Real-Time Connection Monitoring of Ubiquitous Networks for Intrusion Prediction: A SequentialKNN Voting Approach

In the ubiquitous network environment where numerous devices are connecting each other, it is believed that security will play an important role in overall network management. And the wireless sensor network (WSN) is commonly considered to be one of such networks prone to a wide range of attacks due to its inherent characteristics. For the sound operation of WSN, it is important to block malicious connections from the network as early as possible. This paper proposes a novel approach to real-time monitoring of network by using the sequentialKNN voting. When connection data is sequentially recorded on the log, the final result of ongoing behavior is predicted probabilistically with only partial data, which iterates consecutively as additional connection data are accumulated to the log. Once this predicted probability reaches certain preset threshold value for possible network intrusion, then we can do some preventive actions for this ongoing connection. The value of this research lies in that the eventualities are predicted at the early stage of connection with partial information available. Since the prediction uses sequentialKNN voting, the accuracy of our approach can be even more enhanced as with the volume of log grows.

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Rational Programming of Cas12a for Early-Stage Detection of COVID-19 by Lateral Flow Assay and Portable Real-Time Fluorescence Readout Facilities

Biosensors ◽

10.3390/bios12010011 ◽

2021 ◽

Vol 12 (1) ◽

pp. 11

Author(s):

Zhijian Yi ◽

Jean de Dieu Habimana ◽

Omar Mukama ◽

Zhiyuan Li ◽

Nelson Odiwuor ◽

...

Keyword(s):

Real Time ◽

Early Stage ◽

Limited Resource ◽

Lateral Flow ◽

Lateral Flow Assay ◽

Capture Probe ◽

Fluorescence Detector ◽

Lateral Flow Strip ◽

Global Pandemic ◽

Novel Approach

Coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus has led to a global pandemic with a high spread rate and pathogenicity. Thus, with limited testing solutions, it is imperative to develop early-stage diagnostics for rapid and accurate detection of SARS-CoV-2 to contain the rapid transmission of the ongoing COVID-19 pandemic. In this regard, there remains little knowledge about the integration of the CRISPR collateral cleavage mechanism in the lateral flow assay and fluorophotometer. In the current study, we demonstrate a CRISPR/Cas12a-based collateral cleavage method for COVID-19 diagnosis using the Cas12a/crRNA complex for target recognition, reverse transcription loop-mediated isothermal amplification (RT-LAMP) for sensitivity enhancement, and a novel DNA capture probe-based lateral flow strip (LFS) or real-time fluorescence detector as the parallel system readout facility, termed CRICOLAP. Our novel approach uses a customized reporter that hybridizes an optimized complementary capture probe fixed at the test line for naked-eye result readout. The CRICOLAP system achieved ultra-sensitivity of 1 copy/µL in ~32 min by portable real-time fluorescence detection and ~60 min by LFS. Furthermore, CRICOLAP validation using 60 clinical nasopharyngeal samples previously verified with a commercial RT-PCR kit showed 97.5% and 100% sensitivity for S and N genes, respectively, and 100% specificity for both genes of SARS-CoV-2. CRICOLAP advances the CRISPR/Cas12a collateral cleavage result readout in the lateral flow assay and fluorophotometer, and it can be an alternative method for the decentralized field-deployable diagnosis of COVID-19 in remote and limited-resource locations.

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Adaptive Real-Time Wavelet Denoising Architecture Based on Feedback Control Loop

Journal of Engineering Research ◽

10.36909/jer.v9iicrie.11667 ◽

2021 ◽

Vol 9 (ICRIE) ◽

Author(s):

Fars Samann ◽

◽

Serwan Ali Bamerni ◽

Jeeman Ahmed Khorsheed ◽

Ahmed Khorsheed Al-sulaifanie ◽

...

Keyword(s):

Feedback Control ◽

Real Time ◽

Window Size ◽

Threshold Value ◽

Wavelet Denoising ◽

Discrete Wavelet ◽

Control Loop ◽

Denoising Method ◽

Low Snr ◽

Wide Range

The discrete wavelet transform is commonly used as a denoising step for many applications, like biomedical applications which are usually suffering from low SNR of the recorded signal. However, the choice of appropriate threshold value for DWT coefficients plays significant role in reconstructing the denoised signal. This paper presents a design of real-time wavelet denoising architecture which is suitable for wide range of real-time denoising applications. In this design, an adaptive thresholding approach based on feedback control loop is proposed to make the architecture more applicable for real-time wavelet denoising. This thresholding method considers a noise level estimator module based on first detail coefficients level 𝑑1 to calculate the unknown standard deviation of background noise. The proposed architecture is developed using MATLAB to simulate the suggested denoising method. The performance of the proposed denoising method is studied in terms of integral gain 𝐺 of feedback control and window size 𝑀 with respect to the improvement in SNR and settling time. The results imply that the proposed denoising architecture is suitable for real-time denoising applications with acceptable improvement in SNR approximately 8 dB.

