Nowadays traffic congestion has become significantly worse. Not only has it led to economic losses, but also to environmental damages, wastage of time and energy, human stress and pollution. Generally, traffic congestion is a ripple effect of a road congestion on neighbouring roads. When congestion occurs, it will propagate through the road network due to increasing traffic flow. One of the complexities of traffic congestion is unpredictability, thus it is difficult to represent traffic flows by numerical equations. One possible approach is to use the spatial historical data of traffic flow and relate them with traffic condition (congestion or clear) using statistical approach. Studies on traffic flow propagation generally involves visualization with real time GPS trajectory data to help analyze traffic flow propagation using human vision. Our research focuses on traffic flow pattern based on data from sensors without having information about the connected roads. We study spatial and temporal factors that influence traffic flow near a congested road in a neighbouring area. Hence, our study investigates the relationship of roads in a neighbouring area based on the similarity of traffic condition. Roads with high relationship with other neighbouring roads are identified by extracting spatial and temporal features using traffic state clustering. Grey level of co-occurrence matrix (GLCM) is utilized with spectral clustering to cluster road segments that have the same duration of road congestion in terms of day and time intervals. The emission probability is then calculated for prediction of traffic state impact of road congestion in neighboring area using Hidden Markov Model (HMM). We proposed HMM together with our clustering method to predict traffic state impact of road congestion. The experimental results show that the accuracy of prediction using the proposed HMM achieve 89%.

For decades, various algorithms to predict traffic flow have been developed to address traffic congestion. Traffic congestion or traffic jam occurs as a ripple effect from a road congestion in the neighbouring area. Previous research shows that there is a spatial correlation between traffic flow in neighbouring roads. Similar traffic pattern is observed between roads in a neighbouring area with respect to day and time. Currently, time series models and neural network models are widely applied to predict traffic flow and traffic congestion based on historical data. However, studies on relationships between road segments in a neighbouring area are still limited. It is important to investigate these relationships because they can assist drivers in avoiding roads which are impacted by road congestion. Also, the result can be used to improve the accuracy of prediction of traffic flow. Hence, this study investigates relationships of roads in a neighbouring area based on similarity of traffic condition. Traffic condition is influenced by number of vehicles and average speed of vehicles. In our study, clustering method is used to divide the speed of traffic into four (4) categories: very congested, congested, clear and very clear. We used k-means clustering method to cluster condition of traffic flow on road segments. Then, we applied the k-Nearest Neighbour (k-NN) method to classify the traffic condition in neighbouring roads. From the classification of traffic condition in neighbouring roads, we then determine the relationship between road segments. We presented the road with highest relationship on the map and used it as input factor to predict traffic speed of the road using neural network. Results show that combination of k-means and k-NN method produced better results than using both, correlation method and using the k-means method only.

This study examines the pricing methods for educational programmes at institutes of higher learning (IHLs) in Malaysia. Managerial efforts adopted by the IHLs to support their pricing methods are also discussed. Case study approach was used and nine IHLs participated in this study. Cases are chosen based on the Malaysian Qualifying Agency (MQA) Rating system for higher education institutions in Malaysia for 2009 (SETARA '09). Data was collected from selected institutions from the Tier 5 - Outstanding and Tier 4 - Very Good ratings. It is found that IHLs used competition-based and demand-based pricing methods. Managerial efforts include focus on niche, cost management, programme evaluation and financial support. Research findings contribute to the limited pool of studies in the field of pricing for service industry, especially from the Asian region. Findings can also be useful to various stakeholders including Ministry of Higher Education (MOHE), sponsors, corporate bodies, parents and students.

Disaster Information Media in ASEAN Countries: A Paired Comparison Method

This article presents a critical review of revenue recognition practices among the institutions administering waqf, zakah and baitulmal (WZB) in Malaysia. Through a content analysis of financial statements and focus group discussions with the accountants and management of the five WZB institutions, the findings show that different revenue recognition practices have been employed, particularly in recognizing revenues of waqf for specific purpose and in recognizing ‘faraid’ properties or portion of estates of a deceased person that belong to baitulmal. Furthermore, the review of practices of the sample institutions reveals different pointof recognition of ‘luqatah’ as revenue and diverse measurement basis were used in revenues recognition. The findings provide understanding on the current practices of recognition of the various types of revenues administered by the institutions of WZB. The findings can also be used as inputs in developing Islamic accounting standards that will harmonize the accounting and reporting practices among WZB institutions. Keywords: waqf, zakah, baitulmal, revenue recognition, Islamic accounting

Until recently, about three-quarters of all monogenic Parkinson's disease (PD) studies were performed in European/White ancestry, thereby severely limiting our insights into genotype-phenotype relationships at global scale. The first systematic approach to embrace monogenic PD worldwide, The Michael J. Fox Foundation Global Monogenic PD (MJFF GMPD) Project, contacted authors of publications reporting individuals carrying pathogenic variants in known PD-causing genes. In contrast, the Global Parkinson's Genetics Program's (GP2) Monogenic Network took a different approach by targeting PD centers not yet represented in the medical literature. Here, we describe combining both efforts in a "merger project" resulting in a global monogenic PD cohort with build-up of a sustainable infrastructure to identify the multi-ancestry spectrum of monogenic PD and enable studies of factors modifying penetrance and expression of monogenic PD. This effort demonstrates the value of future research based on team science approaches to generate comprehensive and globally relevant results.