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)

Identifying The Role And Mechanism By Which GDF15 And FGF21 Regulate Glucose Homeostasis

As a highly utilized substrate in most cells of the body, glucose homeostasis is strictly regulated by a variety of mechanisms. Under physiological conditions, blood glucose is sophisticatedly maintained by temporal coordination of whole-body glucose uptake, absorption from the digestive system, and endogenous glucose production from the liver and kidney. However, the factors and mechanisms by which plasma glucose is regulated under pathological conditions are not clearly understood. In this Ph.D. thesis, we interrogated the role of two cytokines, Growth Differentiation Factor 15 (GDF15) and fibroblast growth factor-21 (FGF-21), in regulating glucose output under extreme metabolic stress. By employing two clinically relevant hypoglycemic models, the insulin tolerance test and hyperinsulinemic-hypoglycemic clamp, we found that GDF15 can be secreted from the S3 segment of the proximal tubules. For the first time, we identified the counterregulatory role of GDF15 during hypoglycemia by combining tracer infusion with a genetic knock-out mouse model, which shows the GDF15 can increase liver gluconeogenesis in an intrahepatic lipolysis-dependent manner. Regarding the diabetic condition, we found that recurrent hypoglycemia, which is frequently a concern in T1DM and advanced T2DM patients, can diminish the production of GDF15 in a rodent model. In accordance with this finding, T1DM patients showed attenuated GDF15 induction during the hypoglycemic clamp. To investigate the contribution of the liver and the kidney separately, REnal Gluconeogenesis Analytical Leads (REGAL) was developed based on the previous PINTA method1. This non-invasive and accurate method enables us to measure kidney gluconeogenesis flux, liver gluconeogenesis, and glycogenolysis by stable isotope infusion. We found that kidney glucose production is elevated in response to an unsatisfied systemic glucose demand during metabolic challenges. Furthermore, we identified that FGF21, which was previously considered as a catabolic cytokine, can increase renal glucose output via a liver-brain-kidney axis. Our genetic knock-out and pharmacological intervention model revealed that the critical function of FGF21 relies on the beta 2 adrenergic receptor (Adrb2) and intra-renal lipolysis. In addition, we showed that renal cell carcinoma can hijack this mechanism by inducing FGF21 production in the liver, possibly in a VGEF-dependent manner, to create a favorable microenvironment for itself. Taken together, we developed a method to understand the regulation of glucose output from the liver and kidney separately. Simply requiring jugular vein catheterization and stable isotope tracers, this method can be applied to a broad spectrum of rodent models, or even human subjects, with a high success rate. These studies give insight to how the two main sources of endogenous glucose production communicate with each other to maintain systemic glucose supply and how kidney tumor cells take advantage of this mechanism to support their own survival. Finally, the abnormal GDF15 production in T1DM patients, the anabolic role of FGF21 during metabolic stress, and the induction of FGF21 in RCC show the translational value of the study and inspire us to investigate the clinical application of GDF15 and FGF21 in insulin-related hypoglycemia, liver dysfunction, and renal cell carcinoma

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

A Survey of Deep Causal Models and Their Industrial Applications

The notion of causality assumes a paramount position within the realm of human cognition. Over the past few decades, there has been significant advancement in the domain of causal effect estimation across various disciplines, including but not limited to computer science, medicine, economics, and industrial applications. Given the continous advancements in deep learning methodologies, there has been a notable surge in its utilization for the estimation of causal effects using counterfactual data. Typically, deep causal models map the characteristics of covariates to a representation space and then design various objective functions to estimate counterfactual data unbiasedly. Different from the existing surveys on causal models in machine learning, this review mainly focuses on the overview of the deep causal models based on neural networks, and its core contributions are as follows: 1) we cast insight on a comprehensive overview of deep causal models from both timeline of development and method classification perspectives; 2) we outline some typical applications of causal effect estimation to industry; 3) we also endeavor to present a detailed categorization and analysis on relevant datasets, source codes and experiments

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

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

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

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

Shorter SPECT Scans Using Self-supervised Coordinate Learning to Synthesize Skipped Projection Views

Purpose: This study addresses the challenge of extended SPECT imaging duration under low-count conditions, as encountered in Lu-177 SPECT imaging, by developing a self-supervised learning approach to synthesize skipped SPECT projection views, thus shortening scan times in clinical settings. Methods: We employed a self-supervised coordinate-based learning technique, adapting the neural radiance field (NeRF) concept in computer vision to synthesize under-sampled SPECT projection views. For each single scan, we used self-supervised coordinate learning to estimate skipped SPECT projection views. The method was tested with various down-sampling factors (DFs=2, 4, 8) on both Lu-177 phantom SPECT/CT measurements and clinical SPECT/CT datasets, from 11 patients undergoing Lu-177 DOTATATE and 6 patients undergoing Lu-177 PSMA-617 radiopharmaceutical therapy. Results: For SPECT reconstructions, our method outperformed the use of linearly interpolated projections and partial projection views in relative contrast-to-noise-ratios (RCNR) averaged across different downsampling factors: 1) DOTATATE: 83% vs. 65% vs. 67% for lesions and 86% vs. 70% vs. 67% for kidney, 2) PSMA: 76% vs. 69% vs. 68% for lesions and 75% vs. 55% vs. 66% for organs, including kidneys, lacrimal glands, parotid glands, and submandibular glands. Conclusion: The proposed method enables reduction in acquisition time (by factors of 2, 4, or 8) while maintaining quantitative accuracy in clinical SPECT protocols by allowing for the collection of fewer projections. Importantly, the self-supervised nature of this NeRF-based approach eliminates the need for extensive training data, instead learning from each patient's projection data alone. The reduction in acquisition time is particularly relevant for imaging under low-count conditions and for protocols that require multiple-bed positions such as whole-body imaging.Comment: 25 pages, 5568 word