Miracle-dz/UniFAR-FTUs huggingface dataset:
Terrestrial deuterium-to-hydrogen ratio in water in hyperactive comets

The D/H ratio in cometary water has been shown to vary between 1 and 3 times the Earth’s oceans value, in both Oort cloud comets and Jupiter-family comets originating from the Kuiper belt. This has been taken as evidence that comets contributed a relatively small fraction of the terrestrial water. We present new sensitive spectroscopic observations of water isotopologues in the Jupiter-family comet 46P/Wirtanen carried out using the GREAT spectrometer aboard the Stratospheric Observatory for Infrared Astronomy (SOFIA). The derived D/H ratio of (1.61 ± 0.65)×10−4 is the same as in the Earth’s oceans. Although the statistics are limited, we show that interesting trends are already becoming apparent in the existing data. A clear anti-correlation is seen between the D/H ratio and the active fraction, defined as the ratio of the active surface area to the total nucleus surface. Comets with an active fraction above 0.5 typically have D/H ratios in water consistent with the terrestrial value. These hyperactive comets, such as 46P/Wirtanen, require an additional source of water vapor in their coma, explained by the presence of subliming icy grains expelled from the nucleus. The observed correlation may suggest that hyperactive comets belong to a population of ice-rich objects that formed just outside the snow line, or in the outermost regions of the solar nebula, from water thermally reprocessed in the inner disk that was transported outward during the early disk evolution. The observed anti-correlation between the active fraction and the nucleus size seems to argue against the first interpretation, as planetesimals near the snow line are expected to undergo rapid growth. Alternatively, isotopic properties of water outgassed from the nucleus and icy grains may be different due to fractionation effects at sublimation. In this case, all comets may share the same Earth-like D/H ratio in water, with profound implications for the early solar system and the origin of Earth’s oceans.
Systematic Review of Photodynamic Therapy in Gliomas

Simple Summary Malignant gliomas are of the deadliest, most hard-to-treat cancers, given their various characteristics of aggression and infiltration as well as the location of growth. Photodynamic therapy (PDT) is a promising avenue for localized cancer therapy. A therapeutic effect is achieved by systemic drug delivery followed by localized wavelength-specific illumination. This review extensively explores the use of photosensitizers in gliomas, from their first use to the present, and details their mechanisms, and pre-clinical and clinical findings. Further discussion is provided on its limitations and future directions. Abstract Over the last 20 years, gliomas have made up over 89% of malignant CNS tumor cases in the American population (NIH SEER). Within this, glioblastoma is the most common subtype, comprising 57% of all glioma cases. Being highly aggressive, this deadly disease is known for its high genetic and phenotypic heterogeneity, rendering a complicated disease course. The current standard of care consists of maximally safe tumor resection concurrent with chemoradiotherapy. However, despite advances in technology and therapeutic modalities, rates of disease recurrence are still high and survivability remains low. Given the delicate nature of the tumor location, remaining margins following resection often initiate disease recurrence. Photodynamic therapy (PDT) is a therapeutic modality that, following the administration of a non-toxic photosensitizer, induces tumor-specific anti-cancer effects after localized, wavelength-specific illumination. Its effect against malignant glioma has been studied extensively over the last 30 years, in pre-clinical and clinical trials. Here, we provide a comprehensive review of the three generations of photosensitizers alongside their mechanisms of action, limitations, and future directions.
A Model for the Influence of Media on the Ideology of Content in Online Social Networks

