Miracle-dz/UniFAR-FTUs huggingface dataset:
An Exponential Decay Model for the Deterministic Correlations in Axial Compressors

The unsteady blade row interaction (UBRI) is inherent and usually has a large effect on performance in multistage axial compressors. The effect could be considered by using the average-passage equation system (APES) in steady-state environment by introducing the deterministic correlations (DC). How to model the DC is the key in APES method. The primary purpose of this study is to develop a DC model for compressor routine design. The APES technique is investigated by using a 3D viscous unsteady and time-averaging Computational fluid dynamics (CFD) flow solver developed in our previous studies. Based on DC characteristics and its effects on time-averaged flow, an exponential decay DC model is proposed and implemented into the developed time-averaging solver. Steady, unsteady, and time-averaging simulations are conducted on the investigation of the UBRI and the DC model in the first transonic stage of NASA 67 and the first two stages of a multistage compressor. The DC distributions and mean flow fields from the DC model are compared with the unsteady simulations. The comparison indicates that the proposed model can take into account the major part of UBRI and provide significant improvements for predicting compressor characteristics and spanwise distributions of flow properties in axial compressors, compared with the steady mixing plane method.
Disturbance-Parametrized Robust Lattice-Based Motion Planning

This article introduces a disturbance-parametrized (DP) robust lattice-based motion-planning framework for nonlinear systems affected by bounded disturbances. A key idea in this work is to rigorously exploit the available knowledge about the disturbance, starting already offline at the time when a library of DP motion primitives is computed and ending not before the motion has been executed online. Given an up-to-date-estimate of the disturbance, the lattice-based motion planner performs a graph search online, to non-conservatively compute a disturbance aware optimal motion plan with formally motivated margins to obstacles. This is done utilizing the DP motion primitives, around which tubes are generated utilizing a suitably designed robust controller. The sizes of the tubes are dependent on the upper bounds of the disturbance appearing in the error between the actual system trajectory and the DP nominal trajectory, which in turn along with the overall optimality of the plan is dependant on the user-selected resolution of the available disturbance estimates. Increasing the resolution of the disturbance parameter results in smaller sizes of tubes around the motion primitives and can significantly reduce the conservativeness compared to traditional approaches, thus increasing the performance of the computed motion plans. The proposed strategy is implemented on an Euler-Lagrange dynamics based ship model which is affected by a significant wind disturbance and the efficiency of the strategy is validated through a suitable simulation example.
Validity of the SARC-F questionnaire in assessing sarcopenia in patients with chronic kidney disease: a cross-sectional study

Objective To examine the validity of the 5-component SARC-F questionnaire for screening sarcopenia among patients with chronic kidney disease (CKD). Methods Eligible participants were enrolled from the Department of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine from March 2019 to November 2019. Evaluations were performed using the self-administered SARC-F questionnaire. Sarcopenia was diagnosed by grip strength, the chair stand test and appendicular skeletal muscle mass. The severity of sarcopenia was evaluated by gait speed. We calculated the sensitivity and specificity of the SARC-F to evaluate construct validity. Moreover, receiver operating characteristic (ROC) curve analysis was performed to identify the cutoff value for nondialysis-dependent (NDD) CKD patients’ and maintenance hemodialysis (MHD) patients’ scores. Results A total of 105 NDD-CKD patients and 125 MHD patients were included, and the prevalence of sarcopenia was 5.7 and 31.2%, respectively. Among them, there were 21 (16.8%) MHD patients with severe sarcopenia but no NDD-CKD patients with severe sarcopenia. The sensitivity and specificity of the SARC-F were 16.7 and 98.0% for NDD-CKD patients, and 48.7 and 89.5% for MHD patients, respectively. For NDD-CKD patients, the area under the receiver operating characteristic curve (AUROC) of the total SARC-F score was 0.978 (95% confidence interval (CI): 0.929–0.997, p < 0.001), and the cutoff value of 1 reached the highest Youden index of 0.950 and max ROC curve area of 0.974. For MHD patients, the AUROC of the total SARC-F score was 0.730 (95% CI: 0.644–0.806, p < 0.001), and the cutoff value of 4 reached the highest Youden index of 0.383 and max ROC curve area of 0.691. Conclusion CKD patients, especially MHD patients, were at high risk of suffering sarcopenia. The SARC-F had low-to-moderate sensitivity but high specificity for screening sarcopenia among patients with CKD. The best cutoff values of the SARC-F score were different for screening sarcopenia among NDD-CKD and MHD patients.
Metrology and control of electromagnetically actuated cantilevers using optical beam deflection method

