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All-electric ships (AES) are regarded as a promising solution for decreasing greenhouse gas emissions since they are able to utilize clean technologies like fuel cells instead of fossil fuel. A zero-emission hybrid energy system comprising the Proton Exchange Membrane (PEM) fuel cell, battery, cold-ironing, and Recuperative organic Rankine cycle (RORC) is scrutinized in this paper. To use the waste heat produced by PEM fuel cells, a hybrid system which is composed of PEM fuel cells and RORC is suggested. In order to analyze this zero-emission all-electric ship, the real data of a ferry boat, containing the load profiles and routes is used to appraise the possibility of the suggested hybrid system. The structure of the boat and energy sources, along with mathematical models, are represented and examined. Eventually, the hourly energy management of the boat for one specific day is analyzed, and also, to optimize the power dispatch, the Deep Deterministic Policy Gradient (DDPG) is applied in actor-critic architecture. To corroborate the proposed models, the MATLAB software is employed. According to obtained results, the proposed models could ensure effective and efficient ways in marine vessels due to not only their high performances but also permissible energy cost to be a zero-emission ship.

Fuel cell-based hybrid electric vehicles are one of the most promising options to achieve zero-emission city buses. Efficient Energy Management (EM) plays a critical role to make such buses more efficient and practical. In this research, an available all-electric bus consisting of fuel cell (FC) and battery is considered and the efficiency of adding a Wireless Power Transfer (WPT) system to it is assessed. The proposed WPT system is only capable to receive energy in bus stations and use it to supply loads or charge the battery. To this end, the actual data of a city bus, its route and load profile were collected and utilized to ensure a realistic assessment. A full mathematical model of the energy system as well as the constraints governing the management issue is extracted and a Deep Deterministic Policy Gradient (DDPG) method is used to optimally manage the energy flows for the entire journey. All models are implemented in MATLAB software and the efficiency of the proposed system is investigated from economic and technical aspects. The results illustrate a high efficiency for the proposed WPT technique to be used in actual all-electric city buses.

The negative impedance characteristic of the constant power loads (CPLs) causes instability in DC/DC converters in the DC microgrids. To improve the stability of the DC/DC converter feeding CPLs, a robust and fast controller is required. This paper presents a robust pulse-width modulation-based type-II fuzzy controller for a DC/DC boost converter feeding the CPL in a DC microgrid. Theoretical analysis and real-time simulation are presented to demonstrate the effectiveness of the proposed non-integer intelligent controller. Finally, the experimental results demonstrate that the proposed intelligent controller for the DC/DC converter has a faster and more robust response in comparison to the previously suggested control techniques.

Today’s remarkable challenge of maritime transportation industry is the detrimental contamination generation from fossil fuels. To tackle such a challenge and reduce the contribution into air pollution, different power solutions have been considered; among others, hybrid energy-based solutions are powering many ferry boats. This paper introduces an energy management strategy (EMS) for a hybrid energy system (HES) of a ferry boat with the goal to optimize the performance and reduce the operation cost. HES considered for the ferry boat consists of different devices such as proton exchange membrane fuel cell (PEMFC), LI-ION battery bank, and cold ironing (CI). PEMFC systems are appropriate to employ as they are not polluting. The battery bank compensates for the abrupt variations of the load as the fuel cell has a slow dynamic against sudden changes of the load. Also, CI systems can improve the reduction of the expenses of energy management, during hours where the ferry boat is located at the harbor. To study the performance, cost and the pollution contribution CO2, NOX, SOX of the proposed hybrid energy management strategy (HEMS), we compare it against three various types of HEM from the state-of-the-art and also available rule-based methods in the literature. The analysis results show a high applicability of the proposed HES. All results in this paper have been obtained in the MATLAB software environment.

