Machine learning in dam water research: an overview of applications and approaches

Dam plays a crucial role in water security. A sustainable dam intends to balance a range of resources involves within a dam operation. Among the factors to maintain sustainability is to maintain and manage the water assets in dams. Water asset management in dams includes a process to ensure the planned maintenance can be conducted and assets such as pipes, pumps and motors can be mended, substituted, or upgraded when needed within the allocated budgetary. Nowadays, most water asset management systems collect and process data for data analysis and decision-making. Machine learning (ML) is an emerging concept applied to fulfill the requirement in engineering applications such as dam water researches. ML can analyze vast volumes of data and through an ML model built from algorithms, ML can learn, recognize and produce accurate results and analysis. The result brings meaningful insights for water asset management specifically to strategize the optimal solution based on the forecast or prediction. For example, a preventive maintenance for replacing water assets according to the prediction from the ML model. We will discuss the approaches of machine learning in recent dam water research and review the emerging issues to manage water assets in dams in this paper

The causal effects of global supply chain disruptions on macroeconomic outcomes: evidence and theory

We study the causal effects and policy implications of global supply chain disruptions. We construct a new index of supply chain disruptions from the mandatory automatic identification system data of container ships, developing a novel spatial clustering algorithm that determines real-time congestion from the position, speed, and heading of container ships in major ports around the globe. We develop a model with search frictions between producers and retailers that links spare productive capacity with congestion in the goods market and the responses of output and prices to supply chain shocks. The co-movements of output, prices, and spare capacity yield unique identifying restrictions for supply chain disturbances that allow us to study the causal effects of such disruptions. We document how supply chain shocks drove inflation during 2021 but that, in 2022, traditional demand and supply shocks also played an important role in explaining inflation. Finally, we show how monetary policy is more effective in taming inflation after a global supply chain shock than in regular circumstances

An Automated Method for Segmenting White Matter Lesions through Multi-Level Morphometric Feature Classification with Application to Lupus

We demonstrate an automated, multi-level method to segment white matter brain lesions and apply it to lupus. The method makes use of local morphometric features based on multiple MR sequences, including T1-weighted, T2-weighted, and fluid attenuated inversion recovery. After preprocessing, including co-registration, brain extraction, bias correction, and intensity standardization, 49 features are calculated for each brain voxel based on local morphometry. At each level of segmentation a supervised classifier takes advantage of a different subset of the features to conservatively segment lesion voxels, passing on more difficult voxels to the next classifier. This multi-level approach allows for a fast lesion classification method with tunable trade-offs between sensitivity and specificity producing accuracy comparable to a human rater