Knowledge Management (KM) addresses the critical issues of organizational adoption, survival and competence in the face of an increasingly changing environment. KM embodies organizational processes that seek a synergistic combination of the data and information processing capabilities of information and communication technologies (ICT), and the creative and innovative capacity of human beings to improve ICT In that role, knowledge management will improve quality management and avoid or minimize losses and weakness that usually come from poor performance as well as increase the competitive level of the company and its ability to survive in the global marketplace. To achieve quality, all parties including the clients, company consultants, contractors, entrepreneurs, suppliers, and the governing bodies (i.e., all involved stake-holders) need to collaborate and commit to achieving quality. The design based organizations in major business and construction companies have to be quality driven to support healthy growth in today's competitive market. In the march towards vision 2020 and globalization (i.e., the one world community) of many companies, their design based organizations need to have superior quality management and knowledge management to anticipate changes. The implementation of a quality system such as the ISO 9000 Standards, Total Quality Management, or Quality Function Deployment (QFD) focuses the company's resources towards achieving faster and better results in the global market with less cost. To anticipate the needs of the marketplace and clients as the world and technology change, a new system, which we call Power Quality System (PQS), has been designed. PQS is a combination of information and communication technologies (ICT) and the creative and innovative capacity of human beings to meet the challenges of the new world business and to develop high quality products.
Publication year= 2001 Jan-2002 Mar
Over the years, maximum likelihood estimation and Bayesian method became popular statistical tools in which applied to fit finite mixture model. These trends begin with the advent of computer technology during the last decades. Moreover, the asymptotic properties for both statistical methods also act as one of the main reasons that boost the popularity of the methods. The difference between these two approaches is that the parameters for maximum likelihood estimation are fixed, but unknown meanwhile the parameters for Bayesian method act as random variables with known prior distributions. In the present paper, both the maximum likelihood estimation and Bayesian method are applied to investigate the relationship between exchange rate and the rubber price for Malaysia, Thailand, Philippines and Indonesia. In order to identify the most plausible method between Bayesian method and maximum likelihood estimation of time series data, Akaike Information Criterion and Bayesian Information Criterion are adopted in this paper. The result depicts that the Bayesian method performs better than maximum likelihood estimation on financial data.
The Covid-19 outbreak has spread over the world, limiting population and trade, causing job losses, and forcing businesses to close. The study's goal is to look at Covid-19's pandemic and consumer survival as a mediator for the future of running a business when FMCG companies are doing well. The researchers employed a basic random sampling strategy to do a layered transverse evaluation of samples. EFA, CFA, and SEM are used to choose data collection techniques for participants at Covid-19. According to the data, Malaysia has resulted in job losses, business growth, and customer satisfaction retention, as well as an increase in unemployment, company closures, and a drop in overall GDP. The impact of the Covid-19 epidemic on survival, production, and GDP has been demonstrated. Incorporating technology into all aspects of a company's working practices reveals the necessity and capacity of the organization to adapt to new scenarios.
This study develops a set of measures to address the interrelationship among circular waste-based bioeconomy (CWBE) attributes, including those of government strategy, digital collaboration, supply chain integration, smart operations, and a green supply chain, to build a circular bioeconomy that feeds fish waste back into the economy. CWBE development is a potential solution to the problem of waste reuse in the fish supply chain; however, this potential remains untapped, and prior studies have failed to provide the criteria to guide its practices. Such an analytical framework requires qualitative assessment, which is subject to uncertainty due to the linguistic preferences of decision makers. Hence, this study adopts the fuzzy Delphi method to obtain a valid set of attributes. A fuzzy decision-making trial and evaluation was applied to address the attribute relationships and determine the driving criteria of CWBE development. The results showed that government strategies play a causal role in CWBE development and drive digital collaboration, smart operations, and supply chain integration. The findings also indicated that smart manufacturing technology, organizational policies, market enhancement, supply chain analytics, and operational innovation are drivers of waste integration from fisheries into the circular economy through waste-based bioeconomy processes.
There is widespread agreement among experts that a fundamental reorientation of global, regional, national and local food systems is needed to achieve the UN Sustainable Development Goals Agenda and address the linked challenges of undernutrition, obesity and climate change described as the Global Syndemic. Recognising the urgency of this imperative, a wide range of global stakeholders - governments, civil society, academia, agri-food industry, business leaders and donors - convened at the September 2021 UN Food Systems Summit to coordinate numerous statements, commitments and declarations for action to transform food systems. As the dust settles, how will they be pieced together, how will governments and food corporations be held to account and by whom? New data, analytical methods and global coalitions have created an opportunity and a need for those working in food systems monitoring to scale up and connect their efforts in order to inform and strengthen accountability actions for food systems. To this end, we present - and encourage stakeholders to join or support - an Accountability Pact to catalyse an evidence-informed transformation of current food systems to promote human and ecological health and wellbeing, social equity and economic prosperity.
