Miracle-dz/UniFAR-FTUs huggingface dataset:
THE ADOPTION OF LINKAJA MOBILE PAYMENT USING UTAUT MODEL

Mobile payment is one of the alternative innovative payment methods that trigger the payment revolution in Indonesia. LinkAja, as one of the mobile payments in Indonesia, still has low brand awareness compared to other mobile payments. This study aims to examine the adoption of LinkAja referring to the Unified Theory of Acceptance and Use of Technology (UTAUT) that analyse the effect of performance expectancy, effort expectancy, social influence
Modelling Performance Loss due to Thread Imbalance in Stochastic Variable-Length SIMT Workloads

When designing algorithms for single-instruction multiple-thread (SIMT) devices such as general purpose graphics processing units (GPGPUs), thread imbalance is an important performance consideration. Thread imbalance can emerge in iterative applications where workloads are of variable length, because threads processing larger amounts of work will cause threads with less work to idle. This form of thread imbalance influences the design space of algorithms-particularly in terms of processing granularity-but we lack models to quantify its impact on application performance. In this paper, we present a statistical model for quantifying the performance loss due to thread imbalance for iterative SIMT applications with stochastic, variable-length workloads. Our model is designed to operate with minimal knowledge of the implementation details of the algorithm, relying solely on an understanding of the probability distribution of the lengths of the workloads. We validate our model against a synthetic benchmark based on a Monte Carlo simulation of matrix exponentiation, and show that our model achieves nearly perfect accuracy. Compared to empirical data extracted from real hardware, our model maintains a high degree of accuracy, predicting mean performance loss within a margin of 2%.
Automated Tongue Image Analysis for autoimmune disease detection with supervised machine learning models

Tongue diagnosis is considered an effective and non-invasive technique commonly used to carry out secondary diagnosis at any time and anywhere. Automated supervised machine and deep learning models have become a disruptive technology that leverages computerized algorithms to dissect complex data. Among the many clinical applications of such models, diagnostic imaging has the most promise due to its cognizant detection and ability to quantify various diseases. However, with the existence of various models adopting diverse strategies for distinct diseases, the search for the best fitting supervised machine and deep learning model becomes a strenuous task. This paper focuses on diagnosing a prevalent autoimmune disease known as chronic gastritis. Contrary to the standard invasive methods of diagnosis, we perform automated tongue image analysis on a tongue image dataset using a set of well-established supervised machine learning models and a pre-trained deep neural network. The primary objective of this paper is to accurately distinguish and classify images of tongues infected with chronic gastritis from healthy ones using supervised machine learning models. The secondary objective of the paper is to perform a comparative study between the performance of the tested models, thus answering the research question: “Which supervised machine / deep learning model yields the best performance in diagnosing autoimmune diseases via tongue image analysis? ”. The resultant experimental values of the pre-trained ResNet-50 convolutional neural network revealed the highest average accuracy of 90% among the supervised machine learning models. The model produced a precision of 81.81%, recall of 90%, F1 score of 85.71%, and an AUC of 0.9. It yielded the best performance for solving our binary classification problem. KeywordsSupervised machine learning, Deep learning, Chronic gastritis, Autoimmune diseases, Tongue image analysis, Deep neural network, Convolutional neural network (CNN), Diagnostic imaging.
Molecular Characteristics and the Effect of KRAS Mutation on the Prognosis of Immunotherapy in Non-Small Cell Lung Cancer in Xinjiang, China

