Organizational capability and evolution in China's independent colleges

Independent colleges, as transitional form of higher education and spin off organizations in the Chinese context, are inseparable from the dependence on the resources of parent organizations. Meanwhile, independent colleges' dual role comes from considering not only the public good nature of education but also the for profit of investment returns. In view of the dual role feature and heavy dependence on external resources, this study endeavors to define the organizational capability of independent colleges and how it is formed and developed. From the perspective of the paradox lens and taken Xinhua College as research object, this study analyzes the capability building process of spin off organizations and their innovative pattern. The findings are : (1) The process of organizational capability building of independent colleges can be revealed through the dynamic interactive pattern of internal replication and internal innovation. (2) The evolutionary path of the independent colleges' organizational capability is a cyclic construction process with three stages, namely , "fr om external to internal", "capability retention" and "from internal to external". (3) Affected by environmental pressures and opportunities, independent colleges explore balances between public welfare and commercial behaviors. Meanwhile, under the joint promotion of environmental cognition and paradox balance system, independent colleges build the internal innovation capability and complete the cyclical construction of organizational capabilities. The main contributions are: (1) It explores the organizational capabilities of independent colleges in China and deepens the research on organizational capabilities of independent colleges; (2) After exploring the evolutionary process of organizational capabilities of independent college s , this study concludes propositions on the evolution of organizational capability process; (3) It explores the interaction pattern between the paradox balance system and the organization’s dual formation role of organizational capabilities. (4) It reveals the innovation pattern which promotes the innovative motivation of spin off organizations

Self organizing fuzzy sliding mode controller for the position control of a permanent magnet synchronous motor drive

AbstractIn this paper, a self organizing fuzzy sliding mode controller (SOFSMC) which emulates the fuzzy controller with gain auto-tuning is proposed for a permanent magnet synchronous motor (PMSM) drive. The proposed controller is used for the position control of the PMSM drive. The performance and robustness of the control system is tested for nonlinear motor load torque disturbance and parameter variations. It has a novel gain self organizing strategy in response to the transient or tracking responses requirement. To illustrate the performance of the proposed controller, the simulation studies are presented separately for the SOFSMC and the fuzzy controller with gain auto-tuning. The results are compared with each other and discussed in detail. Simulation results showing the effectiveness of the proposed control system are confirmed under the different position changes

Higher-order Motif-based Time Series Classification for Forced Oscillation Source Location in Power Grids

Time series motifs are used for discovering higher-order structures of time series data. Based on time series motifs, the motif embedding correlation field (MECF) is proposed to characterize higher-order temporal structures of dynamical system time series. A MECF-based unsupervised learning approach is applied in locating the source of the forced oscillation (FO), a periodic disturbance that detrimentally impacts power grids. Locating the FO source is imperative for system stability. Compared with the Fourier analysis, the MECF-based unsupervised learning is applicable under various FO situations, including the single FO, FO with resonance, and multiple sources FOs. The MECF-based unsupervised learning is a data-driven approach without any prior knowledge requirement of system models or typologies. Tests on the UK high-voltage transmission grid illustrate the effectiveness of MECF-based unsupervised learning. In addition, the impacts of coupling strength and measurement noise on locating the FO source by the MECF-based unsupervised learning are investigated

How good are jobs in New Zealand?

Based on an analysis of the New Zealand data in the Work Orientation module of the International Social Survey Programme (ISSP) across three rounds (1997, 2005 and 2015), this paper examines how workers in New Zealand perceive their job quality. These surveys imply that New Zealanders have relatively good jobs, as shown in healthy levels of job quality and job satisfaction. They rate highly the quality of their collegial relationships at work and typically perceive the intrinsic quality of their job as better than the extrinsic quality. A key issue in relation to the latter is that they generally do not rate their advancement opportunities as high. While men, full-timers and graduates have some advantages over women, part-timers and non-graduates in extrinsic job quality, the intrinsic quality of work is more evenly experienced. In terms of intrinsic issues, the rising level of stress from 2005 to 2015 poses a concern and there is no evidence that graduates enjoy any kind of premium in the intrinsic quality of work apart from a lower level of hard physical effort

