Model-driven decision support system for estimating number of ambulances required during earthquake disaster relief operation

Most of human life has been encountered danger due to natural disasters nowadays. One of these natural disasters that endanger human lives and which causes lot of damages is earthquake. A proper emergency response after an earthquake happening is important and has high priority in earthquake emergency management to reduce number of damages. Decision making for critical resources in the phase of response, is one of the main concerns for managers. Ambulance, as one of the critical resource that can help to reduce earthquake losses and costs, needs to be planned. Confusion in the number of victims in the early stages of earthquake, access complexity to the required data of different organizations by the pressing time, complicated nature of estimation, diversity of models and limitation of time for decision making are the main problems associated with estimating ambulances during earthquake disaster which makes estimation too difficult. In addition, there is a call for research in determining the number of required ambulances during earthquake emergency management, due to high error in estimating the number of ambulances in the current methods, which leads to unnecessary expenses and thereby helping to ensure that disaster sites are not overcrowded with emergency workers impeding each other's effectiveness. Such complexity suggests the introduction of Decision Support System (DSS). More accurate estimation of the number of required ambulances using a decision support system can help managers to speed up the process of decision making and thus reducing error and costs. Since the number of ambulances needed during a disaster is directly proportional to the number of victims requiring hospital treatment and in order to reach the first objective of this study, factors determining the number of human casualties in earthquake disaster i.e. population, modified Mercalli, age, time, building occupancy and gender are selected as the most relevant factors which have high probability in creating human casualties. The collected data from various relevant sources is used in proposing the model of this research. After testing different approaches, Fuzzy rule-based approach is being used, after defining the rules for each aforementioned factors and optimization is conducted in order to minimize the error for estimating the number of human casualties. Finally, by using de Boer formula and obtained number of human casualties, the number of required ambulances is estimated accurately. The results indicate that the error is decreased by more than 50% in the proposed method. A prototype of Model-Driven Decision Support System was developed based on the proposed model that can be used to aid emergency response planners for their decision making process prior to take any action during earthquake emergency management

ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems

Regularization of neural machine translation is still a significant problem, especially in low-resource settings. To mollify this problem, we propose regressing word embeddings (ReWE) as a new regularization technique in a system that is jointly trained to predict the next word in the translation (categorical value) and its word embedding (continuous value). Such a joint training allows the proposed system to learn the distributional properties represented by the word embeddings, empirically improving the generalization to unseen sentences. Experiments over three translation datasets have showed a consistent improvement over a strong baseline, ranging between 0.91 and 2.54 BLEU points, and also a marked improvement over a state-of-the-art system.Comment: Accepted at NAACL-HLT 201

Negotiating academic conflict in discussion sections of doctoral dissertations

This is the author accepted manuscript. The final version is available from John Benjamins Publishing Company via the DOI in this recordThis study explores how doctoral students negotiated academic conflict (AC) in discussion section of their dissertations and what engagement resources they utilized to convey academic conflict. To this end, discussion chapters of 30 doctoral dissertations in Applied Linguistics (15 samples by each writer group) were analyzed using Huston’s (1991) academic conflict framework and Martin and White’s (2005) engagement system of Appraisal Theory. The functional analysis constituted discovering components of academic conflict and engagement resources in the discussions. We found that components of academic conflict determined engagement values used to convey them. The linguistic background of the authors was less of an issue in resolving conflicts. The two writer groups managed academic conflict and related engagement resources more or less similarly in different components of academic conflict. They mainly expressed their novel contribution readily and identified the flaws of previous research; however, both writer groups showed little tendency to explain controversial points. The findings have pedagogical implications for academic writing courses highlighting the importance of developing awareness of AC and resolving the conflicts

Magnetron sputtering technique for analyzing the influence of RF sputtering power on microstructural surface morphology of aluminum thin films deposited on SiO2/Si substrates

In this research, aluminum (Al) thin films were deposited on SiO2/Si substrates using RF magnetron sputtering technique for analyzing the influence of RF sputtering power on microstructural surface morphologies. Different sputtering RF powers (100–400 W) were employed to form Al thin films. The characteristics of deposited Al thin films are investigated using X-ray diffraction pattern (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM) and Fourier-transforms infrared (FTIR) spectroscopy. The X-ray diffraction (XRD) results demonstrate that the deposited films in low sputtering power have amorphous nature. By increasing the sputtering power, crystallization is observed. AFM analysis results show that the RF power of 300 W is the optimum sputtering power to grow the smoothest Al thin films. FTIR results show that the varying RF power affect the chemical structure of the deposited films. The SEM results show that by increasing the sputtering power leads to the formation of isolated texture on the surface of substrate. In conclusion, RF power has a significant impact on the properties of deposited films, particularly crystallization and shape

Evaluation of Streamflow Hydrograph using ERA5 Precipitation Data in HEC-HMS Model

