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Due to the scope and volume of activities, the petrochemical industry has a high potential for risk to humans and the environment. This study aimed to evaluate the environmental risks caused by the ammonia and urea production process. To screen the risks identified in the follow-up phase, the process hazard analysis (PHA) was used. The environmental aspects were also assessed using environmental failure mode and effects analysis (EFMEA). The most significant environmental aspect with a Risk Priority Number (RPN) of 100 was related to CO

A major challenge in cross-language information retrieval (CLIR) is the adoption of translation knowledge in retrieval models, as it affects term weighting which is known to highly impact the retrieval performance. Despite its importance, how different approaches for integration of translation knowledge into retrieval models relatively perform has not been analytically examined. In this paper, we present an analytical investigation of using translation knowledge in CLIR. In particular, by adopting the axiomatic analysis framework, we formulate impacts of using translation knowledge on document ranking as constraints that any cross-language retrieval model should satisfy. We then consider state-of-the-art CLIR methods and check whether they satisfy these constraints. Our study shows that none of the existing methods satisfies all constraints. Based on the defined constraints, we propose the hierarchical query modeling method for CLIR which satisfies more constraints and achieves a higher CLIR performance, compared to the existing methods.

Rice is a major diet and staple food in Iran. The most common organophosphorus pesticide used in rice paddies of this area is diazinon. The objective of this research is to determine the concentration of diazinon in soil and rice grains and specify health risk assessment of diazinon in rice when marketed. 30 mixed samples were collected from paddies. High Performance Liquid Chromatography was used to distinguish diazinon pesticide in soil and rice. The results show that diazinon was detected in 96.66 percent of the total samples. The average level of this diazinon in rice was 0.4±0.43 (mg/kg-1) three months after harvesting. There was a significant correlation between diazinon concentration of rice grains in all samples (P 0.05). The result indicates that this amount is higher than the MRLs allowed by the European Union and Iran. Target Hazard Quotient was applied to assess human health risk for adults, women and children. Although estimated THQ for all target population is less than 1 that can be a slight non-carcinogen health risk for human.

We propose AutoName, an unsupervised framework that extracts a name for a set of query entities from a large-scale text corpus. Entity-set naming is useful in many tasks related to natural language processing and information retrieval such as session-based and conversational information seeking. Previous studies mainly extract set names from knowledge bases which provide highly reliable entity relations, but suffer from limited coverage of entities and set names that represent broad semantic classes. To address these problems, AutoName generates hypernym-anchored candidate phrases via probing a pre-trained language model and the entities' context in documents. Phrases are then clustered to identify ones that describe common concepts among query entities. Finally, AutoName ranks refined phrases based on the co-occurrences of their words with query entities and the conceptual integrity of their respective clusters. We built a new benchmark dataset for this task, consisting of 130 entity sets with name labels. Experimental results show that AutoName generates coherent and meaningful set names and significantly outperforms all baselines.

We propose using learning-to-rank for entity set expansion (ESE) from unstructured data, the task of finding "sibling" entities within a corpus that are from the set characterized by a small set of seed entities. We present a two-channel neural re-ranking model, NESE, that jointly learns exact and semantic matching of entity contexts through entity interaction features. Although entity set expansion has drawn increasing attention in the IR and NLP communities for its various applications, the lack of massive annotated entity sets has hindered the development of neural approaches. We describe DBpedia-Sets, a toolkit that automatically extracts entity sets from a plain text collection, thus providing a large amount of distant supervision data for neural model training. Experiments on real datasets of different scales from different domains show that NESE outperforms state-of-the-art approaches in terms of precision and MAP. Furthermore, evaluation through human annotations shows that the knowledge learned from the training data is generalizable.

The present study aimed to develop a practical framework consisting of GIS and MCDM to assess the development sustainability of Karaj, a city in the west of Tehran, Iran. Firstly, the influencing factors on sustainable urban development including seven criteria and 24 sub-criteria were screened and selected based on literature review, interviews with experts and Delphi technique. Then, the criteria were weighted and prioritized using Group Fuzzy BWM (GFBWM) and Analytic Hierarchy Process (AHP) methods. Fuzzy logic and Weighted Linear Combination (WLC) methods in GIS were used to determine the sustainability of Karaj for urban development. Finally, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to rank the identified areas. Our findings showed that the socioeconomic criterion and employment sub-criterion were the most important in AHP and GF-BWM methods. Both methods estimated that about 30% of the entire study area was highly sustainable in terms of urban development with no significant difference between the two methods. Finally, TOPSIS selected the center and north of the city as those with the highest priority. The integrated approach used in this study provided a flexible tool for analysis of the sustainable development, useful for planners and city managers.

