Miracle-dz/UniFAR-FTUs huggingface dataset:
A Bayesian approach to improving the Born approximation for inverse scattering with high-contrast materials

Time harmonic inverse scattering using accurate forward models is often computationally expensive. On the other hand, the use of computationally efficient solvers, such as the Born approximation, may fail if the targets do not satisfy the assumptions of the simplified model. In the Bayesian framework for inverse problems, one can construct a statistical model for the errors that are induced when approximate solvers are used, and hence increase the domain of applicability of the approximate model. In this paper, we investigate the error structure that is induced by the Born approximation and show that the Bayesian approximation error approach can be used to partially recover from these errors. In particular, we study the model problem of reconstruction of the index of refraction of a penetrable medium from measurements of the far field pattern of the scattered wave.
On the inverse spectrum problem of neutron stars

In this work we revisit axial perturbations of spherically symmetric and non-rotating neutron stars. Although it has been object of many studies, it still offers new insights that are of potential interest for more realistic scenarios or in the study exotic compact objects, which have drawn much attention recently. By using WKB theory, we first derive a new Bohr–Sommerfeld rule that allows to investigate the quasi-normal mode spectrum and address the inverse spectrum problem. The pure analytical treatment of the wave equation is rather involved, because it requires the solution of the TOV equations and the non-trivial tortoise coordinate transformation depending on the underlying space-time. Therefore we provide an easy way to construct potentials that simplifies the analytical treatment, but still captures the relevant physics. The approximated potential can be used for calculations of the axial perturbation spectrum. These results are also useful in the treatment of the inverse problem. We demonstrate this by reconstructing the time–time component of the metric throughout the star and constraining the equation of state in the central region. Our method also provides an analytical explanation of the empirically known asteroseismology relation that connects the fundamental QNM and radius of a neutron star with its compactness.
Efficacy of a Probiotic and Herbal Supplement in Models of Lung Inflammation

Background: Gut microbiome dysbiosis is associated with lung disease through the gut-lung axis. Abundant proteobacteria increase MMP-9 and contribute to tissue proteolysis followed by neutrophil recruitment, lung tissue injury, and perpetuation of chronic lung disease. We sought to determine if a scientifically formulated probiotic and herbal supplement could attenuate neutrophilic inflammation and improve lung structure and function in models of lung inflammation. Methods: For in vitro experiments, epithelial cells exposed to proteobacteria were treated with resB—a blend of three probiotic Lactobacillus strains and turmeric, holy basil, and vasaka herbal extracts. For in vivo experimentation, mice exposed to pulmonary proteobacteria-derived lipopolysaccharide were treated by gavage with resB. Results: In vitro, the bacterial and herbal components of resB decreased activity of the MMP-9 pathway. Mice exposed to LPS and pre- and post-treated with resB had decreased neutrophil recruitment and inflammatory biomarkers in bronchoalveolar lavage fluid, serum, and lung tissue compared to untreated mice. Conclusions: This study describes the mechanisms and efficacy of probiotic and herbal blend in pre-clinical models of lung injury and inflammation.
Irreversible Electroporation Mediates Glioma Apoptosis via Upregulation of AP-1 and Bim: Transcriptome Evidence

The heat-sink effect and thermal damage of conventional thermal ablative technologies can be minimized by irreversible electroporation (IRE), which results in clear ablative boundaries and conservation of blood vessels, facilitating maximal safe surgical resection for glioblastoma. Although much comparative data about the death forms in IRE have been published, the comprehensive genetic regulatory mechanism for apoptosis, among other forms of regulatory cell death (RCD), remains elusive. We investigated the electric field intensity threshold for apoptosis/necrosis (YO-PRO-1/PI co-staining) of the U251 human malignant glioma cell line with stepwise increased uniform field intensity. Time course samples (0–6 h) of apoptosis induction and sham treatment were collected for transcriptome sequencing. Sequencing showed that transcription factor AP-1 and its target gene Bim (Bcl2l11), related to the signaling pathway, played a major role in the apoptosis of glioma after IRE. The sequencing results were confirmed by qPCR and Western blot. We also found that the transcription changes also implicated three other forms of RCD: autophagy, necroptosis, and immunogenic cell death (ICD), in addition to apoptosis. These together imply that IRE possibly mediates apoptosis by the AP-1-Bim pathway, causes mixed RCD simultaneously, and has the potential to aid in the generation of a systemic antitumor immune response.
Lipid-Derived Aldehydes: New Key Mediators of Plant Growth and Stress Responses

