Miracle-dz/UniFAR-FTUs huggingface dataset:
A Bayesian approach to mixture cure models with spatial frailties for population‐based cancer relative survival data

As the treatments of cancer progress, a certain number of cancers are curable if diagnosed early. In population‐based cancer survival studies, cure is said to occur when mortality rate of the cancer patients returns to the same level as that expected for the general cancer‐free population. The estimates of cure fraction are of interest to both cancer patients and health policy makers. Mixture cure models have been widely used because the model is easy to interpret by separating the patients into two distinct groups. Usually parametric models are assumed for the latent distribution for the uncured patients. The estimation of cure fraction from the mixture cure model may be sensitive to misspecification of latent distribution. We propose a Bayesian approach to mixture cure model for population‐based cancer survival data, which can be extended to county‐level cancer survival data. Instead of modeling the latent distribution by a fixed parametric distribution, we use a finite mixture of the union of the lognormal, loglogistic, and Weibull distributions. The parameters are estimated using the Markov chain Monte Carlo method. Simulation study shows that the Bayesian method using a finite mixture latent distribution provides robust inference of parameter estimates. The proposed Bayesian method is applied to relative survival data for colon cancer patients from the Surveillance, Epidemiology, and End Results (SEER) Program to estimate the cure fractions. The Canadian Journal of Statistics 40: 40–54; 2012 © 2012 Statistical Society of Canada
The Association between Threatened Abortion and the Risk of Autism Spectrum Disorders among Children: A Meta-Analysis

Background The current study is aimed at updating the observational studies on the relationship between threatened abortion and the risk of ASD. Methods The search keywords were covered in three electronic databases PubMed, Web of Science, and Scopus up to April 2022. The modified Newcastle–Ottawa scale (NOS) was applied to detect the quality of epidemiological studies. We used the chi-square test and the I2 statistic to show the heterogeneity among articles. I2 more than 50% was considered high heterogeneity. Egger's and Begg's line regression tests were used for evaluating the publication bias. The random-effects model was applied for the analysis of the findings. The Stata 13.0 software package was applied for analysis and indicated p value less than 0.05 as a significant level. Results The pooled analysis reported significant differences between threatened abortion and the risk of ASD in adjusted studies (OR = 1.93; 95% CI: 1.12, 2.73; I2 = 59.5.0%) and in crude studies (OR = 2.17; 95% CI: 1.46, 2.88; I2 = 39.5%). The evidence of publication bias was not found. Conclusions The findings suggest that threatened abortion is a risk factor for ASD. As a result, screening tools to detect are necessary in mothers facing a threatened abortion.
The Mechanism of Aerobic Exercise Regulating the PI3K/Akt-mTOR Signaling Pathway Intervenes in Hippocampal Neuronal Apoptosis in Vascular Dementia Rats

Background: The purpose of this paper is to explore the mechanism of aerobic exercise regulating autophagy through the PI3K/Akt-mTOR signaling pathway and its participation in apoptosis, to protect the hippocampal nerves from damage in vascular dementia rats. Methods: Thirty-six healthy male SD rats were randomly divided into a sham group, a model group, and a model exercise group. A neurobehavioral assessment was used to determine the memory and exploration abilities of the rats. A TUNEL assay was used to detect hippocampal neuron apoptosis. Immunohistochemical and Western blot analyses were used to analyze LC3Ⅱ and the beclin-1 protein. An RT-PCR detected the differential expression of mRNA. Results: The results of the neurobehavioral tests showed that the platform latency time of the rats with vascular dementia was prolonged. Aerobic exercise significantly shortens the swimming time of rats in platform latency. The TUNEL results showed that the TUNEL-positive cells of the hippocampal neurons in the model group increased; the expression of pro-apoptotic genes caspase-3 and Bax mRNA was up-regulated, and the expression of Bcl-2 mRNA was down-regulated. Aerobic exercise reduced hippocampal neuronal apoptosis, up-regulated Bcl-2 mRNA, and down-regulated caspase-3 and Bax mRNA. The LC3Ⅱ and Beclin-1 proteins, detected by immunohistochemistry and a Western blot analysis, showed that the protein expression in the hippocampi of rats with vascular dementia increased. Aerobic exercise reduced LC3Ⅱ and Beclin-1 protein expression. The results of the RT-PCR showed similar changes. Conclusions: Aerobic exercise could improve the learning and memory abilities of vascular dementia rats, moderately regulate the process of autophagy, reduce the TUNEL-positive cells of hippocampal neurons, repair damaged hippocampal neurons by regulating the autophagy signaling pathway PI3K/Akt-mTOR, and improve hippocampal function.
Algorithms for efficient and accurate edge detection and localization

