Miracle-dz/UniFAR-FTUs huggingface dataset:
Erratum: Effects of meteoric debris on stratospheric aerosols and gases

We consider the interactions of meteoric dust particles and metal vapors with stratospheric aerosols and gases. We utilize the detailed calculations of meteor ablation and recondensation rates made by Hunten et al. (1980) to obtain a fairly precise characterization of meteoric dust height and size distributions. We quantify the contribution of meteor residues to aerosol composition, the role of meteoric dust as condensation nuclei, and the effects of meteor debris on aerosol size distributions. We give estimates of particle surface areas, and discuss the potential importance of heterogeneous chemistry for stratospheric trace gases. We also investigate the interaction between H2SO4 vapor, which is associated with stratospheric aerosols, and meteor metal vapors, which are responsible for stratospheric metal ion composition. Our model predictions compare favorably with observational data on the elemental composition of aerosols, stratospheric particle size distributions, condensation nuclei abundances and sulfate mass mixing ratios. We conclude that meteoric particles may dominate the natural stratospheric aerosols at small ( 1.0 μm) sizes under normal conditions. We show that metallic meteoric elements may act to neutralize sulfuric acid vapor above 20 km, significantly reducing ambient H2SO4 abundances. We also demonstrate that model calculations which neglect particle interactions can seriously overestimate stratospheric metal vapor concentrations.
Transdimensional approaches to geophysical inverse problems

In geophysical inversion the model parameterisation, the number of unknown parameters, the level of smoothing and the required level of data fit are usually arbitrarily determined by the user prior to the inversion. These quantities are related to each other and define the formulation of the inverse problem; by definition they affect the final solution. They are often manually ‘tuned’ by means of subjective trial-and-error procedures, and this represents a recurring problem in geophysical inversion. In this thesis this issue is addressed by proposing an alternative inversion strategy. Different methodologies recently developed in the area of Bayesian statistics are combined to produce a general inversion algorithm, which lets the data themself formulate the inverse problem. This is done by treating the tunable quantities as unknowns to be constrained directly by the data. A major focus is on situations where data constrain a 2D spatially varying field, particularly seismic tomography. A variable parametrisation consisting of Voronoi cells with mobile geometry, shape and number, is treated as a set unknowns in the inversion. The reversible jump algorithm is used to sample the transdimensional model space within a Bayesian framework which avoids global damping procedures and the need to tune regularisation parameters. The method developed in this thesis is an ensemble inference approach, where many potential solutions are generated with variable numbers of cells. Information is extracted from the ensemble as a whole by performing Monte Carlo integration to obtain an expected Earth model. The inherent model averaging process naturally smooths out unwarranted structure in the Earth model, but maintains local discontinuities if well constrained by the data. As a by product, uncertainty estimates are obtained for any point in the medium. In a transdimensional approach, the level of data uncertainty directly determines the model complexity needed to satisfy the data. Intriguingly, the Bayesian formulation can be extended to the case where data uncertainty is also uncertain. It is possible to estimate the level of data noise while at the same time controlling model complexity in an automated fashion. For 2D seismic tomography, this novel procedure gives promising results in situations where the ray coverage is far from ideal, as it performs better compared to standard methods that use regular parameterisations. The method is also applied to the inversion of three ambient noise datasets that span the Australian continent at different scales. A multiscale tomographic image of Rayleigh wave group velocity
DOSE TITRATION AND GLYCEMIC CONTROL WITH INSULIN GLARGINE 100 U/ML IN PREVIOUSLY UNCONTROLLED PATIENTS WITH TYPE 2 DIABETES: A 6-MONTH PROSPECTIVE OBSERVATIONAL STUDY IN PRIMARY CARE IN LEBANON.