Introduction Precipitation is one of the most important input parameters of the hydrological models for rainfall-runoff simulation, which due to the lack of proper dispersion of rain gauge stations and the newly established some of these stations in most basins of the country, the use of these precipitation data faces serious challenges. Therefore, the use of remote-sensing methods is one of the ways that can be used for the streamflow simulation using hydrological models. Runoff is also one of the most important hydrological variables and rainfall-runoff modeling is one of the key items in hydrological sciences to estimate runoff characteristics such as volume, peak flow and arrival time to peak flow. In the present study, we used reanalyzed precipitation data and then evaluated the simulated streamflow using this precipitation data in the Zoshk subbasin. The precipitation data was validated with in situ data, of Kashafrood basin.Materials and Methods The reanalysis precipitation data was selected from the ERA5 precipitation data, and the HEC-HMS was used for the rainfall-runoff simulation. The basin parameters were calculated by the GIS menu. This menu is the newest option in the HEC-HMS software that needs only the DEM basin for calculating the basin parameters. In the present study, we should validate the ERA5 reanalysis precipitation data with in situ data, so we did that in the Kashafrood basin. The number of the rain gauge stations were 34, but some of the stations didn't have complete data and omitted them from the list of the rain gauge stations. For the validation ERA5 reanalysis precipitation data was used from the R, NSE, RMSE, Bias, FAR, POD and TS statistical indicators. These indicators were calculated by programming in EXCEL Visual Basic. The ERA5 precipitation data was evaluated for the Kashfarood basin at daily and monthly time steps. The DEM Zoshk was downloaded with the spatial resolution of 12.5 meters from ALOS-PALSAR satellite and then the basin parameters were calculated by the GIS menu. The SCS curve number was selected as a loss method. In this method, the calculations related to the percentage of impermeability and the average curve number of each sub-basin were obtained through land use and curve number layers, respectively. The SCS unit hydrograph was selected as a transform method. The recession method was selected as a base flow method. NSE and PBias were used for the calibration and validation events in HEC-HMS. In this way, at first the HEC-HMS model was calibrated by tow in situ rainfall-runoff events (91/1/11 and 91/2/6), and then validated by one in situ rainfall-runoff event (99/1/23). For validation streamflow of the ERA5 reanalysis precipitation data, the event on 99/1/23 was used and their streamflow hydrographs were evaluated with each other in Zoshk station.Results and Discussion The results showed that the reanalysis precipitation data of ERA5 had underestimation in daily and monthly time steps. Also in monthly time step, the accuracy of these precipitation dataset in detecting precipitation events (in terms of FAR, TS, and POD indices) was higher than a daily one. In addition, in monthly time steps it had worse accuracy in summer months than the rest of the year in detecting precipitation events (in terms of FAR, TS, and POD indices). For streamflow evaluation, in the calibration phase both NSE was in very good and good ranges, and PBias was in very good, good and acceptable ranges. In addition, the model underestimated the observational one. Finally the ERA5 reanalysis precipitation data was compared by 99/1/23 hydrograph event. The streamflow hydrograph from the ERA5 reanalysis precipitation data was underestimated due to ERA5 underestimation of the precipitation at the Zoshk rain gauge on the days corresponding to the 23/6/99 incident. The ERA5 reanalyzed precipitation data with NSE and Bias percentage coefficients in unacceptable range (NSE≤0.5 and PBias≤±25), compared to flow hydrograph obtained from Zoshk station precipitation data, the efficiency of this precipitation dataset is low. The range of the streamflow hydrograph from the ERA5 precipitation data was unsatisfactory in compared to the observational hydrograph (NSE = -0.47 and PBias = -55.16).Conclusion In general, the accuracy of the flow hydrograph of this product compared to the flow hydrograph of the precipitation data of Zoshk station (NSE = 0.64 and PBias = -15.82), cannot be a relatively reliable source instead of in situ rainfall data in hydrological simulation. The suggestion for future studies is to evaluate other rainfall data based on remote sensing methods in hydrological modeling

Investigating Risk Factors and Predicting Complications in Deep Brain Stimulation Surgery with Machine Learning Algorithms

© 2019 Elsevier Inc. Background: Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurologic symptoms. DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorithms may be used to effectively predict these outcomes. The aims of this study were to 1) investigate preoperative clinical risk factors and 2) build machine learning models to predict adverse outcomes. Methods: This multicenter registry collected clinical and demographic characteristics of patients undergoing DBS surgery (n = 501) and tabulated occurrence of complications. Logistic regression was used to evaluate risk factors. Supervised learning algorithms were trained and validated on 70% and 30%, respectively, of both oversampled and original registry data. Performance was evaluated using area under the receiver operating characteristics curve (AUC), sensitivity, specificity, and accuracy. Results: Logistic regression showed that the risk of complication was related to the operating institution in which the surgery was performed (odds ratio [OR] = 0.44, confidence interval [CI] = 0.25–0.78), body mass index (OR = 0.94, CI = 0.89–0.99), and diabetes (OR = 2.33, CI = 1.18–4.60). Patients with diabetes were almost 3× more likely to return to the operating room (OR = 2.78, CI = 1.31–5.88). Patients with a history of smoking were 4× more likely to experience postoperative infection (OR = 4.20, CI = 1.21–14.61). Supervised learning algorithms demonstrated high discrimination performance when predicting any complication (AUC = 0.86), a complication within 12 months (AUC = 0.91), return to the operating room (AUC = 0.88), and infection (AUC = 0.97). Age, body mass index, procedure side, gender, and a diagnosis of Parkinson disease were influential features. Conclusions: Multiple significant complication risk factors were identified, and supervised learning algorithms effectively predicted adverse outcomes in DBS surgery

