THE OPPORTUNITY COST OF FOOD SAFETY REGULATION - AN OUTPUT DIRECTIONAL DISTANCE FUNCTION APPROACH

This paper provides a novel methodology to measure the impact of food safety regulation. An output directional distance function approach is applied to estimate the opportunity cost of food safety regulation and the shadow price of food risk. Such measures should be included as part of the overall cost of compliance for a more precise comparison of the benefits and costs of food safety regulation. Further, comparing the implicit shadow price of food risk and willingness to pay for food safety can bridge the gap of understanding how valuable safer foods are from the perspective of two different market participants - consumers and firms respectively.Food Consumption/Nutrition/Food Safety,

MEASURING THE IMPACT OF FOOD SAFETY REGULATION-AN OUTPUT DIRECTIONAL DISTANCE FUNCTION APPROACH

This paper provides a novel methodology to measure the impact of food safety regulation. An output directional distance function approach is applied to estimate the opportunity cost of food safety regulation. Such a measure should be included as part of the overall cost for a more precise benefit-cost analysis of food safety regulation. Using US Census and food safety recall data, the value of potential output loss due to food safety regulation is suggested $2.5 billion.Food Consumption/Nutrition/Food Safety,

Impact of subject related factors and position of flight control stick on acquisition of simulated flying skills using a flight simulator

Increasing demand on aviation industry calls for more pilots. Thus, pilot training systems and pilot-candidate screening systems are essential for civil and military flying training institutes. Before actual flight training, it is not easy to determine whether a flight trainee will be successful in the training. Due to the high cost of actual flight training, it would be better if there were low cost methods for screening and training candidates prior to the actual flight training. This study intended to determine if subject related factors and flight control stick position have an impact on acquisition of simulated flying skills using a PC-based flight simulator. The experimental model was a factorial design with repeated measures. Sixty-four subjects participated in the experiment and were divided into 8 groups. Experiment consisted of 8 sessions in which performance data, such as heading, altitude and airspeed were collected every 15 seconds. Collected data were analyzed using SAS statistical program. Result of multivariate analysis of variance indicated that the three independent variables: nationality, computer game experience, and flight stick position have significant impact on acquiring simulated flying skill. For nationality, Americans recorded higher scores in general (mean: 81.7) than Koreans (mean: 78.9). The difference in mean scores between Americans and Koreans was 2.8 percent. Regarding computer game experience, the difference between high experience group (82.3) and low experience group (78.3) is significant. For high experience group, American side-stick group recorded the highest (mean: 85.6), and Korean side-stick group (mean: 77.2) scored the lowest. For the low experience group, American center-stick group scored the highest (80.6), and the Korean side-stick group (74.2) scored the lowest points. Therefore, there is a significant difference between high experience group and low experience group. The results also reveal that the center-stick position is easier to learn than side-stick position. The difference in performance score between group of center-stick (mean: 82.1) and side-stick (mean: 76.8) is considerable

A Reportcard on Rural and Urban Ohio

The policy brief provides a comparison of rural and urban communities in the U.S. and Ohio over the last decade. In addition, it provides a discussion of differences across rural communities in Ohio and efforts to strengthen these communities

Identification Of Continuous-Time Model Of Hammerstein System Using Archimedes Optimization Algorithm

This thesis proposed a novel identification method known as the improved archimedes optimization algorithm (IAOA) for identifying the continuous-time Hammerstein model. Two modifications were employed to solve several demerits of the original archimedes optimization algorithm (AOA). The first modification was an alteration of the density decreasing factor to solve the imbalance of the exploration and exploitation phases. The second one was the introduction of safe updating mechanism to solve the local optima issue. Next, the proposed method was utilized in identifying the variables of the linear and nonlinear subsystems in a continuous-time Hammerstein model using the given input and output data. To verify the efficiency of the proposed method, a numerical example and two real-world experiments, namely the twin-rotor system and the electromechanical positioning system were carried out. The results were analysed in terms of the convergence curve of the fitness function, the variable deviation index, time-domain and frequency-domain responses of the identified model, and the Wilcoxon’s rank-sum test. The obtained results showed that the proposed method, yields solutions with better accuracy and consistency when compared with other well-known metaheuristics methods such as the Particle Swarm Optimizer, Grey Wolf Optimizer, Multi-Verse Optimizer, Archimedes Optimization Algorithm and a hybrid method named the Average Multi-Verse Optimizer and Sine Cosine Algorithm