Cyclic olefin polymer as a novel membrane material for membrane distillation applications

A first attempt was made to prepare cyclic olefin polymer/copolymer (COP/COC) flat-sheet porous membranes by the well-known non-solvent induced phase separation method. In this study, two solvents (chloroform and 1,2,4-trichlorobenzene), different additives (polyvinylpyrrolidone, PVP, polyethylene glycol, PEG400, polyethylene oxide, PEO, and Sorbitan monooleate, Span 80) and coagulants (acetone and 70/30 wt% acetone/water mixture) were employed. The prepared membranes were characterized in terms of the thickness (70-85 mu m), porosity (-50-80%), liquid entry pressure (1.16-4.55 bar), water contact angle (similar to 86 degrees - 111 degrees), mean pore size (158-265 nm), mechanical properties (tensile strength: 0.74-5.51 MPa, elongation at break: 3.34%-7.94% and Young's modulus: 29-237 MPa), morphological and topographical characteristics. Short-term direct contact membrane distillation (DCMD) tests showed maximum permeate fluxes of 20 kg m(-2) h(-1) and 15 kg m(-2) h(-1) when using as feed distilled water and 30 g/L sodium chloride aqueous solution, respectively, with a high salt separation factor (99.99%). Long-term DCMD tests of some selected membranes carried out during 24-50 h showed that the membranes prepared with PEG additive exhibited more stable DCMD performance. In general, it was proved that COP can be successfully used as a novel polymer candidate in membrane distillation (MD) applications

A fuzzy method for improving the functionality of search engines based on user's web interactions

Web mining has been widely used to discover knowledge from various sources in the web. One of the important tools in web mining is mining of web user’s behavior that is considered as a way to discover the potential knowledge of web user’s interaction. Nowadays, Website personalization is regarded as a popular phenomenon among web users and it plays an important role in facilitating user access and provides information of users’ requirements based on their own interests. Extracting important features about web user behavior plays a significant role in web usage mining. Such features are page visit frequency in each session, visit duration, and dates of visiting a certain pages. This paper presents a method to predict user’s interest and to propose a list of pages based on their interests by identifying user’s behavior based on fuzzy techniques called fuzzy clustering method. Due to the user’s different interests and use of one or more interest at a time, user’s interest may belong to several clusters and fuzzy clustering provide a possible overlap. Using the resulted cluster helps extract fuzzy rules. This helps detecting user’s movement pattern and using neural network a list of suggested pages to the users is provided

Sample reduction strategies for protein secondary structure prediction

Predicting the secondary structure from protein sequence plays a crucial role in estimating the 3D structure, which has applications in drug design and in understanding the function of proteins. As new genes and proteins are discovered, the large size of the protein databases and datasets that can be used for training prediction models grows considerably. A two-stage hybrid classifier, which employs dynamic Bayesian networks and a support vector machine (SVM) has been shown to provide state-of-the-art prediction accuracy for protein secondary structure prediction. However, SVM is not efficient for large datasets due to the quadratic optimization involved in model training. In this paper, two techniques are implemented on CB513 benchmark for reducing the number of samples in the train set of the SVM. The first method randomly selects a fraction of data samples from the train set using a stratified selection strategy. This approach can remove approximately 50% of the data samples from the train set and reduce the model training time by 73.38% on average without decreasing the prediction accuracy significantly. The second method clusters the data samples by a hierarchical clustering algorithm and replaces the train set samples with nearest neighbors of the cluster centers in order to improve the training time. To cluster the feature vectors, the hierarchical clustering method is implemented, for which the number of clusters and the number of nearest neighbors are optimized as hyper-parameters by computing the prediction accuracy on validation sets. It is found that clustering can reduce the size of the train set by 26% without reducing the prediction accuracy. Among the clustering techniques Ward’s method provided the best accuracy on test data

An epidemiological study of hospitalised patients with burns in Imam Reza hospital in Birjand between2007 and 2013