Grace Abi Rizk. MD,Hotel-Dieu De France Hospital, Beirut, Lebanon, Family Medicine Department. Manuscript Info Abstract . Manuscript History Received: 14 November 2018 Final Accepted: 16 December 2018 Published: January 2019 Objective:the rapid increase in diabetes prevalence in lebanon is alarming, with 14.5% of the population affected in 2013. Early and adequate use of individualized insulin treatment strategies is effective to prevent or delay the onset of long-term complications. This study aimed at exploring the effectiveness of insulin glargine 100 u/ml (gla100)-based strategies initiated at the discretion of primary care physicians (pcps) in people with type 2 diabetes mellitus uncontrolled on oral antidiabetic drugs (oads). Research design and methods: in this national, multicenter, prospective study, 601 patients with previously uncontrolled type 2 diabetes and prescribed gla-100 were enrolled. Glycated hemoglobin (hba1c), fasting plasma glucose (fpg) and the changes in treatment schemes to reach control were analyzed after three and six months. Safety data were also reported. Results:the majority (85%) of patients were overweight or obese, and over 30% had diabetes complications. About 60% of patients used a self-titration scheme and reported adherence to gla-100 therapy at both study visits. Hba1c decreased from 9.8 ± 1.3% (84 mmol/mol) to 7.1 ± 0.9% (54 mmol/mol) from baseline to 6 months (p<0.0001). Similarly, fpg levels significantly improved. People lost body weight of 1.7 kg at 6 months and only 1.4% reported minor hypoglycemic episodes. Conclusion:this study demonstrated that introduction of gla-100 to type 2 diabetes patients uncontrolled on oad therapy significantly improved glycemic control with low risk of hypoglycemia and no weight gain. In addition, the study indicated that pcps in lebanon were successful in managing type 2 diabetes and monitoring patients’ selftitration schemes.
Application of neural network in Infrared-Radar Dual-mode Guidance System

In this paper a novel target tracking information differentiating system with the capability of real time tracking and accurately identifying is developed in infrared-radar dual-mode guidance system. We take advantage of neural network's well known capability of learning to perform the required classification without the assumption of probabilistic models for the input models to substitute the fuzzy rules in the information differentiating system. Since the neural network training based on expert knowledge database is conducted off-line, significant benefits of developing real time tracking capabilities are possible. Her supervising trained neural network is applied in the dual-mode guidance system, which outputs the confidence degree denoted as the weight value of target information in data fusion center according to the two input variables of measure noise and tracking error. The validity of this method is proved by simulation.
Decentralized adaptive output-feedback stabilization of large-scale time-delay nonlinear systems with unknown high-frequency-gain signs

In this paper, we propose a decentralized adaptive output-feedback controller for a class of large-scale time-delay nonlinear systems with unknown high-frequency-gain signs. We establish global asymptotic stabilization results, without using Razumikhin Theorem or constructing Lyapunov-Krasovskii functionals. The paper enlarges the class of large-scale nonlinear systems for which global decentralized adaptive output- feedback controller can be designed.
Analysis of Pseudomomentum Wave-Activity Density in a Heavy Rainfall Event in East China

Abstract Taking into account moisture in virtue of general potential temperature, the author derive a three-dimensional (3D) pseudomomentum wave-activity relation for the moist atmosphere from the primitive equations in Cartesian coordinates using the Momentum-Casimir method. Since the wave-activity relation is constructed in an ageostrophic and non-hydrostatic dynamical framework, it may be applicable to diagnosing the evolution and propagation of mesoscale systems leading to heavy rainfall. The theoretical analysis shows that, besides the local change of wave-activity flux divergence and source or sink, the wave-activity relation includes two additional forcing terms. The first is the zonal gradient of difference between general potential temperature and potential temperature perturbations, and the second is the covariance of the solenoid and gradient of water vapor, denoting the direct influence of moisture on wave-activity density. The wave-activity density was applied to a heavy precipitation event occurring in the Jianghuai region of China. The calculation showed that the wave-activity density was consistent with 6-h accumulated precipitation observations, in terms of both spatial distribution and temporal tendency. This suggested that the disturbance represented by wave-activity density was closely related to the heavy precipitation. Although the wave-activity flux divergence and the covariance of the solenoid and gradient of water vapor made the primary contribution to the local change of wave-activity density, the covariance was more remarkable. The zonal gradient of difference between general potential temperature and potential temperature perturbations made a weaker contribution to the wave-activity density.
Improved Low-Frequency Operation of Hybrid Inverter for Medium-Voltage Induction Motor Drive Under V/f and Vector Control Mode of Operation

