Relevant bibliographies by topics / Cross-scale knowledge

Academic literature on the topic 'Cross-scale knowledge'

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Published: 14 March 2023

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Journal articles on the topic "Cross-scale knowledge"

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Li, Zhi, Zhefeng Wang, Zhicheng Wei, Xiangguang Zhou, Yijun Wang, Baoxing Huai, Qi Liu, Nicholas Jing Yuan, Renbin Gong, and Enhong Chen. "Cross-Oilfield Reservoir Classification via Multi-Scale Sensor Knowledge Transfer." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4215–23. http://dx.doi.org/10.1609/aaai.v35i5.16545.

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Reservoir classification is an essential step for the exploration and production process in the oil and gas industry. An appropriate automatic reservoir classification will not only reduce the manual workloads of experts, but also help petroleum companies to make optimal decisions efficiently, which in turn will dramatically reduce the costs. Existing methods mainly focused on generating reservoir classification in a single geological block but failed to work well on a new oilfield block. Indeed, how to transfer the subsurface characteristics and make accurate reservoir classification across the geological oilfields is a very important but challenging problem. To that end, in this paper, we present a focused study on the cross-oilfield reservoir classification task. Specifically, we first propose a Multi-scale Sensor Extraction (MSE) to extract the multi-scale feature representations of geological characteristics from multivariate well logs. Furthermore, we design an encoder-decoder module, Specific Feature Learning (SFL), to take advantage of specific information of both oilfields. Then, we develop a Knowledge-Attentive Transfer (KAT) module to learn the feature-invariant representation and transfer the geological knowledge from a source oilfield to a target oilfield. Finally, we evaluate our approaches by conducting extensive experiments with real-world industrial datasets. The experimental results clearly demonstrate the effectiveness of our proposed approaches to transfer the geological knowledge and generate the cross-oilfield reservoir classifications.
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Ahmad Khan, Aatif, and Sanjay Kumar Malik. "Assessing Large-Scale, Cross-Domain Knowledge Bases for Semantic Search." Mehran University Research Journal of Engineering and Technology 39, no. 3 (July 1, 2020): 595–602. http://dx.doi.org/10.22581/muet1982.2003.14.

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Semantic Search refers to set of approaches dealing with usage of Semantic Web technologies for information retrieval in order to make the process machine understandable and fetch precise results. Knowledge Bases (KB) act as the backbone for semantic search approaches to provide machine interpretable information for query processing and retrieval of results. These KB include Resource Description Framework (RDF) datasets and populated ontologies. In this paper, an assessment of the largest cross-domain KB is presented that are exploited in large scale semantic search and are freely available on Linked Open Data Cloud. Analysis of these datasets is a prerequisite for modeling effective semantic search approaches because of their suitability for particular applications. Only the large scale, cross-domain datasets are considered, which are having sizes more than 10 million RDF triples. Survey of sizes of the datasets in triples count has been depicted along with triples data format(s) supported by them, which is quite significant to develop effective semantic search models.
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Jin, Hailong, Chengjiang Li, Jing Zhang, Lei Hou, Juanzi Li, and Peng Zhang. "XLORE2: Large-scale Cross-lingual Knowledge Graph Construction and Application." Data Intelligence 1, no. 1 (March 2019): 77–98. http://dx.doi.org/10.1162/dint_a_00003.

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Knowledge bases (KBs) are often greatly incomplete, necessitating a demand for KB completion. Although XLORE is an English-Chinese bilingual knowledge graph, there are only 423,974 cross-lingual links between English instances and Chinese instances. We present XLORE2, an extension of the XLORE that is built automatically from Wikipedia, Baidu Baike and Hudong Baike. We add more facts by making cross-lingual knowledge linking, cross-lingual property matching and fine-grained type inference. We also design an entity linking system to demonstrate the effectiveness and broad coverage of XLORE2.
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Talluto, Matthew V., Isabelle Boulangeat, Aitor Ameztegui, Isabelle Aubin, Dominique Berteaux, Alyssa Butler, Frédérik Doyon, et al. "Cross-scale integration of knowledge for predicting species ranges: a metamodelling framework." Global Ecology and Biogeography 25, no. 2 (October 29, 2015): 238–49. http://dx.doi.org/10.1111/geb.12395.

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Li, Mingyong, Qiqi Li, Lirong Tang, Shuang Peng, Yan Ma, and Degang Yang. "Deep Unsupervised Hashing for Large-Scale Cross-Modal Retrieval Using Knowledge Distillation Model." Computational Intelligence and Neuroscience 2021 (July 17, 2021): 1–11. http://dx.doi.org/10.1155/2021/5107034.

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Cross-modal hashing encodes heterogeneous multimedia data into compact binary code to achieve fast and flexible retrieval across different modalities. Due to its low storage cost and high retrieval efficiency, it has received widespread attention. Supervised deep hashing significantly improves search performance and usually yields more accurate results, but requires a lot of manual annotation of the data. In contrast, unsupervised deep hashing is difficult to achieve satisfactory performance due to the lack of reliable supervisory information. To solve this problem, inspired by knowledge distillation, we propose a novel unsupervised knowledge distillation cross-modal hashing method based on semantic alignment (SAKDH), which can reconstruct the similarity matrix using the hidden correlation information of the pretrained unsupervised teacher model, and the reconstructed similarity matrix can be used to guide the supervised student model. Specifically, firstly, the teacher model adopted an unsupervised semantic alignment hashing method, which can construct a modal fusion similarity matrix. Secondly, under the supervision of teacher model distillation information, the student model can generate more discriminative hash codes. Experimental results on two extensive benchmark datasets (MIRFLICKR-25K and NUS-WIDE) show that compared to several representative unsupervised cross-modal hashing methods, the mean average precision (MAP) of our proposed method has achieved a significant improvement. It fully reflects its effectiveness in large-scale cross-modal data retrieval.
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Keeley, Annika T. H., Alexander K. Fremier, Pascale A. L. Goertler, Patrick R. Huber, Anna M. Sturrock, Samuel M. Bashevkin, Blake A. Barbaree, et al. "Governing Ecological Connectivity in Cross-Scale Dependent Systems." BioScience 72, no. 4 (January 25, 2022): 372–86. http://dx.doi.org/10.1093/biosci/biab140.

