PVA-PSSA Membrane with Interpenetrating Networks and its Methanol Crossover Mitigating Effect in DMFCs

A membrane with interpenetrating networks between poly(vinyl alcohol) (PVA) and poly(styrene sulfonic acid) (PSSA) coupled with a high proton conductivity is realized and evaluated as a proton exchange membrane electrolyte for a direct methanol fuel cell (DMFC). Its reduced methanol permeability and improved performance in DMFCs suggest the new blend as an alternative membrane to Nafion membranes. The membrane has been characterized by powder X-ray diffraction, scanning electron microscopy, time-modulated differential scanning calorimetry, and thermogravimetric analysis in conjunction with its mechanical strength. The maximum proton conductivity of 3.3×10−2 S/cm for the PVA–PSSA blend membrane is observed at 373 K. From nuclear magnetic resonance imaging and volume localized spectroscopy experiments, the PVA–PSSA membrane has been found to exhibit a promising methanol impermeability, in DMFCs. On evaluating its utility in a DMFC, it has been found that a peak power density of 90 mW/cm2 at a load current density of 320 mA/cm2 is achieved with the PVA–PSSA membrane compared to a peak power density of 75 mW/cm2 at a load current density of 250 mA/cm2 achievable for a DMFC employing Nafion membrane electrolyte while operating under identical conditions; this is attributed primarily to the methanol crossover mitigating property of the PVA–PSSA membrane.

Detecting Earnings Management Using Neural Networks

Detecting Earnings Management Using Neural Networks. Trying to balance between relevant and reliable accounting data, generally accepted accounting principles (GAAP) allow, to some extent, the company management to use their judgment and to make subjective assessments when preparing financial statements. The opportunistic use of the discretion in financial reporting is called earnings management. There have been a considerable number of suggestions of methods for detecting accrual based earnings management. A majority of these methods are based on linear regression. The problem with using linear regression is that a linear relationship between the dependent variable and the independent variables must be assumed. However, previous research has shown that the relationship between accruals and some of the explanatory variables, such as company performance, is non-linear. An alternative to linear regression, which can handle non-linear relationships, is neural networks. The type of neural network used in this study is the feed-forward back-propagation neural network. Three neural network-based models are compared with four commonly used linear regression-based earnings management detection models. All seven models are based on the earnings management detection model presented by Jones (1991). The performance of the models is assessed in three steps. First, a random data set of companies is used. Second, the discretionary accruals from the random data set are ranked according to six different variables. The discretionary accruals in the highest and lowest quartiles for these six variables are then compared. Third, a data set containing simulated earnings management is used. Both expense and revenue manipulation ranging between -5% and 5% of lagged total assets is simulated. Furthermore, two neural network-based models and two linear regression-based models are used with a data set containing financial statement data from 110 failed companies. Overall, the results show that the linear regression-based models, except for the model using a piecewise linear approach, produce biased estimates of discretionary accruals. The neural network-based model with the original Jones model variables and the neural network-based model augmented with ROA as an independent variable, however, perform well in all three steps. Especially in the second step, where the highest and lowest quartiles of ranked discretionary accruals are examined, the neural network-based model augmented with ROA as an independent variable outperforms the other models.

Collective Network Capability in International Project Business Networks - A Case Study of the Business Network for the Ashanti Electrification Project in Ghana

Despite thirty years of research in interorganizational networks and project business within the industrial networks approach and relationship marketing, collective capability of networks of business and other interorganizational actors has not been explicitly conceptualized and studied within the above-named approaches. This is despite the fact that the two approaches maintain that networking is one of the core strategies for the long-term survival of market actors. Recently, many scholars within the above-named approaches have emphasized that the survival of market actors is based on the strength of their networks and that inter-firm competition is being replaced by inter-network competition. Furthermore, project business is characterized by the building of goal-oriented, temporary networks whose aims, structures, and procedures are clarified and that are governed by processes of interaction as well as recurrent contracts. This study develops frameworks for studying and analysing collective network capability, i.e. collective capability created for the network of firms. The concept is first justified and positioned within the industrial networks, project business, and relationship marketing schools. An eclectic source of conceptual input is based on four major approaches to interorganizational business relationships. The study uses qualitative research and analysis, and the case report analyses the empirical phenomenon using a large number of qualitative techniques: tables, diagrams, network models, matrices etc. The study shows the high level of uniqueness and complexity of international project business. While perceived psychic distance between the parties may be small due to previous project experiences and the benefit of existing relationships, a varied number of critical events develop due to the economic and local context of the recipient country as well as the coordination demands of the large number of involved actors. The study shows that the successful creation of collective network capability led to the success of the network for the studied project. The processes and structures for creating collective network capability are encapsulated in a model of governance factors for interorganizational networks. The theoretical and management implications are summarized in seven propositions. The core implication is that project business success in unique and complex environments is achieved by accessing the capabilities of a network of actors, and project management in such environments should be built on both contractual and cooperative procedures with local recipient country parties.