Information about asbestos issues at the national level was compiled for ten Asian countries (China, Indonesia, Japan, Korea, Malaysia, Philippines, Singapore, Taiwan, Thailand, and Vietnam) regarding 1) bans and consumption levels; 2) occupational exposure limits (OELs) and medical follow-up schemes; and 3) statistics and compensation status of asbestosis and mesothelioma victims. Only Singapore and recently Japan have adopted a total ban an asbestos. China, a major producer of chrysotile, showed an increasing consumption trend, which was typical of the less industrialized countries. Considerable differences between countries existed in OELs (0.1 to 5.0 fibers/mL) and medical follow-up of exposed workers. National statistics for asbestosis and mesothelioma were available for only the industrialized countries, where reported cases as well as compensated cases were relatively few. There is need to improve the quality and quantity of information, but the available information attests to unfavorable conditions in the less industrialized countries. Hence the experience of industrialized countries regarding asbestos and its use should be utilized to the fullest to improve the situation worldwide.
Corporate governance is the way of governing a firm in order to increase its accountability and to avoid any massive damage before it occurs. The aim of this paper is to investigate the impact of capital structure, firms' size, and competitive advantages of firms as control variables on credit ratings. We investigate the role of corporate governance in improving the firms' credit rating using a sample of Jordanian listed firms. We split firms into four categories according to WVB credit rating. We use both the binary logistic regression (LR) and the ordinal logistic regression (OLR) to model credit ratings in Jordanian environment. The empirical results show that the control variables are strong determinants of credit ratings. When we evaluate the relationship between the governance variables and credit ratings, we found interesting results. The board stockholders and board expertise are moderately significant. The board independence and role duality are weakly significant, while board size is insignificant.
Mass valuation of properties is important for purposes like property tax, price indices construction, and understanding market dynamics. There are several ways that the mass valuation can be carried out. This paper reviews the conventional MRA and several other advanced methods such as SAR, Kriging, GWR, and MWR. SAR and Kriging are good for modeling spatial dependence while GWR and MWR are good for modeling spatial heterogeneity. The difference between SAR and Kriging is the calculation of weights. Kriging weights are based on the spatial dependence or so called the semi-variogram analysis of the price data whereas the weights in SAR are based on the spatial contiguity between the sample data. MWR and GWR are special types of regression where study region is subdivided into local sections to increase the accuracy of prediction through neutralizing the heterogeneity of autocorrelations. MWR assigns equal weights for observations within a window while GWR uses distance decay functions. The merits and drawbacks of each method are discussed.
In this article we present a standardized dataset on 6659 songbirds (Passeriformes) highlighting information relevant to species conservation prioritization with a main focus to support the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). Data were collected from both scientific and grey literature as well as several online databases. The data are structured into six knowledge categories: Conventions and Treaties, Human Use, Extinction Risk, Management Opportunities, Biological Information, and Intrinsic Values. The Conventions and Treaties category includes the listings for two international conventions, CITES and the Convention on the Conservation of Migratory Species of Wild Animals (CMS), as well as EU listings for the EU Wildlife Trade Regulations and the EU Birds Directive. The Human Use category contains information on both regulated trade collected from the CITES Trade Database and the United States' Law Enforcement Management Information System (LEMIS), and highly aggregated data on seizures which we obtained from TRAFFIC, the United Nations Office on Drugs and Crime (UNODC) and two data sources on traditional medicine. We also present, for the first time, the complete Songbirds in Trade Database (SiTDB), a trade database curated by taxon expert S. Bruslund based on expert knowledge, literature review, market surveys and sale announcements. Data on the types of human use, including traditional medicine are also provided. The knowledge area on Extinction Risk contains data on the species' IUCN Red List status, the Alliance for Zero Extinction Trigger Species status, site and population at the site, the species' IUCN Climate Change Vulnerability Assessment, and the listing of priority species at the Asian Songbird Crisis Summit. In the Management Opportunities category, we gathered data on ex-situ management from Species360 zoo holdings as well as species management plans from the European and North American Zoo Associations (EAZA and AZA, respectively). Biological Information includes data on body mass, clutch size, diet, availability of data from the IUCN Red List on habitat systems, extent of occurrence, generation length, migration pattern, distribution, and biological data from the Demographic Species Knowledge Index, number of occurrences recorded by the Global Biodiversity Information Facility (GBIF) as well as genomic data from the Bird 10 000K Genomes (B10K) project, Vertebrate Genome Project (VGP) and GenBank. Information on invasive species is also part of this knowledge area. The Intrinsic Value category refers to two measures of the species' intrinsic value, namely Ecological and Evolutionary Distinctiveness. In order to make these knowledge areas comparable, we standardized data following the taxonomy of the Handbook of the Birds of the World and Birdlife (Version 4, 2019). The data enable a broad spectrum of analyses and will be useful to scientists for further research and to policymakers, zoos and other conservation stakeholders for future prioritization decisions.
