When cost-efficient technologies meet politics:a case study of radical wireless network implementation

Cost efficiency has been a dominant perspective in the traditional IT literature. However, in complex technology and business environment, the widely recognized cost efficient assumption of information technology has been increasingly challenged. Drawing from a case study of wireless network implementation situated in a politically sensitive workplace, this paper provided practice insights for IT managers in today’s networked economy. More specifically, stories experienced in the case study illustrated that despite well-calculated cost efficiency of wireless network infrastructure, the radical implementation process in the case organization encountered enormous challenges and opposition due to the fact that administrators failed to consider various stakeholders’ positions and interests. Eventually, the implementation objectives and outcome were considerably undermined. Implications from this empirical case research reemphasized the significance of understanding political forces situated in any business environment where different stakeholders hold conflicting interests. Lessons learned from the case story further encouraged IT managers and policy makers to better strategize emerging information technology in general and wireless networks in particular as the whole global society and business environment are increasingly facing an emerging wireless world.

Independent modulation of engagement and connectivity of the facial network during affect processing by CACNA1C and ANK3 risk genes for bipolar disorder

IMPORTANCE Genome-wide association studies (GWASs) indicate that single-nucleotide polymorphisms in the CACNA1C and ANK3 genes increase the risk for bipolar disorder (BD). The genes influence neuronal firing by modulating calcium and sodium channel functions, respectively. Both genes modulate ?-aminobutyric acid-transmitting interneuron function and can thus affect brain regional activation and interregional connectivity. OBJECTIVE To determine whether the genetic risk for BD associated with 2 GWAS-supported risk single-nucleotide polymorphisms at CACNA1C rs1006737 and ANK3 rs10994336 is mediated through changes in regional activation and interregional connectivity of the facial affect-processing network. DESIGN, SETTING, AND PARTICIPANTS Cross-sectional functional magnetic resonance imaging study at a research institute of 41 euthymic patients with BD and 46 healthy participants, all of British white descent. MAIN OUTCOMES AND MEASURES Blood oxygen level-dependent signal and effective connectivity measures during the facial affect-processing task. RESULTS In healthy carriers, both genetic risk variants were independently associated with increased regional engagement throughout the facial affect-processing network and increased effective connectivity between the visual and ventral prefrontal cortical regions. In contrast, BD carriers of either genetic risk variant exhibited pronounced reduction in ventral prefrontal cortical activation and visual-prefrontal effective connectivity. CONCLUSIONS AND RELEVANCE Our data demonstrate that the effect of CACNA1C rs1006737 and ANK3 rs10994336 (or genetic variants in linkage disequilibrium) on the brain converges on the neural circuitry involved in affect processing and provides a mechanism linking BD to genome-wide genetic risk variants.

Attachment anxiety and friendship group identification under attachment threat:the moderating role of priming support network expectations

We examined the role of priming participants' own network expectations on their subsequent identification with their friendship group. We examined this prime alongside attachment anxiety and attachment threat, as predictors of friendship group identification. Previous research has suggested that attachment anxiety is associated with negative network expectations. In this study, we extended this work to show that when a network expectation prime was absent, higher attachment anxiety was associated with lower group identification under attachment threat, compared to a control condition. However, when expectations of support network were primed, attachment threat no longer affected group identification, so that only attachment anxiety predicted group identification. This suggests that priming participants who are high in attachment anxiety with their own network expectancies (which are negative), results in participants dis-identifying with their friendship group, regardless of whether or not they have experienced attachment threat. © 2012 Elsevier Ltd.

