Search

Monitoring pressure inside hermetically sealed vessels typically relies on devices that have direct contact with the fluid inside. Gaining this access requires a hole through the wall of the vessel, which creates potential for leaks, ruptures, and complete failures. To solve this, noninvasive solutions utilize external sensors that relate vessel-wall behavior to internal pressure. However, existing noninvasive techniques require permanently attaching sensors to a unique vessel and then monitoring for changes in the vessel. We present a noninvasive pressure monitoring technique based on acoustic resonance spectroscopy (ARS) and machine learning (ML) that enables estimating pressure in a vessel similar to those it was trained on and does not require sensors to be permanently attached. We train k-nearest neighbor (KNN) regressor models using experimentally gathered acoustic resonance spectra to estimate the pressure in six stainless-steel vessels. We demonstrate accurate estimation of the pressure inside the vessels when training and testing using spectra taken exclusively from an individual vessel, and when performing cross-validation between vessels. The acoustic technique presented in this paper finds broad applications across industry to monitor pressure in systems where having permanent sensors is undesirable, such as complicated pneumatic systems, vacuum sealed foods, and more.

We present a new method for calculating the temperature profile of high explosive (HE) material using a Convolutional Neural Network (CNN). To train/test the CNN, we have developed a hybrid experiment/simulation method for collecting acoustic and temperature data. We experimentally heat cylindrical containers of HE material until detonation/deflagration, where we continuously measure the acoustic bursts through the HE using multiple acoustic transducers lined around the exterior container circumference. However, measuring the temperature profile in the HE in an experiment would require inserting a large number of thermal probes, which would disrupt the heating process. Thus, we use two thermal probes, one at the HE center and one at the wall. We then use numerical simulation of the heating process to calculate the temperature distribution and correct the simulated temperatures based on the experimental center and wall temperatures. We calculate temperature errors on the order of 15 °C, which is ∼12% of the range of temperatures in the experiment. We also investigate how the algorithm’s accuracy is affected by the number of acoustic receivers used to collect each measurement and the resolution of the temperature prediction. This work provides a means of assessing the safety status of HE material, which cannot be achieved using existing temperature measurement methods. In addition, it has implications for a range of other applications where internal temperature profile measurements would provide critical information. These applications include detecting chemical reactions, observing thermodynamic processes such as combustion, monitoring metal or plastic casting, determining the energy density in thermal storage capsules, and identifying abnormal battery operations.

Bulk wave acoustic time-of-flight (ToF) measurements in pipes and closed containers can be hindered by guided waves with similar arrival times propagating in the container wall, especially when a low excitation frequency is used to mitigate sound attenuation from the material. Convolutional neural networks (CNNs) have emerged as a new paradigm for obtaining accurate ToF in non-destructive evaluation (NDE) and have been demonstrated for such complicated conditions. However, the generalizability of ToF-CNNs has not been investigated. In this work, we analyze the generalizability of the ToF-CNN for broader applications, given limited training data. We first investigate the CNN performance with respect to training dataset size and different training data and test data parameters (container dimensions and material properties). Furthermore, we perform a series of tests to understand the distribution of data parameters that need to be incorporated in training for enhanced model generalizability. This is investigated by training the model on a set of small- and large-container datasets regardless of the test data. We observe that the quantity of data partitioned for training must be of a good representation of the entire sets and sufficient to span through the input space. The result of the network also shows that the learning model with the training data on small containers delivers a sufficiently stable result on different feature interactions compared to the learning model with the training data on large containers. To check the robustness of the model, we tested the trained model to predict the ToF of different sound speed mediums, which shows excellent accuracy. Furthermore, to mimic real experimental scenarios, data are augmented by adding noise. We envision that the proposed approach will extend the applications of CNNs for ToF prediction in a broader range.

Measuring the moisture content in flowing biomass is critical to processes such as liquid biofuel conversion, such as biogasoline, biodiesel, bio jet kerosene, etc. However, biomass tends to flow in aggregates, which results in significant inhomogeneities in the amount of biomass flowing in front of a sensor at a given time, and there can be significant overlap in the material properties of dry vs wet biomass, leading to poor signal-to-noise ratio. We present a technique for identifying biomass moisture content using a series of acoustic pitch-catch measurements to quantify the sound speed and acoustic amplitude through the biomass, in conjunction with classical machine learning techniques, including Naive Bayes, Random Forest, and K-Nearest Neighbors classification. We amplify the differences between the acoustic measurements in different moisture levels by collecting a series of pulse-echo measurements, which we sort in order of ascending sound speed. We test the accuracy of the technique on experimentally-prepared batches of corn stover biomass with specified moisture levels, and measure the average error in the estimated moisture level as a function of the number of pitch-catch measurements used. We observe average estimation errors as low as 6.7% by increasing the number of measurements and optimizing the hyperparameters. This work presents a novel method determining moisture content in flowing biomass with inhomogeneous flow. Additionally, this technique has application in optimizing biomass conversion processes, as well as other fields including, paper production, natural fiber processing, and mineral extraction.

Background: Esophageal ulceration is the most common complication of button battery (BB) ingestion. This can progress to development of tracheo-esophageal fistula (TEF). Operative intervention is the standard, as spontaneous healing is not expected. We describe a patient who had complete spontaneous resolution of caustic TEF by multimodal “complete” esophageal rest without operative repair. Case: A 20-month-old girl ingested a BB which resulted in TEF with mediastinitis. After 8 days, endoscopy demonstrated a large (1.5cm) TEF. “Complete” esophageal rest was imposed with a multimodal approach: nothing by mouth; pharmacologic suppression of oral secretions and gastric acid; and placement of a gastrojejunostomy tube for gastric venting and jejunal feeding.She was discharged home 14 days later, continuing esophageal rest. One month later, endoscopy revealed complete resolution of the TEF. Oral feeding was resumed 3 months following the initial ingestion when vocal fold dysfunction (and aspiration) resolved. There was no suggestion of recurrent fistula or diverticulum on the last endoscopy and exam at 15, 19, and 49 months post injury. Conclusion: A few prior reports of non-operative management of TEF following disc battery ingestion have been published, with length of stays between 28 and 82 days. We report a case suggesting that even with a large caustic TEF, multimodal “complete” esophageal rest can allow complete spontaneous resolution, and with shorter duration of hospitalization. This experience prompts consideration of a novel non-operative approach. Even if complete resolution is not achieved, eventual operation would benefit from partial contracture, resolution of mediastinitis, and time for planning and preparation.

We present a data-driven technique for measuring the time-of-flight through material sealed within a container. Time-of-flight measurement provides a noninvasive means of quantifying the sound speed profile within a material by transmitting an acoustic burst and then measuring the time required for the burst to arrive at an opposing receiver. In a hermetically-sealed cylindrical container, a portion of the acoustic energy propagates through the material as a bulk wave, while the remainder of the acoustic energy propagates around the container walls as guided waves. As a result, interference from the guided waves obscures the bulk arrival, inhibiting measurement of the sound speed. The technique uses a Convolutional Neural Network (CNN) to identify critical features in the measured waveforms and identify bulk wave arrivals. We demonstrate this time-of-flight measurement technique on high explosive-filled containers as they are heated from room temperature to detonation. This is a particularly challenging application for acoustic time-of-flight measurements as the high explosives have significant sound speed gradients as they undergo heating, and they lead to significant attenuation of the bulk wave, as opposed to the guided waves, which do not suffer significant attenuation. We characterize the performance of the CNN as a function of the high explosive temperature and as a function of the CNN hyperparameters. We then provide physical insight into the error trends.

Bulk wave acoustic time-of-flight (ToF) measurements in pipes and closed containers can be hindered by guided waves with similar arrival times propagating in the container wall, especially when a low excitation frequency is used to mitigate sound attenuation from the material. Convolutional neural networks (CNNs) have emerged as a new paradigm for obtaining accurate ToF in non-destructive evaluation (NDE) and have been demonstrated for such complicated conditions. However, the generalizability of ToF-CNNs has not been investigated. In this work, we analyze the generalizability of the ToF-CNN for broader applications, given limited training data. We first investigate the CNN performance with respect to training dataset size and different training data and test data parameters (container dimensions and material properties). Furthermore, we perform a series of tests to understand the distribution of data parameters that need to be incorporated in training for enhanced model generalizability. This is investigated by training the model on a set of small- and large-container datasets regardless of the test data. We observe that the quantity of data partitioned for training must be of a good representation of the entire sets and sufficient to span through the input space. The result of the network also shows that the learning model with the training data on small containers delivers a sufficiently stable result on different feature interactions compared to the learning model with the training data on large containers. To check the robustness of the model, we tested the trained model to predict the ToF of different sound speed mediums, which shows excellent accuracy. Furthermore, to mimic real experimental scenarios, data are augmented by adding noise. We envision that the proposed approach will extend the applications of CNNs for ToF prediction in a broader range. [ABSTRACT FROM AUTHOR]

3-Fluoro-5-nitro-1-(pentafluorosulfanyl)benzene was prepared by three different ways: as a byproduct of direct fluorination of 1,2-bis(3-nitrophenyl)disulfane, by direct fluorination of 4-nitro-1-(pentafluorosulfanyl)benzene, and by fluorodenitration of 3,5-dinitro-1-(pentafluorosulfanyl)benzene. The title compound was subjected to a nucleophilic aromatic substitution of the fluorine atom with oxygen, sulfur and nitrogen nucleophiles affording novel (pentafluorosulfanyl)benzenes with 3,5-disubstitution pattern. Vicarious nucleophilic substitution of the title compound with carbon, oxygen, and nitrogen nucleophiles provided 3-fluoro-5-nitro-1-(pentafluorosulfanyl)benzenes substituted in position four.

Background/objectives Kidney dialysis patients with sarcopenia have increased mortality. Screening for low muscle mass may allow interventions at an earlier stage to help improve outcomes. We wished to determine the prevalence of low muscle mass in a cohort of peritoneal dialysis (PD) patients. Subjects/methods We measured lean body mass index (LBMI) in 490 PD patients by bioimpedance, grading patients using two different classifications of sarcopenia. Results: LBMI was 9.7[plus or minus]1.9 kg/m.sup.2, mean age was 55.3[plus or minus]16.4 years, 53.1% were male, 33.7% were diabetic and 51% were Caucasoid. 98.5% of patients were classified as having sarcopenia based on LBMI cutoffs from NHANES data, whereas 28.8% had moderate and 6.3% severe sarcopenia using a grading correlated with functional disability. Lower muscle mass was associated with increasing co-morbidity ([beta]=0.34, P=0.02) and age ([beta]=0.01, P=0.006), and negatively with body mass index ([beta]=-0.23, P Conclusions: There is currently no agreed universal definition for sarcopenia, and prevalence varied markedly depending on the scoring system. Prevalence was not associated with small solute clearances, but was associated with sex, age co-morbidity, BMI and ethnicity. There was an association with dietary protein intake and urine volume, which may allow for dietary interventions and strategies to preserve urine output to reduce muscle loss in PD patients., Author(s): G H B Greenhall [sup.1] , A Davenport [sup.1] Author Affiliations: (1) UCL Centre for Nephrology, Royal Free Hospital, University College London Medical School, London, UK Introduction Body composition [...]