Simple Summary Aldehydes are inevitably produced through non-enzymatic and enzymatic pathways from fatty acids in both normal and stressed conditions. Different fatty acids result in unique aldehydes with carbon chains of different lengths and levels of unsaturation, which determine their functions and reactivities in plants. The detailed description of the formation, toxic effects, and detoxification mechanisms of aldehydes in plants is highlighted in this review. Moreover, the signaling effects of aldehydes are summarized as well. Abstract Aldehydes, derivatives of lipids, are ubiquitously produced through non-enzymatic and enzymatic pathways in higher plants and participate in many physiological and biological processes. Increasing evidence demonstrates that aldehydes are involved in plants response to many abiotic stresses, such as light, drought, heat and nutrient deficiency. In plant cells, endogenously triggered or exogenously applied high concentrations of aldehydes can damage proteins and nucleic acid, disturb redox homeostasis, and consequently inhibit plant growth; therefore, they are considered cytotoxins. Aldehyde levels are also used as biomarkers to evaluate the health status of plants. Further genetic research shows that several enzymes have strong capacities to detoxify these electrophilic aldehydes. Small molecules, such as carnosine and glutathione, also exhibit the ability to scavenge aldehydes, effectively promoting plant growth. Recently, increasing evidence has shown that certain aldehydes at certain concentrations can upregulate survival genes, activate antioxidant responses, increase defense against pathogens and stimulate plant growth. This review summarizes recent studies of lipid-derived aldehydes in higher plants, mainly focusing on the generation pathway, toxic effects, and detoxification strategies. In addition, the signaling effects of aldehydes in plants are also discussed.
A Neural Network-Based Training Scheme for Probabilistic Support Vector Machine

In this paper a new scheme is proposed for training of the support vector machine (SVM) with probabilistic constraints. In the SVM, a pattern recognition problem is converted to a constraint quadratic programming. Each constraint is related to a training sample thereupon noisy data is caused appearance of incorrect support vector. Probabilistic constraints admit presence probably of samples in each class is applied based on a distribution function for determining suitable support vectors. In this way, it is possible noisy samples have low effect for finding support vectors. In the proposed method, SVM with the probabilistic constraints is converted to form of neural network model which can be described by the nonlinear dynamical system. A set of differential equations are defined for modeling of this dynamic and converges to optimal solution for the SVM with the probabilistic constraints. Another feature of the proposed method is solving both the primal and dual problem of SVM. Experimental results show the capability of the proposed method relative to conventional SVM.
Measuring the State Dependence Effect in Hospital Payment Adjustment

Since FY 2013, as a part of the Affordable Care Act (ACA) program, the Hospital Value-Based Purchasing (HVBP) program has adjusted Medicare’s payments to hospitals based on the total performance score of the hospital. First, the program reduces a portion of the hospital’s Medicare payments in a specific fiscal year, and then, by the end of the same fiscal year, the amount of the payment reductions will be awarded to the hospitals based on the total performance score; thus, the hospitals that do not receive the reward will lose the portion of money reduced by Medicare. In this research, we apply the theory of state dependence and use the dynamic random effect probit model to estimate this effect. The results show that the hospital payment adjustment dynamics have a very significant state dependence effect (0.341); this means that hospitals that received a reward in the previous year are 34.1% more likely to receive a reward this year than the ones that received a penalty in the previous year. Meanwhile, we also find that the state dependence effect varies significantly across hospitals with different ownership (proprietary/government owned/voluntary nonprofit), and the results show that voluntary nonprofit hospitals exhibit the largest effect of state dependence (0.370), while government-owned hospitals exhibit the lowest effect of state dependence (0.293), and proprietary hospitals are in the middle. Among the factors that influence the likelihood that a hospital receives a reward, we find that teaching hospitals with a large number of beds (>400) are less likely be rewarded; in terms of ownership, we find that voluntary nonprofit hospitals are more likely be rewarded; in terms of demographic factors, hospitals where the average household income are higher within the region are more likely be rewarded.
Low-Power Complementary Inverter Based on Graphene/Carbon-Nanotube and Graphene/MoS2 Barristors

