OPTIMAL INVESTMENT IN RESEARCH AND DEVELOPMENT REGARDING A BACKSTOP TECHNOLOGY

We examine the role of investment opportunities on the marginal cost of a backstop technology and the resulting implications for optimal depletion of a non-renewable resource. We consider the case in which two economic agents (individuals, cities, or nations) compete for a non-renewable resource, and investments in research and development will reduce the marginal cost of a backstop technology. We examine the problem in both social optimization and game theory frameworks. We consider three scenarios: 1) The social planner's problem in which the sum of net benefits earned by the two agents (players) is maximized, 2) A scenario in which two players compete for the limited resource, while making investments jointly, and 3) A scenario in which the players compete for the resource and they choose investment levels independently. We examine, in particular, the case of groundwater withdrawals from an aquifer with a very small rate of natural recharge. The backstop technology is desalination. Results describe the optimal paths of investments in knowledge, as the original stock of groundwater is depleted. Groundwater is extracted over a longer interval, and the sum of investments in knowledge is smallest, in the social planner's scenario.Research and Development/Tech Change/Emerging Technologies, Resource /Energy Economics and Policy,

EXAMINING CHANGES IN LAND USE AFTER THE SALE OF DEVELOPMENT RIGHTS ON FARMS IN RHODE ISLAND

Purchasable development rights (PDR) programs are generally considered to provide permanent protection of farmland because development rights are separated from the land in perpetuity. However, the programs do not require that farming activities be maintained in the future. Farming may be discontinued on PDR parcels due to changes in economic conditions or if the parcels are converted to non-farm, rural estates. Such changes may reduce the flow of public goods that citizens seek to obtain by implementing PDR programs. We examine changes in land use on PDR parcels to determine if current activities are consistent with program goals. While changes have occurred in the crops and livestock produced on Rhode Island farms, over time, all of the farms on which development rights were purchased during 1985 through 1999 are currently being farmed by the original owners or by new operators who have either purchased or leased the land.Land Economics/Use,

GAME THEORY ANALYSIS OF COMPETITION FOR GROUNDWATER INVOLVING EL PASO, TEXAS AND CIUDAD JUAREZ, MEXICO

We examine the potential gains from cooperation in the withdrawal of water from the Hueco Bolson aquifer that provides municipal water supply for El Paso, Texas and Ciudad Juarez, Mexico. The aquifer lies beneath the international border, and both cities operate independently regarding pumping rates and withdrawals. We estimate the gains by comparing four scenarios in a dynamic setting: 1) a status quo scenario in which both cities continue extracting groundwater as they are at present, 2) a Nash non-cooperative game scenario, 3) a Nash bargaining scenario, and 4) a scenario that involves maximizing the sum of net benefits in both cities. All scenarios, including the non-cooperative game, provide a longer useful life of the Hueco Bolson aquifer than does the status quo. In the Nash bargaining scenario, both cities gain from cooperation and the sum of net benefits approaches the maximum that can be obtained by maximizing that value explicitly.Resource /Energy Economics and Policy,

ESTIMATING POTENTIAL GAINS TO COOPERATION FOR LIMITED WATER RESOURCES ALONG THE RIO GRANDE

We examine the potential gains from cooperation in the withdrawal of water from the Hueco Bolson aquifer that provides a substantial portion of municipal water supplies in El Paso, Texas and Ciudad Juarez, Mexico. The aquifer lies beneath the international border, and both cities operate independently regarding pumping rates and annual withdrawals. The natural the rate of recharge has been less than the sum of annual withdrawals since the early 1900s, and the resource likely will be depleted if current pumping rates are maintained. Optimal pumping rates and depths are described using a model that maximizes the sum of net benefits obtained from municipal water supplies in both cities. Those results are compared with pumping rates and depths obtained using a dynamic game-theory model of strategic behavior involving the two cities.Resource /Energy Economics and Policy,

Image-to-Graph Convolutional Network for 2D/3D Deformable Model Registration of Low-Contrast Organs

Organ shape reconstruction based on a single-projection image during treatment has wide clinical scope, e.g., in image-guided radiotherapy and surgical guidance. We propose an image-to-graph convolutional network that achieves deformable registration of a three-dimensional (3D) organ mesh for a low-contrast two-dimensional (2D) projection image. This framework enables simultaneous training of two types of transformation: from the 2D projection image to a displacement map, and from the sampled per-vertex feature to a 3D displacement that satisfies the geometrical constraint of the mesh structure. Assuming application to radiation therapy, the 2D/3D deformable registration performance is verified for multiple abdominal organs that have not been targeted to date, i.e., the liver, stomach, duodenum, and kidney, and for pancreatic cancer. The experimental results show shape prediction considering relationships among multiple organs can be used to predict respiratory motion and deformation from digitally reconstructed radiographs with clinically acceptable accuracy

2D/3D Deep Image Registration by Learning 3D Displacement Fields for Abdominal Organs

Deformable registration of two-dimensional/three-dimensional (2D/3D) images of abdominal organs is a complicated task because the abdominal organs deform significantly and their contours are not detected in two-dimensional X-ray images. We propose a supervised deep learning framework that achieves 2D/3D deformable image registration between 3D volumes and single-viewpoint 2D projected images. The proposed method learns the translation from the target 2D projection images and the initial 3D volume to 3D displacement fields. In experiments, we registered 3D-computed tomography (CT) volumes to digitally reconstructed radiographs generated from abdominal 4D-CT volumes. For validation, we used 4D-CT volumes of 35 cases and confirmed that the 3D-CT volumes reflecting the nonlinear and local respiratory organ displacement were reconstructed. The proposed method demonstrate the compatible performance to the conventional methods with a dice similarity coefficient of 91.6 \% for the liver region and 85.9 \% for the stomach region, while estimating a significantly more accurate CT values

Deep Learning Based Lung Region Segmentation with Data Preprocessing by Generative Adversarial Nets

[2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 20-24 July 2020, Montreal, QC, Canada]In endoscopic surgery, it is necessary to understand the three-dimensional structure of the target region to improve safety. For organs that do not deform much during surgery, preoperative computed tomography (CT) images can be used to understand their three-dimensional structure, however, deformation estimation is necessary for organs that deform substantially. Even though the intraoperative deformation estimation of organs has been widely studied, two-dimensional organ region segmentations from camera images are necessary to perform this estimation. In this paper, we propose a region segmentation method using U-net for the lung, which is an organ that deforms substantially during surgery. Because the accuracy of the results for smoker lungs is lower than that for non-smoker lungs, we improved the accuracy by translating the texture of the lung surface using a CycleGAN

Statistical deformation reconstruction using multi-organ shape features for pancreatic cancer localization

Respiratory motion and the associated deformations of abdominal organs and tumors are essential information in clinical applications. However, inter- and intra-patient multi-organ deformations are complex and have not been statistically formulated, whereas single organ deformations have been widely studied. In this paper, we introduce a multi-organ deformation library and its application to deformation reconstruction based on the shape features of multiple abdominal organs. Statistical multi-organ motion/deformation models of the stomach, liver, left and right kidneys, and duodenum were generated by shape matching their region labels defined on four-dimensional computed tomography images. A total of 250 volumes were measured from 25 pancreatic cancer patients. This paper also proposes a per-region-based deformation learning using the non-linear kernel model to predict the displacement of pancreatic cancer for adaptive radiotherapy. The experimental results show that the proposed concept estimates deformations better than general per-patient-based learning models and achieves a clinically acceptable estimation error with a mean distance of 1.2 ± 0.7 mm and a Hausdorff distance of 4.2 ± 2.3 mm throughout the respiratory motion