Miracle-dz/UniFAR-FTUs huggingface dataset:
Temperature and volatile organic compound concentrations as controlling factors for chemical composition of α-pinene-derived secondary organic aerosol

Abstract. This work investigates the individual and combined effects of temperature and volatile organic compound precursor concentrations on the chemical composition of particles formed in the dark ozonolysis of α-pinene. All experiments were conducted in a 5 m3 Teflon chamber at an initial ozone concentration of 100 ppb and initial α-pinene concentrations of 10 and 50 ppb, respectively; at constant temperatures of 20, 0, or −15 ∘C; and at changing temperatures (ramps) from −15 to 20 and from 20 to −15 ∘C. The chemical composition of the particles was probed using a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS). A four-factor solution of a positive matrix factorization (PMF) analysis of the combined HR-ToF-AMS data is presented. The PMF analysis and the elemental composition analysis of individual experiments show that secondary organic aerosol particles with the highest oxidation level are formed from the lowest initial α-pinene concentration (10 ppb) and at the highest temperature (20 ∘C). A higher initial α-pinene concentration (50 ppb) and/or lower temperature (0 or −15 ∘C) results in a lower oxidation level of the molecules contained in the particles. With respect to the carbon oxidation state, particles formed at 0 ∘C are more comparable to particles formed at −15 ∘C than to those formed at 20 ∘C. A remarkable observation is that changes in temperature during particle formation result in only minor changes in the elemental composition of the particles. Thus, the temperature at which aerosol particle formation is induced seems to be a critical parameter for the particle elemental composition. Comparison of the HR-ToF-AMS-derived estimates of the content of organic acids in the particles based on m/z 44 in the mass spectra show good agreement with results from off-line molecular analysis of particle filter samples collected from the same experiments. Higher temperatures are associated with a decrease in the absolute mass concentrations of organic acids (R-COOH) and organic acid functionalities (-COOH), while the organic acid functionalities account for an increasing fraction of the measured particle mass.
Perioperative Anesthetics and Adjuvant Anxiolytics for Cataract Surgery Patients

Cataract Surgery is one of the most common surgeries performed worldwide. It has high rates of success, and minimal adverse outcomes. In spite of the surgery’s effectiveness, patients frequently report perioperative anxiety. This anxiety is often treated with pharmacologic sedation; however, evidence suggests that this is unnecessary, and can lead to poorer patient outcomes. There is evidence that other adjuvant therapies exist which can decrease the need for higher levels of pharmacologic sedation; this paper explores some of these adjuvant therapies.
RC-RNN: Reconfigurable Cache Architecture for Storage Systems Using Recurrent Neural Networks

Solid-State Drives (SSDs) have significant performance advantages over traditional Hard Disk Drives (HDDs) such as lower latency and higher throughput. Significantly higher price per capacity and limited lifetime, however, prevents designers to completely substitute HDDs by SSDs in enterprise storage systems. SSD-based caching has recently been suggested for storage systems to benefit from higher performance of SSDs while minimizing the overall cost. While conventional caching algorithms such as Least Recently Used (LRU) provide high hit ratio in processors, due to the highly random behavior of Input/Output (I/O) workloads, they hardly provide the required performance level for storage systems. In addition to poor performance, inefficient algorithms also shorten SSD lifetime with unnecessary cache replacements. Such shortcomings motivate us to benefit from more complex non-linear algorithms to achieve higher cache performance and extend SSD lifetime. In this article, we propose RC-RNN, the first reconfigurable SSD-based cache architecture for storage systems that utilizes machine learning to identify performance-critical data pages for I/O caching. The proposed architecture uses Recurrent Neural Networks (RNN) to characterize ongoing workloads and optimize itself towards higher cache performance while improving SSD lifetime. RC-RNN attempts to learn characteristics of the running workload to predict its behavior and then uses the collected information to identify performance-critical data pages to fetch into the cache. We implement the proposed architecture on a physical server equipped with a Core-i7 CPU, 256GB SSD, and a 2TB HDD running Linux kernel 4.4. 0. Experimental results show that RC-RNN characterizes workloads with an accuracy up to 94.6 percent for SNIA I/O workloads. RC-RNN can perform similarly to the optimal cache algorithm by an accuracy of 95 percent on average, and outperforms previous SSD caching architectures by providing up to 7x higher hit ratio and decreasing cache replacements by up to 2x.
Predicting Future Cognitive Impairment in De Novo Parkinson's Disease Using Clinical Data and Structural MRI