In this paper, an improved control method has been proposed for hybrid inverter to drive medium-voltage induction motor. This hybrid inverter consists of high-voltage three-level inverter that is operated in quasi-square-wave mode. Low-voltage high-frequency H-bridge cells are connected in series with the three-level inverter. They eliminate harmonic voltages generated by the high-voltage quasi-square-wave inverter. This paper proposes systemic choice of harmonic voltages that the series cells need to generate. The choice of these harmonic voltages is dynamically modified based on operating frequency of the drive. This method ensures that the quality of motor current remains same at all operating frequency of the drive. The naturally regulated dc bus voltage of the series cells are kept low for the pulsewidth modulation (PWM) operation of the series cells. Several issues of adopting vector control strategy for hybrid drive have been addressed in this paper. Based on this analysis, suitable motor current control strategy has been proposed without speed sensor. Experimental results are produced for open-loop and closed-loop operations of this drive. The results show the satisfactory operation of this hybrid inverter-based induction motor drive operating from 5 to 50 Hz.
Isorhamnetin Exerts Antifibrotic Effects by Attenuating Platelet-Derived Growth Factor-BB-induced HSC-T6 Cells Activation via Suppressing PI3K-AKT Signaling Pathway

Background: Currently, liver fibrosis is growing worldwide; unfortunately, there is no definite cure for this disease. Hence, understanding the molecular pathways involved in the development of liver fibrosis can help to find a proper treatment. In this study, we aimed to evaluate the effects of isorhamnetin as an antifibrotic agent on PDGF-BB-activated HSC-T6 cells in a concentration-dependent manner. We have also attempted to assess signaling pathways that may affect liver fibrosis. Methods: PDGF-BB was used to activate the HSC-T6 rat hepatic stellate cell line. The activated cells were treated with Isorhamnetin for 24 h. Finally, we compared the mRNA expression level of COLA1 and α-SMA and also the level of phosphorylated AKT protein with the control group. Results: The obtained data revealed a significant increase in the expression level of the COLA1 and α-SMA genes (p > 0.05), as well as phosphorylated AKT protein, in the cells treated with PDGF-BB. In addition, 75 and 100 µM concentrations of Isorhamnetin markedly declined the COLA1 and α-SMA expression and also the phosphorylated AKT protein level in the HSC-T6 cells. Conclusion: Our findings suggest that Isorhamnetin decreases HSC-T6 activation, the expression of COLA1 and α-SMA, in vitro, which could act as an antifibrotic element to reduce and treat liver fibrosis disease.
Modeling of temporal and spatial distribution of groundwater level in the water table fluctuant belt of the lower reaches of Heihe River: application of FELLOW software.

The groundwater level plays an important role in determining the pattern and distribution of vegetations that stabilize riverine environments.Presently,models simulating distribution of groundwater level become important tools in the groundwater research field.Based on the field investigation of geology and hydrogeology,conductivity and water supply capacity of the aquifer were determined in this study.After spatializing the relative parameters inputted,FEFLLOW software was run and a series of temporal and spatial distribution maps of groundwater level were obtained.By analyzing these maps,we could draw some conclusions:(1) Comparing the estimated and measured values,the mean error was 0.54m in 31 stations with mean minimum error 0.04m and mean maximum error 1.17 m;(2) Spatially,the lowest groundwater table appeared in southern mountain and northern parts between east lake and west lake.Generally,the groundwater level is between 1 m and 4 m in the most parts of the study area.Temporally,variation of groundwater level is not significant;(3) The groundwater level has gradually descended from initial stage of simulation in the study area due to decrease of runoff form the middle reaches of Heihe River.However,the Chinese Government initiated the environmental restoration project in the Heihe River basin in 2000 with water diversions from the Zhengyi gorge reservoir to the lower reaches.From 2000 to 2002 there were 6.8×10~8 m~3 each year has been releases to the lower reaches.The groundwater level has thus risen from 2002.However,the trend of ground level change was different in different parts.The ground level has mounted up in the upper and lower parts of the study area,but dropped in the middle part.
The Equipoise Between the Treatment of Glioblastoma and the Risk of Secondary Acute Myelogenous Leukemia: An Illustrative Case Report