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Abstract Ecosystem management and governance of cross-scale dependent systems require integrating knowledge about ecological connectivity in its multiple forms and scales. Although scientists, managers, and policymakers are increasingly recognizing the importance of connectivity, governmental organizations may not be currently equipped to manage ecosystems with strong cross-boundary dependencies. Managing the different aspects of connectivity requires building social connectivity to increase the flow of information, as well as the capacity to coordinate planning, funding, and actions among both formal and informal governance bodies. We use estuaries in particular the San Francisco Estuary, in California, in the United States, as examples of cross-scale dependent systems affected by many intertwined aspects of connectivity. We describe the different types of estuarine connectivity observed in both natural and human-affected states and discuss the human dimensions of restoring beneficial physical and ecological processes. Finally, we provide recommendations for policy, practice, and research on how to restore functional connectivity to estuaries.
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Delgado-Serrano, María, and Roberto Escalante Semerena. "Gender and Cross-Scale Differences in the Perception of Social-Ecological Systems." Sustainability 10, no. 9 (August 22, 2018): 2983. http://dx.doi.org/10.3390/su10092983.

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The sound management of Social-Ecological Systems (SESs) requires a deep knowledge of the system and its dynamics, but effective strategies also need to include the perceptions of the local actors. These perceptions are specific and might differ for different actors. In this research, we analyzed the gender and across scales differences in the perception of a SES and unveiled the potential reasons that shape the different actors’ understanding. Using structural analysis tools, we analyzed the perceptions of local women, local men, and external stakeholders on the most relevant variables shaping the actual and future sustainable management of a SES. The research was developed in Santiago Comaltepec, an Indigenous community located in the Sierra de Oaxaca (Mexico) that manage their forest under community-based strategies. The gender differences in perception showed the inequalities in agency, voice, and power between women and men. The comparison of the perceptions between community members and external stakeholders showed greater similarities, but still reflected power differences and differences in knowledge and cultural representations. We concluded that sound and resilient SES management need to recognize the gendered and across scales diversity in perception, knowledge, and practices and create bridges and synergies among knowledge systems to shape desirable trajectories.
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Liu, Bi-Yuan, Huai-Xin Chen, Zhou Huang, Xing Liu, and Yun-Zhi Yang. "ZoomInNet: A Novel Small Object Detector in Drone Images with Cross-Scale Knowledge Distillation." Remote Sensing 13, no. 6 (March 21, 2021): 1198. http://dx.doi.org/10.3390/rs13061198.

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Drone-based object detection has been widely applied in ground object surveillance, urban patrol, and some other fields. However, the dramatic scale changes and complex backgrounds of drone images usually result in weak feature representation of small objects, which makes it challenging to achieve high-precision object detection. Aiming to improve small objects detection, this paper proposes a novel cross-scale knowledge distillation (CSKD) method, which enhances the features of small objects in a manner similar to image enlargement, so it is termed as ZoomInNet. First, based on an efficient feature pyramid network structure, the teacher and student network are trained with images in different scales to introduce the cross-scale feature. Then, the proposed layer adaption (LA) and feature level alignment (FA) mechanisms are applied to align the feature size of the two models. After that, the adaptive key distillation point (AKDP) algorithm is used to get the crucial positions in feature maps that need knowledge distillation. Finally, the position-aware L2 loss is used to measure the difference between feature maps from cross-scale models, realizing the cross-scale information compression in a single model. Experiments on the challenging Visdrone2018 dataset show that the proposed method draws on the advantages of the image pyramid methods, while avoids the large calculation of them and significantly improves the detection accuracy of small objects. Simultaneously, the comparison with mainstream methods proves that our method has the best performance in small object detection.
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Sifa, Rafet, Anders Drachen, and Christian Bauckhage. "Large-Scale Cross-Game Player Behavior Analysis on Steam." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 11, no. 1 (June 24, 2021): 198–204. http://dx.doi.org/10.1609/aiide.v11i1.12804.

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Behavioral game analytics has predominantly been confined to work on single games, which means that the cross-game applicability of current knowledge remains largely unknown. Here four experiments are presented focusing on the relationship between game ownership, time invested in playing games, and the players themselves, across more than 3000 games distributed by the Steam platform and over 6 million players, covering a total playtime of over 5 billion hours. Experiments are targeted at uncovering high-level patterns in the behavior of players focusing on playtime, using frequent itemset mining on game ownership, cluster analysis to develop playtime-dependent player profiles, correlation between user game rankings and, review scores, playtime and game ownership, as well as cluster analysis on Steam games. Within the context of playtime, the analyses presented provide unique insights into the behavior of game players as they occur across games, for example in how players distribute their time across games.
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Yunesian, Masud, Alireza Mesdaghinia, Ali Moradi, and Javad Homayoun Vash. "Drivers' Knowledge, Attitudes, and Behavior: A Cross-Sectional Study." Psychological Reports 102, no. 2 (April 2008): 411–17. http://dx.doi.org/10.2466/pr0.102.2.411-417.

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A cross-sectional survey was conducted by random cluster sampling in the city of Tehran. Knowledge of the drivers was assessed with 6 questions about national traffic laws. Attitude was assessed on a 7-item scale of drivers' perceptions. Behavior was observed by a police officer on 13 areas of assessment. Correlations were low among these three scores. Drivers with advanced education had higher scores on behavior. Knowledge, attitude, and behavior of these drivers did not seem associated.
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Dissertations / Theses on the topic "Cross-scale knowledge"

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Melo, André [Verfasser], and Heiko [Akademischer Betreuer] Paulheim. "Automatic refinement of large-scale cross-domain knowledge graphs / André Melo ; Betreuer: Heiko Paulheim." Mannheim : Universitätsbibliothek Mannheim, 2018. http://d-nb.info/1167160584/34.

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Melo, André Verfasser], and Heiko [Akademischer Betreuer] [Paulheim. "Automatic refinement of large-scale cross-domain knowledge graphs / André Melo ; Betreuer: Heiko Paulheim." Mannheim : Universitätsbibliothek Mannheim, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:180-madoc-459801.

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Ocampo, Mascaró Javier, Salazar Vera Jimena Silva, and Costa Bullón Abilio da. "Correlación entre conocimientos sobre consecuencias de la obesidad y grado de actividad física en universitarios." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2015. http://hdl.handle.net/10757/621624.

