5,578 research outputs found

    An empirical investigation of the relationship between integration, dynamic capabilities and performance in supply chains

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    This research aimed to develop an empirical understanding of the relationships between integration, dynamic capabilities and performance in the supply chain domain, based on which, two conceptual frameworks were constructed to advance the field. The core motivation for the research was that, at the stage of writing the thesis, the combined relationship between the three concepts had not yet been examined, although their interrelationships have been studied individually. To achieve this aim, deductive and inductive reasoning logics were utilised to guide the qualitative study, which was undertaken via multiple case studies to investigate lines of enquiry that would address the research questions formulated. This is consistent with the authorโ€™s philosophical adoption of the ontology of relativism and the epistemology of constructionism, which was considered appropriate to address the research questions. Empirical data and evidence were collected, and various triangulation techniques were employed to ensure their credibility. Some key features of grounded theory coding techniques were drawn upon for data coding and analysis, generating two levels of findings. These revealed that whilst integration and dynamic capabilities were crucial in improving performance, the performance also informed the former. This reflects a cyclical and iterative approach rather than one purely based on linearity. Adopting a holistic approach towards the relationship was key in producing complementary strategies that can deliver sustainable supply chain performance. The research makes theoretical, methodological and practical contributions to the field of supply chain management. The theoretical contribution includes the development of two emerging conceptual frameworks at the micro and macro levels. The former provides greater specificity, as it allows meta-analytic evaluation of the three concepts and their dimensions, providing a detailed insight into their correlations. The latter gives a holistic view of their relationships and how they are connected, reflecting a middle-range theory that bridges theory and practice. The methodological contribution lies in presenting models that address gaps associated with the inconsistent use of terminologies in philosophical assumptions, and lack of rigor in deploying case study research methods. In terms of its practical contribution, this research offers insights that practitioners could adopt to enhance their performance. They can do so without necessarily having to forgo certain desired outcomes using targeted integrative strategies and drawing on their dynamic capabilities

    Fairness Testing: A Comprehensive Survey and Analysis of Trends

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    Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and concern among software engineers. To tackle this issue, extensive research has been dedicated to conducting fairness testing of ML software, and this paper offers a comprehensive survey of existing studies in this field. We collect 100 papers and organize them based on the testing workflow (i.e., how to test) and testing components (i.e., what to test). Furthermore, we analyze the research focus, trends, and promising directions in the realm of fairness testing. We also identify widely-adopted datasets and open-source tools for fairness testing

    Generalizable deep learning based medical image segmentation

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    Deep learning is revolutionizing medical image analysis and interpretation. However, its real-world deployment is often hindered by the poor generalization to unseen domains (new imaging modalities and protocols). This lack of generalization ability is further exacerbated by the scarcity of labeled datasets for training: Data collection and annotation can be prohibitively expensive in terms of labor and costs because label quality heavily dependents on the expertise of radiologists. Additionally, unreliable predictions caused by poor model generalization pose safety risks to clinical downstream applications. To mitigate labeling requirements, we investigate and develop a series of techniques to strengthen the generalization ability and the data efficiency of deep medical image computing models. We further improve model accountability and identify unreliable predictions made on out-of-domain data, by designing probability calibration techniques. In the first and the second part of thesis, we discuss two types of problems for handling unexpected domains: unsupervised domain adaptation and single-source domain generalization. For domain adaptation we present a data-efficient technique that adapts a segmentation model trained on a labeled source domain (e.g., MRI) to an unlabeled target domain (e.g., CT), using a small number of unlabeled training images from the target domain. For domain generalization, we focus on both image reconstruction and segmentation. For image reconstruction, we design a simple and effective domain generalization technique for cross-domain MRI reconstruction, by reusing image representations learned from natural image datasets. For image segmentation, we perform causal analysis of the challenging cross-domain image segmentation problem. Guided by this causal analysis we propose an effective data-augmentation-based generalization technique for single-source domains. The proposed method outperforms existing approaches on a large variety of cross-domain image segmentation scenarios. In the third part of the thesis, we present a novel self-supervised method for learning generic image representations that can be used to analyze unexpected objects of interest. The proposed method is designed together with a novel few-shot image segmentation framework that can segment unseen objects of interest by taking only a few labeled examples as references. Superior flexibility over conventional fully-supervised models is demonstrated by our few-shot framework: it does not require any fine-tuning on novel objects of interest. We further build a publicly available comprehensive evaluation environment for few-shot medical image segmentation. In the fourth part of the thesis, we present a novel probability calibration model. To ensure safety in clinical settings, a deep model is expected to be able to alert human radiologists if it has low confidence, especially when confronted with out-of-domain data. To this end we present a plug-and-play model to calibrate prediction probabilities on out-of-domain data. It aligns the prediction probability in line with the actual accuracy on the test data. We evaluate our method on both artifact-corrupted images and images from an unforeseen MRI scanning protocol. Our method demonstrates improved calibration accuracy compared with the state-of-the-art method. Finally, we summarize the major contributions and limitations of our works. We also suggest future research directions that will benefit from the works in this thesis.Open Acces

    Augmented Behavioral Annotation Tools, with Application to Multimodal Datasets and Models: A Systematic Review

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    Annotation tools are an essential component in the creation of datasets for machine learning purposes. Annotation tools have evolved greatly since the turn of the century, and now commonly include collaborative features to divide labor efficiently, as well as automation employed to amplify human efforts. Recent developments in machine learning models, such as Transformers, allow for training upon very large and sophisticated multimodal datasets and enable generalization across domains of knowledge. These models also herald an increasing emphasis on prompt engineering to provide qualitative fine-tuning upon the model itself, adding a novel emerging layer of direct machine learning annotation. These capabilities enable machine intelligence to recognize, predict, and emulate human behavior with much greater accuracy and nuance, a noted shortfall of which have contributed to algorithmic injustice in previous techniques. However, the scale and complexity of training data required for multimodal models presents engineering challenges. Best practices for conducting annotation for large multimodal models in the most safe and ethical, yet efficient, manner have not been established. This paper presents a systematic literature review of crowd and machine learning augmented behavioral annotation methods to distill practices that may have value in multimodal implementations, cross-correlated across disciplines. Research questions were defined to provide an overview of the evolution of augmented behavioral annotation tools in the past, in relation to the present state of the art. (Contains five figures and four tables)

    2023-2024 academic bulletin & course catalog

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    University of South Carolina Aiken publishes a catalog with information about the university, student life, undergraduate and graduate academic programs, and faculty and staff listings

    2023-2024 Undergraduate Catalog

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    2023-2024 undergraduate catalog for Morehead State University