X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learning-based object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis., Comment: 32pages, 20 figures, 12 tables. A literature review paper. Journal

Introduction: Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders diagnosed by impairments in social interaction and communication with repetitive and restrictive stereotyped behavioral patterns. The Prevalence of autism has been reported to be increased in recent years. This study aimed to assess the prevalence of different types of malocclusion among ASD patients in Isfahan in 2018. Materials and Methods: In a descriptive and cross-sectional trial, 92 ASD patients were studied in the age range of 7-18 years at the center for autism patients in Isfahan. Clinical oral examinations of patients are taken to assess the involved malocclusions (Cl I, Cl II and Cl III malocclusions) and malocclusion traits (deep bite, open bite and cross bite) by an educated dental student under the supervision of an orthodontist under natural light. The data are reported using frequency and percentage indices. Results: Class I malocclusion had the highest prevalence 54.3% (50) among ASD patients and the prevalence of class II and class III were found to be 19.6% (18) and 7.6% (7) respectively. The frequency of malocclusions traits of deep bite, cross bite and the open bite were 27.2% (25), 18.5% (17) and 7.6% (7) respectively. Among of the total patients, 65.2% (60) showed normal bite and 18/5% (17) showed Normal occlusion. Conclusion: ASD patients showed class I, class II and class III malocclusions from the most to least frequency and the most frequent malocclusion traits were also deep bite, cross bite and open bite respectively. Keywords: Autism spectrum disorder, Malocclusion, Prevalence.

Introduction: The placement of an orthodontic separator induces pain and discomfort inpatients. The use of Nsaids for its analgesic effect was along with side effects. The aim of study evaluation of the effect of low power laser therapy on pain reduction and NO level in GCF induced by separator force in orthodontic treatment. Materials and Methods: This experimental laboratory study was performed on 30 patients in the age range of 15-35 who required placement of the separator on the right maxillary molars. Patients were randomly divided into two groups of laser and placebo. Gingival groove fluid sampling was performed on 0-3-0 days in both groups. Laser irradiation was performed on days 2 and 4, in the placebo group, the device was used off. The amount of pain was recorded by patients with a VAS questionnaire on a daily basis. Data were analyzed by t-test and ANOVA (α = 0.05). Results: VAS score in laser group was more than placebo group but it wasn’t statistically different (p value = 0.067), NO level further decrease in laser group compared with placebo group but it wasn’t statistically different (p value = 0.286). Conclusion: The effect of low level laser therapy on pain reduction was very low and it wasn’t statistically different Keywords: Laser, Nitric oxide, Orthodontics.

Introduction: Since orthodontic removable appliances might be infected with fungi and bacteria, they need to be cleaned using natural disinfectants and chemicals. This study aimed to compare the clinical impact of thyme essence and Fluconazole solution on the amount of Candida albicans colonies and Streptococcus Mutans accumulated on the removable appliances in orthodontic patients. Materials and Methods: In this descriptive-analytical study, 21 patients were selected who used the wear Hawley’s retainer for 24-hours. Patients were divided into three groups of seven. One group first washed the plaque with soap and water and then spray thyme essence all over it at a 5-centimeter distance (4 times on each side). In the other group, as before, the plaque was washed and from the same distance as before, the fluconazole solution (10 mg/ml) was sprayed on all surfaces 4 times. In the control group, the retainer was washed every night only with soap and water. After one month, the retainers were delivered form patients. The two samples were then separated from the Kami platelets and sent to a laboratory for microbial culture. Data were analyzed by Mann-Whitney, T-test (α = 0.05). Results: The use of thyme essence increases the mean area of Candida albicans inhibition, which was significantly higher than that in the control group (p value = 0.03). The effect of Fluconazole solution on this mean area) Candida albicans inhibition zone( was not significant compared to that observed in the control group (p value = 0.5). In the group in which thyme essence was used, the mean area of Streptococcus mutans inhibition, which was significantly higher than that in the control group (p value = 0.05). When Fluconazole solution was used, the mean area of Streptococcus mutans inhibition, was not significant compared to that observed in the control group (p value = 0.071). Conclusion: Compared to Fluconazole solution, thyme essence functions more effectively in increasing Candida and Streptococcus Mutans inhibition zones. Keywords: Candida albicans, Streptococcus mutans, Orthodontic appliances.

