TY  - JOUR
AU  - Novčić, Đorđe
AU  - Nikolić, Miloš
AU  - Todorović, Dušan
AU  - Karamarković, Rade
AU  - Obradović, Marko
PY  - 2023
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4831
AB  - To achieve as complete fuel burnout with as little excess air as possible, small wood log boilers (< 50 kW) use stage combustion. The first stage is often a process similar to downdraft gasification that consequently produces a flue gas laden with particulates. To prevent the build-up of solids and promote heat transfer in pipes of the convective part of these boilers, wire coils are used. The paper presents their in-situ examination together with CFD analysis. The analysis is carried out in a 460 mm long pipe, with a diameter of 82.5 mm, equipped with different wire coils for flue gas temperatures in the range between 300 °C and 150 °C. The analyzed coils are with and without a conical spring at their free end. The addition of this conical top is economical and should influence the rotation of the core flow. Proper pipe surface cleaning limited the analyzed wire coil designs to the dimensionless pitch, p/d, in the range between 0.36-0.61, dimensionless wire diameter e/d = 0.04-0.1, and pitch to wire diameter ratios p/e = 3.75-14.3, and three different angles (60⁰, 90⁰, and 120⁰) of the conical top. The goals are to find the optimal flue gas velocity for the given operating conditions, pipe, and wire coil dimensions, and to investigate the addition of the conical top on heat transfer enhancement. Several evaluation criteria are used to achieve the goals.
PB  - University of Belgrade, Vinča Institute of Nuclear Sciences
T2  - Thermal Science
T1  - Experimental and CFD analysis of wire coil turbulators in biomass boilers
EP  - 87
IS  - 1A
SP  - 71
VL  - 27
DO  - 10.2298/TSCI2301071N
ER  - 

@article{
author = "Novčić, Đorđe and Nikolić, Miloš and Todorović, Dušan and Karamarković, Rade and Obradović, Marko",
year = "2023",
abstract = "To achieve as complete fuel burnout with as little excess air as possible, small wood log boilers (< 50 kW) use stage combustion. The first stage is often a process similar to downdraft gasification that consequently produces a flue gas laden with particulates. To prevent the build-up of solids and promote heat transfer in pipes of the convective part of these boilers, wire coils are used. The paper presents their in-situ examination together with CFD analysis. The analysis is carried out in a 460 mm long pipe, with a diameter of 82.5 mm, equipped with different wire coils for flue gas temperatures in the range between 300 °C and 150 °C. The analyzed coils are with and without a conical spring at their free end. The addition of this conical top is economical and should influence the rotation of the core flow. Proper pipe surface cleaning limited the analyzed wire coil designs to the dimensionless pitch, p/d, in the range between 0.36-0.61, dimensionless wire diameter e/d = 0.04-0.1, and pitch to wire diameter ratios p/e = 3.75-14.3, and three different angles (60⁰, 90⁰, and 120⁰) of the conical top. The goals are to find the optimal flue gas velocity for the given operating conditions, pipe, and wire coil dimensions, and to investigate the addition of the conical top on heat transfer enhancement. Several evaluation criteria are used to achieve the goals.",
publisher = "University of Belgrade, Vinča Institute of Nuclear Sciences",
journal = "Thermal Science",
title = "Experimental and CFD analysis of wire coil turbulators in biomass boilers",
pages = "87-71",
number = "1A",
volume = "27",
doi = "10.2298/TSCI2301071N"
}

Novčić, Đ., Nikolić, M., Todorović, D., Karamarković, R.,& Obradović, M.. (2023). Experimental and CFD analysis of wire coil turbulators in biomass boilers. in Thermal Science
University of Belgrade, Vinča Institute of Nuclear Sciences., 27(1A), 71-87.
https://doi.org/10.2298/TSCI2301071N

Novčić Đ, Nikolić M, Todorović D, Karamarković R, Obradović M. Experimental and CFD analysis of wire coil turbulators in biomass boilers. in Thermal Science. 2023;27(1A):71-87.
doi:10.2298/TSCI2301071N .

Novčić, Đorđe, Nikolić, Miloš, Todorović, Dušan, Karamarković, Rade, Obradović, Marko, "Experimental and CFD analysis of wire coil turbulators in biomass boilers" in Thermal Science, 27, no. 1A (2023):71-87,
https://doi.org/10.2298/TSCI2301071N . .

