Perception of Risk and Risk Management in Fruit and Vegetable Marketing in Tennessee: The Case of Product Liability Risk

The product liability risk related to fruit and vegetable marketing is that of customer liability associated with injuries caused by harmful products such as contaminated fresh produce. An event associated with product liability risk may have a very low probability of occurrence but may result in a large economic loss. Producers may be unaware of the product liability risk they face, the potential cost of this risk and, therefore their need to adopt measures against this risk. The purpose of this thesis is to examine perceptions of Tennessee fruit and vegetable producers about product liability risk when selling fruits and vegetables, and measures they take to protect themselves against this risk. The data for this thesis was gathered from a survey of Tennessee fruit and vegetable producers. This study examines both fruit and vegetable producer perceptions of product liability risk as a risk face when selling fruits and vegetables and producer adoption of insurance providing product liability coverage. The first essay of the thesis focuses on the evaluation of factors associated with fruit and vegetable producer perceptions of product liability risk. The second essay of this thesis evaluates the factors influencing producer adoption of insurance providing product liability coverage. Factors influencing fruit and vegetable producer perceptions of product liability risk are evaluated using a probit regression. Results suggest that perceptions of product liability risk are associated with producer primary occupation, total household income, whether a farmer produces lettuce or cantaloupes for sale, percentage of farm’s gross annual sales from fresh fruits and vegetables, and the number of farms harvesting vegetables for fresh market in the county where the farming operation is located. Using a probit regression with instrumental variables this study also assesses the factors influencing Tennessee fruit and vegetable producer decision to adopt insurance providing product liability coverage. Results suggest that farmer decision to purchase product liability insurance is associated with the percentage of sales made through retail outlets (e.g., institution, grocery and restaurant)

The Neutralization Epitope of Lactate Dehydrogenase-Elevating Virus Is Located on the Short Ectodomain of the Primary Envelope Glycoprotein

AbstractWe have measured by indirect ELISA the binding of neutralizing and non-neutralizing anti-lactate dehydrogenase-elevating virus (LDV) polyclonal and monoclonal antibodies to synthetic peptides representing unmodified hydrophilic segments of LDV proteins. Using this method a single neutralization epitope has been shown to be located in the very short (about 30 amino acid long) ectodomain of the primary envelope glycoprotein, VP-3P, encoded by ORF 5. Although the neutralization epitopes of neuropathogenic and non-neuropathogenic LDVs differ slightly in amino acid sequences, the neutralizing antibodies bind strongly to the epitopes of both groups of viruses. However, the neutralization epitopes of neuropathogenic and non-neuropathogenic LDVs are associated with different numbers of polylactosaminoglycan chains (1 and 3, respectively) which may affect the binding of neutralizing antibodies to the virions of these LDVs. The ELISA using synthetic peptides containing the neutralization epitope provides a novel, rapid, sensitive, and inexpensive method for quantitating LDV neutralizing antibodies in infected mice

The effect of PID control scheme on the course-keeping of ship in oblique stern waves

Sailing in oblique stern waves causes a ship to make sharp turns and uncontrollable course deviation, which is accompanied by a large heel and sometimes leads to capsizing. Studying the control algorithm in oblique stern waves is imperative because an excellent controller scheme can improve the ship’s course-keeping stability. This paper uses the Maneuvering Modelling Group (MMG) method based on hydrodynamic derivatives and the Computational Fluid Dynamics (CFD)-based self-navigation simulation to simulate ship navigation in waves. This study examines the effect of proportion-integral-derivative (PID) controller schemes on the stability of course maintenance based on hydrodynamic derivatives and 3DOF MMG methods. Then, the optimized PID control parameters are used to simulate the ship’s 6DOF self-propulsion navigation in oblique waves using the CFD method. The nonlinear phenomena during the process, such as side-hull emergency, slamming, and green water, are considered. This study found that the range of the control bandwidth should be optimized based on the ship\u27s heading and wave parameters

Robotic Scene Segmentation with Memory Network for Runtime Surgical Context Inference