Purpose: Non-small cell lung cancer (NSCLC) is a malignant tumor with high mortality. The Kirsten rat sarcoma vial oncogene ( KRAS ) gene can affect patient prognosis. In this study, we aim to explore the impact of KRAS mutation status on the clinical prognosis of NSCLC immunotherapy. Patients and Methods: Collected tumor samples from the Affiliated Cancer Hospital of Xinjiang Medical University and 220 patients with stage III–IV NSCLC were included the study. All patients are on first- or second-line therapy and not on targeted therapy. Based on the molecular profiles and clinical features, we analysis of the effect of KRAS mutation on the treatment outcome of NSCLC. Results: In this study, the main mutant subtypes of KRAS were G12C, G12D, and G12V. In the KRAS mutation group, the highest mutation frequency other than KRAS was TP53, followed by STK11 and KMT2C. We found that among patients received immunotherapy, KRAS -mutant patients were more sensitive to immunotherapy, with an objective response rate (ORR) of 65% and a disease control rate (DCR) of 80%. Survival analysis found that patients with KRAS mutation had better prognosis with immunotherapy than the non- KRAS mutation patients by comparing the overall survival (OS) (median OS: 18.1 months vs 12.2 months, p=0.0032) and progression-free survival (PFS) (media PFS: 7.9 months vs 3.6 months, p=0.01). We found that the patients with KRAS mutation had better prognosis with immunotherapy than with chemotherapy (median OS: 18.1 months vs 12.3 months, p=0.039, PFS 7.9 months vs 4.1 months, p=0.001). Patients with the KRAS G12C mutation had better results with immunotherapy than chemotherapy, but there was no significant difference in outcome between the two groups (OS: p=0.26 PFS: p=0.055). KRAS and TP53 co-mutation and KRAS and KMT2C co-mutation may improve response to immunotherapy. Conclusion: Our results suggested that the gene mutation profile of NSCLC in KRAS mutation group and non- KRAS mutation group were different. The patients with KRAS mutation will have better prognosis with immunotherapy.
Predicting the Proficiency Level of Nonnative Hebrew Authors

We present classifiers that can accurately predict the proficiency level of nonnative Hebrew learners. This is important for practical (mainly educational) applications, but the endeavor also sheds light on the features that support the classification, thereby improving our understanding of learner language in general, and transfer effects from Arabic, French, and Russian on nonnative Hebrew in particular.
Results of the infrared radiometer experiment on Pioneers 10 and 11

The infrared radiometers on the Pioneer 10 and 11 spacecraft have mapped Jupiter in two broad spectral channels centered at wavelengths of 20 and 45 µm. Comparison of Pioneer 10 and 11 data and the results of in-flight and laboratory calibrations indicate an absolute accuracy of ± 8% in the measured intensities. Relative accuracy, for comparing different regions on the planet, is ± 2%. Within the precision of observations, the derived value of effective temperature is independent of assumptions about the Jovian emission spectrum. The global effective temperature is 125 ± 3°K, implying a value of 1.9 ± 0.2 for the ratio of planetary thermal emission to solar energy absorbed. The effective temperatures of belts are greater than those of zones by as much as 3°. 5 K. On a global scale, the poles and equator have the same effective temperature.
A Calibration-Free Evapotranspiration Mapping (CREMAP) Technique

Obtaining spatially distributed accurate evapotranspiration (ET) estimates is crucial in most water balance calculations for the identification of mass and energy fluxes across the area of interest. While routine Bowen-ratio or eddy-covariance measurements of sensible and latent heat fluxes are typically representative of a horizontal scale of a few hundred meters (i.e., the so-called plotor field-scale), many hydrologic models work at regional to continental to global scales. Rather than aggregating from the field scale, which may be difficult not only from a theoretical point of view but also from a practical one when long time-periods (years or decades) and large areas are considered, an ET-estimation technique that directly works at the regional scale, yet, at the same time, spatially distributed may be of high practical value. An ET estimation method had been proposed by Bouchet (1963) almost half a century ago, now called the complementary relationship (CR) of evaporation which was subsequently formulated for practical regional-scale applications by Brutsaert & Stricker (1979), and Morton et al. (1985). The CR states that under constant available energy at the surface (Qn) actual (E) and potential (Ep) evapotranspiration rates are complementary, their sum yielding twice the wet-environment (Ew) evapotranspiration rate, provided Ep is obtained by the Penman (1948) equation (Szilagyi & Jozsa, 2009a) and Ew by the Priestley-Taylor equation (Priestley & Taylor, 1972). In other words
Systematic features of axisymmetric neutrino-driven core-collapse supernova models in multiple progenitors

