The recent technological advancement has proved to be tremendously helpful for medical consultants. However, this advancement has also generated an enormous volume and variety of data, with a high velocity causing an ...The recent technological advancement has proved to be tremendously helpful for medical consultants. However, this advancement has also generated an enormous volume and variety of data, with a high velocity causing an information load for the medical consultants. Information overload can be defined as a difficulty a person can have in comprehending issue and making judgments that are caused by the presence of too much information. Information overload occurs when the amount of input to a system surpasses its processing capability. Decision-makers have a limited cognitive processing ability. Consequently, when information overload happens, it is possible that a decline in decision quality will take place. Decision-makers, such as medical consultants, have fairly limited cognitive processing capacity. Consequently, when information overload occurs, it is likely that a reduction in decision quality will occur. The aim of this study is to assess the impact of information overload on medical consultants’ life, its causes, and potential ways to deal with it. We performed a literature review to find the effects of information overload on medical consultants. Twelve research papers were considered for thematic analysis using NVivo 10 tool. These papers revealed four themes: 1) traditional methods of data collection;2) modern ways of data collection;3) consequences of modern ways of data collection;and 4) the need for handling information overload. This study suggests the development of a Continuing Professional Development course that explains how to deal with information overload, and availing the same through E-Learning mode might be one immediate solution.展开更多
The study endeavors to provide statistical inference for a (1 + 1) cascade system for exponential distribution under joint effect of stress-strength attenuation factors. Estimators of reliability function are obtained...The study endeavors to provide statistical inference for a (1 + 1) cascade system for exponential distribution under joint effect of stress-strength attenuation factors. Estimators of reliability function are obtained using Maximum Likelihood Estimator (MLE) and Uniformly Minimum Variance Unbiased Estimator (UMVUE) of the parameters. Asymptotic distribution of the parameters is also obtained. Comparison between estimators is made using data obtained through simulation experiment.展开更多
Latent Semantic Analysis involves natural language processing techniques for analyzing relationships between a set of documents and the terms they contain, by producing a set of concepts (related to the documents and ...Latent Semantic Analysis involves natural language processing techniques for analyzing relationships between a set of documents and the terms they contain, by producing a set of concepts (related to the documents and terms) called semantic topics. These semantic topics assist search engine users by providing leads to the more relevant document. We develope a novel algorithm called Latent Semantic Manifold (LSM) that can identify the semantic topics in the high-dimensional web data. The LSM algorithm is established upon the concepts of topology and probability. Asearch tool is also developed using the LSM algorithm. This search tool is deployed for two years at two sites in Taiwan: 1) Taipei Medical University Library, Taipei, and 2) Biomedical Engineering Laboratory, Institute of Biomedical Engineering, National Taiwan University, Taipei. We evaluate the effectiveness and efficiency of the LSM algorithm by comparing with other contemporary algorithms. The results show that the LSM algorithm outperforms compared with others. This algorithm can be used to enhance the functionality of currently available search engines.展开更多
The search engines are indispensable tools to find information amidst massive web pages and documents. A good search engine needs to retrieve information not only in a shorter time, but also relevant to the users’ qu...The search engines are indispensable tools to find information amidst massive web pages and documents. A good search engine needs to retrieve information not only in a shorter time, but also relevant to the users’ queries. Most search engines provide short time retrieval to user queries;however, they provide a little guarantee of precision even to the highly detailed users’ queries. In such cases, documents clustering centered on the subject and contents might improve search results. This paper presents a novel method of document clustering, which uses semantic clique. First, we extracted the Features from the documents. Later, the associations between frequently co-occurring terms were defined, which were called as semantic cliques. Each connected component in the semantic clique represented a theme. The documents clustered based on the theme, for which we designed an aggregation algorithm. We evaluated the aggregation algorithm effectiveness using four kinds of datasets. The result showed that the semantic clique based document clustering algorithm performed significantly better than traditional clustering algorithms such as Principal Direction Divisive Partitioning (PDDP), k-means, Auto-Class, and Hierarchical Clustering (HAC). We found that the Semantic Clique Aggregation is a potential model to represent association rules in text and could be immensely useful for automatic document clustering.展开更多
Countries face the risk of natural resource curse because of making their economic growth excessively dependent on natural resources.Although excessive resource dependence causes such a risk,it is inevitable that reso...Countries face the risk of natural resource curse because of making their economic growth excessively dependent on natural resources.Although excessive resource dependence causes such a risk,it is inevitable that resource-rich countries will need resource rent up to a certain level of economic maturity.On the other hand,transferring the wealth achieved after this maturity level to productive investment areas also reduces the resource dependency levels of countries.In this context,countries that capture the possible inverted U-shaped relationship between economic growth and resource dependence can escape the curse.Based on this,the aim of this research is to determine the validity of the Kuznets type relationship between resource dependence and economic growth for the first time in the literature.Nine nations that rely heavily on natural resources are used as a sample for this.The countries with a share of total resource rent in national revenue greater than 25%are taken into consideration throughout the selection process for these countries.Using novel panel data methodologies,the effects of capital accumulation,public spending,foreign direct investment,and economic growth on the dependence on natural resources is examined from 1993 to 2021.The results reveal that capital accumulation reduces resource dependency while foreign investments and government size increases it.In addition,the Resource-Based Kuznets curve concept is supported by empirical data demonstrating an inverted-U-shaped relationship between economic growth and resource dependence for these nations.The thresholds derived from the parameters show that Saudi Arabia and Kazakhstan are well beyond this cutoff.The Democratic Republic of the Congo and the Republic of the Congo,on the other hand,remain a long way from this threshold.Furthermore,Iraq,Mongolia,Iran,and Azerbaijan have national incomes that are close at the threshold.展开更多
文摘The recent technological advancement has proved to be tremendously helpful for medical consultants. However, this advancement has also generated an enormous volume and variety of data, with a high velocity causing an information load for the medical consultants. Information overload can be defined as a difficulty a person can have in comprehending issue and making judgments that are caused by the presence of too much information. Information overload occurs when the amount of input to a system surpasses its processing capability. Decision-makers have a limited cognitive processing ability. Consequently, when information overload happens, it is possible that a decline in decision quality will take place. Decision-makers, such as medical consultants, have fairly limited cognitive processing capacity. Consequently, when information overload occurs, it is likely that a reduction in decision quality will occur. The aim of this study is to assess the impact of information overload on medical consultants’ life, its causes, and potential ways to deal with it. We performed a literature review to find the effects of information overload on medical consultants. Twelve research papers were considered for thematic analysis using NVivo 10 tool. These papers revealed four themes: 1) traditional methods of data collection;2) modern ways of data collection;3) consequences of modern ways of data collection;and 4) the need for handling information overload. This study suggests the development of a Continuing Professional Development course that explains how to deal with information overload, and availing the same through E-Learning mode might be one immediate solution.
