2,336 research outputs found
Reactive dynamics on fractal sets: anomalous fluctuations and memory effects
We study the effect of fractal initial conditions in closed reactive systems
in the cases of both mobile and immobile reactants. For the reaction , in the absence of diffusion, the mean number of particles is shown to
decay exponentially to a steady state which depends on the details of the
initial conditions. The nature of this dependence is demonstrated both
analytically and numerically. In contrast, when diffusion is incorporated, it
is shown that the mean number of particles decays asymptotically as
, the memory of the initial conditions being now carried by the
dynamical power law exponent. The latter is fully determined by the fractal
dimension of the initial conditions.Comment: 7 pages, 2 figures, uses epl.cl
First record of Gonostoma elongatum Günther, 1878 (Osteichthyes: Gonostomatidae) in the North - Western Mediterranean
The first record of Gonostoma elongatum Günther, 1878 (Osteichthyies:
Gonostomatidae) in the North-Western Mediterranean (South West of
Majorca lsland) is reported. lt is a deep mesopelagic fish, with a
circumglobal distribution, tropical and subtropical. It occurs in the Atlantic,
Pacific and Indo-Pacific between 500 to 1200 m. Morphometric and meristic
characteristics are given.Hom dóna la primera cita de Gonostoma elongatum
Günther, 1878 (Osteichthyies: Gonostomatidae) en la Mediterrània Nord
Occidental (Sudoest de Mallorca). Es tracta d'una especie mesopelàgica
d'aigües profundes de distribució circumglobal, tropical i sub tropical. Es
troba al Atlàntic, Pacífic i Indo-Pacífic a profunditats d'entre 500 i 1200 m.
Es donen les principals caracteristiques morfomètriquesPublicado
Class sizes of prime-power order p'-elements and normal subgroups
We prove an extension of the renowned Itô’s theorem on groups having two class sizes in three different directions at the same time: normal subgroups, p′p′-elements and prime-power order elements. Let NN be a normal subgroup of a finite group GG and let pp be a fixed prime. Suppose that |xG|=1|xG|=1 or mm for every qq-element of NN and for every prime q≠pq≠p. Then, NN has nilpotent pp-complements.We are very grateful to the referee, who provided us a significant simplification of the last step of the proof of the main theorem and for many comments which have contributed to improve the paper. C. G. Shao wants to express his deep gratitude for the warm hospitality he has received in the Departamento de Matematicas of the Universidad Jaume I in Castellon, Spain. This research is supported by the Valencian Government, Proyecto PROMETEO/2011/30, by the Spanish Government, Proyecto MTM2010-19938-C03-02. The third author is supported by the research Project NNSF of China (Grant Nos. 11201401 and 11301218) and University of Jinan Research Funds for Doctors (XBS1335 and XBS1336).Beltrán, A.; Felipe Román, MJ.; Shao, C. (2015). Class sizes of prime-power order p'-elements and normal subgroups. Annali di Matematica Pura ed Applicata. 194(5):1527-1533. https://doi.org/10.1007/s10231-014-0432-4S152715331945Akhlaghi, Z., Beltrán, A., Felipe, M.J.: The influence of p -regular class sizes on normal subgroups. J. Group Theory. 16, 585–593 (2013)Alemany, E., Beltrán, A., Felipe, M.J.: Nilpotency of normal subgroups having two G -class sizes. Proc. Am. Math. Soc. 139, 2663–2669 (2011)Alemany, E., Beltrán, A., Felipe, M.J.: Finite groups with two p -regular conjugacy class lengths II. Bull. Aust. Math. Soc. 797, 419–425 (2009)Beltrán, A., Felipe, M.J.: Normal subgroups and class sizes elements of prime-power order. Proc. Am. Math. Soc. 140, 4105–4109 (2012)Beltrán, A.: Action with nilpotent fixed point subgroup. Arch. Math. (Basel) 69, 177–184 (1997)Camina, A.R.: Finite groups of conjugate rank 2. Nagoya Math. J. 53, 47–57 (1974)Casolo, C., Dolfi, S., Jabara, E.: Finite groups whose noncentral class sizes have the same p -part for some prime p . Isr. J. Math. 192, 197–219 (2012)Huppert, B.: Character Theory of Finite groups, vol. 25. De Gruyter Expositions in Mathemathics, Berlin, New York (1998)Kleidman, P., Liebeck, M.: The Subgroup Structure of The Finite Classical Groups. London Mathematical Society Lecture Note Series, 129. Cambridge University Press, Cambridge (1990)Kurzweil, K., Stellmacher, B.: The Theory of Finite Groups. An Introduction. Springer, New York (2004)The GAP Group, GAP—Groups, Algorithms and Programming, Vers. 4.4.12 (2008). http://www.gap-system.orgVasiliev, A.V., Vdovin, E.P.: An adjacency criterion for the prime graph of a finite simple group. Algebra Logic 44(6), 381–406 (2005
Plant-microbe interactions and the new biotechnological methods of plant disease control
Plants constitute an excellent ecosystem for microorganisms. The environmental conditions offered differ considerably between the highly variable aerial plant part and the more stable root system. Microbes interact with plant tissues and cells with different degrees of dependence. The most interesting from the microbial ecology point of view, however, are specific interactions developed by plant-beneficial (either non-symbiotic or symbiotic) and pathogenic microorganisms. Plants, like humans and other animals, also become sick, but they have evolved a sophisticated defense response against microbes, based on a combination of constitutive and inducible responses which can be localized or spread throughout plant organs and tissues. The response is mediated by several messenger molecules that activate pathogen-responsive genes coding for enzymes or antimicrobial compounds, and produces less sophisticated and specific compounds than immunoglobulins in animals. However, the response specifically detects intracellularly a type of protein of the pathogen based on a gene-for-gene interaction recognition system, triggering a biochemical attack and programmed cell death. Several implications for the management of plant diseases are derived from knowledge of the basis of the specificity of plant-bacteria interactions. New biotechnological products are currently being developed based on stimulation of the plant defense response, and on the use of plant-beneficial bacteria for biological control of plant diseases (biopesticides) and for plant growth promotion (biofertilizers)
Isospin Breaking in the Relation Between the tau-->nu_tau pi pi and e^+e^- -->pi^+ pi^- Versions of |F_\pi (s)|^2$ and Implications for (g-2)_mu
We investigate two points related to existing treatments of isospin-breaking
corrections to the CVC relation between the e^+e^- --> pi^+ pi^- cross-section
and dGamma[tau^- --> nu_tau pi^- pi^0]/ds. Implications for the value of the
hadronic contribution to a_mu =(g-2)_mu /2 based on those analyses
incorporating hadronic tau decay data are also considered. We conclude that the
uncertainty on the isospin-breaking correction which must be applied to the tau
decay data should be significantly increased, and that the central value of the
rho-omega ``mixing'' contribution to this correction may be significantly
smaller than indicated by the present standard determination. Such a shift
would contribute to reducing the discrepancy between the tau- and
electroproduction-based determinations of the leading order hadronic
contribution to a_mu.Comment: 15 pages, 1 figur
La falta de homogeneidad del producto (FHP) en las empresas cerámicas y su impacto en la reasignación del inventario
La asignación del producto disponible a prometer (ATP) a pedidos en contextos de fabricación contra almacén (MTS) es de
la máxima importancia ya que puede influir en la satisfacción del cliente y en los beneficios de la empresa. Sin embargo, una
asignación inicial adecuada, puede pasar a ser inadecuada por diversas razones. En estos casos, es necesaria la reasignación
del inventario, la cual será más compleja cuanto más ambiciosos sean los objetivos a alcanzar con ella y mayor el volumen
de información a utilizar. En este sentido, cabe destacar que la falta de homogeneidad en el producto (FHP), presente
en distintos sectores industriales, provoca la atomización del inventario y aumenta la complejidad de la reasignación,
dificultando la obtención de soluciones óptimas. En el presente trabajo se describe la problemática de la FHP, primero de
manera genérica, y luego, particularizada a empresas cerámicas MTS. Posteriormente, se identifican las situaciones en las
que una determinada asignación de ATP puede dejar de ser adecuada en dicho contexto y se propone la reasignación como
una forma de búsqueda de nuevas asignaciones válidas. Finalmente, mediante un caso de estudio de una empresa cerámica,
se analiza el impacto de la FHP en cada una de las situaciones identificadas, observando que la FHP provoca alguna de éstas
situaciones y complica, en todas ellas, la reasignación del inventario a pedidos.Peer reviewe
Kinetic Regimes and Cross-Over Times in Many-Particle Reacting Systems
We study kinetics of single species reactions ("A+A -> 0") for general local
reactivity Q and dynamical exponent z (rms displacement x_t ~ t^{1/z}.) For
small molecules z=2, whilst z=4,8 for certain polymer systems. For dimensions d
above the critical value d_c=z, kinetics are always mean field (MF). Below d_c,
the density n_t initially follows MF decay, n_0 - n_t ~ n_0^2 Q t. A 2-body
diffusion-controlled regime follows for strongly reactive systems (Q>Qstar ~
n_0^{(z-d)/d}) with n_0 - n_t ~ n_0^2 x_t^d. For Q<Qstar, MF kinetics persist,
with n_t ~ 1/Qt. In all cases n_t ~ 1/x_t^d at the longest times. Our analysis
avoids decoupling approximations by instead postulating weak physically
motivated bounds on correlation functions.Comment: 10 pages, 1 figure, uses bulk2.sty, minor changes, submitted to
Europhysics Letter
Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture
[EN] The term "Agri-Food 4.0" is an analogy to the term Industry 4.0; coming from the concept "agriculture 4.0". Since the origins of the industrial revolution, where the steam engines started the concept of Industry 1.0 and later the use of electricity upgraded the concept to Industry 2.0, the use of technologies generated a milestone in the industry revolution by addressing the Industry 3.0 concept. Hence, Industry 4.0, it is about including and integrating the latest developments based on digital technologies as well as the interoperability process across them. This allows enterprises to transmit real-time information in terms behaviour and performance. Therefore, the challenge is to maintain these complex networked structures efficiently linked and organised within the use of such technologies, especially to identify and satisfy supply chain stakeholders dynamic requirements. In this context, the agriculture domain is not an exception although it possesses some specialities depending from the domain. In fact, all agricultural machinery incorporates electronic controls and has entered to the digital age, enhancing their current performance. In addition, electronics, using sensors and drones, support the data collection of several agriculture key aspects, such as weather, geographical spatialization, animals and crops behaviours, as well as the entire farm life cycle. However, the use of the right methods and methodologies for enhancing agriculture supply chains performance is still a challenge, thus the concept of Industry 4.0 has evolved and adapted to agriculture 4.0 in order analyse the behaviours and performance in this specific domain. Thus, the question mark on how agriculture 4.0 support a better supply chain decision-making process, or how can help to save time to farmer to make effective decision based on objective data, remains open. Therefore, in this survey, a review of more than hundred papers on new technologies and the new available supply chains methods are analysed and contrasted to understand the future paths of the Agri-Food domain.Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS "Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems" (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCARISE-2015.Lezoche, M.; Hernández, JE.; Alemany Díaz, MDM.; Panetto, H.; Kacprzyk, J. (2020). Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture. Computers in Industry. 117:1-15. https://doi.org/10.1016/j.compind.2020.103187S115117Ahumada, O., & Villalobos, J. R. (2009). Application of planning models in the agri-food supply chain: A review. European Journal of Operational Research, 196(1), 1-20. doi:10.1016/j.ejor.2008.02.014Ait-Mouheb, N., Bahri, A., Thayer, B. B., Benyahia, B., Bourrié, G., Cherki, B., … Harmand, J. (2018). The reuse of reclaimed water for irrigation around the Mediterranean Rim: a step towards a more virtuous cycle? Regional Environmental Change, 18(3), 693-705. doi:10.1007/s10113-018-1292-zAli, J., & Kumar, S. (2011). Information and communication technologies (ICTs) and farmers’ decision-making across the agricultural supply chain. International Journal of Information Management, 31(2), 149-159. doi:10.1016/j.ijinfomgt.2010.07.008Alzahrani, S. M. (2018). Development of IoT mining machine for Twitter sentiment analysis: Mining in the cloud and results on the mirror. 2018 15th Learning and Technology Conference (L&T). doi:10.1109/lt.2018.8368490Amandeep, Bhattacharjee, A., Das, P., Basu, D., Roy, S., Ghosh, S., … Rana, T. K. (2017). Smart farming using IOT. 2017 8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). doi:10.1109/iemcon.2017.8117219Annosi, M. C., Brunetta, F., Monti, A., & Nati, F. (2019). Is the trend your friend? An analysis of technology 4.0 investment decisions in agricultural SMEs. Computers in Industry, 109, 59-71. doi:10.1016/j.compind.2019.04.003Baio, F. H. R. (2011). Evaluation of an auto-guidance system operating on a sugar cane harvester. Precision Agriculture, 13(1), 141-147. doi:10.1007/s11119-011-9241-6Belaud, J.-P., Prioux, N., Vialle, C., & Sablayrolles, C. (2019). Big data for agri-food 4.0: Application to sustainability management for by-products supply chain. Computers in Industry, 111, 41-50. doi:10.1016/j.compind.2019.06.006Nicolaas Bezuidenhout, C., Bodhanya, S., & Brenchley, L. (2012). An analysis of collaboration in a sugarcane production and processing supply chain. British Food Journal, 114(6), 880-895. doi:10.1108/00070701211234390Bhatt, M. R., & Buch, S. (2015). Prediction of formability for sheet metal component using artificial intelligent technique. 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN). doi:10.1109/spin.2015.7095356Birkel, H. S., & Hartmann, E. (2019). Impact of IoT challenges and risks for SCM. Supply Chain Management: An International Journal, 24(1), 39-61. doi:10.1108/scm-03-2018-0142Boehlje, M. (1999). Structural Changes in the Agricultural Industries: How Do We Measure, Analyze and Understand Them? American Journal of Agricultural Economics, 81(5), 1028-1041. doi:10.2307/1244080Bonney, L., Clark, R., Collins, R., & Fearne, A. (2007). From serendipity to sustainable competitive advantage: insights from Houston’s Farm and their journey of co‐innovation. Supply Chain Management: An International Journal, 12(6), 395-399. doi:10.1108/13598540710826326Boshkoska, B. M., Liu, S., Zhao, G., Fernandez, A., Gamboa, S., del Pino, M., … Chen, H. (2019). A decision support system for evaluation of the knowledge sharing crossing boundaries in agri-food value chains. Computers in Industry, 110, 64-80. doi:10.1016/j.compind.2019.04.012Brewster, C., Roussaki, I., Kalatzis, N., Doolin, K., & Ellis, K. (2017). IoT in Agriculture: Designing a Europe-Wide Large-Scale Pilot. IEEE Communications Magazine, 55(9), 26-33. doi:10.1109/mcom.2017.1600528Bronson, K., & Knezevic, I. (2016). Big Data in food and agriculture. Big Data & Society, 3(1), 205395171664817. doi:10.1177/2053951716648174Brown, K. (2013). Global environmental change I. Progress in Human Geography, 38(1), 107-117. doi:10.1177/0309132513498837Chilcanan, D., Navas, P., & Escobar, S. M. (2017). Expert system for remote process automation in multiplatform servers, through human machine conversation. 2017 12th Iberian Conference on Information Systems and Technologies (CISTI). doi:10.23919/cisti.2017.7975913Choi, J., In, Y., Park, C., Seok, S., Seo, H., & Kim, H. (2016). Secure IoT framework and 2D architecture for End-To-End security. The Journal of Supercomputing, 74(8), 3521-3535. doi:10.1007/s11227-016-1684-0Cohen, W. M., & Levinthal, D. A. (1990). Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly, 35(1), 128. doi:10.2307/2393553Dabbene, F., Gay, P., & Tortia, C. (2014). Traceability issues in food supply chain management: A review. Biosystems Engineering, 120, 65-80. doi:10.1016/j.biosystemseng.2013.09.006Del Borghi, A., Gallo, M., Strazza, C., & Del Borghi, M. (2014). An evaluation of environmental sustainability in the food industry through Life Cycle Assessment: the case study of tomato products supply chain. Journal of Cleaner Production, 78, 121-130. doi:10.1016/j.jclepro.2014.04.083Devarakonda, R., Shrestha, B., Palanisamy, G., Hook, L., Killeffer, T., Krassovski, M., … Lazer, K. (2014). OME: Tool for generating and managing metadata to handle BigData. 2014 IEEE International Conference on Big Data (Big Data). doi:10.1109/bigdata.2014.7004476Nascimento, A. F. do, Mendonça, E. de S., Leite, L. F. C., Scholberg, J., & Neves, J. C. L. (2012). Calibration and validation of models for short-term decomposition and N mineralization of plant residues in the tropics. Scientia Agricola, 69(6), 393-401. doi:10.1590/s0103-90162012000600008Dolan, C., & Humphrey, J. (2000). Governance and Trade in Fresh Vegetables: The Impact of UK Supermarkets on the African Horticulture Industry. Journal of Development Studies, 37(2), 147-176. doi:10.1080/713600072Dragincic, J., Korac, N., & Blagojevic, B. (2015). Group multi-criteria decision making (GMCDM) approach for selecting the most suitable table grape variety intended for organic viticulture. Computers and Electronics in Agriculture, 111, 194-202. doi:10.1016/j.compag.2014.12.023Dworak, V., Selbeck, J., Dammer, K.-H., Hoffmann, M., Zarezadeh, A., & Bobda, C. (2013). Strategy for the Development of a Smart NDVI Camera System for Outdoor Plant Detection and Agricultural Embedded Systems. Sensors, 13(2), 1523-1538. doi:10.3390/s130201523Eisele, M., Kiese, R., Krämer, A., & Leibundgut, C. (2001). Application of a catchment water quality model for assessment and prediction of nitrogen budgets. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 26(7-8), 547-551. doi:10.1016/s1464-1909(01)00048-xElsayed, K. M. F., Ismail, T., & S. Ouf, N. (2018). A Review on the Relevant Applications of Machine Learning in Agriculture. IJIREEICE, 6(8), 1-17. doi:10.17148/ijireeice.2018.681Esteso, A., Alemany, M. M. E., & Ortiz, A. (2017). Métodos y Modelos Deterministas e Inciertos para la Gestión de Cadenas de Suministro Agroalimentarias. Dirección y Organización, 41-46. doi:10.37610/dyo.v0i0.509Esteso, A., Alemany, M. M. E., & Ortiz, A. (2018). Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models. International Journal of Production Research, 56(13), 4418-4446. doi:10.1080/00207543.2018.1447706GERHARDS, R., GUTJAHR, C., WEIS, M., KELLER, M., SÖKEFELD, M., MÖHRING, J., & PIEPHO, H. P. (2011). Using precision farming technology to quantify yield effects attributed to weed competition and herbicide application. Weed Research, 52(1), 6-15. doi:10.1111/j.1365-3180.2011.00893.xGovindan, K., Jafarian, A., Khodaverdi, R., & Devika, K. (2014). Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food. International Journal of Production Economics, 152, 9-28. doi:10.1016/j.ijpe.2013.12.028Gumaste, S. S., & Kadam, A. J. (2016). Future weather prediction using genetic algorithm and FFT for smart farming. 2016 International Conference on Computing Communication Control and automation (ICCUBEA). doi:10.1109/iccubea.2016.7860028Hashem, H., & Ranc, D. (2016). A review of modeling toolbox for BigData. 2016 International Conference on Military Communications and Information Systems (ICMCIS). doi:10.1109/icmcis.2016.7496565Hefnawy, A., Elhariri, T., Cherifi, C., Robert, J., Bouras, A., Kubler, S., & Framling, K. (2017). Combined use of lifecycle management and IoT in smart cities. 2017 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA). doi:10.1109/skima.2017.8294112Hosseini, S. H., Tang, C. Y., & Jiang, J. N. (2014). Calibration of a Wind Farm Wind Speed Model With Incomplete Wind Data. IEEE Transactions on Sustainable Energy, 5(1), 343-350. doi:10.1109/tste.2013.2284490Hu, Y., Zhang, L., Li, J., & Mehrotra, S. (2016). ICME 2016 Image Recognition Grand Challenge. 2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). doi:10.1109/icmew.2016.7574663A. Irmak, J. W. Jones, W. D. Batchelor, S. Irmak, K. J. Boote, & J. O. Paz. (2006). Artificial Neural Network Model as a Data Analysis Tool in Precision Farming. Transactions of the ASABE, 49(6), 2027-2037. doi:10.13031/2013.22264Jeon, S., Kim, B., & Huh, J. (2017). Study on methods to determine rotor equivalent wind speed to increase prediction accuracy of wind turbine performance under wake condition. Energy for Sustainable Development, 40, 41-49. doi:10.1016/j.esd.2017.06.001Joly, P.-B. (2005). Resilient farming systems in a complex world — new issues for the governance of science and innovation. Australian Journal of Experimental Agriculture, 45(6), 617. doi:10.1071/ea03252Joshi, R., Banwet, D. K., & Shankar, R. (2009). Indian cold chain: modeling the inhibitors. British Food Journal, 111(11), 1260-1283. doi:10.1108/00070700911001077Kamata, T., Roshanianfard, A., & Noguchi, N. (2018). Heavy-weight Crop Harvesting Robot - Controlling Algorithm. IFAC-PapersOnLine, 51(17), 244-249. doi:10.1016/j.ifacol.2018.08.165Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2020). Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics, 219, 179-194. doi:10.1016/j.ijpe.2019.05.022Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23-37. doi:10.1016/j.compag.2017.09.037Kelepouris, T., Pramatari, K., & Doukidis, G. (2007). RFID‐enabled traceability in the food supply chain. Industrial Management & Data Systems, 107(2), 183-200. doi:10.1108/02635570710723804Khan, S. F., & Ismail, M. Y. (2018). An Investigation into the Challenges and Opportunities Associated with the Application of Internet of Things (IoT) in the Agricultural Sector-A Review. Journal of Computer Science, 14(2), 132-143. doi:10.3844/jcssp.2018.132.143Kladivko, E. J., Helmers, M. J., Abendroth, L. J., Herzmann, D., Lal, R., Castellano, M. J., … Villamil, M. B. (2014). Standardized research protocols enable transdisciplinary research of climate variation impacts in corn production systems. Journal of Soil and Water Conservation, 69(6), 532-542. doi:10.2489/jswc.69.6.532Ko, T., Lee, J., & Ryu, D. (2018). Blockchain Technology and Manufacturing Industry: Real-Time Transparency and Cost Savings. Sustainability, 10(11), 4274. doi:10.3390/su10114274KÖK, M. S. (2009). Application of Food Safety Management Systems (ISO 22000/HACCP) in the Turkish Poultry Industry: A Comparison Based on Enterprise Size. Journal of Food Protection, 72(10), 2221-2225. doi:10.4315/0362-028x-72.10.2221Kvíz, Z., Kroulik, M., & Chyba, J. (2014). Machinery guidance systems analysis concerning pass-to-pass accuracy as a tool for efficient plant production in fields and for soil damage reduction. Plant, Soil and Environment, 60(No. 1), 36-42. doi:10.17221/622/2012-pseLamsal, K., Jones, P. C., & Thomas, B. W. (2016). Harvest logistics in agricultural systems with multiple, independent producers and no on-farm storage. Computers & Industrial Engineering, 91, 129-138. doi:10.1016/j.cie.2015.10.018Laube, P., Duckham, M., & Palaniswami, M. (2011). Deferred decentralized movement pattern mining for geosensor networks. International Journal of Geographical Information Science, 25(2), 273-292. doi:10.1080/13658810903296630Li, F.-R., Gao, C.-Y., Zhao, H.-L., & Li, X.-Y. (2002). Soil conservation effectiveness and energy efficiency of alternative rotations and continuous wheat cropping in the Loess Plateau of northwest China. Agriculture, Ecosystems & Environment, 91(1-3), 101-111. doi:10.1016/s0167-8809(01)00265-1Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors, 18(8), 2674. doi:10.3390/s18082674Meichen, L., Jun, C., Xiang, Z., Lu, W., & Yongpeng, T. (2018). Dynamic obstacle detection based on multi-sensor information fusion. IFAC-PapersOnLine, 51(17), 861-865. doi:10.1016/j.ifacol.2018.08.086Louwagie, G., Northey, G., Finn, J. A., & Purvis, G. (2012). Development of indicators for assessment of the environmental impact of livestock farming in Ireland using the Agri-environmental Footprint Index. Ecological Indicators, 18, 149-162. doi:10.1016/j.ecolind.2011.11.003Luque, A., Peralta, M. E., de las Heras, A., & Córdoba, A. (2017). State of the Industry 4.0 in the Andalusian food sector. Procedia Manufacturing, 13, 1199-1205. doi:10.1016/j.promfg.2017.09.195Malhotra, S., Doja, M. ., Alam, B., & Alam, M. (2017). Bigdata analysis and comparison of bigdata analytic approches. 2017 International Conference on Computing, Communication and Automation (ICCCA). doi:10.1109/ccaa.2017.8229821Mayer, J., Gunst, L., Mäder, P., Samson, M.-F., Carcea, M., Narducci, V., … Dubois, D. (2015). «Productivity, quality and sustainability of winter wheat under long-term conventional and organic management in Switzerland». European Journal of Agronomy, 65, 27-39. doi:10.1016/j.eja.2015.01.002McGuire, S., & Sperling, L. (2013). Making seed systems more resilient to stress. Global Environmental Change, 23(3), 644-653. doi:10.1016/j.gloenvcha.2013.02.001Mekala, M. S., & Viswanathan, P. (2017). A Survey: Smart agriculture IoT with cloud computing. 2017 International conference on Microelectronic Devices, Circuits and Systems (ICMDCS). doi:10.1109/icmdcs.2017.8211551Mishra, S., Mishra, D., & Santra, G. H. (2016). Applications of Machine Learning Techniques in Agricultural Crop Production: A Review Paper. Indian Journal of Science and Technology, 9(38). doi:10.17485/ijst/2016/v9i38/95032Mocnej, J., Seah, W. K. G., Pekar, A., & Zolotova, I. (2018). Decentralised IoT Architecture for Efficient Resources Utilisation. IFAC-PapersOnLine, 51(6), 168-173. doi:10.1016/j.ifacol.2018.07.148Mohanraj, I., Gokul, V., Ezhilarasie, R., & Umamakeswari, A. (2017). Intelligent drip irrigation and fertigation using wireless sensor networks. 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR). doi:10.1109/tiar.2017.8273682Montecinos, J., Ouhimmou, M., Chauhan, S., & Paquet, M. (2018). Forecasting multiple waste collecting sites for the agro-food industry. Journal of Cleaner Production, 187, 932-939. doi:10.1016/j.jclepro.2018.03.127Yandun Narváez, F., Gregorio, E., Escolà, A., Rosell-Polo, J. R., Torres-Torriti, M., & Auat Cheein, F. (2018). Terrain classification using ToF sensors for the enhancement of agricultural machinery traversability. Journal of Terramechanics, 76, 1-13. doi:10.1016/j.jterra.2017.10.005Nguyen, T., ZHOU, L., Spiegler, V., Ieromonachou, P., & Lin, Y. (2018). Big data analytics in supply chain management: A state-of-the-art literature review. Computers & Operations Research, 98, 254-264. doi:10.1016/j.cor.2017.07.004Nilsson, E., Hochrainer-Stigler, S., Mochizuki, J., & Uvo, C. B. (2016). Hydro-climatic variability and agricultural production on the shores of Lake Chad. Environmental Development, 20, 15-30. doi:10.1016/j.envdev.2016.09.001Nolan, P., Paley, D. A., & Kroeger, K. (2017). Multi-UAS path planning for non-uniform data collection in precision agriculture. 2017 IEEE Aerospace Conference. doi:10.1109/aero.2017.7943794Oberholster, C., Adendorff, C., & Jonker, K. (2015). Financing Agricultural Production from a Value Chain Perspective. Outlook on Agriculture, 44(1), 49-60. doi:10.5367/oa.2015.0197Opara, L. U., & Mazaud, F. (2001). Food Traceability from Field to Plate. Outlook on Agriculture, 30(4), 239-247. doi:10.5367/000000001101293724Ott, K.-H., Aranı́bar, N., Singh, B., & Stockton, G. W. (2003). Metabonomics classifies pathways affected by bioactive compounds. Artificial neural network classification of NMR spectra of plant extracts. Phytochemistry, 62(6), 971-985. doi:10.1016/s0031-9422(02)00717-3Panetto, H. (2007). Towards a classification framework for interoperability of enterprise applications. International Journal of Computer Integrated Manufacturing, 20(8), 727-740. doi:10.1080/09511920600996419Paulraj, G. J. L., Francis, S. A. J., Peter, J. D., & Jebadurai, I. J. (2018). Resource-aware virtual machine migration in IoT cloud. Future Generation Computer Systems, 85, 173-183. doi:10.1016/j.future.2018.03.024Pilli, S. K., Nallathambi, B., George, S. J., & Diwanji, V. (2015). eAGROBOT — A robot for early crop disease detection using image processing. 2015 2nd International Conference on Electronics and Communication Systems (ICECS). doi:10.1109/ecs.2015.7124873Pinho, P., Dias, T., Cruz, C., Sim Tang, Y., Sutton, M. A., Martins-Loução, M.-A., … Branquinho, C. (2011). Using lichen functional diversity to assess the effects of atmospheric ammonia in Mediterranean woodlands. Journal of Applied Ecology, 48(5), 1107-1116. doi:10.1111/j.1365-2664.2011.02033.xPrathibha, S. R., Hongal, A., & Jyothi, M. P. (2017). IOT Based Monitoring System in Smart Agriculture. 2017 International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT). doi:10.1109/icraect.2017.52Reardon, T., Echeverria, R., Berdegué, J., Minten, B., Liverpool-Tasie, S., Tschirley, D., & Zilberman, D. (2019). Rapid transformation of food systems in developing regions: Highlighting the role of agricultural research & innovations. Agricultural Systems, 172, 47-59. doi:10.1016/j.agsy.2018.01.022Ribarics, P. (2016). Big Data and its impact on agriculture. Ecocycles, 2(1), 33-34. doi:10.19040/ecocycles.v2i1.54Rosell, J. R., & Sanz, R. (2012). A review of methods and applications of the geometric characterization of tree crops in agricultural activities. Computers and Electronics in Agriculture, 81, 124-141. doi:10.1016/j.compag.2011.09.007Roshanianfard, A., Kamata, T., & Noguchi, N. (2018). Performance evaluation of harvesting robot for heavy-weight crops. IFAC-PapersOnLine, 51(17), 332-338. doi:10.1016/j.ifacol.2018.08.200Routroy, S., & Behera, A. (2017). Agriculture supply chain. Journal of Agribusiness in Developing and Emerging Economies, 7(3), 275-302. doi:10.1108/jadee-06-2016-0039Ruiz-Garcia, L., Steinberger, G., & Rothmund, M. (2010). A model and prototype implementation for tracking and tracing agricultural batch products along the food chain. Food Control, 21(2), 112-121. doi:10.1016/j.foodcont.2008.12.003Saggi, M. K., & Jain, S. (2018). A survey towards an integration of big
Update of the search for supersymmetric particles in light gravitino scenarios at = 204-208 GeV
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