State switching in multi-stable systems: control and optimisation.

This thesis studies state-switching in multistable systems, so that they can switch from inefficient operating states to a more efficient one, in order to achieve performance enhancement in real-life engineering systems. Multistable systems have more than one stable state under a set of parameters and the process of switching from an undesired state to a desired state is achieved by the proposed PD-like controller. It exploits the difference of the displacement and velocity between the undesired and the desired stable conditions for feedback in state switching. Three test systems are used for investigating the performance of this PD-like controller, namely: the Duffing oscillator, which is a typical smooth multistable system; the non-smooth soft-impact oscillator; and the soft-impact oscillator with a drift. A randomised triangular subdivision algorithm is proposed to reconstruct the basins of attraction of the target multistable systems, in order to identify the desired state for switching. Due to the limited capacity of physical actuators, behaviours of the constrained PD-like controller are investigated using extensive simulation on the test systems. Moreover, optimisation of the controller (based on multiple performance objectives) can further improve system performance. Two performance objectives - maximum peak of control input and switching duration - are adopted in optimising the proposed PD-like controller. The first objective is minimised in order to avoid output limit and reduce energy consumption in the actuator, while the second objective is minimised in order to shorten the time required for state switching. These two performance objectives are considered independently in performance optimisation, using particle swarm optimisation (PSO). Since these two objectives are in conflict with each other, both objectives are minimised simultaneously in multiobjective optimisation of the performance of the PD-like controller using Non-Dominated Sorting Genetic Algorithms-II (NSGA-II). A trade-off in performance enhancement is achieved through selecting control parameters from the Pareto optimal set

When Service Recovery Becomes Visible: Effects Of Recovery Strategies On Electronic Word-of-mouth Receiver

The emergence of social media stimulates electronic word-of-mouth (eWOM) and makes consumers’ service encounter experiences ‘visible’ to a wide range of information receivers. In the existing literature, eWOM is mostly studied as the consequences of service failure and service recovery. This study extends prior research by bridging the service recovery experiences of eWOM communicators and the attitudes and behaviors of eWOM receivers. Using scenario-based experiments, this study tests eWOM receiver’s responses to the communicator’s experience and compares the impacts of different service recovery strategies. The results confirm that, for eWOM receivers, vicarious distributive, procedural and interactional justices influence their brand attitudes, which further affect their purchase intentions. Moreover, results support that the relationship between brand attitude and purchase intention is moderated by the perceived information credibility. It is also found that, compared to observing apology, eWOM receivers tend to have more positive justice perceptions and brand attitudes and stronger purchase intentions when they observe compensation. However, whether the recovery is conducted at the service failure scene or after eWOM are not significantly different. This study contributes to the previous literature on service recovery by incorporating vicarious justice in understanding how eWOM receivers develop attitudes and behavioral intentions from others’ service recovery experiences in social media. The findings can be used by service providers to guide their recovery strategies

ERP and ITS Application in Mainland China

Enterprise Resource Planning (ERP) is a good technology for planning and controlling of a manufacturing company. Because of the “Manufacturing Fundamental Equation”, ERP is applicable widely. But, the data foundations of the companies must be accurate and complete for using ERP, and different methods of thinking in management must be needed. For years, the application status of ERP is not as good as we expected in Mainland China. But we believe that the situation will be improved if leaders and staffs of manufacturing companies have the common opinion of holding on to the implementation and application of ERP. ERP is continuously developing. Now, integrating ERP with other management theories and methods is the tren

Design and Development of Water-splitting Electrocatalysts Based on Conjugated Triazine Frameworks

Covalent triazine frameworks (CTFs) with rich nitrogen atoms and permanent porosity have been widely used in the field of opto/electronics as supports. In this study, two CTFs with different pore sizes (single pore and heteropore) were synthesized, after which Cu2+, Co2+, Ni2+, Pd2+, Pt2+, and the corresponding metal cluster were introduced into the CTFs as catalytic active sites through the confinement effect of the pores. Among a series of CTFs-based electrocatalysts, DCP-CTF-Pt2+ displays an outstanding electrocatalytic performance with an overpotential of 46 mV and a Tafel slope of 30.2 mV dec-1. Catalytic kinetics analysis indicates that electrocatalytic performance is closely relevant to hierarchical pore and metal size

State switching of a multistable impacting system using PD-like control.

A new PD-like control law is proposed in this paper to control a multistable impacting system. This control law can switch the system from a current, undesired state to a desired one by using the differences of the displacement and velocity between the current and desired states. The control law can control the multistable system without affecting its original dynamics, and its stability is proved by using the Lyapunov direct method. Numerical results are compared with the results obtained by using the intermittent control studied in [1]. The proposed PD-like control shows a better performance in terms of the smoothness of its control signal, which is easier to be implemented in practical applications

Lung inflammation by fungus, Bjerkandera adusta isolated from Asian sand dust (ASD) aerosol and enhancement of ovalbumin-induced lung eosinophilia by ASD and the fungus in mice.

BackgroundBjerkandera adusta (B. adusta) is one of the most important etiological fungi associated with chronic cough. However, precise details of the inflammatory response to exposure are not well understood yet. B. adusta was recently identified in Asian sand dust (ASD) aerosol. Therefore, in the present study the exacerbating effects of ASD on B. adusta-induced lung inflammation and B. adusta + ASD on ovalbumin (OVA)-induced murine lung eosinophilia were investigated using experimental mice.MethodsIn order to prepare testing samples, B. adusta obtained from ASD aerosol was inactivated by formalin and ASD collected from the atmosphere was heated to remove toxic organic substances (H-ASD). CD-1 mice were instilled intratracheally with 12 different samples prepared with various combinations of B. adusta, H-ASD, and OVA in a normal saline solution. The lung pathology, cytological profiles in bronchoalveolar lavage fluid (BALF), and the levels of inflammatory cytokines/chemokines in BALF were investigated.ResultsH-ASD aggravated the lung eosinophilia induced by B. adusta alone, which also aggravated the lung eosinophilia induced by OVA. The mixture of OVA, H-ASD, and B. adusta caused serious fibrous thickening of the subepithelial layer, eosinophil infiltration, and proliferation of goblet cells in the airways along with remarkable increases of IL-13, eotaxin, IL-5, and MCP-3 in BALF.ConclusionsThe results of the present study demonstrated that B. adusta isolated from ASD aerosol induces allergic lung diseases. H-ASD enhanced allergic reactions caused by OVA or B. adusta. A mixture of B. adusta, H-ASD, and OVA caused the most remarkable exacerbation to the allergic airway inflammation via remarkable increases of pro-inflammatory mediators

Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews

This study aims to investigate the contributions of online promotional marketing and online reviews as predictors of consumer product demands. Using electronic data from Amazon.com, we attempt to predict if online review variables such as valence and volume of reviews, the number of positive and negative reviews, and online promotional marketing variables such as discounts and free deliveries, can influence the demand of electronic products in Amazon.com. A Big Data architecture was developed and Node.JS agents were deployed for scraping the Amazon.com pages using asynchronous Input/Output calls. The completed Web crawling and scraping data-sets were then preprocessed for Neural Network analysis. Our results showed that variables from both online reviews and promotional marketing strategies are important predictors of product demands. Variables in online reviews in general were better predictors as compared to online marketing promotional variables. This study provides important implications for practitioners as they can better understand how online reviews and online promotional marketing can influence product demands. Our empirical contributions include the design of a Big Data architecture that incorporate Neural Network analysis which can used as a platform for future researchers to investigate how Big Data can be used to understand and predict online consumer product demands