SCOPE AND SCALE ECONOMIES FOR MULTI-PRODUCT FARMS: FIRM-LEVEL PANEL DATA ANALYSIS

This study used the flexible fixed cost quadratic function to analyze the cost structure of multi-product farms using farm-level panel data. The robustness of estimated parameters are examined using four panel data estimators. Results suggest that scale economies remain significant in Illinois farming. An increase in soybean acreage reduces the marginal cost of producing corn. Firm-specific effects, that indicate the levels of fixed costs, are found to be positive and significant.Farm Management,

ANALYSIS OF CONSUMERS' INTEREST IN ALTERNATIVE FOOD DELIVERY SYSTEMS: RESULTS FROM A CONSUMERS' SURVEY IN NEW JERSEY

Consumer/Household Economics,

Lithuania's Food Demand During Economic Transition

The linear approximate version of the almost ideal demand system (LA-AIDS) model is estimated using data from the Lithuanian household budget survey (HBS) covering the period from July 1992 to December 1994. Price and real expenditure elasticities for 12 food groups were estimated based on the estimated coefficients of the model. Very little or nothing is known about the demand parameters of Lithuania and other former socialist countries, so the results are of intrinsic interest. Estimated expenditure elasticities were positive and statistically significant for all food groups, while all own-price elasticities were negative and statistically significant, except for that of eggs which was insignificant. Results suggest that Lithuanian household consumption did respond to price and real income changes during their transition to a market-oriented economy.

Computational Approaches for Remote Monitoring of Symptoms and Activities

We now have a unique phenomenon where significant computational power, storage, connectivity, and built-in sensors are carried by many people willingly as part of their life style; two billion people now use smart phones. Unique and innovative solutions using smart phones are motivated by rising health care cost in both the developed and developing worlds. In this work, development of a methodology for building a remote symptom monitoring system for rural people in developing countries has been explored. Design, development, deployment, and evaluation of e-ESAS is described. The system’s performance was studied by analyzing feedback from users. A smart phone based prototype activity detection system that can detect basic human activities for monitoring by remote observers was developed and explored in this study. The majority voting fusion technique, along with decision tree learners were used to classify eight activities in a multi-sensor framework. This multimodal approach was examined in details and evaluated for both single and multi-subject cases. Time-delay embedding with expectation-maximization for Gaussian Mixture Model was explored as a way of developing activity detection system using reduced number of sensors, leading to a lower computational cost algorithm. The systems and algorithms developed in this work focus on means for remote monitoring using smart phones. The smart phone based remote symptom monitoring system called e-ESAS serves as a working tool to monitor essential symptoms of patients with breast cancer by doctors. The activity detection system allows a remote observer to monitor basic human activities. For the activity detection system, the majority voting fusion technique in multi-sensor architecture is evaluated for eight activities in both single and multiple subjects cases. Time-delay embedding with expectation-maximization algorithm for Gaussian Mixture Model was studied using data from multiple single sensor cases

Current account determination in the intertemporal framework: an empirical analysis

Traditional analysis of the determination of the current account balance of a country is based on static Keynesian models of saving and investment. In the early 1980s, several authors challenged the appropriateness of the traditional model by arguing that the observed movements in the current account balance of a country are the outcome of saving and investment decisions by economic agents. Saving and investment decisions are inherently dynamic in nature in the sense that they involve intertemporal choice which is affected by current as well as expected future movements in economic variables. Therefore, static models in general, and Keynesian models in particular, are incapable of accommodating the dynamic nature of the decisions involved in saving and investment. These authors present explicit dynamic optimizing framework in which they distinguish between the effects of transitory and permanent changes in income and relative price on the current account balance of a country;Despite their elegance, empirical testing of the intertemporal models of current account determination has been limited by our inability to identify the transitory and permanent components in observed economic time series. However, recent developments in time series econometrics provide ways in which one may attempt to decompose an observed nonstationary time series into a transitory and a permanent component. Such a decomposition opens the opportunity to empirically test whether real world data support the predictions of the intertemporal models of current account determination. In this study, two different methods have been used to obtain such decomposition of nonstationary economic variables. Cointegration analysis has been used to examine the long-run relationship among the variables. Then Vector Autoregression (VAR) technique is used to investigate the short-run dynamic behavior of current account balance in response to shocks to transitory and permanent components in income and real exchange rate. The analysis is performed for two countries: the United States vis-a-vis the rest of the world, and Japan vis-a-vis the rest of the world. The results of the empirical analysis are inconclusive. Results for Japanese data are more supportive of the intertemporal models than those for U.S. data

