Optimal Data Placement on Networks With Constant Number of Clients

We introduce optimal algorithms for the problems of data placement (DP) and page placement (PP) in networks with a constant number of clients each of which has limited storage availability and issues requests for data objects. The objective for both problems is to efficiently utilize each client's storage (deciding where to place replicas of objects) so that the total incurred access and installation cost over all clients is minimized. In the PP problem an extra constraint on the maximum number of clients served by a single client must be satisfied. Our algorithms solve both problems optimally when all objects have uniform lengths. When objects lengths are non-uniform we also find the optimal solution, albeit a small, asymptotically tight violation of each client's storage size by $\epsilon$lmax where lmax is the maximum length of the objects and $\epsilon$ some arbitrarily small positive constant. We make no assumption on the underlying topology of the network (metric, ultrametric etc.), thus obtaining the first non-trivial results for non-metric data placement problems

The New Keynesian Phillips Curve and Lagged Inflation: A Case of Spurious Correlation?

The New Keynesian Phillips Curve (NKPC) specifies a relationship between inflation and a forcing variable and the current period’s expectation of future inflation. Most empirical estimates of the NKPC, typically based on Generalized Method of Moments (GMM) estimation, have found a significant role for lagged inflation, producing a “hybrid” NKPC. Using U.S. quarterly data, this paper examines whether the role of lagged inflation in the NKPC might be due to the spurious outcome of specification biases. Like previous investigators, we employ GMM estimation and, like those investigators, we find a significant effect for lagged inflation. We also use time varying-coefficient (TVC) estimation, a procedure that allows us to directly confront specification biases and spurious relationships. Using three separate measures of expected inflation, we find strong support for the view that, under TVC estimation, the coefficient on expected inflation is near unity and that the role of lagged inflation in the NKPC is spurious.New Keynesian Phillips Curve; time-varying coefficients; spurious relationships

A Portfolio Balance Approach to Euro-Area Money Demand in a Time-Varying Environment

As part of its monetary policy strategy, the European Central Bank has formulated a reference value for M3 growth. A pre-requisite for the use of a reference value for M3 growth is the existence of a stable demand function for that aggregate. However, a large empirical literature has emerged showing that, beginning in 2001, essentially all euro area M3 demand functions have exhibited instability. This paper argues that a proper understanding of the determination of money requires a portfolio analysis where the demand for broad money is seen as just one element in the wealth portfolio. Under this framework, wealth is the variable that constitutes the total budget constraint on the holdings of assets, including money, and changes in equity prices are a key transmission channel of monetary policy. Understanding money behaviour thus requires good data on euro area wealth which at present do not exist. Our basic premise is that there is a stable demand-for-money function but that the models that have been used until now to estimate euro area money-demand are not well-specified because they do not include a measure of wealth. Using two empirical methodologies - - a co-integrated vector equilibrium correction (VEC) approach and a time-varying coefficient (TVC) approach - - we find that a demand-for-money function that includes wealth is stable. The upshot of our findings is that M3 behaviour continues to provide useful information about medium-term developments on inflation.Money demand; VEC, time varying coefficient estimation; Euro area

Estimation of Parameters in the Presence of Model misspecification and Measurement Error

Misspecifications of econometric models can lead to biased coefficients and error terms, which in turn can lead to incorrect inference and incorrect models. There are specific techniques such as instrumental variables which attempt to deal with some individual forms of model misspecification. However these can typically only address one problem at a time. This paper proposes a general method for estimating underlying parameters in the presence of a range of unknown model misspecifications. It is argued that this method can consistently estimate the direct effect of an independent variable on a dependent variable with all of its other determinants held constant even in the presence of a misspecified functional form, measurement error and omitted variables.Misspecified model; Correct interpretation of coefficients; Appropriate assumption; Time-varying coefficient model; Coefficient driver

