Search Results - [acs] data analysis: algorithms and implementation
-
1
Authors: et al.
Source: International Journal of General Medicine; Jun2025, Vol. 18, p2957-2972, 16p
-
2
Authors: Zhu, Wei
Source: Molecular & Cellular Biomechanics; 2025, Vol. 22 Issue 4, p1-17, 17p
Subject Terms: BIOSENSORS, BIOMECHANICS, DATA analysis, ATHLETIC ability, BIOMARKERS, PREVENTION of injury, MACHINE learning, SPORTS medicine
-
3
Authors: et al.
Source: Veterinary Medicine & Science. Nov2025, Vol. 11 Issue 6, p1-20. 20p.
Subjects: Lumpy skin disease, Deep learning, Ensemble learning, Veterinary clinical pathology, Health of cattle, Transformer models, Machine learning
HTML Full Text PDF Full Text -
4
Authors:
Source: Molecular & Cellular Biomechanics; 2024, Vol. 21 Issue 3, p1-19, 19p
-
5
Authors: et al.
Source: Veterinary Oncology (3004-9814). 11/14/2025, Vol. 2 Issue 1, p1-11. 11p.
HTML Full Text PDF Full Text -
6
Authors: et al.
Source: Open Veterinary Journal. 2025, Vol. 15 Issue 5, p1880-1894. 15p.
Subjects: Avian influenza A virus, Poultry diseases, Data analytics, Artificial intelligence, Poultry industry
PDF Full Text -
7
Authors:
Source: Water (20734441); Nov2025, Vol. 17 Issue 22, p3268, 56p
-
8
Authors: et al.
Source: Nursing & health sciences [Nurs Health Sci] 2024 Dec; Vol. 26 (4), pp. e70014.
Publication Type: Journal Article
Journal Info: Publisher: Blackwell Science Asia Country of Publication: Australia NLM ID: 100891857 Publication Model: Print Cited Medium: Internet ISSN: 1442-2018 (Electronic) Linking ISSN: 14410745 NLM ISO Abbreviation: Nurs Health Sci Subsets: MEDLINE
MeSH Terms: Persons with Disabilities*/statistics & numerical data , Home Care Services*/statistics & numerical data , Home Care Services*/standards , Latent Class Analysis* , Comorbidity*, Humans ; Male ; Female ; Cross-Sectional Studies ; Aged ; China/epidemiology ; Aged, 80 and over ; Middle Aged
-
9
Authors: et al.
Source: Veterinary Pathology. Nov2025, Vol. 62 Issue 6, p867-877. 11p.
HTML Full Text PDF Full Text -
10
Authors:
Source: Environmental monitoring and assessment [Environ Monit Assess] 2025 Dec 10; Vol. 198 (1), pp. 33. Date of Electronic Publication: 2025 Dec 10.
Publication Type: Journal Article; Review
Journal Info: Publisher: Springer Country of Publication: Netherlands NLM ID: 8508350 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-2959 (Electronic) Linking ISSN: 01676369 NLM ISO Abbreviation: Environ Monit Assess Subsets: MEDLINE
-
11
Authors:
Source: Microorganisms; Jul2025, Vol. 13 Issue 7, p1473, 31p
-
12
Authors: et al.
Source: Molecular genetics and genomics : MGG [Mol Genet Genomics] 2025 Dec 05; Vol. 300 (1), pp. 111. Date of Electronic Publication: 2025 Dec 05.
Publication Type: Journal Article; Review
Journal Info: Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 101093320 Publication Model: Electronic Cited Medium: Internet ISSN: 1617-4623 (Electronic) Linking ISSN: 16174623 NLM ISO Abbreviation: Mol Genet Genomics Subsets: MEDLINE
-
13
Authors:
Source: Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2025; Vol. 2952, pp. 335-367.
Publication Type: Journal Article
Journal Info: Publisher: Humana Press Country of Publication: United States NLM ID: 9214969 Publication Model: Print Cited Medium: Internet ISSN: 1940-6029 (Electronic) Linking ISSN: 10643745 NLM ISO Abbreviation: Methods Mol Biol Subsets: MEDLINE
-
14
Authors:
Source: Big Data and Cognitive Computing, Vol 3, Iss 3, p 36 (2019)
Subject Terms: Air Conditioners (AC), Artificial Neural Network (ANN), big data analysis, cooling demand, energy consumption, Demand Response (DR), Load Forecasting (LF), Levenberg–Marquardt Algorithm (LMA), Technology
Relation: https://www.mdpi.com/2504-2289/3/3/36; https://doaj.org/toc/2504-2289; https://doaj.org/article/9cd8c56c708648688fe3cd20c5b7c2c7
-
15
Authors: et al.