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Introducción. El objetivo del presente estudio es determinar si existe correlación entre los conocimientos sobre las consecuencias de la obesidad y el grado de actividad física de las personas. Métodos. Se realizó un estudio transversal analítico durante los años 2013 y 2014. Participaron 215 alumnos de pregrado seleccionados por conveniencia no relacionados a carreras del campo de la salud en una universidad de Lima, Perú. Se evaluó el grado de actividad física utilizando el International Physical Activity Questionnaire (IPAQ) y el nivel de conocimientos sobre consecuencias de la obesidad utilizando la escala Obesity Risk Knowledge-10 (ORK-10). También, se consignó las fuentes de información de donde obtuvieron el conocimiento para responder dicho cuestionario. Resultados. La mediana de edad fue 20 (rango intercuartílico=4) y 63% eran mujeres. De acuerdo al IPAQ, 53,9% realizaban actividad física alta, 35,4%, moderada y 10,7%, leve. Se encontró una correlación muy baja (rs=0,06) entre el puntaje del ORK-10 y la cantidad de equivalentes metabólicos/minuto consumidos por semana, pero no era significativa (p=0,38). Las personas informadas por medios de comunicación y por personal de salud obtuvieron mayores puntajes en el ORK- 10 que quienes se informaron por otras vías (p<0,05). Conclusiones. La correlación entre los conocimientos sobre consecuencias de la obesidad y el grado de actividad física es muy baja. Es necesario utilizar enfoques multidisciplinarios que incluyan todos los determinantes de la realización de actividad física para poder lograr cambios en la conducta de la población.
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"Knowledge management, a resource-based perspective: scale development and cross validation." 2004. http://library.cuhk.edu.hk/record=b5892068.

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Maggie Chu Ying-ying.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.
Includes bibliographical references (leaves 80-89).
Abstracts in English and Chinese.
Chapter Chapter 1 --- Introduction
Chapter 1.1 --- Background --- p.1
Chapter 1.2 --- Research Objective --- p.4
Chapter 1.3 --- Outline of This Study --- p.4
Chapter Chapter 2 --- Backgrounds and Previous Research
Chapter 2.1 --- The Resource-based View and the Environmental Models --- p.5
Chapter 2.2 --- The Resource-based View and Knowledge --- p.6
Chapter Chapter 3 --- Conceptualization of KM
Chapter 3.1 --- Knowledge --- p.8
Chapter 3.2 --- Knowledge Management --- p.11
Chapter 3.2.1 --- Technological Perspective --- p.11
Chapter 3.2.2 --- Human Perspective --- p.12
Chapter 3.2.3 --- Process-oriented Perspective --- p.13
Chapter 3.2.4 --- Intellectual Capital Perspective --- p.14
Chapter 3.2.5 --- "Confluence of Definitions and the Components of KM," --- p.15
Chapter 3.3 --- Components of KM --- p.16
Chapter 3.3.1 --- Organization Culture --- p.21
Chapter 3.3.2 --- Structural Capital --- p.22
Chapter 3.3.3 --- Human Resource --- p.23
Chapter 3.3.4 --- Social Capital --- p.24
Chapter 3.3.5 --- Knowledge Acquisition --- p.25
Chapter 3.3.6 --- Knowledge Dissemination --- p.26
Chapter 3.3.7 --- Knowledge Application --- p.27
Chapter Chapter 4 --- Research Methodology
Chapter 4.1 --- Overview --- p.28
Chapter 4.2 --- Exploratory versus Confirmatory Factor Analysis --- p.28
Chapter 4.3 --- Item Generation and Content Validity --- p.29
Chapter 4.4 --- Pilot Study --- p.30
Chapter 4.5 --- Main Study --- p.34
Chapter 4.5.1 --- Sample and Data Collection --- p.34
Chapter 4.5.2 --- Scale Calibration and Validation --- p.41
Chapter 4.5.2.1 --- Dimensionality Assessment --- p.41
Chapter 4.5.2.2 --- Reliability Assessment --- p.47
Chapter 4.5.2.3 --- Validity Assessment --- p.48
Chapter 4.5.2.3.1 --- "Convergent Validity," --- p.48
Chapter 4.5.2.3.2 --- Discriminant Validity --- p.52
Chapter 4.5.2.3.3 --- Nomological Validity --- p.53
Chapter 4.5.2.3.4 --- "Cross-validation," --- p.56
Chapter 4.6. --- Comparison across Industries --- p.59
Chapter 4.6.1 --- Impact of KM on Business Performance --- p.59
Chapter 4.6.2 --- Level of KM exhibited across Industries --- p.67
Chapter 4.6.3 --- The Relative Importance of KM Components --- p.69
Chapter Chapter 5 --- Discussion
Chapter 5.1 --- Academic Contributions --- p.70
Chapter 5.2 --- Managerial Implications --- p.72
Chapter 5.3 --- Limitations and Future Research --- p.74
Appendix
Chapter A. --- Constitutive Definitions and Operative Definitions of each Dimension of KM --- p.76
Chapter B. --- (a) Items used in Pilot Study --- p.77
(b) Items of the Final Scales --- p.79
References --- p.80
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Su, Cheng-Hung, and 蘇政宏. "Construction and Measurement for Knowledge Management Performance：A Cross-functional Scale Design and Empirical Survey." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/87533124996505462231.

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碩士
義守大學
管理科學研究所
90
While the era of "knowledge economy" is being ignited, our practitioners and academicians begin to focus on the issues of knowledge management (KM). But after reviewing most of the KM literature, we may find that they tend to work on the "operational perspective" of KM, and neglect researching "strategic perspective (KS)". Moreover, most literature sorely explored the KM issues of those advantageous hi-tech industries, and failed to study the industries of different advantage stages, such as advantage- sustaining, advantage- transition, advantage- growing and advantage- declining industries. Besides, related literature in question still failed to construct and measure the Knowledge Performance (KP), and neglected to explore how the KM activities influence KP. Thus, this study attempts to work on the following topics: 1.This study will conduct "strategic perspectives" into the KM study, and explore how knowledge strategy (KS) will affect KP. 2.Since most KM articles tended to employ case study or small-scale survey as the objectives of study, this study uses larger-sample sampling to clarify the previous topics. This study attempts to construct and measure KP, and accordingly explores how the above issues influence KP. 3.This study not only takes those hi-tech industries as the samples, but also involve the industries that locate in different stages of advantage, i.e., advantage-sustaining, advantage-transition, advantage-growing and advantage-declining industries. It is reasonable to make a comparative study. 　　The primary goal of this study is to understand the KP’s model, including the measurement and construction to point out the shortcomings of the present situation and to make suggestions for the future direction. After sampling Top 1000’s firms ranked by the Common Wealth Magazine and mailing out 600 questionnaires, this study presented a detailed analysis of its result with collected 23.83% of questionnaires, which were processed by SAS software, and obtained the findings as follows. 　　The attributes of knowledge strategy have come to an evident correlation with knowledge performance. As for the performances of the four functions this study explores, strong correlation with knowledge performance are also found in human resource, production, and R&D, except in marketing. Besides, there is only partly difference between the attributes of knowledge strategy among the industries that locate in different stages, and no correlation with the difference of knowledge performance. Further, the study also presents some research suggestions as reference for the future researchers in business and academic circles. Hopefully, it may bring contribution towards academic and practical concerns through extensive exploration of knowledge management and clarification for the correlation between knowledge performance and functional performance of enterprises.
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Books on the topic "Cross-scale knowledge"

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Halvorsen, Tor, Hilde Ibsen, and Vyvienne RP M’kumbuzi. Knowledge for a Sustainable World: A Southern African-Nordic contribution. African Minds, 2015. http://dx.doi.org/10.47622/9781928331049.

