69 research outputs found

    Acoustics and oceanographic observations collected during the QPE Experiment by Research Vessels OR1, OR2 and OR3 in the East China Sea in the Summer of 2009

    Get PDF
    This document describes data, sensors, and other useful information pertaining to the ONR sponsored QPE field program to quantify, predict and exploit uncertainty in observations and prediction of sound propagation. This experiment was a joint operation between Taiwanese and U.S. researchers to measure and assess uncertainty of predictions of acoustic transmission loss and ambient noise, and to observe the physical oceanography and geology that are necessary to improve their predictability. This work was performed over the continental shelf and slope northeast of Taiwan at two sites: one that was a relatively flat, homogeneous shelf region and a more complex geological site just shoreward of the shelfbreak that was influenced by the proximity of the Kuroshio Current. Environmental moorings and ADCP moorings were deployed and a shipboard SeaSoar vehicle was used to measure environmental spatial structure. In addition, multiple bottom moored receivers and a horizontal hydrophone array were deployed to sample transmission loss from a mobile source and ambient noise. The acoustic sensors, environmental sensors, shipboard resources, and experiment design, and their data, are presented and described in this technical report.Funding was provided by the Office of Naval Research under Contract No. N00014-08-1-076

    Enhancing the sustainability performance of Agri-Food Supply Chains by implementing Industry 4.0