We present an overview of two-dimensional (2D) core-collapse supernova simulations employing neutrino transport scheme by the isotropic diffusion source approximation. We study 101 solar-metallicity, 247 ultra metal-poor, and 30 zero-metal progenitors covering zero-age main sequence mass from 10.8M⊙ to 75.0M⊙. Using the 378 progenitors in total, we systematically investigate how the differences in the structures of these multiple progenitors impact the hydrodynamics evolution. By following a long-term evolution over 1.0 s after bounce, most of the computed models exhibit neutrino-driven revival of the stalled bounce shock at ∼ 200 - 800 ms postbounce, leading to the possibility of explosion. Pushing the boundaries of expectations in previous one-dimensional (1D) studies, our results confirm that the compactness parameter ξ that characterizes the structure of the progenitors is also a key in 2D to diagnose the properties of neutrino-driven explosions. Models with high ξ undergo high ram pressure from the accreting matter onto the stalled shock, which affects the subsequent evolution of the shock expansion and the mass of the protoneutron star under the influence of neutrino-driven convection and the standing accretion-shock instability. We show that the accretion luminosity becomes higher for models with high ξ, which makes the growth rate of the diagnostic explosion energy higher and the synthesized nickel mass bigger. We find that these explosion characteristics tend to show a monotonic increase as a function of the compactness parameter ξ.
Understanding the How and the Why: Exploring Secure Development Practices through a Course Competition

This paper presents the results of in-depth study of 14 teams' development processes during a three-week undergraduate course organized around a secure coding competition. Contest participants were expected to first build code to a specification---emphasizing correctness, performance, and security---and then to find vulnerabilities in other teams' code while fixing discovered vulnerabilities in their own code. Our study aimed to understand why developers introduce different vulnerabilities, the ways they evaluate programs for vulnerabilities, and why different vulnerabilities are (not) found and (not) fixed. We used iterative open coding to systematically analyze contest data including code, commit messages, and team design documents. Our results point to the importance of existing best practices for secure development, the use of security tools, and development team organization.
Parameter Inference of Gravitational Waves using Inverse Autoregressive Spline Flow

In this research, we investigate the use of inverse autoregressive spline flows to infer the parameters of binary black hole systems from a set of simulated gravitational waves. A flow network is an invertible mapping from a simple probability distribution to a more complex one. If the simple distribution can be rapidly sampled and its density evaluated, then so can the complex one. The network is trained on an artificially created dataset, consisting of a set of the parameters (q,M, χ1, χ2) and signal, conditioned on a signal strain data of the IMRPhenomPv2 waveform model. The results show that the model’s predictions about the true parameters are highly accurate, and it’s sampling them at a very high speed, 100000 samples take about a second.
Generating Sex Trafficking Networks From Text Documents

Qualitative coding is a long and strenuous process that requires a well-skilled investigator. Natural language processing techniques have made leaps and bounds as far as usability and application domain, although it does not work for every task. In this work, we have created a natural language processing framework to help qualitative coders automatically obtain the nodes and node arcs from federal case files, dockets, and indictments within a sex trafficking network. The produced nodes and arcs allows us to perform network modeling by providing us with the information needed to create network structures that can then be used for interdiction simulation. The network models can also be analyzed for patterns, trends, and contrasts. Another goal for these networks is to apply Operations Research (OR) methods to better understand the operations of sex trafficking networks. Results fared better for the node extraction task, begging the question, does automation belong in the process of coding sex trafficking networks? If yes, then future implementations should avoid rule-based matching, despite the high structure of court documents. Additionally, more data would help improve accuracy of a model; however, obtaining ground truth data requires human coders. This thesis helps to address the question of how automated techniques, such as natural language processing and machine learning, can play a role in qualitative coding and thematic analysis. Further, by focusing on obtaining networks from text documents, it provides a basis for inputs into operations research models.
Inhibiting Surface Flashover in Vacuum with High Gradient Insulators

