Predicting Group Choices from Group Profiles

Group recommender systems (GRSs) identify items to recommend to a group of people by aggregating group members' individual preferences into a group profile, and selecting the items that have the largest score in the group profile. The GRS predicts that these recommendations would be chosen by the group, by assuming that the group is applying the same preference aggregation strategy as the one adopted by the GRS. However, predicting the choice of a group is more complex since the GRS is not aware of the exact preference aggregation strategy that is going to be used by the group. To this end, the aim of this paper is to validate the research hypothesis that, by using a machine learning approach and a data set of observed group choices, it is possible to predict a group's final choice, better than by using a standard preference aggregation strategy. Inspired by the Decision Scheme theory, which first tried to address the group choice prediction problem, we search for a group profile definition that, in conjunction with a machine learning model, can be used to accurately predict a group choice. Moreover, to cope with the data scarcity problem, we propose two data augmentation methods, which add synthetic group profiles to the training data, and we hypothesize they can further improve the choice prediction accuracy. We validate our research hypotheses by using a data set containing 282 participants organized in 79 groups. The experiments indicate that the proposed method outperforms baseline aggregation strategies when used for group choice prediction. The method we propose is robust with the presence of missing preference data and achieves a performance superior to what humans can achieve on the group choice prediction task. Finally, the proposed data augmentation method can also improve the prediction accuracy

Patient doses of CT examinations in Western and Eastern Azerbyjan provinces of Iran

Medical X-rays are the largest man-made source of public exposure to ionizing radiation. While the benefits of computed tomography (CT) are well known in accurate diagnosis, those benefits are not risk free. CT is a device with higher patient dose in comparison with other conventional radiation procedures. So it is important to avoid conditions where the amount of radiation used is more than that needed for the procedure. Since that there is not any report on the radiation doses received by patients in CT scan wards in hospitals under control of Eastern and Western Azerbyjan medical sciences university, in the North West of Iran; this study was a part of national project to establish and optimize local and national diagnostic guidance levels. This work intends to calculate CT Dose Index (CTDI) and Dose Length Product (DLP) in common CT procedures in two north western provinces of country. Two hospitals got involved in the present study. CTDI and DLP measurements were done according to AAPM report no. 96 for head, chest and abdomen CT procedures. The mean CTDIw for head (base), sinus, chest and abdomen were 12.22, 13.13, 13.3 and 7.6 mGy, respectively.Patient dose levels in CTDI and DLP in our study aren't higher than those in developed countries

Water quality assessment of the kopal river (Iran)

The Kopal River in the Khozestan province, IRAN, is the most important river on the plain and water capability for agriculture in this plain was provided from this river. It is planning to construct a reservoir on the river in the Haftgel area in order to supply the agriculture and drinking consumptions in that region. Therefore, the study on the water quality of this river is very important role in take any decision. In this paper the water quality parameters such as HCO3- SO4-2 CL-1 K+ Na+ Mg+2 Ca+2 and SO4-2 are evaluated base on the sampled data which taken in the Hydrometric station in the period from 1981-2001. In general, 162 series data are used. For assessment of the water the Wilcox diagram are used. Base on this criterion, the water of Kopal River has high harness and it is not suitable for dinking uses. Also the water is classified as C4S2, C4S3 and C4S4; therefore it is not suitable for the irrigation consumptions

Neural networks for predicting flow discharge in the balarood river (Iran)

In this study an artificial neural networks (ANNs) model, multi-layer perception using back-propagation algorithm (MLP/BP) was used for predicting flow discharge in the Balarood River which located in Khozestan province, Iran. The rain and temperature data as monthly collected at the five meteorology stations near the Balarood basin, and corresponding them the measured discharge at the Dokohe hydrometric station on the Balarood river were used to train and validate the ANN model. The ANN model was performed by varying the network parameters to minimize the prediction error and determine the optimum network configuration. The results show that the best architecture for the MLP/BP model comprised of 10 neurons in the hidden layer and a learning rate of 0.01. Overall, the performance of the MLP/BP neural network was good in predicting the discharge of Balarood River. This information can be used for proper water management studies in that area

