Identifying Quality, Novel, and Creative Ideas: Constructs and Scales for Idea Evaluation

Researchers and practitioners have an abiding interest in improving tools and methods to support idea generation. In studies that go beyond merely enumerating ideas, researchers typically select one or more of the following three constructs, which are often operationalized as the dependent variable(s): 1) idea quality, 2) idea novelty, which is sometimes referred to as rarity or unusualness, and 3) idea creativity. It has been chronically problematic to compare findings across studies because these evaluation constructs have been variously defined and the constructs have been sampled in different ways. For example, some researchers term an idea \u27creative\u27 if it is novel, while others consider an idea to be creative only if it is also applicable, effective, and implementable. This paper examines 90 studies on creativity and idea generation. Within the creativity studies considered here, the novelty of ideas was always measured, but in some cases the ideas had to also meet additional requirements to be considered creative. Some studies that examined idea quality also assessed novelty, while others measured different quality attributes, such as effectiveness and implementability, instead. This paper describes a method for evaluating ideas with regard to four dimensions--novelty, workability, relevance, and specificity--and has identified two measurable sub-dimensions for each of the four main dimensions. An action-research approach was used to develop ordinal scales anchored by clearly differentiable descriptions for each sub-dimension. Confirmatory factor analysis revealed high loadings among the sub-dimensions that comprise each dimension as well as high discriminant validity between dimensions. Application of this method resulted in high inter-rater reliability even when the method was applied by different raters to different problems and to ideas produced by both manual methods and group support systems (GSS)

Hotspots and drivers of compound marine heatwaves and low net primary production extremes

Extreme events can severely impact marine organisms and ecosystems. Of particular concern are multivariate compound events, namely when conditions are simultaneously extreme for multiple ocean ecosystem stressors. In 2013–2015 for example, an extensive marine heatwave (MHW), known as the Blob, co-occurred locally with extremely low net primary productivity (NPPX) and negatively impacted marine life in the northeast Pacific. Yet, little is known about the characteristics and drivers of such multivariate compound MHW–NPPX events. Using five different satellite-derived net primary productivity (NPP) estimates and large-ensemble-simulation output of two widely used and comprehensive Earth system models, the Geophysical Fluid Dynamics Laboratory (GFDL) ESM2M-LE and Community Earth System Model version 2 (CESM2-LE), we assess the present-day distribution of compound MHW–NPPX events and investigate their potential drivers on the global scale. The satellite-based estimates and both models reveal hotspots of frequent compound events in the center of the equatorial Pacific and in the subtropical Indian Ocean, where their occurrence is at least 3 times higher (more than 10 d yr−1) than if MHWs (temperature above the seasonally varying 90th-percentile threshold) and NPPX events (NPP below the seasonally varying 10th-percentile threshold) were to occur independently. However, the models show disparities in the northern high latitudes, where compound events are rare in the satellite-based estimates and GFDL ESM2M-LE (less than 3 d yr−1) but relatively frequent in CESM2-LE. In the Southern Ocean south of 60∘ S, low agreement between the observation-based estimates makes it difficult to determine which of the two models better simulates MHW–NPPX events. The frequency patterns can be explained by the drivers of compound events, which vary among the two models and phytoplankton types. In the low latitudes, MHWs are associated with enhanced nutrient limitation on phytoplankton growth, which results in frequent compound MHW–NPPX events in both models. In the high latitudes, NPPX events in GFDL ESM2M-LE are driven by enhanced light limitation, which rarely co-occurs with MHWs, resulting in rare compound events. In contrast, in CESM2-LE, NPPX events in the high latitudes are driven by reduced nutrient supply that often co-occurs with MHWs, moderates phytoplankton growth, and causes biomass to decrease. Compound MHW–NPPX events are associated with a relative shift towards larger phytoplankton in most regions, except in the eastern equatorial Pacific in both models, as well as in the northern high latitudes and between 35 and 50∘ S in CESM2-LE, where the models suggest a shift towards smaller phytoplankton, with potential repercussions on marine ecosystems. Overall, our analysis reveals that the likelihood of compound MHW–NPPX events is contingent on model representation of the factors limiting phytoplankton production. This identifies an important need for improved process understanding in Earth system models used for predicting and projecting compound MHW–NPPX events and their impacts.</p

