Vibro-Acoustic Codling Moth Larvae Infestation Detection in Apples

Within recent years, the demand for organic produce has greatly increased due to many factors, including increasing knowledge about such things as dietary fiber and balanced gastrointestinal bacterial ecosystems. This increase in demand, coupled with the financial penalties for sending invasive species and pests across borders, presents a need for a scalable and accurate system to non-destructively detect infestation. The proposed work addresses this problem by testing the performance of a non-destructive vibro-acoustic method for detecting lava activity in apples. This involved 3 steps; design a mechanical data collection prototype for testing apples, a evaluate a set of features, and test the detection performance using machine learning algorithms. The mechanical data collection prototype aims to solve some of the issues that arose when collecting repeatable vibro-acoustic data from apples. The second piece aims to show the feasibility of a scalable model which takes vibro-acoustic data, performs multi-domain feature extraction, and then utilizes a SVM/ANN backend to detect codling moth infestation in apples. The final piece describes a procedure in which a novel CNN architecture pair is created to assess the quality of results with and without an acoustic reference channel. The data collection prototype produced higher quality data than previous setups. The feature extraction and SVM/ANN showed the ability to characterize patterns and detect infestation. The best of these was an SVM which had 87.34% accuracy on classifying 5 second segments from apples not in the training set, which was run on one iteration of a randomized dataset split. The CNN architectures showed potential for further development, with the noise-inclusive model performing over 8% better. However, both models show limited potential for generalizing to new apples with accuracies of (35.15% without noise, 43.92% with noise). The lower detection rates were limited by the intermittent larval activity rates, since the low accuracy rates were driven primarily by missed detections in the 5 second windows on apples labeled as infested. If the percentage of activity in any five second window is too low, then the “infested” sample will get classified as healthy due to that window having no larval sounds. The other notable issue regarding generalization potential was the sample size: the number of distinct apples used was too small, especially for deep learning applications. A much larger number of apples will be needed for future work

Abstracts of Invited and Selected Papers and Organized Symposia, WAEA Annual Meetings, San Francisco, California, July 6-8, 2005

Teaching/Communication/Extension/Profession,

The Effectiveness of University Knowledge Spill-Overs: Performance Differences between University Spin-Offs and Corporate Spin-Offs

While much prior research has focused upon how the Technology Transfer Offices and other contextual characteristics shape the level of university spin-offs (USO), there is little research on entrepreneurial potential among individual academics, and to the best of our knowledge, no comparative studies with other types of spin-offs exist to date. In this paper we suggest that knowledge transfer from academic research may flow indirectly to entrepreneurship by individuals with a university education background who become involved in new venture creation by means of corporate spin-offs (CSO) after gaining industrial experience, rather than leaving university employment to found a new venture as an academic spinoff. In fact, the commercial knowledge gained by industry experience is potentially more valuable for entrepreneurial performance compared to the academic knowledge gained by additional research experience at a university. This leads us to posit that not only will the average performance of CSOs be higher than comparable USOs, but the gains from founder’s prior experiences will also be higher among CSOs. We investigate these propositions in a comparative study tracking the complete population of USOs and CSOs among the Swedish knowledge-intensive sectors between 1994 and 2002.Spill-Overs

Tortious Toxics

In this Article we offer one small idea with potentially large implications. We propose the recognition arid development of a special tort for toxic exposures, where the exposures have not yet led to a physical illness such as cancer. We argue, in brief, that this new tort would, in one simple step, accomplish three things: it would address many of the problems with the courts\u27 current handling of toxic torts; it would consolidate the many overlapping causes of action now pressed in toxic tort cases into one single claim; and it would give expression to the real injury motivating these cases - a dignitary and autonomy-based harm, not a physical one

Making apples from oranges: Comparing noncollapsible effect estimators and their standard errors after adjustment for different covariate sets

We revisit the well‐known but often misunderstood issue of (non)collapsibility of effect measures in regression models for binary and time‐to‐event outcomes. We describe an existing simple but largely ignored procedure for marginalizing estimates of conditional odds ratios and propose a similar procedure for marginalizing estimates of conditional hazard ratios (allowing for right censoring), demonstrating its performance in simulation studies and in a reanalysis of data from a small randomized trial in primary biliary cirrhosis patients. In addition, we aim to provide an educational summary of issues surrounding (non)collapsibility from a causal inference perspective and to promote the idea that the words conditional and adjusted (likewise marginal and unadjusted) should not be used interchangeably