Entropy serves as a measure of chaos in systems by representing the average rate of information loss about a phase point’s position on the attractor. When dealing with a multifractal system, a single ...exponent cannot fully describe its dynamics, necessitating a continuous spectrum of exponents, known as the singularity spectrum. From an investor’s point of view, a rise in entropy is a signal of abnormal and possibly negative returns. This means he has to expect the unexpected and prepare for it. To explore this, we analyse the New York Stock Exchange (NYSE) U.S. Index as well as its constituents. Through this examination, we assess their multifractal characteristics and identify market conditions (bearish/bullish markets) using entropy, an effective method for recognizing fluctuating fractal markets. Our findings challenge conventional beliefs by demonstrating that price declines lead to increased entropy, contrary to some studies in the literature that suggest that reduced entropy in market crises implies more determinism. Instead, we propose that bear markets are likely to exhibit higher entropy, indicating a greater chance of unexpected extreme events. Moreover, our study reveals a power-law behaviour and indicates the absence of variance.
We ask whether empirical finance market data (Financial Stress Index, swap and equity, emerging and developed, corporate and government, short and long maturity), with their recently observed ...alternations between calm periods and financial turmoil, could be described by a low-dimensional deterministic model, or whether this requests a stochastic approach. We find that a deterministic model performs at least as well as one of the best stochastic models, but may offer additional insight into the essential mechanisms that drive financial markets.
This research aims to propose the so-called CIR#, which takes its cue from the well- known Cox-Ingersoll-Ross (CIR) model originally devised for pricing, as a general econometric model. To this end, ...we present the results on two very different time series such as Polish interest rates (subject to market sentiments) and seasonal tourism (subject to pandemic lock-down measures). For interest rates, as reference models, we consider an improved version of the CIR model (denoted CIRadj), the Hull and White model, the exponentially weighted moving average (EWMA) which is often adopted whenever no structure is assumed in the data and a popular machine learning model such as the short-term memory network (LSTM). For tourism, as a benchmark, we consider seasonal autoregressive integrated moving average (SARIMA) complemented by the generalized autoregressive conditional heteroskedasticity (GARCH) for modelling the variance, the classic Holt-Winters model and the aforementioned LSTM. Results support the claim that the CIR# performs better than the other models in all considered cases being able to deal with erratic behaviour in data.
Many processes in nature are the result of many coupled individual subsystems (like population dynamics or neurosystems). Not always such systems exhibit simple stable behaviors that in the past ...science has mostly focused on. Often, these systems are characterized by bursts of seemingly stochastic activity, interrupted by quieter periods. The hypothesis is that the presence of a strong deterministic ingredient is often obscured by the stochastic features. We test this by modeling classically stochastic considered real-world data from both, the stochastic as well as the deterministic approaches to find that the deterministic approach's results level with those from the stochastic side. Moreover, the deterministic approach is shown to reveal the full dynamical systems landscape, which can be exploited for steering the dynamics into a desired regime.
In this paper, a novel data-driven control algorithm based on model-free adaptive control is presented, addressing general discrete-time single-input single-output nonlinear systems, approximated by ...an equivalent dynamic linearization model using pseudo-partial derivatives. The closed loop stability is proved, showing that the tracking error asymptotically vanishes. Moreover, the proposed approach has been applied to a 5 MW wind turbine, considering as control target the efficiency optimization issue when the turbine operates under medium wind speed conditions. Validation of the technique has been performed, testing the overall control system by simulation using the tool FAST developed by the National Renewable Energy Laboratory (NREL).
This special issue of World Journal of Gastroenterology has been conceived to illustrate to gastroenterology operators the role that regenerative medicine（RM） will have in the progress of ...gastrointestinal（GI） medicine.RM is a multidisciplinary field aiming to replace,regenerate or repair diseased tissues or organs.The past decade has been marked by numerous ground-breaking achievements that led experts in the field to manufacture functional substitutes of relatively simple organs.This progress is paving the ground for investigations that aims to the bioengineering and regeneration of more complex organs like livers,pancreas and intestine.In this special issue,the reader will be introduced,hand-in-hand,to explore the field of RM and will be educated on the progress,pitfalls and promise of RM technologies as applied to GI medicine.
The aim of this work was to test how returns are distributed across multiple asset classes, markets and sampling frequency. We examine returns of swaps, equity and bond indices as well as the ...rescaling by their volatilities over different horizons (since inception to Q2-2020). Contrarily to some literature, we find that the realized distributions of logarithmic returns, scaled or not by the standard deviations, are skewed and that they may be better fitted by t-skew distributions. Our finding holds true across asset classes, maturity and developed and developing markets. This may explain why models based on dynamic conditional score (DCS) have superior performance when the underlying distribution belongs to the t-skew family. Finally, we show how sampling and distribution of returns are strictly connected. This is of great importance as, for example, extrapolating yearly scenarios from daily performances may prove not to be correct.
The achievement of an immunosuppression (IS)-free state after transplantation represents the ultimate goal of any immunosuppressive regimen. While clinical operational tolerance (COT) remains the ...exception after other types of solid organ transplantation, several cases of COT have been described after liver transplantation (LT). Overall, the experience gained so far worldwide demonstrates that COT can be achieved safely in one quarter of selected individuals, irrespective of the immunological background of donor and recipient, patient age, indication for LT, study endpoint, length of the weaning period and of pre/post-weaning follow-up, presence or not of chimerism. However, most transplant physicians still believe that the achievement of COT is still out of reach for the majority of LT recipients because of the potential risk for transplant survival, the non-randomized nature of most of the studies reported so far, and the selective nature of the patients enrolled in such studies, making them non-representative of the whole population of LT recipients. Despite these concerns, the present article demonstrates that this attitude is potentially no longer justified, given the growing evidence that a permanent and stable IS-free state can be achieved in a proportion of individuals who have received a LT for non-immune mediated liver diseases.
We propose an implicit Discontinuous Galerkin (DG) discretization for incompressible two-phase flows using an artificial compressibility formulation. The conservative level set (CLS) method is ...employed in combination with a reinitialization procedure to capture the moving interface. A projection method based on the L-stable TR-BDF2 method is adopted for the time discretization of the Navier-Stokes equations and of the level set method. Adaptive Mesh Refinement (AMR) is employed to enhance the resolution in correspondence of the interface between the two fluids. The effectiveness of the proposed approach is shown in a number of classical benchmarks. A specific analysis on the influence of different choices of the mixture viscosity is also carried out.
Despite advances in tumor treatment, the inconsistent response is a major challenge among glioblastoma multiform (GBM) that lead to different survival time. Our aim was to integrate multimodal MRI ...with non-supervised and supervised machine learning methods to predict GBM patients' survival time. To this end, we identified different compartments of the tumor and extracted their features. Next, we applied Random Forest-Recursive Feature Elimination (RF-RFE) to identify the most relevant features to feed into a GBoost machine. This study included 29 GBM patients with known survival time. RF-RFE GBoost model was evaluated to assess the survival prediction performance using optimal features. Furthermore, overall survival (OS) was analyzed using univariate and multivariate Cox regression analyses, to evaluate the effect of ROIs and their features on survival. The results showed that a RF-RFE Gboost machine was able to predict survival time with 75% accuracy. The results also revealed that the rCBV in the low perfusion area was significantly different between groups and had the greatest effect size in terms of the rate of change of the response variable (survival time). In conclusion, not only integration of multi-modality MRI but also feature selection method can enhance the classifier performance.
PDF
