Enhanced H-filter based on F?hr?us-Lindqvist effect for efficient and robust dialysis without membrane

A novel microfluidic device for highly efficient and robust dialysis without membrane is highly desired for the development of portable or wearable microdialyzer. Here we report an enhanced H-filter with pillar array based on Fåhræus-Lindqvist effect (F-L effect) for highly efficient and robust membraneless dialysis of simplified blood for the first time. The H-filter employs two fluids laminarly flowing in the microchannel for continuously membraneless dialysis. With pillar array in the microchannel, the two laminar flows, with one containing blood cells and small molecules and another containing dialyzate solution, can form a cell-free layer at the interface as selective zones for separation. This provides enhanced mixing yet extremely low shear for extraction of small molecules from the blood-cell-containing flow into the dialyzate flow, resulting in robust separation with reduced cell loss and improved efficiency. We demonstrate this by first using Chlorella pyrenoidosa as model cells to quantitatively study the separation performances, and then using simplified human blood for dialysis. The advanced H-filter, with highly efficient and robust performance for membraneless dialysis, shows great potential as promising candidate for rapid blood analysis/separation, and as fundamental structure for portable dialyzer.     

The effect mechanism of leaders＇ linguistic frame on followers＇ creativity based on regulatory focus theory

尝试把调节焦点理论引入领导对下属创造力影响的研究领域,构建并验证了基于该理论的领导语言框架对下属创造力的影响机理模型。以143名学生为样本,采用实验研究方法,对领导语言框架影响下属创造力的作用机制进行了深入考察,实验结果表明,下属情境调节焦点部分中介了领导语言框架与下属创造力间的关系,下属特质调节焦点则调节了情境调节焦点与下属创造力间的关系。最后,就论文的主要研究结论、主要贡献及不足之处进行了总结。     

Nonlinear control design based on generalized Takagi–Sugeno fuzzy systems

This paper is concerned with control design for a generalized Takagi–Sugeno fuzzy system. The Takagi–Sugeno fuzzy system generally describes nonlinear systems by employing local linear system representations, while a generalized fuzzy system to be considered in this paper describes even a wider class of nonlinear systems by representing locally nonlinear systems. For such a generalized system, a stabilizing controller design method is proposed by introducing a new class of non-PDC controllers. A non-PDC controller is a generalized controller of PDC one, which is a traditional fuzzy controller. Stabilizing controller design conditions are given in terms of a set of linear matrix inequalities (LMIs), which are easily numerically solvable. A relaxation method is used to reduce the conservatism of design conditions. Finally, numerical examples are given to illustrate our nonlinear control design and to show the effectiveness over other existing results.     

An electrostatic microwell–based biochip for phytoplanktonic cell trapping

A simple microwell-based microfluidic chip for microalgal cells trapping was fabricated. An electrostatic cell trapping mechanism, enabled by a positively charged glass surface, was used. The chip was capable of capturing multiple algal cell types. In the case of filamentous Spirulina platensis, we observed single filament occupancy of up to ∼30% available wells, as high as some previously proposed methods. Captured filaments were not of any preferential size, suggesting well randomized cell trapping. It was found that the electrostatic attraction did not affect the cell growth. Total replacement of liquid inside the wells could be achieved by pumping new solutions via the inlet, making single cell experiments in controlled chemical conditions possible. After the top layer of the chip was removed, cells in the wells could be simply transferred using a micropipette, turning the chip into a platform for strain selection.     

Adaptive T–S fuzzy controller using reinforcement learning based on Lyapunov stability

In this paper, an adaptive Takagi–Sugeno (T–S) fuzzy controller based on reinforcement learning for controlling the nonlinear dynamical systems is proposed. The parameters of the T–S fuzzy system are learned using the reinforcement learning based on the actor-critic method. This on-line learning algorithm improves the controller performance over the time, which it learns from its own faults through the reinforcement signal from the external environment and tries to reinforce the T–S fuzzy system parameters to converge. The updating parameters are developed using the Lyapunov stability criterion. The proposed controller is faster in learning than the T–S fuzzy that parameters learned using the gradient descent method under the same conditions. Moreover, it is able to handle the load changes and the system uncertainties. The test is carried out based on two mathematical models. In addition, the proposed controller is applied practically for controlling a direct current (DC) shunt machine. The results indicate that the response of the proposed controller has a good performance compared with other controllers.     

