GMO Testing Strategies and Implications for Trade: A Game Theoretic Approach

Since their commercial introduction in 1996, genetically modified (GM) crops have been quickly adopted world wide, but some GM crops/varieties have not received regulatory approval for use in some importing countries, leading to asynchronicity in regulatory approvals. In this context, the international agricultural trade relied on analytical GMO testing which is a statistical process, along with identity preserved systems to segregate GM and non-GM crops. This led to a situation where measurement uncertainty became an important issue as it can lead to potential holdups at the point of import. In this background, this paper examines the implications of measurement uncertainty associated with GMO testing on the behavior of importers and exporters in a game theoretic framework. The results indicate that relative size of identity preservation costs, testing and rejection costs, the premiums offered in the non- GM markets and measurement uncertainty all have direct impacts on the behavior of importers and exporters.GMO testing, measurement uncertainty, identity preservation systems, agricultural trade, International Relations/Trade,

Optimum Cropping Pattern for Sericulture-dominant Farms in Southern Dry Zone of Karnataka

Sericulture is labour-intensive and well-suited to small and marginal farms with surplus labour, especially female labour. Ample labour and a small land-base encourage farmers to practise sericulture as a subsidiary occupation. While income from crop production is seasonal, sericulture provides a year-round income, which is an important incentive for small farmers to take up sericulture. The agricultural production is seasonal, while consumption is evenly spread over the years. Under such circumstances, the planners and policymakers are confronted with the challenge of formulating a suitable agricultural production policy with which the desired growth of agricultural production can be achieved. In the present study, optimum cropping patterns for different categories of sericulturists have been suggested by selecting Siddlaghatta in Kolar and Kollegal talukas in Mysore as study areas. The primary data have been collected using the personal interview method. The deterministic linear programming technique has been employed to work out the maximum attainable returns by small, medium and large farmers through the optimum allocation of various crops, sericulture and livestock (dairy), using the available resources. The model has suggested fewer crops in the cropping pattern of both the areas. The model has also suggested shifting of the cropping pattern from subsistence-dominated crops like ragi to commercial crops like bivoltine sericulture in the Kolar area and crossbreed sericulture in the Musore area. The suggested cropping patterns have increased the gross income in the range of 83.55 to 388.68 per cent in the Kolar area and 2.71 to 10.70 per cent in the Kollegal area.Agricultural and Food Policy,

Job Satisfaction and Expectations of LIS Professionals in India: A study

Psychological aspects are always playing vital roles in any field of work. Being a service sector, the satisfaction level of Library and Information professionals has a direct impact on the services which are rendered to users. In the purview of this aspect, authors have tried to assess the level job satisfaction and expectations of Library and Information Science professionals from the Indian original aspects

Constructive Multiuser Interference in Symbol Level Precoding for the MISO Downlink Channel

This paper investigates the problem of interference among the simultaneous multiuser transmissions in the downlink of multiple antennas systems. Using symbol level precoding, a new approach towards the multiuser interference is discussed along this paper. The concept of exploiting the interference between the spatial multiuser transmissions by jointly utilizing the data information (DI) and channel state information (CSI), in order to design symbol-level precoders, is proposed. In this direction, the interference among the data streams is transformed under certain conditions to useful signal that can improve the signal to interference noise ratio (SINR) of the downlink transmissions. We propose a maximum ratio transmission (MRT) based algorithm that jointly exploits DI and CSI to glean the benefits from constructive multiuser interference. Subsequently, a relation between the constructive interference downlink transmission and physical layer multicasting is established. In this context, novel constructive interference precoding techniques that tackle the transmit power minimization (min power) with individual SINR constraints at each user's receivers is proposed. Furthermore, fairness through maximizing the weighted minimum SINR (max min SINR) of the users is addressed by finding the link between the min power and max min SINR problems. Moreover, heuristic precoding techniques are proposed to tackle the weighted sum rate problem. Finally, extensive numerical results show that the proposed schemes outperform other state of the art techniques.Comment: Submitted to IEEE Transactions on Signal Processin

Symbol Based Precoding in The Downlink of Cognitive MISO Channels

This paper proposes symbol level precoding in the downlink of a MISO cognitive system. The new scheme tries to jointly utilize the data and channel information to design a precoding that minimizes the transmit power at a cognitive base station (CBS); without violating the interference temperature constraint imposed by the primary system. In this framework, the data information is handled at symbol level which enables the characterization the intra-user interference among the cognitive users as an additional source of useful energy that should be exploited. A relation between the constructive multiuser transmissions and physical-layer multicast system is established. Extensive simulations are performed to validate the proposed technique and compare it with conventional techniques.Comment: CROWNCOM 201

Exploring Predicate Based Access Control for Cloud Workflow Systems

Authentication and authorization are the two crucial functions of any modern security and access control mechanisms. Authorization for controlling access to resources is a dynamic characteristic of a workflow system which is based on true business dynamics and access policies. Allowing or denying a user to gain access to a resource is the cornerstone for successful implementation of security and controlling paradigms. Role based and attribute based access control are the existing mechanisms widely used. As per these schemes, any user with given role or attribute respectively is granted applicable privileges to access a resource. There is third approach known as predicate based access control which is less explored. We intend to throw light on this as it provides more fine-grained control over resources besides being able to complement with existing approaches. In this paper we proposed a predicate-based access control mechanism that caters to the needs of cloud-based workflow systems

Application of Entropy Techniques in Analyzing Heart Rate Variabilityusing ECG Signals

The variation of the heart rate about a mean value is the Heart Rate Variability (HRV). HRV reflects the functioning of cardio-respiratory control system. It is used as one of the diagnostic measures to detect heart disorders. In the proposed work, HRV analysis using entropy measures is carried out on healthy, Congestive Heart Failure (CHF) and Atrial Fibrillations (AF) subjects using their ECG signals. The entropy methods used in the work are Approximate entropy (ApE), Symbolic entropy (SyE) and Spectral entropy (SpE). ECG signals of 20 healthy subjects in the age group of 21 – 30 years were acquired using dry electrode at a sampling rate of 500 Hz for 10 minutes. Signal processing algorithms for removal of baseline wandering, power line interference and motion artefacts were applied for the raw ECG signal. The ECG signals for CHF and AF subjects in the age group of 30 – 75 years were obtained from the Physionet database. From the analysis it was found that values of ApE and SyE were highest for AF subjects and for SpE, the value was highest for healthy subjects. Further, values of all the three entropies were lowest for CHF subjects. In conclusion, it indicates that the entropy techniques are useful tools in diagnosing patients having heart disorders

BSRS: Best Stable Route Selection Algorithm for Wireless Sensor Network Applications

Topological changes in sensor networks frequently render routing paths unusable. Such recurrent path failures have detrimental effects on the network ability to support QoS-driven services. Because of connectivity richness in sensor networks, there often exist multiple paths between a source and a destination. Since many applications require uninterrupted connectivity of a session, the ability to find long-living paths can be very useful. In this paper, we propose Best Stable Route Selection (BSRS) approach based on Artificial Bee Colony based search algorithm, ensures that contributes stable quality performance of network and to calculate the best stable path services randomly based on QoS parameter requirements and existing circulation load; so that efficient route selection can easily capture by designing of proposed BSRS approach. The implementation of the proposed BSRS technique is implemented using NS2 simulation environment and the AODV routing protocol is used to incorporate the proposed algorithm. The experimental results are measured in terms of end to end delay, throughput, packet delivery ratio, and energy consumption and routing overhead. The results show the proposed BSRS algorithm improves the flexibility of network node and performance of network when multiple inefficient paths exist