Modern Scientific and Technological Discoveries: A New Era of Possibilities
Impacts of Modern Scientific and Technological Discoveries on Healthcare, Energy, and Space Exploration
DOI:
https://doi.org/10.5281/Abstract
Modern scientific and technological discoveries have ushered in a new era of innovation and transformation in various human endeavors. These rapid improvements are transforming how we live, communicate, work, and experience the globe through the twenty-first century. From startling genetic discoveries to the unstoppable advancement of artificial intelligence, the panorama of human knowledge is expanding at an unparalleled rate. Artificial Intelligence and Machine Learning (AI and ML) have significantly advanced in natural language processing, computer vision, and reinforcement learning. GPT-3 technology is a significant example of advancement in the AI language model. Revolutionary breakthroughs in healthcare and medicine have permitted personalized treatments and interventions. Precision medicine, which tailors treatment procedures to an individual's genetic composition, has been made possible by decoding the human genome. Furthermore, advances in minimally invasive surgical methods, better prosthetics, and telemedicine have improved patient outcomes and access to care.
References
Alberts, B., Johnson, A., Lewis, J., Raff, M., Roberts, K., & Walter, P. (2002). Molecular biology of the cell (4th ed.). Garland Science.
Alghoul, M. A., Sulaiman, M. Y., Azmi, B. Z., Wahab, & Abd, M. (2007). Wind energy potential of Jordan. International Energy Journal, 8, 71.
Alsaad, M. A. (2013). Wind energy potential in selected areas in Jordan. Energy Conversion and Management, 65, 704–708. https://doi.org/10.1016/j.enconman.2011.12.037
Al-Soud, M. S., & Hrayshat, E. S. (2009). A 50 MW concentrating solar power plant for Jordan. Journal of Cleaner Production, 17(6), 625–635. https://doi.org/10.1016/j.jclepro.2008.11.002
Arrazola, J. M., Delgado, A., Bardhan, B. R., & Lloyd, S. (2020). Quantum-inspired algorithms in practice. Quantum, 4, 307. https://doi.org/10.22331/q-2020-08-13-307
Ashari, M., & Nayar, C. V. (1999). An optimum dispatch strategy using set points for a photovoltaic (PV)-diesel-battery hybrid power system. Solar Energy, 66(1), 1–9. https://doi.org/10.1016/S0038-092X(99)00016-X
Bajpai, P., & Dash, V. (2012). Hybrid renewable energy systems for power generation in stand-alone applications: A review. Renewable and Sustainable Energy Reviews, 16(5), 2926–2939. https://doi.org/10.1016/j.rser.2012.02.009
Broeren, J., Claesson, L., Goude, D., Rydmark, M., & Sunnerhagen, K. S. (2008). Virtual rehabilitation in an activity centre for community- dwelling persons with stroke: The possibilities of 3- dimensional computer games. Cerebrovascular Diseases, 26(3), 289–296. https://doi.org/10.1159/000149576
Bryson, B. (2003). A short history of nearly everything. Broadway Books.
Cascio, W. F., & Montealegre, R. (2016). How technology is changing work and organizations. Annual Review of Organizational Psychology and Organizational Behavior, 3(1), 349–375. https://doi.org/10.1146/annurev-orgpsych-041015-062352
Chiang, M. F., Jiang, L., Gelman, R., Du, Y. E., & Flynn, J. T. (2007). Interexpert agreement of plus disease diagnosis in retinopathy of prematurity. Archives of Ophthalmology, 125(7), 875–880. https://doi.org/10.1001/archopht.125.7.875
Dang, L., White, D. W., Gross, S., Bennett, B. D., Bittinger, M. A., Driggers, E. M., Fantin, V. R., Jang, H. G., Jin, S., Keenan, M. C., Marks, K. M., Prins, R. M., Ward, P. S., Yen, K. E., Liau, L. M., Rabinowitz, J. D., Cantley, L. C., Thompson, C. B., Vander Heiden, M. G., & Su, S. M. (2009). Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature, 462(7274), 739–744. https://doi.org/10.1038/nature08617
Dong, D., Chen, C., Li, H., & Tarn, T.-J. (2008). Quantum reinforcement learning. IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics, 38(5), 1207–1220. https://doi.org/10.1109/TSMCB.2008.925743.Elizabeth I. Suresh, K., V., Carlos, S., & Sorelle, F. (2020). Proceedings of the 37th International Conference on Machine Learning, PMLR, 119 (pp. 5491–5500). https://proceedings.mlr.press/v119/kumar20e.html
Doudna, J. A., & Charpentier, E. (2014). Genome editing. The new frontier of genome engineering with CRISPR-Cas9. Science, 346(6213), Article 1258096. https://doi.org/10.1126/science.1258096
Feynman, R. P. (1964). The Feynman lectures on physics. Addison-Wesley.