Surgical context inference has recently garnered significant attention in robot-assisted surgery as it can facilitate workflow analysis, skill assessment, and error detection. However, runtime context inference is challenging since it requires timely and accurate detection of the interactions among the tools and objects in the surgical scene based on the segmentation of video data. On the other hand, existing state-of-the-art video segmentation methods are often biased against infrequent classes and fail to provide temporal consistency for segmented masks. This can negatively impact the context inference and accurate detection of critical states. In this study, we propose a solution to these challenges using a Space Time Correspondence Network (STCN). STCN is a memory network that performs binary segmentation and minimizes the effects of class imbalance. The use of a memory bank in STCN allows for the utilization of past image and segmentation information, thereby ensuring consistency of the masks. Our experiments using the publicly available JIGSAWS dataset demonstrate that STCN achieves superior segmentation performance for objects that are difficult to segment, such as needle and thread, and improves context inference compared to the state-of-the-art. We also demonstrate that segmentation and context inference can be performed at runtime without compromising performance.Comment: accepted at The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 202

Evaluating the Task Generalization of Temporal Convolutional Networks for Surgical Gesture and Motion Recognition using Kinematic Data

Fine-grained activity recognition enables explainable analysis of procedures for skill assessment, autonomy, and error detection in robot-assisted surgery. However, existing recognition models suffer from the limited availability of annotated datasets with both kinematic and video data and an inability to generalize to unseen subjects and tasks. Kinematic data from the surgical robot is particularly critical for safety monitoring and autonomy, as it is unaffected by common camera issues such as occlusions and lens contamination. We leverage an aggregated dataset of six dry-lab surgical tasks from a total of 28 subjects to train activity recognition models at the gesture and motion primitive (MP) levels and for separate robotic arms using only kinematic data. The models are evaluated using the LOUO (Leave-One-User-Out) and our proposed LOTO (Leave-One-Task-Out) cross validation methods to assess their ability to generalize to unseen users and tasks respectively. Gesture recognition models achieve higher accuracies and edit scores than MP recognition models. But, using MPs enables the training of models that can generalize better to unseen tasks. Also, higher MP recognition accuracy can be achieved by training separate models for the left and right robot arms. For task-generalization, MP recognition models perform best if trained on similar tasks and/or tasks from the same dataset.Comment: 8 pages, 4 figures, 6 tables. To be published in IEEE Robotics and Automation Letters (RA-L

Training End-to-End Unrolled Iterative Neural Networks for SPECT Image Reconstruction

Training end-to-end unrolled iterative neural networks for SPECT image reconstruction requires a memory-efficient forward-backward projector for efficient backpropagation. This paper describes an open-source, high performance Julia implementation of a SPECT forward-backward projector that supports memory-efficient backpropagation with an exact adjoint. Our Julia projector uses only ~5% of the memory of an existing Matlab-based projector. We compare unrolling a CNN-regularized expectation-maximization (EM) algorithm with end-to-end training using our Julia projector with other training methods such as gradient truncation (ignoring gradients involving the projector) and sequential training, using XCAT phantoms and virtual patient (VP) phantoms generated from SIMIND Monte Carlo (MC) simulations. Simulation results with two different radionuclides (90Y and 177Lu) show that: 1) For 177Lu XCAT phantoms and 90Y VP phantoms, training unrolled EM algorithm in end-to-end fashion with our Julia projector yields the best reconstruction quality compared to other training methods and OSEM, both qualitatively and quantitatively. For VP phantoms with 177Lu radionuclide, the reconstructed images using end-to-end training are in higher quality than using sequential training and OSEM, but are comparable with using gradient truncation. We also find there exists a trade-off between computational cost and reconstruction accuracy for different training methods. End-to-end training has the highest accuracy because the correct gradient is used in backpropagation; sequential training yields worse reconstruction accuracy, but is significantly faster and uses much less memory.Comment: submitted to IEEE TRPM

COMPASS: A Formal Framework and Aggregate Dataset for Generalized Surgical Procedure Modeling

Purpose: We propose a formal framework for the modeling and segmentation of minimally-invasive surgical tasks using a unified set of motion primitives (MPs) to enable more objective labeling and the aggregation of different datasets. Methods: We model dry-lab surgical tasks as finite state machines, representing how the execution of MPs as the basic surgical actions results in the change of surgical context, which characterizes the physical interactions among tools and objects in the surgical environment. We develop methods for labeling surgical context based on video data and for automatic translation of context to MP labels. We then use our framework to create the COntext and Motion Primitive Aggregate Surgical Set (COMPASS), including six dry-lab surgical tasks from three publicly-available datasets (JIGSAWS, DESK, and ROSMA), with kinematic and video data and context and MP labels. Results: Our context labeling method achieves near-perfect agreement between consensus labels from crowd-sourcing and expert surgeons. Segmentation of tasks to MPs results in the creation of the COMPASS dataset that nearly triples the amount of data for modeling and analysis and enables the generation of separate transcripts for the left and right tools. Conclusion: The proposed framework results in high quality labeling of surgical data based on context and fine-grained MPs. Modeling surgical tasks with MPs enables the aggregation of different datasets and the separate analysis of left and right hands for bimanual coordination assessment. Our formal framework and aggregate dataset can support the development of explainable and multi-granularity models for improved surgical process analysis, skill assessment, error detection, and autonomy.Comment: 22 pages, 6 figures, 12 table

Nonlinear robust control of tail-sitter aircrafts in flight mode transitions

© 2018 Elsevier Masson SAS In this paper, a nonlinear robust controller is proposed to deal with the flight mode transition control problem of tail-sitter aircrafts. During the mode transitions, the control problem is challenging due to the high nonlinearities and strong couplings. The tail-sitter aircraft model can be considered as a nominal part with uncertainties including nonlinear terms, parametric uncertainties, and external disturbances. The proposed controller consists of a nominal H∞controller and a nonlinear disturbance observer. The nominal H∞controller based on the nominal model is designed to achieve the desired trajectory tracking performance. The uncertainties are regarded as equivalent disturbances to restrain their influences by the nonlinear disturbance observer. Theoretical analysis and simulation results are given to show advantages of the proposed control method, compared with the standard H∞control approach

Region Normalization for Image Inpainting

Feature Normalization (FN) is an important technique to help neural network training, which typically normalizes features across spatial dimensions. Most previous image inpainting methods apply FN in their networks without considering the impact of the corrupted regions of the input image on normalization, e.g. mean and variance shifts. In this work, we show that the mean and variance shifts caused by full-spatial FN limit the image inpainting network training and we propose a spatial region-wise normalization named Region Normalization (RN) to overcome the limitation. RN divides spatial pixels into different regions according to the input mask, and computes the mean and variance in each region for normalization. We develop two kinds of RN for our image inpainting network: (1) Basic RN (RN-B), which normalizes pixels from the corrupted and uncorrupted regions separately based on the original inpainting mask to solve the mean and variance shift problem; (2) Learnable RN (RN-L), which automatically detects potentially corrupted and uncorrupted regions for separate normalization, and performs global affine transformation to enhance their fusion. We apply RN-B in the early layers and RN-L in the latter layers of the network respectively. Experiments show that our method outperforms current state-of-the-art methods quantitatively and qualitatively. We further generalize RN to other inpainting networks and achieve consistent performance improvements.Comment: Accepted by AAAI-202

An analytic model of typhoon wind field and simulation of storm tides

Storm tides have intensified due to global climate warming, with limited attention given to storm current velocity (SCV) due to data scarcity during hurricanes/typhoons and limitations in existing wind models’ accuracy. We propose an analytic model incorporating sea-surface resistance into the gradient wind equation, offering a theoretically robust approach. Through rigorous verification against measured data, our model demonstrates significant accuracy improvement compared to established models. Simulating storm tides during Typhoon Rammasun using our approach reveals strong agreement between calculated SCVs and measured data, surpassing the performance of the Holland model. Notably, typhoon storm surges primarily respond to pressure, while SCVs are predominantly governed by wind speed in open sea. The highest water level aligns with the lowest pressure, with maximum SCVs trailing the maximum wind radius. SCVs significantly exceed astronomical tidal current velocities (ACVs) in the open sea, reaching a maximum of 3.57 m/s. Areas where the SCV-to-ACV ratio exceeds 3 constitute 21.4% of the study area. Combining our wind model with Typhoon SCV simulations provides valuable insights into storm tide dynamics, advancing our understanding of storm tide mechanisms and informing mitigation strategies