Introduction: Anesthetic gases are very important for health among health care worker (HCWs) and patients in medical centers. Operating rooms (ORs) is the most important ward that use anesthetic gases. Isoflurane gases is very dangerous for HCWs. Objective: In this study, we have associated the concentration of anesthetic toxic isoflurane gases (ppm) and the health risk assessment due to exposure to common anesthetic gases in Valiasr and Shahid Beheshti teaching hospital operating room during 2018. Methods: In this study, we used the active sampling system by portable pump SKC and tubes (sorbent Tube Tenax TA 250 mg) for detection of isoflurane concentration (ppm). Different points of the operating rooms were selected for sampling. Hazard index (HI) quantified by calculating the non-cancer causing anesthetic toxic isoflurane gases. Results: According result this study, the Valiasr and Shahid Beheshti had the lowest and the highest level of isoflurane. Based on result this study, level of isoflurane on indoor air quality in the operation room in Valiasr and Shahid Beheshti hospital were 2.129 and 2.436 ppm, respectively. According to the results from the current study, hazard index was under 1.0 and it amount showed that no significant risk of adverse health endpoint attributed to exposure to level of isoflurane in Valiasr and Shahid Beheshti teaching hospital operating room during 2018. Conclusion: According Result this study the average concentration of isoflurane and the health risk assessment in Valiasr and Shahid Beheshti teaching hospital operating room during 2018 because of flaw in the ventilation system was significantly higher than standard.

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This paper presents the development of a metal oxide semiconductor (MOS) sensor for the detection of volatile organic compounds (VOCs) which are of great importance in many applications involving either control of hazardous chemicals or noninvasive diagnosis. In this study, the sensor is fabricated based on tin dioxide (SnO2) and poly(ethylene oxide) (PEO) using electrospinning. The sensitivity of the proposed sensor is further improved by calcination and gold doping. The gold doping of composite nanofibers is achieved using sputtering, and the calcination is performed using a high-temperature oven. The performance of the sensor with different doping thicknesses and different calcination temperatures is investigated to identify the optimum fabrication parameters resulting in high sensitivity. The optimum calcination temperature and duration are found to be 350 °C and 4 h, respectively and the optimum thickness of the gold dopant is found to be 10 nm. The sensor with the optimum fabrication process is then embedded in a microchannel coated with several metallic and polymeric layers. The performance of the sensor is compared with that of a commercial sensor. The comparison is performed for methanol and a mixture of methanol and tetrahydrocannabinol (THC) which is the primary psychoactive constituent of cannabis. It is shown that the proposed sensor outperforms the commercial sensor when it is embedded inside the channel.

Autonomous Land Vehicles (ALV) shall efficiently recognize the ground in unknown environments. A novel GPbased method is proposed for the ground segmentation task in rough driving scenarios. The non-stationary covariance function proposed by [1] is utilized as the kernel for the GP. The ground surface behavior is assumed to only demonstrate local-smoothness. Thus, point estimates of the kernel's length-scales are obtained. Thus, two Gaussian processes are introduced to separately model the observation and local characteristics of the data. While, the observation process is used to model the ground, the latent process is put on lengthscale values to estimate point values of length-scales at each input location. Input locations for this latent process are chosen in a physically-motivated procedure to represent an intuition about ground condition. Furthermore, an intuitive guess of length-scale value is represented by assuming the existence of hypothetical surfaces in the environment that every bunch of data points may be assumed to be resulted from measurements from this surfaces. Bayesian inference is implemented using maximum a Posteriori criterion. The log-marginal likelihood function is assumed to be a multitask objective function, to represent a whole-frame unbiased view of the ground at each frame. Simulation results shows the effectiveness of the proposed method even in an uneven, rough scene which outperforms similar Gaussian process based ground segmentation methods. While adjacent segments do not have similar ground structure in an uneven scene, the proposed method gives an efficient ground estimation based on a whole-frame viewpoint instead of just estimating segmentwise probable ground surfaces.

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