Background: Exposure to news on coronavirus disease 2019 (COVID-19) pandemic is inevitable. This study aimed to explore associations between exposure to COVID-19 news on social media and psychological and behavioral consequences among Iranians.Methods: This was a telephone-based survey carried out in April 2020 in Iran. Iranian adults aged 18 and over were randomly selected from the postal codes and using their mobile phones. A self-designed questionnaire was administered to collect data on participants’ characteristics and questions to address their psychological and behavioral responses regarding the COVID-19 pandemic. A multivariate logistic regression model was performed to assess the relationship between anxiety, fear, and behavioral responses and independent variables including exposure to news.Results: In all 1563 adults participated in the study. The mean age of respondents was 39.17±13.5 years. Moderate to high-level anxiety was reported by 55.4% of participants, while fear of being affected by coronavirus was reported by 54.1%. Overall 88% reported that they changed their behaviors to some extent. Exposure to the COVID-19 news on social media was by far the most influencing variable on anxiety (OR= 2.21, 95% CI: 1.62-3.04; P < 0.0001), fear (OR = 1.95, 95% CI: 1.49-2.56; P < 0.0001), and change in health behaviors (OR = 2.02, 95% CI: 1.28-3.19; P = 0.003) in the regression model. While independent variables did not show significant associations with anxiety and behavior change, the fear of being infected by the COVID19 was associated with the female gender and some socio-economic characteristics of the participants.Conclusion: Although exposure to the COVID-19 news on social media seemed to be associated with excessive anxiety and fear among people, it also, to some extent, positively has changed people’s health behaviors towards preventive measures.

Introduction: Antidepressant drugs are the most commonly prescribed classes of pharmacologic agents in the public. The drugs have been shown to have a role in the regulation of bone cell function and as a result affecting the orthodontic tooth movements. The aim of this study was to determine the effect of Amitriptyline and Sertraline on the tooth movement, root resorption and alveolar bone remodeling after load application in dogs. Materials and Methods: In this experimental study, 9 male dogs were randomly divided into three groups, first group sertraline, second group Amitriptyline and the control group (normal saline). A nickel titanium spring (200 gr) was used between second premolar and canine after 1st premolar extraction. After 2 months, the reduction of distance between 2nd premolar and canine was measured. The percentages of root resorption and bone formation were determined. The data were analyzed using repeated measures analysis at significance level of 0.05. Results: In the three groups of amitriptyline, sertraline and control, the mean of teeth movement (p value = 0.483), external root resorption (p value = 0.608), total bone mineral density (p value = 0.078), bone formation percentage (p value = 0.616) and immature and lamellar bone formation (p value = 0.083), there was no statistical difference in any group and in premolar teeth Conclusions: The rate of tooth movement and the percentages of bone formation and root resorption in dogs decreased with systemic administration of amitriptyline and sertraline; although this reduction was not statistically significant in comparison with control group. Key words: Bone remodeling, Antidepressive Agents, Root resorption

The power system of an all-electric ship (AES) establishes an independent microgrid using the distributed energy resources, energy storage devices, and power electronic converters. As a hybrid energy system (HES), the power system of an AES works as a unified system where each part can affect the reliability of the other parts. The systemic reliability centered maintenance (SRCM), which efficiently enhances the reliability and safety of the AES by identifying optimal maintenance tasks of the AES, is considered in this article to apply to the entire system. In order to calculate the reliability and optimal maintenance schedule, the Markov process and Enhanced JAYA (EJAYA) are utilized. A layer of protection analysis (LOPA), which is a risk management technique, is adopted to assess the safety of the system. A hybrid molten carbonate fuel cell, photovoltaic (PV), and lithium-ion battery are considered as energy sources of the AES. Based on two common standards, DNVGL-ST-0033 and DNVGL-ST-0373, the suggested maintenance planning method can be used in industrial applications. Eventually, in order to validate the proposed method, a model-in-the-loop real-time simulation using dSPACE is carried out. The obtained results show the applicability and efficiency of the proposed method for improving reliability and safety.