Optimal Inter-area Oscillation Damping Control: A Transfer Deep Reinforcement Learning Approach with Switching Control Strategy

Wide-area damping control for inter-area oscillation (IAO) is critical to modern power systems. The recent breakthroughs in deep learning and the broad deployment of phasor measurement units (PMU) promote the development of datadriven IAO damping controllers. In this paper, the damping control of IAOs is modeled as a Markov Decision Process (MDP) and solved by the proposed Deep Deterministic Policy Gradient (DDPG) based deep reinforcement learning (DRL) approach. The proposed approach optimizes the eigenvalue distribution of the system, which determines the IAO modes in nature. The eigenvalues are evaluated by the data-driven method called dynamic mode decomposition. For a given power system, only a subset of generators selected by participation factors needs to be controlled, alleviating the control and computing burdens. A Switching Control Strategy (SCS) is introduced to improve the transient response of IAOs. Numerical simulations of the IEEE-39 New England power grid model validate the effectiveness and advanced performance of the proposed approach as well as its robustness against communication delays. In addition, we demonstrate the transfer ability of the DRL model trained on the linearized power grid model to provide effective IAO damping control in the non-linear power grid model environment

Parent training for preschool ADHD: a randomized controlled trial of specialized and generic programs

BackgroundThe New Forest Parenting Package' (NFPP), an 8-week home-based intervention for parents of preschoolers with attention-deficit/hyperactivity disorder (ADHD), fosters constructive parenting to target ADHD-related dysfunctions in attention and impulse control. Although NFPP has improved parent and laboratory measures of ADHD in community samples of children with ADHD-like problems, its efficacy in a clinical sample, and relative to an active treatment comparator, is unknown. The aims are to evaluate the short- and long-term efficacy and generalization effects of NFPP compared to an established clinic-based parenting intervention for treating noncompliant behavior [Helping the Noncompliant Child' (HNC)] in young children with ADHD. MethodsA randomized controlled trial with three parallel arms was the design for this study. A total of 164 3-4-year-olds, 73.8% male, meeting DSM-IV ADHD diagnostic criteria were randomized to NFPP (N=67), HNC (N=63), or wait-list control (WL, N=34). All participants were assessed at post-treatment. NFPP and HNC participants were assessed at follow-up in the next school year. Primary outcomes were ADHD ratings by teachers blind to and uninvolved in treatment, and by parents. Secondary ADHD outcomes included clinician assessments, and laboratory measures of on-task behavior and delay of gratification. Other outcomes included parent and teacher ratings of oppositional behavior, and parenting measures. (Trial name: Home-Based Parent Training in ADHD Preschoolers; Registry: ClinicalTrials.gov Identifier: NCT01320098; URL: ). ResultsIn both treatment groups, children's ADHD and ODD behaviors, as well as aspects of parenting, were rated improved by parents at the end of treatment compared to controls. Most of these gains in the children's behavior and in some parenting practices were sustained at follow-up. However, these parent-reported improvements were not corroborated by teacher ratings or objective observations. NFPP was not significantly better, and on a few outcomes significantly less effective, than HNC. ConclusionsThe results do not support the claim that NFPP addresses putative dysfunctions underlying ADHD, bringing about generalized change in ADHD, and its underpinning self-regulatory processes. The findings support documented difficulties in achieving generalization across nontargeted settings, and the importance of using blinded measures to provide meaningful assessments of treatment effects

Research on Traffic Optimization of Urban Four-Phase Intersections Based on VISSIM

In order to effectively solve the congestion problem of mixed intersections, improve traffic efficiency, and improve the current congestion situation of urban road network, this paper takes the mixed traffic flow intersection of Shanghai Road and Guyan Street in Yuanzhou District of Guyuan City as an example to carry out optimization research, collect traffic flow and signal timing data during peak hours through field investigation, and establish a microscopic road model by using VISSIM software. Through simulation, the indexes such as vehicle delay and queue length are obtained. Then the lane function of the model is divided, the signal timing is optimized by Webster algorithm and Fuzzy algorithm, and the comparison analysis is carried out. The simulation results show that the vehicle delay and queue length after Fuzzy optimization are greatly reduced, and the traffic running condition is significantly improved, thus verifying the scientificity and rationality of the optimization scheme