1,3-Dipolar cycloaddition polymerization reactions of novel macromolecules containing sym-tetrazine rings

We report the synthesis of sym-1,4-diphenyl-1,4-dihydro-1,2,4,5-polytetrazine through 1,3-dipolar cycloaddition polymerization reactions where bis-hydrazonoyl chloride was converted to a tetrazine based polymer through bis-nitrilimine intermediates. Polymer molecular weights approached 90,000 g/mol under optimized reaction conditions with low polydispersity indices of approximately 1.05. The polymers are soluble in a variety of organic solvents and the reactions were characterized through a series of spectral, thermal and chromatographic techniques. The tetrazine based polymers display high complexation potential with cobalt chloride demonstrating metal complexation capability.     

Prediction and early detection of mycotoxigenic Fusarium culmorum in wheat by direct PCR-based procedure

Cereals contamination with mycotoxigenic species of Fusarium is considered as a major source of trichothecenes and other mycotoxin groups which cause severe yield losses and serious diseases in human and animal health. Early detection of Fusarium species could be for a great interest to prevent mycotoxin contaminating agro-products.We have established for the first time a direct polymerase chain reaction (DPCR) protocol to detect contamination with trichothecene-producing F. culmorum in wheat samples. We have successfully amplified fungal genomic DNA using specific primers targeting the trichothecenes biosynthetic Tri5 gene. We further investigated a versatile multiplex-DPCR on the basis of Tri5 gene and IGS (Intergenic Spacer of rDNA) specific sequence of F. culmorum for its identification at specie level and prediction of its potential trichothecenes production simultaneously. Our protocol allowed amplification directly from crude templates with no need of DNA extraction or purification methods and did not require any culture-based approach.These DPCR assays represent a reliable tool for high throughput screening, detection and rapid characterization of mycotoxigenic isolates as well as diverse applications in food industry.     

Verifying distributed real-time properties of embedded systems via graph transformations and model checking

Component middleware provides dependable and efficient platforms that support key functional, and quality of service (QoS) needs of distributed real-time embedded (DRE) systems. Component middleware, however, also introduces challenges for DRE system developers, such as evaluating the predictability of DRE system behavior, and choosing the right design alternatives before committing to a specific platform or platform configuration. Model-based technologies help address these issues by enabling design-time analysis, and providing the means to automate the development, deployment, configuration, and integration of component-based DRE systems. To this end, this paper applies model checking techniques to DRE design models using model transformations to verify key QoS properties of component-based DRE systems developed using Real-time CORBA. We introduce a formal semantic domain for a general class of DRE systems that enables the verification of distributed non-preemptive real-time scheduling. Our results show that model-based techniques enable design-time analysis of timed properties and can be applied to effectively predict, simulate, and verify the event-driven behavior of component-based DRE systems. This research was supported by the NSF Grants CCR-0225610 and ACI-0204028 Gabor Madl is a Ph.D. student and a graduate student researcher at the Center for Embedded Computer Systems at the University of California, Irvine. His advisor is Nikil Dutt. His research interests include the formal verification, optimization, component-based composition, and QoS management of distributed real-time embedded systems. He received his M.S. in computer science from Vanderbilt University and in computer engineering from the Budapest University of Technology and Economics. Dr. Sherif Abdelwahed received his Ph.D. degree in Electrical and Computer Engineering from the University of Toronto, Canada, in 2001. During 2000–2001, he was a research scientist with the system diagnosis group at the Rockwell Scientific Company. Since 2001 he has been with the Department of Electrical Engineering and Computer Science at Vanderbilt University as a Research Assistant Professor. His research interests include verification and control of distributed real-time systems, and model-based diagnosis of discrete-event and hybrid systems. Dr. Douglas C. Schmidt is a Professor of Computer Science, Associate Chair of the Computer Science and Engineering program, and a Senior Researcher in the Institute for Software Integrated Systems (ISIS) all at Vanderbilt University. He has published over 300 technical papers and 6 books that cover a range of research topics, including patterns, optimization techniques, and empirical analyses of software frameworks and domain-specific modeling environments that facilitate the development of distributed real-time and embedded (DRE) middleware and applications. Dr. Schmidt has served as a Deputy Office Director and a Program Manager at DARPA, where he lead the national R&D effort on middleware for DRE systems. In addition to his academic research and government service, Dr. Schmidt has over fifteen years of experience leading the development of ACE, TAO, CIAO, and CoSMIC, which are widely used, open-source DRE middleware frameworks and model-driven tools that contain a rich set of components and domain-specific languages that implement patterns and product-line architectures for high-performance DRE systems.     

Secure Irrigation System for Olive Orchards Using Internet of Things

Smart irrigation system, also referred as precision irrigation system, is an attractive solution to save the limited water resources as well as to improve crop productivity and quality. In this work, by using Internet of things (IoT), we aim to design a smart irrigation system for olive groves. In such IoT system, a huge number of low-power and low-complexity devices (sensors, actuators) are interconnected. Thus, a great challenge is to satisfy the increasing demands in terms of spectral efficiency. Moreover, securing the IoT system is also a critical challenge, since several types of cybersecurity threats may pose. In this paper, we address these issues through the application of the massive multiple-input multiple-output (M-MIMO) technology. Indeed, M-MIMO is a key technology of the fifth generation (5G) networks and has the potential to improve spectral efficiency as well as the physical layer security. Specifically, by exploiting the available M-MIMO channel degrees of freedom, we propose a physical layer security scheme based on artificial noise (AN) to prevent eavesdropping. Numerical results demonstrate that our proposed scheme outperforms traditional ones in terms of spectral efficiency and secrecy rate.     

