The role of the Arabidopsis AHL15/REJUVENATOR gene in developmental phase transitions

This thesis describes the functional analysis of the Arabidopsis AHL15 gene. We showed that AHL15 and plays major roles in directing plant cell totipotency. Furthermore, our data show that AHL15 has a role in opening of chromatin, leading to endomitosis and polyploidy in embryonic cells and that AHL15 overexpression can lead to polyploid SEs and plants, probable by endomitotic events caused by incomplete chromatin condensation during cell division. More detailed analyses revealed that AHL15 is not specifically involved in the embryogenesis program, but that, surprisingly, the protein rather is involved in post embryonic development. Our analyses in Arabidopsis and tobacco showed that AHL15act as suppressors of developmental phase changes. In Arabidopsis, reduced AHL15 expression coincided with a faster progression from the vegetative to the reproductive phase. By contrast, AHL15 overexpression delayed the vegetative to reproductive phase change in both Arabidopsis and tobacco, causing some axillary meristems to be maintained in the vegetative phase, thereby allowing polycarpic development in these monocarpic annuals. By using RNA sequencing analysis, an extensive reprogramming of the transcriptome detected after AHL15 activation. Rapid decondensation of heterochromatin was observed after AHL15 activation, indicating that the global reprogramming of the transcriptome by AHL15 might at least in part be caused by extensive modulation of the chromatin configuration.  Plant science

Soft Magnetoactive Laminates:Large Deformations, Transverse Elastic Waves and Band Gaps Tunability by a Magnetic Field

We investigate the behavior of soft magnetoactive periodic laminates under remotely applied magnetic field. We derive explicit formulae for the induced deformation due to magnetic excitation of the laminates with hyperelastic magnetoactive phases. Next, we obtain the closed-form formulas for the velocities of long transverse waves. We show the dependence of the wave velocity on the applied magnetic intensity and induced strains, as well as on the wave propagation direction. Based on the long wave analysis, we derive closed form formulae for the critical magnetic field corresponding to loss of macroscopic stability. Finally, we analyze the transverse wave band gaps appearing in magnetoactive laminates in direction normal to the layers. We illustrate the band gap tunability – width and position – by magnetically induced deformation

A Wormhole Attack Detection and Prevention Technique in Wireless Sensor Networks

Security is one of the major and important issues surrounding network sensors because of its inherent liabilities, i.e. physical size. Since network sensors have no routers, all nodes involved in the network must share the same routing protocol to assist each other for the transmission of packets. Also, its unguided nature in dynamic topology makes it vulnerable to all kinds of security attack, thereby posing a degree of security challenges. Wormhole is a prominent example of attacks that poses the greatest threat because of its difficulty in detecting and preventing. In this paper, we proposed a wormhole attach detection and prevention mechanism incorporated AODV routing protocol, using neighbour discovery and path verification mechanism. As compared to some preexisting methods, the proposed approach is effective and promising based on applied performance metrics

Reconstructing interacting new agegraphic polytropic gas model in non-flat FRW universe

We study the correspondence between the interacting new agegraphic dark energy and the polytropic gas model of dark energy in the non-flat FRW universe. This correspondence allows to reconstruct the potential and the dynamics for the scalar field of the polytropic model, which describe accelerated expansion of the universe.Comment: 9 page

A fuzzy multi-criteria decision method for locating ecotourism development

The County of Khorram-Abad enjoys a high potential for ecotourism because of its mountains, forests, natural mineral springs, natural waterfalls and diversity in folks and cultures. But, un-planned and uncontrolled ecotourism can have negative effects on environment, economy, culture and even the security of eco-tourists. The main purpose of this study is to present a fuzzy multi-criteria decision making (FMCDM) method for ecotourism development location selection. In this study we created 5 main criteria and 14 sub-criteria for locating the suitable areas for ecotourism development based on literature reviews and experts’ opinions. Delphi method was used to obtain the significant criteria and sub-criteria for ecotourism development by interviewing the foregoing experts and related managers. Then, the methods of fuzzy set theory, linguistic value, hierarchical structure analysis, and fuzzy analytic hierarchy process (FAHP) were applied to find the relative weights or importance degree of each criterion and rank the overall criteria as the measurable indices for ecotourism development. Different layers were prepared and were combined using weighted linear combination (WLC) method in GIS environment. The results showed that 6.57 and 38.65 percentages of the area have an excellent and good potential for the ecotourism development. In addition, the study confirms that FAHP and GIS could be a powerful combination to apply for different land use planning

