http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Danish,Recep Ulucak,Seyfettin Erdogan 한국원자력학회 2022 Nuclear Engineering and Technology Vol.54 No.4
The earlier studies have analyzed theoretical links between nuclear energy and carbon dioxide (CO2)emissions concerning territorial (or production-based) emissions. Here using the latest available dataset,this study explores the impacts of nuclear energy on production-based and consumption-based CO2emission in the era of globalization for the Organization for Economic Co-operation and Development(OECD) countries. The Driscoll-Kraay regression method reveals that nuclear energy is beneficial for thereduction of production-based CO2 emissions. However, it is revealed that nuclear energy does notreduce consumption-based CO2 emissions that are traded internationally and hence not comprised inconventional production-based emissions (territory) inventories. Globalization tends to reduce bothproduction-based and demand-based carbon emissions. Finally, Environmental Kuznets Curve (EKC) isvalidated for both kinds of CO2 emissions. The findings may deliver practical policy implications relatedto nuclear energy and CO2 emissions for selected countries.
Danish, Danish,Ozcan, Burcu,Ulucak, Recep Korean Nuclear Society 2021 Nuclear Engineering and Technology Vol.53 No.6
The transition toward clean energy is an issue of great importance with growing debate in climate change mitigation. The complex nature of nuclear energy-CO<sub>2</sub> emissions nexus makes it difficult to predict whether or not nuclear acts as a clean energy source. Hence, we examined the relationship between nuclear energy consumption and CO<sub>2</sub> emissions in the context of the IPAT and Environmental Kuznets Curve (EKC) framework. Dynamic Auto-regressive Distributive Lag (DARDL), a newly modified econometric tool, is employed for estimation of long- and short-run dynamics by using yearly data spanning from 1971 to 2018. The empirical findings of the study revealed an instantaneous increase in nuclear energy reduces environmental pollution, which highlights that more nuclear energy power in the Indian energy system would be beneficial for climate change mitigation. The results further demonstrate that the overarching effect of population density in the IPAT equation stimulates carbon emissions. Finally, nuclear energy and population density contribute to form the EKC curve. To achieving a cleaner environment, results point out governmental policies toward the transition of nuclear energy that favours environmental sustainability.
Danish, Danish,Ud-Din Khan, Salah,Ahmad, Ashfaq Korean Nuclear Society 2021 Nuclear Engineering and Technology Vol.53 No.8
The environmental effects of China's nuclear energy consumption in a dynamic framework of the pollution haven hypothesis are examined. This study uses a dynamic autoregressive distributed lag simulation approach. Empirical evidence confirms that the pollution haven hypothesis does not exist for China; i.e., foreign direct investment plays a promising role in influencing environmental outcomes. Furthermore, empirical results concluded positive contribution of nuclear energy in pollution mitigation. From the results it is expected that encouraging foreign investment to increase generation of nuclear energy would benefit environmental quality by reducing CO<sub>2</sub> emissions.
Application of Fuzzy Logic for Predicting of Mine Fire in Underground Coal Mine
Danish, Esmatullah,Onder, Mustafa Occupational Safety and Health Research Institute 2020 Safety and health at work Vol.11 No.3
Background: Spontaneous combustion of coal is one of the factors which causes direct or indirect gas and dust explosion, mine fire, the release of toxic gases, loss of reserve, and loss of miners' life. To avoid these incidents, the prediction of spontaneous combustion is essential. The safety of miner's in the mining field can be assured if the prediction of a coal fire is carried out at an early stage. Method: Adularya Underground Coal Mine which is fully mechanized with longwall mining method was selected as a case study area. The data collected for 2017, by sensors from ten gas monitoring stations were used for the simulation and prediction of a coal fire. In this study, the fuzzy logic model is used because of the uncertainties, nonlinearity, and imprecise variables in the data. For coal fire prediction, CO, O<sub>2</sub>, N<sub>2</sub>, and temperature were used as input variables whereas fire intensity was considered as the output variable.The simulation of the model is carried out using the Mamdani inference system and run by the Fuzzy Logic Toolbox in MATLAB. Results: The results showed that the fuzzy logic system is more reliable in predicting fire intensity with respect to uncertainties and nonlinearities of the data. It also indicates that the 1409 and 610/2B gas station points have a greater chance of causing spontaneous combustion and therefore require a precautional measure. Conclusion: The fuzzy logic model shows higher probability in predicting fire intensity with the simultaneous application of many variables compared with Graham's index.
Prediction Model for Gastric Cancer via Class Balancing Techniques
Danish, Jamil,Sellappan, Palaniappan,Sanjoy Kumar, Debnath,Muhammad, Naseem,Susama, Bagchi,Asiah, Lokman International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.1
Many researchers are trying hard to minimize the incidence of cancers, mainly Gastric Cancer (GC). For GC, the five-year survival rate is generally 5-25%, but for Early Gastric Cancer (EGC), it is almost 90%. Predicting the onset of stomach cancer based on risk factors will allow for an early diagnosis and more effective treatment. Although there are several models for predicting stomach cancer, most of these models are based on unbalanced datasets, which favours the majority class. However, it is imperative to correctly identify cancer patients who are in the minority class. This research aims to apply three class-balancing approaches to the NHS dataset before developing supervised learning strategies: Oversampling (Synthetic Minority Oversampling Technique or SMOTE), Undersampling (SpreadSubsample), and Hybrid System (SMOTE + SpreadSubsample). This study uses Naive Bayes, Bayesian Network, Random Forest, and Decision Tree (C4.5) methods. We measured these classifiers' efficacy using their Receiver Operating Characteristics (ROC) curves, sensitivity, and specificity. The validation data was used to test several ways of balancing the classifiers. The final prediction model was built on the one that did the best overall.