Hybrid systems, which combine statistical and machine learning (ML) techniques using residual (error forecasting) modeling, have been highlighted in the literature due to their accuracy and ability ...to forecast time series with different characteristics. In these architectures, a crucial task is the proper modeling of the residuals since they may present random fluctuations, complex nonlinear patterns, and heteroscedastic behavior. Hence, the selection, specification, and training of one ML model to forecast the residuals are costly and challenging tasks since issues, such as underfitting, overfitting, and misspecification, can lead to a system with low accuracy or even deteriorate the linear forecast of the time series. This article proposes a hybrid system, named dynamic residual forecasting (DReF), that employs a modified dynamic selection (DS) algorithm to decide: the most suitable ML model to forecast a pattern of the residual series and if it is a promising candidate to increase the accuracy of the time series forecast from the linear combination. Thus, the DReF aims to reduce the uncertainty of the ML model selection and avoid the deterioration of the time series forecast. Furthermore, the proposed system searches for the most suitable parameters of the DS algorithm for each data set. In this article, the proposed method uses a pool of five ML models widely adopted in the literature: multilayer perceptron, support vector regression, radial basis function, long short-term memory, and convolutional neural network. An experimental evaluation was conducted using ten well-known time series. The results show that the DReF obtains superior results for the majority of the data sets compared with single and hybrid models of the literature.
Endovascular thrombectomy has been effective in reducing stroke-related disability in high-income countries. This trial in the public health care system of Brazil, a developing country, showed ...similar effectiveness in patients treated within 8 hours after the onset of symptoms.
The development of accurate forecasting systems can be challenging in real-world applications. The modeling of real-world time series is a particularly difficult task because they are generally ...composed of linear and nonlinear patterns that are combined in some form. Several hybrid systems that combine linear and nonlinear techniques have obtained relevant results in terms of accuracy in comparison with single models. However, the best combination function of the forecasting of the linear and nonlinear patterns is unknown, which makes this modeling an open question. This work proposes a hybrid system that searches for a suitable function to combine the forecasts of linear and nonlinear models. Thus, the proposed system performs: (i) linear modeling of the time series; (ii) nonlinear modeling of the error series; and (iii) a data-driven combination that searches for: (iii.a) the most suitable function, between linear and nonlinear formalisms, and (iii.b) the number of forecasts of models (i) and (ii) that maximizes the performance of the combination. Two versions of the hybrid system are evaluated. In both versions, the ARIMA model is used in step (i) and two nonlinear intelligent models – Multi-Layer Perceptron (MLP) and Support Vector Regression (SVR) – are used in steps (ii) and (iii), alternately. Experimental simulations with six real-world complex time series that are well-known in the literature are evaluated using a set of popular performance metrics. Our results show that the proposed hybrid system attains superior performance when compared with single and hybrid models in the literature.
Wastewater from the oil industry can be considered a dangerous contaminant for the environment and needs to be treated before disposal or re-use. Currently, membrane separation is one of the most ...used technologies for the treatment of produced water. Therefore, the present work aims to study the process of separating oily water in a module equipped with a ceramic membrane, based on the Eulerian-Eulerian approach and the Shear-Stress Transport (SST k-ω) turbulence model, using the Ansys Fluent
15.0. The hydrodynamic behavior of the water/oil mixture in the filtration module was evaluated under different conditions of the mass flow rate of the fluid mixture and oil concentration at the entrance, the diameter of the oil particles, and membrane permeability and porosity. It was found that an increase in the feed mass flow rate from 0.5 to 1.5 kg/s significantly influenced transmembrane pressure, that varied from 33.00 to 221.32 kPa. Besides, it was observed that the particle diameter and porosity of the membranes did not influence the performance of the filtration module; it was also verified that increasing the permeability of the membranes, from 3 × 10
to 3 × 10
m
, caused transmembrane pressure reduction of 22.77%. The greater the average oil concentration at the permeate (from 0.021 to 0.037 kg/m
) and concentrate (from 1.00 to 1.154 kg/m
) outlets, the higher the average flow rate of oil at the permeate outlets. These results showed that the filter separator has good potential for water/oil separation.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Objective
To analyze the effects of androgen therapy on the thyroarytenoid (TA) muscle, expression of androgen receptors (ARs) and hyaluronic acid (HA) concentration in the vocal folds (VFs) of adult ...female rats.
Methods
Twenty‐one adult female Wistar rats were divided into experimental and control groups. The experimental group received weekly intramuscular injections of nandrolone decanoate for 9 weeks. Following euthanasia and dissection of the VFs, histomorphometric analysis of the TA muscle, immunohistochemical evaluation of ARs, and measurement of HA concentration using the ELISA‐like fluorimetric method were performed.