Norm-dependent Random Matrix Ensembles in External Field and Supersymmetry

The class of norm-dependent Random Matrix Ensembles is studied in the presence of an external field. The probability density in those ensembles depends on the trace of the squared random matrices, but is otherwise arbitrary. An exact mapping to superspace is performed. A transformation formula is derived which gives the probability density in superspace as a single integral over the probability density in ordinary space. This is done for orthogonal, unitary and symplectic symmetry. In the case of unitary symmetry, some explicit results for the correlation functions are derived.Comment: 19 page

The Chemical Compositions of the Type II Cepheids -- The BL Her and W Vir Variables

Abundance analyses from high-resolution optical spectra are presented for 19 Type II Cepheids in the Galactic field. The sample includes both short-period (BL Her) and long-period (W Vir) stars. This is the first extensive abundance analysis of these variables. The C, N, and O abundances with similar spreads for the BL Her and W Vir show evidence for an atmosphere contaminated with $3\alpha$-process and CN-cycling products. A notable anomaly of the BL Her stars is an overabundance of Na by a factor of about five relative to their presumed initial abundances. This overabundance is not seen in the W Vir stars. The abundance anomalies running from mild to extreme in W Vir stars but not seen in the BL Her stars are attributed to dust-gas separation that provides an atmosphere deficient in elements of high condensation temperature, notably Al, Ca, Sc, Ti, and $s$-process elements. Such anomalies have previously been seen among RV Tau stars which represent a long-period extension of the variability enjoyed by the Type II Cepheids. Comments are offered on how the contrasting abundance anomalies of BL Her and W Vir stars may be explained in terms of the stars' evolution from the blue horizontal branch.Comment: 41 pages including 11 figures and 4 tables; Accepted for publication in Ap

Development of a β-Lactoglobulin sensor based on SPR for milk allergens detection

A sensitive and label-free surface plasmon resonance (SPR) based sensor was developed in this work for the detection of milk allergens. β-lactoglobulin (BLG) protein was used as the biomarker for cow milk detection. This is to be used directly in final rinse samples of cleaning in-place (CIP) systems of food manufacturers. The affinity assay was optimised and characterised before a standard curve was performed in pure buffer conditions, giving a detection limit of 0.164 µg mL−1 as a direct binding assay. The detection limit can be further enhanced through the use of a sandwich assay and amplification with nanomaterials. However, this was not required here, as the detection limit achieved exceeded the required allergen detection levels of 2 µg mL−1 for β-lactoglobulin. The binding affinities of the polyclonal antibody for BLG, expressed by the dissociation constant (KD), were equal to 2.59 × 10−9 M. The developed SPR-based sensor offers several advantages in terms of label-free detection, real-time measurements, potential on-line system and superior sensitivity when compared to ELISA-based techniques. The method is novel for this application and could be applied to wider food allergen risk management decision(s) in food manufacturing

Synthesis of molecularly imprinted polymer nanoparticles for α-casein detection using surface plasmon resonance as a milk allergen sensor

Food recalls due to undeclared allergens or contamination are costly to the food manufacturing industry worldwide. As the industry strives for better manufacturing efficiencies over a diverse range of food products, there is a need for the development of new analytical techniques to improve monitoring of the presence of unintended food allergens during the food manufacturing process. In particular, the monitoring of wash samples from cleaning in place systems (CIP), used in the cleaning of food processing equipment, would allow for the effective removal of allergen containing ingredients in between food batches. Casein proteins constitute the biggest group of proteins in milk and hence are the most common milk protein allergen in food ingredients. As such, these proteins could present an ideal analyte for cleaning validation. In this work, molecularly imprinted polymer nanoparticles (nanoMIPs) with high affinity toward bovine α-casein were synthesized using a solid-phase imprinting method. The nanoMIPs were then characterized and incorporated into label free surface plasmon resonance (SPR) based sensor. The nanoMIPs demonstrated good binding affinity and selectivity toward α-casein (KD ∼ 10 × 10–9 M). This simple affinity sensor demonstrated the quantitative detection of α-casein achieving a detection limit of 127 ± 97.6 ng mL–1 (0.127 ppm) which is far superior to existing commercially available ELISA kits. Recoveries from spiked CIP wastewater samples were within the acceptable range (87–120%). The reported sensor could allow food manufacturers to adequately monitor and manage food allergen risk in food processing environments while ensuring that the food produced is safe for the consumer

