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Accurate prediction of the rate of penetration (ROP) is a difficult issue in the drilling process, especially under complex formation conditions. Many methods, such as mechanism and machine learning, were introduced to investigate it. However, most of them are offline prediction methods which may not be capable of capturing the online trend of ROP. In this paper, a novel dynamic model for ROP prediction is proposed considering the process characteristics, which consists of three stages. In the first stage, the correlations between ROP and eight drilling parameters are analyzed, and the rotational speed, weight on bit, depth are selected as the model inputs. In the second stage, the drilling data are pre-processed by using the filtering and re-sampling techniques. In the last stage, the moving window strategy, extreme learning machine, and 10-fold cross validation are used to establish the ROP model. Our main idea of online prediction of ROP lies in this last stage. Specifically, two steps (modeling and prediction) are executed alternately in the moving drilling depth windows so as to predict the ROP more accurately. Finally, the proposed ROP prediction model is applied to the drilling well ZK3 in Xiangyang area, Central China. The prediction accuracy is improved by at least 7% compared with seven well-known ROP prediction methods, two online and five offline, which validates the effectiveness of the proposed method. It is believed that the proposed model provides a basis for intelligent optimization control in drilling process. • A novel intelligent dynamic model is proposed for the online prediction of drilling ROP. • The influence of the length of moving window and distance between two windows are investigated thoroughly. • The proposed model can learn autonomously in the drilling process and has a fast computation speed. • The proposed method has a higher prediction accuracy than two online and five offline well-known conventional methods. [ABSTRACT FROM AUTHOR]

An ensemble visual-audio emotion recognition framework is proposed based on multi-task and blending learning with multiple features in this paper. To solve the problem that existing features can not accurately identify different emotions, we extract two kinds features, i. e., Interspeech 2010 and deep features for audio data, LBP and deep features for visual data, with the intent to accurately identify different emotions by using different features. Owing to the diversity of features, SVM classifiers and CNN are designed for manual features, i.e., Interspeech 2010 features and local LBP features, and deep features, through which four sub-models are obtained. Finally, the blending ensemble algorithm is used to fuse sub-models to improve the recognition performance of visual-audio emotion recognition. In addition, multi-task learning is applied in the CNN model for deep features, which can predict multiple tasks at the same time with fewer parameters and improve the sensitivity of the single recognition model to user's emotion by sharing information between different tasks. Experiments are performed using eNTERFACCE database, from which the results indicate that the recognition of multi-task CNN increased by 3% and 2% on average over CNN model in speaker-independent and speaker-dependent experiments, respectively. And emotion recognition accuracy of visual-audio by our method reaches 81.36% and 78.42% in speaker-independent and speaker-dependent experiments, respectively, which maintain higher performance than some state-of-the-art works. [ABSTRACT FROM AUTHOR]

Abstract: This article presents an approach based on differential evolution (DE) for extracting coupling matrix (CM) and the uneven unloaded Qs from measured S‐parameters of a narrow band coaxial‐resonator filter with losses. Different from analytical extraction methods and traditional optimization methods, nonideal effects and uneven‐Qs are chosen as unknown parameters to be optimized. In the optimization process, the polynomials of the S‐parameters of a filter can be obtained by the Cauchy method after the unknown parameters are given. Once the rational polynomials having been obtained; the CM with an assigned topology can be extracted. The DE approach will obtain optimal parameters when the difference between the measured S‐parameters and the extracted S‐parameters is minimized, and then the CM and the unloaded Qs of each resonator can be determined using well established techniques. The approach is useful and can be used in computer‐aided turning of microwave filters. Two examples are presented to illustrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

