Validating Institutional Commitment to Outreach at Land-Grant Universities: Listening to the Voices of Community Partners

The need for public understanding and awareness of the value of university Extension and outreach is at an all-time high due to flattening Extension budgets and recent criticisms about higher education\u27s commitment to public service. Drawing on interviews from community partners in three states, this article examines how community partners formulate their perceptions about an institution\u27s commitment to its outreach mission. Community partners form their opinions about institutional commitment to engagement through a combination of three factors: language and symbolic actions of campus leadership, personal experiences with faculty and staff, and success in navigating the complex structures of the university

Prediction error identification of linear dynamic networks with rank-reduced noise

Dynamic networks are interconnected dynamic systems with measured node signals and dynamic modules reflecting the links between the nodes. We address the problem of \red{identifying a dynamic network with known topology, on the basis of measured signals}, for the situation of additive process noise on the node signals that is spatially correlated and that is allowed to have a spectral density that is singular. A prediction error approach is followed in which all node signals in the network are jointly predicted. The resulting joint-direct identification method, generalizes the classical direct method for closed-loop identification to handle situations of mutually correlated noise on inputs and outputs. When applied to general dynamic networks with rank-reduced noise, it appears that the natural identification criterion becomes a weighted LS criterion that is subject to a constraint. This constrained criterion is shown to lead to maximum likelihood estimates of the dynamic network and therefore to minimum variance properties, reaching the Cramer-Rao lower bound in the case of Gaussian noise.Comment: 17 pages, 5 figures, revision submitted for publication in Automatica, 4 April 201

The CLIC Programme: Towards a Staged e+e- Linear Collider Exploring the Terascale : CLIC Conceptual Design Report

This report describes the exploration of fundamental questions in particle physics at the energy frontier with a future TeV-scale e+e- linear collider based on the Compact Linear Collider (CLIC) two-beam acceleration technology. A high-luminosity high-energy e+e- collider allows for the exploration of Standard Model physics, such as precise measurements of the Higgs, top and gauge sectors, as well as for a multitude of searches for New Physics, either through direct discovery or indirectly, via high-precision observables. Given the current state of knowledge, following the observation of a 125 GeV Higgs-like particle at the LHC, and pending further LHC results at 8 TeV and 14 TeV, a linear e+e- collider built and operated in centre-of-mass energy stages from a few-hundred GeV up to a few TeV will be an ideal physics exploration tool, complementing the LHC. In this document, an overview of the physics potential of CLIC is given. Two example scenarios are presented for a CLIC accelerator built in three main stages of 500 GeV, 1.4 (1.5) TeV, and 3 TeV, together with operating schemes that will make full use of the machine capacity to explore the physics. The accelerator design, construction, and performance are presented, as well as the layout and performance of the experiments. The proposed staging example is accompanied by cost estimates of the accelerator and detectors and by estimates of operating parameters, such as power consumption. The resulting physics potential and measurement precisions are illustrated through detector simulations under realistic beam conditions.Comment: 84 pages, published as CERN Yellow Report https://cdsweb.cern.ch/record/147522

Proton and cadmium adsorption by the archaeon Thermococcus zilligii: Generalising the contrast between thermophiles and mesophiles as sorbents

Adsorption by microorganisms can play a significant role in the fate and transport of metals in natural systems. Surface complexation models (SCMs) have been applied extensively to describe metal adsorption by mesophilic bacteria, and several recent studies have extended this framework to thermophilic bacteria. We conduct acid-base titrations and batch experiments to characterise proton and Cd adsorption onto the thermophilic archaeon Thermococcus zilligii. The experimental data and the derived SCMs indicate that the archaeon displays significantly lower overall sorption site density compared to previously studied thermophilic bacteria such Anoxybacillus flavithermus, Geobacillus stearothermophilus, G. thermocatenulatus, and Thermus thermophilus. The thermophilic bacteria and archaea display lower sorption site densities than the mesophilic microorganisms that have been studied to date, which points to a general pattern of total concentration of cell wall adsorption sites per unit biomass being inversely correlated to growth temperature

