Overrated gaps: Inter-speaker gaps provide limited information about the timing of turns in conversation

Corpus analyses have shown that turn-taking in conversation is much faster than laboratory studies of speech planning would predict. To explain fast turn-taking, Levinson and Torreira (2015) proposed that speakers are highly proactive: They begin to plan a response to their interlocutor's turn as soon as they have understood its gist, and launch this planned response when the turn-end is imminent. Thus, fast turn-taking is possible because speakers use the time while their partner is talking to plan their own utterance. In the present study, we asked how much time upcoming speakers actually have to plan their utterances. Following earlier psycholinguistic work, we used transcripts of spoken conversations in Dutch, German, and English. These transcripts consisted of segments, which are continuous stretches of speech by one speaker. In the psycholinguistic and phonetic literature, such segments have often been used as proxies for turns. We found that in all three corpora, large proportions of the segments comprised of only one or two words, which on our estimate does not give the next speaker enough time to fully plan a response. Further analyses showed that speakers indeed often did not respond to the immediately preceding segment of their partner, but continued an earlier segment of their own. More generally, our findings suggest that speech segments derived from transcribed corpora do not necessarily correspond to turns, and the gaps between speech segments therefore only provide limited information about the planning and timing of turns

Explicit wave overtopping formula for mound breakwaters with crown walls using CLASH neural network-derived data

Based on the Crest Level Assessment of Coastal Structures (CLASH) Neural Network Overtopping prediction method, a new 16-parameter overtopping estimator (Q6) was developed for conventional mound breakwaters with crown walls, both with and without toe berms. Q6 was built up using the overtopping estimations given by the CLASH Neural Network and checked using the CLASH database. Q6 was compared to other conventional overtopping formulas, and the Q6 obtained the lowest prediction errors. Q6 provides overtopping predictions similar to the CLASH Neural Network for conventional mound breakwaters but using only six explanatory dimensionless variables (Rc=Hm0; Ir; Rc=h;Gc=Hm0; Ac=Rc, and a toe berm variable based on Rc=h) and two reduction factors (g f and g b ). Q6 describes explicit relationships between input variables and overtopping discharge, and hence it facilitates use in engineering design to identify costeffective solutions and to quantify the influence of variations in wave and structural parameters.The authors are grateful for financial support from the Spanish Ministerio de Economia y Competitividad (Grant BIA2012-33967). The first author was funded through the FPU program (Formacion del Profesorado Universitario, Grant AP2010-4366) by the Spanish Ministerio de Educacion, Cultura y Deporte. The authors also thank Debra Westall for revising the manuscript.Molines, J.; Medina, JR. (2016). Explicit wave overtopping formula for mound breakwaters with crown walls using CLASH neural network-derived data. Journal of Waterway Port Coastal and Ocean Engineering. 142(3). https://doi.org/10.1061/(ASCE)WW.1943-5460.0000322S142

Tendinopathy—from basic science to treatment

Chronic tendon pathology (tendinopathy), although common, is difficult to treat. Tendons possess a highly organized fibrillar matrix, consisting of type I collagen and various 'minor' collagens, proteoglycans and glycoproteins. The tendon matrix is maintained by the resident tenocytes, and there is evidence of a continuous process of matrix remodeling, although the rate of turnover varies at different sites. A change in remodeling activity is associated with the onset of tendinopathy. Major molecular changes include increased expression of type III collagen, fibronectin, tenascin C, aggrecan and biglycan. These changes are consistent with repair, but they might also be an adaptive response to changes in mechanical loading. Repeated minor strain is thought to be the major precipitating factor in tendinopathy, although further work is required to determine whether it is mechanical overstimulation or understimulation that leads to the change in tenocyte activity. Metalloproteinase enzymes have an important role in the tendon matrix, being responsible for the degradation of collagen and proteoglycan in both healthy patients and those with disease. Metalloproteinases that show increased expression in painful tendinopathy include ADAM (a disintegrin and metalloproteinase)-12 and MMP (matrix metalloproteinase)-23. The role of these enzymes in tendon pathology is unknown, and further work is required to identify novel and specific molecular targets for therapy

