Isolation and Sequence Analysis of LRR-RLK (Leucine-Rich Repeat Receptor-Like Protein Kinase) Gene of Kelampayan (Neolamarckia Cadamba)

Neolamarckia cadamba or locally known as kelampayan, belongs to the Rubiaceae family. N. cadamba is a fastgrowing tree species and has economic importance in timber industry. Leucine-rich repeat receptor-like protein kinase (LRR-RLK) genes are the largest group of receptor-like kinase (RLK) encoding genes. LRR-RLKs have been studied comprehensively for their essential roles in plant development and stress responses. This rises the needs to determine the gene sequence of LRR-RLK in N cadamba and the identity of the gene sequence as compared to the similar gene in other plant species. Total genomic DNA was extracted from the leaves of N. cadamba using cetyltrimethylammonium bromide (CTAB) followed by DNA purification and integrity assessment. Four EST sequences were retrieved from N. cadamba EST database for in silico analysis. Primers were designed based on comparison of LRR-RLK EST sequences of N. cadamba with genomic sequence of another Rubiaceae plant. Gradient polymerase chain reaction (PCR) was utilized to amplify the LRR-RLK partial gene and amplicons with desired size of approximately 619 bp were obtained. The PCR products of LRR-RLK partial gene was sequenced and in silico analysis on these sequences was conducted. This effort could provide data for tissuespecific expression model, for selection of tree genetic properties and functional analysis of LRR-RLK genes in N. cadamba

Industrial Electrical Energy Consumption Forecasting by using Temporal Convolutional Neural Networks

In conjunction with the 4th Industrial Revolution, many industries are implementing systems to collect data on energy consumption to be able to make informed decision on scheduling processes and manufacturing in factories. Companies can now use this historical data to forecast the expected energy consumption for cost management. This research proposes the use of a Temporal Convolutional Neural Network (TCN) with dilated causal convolutional layers to perform forecasting instead of conventional Long-Short Term Memory (LSTM) or Recurrent Neural Networks (RNN) as TCN exhibit lower memory and computational requirements. This approach is also chosen due to traditional regressive methods such as Autoregressive Integrated Moving Average (ARIMA) fails to capture non-linear patterns and features for multi-step time series data. In this research paper, the electrical energy consumption of a factory will be forecasted by implementing a TCN to extract the features and to capture the complex patterns in time series data such daily electrical energy consumption with a limited dataset. The neural network will be built using Keras and TensorFlow libraries in Python. The energy consumption data as training data will be provided by GoAutomate Sdn Bhd. Then, the historical data of economic factors and indexes such as the KLCI will be included alongside the consumption data for neural network training to determine the effects of the economy on industrial energy consumption. The forecasted results with and without the economic data will then be compared and evaluated using Weighted Average Percentage Error (WAPE) and Mean Absolute Percentage Error (MAPE) metrics. The parameters for the neural network will then be evaluated and fined tuned accordingly based on the accuracy and error metrics. This research is able create a CNN to forecast electrical energy consumption with WAPE = 0.083 & MAPE = 0.092, of a factory one (1) week ahead with a small scale dataset with only 427 data points, and has determined that the effects of economic index such as the Bursa Malaysia has no meaningful impact on industrial energy consumption that can be then applied to the forecasting of energy consumption of the factory

Real-time Machine Health Monitoring System using Machine Learning with IoT Technology

Machine health monitoring is the main focal point for now as many industries are evolving to industry 4.0. Industry 4.0 is the revolution in industrial that involve the Internet of Things (IoT) and artificial intelligence toward automation and data sharing for production efficiency improvement. The existing established methods for machine health monitoring were not in real-time and there was no real-time correction of data from the load and processing of data on the computer. In tracking machine health efficiency this approach wasn’t very successful. Real-time machine health monitoring can improve overall equipment effectiveness (OEE), reduce electricity consumption, minimize unplanned downtime, and extend machine lifetime. In this research paper, we propose to design a real-time machine health monitoring system using machine learning with IoT technology that can analyze the supply balancing condition on a 3-phase system. This system is built with compact physical hardware and can capture the electrical data from the load then send it to the server. The server will progress data and train the data using machine learning. The system was installed on a blender machine in a factory. In this research, a system which is able to monitor the machine operation and classify the operation stages of the machine was developed. Besides that, the system also capable to monitor the load balancing condition of the machine

Spatial and Temporal Analysis of Plasmodium knowlesi Infection in Peninsular Malaysia, 2011 to 2018

The life-threatening zoonotic malaria cases caused by Plasmodium knowlesi in Malaysia has recently been reported to be the highest among all malaria cases; however, previous studies have mainly focused on the transmission of P. knowlesi in Malaysian Borneo (East Malaysia). This study aimed to describe the transmission patterns of P. knowlesi infection in Peninsular Malaysia (West Malaysia). The spatial distribution of P. knowlesi was mapped across Peninsular Malaysia using Geographic Information System techniques. Local indicators of spatial associations were used to evaluate spatial patterns of P. knowlesi incidence. Seasonal autoregressive integrated moving average models were utilized to analyze the monthly incidence of knowlesi malaria in the hotspot region from 2012 to 2017 and to forecast subsequent incidence in 2018. Spatial analysis revealed that hotspots were clustered in the central-northern region of Peninsular Malaysia. Time series analysis revealed the strong seasonality of transmission from January to March. This study provides fundamental information on the spatial distribution and temporal dynamic of P. knowlesi in Peninsular Malaysia from 2011 to 2018. Current control policy should consider different strategies to prevent the transmission of both human and zoonotic malaria, particularly in the hotspot region, to ensure a successful elimination of malaria in the future

Simian malaria: a narrative review on emergence, epidemiology and threat to global malaria elimination

Simian malaria from wild non-human primate populations is increasingly recognised as a public health threat and is now the main cause of human malaria in Malaysia and some regions of Brazil. In 2022, Malaysia became the first country not to achieve malaria elimination due to zoonotic simian malaria. We review the global distribution and drivers of simian malaria and identify priorities for diagnosis, treatment, surveillance, and control. Environmental change is driving closer interactions between humans and wildlife, with malaria parasites from non-human primates spilling over into human populations and human malaria parasites spilling back into wild non-human primate populations. These complex transmission cycles require new molecular and epidemiological approaches to track parasite spread. Current methods of malaria control are ineffective, with wildlife reservoirs and primarily outdoor-biting mosquito vectors urgently requiring the development of novel control strategies. Without these, simian malaria has the potential to undermine malaria elimination globally