Supply chains (SC) span many geographies, modes and industries and involve several phases where data flows in both directions from suppliers, manufacturers, distributors, retailers, to customers. This data flow is necessary to support critical business decisions that may impact product cost and market share. Current SC information systems are unable to provide validated, pseudo real-time shipment tracking during the distribution phase. This information is available from a single source, often the carrier, and is shared with other stakeholders on an as-needed basis. This paper introduces an independent, crowd-validated, online shipment tracking framework that complements current enterprise-based SC management solutions. The proposed framework consists of a set of private distributed ledgers and a single blockchain public ledger. Each private ledger allows the private sharing of custody events among the trading partners in a given shipment. Privacy is necessary, for example, when trading high-end products or chemical and pharmaceutical products. The second type of ledger is a blockchain public ledger. It consists of the hash code of each private event in addition to monitoring events. The latter provide an independently validated immutable record of the pseudo real-time geolocation status of the shipment from a large number of sources using commuters-sourcing.
Our aim is to develop a machine learning (ML) model that can predict dementia in a general patient population from multiple health care institutions one year and three years prior to the onset of the disease without any additional monitoring or screening. The purpose of the model is to automate the cost-effective, non-invasive, digital pre-screening of patients at risk for dementia. Towards this purpose, routine care data, which is widely available through Electronic Medical Record (EMR) systems is used as a data source. These data embody a rich knowledge and make related medical applications easy to deploy at scale in a cost-effective manner. Specifically, the model is trained by using structured and unstructured data from three EMR data sets: diagnosis,
This paper advocates a data summarization approach based on distance rather than the traditional time period when developing individualized machine learning models for fuel consumption. This approach is used in conjunction with seven predictors derived from vehicle speed and road grade to produce a highly predictive neural network model for average fuel consumption in heavy vehicles. The proposed model can easily be developed and deployed for each individual vehicle in a fleet in order to optimize fuel consumption over the entire fleet. The predictors of the model are aggregated over fixed window sizes of distance traveled. Different window sizes are evaluated and the results show that a 1 km window is able to predict fuel consumption with a 0.91 coefficient of determination and mean absolute peak-to-peak percent error less than 4% for routes that include both city and highway duty cycle segments.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.