How believable are published laboratory data? A deeper look into system-compliance and elastic modulus

Elastic modulus(E) interpretation is debatable with limited literature detailing the impact of systemcompliance.To address this impact,a comprehensive testing schedule using an aluminium 6061(Al)sample is carried out on several systems under various test setups.Al is chosen as it is extruded and adheres to well defined shape tolerances and elastic properties.A robust method,using the SavitzkyGolay filter,is introduced to identify significant slope changes in the stress-strain curve.Since the load in the test system is well defined,the recorded deformation is corrected to the expected value of Al resulting in a system-compliance factor.The results across the testing systems and test setups showed significant variance,with the recorded E always lower than the anticipated E_(Al).The number of components within the system over which the deformation is measured had the most significant impact,lowering the expected E by up to 50%.Additionally,the system-compliance factor is inconsistent across different systems and setups.Thus,it is evidently proved that each setup must be separately evaluated for its system-compliance and that no single value exists across systems and setups.The findings are then projected onto a series of uniaxial compressive strength(UCS) tests carried out on Stanstead granite(SS GR) samples.The corrected E_(t50) and E_(avg) values for system-compliance of the samples are within ±1%for each system as opposed to being ±50% pre-correction.The findings conclude that it is deemed necessary and of utmost importance that the deformation be corrected to accommodate the systemcompliance to obtain reliable results.     

Data Association in Multi-target Tracking Using Belief Function

In this paper, we present a new method for data association in multi-target tracking. The representation and the fusion of the information in our method are based on the use of belief function. The proposal generates the basic belief mass assignment using a modified Mahalanobis distance. While the decision making process is based on the extension of the frame of hypotheses. Our method has been tested for a nearly constant velocity target and compared with both the nearest neighbor filter and the joint probabilistic data associations filter in highly ambiguous cases. The results demonstrate the feasibility of the proposal and show improved performance compared to the aforementioned alternative commonly used methods.     

Widespread infection of lake whitefish Coregonus clupeaformis with the swimbladder nematode Cystidicola farionis in northern lakes Michigan and Huron

We estimated the prevalence, intensity, and abundance of swimbladder nematode infection in 1281 lake whitefish (Coregonus clupeaformis) collected from four sites in northern lakes Huron (Cheboygan and DeTour Village) and Michigan (Big Bay de Noc and Naubinway) from fall 2003 through summer 2006. Morphological examination of nematode egg, larval, and mature stages through light and scanning electron microscopy revealed characteristics consistent with that of Cystidicola farionis Fischer 1798. Total C. farionis prevalence was 26.94%, while the mean intensity and abundance of infection was 26.72 and 7.21 nematodes/fish, respectively. Although we detected C. farionis in all four stocks that were examined, Lake Huron stocks generally had higher prevalence, intensity, and abundance of infection than Lake Michigan stocks. A distinct seasonal fluctuation in prevalence, abundance, and intensity of C. farionis was observed, which does not coincide with reported C. farionis development in other fish species. Lake whitefish that were heavily infected with C. farionis were found to have thickened swimbladder walls with deteriorated mucosa lining, which could affect swimbladder function. Whether C. farionis infection may be negatively impacting lake whitefish stocks in the Great Lakes is unclear; continued monitoring of C. farionis infection should be conducted to measure responses of lake whitefish stocks to infection levels.     

Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

Arrhythmia has been classified using a variety of methods. Because of the dynamic nature of electrocardiogram (ECG) data, traditional handcrafted approaches are difficult to execute, making the machine learning (ML) solutions more appealing. Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives. Cardiac arrhythmia classification and prediction have greatly improved in recent years. Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish. Every year, it is one of the main reasons of mortality for both men and women, worldwide. For the classification of arrhythmias, this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors (KNN) classifier. The proposed method makes advantage of the UCI repository, which has a 279-attribute high-dimensional cardiac arrhythmia dataset. The proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s features. The purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia patients. This method of comprehensive arrhythmia classification outperforms earlier methods presented in the literature. The achieved classification accuracy using the proposed approach is 99.8%.     

Berries anthocyanins as potential SARS-CoV–2 inhibitors targeting the viral attachment and replication; molecular docking simulation

The viral respiratory disease, severe acute respiratory syndrome (SARS), has turned into a global health concern. Till now, there is no drug or vaccine has yet been specifically approved for SARS-CoV-2. One of the urgent solutions against the recent COVID-19 disease is the use of dietary molecules, which can be found abundantly in functional food. In the current study, we have conducted a molecular docking approach for eighteen dietary molecules belong to the subclass of anthocyanins, as potential inhibitors of the main protease and spike glycoprotein of SARS-CoV-2. Both selected targets, playing a vital role in attachment and replication of the virus. The results indicated that cyanidin-3-arabinoside exhibited the lowest binding energy and located onto the pocket through a sufficient number of hydrogen bonds with the main protease virus. However, pelargonidin-3-glucoside and pelargonidin 3-rhamnoside display significant binding energy with the spike glycoprotein of SARS-CoV-2. All compounds mentioned above shown high drug-likeness and fulfils the Lipinski’s rule of five, as well as confer favorable toxicity parameters, in addition to ADME values. Considering the obtained results, regular consumption of berry fruits, which are rich in anthocyanin compounds, should be supportive to inhibit viral infectious by reducing of propagation and pathogenicity of SARS-CoV–2.     

Application of TSVR algorithm in 5G mmWave indoor networks

A robust algorithm based on Twin Support Vector Regression and discrete wavelet transform applied to millimetric wave (mmWave) channel prediction is proposed in this work. The 60 GHz band is appropriate for small-scale high-speed data transmission applications in future 5G indoor network solutions. The experimentation takes place in an enclosed complex conference room setting with furniture and computer equipment. The proposed algorithm is applied to mmWave multipath channel with higher order modulation scheme with receiver sensitivity thresholds being ? 80 dBm, ? 90 dBm, ? 100 dBm and ? 110 dBm corresponding to 41, 89, 195 and 250 paths, respectively. The Channel Impulse Response of 60 GHz multipath wireless system is generated by the “Wireless InSite” ray tracer by Remcom. Compared to other traditional algorithms, numeric experiments confirm the effectiveness of the proposed solution under several multipath configurations.

     

Towards the Development of Realistic DoS Dataset for Intelligent Transportation Systems

Wireless Personal Communications - Vehicular ad-hoc networks (VANETs) present security vulnerabilities, which make them prone to diverse cyberattacks. Denial of Service (DoS) is one of the most...     

Stepanov-Like Pseudo Almost Periodic Solution of Quaternion-Valued for Fuzzy Recurrent Neural Networks with Mixed Delays

Neural Processing Letters - Real-valued neural networks or complex-valued neural networks are sometimes inappropriate for some engineering and research problems for instance where the data is...