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In-house reverse transcriptase polymerase chain reaction for detection of SARS-CoV-2 with increased sensitivity

Scientific Reports ◽

10.1038/s41598-021-97502-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Manash Jyoti Kalita ◽

Kalpajit Dutta ◽

Gautam Hazarika ◽

Ridip Dutta ◽

Simanta Kalita ◽

...

Keyword(s):

Multiplex Pcr ◽

Real Time ◽

Real Time Pcr ◽

Cost Effective ◽

Sybr Green ◽

Green Approach ◽

Novel Approach ◽

Effective Manner ◽

Wide Range ◽

Thermal Cycler

AbstractAs the COVID-19 infection continues to ravage the world, the advent of an efficient as well as the economization of the existing RT-PCR based detection assay essentially can become a blessing in these testing times and significantly help in the management of the pandemic. This study demonstrated an innovative and rapid corroboration of COVID-19 test based on innovative multiplex PCR. An assessment of optimal PCR conditions to simultaneously amplify the SARS-CoV-2 genes E, S and RdRp has been made by fast-conventional and HRM coupled multiplex real-time PCR using the same sets of primers. All variables of practical value were studied by amplifying known target-sequences from ten-fold dilutions of archived positive samples of COVID-19 disease. The multiplexing with newly designed E, S and RdRp primers have shown an efficient amplification of the target region of SARS-CoV-2. A distinct amplification was observed in 37 min using thermal cycler while it took 96 min in HRM coupled real time detection using SYBR green over a wide range of template concentrations. Our findings revealed decent concordance with other commercially available detection kits. This fast HRM coupled multiplex real-time PCR with SYBR green approach offers rapid and sensitive detection of SARS-CoV-2 in a cost-effective manner apart from the added advantage of primer compatibility for use in conventional multiplex PCR. The highly reproducible novel approach can propel extended applicability for developing sustainable commercial product besides providing relief to a resource limited setting.

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Konkurrenz zwischen Stieleiche und Buche auf Lothar-Sturmflächen | Competition between pedunculate oak and European beech on Lothar windthrow areas

Schweizerische Zeitschrift fur Forstwesen ◽

10.3188/szf.2009.0114 ◽

2009 ◽

Vol 160 (5) ◽

pp. 114-123 ◽

Cited By ~ 4

Author(s):

Daniel Otto ◽

Sven Wagner ◽

Peter Brang

Keyword(s):

Large Scale ◽

Early Stage ◽

Tree Height ◽

European Beech ◽

Threshold Value ◽

Test Methods ◽

Permanent Plots ◽

Pedunculate Oak ◽

Competitive Pressure ◽

Competition Index

The competitive pressure of naturally regenerated European beech (Fagus sylvatica) saplings on planted pedunculate oak (Quercus robur) was investigated on two 1.8 ha permanent plots near Habsburg and Murten (Switzerland). The plots were established with the aim to test methods of artificial oak regeneration after large-scale windthrow. On both plots, 80 oaks exposed to varying levels of competitive pressure from at most 10 neighbouring beech trees were selected. The height of each oak as well as stem and branch diameters were measured. The competitive pressure was assessed using Schütz's competition index, which is based on relative tree height, crown overlap and distance from competing neighbours. Oak trees growing without or with only slight competition from beech were equally tall, while oaks exposed to moderate to strong competition were smaller. A threshold value for the competition index was found above which oak height decreased strongly. The stem and branch diameters of the oaks started to decrease even if the competition from beech was slight, and decreased much further with more competition. The oak stems started to become more slender even with only slight competition from beech. On the moderately acid beech sites studied here, beech grow taller faster than oak. Thus where beech is competing with oak and the aim is to maintain the oak, competitive pressure on the oak must be reduced at an early stage. The degree of the intervention should, however, take the individual competitive interaction into account, with more intervention if the competition is strong.

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A novel approach to the real-time modelling of large urban drainage systems

Water Science & Technology ◽

10.2166/wst.1997.0638 ◽

1997 ◽

Vol 36 (8-9) ◽

pp. 19-24 ◽

Cited By ~ 6

Author(s):

Richard Norreys ◽

Ian Cluckie

Keyword(s):

Real Time ◽

Dynamic Models ◽

Drainage System ◽

Radar Data ◽

Urban Drainage ◽

Real Time Control ◽

Empirical Modelling ◽

Time Control ◽

Novel Approach ◽

Non Linear Systems

Conventional UDS models are mechanistic which though appropriate for design purposes are less well suited to real-time control because they are slow running, difficult to calibrate, difficult to re-calibrate in real time and have trouble handling noisy data. At Salford University a novel hybrid of dynamic and empirical modelling has been developed, to combine the speed of the empirical model with the ability to simulate complex and non-linear systems of the mechanistic/dynamic models. This paper details the ‘knowledge acquisition module’ software and how it has been applied to construct a model of a large urban drainage system. The paper goes on to detail how the model has been linked with real-time radar data inputs from the MARS c-band radar.

