Susceptibility of Field-Collected Pupations of the Corn Earworm, Helicoverpa zea (Boddie) (Lepidoptera: Noctuidae) from Three Southern States of the U.S. to Cry1A.105 and Cry2Ab2 Proteins

The corn earworm (CEW), Helicoverpa zea (Boddie) (Lepidoptera: Noctuidae), is a major target pest of pyramided Bt corn and Bt cotton in the U.S. In 2016 and 2017, notable corn ear damage and larval survival of CEW were observed on pyramided Cry1A.105/Cry2Ab2 corn in some fields in northeast Louisiana. The objectives of this study were 1) to determine if the ear damage and larval survival observed in the area were due to resistance development to the Bt proteins in the plants, and 2) if resistance had occurred, to determine the approximate distributions of the resistance in the southern region of the U.S. To accomplish the proposed objectives, 12 populations of CEW were collected from Bt and non-Bt corn plants in multiple locations in Louisiana, Georgia, and Florida. Diet-overlay bioassays were conducted to examine the susceptibility of the progeny produced from the field-collected populations to Cry1A.105 and Cry2Ab2. Results of the bioassays showed that the median lethal concentrations (LC50s) of Cry1A.105 and Cry2Ab2 for the populations collected from the areas with control problem occurrence were as much as \u3e909-fold and \u3e25-fold greater than that of a known Bt-susceptible strain, respectively. The results documented that the observed field control problems of Cry1A.105/Cry2Ab2 corn in northeast Louisiana was due to resistance development of the insect to the Bt proteins in the plants. This is the first documentation of field resistance to Bt corn in any target insect species in the U.S. mid-south region. However, susceptibility levels to Cry1A.105 and Cry2Ab2 varied greatly among the CEW populations collected from the three states, suggesting a mosaic distribution of the resistance in the region. Several factors could have contributed to the rapid development of the resistance to Cry1A.105/Cry2Ab2 corn plants in the insect. The documentation of the field resistance to Cry1A.105/Cry2Ab2 corn in CEW should have important implication for development of effective resistance management strategies for the sustainable use of Bt crop technology in the region

To Enhance the OTP Generation Process for Cloud Data Security using Diffie-Hellman and HMAC

Cloud computing is an innovation or distributed network where user can move their data and any application programming on it. In any case, there is a few issues in cloud computing, the main one is security on the grounds that each user store their helpful data on the network so they need their data ought to be protected from any unapproved access, any progressions that is not done for user's benefit. There are diverse encryption methods utilized for security reason like FDE and FHE. To tackle the issue of Key management, Key Sharing different plans have been proposed. The outsider auditing plan will be fizzled, if the outsider's security is bargained or of the outsider will be malicious. To tackle this issue, we will chip away at to design new modular for key sharing and key management in completely Homomorphic Encryption plan. In this paper, we have utilized the symmetric key understanding algorithm named Diffie Hellman, it is key trade algorithm with make session key between two gatherings who need to speak with each other and HMAC for the data integrity OTP(One Time Password) is made which gives more security. Because of this the issue of managing the key is expelled and data is more secured

Sikhism: A Targeted Religion

This paper hopes to explore targeted racist crimes happening against the Sikh community. The paper goes over why these incidents seem to be happening, what affect it has on the Sikh community, and finally what the Sikh community is doing to educate more individuals on what Sikhism is

A Study of Feature Reduction Techniques and Classification for Network Anomaly Detection

Due to the launch of new applications the behavior of internet traffic is changing. Hackers are always looking for sophisticated tools to launch attacks and damage the services. Researchers have been working on intrusion detection techniques involving machine learning algorithms for supervised and unsupervised detection of these attacks. However, with newly found attacks these techniques need to be refined. Handling data with large number of attributes adds to the problem. Therefore, dimensionality based feature reduction of the data is required. In this work three reduction techniques, namely, Principal Component Analysis (PCA), Artificial Neural Network (ANN), and Nonlinear Principal Component Analysis (NLPCA) have been studied and analyzed. Secondly, performance of four classifiers, namely, Decision Tree (DT), Support Vector Machine (SVM), K Nearest Neighbor (KNN) and Naïve Bayes (NB) has been studied for the actual and reduced datasets. In addition, novel performance measurement metrics, Classification Difference Measure (CDM), Specificity Difference Measure (SPDM), Sensitivity Difference Measure (SNDM), and F1 Difference Measure (F1DM) have been defined and used to compare the outcomes on actual and reduced datasets. Comparisons have been done using new Coburg Intrusion Detection Data Set (CIDDS-2017) dataset as well widely referred NSL-KDD dataset. Successful results were achieved for Decision Tree with 99.0 percent and 99.8 percent accuracy on CIDDS and NSLKDD datasets respectively