The detection of edges in digital imagery based on localizing the zero crossings of filtered image data has been investigated. Truncated time or frequency sampled forms (TSF/FSF) of the Laplacian of Gaussian (LOG) filter are employed in the transform domain. Samples of the image are transformed using the discrete symmetric cosine transform (DSCT) prior to adaptive filtering and the isolation of zero crossings. The DSCT facilitates both control of the edge localization accuracy as well as modular implementation. The adaptive strategy for accepting/rejecting edge transitions at the appropriate locations in the image is based on estimates of the local gradient. This paper evaluates block-based filtering procedures to identify edges in terms of achievable edge localization, signal-to-noise ratio (SNR) around the edge, and computational benefits.
Schottky barrier between 6H-SiC and graphite: Implications for metal/SiC contact formation

Using photoelectron spectroscopy we have determined the Schottky barrier between 6H-SiC(0001) and graphite layers grown by solid state graphitization. For n-type 6H-SiC(0001) we find a low Schottky barrier of ϕbn=0.3±0.1eV. For p-type SiC(0001) a rather large value of ϕbp=2.7±0.1eV was determined. It is proposed that these extreme values are likely to have an impact on the electrical behavior of metal/SiC contacts subjected to postdeposition anneals.
A General Approach to Compute Standard Risk Measures

This article focuses on the computation of VaR and CTE. It provides a very accurate and fast method, based on Fourier analysis and following Boyarchenko and Levendorskii (2000). Once the characteristic function of a marginal law is known, the computation of VaR or CTE is performed using a Fast Fourier Transform. We use different Levy processes, namely a Kou process and a CGMY process which are representative of processes with finite and infinite activity respectively. We also consider a specific regime switching model. We show the method accuracy and rapidity. The results are obtained instantaneously as if we had a closed form solution. We give as a synthesis of this method and as an illustration, the computations of VaR and CTE for a simplified variable annuities contract
Implementation and Partial Provability

We extend implementation theory by allowing the social choice function to depend on more than just the pro le of preferences of the agents and by allowing agents to support their statements with hard evidence. We show that a simple condition on the evidence structure which is necessary for the implementation of a social choice function f when the preferences of the agents are state independent is also sufficient for implementation for any preferences (including state dependent) if the social planner can perform small monetary transfers and there are at least three players. If transfers can be large, f can be implemented in a game with perfect information when there are at least two players under an additional boundedness assumption. In both cases, transfers only occur off the equilibrium path. Finally, in the special but important case of allocation problems, under weak conditions, f can be implemented in a perfect information game with at least two players and no transfers. In all cases, the use of evidence enables implementation which is robust in the sense that the social planner needs very little information about the preferences, beliefs, and evidence of the agents and the agents need little information about each others' preferences.
Compensating for pupil rotation in the W.M. Keck Observatory adaptive optics system

The non-circular shape of the Keck telescopes primary mirror, combined with the fact that the telescope is on an alt/az mount with the adaptive optics system on the left Nasmyth platform, means that as the telescope tracks a star field the hexagonal pupil image rotates on the wavefront sensor camera and on the deformable mirror. This creates a need for a dynamic control strategy. The control matrix, the list of which subapertures are sufficiently illuminated and the list of which actuators are active and which are slaved, all must change on the fly, with the loops closed and without perturbing a science observation in progress. This control strategy must also be robust in the presence of modest pupil translation. These needs are met in the Keck AO system by the Pupil Rotation Compensation (PRC) software tool. The PRC software is responsible for maintaining system parameters that are functions of the pupil position and orientation. This paper will present the details of the PRC software design including algorithms used for selecting active subapertures, dealing with variable subaperture illumination, calculating control matrices, and slaving actuators outside the pupil illuminated area on the deformable mirror.
Environmental policies in a Stackelberg differential game

We develop a Stackelberg differential game to analyze the economic effects of the reduction plan through two policy instruments, tradable permits and taxes on emissions. Emissions are a by‐product of firm output. The authority acts as a Stackelberg leader, able to set the optimal instrument's level in the light of a finite‐horizon environmental target. We show that the optimal solution of the game is dynamically consistent. Moreover, optimal environmental policies substantially impact the level and composition of economic activity. The differentiation between “clean” and “dirty” firms allows us to assess distributional effects and how environmental technology may influence the game's outcome. Results are shown to be robust under different parameterizations.
Adaptive control of flexible joint manipulators

A reduced-order flexible joint model that based on a singular perturbation formulation of the manipulator equations of motion is constructed. The concept of an integral manifold is utilized to construct the dynamics of a slow subsystem. A fast subsystem is constructed to represent the fast dynamics of the elastic forces at the joints. A composite control scheme featuring online identification of the manipulator parameters is developed. It takes into account the effect of certain unmodeled dynamics and parameter variations. Stability analysis of the resulting closed-loop full-order system is presented. Numerical simulation results for a single-link flexible-joint manipulator are given to illustrate the applicability of the proposed algorithm. <<ETX>>
Gen-Z Muslims’ purchase intention of halal food: Evidence from Indonesia