Reconfiguration of supply chain network:an ISM-based roadmap to performance

Purpose: Short product life cycle and/or mass customization necessitate reconfiguration of operational enablers of supply chain (SC) from time to time in order to harness high levels of performance. The purpose of this paper is to identify the key operational enablers under stochastic environment on which practitioner should focus while reconfiguring a SC network. Design/methodology/approach: The paper used interpretive structural modeling (ISM) approach that presents a hierarchy-based model and the mutual relationships among the enablers. The contextual relationship needed for developing structural self-interaction matrix (SSIM) among various enablers is realized by conducting experiments through simulation of a hypothetical SC network. Findings: The research identifies various operational enablers having a high driving power towards assumed performance measures. In this regard, these enablers require maximum attention and of strategic importance while reconfiguring SC. Practical implications: ISM provides a useful tool to the SC managers to strategically adopt and focus on the key enablers which have comparatively greater potential in enhancing the SC performance under given operational settings. Originality/value: The present research realizes the importance of SC flexibility under the premise of reconfiguration of the operational units in order to harness high value of SC performance. Given the resulting digraph through ISM, the decision maker can focus the key enablers for effective reconfiguration. The study is one of the first efforts that develop contextual relations among operational enablers for SSIM matrix through integration of discrete event simulation to ISM. © Emerald Group Publishing Limited.

Numerical analysis of artificial neural network and volterra-based nonlinear equalizers for coherent optical OFDM

One major drawback of coherent optical orthogonal frequency-division multiplexing (CO-OFDM) that hitherto remains unsolved is its vulnerability to nonlinear fiber effects due to its high peak-to-average power ratio. Several digital signal processing techniques have been investigated for the compensation of fiber nonlinearities, e.g., digital back-propagation, nonlinear pre- and post-compensation and nonlinear equalizers (NLEs) based on the inverse Volterra-series transfer function (IVSTF). Alternatively, nonlinearities can be mitigated using nonlinear decision classifiers such as artificial neural networks (ANNs) based on a multilayer perceptron. In this paper, ANN-NLE is presented for a 16QAM CO-OFDM system. The capability of the proposed approach to compensate the fiber nonlinearities is numerically demonstrated for up to 100-Gb/s and over 1000km and compared to the benchmark IVSTF-NLE. Results show that in terms of Q-factor, for 100-Gb/s at 1000km of transmission, ANN-NLE outperforms linear equalization and IVSTF-NLE by 3.2dB and 1dB, respectively.

The PHAR-QA project: competency framework for pharmacy practice—first steps, the results of the European Network Delphi round 1

PHAR-QA, funded by the European Commission, is producing a framework of competences for pharmacy practice. The framework is in line with the EU directive on sectoral professions and takes into account the diversity of the pharmacy profession and the on-going changes in healthcare systems (with an increasingly important role for pharmacists), and in the pharmaceutical industry. PHAR-QA is asking academia, students and practicing pharmacists to rank competences required for practice. The results show that competences in the areas of drug interactions, need for drug treatment and provision of information and service were ranked highest whereas those in the areas of ability to design and conduct research and development and production of medicines were ranked lower. For the latter two categories, industrial pharmacists ranked them higher than did the other five groups

Improved methodology to evaluate fracture properties of warm-mix asphalt using overlay test

This study developed a reliable and repeatable methodology to evaluate the fracture properties of asphalt mixtures with an overlay test (OT). In the proposed methodology, first, a two-step OT protocol was used to characterize the undamaged and damaged behaviors of asphalt mixtures. Second, a new methodology combining the mechanical analysis of viscoelastic force equilibrium in the OT specimen and finite element simulations was used to determine the undamaged properties and crack growth function of asphalt mixtures. Third, a modified Paris's law replacing the stress intensity factor by the pseudo J-integral was employed to characterize the fracture behavior of asphalt mixtures. Theoretical equations were derived to calculate the parameters A and n (defined as the fracture properties) in the modified Paris's law. The study used a detailed example to calculate A and n from the OT data. The proposed methodology was successfully applied to evaluate the impact of warm-mix asphalt (WMA) technologies on fracture properties. The results of the tested specimens showed that Evotherm WMA technology slightly improved the cracking resistance of asphalt mixtures, while foaming WMA technology provided comparable fracture properties. In addition, the study found that A decreased with the increase in n in general. A linear relationship between 2log(A) and n was established.

