Reconfigurable Architecture for Elliptic Curve Cryptography Using FPGA

The high performance of an elliptic curve (EC) crypto system depends efficiently on the arithmetic in the underlying finite field. We have to propose and compare three levels of Galois Field , , and . The proposed architecture is based on Lopez-Dahab elliptic curve point multiplication algorithm, which uses Gaussian normal basis for field arithmetic. The proposed is based on an efficient Montgomery add and double algorithm, also the Karatsuba-Ofman multiplier and Itoh-Tsujii algorithm are used as the inverse component. The hardware design is based on optimized finite state machine (FSM), with a single cycle 193 bits multiplier, field adder, and field squarer. The another proposed architecture is based on applications for which compactness is more important than speed. The FPGA’s dedicated multipliers and carry-chain logic are used to obtain the small data path. The different optimization at the hardware level improves the acceleration of the ECC scalar multiplication, increases frequency and the speed of operation such as key generation, encryption, and decryption. Finally, we have to implement our design using Xilinx XC4VLX200 FPGA device

Troubles with Reality and Order through the Imagination in a World of Disbelief and Disorder: Wallace Stevens and Supreme Fiction

The voice of the intellect is a soft one but it does not rest until it has gained a hearing - Sigmu nd Freud There have been a lot of writers throughout history who praised or criticized the world s beauty and ways They all had their ideas and advice for humanity But America s most celebrated twentiethcentury poet Wallace Stevens did not just rain down his ideas in his writings but took them as his motto for life And he called the sum of his ideas Supreme Fiction This Supreme Fiction according to Stevens is a supreme level of poetry that cleanses the mind and soul of its readers and reduces the hardships they have to face in real life In other words Supreme Fiction is Stevens replacement for the idea of God Deeply influenced by the Nietzschean idea of the Death of God Stevens wants to create a replacement for God for people to find comfort in a world of disbelief and disorder in the twentieth centur

Modified Design Hardware and Wireless Sensors Networks for Designing Future Combat System

Future combat system (FCS) is a network, which connects eighteen individual weapon scheme connected with the soldier all the way through the network.Supporting the soldier with real time situational awareness and cooperative operability among forces to entire a mission successfully with low mortality rate is the fundamental thought, there by enabling the soldier to see first, understand first and take action decisively. Since, most of the system operates in remote environment with battery support, power consumption is a serious issue. System with many nodal points has to be encrypted strictly to prevent the network from attacks (simply hacking). In this paper, an analysis was made on use of instruments with less power consumption, particularly custom design hardware and wireless sensors or sensors to tackle this difficulty

An Enhanced Distributed Clustering Methodology and Data Aggregation in Connecting Dissimilar Wireless Sensor Networks

One of the major advantages of wireless sensor network is their ability to operate in unattended, harsh environments in which existing human-in-the-loop monitoring schemes are uncertain, inefficient and sometimes impossible. Therefore, wireless sensors are expected to be deployed randomly in thepredetermined area of interest by a relatively uncontrolled manner. Given the huge area to be covered, the short lifespan of the battery-operated wireless sensors and the possibility of having damaged sensor nodes during deployment, large population of sensors are expected in the majority of wireless sensor applications. In centralized clustering, the cluster head isfixed. The rest of the nodes in the cluster act as member nodes. In distributed clustering, the cluster head is not fixed. The cluster head keeps onshiftingformnodetonodewithintheclusteronthebasisofsomeparameters.Hybridclusteringisthecombinationofbothcentralizedclusteringanddistributed clustering mechanisms. This paper gives a brief overview on clustering process in wireless sensor networks and an enhanced distributed clustering methodology and data aggregation in connecting dissimilar wireless sensor Networks. The proposed method is compared with LEACH and HEED Clustering methods

Distribution of Wireless Sensor Network for Efficient Environmental Monitoring and Greenhouse Control

Nowadays, the applications of these networks are copious, varied and the applications in agriculture are still budding. One interesting purpose is in environmental monitoring and greenhouse control, where the crop conditions such as weather and soil do not depend on natural agents. To control and observe the environmental factors, sensors and actuators are necessary. Under these conditions, these devices must be used to make a distributed measure, scattering sensors all over the greenhouse using distributed clustering mechanism. This paper reveals an initiative of environmental monitoring and greenhouse control using a sensor network

Integrative taxonomic approach to the systematics of the genus Pampus

In capture fisheries management, fish populations play a critical role in deciding management units employed. Projects like FishPopTrace have been employed in Europe, facilitating use of genetic data and morphological markers, including otoliths for this purpose. Multispecies, multi-gear fisheries landing closely resembling species mixes found in a common ecosystem or fishing ground is widespread in the Indian EEZ. Pomfrets are a low -volume, high value marine fishery in India and an important targeted fishery resource in the Indian Ocean region. However, the taxonomy of the genus Pampus has remained complex till date, with literature indicating several cryptic species leading to misidentification and nomenclature issues. This has serious ramifications for traceability concerns in seafood trade and supply chains as well to address sustainability concerns while preparing species specific fisheries management plans. A recent study involving an integrative taxonomic approach with conventional taxonomy tools as well as genetic data and otolith structure, has helped to clarify the taxonomic status of the genus Pampus in the Indian EEZ and the new findings with a global outlook is summarized below

