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Carbon capture and storage (CCS) is proved to be effective measure for reducing CO2 emissions. whilst the world still highly depends on the use of fossil fuel energy, this method is necessary for reaching the world’s 1.5 °C goal. Saline aquifers among all possible underground formations are most common targeted ones for CO2 storage due to their frequent presence, and large storage capacity. However, this storage option suffers from sufficient well injectivity to inject large volumes of CO2 at acceptable rates through a minimum number of wells. The injectivity impairment/reinforcement happens through mineral dissolution, fine particle movement, salt precipitation and hydrate formation (known so far). Each of these mechanisms will be more dominant in injectivity alteration at different distance from the injection point depending on reservoir pressure and temperature, formation water salinity, rock mineralogy, and flow rate of CO2 injection as well as its dryness. In this study we have chosen a commercial software Eclipse 300 together with an open-source code to investigate the impact of formation characteristics, CO2 -Brine-Rock interaction, pressure, temperature as well as injection rate on injectivity alteration. The goal for this work is to provide a workflow which can help predicting injectivity alteration using the existing tools. Simulation results show that permeability is affected severely by salt precipitation during CO2 injection. Combined static and dynamic parameter study demonstrate that the injection rate plays a crucial role in size and expansion of CO2 plume as well as growth rate of dry out zone length, amount of salt precipitation and length of equilibrium region. The higher the injection rate, the quicker activation of the capillary and gravity force which leads to drag more brine to near well-bore resulting in higher volume fraction of salt precipitation. However, low injection rate could result in smaller CO2 plume, shorter dry out zone and longer equilibrium region in term of distance from injection point. Thus, optimizing the injection rate regarding reservoir parameters i.e., temperature, pressure and in-situ salinity, will lead to higher storage capacity as well as well performance and maintenance.

This study represents the detailed calculation of damage by Auger electrons of radionuclides 99 m T c , 111 I n , 125 I and 123 I to DNA molecules at cellular scales, using Geant4-DNA toolkit. The average number of breaks in DNA are shown as a function of distance from the center of the DNA axis to position of Auger electron decay and compared with results obtained for different models of DNA used by others. The highest damage occurs for electrons with energies below 1 keV, and in proximity of DNA molecule. The calculation of DSB yield per Gy per Dalton is carried out for above radionuclides. The most considerable damage occurs when the distance between an Auger electron-emitting atom and DNA is about 2.5 nm, and 50% decreases happen when the distance is about 3.5 to 4 nm. DSB yield are comparable with other experimental and simulation data, and show a good agreement between our results and other works.

Background: Concerns about the environment have persisted over decades, and governments determinedly pursue the issue of environmental education. Designing an integrated curriculum is an effective approach to learning, participation, behavior, and critical thinking. Objectives: The presented study aimed to propose and validate an integrated model of environmental education curriculum for general medicine. Methods: This study was conducted using a mixed method (quantitative and qualitative). Data were collected using a questionnaire and via interviews. By using the Morgan’s table, the target population was determined to be 280 in Isfahan University of Medical Sciences (15 professors and 265 students). Results: The students had inadequate knowledge and low positive attitudes toward environmental education. Given the maximum consistency of the data obtained and the curriculum models provided by Silver and Alexander and Taylor and Kelvin, the integrated environmental education curriculum model was designed in 11 stages, including needs assessment, objectives, content, area determination, learning materials and resources, learning-teaching strategies, implementation and the role of professors, grouping, time, place, and evaluation. The proposed model was validated based on experts’ comments, and the content validity ratio was considered appropriate. Conclusions: Considering the integrated features of the proposed model, it applies to all stages of medical education and could foster effective interactions between students and environmental education during the seven-year study period, which confirms the efficacy of the model if implemented correctly. Considering the subjects of environmental education based on health-centered environment and the effects on physical, mental, and social health, the proposed model recognizes the contents of the current medical curriculum (i.e., disease prevention and treatment and the position of integration in organizing the content). In addition, it selects the contents of the model, which are reflected in its integrated approaches. The model composes of thematic knowledge, efficient knowledge, and functional knowledge in environmental education in medicine in the form of an integrated curriculum.