Term discrimination value is among the three basic heuristics exploited, directly or indirectly, in almost all ranking models for ad-hoc Information Retrieval (IR). Query term discrimination in monolingual IR is usually estimated based on document or collection frequency of terms. In the query translation approach for CLIR, the discrimination value of a query term needs to be estimated based on document or collection frequencies of its translations, which is more challenging. We show that the existing estimation models do not correctly estimate and adequately reflect the difference between the discrimination power of query terms, which hurts retrieval performance. We then propose a new model to estimate discrimination values of query terms for CLIR and empirically demonstrate its impact in improving the CLIR performance.

Proposing a language modeling method to extract translations from comparable corpora.Comparing two similarity functions for deriving bilingual word correlations.Improving translation quality by integrating co-occurrence relations into word models.Comparing different estimations of translation probabilities from word correlations.Showing the significant impact of probability estimation methods on CLIR performance. A main challenge in Cross-Language Information Retrieval (CLIR) is to estimate a proper translation model from available translation resources, since translation quality directly affects the retrieval performance. Among different translation resources, we focus on obtaining translation models from comparable corpora, because they provide appropriate translations for both languages and domains with limited linguistic resources. In this paper, we employ a two-step approach to build an effective translation model from comparable corpora, without requiring any additional linguistic resources, for the CLIR task. In the first step, translations are extracted by deriving correlations between sourcetarget word pairs. These correlations are used to estimate word translation probabilities in the second step. We propose a language modeling approach for the first step, where modeling based on probability distribution provides two key advantages. First, our approach can be tuned easier in comparison with heuristically adjusted previous work. Second, it provides a principled basis for integrating additional lexical and translational relations to improve the accuracy of translations from comparable corpora. As an indication, we integrate monolingual relations of word co-occurrences into the process of translation extraction, which helps to extract more reliable translations for low-frequency words in a comparable corpus. Experimental results on an EnglishPersian comparable corpus show that our method outperforms the previous approaches in terms of both translation quality and the performance of CLIR. Indeed, the proposed method is naturally applicable to any comparable corpus, regardless of its languages. In addition, we demonstrate the significant impact of word translation probabilities, estimated in the second step of our approach, on the performance of CLIR.

This paper introduces a framework comprising Geographic Information System (GIS) techniques and fuzzy Multi-Criteria Decision-Making (MCDM) methods for application in landfill site selection problem. The developed methodology is utilized in an environmentally critical area related to Mahallat city located in the northwest of Iran. In this process, fourteen effective parameters related to sustainability of the location of landfills are identified and categorized into three groups including environmental, economic, and social criteria. First, criteria weights are obtained by applying the group fuzzy Best-Worst Method (BWM) and then suitability maps are generated based on GIS analysis. From the spatial analysis, eleven potential sites are identified as the most suitable alternatives. These sites are then analyzed and ranked using group fuzzy MULTIMOORA method. This study is different from the previous researches applied the GIS-MCDM approach, in that group fuzzy BWM for weighting criteria is more reliable compared to the other common methods. The group fuzzy MULTIMOORA method, and its three subordinate approaches, select a same candidate, “Site A8” a region near urban area of Mahallat, as the most suitable location for disposing municipal solid waste.

Background Cystic fibrosis (CF) is the most common autosomal recessive disease in Caucasians, caused by mutation in cystic fibrosis transmembrane conductance regulator ( CFTR ). The type and distribution of mutations vary widely between different countries and ethnic groups. We therefore aimed to perform a comprehensive analysis of the CFTR gene in northern Iranian CF patients. Methods Forty northern Iranian CF patients were analyzed for mutations in introns 11 to 14a of their CFTR genes, using sequencing and reverse dot blot methods. Five normal subjects were also analyzed as normal control. Results One mutation and seven polymorphisms were identified. Of the eighty alleles studied, c.2043delG in exon 13 represented 12.5% of mutant alleles and was associated with two distinct haplotypes. rs1042077T>G, rs4148712delAT, rs4148711T>A and rs3808183 T>C with frequencies varying between 29.2% and 6.9% for the least common allele, as well as three new polymorphisms c.1680-224C>A (11.1%), c.2491-275T>G (14.1%) and c.2491-274C>G (35.9%) were detected. Conclusion These findings suggest a founder effect for c.2043delG in the Middle East and will assist in genetic counseling, prenatal diagnosis and future screening of CF in Iran.

