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Iron-based coagulants are widely used in wastewater treatment due to their high positively charged ion that effectively destabilise colloidal suspension, and thus contribute to the formation of insoluble flocs. Ferric chloride, ferrous sulphate, and poly-ferric sulphate (PFS) are examples of iron-based coagulants that are highly available, and are beneficial in producing denser flocs, thereby improving settling characteristics. This work aims to review the preparation methods of PFS and critically discuss the influence of these methods on the PFS properties and performance as a chemical coagulant for water and wastewater treatment. In polymeric form, PFS is one of the pre-hydrolysing metallic salts with the chemical formula [Fe2(OH)n(SO4)3−n/2]m (where, n 10) and has a dark brownish red colour as well as is more viscous and less corrosive. PFS has an amorphous structure with small traces of crystallinity, containing both hydroxyl and sulphate functional groups. It has been applied in many industries including water or wastewater treatment which is also discussed in this study. It has the ability to remove pollutants contained in water or wastewater, such as turbidity, colour, chemical and biological oxygen demand, phosphorus, and others. This study also provides a review on the combination of PFS with other chemical coagulants or flocculants in the coagulation/flocculation process, and also flocs formed after a more stable treatment process.

Myocardial Infarction (MI) is the death of the heart muscle caused by lack of oxygenated blood flow to the heart muscle. It has been the main cause of death worldwide. The fastest way to detect MI is by using an electrocardiogram (ECG) device, which generates graphs of heartbeats morphology over a certain period of time. Patients with MI need fast intervention as delay will lead to worsening heart conditions or failure. To improve MI diagnosis, much research has been carried out to come up with a fast and reliable system to aid automatic MI detection and prediction from ECG readings. Recurrent Neural Network (RNN) with memory has produced more accurate results in predicting time series problems. Convolutional neural networks have also shown good results in terms of solving prediction problems. However, CNN models do not have the capability of remembering temporal information. This research proposes hybrid models of CNN and RNN techniques to predict MI. Specifically, CNN-LSTM and CNN-BILSTM models have been developed. The PTB XL dataset is used to train the models. The models predict ECG input as representing MI symptoms, healthy heart conditions or other cardiovascular diseases. Deep learning models offer automatic feature extraction, and our models take advantage of automatic feature extraction. The other superior models used their own feature extraction algorithm. This research proposed a straightforward architecture that depends mostly on the capability of the deep learning model to learn the data. Performance evaluation of the models shows overall accuracy of 89% for CNN LSTM and 91% for the CNN BILSTM model. [ABSTRACT FROM AUTHOR]

High Performance Fiber Reinforced Concrete (HPFRC) has been proposed to be used as a repair material for the deterioration of concrete structure since its very low porosity that leads to a low permeability and high durability. This characteristic makes it suitable for rehabilitation and retrofitting reinforced concrete structures or for as a new repair material. The bond strength between HPFRC and old concrete should have a good bond strength, thus surface preparation method and curing method can help strengthen the bond strength between HPFRC and normal concrete. This paper was performed to study the effect of surface preparation and curing method on the bond strength between HPFRC and normal concrete. In this study, three surface preparations were prepared: sandblasting, grooved and drill hole. Then, the curing methods that were performed in this study are ambient curing and water curing. The tests that were conducted to evaluate the bond strength between HPFRC and normal concrete are slant shear test and splitting tensile test. The result from this study shows that sandblasting gave the highest bond strength result between normal concrete and HPFRC. For the curing method, water curing gives the highest bond strength between normal concrete and HPFRC.

Soil compaction plays an important role in every construction activities to reduce risks of any damage. Traditionally, methods of assessing compaction include field tests and invasive penetration tests for compacted areas have great limitations, which caused time-consuming in evaluating large areas. Thus, this study proposed the possibility of using non-invasive surface wave method like Multi-channel Analysis of Surface Wave (MASW) as a useful tool for assessing soil compaction. The aim of this study was to determine the shear wave velocity profiles and field density of compacted soils under varying compaction efforts by using MASW method. Pre and post compaction of MASW survey were conducted at Pauh Campus, UniMAP after applying rolling compaction with variation of passes (2, 6 and 10). Each seismic data was recorded by GEODE seismograph. Sand replacement test was conducted for each survey line to obtain the field density data. All seismic data were processed using SeisImager/SW software. The results show the shear wave velocity profiles increase with the number of passes from 0 to 6 passes, but decrease after 10 passes. This method could attract the interest of geotechnical community, as it can be an alternative tool to the standard test for assessing of soil compaction in the field operation.