The piston effect due to train movement in a subway system significantly affects the ventilation system in the stations and connecting tunnels. In this study the train movement in the subway tunnels ...and stations is simulated using a 3-D computational model and the transient airflow field around the train is studied. The unsteady RANS, and energy equations are solved for three dimensional compressible turbulent airflows using the dynamic mesh technique. The numerical model is validated by comparing the predicted results with the experimental data available in the literature. The computational domain consists of four stations and the connecting tunnels. The results show that the airflow rate in the main tunnel in which the train is moving can be quadrupled with respect to the stationary situation and the flow rate in the neighbor tunnel connecting to the next station can be tripled. The results also reveal that the airflow velocity in certain region of the platform can become too high and might give rise to the wind speed discomfort for waiting passengers. Finally, a simplified equivalent axisymmetric model is proposed and the results are compared with those obtained from the 3D model, good agreement is found especially in the tunnels.
•Due to the piston effect, the airflow rate changes in the main and neighbor tunnels.•The piston effect changes flow direction around fan openings; even in neighbor tunnels.•For a higher train acceleration, a larger first pressure rise in tunnel is obtained.•The airflow velocity in some regions of the platform gives rise to the wind discomfort.•A simplified equivalent axisymmetric model can approximately represent a 3D model.
The train movement in a model of the subway tunnels and stations with a side-platform was simulated using a 3D computational model. The impact of airflow mainly due to piston effect on distribution ...of particles generated due to the train braking was investigated. The unsteady RANS and energy equations were solved using the dynamic mesh technique with an Eulerian approach. The particle trajectories were evaluated by the Lagrangian approach. The particle concentration in different points of the station emitted due to the train braking is obtained and discussed. The results showed that when the train enters the station while braking, a fraction of particles is transported and spread into the platform areas. Some of the generated particles, however, were accumulated in the rear of the train. When the train stops, these particles entered into the station due to continuation of the piston effect. The simulations showed that particles would spread in the platform and penetrate into the staircases and ticket hall, and even disperse into the outside ambient. The results also demonstrated that some of the particles released from the braking system of the train could penetrate into the train through the ventilation system of the passenger cars.
•The impact of piston effect on distribution of braking particles in subway system is studied.•Most of the particles were accumulated in the train rear and entered the station due to the piston effect.•Particles spread in the platform, ticket hall, and even reach to the outside ambient.•Particles could penetrate into the train through the ventilation system of passenger cars.•The number of entering particles is reduced along the train from the rear to the head.
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•Indoor ventilation grill cannot well reduce the peak concentration.•UPE system can effectively decrease the particle concentration in subway station.•UPE system can decrease the ...particle concentration by more than 50 percent.
Thermal comfort has been the main target of the ventilation in subway systems. However, pollutant concentration and aerosol dispersion could be the leading health issues in underground metro stations. This study numerically simulated a train movement inside a subway system using the Dynamic Mesh Technique for a 3-D computational domain consisting of four stations and connecting tunnels. The effects of both the ventilation system and the train-induced fluid flow inside the subway system were investigated. Then, the particle generation and dispersion due to train braking are considered, and the impact of the ventilation system on reducing the particle concentration inside the station was investigated. It is shown that the airflow inside the subway system is entirely affected by the piston effect. The airflow generated by the train movement is much higher than that generated by the operation of the ventilation system when only one train passes through the tunnel. The results show that the ventilation system, consisting of the supply and exhaust fans inside the tunnel and supply grilles inside the platform, can reduce the particle concentration by half, except for the platform beside the stopped train when the train enters the station and during half of the train stop time. The other design concept demonstrates that the under-platform exhaust system considerably reduces the concentration of the particles released by the train braking system on the trackside platform.
In this research the train movement along the tunnel was simulated numerically, and the time evolution of the flow field in the tunnel was evaluated. The unsteady forms of continuity, Navier–Stokes, ...and energy equations were applied to solve the compressible viscous flow using the dynamic mesh technique. The importance of using a compressible flow model, even for the low Mach number flow case, was demonstrated. It was shown that even in low train speeds; a compressible model is preferred, as it can predict the pressure fluctuations, while an incompressible model may only be used for predicting the mean pressure. However, in cases where pressure fluctuations are not important, the incompressible model can be used especially for a train with low acceleration. When the trains accelerate, it was found that the initial slope of the pressure curve is proportional to the train acceleration. Therefore, the increase in the train acceleration increases the maximum pressure rise. Also, the first pressure rise depends on the velocity of the train tail as it enters the tunnel. In addition, the earlier equations in the literature for computing the maximum pressure rise, can properly predict the pressure rise of accelerating trains if the train tail velocity, as it enters the tunnel, is used as the velocity scale.
