Community detection forecasts material failure in a sheared granular material

The stability of a granular material is a collective phenomenon controlled by individual particles through their interactions. Forecasting when granular materials will undergo an abrupt failure is an ongoing challenge due to the intricate interactions between particles. Here, we report experiments on photoelastic disks undergoing intermittent stick-slip dynamics in a quasi-2D annular shear apparatus, with the evolving network of contact forces made visible via polarized light. We characterize the system by interpreting the interparticle forces as a multilayer network, and apply GenLouvin community detection to identify strongly correlated groups of particles. We observe that the community structure becomes increasingly volatile as the material approaches failure, and that this volatility provides a forecast that precedes what is detectable by considering the forces alone. We additionally observe that both weak and strong forces contribute to the strength of this forecast. These findings provide a new approach to detect patterns of causality and forecast impending failures

Image analysis methods in the measurement of ice loads on structures

The icing of marine vessels and offshore structures causes significant reductions in their efficiency and creates unsafe working conditions. Ice detection and removal play important roles to reduce the risk of hazards and increase operational efficiency. Ice detection and measurement on structures are a challenge in marine industries, due to a lack of studies in this field. In this research, image processing methods are developed to measure ice loads on structures. Image processing algorithms are used to detect the ice accumulated on the structures and then the ice loads are calculated. The combination of thermal and visual imaging is suggested to detect ice, in order to reduce drawbacks occurring in these types of imaging. Also, the ice load is calculated on a known structure based on the structure information and the ice detection results. Experiments are conducted to verify the results of ice load measurements obtained by the algorithms. Ice loads are calculated in a variety of situations, such as using different imaging types, changing camera positions and angles of view and using different ice load values. The calculated ice load results show good coherence with the actual values obtained by measuring the samples which are used in the experimental setups

Diversity, Trust and Conformity: a Simulation Study

Previous simulation models have found positive effects of cognitive diversity on group performance, but have not explored effects of diversity in demographics (e.g., gender, ethnicity). In this paper, we present an agent-based model that captures two empirically supported hypotheses about how demographic diversity can improve group performance. The results of our simulations suggest that, even when social identities are not associated with distinctive task-related cognitive resources, demographic diversity can, in certain circumstances, benefit collective performance by counteracting two types of conformity that can arise in homogeneous groups: those relating to group-based trust and those connected to normative expectations towards in-groups

The Many Faces of Attention: why precision optimization is not attention

The predictive coding (PC) theory of attention identifies attention with the optimization of the precision weighting of prediction error. Here we provide some challenges for this identification. On the one hand, the precision weighting of prediction error is too broad a phenomenon to be identified with attention because such weighting plays a central role in multimodal integration. Cases of crossmodal illusions such as the rubber hand illusion and the McGurk effect involve the differential precision weighting of prediction error, yet attention does not shift as one would predict. On the other hand, the precision weighting of prediction error is too narrow a phenomenon to be identified with attention, because it cannot accommodate the full range of attentional phenomena. We review criticisms that PC cannot account for volitional attention and affect-biased attention, and we propose that it may not be able to account for feature-based and intellectual attention

Fair machine learning under partial compliance

Typically, fair machine learning research focuses on a single decision maker and assumes that the underlying population is stationary. However, many of the critical domains motivating this work are characterized by competitive marketplaces with many decision makers. Realistically, we might expect only a subset of them to adopt any non-compulsory fairness-conscious policy, a situation that political philosophers call partial compliance. This possibility raises important questions: how does partial compliance and the consequent strategic behavior of decision subjects affect the allocation outcomes? If k% of employers were to voluntarily adopt a fairness-promoting intervention, should we expect k% progress (in aggregate) towards the benefits of universal adoption, or will the dynamics of partial compliance wash out the hoped-for benefits? How might adopting a global (versus local) perspective impact the conclusions of an auditor? In this paper, we propose a simple model of an employment market, leveraging simulation as a tool to explore the impact of both interaction effects and incentive effects on outcomes and auditing metrics. Our key findings are that at equilibrium: (1) partial compliance by k% of employers can result in far less than proportional (k%) progress towards the full compliance outcomes; (2) the gap is more severe when fair employers match global (vs local) statistics; (3) choices of local vs global statistics can paint dramatically different pictures of the performance vis-a-vis fairness desiderata of compliant versus non-compliant employers; (4) partial compliance based on local parity measures can induce extreme segregation. Finally, we discuss implications for auditors and insights concerning the design of regulatory frameworks

