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Meeting the rising energy demand and limiting its environmental impact are the two intertwined issues faced in the 21st century. Governments in different countries have been engaged in developing regulations and related policies to encourage environment friendly renewable energy generation along with conservation strategies and technological innovations. It is important to develop sustainable energy policies and provide relevant and suitable policy recommendations for end-users. This study presents a review on sustainable energy policy for promotion of renewable energy by introducing the development history of energy policy in five countries, i.e., the United States, Germany, the United Kingdom, Denmark and China. A survey of the articles aimed at promoting the development of sustainable energy policies and their modelling is carried out. It is observed that energy-efficiency standard is one of the most popular strategy for building energy saving, which is dynamic and renewed based on the current available technologies. Feed-in-tariff has been widely applied to encourage the application of renewable energy, which is demonstrated successfully in different countries. Building energy performance certification schemes should be enhanced in terms of reliable database system and information transparency to pave the way for future net-zero energy building and smart cities.

This paper presents eight hybrid renewable energy (RE) systems that are derived from solar, wind and biomass, with energy storage, to meet the energy demands of an average household in the six geopolitical zones of Nigeria. The resource assessments show that the solar insolation, wind speed (at 30 m hub height) and biomass in the country range, respectively, from 4.38–6.00 kWh/m2/day, 3.74 to 11.04 m/s and 5.709–15.80 kg/household/day. The HOMER software was used to obtain optimal configurations of the eight hybrid energy systems along the six geopolitical zones' RE resources. The eight optimal systems were further subjected to a multi-criteria decision making (MCDM) analysis, which considers technical, economic, environmental and socio-cultural criteria. The TOPSIS-AHP composite procedure was adopted for the MCDM analysis in order to have more realistic criteria weighting factors. In all the eight techno-economic optimal system configurations considered, the biomass generator-solar PV-battery energy system (GPBES) was the best system for all the geopolitical zones. The best system has the potential of capturing carbon from the atmosphere, an attribute that is desirous for climate change mitigation. The cost of energy (COE) was seen to be within the range of 0.151–0.156 US$/kWh, which is competitive with the existing electricity cost from the national grid, average 0.131 US$/kWh. It is shown that the Federal Government of Nigeria favorable energy policy towards the adoption of biomass-to-electricity systems would make the proposed system very affordable to the rural households.

Colon cancer is one of the world's three most deadly and severe cancers. As with any cancer, the key priority is early detection. Deep learning (DL) applications have recently gained popularity in medical image analysis due to the success they have achieved in the early detection and screening of cancerous tissues or organs. This paper aims to explore the potential of deep learning techniques for colon cancer classification. This research will aid in the early prediction of colon cancer in order to provide effective treatment in the most timely manner. In this exploratory study, many deep learning optimizers were investigated, including stochastic gradient descent (SGD), Adamax, AdaDelta, root mean square prop (RMSprop), adaptive moment estimation (Adam), and the Nesterov and Adam optimizer (Nadam). According to the empirical results, the CNN-Adam technique produced the highest accuracy with an average score of 82% when compared to other models for four colon cancer datasets. Similarly, Dataset_1 produced better results, with CNN-Adam, CNN-RMSprop, and CNN-Adadelta achieving accuracy scores of 0.95, 0.76, and 0.96, respectively.