The recent report of a p-type graphene(Gr)/carbon-nanotube(CNT) barristor facilitates the application of graphene barristors in the fabrication of complementary logic devices. Here, a complementary inverter is presented that combines a p-type Gr/CNT barristor with a n-type Gr/MoS2 barristor, and its characteristics are reported. A sub-nW (~0.2 nW) low-power inverter is demonstrated with a moderate gain of 2.5 at an equivalent oxide thickness (EOT) of ~15 nm. Compared to inverters based on field-effect transistors, the sub-nW power consumption was achieved at a much larger EOT, which was attributed to the excellent switching characteristics of Gr barristors.
The Role of the Epidermal Growth Factor Receptor in Diabetic Kidney Disease

The epidermal growth factor receptor (EGFR) is expressed in numerous cell types in the adult mammalian kidney and is activated by a family of EGF-like ligands. EGFR activation has been implicated in a variety of physiologic and pathophysiologic functions. There is increasing evidence that aberrant EGFR activation is a mediator of progressive kidney injury in diabetic kidney disease. This review will highlight recent studies indicating its potential role and mechanisms of injury of both glomerular and tubular cells in development and progression of diabetic kidney disease.
Assaying Microglia Functions In Vitro

Microglia, the main immune modulators of the central nervous system, have key roles in both the developing and adult brain. These functions include shaping healthy neuronal networks, carrying out immune surveillance, mediating inflammatory responses, and disposing of unwanted material. A wide variety of pathological conditions present with microglia dysregulation, highlighting the importance of these cells in both normal brain function and disease. Studies into microglial function in the context of both health and disease thus have the potential to provide tremendous insight across a broad range of research areas. In vitro culture of microglia, using primary cells, cell lines, or induced pluripotent stem cell derived microglia, allows researchers to generate reproducible, robust, and quantifiable data regarding microglia function. A broad range of assays have been successfully developed and optimised for characterizing microglial morphology, mediation of inflammation, endocytosis, phagocytosis, chemotaxis and random motility, and mediation of immunometabolism. This review describes the main functions of microglia, compares existing protocols for measuring these functions in vitro, and highlights common pitfalls and future areas for development. We aim to provide a comprehensive methodological guide for researchers planning to characterise microglial functions within a range of contexts and in vitro models.
Geophysical and Geochemical Constraints on Magma Storage Depths Along the Cascade Arc: Knowns and Unknowns

The iconic volcanoes of the Cascade arc stretch from Lassen Volcanic Center in northern California, through Oregon and Washington, to the Garibaldi Volcanic Belt in British Columbia. Recent studies have reviewed differences in the distribution and eruptive volumes of vents, as well as variations in geochemical compositions and heat flux along strike (amongst other characteristics). We investigate whether these along‐arc trends manifest as variations in magma storage conditions. We compile available constraints on magma storage depths from InSAR, geodetics, seismic inversions, and magnetotellurics for each major edifice and compare these to melt inclusion saturation pressures, pressures calculated using mineral‐only barometers, and constraints from experimental petrology. The availability of magma storage depth estimates varies greatly along the arc, with abundant geochemical and geophysical data available for some systems (e.g., Lassen Volcanic Center, Mount St. Helens) and very limited data available for other volcanoes, including many which are classified as “very high threat” by the USGS (e.g., Glacier Peak, Mount Baker, Mount Hood, Three Sisters). Acknowledging the limitations of data availability and the large uncertainties associated with certain methods, available data are indicative of magma storage within the upper 15 km of the crust (∼2 ± 2 kbar) beneath the main edifices. These findings are consistent with previous work recognizing barometric estimates cluster within the upper crust in many arcs worldwide. There are no clear offsets in magma storage between arc segments that are in extension, transtension or compression, although substantially more petrological work is needed for fine scale evaluation of storage pressures.
A Data-Driven Method for the Estimation of Truck-State Parameters and Braking Force Distribution