We present a case report of a 56-year-old woman who was diagnosed with biopsy-proven left thalamic glioblastoma multiforme (GBM). She was treated with standard concurrent chemotherapy and radiation, as well as a 2-year period of adjuvant temozolomide. She relapsed 2 ½ years after starting her initial therapy and was treated with bevacizumab and lomustine, but she relapsed. She was then placed on a phase 1/2 clinical trial that included KHK2455 and mogamulizumab-kpkc individually and in combination for almost 4 years. She had a rapid demise due to the development of a neutropenic pneumonia and treatment-induced acute myeloid leukemia (AML) and elected for hospice care.
Antibody-Functionalized Polymer Nanoparticles for Targeted Antibiotic Delivery in Models of Pathogenic Bacteria Infecting Human Macrophages

The efficacy of antibody-functionalized poly(d,l-lactide-co-glycolide) (PLGA) nanoparticles (NPs), prepared by nanoprecipitation, carrying rifampicin (RIF) against planktonic, sessile, and intracellular Staphylococcus aureus and Escherichia coli is reported here. A biotinylated anti-S. aureus polyclonal antibody, which binds to structural antigens of the whole bacterium, was functionalized on the surface of RIF-loaded PLGA-based NPs by using the high-affinity avidin–biotin complex. This general strategy allows the binding of commercially available biotinylated antibodies. Coculture models of S. aureus ATCC 25923 and Escherichia coli S17 were used to demonstrate the preferential selectivity of the antibody-functionalized NPs against the Gram-positive bacterium only. At 0.2 μg/mL, complete S. aureus eradication was observed for the antibody-functionalized RIF-loaded NPs, whereas only a 5-log reduction was observed for the nontargeted RIF-loaded NPs. S. aureus is a commensal facultative pathogen having part of its live cycle intracellularly in both phagocytic and nonphagocytic cells. Those intracellular bacterial persisters, named small colony variants, have been postulated as reservoirs of relapsed episodes of infection and consequent treatment failure. At 0.5 μg/mL, the RIF-loaded NPs reduced in 2-log intracellular S. aureus-infecting human macrophages. The ability of those antibody-functionalized nanoparticles to prevent biofilm formation or to reduce the bacterial burden in already-formed mature biofilms is also reported here using S. aureus and E. coli single and cocultured biofilms. In the prevention of S. aureus biofilm formation, the antibody-functionalized NPs exerted a superior inhibition of bacterial growth (up to 2 logs) compared to the nonfunctionalized ones. This study demonstrates the selectivity of the synthesized immunonanoparticles and their antimicrobial efficacy in different scenarios, including planktonic cultures, sessile conditions, and even against intracellular infective pathogens.
FRGNN: Mitigating the Impact of Distribution Shift on Graph Neural Networks via Test-Time Feature Reconstruction

Due to inappropriate sample selection and limited training data, a distribution shift often exists between the training and test sets. This shift can adversely affect the test performance of Graph Neural Networks (GNNs). Existing approaches mitigate this issue by either enhancing the robustness of GNNs to distribution shift or reducing the shift itself. However, both approaches necessitate retraining the model, which becomes unfeasible when the model structure and parameters are inaccessible. To address this challenge, we propose FR-GNN, a general framework for GNNs to conduct feature reconstruction. FRGNN constructs a mapping relationship between the output and input of a well-trained GNN to obtain class representative embeddings and then uses these embeddings to reconstruct the features of labeled nodes. These reconstructed features are then incorporated into the message passing mechanism of GNNs to influence the predictions of unlabeled nodes at test time. Notably, the reconstructed node features can be directly utilized for testing the well-trained model, effectively reducing the distribution shift and leading to improved test performance. This remarkable achievement is attained without any modifications to the model structure or parameters. We provide theoretical guarantees for the effectiveness of our framework. Furthermore, we conduct comprehensive experiments on various public datasets. The experimental results demonstrate the superior performance of FRGNN in comparison to multiple categories of baseline methods.
Video-Instrument Synergistic Network for Referring Video Instrument Segmentation in Robotic Surgery

