Re-Examining the Publicity, Advertising and Marketing of Legal Profession in Malaysia

The legal practitioners in Malaysia are restricted from publicising, advertising and marketing themselves on the grounds of fiduciary relationship with clients, the duty to serve the public and it is professionally undignified. Despite the advancement of the Information, Communication and Technology, lawyers are restricted in utilising it for publicity, advertising and marketing. At the same time, the public is deprived of information to engage the best lawyers of their choice. Furthermore, while other countries such as European Union, United Kingdom, Singapore and Australia have moved forward, the Malaysian legal profession remains unchanged. This concept paper investigates the adequacy of the Legal Profession (Publicity) Rules 2001(“LPPR 2001”) in legalising publicity, advertising and marketing. This paper adopts a qualitative research methodology with doctrinal and comparative approaches. Firstly, this paper focuses on content analysis of statutes as the primary source of law. Secondly, content analysis on secondary sources of law including journal articles, and online sources. Thirdly, conducting a comparative study by analysing the primary and secondary sources of law in other jurisdictions. This paper explains that lawyers must be allowed to innovate into new methods in publicising, advertising and marketing themselves. Society will greatly benefit from this as they will be more informed and knowledgeable in engaging the service of lawyers of their choice. This paper ends by suggesting that there is a dire need to legalise the publicity, advertising and marketing of the legal profession in Malaysia. Thus, this research is significant to the development of the legal profession in Malaysia

Isu dan Cabaran Pelaksanaan Pendidikan Asas Vokasional (PAV) di Sekolah Menengah Harian, Malaysia

Kertas konsep ini membincangkan pelbagai isu berkaitan mata pelajaran baru di sekolah menengah akademik yang dikenali sebagai Pendidikan Asas Vokasional (PAV) yang baru dilaksanakan pada tahun 2013. PAV merupakan matapelajaran berasaskan kompetensi (Competency Based Education/ CBE) yang merangkumi pelaksanaan pengajaran dan pembelajaran serta pentaksiran berasaskan keterampilan. Justeru, fokus utama kertas ini ialah untuk mengupas mengenai isu serta cabaran terhadap pelaksanaan PAV daripasa aspek penyediaan dan latihan guru, proses pengajaran dan pembelajaran, penyediaan sumber, bahan dan peralatan, serta sistem penilaian dan pentaksiran. Adalah diharapkan kertas konsep ini dapat memperluaskan perspektif dan pemahaman terhadap berbagai isu berkaitan dengan proses pelaksanaan program PAV di sekolah-sekolah menengah harian biasa di Negara ini

Machine Learning Methods for Better Water Quality Prediction

In any aquatic system analysis, the modelling water quality parameters are of considerable significance. The traditional modelling methodologies are dependent on datasets that involve large amount of unknown or unspecified input data and generally consist of time-consuming processes. The implementation of artificial intelligence (AI) leads to a flexible mathematical structure that has the capability to identify non-linear and complex relationships between input and output data. There has been a major degradation of the Johor River Basin because of several developmental and human activities. Therefore, setting up of a water quality prediction model for better water resource management is of critical importance and will serve as a powerful tool. The different modelling approaches that have been implemented include: Adaptive Neuro-Fuzzy Inference System (ANFIS), Radial Basis Function Neural Networks (RBF-ANN), and Multi-Layer Perceptron Neural Networks (MLP-ANN). However, data obtained from monitoring stations and experiments are possibly polluted by noise signals as a result of random and systematic errors. Due to the presence of noise in the data, it is relatively difficult to make an accurate prediction. Hence, a Neuro-Fuzzy Inference System (WDT-ANFIS) based augmented wavelet de-noising technique has been recommended that depends on historical data of the water quality parameter. In the domain of interests, the water quality parameters primarily include ammoniacal nitrogen (AN), suspended solid (SS) and pH. In order to evaluate the impacts on the model, three evaluation techniques or assessment processes have been used. The first assessment process is dependent on the partitioning of the neural network connection weights that ascertains the significance of every input parameter in the network. On the other hand, the second and third assessment processes ascertain the most effectual input that has the potential to construct the models using a single and a combination of parameters, respectively. During these processes, two scenarios were introduced: Scenario 1 and Scenario 2. Scenario 1 constructs a prediction model for water quality parameters at every station, while Scenario 2 develops a prediction model on the basis of the value of the same parameter at the previous station (upstream). Both the scenarios are based on the value of the twelve input parameters. The field data from 2009 to 2010 was used to validate WDT-ANFIS. The WDT-ANFIS model exhibited a significant improvement in predicting accuracy for all the water quality parameters and outperformed all the recommended models. Also, the performance of Scenario 2 was observed to be more adequate than Scenario 1, with substantial improvement in the range of 0.5% to 5% for all the water quality parameters at all stations. On validating the recommended model, it was found that the model satisfactorily predicted all the water quality parameters (R2 values equal or bigger than 0.9). © 201

