Control Infrastructure for a Pulsed Ion Accelerator

We report on updates to the accelerator controls for the Neutralized Drift Compression Experiment II, a pulsed induction-type accelerator for heavy ions. The control infrastructure is built around a LabVIEW interface combined with an Apache Cassandra backend for data archiving. Recent upgrades added the storing and retrieving of device settings into the database, as well as ZeroMQ as a message broker that replaces LabVIEW's shared variables. Converting to ZeroMQ also allows easy access via other programming languages, such as Python

Control System for the LEDA 6.7-MeV Proton Beam Halo Experiment

Measurement of high-power proton beam-halo formation is the ongoing scientific experiment for the Low Energy Demonstration Accelerator (LEDA) facility. To attain this measurement goal, a 52-magnet beam line containing several types of beam diagnostic instrumentation is being installed. The Experimental Physics and Industrial Control System (EPICS) and commercial software applications are presently being integrated to provide a real-time, synchronous data acquisition and control system. This system is comprised of magnet control, vacuum control, motor control, data acquisition, and data analysis. Unique requirements led to the development and integration of customized software and hardware. EPICS real-time databases, Interactive Data Language (IDL) programs, LabVIEW Virtual Instruments (VI), and State Notation Language (SNL) sequences are hosted on VXI, PC, and UNIX-based platforms which interact using the EPICS Channel Access (CA) communication protocol. Acquisition and control hardware technology ranges from DSP-based diagnostic instrumentation to the PLC-controlled vacuum system. This paper describes the control system hardware and software design, and implementation.Comment: LINAC2000 Conference, 4 pg

Tyre wear particles are toxic for us and the environment

This briefing paper discusses the current knowledge on the effects of tyre wear particles on our health and environment, highlights the need for an ambitious research agenda to build further understanding of the impacts on people and nature and develop solutions, and includes recommendations for policymakers

Association of 1,5-Anhydroglucitol and 2-h Postprandial Blood Glucose in Type 2 Diabetic Patients

OBJECTIVE—To assess the association of 1,5-anhydroglucitol (1,5-AG) with 2-h postprandial glucose values in type 2 diabetic patients followed over 12 months in an outpatient setting

Die maximal tragbare Radlast – eine zweckmässige Kenngrösse für die Praxis

Hohe Radlasten führen vor allem in Unterböden zu Druckbelastungen, die dauerhafte Verformungen und damit Beeinträchtigungen der Bodenfunktionen und der Ertragsfähigkeit verursachen. Die maximal tragbare Radlast ist jene Radlast, bei der die Bodenbelastung gerade noch unterhalb der Bodenfestigkeit (bestimmt als Vorbelastung) liegt und die Bodenstruktur deshalb noch nicht dauerhaft verformt wird. Wir berechneten die saisonalen Veränderungen der maximal tragbaren Radlast für die beiden Anbausysteme Direktsaat und Pflug sowie für den Dauergrünlandstreifen zwischen den Versuchsparzellen der Dauerbeobachtungsfläche Oberacker (sandiger Lehm). Gemessen wurde die Bodenfeuchtigkeit (als Matrixpotenzial) in situ und die Vorbelastung bei verschiedenen Matrixpotenzialen an ungestörten Zylinderproben im Labor. Die Simulationen wurden für eine Referenztiefe von 35 cm sowohl für Standardreifen als auch für Niederdruckreifen durchgeführt. Es zeigte sich, dass sowohl die Vorbelastung als auch die maximal tragbare Radlast stark von der Bodenfeuchtigkeit abhängig sind. Bei Niederdruckreifen ist die maximal tragbare Radlast höher als bei Standardreifen. Die Anzahl Tage, an denen der Boden ohne Verdichtungsrisiko befahren werden kann, schwankt stark von Jahr zu Jahr, ist beim Pflugsystem leicht höher als beim Direktsaatsystem und nimmt mit steigender Radlast ab. Die Darstellung des Verdichtungsrisikos mit dem Parameter «maximal tragbare Radlast» ist einfach zu interpretieren und deshalb nützlich für die Prävention von Bodenverdichtungen

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

OBJECTIVES: Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features. METHODS: We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening. RESULTS: The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk. CONCLUSIONS: The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA

Associations of the distance-saturation product and low-attenuation area percentage in pulmonary computed tomography with acute exacerbation in patients with chronic obstructive pulmonary disease