In this article, an advanced technique is developed to combine multi-output least-squares support vector regression (MLS-SVR) and pole-residue-based transfer function models for microwave filter parametric modeling. MLS-SVR is trained to learn the relationship between the length of tuning screws and the pole/residues of the transfer function, where MLS-SVR is an effective method to cope with the multi-output case unlike the traditional approach. Traditional approach treats the different outputs separately in the multi-output case and it cannot model the relation between different outputs. Another important element for modeling is feature parameters. Extracted feature parameters have an important influence on the accuracy of modeling. For the purpose of establishing a more accurate model, the complex system poles and residues from Y-parameters are chosen as the outputs of modeling, which are obtained by vector fitting (VF). Then we give a solution to obtaining pole/residues extracted by VF when the filter is in high detuned. After the proposed modeling process, trained model can be used to provide an accurate and fast prediction of the behavior of microwave filter with the length of tuning screws as variables, and model the electromagnetic simulation (or actual) microwave filter tuning. The methodology is applied to a narrow band coaxial-resonator filter modeling, and more accurate results are achieved compared with the other methods. An example is presented to illustrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Plasmacytoid dendritic cells (pDCs) are crucial for control of chronic hepatitis B (CHB) virus infection. In this study, we evaluated the frequencies of pDCs and expression of functional molecules on pDCs in patients treated with PEG-IFN-α-2a or entecavir (ETV) and investigated changes during treatment. The mean fluorescence intensity of CD86 (CD86MFI) on the surface of pDCs and frequencies of pDCs and CD86+ pDCs in peripheral blood were measured. Compared with baseline, CD86+ pDC% and CD86MFI increased obviously after PEG-IFN-α-2a treatment for 12 and 24 weeks. For patients treated with ETV, only pDC% increased observably after treatment weeks 12 and 24 (P < 0.001) compared with baseline. Hepatitis B surface antigen (HBsAg) decline was significantly associated with elevated CD86+ pDC% (r = 0.348, P = 0.015) during PEG-IFN-α-2a treatment. In the HBsAg response group, CD86+ pDC% and CD86MFI (P < 0.001) increased observably after PEG-IFN-α-2a therapy, whereas only CD86MFI had a statistically significant difference after therapy compared with baseline (12 weeks versus 0 weeks, P = 0.022; 24 weeks versus 0 weeks, P = 0.015) in the HBsAg nonresponse group. CD86+ pDC% between the 2 groups had statistically significant differences at baseline (P = 0.001) and at the treatment time points of 12 and 24 weeks (P < 0.001), respectively. For patients receiving ETV therapy, pDC% increased observably, but CD86+ pDC% decreased significantly (P < 0.001) in the HBV DNA nonresponse group during early treatment with ETV. In CHB patients, HBsAg response in PEG-IFN-α-2a therapy correlated with the increase of CD86+ pDC% and HBV DNA nonresponse in ETV treatment correlated with the decrease of CD86+ pDC%. [ABSTRACT FROM AUTHOR]

Based on hazard assessment and vulnerability assessment, this paper applies meteorological data and insurance loss data to evaluate the annual expected loss rate for enterprise property. The applicability of the assessment procedure is demonstrated by a case study of Taizhou city, Zhejiang province. The typhoon hazard index induced to enterprise property loss is analyzed, and it is found that process precipitation is the optimal index to measure typhoon intensity. The nonlinear relationship between process precipitation and enterprise loss rate is established using the self-study discrete regression method, which has higher estimation accuracy in small sample cases. The results can provide an important basis for risk management. [ABSTRACT FROM AUTHOR]

Abstract: Disinhibition of DMH neurons with the GABAA receptor antagonist, bicuculline, increases heart rate (HR) and augments both brown adipose tissue sympathetic nerve activity (BAT SNA) and renal SNA (RSNA) contributing to the evoked increases in BAT thermogenesis and arterial pressure (AP). We determined the role of glutamate receptor activation in the rostral raphe pallidus (RPa) in mediating the sympathoexcitatory responses in HR, BAT SNA and RSNA following disinhibition of DMH neurons in urethane/chloralose anesthetized, artificially ventilated rats. Microinjections of either the selective NMDA receptor agonist, NMDA, or the selective non-NMDA receptor agonist, kainic acid (KA), into the RPa produced increases in BAT SNA (peak: +502% and +408% of control, respectively) and BAT temperature (peak: +0.6°C and +1.0°C) accompanied by rises in HR (peak: +38 and +63bpm), RSNA (peak: +57% and +58% of control) and MAP (peak: +12 and 15mmHg). These responses were reversed by subsequent microinjection into RPa of the respective selective glutamate receptor antagonists, AP5 and CNQX. Microinjections of the non-selective glutamate receptor antagonist, kynurenic acid (Kyn), the NMDA receptor antagonist, AP5, or the non-NMDA receptor antagonist, CNQX, were effective in reversing the increases in BAT SNA (for Kyn, from peak of +419% of control to +9% of control) and BAT temperature, but not those in HR, MAP or RSNA (for Kyn, from peak of +143% of control to +124% of control) evoked by unilateral microinjection of bicuculline into the DMH. These results indicate that both NMDA and non-NMDA glutamate receptors in the RPa play a significant role in mediating the excitatory synaptic transmission producing the activation of BAT thermogenesis following disinhibition of DMH neurons. Glutamate receptors in the RPa may not be important for transmitting cardiovascular responses induced by activation of the DMH neurons. [Copyright &y& Elsevier]

