Scalable Deep Traffic Flow Neural Networks for Urban Traffic Congestion Prediction

Tracking congestion throughout the network road is a critical component of Intelligent transportation network management systems. Understanding how the traffic flows and short-term prediction of congestion occurrence due to rush-hour or incidents can be beneficial to such systems to effectively manage and direct the traffic to the most appropriate detours. Many of the current traffic flow prediction systems are designed by utilizing a central processing component where the prediction is carried out through aggregation of the information gathered from all measuring stations. However, centralized systems are not scalable and fail provide real-time feedback to the system whereas in a decentralized scheme, each node is responsible to predict its own short-term congestion based on the local current measurements in neighboring nodes. We propose a decentralized deep learning-based method where each node accurately predicts its own congestion state in real-time based on the congestion state of the neighboring stations. Moreover, historical data from the deployment site is not required, which makes the proposed method more suitable for newly installed stations. In order to achieve higher performance, we introduce a regularized Euclidean loss function that favors high congestion samples over low congestion samples to avoid the impact of the unbalanced training dataset. A novel dataset for this purpose is designed based on the traffic data obtained from traffic control stations in northern California. Extensive experiments conducted on the designed benchmark reflect a successful congestion prediction

SiamixFormer: a fully-transformer Siamese network with temporal Fusion for accurate building detection and change detection in bi-temporal remote sensing images

Building detection and change detection using remote sensing images can help urban and rescue planning. Moreover, they can be used for building damage assessment after natural disasters. Currently, most of the existing models for building detection use only one image (pre-disaster image) to detect buildings. This is based on the idea that post-disaster images reduce the model's performance because of presence of destroyed buildings. In this paper, we propose a siamese model, called SiamixFormer, which uses pre- and post-disaster images as input. Our model has two encoders and has a hierarchical transformer architecture. The output of each stage in both encoders is given to a temporal transformer for feature fusion in a way that query is generated from pre-disaster images and (key, value) is generated from post-disaster images. To this end, temporal features are also considered in feature fusion. Another advantage of using temporal transformers in feature fusion is that they can better maintain large receptive fields generated by transformer encoders compared with CNNs. Finally, the output of the temporal transformer is given to a simple MLP decoder at each stage. The SiamixFormer model is evaluated on xBD, and WHU datasets, for building detection and on LEVIR-CD and CDD datasets for change detection and could outperform the state-of-the-art

Thallium Intoxication in Relation to Drug Abuse and Cigarette Smoking in Iran

Thallium (Tl) is a highly toxic heavy metal with atomic number 81. It is a soft, bluish-white or gray water-insoluble metal but the salt forms are colorless, tasteless, and odorless. Tl is readily absorbed via ingestion, inhalation, and dermal contact. Any amount of Tl in the body is abnormal. The clinical manifestation of thallotoxicosis has a wide spectrum but painful ascending peripheral neuropathy, gastrointestinal, and dermatologic manifestations are major characteristics in Tl toxicity. Tl intoxication has been identified in drug abuse and cigarette smoking leading to various signs and symptoms. Substance abuse and cigarette smoke are a major public health hazard across the world

Evaluation and Prediction of the Scour Depth of Bridge Foundations with HEC-RAS Numerical Model and Empirical Equations (Case Study: Bridge of Simineh Rood Miandoab, Iran)

Today, scouring is one of the major issues in the river and coastal engineering. Each year, many bridges around the world are destroyed due to neglecting hydraulic elements. In the present study, scour depth around the piers of the Simineh Rood Bridge in Miandouab, Iran were investigated using empirical relationships and the HEC-RAS numerical model, and the results are compared with each other. Firstly, a hydraulic software model was created from the river where the bridge was located using field data. Then, by entering the scouring data of bridge piers for discharges with a return period of 5 to 1000 years, changes in flow discharge were investigated for scouring around the middle and lateral sides of the bridge. Results of the empirical equations showed that some of the equations are not sensitive to increases in flow discharge, and for each return period, the results are near each other. Also, numerical model results showed that with an increase in discharge, scouring increases in the bridge’s middle and lateral piers. In all discharges, the first and the seventh pier had the lowest and highest scour depth, respectively. Also, the left and right abutments are heavily influenced by increasing discharge. In discharges with a return period of 1000 years, the scour depth was 11.19 and 6.32 m. The Frohlich method is not as sensitive as the CSU method to an increase in discharge when calculating scour depth. Finally, the results of the numerical model were compared with experimental empirical equation