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The search for answers to the issue of global sustainability has become increasingly urgent. In the context of higher education, many universities and academics are seeking new insights that can shift our dependence on ways of living that rely on the exploitation of so many and the degradation of so much of our planet. This is the vision that drives SANORD and many of the researchers and institutions within its network. Although much of the research is on a relatively small scale, the vision is steadily gaining momentum, forging dynamic collaborations and pathways to new knowledge. The contributors to this book cover a variety of subject areas and offer fresh insights about chronically under-researched parts of the world. Others document and critically reflect on innovative approaches to cross-continental teaching and research collaborations. This book will be of interest to anyone involved in the transformation of higher education or the practicalities of cross-continental and cross-disciplinary academic collaboration. The Southern African-Nordic Centre (SANORD) is a network of higher education institutions from Denmark, Finland, Iceland, Norway, Sweden, Botswana, Namibia, Malawi, South Africa, Zambia and Zimbabwe. Universities in the southern African and Nordic regions that are not yet members are encouraged to join.
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Halvorsen, Tor, Hilde Ibsen, Henri-Count Evans, and Sharon Penderis. Knowledge for Justice: Critical Perspectives from Southern African-Nordic Research Partnerships. African Minds, 2017. http://dx.doi.org/10.47622/9781928331636.

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With the adoption of the United Nations Sustainable Development Goals (SDGs) and the Paris Agreement, the purpose of development is being redefined in both social and environmental terms. Despite pushback from conservative forces, change is accelerating in many sectors. To drive this transformation in ways that bring about social, environmental and economic justice at a local, national, regional and global levels, new knowledge and strong cross-regional networks capable of foregrounding different realities, needs and agendas will be essential. In fact, the power of knowledge matters today in ways that humanity has probably never experienced before, placing an emphasis on the roles of research, academics and universities. In this collection, an international diverse collection of scholars from the southern African and Nordic regions critically review the SDGs in relation to their own areas of expertise, while placing the process of knowledge production in the spotlight. In Part I, the contributors provide a sober assessment of the obstacles that neo-liberal hegemony presents to substantive transformation. In Part Two, lessons learned from North-South research collaborations and academic exchanges are assessed in terms of their potential to offer real alternatives. In Part III, a set of case studies supply clear and nuanced analyses of the scale of the challenges faced in ensuring that no one is left behind. This accessible and absorbing collection will be of interest to anyone interested in North-South research networks and in the contemporary debates on the role of knowledge production. The Southern African-Nordic Centre (SANORD) is a network of higher education institutions that stretches across Denmark, Finland, Iceland, Norway, Sweden, Botswana, Namibia, Malawi, South Africa, Zambia and Zimbabwe. Universities in the southern African and Nordic regions that are not yet members are encouraged to join.
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Polanczyk, Guilherme V. Epidemiology. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198739258.003.0013.

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This chapter initially reviews the main methodological aspects of ADHD prevalence studies, specifically study design, case definition, and ascertainment to subsequently address meta-analyses summarizing the prevalence of the disorder on children, adolescents, and adults. Results of meta-regression in the context of meta-analysis have investigated the effect of year of publication, sample location, and methodological characteristics of studies on heterogeneity of results. Studies on the course of the disorder, following up clinical and community samples, are discussed, as well as cultural influences on epidemiological findings. Large-scale cross-national studies and longitudinal studies following non-referred samples are necessary to further advance the knowledge on the epidemiology of ADHD.
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Biesel, Kay, Judith Masson, Nigel Parton, and Tarja Pösö, eds. Errors and Mistakes in Child Protection. Policy Press, 2020. http://dx.doi.org/10.1332/policypress/9781447350705.001.0001.

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This comprehensive international study provides a cross-national analysis of different understandings of errors and mistakes in child protection practice and lessons to avoid and handle them, using research and knowledge from eleven countries in Europe and North America. Divided into country-specific chapters, each examines the pathways that led to mistakes, the scale of their impact, how responsibilities and responses are decided and how practice and policy subsequently changed. Considering the complexities of evolving practice contexts, this authoritative, future-oriented study is an invaluable text for practitioners, researchers and policy makers wishing to understand why child protection fails – and offers a springboard for fresh thinking about strategies to reduce future risk.
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Conca, Ken, and Erika Weinthal, eds. The Oxford Handbook of Water Politics and Policy. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199335084.001.0001.

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This handbook gathers a diverse group of leading scholars of water politics and policy. Authors were tasked to present forward-looking chapters in their areas of expertise, flagging key trends in both research and practice. The volume is organized into six sections: poverty, rights, and ethics; food, energy, and water; water and the politics of scale; law, economics, and water management; the politics of transboundary water; and the politics of water knowledge. Cross-cutting themes include governance challenges rooted in the mobility, unpredictability, and public-goods dimensions of water; tensions and synergies among equity, efficiency, and sustainability; the distributive consequences of water governance; the design and performance of water institutions; and the implications of climate change.
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Egedi, Barbara, and Veronika Hegedűs, eds. Functional Heads Across Time. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780198871538.001.0001.

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Abstract This volume brings together studies that contribute to our knowledge about the role functional elements play in syntactic changes, and the semantic and functional features that are the driving force behind the changes. Parameter resettings, structural reanalyses, and changes in the feature specification of functional heads are explored related to the functional sequence of the clausal as well as the nominal and adpositional domains. The chapters in this book discuss ‘microdiachronic’ syntactic changes that often have implications for large-scale syntactic effects, such as word order variation and change, the emergence (and lexicalization) of syntactic projections, grammaticalization, and changes in information structural properties. The volume contains case studies of individual languages (English, German, Hungarian, Icelandic, Italian, Latin, Portuguese, and Romanian are included) as well as discussions of cross-linguistic phenomena. The studies heavily rely on digital corpora of historical or dialectal data. The chapters are organized in an order that essentially reflects the hierarchy of projections in the clausal functional sequence and the other distinguished ph(r)asal projections from CP to DP.
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Ramraj, Victor V., ed. Covid-19 in Asia. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197553831.001.0001.