Multidimensionality of Actors in Business Networks: The Influence of Social Action in Pharmacy Networks in Finland

In the markets-as-networks approach business networks are conceived as dynamic actor structures, giving focus to exchange relationships and actors’ capabilities to control and co-ordinate activities and resources. Researchers have shared an understanding that actors’ actions are crucial for the development of business networks and for network dynamics. However, researchers have mainly studied firms as business actors and excluded individuals, although both firms and individuals can be seen as business actors. This focus on firms as business actors has resulted in a paucity of research on human action and the exchange of intangible resources in business networks, e.g. social exchange between individuals in social networks. Consequently, the current conception of business networks fails to appreciate the richness of business actors, the human character of business action and the import of social action in business networks. The central assumption in this study is that business actors are multidimensional and that their specific constitution in any given situation is determined by human interaction in social networks. Multidimensionality is presented as a concept for exploring how business actors act in different situations and how actors simultaneously manage multiple identities: individual, organisational, professional, business and network identities. The study presents a model that describes the multidimensionality of actors in business networks and conceptualises the connection between social exchange and human action in business networks. Empirically the study explores the change that has taken place in pharmaceutical retailing in Finland during recent years. The phenomenon of emerging pharmacy networks is highly contemporary in the Nordic countries, where the traditional license-based pharmacy business is changing. The study analyses the development of two Finnish pharmacy chains, one integrated and one voluntary chain, and the network structures and dynamics in them. Social Network Analysis is applied to explore the social structures within the pharmacy networks. The study shows that emerging pharmacy networks are multifaceted phenomena where political, economic, social, cultural, and historical elements together contribute to the observed changes. Individuals have always been strongly present in the pharmacy business and the development of pharmacy networks provides an interesting example of human actors’ influence in the development of business networks. The dynamics or forces driving the network development can be linked to actors’ own economic and social motives for developing the business. The study highlights the central role of individuals and social networks in the development of the two studied pharmacy networks. The relation between individuals and social networks is reciprocal. The social context of every individual enables multidimensional business actors. The mix of various identities, both individual and collective identities, is an important part of network dynamics. Social networks in pharmacy networks create a platform for exchange and social action, and social networks enable and support business network development.

Artificial neural networks and multicriterion analysis for sustainable irrigation planning

The objective of the present paper is to select the best compromise irrigation planning strategy for the case study of Jayakwadi irrigation project, Maharashtra, India. Four-phase methodology is employed. In phase 1, separate linear programming (LP) models are formulated for the three objectives, namely. net economic benefits, agricultural production and labour employment. In phase 2, nondominated (compromise) irrigation planning strategies are generated using the constraint method of multiobjective optimisation. In phase 3, Kohonen neural networks (KNN) based classification algorithm is employed to sort nondominated irrigation planning strategies into smaller groups. In phase 4, multicriterion analysis (MCA) technique, namely, Compromise Programming is applied to rank strategies obtained from phase 3. It is concluded that the above integrated methodology is effective for modeling multiobjective irrigation planning problems and the present approach can be extended to situations where number of irrigation planning strategies are even large in number. (c) 2004 Elsevier Ltd. All rights reserved.

Nonlinear mixture autoregressive model: economic applications

The aim of this dissertation is to model economic variables by a mixture autoregressive (MAR) model. The MAR model is a generalization of linear autoregressive (AR) model. The MAR -model consists of K linear autoregressive components. At any given point of time one of these autoregressive components is randomly selected to generate a new observation for the time series. The mixture probability can be constant over time or a direct function of a some observable variable. Many economic time series contain properties which cannot be described by linear and stationary time series models. A nonlinear autoregressive model such as MAR model can a plausible alternative in the case of these time series. In this dissertation the MAR model is used to model stock market bubbles and a relationship between inflation and the interest rate. In the case of the inflation rate we arrived at the MAR model where inflation process is less mean reverting in the case of high inflation than in the case of normal inflation. The interest rate move one-for-one with expected inflation. We use the data from the Livingston survey as a proxy for inflation expectations. We have found that survey inflation expectations are not perfectly rational. According to our results information stickiness play an important role in the expectation formation. We also found that survey participants have a tendency to underestimate inflation. A MAR model has also used to model stock market bubbles and crashes. This model has two regimes: the bubble regime and the error correction regime. In the error correction regime price depends on a fundamental factor, the price-dividend ratio, and in the bubble regime, price is independent of fundamentals. In this model a stock market crash is usually caused by a regime switch from a bubble regime to an error-correction regime. According to our empirical results bubbles are related to a low inflation. Our model also imply that bubbles have influences investment return distribution in both short and long run.