    Full text link
    [EN] In order to enhance the sustainability in the supply chain, its members should define and pursue common objectives in the three dimensions of the sustainability (economic, environmental and social). The Agri-Food Supply Chain (AFSC) is a network of different members such as farmers (producers), processors and distributors (wholesales, retailers.), etc.. In order to achieve the performance objectives of the AFSC, Industry 4.0 technologies can be implemented. The aim of this paper is to present a classification of these technologies according to two criteria: objective to be achieved (environmental or social) specified in the main issues to be covered in each objective and member of the AFSC supply chain where it is implemented. In this work, we focus on technologies that deal with environmental and social sustainability because economic sustainability will depend on the specific characteristics of the business (a supply chain using a specific Industry 4.0 technology may be profitable while others do not).This work has been funded by the Project GV/2017/065 "Development of a decision support tool for the management and improvement of sustainability in supply chains" funded by the Regional Government of Valencia. Authors also acknowledge the Project 691249, RUC-APS: Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems.Pérez Perales, D.; Verdecho Sáez, MJ.; Alarcón Valero, F. (2019). Enhancing the sustainability performance of Agri-Food Supply Chains by implementing Industry 4.0. IFIP Advances in Information and Communication Technology. 568:496-503. https://doi.org/10.1007/978-3-030-28464-0_43S496503568Camarinha-Matos, L.M., Fornasiero, R., Afsarmanesh, H.: Collaborative networks as a core enabler of Industry 4.0. In: Camarinha-Matos, L.M., Afsarmanesh, H., Fornasiero, R. (eds.) PRO-VE 2017. IAICT, vol. 506, pp. 3–17. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65151-4_1Stich, V., Gudergan, G., Zeller, V.: Need and solution to transform the manufacturing industry in the age of Industry 4.0 – a capability maturity index approach. In: Camarinha-Matos, L.M., Afsarmanesh, H., Rezgui, Y. (eds.) PRO-VE 2018. IAICT, vol. 534, pp. 33–42. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99127-6_3Flores, M., Maklin, D., Golob, M., Al-Ashaab, A., Tucci, C.: Awareness towards Industry 4.0: key enablers and applications for internet of things and big data. In: Camarinha-Matos, L.M., Afsarmanesh, H., Rezgui, Y. (eds.) PRO-VE 2018. IAICT, vol. 534, pp. 377–386. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99127-6_32Seuring, S., Müller, M.: From a literature review to a conceptual framework for sustainable supply chain management. J. Clean. Prod. 16, 1699–1710 (2008)Prima, W.A., Xing, K., Amer, Y.: Collaboration and sustainable agri-food supply chain: a literature review. In: MATEC (2016). https://doi.org/10.1051/matecconf/20165802004Pérez Perales, D., Alarcón Valero, F., Drummond, C., Ortiz, Á.: Towards a sustainable agri-food supply chain model. The case of LEAF. In: Ortiz, Á., Andrés Romano, C., Poler, R., García-Sabater, J.-P. (eds.) Engineering Digital Transformation. LNMIE, pp. 333–341. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-96005-0_40Savastano, M., Amendola, C., Bellini, F., D’Ascenzo, F.: Contextual impacts on industrial processes brought by the digital transformation of manufacturing: a systematic review. Sustainability 11, 891 (2019)Varela, L., Araújo, A., Ávila, P., Castro, H., Putnik, G.: Evaluation of the relation between lean manufacturing, Industry 4.0, and sustainability. Sustainability 11, 1439 (2019)Bonilla, S.H., Silva, H.R.O., da Silva, M.T., Gonçalves, R.F., Sacomano, J.B.: Industry 4.0 and sustainability implications: a scenario-based analysis of the impacts and challenges. Sustainability 10, 3740 (2018)Bányai, T., Tamás, P., Illés, B., Stankeviciute, Z., Bányai, A.: Optimization of municipal waste collection routing: impact of Industry 4.0 technologies on environmental awareness and sustainability. Int. J. Environ. Res. Public Health. 16, 634 (2019)Lin, K.C., Shyu, J.Z., Ding, K.: A cross-strait comparison of innovation policy under Industry 4.0 and sustainability development transition. Sustainability 9, 786 (2017)Kamble, S.: Sustainable Industry 4.0 framework: a systematic literature review identifying the current trends and future perspectives. In: Process Safety and Environmental Protection Transactions of the Institution of Chemical Engineers, Part B, vol. 117, pp. 408–25. Institution of Chemical Engineers (2018)Franciosi, C., Iung, B., Miranda, S., Riemma, S.: Maintenance for sustainability in the Industry 4.0 context: a scoping literature review. IFAC-Pap. Online 51(11), 903–908 (2018)Bocken, N.M.P., Short, S.W., Rana, P., Evans, S.: A literature and practice review to develop sustainable business model archetypes. J. Clean. Prod. 65, 42–56 (2014)Bourlakis, M., Maglaras, G., Aktas, E., Gallear, D., Fotopoulos, C.: Firm size and sustainable performance in food supply chains: insights from Greek SMEs. Int. J. Prod. Econ. 152, 112–130 (2014)Garbie, I.H.: An analytical technique to model and assess sustainable development index in manufacturing enterprises. Int. J. Prod. Res. 52(16), 4876–4915 (2014)Beier, G., Niehoff, S., Ziems, T., Xue, B.: Sustainability aspects of a digitalized industry - a comparative study from China and Germany. Int. J. Precis. Eng. Manuf. Green Technol. 