Feature Subset Selection with Artificial Intelligence-Based Classification Model for Biomedical Data

Recently, medical data classification becomes a hot research topic among healthcare professionals and research communities, which assist in the disease diagnosis and decision making process. The latest developments of artificial intelligence (AI) approaches paves a way for the design of effective medical data classification models. At the same time, the existence of numerous features in the medical dataset poses a curse of dimensionality problem. For resolving the issues, this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data (FSS-AICBD) technique. The FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier results. Primarily, the FSS-AICBD technique undergoes min-max normalization technique to prevent data complexity. In addition, the information gain (IG) approach is applied for the optimal selection of feature subsets. Also, group search optimizer (GSO) with deep belief network (DBN) model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GSO algorithm. The choice of IG and GSO approaches results in promising medical data classification results. The experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare datasets. The simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures.     

Full band waveguide turnstile junction orthomode transducer with phase matched outputs

A >40% bandwidth fully scalable turnstile‐based waveguide orthomode transducer having excellent phase performance is described for the WR75 standard rectangular waveguide. Flexible bandwidth tuning is achieved through the use of an interchangeable stepped scattering element. Reduced height waveguide topology provides a simple, compact, and robust design against mechanical tolerances. The intrinsic broadband nature of half‐height E‐plane bends and single‐step power combiners assures high order mode free increased bandwidth in balanced phase operation. The designed orthomode transducer exhibits a return loss better than 23 dB at any port, an insertion loss less than 0.06 dB, and an isolation of 50 dB over the full bandwidth. Moreover, the phase difference between orthogonal polarizations is lower than 0.7° over the band, thus enabling applications where phase‐matched outputs are required. This design has been chosen for the QUIJOTE cosmic microwave background experiment due to its cost‐effective, compact design, and high‐quality performance as well as being readily scalable to the WR51 and WR28 waveguide bands. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.     

Improved Ant Lion Optimizer with Deep Learning Driven Arabic Hate Speech Detection

Arabic is the world’s first language, categorized by its rich and complicated grammatical formats. Furthermore, the Arabic morphology can be perplexing because nearly 10,000 roots and 900 patterns were the basis for verbs and nouns. The Arabic language consists of distinct variations utilized in a community and particular situations. Social media sites are a medium for expressing opinions and social phenomena like racism, hatred, offensive language, and all kinds of verbal violence. Such conduct does not impact particular nations, communities, or groups only, extending beyond such areas into people’s everyday lives. This study introduces an Improved Ant Lion Optimizer with Deep Learning Dirven Offensive and Hate Speech Detection (IALODL-OHSD) on Arabic Cross-Corpora. The presented IALODL-OHSD model mainly aims to detect and classify offensive/hate speech expressed on social media. In the IALODL-OHSD model, a three-stage process is performed, namely pre-processing, word embedding, and classification. Primarily, data pre-processing is performed to transform the Arabic social media text into a useful format. In addition, the word2vec word embedding process is utilized to produce word embeddings. The attention-based cascaded long short-term memory (ACLSTM) model is utilized for the classification process. Finally, the IALO algorithm is exploited as a hyperparameter optimizer to boost classifier results. To illustrate a brief result analysis of the IALODL-OHSD model, a detailed set of simulations were performed. The extensive comparison study portrayed the enhanced performance of the IALODL-OHSD model over other approaches.     

Optimal Deep Learning Based Ransomware Detection and Classification in the Internet of Things Environment

With the advent of the Internet of Things (IoT), several devices like sensors nowadays can interact and easily share information. But the IoT model is prone to security concerns as several attackers try to hit the network and make it vulnerable. In such scenarios, security concern is the most prominent. Different models were intended to address these security problems; still, several emergent variants of botnet attacks like Bashlite, Mirai, and Persirai use security breaches. The malware classification and detection in the IoT model is still a problem, as the adversary reliably generates a new variant of IoT malware and actively searches for compromise on the victim devices. This article develops a Sine Cosine Algorithm with Deep Learning based Ransomware Detection and Classification (SCADL-RWDC) method in an IoT environment. In the presented SCADL-RWDC technique, the major intention exists in recognizing and classifying ransomware attacks in the IoT platform. The SCADL-RWDC technique uses the SCA feature selection (SCA-FS) model to improve the detection rate. Besides, the SCADL-RWDC technique exploits the hybrid grey wolf optimizer (HGWO) with a gated recurrent unit (GRU) model for ransomware classification. A widespread experimental analysis is performed to exhibit the enhanced ransomware detection outcomes of the SCADL-RWDC technique. The comparison study reported the enhancement of the SCADL-RWDC technique over other models.