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Covid-19 in Asia: Law and Policy Contexts is an edited collection of original essays on Asia’s legal and policy responses to the Covid-19 pandemic, which, in a matter of months, swept around the globe, infecting millions. In a matter of weeks, the unimaginable became ordinary: lockdowns of cities and entire countries, physical distancing and quarantines, travel restrictions and border controls, movement-tracking technology, mandatory closures of all but essential services, economic devastation and mass unemployment, and government assistance programs on record-breaking scales. Yet a pandemic on this scale, under contemporary conditions of globalization, has left governments and their advisors scrambling to improvise solutions, often themselves unprecedented in modern times, such as the initial lockdown of Wuhan. Identifying cross-cutting themes and challenges, this collection of essays taps the collective knowledge of an interdisciplinary team of sixty-one researchers. Beginning with an epidemiological overview and survey of the law and policy themes, it covers five topics: first wave containment measures; emergency powers; technology, science, and expertise; politics, religion, and governance; and economy, climate, and sustainability.
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McGreavy, Bridie, and David Hart. Sustainability Science and Climate Change Communication. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.563.

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Direct experience, scientific reports, and international media coverage make clear that the breadth, severity, and multiple consequences from climate change are far-reaching and increasing. Like many places globally, the northeastern United States is already experiencing climate change, including one of the world’s highest rates of ocean warming, reduced durations of winter ice cover on lakes, a marked increase in the frequency of extreme precipitation events, and climate-mediated ecological disruptions of invasive species. Given current and projected changes in ecosystems, communities, and economies, it is essential to find ways to anticipate and reduce vulnerabilities to change and, at the same time, promote sustainable economic development and human well-being.The emerging field of sustainability science offers a promising conceptual and analytic framework for accelerating progress towards sustainable development. Sustainability science aims to be use-inspired and to connect basic and applied knowledge with solutions for societal benefit. This approach draws from diverse disciplines, theories, and methods organized around the broad goal of maintaining and improving life support systems, ecosystem health, and human well-being. Partners in New England have been using sustainability science as a framework for stakeholder-engaged, interdisciplinary research that has generated use-inspired knowledge and multiple solutions for more than a decade. Sustainability science has helped produce a landscape-scale approach to wetland conservation; emergency response plans for invasive species that threaten livelihoods and cultures; decision support tools for improved water quality management and public health for beach use and shellfish consumption; and the development of robust partnership networks across disciplines and institutions. Understanding and reducing vulnerability to climate change is a central motivating factor in this portfolio of projects because linking knowledge about social-ecological systems with effective policy action requires a holistic view that addresses complex intersecting stressors.One common theme in these varied efforts is the way that communication fundamentally shapes collaborative research and social, technical, and policy outcomes from sustainability science. Communication as a discipline has, for more than two thousand years, sought to understand how environments and symbols shape human life, forms of social organization, and collective decision making. The result is a body of scholarship and practical techniques that are diverse and well adapted to meet the complexity of contemporary sustainability challenges. The complexity of the issues that sustainability science aspires to solve requires diversity and flexibility to be able to adapt approaches to the specific needs of a situation. Long-term, cross-scale, and multi-institutional sustainability science collaborations show that communication research and practice can help build communities and networks, and advance technical and policy solutions to confront the challenges of climate change and promote sustainability now and in future.
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Henry, Laura A., and Lisa McIntosh Sundstrom. Bringing Global Governance Home. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197530238.001.0001.

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The world’s problems are increasingly global in scale. Climate change, pandemics, and the actions of multinational corporations are all beyond the ability of any single state to address. States and civil society actors have joined a growing number of global governance institutions to address these challenges collectively. While global governance is initiated at the international level, the effects of global governance occur at the domestic level and depend upon the actions of domestic actors. NGOs act as “mediators” between global and domestic political arenas, translating and adapting global norms for audiences at home. However, NGO participation in global governance varies significantly by country and by issue area. The role of domestic NGOs in global governance has been relatively neglected—a puzzling gap since domestic implementation determines whether global “best practices” are applied for the common good or languish as words on the pages of international reports. The BRICS states (Brazil, Russia, India, China, and South Africa) offer excellent cases for delving into contention around NGOs’ role as mediators due to their shared aspiration to shape global governance and their varied political and economic characteristics. Bringing Global Governance Home: NGO Mediation in the BRICS States fills gaps in our knowledge by identifying and explaining significant cross-national variation in NGO participation in global governance based on an original dataset. Moreover, it combines insights from international relations and comparative politics to explain the dilemmas and strategies of NGO mediation in case studies on HIV/AIDS, climate change, sustainable forestry, and corporate social responsibility.
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Ergas, Christina. Surviving Collapse. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197544099.001.0001.

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As environmental crises loom, this book makes an argument for radical changes in the ways in which people live in order to stave off a dystopian future. A possible way forward is radical sustainable development, which emphasizes environmental and social justice concerns that are at once transformative, or egalitarian toward total liberation, and regenerative, or restorative to heal the health of people and the planet. Radical sustainability is distinguished from weak sustainability—a critique of the neoliberal, sustainable development project that, in practice, prioritizes economic growth over people and the planet—using theories from ecofeminist, environmental justice, and postcolonial scholars. The prevailing notion of sustainable development has remained ineffective at reducing environmental degradation and social inequalities. To gauge possible solutions to these problems, the book examines two alternative, community-scale, socioecological models of development with small environmental footprints and more egalitarian social practices. Methods employed are qualitative, cross-national, and comparative. The cases are an urban ecovillage in the Pacific Northwest, United States and a Cuban urban farm in Havana. These cases are important reminders that elegant, low-cost solutions already exist for environmental harm mitigation as well as social equity and adaptation. Findings highlight that each case uses community-oriented, low-tech practices and integrates ancestral, Indigenous, and local ecological knowledges. They prioritize social and ecological efficiency and subsume economic rationality towards those ends. While neither is a panacea, both provide examples for how communities can move toward stronger forms of sustainable development and empower readers to imagine, and possibly build, more resilient futures.
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Book chapters on the topic "Cross-scale knowledge"

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Pede, Elena. "Improving Resilience through Cross-Scale Knowledge Sharing." In Resilient Cities, 93–107. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76944-8_7.

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He, Dehong, Song Wu, Jinpeng Liu, and Guoqiang Xiao. "Cross Transformer Network for Scale-Arbitrary Image Super-Resolution." In Knowledge Science, Engineering and Management, 633–44. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10986-7_51.

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Su, Yonghao, Chi Zhang, Jinyang Li, Chengyu Wang, Weining Qian, and Aoying Zhou. "Cross-Lingual Entity Query from Large-Scale Knowledge Graphs." In Web Technologies and Applications, 139–50. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28121-6_13.

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Li, Yi, Yitao Duan, and Wei Xu. "PEM: A Practical Differentially Private System for Large-Scale Cross-Institutional Data Mining." In Machine Learning and Knowledge Discovery in Databases, 89–105. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71246-8_6.

