scholarly journals Real-Time Ozone Sensor Based on Selective Oxidation of Methylene Blue in Mesoporous Silica Films

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3508 ◽  
Author(s):  
Christelle Ghazaly ◽  
Marc Hébrant ◽  
Eddy Langlois ◽  
Blandine Castel ◽  
Marianne Guillemot ◽  
...  
Keyword(s):  
Methylene Blue ◽  
Mesoporous Silica ◽  
Real Time ◽  
Detection System ◽  
Personal Exposure ◽  
Measurement Range ◽  
Robust Detection ◽  
Limit Value ◽  
Exposure Limit ◽  
The Usa

Sensitive and selective personal exposure monitors are needed to assess ozone (O3) concentrations in the workplace atmosphere in real time for the analysis and prevention of health risks. Here, a cumulative gas sensor using visible spectroscopy for real-time O3 determination is described. The sensing chip is a mesoporous silica thin film deposited on transparent glass and impregnated with methylene blue (MB). The sensor is reproducible, stable for at least 50 days, sensitive to 10 ppb O3 (one-tenth of the occupational exposure limit value in France, Swiss, Canada, U.K., Japan, and the USA) with a measurement range tested up to 500 ppb, and insensitive to NO2 and to large variation in relative humidity. A model and its derivative as a function of time are proposed to convert in real time the sensor response to concentrations, and an excellent correlation was obtained between those data and reference O3 concentrations. This sensor is based on a relatively cheap sensing material and a robust detection system, and its analytical performance makes it suitable for monitoring real-time O3 concentrations in workplaces to promote a safer environment for workers.

scholarly journals Assessment of Occupational Dust and Silica Exposure in Indian Stone Mining and Crushing Unit- A Case Study

2017 ◽  
Vol 12 (3) ◽  
pp. 661-669
Author(s):  
Anand Deshmukh ◽  
Nikhil Pradip ◽  
Sarang Dhatrak ◽  
Subroto Nandi
Keyword(s):  
Personal Exposure ◽  
Mining Area ◽  
Silica Content ◽  
Vital Role ◽  
Dust Exposure ◽  
Silica Exposure ◽  
Free Silica ◽  
Limit Value ◽  
Exposure Limit ◽  
Precautionary Measures

Stone crushing industry plays a vital role in the economy and urban development of fast developing countries like India. Stone mines and crushers in India are located around major cities and roughly employ around 5,00,000 peoples throughout the country. However this employment generating industry also happens to be one of the most dust generating activity and also a precursor to the respiratory disease, silicosis. This study was undertaken with an objective to estimate the personal exposure of the workers to silica laden dust in this industry sector. Personal dust sampling (n=11) and (n=6) was carried out in stone crushing and stone mining (quarry)areas respectively over a period of three consecutive days in selected units in a suburban area of Nalgonda district of Telangana state in India. The respirable dust exposure and free silica content was then estimated. It was observed that three (3) samples of crusher helper from the Crushing Unit had exposures exceeding the Permissible Limit Value (PLV) of Indian Factories Act1948. Two (2) Heavy Earth Moving Machineries (HEMM) operators from stone mining area were observed to have exceeded the Permissible Maximum Exposure Limit (PMEL) prescribed by the Indian Mines Act 1952 and subsequent rules their under. The remaining samples of HEMM operators from mining area and of the crusher helper from the crusher plant were observed to be within the prescribed limits of respective guidelines prescribed by the Indian statutory agencies. Two different acts were considered, because of the fact that stone mining is regulated by the Indian mining act and under the overall control of Directorate of Mines and Safety (DGMS), Government of India. On the other hand Crusher plant comes under the ambit of Model Factory Rule 120 under section 87 of Indian Factories Act 1948 under the overall control of Directorate General Factory Service and Labour Institute (DGFASLI) Government of India. Post the study it could be concluded that, stone crushers are dustier as compared to stone mining area. Workers in stone mining and crushing units of study area are indeed exposed to high levels of respirable and silica laden dust. It was observed that safety and precautionary measures towards dust and silica exposure are not implemented necessitating to be taken by unit operators.

scholarly journals Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Masayoshi Yamada ◽  
Yutaka Saito ◽  
Hitoshi Imaoka ◽  
Masahiro Saiko ◽  
Shigemi Yamada ◽  
...  
Keyword(s):  
Real Time ◽  
Detection System ◽  
Area Under The Curve ◽  
Learning Technology ◽  
Optical Colonoscopy ◽  
Robust Detection ◽  
Endoscopic Image ◽  
Image Diagnosis ◽  
High Detection ◽  
Validation Set

Abstract Gaps in colonoscopy skills among endoscopists, primarily due to experience, have been identified, and solutions are critically needed. Hence, the development of a real-time robust detection system for colorectal neoplasms is considered to significantly reduce the risk of missed lesions during colonoscopy. Here, we develop an artificial intelligence (AI) system that automatically detects early signs of colorectal cancer during colonoscopy; the AI system shows the sensitivity and specificity are 97.3% (95% confidence interval [CI] = 95.9%–98.4%) and 99.0% (95% CI = 98.6%–99.2%), respectively, and the area under the curve is 0.975 (95% CI = 0.964–0.986) in the validation set. Moreover, the sensitivities are 98.0% (95% CI = 96.6%–98.8%) in the polypoid subgroup and 93.7% (95% CI = 87.6%–96.9%) in the non-polypoid subgroup; To accelerate the detection, tensor metrics in the trained model was decomposed, and the system can predict cancerous regions 21.9 ms/image on average. These findings suggest that the system is sufficient to support endoscopists in the high detection against non-polypoid lesions, which are frequently missed by optical colonoscopy. This AI system can alert endoscopists in real-time to avoid missing abnormalities such as non-polypoid polyps during colonoscopy, improving the early detection of this disease.

