Hierarchy as a barrier to advancement for women in academic medicine

Background: Research on barriers to professional advancement for women in academic medicine has not adequately considered the role of environmental factors and how the structure of organizations affects professional advancement and work experiences. This article examines the impact of the hierarchy, including both the organization's hierarchical structure and professionals' perceptions of this structure, in medical school organization on faculty members' experience and advancement in academic medicine. Methods: As part of an inductive qualitative study of faculty in five disparate U.S. medical schools, we interviewed 96 medical faculty at different career stages and in diverse specialties, using in-depth semistructured interviews, about their perceptions about and experiences in academic medicine. Data were coded and analysis was conducted in the grounded theory tradition. Results: Our respondents saw the hierarchy of chairs, based on the indeterminate tenure of department chairs, as a central characteristic of the structure of academic medicine. Many faculty saw this hierarchy as affecting inclusion, reducing transparency in decision making, and impeding advancement. Indeterminate chair terms lessen turnover and may create a bottleneck for advancement. Both men and women faculty perceived this hierarchy, but women saw it as more consequential. Conclusions: The hierarchical structure of academic medicine has a significant impact on faculty work experiences, including advancement, especially for women. We suggest that medical schools consider alternative models of leadership and managerial styles, including fixed terms for chairs with a greater emphasis on inclusion. This is a structural reform that could increase opportunities for advancement especially for women in academic medicine. © 2010 Copyright Mary Ann Liebert, Inc.

Managing Future offshore wind power variability in a European Supergrid

The future European power system will have a hierarchical structure created by layers of system control from a Supergrid via regional high-voltage transmission through to medium and low-voltage distribution. Each level will have generation sources such as large-scale offshore wind, wave, solar thermal, nuclear directly connected to this Supergrid and high levels of embedded generation, connected to the medium-voltage distribution system. It is expected that the fuel portfolio will be dominated by offshore wind in Northern Europe and PV in Southern Europe. The strategies required to manage the coordination of supply-side variability with demand-side variability will include large scale interconnection, demand side management, load aggregation and storage in the context of the Supergrid combined with the Smart Grid. The design challenge associated with this will not only include control topology, data acquisition, analysis and communications technologies, but also the selection of fuel portfolio at a macro level. This paper quantifies the amount of demand side management, storage and so-called 'back-up generation' needed to support an 80% renewable energy portfolio in Europe by 2050. © 2013 IEEE.

Managing the variability of supply in the European power system with high wind energy penetration

The power system of the future will have a hierarchical structure created by layers of system control from via regional high-voltage transmission through to medium and low-voltage distribution. Each level will have generation sources such as large-scale offshore wind, wave, solar thermal, nuclear directly connected to this Supergrid and high levels of embedded generation, connected to the medium-voltage distribution system. It is expected that the fuel portfolio will be dominated by offshore wind in Northern Europe and PV in Southern Europe. The strategies required to manage the coordination of supply-side variability with demand-side variability will include large scale interconnection, demand side management, load aggregation and storage in the concept of the Supergrid combined with the Smart Grid. The design challenge associated with this will not only include control topology, data acquisition, analysis and communications technologies, but also the selection of fuel portfolio at a macro level. This paper quantifies the amount of demand side management, storage and so-called ‘back-up generation’ needed to support an 80% renewable energy portfolio in Europe by 2050.

A hierarchical physical model-based approach to predictive control of a thermal power plant for efficient plant-wide disturbance rejection

Extending the work presented in Prasad et al. (IEEE Proceedings on Control Theory and Applications, 147, 523-37, 2000), this paper reports a hierarchical nonlinear physical model-based control strategy to account for the problems arising due to complex dynamics of drum level and governor valve, and demonstrates its effectiveness in plant-wide disturbance handling. The strategy incorporates a two-level control structure consisting of lower-level conventional PI regulators and a higher-level nonlinear physical model predictive controller (NPMPC) for mainly set-point manoeuvring. The lower-level PI loops help stabilise the unstable drum-boiler dynamics and allow faster governor valve action for power and grid-frequency regulation. The higher-level NPMPC provides an optimal load demand (or set-point) transition by effective handling of plant-wide interactions and system disturbances. The strategy has been tested in a simulation of a 200-MW oil-fired power plant at Ballylumford in Northern Ireland. A novel approach is devized to test the disturbance rejection capability in severe operating conditions. Low frequency disturbances were created by making random changes in radiation heat flow on the boiler-side, while condenser vacuum was fluctuating in a random fashion on the turbine side. In order to simulate high-frequency disturbances, pulse-type load disturbances were made to strike at instants which are not an integral multiple of the NPMPC sampling period. Impressive results have been obtained during both types of system disturbances and extremely high rates of load changes, right across the operating range, These results compared favourably with those from a conventional state-space generalized predictive control (GPC) method designed under similar conditions.

