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Mohammadreza Shabgard,Samad Nadimi Bavil Oliaei,Mirsadegh Seyedzavvar,Ahmad Najadebrahimi 대한기계학회 2011 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.25 No.12
An axisymmetric three-dimensional model for temperature distribution in the electrical discharge machining process has been developed using the finite element method to estimate the surface integrity characteristics of AISI H13 tool steel as workpiece. White layer thickness, depth of heat affected zone, and arithmetical mean roughness consisting of the studied surface integrity features on which the effect of process parameters, including pulse on-time and pulse current were investigated. Additionally, the experiments were carried out under the designed full factorial procedure to validate the numerical results. Both numerical and experimental results show that increasing the pulse on-time leads to a higher white layer thickness, depth of heat affected zone, and the surface roughness. On the other hand,an increase in the pulse current results in a slight decrease of the white layer thickness and depth of heat affected zone, but a coarser surface roughness. Generally, there is a good agreement between the experimental and the numerical results.
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Rahim Jafari,Müge Kahya,Samad Nadimi Bavil Oliaei,Hakkı Özgür Ünver,Tuba Okutucu Özyurt 대한기계학회 2017 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.31 No.11
Microchannel heat exchangers are used to remove the high heat fluxes generated in compact electronic devices. The roughness of the microchannels has a significant effect on the heat transfer characteristics, especially the nucleate boiling and pumping power. Therefore, development of predictive models of surface texture is of significant importance in controlling heat transfer characteristics of these devices. In this study, micro-Wire electrical discharge machining (µ-WEDM) was employed to fabricate metal-based microchannel heat sinks with different surface textures. First, experiments were conducted to achieve the desired surface roughness values. Oxygen-free copper is a common material in the cooling systems of electronic devices because of its high thermal conductivity and low cost. Design of experiment approach based on the Taguchi technique was used to find the optimum set of process parameters. An analysis of variance is also performed to determine the significance of process parameters on the surface texture. An artificial neural network model is utilized to assess the variation of the surface roughness with process parameters. The predictions are in very good agreement with results yielding a coefficient of determination of 99.5 %. The results enable to determine µ-WEDM parameters which can result in the desired surface roughness, to have a well-controlled flow and heat transfer characteristics for the microchannels.
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Mohammad-Rahimi Hossein,Motamadian Saeed Reza,Nadimi Mohadeseh,Hassanzadeh-Samani Sahel,Minabi Mohammad A. S.,Mahmoudinia Erfan,Lee Victor Y.,Rohban Mohammad Hossein 대한치과교정학회 2022 대한치과교정학회지 Vol.52 No.2
Objective: This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs. Methods: The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model’s performance using weighted kappa and Cohen’s kappa statistical analyses. Results: The model’s validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model’s validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model. Conclusions: The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.
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Correlation between weather conditions and COVID-19 pandemic in the southeast area of Iran
Najmeh Parvaz,Fatemeh Amin,Ali Esmaeili Nadimi,Hadi Eslami 대한공간정보학회 2023 Spatial Information Research Vol.31 No.6
The Coronavirus disease 2019 (COVID-19) has influenced the life of all people around the world. This study analyzed the relationship between the weather elements (daily temperature, wind speed and humidity) and daily active, recovered and dead cases of COVID-19 in Rafsanjan, southeast area of Iran. COVID-19 data and meteorological variables were obtained from 29 February 2020 to 20 March 2021 (386 days) from Rafsanjan University of Medical Sciences and Meteorological Organization of Iran, respectively. The results showed that there is a significant inverse association between daily average temperature with the number of daily active cases (r: − 0.293), recovered cases (r: − 0.301) and dead cases (r: − 0.198) of COVID-19 (p < 0.01). With decreasing the average wind speed, the number of daily active cases (r: − 0.224), recovered cases (r: − 0.232) and dead cases (r: − 0.169) of COVID-19 has been increased (p < 0.01). A non-significant positive correlation was observed between daily humidity and active cases (r: 0.033, p = 0.518) and recovered cases (r: 0.044, p = 0.390), and significant positive correlation with the daily dead cases (r: 0.254, p < 0.01). Therefore, temperature and wind speed can be considered as affective factors in COVID-19 transmission as an auxiliary solution.
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