In the study of braking force distribution of trucks, the accurate estimation of the state parameters of the vehicle is very critical. However, during the braking process, the state parameters of the vehicle present a highly nonlinear relationship that is difficult to estimate accurately and that seriously affects the accuracy of the braking force distribution strategy. To solve this problem, this paper proposes a machine-learning-based state-parameter estimation method to provide a solid data base for the braking force distribution strategy of the vehicle. Firstly, the actual collected complete vehicle information is processed for data; secondly, random forest is applied for the feature screening of data to reduce the data dimensionality; subsequently, the generalized regression neural network (GRNN) model is trained offline, and the vehicle state parameters are estimated online; the estimated parameters are used to implement the four-wheel braking force distribution strategy; finally, the effectiveness of the method is verified by joint simulation using MATLAB/Simulink and TruckSim.
Experimental study of UV induced tensile properties deterioration and chemical aging of polyurea–POSS composites

Ultraviolet (UV) radiation present in natural sunlight degrades the chemical and mechanical properties of polymeric matrices in composites. Polyurea possess a unique set of chemical and mechanical properties due to its complex microstructure comprising of hard and soft segments (phases). The primary objective of the work presented here is to characterize the chemical and mechanical degradation of pure polyurea and polyurea - polyhedral oligomeric silsesquioxane (POSS) nanocomposites subjected to UV radiation exposure for 45 days. Control specimen in as-received condition (reference specimen) and UV-aged tensile test specimen are tested in the study. Analysis of Fourier transformed infrared (FTIR) spectra for UV-aged polyurea and polyurea-POSS composites reveals that the addition of POSS nanoparticles accelerate the deterioration of polyurea matrix by breaking the hard phase network and altering the mechanism of degradation for the soft phase present in polyurea microstructure. The deterioration of hard and soft segments cause a significant reduction in tensile properties of polyurea-POSS composites.
A Survey on NoSQL Databases

NoSQL databases have gained popularity in the recent years and have been successful in many production systems. The goal of this document is to understand the current needs that have led to the evolution of NoSQL data stores, why relational database systems were not able to meet these requirements and a brief discussion of some of the successful NoSQL data stores. We will study the common concepts underlying these data stores and how they compromise on ACID properties to achieve high scalability and availability. We also look at how the database community looks at this evolution: will it supersede the RDBMS (or) just a passing cloud?
Suites presque scindées d'algèbres intérieures et algèbres intérieures des suites presque scindées

Dans le cadre de l'etude des representations modulaires d'un groupe fini sur un corps, nous generalisons la notion, introduite par auslander et reiten, de "suite exacte presque scindee" de nodules (sur l'algebre de groupe), en une notion de "systemes d'idempotents" (dits systemes de auslander-reiten) dans une algebre "interieure" (du point de vue de l'operation du groupe) symetrique quelconque. Raisonnant a l'interieur des algebres, nous donnons, comme dans la theorie classique, un resultat d'"existence et unicite" des systemes de auslander-reiten. D'autre part le point de vue de l'"algebre commutante" des systemes nous permet de decrire, par le biais des groupes pointes, la restriction des systemes de auslander-reiten au vortex de leur terme extreme, et de donner un critere pour que ce vortex coincide avec celui du systeme. En particulier on a egalite pour les suites presque scindees se terminant par un module simple
Comptonization of cosmic microwave background photons in dwarf spheroidal galaxies

