Time to Raise the Bar: The Real Corporate Social Responsibility Report for the Hershey Company

This document is part of a digital collection provided by the Martin P. Catherwood Library, ILR School, Cornell University, pertaining to the effects of globalization on the workplace worldwide. Special emphasis is placed on labor rights, working conditions, labor market changes, and union organizing.ILRF_Time_to_Raise_the_Bar_Hershey.pdf: 2396 downloads, before Oct. 1, 2020

Medical Cannabis Bike Tour funds glioblastoma clinical trial

Independent medical research into glioblastoma funded by business people in the legal European cannabis industry. A study of sporting philanthropy that shows personal motivations to bring change to the law and medical research

Resilience Planning: Forging a New Planning Paradigm

The environment has been a significant focus for planning education since the 1960’s. This paper traces the transition of environmental planning through the sustainability era to the emergence of a new and more accelerated transition that increasingly is termed resilience. It outlines the emerging characteristics of resilience and suggests they need to become part of a new paradigm in planning education – resilience planning

Point-based metric and topological localisation between lidar and overhead imagery

In this paper, we present a method for solving the localisation of a ground lidar using overhead imagery only. Public overhead imagery such as Google satellite images are readily available resources. They can be used as the map proxy for robot localisation, relaxing the requirement for a prior traversal for mapping as in traditional approaches. While prior approaches have focused on the metric localisation between range sensors and overhead imagery, our method is the first to learn both place recognition and metric localisation of a ground lidar using overhead imagery, and also outperforms prior methods on metric localisation with large initial pose offsets. To bridge the drastic domain gap between lidar data and overhead imagery, our method learns to transform an overhead image into a collection of 2D points, emulating the resulting point-cloud scanned by a lidar sensor situated near the centre of the overhead image. After both modalities are expressed as point sets, point-based machine learning methods for localisation are applied

RSL-Net: Localising in Satellite Images From a Radar on the Ground

This paper is about localising a vehicle in an overhead image using FMCW radar mounted on a ground vehicle. FMCW radar offers extraordinary promise and efficacy for vehicle localisation. It is impervious to all weather types and lighting conditions. However the complexity of the interactions between millimetre radar wave and the physical environment makes it a challenging domain. Infrastructure-free large-scale radar-based localisation is in its infancy. Typically here a map is built and suitable techniques, compatible with the nature of sensor, are brought to bear. In this work we eschew the need for a radar-based map; instead we simply use an overhead image -- a resource readily available everywhere. This paper introduces a method that not only naturally deals with the complexity of the signal type but does so in the context of cross modal processing.Comment: Accepted to IEEE Robotics and Automation Letters (RA-L

Self-Supervised Localisation between Range Sensors and Overhead Imagery

Publicly available satellite imagery can be an ubiquitous, cheap, and powerful tool for vehicle localisation when a prior sensor map is unavailable. However, satellite images are not directly comparable to data from ground range sensors because of their starkly different modalities. We present a learned metric localisation method that not only handles the modality difference, but is cheap to train, learning in a self-supervised fashion without metrically accurate ground truth. By evaluating across multiple real-world datasets, we demonstrate the robustness and versatility of our method for various sensor configurations. We pay particular attention to the use of millimetre wave radar, which, owing to its complex interaction with the scene and its immunity to weather and lighting, makes for a compelling and valuable use case.Comment: Robotics: Science and Systems (RSS) 202

Stochastic oscillations of adaptive networks: application to epidemic modelling

Adaptive-network models are typically studied using deterministic differential equations which approximately describe their dynamics. In simulations, however, the discrete nature of the network gives rise to intrinsic noise which can radically alter the system's behaviour. In this article we develop a method to predict the effects of stochasticity in adaptive networks by making use of a pair-based proxy model. The technique is developed in the context of an epidemiological model of a disease spreading over an adaptive network of infectious contact. Our analysis reveals that in this model the structure of the network exhibits stochastic oscillations in response to fluctuations in the disease dynamic.Comment: 11 pages, 4 figure

A Validated Injury Surveillance and Monitoring Tool for Fast Jet Aircrew: Translating Sports Medicine Paradigms to a Military Population

BACKGROUND: Military populations, including fast jet aircrew (FJA - aka fighter aircrew/pilots), commonly suffer from musculoskeletal complaints, which reduce performance and operational capability. Valid surveillance tools and agreed recordable injury definitions are lacking. Our objective was to develop and then evaluate the validity of a musculoskeletal complaints surveillance and monitoring tool for FJA. METHODS: A Delphi study with international experts sought consensus on recordable injury definitions and important content for use in a surveillance and monitoring tool for FJA. Using these results and feedback from end-users (FJA), the University of Canberra Fast Jet Aircrew Musculoskeletal Questionnaire (UC-FJAMQ) was developed. Following its use with 306 Royal Australian Air Force (RAAF) FJA over 4 × five-month reporting periods, validity of the UC-FJAMQ was evaluated via multi-level factor analysis (MFA) and compared with routine methods of injury surveillance. RESULTS: Consensus was achieved for: eight words/descriptors for defining a musculoskeletal complaint; six definitions of recordable injury; and 14 domains important for determining overall severity. The UC-FJAMQ was developed and refined. MFA identified three distinct dimensions within the 11 items used to determine severity: operational capability, symptoms, and care-seeking. MFA further highlighted that symptom severity and seeking medical attention were poor indicators of the impact musculoskeletal complaints have upon operational capability. One hundred and fifty-two episodes of time loss were identified, with the UC-FJAMQ identifying 79% of these, while routine methods identified 49%. Despite modest weekly reporting rates (61%), the UC-FJAMQ outperformed routine surveillance methods. CONCLUSIONS: The UC-FJAMQ was developed to specifically address the complexities of injury surveillance with FJA, which are similar to those noted in other military and sporting populations. The results demonstrated the UC-FJAMQ to be sensitive and valid within a large group of FJA over 4 × five-month reporting periods. Adoption of consistent, sensitive, and valid surveillance methods will strengthen the FJA injury prevention literature, ultimately enhancing their health, performance, and operational capability. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40798-022-00484-1