Construction of symmetry triangular fuzzy number procedure (STFNP) using statistical information for autoregressive forecasting

Single-point data are used for data collection. However, data collected by various data collection methods are often exposed to uncertainties that may affect the information presented by the quantitative results. This also causes the forecast model developed to be less precise because of the uncertainties contained in the input data. It is essential to describe the uncertainty in data to obtain a realistic result from data analysis. However, most studies focus on model uncertainty regardless of data uncertainty. The data processing carried out may not always take care of uncertainty. When uncertainties in the raw data are not sufficiently handled, this creates more errors that are included in the predicted model. Standard procedures are also very limited to be followed in order to transform a single-point value into Triangular Fuzzy Number (TFN), which addresses the uncertainty. Thus, the data preparation procedure of Symmetry Triangular Fuzzy Number (STFN) is presented in this study to build an improved autoregressive model for time series forecasting. This study presents the proposed Symmetry Triangular Fuzzy Number Procedure (STFNP) using percentage error method and standard deviation method for first-order autoregressive forecasting. Percentage error rate method involves three different percentage rates, while the second method uses the standard deviation of the data. Simulations and verification procedures are presented and are accompanied with numerical examples using actual datasets of Air Pollutant Index and stock markets of selected ASEAN countries. This study reveals that the percentage error and standard deviation methods, which were used to construct the TFN, can achieve the same or better accuracy as compared to a single-point procedure. The results of the simulations and experiments show that the standard deviation method produces better results compared to the other proposed approaches and the conventional approach. Besides, the systematic procedure to construct the TFN does not deviate from single-point procedures. Importantly, uncertain data being treated avoids more uncertainties that would have been brought to the outcome of the forecast model and consequently improves prediction accuracy

Molecular Characterization of Clinical Isolates of Enteropathogenic Escherichia Coli from Miri Sarawak

A total of thirty two strains of clinical enteropathogenic Escherichia coli (EPEC) isolated from Hospital Miri, Sarawak were examined and further characterized by various molecular techniques. These techniques include the plasmid profiling, antimicrobial resistance, resistance and virulence genes detection by multiplex PCR, RAPD, ERIC and PFGE genomic fingerprinting. All the strains studied were found to exhibit multiple antibiotics resistance patterns to twelve antibiotics [penicillin (100%), teicoplanin (100%), vancomycin (100%), bacitrasin (97%), methicillin (97%), erythromycin (69%), ampicillin (63%), cephalothin (47%), streptomycin (25%), chloramphenicol (16%), kanamycin (6%) and nalidixic acid (3%)] used. Thirteen EPEC isolates were shown to encode ampicillin resistance by means of the blaTEM gene respectively, and none of the EPEC isolates showed the presence of the sipB/C, cmlA/tetR and blaPSE-1 genes. The plasmid profiles obtained ranged in size from 1.8 MDa to 57 MDa. Two types of specific primer encoding the Shiga-like Toxin gene, the SLTII (584 bp) gene and SLTI (348 bp) were utilized in the multiplex PCR assay. Analysis carried out demonstrated that all were positive for the presence of the SLTII and SLTI gene. Two EPEC isolates analysed by PCR were confirmed to be the O157:H7 serogroup as determined by agglutination tests with specific antisera. Three 50% G+C contents 10-mer random primers, the Gen 1-50-02 (5’- CCAAACTGCT-3’), Gen 1-50-08 (5’-GAGATGACGA-3’), and Gen1-50-09 (5’- TCGCTATCTC-3’) were chosen after screening through ten random primers. In PFGE technique carried out, two kinds of restriction enzymes, the SpeI (5’-A CTAGT-3’) and XbaI (5’- T CTAGA-3’) were used to check for the in-situ DNA digestion pattern due to their inherit advantages of the short sequence of these enzymes. Both the RAPD polymorphism pattern and PFGE profile obtained showed a significant discriminatory fingerprinting among the 32 isolates under studied. A respective dendrogram was constructed from the binary data matrix obtained from the RAPD, ERIC and PFGE fingerprints to compare the diversity relationship among the 32 isolates. All the dendrograms were constructed utilizing the RAPDistance software package based on the data retrieved from the presence or absence of banding pattern. All the three molecular techniques of RAPD-, ERIC-, and PFGE genotyping showed a significant correlation whereby the first 16 and the second 16 strains of EPEC used in this study showed a closer relationship in the respective cluster groups as shown in the constructed dendrograms. From the overall results obtained both the RAPD and ERIC analysis showed greater discriminatory power compared to the other phenotypic and molecular characterization techniques used in this study. Our results demonstrate that the antimicrobial resistance,presence of resistance and virulence genes, plasmid profiling, multiplex PCR, RAPD-PCR fingerprinting, ERIC and PFGE profiling methods are useful as a suitable analysis tools for a rapid and reliable molecular typing and identification of EPEC

Continuity and change : dealing with political volatility to advance climate change mitigation strategies ; examples from the transport sector

As the recent withdrawal of the United States from the Paris Agreement has shown, political volatility directly affects climate change mitigation policies, in particular in sectors, such as transport associated with long-term investments by individuals (vehicles) and by local and national governments (urban form and transport infrastructure and services). There is a large potential for cost-effective solutions to reduce greenhouse gas emissions and to improve the sustainability of the transport sector that is yet unexploited. Considering the cost-effectiveness and the potential for co-benefits, it is hard to understand why efficiency gains and CO2 emission reductions in the transport sector are still lagging behind this potential. Particularly interesting is the fact that there is substantial difference among countries with relatively similar economic performances in the development of their transport CO2 emissions over the past thirty years despite the fact that these countries had relatively similar access to efficient technologies and vehicles. This study aims to explore some well-established political science theories on the particular example of climate change mitigation in the transport sector in order to identify some of the factors that could help explain the variations in success of policies and strategies in this sector. The analysis suggests that institutional arrangements that contribute to consensus building in the political process provide a high level of political and policy stability which is vital to long-term changes in energy end-use sectors that rely on long-term investments. However, there is no direct correlation between institutional structures, e.g., corporatism and success in reducing greenhouse gas emissions in the transport sector. Environmental objectives need to be built into the consensus-based policy structure before actual policy progress can be observed. This usually takes longer in consensus democracies than in politically more agile majoritarian policy environments, but the policy stability that builds on corporatist institutional structures is likely to experience changes over a longer-term, in this case to a shift towards low-carbon transport that endures

Developing professional learning in k-based economy and k-society using action learning and action research approaches

Following rapid economic and social advancement, Malaysia has placed a great emphasis on Information and ommunication Technology (ICT) education and training, and particularly on the development of a knowledge or k-based economy and k-society. In the wake of new technologies and k-based activities, there has arisen an enhanced need for learning approaches that will develop professional knowledge and skills in line with new approaches to knowledge creation and utilization. Action Learning (AL) and Action Research (AR) approaches have received recognition as effective strategies for facilitating change in a variety of organizational learning and change-management contexts. This paper examines the basic concepts of AL and AR approaches that are widely used and suggests relevant learning strategies to develop work place or on-the-job learning in a k-based economy and k-society. A few critical success factors in AL and AR approaches are discussed in order to highlight human capacity building for k-workers and k-professionals