Source: Nature reviews. Chemistry [Nat Rev Chem] 2025 Sep; Vol. 9 (9), pp. 601-616. Date of Electronic Publication: 2025 Jul 18.
Publication Type: Journal Article; Review
Journal Info: Publisher: Springer Nature Country of Publication: England NLM ID: 101703631 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2397-3358 (Electronic) Linking ISSN: 23973358 NLM ISO Abbreviation: Nat Rev Chem Subsets: PubMed not MEDLINE; MEDLINE
-
16
Authors: et al.
Source: Diagnostics (2075-4418); Jun2024, Vol. 14 Issue 11, p1103, 64p
Subject Terms: MACHINE learning, ARTIFICIAL intelligence, PULMONARY embolism, CARDIAC pacing, SCIENTIFIC literature, BIG data
-
17
Authors:
Source: Revista CEA; Vol. 9 No. 20 (2023); e2448 ; Revista CEA; Vol. 9 Núm. 20 (2023); e2448 ; 2422-3182 ; 2390-0725
Subject Terms: Big data, supply chain, Logistics 4.0, technology, Industry 4.0, cadenas de suministros, logística 4.0, tecnología, industria 4.0
File Description: application/pdf; application/zip; text/xml; text/html
Relation: https://revistas.itm.edu.co/index.php/revista-cea/article/view/2448/2900; https://revistas.itm.edu.co/index.php/revista-cea/article/view/2448/2948; https://revistas.itm.edu.co/index.php/revista-cea/article/view/2448/2949; https://revistas.itm.edu.co/index.php/revista-cea/article/view/2448/2959; https://revistas.itm.edu.co/index.php/revista-cea/article/view/2448/2901; Acevedo Meneses, J. P., Robledo Giraldo, S., y Sepúlveda Angarita, M. Z. (2020). Subáreas de internacionalización de emprendimientos: una revisión bibliográfica. Económicas CUC, 42(1), 249–268. https://doi.org/10.17981/econcuc.42.1.2021.org.7; Addo-Tenkorang, R., y Helo, P. T. (2016). Big data applications in operations/supply-chain management: A literature review. Computers & Industrial Engineering, 101, 528–543. https://doi.org/10.1016/j.cie.2016.09.023; Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., y Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, 182, 113–131. https://doi.org/10.1016/j.ijpe.2016.08.018; Aria, M., y Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007; Aria, M., Misuraca, M., y Spano, M. (2020). Mapping the Evolution of Social Research and Data Science on 30 Years of Social Indicators Research. Social indicators research, 149(3), 803–831. https://doi.org/10.1007/s11205-020-02281-3; Arunachalam, D., Kumar, N., y Kawalek, J. P. (2018). Understanding Big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part E: Logistics and Transportation Review, 114, 416–436. https://doi.org/10.1016/j.tre.2017.04.001; Aslam, S., Michaelides, M. P., y Herodotou, H. (2020). Internet of Ships: A Survey on Architectures, Emerging Applications, and Challenges. IEEE Internet of Things Journal, 7(10), 9714–27. https://doi.org/10.1109/JIOT.2020.2993411; Bar-Ilan, J. (2008). Which h-index? — A comparison of WoS, Scopus and Google Scholar. Scientometrics, 74, 257–271. https://doi.org/10.1007/s11192-008-0216-y; Barrera Rubaceti, N. A., Robledo Giraldo, S., y Sepulveda, M. Z. (2022). Una revisión bibliográfica del Fintech y sus principales subáreas de estudio. Económicas CUC, 43(1), 83-100. https://doi.org/10.17981/econcuc.43.1.2022.Econ.4; Bastian, M., Heymann, S., y Jacomy, M. (2009). Gephi: an open source software for exploring and manipulating networks. En International AAAI Conference on Weblogs and Social Media. https://gephi.org/users/publications/; Benabdellah, A. C., Benghabrit, A., Bouhaddou, I., y Zemmouri, E. M. (2016). Big data for supply chain management: Opportunities and challenges. En 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), 1–6. https://doi.org/10.1109/AICCSA.2016.7945828; Blondel, V. D., Guillaume, J.