Alternative News Sources as 'Collaboratory' Journalism. A case study of Helsingin Sanomat and its relation with the Youth Voice Editorial Board

The new paradigm of connectedness and empowerment brought by the interactivity feature of the Web 2.0 has been challenging the traditional centralized performance of mainstream media. The corporation has been able to survive the strong winds by transforming itself into a global multimedia business network embedded in the network society. By establishing networks, e.g. networks of production and distribution, the global multimedia business network has been able to sight potential solutions by opening the doors to innovation in a decentralized and flexible manner. Under this emerging context of re-organization, traditional practices like sourcing need to be re- explained and that is precisely what this thesis attempts to tackle. Based on ICT and on the network society, the study seeks to explain within the Finnish context the particular case of Helsingin Sanomat (HS) and its relations with the youth news agency, Youth Voice Editorial Board (NÄT). In that sense, the study can be regarded as an explanatory embedded single case study, where HS is the principal unit of analysis and NÄT its embedded unit of analysis. The thesis was able to reach explanations through interrelated steps. First, it determined the role of ICT in HS’s sourcing practices. Then it mapped an overview of the HS’s sourcing relations and provided a context in which NÄT was located. And finally, it established conceptualized institutional relational data between HS and NÄT for their posterior measurement through social network analysis. The data set was collected via qualitative interviews addressed to online and offline editors of HS as well as interviews addressed to NÄT’s personnel. The study concluded that ICT’s interactivity and User Generated Content (UGC) are not sourcing tools as such but mechanism used by HS for getting ideas that could turn into potential news stories. However, when it comes to visual communication, some exemptions were found. The lack of official sources amidst the immediacy leads HS to rely on ICT’s interaction and UGC. More than meets the eye, ICT’s input into the sourcing practice may be more noticeable if the interaction and UGC is well organized and coordinated into proper and innovative networks of alternative content collaboration. Currently, HS performs this sourcing practice via two projects that differ, precisely, by the mode they are coordinated. The first project found, Omakaupunki, is coordinated internally by Sanoma Group’s owned media houses HS, Vartti and Metro. The second project found is coordinated externally. The external alternative sourcing network, as it was labeled, consists of three actors, namely HS, NÄT (professionals in charge) and the youth. This network is a balanced and complete triad in which the actors connect themselves in relations of feedback, recognition, creativity and filtering. However, as innovation is approached very reluctantly, this content collaboration is a laboratory of experiments; a ‘COLLABORATORY’.

Multi-hop Cooperative Wireless Networks: Diversity Multiplexing Tradeoff and Optimal Code Design

We consider single-source single-sink (ss-ss) multi-hop relay networks, with slow-fading links and single-antenna half-duplex relay nodes. While two-hop cooperative relay networks have been studied in great detail in terms of the diversity-multiplexing tradeoff (DMT), few results are available for more general networks. In this paper, we identify two families of networks that are multi-hop generalizations of the two-hop network: K-Parallel-Path (KPP)networks and layered networks.KPP networks, can be viewed as the union of K node-disjoint parallel relaying paths, each of length greater than one. KPP networks are then generalized to KPP(I) networks, which permit interference between paths and to KPP(D) networks, which possess a direct link from source to sink. We characterize the DMT of these families of networks completely for K > 3. Layered networks are networks comprising of layers of relays with edges existing only between adjacent layers, with more than one relay in each layer. We prove that a linear DMT between the maximum diversity dmax and the maximum multiplexing gain of 1 is achievable for single-antenna fully-connected layered networks. This is shown to be equal to the optimal DMT if the number of relaying layers is less than 4.For multiple-antenna KPP and layered networks, we provide an achievable DMT, which is significantly better than known lower bounds for half duplex networks.For arbitrary multi-terminal wireless networks with multiple source-sink pairs, the maximum achievable diversity is shown to be equal to the min-cut between the corresponding source and the sink, irrespective of whether the network has half-duplex or full-duplex relays. For arbitrary ss-ss single-antenna directed acyclic networks with full-duplex relays, we prove that a linear tradeoff between maximum diversity and maximum multiplexing gain is achievable.Along the way, we derive the optimal DMT of a generalized parallel channel and derive lower bounds for the DMT of triangular channel matrices, which are useful in DMT computation of various protocols. We also give alternative and often simpler proofs of several existing results and show that codes achieving full diversity on a MIMO Rayleigh fading channel achieve full diversity on arbitrary fading channels. All protocols in this paper are explicit and use only amplify-and-forward (AF) relaying. We also construct codes with short block-lengths based on cyclic division algebras that achieve the optimal DMT for all the proposed schemes.Two key implications of the results in the paper are that the half-duplex constraint does not entail any rate loss for a large class of cooperative networks and that simple AF protocols are often sufficient to attain the optimal DMT

