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The commercial stainless steels have been used extensively in the biomedicine application and their electrochemical behaviour in the simulated body fluid (SBF) are not uncovered obviously. In this research, the corrosion resistance of the commercial stainless steel of Fe–17Cr–xNi alloys (x = 4, 8, 10 and 14) has been studied. This study aims to evaluate the rate of corrosion and corrosion resistance of some Fe–Cr–Ni alloys in SBF at 37°C.

In this research, the corrosion resistance of the commercial stainless steel of Fe–17Cr–xNi alloys has been studied using open circuit potential, electrochemical impedance spectroscopy and potentiodynamic polarization in the SBF at 37°C and pH 7.4 for a week. Also, the surface morphology of the four alloys was investigated using scanning electron microscopy, elemental composition was obtained via energy dispersive spectroscopy and the crystal lattice structure of Fe–17Cr–xNi alloys was obtained using X-ray diffraction technique. The chemical structure of the protective oxide film has been examined by X-ray photoelectron spectroscopy (XPS) and metals ions released into the solution have been detected after different immersion time using atomic absorption spectroscopy.

The results revealed that the increase of the Ni content leads to the formation of the stable protective film on the alloys such as the Fe–17Cr–10Ni and Fe–17Cr–14Ni alloys which possess solid solution properties. The Fe–17Cr–14Ni alloy displayed highest resistance of corrosion, notable resistance for localized corrosion and the low corrosion rate in SBF because of the formation of a homogenously protective oxide film on the surface. The XPS analysis showed that the elemental Fe, Cr and Ni react with the electrolyte medium and the passive film is mainly composed of Cr₂O₃ with some amounts of Fe(II) hydroxide at pH 7.4.

This work includes important investigation to use commercial stainless steel alloys for biomedical application.

The study aimed to use geographic information system (GIS) based multi-criteria decision making analysis (GIS-MCDA) to select areas suitable for siting landfills in Ashanti region. It also sought to ascertain variables most sensitive to the siting of landfill in the region.

This study utilized GIS-based Multi-criteria decision making analysis –AHP to model and select areas most suitable to siting landfills within the region. Overall, 16 variables including wind speed and hydraulic conductivity (which were previously neglected in landfill siting in Ghana) were identified through comprehensive literature review. These variables were weighted using AHP method and integrated using the weighted linear combination (WLC) in GIS environment to develop five sub-models: the physical environmental, sociocultural, economic/technical, climatic and hydrogeological sub-models. These sub-models were further weighted and then integrated to derive the final suitability model.

Results show that 13% (3,067 km²) of the region was identified as most suitable to siting engineered landfills. The study also identified 11 sites which are considered most suitable for situating landfills. On a sensitivity angle, hydrogeological (R² = 0.5923; p = 0.003) and physical environmental sub-model (R² = 0.254; p = 0.034) significantly predicted the final suitability model developed.

Ghana's Landfill Guidelines seeks to optimize site selection and ancillary services that culminate into achieving sanitary landfills by 2020. Evidence still abounds on the unsuitability of existing and in some cases, new landfill sites presenting environmental and social negative impacts. The comprehensive evaluation of most crucial variables – social and environmental factors that determine an optimal landfill location – will be of immense help to policy planners like the Environmental Protection Agency (EPA) towards upgraded landfills. The authors hope that, concerned agencies will adopt the model in the study and integrate into their existing landfill suitability modeling techniques to provide a more grounded framework that optimizes landfill site selection within the study area.

This study is the first attempt to consider a regional-level waste collection site selection in Ghana using comprehensive sets of social and environmental factors and will therefore contribute immensely to EPA's goal of achieving upgraded landfills by 2022.

Modern meat processing requires automation and robotisation to remain sustainable and adapt to future challenges, including those brought by global infection events. Automation of all or many processes is seen as the way forward, with robots performing various tasks instead of people. Meat cutting is one of these tasks. Smart novel solutions, including smart knives, are required, with the smart knife being able to analyse and predict the meat it cuts. This paper aims to review technologies with the potential to be used as a so-called “smart knife” The criteria for a smart knife are also defined.

This paper reviews various technologies that can be used, either alone or in combination, for developing a future smart knife for robotic meat cutting, with possibilities for their integration into automatic meat processing. Optical methods, Near Infra-Red spectroscopy, electrical impedance spectroscopy, force sensing and electromagnetic wave-based sensing approaches are assessed against the defined criteria for a smart knife.

Optical methods are well established for meat quality and composition characterisation but lack speed and robustness for real-time use as part of a cutting tool. Combining these methods with artificial intelligence (AI) could improve the performance. Methods, such as electrical impedance measurements and rapid evaporative ionisation mass spectrometry, are invasive and not suitable in meat processing since they damage the meat. One attractive option is using athermal electromagnetic waves, although no commercially developed solutions exist that are readily adaptable to produce a smart knife with proven functionality, robustness or reliability.

This paper critically reviews and assesses a range of sensing technologies with very specific requirements: to be compatible with robotic assisted cutting in the meat industry. The concept of a smart knife that can benefit from these technologies to provide a real-time “feeling feedback” to the robot is at the centre of the discussion.