Many people rely on online social networks as sources of news and information, and the spread of media content with ideologies across the political spectrum influences online discussions and impacts actions offline. To examine the impact of media in online social networks, we generalize bounded-confidence models of opinion dynamics by incorporating media accounts as influencers in a network. We quantify partisanship of content with a continuous parameter on an interval, and we formulate higher-dimensional generalizations to incorporate content quality and increasingly nuanced political positions. We simulate our model with one and two ideological dimensions, and we use the results of our simulations to quantify the "entrainment" of content from non-media accounts to the ideologies of media accounts in a network. We maximize media impact in a social network by tuning the number of media accounts that promote the content and the number of followers of the accounts. Using numerical computations, we find that the entrainment of the ideology of content spread by non-media accounts to media ideology depends on a network's structural features, including its size, the mean number of followers of its nodes, and the receptiveness of its nodes to different opinions. We then introduce content quality --- a key novel contribution of our work --- into our model. We incorporate multiple media sources with ideological biases and quality-level estimates that we draw from real media sources and demonstrate that our model can produce distinct communities ("echo chambers") that are polarized in both ideology and quality. Our model provides a step toward understanding content quality and ideology in spreading dynamics, with ramifications for how to mitigate the spread of undesired content and promote the spread of desired content.
Visualization of oxygen transportation in microcirculation by sidestream dark-field oximetry

The sidestream dark-field (SDF) imaging allows direct visualization of red blood cells in microvessels near tissue surfaces. We have developed an image-based oximetry method using two-band images obtained by SDF imaging (SDF oximetry) and a trial SDF device with light-emitting diodes to obtain band images. In this study, we propose a technique of producing oxygen saturation (SO2) maps from SDF images and perform animal experiments in vivo. To produce SO2 maps, we use spectral analysis using two band images obtained with our SDF device. As an image processing, the combination of both the Hessian-based and pixel value-based techniques as blood vessel extraction from an SDF image is used. From the experiment with the surface of rat small intestines, we can produce SO2 maps and find that the map represents arterioles and venules those were determined based on the blood ow from SDF images. Moreover, we find the variation of SO2 along a blood vessel running direction.
Rate-Splitting Multiple Access in Wireless Backhaul HetNets: A Decentralized Spectral Efficient Approach

In this paper, we investigate the application of rate-splitting multiple access (RSMA) in a two-tier wireless backhaul heterogeneous network (HetNet), where a macro base station (MBS) simultaneously transmits wireless access signals to multiple macro-cell users (MCUs) and wireless backhaul signals to small base stations (SBSs) by leveraging RSMA. Furthermore, to explore the potential advantage of common streams in RSMA systems, we develop a “Hybrid RSMA” scheme in which the MBS only employs RSMA to encode the backhaul messages while each MCU’s message is directly encoded without rate-splitting. We formulate an optimization problem to maximize the system’s spectral efficiency (SE) by jointly considering transmit precoding and rate allocation at the MBS and SBSs. To solve the formulated non-convex problem, we first propose an iterative centralized algorithm based on successive convex approximation (SCA). Then, we further develop an efficient decentralized algorithm that can be executed in parallel at the MBS and each SBS based on the local channel state information with fewer signaling exchanges. Simulation results show that the application of RSMA can achieve higher SE over conventional non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) under different network loads. Particularly, “Hybrid RSMA” has a greater performance improvement than “RSMA” in the underloaded system.
Hopfield Neural Network Based Uplink/Downlink Transmission Order Optimization for Dynamic Indoor TDD Femtocells

The Uplink/Downlink transmission mode or Transmission Order (TO) optimization has recently appeared as a new optimization domain in radio resource management. Such optimization is a combinatorics problem and requires good heuristic algorithm to be approximately solved within short time for the dynamic radio environment. This paper shows how the TO optimization problem in Time Division Duplex (TDD) indoor femtocells can be formulated and solved by the Hopfield Neural Network (HNN) based TO schedulers. Both centralized and distributed versions are analyzed in the context of indoor femtocells. We also examine proposed TO schedulers’ system performance in TDD indoor femtocells environment by extensive simulation campaigns. Our simulation results for a large 3-story building including 120 femtocells show that (i) the indoor femtocell system performance is improved up to 13 to 20 percent by the proposed HNN schedulers depending on the number of femtocells, (ii) the proposed TO schedulers converge within the first few epochs. (iii) The performance of the proposed schedulers are justified by a time-consuming but a thorough Genetic Algorithm Scheduler.
Stochastically modeled weakly reversible reaction networks with a single linkage class

