Addressing Antibiotic Resistance From Farm-Raised Fish Imported To The United States

Misuse of medically important antibiotics in animal production threatens the effectiveness of drugs that are vital in combating disease and infections. Recently, the FDA implemented regulations to limit the use of and access to veterinary drugs. However, these regulations only affect domestic production operations. Because over 90% of seafood consumed in the U.S. is imported from countries with different regulatory standards and because the U.S. has an import inspection rate of less than 1%, antibiotic resistance stemming from imported aquaculture is still a risk that is not sufficiently accounted for. This research investigates how the U.S. has reacted to the growing issue of antibiotic resistance and explores how to further reduce bacterial resistance contributed by aquaculture imports. In addition, it demonstrates that imported fish from aquaculture production presents a risk to human health, undermining progress in U.S. regulatory control of antibiotic use. Actions taken by the FDA in recent years to curb antibiotic use is presented to illustrate the United States’ overall response and current strategy. Then a review of scientific research identifying evidence of antibiotic use and import rejection data from the FDA was used to assess the threat of resistance from imported aquaculture. The research verifies that medically important drugs are being used in aquaculture by the countries from which the U.S. imports most of its seafood. Drawing recommendations based on current E.U. policy, to better account for antibiotic resistance from imported aquaculture, the U.S. should involve exporting country authorities in trade operations to increase the degree to which U.S.-bound seafood is inspected. Greater funding to increase inspection upon arrival in the U.S. should also be allocated. Additionally, research on antibiotic alternatives should also be heavily supported as well advocacy for veterinary-style legislation in countries that do not already moderate the use of and access to antibiotics for aquaculture. Bacterial resistance from imported aquaculture is still an issue is of concern that is adequately addressed under U.S. regulations

Suicide and Laser Refractive Surgery

This is a Letter to the Editor and does not have an abstract

State estimation of medium voltage distribution networks using smart meter measurements

Distributed generation and low carbon loads are already leading to some restrictions in the operation of distribution networks and higher penetrations of e.g. PV generation, heat pumps and electric vehicles will exacerbate such problems. In order to manage the distribution network effectively in this new situation, increased real-time monitoring and control will become necessary. In the future, distribution network operators will have smart meter measurements available to them to facilitate safe and cost-effective operation of distribution networks. This paper investigates the application of smart meter measurements to extend the observability of distribution networks. An integrated load and state estimation algorithm was developed and tested using residential smart metering measurements and an 11 kV residential distribution network. Simulation results show that smart meter measurements, both real-time and pseudo measurements derived from them, can be used together with state estimation to extend the observability of a distribution network. The integrated load and state estimation algorithm was shown to produce accurate voltage magnitudes and angles at each busbar of the network. As a result, the algorithm can be used to enhance distribution network monitoring and contro

k-means based load estimation of domestic smart meter measurements

A load estimation algorithm based on kk-means cluster analysis was developed. The algorithm applies cluster centres – of previously clustered load profiles – and distance functions to estimate missing and future measurements. Canberra, Manhattan, Euclidean, and Pearson correlation distances were investigated. Several case studies were implemented using daily and segmented load profiles of aggregated smart meters. Segmented profiles cover a time window that is less than or equal to 24 h. Simulation results show that Canberra distance outperforms the other distance functions. Results also show that the segmented cluster centres produce more accurate load estimates than daily cluster centres. Higher accuracy estimates were obtained with cluster centres in the range of 16–24 h. The developed load estimation algorithm can be integrated with state estimation or other network operational tools to enable better monitoring and control of distribution networks

E-mail advertising: a Middle Eastern perspective and the moderating role of gender

This study assesses Middle Eastern consumers’ beliefs regarding attitude and behavioural responses toward e-mail advertising. To date, little is known about Middle Eastern consumers’ attitudes and behavioural responses towards email advertising. Our findings show that gender moderates the relationship between beliefs and attitudes, and responses to email advertising. A cross-sectional survey was conducted among 1200 respondents and yielded 321 valid responses. The hypothesized model was tested using structural equation modelling. Our findings show that both informativeness and entertainment beliefs positively predicted Middle Eastern consumers’ attitudes toward e-mail advertising. Additionally, attitude was found to fully mediate the relationship between beliefs regarding and behavioural responses toward e-mail advertising. Notably, females were found to react more intensely when exposed to email ads. Our findings suggest that advertisers promoting in a Middle Eastern context should take further steps to enhance the quality of information and the perceived entertainment value that could be delivered to Middle Eastern consumers through e-mail advertising specially for female internet users

Modified Flower Pollination Algorithm for Energy Forecasting and Demand Management Coupled with Improved Battery Life for Smart Building Micro-Grid

This paper presents the Modified Flower Pollination Algorithm-based Multi-Layer Perceptron Neural Network (MFPA-MLPNN) as an optimization technique for efficient power flow management in a Smart Building Microgrid (SBMG) integrated with solar and wind generation, and Electric Vehicle Batteries (EVBs) within grid connected structure while concurrently reducing optimization processing time. To achieve both technical and economic superiority, two optimization objectives are addressed. Firstly, a Demand Response (DR) framework is harnessed to accommodate the stochastic behavior and forecasting errors associated with intermittent sources. Secondly, the degradation of EVBs is considered, ensuring an economically viable power flow proposed strategy for both EV owners and microgrid (MG) authorities. Power generation of Variable Renewable Energy Sources (VRES) has been forecasted using MLPNN. Battery degradation and system stability under the action of the proposed topology have been evaluated using a simulation-based environment. Results show a significant decrease in battery degradation and processing time using the proposed MFPA-MLPNN optimization architectur