Placement, porosity and randomness of cube and Cubipod armor layers

Although little attention is usually given to the armor porosity and armor randomness of randomly placed concrete armor units in mound breakwaters, significant model effects may occur if armor porosity and randomness are different for prototype and small-scale models. Armor randomness and porosity are easier to control in small-scale models because they are generally constructed by hand in dry and perfect viewing conditions; equipment and environmental constraints make control at prototype scale more difficult. Results from three-dimensional small-scale placement tests are analyzed when cube and Cubipod units are placed with a small-scale crawler crane and pressure clamps. Armor porosity was not workable below 37% for cubes and 35% for Cubipods; placement grids were obtained for feasible armor porosities, considering row settlements during construction as well. Amethodology to measure armor randomness using high-precision laser scanning, similar to terrestrial LIDAR, was tested with small-scale cube and Cubipod armor. Three armor randomness indexes (ARIs) measured the randomness of cube and Cubipod armor; the values for ARIs were higher for Cubipod armor than for cube armor. (C) 2014 American Society of Civil EngineersThe authors would like to acknowledge the financial support received from the CDTI (CUBIPOD and CLIOMAR Projects), SATO-OHL Group (CLIOMAR Project), and the Spanish Ministry of Economy and Competitiveness (Grant BIA2012-33967). The third author was financially supported through the FPU program (Formacion del Profesorado Universitario) funded by the Spanish Ministry of Education (Ministerio de Educacion, Cultura y Deporte). The authors thank Tomas J. Perez for assisting with the 3D placement tests and processing the laser-scanner data, and Debra Westall for revising the manuscript.Pardo De Gregorio, V.; Herrera Gamboa, MP.; Molines Llodra, J.; Medina Folgado, JR. (2014). Placement, porosity and randomness of cube and Cubipod armor layers. Journal of Waterway, Port, Coastal, and Ocean Engineering. 140(5). doi:10.1061/(ASCE)WW.1943-5460.0000245S140

Overcoming failure in infrastructure risk governance implementation: large dams journey