In this paper, we present metrology and control methods and techniques for electromagnetically actuated microcantilevers. The electromagnetically actuated cantilevers belong to the micro electro mechanical systems (MEMS), which can be used in high resolution force and mass change investigations. In the described experiments, silicon cantilevers with an integrated Lorentz current loop were investigated. The electromag-netically actuated cantilevers were characterized using a modiﬁed optical beam deﬂection (OBD) system, whose architecture was optimized in order to increase its resolution. The sensitivity of the OBD system was calibrated using a reference cantilever, whose spring constant was determined through thermomechanical noise analysis registered interferometrically. The optimized and calibrated OBD system was used to observe the resonance and bidirectional static deﬂection of the electromagnetically deﬂected cantilevers. After theoretical analysis and further experiments, it was possible to obtain setup sensitivity equal to 5.28 mV/nm.
Propensity Score Matching in Observational Studies with Multiple Time Points

Inferring causal effects from observational studies is desirable when the randomized experiments are not feasible or are unethical. Due to the lack of randomization in observational studies, treatment selection depends on various covariates that are associated with outcomes of interest. In multiple-time-point observational studies, in particular, temporal features may potentially affect subjects differently, resulting in time-varying confounders. Using conventional methods for cross-sectional observational studies is inappropriate, since they fail to compensate for the biases arising from the time component. This dissertation focuses on two types of multiple-timepoint study designs, and proposes a propensity score matching method for valid causal inference. Chapter 2 deals with the repeated cross-sectional observational study, where the intervention of primary interest is conducted repeatedly on different cohorts, and a second intervention occurs between two time points. However, the covariates may be imbalanced for subjects in different intervention groups and for subjects participating at different time points. We extend from Rubin’s causal model, and establish a potential outcomes framework for this study design. The assumptions for identification of various causal effects are discussed. We propose two matching algorithms for multigroup matching, and compare them via simulation studies. We further provide two
A Class of Symmetric Factored Approximate Inverses and Hybrid Two-Level Solver

A new class of symmetric factored approximate inverses is proposed and used in conjunction with the Preconditioned Conjugate Gradient method for solving sparse symmetric linear systems. Additionally, a new hybrid two-level solver is proposed utilizing a block independent set reordering, in order to create the two level hierarchy. The Schur complement is formed explicitly by inverting the blocks created by reordering. Then, the preconditioned conjugate gradient method is used in conjunction with the symmetric factored approximate inverse to solve the reduced order linear system. Furthermore, numerical results on the performance and convergence behavior for solving various model problems are presented.
Assessing Asymmetrical Rates in Multivariate Phylogenetic Trait Evolution: An Extension of Statistical Models for Heterogeneous Rate Estimation

Understanding the rate of evolution provides insight into how rapidly species have historically evolved. We investigate the often-overlooked concept of asymmetry in evolutionary rates. We observe the variation in the rates at which different traits within the same organism, or the same traits across different organisms, evolve. Influenced by factors such as environmental pressures and genetic constraints, this asymmetry might lead to inconsistent rates of biological changes. To capture these diverse rates, we propose three advanced statistical models, transcending the traditionally employed Brownian motion model. These models—the phylogenetic multivariate Ornstein–Uhlenbeck model, the early burst model, and the mixed model—were applied to body length, forelimbs, and head length in salamanders. The results from our substantial dataset show these models’ effectiveness in highlighting the asymmetrical patterns of trait evolution, enhancing our understanding of the complex dynamics in species evolution. Therefore, our study underscores the importance of considering asymmetry when studying evolutionary rates.
Marketing Endorsement Activities on the Level of Sales and the Existence of Online Shop Tokikubytata

Nowadays, social media is quite famous for promotional media. Social media is now a business land for someone to promote and sell products, goods or services to social media users known as an online shop. The use of endorsements is one of the creative strategies in advertising that online shop sellers often use to introduce and disseminate product information that is owned widely and quickly. This research will focus on the influence of using endorsements on the sale of online shop businesses. This study examines the usefulness of endorsements to increase online shop sales for Tokikubytata. The research method used is descriptive with a qualitative data approach obtained from consumers and online shop owners through written interviews with questionnaires. The research process has several stages, starting from reading books about marketing endorsements, literature and research supporting references, understanding research backgrounds, collecting data, conducting a series of qualitative data analysis processes, and conducting written interviews and field observations. The result of this study show that endorsement is very influential in increasing the number of followers and buyers for Tokikubytata. Endorsement will help in increasing consumer confidence in the products marketed by Tokikubytata. Phenomenological studies in this study are a method used to explore that the phenomenon of endorsement can increase the number of followers for online shop. Keywords: endorsement, Tokikubytata, online shop
Dynamic Parallel Machine Scheduling With Deep Q-Network

