scholarly journals Development of a cost-effective remote explosives detection system based on Raman spectroscopy

2021 ◽  
Vol 1719 (1) ◽  
pp. 012080
Author(s):  
S Thongrom ◽  
P Kalasuwan ◽  
P van Dommelen ◽  
C Daengngam
Keyword(s):  
Raman Spectroscopy ◽  
Detection System ◽  
Cost Effective ◽  
Explosives Detection

scholarly journals CRISPR/Cas12a-Based Ultrasensitive and Rapid Detection of JAK2 V617F Somatic Mutation in Myeloproliferative Neoplasms

Biosensors ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 247
Author(s):  
Miaomiao Chen ◽  
Chunhua Zhang ◽  
Zhiqing Hu ◽  
Zhuo Li ◽  
Menglin Li ◽  
...  
Keyword(s):  
Detection System ◽  
Myeloproliferative Neoplasms ◽  
Cost Effective ◽  
Jak2 V617f ◽  
Jak2 V617f Mutation ◽  
Minimum Detectable Concentration ◽  
Lateral Flow Strip ◽  
Whole Process ◽  
Detectable Concentration ◽  
V617f Mutation

The JAK2 V617F mutation is a major diagnostic, therapeutic, and monitoring molecular target of Philadelphia-negative myeloproliferative neoplasms (MPNs). To date, numerous methods of detecting the JAK2 V617F mutation have been reported, but there is no gold-standard diagnostic method for clinical applications. Here, we developed and validated an efficient Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR associated protein 12a (Cas12a)-based assay to detect the JAK2 V617F mutation. Our results showed that the sensitivity of the JAK2 V617F/Cas12a fluorescence detection system was as high as 0.01%, and the JAK2 V617F/Cas12a lateral flow strip assay could unambiguously detect as low as 0.5% of the JAK2 V617F mutation, which was much higher than the sensitivity required for clinical application. The minimum detectable concentration of genomic DNA achieved was 0.01 ng/μL (~5 aM, ~3 copies/μL). In addition, the whole process only took about 1.5 h, and the cost of an individual test was much lower than that of the current assays. Thus, our methods can be applied to detect the JAK2 V617F mutation, and they are highly sensitive, rapid, cost-effective, and convenient.

Graphene Oxide as a Substrate for Biosensors: Synthesis and Characterization

2021 ◽  
Vol 324 ◽  
pp. 87-93
Author(s):  
Mohamed Adel ◽  
Abdel Hady A. Abdel-Wahab ◽  
Ahmed Abdel-Mawgood ◽  
Ahmed Osman Egiza
Keyword(s):  
Raman Spectroscopy ◽  
Graphene Oxide ◽  
Acidic Solution ◽  
Cost Effective ◽  
Sem Analysis ◽  
Visible Spectroscopy ◽  
Sem Images ◽  
Hummers Method ◽  
Uv Visible ◽  
Modified Hummers Method

Graphene oxide (GO) is an oxidized nanosheets of graphite with a 2D planar structure. GO could be readily complexed with bio-entities as it possesses many oxygen-containing functionalities on its surface. The preparation process is fast, easy, and cost-effective. It was prepared using modified Hummers’ method in acidic solution as a primary solvent and potassium permanganate as an oxidizing agent. Afterwards, it was successfully characterized by FTIR, UV-visible spectroscopy, as well as XRD and Raman spectroscopy, and finally, SEM analysis. It was observed that the formed GO is mainly composed of carbon and oxygen elements rich in oxygen functional groups. Furthermore, the existence of (001) plane in XRD interprets the complete oxidation of graphite with d-spacing 9 Å. Moreover, Raman spectroscopy displayed the sp3 carbon hybridization, besides, the ID/IG ratio is found to be 0.84, which confirms the disorder between graphene oxide layers. The SEM images also pointed out that graphene oxide sheets were regularly stacked together as flake-like structures. Accordingly, the richness of oxygen-containing functionalities was confirmed. Hence, it is appropriate to be used as a base transducer for biosensing applications.

scholarly journals Automated Quality Assessment of Crops Using CNN - Keras

2021 ◽  
pp. 641-645
Author(s):  
Meghashree ◽  
Alwyn Edison Mendonca ◽  
Ashika S Shetty
Keyword(s):  
Image Classification ◽  
Agricultural Production ◽  
Detection System ◽  
Cost Effective ◽  
Farming System ◽  
Modern Method ◽  
Innovative Technology ◽  
Disease Detection ◽  
Agricultural Field ◽  
Plant Leaf

