From Novelty to Normalcy: Polling in Myanmars Democratic Transition

Since the government of Myanmar* announced a transition from military rule to democracy in 2010, both domestic and international stakeholders have turned to polling to discover public opinion on a range of issues. This report examines the state of opinion research in Myanmar, identifies challenges, and makes recommendations for improvements.Although Myanmar has a decades-long history of market surveys, political polling is a relatively new phenomenon. Organizations operating in this field face four major challenges. The first is selecting a sample in a country that lacks reliable census or voter registration data, and lacks comprehensive access to telephones or the internet. The second is how to provide survey questionnaires in several languages to accommodate Myanmar's numerous ethnic groups. The third challenge relates to interviewers, both to their training and to accounting for possible response bias based on the interaction between the interviewer's sociodemographic background and the respondent's. Finally, polling groups and interviewers must ensure respondents' confidentiality.These problems are not unique to Myanmar. Pollsters around the world regularly grapple with similar dilemmas. What makes their task more challenging in Myanmar is the novelty of polling. Few people (even in civil society and political parties) understand its nature, and many are quick to dismiss the whole exercise when they do not like some of a poll's results. The report examines and refutes several of their criticisms

Entrepreneurial Specific Factors, Support Factors and Micro enterprises Performance: The Case of Malaysian Microcredit Program

The study's objective is to analyze the influence of several elements related to entrepreneurial-specific factors and support factors on the performance of micro-enterprises. The study sample consisted of a total of 756 micro-entrepreneurs who were involved in two main microcredit programs in Malaysia, namely Amanah Ikhtiar Malaysia (AIM) and The Venture Group Economic Fund (TEKUN). The study’s findings, obtained through multiple regression analysis, revealed that factors such as personal entrepreneurial competencies, management practice, microcredit programs and government support exert a significant influence on the performance of micro-enterprises. This finding is consistent with the Resource Base View (RBV) theory which links the importance of internal and external resources as a catalyst for competitive advantage and the performance of a business. The influence of family and commitment to religious principles is less significant. It was associated with the attitude factor of micro-entrepreneurs, who frequently disregard the fundamental principles of religion while making judgments. The impact of family influence on micro enterprises' performance was determined to be negligible. Family members make minimal contributions in terms of finances, energy, or moral support. The formulation of policies is crucial for these insights, which highlight the importance of providing entrepreneurship training and implementing excellent management practices. Additionally, financial support is necessary to promote the growth of micro-enterprises in Malaysia. The government plays a crucial role in facilitating the growth of micro companies by implementing various support programs. An efficient distribution system can minimize the leakage of programs and maximize benefits for the recipients who are intended

Exploring YouTube Comments to Understand Public Sentiment on COVID-19 Vaccines through Deep Learning-based Sentiment Analysis

COVID-19 was first found in China in 2019. Since then, it has quickly spread around the world, which has led to a lot of news stories and social media posts about the pandemic. YouTube, a popular video-sharing website, has become a valuable source of information on COVID-19 and other topics. However, it can be difficult to extract useful insights from the vast array of user comments that accompany these videos. One potential method for understanding public sentiment is to use sentiment analysis, which involves classifying text as positive, negative, or neutral. In this study, the dataset of over 44,000 YouTube comments related to COVID-19 vaccines was used, which was filtered to a total of 16,073 comments for analysis. The data was cleaned and organised using NeatText and then processed using GloVe word embedding, a technique for establishing statistical relationships between words. Based on the experiment, the performances of three different types of deep learning techniques: recurrent neural networks (RNN), gated recurrent units (GRU) and long short-term memory (LSTM) are compared in accurately classifying the sentiment of the comments. The study found that the GRU had the highest accuracy of 80.19%, followed by the LSTM with 79.00% accuracy, and the RNN with 67.15% accuracy

Optimization of fermentative hydrogen production from palm oil mill effluent in an up-flow anaerobic sludge blanket fixed film bioreactor

AbstractFermentative hydrogen production from palm oil mill effluent (POME) in an upflow anaerobic sludge blanket fixed film bioreactor was optimized using response surface methodology with a central composite design. The simultaneous effects of two independent operating variables, i.e. feed flow rate (QF) and up-flow velocity (Vup) on biological hydrogen production was investigated. The operating variables were varied to cover a wide range of organic loading rates from 10 to 60 g COD L−1 d−1. The dependent parameters as multiple responses were evaluated. Experimental results showed the highest value of yield at 0.31 L H2 g−1 COD was obtained at QF and Vup of 1.7 L d−1 and 0.5 m h−1, respectively. The optimum conditions for the fermentative hydrogen production using pre-settled POME were QF = 2.0–3.7 L d−1 and Vup = 1.5–2.3 m h−1. The experimental results agreed very well with the model prediction