Patient doses in radiographic examinations in Western and Eastern Azerbyjan provinces of Iran

     This study was a part of national project to establish and optimize local and national diagnostic guidance levels. This work intends to evaluate image quality and entrance surface air kerma (ESAK) for patients' radiographic examinations in two north western provinces of country. Two hospitals got involved in the present study. The rate of the rejected images and image quality grades were determined. The ESAK were calculated by X-ray tube output measurements and X-ray exposure parameters (kVp, mAS, FFD, as well as patients thicknesses) for common radiographic examinations including: chest, skull, thoracic, lumbar in two projections and also abdomen and pelvis in one projection. The rate of images categorized as poor was 40%. Patients' dose in radiographic examination varied by a factor of up to 6.9, 13.84, 9.76, 11.33, 6.15, 8.69, 2.85, 3.05, 12.41, and 5.51 in chest (PA), chest (LAT), lumbar (AP), lumbar (LAT), thoracic (AP), thoracic (LAT), skull (PA), skull (LAT), abdomen and pelvis, respectively. The mean ESAK values for above mentioned techniques were 0.3, 0.7, 2.85, 6.87, 2.3, 4.9, 1.32, 1.05, 2.9 and 2.2 mGy, respectively. Poor image quality plays a major role in unnecessary radiation dose to the patients but in compare with other studies stated that patient dose levels in radiographic examinations in our study aren't higher than those in developed countries.

Experimental Investigation on the Deviated Sediment and Flow to Sediment Bypass Tunnels (SBTs) Using Submerged Plates

Sediment Bypass Tunnels (SBTs) are deviant channels that convey the current containing sediments from the upstream of the reservoir to the downstream of the dam. In this research, by applying submerged plates on the entrance of a 90-degree diversion channel for sediment transport, the effect of hydraulic parameters of flow and changes in the angle of plates on sediment transport and deviated flow are studied and compared with the state without using submerged plates. The experiments were conducted on a 10-meter-long Laboratory Flume, with a main channel of 60 cm width and a secondary channel of 30 cm width and a height of 75 cm. In this regard, the variables of Froude number and flow depth in three angles of 30, 45 and 60 degrees were considered. The results of this study highlight that an increase in Froude number on average would result in reduction of 22.2% of the channel deviated flow and reduction of 53.3% of the deviated sediment to the secondary channel. The 60-degree angle of the plates is effective in decreasing the deviated flow while the 30-degree angle is responsible for the increased deviated sediments. With a decrease in Froude number and depth along with submerged plates with a 30 degree angle, the optimum condition in conveying sediments is achieved where the maximum amount of sediments are conveyed in the minimum flow rate. Based on prediction results, the best equation to calculate the deviated sedimentation flow using the Genetic Algorithm (GA) is suggested

Investigating the Effect of Wavelet Decomposition on the Performance of the Optimized Support Vector Regression in Precipitation Simulation