Biometric multimodal security simulation on schedule Ii controlled drug

The paper present a multimodal (multi biometrics) security system focusing on the implementation of fingerprint recognition and facial feature recognition to enhance the existing method of security using password or personal identification number (PIN). This project is operated through a personal computer where all the identification for fingerprint and facial feature are done by using specific software. Successful identification will send a signal through a serial communication circuit and open an application. In this project, the final application should be a cupboard that store and secure schedule II controlled drug in hospital. Due to some problem, the final application was replaced by using a light emitting diode (LED) simulation circuit

Occupancy Detection using Wireless Sensor Network in Indoor Environment

Occupancy detection plays an important role in many smart buildings such as reducing building energy usage by controlling heating, ventilation and air conditioning (HVAC) systems, monitoring systems and managing lighting systems, tracking people in hospitals for medical issues, advertising to people in malls, and to search and rescue missions. The global positioning system (GPS) is used most widely as a localization system but highly inaccurate for indoor applications. The indoor environment is difficult to handle because along with the loss of signals, privacy is a major concern. Indoor tracking has many aspects in common with sensor localization in Wireless Sensor Networks (WSN). The contribution of this work is the demonstration of a nonintrusive approach to detect an occupancy in a building using wireless sensor networks to detect energy from cell phones in a secure facility and perform indoor localization based on the minimum mean square error (MMSE). To estimate the occupancy, the detected cellular signals information such as signal amplitude, frequency, power and detection time is sent to a fusion server, matched the detected signals by time and channel information, performed localization to estimate a location, and finally estimated the occupancy of rooms in a building from the estimated locations

Study Of Nanodiamonds Using Molecular Dynamic Simulations With Reactive Potentials

This thesis pertains to the application of classical molecular dynamic simulations using reactive potentials to create a stable nanodiamond with properties as determined by quantum simulations. Nanodiamonds possess large amounts of Structural Bond Energy (SBE) and they have internal pressures as high as 50 GPa. Nanodiamonds could be used as potential energetic materials if their bond energy could be released

Computational Approaches for Remote Monitoring of Symptoms and Activities

We now have a unique phenomenon where significant computational power, storage, connectivity, and built-in sensors are carried by many people willingly as part of their life style; two billion people now use smart phones. Unique and innovative solutions using smart phones are motivated by rising health care cost in both the developed and developing worlds. In this work, development of a methodology for building a remote symptom monitoring system for rural people in developing countries has been explored. Design, development, deployment, and evaluation of e-ESAS is described. The system’s performance was studied by analyzing feedback from users. A smart phone based prototype activity detection system that can detect basic human activities for monitoring by remote observers was developed and explored in this study. The majority voting fusion technique, along with decision tree learners were used to classify eight activities in a multi-sensor framework. This multimodal approach was examined in details and evaluated for both single and multi-subject cases. Time-delay embedding with expectation-maximization for Gaussian Mixture Model was explored as a way of developing activity detection system using reduced number of sensors, leading to a lower computational cost algorithm. The systems and algorithms developed in this work focus on means for remote monitoring using smart phones. The smart phone based remote symptom monitoring system called e-ESAS serves as a working tool to monitor essential symptoms of patients with breast cancer by doctors. The activity detection system allows a remote observer to monitor basic human activities. For the activity detection system, the majority voting fusion technique in multi-sensor architecture is evaluated for eight activities in both single and multiple subjects cases. Time-delay embedding with expectation-maximization algorithm for Gaussian Mixture Model was studied using data from multiple single sensor cases

INCOME OF FARMERS WHO USE DIRECT MARKETING

Policy makers are looking for ways to preserve farmlands, especially near urban areas. Farmers are also trying to find ways to increase their farm income by incorporating non-traditional activities into their farm routine. This paper attempts to quantify the contributions of selected nontraditional activities towards farmers' efforts to enhance their farm income. For farmers involved in direct marketing, a logit model is used to estimate the probability of attaining high income for each activity considered, selling of farm related value-added products, greenhouse operations and urban location of farm markets will increase the chance o attaining high income levels.Farm Management, Marketing,