• Journal of Chest Surgery
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• v.29 no.3
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• pp.303-310
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• 1996

From December of 1994 to April of 1995, we, SHUH Department of Pediatric Thoracic and Cardiovascular Surgery, studied effects of aprotinin. 95 patients were randomly divided into two groups : group I (n=47) with aprotinin and group ll (n=48) without aprotinin. Aprotinin was given as one shot injection to cardiopulmonary bypass perfusion solution with dose of 50,000 KIUikg. Laboratory data such as hemoglobin, hematocrit, BUH, creatinine, fibrinogen, electrolyte concentration, aPTT, PT, and AT R was checked preoperatively, 5 minutes after anesthesia, 5 minutes and 35 minutes after CPB circulation, and 5 minutes, 3 hours, and 24 hours after reperfusion. Also, chest-tube drainage, transfused amount of RBC, platelet concentrate, and fresh frozen plasma within first 24 hours postoperatively were checked and analyzed after transition nn body weight demension. Only RBC transfused postoperatively had statistical significance with P value of less than 0.001. Others had no difference statistical wise. Postoperative side effects of aprotinin was not detected weeks after the surgery and there was no reoperated patient due to postoperative bleeding.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.480-502
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• 2022

This work assesses the degree of satisfaction tourists receive as final recipients in a tourism destination based on the fact that satisfied tourists can make a significant contribution to the growth and continuous improvement of a tourism business. The work considers Pokhara, the tourism capital of Nepal as a prefecture of study. A stratified sampling methodology with open-ended survey questions is used as a primary source of data for a sample size of 1019 for both international and domestic tourists. The data collected through a survey is processed using a data mining tool to perform multi-dimensional analysis to discover information patterns and visualize clusters. Further, supervised machine learning algorithms, kNN, Decision tree, Support vector machine, Random forest, Neural network, Naive Bayes, and Gradient boost are used to develop models for training and prediction purposes for the survey data. To find the best model for prediction purposes, different performance matrices are used to evaluate a model for performance, accuracy, and robustness. The best model is used in constructing a learning-enabled model for predicting tourists as satisfied, neutral, and unsatisfied visitors. This work is very important for tourism business personnel, government agencies, and tourism stakeholders to find information on tourist satisfaction and factors that influence it. Though this work was carried out for Pokhara city of Nepal, the study is equally relevant to any other tourism destination of similar nature.

• Honam Mathematical Journal
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• v.44 no.1
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• pp.135-145
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• 2022

This paper aims to investigate characterizations on parameters k₁, k₂, k₃, k₄, k₅, l₁, l₂, l₃, and l₄ to find relation between the class of 𝓗(k, l, m, n, o) hypergeometric functions defined by $$5_F_4\[{\array{k_1,\;k_2,\;k_3,\;k_4,\;k_5\\l_1,\;l_2,\;l_3,\;l_4}}\;:\;z\]=\sum\limits_{n=2}^{\infty}\frac{(k_1)_n(k_2)_n(k_3)_n(k_4)_n(k_5)_n}{(l_1)_n(l_2)_n(l_3)_n(l_4)_n(1)_n}z^n$$. We need to find k, l, m and n that lead to the necessary and sufficient condition for the function zF([W]), G = z(2 - F([W])) and $H_1[W]=z^2{\frac{d}{dz}}(ln(z)-h(z))$ to be in 𝓢^*(2^-r), r is a positive integer in the open unit disc 𝒟 = {z : |z| < 1, z ∈ ℂ} with $$h(z)=\sum\limits_{n=0}^{\infty}\frac{(k)_n(l)_n(m)_n(n)_n(1+\frac{k}{2})_n}{(\frac{k}{2})_n(1+k-l)_n(1+k-m)_n(1+k-n)_nn(1)_n}z^n$$ and $$[W]=\[{\array{k,\;1+{\frac{k}{2}},\;l,\;m,\;n\\{\frac{k}{2}},\;1+k-l,\;1+k-m,\;1+k-n}}\;:\;z\]$$.