[EN] There is ample recognition of the risk inherent in our very existence and modes of social organization, with a reasonable expectation that implementing risk governance will result in enhanced resilience as a society. Despite this, risk governance is not a mainstream approach in the infrastructure sector, regardless of the increasing number of peer-reviewed published conceptualizations, mature procedures to support its application, or public calls to cope with systemic risks in our modern societies. This paper aims to offer a different view on the issue of risk governance, with focus in the analysis of the root causes of its relatively low degree of implementation in the infrastructure sector. We later analyze the impact of such essential causes, which we have grouped and labeled as the ontology, the concerns, the anathemas, and the forgotten, in the specific field of large dams. Finally, we describe the journey toward risk governance in the specific field of large dams, thus supporting the ultimate objective of this paper to facilitate an evidence-based approach to successful risk governance implementation within and outside the dam sector.This work was supported by Spanish Ministry of Economy and Competitiveness (Ministerio de Economía y Competitividad (España) [grant number BIA2013-48157-C2-1-R].Escuder Bueno, I.; Halpin, E. (2016). Overcoming failure in infrastructure risk governance implementation: large dams journey. Journal of Risk Research. https://doi.org/10.1080/13669877.2016.1215345SAbrahamsen, E. B., & Aven, T. (2012). Why risk acceptance criteria need to be defined by the authorities and not the industry? Reliability Engineering & System Safety, 105, 47-50. doi:10.1016/j.ress.2011.11.004Ardiles, L. D. Sanz, P. Moreno, E. Jenaro, J. Fleitz, and I. Escuder. 2011. “Risk Assessment and Management of 26 Dams Operated by the Duero River Authority in Spain”.Dam Engineering. 21 (4): 313–328. Willmington Publishing. ISSN 0958-9341.Van Asselt, M. B. A., & Renn, O. (2011). Risk governance. Journal of Risk Research, 14(4), 431-449. doi:10.1080/13669877.2011.553730Van Asselt, M., & Vos, E. (2008). Wrestling with uncertain risks: EU regulation of GMOs and the uncertainty paradox. Journal of Risk Research, 11(1), 281-300. doi:10.1080/13669870801990806Aven, T. (2010). Misconceptions of Risk. doi:10.1002/9780470686539Aven, T. (2012). Foundational Issues in Risk Assessment and Risk Management. Risk Analysis, 32(10), 1647-1656. doi:10.1111/j.1539-6924.2012.01798.xAven, T. (2012). The risk concept—historical and recent development trends. Reliability Engineering & System Safety, 99, 33-44. doi:10.1016/j.ress.2011.11.006Aven, T., & Renn, O. (2010). Response to Professor Eugene Rosa’s viewpoint to our paper. Journal of Risk Research, 13(3), 255-259. doi:10.1080/13669870903484369Aven, T., & Renn, O. (2010). Risk Management and Governance. doi:10.1007/978-3-642-13926-0Baecher, G. B., Paté, M. E., & De Neufville, R. (1980). Risk of dam failure in benefit-cost analysis. Water Resources Research, 16(3), 449-456. doi:10.1029/wr016i003p00449Black, J., & Baldwin, R. (2012). When risk-based regulation aims low: Approaches and challenges. Regulation & Governance, 6(1), 2-22. doi:10.1111/j.1748-5991.2011.01124.xBoholm, Å., Corvellec, H., & Karlsson, M. (2012). The practice of risk governance: lessons from the field. Journal of Risk Research, 15(1), 1-20. doi:10.1080/13669877.2011.587886Cox, L. A. (2009). Risk Analysis of Complex and Uncertain Systems. International Series in Operations Research & Management Science. doi:10.1007/978-0-387-89014-2Davis, D., Faber, B. A., & Stedinger, J. R. (2008). USACE Experience in Implementing Risk Analysis for Flood Damage Reduction Projects. Journal of Contemporary Water Research & Education, 140(1), 3-14. doi:10.1111/j.1936-704x.2008.00023.xDe Vries, G., Verhoeven, I., & Boeckhout, M. (2011). Taming uncertainty: the WRR approach to risk governance. Journal of Risk Research, 14(4), 485-499. doi:10.1080/13669877.2011.553728Escuder-Bueno, I., Matheu, E., T. Castillo-Rodríguez, J., & T. Castillo-Rodríguez, J. (Eds.). (2011). Risk Analysis, Dam Safety, Dam Security and Critical Infrastructure Management. doi:10.1201/b11588Ezell, B. C., Bennett, S. P., von Winterfeldt, D., Sokolowski, J., & Collins, A. J. (2010). Probabilistic Risk Analysis and Terrorism Risk. Risk Analysis, 30(4), 575-589. doi:10.1111/j.1539-6924.2010.01401.xForrester, I., & Hanekamp1, J. C. (2006). Precaution, Science and Jurisprudence: a Test Case. Journal of Risk Research, 9(4), 297-311. doi:10.1080/13669870500042974Funabashi, Y., & Kitazawa, K. (2012). Fukushima in review: A complex disaster, a disastrous response. Bulletin of the Atomic Scientists, 68(2), 9-21. doi:10.1177/0096340212440359Hartford, D. N. D., & Baecher, G. B. (2004). Risk and uncertainty in dam safety. doi:10.1680/rauids.32705IRGC (International Risk Governance Council) 2005.Risk Governance: Towards an Integrative Approach, White Paper No. 1, O. Renn with an Annex by P. Graham. Geneva: International Risk Governance Council.Krause, P., Fox, J., Judson, P., & Patel, M. (1998). Qualitative risk assessment fulfils a need. Lecture Notes in Computer Science, 138-156. doi:10.1007/3-540-49426-x_7Kröger, W. (2008). Critical infrastructures at risk: A need for a new conceptual approach and extended analytical tools. Reliability Engineering & System Safety, 93(12), 1781-1787. doi:10.1016/j.ress.2008.03.005Lofstedt, R. E. (2010). Risk communication guidelines for Europe: a modest proposition. Journal of Risk Research, 13(1), 87-109. doi:10.1080/13669870903126176(2008). Journal of Contemporary Water Research & Education, 140(1). doi:10.1111/jcwr.2008.140.issue-1Park, J., Seager, T. P., Rao, P. S. C., Convertino, M., & Linkov, I. (2012). Integrating Risk and Resilience Approaches to Catastrophe Management in Engineering Systems. Risk Analysis, 33(3), 356-367. doi:10.1111/j.1539-6924.2012.01885.xRenn, O., & Walker, K. D. (Eds.). (2008). Global Risk Governance. International Risk Governance Council Bookseries. doi:10.1007/978-1-4020-6799-0Renn, O., Klinke, A., & van Asselt, M. (2011). Coping with Complexity, Uncertainty and Ambiguity in Risk Governance: A Synthesis. AMBIO, 40(2), 231-246. doi:10.1007/s13280-010-0134-0Rosa, E. A. (2010). The logical status of risk – to burnish or to dull. Journal of Risk Research, 13(3), 239-253. doi:10.1080/13669870903484351Slovic, P. (1987). Perception of risk. Science, 236(4799), 280-285. doi:10.1126/science.3563507Vlek, C. (2010). Judicious management of uncertain risks: I. Developments and criticisms of risk analysis and precautionary reasoning. Journal of Risk Research, 13(4), 517-543. doi:10.1080/13669871003629887Zhao, X., Hwang, B.-G., & Low, S. P. (2015). Enterprise Risk Management in International Construction Operations. doi:10.1007/978-981-287-549-

