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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.

Research Libraries UK is a consortium of 37 of the UK and Ireland's largest research libraries with the purpose of convening its members around the key issues that affect them, to represent their collective voice, to support them as they face shared challenges, and to be an effective advocate on their behalf. In fulfilment of these roles, RLUK launched its digital shift manifesto in May 2020, which provides a vision for the research library of 2030 - in relation to the digital shift occurring within research library collections, services, operations, and audience interactions. Centred around the four strands of skills, spaces, scholarship, and stakeholders, the manifesto provides a shared vision of the future and a tangible programme of activities through which this can be achieved. This article will explore how the COVID-19 pandemic has witnessed the digital shift in action. Combining the reflections of individual academic and research libraries, and using RLUK's previous research into the impact of COVID-19 as a foundation, this article will reflect on how realistic and future looking the manifesto was. It will explore the collective experiences of libraries regarding the digital shift, will consider progress made in the implementation of the manifesto against this rapidly changing backdrop, and will provide a series of reflections for the future.

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]

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. [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 [...]

Purpose: To examine the potential impact of fluctuations in sex steroid hormones across the menstrual cycle (MC) on marathon running performance of recreational female athletes. Methods: A survey questionnaire was administered to recreational, nonelite runners who had completed multiple marathons within the last 18 months. Results: A total of 599 questionnaires were returned and deemed viable for review. From these, 185 survey participants were found to have complete information and eligibility to have their surveys used in the statistical analysis. A total of 106 women had their best marathon performance in the luteal phase (high sex steroid hormones) of the MC, and 79 had their best performance in the follicular phase (low sex steroid hormones) of the MC (responses were significantly different; z-score value = 1.11; P <.05). Conclusion: Recreational female runners have varying performances in the marathon across their MC phases, specifically performing better in the luteal phase of the cycle. [ABSTRACT FROM AUTHOR]

Plug‐screw feeders are critical in many industrial processes for compressing slurry materials via a rotating plug‐screw feeder. Over time, increasing plug‐screw feeder wear will eventually lead to catastrophic mechanical failure. Early detection of the wear state can prevent unplanned catastrophic failures resulting in operational shut‐downs, costly repairs, and most importantly the health and safety of workers. We present a theoretical basis for a noninvasive, in operando acoustic resonant technique to monitor the wear state of plug‐screw feeders. The technique is based on tracking the resonant acoustic modes of the plug‐screw feeder, which are sensitive to the plug‐screw feeder geometry, material, and operating conditions. We implemented a multivariate polynomial model to estimate the plug‐screw feeder wear state using multiple acoustic resonances by simulating the acoustic resonant modes for two categories of wear (tip and thread damage) that are common in plug‐screw feeders. Fitting multiple resonances to the polynomial model, we demonstrate accurate estimation of the total mass loss, as well as characterization of the type of damage (i.e., tip vs. thread). Current approaches for monitoring plug‐screw feeder wear rely on shutting down the operation and visually inspecting the plug‐screw feeder. The presented acoustic technique offers a noninvasive, in operando measurement approach that mitigates unplanned catastrophic failures. The acoustic resonance technique presented in the paper has a broad range of industrial applications including the Pharmaceutical, Mining, Integrated Biorefineries (IBR), and Additive Manufacturing industries, to name a few. [ABSTRACT FROM AUTHOR]

Background Urinalysis is a standard component of potential deceased kidney donor assessment in the UK. The value of albuminuria as a biomarker for organ quality is uncertain. We examined the relationship between deceased donor albuminuria and kidney utilization, survival and function. Methods We performed a national cohort study on adult deceased donors and kidney transplant recipients between 2016 and 2020, using data from the UK Transplant Registry. We examined the influence of donor albuminuria, defined as ≥2+ on dipstick testing, on kidney utilization, early graft function, graft failure and estimated glomerular filtration rate (eGFR). Results Eighteen percent (1681/9309) of consented donors had albuminuria. After adjustment for confounders, kidneys from donors with albuminuria were less likely to be accepted for transplantation (74% versus 82%; odds ratio 0.70, 95% confidence interval 0.61 to 0.81). Of 9834 kidney transplants included in our study, 1550 (16%) came from donors with albuminuria. After a median follow-up of 2 years, 8% (118/1550) and 9% (706/8284) of transplants from donors with and without albuminuria failed, respectively. There was no association between donor albuminuria and graft failure (hazard ratio 0.91, 95% confidence interval 0.74 to 1.11). There was also no association with delayed graft function, patient survival or eGFR at 1 or 3 years. Conclusions Our study suggests reluctance in the UK to utilize kidneys from deceased donors with dipstick albuminuria but no evidence of an association with graft survival or function. This may represent a potential to expand organ utilization without negatively impacting transplant outcomes. [ABSTRACT FROM AUTHOR]

We use ultrasound directed self-assembly to organize particles dispersed in a fluid medium into a three-dimensional (3D) user-specified pattern. The technique employs ultrasound transducers that line the boundary of a fluid reservoir to create a standing ultrasound wave field. The acoustic radiation force associated with the wave field drives particles dispersed in the fluid medium into organized patterns, assuming that the particles are much smaller than the wavelength and do not interact with each other. We have theoretically derived a direct solution method to calculate the ultrasound transducer operating parameters that are required to assemble a user-specified 3D pattern of particles in a fluid reservoir of arbitrary geometry. We formulate the direct solution method as a constrained optimization problem that reduces to eigendecomposition. We experimentally validate the solution method by assembling 3D patterns of carbon nanoparticles in a water reservoir and observe good quantitative agreement between theory and experiment. Additionally, we demonstrate the versatility of the solution method by simulating ultrasound directed self-assembly of complex 3D patterns of particles. The method works for any 3D simple, closed fluid reservoir geometry in combination with any arrangement of ultrasound transducers and enables employing ultrasound directed self-assembly in a myriad of engineering applications, including biomedical and materials fabrication processes. [ABSTRACT FROM AUTHOR]

A method based on the total deviation approach, which is used in global positioning system (GPS) operations is described. The increased confidence from the 'total' approach and shorter data processing delays would be important in adequately managing future GPS system events.

