FROM GARDEN CITY TO SPONGE CITY: URBAN GREEN INFRASTRUCTURE POLICY DEVELOPMENT

With rapid urbanization, environmental problems like green space shortage and urban flooding become prevalent. Identifying effective policymaking and implementation is critical in order to solve these problems. This dissertation addresses four theoretical topics in the context of urban green infrastructure: policy entrepreneur, institutional response to club goods, quasi-public-private partnership, and policy goal ambiguity. Each is exemplified by a causal case study. Data were collected through participant observation, field trips, semi-structured interviews, and crowdsourcing. Chapter 1 takes a longitudinal perspective and examines the dual role of policy entrepreneur and policy implementer in reaching the final policy goal of mandating vertical greening in the law in Shanghai (1992-2016). Usually, policy implementer and policy entrepreneur are two distinct identities and studied separately. This paper provides an unusual counterexample, exploring how the two intertwined identities may influence the entrepreneurial strategies and further influence the incremental policymaking process. Chapter 2 illustrates how government involvement may facilitate club-good development by investigating the nascent for-profit shopping mall roof garden (SMRG) development. SMRGs, established by developers to provide an amenity to mall customers, are in nature club goods. Although the government appreciates SMRGs given their positive externalities (e.g., recreation, stormwater mitigation), existing public policies fail to respond to SMRGs’ cross-sector nature, leaving significant financial, legitimacy, and oversight gaps unattended. The research suggests that government involvement can better facilitate club-goods’ sustainable development by creating an enabling institutional environment, which includes optimized policy design and coordinated cross-department collaboration. Chapter 3 focuses on the rarely studied phenomenon of the Quasi-Public Private Partnership (QPPP) in non-liberal societies. This work offers a general definition of Quasi-PPPs and identifies factors that influence the PPP to QPPP transition. In the case of eco-environmental service provision, the PPP-QPPP transition occurred in two stages. First, the eco-environmental service partnerships, initially established as PPPs, became inoperable with inexperienced partners and unsupportive markets. Second, with financial bailouts from the government, the private partner became a subordinated partner in a consortium between private partners and State-Owned Enterprises, and PPPs transitioned to QPPPs. In a non-liberal society, when the three critical PPP assumptions are violated (competent partners, supportive market, and horizontal partner structure), PPPs are more likely to transition to QPPPs. Chapter 4 examines how policy goal ambiguity influences policy implementation outcomes, exemplified by the Sponge City Program (SCP) implementation. SCP is a centrally-initiated program, requiring mainly the use of green instead of gray infrastructure to manage urban stormwater. When implemented top-down, three cross-level, layered goals of sustainability, stormwater management, and resident satisfaction became incoherent and vague in terms of priority and measurement. The research demonstrates that in a program with multiple policy goals, the goal priority ambiguity allows implementers the discretion to decide the order of goals to manage interest conflicts. Moreover, the goal measurement ambiguity allows implementers to decide the degree of their commitment to each goal, and to interpret the desired performance of a goal. Such ambiguity-caused discretions drastically inhibit the achievement of the sustainability policy goal

COMPUTER-SUPPORTED COLLABORATIVE KNOWLEDGE BUILDING IN ENGINEERING DESIGN

Engineering design is defined as a process of devising a technical system, component, or process to satisfy desired needs. Collaborative engineering design (CED) is a knowledge- intensive process that involves multidisciplinary people working jointly, sharing resources and outcomes, and building new knowledge while solving problems. People need to collaborate synchronously or asynchronously, either in the same place or distributed geographically. This thesis proposes that engineering design can be modeled not only as a process of knowledge transformation, but as a process of collaborative knowledge building (CKB). CKB is a goal-driven collaborative process of generating and refining ideas and concepts of value to the community. Properly applied and supported, CKB has the potential to improve both learning and design outcomes resulting from collaborative design projects. Existing collaboration tools have evolved without a clear understanding of designers’ needs, even though a portion of the required functionalities has been achieved separately. This thesis proposes an integrated CKB-orientated model for collaborative engineering design, incorporating the key elements of Stahl’s CKB model, Lu’s ECN-based collaborative engineering model, Nonaka’s knowledge creation theory, and Sim and Duffy’s model of a design activity. Based on the model, a set of specific requirements for collaboration tools are presented and some functionalities not existing currently are identified

A Sarcoplasmic Reticulum Localized Protein Phosphatase Regulates Phospholamban Phosphorylation and Promotes Ischemia Reperfusion Injury in the Heart.