Creating a Tool to Reproducibly Estimate the Ethical Impact of Artificial Intelligence

How can an organization systematically and reproducibly measure the ethical impact of its AI-enabled platforms? Organizations that create applications enhanced by artificial intelligence and machine learning (AI/ML) are increasingly asked to review the ethical impact of their work. Governance and oversight organizations are increasingly asked to provide documentation to guide the conduct of ethical impact assessments. This document outlines a draft procedure for organizations to evaluate the ethical impacts of their work. We propose that ethical impact can be evaluated via a principles-based approach when the effects of platforms’ probable uses are interrogated through informative questions, with answers scaled and weighted to produce a multi-layered score. We initially assess ethical impact as the summed score of a project’s potential to protect human rights. However, we do not suggest that the ethical impact of platforms is assessed exclusively through preservation of human rights alone, a decidedly difficult concept to measure. Instead, we propose that ethical impact can be measured through a similar procedure assessing conformity with other important principles such as: protection of decisional autonomy, explainability, reduction of bias, assurances of algorithmic competence, or safety. In this initial draft paper, we demonstrate the application of our method for ethical impact assessment to the principles of human rights and bias

Commercial refrigeration - An overview of current status

[EN] Commercial Refrigeration comprises food freezing and conservation in retail stores and supermarkets, so, it is one of the most relevant energy consumption sectors, and its relevance is increasing. This paper reviews the most recent developments in commercial refrigeration available in literature and presents a good amount of results provided these systems, covering some advantages and disadvantages in systems and working fluids. Latest researches are focused on energy savings to reduce CO2 indirect emissions due to the burning of fossil fuels. They are focused on system modifications (as dedicated subcooling or the implementation of ejectors), trigeneration technologies (electrical, heating and cooling demand) and better evaporation conditions control. Motivated by latest GWP regulations that are intended to reduce high GWP HFC emissions; R404A and R507 are going to phase out. Besides hydrocarbons and HFO, CO2 appears as one of the most promising HFC replacements because its low contribution to global warming and high efficiencies when used in transcritical and low-stage of cascade systems.The authors thankfully acknowledge "Ministerio de Educacion, Cultura y Deporte" for supporting this work through "Becas y Contratos de Formacion de Profesorado Universitario del Programa Nacional de Formacion de Recursos Humanos de Investigacion del ejercicio 2012".Mota Babiloni, A.; Navarro Esbri, J.; Barragán Cervera, Á.; Moles, F.; Peris, B.; Verdú Martín, GJ. (2015). Commercial refrigeration - An overview of current status. International Journal of Refrigeration. 57:186-196. doi:10.1016/j.ijrefrig.2015.04.013S1861965

Strategies to improve energy and carbon efficiency of luxury hotels in Iran

Luxury hotels generate substantial carbon footprint and scholarly research is urgently required to better understand how it could be effectively mitigated. This study adopts a method of life cycle energy analysis (LCEA) to assess the energy and carbon performance of six luxury, five star, hotels located in Iran. The results of the energy and carbon assessment of luxury hotels in Iran are compared against the energy and carbon values reported in past hotel research. This current study finds that luxury hotels in Iran are up to 3–4 times more energy- and 7 times more carbon-intense than similar hotels examined in past research. Low cost of fossil fuels, international trade sanctions and the lack of governmental and corporate energy conservation targets discourage Iranian hoteliers from carbon footprint mitigation. To counteract poor energy and carbon efficiency of luxury hotels in Iran, it is important to relax economic sanctions, develop alternative energy sources, refine corporate energy conservation targets, regularly benchmark hotel energy performance and enable exchange of good practices amongst Iranian hoteliers