Halal food production is a crucial sector for Muslims, especially Generation Z, who spend time eating in stalls and restaurants instead of eating homemade food. This tendency is higher than in the previous generation. Thus, this study aims to estimate the factors influencing the purchase intention of halal food for Generation Z Muslims in Indonesia. This quantitative study uses a questionnaire distributed randomly online to 352 respondents. Data analysis and hypotheses were examined using the structural equation model (SEM) approach. The results show that religious belief, exposure, and health reasons are essential in increasing halal awareness. Meanwhile, halal awareness and knowledge positively contributed to increasing interest in buying halal food. On the other hand, Islamic brands do not affect the buying interest of Generation Z Muslims in halal products. This shows that Islamic brands in the food sector have no impact on Generation Z Muslims in a Muslim-majority country like Indonesia. This also confirms previous studies conducted in Muslim-majority countries such as Saudi Arabia that Islamic brands do not increase interest in buying halal food. This is because they are used to buying food without seeing the Islamic brand as long as it is halal. However, this result may be different if Muslims live in a non-Islamic country since Islamic branding can provide a comfortable feeling for Muslims in a non-Islamic country.
Testbed for ROADM and WXC based metro WDM networks

A testbed for metro wavelength division multiplexing (WDM) network is realized and tested. The testbed contains a reconfigurable optical add/drop multiplexer (ROADM) node, a 2x2 wavelength cross-connect (WXC) node, and two interconnected two-fiber bidirectional path protected switching ring networks (TF-BPSR). Both the ROADM and WXC node are bidirectional nodes, so they can select channels from the working and the protection ring networks simultaneously, and they support both protected and unprotected services. The ROADM node uses a flexible band tunable filter (FBTF) to drop a waveband from the input WDM signals and send the express channels directly to the output port. As a result, the physical impairment accumulated on the express channels can be minimized. It also has a modular structure, so additional modules can be cascaded to expand the capacity and functionality of the node without any interruption to current services. The WXC node is realized with interconnected ROADM modules that are comprised of wavelength selective switches (WSSes). Arbitrary wavelength or wavelength sets can be either dropped in the node or cross-connected in a non-blocking manner. Multiple services, such as OC-48 and OC-192 SONET signals, gigabit Ethernet streams carrying interactive movie signals, and live video broadcasting services, are carried in the network, dropped in the ROADM and WXC node, and switched between the two ring networks. The testbed is controlled by a websever based network management system that facilitates remote control and monitoring. Experiments demonstrate that the performance of the nodes and the testbed meets the requirement of the services.
Cichorium intybus L. Hairy Roots as a Platform for Antimicrobial Activity

Industrial chicory is an important crop for its high dietary fibre content. Besides inulin, chicory taproots contain interesting secondary metabolite compounds, which possess bioactive properties. Hairy roots are differentiated plant cell cultures that have shown to be feasible biotechnological hosts for the production of several plant-derived molecules. In this study, hairy roots of industrial chicory cultivars were established, and their potential as a source of antimicrobial ingredients was assessed. It was shown that hot water extracts of hairy roots possessed antimicrobial activity against relevant human microbes, whereas corresponding chicory taproots did not show activity. Remarkably, a significant antimicrobial activity of hot water extracts of chicory hairy roots towards methicillin-resistant Staphylococcus aureus was observed, indicating a high potential of hairy roots as a host for production of antimicrobial agents.
Interpolative relations and interpolative preference structures

Relations are very important mathematical objects in different fields of theory and applications. In many real applications, for which gradation of relations is immanent, the classical relations are not adequate. Interpolative relations (I-relations) (as fuzzy relations) are the generalization of classical relations so that the value (intensity) of a relation is an element from a real interval [0, 1] and not only from {0, 1} as in the classical case. The theory of I-relations is crucially different from the theory of fuzzy relations. I-relations are consistent generalizations of classical relations and, contrary to fuzzy relations, all laws of classical relations (set-theoretical laws) are preserved in general case. In this paper, the main characteristics of I-relations are illustrated on the interpolative preference structures (I-preference structures) as consistent generalization of classical preference structures.
Potential Molecular Targeted Therapy for Unresectable Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most prevalent and lethal cancers, representing a serious worldwide health concern. The recurrence incidence of hepatocellular carcinoma (HCC) following surgery or ablation is as high as 70%. Thus, the clinical applicability of standard surgery and other locoregional therapy to improve the outcomes of advanced HCC is restricted and far from ideal. The registered trials did not identify a treatment that prolonged recurrence-free survival, the primary outcome of the majority of research. Several investigator-initiated trials have demonstrated that various treatments extend patients’ recurrence-free or overall survival after curative therapies. In the past decade, targeted therapy has made significant strides in the treatment of advanced HCC. These targeted medicines produce antitumour effects via specific signals, such as anti-angiogenesis or advancement of the cell cycle. As a typical systemic treatment option, it significantly improves the prognosis of this fatal disease. In addition, the combination of targeted therapy with an immune checkpoint inhibitor is redefining the paradigm of advanced HCC treatment. In this review, we focused on the role of approved targeted medicines and potential therapeutic targets in unresectable HCC.
Improving remote sensing of extreme events with machine learning: land surface temperature retrievals from IASI observations

