Interaction tests with covariate-adaptive randomization

Treatment-covariate interaction tests are commonly applied by researchers to examine whether the treatment effect varies across patient subgroups defined by baseline characteristics. The objective of this study is to explore treatment-covariate interaction tests involving covariate-adaptive randomization. Without assuming a parametric data generating model, we investigate usual interaction tests and observe that they tend to be conservative: specifically, their limiting rejection probabilities under the null hypothesis do not exceed the nominal level and are typically strictly lower than it. To address this problem, we propose modifications to the usual tests to obtain corresponding valid tests. Moreover, we introduce a novel class of stratified-adjusted interaction tests that are simple, more powerful than the usual and modified tests, and broadly applicable to most covariate-adaptive randomization methods. The results are general to encompass two types of interaction tests: one involving stratification covariates and the other involving additional covariates that are not used for randomization. Our study clarifies the application of interaction tests in clinical trials and offers valuable tools for revealing treatment heterogeneity, crucial for advancing personalized medicine

An approach to fault diagnosis for gearbox based on reconstructed energy and support vector machine

Normally sensors can only be mounted on the outer shell of gearbox, which induce more difficulties to diagnose gearbox such as serious noise contamination, signal coupling and transmission path effect. Taking into account the unique structural characteristics of gearbox, this paper presents a novel method of using reconstructed energy and Support Vector Machine (SVM) to diagnose various failure or fault modes of gears, shafts and bearings. First, FFT is performed to get the frequency domain information of raw vibration signals. Then, a series of reconstruction filters are designed to remove unwanted information and enhance signal components of interest, which correspond to specific fault information of various elements. Finally, SVM is utilized to classify different faults such as bent shaft, broken gear and defect bearing. The proposed approach has proved to be effective in solving gearbox faults classification of the 2009 PHM Conference Data Analysis Competition

Computer Forensics Model Based on Evidence Ring and Evidence Chain

AbstractIn recent years, with the development of technology, judicial practice involving electronic crime is frequent. To combat this crime, computer forensics bears the irreplaceable role. This is a combination science of law and computer, but there is a “mismatch” phenomenon exists on the research on computer forensics currently, most of them only study the technical aspects of computer or electronic evidence related to legal issues, the two studies combined less. To solve this problem, in this paper, evidence of the general attributes: objectivity, relevance, legitimacy as a criterion to build a computer forensics model based on ring and chain of evidence. In this model, forensic evidence of links forms a ring, in accordance with the forensic to form chain of evidence. In order to ensure the objectivity, legitimacy of evidence, in building a chain of evidence and evidence ring as well as a supervisory chain in supervision, the final forms a electronic evidence forensics system

Flexible and efficient spatial extremes emulation via variational autoencoders

Many real-world processes have complex tail dependence structures that cannot be characterized using classical Gaussian processes. More flexible spatial extremes models exhibit appealing extremal dependence properties but are often exceedingly prohibitive to fit and simulate from in high dimensions. In this paper, we develop a new spatial extremes model that has flexible and non-stationary dependence properties, and we integrate it in the encoding-decoding structure of a variational autoencoder (XVAE), whose parameters are estimated via variational Bayes combined with deep learning. The XVAE can be used as a spatio-temporal emulator that characterizes the distribution of potential mechanistic model output states and produces outputs that have the same statistical properties as the inputs, especially in the tail. As an aside, our approach also provides a novel way of making fast inference with complex extreme-value processes. Through extensive simulation studies, we show that our XVAE is substantially more time-efficient than traditional Bayesian inference while also outperforming many spatial extremes models with a stationary dependence structure. To further demonstrate the computational power of the XVAE, we analyze a high-resolution satellite-derived dataset of sea surface temperature in the Red Sea, which includes 30 years of daily measurements at 16703 grid cells. We find that the extremal dependence strength is weaker in the interior of Red Sea and it has decreased slightly over time.Comment: 30 pages, 8 figure

Effects of turbulence-chemistry interactions on auto-ignition and flame structure for n-dodecane spray combustion

The Engine Combustion Network (ECN) spray A under diesel engine conditions is investigated with a non-adiabatic 5D Flamelet Generated Manifolds (FGM) model with the consideration of detailed chemical kinetic mechanisms. The enthalpy deficit due to droplet vapourisation is considered by employing an additional controlling parameter in the FGM library. In this FGM model, ß-PDF is used for the PDF integration over the control variable space. Validation results in non-reacting conditions indicate relatively good agreement between the predicted and experimental data in terms of liquid and vapour penetrations and mixture fraction spatial distribution. In reacting conditions, the effects of variance of mixture fraction and progress variable were examined. The ignition delay time and the quasi-steady flame structure are both affected by the variances. The variance of mixture fraction delays the ignition process and the variance of progress variable accelerates it. For mixture fraction, the ignition process is quicker at any stage in the case of neglecting variance. While things are more complex for progress variable, the ignition process is advanced in the case of neglecting variance at early times, but surpassed by the case of ß-PDF later and until auto-ignition. When variance of mixture fraction is considered, the OH mass fraction shows a wide spatial distribution. While if not, a very thin flame is observed with a higher peak in OH, and a very large lift-off length. The variance of progress variable has little impact on the global flame structure, but makes the flame lift-off length much shorter. This study confirms the general observation, that the variance of mixture fraction is of higher importance in high temperature non-premixed combustion, however, we found that the variance of progress variable is far from negligible.This work was supported by Major Research Plan of the National Natural Sci-ence Foundation of China (No. 91541205); National Natural Science Foundation of China [grant numbers 51876140]; the project of National Key R&D Program of China (2017YFE0102800); This project has also received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sk lodowska-Curie grant agreement No. 713673. Ambrus Both has received financial support through the ”la Caixa” INPhINIT Fellowship Grant for Doctoral studies at Spanish Research Centres of Excellence, ”la Caixa” Banking Foundation, Barcelona, Spain.Peer ReviewedPostprint (author's final draft

Personalized Estimate of Chemotherapy-Induced Nausea and Vomiting: Development and External Validation of a Nomogram in Cancer Patients Receiving Highly/Moderately Emetogenic Chemotherapy.