Analysis of Interaction Between Hydraulic and Natural Fractures

The behavior of natural fractures at the hydraulic fracturing (HF) treatment is one of the most important considerations in increasing the production from this kind of reservoirs. Therefore, considering the interaction between the natural fractures and hydraulic fractures can have great impact on the analysis and design of fracturing process. Due to the existence of such natural fractures, the perturbation stress regime around the tip of hydraulic fracture leads to some deviation in the propagation of path of hydraulic fracture. Increasing the ratio of transverse stress to the interaction stress results in a reduction in the deviation of hydraulic fracturing propagation trajectory in the vicinity of natural fracture. In this study, we modeled a hydraulic fracture with the extended finite element method (XFEM) using a cohesive-zone technique. The XFEM is used to discrete the equations, allowing for the simulation of induced fracture propagation; no re-meshing of domain is required to model the interaction between hydraulic and natural fractures. XFEM results reveal that the distance and angle of natural fracture with respect to the hydraulic fracture have a direct impact on the magnitude of tensile and shear debonding. The possibility of intersection of natural fracture by the hydraulic fracture will increase with increasing the deviation angle value. At the approaching stage of hydraulic fracture to the natural fracture, hydraulic fracture tip exerts remote compressional and tensile stress on the interface of the natural fracture, which leads to the activation and separation of natural fracture walls

Examining Metabolic Profiles in Opioid-Dependent Patient

Background: Drug abuse is a social burden and a public health disorder. Previous evidencesuggested numerous illicit substances (e.g., opioids, amphetamines, cocaine, & cannabis)affect immune system functions, oxidative stress mechanisms, inflammatory cytokines, andreactive oxygen species production.This study aimed to determine the extent of these metabolic parameters in opioid-dependentpatients. We also compared these patients with a healthy control group.Methods: This study was conducted in Amirie Clinic, Kashan, Iran. Plasma and serumsamples from 50 illicit opioid users (study group) and 50 non-opioid users (control group)were studied. Metabolic levels for MDA, NO, TAC, GSH, Insulin, HOMA-IR, and hs-CRPwere assessed in both research groups (N=100).Results: There was a significant difference in the status of MDA (P=0.003), NO (P=0.01), TAC(P=0.003), GSH (P=0.001), insulin (P=0.04), HOMA-IR (P=0.02), and hs-CRP (P=0.001)between the study and control groups. Furthermore, there was a significant correlation amongthe duration of illicit opioid use and MDA concentrations (r=-0.424, P=0.002), as well as TAClevels (r=0.314, P=0.02).Conclusion: The study results suggested metabolic profiles were impaired in the study group,compared to the controls

The Effects of Quetiapine on Craving and Withdrawal Symptoms in Methamphetamine Abuse: A Randomized, Double-Blind, Placebo-Controlled Trial

Background: Patients with Methamphetamine Abuse (MA) are susceptible to many complications like craving, and withdrawal symptoms. These trials were designed to evaluate the effect of quetiapine administration on craving and withdrawal symptoms in MA abuse.Methods: This trial was conducted on 60 people with MA abuse to receive either 100 mg quetiapine (n=30), or placebo (n=30) every day for 2 months. The Desire for Drug Questionnaire (DDQ) and Amphetamine Withdrawal Questionnaire (AWQ) scores were evaluated at baseline and after 2 months’ intervention. For data analysis, t test, and the Chi-square test were applied in SPSS v. 18.Results: Quetiapine significantly decreased DDQ (P=0.002) and AWQ symptoms (P=0.001) compared to the placebo. Furthermore, there was a significant difference among groups in terms of the frequency of negative urine tests (P<0.001).Conclusion: This trial showed that administration of quetiapine supplements for 2 months in individuals with MA abuse had beneficial effects on craving and withdrawal syndrome