The construction of self-dual normal polynomials over GF(2) and their applications to the Massey-Omura algorithm

Gaussian periods are used to locate a normal element of the finite field GF(2e) of odd degree e and an algorithm is presented for the construction of self-dual normal polynomials over GF(2) for any odd degree. This gives a new constructive proof of the existence of a self-dual basis for odd degree. The use of such polynomials in the Massey-Omura multiplier improves the efficiency and decreases the complexity of the multiplie

Distribution and Attachment of Bryozoans in the Intertidal Region of South Andaman Island

Aiming to collate the distribution and the attachment preference of intertidal bryozoan of Andaman waters, a study was carried out in three intertidal sites (Burmanallah, Kodiyaghat, and Chidiytapu) of southeastern coasts of the Andaman Islands between June and August 2016. The present study is the first exclusive report on bryozoans from the Andaman Islands after a long research gap of nine decades. During our investigation, a total of twelve genera were identified from both calcareous and non-calcareous substratum. Out of the twelve genera, eight genera were new records from the island. The present study showed that the attachment affinity of the Bryozoans is more towards natural substratum particularly on the rocks. The Thalamoporella sp. reported the most abundant species with maximum average colony length of 3.5 cm from the rock substratum

Utjecaj prinosa mlijeka na funkcionalnu aktivnost neutrofila u krava križanki Karan Fries (Holstein Friesian X Tharparkar) tijekom peripartusnog razdoblja.

To study the immunological activities of neutrophils, blood samples were collected from 6 high yielding (HY) and 6 low yielding (LY) Karan Fries (KF) cows on -15,-7, -5, -3, -2, -1 days prepartum, at calving and on 1, 2, 3, 5, 7 and 15 days postpartum. Plasma cortisol levels, phagocytic activity (PA), enzyme (Elastase, Collagenase and Cathepsin G) levels and expression of TLR-2, TLR-4 and IL-8 were also studied. Both HY KF and LY KF cows were found to have increased blood Total leukocyte counts (TLC) and neutrophil percent at calving, but HY KF cows had significantly (P<0.05) higher levels than LY KF cows. The number of band neutrophils were also significantly (P<0.05) higher in HY cows. Significant (P<0.05) immunosuppression in relation to PA was found for HY as compared to LY KF cows throughout the peripartum period, with the lowest immunosuppression at calving in both groups of cows. Cortisol levels were significantly (P<0.01) higher during calving and negatively correlated with neutrophilic functions. The difference between the two groups also remained significant (P<0.05) as higher level of cortisol were found in HY KF cows. Elastase, collagenase and cathepsin were significantly (P<0.05) decreased during parturition. Elastase was reduced approximately 2.5 times on the day of calving in LY KF cows, but no such major reduction was observed for HY KF cows. Collagenase and cathepsin levels were significantly (P<0.05) higher in LY cows. Expression of the TLR-2 gene was significantly (P<0.05) lower in HY cows during the whole peripartum period than in LY cows. Expression of the TLR-4 gene was significantly (P<0.05) lower on days 15 pre- and post-calving in HY cows. IL-8 differed significantly (P<0.05) only during the prepartum days. Lower neutrophilic function in cross bred cows with high production potential, provides lower disease resistance and makes the cows more susceptible to peripartum infection.S ciljem istraživanja imunološke aktivnosti neutrofila, od šest visokoproizvodnih (VP) i šest niskoproizvodnih (NP) Karan Fries (KF) krava prikupljeni su uzorci krvi -15, -7, -5, -3, -2, -1 dana prije partusa, zatim tijekom partusa, te 1, 2, 3, 5, 7 i 15 dana nakon partusa. Analizirana je razina kortizola u plazmi, aktivnost fagocita (AF), razine enzima (elastaze, kolagenaze, katepsina G) te ekspresija TLR-2, TLR-4 i IL-8. Kod obje skupine krava, VP KF i NP KF, utvrđeno je povećanje ukupnog broja leukocita (UBL) i postotka neutrofila pri teljenju, ali razine su kod VP KF krava bile signifikantno više (P<0,05) nego kod NP KF krava. Broj nesegmentiranih neutrofila bio je također signifikantno (P<0,05) viši kod VP krava. Signifikantna (P<0,05) imunosupresija u odnosu na AF utvrđena je kod VP u usporedbi sa NP KF kravama tijekom peripartusnog razdoblja, s najnižom razinom imunosupresije pri teljenju obje skupine krava. Razine kortizola bile su signifikantno (P<0,01) više tijekom teljenja i negativno povezane s funkcijom neutrofila. Razlike između skupina također su ostale signifikantne (P<0,05) i u slučaju više razine kortizola utvrđene kod VP KF krava. Elastaza, kolagenaza i katepsin signifikantno su (P<0,05) opadali tijekom teljenja. Elastaza je smanjena za približno 2,5 puta na dan teljenja NP KF krava dok kod VP KF krava takvo jako smanjenje nije opaženo. Razine kolagenaze i katepsina bile su signifikantno (P<0,05) više kod NP krava. U odnosu na NP krave, ekspresije TLR-2 gena kod VP krava bile su signifikantno (P<0,05) niže tijekom cijelog peripartusnog razdoblja. Ekspresija TLR-4 gena bila je signifikantno (P<0,05) niža 15. dan prije i poslije teljenja VP krava. IL-8 je bio signifikantno različit (P<0,05) samo tijekom prepartusnog razdoblja. Snižena funkcija neutrofila kod krava križanki s visokim proizvodnim potencijalom doprinosi njihovoj manjoj otpornosti na bolesti i čini ihosjetljivijima na infekcije tijekom peripartusnog razdoblja

