Miracle-dz/UniFAR-FTUs huggingface dataset:
Large differences of highly oxygenated organic molecules (HOMs) and low-volatile species in secondary organic aerosols (SOAs) formed from ozonolysis of β-pinene and limonene

Abstract. Secondary organic aerosols (SOAs) play a key role in climate change and public health. However, the oxidation state and volatility of SOAs are still not well understood. Here, we investigated the highly oxygenated organic molecules (HOMs) in SOAs formed from ozonolysis of β-pinene and limonene. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was used to characterize HOMs in aerosol filter samples, and a scanning mobility particle sizer (SMPS) was used to measure the concentration and size distribution of SOA particles. The relative abundance of HOMs (i.e., ratio of summed mass spectrometry peak intensity of HOMs to totally identified organic compounds) in limonene SOA was 14 %–20 %, higher than in β-pinene SOA (3 %–13 %), exhibiting different trends with increasing ozone concentrations. β-pinene oxidation-derived HOMs exhibit higher yield at high ozone concentration, accompanied by substantial formation of ultra-low-volatile organic compounds (ULVOCs). Limonene oxidation-derived HOMs exhibit higher yield at moderate ozone concentrations, with semi-, low-, and extremely low-volatile organic compounds (SVOCs, LOVCs, and ELVOCs) play a major role. Combined experimental evidence and theoretical analysis indicate that oxygen-increasing-based peroxy radical chemistry is a plausible mechanism for the formation of oxygenated organic compounds with 10 carbon atoms. Our findings show that HOMs and low-volatile species in β-pinene and limonene SOA are largely different. The ozone concentration-driven SOA formation and evolution mechanism for monoterpenes is suggested to be considered in future climate or exposure risk models, which may enable more accurate air quality prediction and management.
12CO(J = 1 0) On-the-fly Mapping Survey of the Virgo Cluster Spirals. II. Molecular Gas Properties in Different Density Environments

This study investigated the properties of the molecular gas content and star formation activity of 17 Virgo spirals, 21 Ursa Major (UMa) spirals, 13 Pisces spiral galaxies, and a comparison sample of 11 field spiral galaxies with a spatially resolved gas and stellar distribution. The H i-deficient galaxies with a defH i > 0.4 have a similar range of CO luminosity normalized by the K-band luminosity (LCO/LK) like the field spirals, although their CO content can be smaller by up to a factor of 2. The CO, H i, and stellar disk diameters are closely related to each other for both cluster and field galaxies, and the relative diameters of the CO and H i disks grow monotonically and smoothly as the H i-to-stellar disk diameter ratio decreases. Cluster galaxies have a molecular gas consumption time up to 10 times shorter than that of the field comparison sample, suggesting a significant change in the molecular gas content and star formation activity among all the cluster galaxies, even when they do not show any sign of H i stripping. The strongly H i-stripped Virgo cluster galaxies show only a modestly reduced total gas consumption time, indicating that the star formation activity and gas consumption are a highly local (rather than global) phenomenon. Our finding is that the depletion of cold gas by ram-pressure stripping and/or starvation caused by preprocessing in each cluster environment makes galaxies evolve passively.
Adaptive Back-Stepping Data-Driven Terminal Sliding-Mode Controller for Nonlinear MIMO Systems With Disturbance Observer

This paper presents an Adaptive Back-stepping Data-Driven Terminal Sliding Mode Controller (ABDTSMC) for non-affine MIMO systems with general disturbances including internal uncertainties and external disturbances. The proposed controller with new reaching law is used to reduce the controller’s dependence on the mathematical model and eliminate the chattering phenomenon. Furthermore, to solve the problem of the coupling effect and to estimate the uncertainties and disturbances, the Disturbance Observer (DOB) based on neural network with adaptive weights is utilized. Afterwards, it is implied that the DOB variables are Uniformly Ultimately Bounded (UUB). Next, for the integrated controller, the closed-loop stability based on the Lyapunov and back-stepping theory is investigated and the new adaptive law for the Pseudo Jacobian Matrix (PJM) elements is derived. This method contributes to the reduction of complexity and conservatism, which facilitates analysis of the closed-loop stability. To evaluate performance of the controller, the proposed method is applied to a 2-DOF robot manipulator. The simulation results are compared with Model-Free Adaptive Sliding-Mode Controller (MFASMC) and Model-Free Adaptive controller (MFAC), which are reported recently in related literature. The results demonstrate the precision of the tracking capability is significantly enhanced in the presence of time-varying disturbances. Moreover, the chattering phenomenon is successfully removed. In addition, the number of required data is significantly reduced. Finally, to show practicality of the proposed controller, it is applied to the 2-DOF laboratory manipulator.
Uncertainty Calibration of Passive Microwave Brightness Temperatures Predicted by Bayesian Deep Learning Models

