On the predictions for diffusion-driven evaporation of sessile droplets with interface cooling

The diffusion-driven evaporation of sessile droplets from planar surfaces is influenced by cooling at the air-liquid interface. Here, corrections to the available models for predicting the evaporation process are presented. The mass conservation for diffusion-driven evaporation is resolved by considering the effect of interface cooling on the change in density of saturated vapour along the liquid-vapour interface of sessile droplets. Corrections to the predictions for the spatial distribution of vapour density around a sessile droplet and the evaporative flux of vapour at the interface are obtained. The classical models are recovered from the new predictions if interface cooling is negligible. Comparison between the new and classical predictions for the local surface evaporative flux is obtained using the literature data. Our analysis shows a significant effect of interface cooling which should be considered in predicting diffusion-driven evaporation of sessile droplets on planar surfaces

Maize in Vietnam: Production Systems, Constraints, and Research Priorities

Crop Production/Industries,

Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigation

The work discusses the use of machine learning algorithms for anomaly detection in medical image analysis and how the performance of these algorithms depends on the number of annotators and the quality of labels. To address the issue of subjectivity in labeling with a single annotator, we introduce a simple and effective approach that aggregates annotations from multiple annotators with varying levels of expertise. We then aim to improve the efficiency of predictive models in abnormal detection tasks by estimating hidden labels from multiple annotations and using a re-weighted loss function to improve detection performance. Our method is evaluated on a real-world medical imaging dataset and outperforms relevant baselines that do not consider disagreements among annotators.Comment: This is a short version submitted to the Midwest Machine Learning Symposium (MMLS 2023), Chicago, IL, US

Linear Query Approximation Algorithms for Non-monotone Submodular Maximization under Knapsack Constraint

This work, for the first time, introduces two constant factor approximation algorithms with linear query complexity for non-monotone submodular maximization over a ground set of size $n$ subject to a knapsack constraint, $\mathsf{DLA}$ and $\mathsf{RLA}$. $\mathsf{DLA}$ is a deterministic algorithm that provides an approximation factor of $6+\epsilon$ while $\mathsf{RLA}$ is a randomized algorithm with an approximation factor of $4+\epsilon$. Both run in $O(n \log(1/\epsilon)/\epsilon)$ query complexity. The key idea to obtain a constant approximation ratio with linear query lies in: (1) dividing the ground set into two appropriate subsets to find the near-optimal solution over these subsets with linear queries, and (2) combining a threshold greedy with properties of two disjoint sets or a random selection process to improve solution quality. In addition to the theoretical analysis, we have evaluated our proposed solutions with three applications: Revenue Maximization, Image Summarization, and Maximum Weighted Cut, showing that our algorithms not only return comparative results to state-of-the-art algorithms but also require significantly fewer queries

Role of the cyclic lipopeptide massetolide A in biological control of Phytophthora infestans and in colonization of tomato plants by Pseudomonas fluorescens

Pseudomonas strains have shown promising results in biological control of late blight caused by Phytophthora infestans. However, the mechanism(s) and metabolites involved are in many cases poorly understood. Here, the role of the cyclic lipopeptide massetolide A of Pseudomonas fluorescens SS101 in biocontrol of tomato late blight was examined. Pseudomonas fluorescens SS101 was effective in preventing infection of tomato (Lycopersicon esculentum) leaves by P. infestans and significantly reduced the expansion of existing late blight lesions. Massetolide A was an important component of the activity of P. fluorescens SS101, since the massA-mutant was significantly less effective in biocontrol, and purified massetolide A provided significant control of P. infestans, both locally and systemically via induced resistance. Assays with nahG transgenic plants indicated that the systemic resistance response induced by SS101 or massetolide A was independent of salicylic acid signalling. Strain SS101 colonized the roots of tomato seedlings significantly better than its massA-mutant, indicating that massetolide A was an important trait in plant colonization. This study shows that the cyclic lipopeptide surfactant massetolide A is a metabolite with versatile functions in the ecology of P fluorescens SS101 and in interactions with tomato plants and the late blight pathogen P. infestans

Thyroid disease is a favorable prognostic factor in achieving sustained virologic response in chronic hepatitis C undergoing combination therapy: A nested case control study