Functional observer based controller for stabilizing Takagi–Sugeno fuzzy systems with time-delays

This paper investigates the construction of a fuzzy functional observer for nonlinear systems with time-delays, and the application of the observer to estimate the state functions of the parallel distributed compensation controller for stabilizing the system. Two types of time-delays are considered: constant and time-varying delays with bounded time derivative. Stability conditions are obtained using Lyapunov–Krasovskii functional approach; and the conditions are transformed into linear matrix inequalities with equality constraints so that observer parameters can be calculated using the solution of these inequalities. Functional observer construction procedures are presented considering both constant and time-varying time-delays. Two examples, including one for obtaining a power system stabilizer for a single machine infinite bus system, are presented to illustrate effectiveness of the proposed design procedures.     

LWT–DSR based new robust framework for watermark extraction under intentional attack conditions

This paper proposes a new watermarking approach using dynamic stochastic resonance (DSR) based tuning operation to extract the watermark logo from the watermarked image that has undergone different intentional and signal processing attacks. This method is intended to provide remedies from the shortcomings of the technique proposed by Lin et al. (2008), and invalidates the effect of intentional attacks recently designed by Meerwald et al. (2009). The algorithm incorporates three level image decomposition using lifting wavelet transform (LWT) and low-pass subband is utilized for data hiding purpose. Watermark bits are embedded into the blocks of non-overlapped wavelet coefficients of the cover image by quantizing the two maximum coefficients of the corresponding block. In watermark extraction process, the DSR is applied by performing the tuning operation on coefficient blocks of attacked watermarked image. It is a parameter dependent approach that enhances the performance of watermark extraction, where the parameters of DSR inherently depend on the image properties such as standard deviation or variance. As far as security is concerned, the randomization of wavelet coefficients, blocks, and watermark bits helps the framework to be more secure. The proposed technique is also examined against multiple watermarking attack and successfully proves its authenticity and ownership. Comparison of the proposed technique with recent techniques shows remarkable improvement in terms of robustness and security against various intentional, signal processing, and geometrical attacks.     

Fault detection of discrete-time T–S fuzzy affine systems based on piecewise Lyapunov functions

This paper investigates the problem of robust fault detection for a class of discrete-time nonlinear systems, which are represented by Takagi–Sugeno (T–S) fuzzy affine dynamic models with norm-bounded uncertainties. The objective is to design an admissible fault detection filter guaranteeing the asymptotic stability of the resulting residual system with prescribed performances. It is assumed that the plant premise variables, which are often the state variables or their functions, are not measurable so that the fault detection filter implementation with state-space partition may not be synchronized with the state trajectories of the plant. Based on a piecewise quadratic Lyapunov function combined with S-procedure and some matrix inequality convexification techniques, the results are formulated in the form of linear matrix inequalities. Finally, a simulation example is provided to illustrate the effectiveness of the proposed approach.     

Master-slave synchronization for time-varying delay chaotic Lur’e systems based on the integral-term-related free-weighting-matrices technique

In this paper, the master-slave synchronization of the chaotic Lur’e system (MSSCLS) with time-varying delay is investigated. At first, a Lyapunov-Krasovskii functional (LKF) with nonlinear terms and time-varying delay is constructed. Secondly, when the derivative of LKF is estimated, the improved stability criterion is obtained by considering the relationship between the integral terms generated after using the Bessel-Legendre inequality (BLI) and introducing the integral-term-related free-weighting-matrices. Finally, case studies based on Chua’s circuit are carried out to test and verify the effectiveness of the proposed synchronization criterion, and the results are compared with the latest methods to show the advantages of the proposed synchronization criterion.     

An improved H∞ filtering for Markovian jump system with time-varying delay based on finite frequency domain

In traditional analysis of system performance, Markovian jump system is often considered in the full frequency domain. However, some unknown factors, such as noise interference, often act on a specific frequency domain, so there is a big defect in putting the system analysis in the full frequency domain. In this paper, the finite frequency domain is introduced into the Markovian jump system. By using generalized KYP lemma, the system is divided into three frequency bands: low, medium and high. The disturbance in different frequency domain is analyzed and the H_∞ filter is designed. In addition, in dealing with inequality relations, the improved Wirtinger inequality method is used, and the Projection lemma is used in dealing with filter coupling terms, and time-varying delay problem in finite frequency domain is also considered. Finally, some examples are given to illustrate the effectiveness of our methods and the specific parameters and convergence graph of the finite frequency domain filter are obtained.     