Investigations of migratory bull trout (Salvelinus confluentus) in relation to fish passage at Albeni Falls Dam

Final report prepared for the United States Department of the Army Corps of Engineers, Seattle District. Contract No. DACW68-02-D-00

ETL-0254, Terrain analysis procedural guide for soil, February 1981

This report is one in a series of terrain analysis procedural guides being developed in support of the Topographic Support System (TSS). It was written specifically for a U.S. Army terrain analyst and presents the step-by- step methods needed for extraction, reducing, and recording soil information on a factor overlay and supporting data table. It is a contribution to the Department of Defense terrain intelligence effort. The report contains a detailed bibliography and a lengthy glossary

ETL-0254, Terrain analysis procedural guide for soil, February 1981

This report is one in a series of terrain analysis procedural guides being developed in support of the Topographic Support System (TSS). It was written specifically for a U.S. Army terrain analyst and presents the step-by- step methods needed for extraction, reducing, and recording soil information on a factor overlay and supporting data table. It is a contribution to the Department of Defense terrain intelligence effort. The report contains a detailed bibliography and a lengthy glossary

Making the most of data:An information selection and assessment framework to improve water systems operations

Advances in Environmental monitoring systems are making a wide range of data available at increasingly higher temporal and spatial resolution. This creates an opportunity to enhance real-time understanding of water systems conditions and to improve prediction of their future evolution, ultimately increasing our ability to make better decisions. Yet, many water systems are still operated using very simple information systems, typically based on simple statistical analysis and the operator’s experience. In this work, we propose a framework to automatically select the most valuable information to inform water systems operations supported by quantitative metrics to operationally and economically assess the value of this information. The Hoa Binh reservoir in Vietnam is used to demonstrate the proposed framework in a multiobjective context, accounting for hydropower production and flood control. First, we quantify the expected value of perfect information, meaning the potential space for improvement under the assumption of exact knowledge of the future system conditions. Second, we automatically select the most valuable information that could be actually used to improve the Hoa Binh operations. Finally, we assess the economic value of sample information on the basis of the resulting policy performance. Results show that our framework successfully select information to enhance the performance of the operating policies with respect to both the competing objectives, attaining a 40% improvement close to the target trade-off selected as potentially good compromise between hydropower production and flood control