The New Food Safety Regime in the US: How Will it Affect Canadian Competitiveness

The FSMA appears to be a major undertaking with a very large responsibility placed on the FDA. It would seem that bottlenecks to exporting are bound to appear which will be very frustrating for Canadian firms. It is important for Canadian firms and Canadian policy makers to work hard to ensure that temporary bottlenecks do not become permanent inhibitors of trade. The Canadian government needs to understand industry concerns and use any mechanisms – including those in the NAFTA – to initiate consultations with the US. Given the likely lags in implementation, North American food markets are likely to exhibit considerable disequilibrium over the near term. Trade flows will be affected. As the implementation programs of the FSMA become more transparent, more sophisticated analysis into its effect on Canadian competitiveness in the US market can be undertaken.food, safety, competitiveness, Agribusiness, Agricultural and Food Policy, Food Consumption/Nutrition/Food Safety,

The New Food Safety Regime in the US: How Will it Affect Canadian Competitiveness

The Food Safety Modernization Act (FSMA) which was signed into law in January, 2011 represents a major initiative to improve food safety in the US. The legislation mandates the US Food and Drug Administration with developing a regulatory system to implement the Act. As yet, the full effect of the Act cannot be evaluated because the regulatory requirements are yet to be developed. There is little doubt, however, that those firms, both domestic and foreign, that wish to supply US consumers with food will face a considerable increase in regulatory costs. This paper outlines the major requirements of the FSMA and suggests how the regulatory burden may fall on foreign versus US domestic suppliers. Areas where Canadian firms may be disadvantaged relative to US firms are outlined. Opportunities that may arise from the FSMA for Canadian agri-food firms are discussed, as are the areas where the FSMA may not conform with the international trade commitments of the United States.competitiveness, food safety, regulatory burden, SPS, Agribusiness, Agricultural and Food Policy, Food Consumption/Nutrition/Food Safety, International Relations/Trade,

Multiple convolutional neural network training for Bangla handwritten numeral recognition

Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. The progress of handwritten Bangla numeral is well behind Roman, Chinese and Arabic scripts although it is a major language in Indian subcontinent and is the first language of Bangladesh. Handwritten numeral classification is a high-dimensional complex task and existing methods use distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. In this study, three different CNNs with same architecture are trained with different training sets and combined their decisions for Bangla handwritten numeral recognition. One CNN is trained with ordinary training set prepared from handwritten scan images; and training sets for other two CNNs are prepared with fixed (positive and negative, respectively) rotational angles of original images. The proposed multiple CNN based approach is shown to outperform other existing methods while tested on a popular Bangla benchmark handwritten dataset

Convolutional neural network training with artificial pattern for Bangla handwritten numeral recognition

Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. The progress of handwritten Bangla numeral is well behind Roman, Chinese and Arabic scripts although it is a major language in Indian subcontinent and is the first language of Bangladesh. Handwritten numeral classification is a high dimensional complex task and existing methods use distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. In this study, a CNN based method has been investigated for Bangla handwritten numeral recognition. A moderated pre-processing has been adopted to produce patterns from handwritten scan images. On the other hand, CNN has been trained with the patterns plus a number of artificial patterns. A simple rotation based approach is employed to generate artificial patterns. The proposed CNN with artificial pattern is shown to outperform other existing methods while tested on a popular Bangla benchmark handwritten dataset

Ultimate Strength of a Continuous Decking of Cold-Drawn Low-ductility High Strength Steel

Profiled decking of cold-drawn low-ductility high strength steel is a relatively new introduction to composite floor construction. This type of decking shows high sensitivity to distortional as well as local buckling. Prediction of ultimate strength of such decking in continuous configuration is not adequately covered in any of the analytical methods of modern day codes. Instead, due to inadequate guidance, various design codes currently apply additional restrictions on their design and use. The support moment-rotation and ultimate moment of resistance of such decking are the two most important parameters in designing such decking as continuous structure for the construction stage of a composite floor. The current practices require laboratory testing to determine these parameters, which is costly. Finite element analyses are rarely used to derive these parameters. The present paper deals on prediction of ultimate strength of such a decking in continuous configuration using parameters derived from nonlinear finite element analyses. It is demonstrated that a nonlinear finite element model can give a superior estimates of the parameters needed for ultimate strength design of such a decking