Due to the increasing impacts of ships pollutants on the environment and the preventive laws that are tightening every day, the utilization of all-electric ships is a recent emerging technology. Being a promising technology, the usage of fuel cells as the main energy resource of marine vessels is an interesting choice. In this article, an all-electric hybrid energy system with zero emission based on fuel cell, battery, and cold-ironing is proposed and analyzed. To this end, actual data of a ferry boat, including load profiles and paths, are considered to assess the feasibility of the proposed energy system. The configuration of the boat and energy resources as well as the problem constraints are modeled and analyzed. Finally, the boat's energy management in hourly form for a one-day period is implemented. The improved sine cosine algorithm is used for the power dispatch optimization, and all models are implemented in MATLAB software. Based on the analysis results, the proposed hybrid system and the energy management method have high performance as an applicable method for the marine vessels. In addition, to be a zero-emission ship, the proposed system has an acceptable energy cost.

Environmental pollutants, as a global concern, have led to a general increase in the utilization of renewable energy resources instead of fossil fuels. Accordingly, the penetration of these resources in all-electric ships, as well as power grids, has increased in recent years. In this article, in order to have a zero-emission and cost-effective energy management in an all-electric ferry boat, a new reliable and optimal power scheduling is presented that uses fuel cell and battery energy storage systems. Furthermore, the real information including load profile and paths is considered for the case study to assess the feasibility and superiority of the proposed approach. In addition to the cost of energy management, to have a reliable combination of the proposed resources, the loss of load expectation (LOLE) as a reliability index is considered in the energy management context and the problem is solved by the deep reinforcement learning in a multiobjective manner. The results of the consideration of two common standards, including DNVGL-ST-0033 and DNVGL-ST-0373, demonstrate that the proposed energy management method is applicable in industrial applications. Finally, the real-time simulation-based hardware-in-the-loop (HIL) is conducted to validate the performance and efficacy of the suggested power scheduling for the emission-free ships.

This brief proposes a novel approach to apply reliability centered maintenance (RCM) on a distance protection system in a smart grid. In the proposed approach, instead of focusing on individual equipment, the RCM is implemented for the whole protection system. The approach helps to calculate the reliability of protection systems by overcoming the limitation of their hidden function and also considering the effect of all system equipment on reliability. Markov process and improved Jaya algorithm (IJA) are used to calculate the reliability and find the optimal maintenance plan. The proposed approach has been applied to real data of smart grid distance protection systems, which results in a higher level of reliability and less cost. Furthermore, in order to validate the suggested technique and its efficiency, the simulation is carried out using a real-time digital simulators and hardware-in-the-loop testing equipment known as opal-RT technology.

Due to environmental and economic issues as well as the high performance of marine vessels, efficient energy using has been becoming more demanding. Also, in order to have a zero-emission ship, the utilization of a fuel cell combined with energy storage such as batteries gets more and more attention. In this work, a zero-emission hybrid energy system, including fuel cells, batteries, and cold-ironing, is employed to have an environmentally friendly vessel, and to create condition in which ship operates with high performance, both energy management and components sizing of fuel cells and batteries using real data of ferry boat and intelligent optimization method are done simultaneously. In addition, all constraints related to energy management and component sizing with the topography of the boat and electric power sources are represented and analyzed thoroughly. Ultimately, hourly energy management and component sizing for one specific day are considered in this work, and to optimize this problem, the Improved Sine Cosine Algorithm (ISCA) is utilized. According to obtained results, the proposed energy management and component sizing result in the high-performance ship which could be utilized in the marine industry.