We present theoretical modelling of the electron distribution produced by annihilating neutralino dark matter in dwarf spheroidal galaxies (dSphs). In particular, we follow up the idea of Colafrancesco and find that such electrons distort the cosmic microwave background (CMB) by the Sunyaev-Zeldovich (SZ) effect. For an assumed neutralino mass of 10 GeV and beam size of 1 arcsec, the SZ temperature decrement is of the order of nano-Kelvin for dSph models with a soft core. By contrast, it is of the order of micro-Kelvin for the strongly cusped dSph models favoured by some cosmological simulations. Although this is out of reach of current instruments, it may well be detectable by future mm telescopes, such as the Atacama Large Millimetre Array. We also show that the upscattered CMB photons have energies within reach of upcoming X-ray observatories, but that the flux of such photons is too small to be detectable now. None the less, we conclude that searching for the dark matter induced SZ effect is a promising way of constraining the dark distribution in dSphs, especially if the particles are light.
Roughness enhanced K x-ray emission from femtosecond laser produced copper plasmas

K x-ray emission from copper plasmas produced by 100 fs, 806 nm laser pulses at intensities in the range of 10^15-10^16 W cm^-2 is studied with respect to input laser polarization and target surface conditions. We demonstrate that even moderate surface roughness enhances the characteristic x-ray yield by as much as 70 %. We further show that surface roughness of the targets overrides the effect of polarization state in light coupling to the plasma.
From description to code: a method to predict maintenance codes from maintainer descriptions

Aircraft maintenance crews enter the actions performed, the time required to complete the actions, and process followed to complete the action into a system of record that may be used to support future important operational decisions such as part inventory and staffing levels. Unfortunately, the actions performed by maintainers may not align with structured, predetermined codes for such actions. This discrepancy combined with an overabundance of structured codes has led to incorrect and polluted maintenance data that cannot be used in decision making. Typically, the unstructured textual fields accurately record the maintenance action, but are inaccessible to common reporting approaches. The textual fields can be used to cleanse the structured fields, thereby making more data available to support operational decision making. This paper introduces a natural language processing pipeline to predict C-17 US Air Force maintenance codes from an unstructured, shorthand text record. This research aims to cleanse problematic structured fields for further use in operational efficiency and asset reliability measures. Novel use of text processing, extraction, clustering, and classification approaches was employed to develop a natural language processing pipeline suited to the peculiarities of short, jargon-based text. The pipeline evaluates the frequency of structured field values within the datase and selects an appropriate machine learning model to optimize the predictive accuracy. Three different predictive methods were investigated to determine an optimal approach: a Logistic Regression Classifier, a Random Forrest Classifier, and Unsupervised techniques. This pipeline predicted structured fields with an average accuracy of 93 % across the five maintenance codes.
Crystallization of D-A Conjugated Polymers: A Review of Recent Research

D-A conjugated polymers are key materials for organic solar cells and organic thin-film transistors, and their film structure is one of the most important factors in determining device performance. The formation of film structure largely depends on the crystallization process, but the crystallization of D-A conjugated polymers is not well understood. In this review, we attempted to achieve a clearer understanding of the crystallization of D-A conjugated polymers. We first summarized the features of D-A conjugated polymers, which can affect their crystallization process. Then, the crystallization process of D-A conjugated polymers was discussed, including the possible chain conformations in the solution as well as the nucleation and growth processes. After that, the crystal structure of D-A conjugated polymers, including the molecular orientation and polymorphism, was reviewed. We proposed that the nucleation process and the orientation of the nuclei on the substrate are critical for the crystal structure. Finally, we summarized the possible crystal morphologies of D-A conjugated polymers and explained their formation process in terms of nucleation and growth processes. This review provides fundamental knowledge on how to manipulate the crystallization process of D-A conjugated polymers to regulate their film structure.