Knowledge stocks, knowledge flows and innovation: Evidence from matched patents and innovation panel data

Successful innovation depends on knowledge – technological, strategic and market related. In this paper we explore the role and interaction of firms’ existing knowledge stocks and current knowledge flows in shaping innovation success. The paper contributes to our understanding of the determinants of firms’ innovation outputs and provides new information on the relationship between knowledge stocks, as measured by patents, and innovation output indicators. Our analysis uses innovation panel data relating to plants’ internal knowledge creation, external knowledge search and innovation outputs. Firm-level patent data is matched with this plant-level innovation panel data to provide a measure of firms’ knowledge stock. Two substantive conclusions follow. First, existing knowledge stocks have weak negative rather than positive impacts on firms’ innovation outputs, reflecting potential core-rigidities or negative path dependencies rather than the accumulation of competitive advantages. Second, knowledge flows derived from internal investment and external search dominate the effect of existing knowledge stocks on innovation performance. Both results emphasize the importance of firms’ knowledge search strategies. Our results also re-emphasize the potential issues which arise when using patents as a measure of innovation.

Feedback Networks and Self-Oscillating Objects

A system of self-designed microphones, speakers and transducers creating performable feedback networks and self-oscillating objects. Performance SARC Sonic Lab, Belfast, 18 March 2015

Assessing elements of a family approach to reduce adolescent drinking frequency: parent-adolescent relationship, knowledge management, and keeping secrets

AIMS: To estimate 1) the associations between parent-adolescent relationship, parental knowledge and subsequent adolescent drinking frequency and 2) the influence of alcohol use on parental knowledge.

DESIGN: Path analysis of school based cohort study with annual surveys SETTING: Post primary schools from urban and intermediate/rural areas in Northern Ireland PARTICIPANTS: 4,937 post primary school students aged around 11 years in 2000 followed until around age 16 in 2005.

MEASUREMENTS: Pupil reported measures of: frequency of alcohol use; parental-child relationship quality; sub-dimensions of parental monitoring: parental control, parental solicitation, child disclosure and child secrecy.

FINDINGS: Higher levels of parental control (Ordinal logistic OR 0.86 95% CI 0.78, 0.95) and lower levels of child secrecy (OR 0.83 95% CI 0.75 0.92) were associated with less frequent alcohol use subsequently. Parental solicitation and parent-child relationship quality were not associated with drinking frequency. Weekly alcohol drinking was associated with higher subsequent secrecy (Beta -0.42 95% CI -0.53, -0.32) and lower parental control (Beta -0.15 95% CI -0.26, -0.04). Secrecy was more strongly predictive of alcohol use at younger compared with older ages (P=0.02), and alcohol use was less strongly associated with parental control among families with poorer relationships (P=0.04).

CONCLUSIONS: Adolescent alcohol use appears to increase as parental control decreases and child secrecy increases. Greater parental control is associated with less frequent adolescent drinking subsequently, while parent-child attachment and parental solicitation have little influence on alcohol use. 

Reversibility Iteration Method to Understand Reaction Networks and to Solve Micro-Kinetics in Heterogeneous Catalysis

Solving microkinetics of catalytic systems, which bridges microscopic processes and macroscopic reaction rates, is currently vital for understanding catalysis in silico. However, traditional microkinetic solvers possess several drawbacks that make the process slow and unreliable for complicated catalytic systems. In this paper, a new approach, the so-called reversibility iteration method (RIM), is developed to solve microkinetics for catalytic systems. Using the chemical potential notation we previously proposed to simplify the kinetic framework, the catalytic systems can be analytically illustrated to be logically equivalent to the electric circuit, and the reaction rate and coverage can be calculated by updating the values of reversibilities. Compared to the traditional modified Newton iteration method (NIM), our method is not sensitive to the initial guess of the solution and typically requires fewer iteration steps. Moreover, the method does not require arbitrary-precision arithmetic and has a higher probability of successfully solving the system. These features make it ∼1000 times faster than the modified Newton iteration method for the systems we tested. Moreover, the derived concept and the mathematical framework presented in this work may provide new insight into catalytic reaction networks.

Enabling Robust and Efficient Distributed Computation in Dynamic Peer-to-Peer Networks

Motivated by the need for designing efficient and robust fully-distributed computation in highly dynamic networks such as Peer-to-Peer (P2P) networks, we study distributed protocols for constructing and maintaining dynamic network topologies with good expansion properties. Our goal is to maintain a sparse (bounded degree) expander topology despite heavy {\em churn} (i.e., nodes joining and leaving the network continuously over time). We assume that the churn is controlled by an adversary that has complete knowledge and control of what nodes join and leave and at what time and has unlimited computational power, but is oblivious to the random choices made by the algorithm. Our main contribution is a randomized distributed protocol that guarantees with high probability the maintenance of a {\em constant} degree graph with {\em high expansion} even under {\em continuous high adversarial} churn. Our protocol can tolerate a churn rate of up to $O(n/\poly\log(n))$ per round (where $n$ is the stable network size). Our protocol is efficient, lightweight, and scalable, and it incurs only $O(\poly\log(n))$ overhead for topology maintenance: only polylogarithmic (in $n$) bits needs to be processed and sent by each node per round and any node's computation cost per round is also polylogarithmic. The given protocol is a fundamental ingredient that is needed for the design of efficient fully-distributed algorithms for solving fundamental distributed computing problems such as agreement, leader election, search, and storage in highly dynamic P2P networks and enables fast and scalable algorithms for these problems that can tolerate a large amount of churn.