1. The rostral medullary raphe pallidus contains sympathetic premotor neurons controlling thermogenesis in brown adipose tissue (BAT).2. Disinhibition of neurons in the dorsomedial hypothalamus (DMH) stimulates BAT thermogenesis through activation of neurons in raphe pallidus.3. An increase in BAT sympathetic outflow and BAT thermogenesis following microinjection of prostaglandin E2 into the preoptic area requires activation of both DMH neurons and raphe pallidus neurons.4. DMH contains a population of neurons receiving a tonically- active GABAergic inhibition which mediate increases in BAT thermogenesis through stimulation of BAT sympathetic premotor neurons in raphe pallidus. [Copyright &y& Elsevier]

We determined the cardiovascular effects of microinjecting the GABAA receptor antagonist, bicuculline, into the rostral raphe pallidus (RPa) on arterial pressure (AP), heart rate (HR) and cardiac sympathetic nerve activity (CSNA) in urethane–chloralose anesthetized, artificially-ventilated rats. In 26 animals, microinjection of the GABAA receptor antagonist, bicuculline (2 mM, 30 nl), into RPa increased CSNA (+234±64% of control), HR (+91±10 bpm) and mean AP (+16±3 mmHg). A similar tachycardia was evoked after removal of both adrenal glands, but was absent after β-adrenergic receptor blockade with atenolol. Equivalent percentage increases in CSNA and HR were evoked by disinhibition of the rostral RPa neurons after inhibition of cardiovascular sympathetic premotor neurons in the rostral ventrolateral medulla (RVLM) with bilateral microinjections of the GABAA receptor agonist, muscimol (6 mM, 50 nl) which markedly lowered CSNA, HR and AP. These results indicate that RPa contains a population of neurons, possibly sympathetic premotor neurons with direct projections to spinal cardiac sympathetic preganglionic neurons, that receive a tonic, GABAergic inhibition and thus do not contribute markedly to resting levels of CSNA and HR, but when disinhibited, they can produce large increases in CSNA and HR comparable to those seen during certain stress responses. [Copyright &y& Elsevier]

A spatial model of the 3D formation drillability field is crucial for drilling optimization in the petroleum area. In this paper, a new spatial modeling method is proposed for the 3D formation drillability field, which has two stages. In the first stage, the number of formation modes is determined according to the formation characteristics and these modes are identified by the fuzzy c-means clustering algorithm. In the second stage, random forest models are built separately for all formation modes. X, Y ground coordinates and depth coordinate are selected as the model inputs while the model output is the formation drillability. After that, these spatial 3D formation models are combined into one spatial 3D formation model for the whole drillability field. The proposed method and four compared methods (Random forest, ScatteredInterpolant, Support vector regression, and Kriging) are cross-validated and tested by using the data from eight drilling wells in the Xujiaweizi area, Northeast China. The results indicate the effectiveness of proposed method in spatial 3D formation drillability modeling. • A new spacial modeling method for the 3D formation drillability field is proposed. • The spatial clustering procedure is helpful in increasing the modeling accuracy of the 3D formation drillability field. • The proposed method is useful to the drilling optimization and drilling trajectory design. [ABSTRACT FROM AUTHOR]

Studies respiratory functions in mutant mice deficient in endothelin-1 generated by gene targeting. Mice heterozygous and homozygous for ET-1 mutation; Measurement of blood gas; Ventilation by body plethysmography; Statistical analysis.

Rate of penetration (ROP) prediction is crucial for the optimization and control in drilling process due to its vital role in maximizing the drilling efficiency. This paper proposes a novel intelligent model to predict the drilling ROP considering the process characteristics. First, the geological background and the drilling process of the case study are described. Based on the mechanism and frequency spectrum analysis, the strong nonlinearity and different low-frequency and high-frequency data noises between the data variables are detected. After that, the intelligent model is established via three stages. In the first stage, a wavelet filtering method is introduced to reduce these noises in the drilling data. In the next stage, the model inputs are determined by the mutual information method, which significantly decreased the model redundancy. In the last stage, a hybrid bat algorithm is proposed to optimize the hyper-parameters of the support vector regression model. Finally, the proposed model is validated by using the data from a drilling site in the Shennongjia area, Central China. The results demonstrate that the proposed method outperforms eight well-known methods and another three methods without different data preprocessing procedures in prediction accuracy. • The proposed ROP model considers the effect of strong nonlinearity, different kinds of data noise, and couplings. • A novel hybrid support vector regression method is proposed to predict the drilling ROP. • The proposed method showed an excellent performance in prediction accuracy compared with econventional methods. [ABSTRACT FROM AUTHOR]

