Price Transmission, Market Power and Returns to Scale

In this paper, we aim to model the vertical relation between retailers and suppliers in the food industry whereby retailers exercise seller power in their relation with consumers and buyer power in their relation with producers. We then evaluate the degree of price transmission, relative to the perfectly competitive benchmark, from the farm to the retail sector assuming a supply shock. With the view to evaluating the impact of market power's interaction with industry technology on the degree of price transmission, we assume industry technology to be characterised by variable input proportions and non-constant returns to scale. Our model predicts that, relative to that which obtains when markets are perfectly competitive and industry technology is characterised by constant returns to scale, the degree of price transmission when market power and industry technology interact cannot be unambiguously determined.price transmission, returns to scale, market power, Demand and Price Analysis, Marketing, L11, Q13,

Market Power in UK Food Retailing: Theory and Evidence from Seven Product Groups

Establishing the presence of market power in food chains has become an increasingly pertinent line of enquiry given the trend towards increasing concentration that has been observed in many parts of the world. This paper presents a theoretical model of price transmission in vertically related markets under imperfect competition. The model delivers a quasi-reduced form representation that is empirically tractable using readily available market data to test for the presence of market power. In particular, we show that the hypothesis of perfect competition can be rejected if shocks to the demand and supply function are significant and correctly signed in price transmission equations. Using a cointegrated vector autoregression, we find empirical results that are consistent with downstream market power in six out of seven food products investigated, supporting both the findings of the UK competition authority's recent investigation in to supermarkets and renewed calls for further scrutiny of supermarket behaviour by the UK's Office of Trading.imperfect competition, Cointegrated VARs, UK food industry, Marketing, D4, L81,

Backpropagation Neural Ensemble for Localizing and Recognizing Non-Standardized Malaysia’s Car Plates

In this paper, we describe a research project that autonomously localizes and recognizes non-standardized Malaysian’s car plates using conventional Backpropagation algorithm (BPP) in combination with Ensemble Neural Network (ENN). We compared the results with the results obtained using simple Feed-Forward Neural Network (FFNN). This research aims to solve four main issues; (1) localization of car plates that has the same colour with the vehicle colour, (2) detection and recognition of car plates with varying sizes, (3) detection and recognition of car plates with different font types, and (4) detection and recognition of non-standardized car plates. The non-standardized Malaysian’s car plates are different from the normal plate as they contain italic characters, a combination of cursive characters, and different font types. The experimental results show that the combination of backpropagation and ENN can be effectively used to solve these four issues. The combination of BPP and ENN’s algorithm achieved a localization rate of 98% and a 97% in recognition rate. On the other hand, the combination of backpropagation and simple FFNN recorded a 96% recognition rate

Frequent expansion of Plasmodium vivax Duffy Binding Protein in Ethiopia and its epidemiological significance.

Plasmodium vivax invasion of human erythrocytes depends on the Duffy Binding Protein (PvDBP) which interacts with the Duffy antigen. PvDBP copy number has been recently shown to vary between P. vivax isolates in Sub-Saharan Africa. However, the extent of PvDBP copy number variation, the type of PvDBP multiplications, as well as its significance across broad samples are still unclear. We determined the prevalence and type of PvDBP duplications, as well as PvDBP copy number variation among 178 Ethiopian P. vivax isolates using a PCR-based diagnostic method, a novel quantitative real-time PCR assay and whole genome sequencing. For the 145 symptomatic samples, PvDBP duplications were detected in 95 isolates, of which 81 had the Cambodian and 14 Malagasy-type PvDBP duplications. PvDBP varied from 1 to >4 copies. Isolates with multiple PvDBP copies were found to be higher in symptomatic than asymptomatic infections. For the 33 asymptomatic samples, PvDBP was detected with two copies in two of the isolates, and both were the Cambodian-type PvDBP duplication. PvDBP copy number in Duffy-negative heterozygotes was not significantly different from that in Duffy-positives, providing no support for the hypothesis that increased copy number is a specific association with Duffy-negativity, although the number of Duffy-negatives was small and further sampling is required to test this association thoroughly

Backpropagation neural ensemble for localizing and recognizing non-standardized Malaysia’s car plates

In this paper, we describe a research project that autonomously localizes and recognizes non-standardized Malaysian’s car plates using conventional Backpropagation algorithm (BPP) in combination with Ensemble Neural Network (ENN). We compared the results with the results obtained using simple Feed-Forward Neural Network (FFNN). This research aims to solve four main issues; (1) localization of car plates that has the same colour with the vehicle colour, (2) detection and recognition of car plates with varying sizes, (3) detection and recognition of car plates with different font types, and (4) detection and recognition of non-standardized car plates. The non-standardized Malaysian’s car plates are different from the normal plate as they contain italic characters, a combination of cursive characters, and different font types. The experimental results show that the combination of backpropagation and ENN can be effectively used to solve these four issues. The combination of BPP and ENN’s algorithm achieved a localization rate of 98% and a 97% in recognition rate. On the other hand, the combination of backpropagation and simple FFNN recorded a 96% recognition rate