MoBiMS: A Modular Miniature Mass Analyzer for the Real-Time Monitoring of Gases and Volatile Compounds in Biological Systems

Real-time monitoring of the gold cluster synthesis by electrospray ionization mass spectrometry reveals distinct formation pathways for Au8, Au9 and Au10 clusters.

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A Study of Detecting Broken Rail using the Real-time Monitoring System

Journal of the Korean Society of Safety ◽

10.14346/jkosos.2013.28.4.001 ◽

2013 ◽

Vol 28 (4) ◽

pp. 1-7

Author(s):

Tae Geon Kim ◽

Beom Gyu Eom ◽

Hi Sung Lee

Keyword(s):

Real Time ◽

Monitoring System ◽

Real Time Monitoring ◽

The Real

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The Real-Time Monitoring System of Social Big Data for Disaster Management

Computer Science and its Applications - Lecture Notes in Electrical Engineering ◽

10.1007/978-3-662-45402-2_115 ◽

2015 ◽

pp. 809-815 ◽

Cited By ~ 15

Author(s):

Seonhwa Choi ◽

Byunggul Bae

Keyword(s):

Big Data ◽

Real Time ◽

Monitoring System ◽

Disaster Management ◽

Real Time Monitoring ◽

The Real ◽

Social Big Data

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A Novel Hash Chain-Based Data Availability Monitoring Method for Off-site Disaster Recovery Architecture

Journal of Circuits System and Computers ◽

10.1142/s0218126621502947 ◽

2021 ◽

pp. 2150294

Author(s):

Neng Huang ◽

Junxing Zhu ◽

Chaonian Guo ◽

Shuhan Cheng ◽

Xiaoyong Li

Keyword(s):

Information Systems ◽

Real Time ◽

Data Center ◽

Disaster Recovery ◽

Distributed Storage ◽

Data Availability ◽

Data Consistency ◽

Real Time Monitoring ◽

The Real ◽

Hash Chain

With the rapid development of mobile Internet, there is a higher demand for the real-time, reliability and availability of information systems and to prevent the possible systemic risks of information systems, various business consistency standards and regulatory guidelines have been published, such as Recovery Time Object (RTO) and Recovery Point Object (RPO). Some of the current related researches focus on the standards, methods, management tools and technical frameworks of business consistency, while others study the data consistency algorithms in the cases of large data, cloud computing and distributed storage. However, few researchers have studied on how to monitor the data consistency and RPO of production-disaster recovery, and what architecture and technology should be applied in the monitoring. Moreover, in some information systems, due to the complex structures and distributions of data, it is difficult for traditional methods to quickly detect and accurately locate the first error data. Besides, due to the separation of production data center (PDC) and disaster recovery data center (DRDC), it is difficult to calculate the data difference and RPO between the two centers. This paper first discusses the architecture of remote distributed DRDCs. The architecture can make the disaster recovery (DR) system always online and the data always readable, and support the real-time monitoring of data availability, consistency as well as other related indicators, in this way to make DRDC out-of-the-box in disasters. Second, inspired by blockchain, this paper proposes a method to realize real-time monitoring of data consistency and RTO by building hash chains for PDC and DRDC. Third, this paper evaluates the hash chain operations from the algorithm time complexity, the data consistency, and the validity of RPO monitoring algorithms and since DR system is actually a kind of distributed system, the proposed approach can also be applied to the data consistency detection and data difference monitoring in other distributed systems.

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A highly selective ratiometric fluorescent probe based on naphthalimide for detection and imaging of CYP1A1 in living cells and zebrafish

The Analyst ◽

10.1039/c9an01767d ◽

2019 ◽

Vol 144 (24) ◽

pp. 7390-7397 ◽

Cited By ~ 6

Author(s):

Hefang Ji ◽

Xiuxuan Zhang ◽

Yanpeng Dai ◽

Tianzi Xue ◽

Saima Misal ◽

...

Keyword(s):

Cytochrome P450 ◽

Real Time ◽

Fluorescent Probe ◽

Significant Role ◽

Biological Systems ◽

Living Cells ◽

Cytochrome P450 1A1 ◽

Real Time Monitoring ◽

Practical Tool ◽

Ratiometric Fluorescent Probe

Real-time monitoring of the cytochrome P450 1A1 (CYP1A1) activity in complex biological systems via a practical tool is highly sought after because of its significant role in the metabolism and bioactivation of various xenobiotics.

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An automated apparatus for the real-time monitoring of bioluminescence in plants

Analytical Biochemistry ◽

10.1016/j.ab.2004.11.009 ◽

2005 ◽

Vol 340 (2) ◽

pp. 187-192 ◽

Cited By ~ 15

Author(s):

Kazuhisa Okamoto ◽

Kiyoshi Onai ◽

Norihiko Ezaki ◽

Toru Ofuchi ◽

Masahiro Ishiura

Keyword(s):

Real Time ◽

Real Time Monitoring ◽

The Real

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On-line microfluidic sensor integrated with a micro array electrode and enzyme-modified pre-reactor for the real-time monitoring of blood catecholamine

Electrochemistry Communications ◽

10.1016/j.elecom.2003.10.005 ◽

2003 ◽

Vol 5 (12) ◽

pp. 1037-1042 ◽

Cited By ~ 27

Author(s):

K Hayashi

Keyword(s):

Real Time ◽

Real Time Monitoring ◽

The Real ◽

Array Electrode ◽

Micro Array ◽

On Line

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Dielectric permittivity as a method for the real time monitoring of fungal growth during a solid substrate food fermentation of Quinoa grains

Progress in Biotechnology - Food Biotechnology, Proceedings of an International Symposium organized by the Institute of Technical Biochemistry, Technical University of Lodz, Poland, under the auspices of the Committee of Biotechnology, Polish Academy of Sciences (PAS), Committee of Food Chemistry and Technology, PAS, Working Party on Applied Biocatalysis and Task Group on Public Perception of Biotechnology of the European Federation of Biotechnology, Biotechnology Section of the Polish Biochemical Society ◽

10.1016/s0921-0423(00)80097-0 ◽

2000 ◽

pp. 393-398 ◽

Cited By ~ 1

Author(s):

P. Kaminski ◽

J. Hedger ◽

J. Williams ◽

C. Bucke ◽

I. Swadling

Keyword(s):

Dielectric Permittivity ◽

Real Time ◽

Fungal Growth ◽

Solid Substrate ◽

Real Time Monitoring ◽

Food Fermentation ◽

The Real

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Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

Energies ◽

10.3390/en13153930 ◽

2020 ◽

Vol 13 (15) ◽

pp. 3930 ◽

Cited By ~ 3

Author(s):

Ayaz Hussain ◽

Umar Draz ◽

Tariq Ali ◽

Saman Tariq ◽

Muhammad Irfan ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Real Time ◽

Air Pollutants ◽

Garbage Collection ◽

Air Pollutant ◽

Real Time Monitoring ◽

Collection System ◽

The Real ◽

The Status

Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.

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