Malaysia is in the process of liberalising its electricity supply industry (ESI) further, with the second reform series announced in September 2018. If everything goes as planned, Malaysia would be the third country in the Association of Southeast Asia Nations (ASEAN) to have a fully liberalised ESI after the Philippines and Singapore. A number of initiatives have been in the pipeline to be executed and a lot more will be planned. At this juncture, it is important for Malaysia to look for the best practices and lessons that can be learnt from the experience of other countries that have successfully liberalised their ESIs. Being in the same region, it is believed that there is a lot that Malaysia can learn from the Philippines and Singapore. This paper therefore presents and deliberates on the chronological development of the countries’ progressive journeys in liberalising their ESIs. The aim is to discern the good practices, the challenges as well as the lessons learnt from these transformations. Analysis is being made and discussed from the following four perspectives; legislative framework, implementation phases, market components and impact on renewable energy penetration. Findings from this study would provide useful insight for Malaysia in determining the course of actions to be taken to reform its ESI. Beyond Malaysia, the findings can also serve as the reference for the other ASEAN countries in moving towards liberalising their ESIs.

The manual segmentation of the blood vessels in retinal images has numerous limitations. It is very time consuming and prone to human error, particularly with a very twisted structure of the blood vessel and a vast number of retinal images that needs to be analysed. Therefore, an automatic algorithm for segmenting and extracting useful clinical features from the retinal blood vessels is critical to help ophthalmologists and eye specialists to diagnose different retinal diseases and to assess early treatment. An accurate, rapid, and fully automatic blood vessel segmentation and clinical features measurement algorithm for retinal fundus images is proposed to improve the diagnosis precision and decrease the workload of the ophthalmologists. The main pipeline of the proposed algorithm is composed of two essential stages: image segmentation and clinical features extraction stage. Several comprehensive experiments were carried out to assess the performance of the developed fully automated segmentation algorithm in detecting the retinal blood vessels using two extremely challenging fundus images datasets, named the DRIVE and HRF. Initially, the accuracy of the proposed algorithm was evaluated in terms of adequately detecting the retinal blood vessels. In these experiments, five quantitative performances were measured and calculated to validate the efficiency of the proposed algorithm, which consist of the Acc., Sen., Spe., PPV, and NPV measures compared with current state-of-the-art vessel segmentation approaches on the DRIVE dataset. The results obtained showed a significantly improvement by achieving an Acc., Sen., Spe., PPV, and NPV of 99.55%, 99.93%, 99.09%, 93.45%, and 98.89, respectively.

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