Algal epiphytes of Zostera marina: Variation in assemblage structure from individual leaves to regional scale

Data from a hierarchical study of four Zostera marina beds in Wales were used to identify the spatial scales of variation in epiphyte assemblages. There were significant within and among bed differences in assemblage structure. The differences in assemblage structure with spatial scale generally persisted when species identifications were aggregated into functional groups. There was also significant within and among bed variability in Zostera density and average length. Local variations in Zostera canopy variables at the quadrat scale (total leaf length, average leaf length and leaf density per quadrat) were not related to epiphyte species richness nor to the structure of the assemblage. In contrast, individual leaf length was significantly related to species richness in two of the beds and the structure of epiphyte assemblages was always related to individual leaf lengths. The absence of links between quadrat scale measurements of canopy variables and assemblage structure may reflect the high turnover of individual Zostera leaves. Experimental work is required to discriminate further between the potential causes of epiphyte assemblage variation within and between beds. No bed represented a refuge where a rare species was abundant. If a species was uncommon at the bed scale, it was also uncommon in beds where it occurred. The heterogeneous assemblages found in this study suggest that a precautionary approach to conservation is advisable.

Towards hierarchical management of autonomic components: a case study

We address the issue of autonomic management in hierarchical component-based distributed systems. The long term aim is to provide a modelling framework for autonomic management in which QoS goals can be defined, plans for system adaptation described and proofs of achievement of goals by (sequences of) adaptations furnished. Here we present an early step on this path. We restrict our focus to skeleton-based systems in order to exploit their well-defined structure. The autonomic cycle is described using the Orc system orchestration language while the plans are presented as structural modifications together with associated costs and benefits. A case study is presented to illustrate the interaction of managers to maintain QoS goals for throughput under varying conditions of resource availability.

Hierarchical synthesis of complex DSP functions on FPGAs

SoC systems are now being increasingly constructed using a hierarchy of subsystems or silicon Intellectual Property (IP) cores. The key challenge is to use these cores in a highly efficient manner which can be difficult as the internal core structure may not be known. A design methodology based on synthesizing hierarchical circuit descriptions is presented. The paper employs the MARS synthesis scheduling algorithm within the existing IRIS synthesis flow and details how it can be enhanced to allow for design exploration of IP cores. It is shown that by accessing parameterised expressions for the datapath latencies in the cores, highly efficient FPGA solutions can be achieved. Hardware sharing at both the hierarchical and flattened levels is explored for a normalized lattice filter and results are presented.

Hierarchical Video Summarization in Reference Subspace

In this paper, a hierarchical video structure summarization approach using Laplacian Eigenmap is proposed, where a small set of reference frames is selected from the video sequence to form a reference subspace to measure the dissimilarity between two arbitrary frames. In the proposed summarization scheme, the shot-level key frames are first detected from the continuity of inter-frame dissimilarity, and the sub-shot level and scene level representative frames are then summarized by using K-mean clustering. The experiment is carried on both test videos and movies, and the results show that in comparison with a similar approach using latent semantic analysis, the proposed approach using Laplacian Eigenmap can achieve a better recall rate in keyframe detection, and gives an efficient hierarchical summarization at sub shot, shot and scene levels subsequently.

A hierarchical multiclass support vector machine incorporated with holistic triple learning units

This paper proposes a new hierarchical learning structure, namely the holistic triple learning (HTL), for extending the binary support vector machine (SVM) to multi-classification problems. For an N-class problem, a HTL constructs a decision tree up to a depth of A leaf node of the decision tree is allowed to be placed with a holistic triple learning unit whose generalisation abilities are assessed and approved. Meanwhile, the remaining nodes in the decision tree each accommodate a standard binary SVM classifier. The holistic triple classifier is a regression model trained on three classes, whose training algorithm is originated from a recently proposed implementation technique, namely the least-squares support vector machine (LS-SVM). A major novelty with the holistic triple classifier is the reduced number of support vectors in the solution. For the resultant HTL-SVM, an upper bound of the generalisation error can be obtained. The time complexity of training the HTL-SVM is analysed, and is shown to be comparable to that of training the one-versus-one (1-vs.-1) SVM, particularly on small-scale datasets. Empirical studies show that the proposed HTL-SVM achieves competitive classification accuracy with a reduced number of support vectors compared to the popular 1-vs-1 alternative.

Organizational structure and the periphery of the gene regulatory network in B-cell lymphoma

Background:
The physical periphery of a biological cell is mainly described by signaling pathways which are triggered by transmembrane proteins and receptors that are sentinels to control the whole gene regulatory network of a cell. However, our current knowledge about the gene regulatory mechanisms that are governed by extracellular signals is severely limited.Results: The purpose of this paper is three fold. First, we infer a gene regulatory network from a large-scale B-cell lymphoma expression data set using the C3NET algorithm. Second, we provide a functional and structural analysis of the largest connected component of this network, revealing that this network component corresponds to the peripheral region of a cell. Third, we analyze the hierarchical organization of network components of the whole inferred B-cell gene regulatory network by introducing a new approach which exploits the variability within the data as well as the inferential characteristics of C3NET. As a result, we find a functional bisection of the network corresponding to different cellular components.