Gender Bias and Artificial Intelligence: A Challenge within the Periphery of Human Rights

Technology is advancing at an exponential rate, and artificial intelligence has become a contentious issue of the day. A plethora of fields influencing human life has been impacted by artificial intelligence, whereas the development of artificial intelligence has opened Pandora’s box of legal concerns. Several international organizations, including the United Nations, have identified gender equality as an indispensable constituent of the protection of human rights. The voyage of gender equality has seen a long phase of struggle and persists. This paper aims to analyze, in what manner artificial intelligence is affecting gender equality, raising concerns on the issues regarding the role played by the United Nations in securing gender equality through conventions and resolutions, is artificial intelligence capable of posing a threat to gender equality and what measures can be implemented to secure gender equality about artificial intelligence. 

AN ANALYSIS OF THE COMPONENTS OF SELF ESTEEM IN INDIVIDUAL, TEAM AND DUAL SPORTS PLAYERS

The purpose of the study was to find out the significant differences among Individual, Team and Dual Sport Players on the variable Self Esteem. For the purpose of the present study, two hundred fifty eight (N=258), Male subjects between the age group of 18-25 years volunteered to participate in the study. The subjects were purposively assigned into three groups: Group-A: Guru Nanak Dev University, Amritsar (N1=86); Group-B: Panjab University, Chandigarh (N2=86) and Group-C: Punjabi University, Patiala (N3=86). To measure the level of Self-Esteem of subjects for the present study, the Self-Esteem Inventory (SEI) developed by Prasad and Thakur (1988) was administered. This scale consists of two parameters namely: Personal Perceived Self-Esteem and Social Perceived Self-Esteem. The differences in the mean of each group for selected variable were tested for the significance of difference by One-way Analysis of Variance (ANOVA). For testing the hypotheses, the level of significance was set at 0.05. To conclude, it is significant to mention in relation to Self-Esteem that results of Individual Sport, Team Sports and Dual Sports players with regards to Social Perceived Self-Esteem and Self- Esteem were found statistically insignificant (P > .05) whereas, with regards to Personal Perceived Self Esteem were found statistically significant (P < .05).  Article visualizations

Mixed Pixel Resolution by Evolutionary Algorithm: A Survey

Now a day2019;s Remote Sensing is a mature research area. Remote sensing is defined as a technique for acquiring the information about an object without making physical contact with that image via remote sensors. But the major problem of remotely sensed images is mixed pixel which always degrades the image quality. Mixed pixels are usually the biggest reason for degrading the success in image classification and object recognition. Another major problem is the decomposition of mixed pixels precisely and effectively. Remote sensing data is widely used for the classification of types of features such as vegetation, water body etc but the problem occurs in tagging appropriate class to mixed pixels. In this paper we attempted to present an approach for resolving the mixed pixels by using optimization algorithm i.e. Biogeography based optimization. The main idea is to tag the mixed pixel to a particular class by finding the best suitable class for it using the BBO parameters i.e. Migration and Mutation

An Overview of PAPR Reduction Techniques for an MC-CDMA System

Abstract-MC-CDMA is the most promising technique for high bit rate and high capacity transmission in wireless communication. One of the challenging issues of MC-CDMA system is very high PAPR due to large number of sub-carriers which reduces the system efficiency. This paper describes the various PAPR reduction techniques for MC-CDMA system. Criterion for the selection of PAPR reduction technique and also the comparison between the reduction techniques has been discussed