    ๋ฏธ์„ธ๋จผ์ง€๋กœ ์ธํ•œ ๊ฑด๊ฐ•์˜ํ–ฅ ๋ฐ ์ •์ฑ…๊ฐœ์„ ์˜ ์‚ฌํšŒ์  ํ›„์ƒํšจ๊ณผ ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋†๊ฒฝ์ œ์‚ฌํšŒํ•™๋ถ€(์ง€์—ญ์ •๋ณดํ•™์ „๊ณต), 2023. 2. Hong Sok (Brian) Kim.PM2.5์˜ ๋ฐœ์ƒ์š”์ธ ์ค‘ ์ˆ˜์†ก๋ถ€๋ฌธ์˜ ๋„๋กœ์—์„œ ๋ฐœ์ƒํ•˜๋Š” PM2.5๋Š” ์‹œ๋ฏผ๋“ค์ด ์‰ฝ๊ฒŒ ๋…ธ์ถœ๋˜์–ด ๊ฑด๊ฐ• ์œ„ํ•ด์„ฑ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋Š” ์˜ค์—ผ๋ฌผ์งˆ๋กœ ํ˜ธํก์„ ํ†ตํ•ด ํ๋กœ ์œ ์ž…๋˜๊ณ  ์ธ๊ฐ„์˜ ์งˆ๋ณ‘ ๋ฐ ์‚ฌ๋ง์— ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ์ˆ˜์†ก๋ถ€๋ฌธ ๋ฏธ์„ธ๋จผ์ง€์™€ ๊ด€๋ จํ•œ ์‹ค์ฆ๋ถ„์„ ์—ฐ๊ตฌ๋Š” ๋ฏธ์„ธ๋จผ์ง€ ์ •์ฑ… ๋Œ€์•ˆ์— ๋Œ€ํ•œ ํƒ€๋‹น์„ฑ ๋ฐ ๊ทผ๊ฑฐ๋ฅผ ๋งˆ๋ จํ•˜๋Š” ๋ฐ ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ์ˆ˜์†ก๋ถ€๋ฌธ์˜ ๊ฒฝ์œ ์ฐจ ๋ฏธ์„ธ๋จผ์ง€ ๋ฐœ์ƒ์ด ์งˆ๋ณ‘์œผ๋กœ ์ธํ•œ ์‚ฌ๋ง๋ฅ ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ง€ ํ™•์ธํ•˜๊ณ , PM2.5 ์ €๊ฐ์„ ์œ„ํ•œ ์ •์ฑ…์ดํ–‰ ๋ฐฉํ–ฅ์„ ๊ฒ€ํ† ํ•˜์—ฌ PM ์ •์ฑ… ๊ฐœ์„ ์— ๋”ฐ๋ฅธ ์‚ฌํšŒ์  ํ›„์ƒ ํšจ๊ณผ๋ฅผ ํŒŒ์•…ํ•˜๋Š” ๋ฐ ์žˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” ๊ฒฝ์œ ์ฐจ์˜ PM2.5 ๋ฐฐ์ถœ๋Ÿ‰์ด ํ˜ธํก๊ธฐ์งˆํ™˜ ๋ฐ ํ—ˆํ˜ˆ์„ฑ ์‹ฌ์žฅ์งˆํ™˜์œผ๋กœ ์ธํ•œ ์‚ฌ๋ง๋ฅ ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜๊ณ , ์ด์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ๋“ค์„ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•œ๋‹ค. ์—ฐ๊ตฌ ์ˆ˜ํ–‰์„ ์œ„ํ•ด 2015๋…„๋ถ€ํ„ฐ 2019๋…„๊นŒ์ง€ ์ˆ˜๋„๊ถŒ ์ง€์—ญ์˜ ์ž์น˜๊ตฌ ๋ฐ ์‹œ๏ฝฅ๊ตฐ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์ง€์—ญ ๊ฐ„ ๊ณต๊ฐ„ ์ธ์ ‘์„ฑ์„ ๊ณ ๋ คํ•œ ๊ณต๊ฐ„ ํŒจ๋„ ๋ชจ๋ธ์„ ์ ์šฉํ•˜์˜€์œผ๋ฉฐ, ์—ฐ๊ตฌ์˜ ์ฃผ์š” ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๊ฒฝ์œ ์ฐจ์˜ PM2.5 ๋ฐฐ์ถœ๋Ÿ‰์ด ๋†’์„์ˆ˜๋ก ํ˜ธํก๊ธฐ๊ณ„ ์งˆํ™˜์œผ๋กœ ์ธํ•œ ์‚ฌ๋ง๋ฅ ์ด ๋†’์•„์ง€๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๊ฒฝ์œ ์ฐจ์˜ PM2.5 ๋ฐฐ์ถœ๋Ÿ‰์€ PM2.5 ๋ฐฐ์ถœ์˜ ์›์ธ ์ค‘ ํ•˜๋‚˜์ด๋ฏ€๋กœ PM2.5 ๋ฐฐ์ถœ์ด๋‚˜ PM2.5 ๋†๋„์— ๋น„ํ•˜์—ฌ ํ˜ธํก๊ธฐ๊ณ„ ์งˆํ™˜์œผ๋กœ ์ธํ•œ ์‚ฌ๋ง๋ฅ ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์ ๊ฒŒ ์ค„ ์ˆ˜ ์žˆ๋Š”๋ฐ, ๊ทธ๋Ÿผ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ํ˜ธํก๊ธฐ๋กœ ์ธํ•œ ์‚ฌ๋ง๋ฅ ์— ์œ ์˜ํ•œ ๊ฒฐ๊ณผ๊ฐ€ ๋„์ถœ๋œ ์ ์€ ๊ธด์š”ํ•œ ๋ฐœ๊ฒฌ์ด๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋‘˜์งธ, PM2.5์˜ 2์ฐจ ์ƒ์„ฑ๋ฌผ์ธ SO2๋Š” ํ—ˆํ˜ˆ์„ฑ ์‹ฌ์žฅ์งˆํ™˜์œผ๋กœ ์ธํ•œ ์‚ฌ๋ง๋ฅ ์— ์–‘(+)์˜ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ ๊ณต๊ฐ„๋”๋นˆ๋ชจํ˜• ๋ถ„์„์„ ํ†ตํ•ด ํ•ด๋‹น ์ง€์—ญ์—์„œ ๋ฐœ์ƒํ•˜๋Š” SO2๊ฐ€ ์ธ๊ทผ ์ง€์—ญ์˜ ํ—ˆํ˜ˆ์„ฑ ์‹ฌ์žฅ์งˆํ™˜ ์‚ฌ๋ง๋ฅ ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ณ , ์ธ๊ทผ ์ง€์—ญ์˜ SO2๋Š” ํ•ด๋‹น ์ง€์—ญ์˜ ์ข…์†๋ณ€์ˆ˜์—๋„ ์–‘(+)์˜ ์˜ํ–ฅ์„ ๋ฏธ์นจ์„ ๋ฐํ˜”๋‹ค. ์…‹์งธ, ์ˆ˜๋„๊ถŒ๊ณผ ์ค‘์†Œ๋„์‹œ์˜ ๋”๋ฏธ๋ณ€์ˆ˜๋ฅผ ์ƒ์„ฑํ•œ ํ›„ ๊ฒฝ์œ ์ฐจ์˜ ๋ฏธ์„ธ๋จผ์ง€ ๋ฐฐ์ถœ๋Ÿ‰๊ณผ์˜ ์ƒํ˜ธ์ž‘์šฉ ํ•ญ์„ ์ด์šฉํ•˜์—ฌ ๋น„์ˆ˜๋„๊ถŒ๊ณผ ์ˆ˜๋„๊ถŒ์˜ ์ฐจ์ด๋ฅผ ์‚ดํŽด๋ณธ ๊ฒฐ๊ณผ, ๊ฒฝ์œ ์ฐจ์˜ PM2.5 ๋ฐฐ์ถœ๋Ÿ‰์ด ์ฆ๊ฐ€ํ•  ๋•Œ ๋น„์ˆ˜๋„๊ถŒ์ด ์ˆ˜๋„๊ถŒ๋ณด๋‹ค ํ˜ธํก๊ธฐ ์‚ฌ๋ง๋ฅ ์˜ ์˜ํ–ฅ์„ ์ƒ๋Œ€์ ์œผ๋กœ ๋” ๋งŽ์ด ๋ฐ›๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ๋น„์ˆ˜๋„๊ถŒ๊ณผ ์ˆ˜๋„๊ถŒ์˜ ์ธํ”„๋ผ ์ฐจ์ด๊ฐ€ ์ž‘์šฉํ•œ ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋˜๋ฉฐ, ํ˜ธํก๊ธฐ ์งˆํ™˜์œผ๋กœ ์ธํ•œ ์‚ฌ๋ง๋ฅ ์— ๋‹ค์–‘ํ•œ ์š”์ธ์ด ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ ํ–ฅํ›„ ์˜๋ฃŒ ๋ฐ ํ™˜๊ฒฝ ์ธํ”„๋ผ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ์ง€์—ญ๋ณ„ ์ฐจ์ด๋ฅผ ์‹ฌ์ธต์ ์œผ๋กœ ์‚ดํŽด๋ณผ ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฒฝ์œ ์ฐจ์˜ PM2.5 ๋ฐฐ์ถœ๋Ÿ‰์ด ํ˜ธํก๊ธฐ์™€ ์‹ฌํ˜ˆ๊ด€๊ณ„๋กœ ์ธํ•œ ์‚ฌ๋ง๋ฅ ์ด ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๊ณต๊ฐ„์ƒ๊ด€์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ๋ถ„์„ํ•  ๊ฒฝ์šฐ ๊ธฐ์กด ๋…ผ๋ฌธ๊ณผ ์–ด๋–ค ์ฐจ์ด๊ฐ€ ์žˆ๋Š”์ง€๋ฅผ ๊ฒ€ํ† ํ•˜์˜€์œผ๋ฉฐ, ์—ฐ๊ตฌ์˜ ์˜์˜ ๋ฐ ์ฐจ๋ณ„์„ฑ์€ ์•„๋ž˜์™€ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๋ถ„์„๋ชจํ˜•์—์„œ ๊ณต๊ฐ„์ž๊ธฐ์ƒ๊ด€์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ์ถ”์ •์น˜๊ฐ€ ๊ณผ์†Œ ๋˜๋Š” ๊ณผ๋Œ€ ์ถ”์ •๋  ๊ฐ€๋Šฅ์„ฑ์„ ๋ฐฉ์ง€ํ•˜์˜€๋‹ค. ๊ณต๊ฐ„์ž๊ธฐ์ƒ๊ด€์„ฑ์„ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•ด ์—ฐ์† ๋ฐ์ดํ„ฐ์— ์ ํ•ฉํ•˜๊ณ , ์ง€์—ญ๊ฐ„ ๊ฑฐ๋ฆฌ๊ฐ€ ๊ฐ€๊นŒ์šธ์ˆ˜๋ก ์„œ๋กœ ์ƒํ˜ธ์ž‘์šฉํ•˜๊ฑฐ๋‚˜ ์˜ํ–ฅ์„ ๋ฏธ์น  ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์„ ๋•Œ ์ ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ์—ญ๊ฑฐ๋ฆฌ ๊ฐ€์ค‘์น˜(IDW) ๋ฐฉ์‹์„ ์„ ํƒํ•˜๋‹ค. ๋‘˜์งธ, ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ตญ๊ฐ€๋Œ€๊ธฐ์ •์ฑ… ์ง€์›์‹œ์Šคํ…œ(CAPSS)์˜ ๋„๋กœ ์ด๋™์˜ค์—ผ์›์˜ ๋Œ€๊ธฐ์˜ค์—ผ๋ฌผ์งˆ ๋ฐฐ์ถœ๋Ÿ‰ ์‚ฐ์ •๋ฐฉ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ ๊ฒฝ์œ ์ฐจ์˜ PM2.5 ๋ฐœ์ƒ๋Ÿ‰์„ ์‚ฐ์ถœํ•˜์—ฌ ๋ชจํ˜•์˜ ์ฃผ์š” ๋ณ€์ˆ˜๋กœ ํ™œ์šฉํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์—์„œ ๊ฒฝ์œ ์ฐจ์˜ PM2.5 ๋ฐฐ์ถœ๋Ÿ‰์˜ ๋ณ€์ˆ˜๊ฐ€ ํ˜ธํก๊ธฐ๋กœ ์ธํ•œ ์‚ฌ๋ง๋ฅ ์— ์œ ์˜ํ•œ ์˜ํ–ฅ๊ด€๊ณ„๋ฅผ ๋ฏธ์น˜๊ณ  ์žˆ์Œ์„ ๋ฐํ˜”๋‹ค. ์…‹์งธ, ์˜จ๋„์™€ ์Šต๋„๊ฐ€ ๋†’์„์ˆ˜๋ก ํ˜ธํก๊ธฐ์™€ ์‹ฌํ˜ˆ๊ด€๊ณ„๋กœ ์ธํ•œ ์‚ฌ๋ง๋ฅ ์— ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, SO2์˜ ๊ฒฝ์šฐ ์‹ฌํ˜ˆ๊ด€๊ณ„๋กœ ์ธํ•œ ์‚ฌ๋ง๋ฅ ์—๋งŒ ์œ ์˜ํ•˜๊ฒŒ ์–‘์˜ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ณต๊ฐ„๋”๋นˆ๋ชจํ˜• ๋ถ„์„ ๊ฒฐ๊ณผ์—์„œ ํ•ด๋‹น ์ง€์—ญ์—์„œ ๋ฐœ์ƒํ•˜๋Š” SO2๋Š” ์ธ๊ทผ ์ง€์—ญ์˜ ํ—ˆํ˜ˆ์„ฑ ์‹ฌ์žฅ์งˆํ™˜ ์‚ฌ๋ง๋ฅ ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ณ , ์ธ๊ทผ ์ง€์—ญ์˜ SO2๋Š” ํ•ด๋‹น ์ง€์—ญ์˜ ์ข…์†๋ณ€์ˆ˜์—๋„ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Š” ๊ณต๊ฐ„์ƒ๊ด€์„ฑ์„ ๊ณ ๋ คํ•˜์ง€ ์•Š์€ ์ผ๋ฐ˜ ๊ณ„๋Ÿ‰๋ชจํ˜•์—์„œ๋Š” ๋ฐํ˜€๋‚ด์ง€ ๋ชปํ–ˆ๋˜ ๊ฒƒ์œผ๋กœ, ํŠนํžˆ IDW ๋ฐฉ์‹์„ ํ†ตํ•œ ๊ณต๊ฐ„๊ฐ€์ค‘์น˜ ํ–‰๋ ฌ์„ ๊ตฌ์„ฑํ•  ๋•Œ ๊ทธ ํŠน์ง•์ด ๋ช…ํ™•ํ•ด์ง์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋„ท์งธ, ์ค‘๊ตญ์˜ ๊ธฐ์ƒ์š”์ธ์„ ๊ณ ๋ คํ•˜๊ธฐ ์œ„ํ•ด ์ค‘๊ตญ์˜ ์‚ฐ๋‘ฅ์„ฑ(Shandong Provinces), ํ—ˆ๋ฒ ์ด์„ฑ(Hebei Provinces), ์žฅ์ˆ˜์„ฑ(Jiangsu Provinces)์˜ ๊ธฐ์˜จ, ์ƒ๋Œ€์  ํก๋„ ๋ฐ ๊ฐ•์ˆ˜๋Ÿ‰์˜ ์›”๋ณ„ ํ‰๊ท  ์ž๋ฃŒ๋ฅผ ๊ณ ๋ คํ•˜์˜€๋‹ค. ๊ตญ์™ธ ๋ณ€์ˆ˜์— ๋Œ€ํ•œ ๊ณ ๋ ค๋Š” ๋ฏธ์„ธ๋จผ์ง€ ๊ด€๋ จ ์—ฐ๊ตฌ์—์„œ ์ค‘์š”ํ•˜๋ฉฐ, ํ–ฅํ›„ ์ธ์ ‘ ๊ตญ๊ฐ€ ๊ฐ„ ์—ฐ๊ตฌ๋„ ํ–ฅํ›„ ์ˆ˜ํ–‰๋  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋‹ค์„ฏ์งธ, ๋น„์ˆ˜๋„๊ถŒ์˜ ํ˜ธํก๊ธฐ์— ์˜ํ•œ ์‚ฌ๋ง๋ฅ ์€ ์ˆ˜๋„๊ถŒ๋ณด๋‹ค ๊ฒฝ์œ ์ฐจ์˜ PM2.5 ๋ฐฐ์ถœ๋Ÿ‰์œผ๋กœ๋ถ€ํ„ฐ์˜ ์˜ํ–ฅ์„ ์ƒ๋Œ€์ ์œผ๋กœ ๋” ๋งŽ์ด ๋ฐ›๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Ÿฌํ•œ ์ฐจ์ด๋Š” ๋น„์ˆ˜๋„๊ถŒ๊ณผ ์ˆ˜๋„๊ถŒ์˜ ์˜๋ฃŒยทํ™˜๊ฒฝ ์ธํ”„๋ผ ๋•Œ๋ฌธ์œผ๋กœ์ธ ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋˜๋ฉฐ, ์ง€์—ญ๋ณ„ ์ฐจ์ด๊ฐ€ ๋‚˜ํƒ€๋‚˜๋Š” ์ด์œ ์— ๋Œ€ํ•œ ์ถ”๊ฐ€์ ์ธ ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ์„ ์ œ๊ธฐํ•˜์˜€๋‹ค. ์ถ”๊ฐ€ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๋น„์ˆ˜๋„๊ถŒ์ด ๊ฒฝ์œ ์ฐจ์˜ PM2.5 ๋ฐฐ์ถœ๋Ÿ‰์œผ๋กœ๋ถ€ํ„ฐ์˜ ์˜ํ–ฅ์„ ์ƒ๋Œ€์ ์œผ๋กœ ๋” ๋งŽ์ด ๋ฐ›๋Š” ๊ฒƒ์ด ํ™•์ธ๋œ๋‹ค๋ฉด PM2.5๋กœ๋ถ€ํ„ฐ ์‹œ๋ฏผ๋“ค์˜ ๊ฑด๊ฐ•์„ ๋ณดํ˜ธํ•˜๊ธฐ ์œ„ํ•ด ๋น„์ˆ˜๋„๊ถŒ์— PM2.5 ๋Œ€ํ”ผ์†Œ๋ฅผ ์„ค์น˜ํ•˜๋Š” ๋‹จ๊ธฐ์ ์ธ ๋Œ€์ฑ… ๋งˆ๋ จ์ด ํ•„์š”ํ•  ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ๋‘ ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” ์ฐจ์ข…๋ณ„(์Šน์šฉ, ์Šนํ•ฉ, ํ™”๋ฌผ), ๊ทœ๋ชจ๋ณ„(์†Œํ˜•, ์ค‘ํ˜•, ๋Œ€ํ˜•) ๊ฒฝ์œ ์ž๋™์ฐจ์˜ PM2.5 ๋ฐœ์ƒ๋Ÿ‰์ด ํ•œ๊ตญ์˜ ๋ฏธ์„ธ๋จผ์ง€ ๋†๋„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜์˜€์œผ๋ฉฐ, ๋Œ€๋„์‹œ์™€ ์ค‘์†Œ๋„์‹œ์— ๋”ฐ๋ผ ๊ฒฐ๊ณผ์— ์ฐจ์ด๊ฐ€ ์žˆ๋Š”์ง€ ํŒŒ์•…ํ•˜์˜€๋‹ค. ๋ถ„์„๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๊ฒฝ์œ  ์Šน์šฉ, ์Šนํ•ฉ ๋ฐ ํ™”๋ฌผ์ฐจ์˜ PM2.5 ๋ฐœ์ƒ๋Ÿ‰์€ ๋ชจ๋‘ ํ•œ๊ตญ์˜ ๋ฏธ์„ธ๋จผ์ง€ ๋†๋„์— ์œ ์˜ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋ฉฐ, ์ด ์ค‘ ํ™”๋ฌผ์ฐจ๊ฐ€ ๊ฐ€์žฅ ํฐ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‘˜์งธ, ํ™”๋ฌผ์ฐจ ์ค‘ ์†Œํ˜• ํ™”๋ฌผ์ฐจ์˜ PM2.5 ๋ฐœ์ƒ๋Ÿ‰์ด ์ข…์†๋ณ€์ˆ˜์— ๊ฐ€์žฅ ํฐ ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ์†Œํ˜• ํ™”๋ฌผ์ฐจ๊ฐ€ ์ค‘๋Œ€ํ˜• ํ™”๋ฌผ์ฐจ์— ๋น„ํ•ด 10๋…„ ์ด์ƒ ๋…ธํ›„๋œ ์ฐจ๋Ÿ‰์ด ๊ฐ€์žฅ ๋งŽ๊ณ , ํŠนํžˆ ์˜์—…์šฉ ์†Œํ˜• ํ™”๋ฌผ์ฐจ๋Š” ์ฃผ๋กœ ํƒ๋ฐฐ๋‚˜ ๋ฐฐ๋‹ฌ ์ฐจ๋Ÿ‰์ด ๋งŽ์•„ ์Šน์šฉ์ฐจ ๋Œ€๋น„ ์ฃผํ–‰๊ฑฐ๋ฆฌ๊ฐ€ ๋†’์•„ ๋‚˜ํƒ€๋‚œ ๊ฒฐ๊ณผ๋กœ ํŒ๋‹จ๋œ๋‹ค(์ด๊ทœ์ง„, 2018). ์…‹์งธ, ๋Œ€๋„์‹œ์—์„œ ๋Œ€ํ˜• ์Šน์šฉ์ฐจ, ์†Œํ˜• ์Šนํ•ฉ์ฐจ, ์ค‘ํ˜• ํ™”๋ฌผ์ฐจ์˜ PM2.5 ๋ฐœ์ƒ๋Ÿ‰์€ ์ค‘์†Œ๋„์‹œ๋ณด๋‹ค ํ•œ๊ตญ์˜ ๋ฏธ์„ธ๋จผ์ง€ ๋†๋„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด ํฐ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Š” ์ˆ˜๋„๊ถŒ์„ ํฌํ•จํ•œ ๋Œ€๋„์‹œ๋Š” ์ธ๊ตฌ๊ฐ€ ๋ฐ€์ง‘๋˜์–ด ๊ตํ†ต์ฒด์ฆ์ด ๋ฐœ์ƒํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์•„ ๋‚˜ํƒ€๋‚œ ๊ฒฐ๊ณผ๋กœ ๋ณด์—ฌ์ง€๋ฉฐ, ์ˆ˜๋„๊ถŒ์„ ์ค‘์‹ฌ์œผ๋กœ ๊ณ ๋†๋„ ๋ฏธ์„ธ๋จผ์ง€๊ฐ€ ๋ฐœ์ƒํ•œ๋‹ค๋Š” ํ˜„์‹ค์„ ๋ฐ˜์˜ํ•œ๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์— ๋”ฐ๋ฅธ ์‹œ์‚ฌ์ ์€ ์•„๋ž˜์™€ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๊ฒฝ์œ ์ฐจ์ข… ์ค‘ ํ™”๋ฌผ์ฐจ์˜ PM2.5 ๋ฐœ์ƒ๋Ÿ‰์ด ํ•œ๊ตญ์˜ ๋ฏธ์„ธ๋จผ์ง€ ๋†๋„์— ๋” ํฐ ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ณ  ์žˆ์œผ๋ฏ€๋กœ ์กฐ๊ธฐํ์ฐจ ๋ฐ ์ €๊ณตํ•ด์ฐจ ์šดํ–‰ ๋“ฑ์˜ ๊ฒฝ์œ ์ฐจ ๋Œ€์ฑ…์—์„œ ํ™”๋ฌผ์ฐจ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ์ œ๋„ ๊ฐœ์„ ๊ณผ ์ดํ–‰์ด ํ•„์š”ํ•  ๊ฒƒ์œผ๋กœ ์‚ฌ๋ฃŒ๋œ๋‹ค. ํŠนํžˆ ํ™”๋ฌผ์ฐจ ์‹œ์žฅ์—์„œ ๋‹จ๊ธฐ์ ์œผ๋กœ๋Š” ๊ฒฝ์œ ์ฐจ ๋Œ€์ฒด๊ฐ€ ๊ฐ€๋Šฅํ•œ ์—ฐ๋ฃŒ ์ฐจ๋Ÿ‰์˜ ๋ณด๊ธ‰์„ ํ™•๋Œ€ํ•ด์•ผ ํ•˜๊ณ , ์ค‘์žฅ๊ธฐ์ ์œผ๋กœ๋Š” ๋ฏธ์„ธ๋จผ์ง€ ๋ฌด๋ฐฐ์ถœ ํ™”๋ฌผ์ฐจ๋ฅผ ๋ณด๊ธ‰ํ•˜๋Š” ์ „๋žต์„ ๋ณ‘ํ–‰ํ•ด์•ผ ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค(ํ•œ์ง„์„, 2020). ๋‘˜์งธ, ์ฐจ์ข…๋ณ„ ๊ทœ๋ชจ๋ฅผ ๊ณ ๋ คํ•  ๋•Œ, ์†Œํ˜• ํ™”๋ฌผ์ฐจ์˜ PM2.5 ๋ฐœ์ƒ๋Ÿ‰์ด ํ•œ๊ตญ์˜ ๋ฏธ์„ธ๋จผ์ง€ ๋†๋„์— ๋” ํฐ ์˜ํ–ฅ์„ ์ฃผ๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๋ฏธ์„ธ๋จผ์ง€ ๋Œ€์ฑ…์—์„œ ์ฐจ์ข… ๋ฐ ๊ทœ๋ชจ์— ๋”ฐ๋ผ ์„ธ๋ถ€ ๋Œ€์ฑ…์„ ๋งˆ๋ จํ•ด์•ผ ํ•˜๋ฉฐ, ํŠนํžˆ ๋…ธํ›„๋œ ์ฐจ๋Ÿ‰์ด ๋งŽ์€ ์†Œํ˜• ํ™”๋ฌผ์ฐจ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ๋ฏธ์„ธ๋จผ์ง€ ์ €๊ฐ ๋Œ€์ฑ…์„ ๊ฐœ์„ ํ•ด์•ผ ํ•œ๋‹ค. ์…‹์งธ, ๋Œ€ํ˜• ์Šน์šฉ์ฐจ, ์†Œํ˜• ์Šนํ•ฉ์ฐจ ๋ฐ ์†Œํ˜• ํ™”๋ฌผ์ฐจ์˜ PM2.5 ๋ฐœ์ƒ๋Ÿ‰์ด ๋Œ€๋„์‹œ์—์„œ ํ•œ๊ตญ์˜ PM2.5์— ๋” ํฐ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒฐ๊ณผ๋Š” ์ง€์—ญ๋ณ„ ๋งž์ถคํ˜• ๋ฏธ์„ธ๋จผ์ง€ ์ €๊ฐ ๋Œ€์ฑ…์˜ ์ค‘์š”์„ฑ์„ ์ฆ๋ช…ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์ •๋ถ€๊ฐ€ ์ง€์ž์ฒด๋ณ„๋กœ ๋ฏธ์„ธ๋จผ์ง€ ๊ด€๋ฆฌ ์ˆ˜๋ฆฝ ๋Œ€์ฑ…์„ ๋งˆ๋ จํ•˜๊ณ  ํ‰๊ฐ€ํ•˜๋Š” ์ฒด๊ณ„๋ฅผ ๋งˆ๋ จํ•˜๋Š” ๊ฒƒ์ด ์‹œ๊ธ‰ํ•˜๋‹ค. ์—ฐ๊ตฌ๋Š” ๊ธฐ์กด ์„ ํ–‰์—ฐ๊ตฌ๋“ค์„ ํ†ตํ•ด ๋ถ€๋ฌธ๋ณ„๋กœ ๋Œ€ํ‘œ๋ณ€์ˆ˜๋ฅผ ๋„์ถœํ•˜์—ฌ ๋ถ„์„ํ•˜๊ณ  ํ•œ๊ตญ์˜ ์ •์ฑ…๋ฐฉํ–ฅ์„ ์ ๊ฒ€ํ–ˆ๋‹ค๋Š” ์ ์—์„œ ์˜์˜๋ฅผ ๊ฐ€์ง€๋ฉฐ, ํŠนํžˆ ์ฐจ์ข…๋ณ„(์Šน์šฉ, ์Šนํ•ฉ, ํ™”๋ฌผ), ๊ทœ๋ชจ๋ณ„(์†Œํ˜•, ์ค‘ํ˜•, ๋Œ€ํ˜•)๋กœ ์„ธ๋ถ„ํ™”ํ•˜์—ฌ ๊ฒฝ์œ ์ฐจ์˜ PM2.5 ๋ฐœ์ƒ๋Ÿ‰์ด ํ•œ๊ตญ์— ๋ฏธ์„ธ๋จผ์ง€์— ์ฃผ๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•œ ์ ์€ ์—ฐ๊ตฌ์˜ ์ฐจ๋ณ„์„ฑ์œผ๋กœ ๊ฐ•์กฐ๋  ์ˆ˜ ์žˆ๋‹ค. ์„ธ ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ํ˜„์žฌ ์šฐ๋ฆฌ๋‚˜๋ผ์˜ PM ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ณ  ๋””์ ค์—ฐ๋ฃŒ ๋ฐ ์ฐจ๋Ÿ‰์— ๋Œ€ํ•œ PM ์ •์ฑ… ๊ด€๋ จ ์ •์ฑ… ๋ฐ ๊ด€๋ฆฌ๋ฐฉํ–ฅ์— ๋Œ€ํ•œ ์ „๋ฌธ๊ฐ€(์ •๋ถ€ ๊ณต๋ฌด์› ๋ฐ ์—ฐ๊ตฌ์›)์˜ ์˜๊ฒฌ์„ ๋ถ„๋ฅ˜ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ ๊ฒฝ์œ ์ •์ฑ…์— ๋Œ€ํ•œ ์ „๋ฌธ๊ฐ€๋“ค์˜ ์ธ์‹์€ ๊ฒฝ์œ ๊ทœ์ œ ์˜นํ˜ธํ˜•, ๊ฒฝ์œ ๊ทœ์ œ ์†Œ๊ทนํ˜•, ์ •๋ถ€์˜ˆ์‚ฐ์ง€์›ํ˜•, ์—ฌ๋ก  ๋ฏผ๊ฐ๋Œ€์‘ํ˜•์œผ๋กœ ๊ตฌ๋ถ„๋œ๋‹ค. Q-๋ฐฉ๋ฒ•๋ก ๊ณผ ์‹œ์Šคํ…œ ์‚ฌ๊ณ ๋ถ„์„ ๊ฒฐ๊ณผ๋ฅผ ํ† ๋Œ€๋กœ ๋ณด๋ฉด, ๋น„ํšจ์œจ์ ์ธ ๊ฒฝ์œ ์ •์ฑ…์˜ ์ฃผ์š” ์›์ธ์€ ๋ถ€์ฒ˜์˜ ์—ญํ•  ๋ถ€์žฌ, ์ดํ•ด๊ด€๊ณ„์ž์—๊ฒŒ ์ •๋ณด ์ „๋‹ฌ ๋ถ€์กฑ, ์‹œ๋ฏผ์ฐธ์—ฌ ๋ถˆํ™•์‹ค์„ฑ ๋“ฑ์ด๋‹ค. ์ „๋ฌธ๊ฐ€๋“ค์€ ์ •์ฑ…์— ๋Œ€ํ•œ ๊ตญ๋ฏผ๋“ค์˜ ๋ฐ˜๋ฐœ์— ๋Œ€ํ•ด ์šฐ๋ คํ•˜๋ฉฐ, ์ด๋กœ ์ธํ•ด ์ •์ฑ… ์‹œํ–‰์ด ์ง€์—ฐ๋˜๊ณ  ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์‹œ์‚ฌ์ ์„ ๊ฒ€ํ† ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์ฒซ์งธ, 4๊ฐœ ์œ ํ˜• ์ค‘ ๋งค๊ฐœ์  ์—ญํ• ์„ ํ•  ์ˆ˜ ์žˆ๋Š” ์ง‘๋‹จ์„ ์ฐพ์•„์•ผ ํ•œ๋‹ค. 4๊ฐœ ์œ ํ˜• ์ค‘ ์ •๋ถ€์˜ˆ์‚ฐ์ง€์›ํ˜•์€ ์ •์ฑ…์˜ ์žฅ๋‹จ์ ์„ ๊ฐ•์กฐํ•˜๊ณ  ๊ฐœ์„ ์˜ ํ•„์š”์„ฑ์„ ๋Œ€๋ถ€๋ถ„ ์ˆ˜์šฉํ•  ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ •์ฑ…ํšจ๊ณผ๋ฅผ ๊ธ์ •์ ์œผ๋กœ ํ‰๊ฐ€ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ค‘์žฌ์ž ์—ญํ• ์„ ํ•˜๊ธฐ์— ์ตœ์ ์˜ ๊ทธ๋ฃน์ด๋‹ค. ์ค‘์žฌ์ž ์—ญํ• ์„ ํ•  ์ˆ˜ ์žˆ๋Š” ๋‹จ์ฒด๋ฅผ ์„ ํƒํ•œ ๋’ค ์‹œ๋ฏผ๊ถŒ ์ฐธ์—ฌ๋ฅผ ์›ํ•˜๋Š”์ง€, ์–ด๋–ค ํ˜•ํƒœ์˜ ์ž๊ธˆ์„ ์ง€์›ํ• ์ง€ ํ”ผ๋“œ๋ฐฑ์„ ๋ฐ›์„ ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋‘˜์งธ, ๋ถ€์ฒ˜ ๊ฐ„ ์ดํ•ด์™€ ์ •๋ฆฌ๋ฅผ ๋†’์ด๊ธฐ ์œ„ํ•ด ๋ถ€์ฒ˜์˜ ๊ธฐ๋Šฅ๊ณผ ์—ญํ• ์„ ์žฌ๊ณ ํ•ด์•ผ ํ•œ๋‹ค. ๋˜ํ•œ ์ดํ•ด๊ด€๊ณ„์ž๋“ค ๊ฐ„์˜ ๋ณด๋‹ค ๊ฑด์ „ํ•˜๊ณ  ์ง€์†์ ์ธ ํ˜‘๋ ฅ์„ ์œ„ํ•ด ๋ถ€์ฒ˜ ๊ฐ„ ์—ญํ• ๊ณผ ๊ธฐ๋Šฅ์— ๋Œ€ํ•œ ๋” ๋‚˜์€ ์ดํ•ด๊ฐ€ ๊ณ ๋ ค๋˜๊ณ  ์žฌ์ •์˜๋  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋„ค ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š” ์กฐ๊ฑด๋ถ€ ํ‰๊ฐ€๋ฐฉ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ์ •์ฑ… ๊ฐœ์„ ์„ ํ†ตํ•ด ๋Œ€๊ธฐ์˜ค์—ผ์œผ๋กœ ์ธํ•œ ์‚ฌ๋ง ๊ฐ€๋Šฅ์„ฑ์„ 0.0001 ๊ฐ์†Œ์‹œํ‚ค๋Š” ์‹œ๋‚˜๋ฆฌ์˜ค์— ๋Œ€ํ•œ ์‘๋‹ต์ž์˜ ์ง€๋ถˆ์˜์‚ฌ๊ธˆ์•ก(WTP)๋ฅผ ๋„์ถœํ•˜์˜€๋‹ค. ํ†ต์ œ๋ณ€์ˆ˜๋ฅผ ํฌํ•จํ•˜์—ฌ ์ด์ค‘๊ฒฝ๊ณ„ ์–‘๋ถ„์„ ํƒํ˜•(DBDC) ๋ถ„์„ ๊ฒฐ๊ณผ์— ๋”ฐ๋ฅด๋ฉด ํ‰๊ท  ์ง€๋ถˆ์˜์‚ฌ๊ธˆ์•ก์€ ์—ฐ๊ฐ„ 41,643์›(์›” ํ‰๊ท  3,470์›)์ด๋ฉฐ, ๋‚ฉ๋ถ€๊ฑฐ๋ถ€ ์‘๋‹ต์ž๋ฅผ ํฌํ•จํ•  ๊ฒฝ์šฐ ํ‰๊ท  ์ง€๋ถˆ์˜์‚ฌ๊ธˆ์•ก์€ ์—ฐ๊ฐ„ 22,397์›(์›” ํ‰๊ท  1,866์›)์ด๋‹ค. PM2.5๋กœ ์ธํ•œ ์‚ฌ๋ง๋ฅ ์„ 1๋งŒ๋ถ„์˜ 1 ์ˆ˜์ค€์œผ๋กœ ๊ฐ์†Œ์‹œํ‚ค๊ธฐ ์œ„ํ•œ ๊ฒฝ์ œ์  ๊ฐ€์น˜๋Š” ์—ฐ๊ฐ„ ์•ฝ 8,940์–ต์›(8940๋งŒ90๋งŒ7100์›)์— ๋‹ฌํ•œ๋‹ค. ํ†ต๊ณ„์  ์ƒ๋ช…๊ฐ€์น˜์˜ ๊ฐ€์น˜๋Š” 0.4164์–ต์›์ด๋ฉฐ, 10๋…„ ์ง€๊ธ‰๊ธฐ๊ฐ„์„ ์ ์šฉํ•˜์—ฌ ๋„์ถœ๋œ ํ†ต๊ณ„์  ์ƒ๋ช…๊ฐ€์น˜๋Š” ์•ฝ 41์–ต 643๋งŒ์›์œผ๋กœ ์ถ”์ •๋˜์—ˆ๋‹ค. ์ง€์—ญ ๋ณ„๋กœ ๋ฏธ์„ธ๋จผ์ง€ ์ •์ฑ… ๊ฐœ์„ ์„ ํ†ตํ•ด ์‚ฌ๋ง ๊ฐ€๋Šฅ์„ฑ์„ 0.0001 ๊ฐ์†Œ์‹œํ‚ค๋Š” ์‹œ๋‚˜๋ฆฌ์˜ค์— ๋Œ€ํ•œ ์ง€๋ถˆ์˜์‚ฌ๊ธˆ์•ก์— ์ฐจ์ด๊ฐ€ ์กด์žฌํ•  ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ, ํ–ฅํ›„ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์ฐจ์ด๊ฐ€ ๋ฐœ์ƒํ•˜๋Š” ์›์ธ์— ๋Œ€ํ•ด ๊ทœ๋ช…ํ•ด์•ผ ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋˜ํ•œ ๊ตญ๋‚ด์™ธ ์—ฐ๊ตฌ ๋น„๊ต์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๋ฏธ์„ธ๋จผ์ง€๋กœ ์ธํ•œ ์กฐ๊ธฐ์‚ฌ๋ง๋ฅ  ๊ฐ์†Œ์— ๋Œ€ํ•œ ํšจ์šฉ ์ˆ˜์ค€ ๋ฐ ์œ„์น˜๋ฅผ ํŒŒ์•…ํ•จ์œผ๋กœ์จ ๊ตญ๋‚ด ๋ฏธ์„ธ๋จผ์ง€ ์ €๊ฐ ๋Œ€์ฑ… ๋ฐ ์‹œ๋ฏผ ๊ฑด๊ฐ•๋ณดํ˜ธ๋ฅผ ์œ„ํ•œ ์˜ˆ์‚ฐ ๊ณ„ํš ์‹œ ์ฐธ๊ณ  ์ž๋ฃŒ๋กœ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๋ฏธ์„ธ๋จผ์ง€ ๋ถ„์„ ์—ฐ๊ตฌ์— ๋Œ€ํ•œ ์ผ๋ฐ˜ ์‹œ๋ฏผ๋“ค์˜ ๊ณต๊ฐ๊ณผ ์ดํ•ด๋ฅผ ์ฆ๋Œ€์‹œํ‚ค๊ธฐ ์œ„ํ•ด ๋ฏธ์„ธ๋จผ์ง€ ์ˆ˜์†ก๋ถ€๋ฌธ์˜ ํ˜„ํ™ฉ ๋ฐ ๊ฑด๊ฐ• ์˜ํ–ฅ์— ๋Œ€ํ•œ ์ดํ•ด, ๋ฏธ์„ธ๋จผ์ง€ ์ €๊ฐ์„ ์œ„ํ•œ ์ˆ˜์†ก๋ถ€๋ฌธ ์ •์ฑ… ์ดํ–‰ ๋ฐฉํ–ฅ ์ œ์‹œ, ์ •์ฑ… ๊ฐœ์„ ์— ๋”ฐ๋ฅธ ์‚ฌํšŒ์  ํ›„์ƒํšจ๊ณผ ๋ถ„์„์˜ ๋…ผ๋ฆฌ์  ํ๋ฆ„์œผ๋กœ ๊ตฌ์„ฑํ•˜์˜€์œผ๋ฉฐ, ํ–ฅํ›„ ์ˆ˜์†ก๋ถ€๋ฌธ ๋ฏธ์„ธ๋จผ์ง€ ์ •์ฑ… ๊ฐœ์„  ๋ฐ ์‚ฌํšŒ์ •์ฑ…์  ์ด์Šˆ ๋Œ€์‘์„ ์œ„ํ•ด ๋ฐฉํ–ฅ์„ฑ์„ ํƒ€์ง„ํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ์ดˆ ์ž๋ฃŒ๋กœ ํ™œ์šฉ๋˜๊ธฐ๋ฅผ ๊ธฐ๋Œ€ํ•œ๋‹ค.Among the causes of PM2.5, PM2.5 generated from roads in the transportation sector is a pollutant that can easily be exposed to citizens and cause health risks. It enters the lungs through breathing and negatively affects human diseases and deaths. Empirical analysis studies related to PM2.5 in the transportation sector are critical in establishing the validity and basis for alternatives to PM2.5 policies. This study aims to determine whether the generation of PM2.5 from diesel vehicles in the transportation sector affects the mortality rate of disease and to identify the social welfare effects of PM policy improvement by reviewing the policy implementation direction for PM2.5 reduction. The first study aims to analyze the effect of PM2.5 emissions from diesel vehicles on the mortality rate of respiratory diseases and other cardiovascular disease and to identify the factors that affect them. A spatial panel model was applied in this study to consider the spatial adjacency between regions by using autonomous district, city, and county data in the metropolitan area from 2015 to 2019. The main results of the study are as follows. First, it was confirmed that the higher the PM2.5 emission of diesel vehicles, the higher the mortality rate of respiratory diseases. Since PM2.5 emissions from diesel vehicles are one of the causes of PM2.5 emissions, the effect on the mortality rate of respiratory diseases may be smaller than that of PM2.5 emissions or concentrations. However, it is significant in that it is an important finding about the mortality rate of respiratory diseases. Second, SO2, a secondary product of PM2.5, was found to have a positive (+) effect on the mortality rate of other cardiovascular disease. In addition, through Spatial Durbin Model analysis, it was found that SO2 generated in the area affects the other cardiovascular disease mortality rate in the neighboring area, and SO2 in the neighboring area has a positive (+) effect on the dependent variable in the area. Third, after generating dummy variables for the metropolitan area and small and medium-sized cities, the difference between the non-metropolitan area and the metropolitan area was examined using the interaction term with the PM2.5 emissions of diesel vehicles. It was found to be more affected by respiratory mortality than metropolitan areas when PM2.5 emissions from diesel cars increased. These results are attributed to the difference in infrastructure between non-metropolitan and metropolitan areas since various factors can affect the mortality rate of respiratory diseases. Therefore, it is necessary to examine regional differences in-depth in the future by considering medical and environmental infrastructure. This study examines how the PM2.5 emissions of diesel vehicles differ from existing research papers when analyzing the effect of the mortality rate of respiratory and cardiovascular systems in consideration of spatial correlation. The significance and differentiation of the study are as follows. First, the analysis model prevented the possibility of underestimating or overestimating the estimate by considering spatial autocorrelation. To confirm spatial autocorrelation, we selected an Inverse Distance Weighting (IDW) method that is suitable for continuous data as it can be applied when the closer the distance between regions is, the higher the possibility of interacting or influencing each other. Second, this study calculated PM2.5 emissions of diesel vehicles using the National Clean Air Policy Support System (CAPSS) method of calculating air pollutant emissions from road non-point pollutant sources and used them as the main variable of the model. The study results showed that the variable of PM2.5 emissions of diesel vehicles had a significant effect on the mortality rate from the respiratory tract. Third, the higher the temperature and humidity, the more positive the mortality rate of the respiratory and cardiovascular systems was found. Furthermore, in the case of