202 research outputs found
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 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
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
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
Parallel numerical simulation of impact crater with perfect matched layers
Impact craters are the primary geomorphic features on the surfaces of
celestial bodies such as the Moon, and their formation has significant
implications for the evolutionary history of the celestial body. The study of
the impact crater formation process relies mainly on numerical simulation
methods, with two-dimensional simulations capable of reproducing general
patterns of impact processes while conserving computational resources. However,
to mitigate the artificial reflections of shock waves at numerical boundaries,
a common approach involves expanding the computational domain, greatly reducing
the efficiency of numerical simulations. In this study, we developed a novel
two-dimensional code SALEc-2D that employs the perfect matched layer (PML)
method to suppress artificial reflections at numerical boundaries. This method
enhances computational efficiency while ensuring reliable results.
Additionally, we implemented MPI parallel algorithms in the new code to further
improve computational efficiency. Simulations that would take over ten hours
using the conventional iSALE-2D code can now be completed in less than half an
hour using our code, SALEc-2D, on a standard computer. We anticipate that our
code will find widespread application in numerical simulations of impact
craters in the future.Comment: 17 pages, 8 figure
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
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