A comparative study of long interspersed element-1 protein immunoreactivity in cutaneous malignancies

Background: Skin cancer is the most common cancer worldwide and commonly classified into malignant melanoma (MM) and Nonmelanoma skin cancers (NMSCs), which mainly include basal cell carcinoma (BCC) and squamous cell carcinoma (SCC). The extent to which Long Interspersed Element-1 (LINE-1, L1) ORF1p is expressed in cutaneous malignancies remains to be evaluated. This study aimed to assess LINE-1 ORF1p immunoreactivity in various skin cancer subtypes. Method: The expression level of LINE-1 ORF1p was evaluated in 95 skin cancer specimens comprising 36 (37.9) BCC, 28 (29.5) SCC, and 31 (32.6) melanoma using the tissue microarray (TMA) technique. Then the association between expression of LINE-1 encoded protein and clinicopathological parameters was analyzed. Results: We showed that LINE-1 ORF1p expression level was substantially higher in BCC and SCC patients compared with melanoma samples (p 0.05). Conclusions: According to our observation, LINE-1 ORF1p immunoreactivity in various skin tumor subtypes extends previous studies of LINE-1 expression in different cancers. LINE-1ORF1p overexpression in NMSCs compared with MM can be considered with caution as a tumor-specific antigen for NMSCs. © 2020 The Author(s)

Experimental Evaluation of Mouse Hind Paw Edema Induced by Iranian Naja oxiana Venom

Iranian Naja oxiana (the Elapidae family) known as cobra snake inhabits in the northwestern part of Iran. This study aimed to evaluate the edematogenic potency of the crude venom with intraplantar injection into mice. Additionally, the inhibitory effects of three different drugs (i.e., promethazine, dexamethasone, and piroxicam) on paw edema were examined. Moreover, the gelatinase activity of this venom was assessed using the zymography method. Paw edema was induced by the intraplantar injection of different concentrations of the venom (0.5-5 μg dissolved in 50 μl of normal saline) into the mice (six in each group). It was estimated through the measurement of the increase in the paw thickness (%) with a digital caliper. The paws were pretreated and the rate of changes was measured after the venom injection. Pathological findings in the treated paws were evaluated with hematoxylin and eosin staining. Paw thickness reached its maximum amount within 5 min and resolved after 1 h. This venom had no gelatinase activity using the zymography method ruling out its role in edema. It caused non-hemorrhagic diffuse edema with the infiltration of inflammatory cells (i.e., leukocytes and lymphocytes) in the dermis. Intraperitoneal pretreatment with drugs significantly inhibited the venom-induced (1 μg/paw) edema; however, all the mice died unexpectedly a day after piroxicam injection. This in vitro and in vivo preliminary study demonstrated for the first time that N. oxiana venom-induced non-hemorrhagic edema in a short time. Dexamethasone (phospholipase A2 inhibitor; 1 mg/kg) and promethazine (H1 inhibitor; 5 mg/kg) decreased the venom-induced edema (p <0.001). It is suggested to carry out further studies to identify different mediators in venom-induced edema formation

Constraining Very Heavy Dark Matter Using Diffuse Backgrounds of Neutrinos and Cascaded Gamma Rays

We consider multi-messenger constraints on very heavy dark matter (VHDM) from recent Fermi gamma-ray and IceCube neutrino observations of isotropic background radiation. Fermi data on the diffuse gamma-ray background (DGB) shows a possible unexplained feature at very high energies (VHE), which we have called the "VHE Excess" relative to expectations for an attenuated power law extrapolated from lower energies. We show that VHDM could explain this excess, and that neutrino observations will be an important tool for testing this scenario. More conservatively, we derive new constraints on the properties of VHDM for masses of 10^3-10^10 GeV. These generic bounds follow from cosmic energy budget constraints for gamma rays and neutrinos that we developed elsewhere, based on detailed calculations of cosmic electromagnetic cascades and also neutrino detection rates. We show that combining both gamma-ray and neutrino data is essential for making the constraints on VHDM properties both strong and robust. In the lower mass range, our constraints on VHDM annihilation and decay are comparable to other results; however, our constraints continue to much higher masses, where they become relatively stronger.Comment: 33 pages, 21 figures, accepted for publication in JCA

Light Sterile Neutrinos: A White Paper

This white paper addresses the hypothesis of light sterile neutrinos based on recent anomalies observed in neutrino experiments and the latest astrophysical data