In high voltage systems, insulators may be used to separate conductors and these insulators are typically the limiting factor in the system’s operating voltage. When the voltage between two conductors is too large, the insulators can fail due to surface flashover. As systems become more compact, the threat of failure by insulator flashover increases, making the optimization of insulators a critical task for reliability. Insulators at a vacuum interface are especially vulnerable to surface flashover. Insulators have been shown to holdoff more voltage in vacuum after being baked to remove imbedded surface gases. Ceramic materials are particularly attractive because, unlike polymers, they can be baked at very high temperatures without damage. To further increase the holdoff voltage, ceramic was configured as a high gradient insulator. A high gradient insulator is a stack of alternating layers of insulating and conducting material which has been shown to increase the holdoff voltage, under certain conditions. In this work, two approaches to high gradient insulators are explored. Table of
The eccentricities of the barium stars

We investigate the eccentricities of barium (Ba ii) stars formed via a stellar wind accretion model. We carry out a series of Monte Carlo simulations using a rapid binary evolution algorithm, which incorporates full tidal evolution, mass loss and accretion, and nucleosynthesis and dredge-up on the thermally pulsing asymptotic giant branch. We follow the enhancement of barium in the envelope of the accreting main-sequence companion and dilution into its convective envelope once the star ascends the giant branch. The observed eccentricities of Ba ii stars are significantly smaller than those of an equivalent set of normal red giants but are nevertheless non-zero. We show that such a distribution of eccentricities is consistent with a wind accretion model for Ba ii star production with weak viscous tidal dissipation in the convective envelopes of giant stars. We successfully model the distribution of orbital periods and the number of observed Ba ii stars. The actual distribution of eccentricities is quite sensitive to the strength of the tides, so that we are able to confirm that this strength is close to, but less than, what is expected theoretically and found with alternative observational tests. Two systems – one very short-period but eccentric, and one long-period and highly eccentric – still lie outside the envelope of our models, and so require a more exotic formation mechanism. All our models, even those which were a good fit to the observed distributions, overproduced the number of high-period barium stars, a problem that could not be solved by some combination of the three parameters: tidal strength, tidal enhancement and wind accretion efficiency.
High-dimensional Single-cell Analysis Delineates Peripheral Immune Signature of Coronary Atherosclerosis in Human Blood

Rationale: Pathogenesis of human coronary atherosclerosis is tightly associated with the imbalance of inflammation and resolution in the local immune microenvironment of AS plaques. However, how the peripheral immune system dynamically changes along with disease progression in humans remains unclear. As a result, the minimally-invasive clinical biomarkers that can sensitively distinguish different stages of human coronary atherosclerosis are still lacking. Methods: We performed single-cell Cytometry by Time-Of-Flight (CyTOF) analyses to comprehensively profile the compositions and phenotypes of CD45+ cells derived from 83 human peripheral blood samples with two independent antibody-staining panels (T cell panel and myeloid cell panel). Clinical associations between the frequencies of peripheral immune cell subsets with AS plaque burdens of coronary arteries (Gensini score) and serum lipids were also examined. By integrating immune and clinical features, we established novel CVD risk prediction models to stratify patients in different disease stages. Results: We revealed the disease stage-associated peripheral immune features for patients with coronary atherosclerosis (CAS) and atherosclerotic cardiovascular disease (ASCVD), and also identified the specific peripheral immune cell subsets that were tightly associated with the disease severity of coronary arteries (Gensini score). By integrating these peripheral immune signatures with clinical features, we have established a disease progression prediction (DPP) model that could precisely discriminate CAS patients from ASCVD patients with high prediction accuracy (ROC-AUC = 0.88). Conclusion: The progression of coronary atherosclerosis is accompanied by significant alterations of the peripheral immune system, including the changes in the distributions as well as phenotypic functions of specific immune cell subsets. The indicated stage-specific peripheral immune signatures thus become promising minimally-invasive liquid biomarkers that could help to potentially diagnose and monitor the CVD progression in humans.
Cumulative lifetime acute stressor exposure interacts with reward responsiveness to predict longitudinal increases in depression severity in adolescence