-L., Lambiotte, R., y Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008; Bond, M., Zawacki-Richter, O., y Nichols, M. (2019). Revisiting five decades of educational technology research: A content and authorship analysis of the British Journal of Educational Technology. British Journal of Educational Technology. https://doi.org/10.1111/bjet.12730; Boone, T., Ganeshan, R., Jain, A., y Sanders, N. R. (2019). Forecasting sales in the supply chain: Consumer analytics in the Big data era. International journal of forecasting, 35(1), 170–180. https://doi.org/10.1016/j.ijforecast.2018.09.003; Boyd, D. y Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118X.2012.678878; Brandon-Jones, E., Squire, B., Autry, C. W., y Petersen, K. J. (2014). A contingent resource-based perspective of supply chain resilience and robustness. Journal of Supply Chain Management, 50(3), 55–73. https://doi.org/10.1111/jscm.12050; Brinch, M., Stentoft, J., Jensen, J. K., y Rajkumar, C. (2018). Practitioners understanding of big data and its applications in supply chain management. The International Journal of Logistics Management, 29(2), 555–574. https://doi.org/10.1108/IJLM-05-2017-0115; Buitrago, S., Duque, P. L., y Robledo, S. (2020). Branding Corporativo: una revisión bibliográfica. ECONÓMICAS CUC, 41(1), 143–162. https://doi.org/10.17981/econcuc.41.1.2020.Org.1; Castellano, R., Fiore, U., Musella, G., Perla, F., Punzo, G., Risitano, M., Sorrentino, A., y Zanetti, P. (2019). Do Digital and Communication Technologies Improve Smart Ports? A Fuzzy DEA Approach. IEEE Transactions on Industrial Informatics, 15(10), 5674–5681. https://doi.org/10.1109/TII.2019.2927749; Chalmeta, R., y Santos-deLeón, N. J. (2020). Sustainable Supply Chain in the Era of Industry 4.0 and Big data: A Systematic Analysis of Literature and Research. Sustainability, 12(10), 4108. https://doi.org/10.3390/su12104108; Chen, D. Q., Preston, D. S., y Swink, M. (2015). How the Use of Big data Analytics Affects Value Creation in Supply Chain Management. Journal of Management Information Systems, 32(4), 4–39. https://doi.org/10.1080/07421222.2015.1138364; Chen, H., Chiang, R. H. L., y Storey, V. C. (2012). Business Intelligence and Analytics: From Big data to Big Impact. MIS Quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503; Choi, T.-M., y Chen, Y. (2021). Circular supply chain management with large scale group decision making in the big data era: The macro-micro model. Technological forecasting and social change, 169, 120791. https://doi.org/10.1016/j.techfore.2021.120791; Christopher, M., y Peck, H. (2004). Building the Resilient Supply Chain. The International Journal of Logistics Management, 15(2), 1–14. https://doi.org/10.1108/09574090410700275; Corrêa, J. S., Sampaio, M., y Barros, R. de C. (2020). An Exploratory Study on Emerging Technologies Applied to Logistics 4.0. Gestão & Produção, 27(3), e5468. https://doi.org/10.1590/0104-530X5468-20; Cox, M., y Ellsworth, D. (1997). Application-Controlled Demand Paging for Out-of-Core Visualization. Proceedings. Visualization '97, 235-244. https://doi.org/10.1109/VISUAL.1997.663888; Demiroz, F., y Haase, T. W. (2019). The concept of resilience: a bibliometric analysis of the emergency and disaster management literature. Local Government Studies, 45(3), 308–327. https://doi.org/10.1080/03003930.2018.1541796; Dennehy, D., Oredo, J., Spanaki, K., Despoudi, S., y Fitzgibbon, M. (2021). Supply chain resilience in mindful humanitarian aid organizations: the role of Big data analytics. International Journal of Operations y Production Management, 41(9), 1417–1441. https://doi.org/10.1108/IJOPM-12-2020-0871; Devaraj, S., Krajewski, L., y Wei, J. C. (2007). Impact of eBusiness technologies on operational performance: The role of production information integration in the supply chain. Journal of Operations Management, 25(6), 1199–1216. https://doi.org/10.1016/j.jom.2007.01.002; Dubey, R., Gunasekaran, A., Childe, S. J., Luo, Z., Wamba, S. F., Roubaud, D., y Foropon, C. (2018). Examining the role of Big data and predictive analytics on collaborative performance in context to sustainable consumption and production behaviour. Journal of cleaner production, 196, 