4, 227–234 (2017)Pérez, D., Verdecho, M.J., Alarcón, F: Industry 4.0 for the development of more sustainable decision support tools for agri-food supply chain management. In: 13rd International Conference on Industrial Engineering and Industrial Management, XXIII, Gijón, Spain (2019)Xiaolin, L., Linnan, Y., Lin, P., Wengfeng, L., Limin, Z.: Procedia engineering county soil fertility information management system based on embedded GIS. Procedia Eng. 29, 2388–2392 (2012)Satyanarayana, G.V.: Wireless sensor based remote monitoring system for agriculture using ZigBee and GPS. In: 2013 (CAC2S), pp. 110–114 (2013)Phillips, A.J., Newlands, N.K., Liang, S.H.L., Ellert, B.H.: Integrated sensing of soil moisture at the field-scale: measuring, modeling and sharing for improved agricultural decision support. Comput. Electron. Agric. 107, 73–88 (2014)Liopa-tsakalidi, A., Tsolis, D., Barouchas, P.: Application of mobile technologies through an integrated management system for agricultural production. Procedia Technol. 8, 165–170 (2013). (Haicta)Yerpude, S., Singhal, T.K.: Impact of Internet of Things (IoT) data on demand forecasting. Indian J. Sci. Technol. 10, 5 (2017)Wolfert, S., Ge, L., Verdouw, C., Bogaardt, M.: Big data in smart farming – a review. Agric. Syst. 153, 69–80 (2017)Castka, P., Balzarova, M.A.: ISO 26000 and supply chains-on the diffusion of the social responsibility standard. Int. J. Prod. Econ. 111(2), 274–286 (2008)Stock, T., Obenaus, M., Kunz, S., Kohl, H.: Industry 4.0 as enabler for a sustainable development: A qualitative assessment of its ecological and social potential. Process. Saf. Environ. 118, 254–267 (2018)Verdecho, M.J., Pérez, D., Alarcón F.: Proposal of a customer-oriented sustainable balanced scorecard for agri-food supply chains. In: 12th International Conference on Industrial Engineering and Industrial Management, Girona, Spain, 12–13 July (2018)Valcour, P.M., Hunter, L.W.: Technology, organizations, and work-life integration. In: Kossek, E.E. Lambert, S.J. (eds.), Work and Life Integration: Organizational, Cultural, and Individual Perspectives, pp. 61–84. Lawrence Erlbaum Associates, Mahwah (2005)Arntz, M., Gregory, T., Zierahn, U.: The risk of automation for jobs in OECD countries: a comparative analysis. In: OECD Social, Employment and Migration Working Papers, no. 189. OECD Publishing, Paris (2016)Grubert, J., Langlotz, T., Zollmann, S., Regenbrecht, H.: Towards pervasive augmented reality: context-awareness in augmented reality. IEEE Trans. Vis. Comput. Graph. 23, 1 (2016)Velthuis, A.G.J.: New Approaches to Food-Safety Economics. Kluwer Academic Publishers, Dordrecht (2003)Sándor, Z.P., Csiszár, C.: Development stages of intelligent parking information systems for trucks. Acta Polytechnica Hungarica 10(4), 161–174 (2013)Scognamiglio, V., Arduini, F., Palleschi, G., Rea, G.: Biosensing technology for sustainable food safety. Trends Analyt. Chem. 62, 1–10 (2014)Brynjolfsson, E., McAfee, A.: The Second Machine Age. Work, Progress, and Prosperity in a Time of Brilliant Technologies. W.W. Norton & Company, London (2014)Smith, A., Caiazza, T.: Automation in everyday life (2017). http://assets.pewresearch.org/wpcontent/uploads/sites/14/2017/10/03151500/PI_2017.10.04_Automation_FINAL.pdfHefferon, K.L.: Nutritionally enhanced food crops; progress and perspectives. Int. J. Mol. Sci. 16, 3895–3914 (2015)Glass, S., Fanzo, J.: Genetic modification technology for nutrition and improving diets: an ethical perspective. Curr. Opin. Biotech. 44, 46–51 (2017)Moe, T.: Perspectives on traceability in food manufacture’. Trends Food Sci. Technol. 9(5), 211–214 (1998)Latino, M., Corallo, A., Menegoli, M.: From Industry 4.0 to Agriculture 4.0: how manage product data in agri-food supply chain for voluntary traceability, a framework proposed. In: 20th International Conference on Food and Environment (ICFE), Rome (2018)Linus, U.O.: Traceability in agriculture and food supply chain: a review of basic concepts, technological implications, and future prospects. J. Food Agric. Environ. 1(1), 101–106 (2003)Maumbe, B.M., Okello, J.: Uses of information and communication technology (ICT) in agriculture and rural development in Sub-Saharan Africa: experiences from South Africa and Kenya. IJICTRDA 1(1), 1–22 (2010)Dlodlo, N., Kalezhi, J.: The internet of things in agriculture for sustainable rural development. In: International Conference on Emerging Trends in Networks and Computer Communications (ETNCC) (2015

    Persistent and polarised global actin flow is essential for directionality during cell migration

    Get PDF
    Cell migration is hypothesized to involve a cycle of behaviours beginning with leading edge extension. However, recent evidence suggests that the leading edge may be dispensable for migration, raising the question of what actually controls cell directionality. Here, we exploit the embryonic migration of Drosophila macrophages to bridge the different temporal scales of the behaviours controlling motility. This approach reveals that edge fluctuations during random motility are not persistent and are weakly correlated with motion. In contrast, flow of the actin network behind the leading edge is highly persistent. Quantification of actin flow structure during migration reveals a stable organization and asymmetry in the cell-wide flowfield that strongly correlates with cell directionality. This organization is regulated by a gradient of actin network compression and destruction, which is controlled by myosin contraction and cofilin-mediated disassembly. It is this stable actin-flow polarity, which integrates rapid fluctuations of the leading edge, that controls inherent cellular persistence

    Examining the generalizability of research findings from archival data

    Get PDF
    This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability—for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new samples

    Origins and genetic legacy of prehistoric dogs

    Get PDF
    Dogs were the first domestic animal, but little is known about their population history and to what extent it was linked to humans. We sequenced 27 ancient dog genomes and found that all dogs share a common ancestry distinct from present-day wolves, with limited gene flow from wolves since domestication but substantial dog-to-wolf gene flow. By 11,000 years ago, at least five major ancestry lineages had diversified, demonstrating a deep genetic history of dogs during the Paleolithic. Coanalysis with human genomes reveals aspects of dog population history that mirror humans, including Levant-related ancestry in Africa and early agricultural Europe. Other aspects differ, including the impacts of steppe pastoralist expansions in West and East Eurasia and a near-complete turnover of Neolithic European dog ancestry

    Ancient pigs reveal a near-complete genomic turnover following their introduction to Europe

    Get PDF
    Archaeological evidence indicates that pig domestication had begun by ~10,500 y before the present (BP) in the Near East, and mitochondrial DNA (mtDNA) suggests that pigs arrived in Europe alongside farmers ~8,500 y BP. A few thousand years after the introduction of Near Eastern pigs into Europe, however, their characteristic mtDNA signature disappeared and was replaced by haplotypes associated with European wild boars. This turnover could be accounted for by substantial gene flow from local Euro-pean wild boars, although it is also possible that European wild boars were domesticated independently without any genetic con-tribution from the Near East. To test these hypotheses, we obtained mtDNA sequences from 2,099 modern and ancient pig samples and 63 nuclear ancient genomes from Near Eastern and European pigs. Our analyses revealed that European domestic pigs dating from 7,100 to 6,000 y BP possessed both Near Eastern and European nuclear ancestry, while later pigs possessed no more than 4% Near Eastern ancestry, indicating that gene flow from European wild boars resulted in a near-complete disappearance of Near East ancestry. In addition, we demonstrate that a variant at a locus encoding black coat color likely originated in the Near East and persisted in European pigs. Altogether, our results indicate that while pigs were not independently domesticated in Europe, the vast majority of human-mediated selection over the past 5,000 y focused on the genomic fraction derived from the European wild boars, and not on the fraction that was selected by early Neolithic farmers over the first 2,500 y of the domestication process

    The Comet Interceptor Mission

    Get PDF
    Here we describe the novel, multi-point Comet Interceptor mission. It is dedicated to the exploration of a little-processed long-period comet, possibly entering the inner Solar System for the first time, or to encounter an interstellar object originating at another star. The objectives of the mission are to address the following questions: What are the surface composition, shape, morphology, and structure of the target object? What is the composition of the gas and dust in the coma, its connection to the nucleus, and the nature of its interaction with the solar wind? The mission was proposed to the European Space Agency in 2018, and formally adopted by the agency in June 2022, for launch in 2029 together with the Ariel mission. Comet Interceptor will take advantage of the opportunity presented by ESA’s F-Class call for fast, flexible, low-cost missions to which it was proposed. The call required a launch to a halo orbit around the Sun-Earth L2 point. The mission can take advantage of this placement to wait for the discovery of a suitable comet reachable with its minimum ΔV capability of 600 ms−1. Comet Interceptor will be unique in encountering and studying, at a nominal closest approach distance of 1000 km, a comet that represents a near-pristine sample of material from the formation of the Solar System. It will also add a capability that no previous cometary mission has had, which is to deploy two sub-probes – B1, provided by the Japanese space agency, JAXA, and B2 – that will follow different trajectories through the coma. While the main probe passes at a nominal 1000 km distance, probes B1 and B2 will follow different chords through the coma at distances of 850 km and 400 km, respectively. The result will be unique, simultaneous, spatially resolved information of the 3-dimensional properties of the target comet and its interaction with the space environment. We present the mission’s science background leading to these objectives, as well as an overview of the scientific instruments, mission design, and schedule
    corecore