BACKGROUND Parkinson's disease is the second most common neurodegenerative condition and associated with increasing cognitive dysfunction as the disease progresses. However, subtle cognitive deficits can be detected at diagnosis in 42% of individuals, suggesting that damage may already be present. Our aim was to determine clinical and structural differences in those recently diagnosed with PD who later develop cognitive impairment, and whether these changes predict future cognitive decline. METHODS Clinical and imaging data was acquired from the Parkinson's Progression Markers Initiative for 318 individuals with a diagnosis of Parkinson's disease and baseline 3T T1-weighted MRI. The cohort was divided according to cognitive status over follow-up, with 9 individuals developing Parkinson's disease dementia, 102 developing mild cognitive impairment and 207 remaining cognitively unaffected. FINDINGS At baseline, those who went on to develop cognitive impairment (mild cognitive impairment or dementia) were older with more severe motor and non-motor symptoms (anosmia, rapid eye movement sleep behaviour disorder, depression). Grey matter loss was present in those destined for Parkinson's disease dementia in the precuneus, hippocampi, primary olfactory cortex, lingual gyrus, temporal cortex and cerebellum. Those who later developed mild cognitive impairment had an attenuated but similar pattern of grey matter loss in the temporal lobe, lingual gyrus and cerebellum. Using support vector machines with a feature selection step, future cognitive impairment could be predicted using 11 clinical variables (AUC = 0.81), structural imaging (AUC = 0.72) or a combination of these two modalities (AUC = 0.85). These models more accurately predicted those who developed dementia (subgroup sensitivity 100%). INTERPRETATION Significant abnormalities in cortical structure is present at least three years before dementia manifests in Parkinson's disease, with associated differences in clinical profiles. Combining this data provides a technique to accurately identify future cognitive impairment, providing a non-invasive way to stratify individuals early on.
A third COVID-19 vaccine shot markedly boosts neutralizing antibody potency and breadth

COVID-19 (coronavirus disease 2019) vaccines have been rapidly developed and deployed globally as a measure to combat the disease. These vaccines have been demonstrated to confer significant protection, but there have been reports of temporal decay in antibody titer. Furthermore, several variants have been identified with variable degrees of antibody resistance. These two factors suggest that a booster vaccination may be worthy of consideration. While such a booster dose has been studied as a series of three homologous vaccines in healthy individuals, to our knowledge, information on a heterologous regimen remains unreported, despite the practical benefits of such a scheme. Here, in this observational study, we investigated the serological profile of four healthy individuals who received two doses of the BNT162b2 vaccine, followed by a third booster dose with the Ad26.COV2. S vaccine. We found that while all individuals had spike-binding antibodies at each of the timepoints tested, there was an appreciable drop in titer by four months following the second vaccination. The third vaccine dose robustly increased titers beyond that of two vaccinations, and these elicited antibodies had neutralizing capability against all SARS-CoV-2 strains tested in both a recombinant vesicular stomatitis virus-based pseudovirus assay and an authentic SARS-CoV-2 assay, except for one individual against B.1. 351 in the latter assay. Thus, a third COVID-19 vaccine dose in healthy individuals promoted not just neutralizing antibody potency, but also induced breadth against dominant SARS-CoV-2 variants.
An Adaptive IP Hopping Approach for Moving Target Defense Using a Light-Weight CNN Detector

Scanning attack is normally the first step of many other network attacks such as DDoS and propagation worm. Because of easy implementation and high returns, scanning attack especially cooperative scanning attack is widely used by hackers, which has become a serious threat to network security. In order to defend against scanning attack, this paper proposes an adaptive IP hopping in software defined network for moving target defense (MTD). In order to accurately respond to attacker’s behavior in real time, a light-weight convolutional neural network (CNN) detector composed of three convolutional modules and a judgment module is proposed to sense scanning attack. Input data of the detector is generated via designed packets sampling and data preprocess. The detection result of the detector is used to trigger IP hopping. In order to provide some fault tolerance for the CNN detector, IP hopping can also be triggered by a preset timer. The CNN driving adaptability is applied to a three-level hopping strategy to make the MTD system optimize its behavior according to real time attack. Experiments show that compared with existing technologies, our proposed method can significantly improve the defense effect to mitigate scanning attack and its subsequent attacks which are based on hit list. Hopping frequency of the proposed method is also lower than that of other methods, so the proposed method shows lower system overhead.
Comparison of machine learning algorithms for mortality prediction in intensive care patients on multi-center critical care databases

Current scoring systems for mortality prediction in intensive care patients are usually applied once after 24 hours of admission, as all parameters needed for scoring are not yet available. In addition, several parameters are dynamic and may change according to patient conditions. It is hypothesized that mortality prediction should be made at the earliest when relevant information becomes available and continuously during patient stay. This study focuses on the development of algorithms for mortality prediction from vital signs and laboratory results based on the data from three recent critical care databases, i.e. the eICU collaborative research database, the Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III) database, and the MIMIC-IV database. We employed logistic regression, κ-nearest neighbours, neural networks and tree-based classifiers for such problem. Our models had the area under the receiver operating characteristic curve (AUROC) ranging from 0.67 – 0.95. Reliable mortality prediction can be made as early as the first 4 hours after ICU admission. We provided comprehensive analysis on different time frames used for prediction, models trained with top attributes, models trained with data combination, and missing values. Our results provide guidelines and benchmarks for the development of such algorithm in local narratives.
Intuitionistic fuzzy set-based time series forecasting model via delegeration of hesitancy degree to the major grade de-i-fuzzification and arithmetic rules based on centroid defuzzification