Larvicidal activity of metabolites from the endophytic Podospora sp. against the malaria vector Anopheles gambiae

In a screening for natural products with mosquito larvicidal activities, the endophytic fungus Podospora sp. isolated from the plant Laggera alata (Asteraceae) was conspicuous. Two xanthones, sterigmatocystin (1) and secosterigmatocystin (2), and an anthraquinone derivative (3) 13-hydroxyversicolorin B were isolated after fermentation on M2 medium. These compounds were characterised using spectroscopic and X-ray analysis and examined against third instar larvae of Anopheles gambiae. The results demonstrated that compound 1 was the most potent one with LC50 and LC90 values of 13.3 and 73.5 ppm, respectively. Over 95% mortality was observed at a concentration 100 ppm after 24 h. These results compared farvourably with the commercial larvicide pylarvex® that showed 100% mortality at the same concentration. Compound 3 was less potent and had an LC50 of 294.5 ppm and over 95% mortality was achieved at a concentration of 1,000 ppm. Secosterigmatocystin (2) revealed relatively weak activity and therefore LC values were not determined

SBML Level 3: an extensible format for the exchange and reuse of biological models

Abstract Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution

MEMOTE for standardized genome-scale metabolic model testing

Supplementary information is available for this paper at https://doi.org/10.1038/s41587-020-0446-yReconstructing metabolic reaction networks enables the development of testable hypotheses of an organisms metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Geneproteinreaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols2, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking3. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality.We acknowledge D. Dannaher and A. Lopez for their supporting work on the Angular parts of MEMOTE; resources and support from the DTU Computing Center; J. Cardoso, S. Gudmundsson, K. Jensen and D. Lappa for their feedback on conceptual details; and P. D. Karp and I. Thiele for critically reviewing the manuscript. We thank J. Daniel, T. Kristjánsdóttir, J. Saez-Saez, S. Sulheim, and P. Tubergen for being early adopters of MEMOTE and for providing written testimonials. J.O.V. received the Research Council of Norway grants 244164 (GenoSysFat), 248792 (DigiSal) and 248810 (Digital Life Norway); M.Z. received the Research Council of Norway grant 244164 (GenoSysFat); C.L. received funding from the Innovation Fund Denmark (project “Environmentally Friendly Protein Production (EFPro2)”); C.L., A.K., N. S., M.B., M.A., D.M., P.M, B.J.S., P.V., K.R.P. and M.H. received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 686070 (DD-DeCaF); B.G.O., F.T.B. and A.D. acknowledge funding from the US National Institutes of Health (NIH, grant number 2R01GM070923-13); A.D. was supported by infrastructural funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections; N.E.L. received funding from NIGMS R35 GM119850, Novo Nordisk Foundation NNF10CC1016517 and the Keck Foundation; A.R. received a Lilly Innovation Fellowship Award; B.G.-J. and J. Nogales received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 686585 for the project LIAR, and the Spanish Ministry of Economy and Competitivity through the RobDcode grant (BIO2014-59528-JIN); L.M.B. has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 633962 for project P4SB; R.F. received funding from the US Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant DE-SC0010429; A.M., C.Z., S.L. and J. Nielsen received funding from The Knut and Alice Wallenberg Foundation, Advanced Computing program, grant #DE-SC0010429; S.K.’s work was in part supported by the German Federal Ministry of Education and Research (de.NBI partner project “ModSim” (FKZ: 031L104B)); E.K. and J.A.H.W. were supported by the German Federal Ministry of Education and Research (project “SysToxChip”, FKZ 031A303A); M.K. is supported by the Federal Ministry of Education and Research (BMBF, Germany) within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054); J.A.P. and G.L.M. acknowledge funding from US National Institutes of Health (T32-LM012416, R01-AT010253, R01-GM108501) and the Wagner Foundation; G.L.M. acknowledges funding from a Grand Challenges Exploration Phase I grant (OPP1211869) from the Bill & Melinda Gates Foundation; H.H. and R.S.M.S. received funding from the Biotechnology and Biological Sciences Research Council MultiMod (BB/N019482/1); H.U.K. and S.Y.L. received funding from the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) from the Ministry of Science and ICT through the National Research Foundation (NRF) of Korea; H.U.K. received funding from the Bio & Medical Technology Development Program of the NRF, the Ministry of Science and ICT (NRF-2018M3A9H3020459); P.B., B.J.S., Z.K., B.O.P., C.L., M.B., N.S., M.H. and A.F. received funding through Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517); D.-Y.L. received funding from the Next-Generation BioGreen 21 Program (SSAC, PJ01334605), Rural Development Administration, Republic of Korea; G.F. was supported by the RobustYeast within ERA net project via SystemsX.ch; V.H. received funding from the ETH Domain and Swiss National Science Foundation; M.P. acknowledges Oxford Brookes University; J.C.X. received support via European Research Council (666053) to W.F. Martin; B.E.E. acknowledges funding through the CSIRO-UQ Synthetic Biology Alliance; C.D. is supported by a Washington Research Foundation Distinguished Investigator Award. I.N. received funding from National Institutes of Health (NIH)/National Institute of General Medical Sciences (NIGMS) (grant P20GM125503).info:eu-repo/semantics/publishedVersio