SO2, only the mortality rate of the cardiovascular system was found to have a significant positive effect. In the spatial Durbin model analysis, it was found that SO2 generated in the area affects other cardiovascular disease mortality rate of the nearby area, and SO2 in the neighboring area also affects the dependent variable. This was not found in the general econometric model that did not consider spatial correlation. It was confirmed that the characteristics became clear when constructing the spatial weight matrix through the IDW method. Fourth, to consider meteorological factors in China, monthly average temperature, relative absorption, and precipitation data in Shandong Provinces, Hebei Provinces, and Jiangsu Provinces in China were considered. Consideration of foreign variables is essential in PM2.5-related studies, and studies between neighboring countries need to be conducted in the future. Fifth, it was found that the respiratory mortality rate in non-metropolitan areas was relatively more affected by PM2.5 emissions from diesel vehicles than in metropolitan areas. This difference is likely due to the medical and environmental infrastructure of non-metropolitan and metropolitan areas. The need for additional research on the reason for regional differences has been raised. Suppose further research confirms that the non-metropolitan area is relatively more affected by the PM2.5 emissions of diesel cars. In that case, short-term measures to install PM2.5 shelters in the non-metropolitan area will be needed to protect citizens' health from PM2.5. The second study analyzed the effect of PM2.5 emissions from diesel vehicles by vehicle type (passenger car, van, cargo) and size (small, medium, large) on PM2.5 concentration in Korea and identified whether there were differences in results depending on large and small cities. The analysis results are as follows. First, the amount of PM2.5 generated by diesel passenger cars, vans, and trucks all significantly affected the concentration of PM2.5in Korea, and trucks were found to have the most significant effect. Second, it was found that the amount of PM2.5 generated by small trucks among cargo trucks significantly influenced the dependent variable. This result is believed to be the result of the fact that there are more small trucks that are ten years older compared to medium and large trucks. In particular, small trucks for business use have a higher mileage than passenger cars (Kyujin Lee, 2018). Third, the PM2.5 generation of large passenger cars, small vans, and medium-sized cargo trucks in large cities was found to have a more significant impact on the concentration of PM2.5 in Korea than in small and medium-sized cities. This is seen as a result of the fact that large cities, including the metropolitan area, are densely populated and often experience traffic jams, and reflects the reality that high concentrations of PM2.5 occur in the metropolitan area. The implications of the study results are as follows. First, among diesel vehicles, the amount of PM2.5 generated by trucks significantly impacts the concentration of PM2.5 in Korea. Therefore, improving and implementing the system centered on trucks in diesel vehicle measures such as early scrapping and low-emission vehicle operation is considered necessary. In particular, it is necessary to expand the supply of fuel vehicles that can replace diesel vehicles in the short term in the truck market and to combine strategies to supply PM2.5-free trucks in the mid-to-long term (Hhan, 2020). Second, considering the size of each car type, the PM2.5 generation of small trucks has a more significant impact on Korea's PM2.5 concentration. Therefore, detailed measures should be prepared according to the type and size of PM2.5 measures, especially for small trucks with many old vehicles. Third, the PM2.5 generation of large passenger cars, small vans, and small trucks significantly impact Korea's PM2.5 in large cities, proving the importance of customized PM2.5 reduction measures by region, and the government must establish and evaluate PM2.5 management measures for each local government. The study is significant because it derives and analyzes representative variables by sector through previous studies and examines Korea's policy direction. In particular, the study's differentiation can be emphasized by analyzing the effect of the PM2.5 generation of diesel vehicles on Korea's PM2.5 concentration by vehicle types (passenger car, van, cargo) and