Given the difficulty of a single model in dealing with complex systems. In this study, we propose a tuning model that uses a probabilistic fusion of sub‐optimal back‐propagation neural network based on the Gauss kernel clustering. This study focused mainly three aspects of work compared with the traditional tuning model. First, the calculation of the coupling matrix of scattering parameters is achieved by solving polynomial coefficients after eliminating the inconsistent phase shift and resonant cavity loss. Second, the best clustering center and a number were obtained by mapping the scattered data to high‐dimensional space, and the prediction of multi‐output variables were realized by sub‐model probability fusion. Third, an improved shuffled frog leaping algorithm was introduced to optimize the initial weights of the back‐propagation neural network, and a differential operation significantly improved the diversity of the population and the searchability of the algorithm. Finally, the experiment of nine‐order cross‐coupled filters shows that the proposed method has a better capability to train the weights and thresholds, which improves the generalization performance of the system. [ABSTRACT FROM AUTHOR]

Feature selection is a crucial step in the development of a system for identifying emotions in speech. Recently, the interaction between features generated from the same audio source was rarely considered, which may produce redundant features and increase the computational costs. To solve this problem, feature selection method based on correlation analysis and Fisher is proposed, which can remove the redundant features that have close correlations with each other. To improve the recognition performance of the feature subset after proposal feature selection further, an emotion recognition method based on extreme learning machine (ELM) decision tree is proposed according to the confusion degree among different basic emotions. A framework of speech emotion recognition is proposed and the classification experiments based on proposed classification method by using Chinese speech database from institute of automation of Chinese academy of sciences (CASIA) are performed. And the experimental results show that the proposal achieved 89.6% recognition rate on average. By proposal, it would be fast and efficient to discriminate emotional states of different speakers from speech, and it would make it possible to realize the interaction between speaker-independent and computer/robot in the future. [ABSTRACT FROM AUTHOR]

An underactuated planar acrobot (UPA) is a two-link manipulator with a passive first joint. The control objective for a UPA in a horizontal plane is to move it from an initial position and stabilize it at a target position. Since a UPA is not small-time locally controllable, motion control is a big challenge. This paper presents a new control strategy based on constraints on the motion state. First, constraints on the angles and angular velocities of the two links are devised by integrating the dynamic equation of the UPA. Next, the state constraints are analyzed to discover the motion characteristics. Then, the optimal target position is obtained by solving a motion optimization problem that incorporates those characteristics. Finally, a control strategy based on the Lyapunov function method is devised to achieve the control objective. A numerical example demonstrates the validity of the control strategy. [ABSTRACT FROM AUTHOR]

Abstract: The central melanocortin system plays a critical role in regulation of energy balance, including thermogenesis in brown adipose tissue (BAT). Activation of the hypothalamic melanocortin signaling stimulates sympathetically-mediated interscapular BAT (IBAT) thermogenesis. The rostral raphe pallidus (rRPa) and adjacent area have been proposed as the location of sympathetic premotor neurons for the central nervous system (CNS) control of IBAT thermogenesis. To determine if neuronal activity in rRPa area is required for the central melanocortin-induced thermogenesis, we studied the effects of inhibition of the activity of neurons in the rRPa area on the sympathetic nerve activity (SNA) to IBAT evoked by lateral ventricular injection of the melanocortin 3/4 receptor (MC3/4R) agonist, MTII, in urethane-chloralose-anesthetized rats and the effects on O2 consumption induced by third or fourth ventricular injection of MTII in conscious freely moving mice. Icv injection of MTII (1 nmol) significantly increased rat IBAT SNA (+741% of control). Both third and fourth ventricular injections of MTII (1 nmol) significantly increased O2 consumption in conscious C57BL/6J mice (45% higher than that of saline control for third ventricular injection and 44% higher than that of saline control for fourth ventricular injection). The increases in IBAT SNA and in O2 consumption were reversed by inhibition of neurons in the rRPa and adjacent area with microinjections of glycine or muscimol into rRPa. These results suggest that the neurons in the RPa and its immediate vicinity play an essential role in mediating the increase in IBAT thermogenesis induced by activation of central melanocortin signaling. [Copyright &y& Elsevier]

