Radon (222Rn) concentration has been measured using CN-85 solid-state nuclear track detectors in the dwellings of Northern Areas of Pakistan at different altitudes. The CN-85 detectors, installed in box type radon dosimeters, were exposed for a period of one year. The average radon concentration level has been found at altitudes 3020 m, 3540 m, 3665 m and 4265 m above sea level and the 222Rn concentrations were found to be (16.4±2.5) Bq m-3, (21.7±4.8) Bq m-3, (13.8±3.2) Bq m-3 and (19.3±3.4) Bq m-3, respectively. It has been observed that there are no significant differences in the radon concentrations with different altitudes. However, differences in concentrations were observed with types of rooms attributed to better ventilation, atmospheric parameters and source of radon present in the soil. These results have been compared with radon concentration measurement found in literature. The 222Rn concentration measured in the area under study is found below the permissible level recommended by the International Commission on Radiological Protection (ICRP) for public exposure.

Semantic association computation is the process of quantifying the strength of a semantic connection between two textual units, based on different types of semantic relations. Semantic association computation is a key component of various applications belonging to a multitude of fields, such as computational linguistics, cognitive psychology, information retrieval and artificial intelligence. The field of semantic association computation has been studied for decades. The aim of this paper is to present a comprehensive survey of various approaches for computing semantic associations, categorized according to their underlying sources of background knowledge. Existing surveys on semantic computation have focused on a specific aspect of semantic associations, such as utilizing distributional semantics in association computation or types of spatial models of semantic associations. However, this paper has put a multitude of computational aspects and factors in one picture. This makes the article worth reading for those researchers who want to start off in the field of semantic associations computation. This paper introduces the fundamental elements of the association computation process, evaluation methodologies and pervasiveness of semantic measures in a variety of fields, relying on natural language semantics. Along the way, there is a detailed discussion on the main categories of background knowledge sources, classified as formal and informal knowledge sources, and the underlying design models, such as spatial, combinatorial and network models, that are used in the association computation process. The paper classifies existing approaches of semantic association computation into two broad categories, based on their utilization of background knowledge sources: knowledge-rich approaches; and knowledge-lean approaches. Each category is divided further into sub-categories, according to the type of underlying knowledge sources and design models of semantic association. A comparative analysis of strengths and limitations of various approaches belonging to each research stream is also presented. The paper concludes the survey by analyzing the pivotal factors that affect the performance of semantic association measures.

The cascade-exciton model has been used to observe the dependence of pion induced fission cross sections on the mass of the target. The analysis has been performed at energies 80 MeV, 100 MeV and 150 MeV for both the positive and negative pions. It has been shown that a single value of the ratio af /an can satisfactorily reproduce the experimental findings when compared with the available experimental data in the literature. The general trend of the fission cross sections with mass（fissility parameter） is seen to be low and slowly changing for the lighter nuclei,and it will steeply rise for the heavy nuclei.