Infrequent pattern detection for reliable network traffic analysis using robust evolutionary computation

While anomaly detection is very important in many domains, such as in cybersecurity, there are many rare anomalies or infrequent patterns in cybersecurity datasets. Detection of infrequent patterns is computationally expensive. Cybersecurity datasets consist of many features, mostly irrelevant, resulting in lower classification performance by machine learning algorithms. Hence, a feature selection (FS) approach, i.e., selecting relevant features only, is an essential preprocessing step in cybersecurity data analysis. Despite many FS approaches proposed in the literature, cooperative co-evolution (CC)-based FS approaches can be more suitable for cybersecurity data preprocessing considering the Big Data scenario. Accordingly, in this paper, we have applied our previously proposed CC-based FS with random feature grouping (CCFSRFG) to a benchmark cybersecurity dataset as the preprocessing step. The dataset with original features and the dataset with a reduced number of features were used for infrequent pattern detection. Experimental analysis was performed and evaluated using 10 unsupervised anomaly detection techniques. Therefore, the proposed infrequent pattern detection is termed Unsupervised Infrequent Pattern Detection (UIPD). Then, we compared the experimental results with and without FS in terms of true positive rate (TPR). Experimental analysis indicates that the highest rate of TPR improvement was by cluster-based local outlier factor (CBLOF) of the backdoor infrequent pattern detection, and it was 385.91% when using FS. Furthermore, the highest overall infrequent pattern detection TPR was improved by 61.47% for all infrequent patterns using clustering-based multivariate Gaussian outlier score (CMGOS) with FS

Dynamic and Static Gesture Recognition System Using Moments

Gesture recognition is the novel idea to enhance the signs recognition of those who have speech and hearing disability .Our project discusses an improved method for gesture recognition .The algorithm extracts the gestures from the video given to it and it detects the hand using HSV skin color segmentation in the intent to eliminate the other parts of the body and detect only hands .It distinguishes between static and dynamic gestures and extracts the appropriate feature vector .We used SPHINX parser to form word from set of letters. We strive to enhance the reliability and efficiency by using faster static gesture recognition algorithm

Time series prediction of COVID-19 by mutation rate analysis using recurrent neural network-based LSTM model

SARS-CoV-2, a novel coronavirus mostly known as COVID-19 has created a global pandemic. The world is now immobilized by this infectious RNA virus. As of June 15, already more than 7.9 million people have been infected and 432k people died. This RNA virus has the ability to do the mutation in the human body. Accurate determination of mutation rates is essential to comprehend the evolution of this virus and to determine the risk of emergent infectious disease. This study explores the mutation rate of the whole genomic sequence gathered from the patient's dataset of different countries. The collected dataset is processed to determine the nucleotide mutation and codon mutation separately. Furthermore, based on the size of the dataset, the determined mutation rate is categorized for four different regions: China, Australia, the United States, and the rest of the World. It has been found that a huge amount of Thymine (T) and Adenine (A) are mutated to other nucleotides for all regions, but codons are not frequently mutating like nucleotides. A recurrent neural network-based Long Short Term Memory (LSTM) model has been applied to predict the future mutation rate of this virus. The LSTM model gives Root Mean Square Error (RMSE) of 0.06 in testing and 0.04 in training, which is an optimized value. Using this train and testing process, the nucleotide mutation rate of 400th patient in future time has been predicted. About 0.1% increment in mutation rate is found for mutating of nucleotides from T to C and G, C to G and G to T. While a decrement of 0.1% is seen for mutating of T to A, and A to C. It is found that this model can be used to predict day basis mutation rates if more patient data is available in updated time

Characterizations of the diurnal shapes of OI 630.0 nm dayglow intensity variations: inferences

International audienceMeasurements of OI 630.0 nm thermospheric dayglow emission by means of the Dayglow Photometer (DGP) at Mt. Abu (24.6° N, 73.7° E, dip lat 19.09° N), a station under the crest of Equatorial Ionization Anomaly (EIA), reveal day-to-day changes in the shapes of the diurnal profiles of dayglow intensity variations. These shapes have been characterized using the magnetometer data from equatorial and low-latitude stations. Substantial changes have been noticed in the shapes of the dayglow intensity variations between 10:00?15:00 IST (Indian Standard Time) during the days when normal and counter electrojet events are present over the equator. It is found that the width (the time span corresponding to 0.8 times the maximum dayglow intensity) of the diurnal profile has a linear relationship with the integrated electrojet strength. Occasional deviation from this linear relationship is attributed to the presence of substantial mean meridional wind

Maximum Power Tracking System Based on Power Electronic Topology for Wind Energy Conversion System Applications

Wind energy has emerged as a dominant alternative power generation source. Conventional energy resources are depleting and their increasing cost cannot be ignored. Wind energy has taken an important role in replacing fossil fuels and nuclear energy. However, optimal exploitation of wind energy systems has remained a thought-provoking problem due to the nonlinear behavior of the wind speed. Distribution generation system and isolated micro-grid (MG) are mainly used in a wind energy system in order to utilize maximum potential of wind through the maximum power tracking algorithm based on DC-DC boost power electronic converter. In this paper, authors propose a technique which is effectively workable, highly reliable, of low cost and induces less mechanical stress on the wind generator system (WGS). Experimental results of the proposed system verify that the effectiveness near optimal WGS output power increased approximately up to 50%. Thus, better exploitation of the available wind power is obtained under low wind speeds