Visible and infrared radiance products of geostationary orbiting platforms provide virtually continuous observations of Earth. In contrast, low Earth orbiters observe passive microwave (PMW) radiances at any location much less frequently. Prior literature demonstrates the ability of a Machine Learning (ML) approach to build a link between these two complementary radiance spectra by predicting PMW observations using infrared and visible products collected from geostationary instruments, which could potentially deliver a highly-desirable synthetic PMW product with nearly continuous spatio-temporal coverage. However, current ML models lack the ability to provide a measure of uncertainty of such a product, significantly limiting its applications. In this work, Bayesian Deep Learning is employed to generate synthetic Global Precipitation Measurement (GPM) mission Microwave Imager (GMI) data from Advanced Baseline Imager (ABI) observations with attached uncertainties over the ocean. The study first uses deterministic Residual Networks (ResNets) to generate synthetic GMI brightness temperatures with as little mean absolute error as 1.72 K at the ABI spatio-temporal resolution. Then, for the same task, we use three Bayesian ResNet models to produce a comparable amount of error while providing previously unavailable predictive variance (i.e. uncertainty) for each synthetic data point. We find that the Flipout configuration provides the most robust calibration between uncertainty and error across GMI frequencies, and then demonstrate how this additional information is useful for discarding high-error synthetic data points prior to use by downstream applications.
Multiscale Attention Networks for Pavement Defect Detection

Pavement defects such as cracks, net cracks, and pit slots can cause potential traffic safety problems. The timely detection and identification play a key role in reducing the harm of various pavement defects. Particularly, the recent development in deep learning (DL)-based convolution neural networks (CNNs) has shown competitive performance in image detection and classification. To detect pavement defects automatically and improve effects, a multiscale mobile attention-based network, which we termed MANet, is proposed to perform the detection of pavement defects. The architecture of the encoder–decoder is used in MANet, where the encoder adopts the MobileNet as the backbone network to extract pavement defect features. Instead of the original <inline-formula> <tex-math notation="LaTeX">$3\,\, \times 3$ </tex-math></inline-formula> convolution, the multiscale convolution kernels are used in depthwise separable convolution (DSConv) layers of the network. Furthermore, the hybrid attention mechanism is separately incorporated into the encoder and decoder modules to infer the significance of spatial points and interchannel relationship features for the input intermediate feature maps. The proposed approach achieves state-of-the-art performance on two publicly available benchmark datasets, i.e., the Crack500 (500 crack images with <inline-formula> <tex-math notation="LaTeX">$2000\,\, \times 1500$ </tex-math></inline-formula> pixels) and CFD (118 crack images with <inline-formula> <tex-math notation="LaTeX">$480\,\, \times 320$ </tex-math></inline-formula> pixels) datasets. The mean intersection over union (MIoU) of the proposed approach on these two datasets reaches 0.7219 and 0.7788, respectively. Ablation experiments show that the multiscale convolution and hybrid attention modules can effectively help the model extract high-level feature representations and generate more accurate pavement crack segmentation results. We further test the model on locally collected pavement crack images (131 images with <inline-formula> <tex-math notation="LaTeX">$1024\,\, \times 768$ </tex-math></inline-formula> pixels) and it achieves a satisfactory result. The proposed approach realizes the MIoU of 0.6514 on the local dataset and outperforms other compared baseline methods. Experimental findings demonstrate the validity and feasibility of the proposed approach and it provides a viable solution for pavement crack detection in practical application scenarios. Our code is available at <uri>https://github.com/xtu502/pavement-defects</uri>.
Wave trapping by freely floating obstacles in a discretely stratified fluid

In the context of the linear water wave theory, we model the interaction of time‐harmonic water waves with an infinite array of three‐dimensional freely floating bodies in a multilayer fluid through a spectral boundary value problem, consisting of a differential equation coupled with an algebraic system. We present a variational and operator formulation for the problem and derive sufficient conditions for the existence of trapped waves. We give examples of floating obstacles supporting trapped waves in a discretely stratified fluid in different obstacle configurations with a variable number of layers. Some conclusions regarding the dependence of the sufficient condition on the number of fluid layers are drawn, considering whether the sufficient condition is of the potential or algebraic type.
Noun Sequence Statistics Affect Serial Recall and Order Recognition Memory

Abstract Most theories of verbal working memory recognize that language comprehension and production processes play a role in word memory for familiar sequences, but not for novel lists of nouns. Some language emergent theories propose that language processes can support verbal working memory even for novel sequences. Through corpus analyses, we identify sequences of two nouns that resemble patterns in natural language, even though the sequences are novel. We present 2 experiments demonstrating better recall in college students for these novel sequences over the same words in reverse order. In a third experiment, we demonstrate better recognition of the order of these sequences over a longer time scale. These results suggest verbal working memory and recognition of order over a delay are influenced by language knowledge processes, even for novel memoranda that approximate noun lists typically employed in memory experiments.
The dynamic impact of landscape urbanization on ecosystem service multifunctionality in the Chengdu–Chongqing urban agglomeration: A panel VAR approach