•Health informatics and telematics have been drastically influenced by big data of health IoT devices.•We conducted a qualitative review of scientific literature and a quantitative mapping of ...research trends on IoT Big Data Analytics Paradigm.•Three derivers of IoT BDA convergence are fog computing, data accumulation, and data abstraction.•This paper shows that issues such as security, privacy and data transfer are dominant scientific topics in the fog computing literature.•IoT BDA has sparked the emergence of novel health informatics and telematics systems.
Health informatics and telematics have been drastically influenced by big data of IoT devices. In this paper, we conducted a review of scientific literature and mapping of research trends on IoT Big Data Analytics paradigm (IoTBDA) in healthcare industry. The goal is to identify how the IoT BDA paradigm has impacted the design, development, and application of IoT based innovations in healthcare services. We conducted a qualitative and quantitative review of 46 papers on IoTBDA, and a quantitative review of 84 papers on fog computing in the healthcare industry. This study shows that IoT BDA has impacted the acquisition, storage, retrieval, and use of information in healthcare industry. Consequently, three derivers of IoT BDA convergence are identified. The first driver is computing; which is emerged as a response to reduce data congestion and inefficiencies of emergency systems. As the co-word analysis shows, issues such as security, privacy and data transfer are dominant scientific topics within the domain of fog computing. The second driver of convergence is the storage of IoT big data. This has led the researchers to classify IoT data to critical and non-critical data; while the critical data is sent to fog systems; non-critical data is sent to centralized cloud systems. The third driver of convergence is data abstraction. The study shows that IoT BDA has sparked the emergence of novel health applications and systems. This paper extends the literature on health informatics and telematics and our understanding of everyday practice of these systems in healthcare contexts. Since IoT BDA and fog computing in healthcare are new fields, findings of this study can act as a basis for future studies to determine novel research opportunities on IoT BDA.
Proper monitoring of performance of an alarm system throughout its life cycle is an important factor in safety and reliability of industrial plants. Complexity and extent of modern industrial plants ...and poor design and management of alarm systems, have increased the importance of monitoring of alarm systems. Alarm floods, defined as a large number of alarms triggered in a short interval, is one of the problems that modern complexes are facing regularly. Many researchers have been focusing on this issue both in academia and industry. One approach to deal with alarm flood is analyzing alarms triggered in different floods and finding similar patterns. The identified patterns could help in locating the root cause of an alarm flood. In this paper a modified PrefixSpan sequential pattern recognition algorithm is used to find alarm patterns in different floods. The effectiveness of the algorithm is demonstrated with real alarm floods from a natural gas processing plant.
•Alarm floods (a flood of alarms within a short period of time) are abundant in industry.•It has been observed that some alarm floods are similar; or have similar contributing alarms.•Some methods have been presented to identify similar alarms based on similarity indices and two-by-two comparison.•Presenting a pattern mining algorithm for alarm floods based on PrefixSpan, a sequential pattern mining algorithm.
Background: The high prevalence and associated costs of chronic diseases have challenged global community health. The use of information and communication technology offers patients and healthcare ...providers the ability to respond to healthcare needs. This study develops an information and communication technology use scale for patients with chronic diseases and evaluates its psychometric properties. Methods: This exploratory sequential mixed-method study was conducted in Kashan City, Iran from September 2020 to July 2021. In the qualitative phase, 18 patients with chronic conditions were recruited via the purposeful sampling method. The initial pool of 33 items was extracted in this phase. Psychometric evaluation of the scale was examined through face, content, and construct validity. The internal consistency of the scale was assessed with the Cronbach α and its stability was examined by the test re-test. To evaluate the scale’s construct validity, a cross-sectional study was performed with 300 patients referring to hospital wards and private centers that provide services to patients with chronic diseases. A sequential sampling method was used to select the subjects. The exploratory factor analysis was performed using principal component analysis and the varimax rotation method in the SPSS software, version 16. Results: The initial version of the scale consisted of 33 items. A total of 6 items were removed in the face and content validity evaluation phase. Factor analysis with the removal of 4 items resulted in the extraction of 4 factors that explained 67.45% of the variance in the total scale score. The Cronbach α coefficient and intra-class correlation coefficient were estimated at 0.93 and 0.88, respectively. Conclusion: The scale developed in this study is a valid and reliable tool for measuring formation and communication technology use (including its dimensions) in patients with chronic diseases.
