• Asian Pacific Journal of Cancer Prevention
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• v.16 no.8
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• pp.3443-3446
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• 2015

Background: Testicular tumors constitute a rare type of cancer affecting adolescents and young adults with recent reports confirming an increase in incidence worldwide. The purpose of this study was to estimate the epidemiological characteristics and histological subtypes of testicular tumors in the Lebanese population according to the WHO classification of testicular and paratesticular tumors. Materials and Methods: In this single institutional retrospective study, all patients diagnosed with a testicular tumor in Hotel-Dieu de France Hospital University in Beirut between 1992 and 2014 were enrolled. The age, subtype based on the 2004 WHO classification and body side of tumor were analyzed. Results: A total of two hundred and forty-four (244) patients diagnosed with a testicular tumor in our institution were included in the study. Two hundred and one patients (82.4% of all testicular tumors) had germ cell tumors (TGCT). Among TGCT, 50% were seminomatous tumors, 48% non-seminomatous tumors (NST) and 2% were spermatocytic seminomas. The NST were further divided into mixed germ cell tumors (63.9%), embryonic carcinomas (18.6%), teratomas (15.4%) and yolk sac tumors (2.1%). The mean age for testicular tumors was 32 years. The mean age for germ cell tumors was 31 years and further subtypes such as seminomatous tumors had a mean age of 34 years, 28 years in non-seminomatous tumors and 56 years in spermatocytic seminoma. Patients with right testicular tumor were the predominant group with 55% of patients. Three patients (1.2%) presented with bilateral tumors. Conclusions: The distribution of different subgroups and the mean age for testicular tumors proved comparable to most countries of the world except for some Asian countries. Germ cell tumors are the most common subtype of testicular tumors with seminomatous tumors being slightly more prevalent than non-seminomatous tumors in Lebanese patients.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.3
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• pp.927-932
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• 2016

Bone morphogenetic proteins (BMPs), belonging to the transforming growth factor-${\beta}$ superfamily, regulate many cellular activities including cell migration, differentiation, adhesion, proliferation and apoptosis. Use of recombinant human bone morphogenic protein-2 (rhBMP-2) in oral and maxillofacial surgery has seen a tremendous increase. Due to its role in many cellular pathways, the influence of this protein on carcinogenesis in different organs has been intensively studied over the past decade. BMPs also have been detected to have a role in the development and progression of many tumors, particularly disease-specific bone metastasis. In oral squamous cell carcinoma - the tumor type accounting for more than 90% of head and neck malignancies- aberrations of both BMP expression and associated signaling pathways have a certain relation with the development and progression of the disease by regulating a range of biological functions in the altered cells. In the current review, we discuss the influence of BMPs -especially rhBMP-2- in the development and progression of oral squamous cell carcinoma.

• Advances in biomechanics and applications
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• v.1 no.4
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• pp.279-292
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• 2014

This paper presents some simulations of fluxes and pressures in the coronary network, in the case of very severe coronary disease (several stenoses on the left branches and total occlusion of the right coronary artery). In that case, coronary artery bypass graft surgery is the commonly performed procedure. However, the success of the intervention depends on many factors. Modeling of the coronary circulation is thus important since it can help to understand the influence of all these factors on the coronary haemodynamics. We previously developed an analog electrical model that includes the eventual presence of collateral flows, and can describe the different revascularization strategies (two grafts, three grafts, ...). The aim of the present work is to introduce in our simulations the time-dependence of the coronary microvascular resistances, in order to better represent the effect of the systolic ventricular contraction (which induces an elevation of the resistances because the vessels are squeezed).

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3421-3430
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• 2021

Spherical NaI(Tl) detectors are used in gamma-ray spectrometry, where the gamma emissions come from the nuclei with energies in the range from a few keV up to 10 MeV. A spherical detector is aimed to give a good response to photons, which depends on their direction of travel concerning the detector center. Some distortions in the response of a gamma-ray detector with a different geometry can occur because of the non-uniform position of the source from the detector surface. The present work describes the calibration of a NaI(Tl) spherical detector using both an experimental technique and a numerical simulation method (NSM). The NSM is based on an efficiency transfer method (ETM, calculating the effective solid angle, the total efficiency, and the full-energy peak efficiency). Besides, there is a high probability for a source-to-detector distance less than 15 cm to have pulse coincidence summing (CS), which may occur when two successive photons of different energies from the same source are detected within a very short response time. Therefore, γ-γ ray CS factors are calculated numerically for a ¹⁵²Eu radioactive cylindrical source. The CS factors obtained are applied to correct the measured efficiency values for the radioactive volumetric source at different energies. The results show a good agreement between the NSM and the experimental values (after correction with the CS factors).

• Journal of Electrochemical Science and Technology
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• v.9 no.4
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• pp.320-329
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• 2018

Natural graphite is obtained from an abandoned open-cast mine and purified by a simple, eco-friendly and affordable beneficiation process including ball milling and flotation process. Both raw graphite (55 wt %) and its concentrate (85 wt %) were electrochemically tested in order to evaluate these materials as anode materials for Li-ion and Na-ion batteries. It was found that both raw and purified graphites exhibit good electrochemical activities with respect to lithium and sodium ions through completely different reaction mechanisms. The encouraging results demonstrated in this work suggest that both raw and graphite concentrates after flotation could be used respectively for stationary and embedded applications. This strategy would help in developing local electrical storage systems with a significantly low environmental footprint.