Experimental comparison of artificial neural network and volterra based nonlinear equalization for CO-OFDM

A novel artificial neural network (ANN)-based nonlinear equalizer (NLE) of low complexity is demonstrated for 40-Gb/s CO-OFDM at 2000 km, revealing ∼1.5 dB enhancement in Q-factor compared to inverse Volterra-series transfer function based NLE.

Factors affecting the growth of Cladophora in relation to river pollution

Nuisance growths of Cladophora have been associated with eutrophication. A review of the literature, however, reveals a scarcity of relevant experimental growth studies. Sampling experimental streams reveals that the addition of sewage effluent to good quality water alters the flora from that dominated by Potamogetan crispus to one dominated by CLadophora. Spatial and temporal differences in biomass of taxa present are discussed in the context of accompanying physicochemical data. In laboratory batch culture, growth of unialgal C. glomerata was accompanied by elevation of medium pH - considered largely responsible for the poor growth in such culture. However, appropriate experimental conditions and indices of growth were selected and the effects of various herbicides assessed. Diquat and terbutryne were shown to possess algicidal activity towards Cladophora. A closed continuous culture apparatus was developed: growth proceeded through lag, logarithmic and linear phases. Inoculum size and medium flow rate had significant effects on growth, and were standardized. In continuous culture, specific growth rate increased linearly with increased duration of light per day, up to 24 hours, and increased light intensity, up to 6000 lux - the highest intensity tested. Comparison of field and laboratory results suggests that ammonia toxicity is attributable to the undissociated form. In the laboratory, 185 µg/1 undissociated ammoniacal nitrogen reduced specific growth rate to 50% of that at 10 µg/1 undissociated ammcniacal nitrogen. 0.077-1.057 mg/1 NO2-N had no significant effect on growth. 7.2-15.2 mg/1 NO3-N had no significant effect on specific growth rate. Neither was any nitrate/phosphate interaction significant. At 4.9 mg/1 PO4-1, specific growth rate was only 48% of that at 1.9 g/1 P04-P. The critical medium PO4-P concentration was <0.1 mg/i. Specific growth rate was reduced to 50% of that in natural water by 0.036 mgCu/l, 0.070 mgzn/1 and 1.03 mgPb/l. Metal uptake was evaluated.

A dynamic model of the hypoxia-inducible factor 1α (HIF-1α) network

Activation of the hypoxia-inducible factor (HIF) pathway is a critical step in the transcriptional response to hypoxia. Although many of the key proteins involved have been characterised, the dynamics of their interactions in generating this response remain unclear. In the present study, we have generated a comprehensive mathematical model of the HIF-1a pathway based on core validated components and dynamic experimental data, and confirm the previously described connections within the predicted network topology. Our model confirms previous work demonstrating that the steps leading to optimal HIF-1a transcriptional activity require sequential inhibition of both prolyl- and asparaginyl-hydroxylases. We predict from our model (and confirm experimentally) that there is residual activity of the asparaginyl-hydroxylase FIH (factor inhibiting HIF) at low oxygen tension. Furthermore, silencing FIH under conditions where prolyl-hydroxylases are inhibited results in increased HIF-1a transcriptional activity, but paradoxically decreases HIF-1a stability. Using a core module of the HIF network and mathematical proof supported by experimental data, we propose that asparaginyl hydroxylation confers a degree of resistance upon HIF-1a to proteosomal degradation. Thus, through in vitro experimental data and in silico predictions, we provide a comprehensive model of the dynamic regulation of HIF-1a transcriptional activity by hydroxylases and use its predictive and adaptive properties to explain counter-intuitive biological observations.