In this communication, the computational fluid dynamics (CFD) technique has been employed to study the influence of coiled wire inserts on the Nusselt number, friction coefficient and overall efficiency in double pipe heat-exchangers. For this purpose, some wire coil inserts fitted inside heat-exchangers were meshed and simulated at various Reynolds numbers by using the CFD softwares of Gambit and Fluent. To fulfill reliable and validated results, a big effort was made to generate structural hexahedral meshes over all of the heat-exchanger geometries. The validated models, then, made clear the object and the conditions which could exactly facilitate heat transfer throughout double pipe heat-exchangers. The outcome of this work indicates that taking the advantage of proper wire coils could improve the Nusselt values to 1.77 times. Following the numerical simulation, proper friction coefficient and Nusselt number correlations for various coiled wire inserts with different geometry arrangements under the laminar flow were proposed. Unlike the preceding relations, the correlations of this work are based on the occupied spaces where helical wires take up inside tubes; therefore, the two modified correlations can both be used for non-uniform helical wire insert geometries.

The proper operation of a ventilation system plays a key role in tunnel safety. Foremost, the ventilation system needs to provide acceptable air quality for the safe passage of tunnel users. Further, it needs to provide tenable environment and to facilitate rescue conditions during a smoke or fire event. While accomplishing the first task (normal operation), i.e. providing sufficient fresh air, is relatively straightforward, dealing with the second issue is the subject of considerable debate since defining the best means to ventilate a tunnel during a fire emergency is not always clear. Although fire tests in tunnels have been performed since the early 1960s, and although the topic of fire ventilation was raised in early national and international guidelines, relatively little interest was given to fire ventilation until several big fire events occurred in the 1990s. The tunnel ventilation systems and ventilation methodologies existing at that time proved to work well under normal operation, but failed during fire ventilation. Nowadays, the design and operation of the ventilation system during fire incidents (commonly called ‘fire ventilation’) is a major topic. While the design might follow the well-established principles, the question, ‘how to control tunnel ventilation during a fire event?’, is quite controversial. This paper discusses methods of fire ventilation with a focus on the methodologies themselves as well as on the requirements for sensors and control technologies.

In restructured markets where transactions process is competitive, forecasting of electricity price is inevitably an important available tool for market participants. Due to the sensitivity of forecasting issues in market's performance, and high prediction error resulted from the behavior of price series, nowadays probabilistic forecasting highly attracted participants’ attention. In this paper, a probabilistic approach for the hourly electricity price forecasting is presented. In the proposed method, the uncertainty of predictor model is considered as the uncertainty factor. The bootstrapping technique is used to implement the uncertainty and since the method is needed to be fast and of low computational cost in the daily forecasting, a generalized learning method is applied, which has high accuracy and speed. This newly presented learning method is based on generalized extreme learning machine approach to be used for improved wavelet neural networks. Also in order to reach more accommodation, the predictor model with the changes of price time series, the wavelet preprocessing is used. Effective performance of the proposed model is validated by testing on data of Ontario and Australian electricity markets.

To simplify decision making of market participants, a careful and reliable electricity market price forecasting method is indispensable. Nevertheless, due to the Instability in market clearing prices (MCPs), it is rather tough to forecast MCPs accurately. Using probabilistic forecasting is a new solution to overcome the low accuracy of forecast. Transformation from traditional point forecasts to probabilistic interval forecasts is too important to model the uncertainties of forecasts. Thus the decision making activities of market participants are supported against uncertainties and risks effectively. In this paper a hybrid approach to achieve prediction intervals (PIs) of MCPs is proposed that modified dolphin echolocation optimization algorithm (MDEOA) is applied to estimate point forecasts, model uncertainties, and noise variance. This proposed electricity price probabilistic forecasting method is evaluated by a generalized and comprehensive framework. To test the proposed hybrid method, real price data from Ontario, New England, and, Australian electricity markets are used and effectiveness of the method is validated.