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Li, Mingyang, Yao Shi, Zhigang Wang, and Yongbin Liu. "Building a Large-Scale Cross-Lingual Knowledge Base from Heterogeneous Online Wikis." In Natural Language Processing and Chinese Computing, 413–20. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25207-0_37.

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Wang, Jingchu, Jianyi Liu, Feiyu Chen, Teng Lu, Hua Huang, and Jinmeng Zhao. "Cross-Knowledge Graph Entity Alignment via Neural Tensor Network." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 66–74. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_8.

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AbstractWith the expansion of the current knowledge graph scale and the increase of the number of entities, a large number of knowledge graphs express the same entity in different ways, so the importance of knowledge graph fusion is increasingly manifested. Traditional entity alignment algorithms have limited application scope and low efficiency. This paper proposes an entity alignment method based on neural tensor network (NtnEA), which can obtain the inherent semantic information of text without being restricted by linguistic features and structural information, and without relying on string information. In the three cross-lingual language data sets DBPFR−EN, DBPZH−EN and DBPJP−EN of the DBP15K data set, Mean Reciprocal Rank and Hits@k are used as the alignment effect evaluation indicators for entity alignment tasks. Compared with the existing entity alignment methods of MTransE, IPTransE, AlignE and AVR-GCN, the Hit@10 values of the NtnEA method are 85.67, 79.20, and 78.93, and the MRR is 0.558, 0.511, and 0.499, which are better than traditional methods and improved 10.7% on average.
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Kuenkel, Petra. "Setting the Scene: How to Enhance the Knowledge and Practice of Transformation Literacy." In Transformation Literacy, 221–34. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93254-1_15.

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AbstractThis chapter looks at the scale and modes of transformations needed toward regenerative civilizations. Societal transformations have always happened in human history, and many have been consciously and actively promoted. What is new about the situation at the beginning of the twenty-first century is both scale and depth of transformations required. Institutional and political structures on which our globalized current systems are built, tend to perpetuate the status quo. It is therefore important to create new strategic and communicative structures in the form of cross-sector and cross-institutional collaboration that initiate and facilitate transformations. The chapter suggests that transformation agents need to act more consciously as part of transformation systems around issues such as climate protection, regenerative pathways and economic systems change. The chapter elaborates three strategic core approaches that require conscious attention in transformation literacy: Collective stewardship as the pro-active engagement for a regenerative future in mutually supportive strategies; visionary multiplicity as the acknowledgment of plural approaches to the quality of life; and network leverage as the deliberate and reflective use of power across institutions. The chapter concludes with an overview of the different authors’ chapters.
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Alania, Victor, Alessandro Tibaldi, Fabio L. Bonali, Onise Enukidze, and Elena Russo. "Structural Architecture of the Western Greater Caucasus Orogen: New Data from a Crustal-Scale Structural Cross-Section." In Building Knowledge for Geohazard Assessment and Management in the Caucasus and other Orogenic Regions, 59–71. Dordrecht: Springer Netherlands, 2021. http://dx.doi.org/10.1007/978-94-024-2046-3_5.

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Yan, Peng, and Wei Jin. "Improving Cross-Document Knowledge Discovery Through Content and Link Analysis of Wikipedia Knowledge." In Transactions on Large-Scale Data- and Knowledge-Centered Systems XXI, 161–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47804-2_8.

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Alhamzeh, Alaa, Előd Egyed-Zsigmond, Dorra El Mekki, Abderrazzak El Khayari, Jelena Mitrović, Lionel Brunie, and Harald Kosch. "Empirical Study of the Model Generalization for Argument Mining in Cross-Domain and Cross-Topic Settings." In Transactions on Large-Scale Data- and Knowledge-Centered Systems LII, 103–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-66146-8_5.

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Conference papers on the topic "Cross-scale knowledge"

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Li, Mingyong, and Hongya Wang. "Unsupervised Deep Cross-Modal Hashing by Knowledge Distillation for Large-scale Cross-modal Retrieval." In ICMR '21: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3460426.3463626.

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Abu Helou, Mamoun, and Matteo Palmonari. "Multi-user Feedback for Large-scale Cross-lingual Ontology Matching." In 9th International Conference on Knowledge Engineering and Ontology Development. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006503200570066.

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Liang, Zhehan, Yu Rong, Chenxin Li, Yunlong Zhang, Yue Huang, Tingyang Xu, Xinghao Ding, and Junzhou Huang. "Unsupervised Large-Scale Social Network Alignment via Cross Network Embedding." In CIKM '21: The 30th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3459637.3482310.

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Ciravegna, F., and S. Staab. "Large scale cross-media knowledge acquisition, sharing and reuse in X-media." In 2nd European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology (EWIMT 2005). IET, 2005. http://dx.doi.org/10.1049/ic.2005.0764.

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Viana Soares, Elizabeth, Pedro Henrique Saramago de Lima, and Eduardo Augusto da Silva Erthal Pinto. "Functionality in the ICU in neurological patients-What we found in the evaluation?" In 7th International Congress on Scientific Knowledge. Biológicas & Saúde, 2021. http://dx.doi.org/10.25242/8868113820212403.

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The intensive care environment has been increasing its demand gradually in relation to the number of patients and the severity of the diseases. The functional limitations resulting from patients admitted to the ICU for neurological diseases are numerous and will depend on the severity, location and extent of the injury. The objective is to evaluate the function of neurocritical patients using the PERME scale. A cross-sectional observational study was carried out at the ICU of Hospital Ferreira Machado in Campos dos Goytacazes / RJ from November 2019 to November 2020, including patients with stroke and TBI. The sample consisted of 13 patients, 76.9% of whom were male, with a mean age of ± 51.2 years. The PERME scale had a maximum score of 16 and a minimum of one point, respectively. The present study identified that neurocritical patients have little functionality within the ICU, but identifies that perhaps this scale is not the most suitable for assessing neurocritical patients.
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Huang, Xin, Yuxin Peng, and Mingkuan Yuan. "Cross-modal Common Representation Learning by Hybrid Transfer Network." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/263.