1508–1521. https://doi.org/10.1016/j.jclepro.2018.06.097; Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., Luo, Z., Wamba, S. F., y Roubaud, D. (2019). Can big data and predictive analytics improve social and environmental sustainability? Technological forecasting and social change, 144, 534–545. https://doi.org/10.1016/j.techfore.2017.06.020; Duque, P., Meza, O. E., Giraldo, D., y Barreto, K. (2021). Economía Social y Economía Solidaria: un análisis bibliométrico y revisión de literatura. REVESCO. Revista de Estudios Cooperativos, 138, e75566. https://doi.org/10.5209/reve.75566; Duque, P., Trejos, D., Hoyos, O., y Chica Mesa, J. C. (2021). Finanzas corporativas y sostenibilidad: un análisis bibliométrico e identificación de tendencias. Semestre Económico, 24(56), 25–51. https://doi.org/10.22395/seec.v24n56a1; Duque-Hurtado, P., Samboni-Rodriguez, V., Castro-Garcia, M., Montoya-Restrepo, L. A., y Montoya-Restrepo, I. A. (2020). Neuromarketing:su estado actual y perspectivas de investigación. Estudios Gerenciales, 36(157), 525-539. https://doi.org/10.18046/j.estger.2020.157.3890; Echchakoui, S. (2020). Why and how to merge Scopus and Web of Science during bibliometric analysis: the case of sales force literature from 1912 to 2019. Journal of Marketing Analytics, 8, 165–184. https://doi.org/10.1057/s41270-020-00081-9; Elgendy, A. F. (2021). The mediating effect of big data analysis on the process orientation and information system software to improve supply chain process in Saudi Arabian industrial organizations. International Journal of Data and Network Science, 1(2), 135-142. https://doi.org/10.5267/j.ijdns.2021.1.003; Elgendy, N., Elragal, A., y Päivärinta, T. (2022). DECAS: A modern data-driven decision theory for big data and analytics. Journal of Decision Systems, 31(4), 337-373. https://doi.org/10.1080/12460125.2021.1894674; Feng, J. C.-X., y Kusiak, A. (2006). Data mining applications in engineering design, manufacturing and logistics. International Journal of Production Research, 44(14), 2689-2694. https://doi.org/10.1080/00207540600681072; Fernández, P., Suárez, J. P., Trujillo, A., Domínguez, C., y Santana, J. M. (2018). 3D-Monitoring Big Geo Data on a Seaport Infrastructure Based on FIWARE. Journal of Geographical Systems, 20, 139-157. https://doi.org/10.1007/s10109-018-0269-2; Fosso Wamba, S., y Akter, S. (2015). Big data analytics for supply chain management: A literature review and research agenda. En Lecture Notes in Business Information Processing, (pp. 61–72). Springer International Publishing. https://doi.org/10.1007/978-3-319-24626-0_5; Fosso Wamba, S., Gunasekaran, A., Akter, S., Ren, S. J.-F., Dubey, R., y Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009; Gawankar, S. A., Gunasekaran, A., y Kamble, S. (2020). A study on investments in the big data-driven supply chain, performance measures and organisational performance in Indian retail 4.0 context. International Journal of Production Research, 58(5), 1574–1593. https://doi.org/10.1080/00207543.2019.1668070; George, G., Haas, M. R., y Pentland, A. (2014). Big data and Management. Academy of Management Journal, 57(2), 321–326. https://doi.org/10.5465/amj.2014.4002; Ghalehkhondabi, I., Ahmadi, E., y Maihami, R. (2020). An overview of big data analytics application in supply chain management published in 2010-2019. Production, 30, e20190140. https://doi.org/10.1590/0103-6513.20190140; Gholizadeh, H., Fazlollahtabar, H., y Khalilzadeh, M. (2020). A robust fuzzy stochastic programming for sustainable procurement and logistics under hybrid uncertainty using Big data. Journal of Cleaner Production, 258, 120640. https://doi.org/10.1016/j.jclepro.2020.120640; Gokalp, M. O., Kayabay, K., Akyol, M. A., Eren, P. E., y Koçyiğit, A. (2016). Big data for industry 4.0: A conceptual framework. En 2016 international conference on computational science and computational intelligence (CSCI) (pp. 