Experimental investigation of passively cooled photovoltaic modules on the power output performance

The power output performance of a photovoltaic (PV) module decreases as the temperature increases. The increase in module temperature above the standard test conditions (25 °C) could reduce the average power output by at least 0.2% for each 1 °C rise. Hence, keeping the module temperature low is necessary for PV systems exposed to high solar irradiance throughout the year. Therefore, this study aims to experimentally analyse the eletctrical performance of passively cooled PV modules in the tropics. The developed cooling approach consists of rectangular plate fins made of aluminum 6061, attached to the rear surface of tedlar layer. The results indicated that the average module temperature reduction of 3.25 °C was observed under outdoor exposures. As a result, the heat sink improved the overall power output up to 14.2%. As the PV performances are site-dependent, these findings are beneficial as it provides a thorough explanation of fin heat sink behavior under long-term field exposures of tropics

Development of a valid and reliable scale to assess knowledge, attitude and practice (KAP) on frailty, nutrition and exercise among Malaysian elderly

Introduction: “Frailty Intervention through Nutrition Education and Exercise (FINE)” program is an educational program, an initiative to ameliorate frailty status among elderly due to the alarming number of frailty cases in Malaysia. The current study aims to develop and determine the validity and reliability of the KAP questionnaire on frailty, nutrition and exercise to assess the effectiveness of the “FINE” program among the Malaysian elderly. Methods: The KAP questionnaire was created based on the developed frailty module and education materials. Content and face validity were conducted before the reliability study among five health professionals and 20 elderly, while 79 elderly were involved in a reliability study in three different Projek Perumahan Rakyat (PPR) flats in the Kuala Lumpur area. Data were analysed to determine its internal consistency reliability. Results: Six items were removed during content and face validity, two from each domain. All items in the knowledge section were within an acceptable range of difficulty and discrimination following the item analysis. Yet, item-to-total correlation removes one item for attitude and four items for the practice domain. The analysis found that the internal consistency reliability was 0.852, 0.732 and 0.600 for the KAP section, respectively. Conclusion: The final version of the KAP questionnaire consisted of (11) knowledge, nine (9) attitudes and six (6) practice items proven to be valid and reliable. Thus, it could be used to assess the effectiveness of the “FINE” program among the Malaysian elderly

Uncertainty Prediction Output of a Finite Element Model (FEM) Using Surrogate Modelling Approach

Additive Manufacturing (AM) is a manufacturing approach that can build a three-dimensional object from a computer- aided design model by adding material layer by layer. This method has gained popularity due to its capability to manufacture a product with complex geometries. However, uncertainties exist in its structure as it involves the material properties and geometry parts. A computational approach via Finite Element Method (FEM) is an alternative to overcome these uncertainties. Due to its high computational effort and time consumption, the Machine Learning approach via Surrogate Modelling is another method to produce the output of the simulation results. Surrogate Modelling can generate output with an R2 value of 0.98 intervals when compared with the FEM results. The results demonstrate the potential of Surrogate Modelling to run FEM output via sufficient training data input

Uncertainty Factors of a Finite Element Model using the Fuzzy Analysis Method

The recent advancement in manufacturing technology in the automotive and aerospace sectors has led to the invention of advanced structured material, which is lightweight and a complex geometry model that can be manufactured. As it is related to human safety and hazards, the need for uncertainty analysis in a structure before and after a manufacturing process is a primary concern. Thus, this paper analyzes the uncertainty parameters of a meshed finite element model in the geometry, boundary condition, load, and material properties. An uncertainty analysis numerical tool, the fuzzy analysis method, is applied in Excel-VBA as the simulation platform. Each uncertainty parameter is in a range of numbers, with a maximum and minimum value as the limit. The α-cuts determine the fuzzy analysis output on the membership function. The deterministic value of the variable is implemented for comparison purposes. The simulation result for the von-Mises stress analysis has significantly impacted the uncertainty analysis as its curve has surpassed the yield strength limit of the material. The simulation output for the displacement has a more considerable uncertainty dispersion when compared to the other results. This study helps to find a better security margin of a structure for its sustainability in the future