Compliance, competitiveness and market access : a study on Indian seafood industry

This study attempts to estimate the effects of the sanitary and phytosanitary (SPS) measures in terms of trade elasticity of regulations and competitiveness of exports. In spite of the gener-alized acknowledgment of growing liberalization of trade between countries, there are still numerous obstacles to trade, more of the non-tariff type. This study aims to contribute to the literature on quantifying the eco-nomic impact of health and environmental regulations expressed in the form of SPS measures on international trade in agro-food products, by taking Indian seafood exports as a case study. The gravity analysis, complemented with the constant market share (CMS) model, helped to obtain an insight into the overall dynamics of the export markets, trade flows and competitiveness of fish and fishery products (aggregate level), shrimps and cephalopods. For the regulatory variable, the maxi-mum residue limit (MRL) on cadmium in the model is used as an independent variable. A detailed study on the micro level dynamics of Kerala seafood export sector has been carried out, particularly to understand the industry level changes experienced during the stringent food safety regime. The results indicate that regulations on cadmium appear to be moderately trade restrictive. At the same time, results are divergent at the disaggregate level, which is significant from the point of view of trade policy. The most important aspect of the existing chain in Kerala’s seafood sector is the gradual disappearance of the independent preprocessing sector which has been an important stakeholder of the seafood value chain in Kerala. The preprocessing node of the value chain is getting integrated to the processing sector causing a major restructuring of the existing value chain. Keywords: Competitiveness, Trade models, Seafood Industry, Value Chain JEL Classification: F14, F18, L15, Q17, Q1

Managing Service-Heterogeneity using Osmotic Computing

Computational resource provisioning that is closer to a user is becoming increasingly important, with a rise in the number of devices making continuous service requests and with the significant recent take up of latency-sensitive applications, such as streaming and real-time data processing. Fog computing provides a solution to such types of applications by bridging the gap between the user and public/private cloud infrastructure via the inclusion of a "fog" layer. Such approach is capable of reducing the overall processing latency, but the issues of redundancy, cost-effectiveness in utilizing such computing infrastructure and handling services on the basis of a difference in their characteristics remain. This difference in characteristics of services because of variations in the requirement of computational resources and processes is termed as service heterogeneity. A potential solution to these issues is the use of Osmotic Computing -- a recently introduced paradigm that allows division of services on the basis of their resource usage, based on parameters such as energy, load, processing time on a data center vs. a network edge resource. Service provisioning can then be divided across different layers of a computational infrastructure, from edge devices, in-transit nodes, and a data center, and supported through an Osmotic software layer. In this paper, a fitness-based Osmosis algorithm is proposed to provide support for osmotic computing by making more effective use of existing Fog server resources. The proposed approach is capable of efficiently distributing and allocating services by following the principle of osmosis. The results are presented using numerical simulations demonstrating gains in terms of lower allocation time and a higher probability of services being handled with high resource utilization.Comment: 7 pages, 4 Figures, International Conference on Communication, Management and Information Technology (ICCMIT 2017), At Warsaw, Poland, 3-5 April 2017, http://www.iccmit.net/ (Best Paper Award

A Survey on Security and Privacy of 5G Technologies: Potential Solutions, Recent Advancements, and Future Directions

Security has become the primary concern in many telecommunications industries today as risks can have high consequences. Especially, as the core and enable technologies will be associated with 5G network, the confidential information will move at all layers in future wireless systems. Several incidents revealed that the hazard encountered by an infected wireless network, not only affects the security and privacy concerns, but also impedes the complex dynamics of the communications ecosystem. Consequently, the complexity and strength of security attacks have increased in the recent past making the detection or prevention of sabotage a global challenge. From the security and privacy perspectives, this paper presents a comprehensive detail on the core and enabling technologies, which are used to build the 5G security model; network softwarization security, PHY (Physical) layer security and 5G privacy concerns, among others. Additionally, the paper includes discussion on security monitoring and management of 5G networks. This paper also evaluates the related security measures and standards of core 5G technologies by resorting to different standardization bodies and provide a brief overview of 5G standardization security forces. Furthermore, the key projects of international significance, in line with the security concerns of 5G and beyond are also presented. Finally, a future directions and open challenges section has included to encourage future research.European CommissionNational Research Tomsk Polytechnic UniversityUpdate citation details during checkdate report - A

Vegetation indices based farm-level mustard crop classification for the analysis of cropping pattern in Rabi 2021 and change in crop trend 2019 to 2021 of Kota District, Rajasthan

Remote sensing technology is used to quickly investigate as an innovative, standardized, potentially cost-effective, and faster method for crop acreage estimation. Furthermore, when compared to previous monitoring systems, Sentinel-2 satellite data has tremendous advantages since it delivers five-day interval, topographical, and up-to-date crop info at multiple phases. The main Rabi oil seed crop in Rajasthan is rapeseed and mustard. This study explores the use of the time series NDVI based farm level acreage estimation depending on the condition of the chlorophyll content. It also studies the changes in the cropping patterns and trends in Kota district, Rajasthan using the Google Earth Engine cloud platform along with the NCMS Mobile application for ground truth. Results indicate the reliability of the developed method for estimating acreage down to the farm level. Estimated Results for found to be in close agreement with authenticated government data. Two of the studied sub-districts showed significant cropping patterns. Classification accuracy for mustard ranged between 78-90 percent, while the overall classification accuracy 80-90 percent. The study concludes with the use of technology-based acreage estimations for faster and more reliable results

