119 research outputs found
Legitimacy judgements in business incubators: a perception model of screening entrepreneurial ventures in Chinese business incubator resource decisions
No full textBased on the importance of resources to entrepreneurial ventures and the predominant role of legitimacy theory in explaining resource supports, this dissertation research explores the mechanism of resource decisions with a perceptual model of legitimacy judgment process. The model was then applied to business incubators, where the screening of new ventures plays a critical role under high uncertainties and in need of better theorization.
In study 1, a qualitative analysis with archival information and interviews revealed that opportunity characteristics and entrepreneur characteristics are the two major components of new venture legitimacy in incubator contexts. Study 2 with 170 incubator managers tested the legitimacy judgment model with a vignette study for their effectiveness in explaining judgments formation and resource decisions in business incubators.
Results of the vignette study supported the positive effects of both project novelty and team credibility on incubator managers’ legitimacy judgments, and the positive relationships between legitimacy judgments and managers’ resource decisions. The study also confirmed that both CLJ and ELJ’s mediate the effects of venture characteristics on incubator resource decisions. Study 2 did not support the moderation role of team credibility for the mediated relationships between project novelty and resource decisions. However, the between-group comparison analysis suggested that the composition of venture qualities is one critical factor for new venture legitimacy.
This research addressed the complexity of social dynamics in business incubators and directed further attention to specific contextual factors and individual-level processes (Bergek & Norrman, 2008; Phan et al., 2005). Business incubation context also supplied a ground for examining different objectives and outcomes of legitimation and revealing the interactive dynamics of legitimacy judgment process.Ph.D.Includes bibliographical referencesby Xiangyi Kon
Interactive e-Cube Games for Cognitive Assessment
No full textThis dissertation presents a low-cost adaptive method for cognitive assessment using the e-Cube system. It features six games: Assembly (e-CubeA), Shape-Matching (e-CubeS), Sequence-Memory (e-CubeM1), Spatial-Memory (e-CubeM2), Path-Tracking (e-CubeP), and Maze (e-CubeZ), designed to measure cognitive skills autonomously. Using the embedded play complexity meaures and dynamic item generators, these games are adaptive to an individual���s performance. The evaluation focused on testing the play complexity measures, cognitive assessment, and reliability and usability of the system. The six e-Cube games and three subtests (Block Design, Digit Span, and Matrix Reasoning) of the Wechsler Adult Intelligence Scale - Fourth Edition were administered to 78 participants, of which 41 were assigned to the adaptive e-Cube games and the rest to the fixed games. The computed play complexities were negatively and positively correlated with the mean scores and mean completion time of each item, respectively, providing preliminary evidence for validating the play complexity measures. The preliminary validity of the e-Cube system as a cognitive assessment was confirmed by the significant correlations found between the WAIS-IV subtests and the e-Cube games. The false detection rate was only 0.1% and the average System Usability Scale score was 86.1, indicating e-Cube is reliable and acceptable. To further evaluate the user experience of e-Cube, a genetic algorithm - Support Vector Machine - based facial expression recognition algorithm was embedded in the system. While the potential of the algorithm has been demonstrated, more future work is needed to test its performance in e-Cube
The Role of Artificial Intelligence in Boosting Cybersecurity and Trusted Embedded Systems Performance: A Systematic Review on Current and Future Trends
No full textAs technology becomes increasingly interconnected, ensuring the security of cyber and embedded systems is critical due to escalating vulnerabilities and sophisticated cyber threats. Researchers are exploring artificial intelligence (AI) to improve security mechanisms, yet there is a lack of a comprehensive technical, AI-focused analysis detailing the integration of AI into existing security hardware and frameworks. To address this gap, this article systematically reviews 63 articles on AI in cybersecurity and trusted embedded systems. The reviewed articles are categorized into four application domains: 1) Intrusion Detection and Prevention (IDPS), 2) Malware Detection, 3) Industrial Control and Cyber-Physical Systems (CPS) and 4) Distributed Denial-of-Service (DDoS) Detection and Prevention. We investigated current trends in integrating AI into security domains by summarizing the hardware used, the AI methodologies adopted, and the statistical