Research is presented describing the study of white phase and flicker phase noise using modulated square wave frequency.

The statistical method known as total variance is examined, focusing on its use to improve the ability to estimate frequency stability. A comparison of the total variance method to the standard unbiased estimator is presented.

Donor-transmitted cancer (DTC) has major implications for the affected patient as well as other recipients of organs from the same donor. Unlike heterotopic transplant recipients, there may be limited treatment options for orthotopic transplant recipients with DTC. We systematically reviewed the evidence on DTC in orthotopic solid organ transplant recipients (SOTRs). We searched MEDLINE, EMBASE, PubMed, Scopus, and Web of Science in January 2020. We included cases where the outcome was reported and excluded donor-derived cancers. We assessed study quality using published checklists. Our domains of interest were presentation, time to diagnosis, cancer extent, management, and survival. There were 73 DTC cases in liver (n = 51), heart (n = 10), lung (n = 10) and multi-organ (n = 2) recipients from 58 publications. Study quality was variable. Median time to diagnosis was 8 months; 42% were widespread at diagnosis. Of 13 cases that underwent re-transplantation, three tumours recurred. Mortality was 75%; median survival 7 months. Survival was worst in transmitted melanoma and central nervous system tumours. The prognosis of DTC in orthotopic SOTRs is poor. Although retransplantation offers the best chance of cure, some tumours still recur. Publication bias and clinical heterogeneity limit the available evidence. From our findings, we suggest refinements to clinical practice. [ABSTRACT FROM AUTHOR]

A study was conducted to examine the degradation of the phase of a frequency standard utilizing periodic interrogation and control of a local oscillator. The degradation was carried out by a downconversion of the noise ratio of the oscillator into a loop passband. Results indicated that the basic action of the control loop functions on the oscillator frequency using a filter supporting a frequency response.

Keywords: health status, Parkinson's disease, Parkinson's Disease Questionnaire, summary score Introduction In previous publications we documented the initial development and validation work for the Parkinson's Disease Questionnaire (PDQ-39) [1, 2]. [...]

Summary The purpose of this paper was to document the impact of Parkinson's disease (PD) upon patients using both a generic health status measure (the Short -form 36 health survey [...]

Acoustic time-of-flight (ToF) measurements enable noninvasive material characterization, acoustic imaging, and defect detection and are commonly used in industrial process control, biomedical devices, and national security. When characterizing a fluid contained in a cylinder or pipe, ToF measurements are hampered by guided waves, which propagate around the cylindrical shell walls and obscure the waves propagating through the interrogated fluid. We present a technique for overcoming this limitation based on a broadband linear chirp excitation and cross correlation detection. By using broadband excitation, we exploit the dispersion of the guided waves, wherein different frequencies propagate at different velocities, thus distorting the guided wave signal while leaving the bulk wave signal in the fluid unperturbed. We demonstrate the measurement technique experimentally and using numerical simulation. We characterize the technique performance in terms of measurement error, signal-to-noise-ratio, and resolution as a function of the linear chirp center frequency and bandwidth. We discuss the physical phenomena behind the guided bulk wave interactions and how to utilize these phenomena to optimize the measurements in the fluid. [ABSTRACT FROM AUTHOR]

Aims: To determine whether initiation of treatment with angiotensin converting enzyme inhibitors or angiotensin II receptor blockers (ACEI/ARBs) is associated with a subsequent reduction in haemoglobin in the general population. Methods: We undertook a national cohort study over a 13‐year period (2004–2016), using routine primary healthcare data from the UK Clinical Practice Research Datalink. We compared ACEI/ARB initiation with calcium channel blocker (CCB) initiation, to minimise confounding by indication. We included all first ACEI/ARB or CCB prescriptions in adults with at least 1 haemoglobin result in the 12 months before and 6 months after drug initiation. Our primary outcome was a ≥1 g/dL haemoglobin reduction in the 6 months after drug initiation. Results: We examined 146 610 drug initiation events in 136 655 patients. Haemoglobin fell by ≥1 g/dL after drug initiation in 19.5% (16 936/86 652) of ACEI/ARB initiators and 15.9% (9521/59 958) of CCB initiators. The adjusted odds ratio of a ≥1 g/dL haemoglobin reduction in ACEI/ARB initiators vs CCB initiators was 1.15 (95% confidence interval 1.12–1.19). Conclusion: ACEI/ARBs are associated with a modest increase in the risk of a haemoglobin reduction. For every 100 patients in our study that initiated a CCB, 16 experienced a ≥1 g/dL haemoglobin decline. If the effect is causal, 3 additional patients would have experienced this outcome if they had received an ACEI/ARB. This may have implications for drug choice and monitoring for many patients in primary care. Further research could identify patients at higher risk of this outcome, who may benefit from closer monitoring. [ABSTRACT FROM AUTHOR]