TY  - JOUR
AU  - Marinković, Mladen
AU  - Popović, Marina
AU  - Stojanović-Rundić, Suzana
AU  - Nikolić, Miloš
AU  - Cavić, Milena
AU  - Gavrilović, Dusica
AU  - Teodorović, Dušan
AU  - Mitrović, Nenad
AU  - Mijatovic Teodorović, Ljiljana
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3767
AB  - After primary treatment of localized prostate carcinoma (PC), up to a third of patients have disease recurrence. Different predictive models have already been used either for initial stratification of PC patients or to predict disease recurrence. Recently, artificial intelligence has been introduced in the diagnosis and management of PC with a potential to revolutionize this field. The aim of this study was to analyze machine learning (ML) classifiers in order to predict disease progression in the moment of prostate-specific antigen (PSA) elevation during follow-up. The study cohort consisted of 109 PC patients treated with external beam radiotherapy alone or in combination with androgen deprivation therapy. We developed and evaluated the performance of two ML algorithms based on artificial neural networks (ANN) and naive Bayes (NB). Of all patients, 72.5% was randomly selected for a training set while the remaining patients were used for testing of the models. The presence/absence of disease progression was defined as the output variable. The input variables for models were conducted from the univariate analysis preformed among two groups of patients in the training set. They included two pretreatment variables (UICC stage and Gleason's score risk group) and five posttreatment variables (nadir PSA, time to nadir PSA, PSA doubling time, PSA velocity, and PSA in the moment of disease reevaluation). The area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, and predictive accuracy was calculated to test the models' performance. The results showed that specificity was similar for both models, while NB achieved better sensitivity then ANN (100.0% versus 94.4%). The ANN showed an accuracy of 93.3%, and the matching for NB model was 96.7%. In this study, ML classifiers have shown potential for application in routine clinical practice during follow-up when disease progression was suspected.
PB  - Hindawi Ltd, London
T2  - Biomed Research International
T1  - Comparison of Different Machine Learning Models in Prediction of Postirradiation Recurrence in Prostate Carcinoma Patients
VL  - 2022
DO  - 10.1155/2022/7943609
ER  - 

@article{
author = "Marinković, Mladen and Popović, Marina and Stojanović-Rundić, Suzana and Nikolić, Miloš and Cavić, Milena and Gavrilović, Dusica and Teodorović, Dušan and Mitrović, Nenad and Mijatovic Teodorović, Ljiljana",
year = "2022",
abstract = "After primary treatment of localized prostate carcinoma (PC), up to a third of patients have disease recurrence. Different predictive models have already been used either for initial stratification of PC patients or to predict disease recurrence. Recently, artificial intelligence has been introduced in the diagnosis and management of PC with a potential to revolutionize this field. The aim of this study was to analyze machine learning (ML) classifiers in order to predict disease progression in the moment of prostate-specific antigen (PSA) elevation during follow-up. The study cohort consisted of 109 PC patients treated with external beam radiotherapy alone or in combination with androgen deprivation therapy. We developed and evaluated the performance of two ML algorithms based on artificial neural networks (ANN) and naive Bayes (NB). Of all patients, 72.5% was randomly selected for a training set while the remaining patients were used for testing of the models. The presence/absence of disease progression was defined as the output variable. The input variables for models were conducted from the univariate analysis preformed among two groups of patients in the training set. They included two pretreatment variables (UICC stage and Gleason's score risk group) and five posttreatment variables (nadir PSA, time to nadir PSA, PSA doubling time, PSA velocity, and PSA in the moment of disease reevaluation). The area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, and predictive accuracy was calculated to test the models' performance. The results showed that specificity was similar for both models, while NB achieved better sensitivity then ANN (100.0% versus 94.4%). The ANN showed an accuracy of 93.3%, and the matching for NB model was 96.7%. In this study, ML classifiers have shown potential for application in routine clinical practice during follow-up when disease progression was suspected.",
publisher = "Hindawi Ltd, London",
journal = "Biomed Research International",
title = "Comparison of Different Machine Learning Models in Prediction of Postirradiation Recurrence in Prostate Carcinoma Patients",
volume = "2022",
doi = "10.1155/2022/7943609"
}

Marinković, M., Popović, M., Stojanović-Rundić, S., Nikolić, M., Cavić, M., Gavrilović, D., Teodorović, D., Mitrović, N.,& Mijatovic Teodorović, L.. (2022). Comparison of Different Machine Learning Models in Prediction of Postirradiation Recurrence in Prostate Carcinoma Patients. in Biomed Research International
Hindawi Ltd, London., 2022.
https://doi.org/10.1155/2022/7943609

Marinković M, Popović M, Stojanović-Rundić S, Nikolić M, Cavić M, Gavrilović D, Teodorović D, Mitrović N, Mijatovic Teodorović L. Comparison of Different Machine Learning Models in Prediction of Postirradiation Recurrence in Prostate Carcinoma Patients. in Biomed Research International. 2022;2022.
doi:10.1155/2022/7943609 .

Marinković, Mladen, Popović, Marina, Stojanović-Rundić, Suzana, Nikolić, Miloš, Cavić, Milena, Gavrilović, Dusica, Teodorović, Dušan, Mitrović, Nenad, Mijatovic Teodorović, Ljiljana, "Comparison of Different Machine Learning Models in Prediction of Postirradiation Recurrence in Prostate Carcinoma Patients" in Biomed Research International, 2022 (2022),
https://doi.org/10.1155/2022/7943609 . .