distribution by publication year and region. The key findings of our review indicate that AI significantly enhances security measures by enabling capabilities such as detection, classification, feature selection, data privacy preservation, model combination, data generation, output interpretation, optimization, and adaptation. In addition, the benefits and challenges identified in these studies provide insight into the future potential of AI integration in security. Suggested directions for future work include improving generalization and scalability, exploring continuous or real-time monitoring, and improving AI model performance. This analysis serves as a foundation for advancing AI applications in the effective securing of cyber and embedded systems effectively
The Role of Artificial Intelligence in Boosting Cybersecurity and Trusted Embedded Systems Performance: A Systematic Review on Current and Future Trends
No full textAs technology becomes increasingly interconnected, ensuring the security of cyber and embedded systems is critical due to escalating vulnerabilities and sophisticated cyber threats. Researchers are exploring artificial intelligence (AI) to improve security mechanisms, yet there is a lack of a comprehensive technical, AI-focused analysis detailing the integration of AI into existing security hardware and frameworks. To address this gap, this article systematically reviews 63 articles on AI in cybersecurity and trusted embedded systems. The reviewed articles are categorized into four application domains: 1) Intrusion Detection and Prevention (IDPS), 2) Malware Detection, 3) Industrial Control and Cyber-Physical Systems (CPS) and 4) Distributed Denial-of-Service (DDoS) Detection and Prevention. We investigated current trends in integrating AI into security domains by summarizing the hardware used, the AI methodologies adopted, and the statistical distribution by publication year and region. The key findings of our review indicate that AI significantly enhances security measures by enabling capabilities such as detection, classification, feature selection, data privacy preservation, model combination, data generation, output interpretation, optimization, and adaptation. In addition, the benefits and challenges identified in these studies provide insight into the future potential of AI integration in security. Suggested directions for future work include improving generalization and scalability, exploring continuous or real-time monitoring, and improving AI model performance. This analysis serves as a foundation for advancing AI applications in the effective securing of cyber and embedded systems effectively
Can Growth Compensate Inequality and Risk?---a welfare analysis for Chinese households
No full textIt has been widely observed that China's break-neck growth has not been equally shared between rural and urban areas, with urban households' enjoying a much larger proportion. To further test whether regional inequality exists within urban areas, we measure urban households vulnerability in a risky environment and decompose this measure to quantify China aggregate risks, province-level risks and idiosyncratic risks faced by households situated in 31 provinces. Besides, under this framework of analysis, we are able to make welfare comparisons between growth, inequality and different risks. We find that inequality has very big negative effect on households' welfare, while growth is able to compensate nearly half of it; households seem to be able to smooth consumption against risk in both province and individual level, but unable to do so against China shocks, which affect all the households simultaneously.Community/Rural/Urban Development, Risk and Uncertainty,
Quantum Brownian motion model for the stock market
No full textIt is believed by the majority today that the efficient market hypothesis is imperfect because of market irrationality. Using the physical concepts and mathematical structures of quantum mechanics, we construct an econophysical framework for the stock market, based on which we analogously map massive numbers of single stocks into a reservoir consisting of many quantum harmonic oscillators and their stock index into a typical quantum open system-a quantum Brownian particle. In particular, the irrationality of stock transactions is quantitatively considered as the Planck constant within Heisenberg's uncertainty relationship of quantum mechanics in an analogous manner. We analyze real stock data of Shanghai Stock Exchange of China and investigate fat-tail phenomena and non-Markovian behaviors of the stock index with the assistance of the quantum Brownian motion model, thereby interpreting and studying the limitations of the classical Brownian motion model for the efficient market hypothesis from a new perspective of quantum open system dynamics. (C) 2016 Elsevier B.V. All rights reserved.SCI(E)[email protected]
AI Enabled Wireless Communications with Real Channel Measurements: Channel Feedback