Serum-free process development:improving the yield and consistency of human mesenchymal stromal cell production

Background aims: The cost-effective production of human mesenchymal stromal cells (hMSCs) for off-the-shelf and patient specific therapies will require an increasing focus on improving product yield and driving manufacturing consistency. Methods: Bone marrow-derived hMSCs (BM-hMSCs) from two donors were expanded for 36 days in monolayer with medium supplemented with either fetal bovine serum (FBS) or PRIME-XV serum-free medium (SFM). Cells were assessed throughout culture for proliferation, mean cell diameter, colony-forming potential, osteogenic potential, gene expression and metabolites. Results: Expansion of BM-hMSCs in PRIME-XV SFM resulted in a significantly higher growth rate (P < 0.001) and increased consistency between donors compared with FBS-based culture. FBS-based culture showed an inter-batch production range of 0.9 and 5 days per dose compared with 0.5 and 0.6 days in SFM for each BM-hMSC donor line. The consistency between donors was also improved by the use of PRIME-XV SFM, with a production range of 0.9 days compared with 19.4 days in FBS-based culture. Mean cell diameter has also been demonstrated as a process metric for BM-hMSC growth rate and senescence through a correlation (R² = 0.8705) across all conditions. PRIME-XV SFM has also shown increased consistency in BM-hMSC characteristics such as per cell metabolite utilization, in vitro colony-forming potential and osteogenic potential despite the higher number of population doublings. Conclusions: We have increased the yield and consistency of BM-hMSC expansion between donors, demonstrating a level of control over the product, which has the potential to increase the cost-effectiveness and reduce the risk in these manufacturing processes.

Real-time control of a Tokamak plasma using neural networks

This paper presents results from the first use of neural networks for the real-time feedback control of high temperature plasmas in a Tokamak fusion experiment. The Tokamak is currently the principal experimental device for research into the magnetic confinement approach to controlled fusion. In the Tokamak, hydrogen plasmas, at temperatures of up to 100 Million K, are confined by strong magnetic fields. Accurate control of the position and shape of the plasma boundary requires real-time feedback control of the magnetic field structure on a time-scale of a few tens of microseconds. Software simulations have demonstrated that a neural network approach can give significantly better performance than the linear technique currently used on most Tokamak experiments. The practical application of the neural network approach requires high-speed hardware, for which a fully parallel implementation of the multi-layer perceptron, using a hybrid of digital and analogue technology, has been developed.

Information geometric measurements of generalisation

Neural networks can be regarded as statistical models, and can be analysed in a Bayesian framework. Generalisation is measured by the performance on independent test data drawn from the same distribution as the training data. Such performance can be quantified by the posterior average of the information divergence between the true and the model distributions. Averaging over the Bayesian posterior guarantees internal coherence; Using information divergence guarantees invariance with respect to representation. The theory generalises the least mean squares theory for linear Gaussian models to general problems of statistical estimation. The main results are: (1)~the ideal optimal estimate is always given by average over the posterior; (2)~the optimal estimate within a computational model is given by the projection of the ideal estimate to the model. This incidentally shows some currently popular methods dealing with hyperpriors are in general unnecessary and misleading. The extension of information divergence to positive normalisable measures reveals a remarkable relation between the dlt dual affine geometry of statistical manifolds and the geometry of the dual pair of Banach spaces Ld and Ldd. It therefore offers conceptual simplification to information geometry. The general conclusion on the issue of evaluating neural network learning rules and other statistical inference methods is that such evaluations are only meaningful under three assumptions: The prior P(p), describing the environment of all the problems; the divergence Dd, specifying the requirement of the task; and the model Q, specifying available computing resources.

Neural networks: A principled perspective

Introductory accounts of artificial neural networks often rely for motivation on analogies with models of information processing in biological networks. One limitation of such an approach is that it offers little guidance on how to find optimal algorithms, or how to verify the correct performance of neural network systems. A central goal of this paper is to draw attention to a quite different viewpoint in which neural networks are seen as algorithms for statistical pattern recognition based on a principled, i.e. theoretically well-founded, framework. We illustrate the concept of a principled viewpoint by considering a specific issue concerned with the interpretation of the outputs of a trained network. Finally, we discuss the relevance of such an approach to the issue of the validation and verification of neural network systems.