Purpose: The biological effects of ionizing radiation at the cellular and subcellular scales are studied by the number of breaks in the DNA molecule that provides a quantitative description of the stochastic aspects of energy deposition at cellular scales. The Geant4 code represents a suitable theoretical toolkit in microdosimetry and nanodosimetry. In this study, radiation effects due to Auger electrons emitting radionuclides such as 195mPt 113mIn, 125I and 201Tl are investigated using the Geant4-DNA. Materials and Methods: The Geant4-DNA is the first Open-access software for the simulation of ionizing radiation and biological damage at the DNA scale. Low-energy electrons, especially Auger electron from Auger electron emitting radionuclides during the slowing-down process, deposit their energy within a nanometer volume. Results: The average number of Single-Strand Breaks (SSB) and Double-Strand Breaks (DSB) of DNA as a function of energy and distance from the center of the DNA axis are shown. Conclusion: The highest DSBs yield has occurred at energies less than 1 keV, and induces a higher DSBs yield.

Motion patterns in traffic video can be directly exploited to generate high-level descriptions of video content, which can be used for rule mining and abnormal event detection. The most recent and successful unsupervised methods for complex traffic scene analysis are based on topic models. In this paper, a topic related sparse topical coding framework is proposed for more effectively discovering motion patterns in traffic videos.

A clean and environmentally benign route to 2-amino-4 H -chromenes has been developed via three-component condensation reaction of various benzyl alcohols, malononitrile and 1-naphthol, using a catalytical amount of CAN and a reusable ionic liquid 3,3-(Butane-1,4-diyl)bis(1,2-dimethyl-1 H -imidazole-3-ium) bromide ([BDBDMIm]Br) as a catalyst at room temperature. The present methodology offers several advantages such as solvent-free conditions, excellent yields, simple procedure, mild conditions and reduced environmental consequences. The ionic liquid was recovered and reused. All of synthesized compounds were characterized by IR, NMR and elemental analyses.

In this paper, we introduce a document clustering method based on Sparse Topical Coding, called Cluster-based Sparse Topical Coding. Topic modeling is capable of improving textual document clustering by describing documents via bag-of-words models and projecting them into a topic space. The latent semantic descriptions derived by the topic model can be utilized as features in a clustering process. In our proposed method, document clustering and topic modeling are integrated in a unified framework in order to achieve the highest performance. This framework includes Sparse Topical Coding, which is responsible for topic mining, and K-means that discovers the latent clusters in documents collection. Experimental results on widely-used datasets show that our proposed method significantly outperforms the traditional and other topic model based clustering methods. Our method achieves from 4 to 39% improvement in clustering accuracy and from 2% to more than 44% improvement in normalized mutual information.

Ageism implies negative views and attitudes towards older people. These unconscious behaviors could make the elderly feel as if they were no longer useful for the family and community. Ageism is common in healthcare services. The objective of this study was to develop a tool to assess ageism in nursing care in Kashan/Iran 2015. This psychometric study was carried out in four stages: literature review for item generation; content validity testing; exploratory factor analysis (EFA) for evaluation of construct validity; and reliability assessment. The data analyzed with SPSS version 16. The final questionnaire had 24 items. The total CVI and CVR were 0.88 and 0.57. Explanatory factor analysis identified 3 factors of respecting older adult in nursing care (14 items); age discrimination and negative views towards older adults (5 items); attention to older adult and their basic caring needs (5 items). The three factors could explain 49.54% of variance. The test-re-test correlation stood at 0.9. The internal consistency of the questionnaire was Cronbach’s alpha =0.85. A valid and reliable instrument for the evaluation of ageism in nursing care was developed in this study.

The fatty acid profiles of five main commercial pistachio cultivars, including Ahmad-Aghaei, Akbari, Chrok, Kalle-Ghouchi, and Ohadi, were determined by gas chromatography: palmitic (C16:0), palmitoleic (C16:1), stearic (C18:0), oleic (C18:1), linoleic (C18:2), linolenic (C18:3), arachidic (C20:0), and gondoic (C20:1) acid. Based on the oleic to linoleic acid (O/L) ratio, a quality index was determined for these five cultivars: Ohadi (2.40) &lt, Ahmad-Aghaei (2.60) &lt, Kale-Ghouchi (2.94) &lt, Chrok (3.05) &lt, Akbari (3.66). Principal component analysis (PCA) of the fatty acid data yielded three significant PCs, which together account for 80.0% of the total variance in the dataset. A linear discriminant analysis (LDA) model that was evaluated with cross-validation correctly classified almost all of the samples: the average percent accuracy for the prediction set was 98.0%. The high predictive power for the prediction set shows the ability to indicate the cultivar of an unknown sample based on its fatty acid chromatographic fingerprint.