De-i-fuzzification is a process of converting the intuitionistic fuzzy set into a fuzzy set. It becomes one of the core procedures in fuzzy time series forecasting model based on the intuitionistic fuzzy set. In this paper, we propose a fuzzy time series forecasting model based on intuitionistic fuzzy set via de-i-fuzzification. The de-i-fuzzification approach used is assigning the hesitancy degree to the major grade. The data are partitioned into a few intervals using the frequency density-based method. The data in the fuzzy set form is then transformed into an intuitionistic fuzzy set using the definition of intuitionistic fuzzy set. The arithmetic rules based on centroid defuzzification is used to obtain the forecasted output. The model is implemented on the data of student enrolment at the University of Alabama. The results are then compared to forecasting method using classical fuzzy set and similar de-i-fuzzification approach using max-min operation. The proposed method outperforms the other two methods, thus supports the fact that intuitionistic fuzzy set is a generalization of a classical fuzzy set and gives better performance in forecasting.
Negligible local-factor influences on tree ring cellulose δ18O of Qilian juniper in the Animaqing Mountains of the eastern Tibetan Plateau

Abstract Tree ring cellulose oxygen isotopes (δ18O) were measured on 21 trees of Qilian juniper from the Animaqing Mountains, Tibetan Plateau, to investigate intra- and inter-tree variability, potential juvenile and elevation effects and climatic implications. There are no significant differences in mean and standard deviation of tree ring δ18O values at different heights in individual trees. Tree ring δ18O values from different directions show a high degree of coherence. The mean and standard deviation for vertical and circumferential δ18O time series are very similar, and δ18O data from different heights and directions are highly correlated (r > 0.88). The δ18O values of young trees are lower than those of old trees in the first 10 years of tree growth. Tree ring δ18O data from five different altitudes are highly correlated (r > 0.88) and share similar climatic signals. As such, an altitude effect on tree ring δ18O is not observed. Our results indicate that samples from one site, regardless of sampling height, direction or altitude, can be used to reconstruct a long-term δ18O record. Tree ring δ18O data from the Animaqing Mountains show a significant negative correlation (r = −0.67; p < 0.001) with May–July regional precipitation and appear to be a promising proxy for precipitation reconstruction.
Field nonuniformity of limited-aperture planar SAWs and its implications for designing SSAW acoustofluidics

Acoustofluidics operating at high frequencies usually use surface acoustic waves (SAWs), and those emitted by limited-aperture planar interdigital transducers (IDTs) are prevalent. In such devices, field nonuniformity along the lateral direction is observed by many, but there lacks a study to quantitatively clarifying this effect. Here, by modeling IDTs using dirac-type transfer functions, we examine how field nonuniformity changes at varied aperture length, axial distance, and number of finger pairs of the IDTs. For standing SAW (SSAW) devices, field predictions from numerical analysis are validated through experimental measurements employing laser vibrometry. Finally, particle acoustophoresis is carried out in three SSAW devices, while the results provide some useful implications for future device designs.
Analytical and numerical study on the cyclic behavior of buckling-restrained SMA-based self-centering damper

Owing to the unique superelasticity and satisfactory energy dissipation capacity, shape memory alloys (SMAs) show great potential in earthquake engineering. This study focused on establishing analytical method and finite element (FE) model for the buckling-restrained SMA (BRSMA) based self-centering (SC) dampers. For the BRSMA-based SC damper, the lateral deformation of the slender SMA component is constrained by the surrounding buckling-restrained plates (BRPs). Hence, the SMA component avoids buckling induced instability and the damper accordingly exhibits stable cyclic behavior under tension–compression loadings. Although the compressive behavior has been obtained through cyclic loading tests, an in-depth understanding on the corresponding force-displacement relationship is required. As such, a simple analytical method was proposed for estimating the compressive strength capacity of the BRSMA-based SC damper. Besides, the FE model was built to provide additional information that was difficult to obtain in physical model tests. The accuracy of the analytical method and FE model was confirmed by the testing results. The verified FE model was further used to conduct the parametric analysis. The parameters of interest included the shape of the reduced section, the dimension of the BRSMA components, the gap between the BRSMA components and BRPs and the number of bolts. The findings of this paper not only promote understanding on the cyclic behavior of this new damper, but also provide suggestions and guidelines for future design.
The carbon footprint and environmental impact assessment of desalination