Parallel machine scheduling (PMS) is a common setting in many manufacturing facilities, in which each job is allowed to be processed on one of the machines of the same type. It involves scheduling $n$ jobs on $m$ machines to minimize certain objective functions. For preemptive scheduling, most problems are not only NP-hard but also difficult in practice. Moreover, many unexpected events, such as machine failure and requirement change, are inevitable in the practical production process, meaning that rescheduling is required for static scheduling methods. Deep reinforcement learning (DRL), which combines deep learning and reinforcement learning, has achieved promising results in several domains and has shown the potential to solve large Markov decision process (MDP) optimization tasks. Moreover, PMS problems can be formulated as an MDP problem, inspiring us to devise a DRL method to deal with PMS problems in a dynamic environment. We develop a novel DRL-based PMS method, called DPMS, in which the developed model considers the characteristics of PMS to design states and the reward. The actions involve dispatching rules, so DPMS can be considered a meta-dispatching-rule system that can efficiently select a sequence of dispatching rules based on the current environment or unexpected events. The experimental results demonstrate that DPMS can yield promising results in a dynamic environment by learning from the interactions between the agent and the environment. Furthermore, we conduct extensive experiments to analyze DPMS in the context of developing a DRL to deal with dynamic PMS problems.
Peripheral inflammation is linked with emotion and mental health in people with obesity. A “head to toe” observational study

Background Obesity is a significant worldwide health problem that is linked with mental health. The elucidation of the possible overlapping biochemical mechanism(s) involved in inflammation and oxidative stress is imperative to better understand and address obesity and related metabolic disorders. The aim of the study was to investigate the associations between inflammatory and oxidative stress profiles with parameters that reflect metabolic, emotional, and mental health in a Greek metabolically unhealthy obese cohort. Methods In total, 122 metabolically unhealthy people with obesity were recruited. Anthropometric measurements, biochemical, inflammatory and oxidative stress markers were assessed. Quality of life was evaluated through questionnaires for insomnia, self-esteem, depression, physical and mental health. Results The inflammatory biomarker tumor necrosis factor-alpha (TNF-α) and the ratio oxidized low-density lipoprotein/low-density lipoprotein (oxLDL/LDL) were higher in hypertensive (p=0.002, p=0.001 respectively) and hyperglycemic subjects (p=0.017, p=0.001 respectively). Furthermore TNF-α (p<0.001), oxLDL/LDL (p<0.001) and oxLDL/high-density lipoprotein (HDL) (p=0.016) increased significantly with the increase of metabolic syndrome components. Finally, a negative association between interleukin-6 (IL-6) and Rosenberg Self-Esteem Scale (Beta=-0.019, p=0.019) and a positive association between TNF-α and the Center for Epidemiologic Studies Depression Scale Revised (Beta=0.003, p=0.015) were found. Conclusions The results of the study suggest that obesity-related systemic inflammation is associated with worse self-esteem and depression symptoms, indicating an overlapping mechanism which can be utilized to the management of obesity.
Extreme value laws and mean squared error growth in dynamical systems

Background. Extreme value theory for chaotic deterministic dynamical systems is a rapidly expanding area of research. Given a system and a scalar observable defined on its state space, extreme value theory studies the asymptotic probability distributions of large values attained by the observable along evolutions of the system. Objective. The aim of this paper is to study the relation between the statistics and predictability of extremes. Methods. Predictability is measured by the mean squared error (MSE), which is estimated from the difference of pairs of forecasts conditional on one of the forecasts exceeding a threshold. Results. Under the assumption that pairs of forecast variables satisfy a linear regression model, we show that the MSE can be decomposed into the sum of three terms: a threshold-independent constant, a mean term that always increases with threshold, and a variance term that can either increase, decrease, or stay constant with threshold. Using the generalised Pareto distribution to model excesses over a threshold, we show that the MSE always increases with threshold at sufficiently high threshold. However, when the forecasts have a negative tail index the MSE can be a decreasing function of threshold at lower thresholds. Conclusions. Our method is illustrated by means of four examples: the tent map, the cusp map, and two low-order models for atmospheric regime transitions and the El Ni\~{n}o-Southern Oscillation phenomenon. These examples clearly show that predictability depends on the observable and the invariant measure of the system.
Transport Plans with Domain Constraints

This paper focuses on martingale optimal transport problems when the martingales are assumed to have bounded quadratic variation. First, we give a result that characterizes the existence of a probability measure satisfying some convex transport constraints in addition to having given initial and terminal marginals. Several applications are provided: martingale measures with volatility uncertainty, optimal transport with capacity constraints, and Skorokhod embedding with bounded times. Next, we extend this result to multi-marginal constraints. Finally, we consider an optimal transport problem with constraints and obtain its Kantorovich duality. A corollary of this result is a monotonicity principle which gives a geometric way of identifying the optimizer.
Technical note: Complexity–uncertainty curve (c-u-curve) – a method to analyse, classify and compare dynamical systems

