale thickness, medullary distribution and morphology of cross sections. The conclusions show that there are some common characteristics among cold-resistant animals, i.e., the cross sections are circular or ellipse or anomaly ellipse, scale density ranges from 40 to 100 scales per 1 mm, scale styles vary, and scale angles are small. The experimental results may be used to provide a modification method for functional fibres, especially for thermal-insulation fibres.

The purpose of this paper is to build a novel data envelopment analysis (DEA) model to evaluate the efficiencies of decision making units (DMUs).

Using the Choquet integrals as aggregating tool, the authors give a novel DEA model to evaluate the efficiencies of DMUs.

It extends DEA model to evaluate the DMU with interactive variables (inputs or outputs), the classical DEA model is a special form. At last, the authors use the numerical examples to illustrate the performance of the proposed model.

The proposed DEA model can be used to evaluate the efficiency of the DMUs with multiple interactive inputs and outputs.

This paper introduce a new DEA model to evaluate the DMU with interactive variables (inputs or outputs), the classical DEA model is a special form.

This study aimed to investigate the suitability and cost-benefit of using copper tailings as partial replacement of sand in concrete production. The study was motivated by the accumulation and non-use of copper tailings in dams among them tailing dam 25 also known as TD 25 in Kitwe city of the Copperbelt province in Zambia that take up approximately 111 hectares of unused land.

Laboratory experimental approach of concrete production based on water/cement ratios of 0.3 and 0.5 was used because this was an exploratory study designed to establish the primary performance of concrete. In total, 30 concrete cubes were cast based on the two water-cement ratios. In total, 0% to 30% partial sand replacement with copper tailings was used in both mixes with the 0% copper tailings replacement being the control mix and reference point. Other concrete tests included workability, density, compressive strength and element composition analysis.

Results revealed that copper tailings from TD 25 were suitable for partial replacement of sand in concrete. Thirty per cent of sand replacement with copper tailings was established as the maximum replacement amount to produce optimum compressive strength values from both mixes. The drier mix of 0.3 water-cement ratios produced higher compressive strength results of 23 MPa at 28 days of concrete curing with 2.34% as optimum concrete cost reduction.

The research results provide the cost-benefit analysis and savings that can be attained from using cheaper copper tailings based concrete. The study further provided the quantity of land available for development arising from absorption of copper tailings as a sustainable construction material. The local authority now has statistics and numerical values that it can use to absorb copper tailings as a concrete raw material.

The study provides guidance on optimum concrete grade produced and cost reduction details of copper tailing-based concrete to support for local authorities in suitable land wand waste management using real data.