Considering the climatic changes and the increase of extreme values in recent years, in this study, the effect of time series decomposition based on wavelet transform in improving the performance of the optimized support vector regression model in the simulation of annual precipitation in Dashband and Tapik stations has been discussed and investigated in the Lake Urmia Basin in the period of 1971-2020. In this study, the Ant colony algorithm was used to optimize the parameters of the support vector regression model. Daubechies 4 wavelet with three decomposition levels 1, 2 and 3 was used to decomposition the time series of precipitation in the studied stations. The SVR model takes in annual precipitation data as input, while the decomposition-based models take in decomposed precipitation values. The results of investigation the error rate and efficiency of the 4 investigated models include optimized SVR, W1-SVR (optimized SVR based on level 1 decomposition), W2-SVR (optimized SVR based on level 2 decomposition) and W3-SVR (optimized SVR based on level 3 analysis) showed that the error rate of all 4 mentioned models is acceptable and the observed values are in the 95% confidence interval. The error rate of 5.20 and 6.68 mm in the simulation of precipitation in Dashband and Tapik stations using the optimized SVR model by time series decomposition based on wavelet theory in level 1 decomposition in the mentioned stations, 31 and 35 percent improvement has been found. The level 2 decomposition of the time series of precipitation obtained the lowest error among the different levels of decomposition, which was 3.42 and 3.26 mm in Dashband and Tapik stations, respectively. Considering the increase in simulation complexity with the involvement of wavelet theory, the error rate improvement and model performance are acceptable. The hybrid W-SVR model in this study provides reliable results for precipitation simulation. Analyzing the annual precipitation series makes it possible to develop the dimensions of the optimized SVR model

Estimation of Sediment Transport Rate of Karun River (Iran)

Several types of sediment transport equations have been developed for estimation of the river sediment materials during the past decades. The estimated sediment from these equations is very different, especially when they applied for a specific river. Therefore, choice of an equation for estimation of the river sediment load is not an easy task. In this study 10 important sediment transport equations namely; Meyer-Peter and Muller (1948), Einstein (1950), Bagnold (1966), Engelund and Hansen (1972), Toffaleti (1969), Yang (1996), Van Rijn (2004), Wiuff (1985), Samaga et al. (1986) and Beg (1995) are used to estimate sediment load of the Karun  River in Iran.  The estimated sediment load compared with the measured field data by using statistical criteria such as root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R2). Results showed that Engelund and Hansen formula can provide reliable estimates of sediment load of the Karun River which have high suspended sediment load concentration with RMSE of 3725 ton/day, MAE of 1058.82 ton/day and R2 of 0.41. Bagnold and Wiuff formulas estimated the total sediment load 280 % and 700% more than the measured values and the Van Rijn, Tofaleti and Bagnold formulas estimated the sediment load 99 %, 71% and 93 % lower than the measured values, respectively. The comparison indicated that Samaga, Einstein, Tofaleti and Yang equations with low accuracy are not suitable for estimation of sediment load of the Karun River. The main reason for this difference is related to fact that the Karun River carries fine sediment (wash load) which these equations not considered it

In vivo evaluation of the combination effect of near-infrared laser and 5-fluorouracil-loaded PLGA-coated magnetite nanographene oxide

Magnetite nanographene oxide has exhibited great potential in drug delivery and photothermal therapy (PTT) for cancer treatment. Here we developed 5-fluorouracil-loaded poly (lactic-co-glycolic acid)-coated magnetite nanographene oxide (NGO-SPION-PLGA-5-Fu) to simplify combined PTT and chemotherapy in one complex. The nanocarrier was synthesized using a modified O1/W1/O2/W2 multiple emulsion solvent evaporation method and was characterized for size, zeta potential, drug loading, in vitro and in vivo release. In this paper, in vivo suppression effect of PTT and chemotherapy using this synthesized magnetite nanographene oxide was studied. The in vitro release of 5-Fu from nanoparticles showed that 41.36 of the drug was released within 24�h. In vivo release showed that 5-Fu has a sustained release profile and prolonged lifetime in the rabbit plasma. Remarkably, a single injection of NGO-SPION-PLGA-5-Fu and 808�nm near-infrared laser (NIR) irradiation for 3�min effectively suppressed the growth of tumours compared with 5-Fu alone (p�<�.01). Magnetic resonance imaging (MRI) confirmed that the magnetic nanographene oxide was effectively targeted to the tumour site. Therefore, NGO-SPION-PLGA-5-Fu showed excellent PTT efficacy, magnetic targeting property, and MRI ability, indicating that there is a great potential of NGO-SPION-PLGA-5-Fu for cancer theranostic applications. © 2018 Informa UK Limited, trading as Taylor & Francis Grou