It has been known for nearly a decade that deterministically modeled reaction networks that are weakly reversible and consist of a single linkage class have trajectories that are bounded from both above and below by positive constants (so long as the initial condition has strictly positive components). It is conjectured that the stochastically modeled analogs of these systems are positive recurrent. We prove this conjecture in the affirmative under the following additional assumptions: (i) the system is binary, and (ii) for each species, there is a complex (vertex in the associated reaction diagram) that is a multiple of that species. To show this result, a new proof technique is developed in which we study the recurrence properties of the n-step embedded discrete-time Markov chain.
Data Augmentation Using GANs

In this paper we propose the use of Generative Adversarial Networks (GAN) to generate artificial training data for machine learning tasks. The generation of artificial training data can be extremely useful in situations such as imbalanced data sets, performing a role similar to SMOTE or ADASYN. It is also useful when the data contains sensitive information, and it is desirable to avoid using the original data set as much as possible (example: medical data). We test our proposal on benchmark data sets using different network architectures, and show that a Decision Tree (DT) classifier trained using the training data generated by the GAN reached the same, (and surprisingly sometimes better), accuracy and recall than a DT trained on the original data set.
Assessing the End-of-Semester Examination Papers During the Implementation of the Bologna Process: Bloom’s Taxonomy as a Framework

One of the significant transformations in the Kurdistan Region of Iraq (KRI) is the implementation of the Bologna process. This alteration of the traditional system to the Bologna process system has also impacted the assessment schemes. More weight has been given to formative assessment that can help improve students’ grades and ease success in the courses. Nonetheless, the final exam still carries most of the grades. Therefore, setting appropriate questions that can meet all the cognition levels represented in Bloom’s taxonomy assists in raising students’ cognition to higher levels rather than only assessing bookish knowledge that is located on the baseline of Bloom’s taxonomy. To this intent, the present study endeavored to identify if instructors at colleges of Nursing and Science adhere to the various dimensions of Bloom’s taxonomy that are central to the Bologna process. More importantly, the association of each of the demographic variables to the level of the final examination questions was also examined. The study employed a quantitative method to tackle the topic. Totally, a sample of 75 final examination papers was collected from instructors, including 524 individual questions. The findings revealed that most of the examination papers revolved around low-order thinking questions and the association of the rate of success to the level of the questions was highly statistically significant.
Principled Imputation Made Simple: Multiple Imputation Using Gaussian Copulas

Missing observations are pervasive throughout observational research, especially in the social sciences. Despite multiple approaches to dealing adequately with missing data, many scholars still rely on list-wise deletion. In this article, we present a simple to use approach to multiple imputation. We show that using Gaussian copulas for multiple imputation allows scholars to attain estimation results that have good coverage and small bias. Using simulated as well as observational data from published social science research we compare imputation via Gaussian copulas with two other widely used imputation methods: MICE and Amelia II. The three approaches perform relatively similarly. Importantly, however, imputation via the Gaussian copula is simple and does not require the researcher to undertake any transformation of the data or specification of distributional assumptions for individual variables but returns a valid posterior density of the imputed data.
Stochastic Online Metric Matching

We study the minimum-cost metric perfect matching problem under online i.i. d arrivals. We are given a fixed metric with a server at each of the points, and then requests arrive online, each drawn independently from a known probability distribution over the points. Each request has to be matched to a free server, with cost equal to the distance. The goal is to minimize the expected total cost of the matching. Such stochastic arrival models have been widely studied for the maximization variants of the online matching problem; however, the only known result for the minimization problem is a tight $O(\log n)$-competitiveness for the random-order arrival model. This is in contrast with the adversarial model, where an optimal competitive ratio of $O(\log n)$ has long been conjectured and remains a tantalizing open question. In this paper, we show improved results in the i.i. d arrival model. We show how the i.i. d model can be used to give substantially better algorithms: our main result is an $O((\log \log \log n)^2)$-competitive algorithm in this model. Along the way we give a $9$-competitive algorithm for the line and tree metrics. Both results imply a strict separation between the i.i. d model and the adversarial and random order models, both for general metrics and these much-studied metrics.
The Effect of the Mixed Extract of Kalopanax pictus Nakai and Achyranthes japonica Nakai on the Improvement of Degenerative Osteoarthritis through Inflammation Inhibition in the Monosodium Iodoacetate-Induced Mouse Model