size (small, medium, large). The third study aims to solve the current PM problem in Korea and classify the opinions of experts (government officials and researchers) on policies and management directions related to PM policies for diesel fuel and vehicles. As a result of the study, experts' perceptions of diesel policy are divided into advocacy type, passive regulation type, government budget support type, and public opinion sensitive response type. Based on the Q-methodology and system accident analysis results, the main causes of inefficient diesel policy are the lack of role of ministries, lack of information delivery to stakeholders, and uncertainty of citizen participation. Experts are concerned about the public backlash against the policy, delaying its implementation. Therefore, it is necessary to review the following implications. First, finding a group that can mediate among the four types is necessary. Among the four types, the government budget support type is the best group to serve as a mediator because it not only emphasizes the strengths and weaknesses of the policy and accepts most of the need for improvement but also positively evaluates the policy effects. After selecting an organization that can act as a mediator, it is necessary to receive feedback on whether it wants to participate in citizenship and what funding it will provide. Second, the functions and roles of ministries should be reconsidered to enhance understanding and organization among ministries. Furthermore, a better understanding of inter-ministerial roles and functions must be considered and redefined for more sustained and continuous stakeholder cooperation. In the fourth study, respondents' willingness to pay (WTP) was derived for a scenario that reduces the possibility of death due to air pollution by 0.0001 through policy improvement using the conditional evaluation method. According to the results of the Double Bounded Dichotomous Choice (DBDC) analysis, including control variables, the average willingness to pay is KRW 41,643 per year (on average KRW 3,470 per month). The average willingness to pay is KRW 22,397 per year (on average KRW 1,866 per month), including those who refuse to pay. The economic value to reduce the mortality rate due to PM2.5 to one in ten thousand of the level is about KRW 894 billion (KRW 89.4 million 9007,100) per year. The statistical life value is calculated as KRW 0.4164 billion, and the statistical life value derived by applying a 10-year payment period was estimated to be about KRW 4.114 billion. In future studies, there may be differences in willingness to pay due to the improvement of PM2.5 by region. Furthermore, the cause of such differences needs to be investigated. In addition, if the level of utility for reducing premature mortality rates due to PM2.5 is identified through a comparison of domestic and foreign studies, it can be used as basic data for establishing measures for PM2.5 and health protection. This study consists of understanding the current status and health effects of PM2.5 in transportation, presenting policies for reducing PM2.5, and analyzing social welfare effects according to policy improvement. This study is expected to be used as basic research material to improve PM2.5 policy and respond to social policy issues in the future.Chapter 1. Introduction 1 1.1. Background 1 1.2. Research Scope and Purpose 3 1.3. Research Structure 3 Chapter 2. Is the mortality rate by disease spatially linked to the PM2.5 emission in the transportation sector 7 2.1. Study Background 7 2.2. Literature Review 9 2.3. Research Method 16 2.4. Results 29 2.5. Conclusion and Discussion 42 Chapter 3. Does PM2.5 emission by diesel vehicle type and size have a different effect on PM2.5 concentration 48 3.1. Study Background 48 3.2. Literature Review 50 3.3. Research Method 60 3.4. Results 69 3.5. Conclusion and Discussion 85 Chapter 4. What are the main factors that interfere with the PM2.5 policy for diesel cars 87 4.1. Study Background 87 4.2. Literature Review 90 4.3. Research Method 96 4.4. Results 103 4.5. Conclusion and Discussion 118 Chapter 5. What is the monetary value of the decrease in the probability of death by PM2.5 121 5.1. Study Background 121 5.2. Literature Review 122 5.3. Research Method 131 5.4. Results 142 5.5. Conclusion and Discussion 165 Chapter 6. Conclusion 167 6.1. Summary of Findings and Implications 168 6.2. Limitations of the Studies and Future Research 173 Bibliography 176 Abstract in Korean 188๋ฐ•