Phospholamban (PLN) is a key regulator of sarcolemma calcium uptake in cardiomyocyte, its inhibitory activity to SERCA is regulated by phosphorylation. PLN hypophosphorylation is a common molecular feature in failing heart. The current study provided evidence at molecular, cellular and whole heart levels to implicate a sarcolemma membrane targeted protein phosphatase, PP2Ce, as a specific and potent PLN phosphatase. PP2Ce expression was elevated in failing human heart and induced acutely at protein level by β -adrenergic stimulation or oxidative stress in cardiomyocytes. PP2Ce expression in mouse heart blunted β-adrenergic response and exacerbated ischemia/reperfusion injury. Therefore, PP2Ce is a new regulator for cardiac function and pathogenesis

Fault diagnosis of electro-mechanical actuator based on WPD-STFT time-frequency entropy and PNN

Electro-mechanical actuators (EMAs) are increasingly being used as critical actuation devices of the aircraft. It will cause serious accidents once the fault of EMAs occurs, thus the fault diagnosis of EMAs is essential to maintain the normal operation of aircraft. In this paper, a method based on WPD-STFT time-frequency entropy and PNN is proposed to achieve fault diagnosis of EMAs by processing the vibration signals collected by the accelerometer installed in the EMAs. Firstly, the vibration signals are decomposed by wavelet packet to obtain the signal components of different frequency bands, the signal components are subjected to STFT and spectrograms are obtained. Then, time-frequency entropy is calculated and combined with principal component analysis (PCA) for dimension reduction as the feature vector. Finally, the probabilistic neural network (PNN) classifier is introduced to classify the fault modes. The experimental result shows that this method can accomplish the accurate fault diagnosis of EMAs. Moreover, the performance of the proposed WPD-STFT time-frequency entropy method has an advantage over that of WPD-PCA method or STFT combined with mass-moment entropy method for feature extraction

Rolling Bearing Fault Detection Based on the Teager Energy Operator and Elman Neural Network

This paper presents an approach to bearing fault diagnosis based on the Teager energy operator (TEO) and Elman neural network. The TEO can estimate the total mechanical energy required to generate signals, thereby resulting in good time resolution and self-adaptability to transient signals. These attributes reflect the advantage of detecting signal impact characteristics. To detect the impact characteristics of the vibration signals of bearing faults, we used the TEO to extract the cyclical impact caused by bearing failure and applied the wavelet packet to reduce the noise of the Teager energy signal. This approach also enabled the extraction of bearing fault feature frequencies, which were identified using the fast Fourier transform of Teager energy. The feature frequencies of the inner and outer faults, as well as the ratio of resonance frequency band energy to total energy in the Teager spectrum, were extracted as feature vectors. In order to avoid a frequency leak error, the weighted Teager spectrum around the fault frequency was extracted as feature vector. These vectors were then used to train the Elman neural network and improve the robustness of the diagnostic algorithm. Experimental results indicate that the proposed approach effectively detects bearing faults under variable conditions

V2V Routing in VANET Based on Heuristic Q-Learning

Designing efficient routing algorithms in vehicular ad hoc networks (VANETs) plays an important role in the emerging intelligent transportation systems. In this paper, a routing algorithm based on the improved Q-learning is proposed for vehicle-to-vehicle (V2V) communications in VANETs. Firstly, a link maintenance time model is established, and the maintenance time is taken as an important parameter in the design of routing algorithm to ensure the reliability of each hop link. Aiming at the low efficiency and slow convergence of Q-learning, heuristic function and evaluation function are introduced to accelerate the update of Q-value of current optimal action, reduce unnecessary exploration, accelerate the convergence speed of Q-learning process and improve learning efficiency. The learning task is dispersed in each vehicle node in the new routing algorithm and it maintains the reliable routing path by periodically exchanging beacon information with surrounding nodes, guides the node’s forwarding action by combining the delay information between nodes to improve the efficiency of data forwarding. The performance of the algorithm is evaluated by NS2 simulator. The results show that the algorithm has a good effect on the package delivery rate and end-to-end delay

Rolling bearing fault diagnosis using improved LCD-TEO and softmax classifier

A novel rolling bearing fault diagnosis method based on improved local characteristic-scale decomposition (LCD), Teager energy operator (TEO) and softmax classifier is proposed in this paper. First, vibration signals are decomposed into several intrinsic scale components (ISCs) by using improved LCD; second, TEO and fast Fourier transform (FFT) are respectively used to extract instantaneous amplitude (IA) and frequency spectra of ISC1s, and then FFT is again employed to obtain spectra of IA; third, energy ratio of the resonant frequency band against the total, frequency entropy (FE) in the spectra of ISC1s and several amplitude ratios in the frequency spectra of demodulated ISC1s are extracted as fault feature vectors, and principal components analysis (PCA) is applied for dimensionality reduction; finally, these feature vectors are taken as inputs to train and test softmax classifier. As a new non-stationary signal analysis tool, LCD can decompose adaptively a signal into series of ISCs in different scales and give good results in situations where other methods failed. However, there are two main issues in this method, end effect and mode mixing, possibly leading to unexpected results. In this paper, a slope-based method and noise assisted analysis are applied to restrain the problems respectively. Experimental results show the proposed method performs effectively for bearing fault diagnosis