Phytochemical constituents of some Nigerian medicinal plants

Alkaloids, tannins, saponins, steroid, terpenoid, flavonoids, phlobatannin and cardic glycoside distribution in ten medicinal plants belonging to different families were assessed and compared. The medicinal plants investigated were Cleome nutidosperma, Emilia coccinea, Euphorbia heterophylla, Physalis angulata, Richardia bransitensis, Scopania dulcis, Sida acuta, Spigelia anthelmia, Stachytarpheta cayennensis and Tridax procumbens. All the plants were found to contain alkaloids, tannins and flavonoids except for the absence of tannins in S. acuta and flavonoids in S. cayennsis respectively. The significance of the plants in traditional medicine and the importance of the distribution of these chemical constituents were discussed with respect to the role of these plants in ethnomedicine in Nigeria.African Journal of Biotechnology Vol. 4 (7), pp. 685-688, 200

Modelling of Nicotiana Tabacum L. oil biodiesel production : comparison of ANN and ANFIS

Among the modern computational techniques, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are preferred because of their ability to deal with non-linear modelling and complex stochastic dataset. Nondeterministic models involve some computational complexities while solving real-life problems but would always produce better outcomes. For the first time, this study utilized the ANN and ANFIS models for modelling tobacco seed oil methyl ester (TSOME) production from underutilized tobacco seeds in the tropics. The dataset for the models was obtained from an earlier study which focused on design of the experiment on TSOME production. This study is an an exposition of the influence of transesterification parameters such as reaction duration (T), methanol/oil molar ratio (M:O), and catalyst dosage on the TSOME/biodiesel yield. A multi-layer ANN model with ten hidden layers was trained to simulate the methanolysis process. The ANFIS approach was further implemented to model TSOME production. A comparison of the formulated models was completed by statistical criteria such as coefficient of determination (R2), mean average error (MAE), and average absolute deviation (AAD). The R2 of 0.8979, MAE of 4.34468, and AAD of 6.0529 for the ANN model compared to those of the R2 of 0.9786, MAE of 1.5311, and AAD of 1.9124 for the ANFIS model. The ANFIS model appears to be more reliable than the ANN model in predicting TSOME production in the tropics.http://www.frontiersin.org/Energy_Researcham2022Mechanical and Aeronautical Engineerin

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-

Preliminary phytochemical screening and In vitro antioxidant activities of the aqueous extract of Helichrysum longifolium DC

<p>Abstract</p> <p>Background</p> <p>Many oxidative stress related diseases are as a result of accumulation of free radicals in the body. A lot of researches are going on worldwide directed towards finding natural antioxidants of plants origins. The aims of this study were to evaluate <it>in vitro </it>antioxidant activities and to screen for phytochemical constituents of <it>Helichrysum longifolium </it>DC. [Family Asteraceae] aqueous crude extract.</p> <p>Methods</p> <p>We assessed the antioxidant potential and phytochemical constituents of crude aqueous extract of <it>Helichrysum longifolium </it>using tests involving inhibition of superoxide anions, DPPH, H₂O₂, NO and ABTS. The flavonoid, proanthocyanidin and phenolic contents of the extract were also determined using standard phytochemical reaction methods.</p> <p>Results</p> <p>Phytochemical analyses revealed the presence of tannins, flavonoids, steroids and saponins. The total phenolic content of the aqueous leaf extract was 0.499 mg gallic acid equivalent/g of extract powder. The total flavonoid and proanthocyanidin contents of the plant were 0.705 and 0.005 mg gallic acid equivalent/g of extract powder respectively. The percentage inhibition of lipid peroxide at the initial stage of oxidation showed antioxidant activity of 87% compared to those of BHT (84.6%) and gallic acid (96%). Also, the percentage inhibition of malondialdehyde by the extract showed percentage inhibition of 78% comparable to those of BHT (72.24%) and Gallic (94.82%).</p> <p>Conclusions</p> <p>Our findings provide evidence that the crude aqueous extract of <it>H. longifolium </it>is a potential source of natural antioxidants, and this justified its uses in folkloric medicines.</p

Sinteza, antimikrobno i antitumorsko djelovanje nekoliko novih N-etil, N-benzil i N-benzoil-3-indolil heterocikličkih spojeva