Plant disease is an on-going challenge for the farmers and it has been one of the major threats to the income and the food security. This project is used to classify plant leaf into diseased and healthy leaf,to improve the quality and quantity of agricultural production in the country. The innovative technology that helps in improve the quality and quantity in the agricultural field is the smart farming system. It represented the modern method that provides cost-effective disease detection and deep learning with convolutional neural networks (CNNs) has achieved large successfulness in the categorisation of different plant leaf diseases. CNN reads a really very larger picture in a simple way. CNN nearly utilised to examine visual imagery and are frequently working behind the scenes in image classification. To extract the general features and then classify them under multiple based upon the features detected. This project will help the farmers financially in increasing the production of the crop yield as well as the overall agricultural production. The paper reviews the expected methods of plant leaf disease detection system that facilitates the advancement in agriculture. It includes various phases such as image preprocessing, image classification, feature extraction and detecting healthy or diseased.

scholarly journals Using modelled discharge to develop satellite-based river gauging: a case study for the Amazon Basin

2018 ◽  
Vol 22 (12) ◽  
pp. 6435-6448 ◽  
Author(s):  
Jiawei Hou ◽  
Albert I. J. M. van Dijk ◽  
Luigi J. Renzullo ◽  
Robert A. Vertessy
Keyword(s):  
River Discharge ◽  
Water Resource Management ◽  
Large Scale ◽  
Amazon Basin ◽  
Water Cycle ◽  
Detection System ◽  
Grid Cell ◽  
Cost Effective ◽  
Satellite Observation ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. River discharge measurements have proven invaluable to monitor the global water cycle, assess flood risk, and guide water resource management. However, there is a delay, and ongoing decline, in the availability of gauging data and stations are highly unevenly distributed globally. While not a substitute for river discharge measurement, remote sensing is a cost-effective technology to acquire information on river dynamics in situations where ground-based measurements are unavailable. The general approach has been to relate satellite observation to discharge measured in situ, which prevents its use for ungauged rivers. Alternatively, hydrological models are now available that can be used to estimate river discharge globally. While subject to greater errors and biases than measurements, model estimates of river discharge do expand the options for applying satellite-based discharge monitoring in ungauged rivers. Our aim was to test whether satellite gauging reaches (SGRs), similar to virtual stations in satellite altimetry, can be constructed based on Moderate Resolution Imaging Spectroradiometer (MODIS) optical or Global Flood Detection System (GFDS) passive microwave-derived surface water extent fraction and simulated discharge from the World-Wide Water (W3) model version 2. We designed and tested two methods to develop SGRs across the Amazon Basin and found that the optimal grid cell selection method performed best for relating MODIS and GFDS water extent to simulated discharge. The number of potential river reaches to develop SGRs increases from upstream to downstream reaches as rivers widen. MODIS SGRs are feasible for more river reaches than GFDS SGRs due to its higher spatial resolution. However, where they could be constructed, GFDS SGRs predicted discharge more accurately as observations were less affected by cloud and vegetation. We conclude that SGRs are suitable for automated large-scale application and offer a possibility to predict river discharge variations from satellite observations alone, for both gauged and ungauged rivers.

Application of a webcam camera as a cost-effective sensor with image processing for dual electrochemical – colorimetric detection system

Talanta ◽  
2018 ◽  
Vol 185 ◽  
pp. 160-165 ◽  
Author(s):  
Kajorngai Thajee ◽  
Pathinan Paengnakorn ◽  
Wasin Wongwilai ◽  
Kate Grudpan
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Colorimetric Detection ◽  
Cost Effective

scholarly journals DeepIDS: Deep Learning Approach for Intrusion Detection in Software Defined Networking

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1533 ◽  
Author(s):  
Tuan Anh Tang ◽  
Lotfi Mhamdi ◽  
Des McLernon ◽  
Syed Ali Raza Zaidi ◽  
Mounir Ghogho ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Software Defined Networking ◽  
Cost Effective ◽  
The Internet ◽  
Network Intrusion ◽  
Effective Network ◽  
Control Application

Software Defined Networking (SDN) is developing as a new solution for the development and innovation of the Internet. SDN is expected to be the ideal future for the Internet, since it can provide a controllable, dynamic, and cost-effective network. The emergence of SDN provides a unique opportunity to achieve network security in a more efficient and flexible manner. However, SDN also has original structural vulnerabilities, which are the centralized controller, the control-data interface and the control-application interface. These vulnerabilities can be exploited by intruders to conduct several types of attacks. In this paper, we propose a deep learning (DL) approach for a network intrusion detection system (DeepIDS) in the SDN architecture. Our models are trained and tested with the NSL-KDD dataset and achieved an accuracy of 80.7% and 90% for a Fully Connected Deep Neural Network (DNN) and a Gated Recurrent Neural Network (GRU-RNN), respectively. Through experiments, we confirm that the DL approach has the potential for flow-based anomaly detection in the SDN environment. We also evaluate the performance of our system in terms of throughput, latency, and resource utilization. Our test results show that DeepIDS does not affect the performance of the OpenFlow controller and so is a feasible approach.

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