Gleick, J. (2011). The information: A history, a theory, a flood. Vintage Book Company.
Goddard, K., Roudsari, A., & Wyatt, J. C. (2014). Automation bias: Empirical results assessing influencing factors. International Journal of Medical Informatics, 83(5), 368–375. https://doi.org/10.1016/j.ijmedinf.2014.01.001
Habehh, H., & Gohel, S. (2021). Machine learning in healthcare. Current Genomics, 22(4), 291–300. https://doi.org/10.2174/1389202922666210705124359
Herman, D., Googin, C., Liu, X., Sun, Y., Galda, A., Safro, I., Pistoia, M., & Alexeev, Y. (2023). Quantum computing for finance. Nature Reviews Physics [Review], 5(8), 450–465. https://doi.org/10.1038/s42254-023-00603-1
Jakubik, J., Schöffer, J., Hoge, V., Vössing, M., & Kühl, N. (2023). An empirical evaluation of predicted outcomes as explanations in human-AI decision-making. Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 24, 353–368.
Kurose, J. F., & Ross, K. W. (2017). Computer networking: A top-down approach (7th ed.). Pearson.
Latino, M. E., & Menegoli, M. (2022). Cyber security in the food and beverage industry: A reference framework. Computers in Industry, 141, Article 103702. https://doi.org/10.1016/j.compind.2022.103702
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Emmanuel, M., & Rayudu, R. (2016). Communication technologies for smart grid applications: A survey. Journal of Network and Computer Applications, 74, 133–148. https://doi.org/10.1016/j.jnca.2016.08.012
Mubaarak, S., Zhang, D., Liu, J., Chen, Y., Wang, L., Zaki, S. A., Yuan, R., Wu, J., Zhang, Y., & Li, M. (2020). Potential techno-economic feasibility of hybrid energy systems for electrifying various consumers in Yemen. Sustainability, 13(1), 1–24. https://doi.org/10.3390/su13010228
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019). Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences, 116(44), 22071–22080. https://doi.org/10.1073/pnas.1900654116
Naiseh, M., Al-Thani, D., Jiang, N., & Ali, R. (2023). How the different explanation classes impact trust calibration: The case of clinical decision support systems. International Journal of Human-Computer Studies, 169, Article 102941. https://doi.org/10.1016/j.ijhcs.2022.102941
Preskill, J. (2018). Quantum computing in the NISQ era and beyond. Quantum, 2, 79. https://doi.org/10.22331/q-2018-08-06-79
Pressman, R. S. (2014). Software engineering: A practitioner’s approach (8th ed.). McGraw-Hill.
Rash, I., Helgason, M., Jansons, D., Mitchell, L., & Sakakibara, B. M. (2022). The influence of a virtual reality entertainment program on depressive symptoms and sedentary behaviour in inpatient stroke survivors: A research protocol for a pilot randomized controlled trial. Pilot and Feasibility Studies, 8(1), 230. https://doi.org/10.1186/s40814-022-01189-8
Sagan, C. (1994). Pale Blue dot: A vision of the human future in space. Random House.
Schwab, K. (2017). The Fourth industries Revolution. London: Portfolio penguin.
Stallings, W. (2017). Computer Organization and Architecture: Designing for performance (10th ed.). Pearson.
van der Waa, J., Nieuwburg, E., Cremers, A., & Neerincx, M. (2021). Evaluating XAI: A comparison of rule-based and example-based explanations. Artificial Intelligence, 291, Article 103404. https://doi.org/10.1016/j.artint.2020.103404
Weaver, D. T. (2013). Telemedicine for retinopathy of prematurity. Current Opinion in Ophthalmology, 24(5), 425–431. https://doi.org/10.1097/ICU.0b013e3283645b41
Welin, L., & Xiaobin, Z. (2014). Simulation of the smart grid communications: Challenges, techniques, and future trends. Computer Electronics Engineering, 40(1), 270–288.
Wysocki, O., Davies, J. K., Vigo, M., Armstrong, A. C., Landers, D., Lee, R., & Freitas, A. (2023). Assessing the communication gap between AI models and healthcare professionals: Explainability, utility and trust in AI-driven clinical decision-making. Artificial Intelligence, 316. https://doi.org/10.1016/j.artint.2022.103839
Xie, Q., Luong, M.-T., Hovy, E., & Le, Q. V. (2020). Self-training with noisy student improves imagenet classification. IEEE/CVF. Conference on Computer Vision and Pattern Recognition (pp. 10687–10698).
Yaser, A., Ahmad, B., & Muhammad Al, O. (2009). Solar potential energy in Jordan, ICEGES
Zhang, D., Festag, A., A., Fettweis, G., & G. (2017). Performance of Generalized Frequency Division Multiplexing Based Physical Layer in Vehicular Communication in IEEE. http://ieeexplore.ieee.org/document/7968507/