Relief of pain and suffering : from antiquity to modern times

"An inaugural lecture delivered at the University of Malaya on 10 December 2003

Isolation, Screening and Growth of Monoaromatic Hydrocarbon Utilising Bacteria

A total of 131 isolates originating from different soils were obtained through enrichment and direct plating methods using 0.1% (v/v) of either benzene, toluene or mixture of benzene and toluene as their sole energy and carbon sources. Most of the isolates were from S5 (garden soil) and the least from SI (ESSO refinery soil) obtained by the enrichment culture method. Out of this, 107 motphologicaUy different isolates were rescreened in 1% (v/v) of their respective hydrocarbon of either benzene or toluene. Out of the 34 good isolates grown in varying hydrocarbon concentrations ul> to 50% (v/v), 23 gave good and moderate growth. These isolates were further grown in different concentrations of BTEX. Six isolates (145yw, 113i, 205y, 205w, 113 & 146) exhibited good growth withstanding up to 75 % (v/v) concentration of BTEX. The isolates were also able to grow in 0.4 M NaCl (35 p.s.u.) which is equivalent to sea salinity level. Studies done on the 3 isolates (145yw, 113i & 205y) showed that they were metabolically active throughout their growth in the hydrocarbon spiked media deduced from the !NT stain test, increased oxygen consumption and increased plate COWlts. High colony fonning Wlits / mL percentage (50% or more) were obsetVed in different organic solvents of varying log P values. Isolate 145yw gave the highest growth rate of 0.22 ht in 0.1% (v/v) benzene. Biodegradability of the isolates were further confinned by positive CO2 production and the reduction in the hydrocarbon peaks obsetVed by GC. Extensive degradative profiles were obtained with isolate 113i and mixed culture. Laboratory biodegradation studies showed that all the 3 isolates were able to grow in both single and mixture of site samples

Adversarial Sample Generation using the Euclidean Jacobian-based Saliency Map Attack (EJSMA) and Classification for IEEE 802.11 using the Deep Deterministic Policy Gradient (DDPG)

One of today's most promising developments is wireless networking, as it enables people across the globe to stay connected. As the wireless networks' transmission medium is open, there are potential issues in safeguarding the privacy of the information. Though several security protocols exist in the literature for the preservation of information, most cases fail with a simple spoof attack. So, intrusion detection systems are vital in wireless networks as they help in the identification of harmful traffic. One of the challenges that exist in wireless intrusion detection systems (WIDS) is finding a balance between accuracy and false alarm rate. The purpose of this study is to provide a practical classification scheme for newer forms of attack. The AWID dataset is used in the experiment, which proposes a feature selection strategy using a combination of Elastic Net and recursive feature elimination. The best feature subset is obtained with 22 features, and a deep deterministic policy gradient learning algorithm is then used to classify attacks based on those features. Samples are generated using the Euclidean Jacobian-based Saliency Map Attack (EJSMA) to evaluate classification outcomes using adversarial samples. The meta-analysis reveals improved results in terms of feature production (22 features), classification accuracy (98.75% for testing samples and 85.24% for adversarial samples), and false alarm rates (0.35%).&nbsp

Performance of an Oscillating Water Column Device with Different Bottom Profiles Subjected to Random Waves

Interaction between Offshore Utilisation and the Environmen

A preliminary study on intraparticle diffusion of turbidity through nanomagnetic biocarbon composite (NBC)

The accessibility of safe drinking water is a fundamental element of Sustainable Development Goal 6 (SDG 6). A novel nanomagnetic biocarbon composite (NBC) has been developed utilising coconut shells for purifying raw groundwater. One of the primary concerns associated with groundwater is turbidity, a condition resulting from the presence of clay, dirt, and silt particles. The presence of turbidity in untreated water has a significant effect on both the visual appeal and overall cleanliness of the water. For the purposes of comparison, commercialised activated carbon (CAC) was utilised in this study. According to the Brunauer-Emmett-Teller (BET) analysis, it was observed that the average pore diameter of NBC was smaller compared to commercially available activated carbon (CAC), despite having a higher BET surface (SBET) value of 916.189 m/g compared to CAC. Based on the results of the kinetic study, it was determined that intraparticle diffusion, specifically external film diffusion, exhibited the most suitable fit as the kinetic model for NBC and CAC. This conclusion was supported by the lowest root mean square error (RMSE) values obtained, which were 0.04 for NBC and 0.13 for CAC, surpassing the performance of alternative models. The diffusion coefficient (Di) values for NBC (7.40 x 10–15 cm2/s) and CAC (7.80 x 10–15 cm2/s) demonstrated the phenomenon of bulk diffusion from high to low concentration. Notably, the diffusion coefficient for NBC was found to be lower than that for CAC. Accordingly, it is suggested that average pore diameter played important roles in intraparticle diffusion of an absorbent

Consumer Decision-Making Process in Purchasing Packaging Products in Malaysia

This research paper highlights the consumer decision-making process in purchasing the packaged product in Malaysia. This paper aims to discover the consumer intention when buying the products, specially packaged products. The finding of this research paper can be used by companies that produce product packaging to ensure that their products meet customer’s wants and needs. All data have been analyzed to ascertain the relationship between each variable that is related to packaged products. Results show that all variables are related to each other

What does a typical CNN “see” in an emotional facial image?