Understanding the influence mechanism of landscape urbanization on ecosystem services (ESs) will provide useful guides for ecosystem conservation and urban sustainable management. However, the models used in existing studies tended to evaluate the various impacts of landscape urbanization on ES in the same period and fail to reveal the dynamic response of ES to landscape urbanization and the contribution of each impact variable, which is not conducive to the long‐term planning and management of urban ecosystems. To fill the gap, based on the panel data of the 141 counties in the Chengdu–Chongqing urban agglomeration (CCUA) from 2000 to 2015, this study applied the panel vector autoregressive (PVAR) model to investigate the dynamic impact of different aspects (i.e., urban scale, density, shape, and compactness) of landscape urbanization on ES multifunctionality (ESM) and identified the key impact factors. The results showed that (1) the proportion of high‐level ESM decreased by 9.13% in 2000–2010 and then increased by 19.14% in 2010–2015; (2) the ESM of the CCUA existed significant spatial agglomeration, and the high‐level areas were concentrated in the southwestern mountains, while the low‐level areas were mainly located in the central plains and urban areas; (3) There was a strong non‐linear relationship between landscape urbanization and ESM, with variability in the intensity and direction of ESM's response to landscape urbanization shocks as time. Specifically, urban landscape scale and shape (i.e., percentage of landscape, largest patch index (LPI), and landscape shape index) were generally more influential on the ESM than urban landscape density and compactness (i.e., patch density and pacth cohesion index), with LPI making the greatest contribution. These findings will help to clarify the dynamic relationship between landscape urbanization and ESM, which could provide important guidance for ecosystem conservation and urban planning.
Clinicians’ perceptions of the mental health needs of young people in alternative provision educational settings: An exploratory qualitative analysis

ABSTRACT It is estimated that 1 in 2 young people who are educated in Alternative Provision (AP) educational settings have social, emotional and mental health difficulties. However, the extent to which the mental health needs of these young people are currently being met by Child and Adolescent Mental Health Services (CAMHS) is less clear. In this qualitative study, semi-structured interviews were conducted with 15 CAMHS practitioners who had worked with young people educated in AP settings to explore secondary health care service engagement. We analysed the data using thematic analysis. Findings suggest that young people educated in AP settings have complex mental health needs for which they are currently not receiving adequate support, due to shortcomings within AP schools and numerous external barriers to care. Implications for clinicians and service commissioners include a need for further integration between CAMHS and education services, and better quality support across AP settings.
Influence of multi-wall separation control on normal-shock-induced separation in supersonic duct flows

In this work, separation due to the interaction of normal shock with wall boundary layers in a supersonic duct flow is studied using schlieren, oil flow visualization, and pressure measurements. This study uses separation control devices on three walls of a rectangular duct and investigates the influence of adding control on one wall and on the other walls. It was found that control on any wall has effect on separation on other walls. This effect was found to be due to the change in the size of corner separations. Pressure recovery was found to be steeper with the introduction of more control devices in the duct.
Development of Dual-Actuated Stage for Positioning Applications

Piezoelectric actuators have been widely used in precision positioning applications because of their fast response, fine resolution, and large actuating forces. The displacement of piezoelectric actuators should be precisely controlled to use them in precision actuation stages. However to use these piezoelectric actuators in control systems, it is very much necessary to estimate the displacement of actuator based on their input voltage and also the inverse model. This paper presents Prandtl-Ishlinskii model for describing the nonlinearity of the actuator. Inverse Prandtl-Ishlinskii model will be used in the feed forward loop for the compensation of nonlinearity. This research work also includes development of a dual-actuated single stage to overcome the small travel range of piezoelectric stack actuators. The piezoelectric stack actuator is used for fine positioning and ball screw actuator is used for coarse positioning in the proposed dual-actuated stage. A closed loop control will be designed for the dual positioning stage, and this stage will be later applied for real-time wing flap control of aircraft model.
A nature-inspired algorithm to find community structure in complex networks

Complex networks have in generally communities. These communities are very important. Network’s communities represent sets of nodes, which are very connected. In this research, we developed a new method to find the community structure in networks. Our method is based on flower pollination algorithm (FPA) witch is used in the splitting process. The splitting of networks in our method maximizes a function of quality called modularity. We provide a general framework for implementing our new method to find community structure in networks. We present the effectiveness of our method by comparison with some known methods on computer-generated and real-world networks.
Anesthesia for a Patient With Cerebrovascular Accident with Seizure Disorder Undergoing Medical Termination of Pregnancy: A Case Report