Osteoarthritis is a chronic inflammatory disease, and, due to the lack of fundamental treatment, the main objective is to alleviate pain and prevent cartilage damage. Kalopanax pictus Nakai and Achyranthes japonica Nakai are herbal plants known for their excellent anti-inflammatory properties. The objective of this study is to confirm the potential of a mixture extract of Kalopanax pictus Nakai and Achyranthes japonica Nakai as a functional raw material for improving osteoarthritis through anti-inflammatory effects in macrophages and MIA-induced arthritis experimental animals. In macrophages inflamed by lipopolysaccharide (LPS), treatment of Kalopanax pictus Nakai and Achyranthes japonica Nakai mixture inhibits NF-κB and mitogen-activated protein kinase (MAPK) activities, thereby inhibiting inflammatory cytokine tumor necrosis factor-alpha (TNF-α) and interleukin 6 (IL-6), inflammatory factors PGE2, MMP-2, and MMP-9, and nitric oxide (NO) was reduced. In addition, in an animal model of arthritis induced by MIA (monosodium iodoacetate), administration of Kalopanax pictus Nakai and Achyranthes japonica Nakai mixture reduced blood levels of inflammatory cytokines TNF-α and IL-6, inflammatory factors prostaglandin E2(PGE2), matrix metalloproteinase-2(MMP-2), and NO. Through these anti-inflammatory effects, MIA-induced pain reduction (recovery of clinical index, increase in weight bearing, and increase in area and width of the foot), recovery of meniscus damage, loss of cartilage tissue or inflammatory cells in tissue infiltration reduction, and recovery of the proteglycan layer were confirmed. Therefore, it is considered that Kalopanax pictus Nakai and Achyranthes japonica Nakai mixture has the potential as a functional raw material that promotes joint health.
XCLUMPY: X-Ray Spectral Model from Clumpy Torus and Its Application to the Circinus Galaxy

We construct an X-ray spectral model from the clumpy torus in an active galactic nucleus, designated as “XCLUMPY,” utilizing the Monte Carlo simulation for Astrophysics and Cosmology framework. The adopted geometry of the torus is the same as that in Nenkova et al. who assume a power-law distribution of clumps in the radial direction and a normal distribution in the elevation direction. We investigate the dependence of the X-ray continuum and Fe Kα fluorescence line profile on the torus parameters. Our model is compared with other torus models: MYTorus model, Ikeda model, and CTorus model. As an example, we also present the results applied to the broadband X-ray spectra of the Circinus galaxy observed with XMM-Newton, Suzaku, and NuSTAR. Our model can well reproduce the data, yielding a hydrogen column density along the equatorial plane cm−2, a torus angular width degree, and a 2–10 keV luminosity . These results are discussed in comparison with the observations in other wavelengths.
Retrospective Analysis of Biologic Agent Utilization in Severe Asthma: Impact on Exacerbation Rates, Forced Expiratory Volume in the First Second (FEV1), Eosinophils, and IgE Levels

