The complexity of trait-environment performance landscapes in a local subtropical forest.

That functional traits should affect individual performance and, in turn, determine fitness and population growth, is a foundational assumption of trait-based ecology. This assumption is, however, not supported by a strong empirical base. Here, we measured simultaneously two individual performance metrics (survival and growth), seven traits and 10 environmental properties for each of 3981 individuals of 205 species in a 50-ha stem-mapped subtropical forest. We then modelled survival/growth as a function of traits, environments and trait × environment interactions, and quantified their relative importance at both the species and individual levels. We found evidence of alternative functional designs and multiple performance peaks along environmental gradients, indicating the presence of complicated trait × environment interactions. However, such interactions were relatively unimportant in our site, which had relatively low environmental variations. Moreover, individual performance was not better predicted, and trait × environment interactions were not more likely detected, at the individual level than at the species level. Although the trait × environment interactions might be safely ignored in relatively homogeneous environments, we encourage future studies to test the interactive effects of traits and environments on individual performances and lifelong fitness at larger spatial scales or along experimentally manipulated environmental gradients.

Automated Recognition of Epileptic EEG States Using a Combination of Symlet Wavelet Processing, Gradient Boosting Machine, and Grid Search Optimizer.

Automatic recognition methods for non-stationary electroencephalogram (EEG) data collected from EEG sensors play an essential role in neurological detection. The integrated approaches proposed in this study consist of Symlet wavelet processing, a gradient boosting machine, and a grid search optimizer for a three-class classification scheme for normal subjects, intermittent epilepsy, and continuous epilepsy. Fourth-order Symlet wavelets are adopted to decompose the EEG data into five frequencies sub-bands, such as gamma, beta, alpha, theta, and delta, whose statistical features were computed and used as classification features. The grid search optimizer is used to automatically find the optimal parameters for training the classifier. The classification accuracy of the gradient boosting machine was compared with that of a conventional support vector machine and a random forest classifier constructed according to previous descriptions. Multiple performance indices were used to evaluate the proposed classification scheme, which provided better classification accuracy and detection effectiveness than has been recently reported in other studies on three-class classification of EEG data.

pH-independent dissolution enhancement for multiple poorly water-soluble drugs by nano-sized solid dispersions based on hydrophobic-hydrophilic conjugates.

The objective of this study was to achieve an optimal formulation of hydrophilic-hydrophobic conjugates for nano-sized solid dispersions (SDs) with enhanced dissolution of multiple drugs in different gastrointestinal (GI) tract environments. A new conjugate powder with an optimized process was used to fabricate SDs that contained three poorly water-soluble drugs that were also poorly soluble in different dissolution media. The self-assembled nanoparticle formation, drug crystallinity and SD molecular interactions were investigated by measuring the particle size during dissolution testing and physicochemical property analysis (powder X-ray diffraction and Fourier transform infrared spectroscopy). Drug release studies indicated that SD containing conjugated powder significantly improved the dissolution rates of these poorly water-soluble drugs in the GI tract. In addition, particle size analysis showed nano-sized particles in the dissolution media in the early stage with a tendency to reduce smaller particles over time. Physicochemical characterizations demonstrated almost amorphous drug states and hydrogen bonding interactions between the drugs and conjugates in the SD. This study optimized a promising material for SD, and the material was shown to have a promising performance under various pH medium conditions with poorly water-soluble drugs.

Stochastic Modeling and Analysis of Multiple Nonlinear Accelerated Degradation Processes through Information Fusion.

Accelerated degradation testing (ADT) is an efficient technique for evaluating the lifetime of a highly reliable product whose underlying failure process may be traced by the degradation of the product's performance parameters with time. However, most research on ADT mainly focuses on a single performance parameter. In reality, the performance of a modern product is usually characterized by multiple parameters, and the degradation paths are usually nonlinear. To address such problems, this paper develops a new s-dependent nonlinear ADT model for products with multiple performance parameters using a general Wiener process and copulas. The general Wiener process models the nonlinear ADT data, and the dependency among different degradation measures is analyzed using the copula method. An engineering case study on a tuner's ADT data is conducted to demonstrate the effectiveness of the proposed method. The results illustrate that the proposed method is quite effective in estimating the lifetime of a product with s-dependent performance parameters.

An effective dynamic immune optimization control for the wastewater treatment process.

To resolve the conflict between multiple performance indicators in the complicated wastewater treatment process (WWTP), an effective optimization control scheme based on a dynamic multi-objective immune system (DMOIA-OC) is designed. A dynamic optimization control scheme is first developed in which the control process is divided into a dynamic layer and a tracking control layer. Based on the analysis of the WWTP performance, the energy consumption and effluent quality models are next established adaptively in response to the environment by an optimization layer. An adaptive dynamic immune optimization algorithm is then proposed to optimize the complex and conflicting performance indicators. In addition, a suitable preferred solution is selected from the numerous Pareto solutions to obtain the best set of values for the dissolved oxygen and nitrate nitrogen. Finally, the solution is evaluated on the benchmark simulation platform (BSM1). The results show that the DMOIA-OC method can solve the complex optimization problem for multiple performance indicators in WWTPs and has a competitive advantage in its control effect.

Application of Intelligent Modeling Method to Optimize the Multiple Quality Characteristics of the Injection Molding Process of Automobile Lock Parts.

This study focuses on applying intelligent modeling methods to different injection molding process parameters, to analyze the influence of temperature distribution and warpage on the actual development of auto locks. It explores the auto locks using computer-aided engineering (CAE) simulation performance analysis and the optimization of process parameters by combining multiple quality characteristics (warpage and average temperature). In this experimental design, combinations were explored for each single objective optimization process parameter, using the Taguchi robust design process, with the L18 (21 × 37) orthogonal table. The control factors were injection time, material temperature, mold temperature, injection pressure, packing pressure, packing time, cooling liquid, and cooling temperature. The warpage and temperature distribution were analysed as performance indices. Then, signal-to-noise ratios (S/N ratios) were calculated. Gray correlation analysis, with normalization of the S/N ratio, was used to obtain the gray correlation coefficient, which was substituted into the fuzzy theory to obtain the multiple performance characteristic index. The maximum multiple performance characteristic index was used to find multiple quality characteristic-optimized process parameters. The optimal injection molding process parameters with single objective are a warpage of 0.783 mm and an average temperature of 235.23 °C. The optimal parameters with multi-objective are a warpage of 0.753 mm and an average temperature of 238.71 °C. The optimal parameters were then used to explore the different cooling designs (original cooling, square cooling, and conformal cooling), considering the effect of the plastics temperature distribution and warpage. The results showed that, based on the design of the different cooling systems, conformal cooling obtained an optimal warpage of 0.661 mm and a temperature of 237.62 °C. Furthermore, the conformal cooling system is smaller than the original cooling system; it reduces the warpage by 12.2%, and the average temperature by 0.46%.