Tunable Visible and Near-IR Photoactivation of Light-Responsive Compounds by Using Fluorophores as Light-Capturing Antennas

Although the corrin ring of vitamin B12 is unable to efficiently absorb light beyond 550 nm, it is shown that commercially available fluorophores can be used as antennas to capture long-wavelength light to promote scission of the Co-C bond at wavelengths up to 800 nm. The ability to control the molecular properties of bioactive species with long visible and near-IR light has implications for drug delivery, nanotechnology, and the spatiotemporal control of cellular behavior

Building confidence in projections of the responses of living marine resources to climate change

The Fifth Assessment Report of the Intergovernmental Panel on Climate Change highlights that climate change and ocean acidification are challenging the sustainable management of living marine resources (LMRs). Formal and systematic treatment of uncertainty in existing LMR projections, however, is lacking. We synthesize knowledge of how to address different sources of uncertainty by drawing from climate model intercomparison efforts. We suggest an ensemble of available models and projections, informed by observations, as a starting point to quantify uncertainties. Such an ensemble must be paired with analysis of the dominant uncertainties over different spatial scales, time horizons, and metrics. We use two examples: (i) global and regional projections of Sea Surface Temperature and (ii) projection of changes in potential catch of sablefish (Anoplopoma fimbria) in the 21st century, to illustrate this ensemble model approach to explore different types of uncertainties. Further effort should prioritize understanding dominant, undersampled dimensions of uncertainty, as well as the strategic collection of observations to quantify, and ultimately reduce, uncertainties. Our proposed framework will improve our understanding of future changes in LMR and the resulting risk of impacts to ecosystems and the societies under changing ocean conditions

Neo: an object model for handling electrophysiology data in multiple formats

Neuroscientists use many different software tools to acquire, analyze and visualize electrophysiological signals. However, incompatible data models and file formats make it difficult to exchange data between these tools. This reduces scientific productivity, renders potentially useful analysis methods inaccessible and impedes collaboration between labs. A common representation of the core data would improve interoperability and facilitate data-sharing. To that end, we propose here a language-independent object model, named “Neo,” suitable for representing data acquired from electroencephalographic, intracellular, or extracellular recordings, or generated from simulations. As a concrete instantiation of this object model we have developed an open source implementation in the Python programming language. In addition to representing electrophysiology data in memory for the purposes of analysis and visualization, the Python implementation provides a set of input/output (IO) modules for reading/writing the data from/to a variety of commonly used file formats. Support is included for formats produced by most of the major manufacturers of electrophysiology recording equipment and also for more generic formats such as MATLAB. Data representation and data analysis are conceptually separate: it is easier to write robust analysis code if it is focused on analysis and relies on an underlying package to handle data representation. For that reason, and also to be as lightweight as possible, the Neo object model and the associated Python package are deliberately limited to representation of data, with no functions for data analysis or visualization. Software for neurophysiology data analysis and visualization built on top of Neo automatically gains the benefits of interoperability, easier data sharing and automatic format conversion; there is already a burgeoning ecosystem of such tools. We intend that Neo should become the standard basis for Python tools in neurophysiology.EC/FP7/269921/EU/Brain-inspired multiscale computation in neuromorphic hybrid systems/BrainScaleSDFG, 103586207, GRK 1589: Verarbeitung sensorischer Informationen in neuronalen SystemenBMBF, 01GQ1302, Nationaler Neuroinformatik Knote