Semantic association computation is the process of automatically quantifying the strength of a semantic connection between two textual units based on various lexical and semantic relations such as hyponymy (car and vehicle) and functional associations (bank and manager). Humans have can infer implicit relationships between two textual units based on their knowledge about the world and their ability to reason about that knowledge. Automatically imitating this behavior is limited by restricted knowledge and poor ability to infer hidden relations. Various factors affect the performance of automated approaches to computing semantic association strength. One critical factor is the selection of a suitable knowledge source for extracting knowledge about the implicit semantic relations. In the past few years, semantic association computation approaches have started to exploit web-originated resources as substitutes for conventional lexical semantic resources such as thesauri, machine readable dictionaries and lexical databases. These conventional knowledge sources suffer from limitations such as coverage issues, high construction and maintenance costs and limited availability. To overcome these issues one solution is to use the wisdom of crowds in the form of collaboratively constructed knowledge sources. An excellent example of such knowledge sources is Wikipedia which stores detailed information not only about the concepts themselves but also about various aspects of the relations among concepts. The overall goal of this thesis is to demonstrate that using Wikipedia for computing word association strength yields better estimates of humans' associations than the approaches based on other structured and unstructured knowledge sources. There are two key challenges to achieve this goal: first, to exploit various semantic association models based on different aspects of Wikipedia in developing new measures of semantic associations; and second, to evaluate these measures compared to human performance in a range of tasks. The focus of the thesis is on exploring two aspects of Wikipedia: as a formal knowledge source, and as an informal text corpus. The first contribution of the work included in the thesis is that it effectively exploited the knowledge source aspect of Wikipedia by developing new measures of semantic associations based on Wikipedia hyperlink structure, informative-content of articles and combinations of both elements. It was found that Wikipedia can be effectively used for computing noun-noun similarity. It was also found that a model based on hybrid combinations of Wikipedia structure and informative-content based features performs better than those based on individual features. It was also found that the structure based measures outperformed the informative content based measures on both semantic similarity and semantic relatedness computation tasks. The second contribution of the research work in the thesis is that it effectively exploited the corpus aspect of Wikipedia by developing a new measure of semantic association based on asymmetric word associations. The thesis introduced the concept of asymmetric associations based measure using the idea of directional context inspired by the free word association task. The underlying assumption was that the association strength can change with the changing context. It was found that the asymmetric association based measure performed better than the symmetric measures on semantic association computation, relatedness based word choice and causality detection tasks. However, asymmetric-associations based measures have no advantage for synonymy-based word choice tasks. It was also found that Wikipedia is not a good knowledge source for capturing verb-relations due to its focus on encyclopedic concepts specially nouns. It is hoped that future research will build on the experiments and discussions presented in this thesis to explore new avenues using Wikipedia for finding deeper and semantically more meaningful associations in a wide range of application areas based on humans' estimates of word associations.

Human actions in this world are based on exploiting knowledge of causality. Humans find it easy to connect a cause to the subsequent effect but formal reasoning about causality has proved to be a difficult task in automated NLP applications because it requires rich knowledge of all the relevant events and circumstances. Automated approaches to detecting causal connections attempt to partially capture this knowledge using commonsense reasoning based on lexical and semantics constraints. However, their performance is limited by the lack of sufficient breadth of commonsense knowledge to draw causal inferences. This paper presents a commonsense causality detection system using a new semantic measure based on asymmetric associations on the Choice Of Plausible Alternatives (COPA) task. When evaluated on three COPA benchmark datasets, the causality detection system using asymmetric association based measures demonstrates a superior performance to other symmetric measures.

Semantic relatedness computation is the task of quantifying the degree of relatedness of two concepts. The performance of existing approaches to computing semantic relatedness is highly dependent on particular aspects of relatedness. For instance, taxonomy-based approaches aim at computing similarity, which is a special case of semantic relatedness. On the other hand, corpus-based approaches focus on the associative relations of words by taking their distributional features into account. Based on the assumption that different aspects of knowledge sources cover different kinds of semantic relations, this paper presents a hybrid model for computing semantic relatedness of words using new features extracted from various aspects of Wikipedia. The focus of this paper is on finding the optimal feature combination(s) that enhance the performance of the hybrid model. The empirical evaluation on benchmark datasets has shown that hybrid features perform better than single features by providing a complementary coverage of semantic relations, leading to improved correlation with human judgments.

Semantic relatedness computation is a well known problem with multidisciplinary applications. Existing approaches to computing semantic relatedness ignore the asymmetric associations of words. In the absence of an explicit topical context, these asymmetric associations can be effectively used to represent the relation of words in directional contexts. Motivated by the idea of word associations, this paper presents a new approach to computing semantic relatedness using asymmetric association based probabilities of words extracted from the directional contexts of words based on the Wikipedia corpus. The performance evaluation of the proposed approach on a variety of publicly available benchmark datasets shows that the asymmetric association based measures outperformed not only the baseline symmetric measures but also most of the state-of-art approaches.

Semantic relatedness computation is the task of measuring the degree of relatedness of two concepts. It is a well known problem with applications ranging from computational linguistics to cognitive psychology. In all existing approaches, relatedness is assumed to be symmetric i.e. the relatedness of terms t i and term t j is considered the same as the relatedness of terms t j and t i . However, there are tasks such as free word association, where the association strength assumed to be asymmetric. In free word association, the given term determines the context in which the association strength must be computed. Based on this key observation, the paper presents a new approach to computing term relatedness guided by asymmetric association. The focus of this paper is on using Wikipedia for extracting directional context of each given term and computing the association of input term pair in this context. The proposed approach is generic enough to deal with both symmetric as well as asymmetric relatedness computation problems. Empirical evaluation on multiple benchmark datasets shows encouraging results when our automatically computed relatedness scores are correlated with human judgments.

