• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.21-35
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• 2023

Expectations surrounding generative AI technology and its profound ramifications are sweeping across various industrial domains. Given the anticipated pivotal role of the startup ecosystem in the utilization and advancement of generative AI technology, it is imperative to cultivate a deeper comprehension of the present state and distinctive attributes characterizing venture capital (VC) investments within this domain. The current investigation delves into South Korea's landscape of VC investment deals and prognosticates the projected VC investments by juxtaposing these against the United States, the frontrunner in the generative AI industry and its associated ecosystem. For analytical purposes, a compilation of 286 investment deals originating from 117 U.S. generative AI startups spanning the period from 2008 to 2023, as well as 144 investment deals from 42 South Korean generative AI startups covering the years 2011 to 2023, was amassed to construct new datasets. The outcomes of this endeavor reveal an upward trajectory in the count of VC investment deals within both the U.S. and South Korea during recent years. Predominantly, these deals have been concentrated within the early-stage investment realm. Noteworthy disparities between the two nations have also come to light. Specifically, in the U.S., in contrast to South Korea, the quantum of recent VC deals has escalated, marking an augmentation ranging from 285% to 488% in the corresponding developmental stage. While the interval between disparate investment stages demonstrated a slight elongation in South Korea relative to the U.S., this discrepancy did not achieve statistical significance. Furthermore, the proportion of VC investments channeled into generative AI enterprises, relative to the aggregate number of deals, exhibited a higher quotient in South Korea compared to the U.S. Upon a comprehensive sectoral breakdown of generative AI, it was discerned that within the U.S., 59.2% of total deals were concentrated in the text and model sectors, whereas in South Korea, 61.9% of deals centered around the video, image, and chat sectors. Through forecasting, the anticipated VC investments in South Korea from 2023 to 2029 were derived via four distinct models, culminating in an estimated average requirement of 3.4 trillion Korean won (ranging from at least 2.408 trillion won to a maximum of 5.919 trillion won). This research bears pragmatic significance as it methodically dissects VC investments within the generative AI domain across both the U.S. and South Korea, culminating in the presentation of an estimated VC investment projection for the latter. Furthermore, its academic significance lies in laying the groundwork for prospective scholarly inquiries by dissecting the current landscape of generative AI VC investments, a sphere that has hitherto remained void of rigorous academic investigation supported by empirical data. Additionally, the study introduces two innovative methodologies for the prediction of VC investment sums. Upon broader integration, application, and refinement of these methodologies within diverse academic explorations, they stand poised to enhance the prognosticative capacity pertaining to VC investment costs.

• Magazine of the Korean Society of Agricultural Engineers
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• v.11 no.1
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• pp.1604-1615
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• 1969

This experiment was carried out as one of the basic studies to improve the acid resistance of concrete and it was conducted to investigate some relations among physical properties such as basorption, ratio of water to cement, compressive strength, density and ratio of mix to weight losses of mortar when exposed to 0.1 N solution of hydrochrolic acid. The results obtained from the limited data secured so far in this experiment are summarized as follows: 1. The specimens used in the experiment were made of 5 cubic centimeters of mortar having such various ratios of mix by weight as 1 : 1, 1 : 3, 1 : 5, 1 : 7, 1 : 10. 2. Physical tests included compressive strengths at 7 days, 28 days, 3 months, and 6 month, and 5 hour boiling absorption test. 3. In acid test, every specimen was immersed into 0.1 N solution of hydrochrolic acid. The specimens exposed to the acid solution were weighed to determine the weight losses of the acid-corroded at one week interval for 7 weeks exposure, and the old acid solutions were also changed to fresh one when weighed the weight losses by acid attack at one week interval. 4. The correlative relations were found among physical properties and they are expressed by certain formulas as follows; i) Relation between ratio of mix and absorption Y = 1.036x + 13.53 where Y: absorption(%) X: ratio of mix ii) Relation between ratio of mix and ratio of water-cement Y = 0.204x + 0.214 where Y: ratio of water-cement. X: ratio of mix iii) Relation between ratio of water-cement and absorption Y = 5.01x + 12.53 where Y: absorption(%). X: ratio of water-cement iv) Relation between density and absorption Y = 50.6 - 0.0176X where Y: absorption(%). X: density($kg/m^3$) v) Relation between density and ratio of water cement Y = 7.2183 - 0.0033X where Y: ratio of water-cement . X: density($kg/m^3$) 5. After completing the acid exposure test the specimens were corroded and , the per cent ranges of weight losses varies from a minimum of 20.4 per cent at a 1 : 1 mix to a maximum of 92.0 per cent at a 1:10 mix 6. The correlative relations of physical properties of mortar to weight losses by acid attak were found and they are also expressed by certain formulas as follows: i) Relation between weight losses and ratio of mix Y = 8.59X + 8.63 where Y: weight losses(%), X: ratio of mix ii) Relation between wieght losses and absorption Y = 0.121x + 12.43 where Y: absorption(%). X: weight losses(%) iii) Relation between weight losses and ratio of w/c Y = 0.0226X + 0.07 where Y: ratio of w/c X: weight losses(%) iv) Relation between weight losses and compressive strength LogY = 3.6097 - 0.05058X + 0.00022$X^2$ where Y: compressive strength ($kg/cm^3$) X: weight losses(%) v) Relation between weight losses and density Y = 2153.1 - 6.62X where Y: density($kg/m^3$) X: weigh losses(%) 7. In order to make better acid resistant mortar, it could be concluded that a 1 : 3 mix or richer mixes, adequate mixing water to minnimize the ratio of water-cement considering the workability, 16 per cent or less absorption by 5 hour boiling water, 1,800 kilogram per cubic meter or denser density by absolute weight base and 200 kilogram per square meter or compressive strength at 20 day, etc are required so as to obtain acid-resistant mortar. In addition to the above, it might be recommonded to select the fine aggregate and to use better equipments such as a mechanical vibrator, a mechanical mixer etc. in concrete manufacturing works.