Investigating the business intelligence capabilities’ and network learning effect on the data mining for start-up's function

In recent years, Startups are new concepts in the tech world and are meant to be temporary organizations designed to find a repeatable, scalable business model, while offering a new solution to the problem. What matters most about the success of startups is the right and reasonable targeting. This is something that is usually overlooked by a large percentage of such businesses, causing them to fail within a short period of time. Production of new product or service delivery is not possible without planning. The purpose of this study is to look deeper into how startups should identify and prioritize issues and problems when launching a new product or service. First, startups and subject-related businesses reviewed and then novel and emerging issues, including the status of data mining on startups' performance, the topics of Business Intelligence (BI), innovation and networked learning, and also their role in startups business discussed. Results showed that BI can provide a competitive advantage to startups. With this in mind, these businesses may adapt to the diverse needs of customers in the market and continue to survive, as well as gain greater market share over their competitors. Further, employing technology tools helps companies make their data available seamlessly or securely and by analyzing it, giving managers a better way of making decisions. According to the hypotheses, it was found that BI as a powerful tool in the field of information technology, creates a competitive advantage and it is necessary for start-up managers to accept this tool.     

Identification of Wiener–Hammerstein models based on variational bayesian approach in the presence of process noise

This paper addresses the identification of Wiener–Hammerstein (WH) models in the presence of process and measurement noises, which has not been well studied yet in the existing works. To achieve an unbiased estimation, the model parameters are obtained by maximizing the likelihood function, which is solved in the expectation-maximization framework. Due to the difficulty of computing the posterior distributions of the latent variables of WH models, variational Bayes (VB) is used here, and a method for approximating the posterior distributions based on Monte Carlo integral is proposed in VB framework. To the best of our knowledge, it is the first time to use VB approach for WH model identification. Two simulation examples demonstrate the effectiveness of the proposed method. Moreover, the proposed method is used for a WH benchmark problem, and the results show that it improves the identification performance.     

New robust H∞ control for uncertain stochastic Markovian jumping systems with mixed delays based on decoupling method

This paper considers the problems of robust stochastic stabilization and robust $H_{\infty }$ controller design for a class of stochastic Markovain jumping systems with mixed time delays and polytopic parameter uncertainties. Both the interval time-varying delay and distributed time delay are simultaneously considered. Some new delay-dependent sufficient conditions, which differs greatly from the most existing results, are obtained based on the decoupling method and some advanced techniques. A numerical example is provided to illustrate the effectiveness of the proposed criteria.     

The effect of public subsidies on firms’ investment–cash flow sensitivity: Transient or persistent?

This work analyses the effect of public subsidies on firms’ investments and investment–cash flow sensitivity in a longitudinal sample of 288 Italian unlisted non-venture capital backed owner-managed new-technology-based firms (NTBFs), observed over a 15-year period from 1994 to 2008. Seventy five of these firms received one or more public subsidies in the observation period. We use an error correction model (ECM) specification and system generalised method of moment (GMM) techniques that take into account the endogeneity of public subsidies. First, we find that the investments of small NTBFs are sensitive to internal cash flows, while those of large NTBFs are not. Receipt of public subsidies by small NTBFs results in an increased investment rate and a reduced investment–cash flow sensitivity, in the immediately following year. We interpret these results as an indication of the relaxation of financial constraints. Moreover, while the increase in the investment rate does not persist in the long run, the dependence of investments on cash flow remains negligible after receipt of the first public subsidy. These results support the view that public subsidies can help small NTBFs in persistently removing the financial constraints that bind their investment activity.     

A new idea on almost sure permanence and uniform boundedness for a stochastic predator–prey model

The existence and uniqueness of stationary distribution and ergodic properties of a stochastic system are obtained. Especially, different from the existing methods, a new method is introduced to analyze almost sure permanence and uniform boundedness of the stochastic predator–prey model. This new idea is based on geometric structure of invariant set for a stochastic system. More specifically, we obtain our main conclusions by showing the invariant set for the stochastic population system lies in the interior of the first quadrant. It is interesting and surprising that the stochastic population model can guarantee a uniform boundedness almost surely. Some numerical simulations are carried out to support our results.     

Active fault tolerant control of wind turbine systems based on DFIG with actuator fault and disturbance using Takagi–Sugeno fuzzy model

This paper presents an active fault tolerant control (FTC) for doubly fed induction generator (DFIG) with actuator fault and disturbance using Takagi–Sugeno (TS) fuzzy model. The control structure has two parts: fault and disturbance estimation part and FTC part. First, a TS fuzzy model is used to describe the DFIG system. Using a special linear transformation, the original system is decoupled into three independent subsystems: state subsystem without fault and disturbance, disturbance subsystem without fault, and fault subsystem without disturbance. By solving linear matrix inequalities (LMIs), a TS fuzzy observer is designed for the state subsystem without fault and disturbance. Second, estimations of faults and disturbance are obtained using the other subsystem models. Third, an active FTC scheme is developed to reduce the effect of disturbance and actuator faults. Finally, the performance of the proposed FTC is tested for a wind turbine system based on DFIG with actuator faults and disturbance. The simulation results demonstrate that the new FTC scheme makes possible to obtain an efficient fault and disturbance estimation and to reduce the peak current in the transient process.     

