Subaqueous shrinkage cracks in the Sheepbed mudstone: Implications for early fluid diagenesis, Gale crater, Mars

The Sheepbed mudstone, Yellowknife Bay formation, Gale crater, represents an ancient lakebed now exhumed and exposed on the Martian surface. The mudstone has four diagenetic textures, including a suite of early diagenetic nodules, hollow nodules, and raised ridges and later diagenetic light-toned veins that crosscut those features. In this study, we describe the distribution and characteristics of the raised ridges, a network of short spindle-shaped cracks that crosscut bedding, do not form polygonal networks, and contain two to four layers of isopachous, erosion-resistant cement. The cracks have a clustered distribution within the Sheepbed member and transition laterally into concentrations of nodules and hollow nodules, suggesting that these features formed penecontemporaneously. Because of the erosion-resistant nature of the crack fills, their three-dimensional structure can be observed. Cracks that transition from subvertical to subhorizontal orientations suggest that the cracks formed within the sediment rather than at the surface. This observation and comparison to terrestrial analogs indicate that these are syneresis cracks—cracks that formed subaqueously. Syneresis cracks form by salinity changes that cause sediment contraction, mechanical shaking of sediment, or gas production within the sediment. Examination of diagenetic features within the Sheepbed mudstone favors a gas production mechanism, which has been shown to create a variety of diagenetic morphologies comparable to the raised ridges and hollow nodules. The crack morphology and the isopachous, layered cement fill show that the cracks were filled in the phreatic zone and that the Sheepbed mudstone remained fluid saturated after deposition and through early burial and lithification

Artificial Intelligence in Supply Chain Operations Planning: Collaboration and Digital Perspectives

[EN] Digital transformation provide supply chains (SCs) with extensive accurate data that should be combined with analytical techniques to improve their management. Among these techniques Artificial Intelligence (AI) has proved their suitability, memory and ability to manage uncertain and constantly changing information. Despite the fact that a number of AI literature reviews exist, no comprehensive review of reviews for the SC operations planning has yet been conducted. This paper aims to provide a comprehensive review of AI literature reviews in a structured manner to gain insights into their evolution in incorporating new ICTs and collaboration. Results show that hybrization man-machine and collaboration and ethical aspects are understudied.This research has been funded by the project entitled NIOTOME (Ref. RTI2018-102020-B-I00) (MCI/AEI/FEDER, UE). The first author was supported by the Generalitat Valenciana (Conselleria de Educación, Investigación, Cultura y Deporte) under Grant ACIF/2019/021.Rodríguez-Sánchez, MDLÁ.; Alemany Díaz, MDM.; Boza, A.; Cuenca, L.; Ortiz Bas, Á. (2020). Artificial Intelligence in Supply Chain Operations Planning: Collaboration and Digital Perspectives. IFIP Advances in Information and Communication Technology. 598:365-378. https://doi.org/10.1007/978-3-030-62412-5_30S365378598Lezoche, M., Hernandez, J.E., Alemany, M.M.E., Díaz, E.A., Panetto, H., Kacprzyk, J.: Agri-food 4.0: a survey of the supply chains and technologies for the future agriculture. Comput. Ind. 117, 103–187 (2020)Stock, J.R., Boyer, S.L.: Developing a consensus definition of supply chain management: a qualitative study. Int. J. Phys. Distrib. Logistics Manag. 39(8), 690–711 (2009)Min, H.: Artificial intelligence in supply chain management: theory and applications. Int. J. Logistics Res. Appl. 13(1), 13–39 (2010). https://doi.org/10.1080/13675560902736537Hariri, R.H., Fredericks, E.M., Bowers, K.M.: Uncertainty in big data analytics: survey, opportunities, and challenges. J. Big Data 6(1), 1–16 (2019). https://doi.org/10.1186/s40537-019-0206-3Duan, Y., Edwards, J.S., Dwivedi, Y.K.: Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. Int. J. Inf. Manage. 48(2019), 63–71 (2019). https://doi.org/10.1016/j.ijinfomgt.2019.01.021McCarthy, J., Minsky, M.L., Rochester, N., Shannon, C.E.: A proposal for the dartmouth summer research project on artificial intelligence. AI Mag. 27(4), 12–14 (2006)Barr, A., Feigenbaum, E.A.: The Handbook of Artificial Intelligence, vol. 2. Heuristech: William Kaufmann, Pitman (1982)High-Level Expert Group on Artificial Intelligence, European Commission. A definition of AI: main capabilities and disciplines (2019)Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., De Felice, F.: Artificial intelligence and machine learning applications in smart production: progress, trends, and directions. Sustainability (Switzerland) 12(2) (2020). https://doi.org/10.3390/su12020492Cheng, L., Yu, T.: A new generation of AI: a review and perspective on machine learning technologies applied to smart energy and electric power systems. Int. J. Energy Res. 43(6), 1928–1973 (2019). https://doi.org/10.1002/er.4333Duan, Y., Edwards, J.S., Dwivedi, Y.K.: Artificial intelligence for decision-making in the era of big data. Evolution, challenges and research agenda. Int. J. Inf. Manag. 48, 63–71 (2019)Varshney, S., Jigyasu, R., Sharma, A., Mathew, L.: Review of various artificial intelligence techniques and its applications. IOP Conf. Ser. Mater. Sci. Eng. 594(1) (2019)Cheng, L., Yu, T.: A new generation of AI: a review and perspective on machine learning technologies applied to smart energy and electric power systems. Int. J. Energy Res. 43, 1928–1973 (2019)Seuring, S., Müller, M.: From a literature review to a conceptual framework for sustainable supply chain management. J. Clean. Prod. 16(15), 1699–1710 (2008). https://doi.org/10.1016/j.jclepro.2008.04.020Metaxiotis, K.S., Askounis, D., Psarras, J.: Expert Systems In Production Planning And Scheduling: A State-Of-The-Art Survey. J. Intell. Manuf. 13(4), 253–260 (2002). https://doi.org/10.1023/A:1016064126976Power, Y., Bahri, P.A.: Integration techniques in intelligent operational management: a review. Knowl. Based Syst. 18(2–3), 89–97 (2005). https://doi.org/10.1016/j.knosys.2004.04.009Shen, W., Hao, Q., Yoon, H.J., Norrie, D.H.: Applications of agent-based systems in intelligent manufacturing: an updated review. Adv. Eng. Inform. 20(4), 415–431 (2006). https://doi.org/10.1016/j.aei.2006.05.004Kobbacy, K.A.H., Vadera, S., Rasmy, M.H.: AI and OR in management of operations: history and trends. J. Oper. Res. Soc. 58(1), 10–28 (2007). https://doi.org/10.1057/palgrave.jors.2602132Zhang, W.J., Xie, S.Q.: Agent technology for collaborative process planning: a review. Int. J. Adv. Manuf. Technol. 32(3), 315–325 (2007). https://doi.org/10.1007/s00170-005-0345-xIbáñez, O., Cordón, O., Damas, S., Magdalena, L.: A review on the application of hybrid artificial intelligence systems to optimization problems in operations management. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds.) HAIS 2009. LNCS (LNAI), vol. 5572, pp. 360–367. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02319-4_43Kobbacy, K.A.H., Vadera, S.: A survey of AI in operations management from 2005 to 2009. J. Manuf. Technol. Manag. 22(6), 706–733 (2011). https://doi.org/10.1108/17410381111149602Guo, Z.X., Wong, W.K., Leung, S.Y.S., Li, M.: Applications of artificial intelligence in the apparel industry: a review. Text. Res. J. 81(18), 1871–1892 (2011). https://doi.org/10.1177/0040517511411968Priore, P., Gómez, A., Pino, R., Rosillo, R.: Dynamic scheduling of manufacturing systems using machine learning: an updated review. Artif. Intell. Eng. Des. Anal. Manuf. AIEDAM 28(1), 83–97 (2014). https://doi.org/10.1017/S0890060413000516Renzi, C., Leali, F., Cavazzuti, M., Andrisano, A.: A review on artificial intelligence applications to the optimal design of dedicated and reconfigurable manufacturing systems. Int. J. Adv. Manuf. Technol. 72(1–4), 403–418 (2014). https://doi.org/10.1007/s00170-014-5674-1Ngai, E.W.T., Peng, S., Alexander, P., Moon, K.K.L.: Decision support and intelligent systems in the textile and apparel supply chain: an academic review of research articles. Expert Syst. Appl. 41(1), 81–91 (2014). https://doi.org/10.1016/j.eswa.2013.07.013Rooh, U.A., Li, A., Ali, M.M.: Fuzzy, neural network and expert systems methodologies and applications - a review. J. Mob. Multimedia 11, 157–176 (2015)Bello, O., Teodoriu, C., Yaqoob, T., Oppelt, J., Holzmann, J., Obiwanne, A.: Application of artificial intelligence techniques in drilling system design and operations: a state of the art review and future research pathways. In: Society of Petroleum Engineers - SPE Nigeria Annual International Conference and Exhibition (2016)Arvitrida, N.I.: A review of agent-based modeling approach in the supply chain collaboration context. IOP Conf. Ser. Mater. Sci. Eng. 337(1) (2018). https://doi.org/10.1088/1757-899x/337/1/012015Zanon, L.G., Carpinetti, L.C.R.: Fuzzy cognitive maps and grey systems theory in the supply chain management context: a literature review and a research proposal. In: IEEE International Conference on Fuzzy Systems, July 2018, pp. 1–8 (2018). https://doi.org/10.1109/fuzz-ieee.2018.8491473Burggräf, P., Wagner, J., Koke, B.: Artificial intelligence in production management: a review of the current state of affairs and research trends in academia. In: 2018 International Conference on Information Management and Processing, ICIMP 2018, January 2018, pp. 82–88 (2018). https://doi.org/10.1109/icimp1.2018.8325846Diez-Olivan, A., Del Ser, J., Galar, D., Sierra, B.: Data fusion and machine learning for industrial prognosis: trends and perspectives towards Industry 4.0. Inf. Fusion 50, 92–111 (2019). https://doi.org/10.1016/j.inffus.2018.10.005Ni, D., Xiao, Z., Lim, M.K.: A systematic review of the research trends of machine learning in supply chain management. Int. J. Mach. Learn. Cybernet. 11(7), 1463–1482 (2019). https://doi.org/10.1007/s13042-019-01050-0Ning, C., You, F.: Optimization under uncertainty in the era of big data and deep learning: when machine learning meets mathematical programming. Comput. Chem. Eng. 125, 434–448 (2019). https://doi.org/10.1016/j.compchemeng.2019.03.034Okwu, M.O., Nwachukwu, A.N.: A review of fuzzy logic applications in petroleum exploration, production and distribution operations. J. Petrol. Explor. Prod. Technol. 9(2), 1555–1568 (2018). https://doi.org/10.1007/s13202-018-0560-2Weber, F.D., Schütte, R.: State-of-the-art and adoption of artificial intelligence in retailing. Digit. Policy Regul. Gov. 21(3), 264–279 (2019). https://doi.org/10.1108/DPRG-09-2018-0050Giri, C., Jain, S., Zeng, X., Bruniaux, P.: A detailed review of artificial intelligence applied in the fashion and apparel industry. IEEE Access 7, 95376–95396 (2019). https://doi.org/10.1109/ACCESS.2019.2928979Leo Kumar, S.P.: Knowledge-based expert system in manufacturing planning: State-of-the-art review. Int. J. Prod. Res. 57(15–16), 4766–4790 (2019). https://doi.org/10.1080/00207543.2018.1424372Barua, L., Zou, B., Zhou, Y.: Machine learning for international freight transportation management: a comprehensive review. Res. Transp. Bus. Manag. (2020). https://doi.org/10.1016/j.rtbm.2020.100453Chai, J., Ngai, E.W.T.: Decision-making techniques in supplier selection: recent accomplishments and what lies ahead. Expert Syst. Appl. 140 (2020). https://doi.org/10.1016/j.eswa.2019.112903Usuga Cadavid, J.P., Lamouri, S., Grabot, B., Pellerin, R., Fortin, A.: Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0. J. Intell. Manuf. 31(6), 1531–1558 (2020). https://doi.org/10.1007/s10845-019-01531-7Ekramifard, A., Amintoosi, H., Seno, A.H., Dehghantanha, A., Parizi, R.M.: A systematic literature review of integration of blockchain and artificial intelligence. In: Choo, K.-K.R., Dehghantanha, A., Parizi, R.M. (eds.) Blockchain Cybersecurity, Trust and Privacy. AIS, vol. 79, pp. 147–160. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-38181-3_8Vrbka, J., Rowland, Z.: Using artificial intelligence in company management. In: Ashmarina, S.I., Vochozka, M., Mantulenko, V.V. (eds.) ISCDTE 2019. LNNS, vol. 84, pp. 422–429. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-27015-5_51Leslie, D.: Understanding artificial intelligence ethics and safety: a guide for the responsible design and implementation of AI systems in the public sector. The Alan Turing Institute (2019)Queiroz, M.M., Ivanov, D., Dolgui, A., et al.: Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Ann Oper Res (2020). https://doi.org/10.1007/s10479-020-03685-

Measurements of Charmless Hadronic b->s Penguin Decays in the pi+pi-K+pi- Final State and First Observation of B0 -> rho0K+pi-

We report measurements of charmless hadronic B^0 decays into the pi+pi-K+pi+ final state. The analysis uses a sample of 657x10^6 BBbar pairs collected with the Belle detector at the KEKB asymmetric-energy e+e- collider at the Y(4S) resonance. The decay B^0 -> rho0 Kpi is observed for the first time; the significance is 5.0sigma and the corresponding partial branching fraction for M_Kpi in (0.75,1.20) GeV/c^2 is [2.8 +- 0.5(stat) +-0.5(syst)] x 10^{-6}. We also obtain the first evidence for B^0 -> f0Kpi with 3.5sigma significance and for B^0 -> pi+pi-K*0 with 4.5sigma significance. For the two-body decays B^0 -> rho0K*0 and B^0 -> f0K*0, the significances are 2.7sigma and 2.5sigma, respectively, and the upper limits on the branching fractions are 3.4x10^{-6} and 2.2x10^{-6} at 90% confidence level.Comment: 6 pages, 3 figures. accepted by PRD(RC

Dalitz analysis of B --> K pi psi' decays and the Z(4430)+

From a Dalitz plot analysis of B --> K pi psi' decays, we find a signal for Z(4430)+ --> pi+ psi' with a mass M= (4443(+15-12)(+19-13))MeV/c^2, width Gamma= (107(+86-43)(+74-56))MeV, product branching fraction BR(B0 --> K- Z(4430)+) x BR(Z(4430)+ --> pi+ psi')= (3.2(+1.8-0.9)(+5.3-1.6)) x 10^{-5}, and significance of 6.4sigma that agrees with previous Belle measurements based on the same data sample. In addition, we determine the branching fraction BR(B^0 --> K*(892)^0 psi')= (5.52(+0.35-0.32)(+0.53-0.58)) x 10^{-4} and the fraction of K*(892)^0 mesons that are longitudinally polarized f_L= 44.8(+4.0-2.7)(+4.0-5.3)%. These results are obtained from a 605fb^{-1} data sample that contains 657 million B-anti-B pairs collected near the Upsilon(4S) resonance with the Belle detector at the KEKB asymmetric energy e+e- collider.Comment: Final version published in PRD(RC

Evidence of time-dependent CP violation in the decay B0 to D*+D*-

We report a measurement of the CP-odd fraction and the time-dependent CP violation in B0 to D*+D*- decays, using 657.10^6 BBbar events collected at the Upsilon(4S) resonance with the Belle detector at the KEKB asymmetric-energy e+e- collider. We measure a CP-odd fraction of Rperp=0.125+/-0.043(stat)+/-0.023(syst). From the distributions of the proper-time intervals between a B0to D*+D*- decay and the other B meson in the event, we obtain evidence of CP violation with measured parameters AD*+D*-=0.15+/-0.13(stat)+/-0.04(syst) and SD*+D*-=-0.96+/-0.25(stat)-0.16+0.13(syst).Comment: Published in PR

Study of the B→X(3872)(→D∗0Dˉ0)KB\to X(3872)(\to D^{*0}\bar D^0)K decay

We present a study of $B\to X(3872)K$ with X(3872) decaying to $D^{*0}\bar D^0$ using a sample of 657 million $B\bar B$ pairs recorded at the $\Upsilon(4S)$ resonance with the Belle detector at the KEKB asymmetric-energy $e^+e^-$ collider. Both $D^{*0}\to D^0\gamma$ and $D^{*0}\to D^0\pi^0$ decay modes are used. We find a peak of $50.1^{+14.8}_{-11.1}$ events with a mass of $(3872.9^{+0.6 +0.4}_{-0.4 -0.5}){\rm MeV}/c^2$, a width of $(3.9^{+2.8 +0.2}_{-1.4 -1.1}){\rm MeV}/c^2$ and a product branching fraction ${\cal B}(B\to X(3872)K)\times{\cal B}(X(3872)\to D^{*0}\bar D^0)=(0.80\pm0.20\pm0.10)\times10^{-4}$, where the first errors are statistical and the second ones are systematic. The significance of the signal is $6.4\sigma$. The difference between the fitted mass and the $D^{*0}\bar D^0$ threshold is calculated to be $(1.1^{+0.6 +0.1}_{-0.4 -0.3}){\rm MeV}/c^2$. We also obtain an upper limit on the product of branching fractions ${\cal B}(B\to Y(3940)K)\times{\cal B}(Y(3940)\to D^{*0}\bar D^0)$ of $0.67\times10^{-4}$ at 90% CL.Comment: 7 pages, 3 figures, BELLE-CONF-0832 contributed to ICHEP 2008, revised and submitted to Phys. Rev. D R

Search for the X(1812) in B±→K±ωϕB^{\pm} \to K^{\pm} \omega \phi

We report on a search for the X(1812) state in the decay $B^{\pm} \to K^{\pm} \omega \phi$ with a data sample of $657\times10^6$ $B\bar{B}$ pairs collected with the Belle detector at the KEKB $e^+e^-$ collider. No significant signal is observed. An upper limit ${\cal B}(B^{\pm} \to K^{\pm} X(1812),X(1812) \to \omega \phi)<3.2\times 10^{-7}$ (90% C.L.) is determined. We also constrain the three-body decay branching fraction to be ${\cal B}(B^{\pm} \to K^{\pm} \omega \phi)$ $<$ 1.9 $\times 10^{-6}$ (90% C.L.).Comment: 5pages,2 figures(3 figure files). submitted to PRD(RC

Observation of e+e- to K+K-J/psi via Initial State Radiation at Belle

The process e+e- to K+K-J/psi is observed for the first time via initial state radiation. The cross section of e+e- to K+K-J/psi for center-of-mass energies between threshold and 6.0 GeV is measured using 673 fb^{-1} of data collected with the Belle detector on and off the Upsilon(4S) resonance. We also find evidence for e+e- to K_S K_S J/psi in the same energy region.Comment: Version to appear in Physical Review D (RC

Observation of anomalous Upsilon(1S)pi+pi- and Upsilon(2S)pi+pi- production near the Upsilon(5S) resonance

We report the first observation of e+e- -> Upsilon(1S)pi+pi-, Upsilon(2S)pi+pi-, and first evidence for e+e- -> Upsilon(3S)pi+pi-, Upsilon(1S)K+K-, near the peak of the Upsilon(5S) resonance at sqrt{s}~10.87 GeV. The results are based on a data sample of 21.7 fb^-1 collected with the Belle detector at the KEKB e+e- collider. Attributing the signals to the Upsilon(5S) resonance, the partial widths Gamma(Upsilon(5S) -> Upsilon(1S) pi+pi-) = 0.59 +- 0.04 (stat) +- 0.09 (syst) MeV and Gamma(Upsilon(5S) -> Upsilon(2S) pi+pi-) = 0.85 +- 0.07 (stat) +- 0.16 (syst) MeV are obtained from the observed cross sections. These values exceed by more than two orders of magnitude the previously measured partial widths for dipion transitions between lower Upsilon resonances.Comment: 6 pages, 4 figures, submit to PR

Observation of B0 to p pbar K*0 with a large K*0 polarization

We observe the decay B0 to p pbar K*0 with a branching fraction of (1.18^{+0.29}_{-0.25} (stat.) \pm 0.11 (syst.)) \times 10^{-6}. The statistical significance is 7.2 sigma for the signal in the low ppbar mass region. We study the decay dynamics of B0 to p pbar K*0 and compare it with B+ to p pbar K*+. The K*0 meson is found to be almost 100% polarized (with a fraction of (101 \pm 13 \pm 3)% in the helicity zero state), while the K*+ meson has a (32 \pm 17 \pm 9)% fraction in the helicity zero state. The direct CP asymmetries for B0 to p pbar K*0 and B+ to p pbar K*+ are measured to be -0.08\pm 0.20\pm 0.02 and -0.01\pm 0.19\pm 0.02, respectively. We also study the characteristics of the low mass ppbar enhancements near threshold and the associated angular distributions. In addition, we report improved measurements of the branching fractions BF(B+ to p pbar K*+) = (3.38^{+0.73}_{-0.60} \pm 0.39) \times 10^{-6} and BF(B0 to p pbar K0) = (2.51^{+0.35}_{-0.29} \pm 0.21) \times 10^{-6}, which supersede our previous measurements. These results are obtained from a 492 fb^{-1} data sample collected near the Upsilon(4S) resonance with the Belle detector at the KEKB asymmetric-energy e^+ e^- collider.Comment: 11 pages, 4 figures (8 figure files), submitted to Phys.Rev.Let