Retrieving weather extremes from observations is critical for weather forecasting and climate impact studies. Statistical and machine learning methods are increasingly popular in the remote sensing community. However, these models act as regression tools when dealing with regression problems and as such, they are not always well-suited for the estimation of the extreme weather states. This study firstly introduces two error types that arise from such statistical methods: (a) ‘dampening’ refers to the reduction of the range of variability in the retrieved values, a natural behavior for regression models; (b) ‘inflating’ is the opposite effect (i.e. larger ranges) due to data pooling. We then introduce the concept of localization that intends to better take into account local conditions in the statistical model. Localization largely improves the retrievals of extreme states, and can be used both for retrieval at the pixel level or in image processing techniques. This approach is tested on the retrieval of land surface temperature using infrared atmospheric sounding interferometer observations: the dampening is reduced from 1.9 K to 1.6 K, and the inflating from 1.1 K to 0.5 K, respectively.
Intelligent Demand Side Management for Exhaustive Techno-Economic Analysis of Microgrid System

In a typical microgrid (MG) structure, the requisite of load varies from hour to hour. On the basis of the rise and fall of the load demand curve, the power system utilities fix the rate of electric power at different times of the day. This process is known as time-of-usage (TOU)-based pricing of electricity. The hourly basis load demand can be categorized into elastic hourly load demand and inelastic hourly load demand. For the duration of the peak hours, when the utility charges more, the elastic loads are shifted to low demand hours by the demand side management (DSM) to save the cost. This rebuilds the total demand model on the pillars of demand price elasticity. Keeping in view the fact that the total load in an hour in an MG structure consists of 10% to 40% of elastic loads, the paper proposes an intelligence-technique-based DSM to achieve reduction in the overall cost of using loads in an MG structure. Seven different cases are studied which cover diverse grid participation and electricity market pricing strategies, including DSM programs. The results obtained for all the MGs showcase the applicability and appropriateness of using the proposed DSM strategy in terms of cost savings.
Thermo-optical resonance locking of an optically trapped salt-water microdroplet

We demonstrate that it is possible to lock the radius of an optically trapped salt-water microdroplet to the n=1 whispering gallery resonances (WGRs) at the trapping laser wavelength. The optical properties of the droplet are determined using stimulated Raman scattering. The droplet is in thermodynamic equilibrium with the surrounding vapour and the proposed locking mechanism consists of a balance between bulk heating and WGR heating. Raman measurements allow the size parameter (nka) of the droplet to be determined with a precision of ∼10−5 and the resonance linewidth to be estimated.
Convergence of the Monte Carlo expectation maximization for curved exponential families

The Monte Carlo expectation maximization (MCEM) algorithm is a versatile tool for inference in incomplete data models, especially when used in combination with Markov chain Monte Carlo simulation methods. In this contribution, the almost-sure convergence of the MCEM algorithm is established. It is shown, using uniform versions of ergodic theorems for Markov chains, that MCEM converges under weak conditions on the simulation kernel. Practical illustrations are presented, using a hybrid random walk Metropolis Hastings sampler and an independence sampler. The rate of convergence is studied, showing the impact of the simulation schedule on the fluctuation of the parameter estimate at the convergence. A novel averaging procedure is then proposed to reduce the simulation variance and increase the rate of convergence.
Learning Policies for Automated Racing Using Vehicle Model Gradients

Safe autonomous driving approaches should be capable of quickly and efficiently learning as professional drivers do, while also using all of the available road-tire friction for safety. Inspired by how skilled drivers learn, we demonstrate improvement from an initial optimization-generated racing trajectory using model-based reinforcement learning. By using a simple physics-based dynamics model and gradients of the performance objective, we show that a full-scale automated race car is capable of improving lap time in experiments on high- and low-friction race tracks. Using recorded vehicle data, this approach improves a twenty nine second lap time by almost two full seconds. Beyond improving upon the initial optimization-based solution, it uses only two laps worth of ice track data where conditions can constantly change from lap-to-lap. These results suggest that by combining an approximate model with simple learning techniques, significant improvement to automated racing strategies is possible.