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Summary: In this paper, a new technique is suggested for best electric load forecasting of South Korea. The proposed study is based on an improved metaheuristic methodology. This paper proposes a new improved version of Support Vector Machine for Regression (SVR) base on the proposed algorithm. The algorithm is based on a modified version of Crow Search Algorithm (ACSA), where, by combining by the SVR, generates an efficient classifier. Although, different drawbacks of using original CSA are stated, the proposed ACSA method provides a proper modification for resolving of these shortcomings. The forecasting model has been then performed to a historical data from South Korea to indicate the algorithm efficiency and the results are compared with some other methods, including SVR‐CGA, SVR‐CAS, ANN, and simple Regression to show the suggested method performance. Final results show that MAPD, MSE, and RMSE values of the test data with lower values for South, North, Center, and East, provided better results based on the proposed SVR‐ACSA based on different values of center and kernel of the SVR. Experimental results on the case study shows that the minimum MAPD has been added which is happened in Northern regional is 1.1062, 1.1336, 1.1065, 1.02, 2.23, 1.96, and 1.49 for SVR‐ACSA, SVR‐CGA, SVR‐CAS, ANN, Regression, LSTM‐RNN, and ELM which indicates the method's higher efficiency. [ABSTRACT FROM AUTHOR]

Summary: The fused lasso signal approximator (FLSA) is a smoothing procedure for noisy observations that uses fused lasso penalty on unobserved mean levels to find sparse signal blocks. Several path algorithms have been developed to obtain the whole solution path of the FLSA. However, it is known that the FLSA has model selection inconsistency when the underlying signals have a stair‐case block, where three consecutive signal blocks are either strictly increasing or decreasing. Modified path algorithms for the FLSA have been proposed to guarantee model selection consistency regardless of the stair‐case block. In this paper, we provide a comprehensive review of the path algorithms for the FLSA and prove the properties of the recently modified path algorithms' hitting times. Specifically, we reinterpret the modified path algorithm as the path algorithm for local FLSA problems and reveal the condition that the hitting time for the fusion of the modified path algorithm is not monotone in a tuning parameter. To recover the monotonicity of the solution path, we propose a pathwise adaptive FLSA having monotonicity with similar performance as the modified solution path algorithm. Finally, we apply the proposed method to the number of daily‐confirmed cases of COVID‐19 in Korea to identify the change points of its spread. [ABSTRACT FROM AUTHOR]

Although game‐based learning has been increasingly promoted in education, there is a need to adapt game content to individual needs for personalized learning. Procedural content generation (PCG) offers a solution for difficulty in developing game contents automatically by algorithmic means as it can generate individually customizable game contents applicable to various objectives. In this paper, we advanced a data‐driven PCG approach benefiting from a genetic algorithm and support vector machines to automatically generate educational‐game contents tailored to individuals' abilities. In contrast to other content generation approaches, the proposed method is not dependent on designer's intuition in applying game contents to fit a player's abilities. We assessed this data‐driven PCG approach at length and showed its effectiveness by conducting an empirical study of children who played an educational language‐learning game to cultivate early English‐reading skills. To affirm the efficacy of our proposed method, we evaluated the data‐driven approach against a heuristic‐based approach. Our results clearly demonstrated two things. First, users realized greater performance gains from playing contents tailored to their abilities compared with playing uncustomized game contents. Second, this data‐driven approach was more effective in generating contents closely matching a specific player‐performance target than the heuristic‐based approach. Lay Description: What is already known about this topic Educational games are mostly designed with a fixed task‐progression difficulty. Given the current broad diversity of player background, preferences, and motivations, it is typically difficult to achieve a dynamic difficulty adaptation with any single and fixed progression.Procedural content generation provides a solution for this difficulty by developing game contents automatically through algorithmic means with or without the involvement of a human designer. What this paper adds A data‐driven approach is employed to refine the fitness function for content evaluation and the prediction of players' capabilities.Unlike previous approaches that have predominantly steered the generation process through designer‐defined goals or heuristics, the proposed approach does not depend on the designer intuition in aligning contents with fitness. Implications for practice and/or policy The proposed framework can be applied to other educational games.The proposed approach offers users a different experience with every new game to enhance the game replayability. [ABSTRACT FROM AUTHOR]