Abstract: Adaptive thermogenesis represents one of the important homeostatic mechanisms by which the body maintains appropriate levels of stored energy and its core temperature. Dysregulation of adaptive thermogenesis promotes obesity. The central melanocortin system, in particular the melanocortin 4 receptor (MC4R) signaling pathway, influences the regulation of every aspect of energy balance, including thermogenesis, and plays a critical role in energy homeostasis in both rodent and man. This review will outline our current understanding of adaptive thermogenesis, focusing on the role of the central melanocortin pathway in the regulation of thermogenesis. [Copyright &y& Elsevier]

Background & Aims: Nucleotides with add‐on interferon treatment (NUC‐IFN) provide significantly higher rates of hepatitis B surface antigen (HBsAg) loss in patients with chronic hepatitis B (CHB). This study aimed to investigate the sustainability of HBsAg loss and the prevention of clinical relapse. Methods: Patients with CHB who achieved HBsAg loss and HBV DNA levels <20 IU/ml after IFN or NUC‐IFN therapy were enrolled and followed up for 96 weeks. The primary outcome was HBsAg negativity without viremia at week 96. Secondary outcomes included virological or clinical relapse and predictors of relapse. Results: 420 patients were included in intention‐to‐treat analysis with 290 and 130 in the IFN and NUC‐IFN groups respectively. At week 96, the intention‐to‐treat analysis revealed similar outcomes between groups, including HBsAg seroreversion (24.83% vs. 23.08%, P =.70), viremia (16.90% vs 13.08%, P =.32) and clinical relapse (11.38% vs 10.00%, P =.68); the per‐protocol analyses also showed HBsAg seroreversion, viremia and clinical relapse in IFN group (15.50%, 6.59% and 0.39%) did not differ from those in NUC‐IFN group (15.25%, 4.24% and 0.85%, P >.05). These outcomes were similar between patients who received entecavir and those who received telbivudine/lamivudine/adefovir before the combination therapy. In NUC‐IFN‐treated patients, fibrosis regression was observed at week 96. Baseline HBsAb negativity was independent predictors of HBsAg sero‐reversion and recurrence of viremia in IFN treated group. Conclusion: NUC‐IFN and IFN therapies are equally effective in achieving sustained functional cure and fibrosis regression. (ClinicalTrials.gov, Number NCT02336399). [ABSTRACT FROM AUTHOR]

• Binarization Cascade Convolution Neural Network (WBCCNN) is proposed. This multiple-level and binary structure is coarse-to-fine, which could increase the accuracy and efficiency of eye localization effectively. Eye localization is a key step in the field of face recognition and analysis, which is the premise and breakthrough of drowsiness estimation and auxiliary driving. An eye localization method based on Weight Binarization Cascade Convolution Neural Network (WBCCNN) is proposed, in which the WBCCNN is composed of four levels and the weight is constrained by binarization. It predicts eye positions from coarse-to-fine to improve the performance of eye localization, and binary network not only helps to reduce the storage size of the model, but also speeds up the operation. Experiments on eye localization are performed using Labeled Faces in the Wild (LFW), BioID, and Labeled Face Parts in the Wild (LFPW) Databases, from which the results show that the average detection errors of left eye and right eye by our method are 0.66% and 0.71% on LFW, respectively. The operation speed of binary network is approximately as twice as that of non-binary. In addition, our method requires less storage capacity, which maintains higher performance on BioID and LFPW, compared to some state-of-the-art works. [ABSTRACT FROM AUTHOR]

[Display omitted] • Bare-bone formant structure is used to extract syllable level speech features. • Formant skeleton carries cleaner information about emotional aspects, e.g., stress. • A small neural network outperforms the SOTA methods in cross-corpus tests. Speech emotion recognition systems have high computational requirements for deep learning models and low generalizability mainly because of the poor reliability of emotional measurements across multiple corpora. To solve these problems, we present a speech emotion recognition system based on a reductionist approach of decomposing and analyzing syllable-level features. Mel-spectrogram of an audio stream is decomposed into syllable-level components, which are then analyzed to extract statistical features. The proposed method uses formant attention, noise-gate filtering, and rolling normalization contexts to decrease contextual differences and increase focus the attention on the structure of a formant. A set of syllable-level formant features is extracted and fed into a single hidden layer neural network that makes predictions for each syllable as opposed to the conventional approach of using a sophisticated deep learner to make sentence-wide predictions. The syllable level predictions help to lower the aggregated error in utterance level cross-corpus predictions. The experiments on IEMOCAP (IE), MSP-Improv (MI), and RAVDESS (RA) databases show that the method archives better than the state-of-the-art cross-corpus unweighted accuracy of 47.6% for IE to MI and 56.2% for MI to IE. [ABSTRACT FROM AUTHOR]