Semantic relatedness is a well known problem with its significance ranging from computational linguistics to Natural language Processing applications.Relatedness computation is restricted by the amount of common sense and background knowledge required to relate any two terms.This paper proposes a novel model of relatedness using context profile built on features extracted from encyclopedic knowledge.Proposed research makes use of Wikipedia to represent the context of a word in the high dimensional space of Wikipedia labels.Semantic relatedness of a word pair is then assessed by comparing their corresponding context profiles based on three different weighting schemes using traditional Cosine similarity metrics.To evaluate proposed relatedness approach, three well known benchmark datasets are used and it is shown that Wikipedia article contents can be used effectively to compute term relatedness.The experiments demonstrate that the proposed approach is computationally cheap as well as effective when correlated with human judgments.

This research introduces a new query expansion method that uses Wikipedia and its hyperlink structure to find related terms for reformulating a query. Queries are first understood better by splitting into query aspects. Further understanding is gained through measuring how well each aspect is represented in the original search results. Poorly represented aspects are found to be an excellent source of query improvement. Our main contribution is the way of using Wikipedia to identify aspects and underrepresented aspects, and to weight the expansion terms. Results have shown that our approach improves the original query and search results, and outperforms two existing query expansion methods.

Every term has a meaning but there are terms which have multiple meanings. Identifying the correct meaning of a term in a specific context is the goal of Word Sense Disambiguation (WSD) applications. Identifying the correct sense of a term given a limited context is even harder. This research aims at solving the problem of identifying the correct sense of a term given only one term as its context. The main focus of this research is on using Wikipedia as the external knowledge source to decipher the true meaning of each term using a single term as the context. We experimented with the semantically rich Wikipedia senses and hyperlinks for context disambiguation. We also analyzed the effect of sense filtering on context extraction and found it quite effective for contextual disambiguation. Results have shown that disambiguation with filtering works quite well on manually disambiguated dataset with the performance accuracy of 86%.

This paper proposes a new method of automatically measuring semantic relatedness by exploiting Wikipedia as an external knowledge source. The main contribution of our research is to propose a relatedness measure based on Wikipedia senses and hyperlink structure for computing contextual relatedness of any two terms. We have evaluated the effectiveness of our approach using three datasets and have shown that our approach competes well with other well known existing methods.

Retrieving relevant information from web, containing enormous amount of data, is a highly complicated research area. A landmark research that contributes to this area is web clustering which efficiently organizes a large amount of web documents into a small number of meaningful and coherent groups[1,2]. Various techniques aim at accurately categorizing the web pages into clusters automatically. Suffix Tree Clustering (STC) is a phrase-based, state-of-art algorithm for web clustering that automatically groups semantically related documents based on shared phrases. Research has shown that it has outperformed other clustering algorithms such as K-means and Buckshot due to its efficient utilization of phrases to identify the clusters. Using STC as the baseline, we introduce a new method for ranking base clusters and new similarity measures for comparing clusters. Our STHAC technique combines the Heirarchical Agglomerative clustering method with phrase based Suffix Tree clustering to improve the cluster merging process. Experimental results have shown that STHAC outperforms the original STC as well as ESTC(our precious extended version of STC) with 16% increase in F-measure. This increase in F-measure of STHAC is achieved due to its better filtering of low score clusters, better similarity measures and efficient cluster merging algorithms.

