This paper examines cost efficiency of maize production in Zambia by analyzing the cost of inputs and socio-economic factors that effects farmer�s efficiency. We use the 2019 household survey data, conducted in two provinces of Zambia, that is, Southern and Northern Province. The study applies stochastic frontier approach centered on the Cobb-Douglas cost function. The results indicate that the cost coefficient of all the inputs and outputs were positive and significant, signifying that the cost function monotonically increases with the prices of inputs. Thus, a 1% increase in the cost of seed, fertilizer, chemicals and irrigation will increase the total cost of production by 0.573%, 0.698%, 0.128%, and 0.311% respectively. Similarly, a 1% increase in the cost of labor, harvesting and transportation will correspondingly increase the overall cost of production by 0.218%, 0.255% and 0.014%. The cost efficiency scores span from 1.1 to 4.0 for all sampled farmers. The mean cost efficiency score is found to be at 1.3 which entails that on average famers incurred approximately about 25% cost above the stipulated cost frontier line. This implies that 25% incurred cost was wasted in comparison to the adoption of best farming methods with the same level of technology. However, our findings attest that the majority of famers operated within or close to the cost frontier line which demonstrates that most farmers are cost efficient in their production.
Object detection refers to the potential of computer and software systems to locate objects in an image/scene and identify each object. Object detection has been commonly used to identify face and other objects, detect vehicles, count pedestrians, web images, security systems and driverless cars. Getting to use modern object detection methods in applications and systems, also as building new applications supported these methods isn\'t a simple task. Efficient and accurate object detection has been a crucial topic within the advancement of computer vision systems. With the arrival of deep learning techniques, the accuracy for object detection has increased drastically. This project aims to use state of the art object detection techniques while using transfer learning in order to reduce the time required to train the network and obtain high accuracy. Thus pre-trained convolutional neural network models would be used and trained on the Oxford IIIT pet dataset covering different categories of pet breeds. We will be using the pre-trained faster RCNN Resnet 101 model trained on the coco dataset for this purpose. We retrained the model on the oxford IIIT pet dataset to classify the pet images into different breeds and achieved an overall accuracy of 90% on the test images.
This research aims to highlight the social responsibilities of Saudi listed firms. The researchers had previously studied how the corporate governance and specific factors affected the practices of corporate social responsibility disclosure. Then, the subsequent question was addressed: Does Corporate Governance and specific Factors Affected Saudi Listed firms Practices. Then, this research became a limited assessment of the collective cognitive map of industrial Saudi Arabian listed companies, which was organized based on the idea of a systematic exploration grid completed with the help of company participants. Our results indicate that there is a positive correlation between the practice of CSRD and the proportion of non-executive directors on the board of directors of listed companies in Saudi Arabia; the ownership and size of directors affect the disclosure of corporate social responsibility.