Chemotherapy-induced nausea and vomiting (CINV) is presented in over 30% of cancer patients receiving highly/moderately emetogenic chemotherapy (HEC/MEC). The currently recommended antiemetic therapy is merely based on the emetogenic level of chemotherapy, regardless of patient's individual risk factors. It is, therefore, critical to develop an approach for personalized management of CINV in the era of precision medicine.A number of variables were involved in the development of CINV. In the present study, we pooled the data from 2 multi-institutional investigations of CINV due to HEC/MEC treatment in Asian countries. Demographic and clinical variables of 881 patients were prospectively collected as defined previously, and 862 of them had full documentation of variables of interest. The data of 548 patients from Chinese institutions were used to identify variables associated with CINV using multivariate logistic regression model, and then construct a personalized prediction model of nomogram; while the remaining 314 patients out of China (Singapore, South Korea, and Taiwan) entered the external validation set. C-index was used to measure the discrimination ability of the model.The predictors in the final model included sex, age, alcohol consumption, history of vomiting pregnancy, history of motion sickness, body surface area, emetogenicity of chemotherapy, and antiemetic regimens. The C-index was 0.67 (95% CI, 0.62-0.72) for the training set and 0.65 (95% CI, 0.58-0.72) for the validation set. The C-index was higher than that of any single predictor, including the emetogenic level of chemotherapy according to current antiemetic guidelines. Calibration curves showed good agreement between prediction and actual occurrence of CINV.This easy-to-use prediction model was based on chemotherapeutic regimens as well as patient's individual risk factors. The prediction accuracy of CINV occurrence in this nomogram was well validated by an independent data set. It could facilitate the assessment of individual risk, and thus improve the personalized management of CINV

Experimental Verification of Solid-like and Fluid-like States in the Homogeneous Fluidization Regime of Geldart A Particles

The mechanisms underlying homogeneous fluidization of Geldart A particles have been debated for decades. Some ascribed the stability to interparticle forces, while others insisted a purely hydrodynamic explanation. Valverde et al. (2001) fluidized 8.53-μm (i.e., Geldart C) particles by the addition of fumed silica nanoparticles and found that even during homogeneous fluidization both solid-like and fluid-like behavior can be distinguished. However, it is still unclear whether both states exist for true Geldart A particles. In this paper, the particulate fluidization characteristics of three typical Geldart A powders were studied by camera recording, electrical capacitance tomography, and pressure fluctuation. For the first time, the existence of both a solid-like state dominated by interparticle forces and a fluid-like state dominated by fluid dynamics during homogeneous expansion of Geldart A particles was experimentally verified. Furthermore, the ability and performance of the used measurement techniques to identify different flow regimes were compared.</p

Optimization of liquefaction process based on global meta-analysis and machine learning approach: Effect of process conditions and raw material selection on remaining ratio and bioavailability of heavy metals in biochar

Although liquefaction technology has been extensively applied, plenty of biomass remains tainted with heavy metals (HMs). A meta-analysis of literature published from 2010 to 2023 was conducted to investigate the effects of liquefaction conditions and biomass characteristics on the remaining ratio and chemical speciation of HMs in biochar, aiming to achieve harmless treatment of biomass contaminated with HMs. The results showed that a liquefaction time of 1–3 h led to the largest HMs remaining ratio in biochar, with the mean ranging from 84.09% to 92.76%, compared with liquefaction times of less than 1 h and more than 3 h. Organic and acidic solvents liquefied biochar exhibited the greatest and lowest HMs remaining ratio. The effect of liquefaction temperature on HMs remaining ratio was not significant. The C, H, O, volatile matter, and fixed carbon contents of biomass were negatively correlated with the HMs remaining ratio, and N, S, and ash were positively correlated. In addition, liquefaction significantly transformed the HMs in biochar from bioavailable fractions (F1 and F2) to stable fractions (F3) (P < 0.05) when the temperature was increased to 280–330 ℃, with a liquefaction time of 1–3 h, and organic solvent as the liquefaction solvent. N and ash in biomass were positively correlated with the residue state (F4) of HMs in biochar and negatively correlated with F1 or F2, while H, O, fixed carbon, and volatile matter were negatively correlated with F4 but positively correlated with F3. Machine learning results showed that the contribution of biomass characteristics to HMs remaining ratio was higher than that of liquefaction factor. The most prominent contribution to the chemical speciation changes of HMs was the characteristics of HMs themselves, followed by ash content in biomass, liquefaction time, and C content. The findings of this meta-analysis contribute to factor selection, modification, and application of liquefied biomass to reducing risks