Hot-spot traffic pattern on hierarchical 3D mesh network

A Hierarchical 3D-Mesh (H3DM) Network s a 2D-mesh network of multiple basic modules (BMs), in which the basic modules are 3D-torus networks that are hierarchically interconnected for higher-level networks. In this paper, we evaluate the dynamic communication performance of a H3DM network under hot-spot traffic pattern using a deadlock-free dimension order routing algorithm with minimum number of virtual channels. We have also evaluated the dynamic communication performance of the mesh and torus networks. It is shown that under most imbalance hot-spot traffic pattern H3DM network yields high throughput and low average transfer time than that of mesh and torus networks, providing better dynamic communication performance compared to those networks

Evolving US Food Safety Regulations and International Competitors: Implementation Dynamics

The 2011 US Food Safety Modernization Act (FSMA) represents a major initiative to improve food safety. The legislation mandates the US Food and Drug Administration (FDA) with developing a regulatory system to implement the Act. Both domestic and foreign firms that wish to supply US consumers with food will face a considerable increase in regulatory costs. Implementation has proved challenging for the FDA leading to delays which increase investment risks for foreign suppliers, particulalry from developing countries. This paper sets out the major FSMA requirements and examines how the regulatory burden may fall on foreign versus US suppliers

Physarum-Inspired Bicycle Lane Network Design in a Congested Megacity

Improvement of mobility, especially environment-friendly green mobility, is challenging in existing megacities due to road network complexity and space constraints. Endorsing the bicycle lane network (BLN) in congested megacities is a promising option to foster green mobility. This research presents a novel bioinspired network design method that considers various constraints and preferences related to the megacity for designing an optimal BLN. The proposed method is inspired by natural Physarum polycephalum, a brainless, multi-headed single-celled organism, which is capable of developing a reticulated network of complex foraging behaviors in pursuit of food. The mathematical model of Physarum foraging behavior is adapted to maneuver various BLN constraints in megacity contexts in designing the optimal BLN. The Physarum-inspired BLN method is applied to two case studies on the megacity Dhaka for designing BLNs: the first one covers congested central city area, and the second one covers a broader area that includes major locations of the city. The obtained BLNs were evaluated comparing their available routes between different locations with the existing vehicle routes of the city in terms of distance and required travel times in different time periods, and the BLN routes were found to be suitable alternatives for avoiding congested main roads. The expected travel time using BLNs is shorter than other transport (e.g., car and public bus); additionally, at glance, the average travel speed on BLNs is almost double that of public buses in peak hours. Finally, the designed BLNs are promising for environment-friendly and healthy mobility

Bangla handwritten numeral recognition using convolutional neural network

Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. Although Bangla is a major language in Indian subcontinent and is the first language of Bangladesh study regarding Bangla handwritten numeral recognition (BHNR) is very few with respect to other major languages such Roman. The existing BHNR methods uses distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. It also automatically provides some degree of translation invariance. In this paper, a CNN based BHNR is investigated. The proposed BHNR-CNN normalizes the written numeral images and then employ CNN to classify individual numerals. It does not employ any feature extraction method like other related works. 17000 hand written numerals with different shapes, sizes and variations are used in this study. The proposed method is shown satisfactory recognition accuracy and outperformed other prominent exiting methods

Syzygium Cumini Leaf Extract Showed Vibriocidal Activity on Selected Diarrhea Causing Bacteria

The objective of the study was to investigate the effect of ethanolic leaf extract (ELE) of Syzygium cumini against Vibrio cholerae particularly two serogroups Ogawa and Inaba. The phenolic content of the ELE was found high which is comparable to ascorbic acid. Brine shrimp lethality bioassay was then performed to check the cytotoxic effects of ELE. The lower LC50 value of ELE obtained indicated its less cytotoxic properties. The antimicrobial activity of the extract was then evaluated by the disc diffusion method against multi-drug resistant Vibrio serogroups Ogawa and Inaba. The extract effectively inhibited the growth of both serogroups. Altogether, the results demonstrated that the ELE of S. cumini has a significant vibriocidal activity that might be useful as a drug for the treatment of cholera