Green Mechanical

whitePaper | November 15, 2019

The demands of running global supply chains can be unrelenting – perfection of operations, innovation in support of lastmile delivery, and assurances of sustainability in the pursuit of delivering the “perfect order” as promised. Now, transportation companies face the additional challenge of sharpening their core competencies while increasing the pace of innovation, adapting emerging technologies, and sharing data with their customers to improve transparency and efficiency. Long characterized by capital-intensive assets, forward-looking cargo companies are turning their attention to the data that comes from their own assets and infrastructure as well as from their customers. It is a complex transition, one that requires knowledge-based labor, automation, and predictive planning to turn transportation companies into digital platforms.

whitePaper | August 9, 2020

Even before the COVID-19 pandemic, the pace of change in the financial services industry was exhausting. The challenges just kept coming: from the 2008 financial crisis to a raft of fintech newcomers and established brands pivoting into the industry, a panoply of regulations and the looming threat of a recession — to name a few.

whitePaper | March 30, 2022

The Stanford Institute for Human-Centered Artificial Intelligence (HAI) offers the following submission for consideration in response to the Request for Information(RFI) by the White House Office of Science and Technology to the Update of the National Artificial Intelligence Research and Development Strategic Plan.

whitePaper | August 9, 2022

This document in any form, software or printed matter, contains proprietary information that is the exclusive property of Oracle. Your access to and use of this confidential material is subject to the terms and conditions of your Oracle software license and service agreement, which has been executed and with which you agree to comply.

whitePaper | August 6, 2020

The purpose of any data acquisition system is to gather useful measurement data for characterization, monitoring, or control. The specific parameters of your application will dictate the resolution, accuracy, channel count, and speed requirements of a data acquisition system. A wide assortment of data acquisition components and solutions is available on the market, including low-cost USB modules, benchtop data loggers, and large channel systems. Before you start your search for a data acquisition solution, carefully analyze your application requirements to understand how much capability and performance you need to purchase.

whitePaper | June 7, 2022

Telecoms need to be able to incorporate new technologies and next-generation connectivity such as 5G to customers and end users. To achieve these ambitious goals, they need to optimize their networks – make them more intelligent if you will. Some of the tools needed include artificial intelligence (AI), machine learning (ML) and artificial intelligence operations (AIOps). This document will explore what intelligent networking means to telecoms, vendors and customers, and how AI and ML technologies and tools can be used, the cultural shifts the industry needs to make it a success, and what to bear in mind when deploying machine learning across a telecom network.