• Geomechanics and Engineering
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• v.28 no.4
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• pp.359-374
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• 2022

Geology conditions are crucial in decision-making during the planning and design phase of a tunnel project. Estimation of the geology conditions of road tunnels is subject to significant uncertainties. In this work, the effectiveness of a novel regression method in estimating geological or geotechnical parameters of road tunnel projects was explored. This method, called Gaussian process regression (GPR), formulates the learning of the regressor within a Bayesian framework. The GPR model was trained with data of old tunnel projects. To verify its feasibility, the GPR technique was applied to a road tunnel to predict the state of three geological/geomechanical parameters of Rock Mass Rating (RMR), Rock Structure Rating (RSR) and Q-value. Finally, in order to validate the GPR approach, the forecasted results were compared to the field-observed results. From this comparison, it was concluded that, the GPR is presented very good predictions. The R-squared values between the predicted results of the GPR vs. field-observed results for the RMR, RSR and Q-value were obtained equal to 0.8581, 0.8148 and 0.8788, respectively.

• Geomechanics and Engineering
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• v.28 no.4
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• pp.421-435
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• 2022

The necessity of estimating the time and cost required for tunnel construction has led to extensive research in this regard. Since geological conditions are significant factors in terms of time and cost of road tunnels, considering these conditions is crucial. Uncertainties about the geological conditions of a tunnel alignment cause difficulties in planning ahead of the required construction time and costs. In this paper, the continuous-space, discrete-state Markov process has been used to predict geological conditions. The Monte-Carlo (MC) simulation (MCS) method is employed to estimate the construction time and costs of a road tunnel project using the input data obtained from six tunneling expert questionnaires. In the first case, the input data obtained from each expert are individually considered and in the second case, they are simultaneously considered. Finally, a comparison of these two modes based on the technique presented in this article suggests considering views of several experts simultaneously to reduce uncertainties and ensure the results obtained for geological conditions and the construction time and costs.

• Geomechanics and Engineering
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• v.28 no.1
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• pp.65-75
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• 2022

Time and cost of construction are key factors in decision-making during a tunnel project's planning and design phase. Estimations of time and cost of tunnel construction projects are subject to significant uncertainties caused by uncertain geotechnical and geological conditions. The Gaussian Process Regression (GPR) technique for predicting ground condition and construction time and cost of mountain tunnel projects is used in this work. The GPR model is trained with data from past mountain tunnel projects. The model is applied to a case study in which the predicted time and cost of tunnel construction using the GPR model are compared with the actual construction time and cost for model validation and reducing the uncertainty for the future projects. In addition, the results obtained from the GPR have been compared with to other models of artificial neural network (ANN) and support vector regression (SVR) that the GPR model provides more accurate results.

• Computers and Concrete
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• v.29 no.6
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• pp.433-444
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• 2022

Concrete's compressive strength is widely studied in order to understand many qualities and the grade of the concrete mixture. Conventional civil engineering tests involve time and resources consuming laboratory operations which results in the deterioration of concrete samples. Proposing efficient non-destructive models for the prediction of concrete compressive strength will certainly yield advancements in concrete studies. In this study, the efficiency of using radial basis function neural network (RBFNN) which is not common in this field, is studied for the concrete compressive strength prediction. Complementary studies with back propagation neural network (BPNN), which is commonly used in this field, have also been carried out in order to verify the efficiency of RBFNN for compressive strength prediction. A total of 13 input parameters, including novel ones such as cement's and fly ash's compositional information, have been employed in the prediction models with RBFNN and BPNN since all these parameters are known to influence concrete strength. Three different train: test ratios were tested with both models, while different hidden neurons, epochs, and spread values were introduced to determine the optimum parameters for yielding the best prediction results. Prediction results obtained by RBFNN are observed to yield satisfactory high correlation coefficients and satisfactory low mean square error values when compared to the results in the previous studies, indicating the efficiency of the proposed model.

• Geomechanics and Engineering
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• v.31 no.5
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• pp.453-460
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• 2022

It is essential for geotechnical engineers to conduct studies and make predictions about the stability of slopes, since collapse of a slope may result in catastrophic events. The Gaussian process regression (GPR) approach was carried out for the purpose of predicting the factor of safety (FOS) of the slopes in the study that was presented here. The model makes use of a total of 327 slope cases from Iran, each of which has a unique combination of geometric and shear strength parameters that were analyzed by PLAXIS software in order to determine their FOS. The K-fold (K = 5) technique of cross-validation (CV) was used in order to conduct an analysis of the accuracy of the models' predictions. In conclusion, the GPR model showed excellent ability in the prediction of FOS of slope stability, with an R² value of 0.8355, RMSE value of 0.1372, and MAPE value of 6.6389%, respectively. According to the results of the sensitivity analysis, the characteristics (friction angle) and (unit weight) are, in descending order, the most effective, the next most effective, and the least effective parameters for determining slope stability.