No abstract

The primary objective of this research is to obtain an accurate forecasting model for the US presidential election. To identify a reliable model, artificial neural networks (ANN) and support vector regression (SVR) models are compared based on some specified performance measures. Moreover, six independent variables such as GDP, unemployment rate, the president's approval rate, and others are considered in a stepwise regression to identify significant variables. The president's approval rate is identified as the most significant variable, based on which eight other variables are identified and considered in the model development. Preprocessing methods are applied to prepare the data for the learning algorithms. The proposed procedure significantly increases the accuracy of the model by 50%. The learning algorithms (ANN and SVR) proved to be superior to linear regression based on each method's calculated performance measures. The SVR model is identified as the most accurate model among the other models as this model successfully predicted the outcome of the election in the last three elections (2004, 2008, and 2012). The proposed approach significantly increases the accuracy of the forecast.

The rise of contract farming has transformed millions of farmers' lives. We study a new class of contract farming problems, where the farmer holds superior information and can invest effort to improve productivity over time. Despite their prevalence, the literature offers little guidance on how to manage such farmers with dynamic incentives. We build a game-theoretic model that captures the dynamic incentives of learning and gaming, with hidden action and information. We characterize the optimal contract: it internalizes both the vertical and intertemporal externalities, with performance pay and deferred payment; the performance pay is to motivate the farmer to invest and improve the relationship-specific productivity; the deferred payment is to ensure that the farmer is willing to share information and behave honestly over time. Even with random yield, the optimal contract can still have a simple implementation of a yieldadjusted revenue-sharing policy. Using real data, we show that the learning effect is significant. We then quantify when and how contract farming can improve smallholder farmers' productivity and income, creating shared value. We find when buyers have a long-term perspective and can internalize the benefit of farmer improvement, they will pay higher prices to ensure farmers' long-term viability. Our results inform the policy debate on contract farming: traditional procompetitive policies (based on spot transactions) can be counterproductive for modern agrifood value chains, hurting both buyers and farmers.

This paper studies a design of a puzzle-based storage system. We developed an item retrieval algorithm for our system which has three advantages over the previous counterparts in the literature: (i) we can retrieve items from all sides of our storage system; (ii) the existence of only one empty cell in our system is sufficient to retrieve an item; and (iii) our algorithm never ends in deadlocks. The main feature of our algorithm is to prefer three moves to five moves in the process of moving the seized empty cell toward the optimal side of the requested item. The conventional view in the literature assumes that increasing the number of empty cells always reduces the number of movements required for retrieving items; however, our simulation results show that depending on the size of the puzzle and the number of the requested items, increasing empty cells might make the retrieval process more complicated.

Lower Back Pain is one of the leading causes of disability globally. Its degradation to patients’ standing stability has been reported in the past. The goal of the current study was to evaluate the influence of immediate pain relief enabled by a lumbar facet joint anesthetic injection on patients’ standing stability. A total of 91 chronic LBP patients were recruited, each patient performed standing balance tests both before and after a lumbar facet nerve block treatment while their dynamic center of pressure data were recorded and compared. Results of our study showed that after the injection, participants showed 10% smaller total excursion and up to 30% smaller sway area. These results suggested patients’ balance might be used as an objective measurement to evaluate pain reduction among LBP patients.