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

scholarly journals Validation of a Novel Diagnostic Approach Combining the VersaTREK™ System for Recovery and Real-Time PCR for the Identification of Mycobacterium chimaera in Water Samples

2021 ◽  
Vol 9 (5) ◽  
pp. 1031
Author(s):  
Roberto Zoccola ◽  
Alessia Di Blasio ◽  
Tiziana Bossotto ◽  
Angela Pontei ◽  
Maria Angelillo ◽  
...  
Keyword(s):  
Real Time ◽  
Real Time Pcr ◽  
Limit Of Detection ◽  
Detection System ◽  
Bacterial Load ◽  
Microbial Control ◽  
Hospital Setting ◽  
Sample Volume ◽  
Analytical Sensitivity ◽  
Initial Water

Mycobacterium chimaera is an emerging pathogen associated with endocarditis and vasculitis following cardiac surgery. Although it can take up to 6–8 weeks to culture on selective solid media, culture-based detection remains the gold standard for diagnosis, so more rapid methods are urgently needed. For the present study, we processed environmental M. chimaera infected simulates at volumes defined in international guidelines. Each preparation underwent real-time PCR; inoculates were placed in a VersaTREK™ automated microbial detection system and onto selective Middlebrook 7H11 agar plates. The validation tests showed that real-time PCR detected DNA up to a concentration of 10 ng/µL. A comparison of the isolation tests showed that the PCR method detected DNA in a dilution of ×102 CFU/mL in the bacterial suspensions, whereas the limit of detection in the VersaTREK™ was <10 CFU/mL. Within less than 3 days, the VersaTREK™ detected an initial bacterial load of 100 CFU. The detection limit did not seem to be influenced by NaOH decontamination or the initial water sample volume; analytical sensitivity was 1.5 × 102 CFU/mL; positivity was determined in under 15 days. VersaTREK™ can expedite mycobacterial growth in a culture. When combined with PCR, it can increase the overall recovery of mycobacteria in environmental samples, making it potentially applicable for microbial control in the hospital setting and also in environments with low levels of contamination by viable mycobacteria.

scholarly journals Rapid Foreign Object Detection System on Seaweed Using VNIR Hyperspectral Imaging

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5279
Author(s):  
Dong-Hoon Kwak ◽  
Guk-Jin Son ◽  
Mi-Kyung Park ◽  
Young-Duk Kim
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Hyperspectral Imaging ◽  
High Speed ◽  
Detection System ◽  
Imaging Techniques ◽  
Foreign Object ◽  
Subtraction Method ◽  
Foreign Objects ◽  
Time Operation

The consumption of seaweed is increasing year by year worldwide. Therefore, the foreign object inspection of seaweed is becoming increasingly important. Seaweed is mixed with various materials such as laver and sargassum fusiforme. So it has various colors even in the same seaweed. In addition, the surface is uneven and greasy, causing diffuse reflections frequently. For these reasons, it is difficult to detect foreign objects in seaweed, so the accuracy of conventional foreign object detectors used in real manufacturing sites is less than 80%. Supporting real-time inspection should also be considered when inspecting foreign objects. Since seaweed requires mass production, rapid inspection is essential. However, hyperspectral imaging techniques are generally not suitable for high-speed inspection. In this study, we overcome this limitation by using dimensionality reduction and using simplified operations. For accuracy improvement, the proposed algorithm is carried out in 2 stages. Firstly, the subtraction method is used to clearly distinguish seaweed and conveyor belts, and also detect some relatively easy to detect foreign objects. Secondly, a standardization inspection is performed based on the result of the subtraction method. During this process, the proposed scheme adopts simplified and burdenless calculations such as subtraction, division, and one-by-one matching, which achieves both accuracy and low latency performance. In the experiment to evaluate the performance, 60 normal seaweeds and 60 seaweeds containing foreign objects were used, and the accuracy of the proposed algorithm is 95%. Finally, by implementing the proposed algorithm as a foreign object detection platform, it was confirmed that real-time operation in rapid inspection was possible, and the possibility of deployment in real manufacturing sites was confirmed.

scholarly journals Probe-Based Real-Time qPCR Assays for a Reliable Differentiation of Capripox Virus Species

2021 ◽  
Vol 9 (4) ◽  
pp. 765
Author(s):  
Janika Wolff ◽  
Martin Beer ◽  
Bernd Hoffmann
Keyword(s):  
Real Time ◽  
Animal Health ◽  
Diagnostic Methods ◽  
Virus Species ◽  
Time Saving ◽  
Robust Detection ◽  
Highly Sensitive ◽  
Reliable Differentiation ◽  
World Organization ◽  
Real Time Qpcr

Outbreaks of the three capripox virus species, namely lumpy skin disease virus, sheeppox virus, and goatpox virus, severely affect animal health and both national and international economies. Therefore, the World Organization for Animal Health (OIE) classified them as notifiable diseases. Until now, discrimination of capripox virus species was possible by using different conventional PCR protocols. However, more sophisticated probe-based real-time qPCR systems addressing this issue are, to our knowledge, still missing. In the present study, we developed several duplex qPCR assays consisting of different types of fluorescence-labelled probes that are highly sensitive and show a high analytical specificity. Finally, our assays were combined with already published diagnostic methods to a diagnostic workflow that enables time-saving, reliable, and robust detection, differentiation, and characterization of capripox virus isolates.

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