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Photonic Crystal Nanobeam Cavities for Nanoscale Optical Sensing: A Review

Micromachines ◽

10.3390/mi11010072 ◽

2020 ◽

Vol 11 (1) ◽

pp. 72 ◽

Cited By ~ 2

Author(s):

Da-Quan Yang ◽

Bing Duan ◽

Xiao Liu ◽

Ai-Qiang Wang ◽

Xiao-Gang Li ◽

...

Keyword(s):

Photonic Crystal ◽

Optical Waveguides ◽

Optical Sensing ◽

Early Stage ◽

Disease Diagnosis ◽

Label Free ◽

Quality Factors ◽

Integrated Sensor ◽

Early Stage Disease ◽

Wide Range

The ability to detect nanoscale objects is particular crucial for a wide range of applications, such as environmental protection, early-stage disease diagnosis and drug discovery. Photonic crystal nanobeam cavity (PCNC) sensors have attracted great attention due to high-quality factors and small-mode volumes (Q/V) and good on-chip integrability with optical waveguides/circuits. In this review, we focus on nanoscale optical sensing based on PCNC sensors, including ultrahigh figure of merit (FOM) sensing, single nanoparticle trapping, label-free molecule detection and an integrated sensor array for multiplexed sensing. We believe that the PCNC sensors featuring ultracompact footprint, high monolithic integration capability, fast response and ultrahigh sensitivity sensing ability, etc., will provide a promising platform for further developing lab-on-a-chip devices for biosensing and other functionalities.

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Non-Albumin Proteinuria (NAP) as a Complementary Marker for Diabetic Kidney Disease (DKD)

Life ◽

10.3390/life11030224 ◽

2021 ◽

Vol 11 (3) ◽

pp. 224

Author(s):

Jaehyun Bae ◽

Young Jun Won ◽

Byung-Wan Lee

Keyword(s):

Kidney Disease ◽

Filtration Rate ◽

Diabetic Kidney Disease ◽

Estimated Glomerular Filtration Rate ◽

Early Stage ◽

Screening Tools ◽

Tubular Injury ◽

Current Standard ◽

Diabetic Kidney ◽

Wide Range

Diabetic kidney disease (DKD) is one of the most common forms of chronic kidney disease. Its pathogenic mechanism is complex, and it can affect entire structures of the kidney. However, conventional approaches to early stage DKD have focused on changes to the glomerulus. Current standard screening tools for DKD, albuminuria, and estimated glomerular filtration rate are insufficient to reflect early tubular injury. Therefore, many tubular biomarkers have been suggested. Non-albumin proteinuria (NAP) contains a wide range of tubular biomarkers and is convenient to measure. We reviewed the clinical meanings of NAP and its significance as a marker for early stage DKD.

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Responsive and Personalized Web Layouts with Integer Programming

Proceedings of the ACM on Human-Computer Interaction ◽

10.1145/3461735 ◽

2021 ◽

Vol 5 (EICS) ◽

pp. 1-23

Author(s):

Markku Laine ◽

Yu Zhang ◽

Simo Santala ◽

Jussi P. P. Jokinen ◽

Antti Oulasvirta

Keyword(s):

Integer Programming ◽

Web Design ◽

Web Pages ◽

Web Page Design ◽

Automated Generation ◽

Novel Approach ◽

Wide Range ◽

Responsive Design ◽

Responsive Web Design ◽

Page Design

Over the past decade, responsive web design (RWD) has become the de facto standard for adapting web pages to a wide range of devices used for browsing. While RWD has improved the usability of web pages, it is not without drawbacks and limitations: designers and developers must manually design the web layouts for multiple screen sizes and implement associated adaptation rules, and its "one responsive design fits all" approach lacks support for personalization. This paper presents a novel approach for automated generation of responsive and personalized web layouts. Given an existing web page design and preferences related to design objectives, our integer programming -based optimizer generates a consistent set of web designs. Where relevant data is available, these can be further automatically personalized for the user and browsing device. The paper includes presentation of techniques for runtime adaptation of the designs generated into a fully responsive grid layout for web browsing. Results from our ratings-based online studies with end users (N = 86) and designers (N = 64) show that the proposed approach can automatically create high-quality responsive web layouts for a variety of real-world websites.

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Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Sensors ◽

10.3390/s21144736 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4736

Author(s):

Sk. Tanzir Mehedi ◽

Adnan Anwar ◽

Ziaur Rahman ◽

Kawsar Ahmed

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Intrusion Detection ◽

Real Time ◽

Transfer Learning ◽

Security Requirements ◽

Detection Accuracy ◽

Area Network ◽

Complex Data ◽

Network Intrusion

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

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