The term “total intravenous anesthesia” refers to the preservation of an anesthetic plane with the use of an injectable anesthetic, a sedative that is often given in intermittent boluses. The tendency to have recurrent unprovoked seizures is known as epilepsy. Its prevalence ranges from 0.5% to 1%. The highest incidence rates are in those with anatomical or developmental brain abnormalities, as well as at the extremes of age. The most common triggering factors for epilepsy are stress and fear. The main advantage of total intravenous anesthesia is the patient's rapid recovery and early ambulatory. Conscious sedation is a strategy for giving patients excellent anesthesia and analgesia. In this case report, we will describe a high-risk epileptic patient who required conscious sedation to perform a medical termination.
The Effect of Immersive Reality on Science Learning: A Meta-Analysis

The study aims to investigate the effects of immersive reality applications on students' science achievement. For this purpose, 1,323 articles reporting the effects of immersive reality applications on students' science achievement were examined. The following databases were searched for articles: National Thesis Center, Google scholar, Scopus, ProQuest, SAGE Journals Online, Tylor & Francis, ScienceDirect, and Turkish academic database. 32 effect size values from 23 studies that fit the including criteria determined before the study were included in the meta-analysis. The meta-analysis revealed that the immersive reality applications have a statistically significant effect (Hedge's g = 0.89) on science achievement based on the random effect model. In addition, distribution of the effect sizes was heterogeneous (Q = 300.86, p = 0.000). Therefore, the effects of immersive reality application on achievement within different subgroups were also examined. The subgroups were defined by (i) grade levels in which the studies conducted, (ii) academic discipline in which the study took place, (iii) immersive reality application types, and (iv) location where the study was conducted (in or out of Turkey). Statistical significance between group effect was only observed between the groups defined by the place of study conducted (in vs out of Turkey).
E cient and highly accurate reconstruction of discrete orientation distribution functions by integral approximation of relative probabilities

This paper is concerned with the reconstruction of textures—given as discrete, i.e. piecewise constant, orientation distribution functions (ODFs)—by a limited number, N , of equally weighted orientations. The N orientations are sampled from those N central orientations for which the respective Euler space boxes overlap with the fundamental zone of the discrete original ODF. Taking strong and intermediate experimental and random artificial textures as test-case we compare probabilistic and deterministic reconstruction schemes. The quality of ODF reconstruction is quantified by the root mean squared deviation as well as by the correlation factor of a linear regression between the probabilities in the original and reconstructed ODF. The quality of both reconstruction schemes exhibits a scaling with N =N. In terms of both quality measures, the deterministic sampling scores higher than probabilistic sampling for given N =N. However, if N N deterministic sampling progressively sharpens the reconstructed texture with decreasing N =N due to systematic over-weighting of orientations with originally high probability at the expense of low-probability ones. Therefore, a combined method is proposed, which for N < N draws a random subset of N orientations from a population containing N orientations, where this population is itself generated by prior deterministic sampling from the discrete ODF. The reconstruction quality achieved by this hybrid method is naturally identical to deterministic sampling for N N and asymptotically declines with decreasing N to settle at the levels of probabilistic sampling for N < N=10—without systematic sharpening of the reconstructed texture.
Adaptive Funnel Synchronization Control for Complex Switching Networks in Power Systems

This article investigates the synchronization control issue of complex power networks with switching parameters. In order to better describe the modern power systems with the characteristics of distributivity and uncertainty, we establish a mathematical model of complex power networks with switched parameters, where the dynamics of intermittent sustainable energy sources and flexible loads are taken into account. Motivated by the practical needs for the safety limitations and prescribed performance of node states during the synchronizaiton process, we further propose an adaptive funnel control scheme with prespecified funnel boundary to ensure that the output maintains in prescribed constrained regions. Neural networks approximation is utilized for the controller design to estimate the unknown nonlinearities. By applying the Lyapunov theory, it is guaranteed that the tracking signals are semi-globally, uniformly, and ultimatedly bounded. Eventually, the numerical examples are represented to demonstrate the efficacy of the proposed strategy.
Unraveling a Unique Encounter of Fusobacterium nucleatum With Empyema: A Case Report and Review of Literature

We report a case of Fusobacterium nucleatum (F. nucleatum) empyema in a 34-year-old male with no significant past medical history or obvious risk factors who presented with shortness of breath and chest pain. His imaging showed complicated parapneumonic effusion which grew F. nucleatum. He was started on piperacillin-tazobactam. The patient's clinical condition deteriorated despite initial therapeutic efforts, leading to escalated antibiotic therapy and further investigations. The patient's subsequent clinical course included pigtail catheter placement with drainage of fluid requiring tpa and dornase alpha, leading to significant improvement and eventual discharge on oral amoxicillin-clavulanic acid.