Abstract
Due to recent advances in power electronic systems, direct current (DC) microgrid (MG) topology is considered as a promising solution to unite pollution-free renewable energy sources and DC loads. This paper investigates the issue of finite-time robust adaptive stability and tracking issue of a nonlinear direct current (DC) microgrid (MG) comprising a buck converter, linear resistive loads, and nonlinear constant power loads (CPLs). The developed approach is based on a sliding mode controller (SMC) and a nonlinear and nonsingular sliding surface. It is proved that the tracking error converges to zero in a finite-time in the presence of matched disturbance input and uncertainties. The novel controller manipulates the buck converter of the source side to regulate the DC bus voltage by counteracting the destabilizing effect of CPLs and disturbances. Further, the finite value of the convergence time is presented and the effects of the SMC parameter on the stability and transient performance are evaluated. Lastly, numerical simulations are conducted to illustrate the merits of the developed control approach in the viewpoints of fast reference tracking and robust stability.

Abstract
In recent years data stream mining and learning from imbalanced data have been active research areas. Even though solutions exist to tackle these two problems, most of them are not designed to handle challenges inherited from both problems. As far as we are aware, the few approaches in the area of learning from imbalanced data streams fall in the context of classification, and no efforts on the regression domain have been reported yet. This paper proposes a technique that uses sampling strategies to cope with imbalanced data streams in a regression setting, where the most important cases have rare and extreme target values. Specifically, we employ under-sampling and over-sampling strategies that resort to Chebyshev's inequality value as a heuristic to disclose the type of incoming cases (i.e. frequent or rare). We have evaluated our proposal by applying it in the training of models by four well-known regression algorithms over fourteen benchmark data sets. We conducted a series of experiments with different setups on both synthetic and real-world data sets. The experimental results confirm our approach's effectiveness by showing the models' superior performance trained by each of the sampling strategies compared with their baseline pairs.

Abstract
Knowledge Engineering has become essential in the fields of Medical and Health Care with emphasis for helping citizens to improve their health and quality of life. This includes individual methods and techniques in health-related knowledge acquisition and representation and their application in the construction of intelligent systems capable of using the acquired information to improve the patients' health and/or quality of life. Haemato-oncological diseases can provide significant disability and suffering, with severe symptoms and psychological distress. They can create difficulties in fulfilling professional, family and social roles, affecting an individual's quality of life. Health related quality of life (HRQoL) is a subjective concept but there is also an objective component related to physiological indicators. Some of these physiological indicators can be easily assessed by wearable technology such heart rate variability (HRV). This paper introduces an intelligent system to assess, in real-time, potential HRV indices, that can predict HRQoL in lymphoma patients throughout chemotherapy treatment and to account the individuals' variability. The system is based on wearable technology and intelligent processing of the patients' biometric information to assess some quality of life related parameters. A longitudinal study was conducted among 16 lymphoma patients using this intelligent system. Mixed-effect regression models were performed to investigate predictors for and time effects on HRQoL. There were no significant changes in all HRQoL domains over time. Some quality of life domains revealed similar time trends as HRV indices. These HRV indices also have a significant effect on the domains of quality of life.

Abstract
Context. The formation and evolution of planetary systems impact the evolution of the primordial accretion disk in its dust and gas content. HD 141569 is a peculiar object in this context as it is the only known pre-main sequence star characterized by a hybrid disk. Observations with 8 m class telescopes probed the outer-disk structure showing a complex system of multiple rings and outer spirals. Furthermore, interferometric observations attempted to characterize its inner 5 au region, but derived limited constraints. Aims. The goal of this work was to explore with new high-resolution interferometric observations the geometry, properties, and dynamics of the dust and gas in the internal regions of HD 141569. Methods. We observed HD 141569 on milliarcsecond scales with GRAVITY/VLTI in the near-infrared (IR) at low (R similar to 20) and high (R similar to 4000) spectral resolution. We interpreted the interferometric visibilities and spectral energy distribution with geometrical models and through radiative transfer techniques using the code MCMax to constrain the dust emission. We analyzed the high spectral resolution quantities (visibilities and differential phases) to investigate the properties of the Brackett-gamma (Br gamma) line emitting region. Results. Thanks to the combination of three different epochs, GRAVITY resolves the inner dusty disk in the K band with squared visibilities down to V-2 similar to 0.8. A differential phase signal is also detected in the region of the Br gamma line along most of the six baselines. Data modeling shows that an IR excess of about 6% is spatially resolved and that the origin of this emission is confined in a ring of material located at a radius of similar to 1 au from the star with a width less than or similar to 0.3 au. The MCMax modeling suggests that this emission could originate from a small amount (1.4 x 10(-8) M-circle plus) of quantum-heated particles, while large silicate grain models cannot reproduce at the same time the observational constraints on the properties of near-IR and mid-IR fluxes. The high spectral resolution differential phases in the Br gamma line clearly show an S-shape that can be best reproduced with a gaseous disk in Keplerian rotation, confined within 0.09 au (or 12.9 R-star). This is also hinted at by the double-peaked Br gamma emission line shape, known from previous observations and confirmed by GRAVITY. The modeling of the continuum and gas emission shows that the inclination and position angle of these two components are consistent with a system showing relatively coplanar rings on all scales. Conclusions. With a new and unique observational dataset on HD 141569, we show that the complex disk of this source is composed of a multitude of rings on all scales. This aspect makes HD 141569 a potentially unique source to investigate planet formation and disk evolution in intermediate-mass pre-main sequence stars.

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