Introduction Severe uncontrolled asthma is challenging to manage and impacts lung function and symptoms. Biologic agents targeting inflammatory pathways have transformed asthma management. This retrospective chart review aimed to assess biologic therapy in severe uncontrolled asthma patients and evaluate outcomes. Methods The study analyzed medical records of 30 patients receiving biologic therapy for severe asthma at a tertiary care center in Peshawar, Pakistan, from December 2022 to Jun 2023. Ethical approval was obtained, and patient demographics, biologic agent usage, and clinical parameters were collected. Clinical outcomes were evaluated after six months, including forced expiratory volume in the first second (FEV1), eosinophil count, IgE levels, and exacerbation rates. Results After six months, biologic treatment significantly improved FEV1 (48.7% to 62.4%), reduced eosinophils (540 cells/μL to 290 cells/μL) and IgE levels (410 IU/mL to 280 IU/mL), and decreased exacerbations (4.6 to 1.9). Subgroup analysis based on age and sex showed consistent lung function improvements. Conclusion Biologic agents effectively targeted inflammatory pathways, improving asthma control in severe uncontrolled asthma patients. This study provides valuable insights into biologic therapy for severe asthma, offering new possibilities for patient outcomes. Larger studies are needed to validate findings and optimize personalized treatment strategies.
Slang Words Used by Balinese Generation Z in Instagram Communication

This digital ethnography study identifies the slang words used by the Balinese Generation Z in Instagram Communication. Specifically, this study identifies the slang word types and origin words. The researchers followed Allan and Burridge's types of slang words to identify the slang words. The study data were collected from the comments of the Balinese Generation Z on Instagram. The researchers selected the study samples using a purposive sampling technique and applied content analysis to analyze the collected data. This study found that the slang word types used by the Balinese Generation Z were the acronym, fresh and creative, imitative, clipping, flippant, and mixing. Acronyms are the largest in number. Fresh and creative slang words are in the second position. The fresh and creative slang words found in this study are not new. Those words are coined from the existing words by flipping the letters or reading the letters of the words from back to front. Some slang words found in this study combined clipping and mixing. Even though the mixing is not in Allan and Burridge’s types of slang words, the researchers added it since some are made of two different languages. The origin languages of slang words used by the Balinese generation Z are Indonesian, Indonesian + English, Balinese, and Thai.
Dual frames for causal induction: the normative and the heuristic

ABSTRACT Causal induction in the real world often has to be quick and efficient as well as accurate. We propose that people use two different frames to achieve these goals. The A-frame consists of heuristic processes that presuppose rarity and can detect causally relevant factors quickly. The B-frame consists of analytic processes that can be highly accurate in detecting actual causes. Our dual frame theory implies that several factors affect whether people use the A-frame or the B-frame in causal induction: among these are symmetrical negation, intervention and commitment. This theory is tested and sustained in two experiments. The results also provide broad support for dual process accounts of human thinking in general.
Comments on “Revisiting the Relationship between Eyewall Contraction and Intensification”

AbstractThis comment presents some concerns with the study of Stern et al. and their misinterpretation of the contraction of the radius of the maximum wind (RMW) in tropical cyclones. It is shown that their geometrical RMW contraction model provides little dynamical understanding of the RMW contraction during tropical cyclone intensification, and it differs fundamentally from the RMW contraction model of Willoughby et al. that was derived from the directional derivative concept. Moreover, it is demonstrated that Stern et al. were mistaken in commenting on the derivation of the governing equation for the RMW contraction in Kieu.
Electric sail-based displaced orbits with a refined thrust model

This paper analyzes the performance of an electric solar wind sail for generating and maintaining a heliocentric circular displaced orbit. Previous research on this subject was based on a simplified mathematical model of the spacecraft thrust. However, recent studies have proposed a more accurate algorithm for evaluating both the modulus and the direction of the propulsive thrust as a function of some important parameters related to the spacecraft attitude. Therefore, a reappraisal of the problem is motivated by the need to revise past results, taking into account new information available on the propulsion system. Within this context, this paper focuses on circular displaced orbits that are characterized in terms of orbital period, heliocentric distance and elevation angle. The attitude configuration and the value of the spacecraft characteristic acceleration required for orbital maintenance are calculated. An in-depth analysis of the linear stability of displaced orbits is given. It is shown that displaced orbits are unstable when the elevation angle exceeds about 20°.