Single module identifiability in linear dynamic networks

A recent development in data-driven modelling addresses the problem of identifying dynamic models of interconnected systems, represented as linear dynamic networks. For these networks the notion network identifiability has been introduced recently, which reflects the property that different network models can be distinguished from each other. Network identifiability is extended to cover the uniqueness of a single module in the network model. Conditions for single module identifiability are derived and formulated in terms of path-based topological properties of the network models.Comment: 6 pages, 2 figures, submitted to Control Systems Letters (L-CSS) and the 57th IEEE Conference on Decision and Control (CDC

Delineating the molecular and phenotypic spectrum of the SETD1B-related syndrome

Purpose Pathogenic variants in SETD1B have been associated with a syndromic neurodevelopmental disorder including intellectual disability, language delay, and seizures. To date, clinical features have been described for 11 patients with (likely) pathogenic SETD1B sequence variants. This study aims to further delineate the spectrum of the SETD1B-related syndrome based on characterizing an expanded patient cohort. Methods We perform an in-depth clinical characterization of a cohort of 36 unpublished individuals with SETD1B sequence variants, describing their molecular and phenotypic spectrum. Selected variants were functionally tested using in vitro and genome-wide methylation assays. Results Our data present evidence for a loss-of-function mechanism of SETD1B variants, resulting in a core clinical phenotype of global developmental delay, language delay including regression, intellectual disability, autism and other behavioral issues, and variable epilepsy phenotypes. Developmental delay appeared to precede seizure onset, suggesting SETD1B dysfunction impacts physiological neurodevelopment even in the absence of epileptic activity. Males are significantly overrepresented and more severely affected, and we speculate that sex-linked traits could affect susceptibility to penetrance and the clinical spectrum of SETD1B variants. Conclusion Insights from this extensive cohort will facilitate the counseling regarding the molecular and phenotypic landscape of newly diagnosed patients with the SETD1B-related syndrome.Peer reviewe

Abstractions of linear dynamic networks for input selection in local module identification

In abstractions of linear dynamic networks, selected node signals are removed from the network, while keeping the remaining node signals invariant. The topology and link dynamics, or modules, of an abstracted network will generally be changed compared to the original network. Abstractions of dynamic networks can be used to select an appropriate set of node signals that are to be measured, on the basis of which a particular local module can be estimated. A method is introduced for network abstraction that generalizes previously introduced algorithms, as e.g. immersion and the method of indirect inputs. For this abstraction method it is shown under which conditions on the selected signals a particular module will remain invariant. This leads to sets of conditions on selected measured node variables that allow identification of the target module.Comment: 17 pages, 7 figures. Paper to appear in Automatica, Vol. 117, July 202

Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models

This is the published version. Copyright 2014 American Geophysical UnionThis paper presents a hybrid local-global sensitivity analysis method termed the Distributed Evaluation of Local Sensitivity Analysis (DELSA), which is used here to identify important and unimportant parameters and evaluate how model parameter importance changes as parameter values change. DELSA uses derivative-based “local” methods to obtain the distribution of parameter sensitivity across the parameter space, which promotes consideration of sensitivity analysis results in the context of simulated dynamics. This work presents DELSA, discusses how it relates to existing methods, and uses two hydrologic test cases to compare its performance with the popular global, variance-based Sobol' method. The first test case is a simple nonlinear reservoir model with two parameters. The second test case involves five alternative “bucket-style” hydrologic models with up to 14 parameters applied to a medium-sized catchment (200 km2) in the Belgian Ardennes. Results show that in both examples, Sobol' and DELSA identify similar important and unimportant parameters, with DELSA enabling more detailed insight at much lower computational cost. For example, in the real-world problem the time delay in runoff is the most important parameter in all models, but DELSA shows that for about 20% of parameter sets it is not important at all and alternative mechanisms and parameters dominate. Moreover, the time delay was identified as important in regions producing poor model fits, whereas other parameters were identified as more important in regions of the parameter space producing better model fits. The ability to understand how parameter importance varies through parameter space is critical to inform decisions about, for example, additional data collection and model development. The ability to perform such analyses with modest computational requirements provides exciting opportunities to evaluate complicated models as well as many alternative models