Hepatitis B surface antigen (HBsAg) loss is considered a functional cure in chronic hepatitis B (CHB). However, the durability of HBsAg loss after stopping treatment remains unknown. This study aimed to assess the sustained functional cure achieved by interferon therapy in hepatitis B envelope antigen (HBeAg)‐negative CHB patients. In this prospective study, 176 HBeAg‐negative CHB patients with functional cure were enrolled for 12 weeks of cessation treatment, and treatment information and baseline data were collected. Hepatitis B virus (HBV) biomarkers and clinical biochemical indicators were evaluated every 3 months; liver imaging examinations were performed every 3‐6 months during the 48‐week follow‐up. The sustained functional cure was evaluated. After the 48‐week follow‐up, the sustained functional cure rate was 86.63%. The cumulative rates of HBsAg reversion and HBV DNA reversion were 12.79% and 2.33%, respectively. Consolidation treatment ≥ 12 weeks after HBsAg loss achieved a significantly higher rate of sustained functional cure and significantly lower rate of HBsAg reversion than consolidation treatment < 12 weeks (76.19% vs 90.00%, P = 0.022 and 23.81% vs 9.23%, P = 0.014, respectively). Patients with hepatitis B surface antibody (HBsAb) had higher rate of sustained functional cure than patients achieving HBsAg loss but without HBsAb (89.86% vs 73.53%, P = 0.012). Consolidation treatment ≥ 12 weeks (odds ratio [OR] 16.478; 95% confidence interval [CI], 2.135‐127.151; P = 0.007) and high HBsAb levels (OR 8.312; 95% CI, 1.824‐37.881; P = 0.006) were independent predictors of sustained functional cure. Results suggested that 12 weeks of consolidation therapy after HBsAg clearance and elevated HBsAb levels help to improve functional cure. [ABSTRACT FROM AUTHOR]

Human-robot emotional interaction has developed rapidly in recent years, in which speech emotion recognition plays a significant role. In this paper, a speech emotion recognition method based on an improved brain emotional learning (BEL) model is proposed, which is inspired by the emotional processing mechanism of the limbic system in the brain. The reinforcement learning rule of BEL model, however, makes it have poor adaptation and affects its performance. To solve these problems, Genetic Algorithm (GA) is employed to update the weights of BEL model. The proposal is tested on the CASIA Chinese emotion corpus, SAVEE emotion corpus, and FAU Aibo dataset, in which MFCC related features and their 1st order delta coefficients are extracted. In addition, the proposal is tested on INTERSPEECH 2009 standard feature set, in which three dimensionality reduction methods of Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), and PCA+LDA are used to reduce the dimension of feature set. The experimental results show that the proposed method obtains average recognition accuracy of 90.28% (CASIA), 76.40% (SAVEE), and 71.05% (FAU Aibo) for speaker-dependent (SD) speech emotion recognition and the highest average accuracy of 38.55% (CASIA), 44.18% (SAVEE), 64.60% (FAU Aibo) for speaker-independent (SI) speech emotion recognition are obtained, which shows that the proposal is feasible in speech emotion recognition. [ABSTRACT FROM AUTHOR]

Accumulated evidence suggests that social support is influenced by genetic and environmental factors. However, there are little data that examine this issue from Asian samples. We reported results from a preliminary study that examined familial effects on social support in a Chinese adult twin sample. We administered a 10-item social support instrument that measures three dimensions of social support (i.e., objective support, subjective support, and utilization of support) developed for the Chinese population. Two hundred forty-two same-sex twin pairs, where both members of the pair completed the personal interview, were included in the final analysis. Structural equation modeling was used to estimate additive genetic (A), shared environmental (C), and nonshared environmental (E) effects on each dimension of social support. Familial factors (A+C) explained 56.63% [95% CI = 45.48-65.72%] and 42.42% [95% CI = 29.93-53.25%] of the total phenotypic variances of subjective support and utilization of support, respectively. For the objective support, genetic effects did not exist, but common environmental effect explained 37.56% [95% CI = 26.17-48.28%] of the total phenotypic variances. Neither gender nor age effects were seen on any dimension of social support. Except for objective support, genetic factors probably influence variation in subjective support and utilization of support. Shared environmental factors may influence all dimensions of social support. [ABSTRACT FROM AUTHOR]