Background: Chronic obstructive pulmonary disease (COPD) has high global health concerns, and previous research proposed various indicators to predict mortality, such as the distance-saturation product (DSP), derived from the 6-min walk test (6MWT), and the low-attenuation area percentage (LAA%) in pulmonary computed tomographic images. However, the feasibility of using these indicators to evaluate the stability of COPD still remains to be investigated. Associations of the DSP and LAA% with other COPD-related clinical parameters are also unknown. This study, thus, aimed to explore these associations. Methods: This retrospective study enrolled 111 patients with COPD from northern Taiwan. Individuals’ data we collected included results of a pulmonary function test (PFT), 6MWT, life quality survey [i.e., the modified Medical Research Council (mMRC) scale and COPD assessment test (CAT)], history of acute exacerbation of COPD (AECOPD), and LAA%. Next, the DSP was derived by the distance walked and the lowest oxygen saturation recorded during the 6MWT. In addition, the DSP and clinical phenotype grouping based on clinically significant outcomes by previous study approaches were employed for further investigation (i.e., DSP of 290 m%, LAA% of 20%, and AECOPD frequency of ≥1). Mean comparisons and linear and logistic regression models were utilized to explore associations among the assessed variables. Results: The low-DSP group (<290 m%) had significantly higher values for the mMRC, CAT, AECOPD frequency, and LAA% at different lung volume scales (total, right, and left), whereas it had lower values of the PFT and 6MWT parameters compared to the high-DSP group. Significant associations (with high odds ratios) were observed of the mMRC, CAT, AECOPD frequency, and PFT with low- and high-DSP groupings. Next, the risk of having AECOPD was associated with the mMRC, CAT, DSP, and LAA% (for the total, right, and left lungs). Conclusion: A lower value of the DSP was related to a greater worsening of symptoms, more-frequent exacerbations, poorer pulmonary function, and more-severe emphysema (higher LAA%). These readily determined parameters, including the DSP and LAA%, can serve as indicators for assessing the COPD clinical course and may can serve as a guide to corresponding treatments

Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles.

OBJECTIVE: Obstructive sleep apnea is a global health concern, and several tools have been developed to screen its severity. However, most tools focus on respiratory events instead of sleep arousal, which can also affect sleep efficiency. This study employed easy-to-measure parameters-namely heart rate variability, oxygen saturation, and body profiles-to predict arousal occurrence. METHODS: Body profiles and polysomnography recordings were collected from 659 patients. Continuous heart rate variability and oximetry measurements were performed and then labeled based on the presence of sleep arousal. The dataset, comprising five body profiles, mean heart rate, six heart rate variability, and five oximetry variables, was then split into 80% training/validation and 20% testing datasets. Eight machine learning approaches were employed. The model with the highest accuracy, area under the receiver operating characteristic curve, and area under the precision recall curve values in the training/validation dataset was applied to the testing dataset and to determine feature importance. RESULTS: InceptionTime, which exhibited superior performance in predicting sleep arousal in the training dataset, was used to classify the testing dataset and explore feature importance. In the testing dataset, InceptionTime achieved an accuracy of 76.21%, an area under the receiver operating characteristic curve of 84.33%, and an area under the precision recall curve of 86.28%. The standard deviations of time intervals between successive normal heartbeats and the square roots of the means of the squares of successive differences between normal heartbeats were predominant predictors of arousal occurrence. CONCLUSIONS: The established models can be considered for screening sleep arousal occurrence or integrated in wearable devices for home-based sleep examination

Associations between risk of Alzheimer's disease and obstructive sleep apnea, intermittent hypoxia, and arousal responses: A pilot study.

OBJECTIVES: Obstructive sleep apnea (OSA) may increase the risk of Alzheimer's disease (AD). However, potential associations among sleep-disordered breathing, hypoxia, and OSA-induced arousal responses should be investigated. This study determined differences in sleep parameters and investigated the relationship between such parameters and the risk of AD. METHODS: Patients with suspected OSA were recruited and underwent in-lab polysomnography (PSG). Subsequently, blood samples were collected from participants. Patients' plasma levels of total tau (T-Tau) and amyloid beta-peptide 42 (Aβ42) were measured using an ultrasensitive immunomagnetic reduction assay. Next, the participants were categorized into low- and high-risk groups on the basis of the computed product (Aβ42 × T-Tau, the cutoff for AD risk). PSG parameters were analyzed and compared. RESULTS: We included 36 patients in this study, of whom 18 and 18 were assigned to the low- and high-risk groups, respectively. The average apnea-hypopnea index (AHI), apnea, hypopnea index [during rapid eye movement (REM) and non-REM (NREM) sleep], and oxygen desaturation index (≥3%, ODI-3%) values of the high-risk group were significantly higher than those of the low-risk group. Similarly, the mean arousal index and respiratory arousal index (R-ArI) of the high-risk group were significantly higher than those of the low-risk group. Sleep-disordered breathing indices, oxygen desaturation, and arousal responses were significantly associated with an increased risk of AD. Positive associations were observed among the AHI, ODI-3%, R-ArI, and computed product. CONCLUSIONS: Recurrent sleep-disordered breathing, intermittent hypoxia, and arousal responses, including those occurring during the NREM stage, were associated with AD risk. However, a longitudinal study should be conducted to investigate the causal relationships among these factors

Sensory Measurements: Coordination and Standardization

Do sensory measurements deserve the label of “measurement”? We argue that they do. They fit with an epistemological view of measurement held in current philosophy of science, and they face the same kinds of epistemological challenges as physical measurements do: the problem of coordination and the problem of standardization. These problems are addressed through the process of “epistemic iteration,” for all measurements. We also argue for distinguishing the problem of standardization from the problem of coordination. To exemplify our claims, we draw on olfactory performance tests, especially studies linking olfactory decline to neurodegenerative disorders