The present study deals with measurement of indoor radon concentrations in dwellings of the district Poonch of the state of Azad Jammu and Kashmir, Pakistan. In this context, CR-39-based box-type radon detectors were installed in drawing rooms and bedrooms of 80 selected houses and were exposed to indoor radon for 3 months. After exposure, the CR-39 detectors were etched for 9 h in 6 mol NaOH at 70 degrees C and the observed track densities were related to radon concentrations. Measured indoor radon concentrations in the studied area ranged from 27 +/- 6 to 169 +/- 4, 29 +/- 6 to 196 +/- 4 and 31 +/- 5 to 142 +/- 2 Bq m(-3) in the drawing rooms and 74 +/- 5 to 172 +/- 3, 32 +/- 6 to 191 +/- 4 and 27 +/- 5 to 155 +/- 2 Bq m(-3) in bedrooms of the Abbaspur, Hajira and Rawalakot regions of the district Poonch, respectively; whereas weighted average radon concentration ranged from 93 +/- 6 to 159 +/- 4, 33 +/- 5 to 118 +/- 3 and 31 +/- 6 to 155 +/- 5 Bq m(-3) in the dwellings of Abbaspur, Hajira and Rawalakot, respectively. Estimated doses due to the indoor radon ranged from 2.35 +/- 0.15 to 4.00 +/- 0.10, 0.83 +/- 0.08 to 2.98 +/- 0.08 and 0.78 +/- 0.15 to 3.91 +/- 0.13 mSv y(-1) for Abbaspur, Rawalakot and Hajira, respectively. Comparing the current indoor radon results with those of the Health Protection Agency UK and US EPA (i.e. 200 and 148 Bq m(-3)) limits, majority of the houses surveyed in the present study are within the safe limits.

Indoor radon concentrations have been measured in a limited number of dwellings in the state capital of Azad Jammu and Kashmir, Muzaffarabad city after the devastating earthquake of 2005. Radon detectors(CN-85 based box-type) were placed in the drawing rooms, bedrooms and kitchens of 35 houses, selected on the basis of their location and design as well as willingness and cooperation of householders from mid May to mid July 2007. The average radon concentrations were found to vary from 24 to 518 Bq m−3, 41 to 380 Bq m−3 and 32 to 467 Bq m−3 in the bedrooms, drawing rooms and kitchens, respectively. The weighted average radon concentration in older houses was found to vary from 51 to 334 Bq m−3 and for newly constructed houses a considerable decrease in measured values(ranging from 14 to 102 Bq m−3) have been found. As Pakistan does not currently have a national reference(or action level) for radon in homes, therefore the present data has been compared with the data reported for other countries. Some of the houses studied were found to have higher radon concentrations. These higher values of indoor radon levels may be due to the poor ventilation and influence of the earthquake in creating new fissures and therefore new pathways for radon to enter into the buildings.

OBJECTIVE To conduct a radon awareness survey to examine the level of awareness and risk perception of indoor radon exposure among the general public, medical students, and physicians of the state of Azad Jammu and Kashmir, given that long-term exposure to indoor radon increases lifetime risk of lung cancer and may pose a substantial threat to public health. DESIGN Cross-sectional survey. SAMPLE Households by telephone (500), interviews with menial laborers (200), questionnaires to shopkeepers and government employees (1,000), undergraduates (200), social science graduates (1,500), science graduates (1,500), medical students (325), and physicians (100). MEASURES Familiarity with radioactivity and the nature and health hazards of radiation and radon. ANALYSIS Significance of data trends was measured using the Kruskal-Wallis test. RESULTS About 30% people (excluding medical students and physicians) were aware of radon, and about 6% had knowledgeable awareness of radon. About 80% of the medical students and physicians had heard about radon and about 30.5% of them had knowledgeable awareness about radon and its hazards. CONCLUSION The study suggested a positive relationship of awareness of radon and its hazards with the educational level of people.

This paper presents a detailed study of liquid-metal flow in a curved bend, under the conditions, when the liquid metal flows first parallel and then perpendicular to the magnetic field, while a constant magnetic field also acts in the transverse direction. The duct has conducting vanadium walls, and liquid metals (lithium, sodium, potassium) have been used as coolants. Magneto hydrodynamic (MHD) equations have been developed in three dimensions in the modified toroidal coordinate system. These coupled sets of equations then have been solved using finite difference techniques and an extended SIMPLER algorithm approach. Calculation of MHD pressure drop has been made for three different liquid metals, lithium, sodium and potassium. The results for curved bend indicate an immense axial MHD pressure drop. The axial MHD pressure drop increases for an increase in both kinds of magnetic field for all three liquid metals. It is found that the MHD pressure drop increases as the liquid metal flows more and more transverse to the magnetic field. The MHD pressure drop is found to be maximum for sodium and minimum for lithium.