Kathiravan, Yamunah, Mohd Fahmi Mohamad Amran, Noor Afiza Mat Razali, Mohd Afizi Mohd Shukran, Norshahriah Abdul Wahab, Mohammad Adib Khairuddin, et al.
Privacy has always been a constant concern for many people. Internet users are often worried about the browsing information that is left on their storage media. Web browsers were later introduced with a new feature called private browsing to overcome this issue. The private browsing mode is expected to behave as normal browsing session but without storing any data such as browser cookies, history, cache and passwords on the local machine. Unfortunately, previous researchers concluded web browser often failed to provide the intended privacy protection to their user. Along the way of this reviewing process, the weakness and downside of previous web browser vendors have been identified.
In recent years, the global e-commerce landscape has witnessed rapid growth, with sales reaching a new peak in the past year and expected to rise further in the coming years. Amid this e-commerce boom, accurately predicting user purchase behavior has become crucial for commercial success. We introduce a novel framework integrating three innovative approaches to enhance the prediction model's effectiveness. First, we integrate an event-based timestamp encoding within a time-series attention model, effectively capturing the dynamic and temporal aspects of user behavior. This aspect is often neglected in traditional user purchase prediction methods, leading to suboptimal accuracy. Second, we incorporate Graph Neural Networks (GNNs) to analyze user behavior. By modeling users and their actions as nodes and edges within a graph structure, we capture complex relationships and patterns in user behavior more effectively than current models, offering a nuanced and comprehensive analysis. Lastly, our framework transcends traditional learning strategies by implementing advanced meta-learning techniques. This enables the model to autonomously adjust learning parameters, including the learning rate, in response to new and evolving data environments, thereby significantly enhancing its adaptability and learning efficiency. Through extensive experiments on diverse real-world e-commerce datasets, our model demonstrates superior performance, particularly in accuracy and adaptability in large-scale data scenarios. This study not only overcomes the existing challenges in analyzing e-commerce user behavior but also sets a foundation for future exploration in this dynamic field. We believe our contributions provide significant insights and tools for e-commerce platforms to better understand and cater to their users, ultimately driving sales and improving user experiences.
The trends of safety incident process have been put in the statistical research and development in order to prevent
and mitigate the phenomenon. One of the incidents is known as dust explosion. It represents a constant hazard to
industries which includes any manufacturing using and handling combustible dust materials. Lack of sharing and
know-how on best practices in managing the workplace must be avoided throughout the industries. The severity and
the consequences of not taking the safety precautions at workplace have not been foreseen by the process team. This
present paper discusses the best practices in managing the hazards from the catastrophes to happen again. In addition,
the mitigation response has also been explored thoroughly through database of best practices.
The main purpose of this article is to introduce the technique of panel data analysis in econometrics modeling. The elasticity of labour, capital and economic of scale for twenty two food manufacturing firms covering from 1989 to 1993 is estimated using the Cobb-Douglas model. The three main techniques of panel data analysis discussed are least square dummy variables (LSDV), analysis of covariance (ANCOVA) and generalized least square (GLS). Ordinary Least Square (OLS) method is included as the basis of comparison.
Systematically analysing the relative importance and hierarchical relationships among the influencing factors of the cross-border e-commerce ecosystem holds rich theoretical value and practical significance for the development of this ecosystem. A total of 19 influencing factors covering four aspects affecting the cross-border e-commerce ecosystem are identified by means of the relevant literature, web pages, research, and discussions with relevant experts and scholars, and the decision-making trial and evaluation laboratory (DEMATEL) and interpretative structural modeling (ISM) method is used to analyse the cause-effect correlation of each factor and to obtain a cause-effect diagram and a multi-level recursive structure model. The results show that three factors, i.e., the e-commerce platform development level, cross-border e-commerce competitiveness, and the cross-border e-commerce transaction scale, have a greater degree of influence on the other influencing factors. Additionally, three factors, i.e., the information development level, GDP, and cross-border online shopping demand, are vulnerable to the influence of the other factors. The level of cross-border e-commerce platform development, cross-border e-commerce competitiveness, and inter-firm competition are the root factors and occupy an important position in the cross-border e-commerce ecosystem as influencing factors and influence the stability of the cross-border e-commerce ecosystem by affecting the other factors.