The objective of this research is to understand the current capabilities of artificial neural network algorithms and contrast them to the human visual system, in order to identify the most effective features to support affective automation. This can, in turn, aid in optimisation of resources used for storage and transmission by understanding which level of information can be used to augment and potentially accelerate accurate identification of emotional facial expressions. For the first part of our experiment, which we present in this paper, we focused on evaluating feature selection of facial expression images using machine learning. 70 (10 examples of each basic emotion) randomly selected from the NIMSTIM dataset images were used, which were split into train (56) and test (14) sets. The testing images were then processed using Singular Vector Decomposition to vary the levels of information shown in the image. Next, the training dataset was used to train a Convolutional Neural Network algorithm with 18 layers (with convolutional, max pooling, dropout, flattening and activation layers) and 66,884,615 trainable parameters. The validation accuracy was 45% and the confusion matrix showed that the emotion disgust was predicted at almost 100% accuracy, surprise at 55%, and sorrow/happiness/neutral at 46-47%. As expected, the granularity level of the test images had an effect on the successful predictions. A feature map visualisation was performed to demonstrate what the CNN “sees” (i.e., the feature selection) in the image in order to accurately predict the emotional expression type. For the next phase of our experiment, we plan on contrasting the features and performance to that of the human visual system using an experimental design with eye tracking

Sugarcane bagasse derived nano magnetic adsorbent composite (SCB-NMAC) for removal of Cu2+ from aqueous solution

A novel sugarcane bagasse derived nano magnetic adsorbent composite (SCB-NMAC) was successfully prepared for the removal of Cu2+ in aqueous solution. Characterization of the newly prepared material was obtained through SEM, EDX, particle size analyzer and XRD. Results confirmed the presence of iron oxide coating onto the material. The removal of Cu2+ by SCB-NMAC obeyed the pseudo second order reaction (R2 = 0.982) as opposed to intra particle diffusion (R2 = 0.708), and pseudo first order (R2 = 0.402) model. Langmuir isotherm was found to be more applicable (R2 = 0.996) rather than Freundlich isotherm (R2 = 0.979), which indicated a monolayer adsorption between Cu2+ and SCB-NMAC. The maximum adsorption capacity was calculated as 113.63 mg/g at pH 4. In addition, adsorption-desorption studies indicated that SCB-NMAC displayed high stability for regeneration with good reusability with desorption efficiency up to 60% and reusability efficiency up to 80% for three recurring cycles

The perception on halal label of MAMEE products among consumers in Selangor

The halal label on food products is important to provide health and create awareness among MAMEE consumers. The official halal label on food packaging has been investigated as one of the good health indicators that is beneficial for people who consume it. A shift towards sustainable halal labels should be taken to ensure good absorption of food nutrition. Consumers’ perception of the halal label on MAMEE products has changed due to their awareness of the halal label’s effect on consumer health. This study is conducted to investigate the relationship between demographic factors and the perception of consumers towards MAMEE products based on the halal label. The questionnaire was distributed to 114 respondents in Selangor. The data was analysed using the Statistical Package for Social Science (SPSS). From this study, the consumers are able to give a positive perception towards MAMEE products based on the halal label. The result showed a relationship between demographic factors and the perception of MAMEE consumers in Selangor, with significance values below 0.1. The significance of this study is to be a reference for the MAMEE Company to conduct research and development (R&D) in producing halal food products for the consumers

A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral Squamous Cell Carcinoma

One of the most prevalent cancers is oral squamous cell carcinoma, and preventing mortality from this disease primarily depends on early detection. Clinicians will greatly benefit from automated diagnostic techniques that analyze a patient’s histopathology images to identify abnormal oral lesions. A deep learning framework was designed with an intermediate layer between feature extraction layers and classification layers for classifying the histopathological images into two categories, namely, normal and oral squamous cell carcinoma. The intermediate layer is constructed using the proposed swarm intelligence technique called the Modified Gorilla Troops Optimizer. While there are many optimization algorithms used in the literature for feature selection, weight updating, and optimal parameter identification in deep learning models, this work focuses on using optimization algorithms as an intermediate layer to convert extracted features into features that are better suited for classification. Three datasets comprising 2784 normal and 3632 oral squamous cell carcinoma subjects are considered in this work. Three popular CNN architectures, namely, InceptionV2, MobileNetV3, and EfficientNetB3, are investigated as feature extraction layers. Two fully connected Neural Network layers, batch normalization, and dropout are used as classification layers. With the best accuracy of 0.89 among the examined feature extraction models, MobileNetV3 exhibits good performance. This accuracy is increased to 0.95 when the suggested Modified Gorilla Troops Optimizer is used as an intermediary layer