Give-and-take based peer-to-peer content distribution networks

Content Distribution Networks (CDNs) are widely used to distribute data to large number of users. Traditionally, content is being replicated among a number of surrogate servers, leading to high operational costs. In this context, Peer-to-Peer (P2P) CDNs have emerged as a viable alternative. An issue of concern in P2P networks is that of free riders, i.e., selfish peers who download files and leave without uploading anything in return. Free riding must be discouraged. In this paper, we propose a criterion, the Give-and-Take (G&T) criterion, that disallows free riders. Incorporating the G&T criterion in our model, we study a problem that arises naturally when a new peer enters the system: viz., the problem of downloading a `universe' of segments, scattered among other peers, at low cost. We analyse this hard problem, and characterize the optimal download cost under the G&T criterion. We propose an optimal algorithm, and provide a sub-optimal algorithm that is nearly optimal, but runs much more quickly; this provides an attractive balance between running time and performance. Finally, we compare the performance of our algorithms with that of a few existing P2P downloading strategies in use. We also study the computation time for prescribing the strategy for initial segment and peer selection for the newly arrived peer for various existing and proposed algorithms, and quantify cost-computation time trade-offs.

The two-component signalling networks of Mycobacterium tuberculosis display extensive cross-talk in vitro

Two-component systems (TCSs), which contain paired sensor kinase and response regulator proteins, form the primary apparatus for sensing and responding to environmental cues in bacteria. TCSs are thought to be highly specific, displaying minimal cross-talk, primarily due to the co-evolution of the participating proteins. To assess the level of cross-talk between the TCSs of Mycobacterium tuberculosis, we mapped the complete interactome of the M. tuberculosis TCSs using phosphotransfer profiling. Surprisingly, we found extensive crosstalk among the M. tuberculosis TCSs, significantly more than that in the TCSs in Escherichia coli or Caulobacter crescentus, thereby offering an alternate to specificity paradigm in TCS signalling. Nearly half of the interactions we detected were significant novel cross-interactions, unravelling a potentially complex signalling landscape. We classified the TCSs into specific `one-to-one' and promiscuous `one-to-many' and `many-to-one' circuits. Using mathematical modelling, we deduced that the promiscuous signalling observed can explain several currently confounding observations about M. tuberculosis TCSs. Our findings suggest an alternative paradigm of bacterial signalling with significant cross-talk between TCSs yielding potentially complex signalling landscapes.

Zinc oxide nanowire networks for macroelectronic devices

Highly transparent zinc oxide (ZnO) nanowire networks have been used as the active material in thin film transistors (TFTs) and complementary inverter devices. A systematic study on a range of networks of variable density and TFT channel length was performed. ZnO nanowire networks provide a less lithographically intense alternative to individual nanowire devices, are always semiconducting, and yield significantly higher mobilites than those achieved from currently used amorphous Si and organic TFTs. These results suggest that ZnO nanowire networks could be ideal for inexpensive large area electronics. © 2009 American Institute of Physics.

Optimal Minimum Wage in a Competitive Economy: an Alternative Modelling Approach

This paper analyzes whether a minimum wage can be an optimal redistribution policy when distorting taxes and lump-sum transfers are also available in a competitive economy. We build a static general equilibrium model with a Ramsey planner making decisions on taxes, transfers, and minimum wage levels. Workers are assumed to differ only in their productivity. We find that optimal redistribution may imply the use of a minimum wage. The key factor driving our results is the reaction of the demand for low skilled labor to the minimum wage law. Hence, an optimal minimum wage appears to be most likely when low skilled households are scarce, the complementarity between the two types of workers is large or the difference in productivity is small. The main contribution of the paper is a modelling approach that allows us to adopt analysis and solution techniques widely used in recent public finance research. Moreover, this modelling strategy is flexible enough to allow for potential extensions to include dynamics into the model.

An empirical comparison of the performance of alternative option pricing models

Published as an article in: Investigaciones Economicas, 2005, vol. 29, issue 3, pages 483-523.

Prosocial norms and degree heterogeneity in social networks

We provide empirical evidence to support the claims that social diversity promotes prosocial behavior. We elicit a real-life social network and its members’ adherence to a social norm, namely inequity aversion. The data reveal a positive relationship between subjects’ prosociality and several measures of centrality. This result is in line with the theoretical literature that relates the evolution of social norms to the structure of social interactions and argues that central individuals are crucial for the emergence of prosocial behavior.