Use Of Control Data Research Articles

Resource Efficiency as an environmental performance metric for industry: Exergetic analysis of a clinker manufacturing plant

Control-data exergy-based Resource Efficiency (RE) analysis is applied to investigate resource use and efficiency improvements in the cement industry to understand what insights can be gained into the efficiency of clinker manufacturing and possible decarbonisation options when having full access to material and energy flows from the manufacturing plant control system. Mapping of the resources in and out of the clinker manufacturing section showed that total resource input is dominated by the flow of fuels, with the clinker burning section constituting the most energy and emissions-intensive part of the process. The plant is found to have an annual mean RE of 47.8% in compound operation, and 38.3% in direct operation. Three improvement options are identified: recycling of dust, reduction of heat transfer losses across the kiln, and optimisation of the plant's continuous operation. Heat losses reduction was found to have the largest improvement potential in terms of RE and CO2 emissions reduction. Despite providing a detailed representation of the real-time operation capable of identifying potential improvement interventions, the use of control-data is found to be highly intensive and time-consuming. In the future, it would be interesting to investigate whether the same level of insights into the efficiency and potential for decarbonisation of clinker manufacturing could be gained using less data.

Exploring high-dimensional biological data with sparse contrastive principal component analysis.

Statistical analyses of high-throughput sequencing data have re-shaped the biological sciences. In spite of myriad advances, recovering interpretable biological signal from data corrupted by technical noise remains a prevalent open problem. Several classes of procedures, among them classical dimensionality reduction techniques and others incorporating subject-matter knowledge, have provided effective advances. However, no procedure currently satisfies the dual objectives of recovering stable and relevant features simultaneously. Inspired by recent proposals for making use of control data in the removal of unwanted variation, we propose a variant of principal component analysis (PCA), sparse contrastive PCA that extracts sparse, stable, interpretable and relevant biological signal. The new methodology is compared to competing dimensionality reduction approaches through a simulation study and via analyses of several publicly available protein expression, microarray gene expression and single-cell transcriptome sequencing datasets. A free and open-source software implementation of the methodology, the scPCA R package, is made available via the Bioconductor Project. Code for all analyses presented in this article is also available via GitHub. philippe_boileau@berkeley.edu. Supplementary data are available at Bioinformatics online.

Physiological response to acute stress against confounding factors: a white-box research method

Objectives: Physiological sensors appear a promising tool to monitor psychological stress in the daily life. Yet, despite the increasing know-how developed in affective computing, physiology-driven stress detection algorithms still yield unsatisfactory results on the field. This study intends to highlight physiological patterns that are both stress-sensitive and stress-specific, at the multi-subject scale, in several contexts. Approach: A notional framework was developed and empirically validated, which distinguishes psychological stress from two context-related confounding factors: Background and Activity. A physiological database was elaborated from a multi-task experimental protocol in a standardized environment with skin conductance, electrocardiograph and respiration recordings. Stress-induced variations were isolated in standard physiological features, to select the most stress-sensitive ones independently from confounding factors. Main Results: Most standard features were unable to distinguish acute psychological stress from low cognitive activity (e.g. frequency of skin conductance responses, or spectral power in the low and high frequency bands of the interbeat intervals). After sign-rank tests between the stressful and control conditions of two standard tasks involving arithmetic or speech, their effect sizes remained below 0.3. A number of physiological features also showed task-dependency. Yet, promising correlates were highlighted among cardiac features such as mean heart rate and its standard deviation, or the root mean square of its successive differences (effect sizes above 0.3 for both tasks). Significance: The high sensitivity to context observed in standard features is likely to explain the poor validity on the field of physiology-driven models developed in controlled environments. In particular, mental activity should be considered as confounding factor while elaborating these models to increase their accuracy. The small feature set highlighted in this paper and the use of control data should enhance the generalization abilities of any machine learning algorithm; still, further research is needed to evidence new stress-specific physiological responses.

VGI QUALITY CONTROL

Abstract. This paper presents a framework for considering quality control of volunteered geographic information (VGI). Different issues need to be considered during the conception, acquisition and post-acquisition phases of VGI creation. This includes items such as collecting metadata on the volunteer, providing suitable training, giving corrective feedback during the mapping process and use of control data, among others. Two examples of VGI data collection are then considered with respect to this quality control framework, i.e. VGI data collection by National Mapping Agencies and by the most recent Geo-Wiki tool, a game called Cropland Capture. Although good practices are beginning to emerge, there is still the need for the development and sharing of best practice, especially if VGI is to be integrated with authoritative map products or used for calibration and/or validation of land cover in the future.

ANALYSIS OFKEPLER'S SHORT-CADENCE PHOTOMETRY FOR TrES-2b

We present an analysis of 18 short-cadence (SC) transit light curves of TrES-2b using quarter 0 (Q0) and quarter 1 (Q1) from the Kepler Mission. The photometry is of unprecedented precision, 237 ppm minute−1, allowing for the most accurate determination of the transit parameters yet obtained for this system. Global fits of the transit photometry, radial velocities, and known transit times are used to obtain a self-consistent set of refined parameters for this system, including updated stellar and planetary parameters. Special attention is paid to fitting for limb darkening and eccentricity. We place an upper limit on the occultation depth to be <72.9 ppm to 3σ confidence, indicating TrES-2b has the lowest determined geometric albedo for an exoplanet, of Ag < 0.146. We also produce a transit timing analysis using Kepler's SC data and demonstrate exceptional timing precision at the level of a few seconds for each transit event. With 18 fully sampled transits at such high precision, we are able to produce stringent constraints on the presence of perturbing planets, Trojans, and extrasolar moons. We introduce the novel use of control data to identify phasing effects. We also exclude the previously proposed hypotheses of short-period transit time variation and additional transits but find that the hypothesis of long-term inclination change is neither supported nor refuted by our analysis.

“Illusions of Normality”: a Methodological Critique of Category-Specific Naming

Category-specific disorders are perhaps the archetypal example of domain-specificity — being typically defined by the presence of dissociations between living and nonliving naming ability in people following neurological damage. The methods adopted to quantify naming across categories are therefore pivotal since they provide the criterion for defining whether patients have a category effect and necessarily influence the subsequent direction and the interpretation of testing. This paper highlights a series of methodological concerns relating to how we measure and define category (or any) dissociations. These include the common failure to include control data or the use of control data that is inappropriate e.g. at ceiling, unmatched. A review of past cases shows that the overwhelming majority suffers from these problems and therefore challenges conclusions about the purported empirical demonstrations of dissociations and double dissociations in the category specific literature. This is not a refutation of category deficits, but skepticism about the current existence of any convincing empirical demonstrations of category specific double dissociations. As a potential solution, certain minimal criteria are proposed that might aid with the attempt to document category effects that are more methodologically convincing.

Need for control data in studies of self-esteem and persuasibility.

Of 9 studies involving self-esteem and persuasibility following exposure to a mass communication, none made proper use of control data. This failure leaves unresolved the validity of interpretations which are reasonably alternative to that which supports the research hypothesis. Four of the studies are discussed in detail in order to exemplify the range of interpretations which may be considered alternative to those which, according to the authors, support their research hypotheses. The need for control data in this research area is discussed in the more general context of the purpose of control data.