Probabilistic forecasting is an appropriate tool that helps electricity markets participants to improve their decision making. Due to changes in electricity prices, the point forecasting accuracy cannot be guaranteed. Hence, participants are more interested in results of probabilistic forecasting methods such as prediction intervals method. In this paper, a hybrid approach for probabilistic electricity price forecasting is presented. This model is based on using improved clonal selection algorithm and extreme learning machine for neural networks training process and wavelet preprocess. The wavelet is utilized to decompose data into well behaved subsets, which increases accuracy of the model. Also, due to the high required computational time for training the neural networks, autocorrelation function is used to reduce the number of neural networks inputs. Finally, in order to evaluate the proposed probabilistic forecasting method, the Ontario and Australian electricity markets data are used.

All-electric ships, and especially the hybrid ones with diesel generators and batteries, have attracted the attention of maritime industry in the last years due to their less emission and higher efficiency. The variant emission policies in different sailing areas and the impact of physical and environmental phenomena on ships energy consumption are two interesting and serious concepts in the maritime issues. In this paper, an efficient energy management strategy is proposed for a hybrid vessel that can effectively consider the emission policies and apply the impacts of ship resistant, wind direction and sea state on the ships propulsion. In addition, the possibility and impact of charging and discharging the carried electrical vehicles’ batteries by the ship is investigated. All mentioned matters are mathematically formulated and a general model of the system is extracted. The resulted model and real data are utilized for the proposed energy management strategy. A genetic algorithm is used in MATLAB software to obtain the optimal solution for a specific trip of the ship. Simulation results confirm the effectiveness of the proposed energy management method in economical and reliable operation of the ship considering the different emission control policies and weather condition impacts.

Researchers from all over the places have been reporting their experimental and simulation studies on thermal analysis of enhanced heat-exchangers, such as coiled wire inserts. If those researchers decide to optimize the heat-exchangers' performances under specific conditions, inevitably they will be in need of extracting an empirical or semi-empirical correlation to facilitate their calculations. Developing a correlation for a complex thermal system with loads of variables such as coiled wire inserts is truly hard and somewhat an impossible task. This paper steps forward to reveal the success of artificial neural network (ANN) and genetic algorithm (GA) together in this especial case. To do so, a three dimensional numerical simulation of the fluid flow under non-isothermal condition was initially proposed using computational fluid dynamics (CFD) method. After validation of the numerical model, twelve wire coil inserted tubes were tested through the validated model to get their heat transfer and friction coefficients at specific Reynolds ranges. Then, a prosperous ANN configuration was chosen for simulating the heat-exchangers and then obtaining a continuous function to get valuable output data with any values of input variables. Finally, to answer which type of the heat-exchangers works better, the optimization technique of GA was used along with the ANN's model. Results reveal the fact that while a suitable helical wire empowers the heat transfer efficiency, wrong choice of wire inserts may decrease the overall enhancement efficiency of the heat-exchanger unexpectedly.

Competitive transactions resulting from recent restructuring of the electricity market, have made achieving a precise and reliable load forecasting, especially probabilistic load forecasting, an important topic. Hence, this paper presents a novel hybrid method of probabilistic electricity load forecasting, including generalized extreme learning machine for training an improved wavelet neural network, wavelet preprocessing and bootstrapping. In the proposed method, the forecasting model and data noise uncertainties are taken into account while the output of the model is the load probabilistic interval. In order to validate the method, it is implemented on the Ontario and Australian electricity markets data. Also, in order to remove the influence of model parameters and data on performance validation, Friedman and post-hoc tests, which are non-parametric tests, are applied to the proposed method. The results demonstrate the high performance, accuracy, and reliability of the proposed method. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