We have used electron probe microanalysis to examine Southeast Asian nephrite (jade) artifacts, many archeologically excavated, dating from 3000 B.C. through the first millennium A.D. The research has revealed the existence of one of the most extensive sea-based trade networks of a single geological material in the prehistoric world. Green nephrite from a source in eastern Taiwan was used to make two very specific forms of ear pendant that were distributed, between 500 B.C. and 500 A.D., through the Philippines, East Malaysia, southern Vietnam, and peninsular Thailand, forming a 3,000-km-diameter halo around the southern and eastern coastlines of the South China Sea. Other Taiwan nephrite artifacts, especially beads and bracelets, were distributed earlier during Neolithic times throughout Taiwan and from Taiwan into the Philippines.
This paper mainly forecasts the daily closing price of stock markets. We propose a two-stage technique that combines the empirical mode decomposition (EMD) with nonparametric methods of local linear quantile (LLQ). We use the proposed technique, EMD-LLQ, to forecast two stock index time series. Detailed experiments are implemented for the proposed method, in which EMD-LPQ, EMD, and Holt-Winter methods are compared. The proposed EMD-LPQ model is determined to be superior to the EMD and Holt-Winter methods in predicting the stock closing prices.
Development sociology has used global commodity chains as one way of analyzing the dynamics of power and profit-taking in globalized production networks made up of multiple firms and occurring in multiple national settings. A substantial portion of the alcohol supply in developing countries is now produced through such production networks. Particularly in the beer and spirits trade, a small number of transnational firms control networks of local producers, importers, advertisers and distributors. These networks serve to embed transnational or transnationally backed brands in the local culture, using the tools of market research, product design and marketing to influence local drinking practices. Case materials from Malaysia's beer industry help to illustrate how the transnational firms dominate in those links of the commodity chain in which monopoly or oligopoly control is most likely to be found: the design/recipe and marketing/advertising nodes. Their control of the commodity chains and extraction of monopoly or oligopoly profits from them places substantial resources and influence over drinking settings and practices in foreign hands. The impact of this influence on state efficacy and autonomy in setting alcohol policy is an important subject for future research on the creation and implementation of effective alcohol policies in developing societies.
An estimated 11.6% of the world cigarette market is illicit, representing more than 650 billion cigarettes a year and $40.5 billion in lost revenue. Illicit tobacco trade refers to any practice related to distributing, selling, or buying tobacco products that is prohibited by law, including tax evasion (sale of tobacco products without payment of applicable taxes), counterfeiting, disguising the origin of products, and smuggling. Illicit trade undermines tobacco prevention and control initiatives by increasing the accessibility and affordability of tobacco products, and reduces government tax revenue streams. The World Health Organization (WHO) Protocol to Eliminate Illicit Trade in Tobacco Products, signed by 54 countries, provides tools for addressing illicit trade through a package of regulatory and governing principles. As of May 2015, only eight countries had ratified or acceded to the illicit trade protocol, with an additional 32 needed for it to become international law (i.e., legally binding). Data from multiple international sources were analyzed to evaluate the 10 most commonly used approaches for addressing illicit trade and to summarize differences in implementation across select countries and the European Union (EU). Although the WHO illicit trade protocol defines shared global standards for addressing illicit trade, countries are guided by their own legal and enforcement frameworks, leading to a diversity of approaches employed across countries. Continued adoption of the methods outlined in the WHO illicit trade protocol might improve the global capacity to reduce illicit trade in tobacco products.
This study proposes a novel hybrid computational approach that integrates the artificial dragonfly algorithm (ADA) with the Hopfield neural network (HNN) to achieve an optimal representation of the Exact Boolean kSatisfiability (EBkSAT) logical rule. The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EBkSAT logic representation. To assess the performance of the proposed hybrid computational model, a specific Exact Boolean kSatisfiability problem is constructed, and simulated data sets are generated. The evaluation metrics employed include the global minimum ratio (GmR), root mean square error (RMSE), mean absolute percentage error (MAPE), and network computational time (CT) for EBkSAT representation. Comparative analyses are conducted between the results obtained from the proposed model and existing models in the literature. The findings demonstrate that the proposed hybrid model, ADA-HNN-EBkSAT, surpasses existing models in terms of accuracy and computational time. This suggests that the ADA algorithm exhibits effective compatibility with the HNN for achieving an optimal representation of the EBkSAT logical rule. These outcomes carry significant implications for addressing intricate optimization problems across diverse domains, including computer science, engineering, and business.