The problem of signal detection using a flexible and general model is considered. Owing to applicability and flexibility of sparse signal representation and approximation, it has attracted a lot of attention in many signal processing areas. In this study, the authors propose a new detection method based on sparse decomposition in a union of subspaces model. Their proposed detector uses a dictionary that can be interpreted as a bank of matched subspaces. This improves the performance of signal detection, as it is a generalisation for detectors. Low-rank assumption for the desired signals implies that the representations of these signals in terms of some proper bases would be sparse. Their proposed detector exploits sparsity in its decision rule. They demonstrate the high efficiency of their method in the cases of voice activity detection in speech processing.

Increasing transition in various aspects of society, and extending science and human knowledge; necessitate the review and change curriculum, especially in the higher educational system. The research method was descriptive-survey. The statistical population included professors and undergraduate educational management students in public universities of Tehran. The findings of the study showed the professors and students satisfaction concerning the program in command, their tendency to use integrated curriculum. Referring to the research the possibility of transition in higher education system curriculum is inevitable, and its trend is toward integrated curriculum.

Pica is an eating disorder in which non-nutritional objects are frequently eaten, and may have serious side effects for both the mother and neonate. The aim of this study was to determine the pica and its association with biochemical profiles of pregnant women and its relation to pregnancy outcome. A prospective cohort study was conducted in five health care centers in Zahedan City, Iran, in 2011-2012. Samples of pregnant women (n = 200) in the age groups 18 - 40 years were selected for this study and interviewed using a questionnaire. Maternal biochemical profiles (hemoglobin and ferritin) during pregnancy, mean gestational age and pregnancy outcome namely height, weight, and head circumferences of neonates were compared to pregnant women who reported pica (pica group) and women who did not (without pica group). The results showed pica prevalence categorized by substance as follows: clay (23.3%), ice (53.7%), ice and freezer frost (11.5%) and other substances (11.5%). Women in pica group had lower hemoglobin levels during the three trimesters of pregnancy than without pica group. Head circumferences of neonates in pica groups were significantly lower than those of without pica groups (31.0 ± 0.6 vs. 34.0 ± 0.2 respectively). There were no differences in mean birth weight, length, gestational age of infants born to pregnant women from the pica group and without pica group. The findings suggest that the pica practices during pregnancy are associated with lower maternal hemoglobin during the pregnancy periods, and also significantly lower head circumferences of neonates. Dietitians should ask pregnant women with anemia about pica and should counsel pregnant women who report pica.

With the extensive growth in information, text classification as one of the text mining methods, plays a vital role in organizing and management information. Most text classification methods represent a documents collection as a Bag of Words (BOW) model and then use the histogram of words as the classification features. But in this way, the number of features is very large; therefore performing text classification faces serious computational cost problems. Moreover, the BOW representation is unable to recognize semantic relations between words. Recently, topic-model approaches have been successfully applied for text classification to overcome the problems of BOW. Our main goal in this paper is to investigate the possibility of applying the topic models for Persian text classification and compare between the feature processing techniques of BOW and the topic model based approaches. The experimental results show that the topic-model approach for representing the Persian documents yields at least 9% accuracy improvement compared to the BOW based algorithm.

In this paper, the topic model is adopted to learn traffic phases from video sequence. Phase detection is applied to determine where a video clip is in the traffic light sequence. Each video clip is labeled by a certain traffic phase, based on which, videos are segmented clip by clip. Using topic models, without any prior knowledge of the traffic rules, activities are detected as distributions over quantized optical flow vectors. Then, traffic phases are discovered as clusters over activities according to the traffic signals. We employ the Fully Sparse Topic Model (FSTM) as the topic model. The results show that our method can successfully discover both activities and traffic phases which make veracious description and perception of traffic scenes.

Traffic density estimation plays an integral role in intelligent transportation systems (ITS), using which provides important information for signal control and effective traffic management. In this paper, we present a new framework for traffic density estimation based on topic model, which is an unsupervised model. This framework uses a set of visual features without any need to individual vehicle detection and tracking, and discovers the motion patterns automatically in traffic scenes by using topic model. Then, likelihood value allocated to each video clip enables us to estimate its traffic density. Results on a standard dataset show high classification performance of our proposed approach and robustness to typical environmental and illumination conditions.