431-434). https://doi.org/10.1109/CSCI.2016.0088; Gölgeci, I., y Kuivalainen, O. (2020). Does social capital matter for supply chain resilience? The role of absorptive capacity and marketing-supply chain management alignment. Industrial Marketing Management, 84, 63–74. https://doi.org/10.1016/j.indmarman.2019.05.006; Gubbi, J., Buyya, R., Marusic, S., y Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660. https://doi.org/10.1016/j.future.2013.01.010; Gunasekaran, A., Papadopoulos, T., Dubey, R., Fosso Wamba, S., Childe, S. J., Hazen, B., y Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308–317. https://doi.org/10.1016/j.jbusres.2016.08.004; Gupta, M., y George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049–1064. https://doi.org/10.1016/j.im.2016.07.004; Gurzki, H., y Woisetschläger, D. M. (2017). Mapping the luxury research landscape: A bibliometric citation analysis. Journal of Business Research, 77, 147–166. https://doi.org/10.1016/j.jbusres.2016.11.009; He, B., y Yin, L. (2021). Prediction Modelling of Cold Chain Logistics Demand Based on Data Mining Algorithm. Mathematical Problems in Engineering. https://doi.org/10.1155/2021/3421478; Hofmann, E., Strewe, U. M., y Bosia, N. (2017). Supply Chain Finance and Blockchain Technology: The Case of Reverse Securitisation. Springer International Publishing. https://doi.org/10.1007/978-3-319-62371-9; Huang, S. (2021). Research on basic mathematical models and algorithms of large-scale supply chain design under the background of Big data. En Xu, Z., Parizi, R. M., Loyola-González, O., Zhang, X. (eds) Cyber Security Intelligence and Analytics. CSIA 2021. Advances in Intelligent Systems and Computing (290–297). Springer International Publishing. https://doi.org/10.1007/978-3-030-70042-3_42; Janssen, M., van der Voort, H., y Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of Business Research, 70, 338-345. https://doi.org/10.1016/j.jbusres.2016.08.007; Kittichotsatsawat, Y., Jangkrajarng, V., y Tippayawong, K. Y. (2021). Enhancing Coffee Supply Chain towards Sustainable Growth with Big data and Modern Agricultural Technologies. Sustainability, 13(8), 4593. https://doi.org/10.3390/su13084593; Koot, M., Mes, M. R. K., y Iacob, M. E. (2021). A systematic literature review of supply chain decision making supported by the Internet of Things and Big data Analytics. Computers & Industrial Engineering, 154, 107076. https://doi.org/10.1016/j.cie.2020.107076; Kusi-Sarpong, S., Orji, I. J., Gupta, H., y Kunc, M. (2021). Risks associated with the implementation of big data analytics in sustainable supply chains. Omega, 105, 102502. https://doi.org/10.1016/j.omega.2021.102502; Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity and Variety. META Group.; Li, J. (2019). Optimal design of transportation distance in logistics supply chain model based on data mining algorithm. Cluster Computing, 22(Suppl 2), 3943 - 3952. https://doi.org/10.1007/s10586-018-2544-x; Lin, C., y Lin, M. (2019). Application of Big data in a Multicategory Product-Service System for Global Logistics Support. IEEE Engineering Management Review, 47(4), 108–118. https://doi.org/10.1109/EMR.2019.2953027; Maheshwari, S., Gautam, P., y Jaggi, C. K. (2021). Role of Big data Analytics in supply chain management: current trends and future perspectives. International Journal of Production Research, 59(6), 1875–1900. https://doi.org/10.1080/00207543.2020.1793011; Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., y Byers, A. H. (2015, julio 24). Big data: The next frontier for innovation, competition, and productivity. McKinsey & Company. https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/big-data-the-next-frontier-for-innovation; Mikalef, P., Krogstie, J., Pappas, I. O., y Pavlou, P. (2020). Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information & Management, 57(2), 103169. https://doi.org/10.1016/j.im.2019.05.004; Miller, J. W., Ganster, D. C., y Griffis, S. E. (2018). Leveraging Big data to develop supply chain management theory: The case of panel data. Journal of Business Logistics, 39(3), 182–202. https://doi.org/10.1111/jbl.12188; Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., y Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big data, 2(1), 1-21. https://doi.org/10.1186/s40537-014-0007-7; Narwane, V. S., Raut, R. D., Yadav, Y. S., Cheikhrouhou, N., Narkhede, B. E., y Priyadarshinee, P. (2021). The role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries. Journal of Enterprise Information Management, 34(5), 1452-1480. https://doi.org/10.1108/JEIM-11-2020-0463; Nguyen, T., Zhou, L., Spiegler, V., Ieromonachou, P., y Lin, Y. (2018). Big data analytics in supply chain management: A state-of-the-art literature review. Computers & operations research, 98, 254–264. https://doi.org/10.1016/j.cor.2017.07.004; Nozari, H., Fallah, M., Kazemipoor, H., y Najafi, S. E. (2021). Big data analysis of IoT-based supply chain management considering FMCG industries. Business Informatics, 15(1), 78–96. https://doi.org/10.17323/2587-814x.2021.1.78.96; Ogbuke, N. J., Yusuf, Y. Y., Dharma, K., y Mercangoz, B. A. (2020). Big data supply chain analytics: ethical, privacy and security challenges posed to business, industries and society. Production Planning & Control, 33(2-3), 123-137. https://doi.org/10.1080/09537287.2020.1810764; Oncioiu, I., Bunget, O. C., Türkeș, M. C., Căpușneanu, S., Topor, D. I., Tamaș, A. S., Rakoș, I.-S., y Hint, M. Ș. (2019). The Impact of Big data Analytics on Company Performance in Supply Chain Management. Sustainability, 11(18), 4864. https://doi.org/10.3390/su11184864; Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2020). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. The BMJ, 372(71). https://doi.org/10.1136/bmj.n71; Panetto, H., Iung, B., Ivanov, D., Weichhart, G., y Xiaofan, W. (2019). Challenges for the cyber-physical manufacturing enterprises of the future. Annual reviews in control, 47, 200–213. https://doi.org/10.1016/j.arcontrol.2019.02.002; Papadopoulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S. J., y Fosso-Wamba, S. (2017). The role of Big data in explaining disaster resilience in supply chains for sustainability. Journal of cleaner production, 142(Part. 2), 1108–1118. https://doi.org/10.1016/j.jclepro.2016.03.059; Ramos-Enríquez, V., Duque, P., y Vieira Salazar, J. A. (2021). Responsabilidad Social Corporativa y Emprendimiento: evolución y tendencias de investigación. Desarrollo Gerencial, 13(1), 1–34. https://doi.org/10.17081/dege.13.1.4210; Raut, R. D., Yadav, V.S., Cheikhrouhou, N., Narvwanw, V. S., y Narkhede, B. E. (2021). Big data analytics: Implementation challenges in Indian manufacturing supply chains. Computers in Industry, 125, 103368. https://doi.org/10.1016/j.compind.2020.103368; Razaghi, S., y Shokouhyar, S. (2021). Impacts of big data analytics management capabilities and supply chain integration on global sourcing: a survey on firm performance. The Bottom Line, 34(2), 198–223. https://doi.org/10.1108/BL-11-2020-0071; Rezaei, M., Akbarpour Shirazi, M., y Karimi, B. (2017). IoT-based framework for performance measurement: A real-time supply chain decision alignment. Industrial Management & Data Systems, 117(4), 688–712. https://doi.org/10.1108/imds-08-2016-0331; Robledo, S., Osorio, G., y Lopez, C. (2014). Networking en pequeña empresa: una revisión bibliográfica utilizando la teoria de grafos. Revista vínculos, 11(2), 6–16. https://doi.org/10.14483/2322939X.9664; Sahay, B. S., y Ranjan, J. (2008). Real time business intelligence in supply chain analytics. Information Management & Computer Security, 16(1), 28-48. https://doi.org/10.1108/09685220810862733; Sangari, M. S., y Razmi, J. (2015). Business intelligence competence, agile capabilities, and agile performance in supply chain: An empirical study. International Journal of Logistics Management, 26(2), 356-380. https://doi.org/10.1108/IJLM-01-2013-0012; Schaer, O., Kourentzes, N., y Fildes, R. (2019). Demand forecasting with user-generated online information. International Journal of Forecasting, 35(1), 197–212. https://doi.org/10.1016/j.ijforecast.2018.03.005; Schoenherr, T., y Speier-Pero, C. (2015). Data science, predictive analytics, and Big data in supply chain management: Current state and future potential. Journal of Business Logistics, 36(1), 120–132. https://doi.org/10.1111/jbl.12082; Shen, B., y Chan, H.