    Variability-aware Neo4j for Analyzing a Graphical Model of a Software Product Line

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    A Software product line (SPLs) eases the development of families of related products by managing and integrating a collection of mandatory and optional features (units of functionality). Individual products can be derived from the product line by selecting among the optional features. Companies that successfully employ SPLs report dramatic improvements in rapid product development, software quality, labour needs, support for mass customization, and time to market. In a product line of reasonable size, it is impractical to verify every product because the number of possible feature combinations is exponential in the number of features. As a result, developers might verify a small fraction of products and limit the choices offered to consumers, thereby foregoing one of the greatest promises of product lines โ€” mass customization. To improve the efficiency of analyzing SPLs, (1) we analyze a model of an SPL rather than its code and (2) we analyze the SPL model itself rather than models of its products. We extract a model comprising facts (e.g., functions, variables, assignments) from an SPLโ€™s source-code artifacts. The facts from different software components are linked together into a lightweight model of the code, called a factbase. The resulting factbase is a typed graphical model that can be analyzed using the Neo4j graph database. In this thesis, we lift the Neo4j query engine to reason over a factbase of an entire SPL. By lifting the Neo4j query engine, we enable any analysis that can be expressed in the query language to be applicable to an SPL model. The lifted analyses return variability-aware results, in which each result is annotated with a feature expression denoting the products to which the result applies. We evaluated lifted Neo4j on five real-world open-source SPLs, with respect to ten commonly used analyses of interest. The first evaluation aims at comparing the performance of a post-processing approach versus an on-the-fly approach computing the feature expressions that annotate to variability-aware results of lifted Neo4j. In general, the on-the-fly approach has a smaller runtime compared to the post-processing approach. The second evaluation aims at assessing the overhead of analyzing a model of an SPL versus a model of a single product, which ranges from 1.88% to 456%. In the third evaluation, we compare the outputs and performance of lifted Neo4j to a related work that employs the variability-aware V-Soufflรฉ Datalog engine. We found that lifted Neo4j is usually more efficient than V-Soufflรฉ when returning the same results (i.e., the end points of path results). When lifted Neo4j returns complete path results, it is generally slower than V-Soufflรฉ, although lifted Neo4j can outperform V-Soufflรฉ on analyses that return short fixed-length paths

    Representations of Materials for Machine Learning

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    High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by learning relationships among composition, structure, and properties and by exploiting such relations for design. However, to build these connections, materials data must be translated into a numerical form, called a representation, that can be processed by a machine learning model. Datasets in materials science vary in format (ranging from images to spectra), size, and fidelity. Predictive models vary in scope and property of interests. Here, we review context-dependent strategies for constructing representations that enable the use of materials as inputs or outputs of machine learning models. Furthermore, we discuss how modern ML techniques can learn representations from data and transfer chemical and physical information between tasks. Finally, we outline high-impact questions that have not been fully resolved and thus, require further investigation.Comment: 20 pages, 5 figures, To Appear in Annual Review of Materials Research 5
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