ABSTRACT
Nowadays, electricity has become an integral part of human lives. Most of our daily appliances, tools, and personal belongings are inseparable from electricity. To ensure a proper electricity distribution with an efficient transfer capability, Extra-High Voltage (EHV) transmission towers are needed. To design such a structure, it is of utmost importance to account for the cost of said tower. However, the process to estimate the cost of EHV transmission towers is both time-consuming and strenuous on human labor since a lot of consideration have to be taken. To overcome this, an imperative requirement exists for a prompt, precise, and automated tool to replace the existing manual cost estimation method. This research endeavor aims to craft a tool using support vector regression (SVR) with the capacity to prognosticate construction expenses for projects involving EHV transmission towers. The exploration of pertinent literature has enabled us to amass historical data and delineate the attributes essential for estimating costs linked to EHV transmission tower construction. The investigation delves into a comprehensive dataset spanning the past decade in Taiwan. Within this timeframe, 317 EHV transmission towers were erected between 2009 and 2019. However, 79 of these instances are excluded due to incomplete information, thereby yielding 238 viable datasets (comprising 75 % of the overall total) to underpin the development of the SVR model. By configuring the parameters to C = 0.2 and γ = 0.1, followed by 5-fold cross-validation, the resultant SVR model attains a remarkable prediction accuracy of 97.91 %, on average. As a result, the proposed SVR-based model can effectively and accurately predict the cost of constructing an EHV transmission tower project and reduce the time spent on estimation, thus contributing to the enhancement of the resilience and robustness of the transmission network system.
ABSTRACT
The outbreak of the COVID-19 pandemic poses great challenges to the current government subsidy models in the renewable energy sector for recovering in the post-pandemic economy. Although, many subsidy models have been applied to accelerate renewable energy investment decisions. However, it is important to develop a new model to ensure the sustainability of the renewable energy supply network under disruptions on both the supply and demand sides due to hazardous events. This study investigates different subsidy models (renewable credit, supplier subsidy, and retailer subsidy) to find a win-win subsidy model for sustainable energy supply under disruption risks. The objective is to determine the optimal capacity of renewable energy added to the grid, the optimal wholesale price of the power plant, and the optimal retail price of the aggregator under different subsidy models to maximize the economic, social, and environmental benefits of the whole network. A novel scenario-based robust fuzzy optimization approach is proposed to capture the uncertainties of business-as-usual operations (e.g., some relevant costs and demand) and hazardous events (e.g., COVID-19 pandemic). The proposed model is tested in a case study of the Vietnamese energy market. The results show that for a high negative impact level of hazardous events on the supply side, the renewable credit and supplier subsidy models should be considered to recovery the renewable energy market. Further, the proposed approach has a better performance in improving the power plant's robust profit for most of the hazard scenarios than the robust optimization model.
ABSTRACT
Over the past few months, the COVID-19 pandemic has postponed many renewable energy projects because of disruptions in the technology and finance supply. Additionally, the existing power plants are inefficient because of a record drop in demand for goods and services caused by lockdowns in cities. This situation poses huge challenges to the resilience of renewable energy supply networks in the face of deeply hazardous events, such as the COVID-19 pandemic. Therefore, the purpose of this study was to design a resilient renewable energy supply network considering supply, demand, and payment risks caused by COVID-19. The objective of the proposed model was to determine the optimal amount of electric power generated and stored to meet the demands and the risk-sharing effort index to maximize the total resilient profit of the power plant and determine the optimal price adjustment index to minimize the cost to consumers. A government subsidy-based risk-sharing model was developed to enhance the resilience of the concerned renewable energy supply network under the pandemic. To overcome uncertainties in both random and risk events, a robust fuzzy-stochastic programming model was proposed to solve these research problems. Computational experiments were conducted on the test supply network in Vietnam. The results showed that the resilient energy supply network with the risk-sharing model tended to stabilize the total profit with the different impact levels of COVID-19 compared to the network without risk-sharing. The proposed model efficiently tackled both uncertainties in random and hazardous events and had a higher profit and shorter CPU time compared to the robust optimization mode.
ABSTRACT
A 12-year-old adolescent girl with intractable pneumonia and desaturation was sent to our hospital. An immunocompromised state was highly suspected because of an oral thrush persisting for a year and pneumonia of unusual severity. Laboratory tests confirmed she had human immunodeficiency virus (HIV) infection and full-blown AIDS. She lived with her adopted parents and reported no history of sexual abuse, drug abuse, or blood transfusion. We contacted the Center of Disease Control and discovered that her mother had HIV and had passed away a few years ago, thus confirming that she was a case of vertically transmitted HIV patient who had only developed AIDS recently. Even though her mother had HIV, our public health department failed to follow her as a potential HIV victim, probably because routine HIV examinations for pregnant women only started in 2005, 4 years after she was born.
ABSTRACT
BACKGROUND/PURPOSE: Infectious endocarditis (IE) is a rare, but potentially fatal disease in pediatric patients. In this study, we reviewed the symptoms and signs, etiology, laboratory findings and outcomes of IE patients over the past 10 years. METHODS: Patients (< 18 years old) with definite IE according to the modified Duke criteria, or patients with positive pathological findings, between September 1998 and September 2008 were included in the study. The etiology, symptoms and signs, laboratory findings and outcomes were collected via chart review. RESULTS: Nineteen cases (13 boys and 6 girls) ranging in age from 2.5 months to 18 years (mean = 7.98 years; median = 5 years) were included. Nine out of 17 cases (52.9%) had microscopic hematuria and two out of three (66.7%) cases showed elevated rheumatoid factor levels. Seventeen (89.5%) had fever and seven (36.8%) had major vessel embolic events. Blood cultures yielded Staphylococcus aureus in seven cases, and viridans Streptococci in two cases. The other three cases had Pneumococcus, Pseudomonas aeruginosa and Candida albicans. Two patients died and one was discharged in a critical condition. Two of the seven (28.6%) patients with a positive blood culture for S. aureus died, three (42.9%) had an embolic event and one (14.3%) had central nervous system complications (intracranial hemorrhage). The initial C-reactive protein levels in the blood culture-positive group were significantly higher than those in the blood culture-negative group (p = 0.035). CONCLUSION: S. aureus is one of the most common etiologies in IE patients, while viridans Streptococcus accounts for fewer cases than suggested by previous studies. IE caused by S. aureus seems to carry a higher risk of mortality, and embolic events are associated with increased mortality.