No full textArtificial intelligence (AI) has shown great potential in wireless communications. AI-empowered communication algorithms have beaten many traditional algorithms through simulations. However, the existing works just use the simulated datasets to train and test the algorithms, which can not represent the power of AI in practical communication systems. Therefore, Peng Cheng Laboratory holds an AI competition, National Artificial Intelligence Competition (NAIC): AI+wireless commu-nications, in which one of the topics is AI-empowered channel feedback system design using practical measure-ments. In this paper, we give a baseline neural network design, QuanCsiNet, for this competition, and the details of the channel measurements. QuanCsiNet shows excel-lent performance on channel feedback and the complexity of the neural networks is also given.</p
Genistein adsorption performance and mechanism by metal-organic frameworks based on triangular aromatic ligands
No full textObjective： To improve the adsorption of genistein by zirconium-based metal-organic frameworks （MOFs） with triangle aromatic ligands. Methods： Two MOFs （MOF-808 and PCN-777） containing different sizes of triangle aromatic ligands were synthesized by hydrothermal method for adsorption of genistein. The synthesis of MOFs was determined by a series of characterization methods, and the adsorption performance was compared with that of a linear binary carboxylic acid ligand-constructed MOF （UiO-66）, to evaluate the effects of pore characteristics, hydrophobicity changes, and other factors on the adsorption performance, and to explore the adsorption mechanism by XPS analysis. Results： Compared with linear ligand-constructed UiO-66, the pore sizes of triangular aromatic ligand-constructed MOFs （MOF-808, and PCN-777） increased from 0.65 nm to 1.81 nm and 3.55 nm, respectively, and the water contact angles increased from 47.91° to 110.68° and 128.23°, respectively. The adsorption capacity and adsorption efficiency of genistein in linear ligand-constructed UiO-66 was 40.08 mg/g and 39.98%, respectively, while the adsorption capacity and adsorption efficiency of genistein in MOFs constructed by triangular aromatic ligand （MOF-808, PCN-777） was increased to 61.80 mg/g, 81.75 mg/g and 61.63%, 81.52% respectively. Conclusion： Metal-organic frameworks （MOFs） can be used for the adsorption and enrichment of genistein. Compared with linear ligands, the introduction of triangular aromatic ligands with different sizes changes the pore size and hydrophobicity of MOFs, enhances the internal accessibility of MOFs, and provides more adsorption sites, which improves the adsorption effect of genistein. The adsorption mechanism of genistein in PCN-777 is based on the synergistic combination of metal-chelating interactions, π-π interactions, and hydrophobic interactions
Metformin versus metformin plus pioglitazone on gonadal and metabolic profiles in normal-weight women with polycystic ovary syndrome: a single-center, open-labeled prospective randomized controlled trial
No full textAbstract Objective To investigate the effects of metformin (MET) monotherapy and pioglitazone plus MET (PIOMET) therapy on gonadal and metabolic profiles in normal-weight women with polycystic ovary syndrome (PCOS). Methods Sixty normal-weight women with PCOS were recruited between January and September 2022 at the Shengjing Hospital of China Medical University. They were randomly assigned to the MET or PIOMET groups for 12 weeks of MET monotherapy or PIOMET therapy. Anthropometric measurements, menstrual cycle changes, gonadal profiles, and the oral glucose insulin-releasing test (OGIRT) were performed at baseline and after the 12-week treatment. Results Thirty-six participants completed the trial. MET and PIOMET therapies improved menstrual cycles after the 4- and 12-week treatments; however, there was no statistical difference between the two groups. PIOMET therapy improved luteinizing hormone (LH), luteinizing hormone/follicle stimulating hormone (LH/FSH) ratio, and free androgen index (FAI) levels after the 4-week treatment, whereas MET monotherapy only improved total testosterone (TT) levels compared to baseline (P < 0.05). Both MET and PIOMET therapies improved TT and anti-Mullerian hormone (AMH) levels after the 12-week treatment (P < 0.05). In addition, only PIOMET therapy significantly improved sex hormone-binding globulin (SHBG), FAI, and androstenedione (AND) levels than the baseline (P < 0.05). PIOMET therapy improved SHBG and AMH levels more effectively than MET monotherapy (P < 0.05). Furthermore, PIOMET treatment was more effective in improving blood glucose levels at 120 and 180 min of OGIRT compared to MET monotherapy (P < 0.05). Conclusions In normal-weight women with PCOS, PIOMET treatment may have more benefits in improving SHBG, AMH, and postprandial glucose levels than MET monotherapy, and did not affect weight. However, the study findings need to be confirmed in PCOS study populations with larger sample sizes
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