Abstract Desalination is an important means to meet water needs in many countries. The existing process is costly and energy intensive and further strains the environment with brine disposal and greenhouse gas (GHG) emissions. This paper describes several factors that are to be considered in desalination plants, such as the use of the land, the contamination of groundwater and the marine environment, the use of energy, and noise pollution. One major indirect environmental impact is the production of the energy required to run the desalination plants, particularly from burning oil, which increases GHG emissions. The carbon footprints associated with sea water desalination plants in the United Arab Emirates are assessed along with the other factors affecting human and marine life. There is no standard environmental impact assessment method, but the World Health Organization has begun work to produce one.
Serological Survey and Pathogen Exposure of Adult Female White-tailed Deer in the Western Dakotas

Establishing baseline values for pathogen exposure and nutritional indices is necessary to monitor population health. However, little is known about white-tailed deer (Odocoileus virginianus) pathogen exposure and nutritional condition in the Northern Great Plains. Our objective was to assess pathogen exposure and establish nutritional indices for female white-tailed deer in Dunn and Grant counties, North Dakota and Perkins County, South Dakota. During 2014, we collected blood serum from 150 adult female white-tailed deer. Pathogens with the highest antibody prevalence included West Nile Virus (WNV; 85%), epizootic hemorrhagic disease (48%), and malignant catarrhal fever (32%). Serum values for creatine kinase, globulin, glucose, potassium, and lactate dehydrogenase in all three study areas were higher than reference ranges while sodium was low in Grant County relative to Dunn and Perkins counties. We speculate that high exposure of WNV and high potassium values combined with low sodium values may affect neonate survival in Grant County. However, regional differences in pathogen exposure, their connection to serum values, and their potential interactive effects on survival are not well understood.
REVISIT CLOSED-END FUND PUZZLES VIA DYNAMIC CAPITAL MOBILITY

A theoretic model based on the concepts of constrained arbitrage and capital mobility is proposed to interpret closed-end fund puzzles. Although a discount for a closed-end fund’s price relative to its net asset value is more prevalent, our model never excludes the possibility of a premium, which depends on the relative magnitude of the key parameters for the closed-end fund and its component stocks. Since closed-end funds tend to be more owned by individual investors who are less likely to be active traders due to investor inertia, and investor enthusiasm is usually higher for stocks than for closed-end funds, the aggregated price of component stocks will be more likely higher than the price of the closed-end fund, thus leading to the discount. Our model further shows that a closed-end fund’s discount is negatively related to its expected dividends and the interest rate. The above results are reproduced by simulation.
Breaking the Cognitive Spell: Cognitive Fusion Mediates the Relation of Cognitive Anxiety Sensitivity and Rumination in Undergraduate College Students

Breaking the Cognitive Spell: Cognitive Fusion Mediates the Relation of Cognitive Anxiety Sensitivity and Rumination in Undergraduate College Students Jacey L. Anderberg Director: Christopher R. Berghoff, Ph.D. Rumination (i.e., intrusive and repetitive self-directed thinking) predicts the onset, severity, and maintenance of depression (Galecki & Talarowska, 2017). Ruminative behavior is positively associated with cognitive anxiety sensitivity (i.e., fear of losing internal control; CAS), which may be attributed to cognitive vulnerabilities of depression. However, researchers have not clarified the link between these variables, and mechanisms responsible for change in CAS following treatment are unclear (Tull & Gratz, 2008). Accordingly, clarification of intermediate factors that may be targeted in psychosocial interventions appears warranted. Cognitive fusion (i.e., engaging with thoughts as true reflections of reality rather than products of thinking; CF) may influence this relation, as individuals with high CAS may be attached to and impacted by negative thoughts, leading to ruminative behavior. We hypothesized CF would mediate the CASrumination relation in undergraduate students. Bootstrap analyses suggested CF significantly mediated the CAS-rumination relation, ab = 1.12, 95% CI [0.88, 1.40], indicating CF may partially account for the association of CAS and ruminative behavior. Accordingly, CF may be a productive target to reduce rumination (Bramwell & Richardson, 2018), especially for individuals with high CAS.
RUNNING HEAD: MINDFULNESS, MIND-WANDERING AND ERP The Effects of Mindfulness on Mind-Wandering and ERP Measures of Attention

The present study aimed to examine whether a brief mindfulness breathing exercise would reduce the amount of mind-wandering and used the P300 portion of the ERP peak as a neural measure of attention. Participants (N = 24) listened to a brief recording designed to elicit mindfulness or a control recording and then completed a sustained attention task while EEG was recorded. Though mindful breathing reduced self-reported mind-wandering, P300 peaks to stimuli were not affected by mindfulness or mind-wandering. Future studies should further examine these questions with a larger sample size.