Tran-Switch: A transfer learning approach for sentence level cross-genre author profiling on code-switched English–RomanUrdu Text

Cross-genre author profiling aims to build generalized models for predicting profile traits of authors that can be helpful across different text genres for computer forensics, marketing, and other applications. The cross-genre author profiling task becomes challenging when dealing with low-resourced languages due to the lack of availability of standard corpora and methods. The task becomes even more challenging when the data is code-switched, which is informal and unstructured. In previous studies, the problem of cross-genre author profiling has been mainly explored for mono-lingual texts in highly resourced languages (English, Spanish, etc.). However, it has not been thoroughly explored for the code-switched text which is widely used for communication over social media. To fulfill this gap, we propose a transfer learning-based solution for the cross-genre author profiling task on code-switched (English–RomanUrdu) text using three widely known genres, Facebook comments/posts, Tweets, and SMS messages. In this article, firstly, we experimented with the traditional machine learning, deep learning and pre-trained transfer learning models (MBERT, XLMRoBERTa, ULMFiT, and XLNET) for the same-genre and cross-genre gender identification task. We then propose a novel Trans-Switch approach that focuses on the code-switching nature of the text and trains on specialized language models. In addition, we developed three RomanUrdu to English translated corpora to study the impact of translation on author profiling tasks. The results show that the proposed Trans-Switch model outperforms the baseline deep learning and pre-trained transfer learning models for cross-genre author profiling task on code-switched text. Further, the experimentation also shows that the translation of RomanUrdu text does not improve results.     

A mental state Knowledge–aware and Contrastive Network for early stress and depression detection on social media

Stress and depression detection on social media aim at the analysis of stress and identification of depression tendency from social media posts, which provide assistance for the early detection of mental health conditions. Existing methods mainly model the mental states of the post speaker implicitly. They also lack the ability to mentalise for complex mental state reasoning. Besides, they are not designed to explicitly capture class-specific features. To resolve the above issues, we propose a mental state Knowledge–aware and Contrastive Network (KC-Net). In detail, we first extract mental state knowledge from a commonsense knowledge base COMET, and infuse the knowledge using Gated Recurrent Units (GRUs) to explicitly model the mental states of the speaker. Then we propose a knowledge–aware mentalisation module based on dot-product attention to accordingly attend to the most relevant knowledge aspects. A supervised contrastive learning module is also utilised to fully leverage label information for capturing class-specific features. We test the proposed methods on a depression detection dataset Depression_Mixed with 3165 Reddit and blog posts, a stress detection dataset Dreaddit with 3553 Reddit posts, and a stress factors recognition dataset SAD with 6850 SMS-like messages. The experimental results show that our method achieves new state-of-the-art results on all datasets: 95.4% of F1 scores on Depression_Mixed, 83.5% on Dreaddit and 77.8% on SAD, with 2.07% average improvement. Factor-specific analysis and ablation study prove the effectiveness of all proposed modules, while UMAP analysis and case study visualise their mechanisms. We believe our work facilitates detection and analysis of depression and stress on social media data, and shows potential for applications on other mental health conditions.     

A decomposition analysis on convecting–radiating rectangular plate fins for variable thermal conductivity and heat transfer coefficient

The present study concentrates to make a complete thermal analysis on a rectangular fin with its primary surface by taking into consideration of radiation heat exchange with the surrounding along with the convective mode of heat transfer. The one side of the primary surface is heated by a fluid with high temperature which may be required to dissipate heat quickly by a fin array system. To analyze an actual case study, the thermal conductivity of the fin material and convective heat transfer coefficient over the surface are treated as a variable and they are as a function of the local fin surface temperature. With the aforementioned condition, the energy equations for both the fin and primary surface become nonlinear. The decomposition method is suggested to solve these highly nonlinear equations to obtain a closed form temperature distribution. The result of temperature distribution determined by the present analysis is compared with that of the numerical values. With the adaptation of the simplified case, the present method is also compared with the exactly closed form results. From both of these comparisons, an exact matching of results is found. The fin performances, namely, fin efficiency, surface efficiency and augmentation factor are evaluated for a wide range of thermogeometric parameters.