Reservoir evaporation prediction modeling based on artificial intelligence methods

The current study explored the impact of climatic conditions on predicting evaporation from a reservoir. Several models have been developed for evaporation prediction under different scenarios, with artificial intelligence (AI) methods being the most popular. However, the existing models rely on several climatic parameters as inputs to achieve an acceptable accuracy level, some of which have been unavailable in certain case studies. In addition, the existing AI-based models for evaporation prediction have paid less attention to the influence of the time increment rate on the prediction accuracy level. This study investigated the ability of the radial basis function neural network (RBF-NN) and support vector regression (SVR) methods to develop an evaporation rate prediction model for a tropical area at the Layang Reservoir, Johor River, Malaysia. Two scenarios for input architecture were explored in order to examine the effectiveness of different input variable patterns on the model prediction accuracy. For the first scenario, the input architecture considered only the historical evaporation rate time series, while the mean temperature and evaporation rate were used as input variables for the second scenario. For both scenarios, three time-increment series (daily, weekly, and monthly) were considered

Bat algorithm for dam–reservoir operation

Optimizing reservoir operation rule is considered as a complex engineering problem which requires an efficient algorithm to solve. During the past decade, several optimization algorithms have been applied to solve complex engineering problems, which water resource decision-makers can employ to optimize reservoir operation. This study investigates one of the new optimization algorithms, namely, Bat Algorithm (BA). The BA is incorporated with different rule curves, including first-, second-, and third-order rule curves. Two case studies, Aydoughmoush dam and Karoun 4 dam in Iran, are considered to evaluate the performance of the algorithm. The main purpose of the Aydoughmoush dam is to supply water for irrigation. Hence, the objective function for the optimization model is to minimize irrigation deficit. On the other hand, Karoun 4 dam is designed for hydropower generation. Three different evaluation indices, namely, reliability, resilience, and vulnerability were considered to examine the performance of the algorithm. Results showed that the bat algorithm with third-order rule curve converged to the minimum objective function for both case studies and achieved the highest values of reliability index and resiliency index and the lowest value of the vulnerability index. Hence, the bat algorithm with third-order rule curve can be considered as an appropriate optimization model for reservoir operation

Proceedings of International Technical Postgraduate Conference 2022

This conference proceedings contains articles on the various research ideas of the academic & research communities presented at the International Technical Postgraduate Conference 2022 (TECH POST 2022) that was held at Universiti Malaya, Kuala Lumpur, Malaysia on 24-25 September 2022. TECH POST 2022 was organized by the Faculty of Engineering, Universiti Malaya. The theme of the conference is “Embracing Innovative Engineering Technologies Towards a Sustainable Future”.  TECH POST 2022 conference is intended to foster the dissemination of state-of-the-art research from five main disciplines of Engineering: Electrical Engineering, Biomedical Engineering, Civil Engineering, Mechanical Engineering, and Chemical Engineering. The objectives of TECH POST 2022 are to bring together innovative researchers from all engineering disciplines to a common forum, promote R&D activities in Engineering, and promote the dissemination of scientific knowledge and research know-how between researchers, engineers, and students. Conference Title: International Technical Postgraduate Conference 2022Conference Acronym: TECH POST 2022Conference Date: 24-25 September 2022Conference Location: Faculty of Engineering, Universiti Malaya, Kuala Lumpur Malaysia (Hybrid Mode)Conference Organizers: Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia