147 research outputs found

    Isotope and geochemical characterization of surface and subsurface waters in the semi-arid Sokoto Basin, Nigeria

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    Stable isotopes and geochemical studies have been applied in the investigation of groundwater resources in Sokoto Basin, northwestern Nigeria. Generally, the characteristic hydrochemical classification in the study area is calcium-alkali-bicarbonate. Surface waters are characterized by alkali-calcium-bicarbonate while groundwater is of Ca-Mg-HCO3. The plot of δ18O versus δ2H shows that five isotopic groups can be distinguished. Group I-III is of groundwater origin while group IV and V represent surface water. A combination of the hydrochemical and isotope data (14C, 13C and 3H) reveals the Sokoto basin aquifers generally contains good quality groundwater of Holocene age (100 to 10,000 years BP). Key Words: stable isotopes, geochemical characterization, groundwater, Sokoto Basin. African Journal of Science and Technology Vol.4(2) 2003: 80-8

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

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    [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-

    Solvation free energy profile of the SCN- ion across the water-1,2-dichloroethane liquid/liquid interface. A computer simulation study

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    The solvation free energy profile of a single SCN- ion is calculated across the water-1,2-dichloroethane liquid/liquid interface at 298 K by the constraint force method. The obtained results show that the free energy cost of transferring the ion from the aqueous to the organic phase is about 70 kJ/mol, The free energy profile shows a small but clear well at the aqueous side of the interface, in the subsurface region of the water phase, indicating the ability of the SCN- ion to be adsorbed in the close vicinity of the interface. Upon entrance of the SCN- ion to the organic phase a coextraction of the water molecules of its first hydration shell occurs. Accordingly, when it is located at the boundary of the two phases the SCN- ion prefers orientations in which its bulky S atom is located at the aqueous side, and the small N atom, together with its first hydration shell, at the organic side of the interface

    Molecular dynamics simulation studies of the interactions between ionic liquids and amino acids in aqueous solution

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    Although the understanding of the influence of ionic liquids (ILs) on the solubility behavior of biomolecules in aqueous solutions is relevant for the design and optimization of novel biotechnological processes, the underlying molecular-level mechanisms are not yet consensual or clearly elucidated. In order to contribute to the understanding of the molecular interactions established between amino acids and ILs in aqueous media, classical molecular dynamics (MD) simulations were performed for aqueous solutions of five amino acids with different structural characteristics (glycine, alanine, valine, isoleucine, and glutamic acid) in the presence of 1-butyl-3-methylimidazolium bis(trifluoromethyl)sulfonyl imide. The results from MD simulations enable to relate the properties of the amino acids, namely their hydrophobicity, to the type and strength of their interactions with ILs in aqueous solutions and provide an explanation for the direction and magnitude of the solubility phenomena observed in [IL + amino acid + water] systems by a mechanism governed by a balance between competitive interactions of the IL cation, IL anion, and water with the amino acids

    Determination of Alkali and Halide Monovalent Ion Parameters for Use in Explicitly Solvated Biomolecular Simulations

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    Alkali (Li+, Na+, K+, Rb+, and Cs+) and halide (F−, Cl−, Br−, and I−) ions play an important role in many biological phenomena, roles that range from stabilization of biomolecular structure, to influence on biomolecular dynamics, to key physiological influence on homeostasis and signaling. To properly model ionic interaction and stability in atomistic simulations of biomolecular structure, dynamics, folding, catalysis, and function, an accurate model or representation of the monovalent ions is critically necessary. A good model needs to simultaneously reproduce many properties of ions, including their structure, dynamics, solvation, and moreover both the interactions of these ions with each other in the crystal and in solution and the interactions of ions with other molecules. At present, the best force fields for biomolecules employ a simple additive, nonpolarizable, and pairwise potential for atomic interaction. In this work, we describe our efforts to build better models of the monovalent ions within the pairwise Coulombic and 6-12 Lennard-Jones framework, where the models are tuned to balance crystal and solution properties in Ewald simulations with specific choices of well-known water models. Although it has been clearly demonstrated that truly accurate treatments of ions will require inclusion of nonadditivity and polarizability (particularly with the anions) and ultimately even a quantum mechanical treatment, our goal was to simply push the limits of the additive treatments to see if a balanced model could be created. The applied methodology is general and can be extended to other ions and to polarizable force-field models. Our starting point centered on observations from long simulations of biomolecules in salt solution with the AMBER force fields where salt crystals formed well below their solubility limit. The likely cause of the artifact in the AMBER parameters relates to the naive mixing of the Smith and Dang chloride parameters with AMBER-adapted Åqvist cation parameters. To provide a more appropriate balance, we reoptimized the parameters of the Lennard-Jones potential for the ions and specific choices of water models. To validate and optimize the parameters, we calculated hydration free energies of the solvated ions and also lattice energies (LE) and lattice constants (LC) of alkali halide salt crystals. This is the first effort that systematically scans across the Lennard-Jones space (well depth and radius) while balancing ion properties like LE and LC across all pair combinations of the alkali ions and halide ions. The optimization across the entire monovalent series avoids systematic deviations. The ion parameters developed, optimized, and characterized were targeted for use with some of the most commonly used rigid and nonpolarizable water models, specifically TIP3P, TIP4PEW, and SPC/E. In addition to well reproducing the solution and crystal properties, the new ion parameters well reproduce binding energies of the ions to water and the radii of the first hydration shells

    A conceptual model for the development and management of the Cape Flats aquifer, South Africa

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    This paper provides an integrated approach to the analysis of the geological, hydrological and hydrogeological characteristics of the Cape Flats: a coastal plain sand formed within the mountains of the Cape Town metropolitan area. The study is mainly based on evaluation of available data, on surface water and groundwater, rainfall and selected springs, to describe the Cape Flats aquifer. Qualitative analysis has shown that both surface water and groundwater of the investigated area are of good quality; whereas sources of contamination indicated are restricted to certain parts of the area. Interpretation of hydrogeological data and aquifer parameters revealed that the Cape Flats aquifer has good storage characteristics to support its development for water supply, although the generally unconfined conditions render it highly susceptible to pollution from the surface. From the analysis of long-term climate data in Cape Town, it is evident that fluctuation exists in the pattern ofrainfall; this rainfall pattern has implications for recharge and water management issues in the city. Therefore, a conceptual hydrogeological model was developed to elucidate groundwater flow and recharge mechanisms in the Cape Flats.Keywords: water resource management, Cape Town, water quality, surface-water/groundwater relations, conceptualisatio
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