-L. (2017). Forecast Information Sharing for Managing Supply Chains in the Big data Era: Recent Development and Future Research. Asia-Pacific Journal of Operational Research, 34(01), 1740001. https://doi.org/10.1142/S0217595917400012; Sheng, M. L., y Saide, S. (2021). Supply chain survivability in crisis times through a viable system perspective: Big data, knowledge ambidexterity, and the mediating role of virtual enterprise. Journal of Business Research, 137, 567–578. https://doi.org/10.1016/j.jbusres.2021.08.041; Sodero, A., Jin, Y. H., y Barratt, M. (2019). The social process of Big data and predictive analytics use for logistics and supply chain management. International Journal of Physical Distribution & Logistics Management, 49(7), 706–726. https://doi.org/10.1108/IJPDLM-01-2018-0041; Stock, J. R., y Boyer, S. L. (2009). Developing a consensus definition of supply chain management: A qualitative study. International Journal of Physical Distribution & Logistics, 39(8), 690-711. https://doi.org/10.1108/09600030910996323; Sun, S., Cegielski, C. G., Jia, L., y Hall, D. J. (2018). Understanding the factors affecting the organizational adoption of big data. Journal of Computer Information Systems, 58(3), 193-203. https://doi.org/10.1080/08874417.2016.1222891; Syntetos, A. A., Babai, Z., Boylan, J. E., Kolassa, S., y Nikolopoulos, K. (2016). Supply chain forecasting: Theory, practice, their gap and the future. European Journal of Operational Research, 252(1), 1-26. https://doi.org/10.1016/j.ejor.2015.11.010; Talwar, S., Kaur, P., Fosso Wamba, S., y Dhir, A. (2021). Big data in operations and supply chain management: a systematic literature review and future research agenda. International Journal of Production Research, 59(11), 3509–3534. https://doi.org/10.1080/00207543.2020.1868599; Tani, M., Papaluca, O., y Sasso, P. (2018). The System Thinking Perspective in the Open-Innovation Research: A Systematic Review. Journal of Open Innovation: Technology, Market, and Complexity, 4(3), 38. https://doi.org/10.3390/joitmc4030038; Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., y Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502-517. https://doi.org/10.1016/j.jbusres.2020.09.009; Trkman, P., McCormack, K., de Oliveira, M. P. V., y Ladeira, M. B. (2010). The impact of business analytics on supply chain performance. Decision support systems, 49(3), 318–327. https://doi.org/10.1016/j.dss.2010.03.007; Tu, M. (2018). An exploratory study of Internet of Things (IoT) adoption intention in logistics and supply chain management. International Journal of Logistics Management, 29(1), 131–151. https://doi.org/10.1108/ijlm-11-2016-0274; Uckelmann, D., Harrison, M., y Michahelles, F. (2011). An Architectural Approach Towards the Future Internet of Things. En D. Uckelmann, M. Harrison, y F. Michahelles (Eds.), Architecting the Internet of Things (pp. 1–24). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-19157-2_1; Valencia-Hernandez, D. S., Robledo, S., Pinilla, R., Duque-Méndez, N. D., y Olivar-Tost, G. (2020). SAP Algorithm for Citation Analysis: An improvement to Tree of Science. Ingeniería e Investigación, 40(1), 45–49. https://doi.org/10.15446/ing.investig.v40n1.77718; Vassakis, K., Petrakis, E., y Kopanakis, I. (2018). Big data Analytics: Applications, Prospects and Challenges. En G. Skourletopoulos, G. Mastorakis, C. X. Mavromoustakis, C. Dobre, y E. Pallis (Eds.), Mobile Big data: A Roadmap from Models to Technologies (pp. 3–20). Springer International Publishing. https://doi.org/10.1007/978-3-319-67925-9_1; Vera-Baceta, M-. A., Thelwall, M., y Kousha, K. (2019). Web of Science and Scopus language coverage. Scientometrics, 121, 1803–1813. https://doi.org/10.1007/s11192-019-03264-z; Verdouw, C. N., Wolfert, J., Beulens, A. J. M., y Rialland, A. (2016). Virtualization of food supply chains with the internet of things. Journal of Food Engineering, 176, 128–136. https://doi.org/10.1016/j.jfoodeng.2015.11.009; Waller, M. A., y Fawcett, S. E. (2013). Data science, predictive analytics, and Big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77–84. https://doi.org/10.1111/jbl.12010; Wallis, W. D. (2007). A Beginner’s Guide to Graph Theory. Springer. Ed. https://doi.org/10.1007/978-0-8176-4580-9; Wang, G., Gunasekaran, A., Ngai, E. W. T., y Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98–110. https://doi.org/10.1016/j.ijpe.2016.03.014; Winkelhaus, S., y Grosse, E. H. (2020). Logistics 4.0: A Systematic review towards a new logistics system. International Journal of Production Research, 58(1), 18-43. https://doi.org/10.1080/00207543.2019.1612964; Witkowski, K. (2017). Internet of Things, Big data, Industry 4.0 – Innovative Solutions in Logistics and Supply Chains Management. Procedia Engineering, 182, 763–769. https://doi.org/10.1016/j.proeng.2017.03.197; Wrobel-Lachowska, M., Wisniewski, Z., y Polak-Sopinska, A. (2018). The Role of the Lifelong Learning in Logistics 4.0. En Andre, T. (eds). Advances in Human Factors in Training, Education, and Learning Sciences. AHFE 2017. Advances in Intelligent Systems and Computing (pp. 402-409). Springer. https://doi.org/10.1007/978-3-319-60018-5_39; Zhang, J., y Luo, Y. (2017). Degree Centrality, Betweenness Centrality, and Closeness Centrality in Social Network. En Atlantis Press (Ed.), Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017) (pp. 300–303). https://doi.org/10.2991/msam-17.2017.68; Zhong, R. Y., Xu, C., Chen, C., y Huang, G. Q. (2017). Big data Analytics for Physical Internet-based intelligent manufacturing shop floors. International Journal of Production Research, 55(9), 2610–2621. https://doi.org/10.1080/00207543.2015.1086037; Zhu, J., y Liu, W. (2020). A tale of two databases: the use of Web of Science and Scopus in academic papers. Scientometrics, 123, 321–335. https://doi.org/10.1007/s11192-020-03387-8; Zissis, D. (2017). Intelligent Security on the Edge of the Cloud. En 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC) (pp. 1066-1070). IEEE. https://doi.org/10.1109/ice.2017.8279999; Zupic, I., y Čater, T. (2015). Bibliometric Methods in Management and Organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629; Zuschke, N. (2020). An analysis of process-tracing research on consumer decision-making. Journal of Business Research, 111, 305–320. https://doi.org/10.1016/j.jbusres.2019.01.028
-
18
Authors:
Source: World journal of microbiology & biotechnology [World J Microbiol Biotechnol] 2025 Jul 28; Vol. 41 (8), pp. 278. Date of Electronic Publication: 2025 Jul 28.
Publication Type: Journal Article; Review
Journal Info: Publisher: Springer Country of Publication: Germany NLM ID: 9012472 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-0972 (Electronic) Linking ISSN: 09593993 NLM ISO Abbreviation: World J Microbiol Biotechnol Subsets: In Process; MEDLINE
-
19
Authors: et al.
Source: Bioengineering & Translational Medicine; May2025, Vol. 10 Issue 3, p1-17, 17p
-
20
Authors:
Source: Genes & nutrition [Genes Nutr] 2025 Nov 21. Date of Electronic Publication: 2025 Nov 21.
Publication Type: Journal Article; Review
Journal Info: Publisher: BioMed Central Country of Publication: Germany NLM ID: 101280108 Publication Model: Print-Electronic Cited Medium: Print ISSN: 1555-8932 (Print) Linking ISSN: 15558932 NLM ISO Abbreviation: Genes Nutr
Full Text Finder
Nájsť tento článok vo Web of Science