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

Gene Diversity of Trichomonas vaginalis Isolates

Background: Trichomonas vaginalis is protozoan parasite responsible for trichomoniasis and is more common in high-risk behavior group such as prostitute individuals. Interest in trichomoni­asis is due to increase one's susceptibility to viruses such as herpes, human papillomavirus and HIV. The aim of this study was to find genotypic differences between the isolates.Methods: Forty isolates from prisoners' women in Tehran province were used in this study. The random amplified polymorphic DNA (RAPD) technique was used to determine genetic differ­ences among isolates and was correlated with patient's records. By each primer the banding pat­tern size of each isolates was scored (bp), genetic differences were studied, and the genealogical tree was constructed by using NTSYS software program and UPGMA method.Results: The least number of bands were seen by using primer OPD8 and the most by using OPD3. Results showed no significant difference in isolates from different geographical areas in Iran. By using primer OPD1 specific amplified fragment with length 1300 base pair were found in only 8 isolates. All these isolates were belonged to addicted women; however, six belonged to asymptomatic patients and two to symptomatic ones.Conclusion: There was not much genetic diversity in T vaginalis isolates from three different geo­graphical areas

Leveraging Discourse Rewards for Document-Level Neural Machine Translation

Document-level machine translation focuses on the translation of entire documents from a source to a target language. It is widely regarded as a challenging task since the translation of the individual sentences in the document needs to retain aspects of the discourse at document level. However, document-level translation models are usually not trained to explicitly ensure discourse quality. Therefore, in this paper we propose a training approach that explicitly optimizes two established discourse metrics, lexical cohesion (LC) and coherence (COH), by using a reinforcement learning objective. Experiments over four different language pairs and three translation domains have shown that our training approach has been able to achieve more cohesive and coherent document translations than other competitive approaches, yet without compromising the faithfulness to the reference translation. In the case of the Zh-En language pair, our method has achieved an improvement of 2.46 percentage points (pp) in LC and 1.17 pp in COH over the runner-up, while at the same time improving 0.63 pp in BLEU score and 0.47 pp in F_BERT.Comment: Accepted at COLING 202

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

Wet deposition of hydrocarbons in the city of Tehran-Iran

Air pollution in the city of Tehran has been a major problem for the past three decades. The direct effects of hydrocarbon contaminants in the air are particularly important such as their carcinogenic, mutagenic, and teratogenic effects which can be transported to other environments via dry and wet deposition. In the present study, rainwater samples were collected and analyzed for 16 polycyclic aromatic hydrocarbons (PAHs), benzene, toluene, ethyl benzene, and xylene (BTEX) as well as fuel fingerprints in two ranges of gasoline (C5–C11) and diesel fuel (C12–C20) using a gas chromatograph equipped with a flame ionization detector (GC/FID). Mean concentrations of ∑16 PAHs varied between 372 and 527 µg/L and for BTEX was between 87 and 188 µg/L with maximum of 36 µg/L for toluene. Both gasoline range hydrocarbons (GRH) and diesel range hydrocarbons (DRH) were also present in the collected rainwater at concentrations of 190 and 950 µg/L, respectively. Hydrocarbon transports from air to soil were determined in this wet deposition. Average hydrocarbon transportation for ∑PAHs, BTEX, GRH, and DRH was 2,747, 627, 1,152, and 5,733 µg/m2, respectively