Restoring New Agegraphic Dark Energy in RS II Braneworld

Motivated by recent works [1,2], we investigate new agegraphic model of dark energy in the framework of RS II braneworld. We also include the case of variable gravitational constant in our model. Furthermore, we establish correspondence between the new agegraphic dark energy with other dark energy candidates based on scalar fields.Comment: 13 pages, accepted for publication in IJT

DDoS Hide & Seek:On the effectiveness of a booter services takedown

Booter services continue to provide popular DDoS-as-a-service platforms and enable anyone irrespective of their technical ability, to execute DDoS attacks with devastating impact. Since booters are a serious threat to Internet operations and can cause significant financial and reputational damage, they also draw the attention of law enforcement agencies and related counter activities. In this paper, we investigate booter-based DDoS attacks in the wild and the impact of an FBI takedown targeting 15 booter websites in December 2018 from the perspective of a major IXP and two ISPs. We study and compare attack properties of multiple booter services by launching Gbps-level attacks against our own infrastructure. To understand spatial and temporal trends of the DDoS traffic originating from booters we scrutinize 5 months, worth of inter-domain traffic. We observe that the takedown only leads to a temporary reduction in attack traffic. Additionally, one booter was found to quickly continue operation by using a new domain for its website

Assessment of geostatistical and interpolation methods for mapping forest dieback intensity in Zagros forests

During recent years, oak decline has been widely spread across Brant’s oak (Quercus brantii Lindl.) stands in the Zagros Mountains, Western Iran, which caused large-area forest dieback in several sites. Mapping the intensity and spatial distribution of forest dieback is essential for developing management and control strategies. This study evaluated a range of geostatistical and interpolation methods to explore the spatial structure and provide areabased maps of the intensity of forest dieback across a representative test site - Ilam Province - that was severely affected by Oak decline. The geostatistical analysis provided in-depth measures of the spatial structure amongst the selective sampling units (120 quadratic sample plots of 1200 m2), which eventually resulted in an area-based maps of dieback intensity. The accuracy of the applied methods was assessed by mean error percentage (%ME), root mean squared error percentage (%RMSE) and coefficient of determination (R2). Results showed moderate spatial structure within the sampling units. Moreover, cokriging (associated with soil humidity and aspect as independent variables) approach resulted in the highest accuracy, followed by two other methods of kriging and Radial Basis Function. Results suggested that cokriging can accurately estimate the intensity of dieback and its spatial distribution in the study area. According to this, an average dieback intensity of 18.12 % was estimated within the study area

The generalized second law for the interacting generalized Chaplygin gas model

We investigate the validity of the generalized second law (GSL) of gravitational thermodynamics in a non-flat FRW universe containing the interacting generalized Chaplygin gas with the baryonic matter. The dynamical apparent horizon is assumed to be the boundary of the universe. We show that for the interacting generalized Chaplygin gas as a unified candidate for dark matter (DM) and dark energy (DE), the equation of state parameter can cross the phantom divide. We also present that for the selected model under thermal equilibrium with the Hawking radiation, the GSL is always satisfied throughout the history of the universe for any spatial curvature, independently of the equation of state of the interacting generalized Chaplygin gas model.Comment: 8 page

Training set cleansing of backdoor poisoning by self-supervised representation learning

A backdoor or Trojan attack is an important type of data poisoning attack against deep neural network (DNN) classifiers, wherein the training dataset is poisoned with a small number of samples that each possess the backdoor pattern (usually a pattern that is either imperceptible or innocuous) and which are mislabeled to the attacker's target class. When trained on a backdoor-poisoned dataset, a DNN behaves normally on most benign test samples but makes incorrect predictions to the target class when the test sample has the backdoor pattern incorporated (i.e., contains a backdoor trigger). Here we focus on image classification tasks and show that supervised training may build stronger association between the backdoor pattern and the associated target class than that between normal features and the true class of origin. By contrast, self-supervised representation learning ignores the labels of samples and learns a feature embedding based on images' semantic content. %We thus propose to use unsupervised representation learning to avoid emphasising backdoor-poisoned training samples and learn a similar feature embedding for samples of the same class. Using a feature embedding found by self-supervised representation learning, a data cleansing method, which combines sample filtering and re-labeling, is developed. Experiments on CIFAR-10 benchmark datasets show that our method achieves state-of-the-art performance in mitigating backdoor attacks