A Fog Computing-Based Device-Driven Mobility Management Scheme for 5G Networks

The fog computing-based device-driven network is a promising solution for high data rates in modern cellular networks. It is a unique framework to reduce the generated-data, data management overheads, network scalability challenges, and help us to provide a pervasive computation environment for real-time network applications, where the mobile data is easily available and accessible to nearby fog servers. It explores a new dimension of the next generation network called fog networks. Fog networks is a complementary part of the cloud network environment. The proposed network architecture is a part of the newly emerged paradigm that extends the network computing infrastructure within the device-driven 5G communication system. This work explores a new design of the fog computing framework to support device-driven communication to achieve better Quality of Service (QoS) and Quality of Experience (QoE). In particular, we focus on, how potential is the fog computing orchestration framework? How it can be customized to the next generation of cellular communication systems? Next, we propose a mobility management procedure for fog networks, considering the static and dynamic mobile nodes. We compare our results with the legacy of cellular networks and observed that the proposed work has the least energy consumption, delay, latency, signaling cost as compared to LTE/LTE-A networks

Lens connexins α3Cx46 and α8Cx50 interact with zonula occludens protein-1 (ZO-1)

Connexin α1Cx43 has previously been shown to bind to the PDZ domain–containing protein ZO-1. The similarity of the carboxyl termini of this connexin and the lens fiber connexins α3Cx46 and α8Cx50 suggested that these connexins may also interact with ZO-1. ZO-1 was shown to be highly expressed in mouse lenses. Colocalization of ZO-1 with α3Cx46 and α8Cx50 connexins in fiber cells was demonstrated by immunofluorescence and by fracture-labeling electron microscopy but showed regional variations throughout the lens. ZO-1 was found to coimmunoprecipitate with α3Cx46 and α8Cx50, and pull-down experiments showed that the second PDZ domain of ZO-1 was involved in this interaction. Transiently expressed α3Cx46 and α8Cx50 connexins lacking the COOH-terminal residues did not bind to the second PDZ domain but still formed structures resembling gap junctions by immunofluorescence. These results indicate that ZO-1 interacts with lens fiber connexins α3Cx46 and α8Cx50 in a manner similar to that previously described for α1Cx43. The spatial variation in the interaction of ZO-1 with lens gap junctions is intriguing and is suggestive of multiple dynamic roles for this association

Receiver Design to Employ Simultaneous Wireless Information and Power Transmission with Joint CFO and Channel Estimation

Radio-frequency energy harvesting (EH) is one of the enabling technologies for the next-generation wireless communication systems. EH techniques are specifically used to improve the energy efficiency of the system. Recently, the simultaneous wireless information and power transmission (SWIPT) protocol is adapted for EH. In this paper, we design a new receiver for joint carrier frequency offset (CFO) and channel estimation on single-carrier modulations with frequency-domain equalization along with SWIPT implementation for EH by using the pilot signal. The pilot signal is a highly energized signal, which is superimposed with the information signal. The superimposed signal is used not only to transmit power for EH purposes but also to estimate the CFO and channel conditions. The receiver is designed to accommodate the strong interference levels in the channel estimation and data detection. The proposed scheme offers a flexible design method and efficient resource utilization. We validate our analytical results using simulations

Data driven surrogate model-based optimization of the process parameters in electric discharge machining of D2 steel using Cu-SiC composite tool for the machined surface roughness and the tool wear

Electrical discharge machining (EDM) is mainly utilized for the die manufacturing and also used to machine the hard materials. Pure Copper, Copper based alloys, brass, graphite, steel are the conventional electrode materials for EDM process. While machining with the conventional electrode materials, tool wear becomes the main bottleneck which led to increased machining cost. In the present work, the composite tool tip comprises 80% Copper and 20% silicon carbide was used for the machining of hardened D2 steel. The powder metallurgy route was used to fabricate the composite tool tip. Electrode wear rate and surface roughness were assessed with respect to the different process parameters like input current, gap voltage, pulse on time, pulse off time and dielectric flushing pressure. During the analysis it was found that Input current (I p ), Pulse on time (T on ) and Pulse off time (T off ) were the significant parameters which were affecting the tool wear rate (TWR) while the I p , T on and flushing pressure affected more the surface roughness (SR). SEM micrograph reveals that increase in I p leads to increase in the wear rate of the tool. The data obtained from experiments were used to develop machine learning based surrogate models. Three machine learning (ML) models are random forest, polynomial regression and gradient boosted tree. The predictive capability of ML based surrogate models was assessed by contrasting the R 2 and mean square error (MSE) of prediction of responses. The best surrogate model was used to develop a complex objective function for use in firefly algorithm-based optimization of input machining parameters for minimization of the output responses