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DNN-based cross-modal retrieval is a research hotspot to retrieve across different modalities as image and text, but existing methods often face the challenge of insufficient cross-modal training data. In single-modal scenario, similar problem is usually relieved by transferring knowledge from large-scale auxiliary datasets (as ImageNet). Knowledge from such single-modal datasets is also very useful for cross-modal retrieval, which can provide rich general semantic information that can be shared across different modalities. However, it is challenging to transfer useful knowledge from single-modal (as image) source domain to cross-modal (as image/text) target domain. Knowledge in source domain cannot be directly transferred to both two different modalities in target domain, and the inherent cross-modal correlation contained in target domain provides key hints for cross-modal retrieval which should be preserved during transfer process. This paper proposes Cross-modal Hybrid Transfer Network (CHTN) with two subnetworks: Modal-sharing transfer subnetwork utilizes the modality in both source and target domains as a bridge, for transferring knowledge to both two modalities simultaneously; Layer-sharing correlation subnetwork preserves the inherent cross-modal semantic correlation to further adapt to cross-modal retrieval task. Cross-modal data can be converted to common representation by CHTN for retrieval, and comprehensive experiment on 3 datasets shows its effectiveness.
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Shi, Yuan. "Using Domain Knowledge for Low Resource Named Entity Recognition." In 11th International Conference on Embedded Systems and Applications (EMSA 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120625.

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In recent years, named entity recognition has always been a popular research in the field of natural language processing, while traditional deep learning methods require a large amount of labeled data for model training, which makes them not suitable for areas where labeling resources are scarce. In addition, the existing cross-domain knowledge transfer methods need to adjust the entity labels for different fields, so as to increase the training cost. To solve these problems, enlightened by a processing method of Chinese named entity recognition, we propose to use domain knowledge to improve the performance of named entity recognition in areas with low resources. The domain knowledge mainly applied by us is domain dictionary and domain labeled data. We use dictionary information for each word to strengthen its word embedding and domain labeled data to reinforce the recognition effect. The proposed model avoids large-scale data adjustments in different domains while handling named entities recognition with low resources. Experiments demonstrate the effectiveness of our method, which has achieved impressive results on the data set in the field of scientific and technological equipment, and the F1 score has been significantly improved compared with many other baseline methods.
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Li, Tian, Xiang Chen, Zhen Dong, Kurt Keutzer, and Shanghang Zhang. "Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/585.

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Domain adaptive text classification is a challenging problem for the large-scale pretrained language models because they often require expensive additional labeled data to adapt to new domains. Existing works usually fails to leverage the implicit relationships among words across domains. In this paper, we propose a novel method, called Domain Adaptation with Structured Knowledge (DASK), to enhance domain adaptation by exploiting word-level semantic relationships. DASK first builds a knowledge graph to capture the relationship between pivot terms (domain-independent words) and non-pivot terms in the target domain. Then during training, DASK injects pivot-related knowledge graph information into source domain texts. For the downstream task, these knowledge-injected texts are fed into a BERT variant capable of processing knowledge-injected textual data. Thanks to the knowledge injection, our model learns domain-invariant features for non-pivots according to their relationships with pivots. DASK ensures the pivots to have domain-invariant behaviors by dynamically inferring via the polarity scores of candidate pivots during training with pseudo-labels. We validate DASK on a wide range of cross-domain sentiment classification tasks and observe up to 2.9% absolute performance improvement over baselines for 20 different domain pairs. Code is available at https://github.com/hikaru-nara/DASK.
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Yu, Mengshi, Jian Liu, Yufeng Chen, Jinan Xu, and Yujie Zhang. "Cross-Domain Slot Filling as Machine Reading Comprehension." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/550.

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With task-oriented dialogue systems being widely applied in everyday life, slot filling, the essential component of task-oriented dialogue systems, is required to be quickly adapted to new domains that contain domain-specific slots with few or no training data. Previous methods for slot filling usually adopt sequence labeling framework, which, however, often has limited ability when dealing with the domain-specific slots. In this paper, we take a new perspective on cross-domain slot filling by framing it as a machine reading comprehension (MRC) problem. Our approach firstly transforms slot names into well-designed queries, which contain rich informative prior knowledge and are very helpful for the detection of domain-specific slots. In addition, we utilize the large-scale MRC dataset for pre-training, which further alleviates the data scarcity problem. Experimental results on SNIPS and ATIS datasets show that our approach consistently outperforms the existing state-of-the-art methods by a large margin.
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Zhu, Ziye, Yun Li, Hanghang Tong, and Yu Wang. "CooBa: Cross-project Bug Localization via Adversarial Transfer Learning." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/493.

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Bug localization plays an important role in software quality control. Many supervised machine learning models have been developed based on historical bug-fix information. Despite being successful, these methods often require sufficient historical data (i.e., labels), which is not always available especially for newly developed software projects. In response, cross-project bug localization techniques have recently emerged whose key idea is to transferring knowledge from label-rich source project to locate bugs in the target project. However, a major limitation of these existing techniques lies in that they fail to capture the specificity of each individual project, and are thus prone to negative transfer. To address this issue, we propose an adversarial transfer learning bug localization approach, focusing on only transferring the common characteristics (i.e., public information) across projects. Specifically, our approach (CooBa) learns the indicative public information from cross-project bug reports through a shared encoder, and extracts the private information from code files by an individual feature extractor for each project. CooBa further incorporates adversarial learning mechanism to ensure that public information shared between multiple projects could be effectively extracted. Extensive experiments on four large-scale real-world data sets demonstrate that the proposed CooBa significantly outperforms the state of the art techniques.
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Reports on the topic "Cross-scale knowledge"

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Tulloch, Olivia, Tamara Roldan de Jong, and Kevin Bardosh. Data Synthesis: COVID-19 Vaccine Perceptions in Sub-Saharan Africa: Social and Behavioural Science Data, March 2020-April 2021. Institute of Development Studies (IDS), May 2021. http://dx.doi.org/10.19088/sshap.2028.

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Safe and effective vaccines against COVID-19 are seen as a critical path to ending the pandemic. This synthesis brings together data related to public perceptions about COVID-19 vaccines collected between March 2020 and March 2021 in 22 countries in Africa. It provides an overview of the data (primarily from cross-sectional perception surveys), identifies knowledge and research gaps and presents some limitations of translating the available evidence to inform local operational decisions. The synthesis is intended for those designing and delivering vaccination programmes and COVID-19 risk communication and community engagement (RCCE). 5 large-scale surveys are included with over 12 million respondents in 22 central, eastern, western and southern African countries (note: one major study accounts for more than 10 million participants); data from 14 peer-reviewed questionnaire surveys in 8 countries with n=9,600 participants and 15 social media monitoring, qualitative and community feedback studies. Sample sizes are provided in the first reference for each study and in Table 13 at the end of this document. The data largely predates vaccination campaigns that generally started in the first quarter of 2021. Perceptions will change and further syntheses, that represent the whole continent including North Africa, are planned. This review is part of the Social Science in Humanitarian Action Platform (SSHAP) series on COVID-19 vaccines. It was developed for SSHAP by Anthrologica. It was written by Kevin Bardosh (University of Washington), Tamara Roldan de Jong and Olivia Tulloch (Anthrologica), it was reviewed by colleagues from PERC, LSHTM, IRD, and UNICEF (see acknowledgments) and received coordination support from the RCCE Collective Service. It is the responsibility of SSHAP.
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Nagahi, Morteza, Raed Jaradat, Mohammad Nagahisarchoghaei, Ghodsieh Ghanbari, Sujan Poudyal, and Simon Goerger. Effect of individual differences in predicting engineering students' performance : a case of education for sustainable development. Engineer Research and Development Center (U.S.), May 2021. http://dx.doi.org/10.21079/11681/40700.