Analyzing motion patterns in traffic videos can directly generate some high-level descriptions of the video content which can be further employed in rule mining and abnormal event detection. The most recent and successful unsupervised approaches for complex traffic scene analysis are based on topic models. However, most existing topic models share some key characteristics which could limit their utility. In this paper, based on extracted optical flow features from video clips, we employ Sparse Topical Coding (STC) framework to automatically discover typical motion patterns in traffic scenes. For this purpose, we improve the STC to overcome one of the drawbacks of topic models with the aim of learning the semantic traffic motion patterns. We go beyond the usual word-document paradigm in topic models by taking into account the order of optical flow words during learning. Experimental results show that our proposed method can learn better motion patterns to analyse the traffic video scenes.

Analyzing motion patterns in traffic videos can directly lead to generate some high-level descriptions of the video content. In this paper, an unsupervised method is proposed to automatically discover motion patterns occurring in traffic video scenes. For this purpose, based on optical flow features extracted from video clips, an improved Non-negative Matrix Factorization (NMF) framework is applied for learning of semantic motion patterns. After extracting the motion patterns, each video clip can be sparsely represented as a weighted sum of learned patterns which can further be employed in very large range of applications. Experimental results show that our proposed approach finds accurately the motion patterns and gives a meaningful representation for the video.

Topic modeling can improve document clustering by projecting documents into a topic space. By document, we mean a general concept. Document can be an image, a video, a textual document or each data which can be described in bag-of-words model based on the histogram of its features. In this paper, we introduce a clustering method based on Sparse Topical Coding (STC). In the proposed method, document clustering and topic modeling are integrated into a unified framework and jointly performed to achieve the best clustering performance. Our method clusters the documents based on STC topic modeling used for mining the topics and K-means clustering used for discovering latent groups in document collection. Experimental results show the effectiveness of our proposed clustering approach.

This paper presents a novel approach for Voice Activity Detection (VAD), based on the sparse representation of an input noisy speech over a learned dictionary. For this purpose, we first generate sparse representations of the input noisy speech by Orthogonal Matching Pursuit (OMP) sparse decomposition method with an over-complete speech dictionary learned from clean speech using K-SVD. We then propose a criterion to recognize the speech frames from non-speech frames. Experimental results demonstrate that our VAD approach has a good performance in low SNR conditions and outperforms than current VAD methods.

Analyzing motion patterns in traffic videos can directly lead to generate some high-level descriptions of the video content. In this paper, an unsupervised method is proposed to automatically discover motion patterns occurring in traffic video scenes. For this purpose, based on optical flow features extracted from video clips, an improved Group Sparse Topical Coding (GSTC) framework is applied for learning of semantic motion patterns. Then, each video clip can be sparsely represented as a weighted sum of learned patterns which can further be employed in very large range of applications. Compared to the original GSTC, the proposed improved version of GSTC selects only a small number of relevant words for each topic and hence provides a more compact and precise representation of topic-word relationships. Moreover, in order to deal with large-scale video analysis problems, we develop the online algorithm for improved GSTC, which can not only deal with large video corpora but also can deal with dynamic video streams. Experimental results show that our proposed approach finds accurately the motion patterns and gives a meaningful representation for the video.

Voice activity detection (VAD) can be considered as a binary classification problem and solved using the support vector machine (SVM). This paper presents a robust approach to improve the performance of conventional SVM based VAD methods. To this end, we first generate sparse representations by using a speech dictionary learned from clean speech, and derive some kind of audio features from the sparse representations. Then, we design a SVM to detect speech region and non-speech region based on these features. Experiments show that the proposed approach for noise-robust feature extraction further improves the performance of SVM based VAD methods especially in low SNR noisy environments.

Segmentation of aerial images has been a challenging task in recent years. This paper introduces a simple and efficient method for segmentation and classification of aerial images based on a pixel-level classification. To this end, we use the Gabor texture features in HSV color space as our best experienced features for aerial images segmentation and classification. We test different classifiers including KNN, SVM and a classifier based on sparse representation. Comparison of our proposed method with a sample of segmentation pre-process based classification methods shows that our pixel-wise approach results in higher accuracy results with lower computation time.