Abstract Background Life stress and blunted reward processing each have been associated with the onset and maintenance of major depressive disorder. However, much of this work has been cross-sectional, conducted in separate lines of inquiry, and focused on recent life stressor exposure, despite the fact that theories of depression posit that stressors can have cumulative effects over the lifespan. To address these limitations, we investigated whether acute and chronic stressors occurring over the lifespan interacted with blunted reward processing to predict increases in depression over time in healthy youth. Method Participants were 245 adolescent girls aged 8–14 years old (Mage = 12.4, s.d. = 1.8) who were evaluated at baseline and two years later. The reward positivity (RewP), an event-related potential measure of reward responsiveness, was assessed at baseline using the doors task. Cumulative lifetime exposure to acute and chronic stressors was assessed two years later using the Stress and Adversity Inventory for Adolescents (Adolescent STRAIN). Finally, depressive symptoms were assessed at both baseline and follow-up using the Children's Depression Inventory. Results As hypothesized, greater lifetime acute stressor exposure predicted increases in depressive symptoms over two years, but only for youth exhibiting a blunted RewP. This interaction, however, was not found for chronic stressors. Conclusions Lifetime acute stressor exposure may be particularly depressogenic for youth exhibiting a blunted RewP. Conversely, a robust RewP may be protective in the presence of greater acute lifetime stressor exposure.
Effects of Mineral Oils, Palizin and Buprofezin on Functional and Numerical Responses of Predatory Ladybeetle Cryptolaemus montrouzieri (Mulsant, 1850)

Predatory coccinellid, Cryptolaemus montrouzieri Mulsant is an important biological agent in citrus orchards. The effect of six treatments included: two types of mineral oils, EC and mayonnaise at the concentration of 15 ml/L, buprofezin 0.75 ml/L, buprofezin+EC oil 0.5+5 ml/L, insecticidal soap palizin 2.5 ml/L, and water as the control were investigated on the functional and numerical responses of C. montrouzieri Mulsant under laboratory conditions. The experiment investigated predation rate of female adult coccinellids to varying densities (2, 4, 8, 16, 32 and 64) of mealybug pray, Pseudococcus citri Risso. The relationship between the rate of consumption and pray density was explored with a logistic regression. The results of logistic regressions revealed that the rate of prey consumption by the predator had risen as prey density increased, but eventually leveled off in both the control and all insecticide treatments (following Holling’s type II functional response). Comparison of attack rates showed that the parameter significantly decreased when the predator was exposed to all insecticides but palizin. Predator handling times for oil mayonnaise, buprofezin and buprofezin+ EC oil treatments were significantly longer than that of the control. The longest handling time (1.6065 h) and the lowest attack rate (0.0351 h_1) were estimated for buprofezin+oil and oil EC treatments, respectively. As prey density increased, increase in oviposition and decrease in the food exploitation efficiency were observed. The results confirmed that the functional response of C. montrouzieri was affected by tested compounds reducing the predatory potential. The exception was palizin that had a low effect on functional response events of treated C. montrouzieri.
Pro-categories in homotopy theory

The goal of this paper is to prove an equivalence between the model categorical approach to pro-categories, as studied by Isaksen, Schlank and the first author, and the $\infty$-categorical approach, as developed by Lurie. Three applications of our main result are described. In the first application we use (a dual version of) our main result to give sufficient conditions on an $\omega$-combinatorial model category, which insure that its underlying $\infty$-category is $\omega$-presentable. In the second application we consider the pro-category of simplicial etale sheaves and use it to show that the topological realization of any Grothendieck topos coincides with the shape of the hyper-completion of the associated $\infty$-topos. In the third application we show that several model categories arising in profinite homotopy theory are indeed models for the $\infty$-category of profinite spaces. As a byproduct we obtain new Quillen equivalences between these models, and also obtain an example which settles negatively a question raised by Raptis.