Abstract. We propose and provide a proof of concept of a method to analyse, classify and compare dynamical systems of arbitrary dimensions by the two key features uncertainty and complexity. It starts by subdividing the system's time trajectory into a number of time slices. For all values in a time slice, the Shannon information entropy is calculated, measuring within-slice variability. System uncertainty is then expressed by the mean entropy of all time slices. We define system complexity as “uncertainty about uncertainty” and express it by the entropy of the entropies of all time slices. Calculating and plotting uncertainty “u” and complexity “c” for many different numbers of time slices yields the c-u-curve. Systems can be analysed, compared and classified by the c-u-curve in terms of (i) its overall shape, (ii) mean and maximum uncertainty, (iii) mean and maximum complexity and (iv) characteristic timescale expressed by the width of the time slice for which maximum complexity occurs. We demonstrate the method with the example of both synthetic and real-world time series (constant, random noise, Lorenz attractor, precipitation and streamflow) and show that the shape and properties of the respective c-u-curve clearly reflect the particular characteristics of each time series. For the hydrological time series, we also show that the c-u-curve characteristics are in accordance with hydrological system understanding. We conclude that the c-u-curve method can be used to analyse, classify and compare dynamical systems. In particular, it can be used to classify hydrological systems into similar groups, a pre-condition for regionalization, and it can be used as a diagnostic measure and as an objective function in hydrological model calibration. Distinctive features of the method are (i) that it is based on unit-free probabilities, thus permitting application to any kind of data, (ii) that it is bounded, (iii) that it naturally expands from single-variate to multivariate systems, and (iv) that it is applicable to both deterministic and probabilistic value representations, permitting e.g. application to ensemble model predictions.
Four Novel Species and Two New Records of Boletes from India

Repeated macrofungal explorations, followed by thorough examination of species through morphology and molecular phylogeny, have made it clear that European and American names of wild mushrooms were inadvertently misapplied quite often to Asian lookalikes by mycologists/taxonomists in the past. Therefore, in order to reveal this mushroom treasure, in recent years, taxonomical research on wild mushrooms has been intensified in Asian countries, including India, by undertaking a combined approach of morpho-taxonomy and multigene molecular phylogeny. Boletoid mushrooms (Boletaceae) are no exception. While working on boletoid mushrooms of the Indian Himalayas, authors recently came across six interesting species of boletoid mushrooms. In the present communication, four novel species, namely Leccinellum binderi, Cyanoboletus paurianus, Xerocomus uttarakhandae, and Xerocomellus himalayanus, are established based on morphology and molecular phylogenetic estimations. Moreover, Cyanoboletus macroporus and Xerocomus fraternus are also reported here for the first time in India.
Imagerie microonde d'objets enterrés : modélisations numériques bidimensionnelles et étude de l'extension tridimensionnelle

Dans une premiere partie, une methode de reconstruction du profil de permittivite et de conductivite -- basee sur une methode de bigradient conjugue et une technique de preservation des discontinuites, precedemment developpee dans le cas d'une illumination par ondes planes -- est etendue de facon a prendre en compte le champ proche arbitraire d'antennes emettrices. La robustesse de l'algorithme est etudiee en simulant des erreurs de mesure du champ diffracte. Le choix des differents parametres est examine de facon a apprehender la quantite d'information necessaire pour une reconstruction optimale. Dans une deuxieme partie, on developpe une methode de differences finies pour la resolution des equations de Maxwell en regime harmonique, en deux et trois dimensions. Le maillage est termine par des couches parfaitement adaptees et l'integrale de Kirchhoff est appliquee a la transformation champ proche - champ lointain. Cette methode se prete a une utilisation en imagerie tridimensionnelle.
The effect of morality and Machiavellianism on government fraud: The moderating role of education

The high number of fraud cases in governments has increasingly become a concern among academics and practitioners. Many approaches can be used to minimize fraud cases. However, the role of education in the influence of personality on fraud in the Indonesian government has not been considered. Thus, this study aims to analyze the influence of morality and Machiavellianism as personality factors leading to fraud. Education was tested as a moderating variable. This quantitative study involved a sample of Pekanbaru City government officials. To collect data, questionnaires were sent directly to the respondents. Through the purposive sampling technique, 251 samples were collected. A structural equation with partial least squares was used to test the hypotheses. The test results show that all the proposed hypotheses are accepted. Morality affects fraud with a p-value &lt; 0.001, less than 0.05. Machiavellianism affects fraud with a p-value &lt; 0.001. Education moderates the relationship between morality and fraud with a p-value of 0.048, less than 0.05. Moreover, education moderates the relationship between Machiavellianism and fraud with a p-value &lt; 0.001, less than 0.05. In conclusion, this study contributes to shaping government officials’ behavior to prevent fraud. Character building and high education standards for officials must be integrated to reduce the risk of abuse in Indonesian government.