Results | SCSU Bibliography

Jain, F., Broadbridge, C., Gherasimova, M., & Tang, H. (2023). Front Matter. In Selected topics in electronics and systems (pp. i–ix).

Dodson, J. M. (2023). Christian identity, piety, and politics in early modern England. Reformation, 28(2), 184–186. https://doi.org/10.1080/13574175.2023.2247428

Jain, F., Braodbridge, C., Gherasimova, M., & Hong, X. T. (2023). Nanotechnology in Electronics, Photonics, Biosensors and Energy Systems. https://doi.org/10.1142/13607

Darrow, B. (2023). Briefly Recalling Some Antecedents of Standards-Based Reform and Standardized Assessment in American Mathematics Education. Journal of Mathematics Education at Teachers College, 14(1), 37–42. https://doi.org/10.52214/jmetc.v14i1.10709

Smoyer, A. B. (2023). Book Review: Invisible Mothers: Unseen Yet Hypervisible After Incarceration by Garcia-Hallett, J. Affilia, 088610992311762–088610992311762. https://doi.org/10.1177/08861099231176239

Smith, C. (2023). “Sports! More than just a game.” Connecticut History, 62(1), 72–74. https://doi.org/10.5406/26395991.62.1.05

Germond-duret, C., Garland, M., Heidkamp, C. P., & Morrissey, J. (2023). Blue Economy. In International Encyclopedia of Geography (pp. 1–4).

Krings, A., D’Angelo, K., Ivery, J. M., & Murphy-Nugen, A. B. (2023). Building or Burning: Critical Reflections on Social and Political Change. Journal of Policy Practice and Research, 4(1), 5–10. https://doi.org/10.1007/s42972-023-00079-4

Curry, A. M. T., & Tomczak, S. M. (2023). Macro-Level Social Work Interventions in Schools: Poverty Simulation Programs as Professional Development Opportunities for K–12 Educators. Children & Schools, 45(2), 71–77. https://doi.org/10.1093/cs/cdad004

Kordick, C. (2023). Costa Rica after Coffee: The Co-op Era in History and Memory. Agricultural History, 97(1), 159–161. https://doi.org/10.1215/00021482-10154387

Finch, E. (2023). Experimental Status of the Chiral Magnetic Effect. Epj Web of Conferences, 276, 06018–06018. https://doi.org/10.1051/epjconf/202327606018

Njoku, A., & Marcelin, J. (2023). Covid-19 and environmental racism. In Springer eBooks (pp. 2099–2117). https://doi.org/10.1007/978-3-031-25110-8_132

Supple, B., & Yu, J. (2023). International comparison on economics impact from the COVID-19 pandemic: policy analysis. Medical Research Archives, 11(1), undefined. https://doi.org/10.18103/mra.v11i1.3502

Yang, C. (2023). Towards an integrated understanding of art, spirituality, and well-being. Journal of Management, Spirituality & Religion, undefined. https://doi.org/10.51327/knrz7112

McKee, P., Budnick, C. J., Walters, K. S., & Bind, M.-A. (2024). The role of combat exposure on drinking behavior and subjective well-being: A Rubin Causal Model approach. Psychological Trauma: Theory, Research, Practice, and Policy, 16(4), 610–617. https://doi.org/10.1037/tra0001546

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Romeiko, X. X., Zhang, X., Pang, Y., Gao, F., Xu, M., Lin, S., & Babbitt, C. (2023). A review of machine learning applications in life cycle assessment studies. Science of The Total Environment, 168969. https://doi.org/10.1016/j.scitotenv.2023.168969

Life Cycle Assessment (LCA) is a foundational method for quantitative assessment of sustainability. Increasing data availability and rapid development of machine learning (ML) approaches offer new opportunities to advance LCA. Here, we review current progress and knowledge gaps in applying ML techniques to support LCA, and identify future research directions for LCAs to better harness the power of ML. This review analyzes forty studies reporting quantitative assessment with a combination of LCA and ML methods. We found that ML approaches have been used for generating life cycle inventories, computing characterization factors, estimating life cycle impacts, and supporting life cycle interpretation. Most of the reviewed studies employed a single ML method, with artificial neural networks (ANNs) as the most frequently applied approach. Both supervised and unsupervised ML techniques were used in LCA studies. For studies using supervised ML, training datasets were derived from diverse sources, such as literature, lab experiments, existing databases, and model simulations. Over 70 % of these reviewed studies trained ML models with less than 1500 sample datasets. Although these reviewed studies showed that ML approaches help improve prediction accuracy, pattern discovery and computational efficiency, multiple areas deserve further research. First, continuous data collection and compilation is needed to support more reliable ML and LCA modeling. Second, future studies should report sufficient details regarding the selection criteria for ML models and present model uncertainty analysis. Third, incorporating deep learning models into LCA holds promise to further improve life cycle inventory and impact assessment. Finally, the complexity of current environmental challenges calls for interdisciplinary collaborative research to achieve deep integration of ML into LCA to support sustainable development.

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Benes, S., Oh, S., Peacock, J., & Yan, Z. (2023). Perception of a Peer Health Coaching Intervention Among College Students: Client Experience. American Journal of Health Education, 54(6), 429–438. https://doi.org/10.1080/19325037.2023.2253866

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Jin, S. A., Jung, H. K., Youngbin, K., & Jaeseong, L. (2023). The Escalating Competition Faced by Netflix. Journal of Accounting-Business Dan Management, 30(2), 48–48. https://doi.org/10.31966/jabminternational.v30i2.981

Judd, S. C. (2023). Al-Ḥağğāğ b. Yūsuf and the Umayyad Qurʾān in Syrian Historiography. Archimède. Archéologie et histoire ancienne, 10, 49–59. https://doi.org/10.47245/archimede.0010.ds1.04

Chrissidis, N. (2023). Aspects of Greek-Russian Relations in the Early Modern and Modern Periods (17th-20th Centuries). Annuaire de l’École Pratique Des Hautes Études (EPHE), Section Des Sciences Religieuses. Résumé Des Conférences et Travaux, (130), 365–370. https://doi.org/10.4000/asr.4498

This series of four presentations sought to examine case studies of Greek-Russian relations in the early modern and, mostly, modern period. In the choice of topics, I was guided by two considerations: first, I sought to highlight topics that, to my mind, have not yet attracted the attention they deserve in historiography. For example, although alms collections in the Russian Empire in the early modern period have been discussed repeatedly, this has not been the case until quite recently for t...

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