Multilingual information retrieval (MLIR) provides results that are more comprehensive than those of mono- and cross-lingual retrieval. Methods for MLIR are categorized as: (1) Fusion-based methods that merge results from multiple retrieval runs, and (2) Direct methods that build a unique index for the entire collection. Merging results of individual runs reduces the overall effectiveness, while more effective direct methods suffer from either time complexity and memory overhead, or over-weighting of index terms. In this paper, we propose a direct MLIR approach by using the language modeling framework that includes a novel multilingual language model estimation for documents, and a new way to globally estimate word statistics. These contributions enable ranking documents in multiple languages in one retrieval phase without having the problems of the previous direct methods. Moreover, our approach has the advantage of accommodating multilingual feedback information which helps to prevent query drift, and consequently to improve the performance. Finally, we effectively address the common case of incomplete coverage of translation resources in our proposed estimation methods. Experimental results show that the proposed approach outperforms the previous MLIR approaches.

The problem of solid waste landfills on Caspian Sea coastlines is one of the current concerns of the Iranian environmental authorities. Physical and environmental constraints as well as shortcomings in present municipal solid waste management are the main factors for environmental pollution and natural resources destruction in this geographical region. In order to reduce the above problems, this study has been carried out on the basis of a comparative assessment of the existing condition of landfill sites in natural ecosystems. In this study, 48 cities of the region have been investigated. All of them are located in Gilan and Mazandaran provinces. Total daily waste production in the study area is about 1209 tons that are dumped in open spaces and/or in aquatic and terrestrial ecosystems. Surveying of 53 physical, adaptability and constraint parameters as well as hygiene and environmental parameters in the landfills, based on “Monavari-94 Method” indicate that the main problem in all of them is a high groundwater table. In 42% of landfills, the groundwater level is between from 0.5 to 2 meters. This phenomenon may cause severe risks due to the infiltration of leachate into potable groundwater. Indeed, the results of the study show that 87.5% of the landfills are located in areas with unacceptable conditions. In spite of geographical and environmental problems and constraints and as a final alternative in this respect, the need for sanitary and technical solid waste management is the main need of the study areas.

Most of the current rating methods depend on financial information and solvency level or tangible capitals of insurance companies. Considering intangible capitals in evaluating and rating of the companies has been emphasized by many investigators. A key point in this regard is how the intangible criteria are identified and incorporated along with tangible criteria through a rating process. Application of combined multiple criteria decision making methods have special efficiency. For this, important criteria in rating process are first screened by experts’ opinions. DEMATEL method is then used here to authenticate the identified criteria and retain a logical collection of them. Finally, multiple criteria decision-making method PROMETHEE II is applied. Using data from a sample of insurance companies listed in Tehran Stock Exchange for a period of 2010–2012 and applying the combined method, it was observed that Alborz Company has the highest and Dana Company has the lowest rate. Application of the Content Analysis for identification of intangible capitals constitutes the novelty of our investigation.

A main challenge in Cross-Language information retrieval is to estimate a translation language model, as its quality directly affects the retrieval performance. The translation language model is built using translation resources such as bilingual dictionaries, parallel corpora, or comparable corpora. In general, high quality resources may not be available for scarce-resource languages. For these languages, efficient exploitation of commonly available resources such as comparable corpora is considered more crucial. In this paper, we focus on using only comparable corpora to extract translation information more efficiently. We propose a language modeling approach for estimating the translation language model. The proposed method is based on probability distribution estimation, and can be tuned easier in comparison with heuristically adjusted previous work. Experiment results show a significant improvement in the translation quality and CLIR performance compared to the previous approaches.

As the development in a software project goes on, the structure of the implemented code diverges from the intended architecture. To prevent this, architecture conformance methods are used to check if the source code complies with the architecture. In the development of today's enterprise applications, general-purpose programming languages are used along with a number of domain specific languages. So, there is a need for a conformance checking method to support multi-language source artifacts. We present a model-based approach for checking cross-language architecture conformance rules. Our method is extensible, in the sense that it is independent of the specific set of languages used in the project.

Metasearch engines submit the user query to several underlying search engines and then merge their retrieved results to generate a single list that is more effective to the users information needs. According to the idea behind metasearch engines, it seems that merging the results retrieved from different retrieval models will improve the search coverage and precision. In this study, we have investigated the effect of fusion of different retrieval techniques on the performance of Persian retrieval. We use an extension of Ordered Weighted Average (OWA) operator called IOWA and a weighting schema, NOWA for merging the results. Our experimental results show that merging by OWA operators produces better MAP.