Due Diligence in Oil and Gas Acquisitions

This paper is written from the perspective of a lawyer working with the buyer on an oil and gas asset transaction. It provides an overview of the due diligence process from beginning to end. A discussion highlighting some of the features and differences of the due diligence process for an entity transaction is included for comparative purposes

SHERPA: A Flexible, Modular Spacecraft for Orbit Transfer and On-Orbit Operations

The Department of Defense Space Test Program is responsible for launching small experimental payloads and demonstration technologies as directed by the Space Experiments Review Board (SERB). The Shuttle Expendable Rocket for Payload Augmentation (SHERPA) program will develop a highly functional space vehicle – with several variants – that incorporates a scaleable, modular architecture to support a wide variety of missions, technologies, and configurations. The initial application of SHERPA will be as an orbit transfer vehicle designed to raise a payload from a low Space Transportation System (STS) flight altitude to an orbit with a nominal one-year lifetime. This capability will allow STP to take advantage of the low-cost Space Shuttle launch services and still achieve the mission lifetimes required for experiments. In this paper, analysis and design of the SHERPA scalable, modular architecture will be discussed. In addition, applicable requirements and constraints levied upon the design by the customer, secondary payload deployment mechanisms, such as the Canister for All Payload Ejections (CAPE), STS safety, the concept of operations, and envisioned applications, will be addressed

Automated Ecological Assessment of Physical Activity: Advancing Direct Observation.

Technological advances provide opportunities for automating direct observations of physical activity, which allow for continuous monitoring and feedback. This pilot study evaluated the initial validity of computer vision algorithms for ecological assessment of physical activity. The sample comprised 6630 seconds per camera (three cameras in total) of video capturing up to nine participants engaged in sitting, standing, walking, and jogging in an open outdoor space while wearing accelerometers. Computer vision algorithms were developed to assess the number and proportion of people in sedentary, light, moderate, and vigorous activity, and group-based metabolic equivalents of tasks (MET)-minutes. Means and standard deviations (SD) of bias/difference values, and intraclass correlation coefficients (ICC) assessed the criterion validity compared to accelerometry separately for each camera. The number and proportion of participants sedentary and in moderate-to-vigorous physical activity (MVPA) had small biases (within 20% of the criterion mean) and the ICCs were excellent (0.82-0.98). Total MET-minutes were slightly underestimated by 9.3-17.1% and the ICCs were good (0.68-0.79). The standard deviations of the bias estimates were moderate-to-large relative to the means. The computer vision algorithms appeared to have acceptable sample-level validity (i.e., across a sample of time intervals) and are promising for automated ecological assessment of activity in open outdoor settings, but further development and testing is needed before such tools can be used in a diverse range of settings

Latent profile analysis of accelerometer-measured sleep, physical activity, and sedentary time and differences in health characteristics in adult women.

ObjectivesIndependently, physical activity (PA), sedentary behavior (SB), and sleep are related to the development and progression of chronic diseases. Less is known about how rest-activity behaviors cluster within individuals and how rest-activity behavior profiles relate to health. In this study we aimed to investigate if adult women cluster into profiles based on how they accumulate rest-activity behavior (including accelerometer-measured PA, SB, and sleep), and if participant characteristics and health outcomes differ by profile membership.MethodsA convenience sample of 372 women (mean age 55.38 + 10.16) were recruited from four US cities. Participants wore ActiGraph GT3X+ accelerometers on the hip and wrist for a week. Total daily minutes in moderate-to-vigorous PA (MVPA) and percentage of wear-time spent in SB was estimated from the hip device. Total sleep time (hours/minutes) and sleep efficiency (% of in bed time asleep) were estimated from the wrist device. Latent profile analysis (LPA) was performed to identify clusters of participants based on accumulation of the four rest-activity variables. Adjusted ANOVAs were conducted to explore differences in demographic characteristics and health outcomes across profiles.ResultsRest-activity variables clustered to form five behavior profiles: Moderately Active Poor Sleepers (7%), Highly Actives (9%), Inactives (41%), Moderately Actives (28%), and Actives (15%). The Moderately Active Poor Sleepers (profile 1) had the lowest proportion of whites (35% vs 78-91%, p < .001) and college graduates (28% vs 68-90%, p = .004). Health outcomes did not vary significantly across all rest-activity profiles.ConclusionsIn this sample, women clustered within daily rest-activity behavior profiles. Identifying 24-hour behavior profiles can inform intervention population targets and innovative behavioral goals of multiple health behavior interventions

UAVino

UAVino is a drone solution that uses aerial imagery to determine the overall plant health and water content of vineyards. In general, the system focuses on automating crop inspection by taking aerial imagery of a vineyard, conducting post-processing, and outputting an easily interpreted map of the vineyard\u27s overall health. The project\u27s key innovation is an auto-docking system that allows the drone to automatically return to its launch point and recharge in order to extend mission duration. Long term, UAVino is envisioned as a multi-year, interdisciplinary project involving both the Santa Clara University Robotics Systems Laboratory and local wineries in order to develop a fully functional drone agricultural inspection service

A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines

Information in neural networks is represented as weighted connections, or synapses, between neurons. This poses a problem as the primary computational bottleneck for neural networks is the vector-matrix multiply when inputs are multiplied by the neural network weights. Conventional processing architectures are not well suited for simulating neural networks, often requiring large amounts of energy and time. Additionally, synapses in biological neural networks are not binary connections, but exhibit a nonlinear response function as neurotransmitters are emitted and diffuse between neurons. Inspired by neuroscience principles, we present a digital neuromorphic architecture, the Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex synaptic response functions without requiring additional hardware components. We consider the paradigm of spiking neurons with temporally coded information as opposed to non-spiking rate coded neurons used in most neural networks. In this paradigm we examine liquid state machines applied to speech recognition and show how a liquid state machine with temporal dynamics maps onto the STPU-demonstrating the flexibility and efficiency of the STPU for instantiating neural algorithms.Comment: 8 pages, 4 Figures, Preprint of 2017 IJCN