文摘The study endeavors to provide statistical inference for a (1 + 1) cascade system for exponential distribution under joint effect of stress-strength attenuation factors. Estimators of reliability function are obtained using Maximum Likelihood Estimator (MLE) and Uniformly Minimum Variance Unbiased Estimator (UMVUE) of the parameters. Asymptotic distribution of the parameters is also obtained. Comparison between estimators is made using data obtained through simulation experiment.
文摘Latent Semantic Analysis involves natural language processing techniques for analyzing relationships between a set of documents and the terms they contain, by producing a set of concepts (related to the documents and terms) called semantic topics. These semantic topics assist search engine users by providing leads to the more relevant document. We develope a novel algorithm called Latent Semantic Manifold (LSM) that can identify the semantic topics in the high-dimensional web data. The LSM algorithm is established upon the concepts of topology and probability. Asearch tool is also developed using the LSM algorithm. This search tool is deployed for two years at two sites in Taiwan: 1) Taipei Medical University Library, Taipei, and 2) Biomedical Engineering Laboratory, Institute of Biomedical Engineering, National Taiwan University, Taipei. We evaluate the effectiveness and efficiency of the LSM algorithm by comparing with other contemporary algorithms. The results show that the LSM algorithm outperforms compared with others. This algorithm can be used to enhance the functionality of currently available search engines.
文摘The search engines are indispensable tools to find information amidst massive web pages and documents. A good search engine needs to retrieve information not only in a shorter time, but also relevant to the users’ queries. Most search engines provide short time retrieval to user queries;however, they provide a little guarantee of precision even to the highly detailed users’ queries. In such cases, documents clustering centered on the subject and contents might improve search results. This paper presents a novel method of document clustering, which uses semantic clique. First, we extracted the Features from the documents. Later, the associations between frequently co-occurring terms were defined, which were called as semantic cliques. Each connected component in the semantic clique represented a theme. The documents clustered based on the theme, for which we designed an aggregation algorithm. We evaluated the aggregation algorithm effectiveness using four kinds of datasets. The result showed that the semantic clique based document clustering algorithm performed significantly better than traditional clustering algorithms such as Principal Direction Divisive Partitioning (PDDP), k-means, Auto-Class, and Hierarchical Clustering (HAC). We found that the Semantic Clique Aggregation is a potential model to represent association rules in text and could be immensely useful for automatic document clustering.
文摘Countries face the risk of natural resource curse because of making their economic growth excessively dependent on natural resources.Although excessive resource dependence causes such a risk,it is inevitable that resource-rich countries will need resource rent up to a certain level of economic maturity.On the other hand,transferring the wealth achieved after this maturity level to productive investment areas also reduces the resource dependency levels of countries.In this context,countries that capture the possible inverted U-shaped relationship between economic growth and resource dependence can escape the curse.Based on this,the aim of this research is to determine the validity of the Kuznets type relationship between resource dependence and economic growth for the first time in the literature.Nine nations that rely heavily on natural resources are used as a sample for this.The countries with a share of total resource rent in national revenue greater than 25%are taken into consideration throughout the selection process for these countries.Using novel panel data methodologies,the effects of capital accumulation,public spending,foreign direct investment,and economic growth on the dependence on natural resources is examined from 1993 to 2021.The results reveal that capital accumulation reduces resource dependency while foreign investments and government size increases it.In addition,the Resource-Based Kuznets curve concept is supported by empirical data demonstrating an inverted-U-shaped relationship between economic growth and resource dependence for these nations.The thresholds derived from the parameters show that Saudi Arabia and Kazakhstan are well beyond this cutoff.The Democratic Republic of the Congo and the Republic of the Congo,on the other hand,remain a long way from this threshold.Furthermore,Iraq,Mongolia,Iran,and Azerbaijan have national incomes that are close at the threshold.
