A brain tumour is one of the major reasons for death in humans, and it is the tenth most common type of tumour that affects people of all ages. However, if detected early, it is one of the most treatable types of tumours. Brain tumours are classified using biopsy, which is not usually performed before definitive brain surgery. An image classification technique for tumour diseases is important for accelerating the treatment process and avoiding surgery and errors from manual diagnosis by radiologists. The advancement of technology and machine learning (ML) can assist radiologists in tumour diagnostics using magnetic resonance imaging (MRI) images without invasive procedures. This work introduced a new hybrid CNN-based architecture to classify three brain tumour types through MRI images. The method suggested in this paper uses hybrid deep learning classification based on CNN with two methods. The first method combines a pre-trained Google-Net model of the CNN algorithm for feature extraction with SVM for pattern classification. The second method integrates a finely tuned Google-Net with a soft-max classifier. The proposed approach was evaluated using MRI brain images that contain a total of 1426 glioma images, 708 meningioma images, 930 pituitary tumour images, and 396 normal brain images. The reported results showed that an accuracy of 93.1% was achieved from the finely tuned Google-Net model. However, the synergy of Google-Net as a feature extractor with an SVM classifier improved recognition accuracy to 98.1%.

Early flood warnings are important to allow sufficient time for evacuation.Although warning systems are now in place, key questions remain as to their effectiveness in sending information to the public, which may in part depend on the media used. This paper assesses the effectiveness of warnings disseminated to the public for the December 2014 Kelantan Flood, Malaysia. The flood was the worst in decades making it an appropriate case study with which to assess public awareness and perceptions associated with flood warnings and their dissemination. The effectiveness of warnings issued via different media was assessed by questionnaire.Results show that 56% of respondents received warnings prior to the flood, a majority of them through television and information shared among the public.While the preferred medium of warning is not dependent on age, assessment of peoples' response to warnings shows that with increasing age responsiveness to orders and readiness to evacuate decreases. To increase the number of people receiving the warnings, short message service (SMS) communications sent from the authorities to a wider audience may be considered, as information shared among the public is observed to be most effective in reaching the greatest number of people. K E Y W O R D S community response, disaster management, effective flood warning, flood disaster

No abstract

In order to lower down the fuel consumption and to achieve higher speed of ship, reduction in ship resistance has been the major topic of research for a long time. The most important factor in ship resistance is skin friction resistance. Micro-bubble has been shown to be able to reduce skin friction. This micro-bubble method gives the possibility to lower the friction without any change in the present hull form of a ship. The application of the micro-bubble technique reduces the surface friction by a variation of the viscosity of the fluid around the ship and makes a modification in the structure of the turbulent boundary layer. However, not much is known about the correct size, quantity, area of coverage which can effectively form a skin friction reducing mechanism. There are many established methods, such as Venturi tube type generator, tangential water-jet and forming of dissolve air and also a chemical process, such as electrolysis, may result in bubble production [1]. The use of micro-bubble as reducing agent of drag can lead to the creation of bubbly mixture near the flow surface that can significantly advances in the understanding of the underlying physical process of drag reduction. The current applications of these techniques to surface ships are discussed.

Waste heat recovery from shipboard machineries could be a potential source for heat treatment of ballast water. Similar to a shipboard schematic arrangement, a laboratory-scale engine-heat exchanger set-up harvesting waste heat from jacket water and exhaust gases was erected to test the level of species' mortalities. Mortalities were also assessed under experimental conditions for cultured and natural plankton communities at laboratory level. Effect of pump impellers on species' mortalities were also tested. Exposures between 60°C and 70°C for 60 sec resulted in 80-100% mortalities. Mortalities due to pump impeller effects were observed in the range of 70-100% for zooplankton. On the laboratory-scale arrangement, >95% mortalities of phytoplankton, zooplankton and bacteria were recorded. It was demonstrated that the temperature of tropical sea waters used as secondary coolant can be raised to cause species' mortalities, employing engine exhaust gases. The results also indicated that pump impeller effects will enhance species' mortalities. The limitations of the shipboard application of this method would be the large ballast volumes, flow rates and time for treatment.