Background and Aim: Burn is among the most expensive injuries which occur at all ages. Regarding the notoriety of burns and differences in population, community, economics, and cultural sectors, obtaining particular information from each area is needed to plan for prevention and treatment. Thus, the present study aimed at determining epidemiological factors related to hospitalization in the burns ward of Birjand Imam Reza hospital. Materials and Methods: This cross - sectional retrospective study was done on 1160 burn patients who hospitalized in the burn center during 6 years, (between 21 March 2007 and 21 March 2013). The necessary data was collected by means of a researcher designed questionnaire. The obtained data was analyzed by SPSS software (V: 15.5) and applying descriptive statistics including chi-square and Mann Witnietests (P≤0/05). Results: Mean age of the subjects was 24.6 20.6 years. Length of hospitalisation was 11.6 12.4 days. The majority of the patients were men (58.8 %). Among the cases, 54.1% were single24.1% were unemployed, 71.4% had either no education or only primary education, and 60.4% were provided with health insurance. It was found that the most common causes of burning were kerosene and or gasoline flame (43.9 %) and most burns were due to accidental injuries at home ( 73.1 % ). Among the patients, 37/8 % had burned body surface of 10% – 29%. Most ( 55.9 %) had a combination of 2nd degree and 3rd degree burns, of whom 14.1 % of died. Most patients were admitted in in winters (28.2 %) and autumns (26 %). There was a significant relationship between variables of burns causes in the patients (P=0.001) and the burned body surface percentages (P=0.001) and also with the season of the year. There was also a significant relationship between age groups and the percentage of burned body surface (P=0.001). Conclusion: Burns are more prevalent in men (compared to women) and in children

Script independent offline writer identification from handwriting samples based on texture using wavelet transform in Persian-English languages

Recent advances in information technology and the need for more security have led to the rapid development of intelligent biometric identification systems. Recent studies have proven that handwriting of each person is unique and can be used as one of the authentication methods. There are many researches in the literature for writer identification on a specific language. Unfortunately, there are no necessary data sets for this purpose. In this paper, for the first time, a handwritten data set of 300 persons in both Persian and English languages was collected. The main goal of this paper is to provide a model to identify the writer independent of the language written in Persian and English. After pre-processing stage, each person's handwriting is converted into blocks of a certain size called a texture. Then, using these textures, the desired features are extracted. In order to extract these features, first a two-dimensional wavelet transform is applied to each image and then, using the new algorithm for calculating the fractal dimension, which is used for the first time in this field, the feature vector is obtained. Finally, MLP neural networks are utilized for classification step. The performance of the proposed method is evaluated in different scenarios

The Association between Birth Weight, Height and Some Maternal Risk Factors

Background and purpose: Since a large proportion of fetal mortality is associated with low birth weight (LBW) and regarding that fetal development is a vulnerable process influenced by maternal risk factors, this study examined some maternal risk factors associated with LBW infants. Materials and Methods: This cross-sectional study was conducted based on the medical files of 300 infants born in hemoglobin and hematocrit levels Sajjadieh Arjomand Health Care Center, Kerman County, Iran. The required data were registered in a predeveloped checklist. The data were analyzed by SPSS Software using descriptive and inferential statistics. Results: The mean weight of the infants was 3.22 &plusmn; 0.36 kg, the mean height 48.4 &plusmn; 0.3 cm, and the mean head circumference 35.00 &plusmn; 1.74 cm. The results indicated a significant association of the parity, maternal weight gain, pregnancy-induced hypertension, type of pregnancy (planned or unplanned), and abortion history with the birth weight (P < 0.050). Conclusion: Regarding the findings of this study, health centers should study the risk factors before and during pregnancy more seriously. Many risks for LBW can be identified before pregnancy occurs

Spatial Network-Wide Traffic Flow Imputation With Graph Neural Network

Traffic data plays an essential role in Intelligent Transportation Systems (ITS) and offers numerous advantages, including efficient traffic control and system performance improvement. However, due to the scarcity of data collection systems, missing data in traffic datasets is inevitable. Therefore, traffic data imputation becomes an essential task. Graph Neural Network (GNN) is a type of neural network that operates on graph-structured data and has shown potential in handling traffic network related tasks such as traffic prediction and traffic data imputation. In this paper, we contribute to the body of knowledge with two aspects. First, we focus on traffic data imputation using solely spatial information. Most of the studies in the literature address spatio-temporal traffic data imputation, which is a distinct task from our research. Second, Since most GNN models operates on node features, we propose an approach to construct node features for nodes in traffic networks, by leveraging available link flows. To investigate the effectiveness of the proposed method, we implement two missing scenarios, random missing (RM) and block missing (BM). We evaluate the performance of the proposed method on three different sized real-world networks: Sioux Falls, Anaheim, and Chicago. The evaluation results demonstrate that GNN models outperform other baselines for most of the missing patterns.  </p

Intelligent Agents: Multi-Agent Systems

The article introduces key selected concepts in the area of agents and multi-agent systems. It discusses the possible use of agents in modeling and simulation of complex systems, and the use of the agent paradigm for distributing a problem among a number of reactive, autonomous, deliberative, pro-active, adaptive, possibly mobile, flexible and collaborative entities