Conclusions:
Overall, our study allows to highlight the peripheral gene regulatory network of B-cells and shows that it is centered around hub transmembrane proteins located at the physical periphery of the cell. In addition, we identify a variety of novel pathological transmembrane proteins such as ion channel complexes and signaling receptors in B-cell lymphoma. © 2012 Simoes et al.; licensee BioMed Central Ltd.

Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data

The relationships among organisms and their surroundings can be of immense complexity. To describe and understand an ecosystem as a tangled bank, multiple ways of interaction and their effects have to be considered, such as predation, competition, mutualism and facilitation. Understanding the resulting interaction networks is a challenge in changing environments, e.g. to predict knock-on effects of invasive species and to understand how climate change impacts biodiversity. The elucidation of complex ecological systems with their interactions will benefit enormously from the development of new machine learning tools that aim to infer the structure of interaction networks from field data. In the present study, we propose a novel Bayesian regression and multiple changepoint model (BRAM) for reconstructing species interaction networks from observed species distributions. The model has been devised to allow robust inference in the presence of spatial autocorrelation and distributional heterogeneity. We have evaluated the model on simulated data that combines a trophic niche model with a stochastic population model on a 2-dimensional lattice, and we have compared the performance of our model with L1-penalized sparse regression (LASSO) and non-linear Bayesian networks with the BDe scoring scheme. In addition, we have applied our method to plant ground coverage data from the western shore of the Outer Hebrides with the objective to infer the ecological interactions. (C) 2012 Elsevier B.V. All rights reserved.

Hierarchical zeolites prepared by organosilane templating: A study of the synthesis mechanism and catalytic activity

The crystallization of hierarchical ZSM-5 in the presence of the organosilane octadecyl-dimethyl-(3-trimethoxysilyl-propyl)-ammonium chloride as the mesoporogen was investigated as a function of time and temperature. The synthesis by this method proceeds in two steps. The rapid formation of a predominantly amorphous disordered mesoporous aluminosilicate precursor phase is followed by the formation of globular highly mesoporous zeolite particles involving dissolution of the precursor phase. It is difficult to completely convert the initial phase into the final hierarchical zeolite. This limits the amount of aluminium built into the MFI network and the resulting Bronsted acidity. In the presence of iron, more crystalline hierarchical zeolite is obtained. These Fe-containing zeolites are excellent catalysts for the selective oxidation of benzene to phenol. Their hierarchical pore structure leads to higher reaction rates due to increased mass transfer and increased catalyst longevity despite more substantial coke formation. (C) 2011 Elsevier B.V. All rights reserved.

Coastal upwelling drives intertidal assemblage structure and trophic ecology

ecosystems. Coastal oceanic upwelling, for example, has been associated with elevatedbiomass and abundance patterns of certain functional groups, e.g., corticated macroalgae.In the upwelling system of Northern Chile, we examined measures of intertidal macrobenthiccomposition, structure and trophic ecology across eighteen shores varying in theirproximity to two coastal upwelling centres, in a hierarchical sampling design (spatial scalesof >1 and >10 km). The influence of coastal upwelling on intertidal communities was confirmedby the stable isotope values (δ13C and δ15N) of consumers, including a dominantsuspension feeder, grazers, and their putative resources of POM, epilithic biofilm, andmacroalgae. We highlight the utility of muscle δ15N from the suspension feeding mussel,Perumytilus purpuratus, as a proxy for upwelling, supported by satellite data and previousstudies. Where possible, we used corrections for broader-scale trends, spatial autocorrelation,ontogenetic dietary shifts and spatial baseline isotopic variation prior to analysis. Ourresults showed macroalgal assemblage composition, and benthic consumer assemblagestructure, varied significantly with the intertidal influence of coastal upwelling, especiallycontrasting bays and coastal headlands. Coastal topography also separated differences inconsumer resource use. This suggested that coastal upwelling, itself driven by coastlinetopography, influences intertidal communities by advecting nearshore phytoplankton populationsoffshore and cooling coastal water temperatures. We recommend the isotopic valuesof benthic organisms, specifically long-lived suspension feeders, as in situ alternativesto offshore measurements of upwelling influence

Latent and Explicit Authority in Hierarchical Organizations

We consider the problem of the exercise of authority within social production organizations, embedding the decision makers into a structure of formal authority relationships. We distinguish two types of behavior. First, we introduce an equilibrium notion implementing latent authority under which subordinates submit themselves to authority even though such authority is not en- forced explicitly. Second, we compare this with a non-cooperative equilibrium concept describing explicit exercise of authority. We show that for low enough enforcement costs both forms of authority will be exercised in equilibrium, but for higher enforcement costs latent authority will be exercised while explicit authority will not.