A millimetre‐wave cloud radar has limitations for observing heavy rainfall because the short wavelength leads to strong attenuation from raindrops. However, recent studies have attempted to estimate the rain rate using attenuation, which becomes greater as the rain intensity increases. The rain‐rate‐retrieval algorithm is developed in the present paper using the Ka‐band cloud radar (KaCR) installed at the Boseong Global Standard Observatory (BGSO) in the Republic of Korea. First, rain profiles were identified using the threshold of reflectivity (Z) and Doppler velocity (DV) averaged for the analysis of layer that minimized the effects of the receiver saturation and classified as low or high rain‐rate cases using the averaged DV. The reflectivity and rain‐rate (Z‐R) relationship was then derived for low rain‐rate cases that showed insignificant effects for signal attenuation. On the other hand, the attenuation and rain‐rate (A‐R) relationship was derived using high rain‐rate cases that showed the dominant effects of attenuation. The attenuation was calculated using a reflectivity gradient between the upper and lower boundaries of the attenuated layer. Finally, the rain‐rate‐retrieval algorithm was designed using the derived Z‐R and A‐R relationship and then applied back to the KaCR. The estimated rain rate in the KaCR was a similar trend to the observed rain rate in the optical rain gauge (ORG), but slightly underestimated. [ABSTRACT FROM AUTHOR]

Aim: This cross‐sectional study aimed to examine the diagnostic ability of salivary matrix metalloproteinase (MMP)‐9 lateral flow test (LFT) point‐of‐care (POC) kit and develop an algorithm for diagnosis of periodontitis. Materials and Methods: Through Seoul National Dental Hospital, 137 participants (46 LFT negatives, 91 LFT positives) were recruited. For salivary diagnostics, 150 μl of the unstimulated saliva was applied to LFT‐POC kit. To make a diagnosis of periodontitis, stage II‐IV in modified new international classification system was used. Covariates encompassing age, sex, smoking and obesity were evaluated through face‐to‐face interview. Enzyme‐linked immunosorbent assay was used for quantification of salivary MMP‐9. To develop a diagnostic algorithm, multivariable logistic regression analysis was used. Receiver operating characteristic curve was applied for evaluating diagnostic ability. Results: Diagnostic ability of salivary MMP‐9 LFT‐POC test was 0.82 (sensitivity of 0.92, specificity of 0.72) in total participants. Diagnostic algorithm using POC test resulted in a response equation, that is algorithm score = −3.675 + 2.877*LFT + 0.034*age + 0.121*sex + 0.372*smoking + 0.192*obesity. Diagnostic ability of the algorithm was 0.88 (sensitivity of 0.92, specificity of 0.85) with cut‐off score of 0.589. Conclusions: Salivary MMP‐9 LFT‐POC kit showed appropriate diagnostic ability for periodontitis and would be an efficient tool for screening of periodontitis. [ABSTRACT FROM AUTHOR]

YouTube is a successful social network that people use to upload, watch, and comment on videos. We believe comments left on these videos can provide insight into user interests, but to this point have not been used to map out a specific video community. Our study investigates whether and how user commenting behavior impacts the topology of the K-pop video community through analysis of co-commenting behavior on these videos. We apply a traditional author cocitation analysis to this behavior, in a process we refer to as co-comment analysis, to detect the topology of this community. This involves: a) an analysis of user co-comments to elicit the inclination of user homophily within the community; b) an analysis of user co-comments, weighted frequency of co-comments, to detect user interests in the community; and c) an analysis of user co-comments, weighted sentiment scores, to capture user opinions by polarity. The results indicate that users who comment on specific K-pop videos also tend to comment on topically similar YouTube videos. We also find that the number of comments made by users correlates with the degree of positivity of their comments. Conversely, users who comment negatively on K-pop videos are not inclined to form specific user groups, but rather present only their opinions individually. [ABSTRACT FROM AUTHOR]

ABSTRACT Background In Korea, regular screening for delirium is not considered essential. In addition, delirium is often associated with vague concepts, making it harder to identify high-risk patients and impeding decision-making. Aims To assess the impact of the Automatic PREdiction of DELirium in Intensive Care Units ( APREDEL-ICU) system on nursing-sensitive and patient outcomes for surgical ICU patients and to evaluate nurse satisfaction with the system and its usability. Methods A pre-post research design was adopted. Our study included 724 patients admitted before the implementation of the APREDEL-ICU (January to December 2010) and 1111 patients admitted after the system was installed (May 2011 to April 2012). The APREDEL-ICU uses a pop-up window message to inform the nursing staff of patients at risk for delirium, allowing evidence-based nursing interventions to be applied to the identified patients. A total of 42 nurses were surveyed to determine the system's usability and their level of satisfaction with it. Results After the implementation of APREDEL-ICU, high-risk patients, determined using a prediction algorithm, showed a slight decrease in the incidence of delirium, but the changes were not significant. However, significant decreases in the number and duration of analgesic/narcotic therapies were observed after the implementation of the system. Nurse self-evaluation results showed an improvement in all categories of knowledge regarding delirium care. Conclusion The use of a prediction and alerting system for ICU patients at high risk of delirium showed a potential increase in the quality of delirium care, including early detection and proper intervention. [ABSTRACT FROM AUTHOR]