3D Flower-Like Gadolinium Molybdate Catalyst for Efficient Detection and Degradation of Organophosphate Pesticide (Fenitrothion)

2018 ◽  
Vol 10 (18) ◽  
pp. 15652-15664 ◽  
Author(s):  
Jeyaraj Vinoth Kumar ◽  
Raj Karthik ◽  
Shen-Ming Chen ◽  
Karikalan Natarajan ◽  
Chelladurai Karuppiah ◽  
...  
Keyword(s):  
Organophosphate Pesticide ◽  
Efficient Detection ◽  
Gadolinium Molybdate

Efficient Detection of Organophosphate Pesticide Based on a Metal−Organic Framework Derived from Biphenyltetracarboxylic Acid

2010 ◽  
Vol 10 (7) ◽  
pp. 2835-2838 ◽  
Author(s):  
Li-Li Wen ◽  
Feng Wang ◽  
Xiao-Ke Leng ◽  
Cheng-Gang Wang ◽  
Lian-Yi Wang ◽  
...  
Keyword(s):  
Metal Organic Framework ◽  
Organophosphate Pesticide ◽  
Efficient Detection ◽  
Metal Organic ◽  
Organic Framework

Efficient Detection of Suspected areas in Mammographic Breast Cancer Images

2015 ◽  
Vol 1 (4) ◽  
pp. 1-10 ◽  
Author(s):  
Bhagwati Charan Patel ◽  
◽  
Dr.G.R. Sinha ◽  
Keyword(s):  
Breast Cancer ◽  
Efficient Detection

scholarly journals Online Judging Platform Utilizing Dynamic Plagiarism Detection Facilities

Computers ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 47
Author(s):  
Fariha Iffath ◽  
A. S. M. Kayes ◽  
Md. Tahsin Rahman ◽  
Jannatul Ferdows ◽  
Mohammad Shamsul Arefin ◽  
...  
Keyword(s):  
Source Code ◽  
Large Data ◽  
Large Data Sets ◽  
Detection Technique ◽  
Data Sets ◽  
Plagiarism Detection ◽  
Source Codes ◽  
Efficient Detection ◽  
Mathematical Problems ◽  
Automatic Scoring

A programming contest generally involves the host presenting a set of logical and mathematical problems to the contestants. The contestants are required to write computer programs that are capable of solving these problems. An online judge system is used to automate the judging procedure of the programs that are submitted by the users. Online judges are systems designed for the reliable evaluation of the source codes submitted by the users. Traditional online judging platforms are not ideally suitable for programming labs, as they do not support partial scoring and efficient detection of plagiarized codes. When considering this fact, in this paper, we present an online judging framework that is capable of automatic scoring of codes by detecting plagiarized contents and the level of accuracy of codes efficiently. Our system performs the detection of plagiarism by detecting fingerprints of programs and using the fingerprints to compare them instead of using the whole file. We used winnowing to select fingerprints among k-gram hash values of a source code, which was generated by the Rabin–Karp Algorithm. The proposed system is compared with the existing online judging platforms to show the superiority in terms of time efficiency, correctness, and feature availability. In addition, we evaluated our system by using large data sets and comparing the run time with MOSS, which is the widely used plagiarism detection technique.

scholarly journals Highly-Efficient Sulfonated UiO-66(Zr) Optical Fiber for Rapid Detection of Trace Levels of Pb2+

2021 ◽  
Vol 22 (11) ◽  
pp. 6053
Author(s):  
Marziyeh Nazari ◽  
Abbas Amini ◽  
Nathan T. Eden ◽  
Mikel C. Duke ◽  
Chun Cheng ◽  
...  
Keyword(s):  
Optical Fiber ◽  
Rapid Detection ◽  
Chemical Sensor ◽  
Metal Organic Framework ◽  
Aqueous System ◽  
Icp Oes ◽  
Efficient Detection ◽  
Low Concentrations ◽  
Fabry Perot ◽  
Metal Organic