Background and purpose: Pedestrians are the most vulnerable people in the traffic system, and every year, thousands of pedestrians are injured or killed due to traffic accidents. The behavior of ...pedestrians is one of the human factors involved in traffic accidents. In the meantime, the damage is more in the age groups of 60 years and older. Therefore, the present study was conducted to determine the relationship between fear of falling and functional status with the traffic behavior of older pedestrians in Sari City. Materials and methods: The present study is a descriptive-analytical cross-sectional study conducted in Sari City in 2022-2023. The research population included all the older adults aged 60 years and above receiving care in the health centers of Sari City. Considering the inclusion and exclusion criteria and using multistage random sampling, 630 people were determined as the sample size. In order to collect information, a demographic characteristics questionnaire, Womac functional status questionnaire, fear of falling (FES), and traffic behavior were used. The data was analyzed using SPSS-22 statistical software. The significance level of the tests was considered to be 0.05. Results: The average age of the older participants was 67.34±7.61 years. 53.2% of the participants were male, and 71.4% were married. The mean and standard deviation of functional state scores, fear of falling, and traffic behavior were 56.65±17.62, 30.69±12.56, and 106.06±12.01, respectively. Correlation results showed that functional status has a significant inverse correlation with fear of falling (P<0.001 and r=-0.22) and a positive significant correlation with traffic behavior (P<0.001 and r=0.32). No significant relationship was observed between fear of falling and traffic behavior. Gender, marital status, duration of walking, means of transportation, and age had a significant relationship with fear of falling and functional status (P<0.001). Walking time was significantly related to traffic behavior (P<0.001). Conclusion: According to the results of the study, the fear of falling and the physical condition of the older have an effect on their traffic behavior, which may increase the number of accidents in this group, so it is important to pay attention to this issue among drivers.
Curcumin is a multitherapeutic agent with great therapeutic potential in central nervous system (CNS) diseases. In the current study, curcumin was encapsulated in solid lipid nanoparticles (SLNs) and ...nanostructured lipid carriers (NLCs) for the purpose of increasing brain accumulation. The preparation processes have been optimized using experimental design and multiobjective optimization methods. Entrapment efficiency of curcumin in SLNs and NLCs was found to be 82% ± 0.49 and 94% ± 0.74, respectively. The pharmacokinetic studies showed that the amount of curcumin available in the brain was significantly higher in curcumin-loaded NLCs (AUC0‑t = 505.76 ng/g h) compared to free curcumin (AUC0‑t = 0.00 ng/g h) and curcumin-loaded SLNs (AUC0‑t = 116.31 ng/g h) (P < 0.005), after intravenous (IV) administration of 4 mg/kg dose of curcumin in rat. The results of differential scanning calorimetry and X-ray diffraction showed that curcumin has been dispersed as amorphous in the nanocarriers. Scanning electron microscopy images confirmed the nanoscale size and spherical shape of the nanoparticles. The DPPH (2,2-diphenyl-1-picrylhydrazyl) free radical scavenging study indicated that preparation processes do not have any significant effect on the antioxidant activity of curcumin. The results of this study are promising for the use of curcumin-loaded NLCs in more studies and using curcumin in the treatment of CNS diseases.
Background: Non-adherence to treatment prevents the effectiveness of therapeutic interventions. This study aims to evaluate the role of Information and Communication Technology (ICT) use in ...predicting adherence to treatment in patients with Coronary Artery Disease (CAD). Methods: This cross-sectional study with a predictive correlational design was performed on 400 patients with CAD referred to public and private cardiology clinics in Kashan, Iran, from September 2020 to July 2021. A consecutive sampling technique was used to select the samples. The treatment adherence questionnaire and a researcher-made Information and Communication Technology Use (ICTU) scale were used to collect the data. Data were analyzed in SPSS version 16 using independent t-test, Pearson correlation test, and linear regression analysis. Results: The mean scores of treatment adherence and ICTU was 64.5±13.4 and 42.22±16.6, respectively. Linear regression analysis showed that the two ICTU domains of familiarity with ICT and the utilization of ICT together predicted 27% of variance in treatment adherence of CAD patients (P<0.001). Conclusion: Adherence to treatment is higher in patients who use ICT tools for health purposes. It is recommended that health professionals and policymakers develop appropriate interventions to increase the ability of CAD patients to use ICT.
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