safety is considered as one of the major objectives in design, construction and specially tunnel operation. The hot and toxic smoke propagation is a fundamental reason of human losses during a fire inside the tunnels. Therefore, an exact analytical investigation on the probable fire scenarios and its destructive effects for passengers, vehicles and structure are the main part of the tunnel safety measures. The complications of tunnel fires can be reduced, using a suitable hot smoke management regime through the standard safety plan. In a unidirectional traffic tunnel, upstream of the fire is usually a place where people and vehicles are trapped and can leave the tunnel during a fire only via egress ways. Meanwhile, downstream the fire people and vehicles in most cases will have a chance to leave the tunnel. In order to have a full view of the problem and to find a proper solution, numerous effective parameters in smoke control and temperature distribution have to be considered in design phases. A sufficient longitudinal air speed is one of the most important parameters in the hot smoke management. Mentioned longitudinal airflow creates a safe place upstream the fire through preventing smoke back-layering from the location of the fire. The critical velocity is known as a required volume flow to prevent smoke back-layering from the location of the fire. The mentioned critical velocity is influenced by the heat release rate (HRR) of the fire, tunnel slope and structural characteristics. The implementation of small size fire tests is an easy and cheap way to see the performance of safety installations, smoke back-layering, and airflow and temperature distributions. However, as in most cases a full- scale fire tests is not easily feasible, CFD simulations can be a great opportunity to investigate the tunnel ventilation in a fire incident. In this numerical study, CFD simulation is employed, to demonstrate the effect of the required critical velocity during the fire with high heat release rates, in a tunnel with different longitudinal slopes. The results are compared with other experimental data and numerical studies. Keywordsventilation, Smoke distribution, Critical velocity

mechanical ventilation system plays a major role in tunnels safety. Over the world, different ventilation systems regarding to the tunnel geometry and other parameters are used for tunnel ventilation in the normal and fire operations. However, in short tunnels depending on the tunnel geometry and traffic condition, there is the possibility of passing traffic through a tunnel without mechanical ventilation. Normally, natural ventilation is applied in short tunnels. In longer tunnels, additional measures in geometry such as openings or shafts are necessary to support natural ventilation. Due to the high costs of the electro-mechanical installations, maintenance and electricity power consumption for mechanical ventilation, having tunnels with natural ventilation system will be the best option. But, reliability of such ventilation systems in case of emergency must be substantiated before applying to tunnels. The main concern is a proper smoke evacuation in a fire case. In this survey, in order to set up a suitable CFD model, numerical simulation of a pre-conducted fire test by using same boundary conditions is done. These simulations were further extended to fires with higher capacities to show smoke and temperature behavior during the higher fires. The newest version of the FDS software (Fire Dynamic Simulator 6) was employed for simulations. Keywordsventilation, Tunnel fire, Fire simulation, Smoke propagation, FDS 6

Background: Antidepressant drugs such as fluoxetine are of the most commonly used drugs among the public. These drugs may impact the regulation of bone cell functioning, and thus affect orthodontic tooth movement. The aim of this study was to determine the effect of fluoxetine on tooth movements during orthodontic treatment in rats. Materials and Methods: In this study, 30 male rats were randomly assigned into two groups and injected with fluoxetine 10 mg/kg (experimental group) and normal saline (control group) for a period of 1-month intraperitoneally 5 times/week. Then, the rats were anesthetized and a nickel-titanium closed-coil spring was placed between the left maxillary first molar and left maxillary central incisors of all samples, and then fluoxetine (experimental group) and normal saline (control group) were injected for another 3 weeks by the same method. After measuring tooth movements, rats were sacrificed, and histomorphometric analyses were conducted and the obtained data were statistically analyzed using independent t-test and the significance was set at 0.05. Results: Following the fluoxetine injection, the mean amount of tooth movements in the experimental group was reduced compared to the control group, which was not statistically significant (P = 0.14). There was no significant difference between the two groups regarding bone apposition rate (P = 0.83), external root resorption rate (P = 0.1), and mean number of root resorption lacunae (P = 0.16). Conclusion: Within the limitations of this study, systemic use of fluoxetine may cause insignificant reduction of tooth movement rate in rats; however, this subject needs more evaluations.