Host-based P2P flow identification and use in real-time

Data identification is a key task for any Internet Service Provider (ISP) or network administrator. As port fluctuation and encryption become more common in P2P traffic wishing to avoid identification, new strategies must be developed to detect and classify such flows. This paper introduces a new method of separating P2P and standard web traffic that can be applied as part of a data mining process, based on the activity of the hosts on the network. Unlike other research, our method is aimed at classifying individual flows rather than just identifying P2P hosts or ports. Heuristics are analysed and a classification system proposed. The accuracy of the system is then tested using real network traffic from a core internet router showing over 99% accuracy in some cases. We expand on this proposed strategy to investigate its application to real-time, early classification problems. New proposals are made and the results of real-time experiments compared to those obtained in the data mining research. To the best of our knowledge this is the first research to use host based flow identification to determine a flows application within the early stages of the connection.

Application of auto-associative neural networks to transient fault detection in an IC engine

The tailpipe emissions from automotive engines have been subject to steadily reducing legislative limits. This reduction has been achieved through the addition of sub-systems to the basic four-stroke engine which thereby increases its complexity. To ensure the entire system functions correctly, each system and / or sub-systems needs to be continuously monitored for the presence of any faults or malfunctions. This is a requirement detailed within the On-Board Diagnostic (OBD) legislation. To date, a physical model approach has been adopted by me automotive industry for the monitoring requirement of OBD legislation. However, this approach has restrictions from the available knowledge base and computational load required. A neural network technique incorporating Multivariant Statistical Process Control (MSPC) has been proposed as an alternative method of building interrelationships between the measured variables and monitoring the correct operation of the engine. Building upon earlier work for steady state fault detection, this paper details the use of non-linear models based on an Auto-associate Neural Network (ANN) for fault detection under transient engine operation. The theory and use of the technique is shown in this paper with the application to the detection of air leaks within the inlet manifold system of a modern gasoline engine whilst operated on a pseudo-drive cycle. Copyright © 2007 by ASME.

Social exclusion and social capital: A comparison and critique

Social exclusion and social capital are widely used concepts with multiple and ambiguous definitions. Their meanings and indicators partially overlap, and thus they are sometimes used interchangeably to refer to the inter-relations of economy and society. Both ideas could benefit from further specification and differentiation. The causes of social exclusion and the consequences of social capital have received the fullest elaboration, to the relative neglect of the outcomes of social exclusion and the genesis of social capital. This article identifies the similarities and differences between social exclusion and social capital. We compare the intellectual histories and theoretical orientations of each term, their empirical manifestations and their place in public policy. The article then moves on to elucidate further each set of ideas. A central argument is that the conflation of these notions partly emerges from a shared theoretical tradition, but also from insufficient theorizing of the processes in which each phenomenon is implicated. A number of suggestions are made for sharpening their explanatory focus, in particular better differentiating between cause and consequence, contextualizing social relations and social networks, and subjecting the policy 'solutions' that follow from each perspective to critical scrutiny. Placing the two in dialogue is beneficial for the further development of each.

An Antennas and Propagation Approach to Improving Physical Layer Performance in Wireless Body Area Networks

A combined antennas and propagation study has been undertaken with a view to directly improving link conditions for wireless body area networks. Using tissue-equivalent numerical and experimental phantoms representative of muscle tissue at 2.45 GHz, we show that the node to node [S-21] path gain performance of a new wearable integrated antenna (WIA) is up to 9 dB better than a conventional compact Printed-F antenna, both of which are suitable for integration with wireless node circuitry. Overall, the WIA performed extremely well with a measured radiation efficiency of 38% and an impedance bandwidth of 24%. Further benefits were also obtained using spatial diversity, with the WIA providing up to 7.7 dB of diversity gain for maximal ratio combining. The results also show that correlation was lower for a multipath environment leading to higher diversity gain. Furthermore, a diversity implementation with the new antenna gave up to 18 dB better performance in terms of mean power level and there was a significant improvement in level crossing rates and average fade durations when moving from a single-branch to a two-branch diversity system.

A Ligand predication tool based on modeling and reasoning with imprecise probabilistic knowledge

Ligand prediction has been driven by a fundamental desire to understand more about how biomolecules recognize their ligands and by the commercial imperative to develop new drugs. Most of the current available software systems are very complex and time-consuming to use. Therefore, developing simple and efficient tools to perform initial screening of interesting compounds is an appealing idea. In this paper, we introduce our tool for very rapid screening for likely ligands (either substrates or inhibitors) based on reasoning with imprecise probabilistic knowledge elicited from past experiments. Probabilistic knowledge is input to the system via a user-friendly interface showing a base compound structure. A prediction of whether a particular compound is a substrate is queried against the acquired probabilistic knowledge base and a probability is returned as an indication of the prediction. This tool will be particularly useful in situations where a number of similar compounds have been screened experimentally, but information is not available for all possible members of that group of compounds. We use two case studies to demonstrate how to use the tool.