Results
The experimental group exhibited a significantly larger mean fiber cross‐sectional area in the TA muscle compared to the control group (434.3 ± 68.6 μm2 versus 305.7 ± 110.1 μm2; p = 0.029), indicating muscle hypertrophy. There was no significant difference in the number of muscle fibers. The experimental group showed higher expression of ARs in the lamina propria (62.0% ± 30.3% versus 22.0% ± 22.8%; p = 0.046) and in the TA muscle (45.0% ± 22.6% versus 18.3% ± 9.8%; p = 0.024). There was no significant difference in the concentration of HA.
Conclusion
Exposure of adult female rats to androgen therapy resulted in hypertrophy of the TA muscle and increased expression of ARs in the VFs. The TA muscle seems to be the primary target of testosterone action in the VF, and the up‐regulation of ARs might contribute to the persistent deepening of the voice.
Level of Evidence
NA Laryngoscope, 134:2316–2321, 2024
Exposure of adult female rats to androgen therapy resulted in hypertrophy of the thyroarytenoid (TA) muscle and increased expression of androgen receptors in the vocal folds. The TA muscle seems to be the primary target of testosterone action in the larynx, and the up‐regulation of androgen receptors may contribute to the permanent deepening of the voice.
Solar irradiance forecasting has been an essential topic in renewable energy generation. Forecasting is an important task because it can improve the planning and operation of photovoltaic systems, ...resulting in economic advantages. Traditionally, single models are employed in this task. However, issues regarding the selection of an inappropriate model, misspecification, or the presence of random fluctuations in the solar irradiance series can result in this approach underperforming. This paper proposes a heterogeneous ensemble dynamic selection model, named HetDS, to forecast solar irradiance. For each unseen test pattern, HetDS chooses the most suitable forecasting model based on a pool of seven well-known literature methods: ARIMA, support vector regression (SVR), multilayer perceptron neural network (MLP), extreme learning machine (ELM), deep belief network (DBN), random forest (RF), and gradient boosting (GB). The experimental evaluation was performed with four data sets of hourly solar irradiance measurements in Brazil. The proposed model attained an overall accuracy that is superior to the single models in terms of five well-known error metrics.
Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) ...information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.
CASZ1 is a conserved transcription factor involved in neural development, blood vessel assembly and heart morphogenesis. CASZ1 has been implicated in cancer, either suppressing or promoting tumor ...development depending on the tissue. However, the impact of CASZ1 on hematological tumors remains unknown. Here, we show that the T-cell oncogenic transcription factor TAL1 is a direct positive regulator of CASZ1, that T-cell acute lymphoblastic leukemia (T-ALL) samples at diagnosis overexpress CASZ1b isoform, and that CASZ1b expression in patient samples correlates with PI3KAKT- mTOR signaling pathway activation. In agreement, overexpression of CASZ1b in both Ba/F3 and T-ALL cells leads to the activation of PI3K signaling pathway, which is required for CASZ1b-mediated transformation of Ba/F3 cells in vitro and malignant expansion in vivo. We further demonstrate that CASZ1b cooperates with activated NOTCH1 to promote T-ALL development in zebrafish, and that CASZ1b protects human T-ALL cells from serum deprivation and treatment with chemotherapeutic drugs. Taken together, our studies indicate that CASZ1b is a TAL1-regulated gene that promotes T-ALL development and resistance to chemotherapy.
In this work, we study the polarization time series obtained from experimental observation of a group of zebrafish (
Danio rerio
) confined in a circular tank. The complex dynamics of the individual ...trajectory evolution lead to the appearance of multiple characteristic scales. Employing the Multifractal Detrended Fluctuation Analysis (MF-DFA), we found distinct behaviors according to the parameters used. The polarization time series are multifractal at low fish densities and their average scales with
ρ
-
1
/
4
. On the other hand, they tend to be monofractal, and their average scales with
ρ
-
1
/
2
for high fish densities. These two regimes overlap at critical density
ρ
c
, suggesting the existence of a phase transition separating them. We also observed that for low densities, the polarization velocity shows a non-Gaussian behavior with heavy tails associated with long-range correlation and becomes Gaussian for high densities, presenting an uncorrelated regime.
Graphical abstract
Estimating future streamflows is a key step in producing electricity for countries with hydroelectric plants. Accurate predictions are particularly important due to environmental and economic impact ...they lead. In order to analyze the forecasting capability of models regarding monthly seasonal streamflow series, we realized an extensive investigation considering: six versions of unorganized machines—extreme learning machines (ELM) with and without regularization coefficient (RC), and echo state network (ESN) using the reservoirs from Jaeger’s and Ozturk et al., with and without RC. Additionally, we addressed the ELM as the combiner of a neural-based ensemble, an investigation not yet accomplished in such context. A comparative analysis was performed utilizing two linear approaches (autoregressive model (AR) and autoregressive and moving average model (ARMA)), four artificial neural networks (multilayer perceptron, radial basis function, Elman network, and Jordan network), and four ensembles. The tests were conducted at five hydroelectric plants, using horizons of 1, 3, 6, and 12 steps ahead. The results indicated that the unorganized machines and the ELM ensembles performed better than the linear models in all simulations. Moreover, the errors showed that the unorganized machines and the ELM-based ensembles reached the best general performances.
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