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The academic performance of engineering students continues to receive attention in the literature. Despite that, there is a lack of studies in the literature investigating the simultaneous relationship between students' systems thinking (ST) skills, Five-Factor Model (FFM) personality traits, proactive personality scale, academic, demographic, family background factors, and their potential impact on academic performance. Three established instruments, namely, ST skills instrument with seven dimensions, FFM traits with five dimensions, and proactive personality with one dimension, along with a demographic survey, have been administrated for data collection. A cross-sectional web-based study applying Qualtrics has been developed to gather data from engineering students. To demonstrate the prediction power of the ST skills, FFM traits, proactive personality, academic, demographics, and family background factors on the academic performance of engineering students, two unsupervised learning algorithms applied. The study results identify that these unsupervised algorithms succeeded to cluster engineering students' performance regarding primary skills and characteristics. In other words, the variables used in this study are able to predict the academic performance of engineering students. This study also has provided significant implications and contributions to engineering education and education sustainable development bodies of knowledge. First, the study presents a better perception of engineering students' academic performance. The aim is to assist educators, teachers, mentors, college authorities, and other involved parties to discover students' individual differences for a more efficient education and guidance environment. Second, by a closer examination at the level of systemic thinking and its connection with FFM traits, proactive personality, academic, and demographic characteristics, understanding engineering students' skillset would be assisted better in the domain of sustainable education.
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Tulloch, Olivia, Tamara Roldan de Jong, and Kevin Bardosh. Data Synthesis: COVID-19 Vaccine Perceptions in Africa: Social and Behavioural Science Data, March 2020-March 2021. Institute of Development Studies (IDS), May 2021. http://dx.doi.org/10.19088/sshap.2021.030.

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Safe and effective vaccines against COVID-19 are seen as a critical path to ending the pandemic. This synthesis brings together data related to public perceptions about COVID-19 vaccines collected between March 2020 and March 2021 in 22 countries in Africa. It provides an overview of the data (primarily from cross-sectional perception surveys), identifies knowledge and research gaps and presents some limitations of translating the available evidence to inform local operational decisions. The synthesis is intended for those designing and delivering vaccination programmes and COVID-19 risk communication and community engagement (RCCE). 5 large-scale surveys are included with over 12 million respondents in 22 central, eastern, western and southern African countries (note: one major study accounts for more than 10 million participants); data from 14 peer-reviewed questionnaire surveys in 8 countries with n=9,600 participants and 15 social media monitoring, qualitative and community feedback studies. Sample sizes are provided in the first reference for each study and in Table 13 at the end of this document. The data largely predates vaccination campaigns that generally started in the first quarter of 2021. Perceptions will change and further syntheses, that represent the whole continent including North Africa, are planned. This review is part of the Social Science in Humanitarian Action Platform (SSHAP) series on COVID-19 vaccines. It was developed for SSHAP by Anthrologica. It was written by Kevin Bardosh (University of Washington), Tamara Roldan de Jong and Olivia Tulloch (Anthrologica), it was reviewed by colleagues from PERC, LSHTM, IRD, and UNICEF (see acknowledgments) and received coordination support from the RCCE Collective Service. It is the responsibility of SSHAP.
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Wozniakowska, P., D. W. Eaton, C. Deblonde, A. Mort, and O. H. Ardakani. Identification of regional structural corridors in the Montney play using trend surface analysis combined with geophysical imaging, British Columbia and Alberta. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328850.

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The Western Canada Sedimentary Basin (WCSB) is a mature oil and gas basin with an extraordinary endowment of publicly accessible data. It contains structural elements of varying age, expressed as folding, faulting, and fracturing, which provide a record of tectonic activity during basin evolution. Knowledge of the structural architecture of the basin is crucial to understand its tectonic evolution; it also provides essential input for a range of geoscientific studies, including hydrogeology, geomechanics, and seismic risk analysis. This study focuses on an area defined by the subsurface extent of the Triassic Montney Formation, a region of the WCSB straddling the border between Alberta and British Columbia, and covering an area of approximately 130,000 km2. In terms of regional structural elements, this area is roughly bisected by the east-west trending Dawson Creek Graben Complex (DCGC), which initially formed in the Late Carboniferous, and is bordered to the southwest by the Late Cretaceous - Paleocene Rocky Mountain thrust and fold belt (TFB). The structural geology of this region has been extensively studied, but structural elements compiled from previous studies exhibit inconsistencies arising from distinct subregions of investigation in previous studies, differences in the interpreted locations of faults, and inconsistent terminology. Moreover, in cases where faults are mapped based on unpublished proprietary data, many existing interpretations suffer from a lack of reproducibility. In this study, publicly accessible data - formation tops derived from well logs, LITHOPROBE seismic profiles and regional potential-field grids, are used to delineate regional structural elements. Where seismic profiles cross key structural features, these features are generally expressed as multi-stranded or en echelon faults and structurally-linked folds, rather than discrete faults. Furthermore, even in areas of relatively tight well control, individual fault structures cannot be discerned in a robust manner, because the spatial sampling is insufficient to resolve fault strands. We have therefore adopted a structural-corridor approach, where structural corridors are defined as laterally continuous trends, identified using geological trend surface analysis supported by geophysical data, that contain co-genetic faults and folds. Such structural trends have been documented in laboratory models of basement-involved faults and some types of structural corridors have been described as flower structures. The distinction between discrete faults and structural corridors is particularly important for induced seismicity risk analysis, as the hazard posed by a single large structure differs from the hazard presented by a corridor of smaller pre-existing faults. We have implemented a workflow that uses trend surface analysis based on formation tops, with extensive quality control, combined with validation using available geophysical data. Seven formations are considered, from the Late Cretaceous Basal Fish Scale Zone (BFSZ) to the Wabamun Group. This approach helped to resolve the problem of limited spatial extent of available seismic data and provided a broader spatial coverage, enabling the investigation of structural trends throughout the entirety of the Montney play. In total, we identified 34 major structural corridors and number of smaller-scale structures, for which a GIS shapefile is included as a digital supplement to facilitate use of these features in other studies. Our study also outlines two buried regional foreland lobes of the Rocky Mountain TFB, both north and south of the DCGC.
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