<p>Abstract</p> <p>Background</p> <p>Interferon-α in combination with ribavirin is the current gold standard for treatment of chronic hepatitis C. It is unknown if the development of autoimmune thyroid disease (TD) during treatment confers an improved chance of achieving sustained virologic response. The aim of this study is to assess the chance of achieving sustained virologic response (SVR) in patients who developed TD during treatment when compared with those who did not.</p> <p>Methods</p> <p>We performed a tertiary hospital-based retrospective nested case-control analysis of 19 patients treated for hepatitis C who developed thyroid disease, and 76 controls (matched for age, weight, gender, cirrhosis and aminotransferase levels) who did not develop TD during treatment. Multivariate logistic-regression models were used to compare cases and controls.</p> <p>Results</p> <p>The development of TD was associated with a high likelihood of achieving SVR (odds ratio, 6.0; 95% confidence interval, 1.5 to 24.6) for the pooled group containing all genotypes. The likelihood of achieving SVR was increased in individuals with genotype 1 HCV infection who developed TD (odds ratio, 5.2; 95% confidence interval, 1.2 to 22.3), and all genotype 3 patients who developed TD achieved SVR.</p> <p>Conclusions</p> <p>Development of TD during treatment for hepatitis C infection is associated with a significantly increased chance of achieving SVR. The pathophysiogical mechanisms for this observation remain to be determined.</p> <p>Trial Registration</p> <p><it>The Australian New Zealand Clinical Trials Registry (ANZCTR)</it>: <a href="http://www.anzctr.org.au/ACTRB12610000830099.aspx">ACTRB12610000830099</a></p

Pharmacist-Led Intervention to Enhance Medication Adherence in Patients With Acute Coronary Syndrome in Vietnam:A Randomized Controlled Trial

Background: Patient adherence to cardioprotective medications improves outcomes of acute coronary syndrome (ACS), but few adherence-enhancing interventions have been tested in low-income and middle-income countries. Objectives: We aimed to assess whether a pharmacist-led intervention enhances medication adherence in patients with ACS and reduces mortality and hospital readmission. Methods: We conducted a randomized controlled trial in Vietnam. Patients with ACS were recruited, randomized to the intervention or usual care prior to discharge, and followed 3 months after discharge. Intervention patients received educational and behavioral interventions by a pharmacist. Primary outcome was the proportion of adherent patients 1 month after discharge. Adherence was a combined measure of self-reported adherence (the 8-item Morisky Medication Adherence Scale) and obtaining repeat prescriptions on time. Secondary outcomes were (1) the proportion of patients adherent to medication; (2) rates of mortality and hospital readmission; and (3) change in quality of life from baseline assessed with the European Quality of Life Questionnaire - 5 Dimensions - 3 Levels at 3 months after discharge. Logistic regression was used to analyze data. Registration: ClinicalTrials.gov (NCT02787941). Results: Overall, 166 patients (87 control, 79 intervention) were included (mean age 61.2 years, 73% male). In the analysis excluding patients from the intervention group who did not receive the intervention and excluding all patients who withdrew, were lost to follow-up, died or were readmitted to hospital, a greater proportion of patients were adherent in the intervention compared with the control at 1 month (90.0% vs. 76.5%; adjusted OR = 2.77; 95% CI, 1.01-7.62) and at 3 months after discharge (90.2% vs. 77.0%; adjusted OR = 3.68; 95% CI, 1.14-11.88). There was no significant difference in median change of EQ-5D-3L index values between intervention and control [0.000 (0.000; 0.275) vs. 0.234 (0.000; 0.379); p = 0.081]. Rates of mortality, readmission, or both were 0.8, 10.3, or 11.1%, respectively; with no significant differences between the 2 groups. Conclusion: Pharmacist-led interventions increased patient adherence to medication regimens by over 13% in the first 3 months after ACS hospital discharge, but not quality of life, mortality and readmission. These results are promising but should be tested in other settings prior to broader dissemination

Iron-induced acceptor centers in the gallium nitride high electron mobility transistor: thermal simulation and analysis

The effect of the presence of iron-induced acceptor centers in the gallium nitride high electron mobility transistor is studied using device physics simulations at elevated temperatures (up to 600 K), as a lattice heat flow equation is solved self-consistently with the Poisson and the continuity equations to account for self-heating effects. It is shown that the acceptor centers intentionally introduced in the buffer layer of the device cause a shift of the input characteristics in the positive direction

Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes

Hogwild! implements asynchronous Stochastic Gradient Descent (SGD) where multiple threads in parallel access a common repository containing training data, perform SGD iterations and update shared state that represents a jointly learned (global) model. We consider big data analysis where training data is distributed among local data sets in a heterogeneous way -- and we wish to move SGD computations to local compute nodes where local data resides. The results of these local SGD computations are aggregated by a central "aggregator" which mimics Hogwild!. We show how local compute nodes can start choosing small mini-batch sizes which increase to larger ones in order to reduce communication cost (round interaction with the aggregator). We improve state-of-the-art literature and show $O(\sqrt{K}$) communication rounds for heterogeneous data for strongly convex problems, where $K$ is the total number of gradient computations across all local compute nodes. For our scheme, we prove a \textit{tight} and novel non-trivial convergence analysis for strongly convex problems for {\em heterogeneous} data which does not use the bounded gradient assumption as seen in many existing publications. The tightness is a consequence of our proofs for lower and upper bounds of the convergence rate, which show a constant factor difference. We show experimental results for plain convex and non-convex problems for biased (i.e., heterogeneous) and unbiased local data sets.Comment: arXiv admin note: substantial text overlap with arXiv:2007.09208 AISTATS 202