A series of 1-(N-substituted-1H-indol-3-yl)-3-arylprop-2-ene-1-ones (2a,b-4a,b) were prepared and allowed to react with urea, thiourea or guanidine to give pyrimidine derivatives 5a,b–13a,b. Reaction of 2a,b-4a,b with ethyl acetoacetate in the presence of a base gave cyclohexanone derivatives 14a,b-16a,b. Reaction of the latter compounds with hydrazine hydrate afforded indazole derivatives 17a,b-19a,b. On the other hand, reaction of 2a,b-4a,b with some hydrazine derivatives, namely hydrazine hydrate, acetyl hydrazine, phenyl- hydrazine and benzylhydrazine hydrochloride, led to the formation of pyrazole derivatives 20a,b-31a,b. Moreover, reaction of 2a,b-4a,b with hydroxylamine hydrochloride gave isoxazole derivatives 32a,b-34a,b. The newly synthesized compounds were tested for their antimicrobial activity and showed that 4-(N-ethyl-1H-indol-3-yl)-6-(p-chlorophenyl)-pyrimidine-2-amine (11b) was the most active of all the test compounds towards Candida albicans compared to the reference drug cycloheximide. Eighteen new compounds, namely pyrimidin-2(1H)-ones 5a,b-7a,b, pyrimidin-2(1H)-thiones 8a,b-10a,b and pyrimidin-2-amines 11a,b-13a,b derivatives, were tested for their in vitro antiproliferative activity against HEPG2, MCF7 and HCT-116 cancer cell lines. 4-(N-ethyl-1H-indol-3-yl)-6-(p-methoxyphenyl)-pyrimidin-2-amine (11a) was found to be highly active with IC50 of 0.7 µmol L1.Sintetizirana je serija 1-(N-supstituiranih-1H-indol-3-il)-3-arilprop-2-en-1-ona (2a,b-4a,b) i podvrgnuta reakciji s ureom, tioureom ili gvanidinom, pri čemu su nastali derivati pirimidina 5a,b–13a,b. Reakcijom 2a,b-4a,b s etil-acetoacetatom u prisutnosti baze nastali su derivati cikloheksanona 14a,b-16a,b. Njihovom reakcijom s hidrazin hidratom dobiveni su derivati indazola 17a,b-19a,b. S druge strane, reakcijom 2a,b-4a,b s određenim derivatima hidrazina, tj. s hidrazin hidratom, acetil hidrazinom, fenilhidrazinom i benzilhidrazin hidrokloridom, nastali su derivati pirazola 20a,b-31a,b. Nadalje, reakcijom 2a,b-4a,b s hidroksilamin hidrokloridom dobiveni su derivati izoksazola 32a,b-34a,b. Pripravljeni spojevi ispitani su na antimikrobno djelovanje. Pokazalo se da je 4-(N-etil-1H-indol-3-il)-6-(p-klorfenil)-pirimidin-2-amin (11b) najaktivniji spoj za Candida albicans (ATCC 10231) uz cikloheksimid kao poredbeni lijek. Testirano je antitumorsko djelovanje in vitro osamnaest novih spojeva, tj. pirimidin-2(1H)-ona 5a,b-7a,b, pirimidin-2(1H)-tiona 8a,b-10a,b i pirimidin-2-amina 11a,b-13a,b na tumorske stanice HEPG2, MCF7 i HCT-116. Najaktivniji spoj bio je 4-(N-etil-1H-indol-3-il)-6-(p-metoksifenil)-pirimidin-2-amin (11a) uz IC50 0,7 µmol L1

Intraperitoneal drain placement and outcomes after elective colorectal surgery: international matched, prospective, cohort study

Despite current guidelines, intraperitoneal drain placement after elective colorectal surgery remains widespread. Drains were not associated with earlier detection of intraperitoneal collections, but were associated with prolonged hospital stay and increased risk of surgical-site infections.Background Many surgeons routinely place intraperitoneal drains after elective colorectal surgery. However, enhanced recovery after surgery guidelines recommend against their routine use owing to a lack of clear clinical benefit. This study aimed to describe international variation in intraperitoneal drain placement and the safety of this practice. Methods COMPASS (COMPlicAted intra-abdominal collectionS after colorectal Surgery) was a prospective, international, cohort study which enrolled consecutive adults undergoing elective colorectal surgery (February to March 2020). The primary outcome was the rate of intraperitoneal drain placement. Secondary outcomes included: rate and time to diagnosis of postoperative intraperitoneal collections; rate of surgical site infections (SSIs); time to discharge; and 30-day major postoperative complications (Clavien-Dindo grade at least III). After propensity score matching, multivariable logistic regression and Cox proportional hazards regression were used to estimate the independent association of the secondary outcomes with drain placement. Results Overall, 1805 patients from 22 countries were included (798 women, 44.2 per cent; median age 67.0 years). The drain insertion rate was 51.9 per cent (937 patients). After matching, drains were not associated with reduced rates (odds ratio (OR) 1.33, 95 per cent c.i. 0.79 to 2.23; P = 0.287) or earlier detection (hazard ratio (HR) 0.87, 0.33 to 2.31; P = 0.780) of collections. Although not associated with worse major postoperative complications (OR 1.09, 0.68 to 1.75; P = 0.709), drains were associated with delayed hospital discharge (HR 0.58, 0.52 to 0.66; P &lt; 0.001) and an increased risk of SSIs (OR 2.47, 1.50 to 4.05; P &lt; 0.001). Conclusion Intraperitoneal drain placement after elective colorectal surgery is not associated with earlier detection of postoperative collections, but prolongs hospital stay and increases SSI risk

Historical Missionary Activity, Schooling, and the Reversal of Fortunes: Evidence from Nigeria

This paper shows that historical missionary activity has had a persistent effect on schooling outcomes, and contributed to a reversal of fortunes wherein historically richer ethnic groups are poorer today. Combining contemporary individual-level data with a newly constructed dataset on mission stations in Nigeria, we find that individuals whose ancestors were exposed to greater missionary activity have higher levels of schooling. This effect is robust to omitted heterogeneity, ethnicity fixed effects, and reverse causation. We find inter-generational factors and the persistence of early advantages in educational infrastructure to be key channels through which the effect has persisted. Consistent with theory, the effect of missions on current schooling is larger for population subgroups that have historically suffered disadvantages in access to education