Robot-assisted surgery has made significant progress, with instrument segmentation being a critical factor in surgical intervention quality. It serves as the building block to facilitate surgical robot navigation and surgical education for the next generation of operating intelligence. Although existing methods have achieved accurate instrument segmentation results, they simultaneously generate segmentation masks for all instruments, without the capability to specify a target object and allow an interactive experience. This work explores a new task of Referring Surgical Video Instrument Segmentation (RSVIS), which aims to automatically identify and segment the corresponding surgical instruments based on the given language expression. To achieve this, we devise a novel Video-Instrument Synergistic Network (VIS-Net) to learn both video-level and instrument-level knowledge to boost performance, while previous work only used video-level information. Meanwhile, we design a Graph-based Relation-aware Module (GRM) to model the correlation between multi-modal information (i.e., textual description and video frame) to facilitate the extraction of instrument-level information. We are also the first to produce two RSVIS datasets to promote related research. Our method is verified on these datasets, and experimental results exhibit that the VIS-Net can significantly outperform existing state-of-the-art referring segmentation methods. Our code and our datasets will be released upon the publication of this work.
Reproducing Kernel Hilbert Space Pruning for Sparse Hyperspectral Abundance Prediction

Hyperspectral measurements from long range sensors can give a detailed picture of the items, materials, and chemicals in a scene but analysis can be difficult, slow, and expensive due to high spatial and spectral resolutions of state-of-the-art sensors. As such, sparsity is important to enable the future of spectral compression and analytics. It has been observed that environmental and atmospheric effects, including scattering, can produce nonlinear effects posing challenges for existing source separation and compression methods. We present a novel transformation into Hilbert spaces for pruning and constructing sparse representations via non-negative least squares minimization. Then we introduce max likelihood compression vectors to decrease information loss. Our approach is benchmarked against standard pruning and least squares as well as deep learning methods. Our methods are evaluated in terms of overall spectral reconstruction error and compression rate using real and synthetic data. We find that pruning least squares methods converge quickly unlike matching pursuit methods. We find that Hilbert space pruning can reduce error by as much as 40% of the error of standard pruning and also outperform neural network autoencoders.
Training-Free Energy Beamforming Assisted by Wireless Sensing

This paper studies the transmit energy beamforming in a multi-antenna wireless power transfer (WPT) system, in which an access point (AP) equipped with a uniform linear array (ULA) sends radio signals to wirelessly charge multiple single-antenna energy receivers (ERs). Different from conventional energy beamforming designs that require the AP to acquire the channel state information (CSI) via training and feedback, we propose a new training-free energy beamforming approach assisted by wireless radar sensing, which is implemented based on the following two-stage protocol. In the first stage, the AP performs wireless radar sensing to estimate the path gain and angle parameters of the ERs for constructing the corresponding CSI. In the second stage, the AP implements the transmit energy beamforming based on the constructed CSI to efficiently charge these ERs in a fair manner. Under this setup, first, we jointly optimize the sensing beamformers and duration in the first stage to minimize the sensing duration, while ensuring a given accuracy threshold for parameters estimation subject to the maximum transmit power constraint at the AP. Next, we optimize the energy beamformers in the second stage to maximize the minimum harvested energy by all ERs. In this approach, the estimation accuracy threshold for the first stage is properly designed to balance the resource allocation between the two stages for optimizing the ultimate energy harvesting performance. Finally, numerical results show that the proposed training-free energy beamforming design performs close to the performance upper bound with perfect CSI, and outperforms the benchmark schemes without such joint optimization and that with isotropic transmission.