An Approach to Drought Vulnerability Assessment Focused on Groundwater Wells in Upland Cultivation Areas of South Korea

This study aimed to present an approach that identifies priority management areas to drought focused on field crops and groundwater wells using the MCDM method. Groundwater wells are the primary source of water during drought for field crops. Nevertheless, the systematic management of groundwater wells has not been achieved. Thus, this paper intends to establish a plan that can manage groundwater wells through a vulnerability assessment. This study used TOPSIS, a widely applied multi-criterion decision-making algorithm, to evaluate 158 cities and counties in Korea. This study chose the assessment factors by focusing on drought and classifying the positive and negative elements of the wells. Precipitation, groundwater level, and pumping capacity were considered to have positive effects, while cultivated area, the number of consecutive days without rain, and the proportion of private groundwater wells were considered as negative factors. As a result, the pumping capacity per cultivation area is the major factor affecting management priorities and groundwater well vulnerability. This study presents an approach to assess the drought vulnerability of field crops focused on groundwater wells and select a priority management area, which facilitates efficient well management and reduces damage to crops caused by local droughts

Combining clinical and imaging data for predicting functional outcomes after acute ischemic stroke: an automated machine learning approach

Abstract This study aimed to develop and validate an automated machine learning (ML) system that predicts 3-month functional outcomes in acute ischemic stroke (AIS) patients by combining clinical and neuroimaging features. Functional outcomes were categorized as unfavorable (modified Rankin Scale ≥ 3) or not. A clinical model employing optimal clinical features (Model_A), a convolutional neural network model incorporating imaging data (Model_B), and an integrated model combining both imaging and clinical features (Model_C) were developed and tested to predict unfavorable outcomes. The developed models were compared with each other and with traditional risk-scoring models. The dataset comprised 4147 patients from a multicenter stroke registry, with 1268 (30.6%) experiencing unfavorable outcomes. Age, initial NIHSS, and early neurologic deterioration were identified as the most important clinical features. The ML model prediction achieved an area under the curves of 0.757 (95% CI 0.726–0.789) for Model_A, 0.725 (95% CI 0.693–0.755) for Model_B, and 0.786 (95% CI 0.757–0.814) for Model_C in the test set. The integrated models outperformed traditional risk-scoring models by 0.21 (95% CI 0.16–0.25) for HIAT and 0.15 (95% CI 0.11–0.19) for THRIVE. In conclusion, the integrated ML system enhanced stroke outcome prediction by combining imaging data and clinical features, outperforming traditional risk-scoring models

Stock Market Reaction to Mergers and Acquisitions in Anticipation of a Subsequent Related Significant Event: Evidence from the Korean Telecommunications Industry

The objective of this paper is to assess the effect of mergers and acquisitions on shareholder wealth when subsequent related significant events are anticipated. We identify a particular merger and acquisition between telecommunication companies in Korea and examine whether it conveys good or bad news to stock market participants. We hypothesize and find that mergers and acquisitions are interpreted as good news by the marketplace when they are expected to be accompanied by a subsequent related significant event, in our case granting of a government license for the IMT-2000 mobile service.Mergers and acquisitions, telecommunications industry, IMT-2000, mobile service