Lead detection for biological environments, aqueous resources, and medicinal compounds, rely mainly on either utilizing bulky lab equipment such as ICP-OES or ready-made sensors, which are based on colorimetry with some limitations including selectivity and low interference. Remote, rapid and efficient detection of heavy metals in aqueous solutions at ppm and sub-ppm levels have faced significant challenges that requires novel compounds with such ability. Here, a UiO-66(Zr) metal-organic framework (MOF) functionalized with SO3H group (SO3H-UiO-66(Zr)) is deposited on the end-face of an optical fiber to detect lead cations (Pb2+) in water at 25.2, 43.5 and 64.0 ppm levels. The SO3H-UiO-66(Zr) system provides a Fabry–Perot sensor by which the lead ions are detected rapidly (milliseconds) at 25.2 ppm aqueous solution reflecting in the wavelength shifts in interference spectrum. The proposed removal mechanism is based on the adsorption of [Pb(OH2)6]2+ in water on SO3H-UiO-66(Zr) due to a strong affinity between functionalized MOF and lead. This is the first work that advances a multi-purpose optical fiber-coated functional MOF as an on-site remote chemical sensor for rapid detection of lead cations at extremely low concentrations in an aqueous system.

DAP: Efficient Detection Against Probabilistic Cloning Attacks in Anonymous RFID Systems

2021 ◽  
pp. 1-1
Author(s):  
Honglong Chen ◽  
Xin Ai ◽  
Kai Lin ◽  
Na Yan ◽  
Zhibo Wang ◽  
...  
Keyword(s):  
Rfid Systems ◽  
Efficient Detection

scholarly journals Biosensors Based on Isothermal DNA Amplification for Bacterial Detection in Food Safety and Environmental Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 602
Author(s):  
Sandra Leonardo ◽  
Anna Toldrà ◽  
Mònica Campàs
Keyword(s):  
Food Safety ◽  
Environmental Monitoring ◽  
Rolling Circle Amplification ◽  
Dna Amplification ◽  
Isothermal Amplification ◽  
In Situ Monitoring ◽  
Strand Displacement ◽  
Strand Displacement Amplification ◽  
Bacterial Detection ◽  
Efficient Detection

The easy and rapid spread of bacterial contamination and the risk it poses to human health makes evident the need for analytical methods alternative to conventional time-consuming laboratory-based techniques for bacterial detection. To tackle this demand, biosensors based on isothermal DNA amplification methods have emerged, which avoid the need for thermal cycling, thus facilitating their integration into small and low-cost devices for in situ monitoring. This review focuses on the breakthroughs made on biosensors based on isothermal amplification methods for the detection of bacteria in the field of food safety and environmental monitoring. Optical and electrochemical biosensors based on loop mediated isothermal amplification (LAMP), rolling circle amplification (RCA), recombinase polymerase amplification (RPA), helicase dependent amplification (HDA), strand displacement amplification (SDA), and isothermal strand displacement polymerisation (ISDPR) are described, and an overview of their current advantages and limitations is provided. Although further efforts are required to harness the potential of these emerging analytical techniques, the coalescence of the different isothermal amplification techniques with the wide variety of biosensing detection strategies provides multiple possibilities for the efficient detection of bacteria far beyond the laboratory bench.

Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm

2021 ◽  
pp. 1-17
Author(s):  
Ahmed Al-Tarawneh ◽  
Ja’afer Al-Saraireh
Keyword(s):  
Machine Learning ◽  
Complex Networks ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Efficient Detection ◽  
Suggested Keywords

Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks technique with adapted machine learning algorithms. This work presents a methodology that collects a list of users with their followers who are sharing their posts that have similar interests from a hackers’ community on Twitter. The list is built based on a set of suggested keywords that are the commonly used terms by hackers in their tweets. After that, a complex network is generated for all users to find relations among them in terms of network centrality, closeness, and betweenness. After extracting these values, a dataset of the most influential users in the hacker community is assembled. Subsequently, tweets belonging to users in the extracted dataset are gathered and classified into positive and negative classes. The output of this process is utilized with a machine learning process by applying different algorithms. This research build and investigate an accurate dataset containing real users who belong to a hackers’ community. Correctly, classified instances were measured for accuracy using the average values of K-nearest neighbor, Naive Bayes, Random Tree, and the support vector machine techniques, demonstrating about 90% and 88% accuracy for cross-validation and percentage split respectively. Consequently, the proposed network cyber Twitter model is able to detect hackers, and determine if tweets pose a risk to future institutions and individuals to provide early warning of possible attacks.

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