Effect of Root Opening on Distortion of Butt-Joints in Submerged Arc Welding

The dimensional differences in steel bridge manufacturing caused by weld deformation often occur for butt joints of thin plates. The problems of distortion, residual stresses and reduced strength of structure in and around a welded joint are of major concern in the shipbuilding industry and other similar manufacturing industries. The various distortions induced by welding process and restriction of these distortions may lead to higher residual stresses. The prediction of distortions in ship panels are of great importance from the point of view of dimensional control. In view of this, it has been investigated the effect of root opening on the transverse shrinkage, longitudinal shrinkage and angular distortions of butt joints. The experimental investigations are carried out using Submerged Arc Welding with zero mm, 1 mm and 2 mm root opening for constant heat input. The transverse and longitudinal shrinkage increase but the angular distortion decreases with increase in the root opening

DATA Analytics of Agriculture Production, Wages and Income in Rural Areas of India using Big Data and Python Matplot Lib

Agriculture Sector is the major contribution in GDP growth rate of India and Most of the Rural India it will become major resource of Income generator it contains different sectors like paddy, poultry, fisheries, Milk, and other crops. In this paper we studied general, commercial, dairy and other related Agricultural out comes and their Incomes and wages. In this paper we are performing different Data Analytics by taking parameters Daily wages, Income and production of Rural India. In this we are using Big Data Hive and Python Matplotlib to produce Graphical Analytical Reports. and finding results of different crops and daily wages of rural workers. The results we are finding year of production, crop wise production, crop wise and sector wise wages and Income of different crops. In this paper we collected Data and sample Analytical Reports from Agriculture Statistics Ministry of Agriculture, Co operation & Farmers Welfare and Data.gov.in . we are revealing different Analytical Reports regarding wages, Income and Production

A Review on Enhanced GPSR protocol For Wireless Sensor Networks

Greedy Perimeter Stateless Routing (GPSR), a novel routing protocol for wireless datagram networks that uses the positions of routers and a packet's destination to make packet forwarding decisions. It also offers routing support for Wireless Sensor Network (WSN). However, GPSR was designed for the symmetric links (bidirectional reachable), but sensor networks are often asymmetric in nature. So, when the destination is outside the boundary GPSR suffers by energy inefficiency as it has to trace through all the nodes in the boundary for reaching the destination. In this paper a modified version of GPSR is proposed which identifies optimal route based on energy utilization and overcome problems in GPSR so that the feasibility of using GPSR in asymmetric WSN can be increased. The simulation results prove that the energy and delay is minimized and hence the proposed protocol outperforms the existing routing protocol for WSN

Efficient Garbage Disposal Management in Metropolitan Cities Using VANETs

Rapid increase in population, has led to improper waste management in metropolitan cities resulting in increased pests and spreading of diseases. An efficient method to dispose this waste has been designed with Wireless Sensor Networks (WSN) using VANETs. IEEE 802.11p has been adopted and multicast routing has been proposed to be implemented in Garbage Collecting Vehicle’s (GCV) On Board Units (OBU) for effective communication. Road Side Units (RSU) and sensors have been made use of in the response system. Filling up of multiple bins at the same time and usage of reserve GCVs has been considered. The prototype VANET based efficient garbage disposal system is induced in a metropolitan city environment and has been simulated in NS2and the results are encouraging for implementation

Utilizing the protected learning calculation method to forestall the Black Hole Attacks in Mobile ad-hoc networks

Mobile Ad-hoc Networks (MANETs) are a gathering of portable hosts which speak with each other with no focal system power or altered foundation. Because of its attributes like portability furthermore, heterogeneity ad-hoc networks are more defenseless to assaults. Black hole is an assault where every one of the bundles sent to assailant hub, by neighboring hubs, are dropped purposefully. In this thesis, we propose a secure learning calculation method which intends to identify and securing the black hole by considering the bundle drop reasons in needless mode. Presented AODV direction convention is adjusted to distinguish and securing the black hole assault. The investigation results demonstrate that our proposed calculation secure the AODV against black hole assault in MANETs