Trackable CEMB-Klean Cotton Transgenic Technology: Affordable Climate Neutral Agri-biotech Industrialization for Developing Countries

Background: Transgenic technology reflects the incorporation of novel useful traits in crop plants like cotton for economic benefits by overcoming the problems including insects’ pests and weeds in special. The present study is the success story of the continuous effort of CEMB team started back in the 1990s.Methods: This study includes characterization of a large number of Bacillus thuringiensis (Bt) strains taken from local soil and subjected to direct transformation of isolated BT genes into local cotton cultivars. Protocols for transformation into cotton plants were optimized and validated by the development of double gene codon optimized (Cry1Ac and Cry2A) transgenic cotton varieties.Results: The resulting GMOs in the form of CEMB-33, CA-12, CEMB-66 have been approved by Punjab Seed Council in 2013 and 2016 respectively. Double Bt and weedicide resistant cotton harboring CEMB-Modified and codon optimized cp4EPSPS (GTGene). These varieties can tolerate glyphosate spray @ 1900ml per acre without the appearance of necrotic spots/shedding and complete removal of all surrounding weeds in the cotton field is a significant advance to boost cotton production without spending much on insecticides and herbicides.Conclusion: In the current report, two unique sets of primers which amplify 1.1 Kb for CEMB-double Bt genes and 660 bp product for CEMB-Modified cp4EPSPS (GTGene) were tested. CEMB cotton variety CKC-01 is specially designed as low cost and easy to use by local farmer’s technology has the potential to revolutionize the cotton growing culture of the country.Keywords: Event detection; Bt Cotton; CEMB transgenic technology; GTGen

A Machine Learning-Based Anomaly Prediction Service for Software-Defined Networks

Software-defined networking (SDN) has gained tremendous growth and can be exploited in different network scenarios, from data centers to wide-area 5G networks. It shifts control logic from the devices to a centralized entity (programmable controller) for efficient traffic monitoring and flow management. A software-based controller enforces rules and policies on the requests sent by forwarding elements; however, it cannot detect anomalous patterns in the network traffic. Due to this, the controller may install the flow rules against the anomalies, reducing the overall network performance. These anomalies may indicate threats to the network and decrease its performance and security. Machine learning (ML) approaches can identify such traffic flow patterns and predict the systems’ impending threats. We propose an ML-based service to predict traffic anomalies for software-defined networks in this work. We first create a large dataset for network traffic by modeling a programmable data center with a signature-based intrusion-detection system. The feature vectors are pre-processed and are constructed against each flow request by the forwarding element. Then, we input the feature vector of each request to a machine learning classifier for training to predict anomalies. Finally, we use the holdout cross-validation technique to evaluate the proposed approach. The evaluation results specify that the proposed approach is highly accurate. In contrast to baseline approaches (random prediction and zero rule), the performance improvement of the proposed approach in average accuracy, precision, recall, and f-measure is (54.14%, 65.30%, 81.63%, and 73.70%) and (4.61%, 11.13%, 9.45%, and 10.29%), respectively

DOLPHIN: Dynamically Optimized and Load Balanced PatH for INter-domain SDN Communication

Software-Defined Networking has become an integral technology for large scale networks that require dynamic flow management. It separates the control function from data plane devices and centralizes it in a domain controller. However, only a limited number of switches can be managed by a single and centralized controller which introduces challenges such as scalability, reliability, and availability. Distributed controller architecture resolves these issues but also introduces new challenges of uneven load and traffic management across domains. As real-world networks have redundant links, hence a significant challenge is to distribute traffic flows on multiple paths, within a domain, and across multiple independent domains. The selection of ingress and egress switches becomes even more problematic if the intermediate domain is non-cooperative. In this work, we propose a Dynamically Optimized and Load-balanced Path for Inter-domain (DOLPHIN) communication system, a customized solution for different SDN controllers. It provides control beyond the virtual switch elements in intra and inter-domain communication and extends the range of programmability to wireless devices, such as the Internet of Things or vehicular networks. Extensive simulation results show that the traffic load is distributed evenly on multiple links connecting different domains. We model data center communication and 5G vehicular network communication to show that, by load balancing the flow completion times of the different types of network traffic can be significantly improved

Process Simulations of CO₂ Desorption in the Interaction between the Novel Direct Steam Stripping Process and Solvents

Intensive energy consumption is one of the major challenges for large-scale implementation of amine-based CO₂ chemical absorption processes. The study of diverse typical solvents in a novel process can effectively exploit the potential of energy saving to the utmost. This work focuses on the optimization of a novel direct steam stripping process using three typical solvents, monoethanolamine (MEA), 2-amino-2-methyl-1-propanol (AMP), and piperazine (PZ). A rate-based model was established to investigate the regeneration process. The direct steam stripping process exhibits 20–30% reduction in energy penalty for different solvents. Detailed information on the concentration and pressure profiles along the stripping column was presented in the direct steam stripping process. There is a strong flash process at the top of the column, which results in an enormous reduction of the latent heat. Therefore, using MEA as the absorbent shows the best performance on reducing energy penalty as a result of its best property of vapor–liquid equilibrium at high CO₂ loading. Capacity and size variations of absorber and heat exchangers were also analyzed to evaluate the influence on primary devices in the new process. The relatively lower CO₂ loading of the lean solvents can result in a significant decrease of the packing height of the absorber, which would achieve a 31% reduction in the packing height for AMP solvent, hence alleviating the high initial investment of the whole process

Techno-Economic Analysis of Hydrogen and Electricity Production by Biomass Calcium Looping Gasification

Combined cycle, biomass calcium looping gasification is proposed for a hydrogen and electricity production (CLGCC–H) system. The process simulation Aspen Plus is used to conduct techno-economic analysis of the CLGCC–H system. The appropriate detailed models are set up for the proposed system. Furthermore, a dual fluidized bed is optimized for hydrogen production at 700 °C and 12 bar. For comparison, calcium looping gasification with the combined cycle for electricity (CLGCC) is selected with the same parameters. The system exergy and energy efficiency of CLGCC–H reached as high as 60.79% and 64.75%, while the CLGCC system had 51.22% and 54.19%. The IRR and payback period of the CLGCC–H system, based on economic data, are calculated as 17.43% and 7.35 years, respectively. However, the CLGCC system has an IRR of 11.45% and a payback period of 9.99 years, respectively. The results show that the calcium looping gasification-based hydrogen and electricity coproduction system has a promising market prospect in the near future